Applying the global SCC estimates to the estimated increases in CO2 emissions for the range of policy scenarios, we estimate the dollar value of the climate-related disbenefits captured by the models for each analysis year. For internal consistency, the annual disbenefits are discounted back to NPV terms using the same discount rate as each SCC estimate (i.e., 5%, 3%, and 2.5%) rather than 3% and 7%. These estimates are provided in Table 7-10.
Table 7-10. Monetized SCC-Derived Disbenefits of CO2 Emission Increases in 2014 (millions of 2008$)[a]
Discount Rate and Statistic
Selected Option (Major)
Alternative Option (Major)
Proposed MACT Approach (Area)
Final MACT/GACT Approach (Area)
Tons of CO2
                                    911,048
                                   1,524,572
                                    24,936
                                    22,191
5% (Average)
                                     $5.2
                                     $8.7
                                     $0.1
                                     $0.1
3% (Average)
                                      $22
                                      $37
                                     $0.6
                                     $0.5
2.5% (Average)
                                      $36
                                      $60
                                     $1.0
                                     $0.9
3% (95[th] percentile)
                                      $67
                                     $113
                                     $1.8
                                     $1.6
[a]	The SCC values are dollar-year and emissions-year specific. SCC values represent only a partial accounting of climate impacts.
Unquantified or Nonmonetized Benefits
The monetized benefits estimated in this RIA only reflect the portion of benefits attributable to the health impacts associated with exposure to ambient fine particles. Data, resource, and methodological limitations prevented EPA from quantifying or monetizing the benefits from several important benefit categories, including benefits from reducing toxic emissions, ecosystem effects, and visibility impairment. The direct health benefits from reducing HAPs have not been monetized in this analysis. In addition to being a PM2.5 precursor, SO2 emissions also contribute to adverse effects from acidic deposition in aquatic and terrestrial ecosystems, increase mercury methylation, as well as visibility impairment. In addition to health effects, ozone is associated with adverse vegetation effects to forests and crops.
Other SO2 Benefits
In addition to being a precursor to PM2.5, SO2 emissions are also associated with a variety of respiratory health effects. Unfortunately, we were unable to estimate the health benefits associated with reduced SO2 exposure in this analysis because we do not have air quality modeling data available. Without knowing the location of the emission reductions and the resulting ambient concentrations, we were unable to estimate the exposure to SO2 for nearby populations. Therefore, this analysis only quantifies and monetizes the PM2.5 benefits associated with the reductions in SO2 emissions.
Following an extensive evaluation of health evidence from epidemiologic and laboratory studies, the Integrated Science Assessment (ISA) for Sulfur Dioxide concluded that there is a causal relationship between respiratory health effects and short-term exposure to SO2 (U.S. EPA, 2008a). According to summary of the ISA in EPA's risk and exposure assessment (REA) for the SO2 NAAQS "the immediate effect of SO2 on the respiratory system in humans is bronchoconstriction" (U.S. EPA, 2009b). In addition, the REA summarized from the ISA that "asthmatics are more sensitive to the effects of SO2 likely resulting from preexisting inflammation associated with this disease." A clear concentration-response relationship has been demonstrated in laboratory studies following exposures to SO2 at concentrations between 20 and 100 ppb, both in terms of increasing severity of effect and percentage of asthmatics adversely affected (U.S. EPA, 2009b). Based on our review of this information, we identified four short-term morbidity endpoints that the SO2 ISA identified as a "causal relationship": asthma exacerbation, respiratory-related emergency department visits, and respiratory-related hospitalizations. The differing evidence and associated strength of the evidence for these different effects is described in detail in the SO2 ISA. The SO2 ISA also concluded that the relationship between short-term SO2 exposure and premature mortality was "suggestive of a causal relationship" because it is difficult to attribute the mortality risk effects to SO2 alone. Although the SO2 ISA stated that studies are generally consistent in reporting a relationship between SO2 exposure and mortality, there was a lack of robustness of the observed associations to adjustment for pollutants.
SO2 emissions also contribute to adverse welfare effects from acidic deposition, visibility impairment, and enhanced mercury methylation (U.S. EPA, 2008c). Deposition of sulfur causes acidification, which can cause a loss of biodiversity of fishes, zooplankton, and macro invertebrates in aquatic ecosystems, as well as a decline in sensitive tree species, such as red spruce (Picea rubens) and sugar maple (Acer saccharum) in terrestrial ecosystems. In the northeastern United States, the surface waters affected by acidification are a source of food for some recreational and subsistence fishermen and for other consumers and support several cultural services, including aesthetic and educational services and recreational fishing. Biological effects of acidification in terrestrial ecosystems are generally linked to aluminum toxicity, which can cause reduced root growth, which restricts the ability of the plant to take up water and nutrients. These direct effects can, in turn, increase the sensitivity of these plants to stresses, such as droughts, cold temperatures, insect pests, and disease leading to increased mortality of canopy trees. Terrestrial acidification affects several important ecological services, including declines in habitat for threatened and endangered species (cultural), declines in forest aesthetics (cultural), declines in forest productivity (provisioning), and increases in forest soil erosion and reductions in water retention (cultural and regulating)
Carbon Monoxide Benefits
Carbon monoxide (CO) exposure is associated with a variety of health effects. Without knowing the location of the emission reductions and the resulting ambient concentrations using fine-scale air quality modeling, we were unable to estimate the exposure to CO for nearby populations. Due to methodological and time limitations under the court-ordered schedule, we were unable to estimate the benefits associated with the reductions in CO emissions that would occur as a result of this rule.
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, 2010b) 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.
Visibility Benefits
Reducing SO2 and PM emissions would improve the level of visibility throughout the United States (U.S. EPA, 2009c). Fine particles with significant light-extinction efficiencies include sulfates, nitrates, organic carbon, elemental carbon, and soil (Sisler, 1996). These suspended particles and gases degrade visibility by scattering and absorbing light. Higher visibility impairment levels in the East are due to generally higher concentrations of fine particles, particularly sulfates, and higher average relative humidity levels. In fact, particulate sulfate is the largest contributor to regional haze in the eastern U.S. (i.e., 40% or more annually and 75% during summer). In the western U.S., particulate sulfate contributes to 20 - 50% of regional haze (U.S. EPA, 2009c). 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. Due to time limitations under the court-ordered schedule, we were unable to estimate the monetized benefits associated with visibility improvements. Previous analyses (U.S. EPA, 2006; U.S. EPA, 2010e) show that visibility benefits are a significant welfare benefit category. As shown in Section 6.5.4 of this RIA, an average visibility improvement of 0.51 deciviews in annual 20% worst visibility days over all Class I area monitors is anticipated as a result of these rules.
Ozone Vegetation Benefits
Exposure to ozone has been associated with a wide array of vegetation and ecosystem effects in the published literature (U.S. EPA, 2010a). Sensitivity to ozone is highly variable across species, with over 65 plan species identified as "ozone-sensitive," many of which occur in state and national parks and forests. These effects include those that damage or impair the intended use of the plant or ecosystem. Such effects are considered adverse to the public welfare and can include reduced growth and/or biomass production in sensitive plant species, including forest trees, reduced crop yields, visible foliar injury, reduced plant vigor (e.g., increased susceptibility to harsh weather, disease, insect pest infestation, and competition), species composition shift, and changes in ecosystems and associated ecosystem services.
Direct HAP Benefits
Americans are exposed to ambient concentrations of air toxics at levels which have the potential to cause adverse health effects. The levels of air toxics to which people are exposed vary depending on where people live and work and the kinds of activities in which they engage. In order to identify and prioritize air toxics, emission source types and locations that are of greatest potential concern, U.S. EPA conducts the National-Scale Air Toxics Assessment (NATA). The most recent NATA was conducted for calendar year 2002, and was released in June 2009. NATA for 2002 includes four steps:
1.	Compiling a national emissions inventory of air toxics emissions from outdoor sources
2.	Estimating ambient concentrations of air toxics across the United States
3.	Estimating population exposures across the United States
4.	Characterizing potential public health risk due to inhalation of air toxics including both cancer and noncancer effects
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 2002 NATA, nearly the entire U.S. population was exposed to an average concentration of air toxics that has the potential for adverse noncancer respiratory health effects. Figures 7-17 and 7-18 depict estimated county-level carcinogenic risk and noncancer respiratory hazard from the assessment. Results from the 2002 NATA suggest that acrolein is the primary driver for noncancer respiratory risk. Large reductions in HAP emissions may not necessarily translate into significant reductions in health risk because toxicity varies by pollutant and whether or not there are exposures at or above levels of concern is not known. 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 than that for acetaldehyde, suggesting that acrolein could be potentially more toxic than acetaldehyde. Thus, it is important to

Figure 7-17.	Estimated County Level Carcinogenic Risk from HAP exposure from outdoor sources (2002 NATA)

Figure 7-18.	Estimated County Level Noncancer (Respiratory) Risk from HAP exposure from outdoor sources (2002 NATA)

account for the toxicity and exposure, as well as the mass of the targeted emissions when designing reduction strategies to maximize health benefits.
Due to methodology and time limitations under the court-ordered schedule, 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 HAPs, 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 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, 2002). Since this time, EPA has continued to develop better methods for analyzing the benefits of reductions in HAPs.
In the Second Prospective 812 Analysis, 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 HAPs. 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 HAPs, and estimates of the value of an avoided case of cancer (fatal and non-fatal). Due to methodology and time limitations under the court-ordered schedule, we do not have sufficient information on emissions from specific sources and thus are unable to model changes in population exposures to ambient concentrations of HAPs. For this reason, we did not attempt to quantify or monetized the health benefits of reductions in HAPs in this analysis. Instead, we provide a qualitative analysis of the health effects associated with the HAPs 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.
Available emissions data show that boilers emit several different HAPs, either contained within the fuel burned or formed during the combustion process. Although numerous HAPs may be emitted from boilers, a few HAPs account for the majority of the total mass of HAPs emissions. See Table 7-11 for the list of the major HAPs for each fuel type. These rules are anticipated to reduce a combined 113,000 tons of carbon monoxide, 30,000 tons of HCl, 830 tons of HF, 2,900 pounds of mercury, 3,000 tons of other metals, and 23 grams of dioxins/furans (TEQ) each year. We discuss the health effects associated with these top HAPs as well as the HAPs for which we have emission reduction estimates.
Table 7-11.	Top HAPs by Mass from Boilers by Fuel Type
Coal
Gas
Biomass
Oil
                                    68% HCl
                               44% Formaldehyde
                               32% Acetaldehyde
                                  28% Nickel
                                     5% HF
                                    25% PAH
                                    28% HCl
                                 19% Manganese

                                  3% Toluene
                               25% Formaldehyde

Mercury
Mercury is a highly neurotoxic contaminant that enters the food web as a methylated compound, methylmercury (U.S. EPA, 2008c). The contaminant is concentrated in higher trophic levels, including fish eaten by humans. Experimental evidence has established that only inconsequential amounts of methylmercury can be produced in the absence of sulfate (U.S. EPA, 2008c). Current evidence indicates that in watersheds where mercury is present, increased sulfate deposition very likely results in methylmercury accumulation in fish (Drevnick et al., 2007; Munthe et al., 2007). The NOx SOX Ecological ISA concluded that evidence is sufficient to infer a casual relationship between sulfur deposition and increased mercury methylation in wetlands and aquatic environments (U.S. EPA, 2008c).
In addition to the role of sulfate deposition on methylation, these rules would also reduce mercury emissions. Mercury is emitted to the air from various man-made and natural sources. These emissions transport through the atmosphere and eventually deposit to land or water bodies. This deposition can occur locally, regionally, or globally, depending on the form of mercury emitted and other factors such as the weather. The form of mercury emitted varies depending on the source type and other factors. Available data indicate that the mercury emissions from these sources are a mixture of gaseous elemental mercury (25%), inorganic divalent mercury (reactive gas phase mercury) (50%), and particulate bound mercury (25%) (U.S. EPA, 2010f). Gaseous elemental mercury can be transported very long distances, even globally, to regions far from the emissions source (becoming part of the global "pool") before deposition occurs. Inorganic divalent and particulate bound mercury have a shorter atmospheric lifetime and can deposit to land or water bodies closer to the emissions source. Furthermore, elemental mercury in the atmosphere can undergo transformation into divalent mercury, providing a significant pathway for deposition of emitted elemental mercury.
Potential exposure routes to mercury emissions include both direct inhalation and consumption of fish containing methylmercury. The primary route of human exposure to mercury emissions from industrial sources is generally indirectly through the consumption of fish containing methylmercury. As described above, mercury that has been emitted to the air eventually settles into water bodies or onto land where it can either move directly or be leached into waterbodies. Once deposited, certain microorganisms can change it into methylmercury, a highly toxic form that builds up in fish, shellfish and animals that eat fish. Consumption of fish and shellfish are the main sources of methylmercury exposure to humans. Methylmercury builds up more in some types of fish and shellfish than in others. The levels of methylmercury in fish and shellfish vary widely depending on what they eat, how long they live, and how high they are in the food chain. Most fish, including ocean species and local freshwater fish, contain some methylmercury. For example, in recent studies by EPA and the U.S. Geological Survey (USGS) of fish tissues, every fish sampled from 291 streams across the country contained some methylmercury (Scudder, 2009).
The majority of fish consumed in the U.S. are ocean species. The methylmercury concentrations in ocean fish species are primarily influenced by the global mercury pool. However, the methylmercury found in local fish can be due, at least partly, to mercury emissions from local sources. Research shows that most people's fish consumption does not cause a mercury-related health concern. However, certain people may be at higher risk because of their routinely high consumption of fish (e.g., tribal and other subsistence fishers and their families who rely heavily on fish for a substantial part of their diet). It has been demonstrated that high levels of methylmercury in the bloodstream of unborn babies and young children may harm the developing nervous system, making the child less able to think and learn. Moreover, mercury exposure at high levels can harm the brain, heart, kidneys, lungs, and immune system of people of all ages.
Several studies suggest that the methylmercury content of fish may reduce cardio-protective effects of fish consumption. Some of these studies also suggest that methylmercury may cause adverse effects to the cardiovascular system. For example, the NRC (2000) review of the literature concerning methylmercury health effects took note of two epidemiological studies that found an association between dietary exposure to methylmercury and cardiovascular effects. In a study of 1,833 males in Finland aged 42 to 60 years, Solonen et al. (1995) observed a relationship between methylmercury exposure via fish consumption and acute myocardial infarction (AMI or heart attacks), death from coronary heart disease or cardiovascular disease. The NRC also noted a study of 917 seven-year old children in the Faroe Islands, whose initial exposure to methylmercury was in utero although postnatal exposures may have occurred as well. At seven years of age, these children exhibited an increase in blood pressure and a decrease in heart rate variability. Based on these and other studies, NRC concluded in 2000 that, "Although the data base is not as extensive as it is for other end points (i.e., neurologic effects) the cardiovascular system appears to be a target for methylmercury toxicity in humans and animals." NRC also stated that "additional studies are needed to better characterize the effect of methylmercury exposure on blood pressure and cardiovascular function at various stages of life."
Since publication of the NRC report there have been some 30 published papers presenting the findings of studies that have examined the possible cardiovascular effects of methylmercury exposure. These studies include epidemiological, toxicological, and toxicokinetic investigations. Over a dozen review papers have also been published. If there were a causal relationship between methylmercury exposure and adverse cardiovascular effects, then reducing exposure to methylmercury would result in public health benefits from reduced cardiovascular effects.
In early 2010, EPA sponsored a workshop in which a group of experts were asked to assess the plausibility of a causal relationship between methylmercury exposure and cardiovascular health effects and to advise EPA on methodologies for estimating population level cardiovascular health impacts of reduced methylmercury exposure. The report from that workshop is in preparation.
Baseline emissions for major and area source boilers in the U.S. are about 11 tons of mercury in the air. Based on the EPA's National Emission Inventory, about 103 tons of mercury were emitted from all anthropogenic sources in the U.S. in 2005. Moreover, the United Nations has estimated that about 2,100 tons of mercury were emitted worldwide by anthropogenic sources in 2005. We believe that total mercury emissions in the U.S. and globally in 2008 were about the same magnitude in 2005. Therefore, we estimate that in 2008, these sources emitted about 11% of the total anthropogenic mercury emissions in the U.S. and about 0.5% of the global emissions.
Overall, the major and area source rules would reduce mercury emissions by about 1.5 tons (2,900 pounds, or a 13% reduction) per year from current levels, and therefore, contribute to reductions in mercury exposures and health effects. Due to time limitations under the court-ordered schedule for this rule, we were unable to model mercury methylation, bioaccumulation in fish tissue, and human consumption of mercury-contaminated fish that would be needed in order to estimate the human health benefits from reducing mercury emissions. However, we were able to model the change in mercury deposition using CAMx for the final Boiler MACT and Boiler Area Source Rule. These modeling results indicate significantly reduced total mercury deposition (wet and dry forms) in some areas, including reducing total deposition from all sources by an average of 1.4% in the East and 0.5% in the West in the analysis year. This modeling indicates that mercury deposition reductions tend to be greatest nearest the sources. Figure 7-19 shows the percentage reduction in total mercury deposition as a result of the final Boiler MACT and Boiler Area Source Rule in the Eastern U.S., and Figure 7-20 shows the percentage reduction in mercury deposition in the Western U.S. based on the air quality modeling conducted for these rules.

Figure 7-19.	Percentage Reduction in Total Mercury Deposition in the Eastern U.S.

Figure 7-20.	Percentage Reduction in Total Mercury Deposition in the Western U.S.

Hydrogen Chloride (HCl)
Hydrogen chloride gas is intensely irritating to the mucous membranes of the nose, throat, and respiratory tract. Brief exposure to 35 ppm causes throat irritation, and levels of 50 to 100 ppm are barely tolerable for 1 hour. The greatest impact is on the upper respiratory tract; exposure to high concentrations can rapidly lead to swelling and spasm of the throat and suffocation. Most seriously exposed persons have immediate onset of rapid breathing, blue coloring of the skin, and narrowing of the bronchioles. Patients who have massive exposures may develop an accumulation of fluid in the lungs. Exposure to hydrogen chloride can lead to Reactive Airway Dysfunction Syndrome (RADS), a chemically- or irritant-induced type of asthma. Children may be more vulnerable to corrosive agents than adults because of the relatively smaller diameter of their airways. Children may also be more vulnerable to gas exposure because of increased minute ventilation per kg and failure to evacuate an area promptly when exposed. Hydrogen chloride has not been classified for carcinogenic effects.
Chlorine Gas (Cl2)
Chlorine gas is irritating and corrosive to the eyes, skin, and respiratory tract. Exposure to chlorine may cause burning of the eyes, nose, and throat; cough as well as constriction and edema of the airway and lungs can occur.
Hydrogen Cyanide (HCN)
Hydrogen cyanide is highly toxic by all routes of exposure and may cause abrupt onset of profound central nervous system, cardiovascular, and respiratory effects, leading to death within minutes. Exposure to lower concentrations of hydrogen cyanide may produce eye irritation, headache, confusion, nausea, and vomiting followed in some cases by coma and death. Hydrogen cyanide acts as a cellular asphyxiant. By binding to mitochondrial cytochrome oxidase, it prevents the utilization of oxygen in cellular metabolism. The central nervous system and myocardium are particularly sensitive to the toxic effects of cyanide.
Hydrogen Fluoride (HF)
Acute (short-term) inhalation exposure to gaseous hydrogen fluoride can cause severe respiratory damage in humans, including severe irritation and pulmonary edema. Chronic (long-term) exposure to fluoride at low levels has a beneficial effect of dental cavity prevention and may also be useful for the treatment of osteoporosis. Exposure to higher levels of fluoride may cause dental fluorosis. One study reported menstrual irregularities in women occupationally exposed to fluoride. The EPA has not classified hydrogen fluoride for carcinogenicity.
Toluene
Toluene is found in evaporative as well as exhaust emissions from motor vehicles. Under the 2005 Guidelines for Carcinogen Risk Assessment, there is inadequate information to assess the carcinogenic potential of toluene because studies of humans chronically exposed to toluene are inconclusive, toluene was not carcinogenic in adequate inhalation cancer bioassays of rats and mice exposed for life, and increased incidences of mammary cancer and leukemia were reported in a lifetime rat oral bioassay.
The central nervous system (CNS) is the primary target for toluene toxicity in both humans and animals for acute and chronic exposures. CNS dysfunction (which is often reversible) and narcosis have been frequently observed in humans acutely exposed to low or moderate levels of toluene by inhalation; symptoms include fatigue, sleepiness, headaches, and nausea. Central nervous system depression has been reported to occur in chronic abusers exposed to high levels of toluene. Symptoms include ataxia, tremors, cerebral atrophy, nystagmus (involuntary eye movements), and impaired speech, hearing, and vision. Chronic inhalation exposure of humans to toluene also causes irritation of the upper respiratory tract, eye irritation, dizziness, headaches, and difficulty with sleep.
Human studies have also reported developmental effects, such as CNS dysfunction, attention deficits, and minor craniofacial and limb anomalies, in the children of women who abused toluene during pregnancy. A substantial database examining the effects of toluene in subchronic and chronic occupationally exposed humans exists. The weight of evidence from these studies indicates neurological effects (i.e., impaired color vision, impaired hearing, decreased performance in neurobehavioral analysis, changes in motor and sensory nerve conduction velocity, headache, and dizziness) as the most sensitive endpoint.
Formaldehyde
Since 1987, EPA has classified formaldehyde as a probable human carcinogen based on evidence in humans and in rats, mice, hamsters, and monkeys. EPA is currently reviewing recently published epidemiological data. For instance, research conducted by the National Cancer Institute (NCI) 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 exposures. A recent National Institute of Occupational Safety and Health (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 CIIT Centers for Health Research (formerly the Chemical Industry Institute of Toxicology), 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. 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." EPA is reviewing the recent work cited above from the NCI and NIOSH, as well as the analysis by the CIIT Centers for Health Research and other studies, as part of a reassessment of the human hazard and dose-response associated with formaldehyde.
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 eosinophil infiltration into the airways. There are several studies that suggest that formaldehyde may increase the risk of asthma -- particularly in the young.[,]
Polycyclic Aromatic Hydrocarbons (PAHs)
At least seven PAH compounds are classified by EPA as probable human carcinogens based on animal data, including benzo(a)anthracene, benzo(b)fluoranthene, benzo(k)fluoranthene, benzo(a)pyrene, chrysene, dibenz(a,h)anthracene, and indeno(1,2,3-cd)pyrene. 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 at age three.[,]  EPA has not yet evaluated these recent studies.
Acetaldehyde
Acetaldehyde is classified in EPA's IRIS database as a probable human carcinogen, based on nasal tumors in rats, and is considered toxic by the inhalation, oral, and intravenous routes. Acetaldehyde is reasonably anticipated to be a human carcinogen by the U.S. DHHS in the 11[th] Report on Carcinogens and is classified as possibly carcinogenic to humans (Group 2B) by the IARC.[,]  EPA is currently conducting a reassessment of cancer risk from inhalation exposure to acetaldehyde.
The primary noncancer effects of exposure to acetaldehyde vapors include irritation of the eyes, skin, and respiratory tract. In short-term (4 week) rat studies, degeneration of olfactory epithelium was observed at various concentration levels of acetaldehyde exposure. Data from these studies were used by EPA to develop an inhalation reference concentration. Some asthmatics have been shown to be a sensitive subpopulation to decrements in functional expiratory volume (FEV1 test) and bronchoconstriction upon acetaldehyde inhalation. The agency is currently conducting a reassessment of the health hazards from inhalation exposure to acetaldehyde.
Nickel
Nickel is an essential element in some animal species, and it has been suggested it may be essential for human nutrition. Nickel dermatitis, consisting of itching of the fingers, hand and forearms, is the most common effect in humans from chronic (long-term) skin contact with nickel. Respiratory effects have also been reported in humans from inhalation exposure to nickel. No information is available regarding the reproductive or developmental effects of nickel in humans, but animal studies have reported such effects. Human and animal studies have reported an increased risk of lung and nasal cancers from exposure to nickel refinery dusts and nickel subsulfide. Animal studies of soluble nickel compounds (i.e., nickel carbonyl) have reported lung tumors. The EPA has classified nickel refinery subsulfide as Group A, human carcinogens and nickel carbonyl as a Group B2, probable human carcinogen.
Manganese
Health effects in humans have been associated with both deficiencies and excess intakes of manganese. Chronic (long-term) exposure to low levels of manganese in the diet is considered to be nutritionally essential in humans, with a recommended daily allowance of 2 to 5 milligrams per day. Chronic exposure to high levels of manganese by inhalation in humans results primarily in CNS effects. Visual reaction time, hand steadiness, and eye-hand coordination were affected in chronically exposed workers. Manganism, characterized by feelings of weakness and lethargy, tremors, a masklike face, and psychological disturbances, may result from chronic exposure to higher levels. Impotence and loss of libido have been noted in male workers afflicted with manganism attributed to inhalation exposures. The EPA has classified manganese in Group D, not classifiable as to carcinogenicity in humans.
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.
Furans (Chlorinated dibenzofurans (CDFs))
Most of the information on the adverse health effects comes from studies in people who were accidentally exposed to food contaminated with CDFs. The amounts that these people were exposed to were much higher than are likely from environmental exposures or from a normal diet. Skin and eye irritations, especially severe acne, darkened skin color, and swollen eyelids with discharge, were the most obvious health effects of the CDF poisoning. CDF poisoning also caused vomiting and diarrhea, anemia, more frequent lung infections, numbness, effects on the nervous system, and mild changes in the liver. Children born to exposed mothers had skin irritation and more difficulty learning, but it is unknown if this effect was permanent or caused by CDFs alone or CDFs and polychlorinated biphenyls in combination.
Many of the same effects that occurred in people accidentally exposed also occurred in laboratory animals that ate CDFs. Animals also had severe weight loss, and their stomachs, livers, kidneys, and immune systems were seriously injured. Some animals had birth defects and testicular damage, and in severe cases, some animals died. These effects in animals were seen when they were fed large amounts of CDFs over a short time, or small amounts over several weeks or months. Nothing is known about the possible health effects in animals from eating CDFs over a lifetime.
Other Air Toxics
In addition to the compounds described above, other compounds in gaseous hydrocarbon and PM emissions would be affected by these rules, including metal and organic HAPs. Information regarding the health effects of these compounds can be found in EPA's IRIS database.
Limitations and Uncertainties
The National Research Council (NRC) (2002) concluded that EPA's general methodology for calculating the benefits of reducing air pollution is reasonable and informative in spite of inherent uncertainties. To address these inherent uncertainties, NRC highlighted the need to conduct rigorous quantitative analysis of uncertainty and to present benefits estimates to decisionmakers in ways that foster an appropriate appreciation of their inherent uncertainty. In response to these comments, EPA's Office of Air and Radiation (OAR) is developing a comprehensive strategy for characterizing the aggregate impact of uncertainty in key modeling elements on both health incidence and benefits estimates. Components of that strategy include emissions modeling, air quality modeling, health effects incidence estimation, and valuation.
In this analysis, we use three methods to assess uncertainty quantitatively: Monte Carlo analysis, LML assessment, and alternate concentration-response functions for PM- and ozone-related mortality. We also provide a qualitative assessment for those aspects that we are unable to address quantitatively in this analysis. Each of these analyses is described in the following sections.
This analysis includes many data sources as inputs, including emission inventories, air quality data from models (with their associated parameters and inputs), population data, health effect estimates from epidemiology studies, and economic data for monetizing benefits. Each of these inputs may be uncertain and would affect the benefits estimate. When the uncertainties from each stage of the analysis are compounded, small uncertainties can have large effects on the total quantified benefits. In this analysis, we are unable to quantify the cumulative effect of all of these uncertainties, but we provide the following analyses to characterize many of the largest sources of uncertainty.
Monte Carlo Analysis
Similar to other recent RIAs, we used Monte Carlo methods for characterizing random sampling error associated with the concentration response functions and economic valuation functions. Monte Carlo simulation uses random sampling from distributions of parameters to characterize the effects of uncertainty on output variables, such as incidence of morbidity. Specifically, we used Monte Carlo methods to generate confidence intervals around the estimated health impact and monetized benefits. The reported standard errors in the epidemiological studies determined the distributions for individual effect estimates, as shown in Tables 7-4 and 7-5. The confidence intervals around the monetized benefits in Tables 7-6 and 7-7 incorporate the epidemiology standard errors as well as the distribution of the valuation function. These confidence intervals do not reflect other sources of uncertainty inherent within the boiler-specific BPT estimates, nor do they reflect baseline incidence rates, populations exposed and transferability of the effect estimate to diverse locations. As a result, the reported confidence intervals and range of estimates give an incomplete picture about the overall uncertainty in the benefits estimates.
LML Assessment for PM2.5
PM2.5 mortality benefits are the largest benefit category that we monetized in this analysis. To better characterize the uncertainty associated with mortality impacts that are estimated to occur in areas with low baseline levels of PM2.5, we included the LML assessment. We have more confidence in the mortality impacts among populations exposed to levels of PM2.5 above the lowest LML of the large cohort studies, and our confidence in the results diminish as we model that are lower than the LML. While an LML assessment provides some insight into the level of uncertainty in the estimated PM mortality benefits, EPA does not view the LML as a threshold and continues to quantify PM-related mortality impacts using a full range of modeled air quality concentrations. It is important to emphasize that just because we have greater confidence in the benefits above the LML, this does not mean that we have no confidence that benefits occur below the LML. In Section 7.3, we provide the results of the LML assessment in Figures 7-15 and 7-16.
Alternate Concentration-response Functions for PM Mortality
PM2.5 mortality benefits are the largest benefit category that we monetized in this analysis. To better understand the concentration-response relationship between PM2.5 exposure and premature mortality, EPA conducted an expert elicitation in 2006 (Roman et al., 2008; IEc, 2006). In general, the results of the expert elicitation support the conclusion that the benefits of PM2.5 control are very likely to be substantial. In previous RIAs, EPA presented benefits estimates using concentration response functions derived from the PM2.5 Expert Elicitation as a range from the lowest expert value (Expert K) to the highest expert value (Expert E). However, this approach did not indicate the agency's judgment on what the best estimate of PM benefits may be, and EPA's Science Advisory Board described this presentation as misleading. Therefore, we began to present the cohort-based studies (Pope et al., 2002; and Laden et al., 2006) as our core estimates in the proposal RIA for the Portland Cement NESHAP (U.S. EPA, 2009a). Using alternate relationships between PM2.5 and premature mortality supplied by experts, higher and lower benefits estimates are plausible, but most of the expert-based estimates fall between the two epidemiology-based estimates (Roman et al., 2008).
In this analysis, we present the results derived from the expert elicitation as indicative of the uncertainty associated with a major component of the health impact functions, and we provide the independent estimates derived from each of the twelve experts to better characterize the degree of variability in the expert responses. In this section, we provide the results using the concentration-response functions derived from the expert elicitation in both tabular (Tables 7-7 and 7-8) and graphical form (Figure 7-10). Please note that these results are not the direct results from the studies or expert elicitation; rather, the estimates are based in part on the concentration-response function provided in those studies.
Alternate Concentration-response Functions for Ozone Mortality
While particulate matter is the criteria pollutant most clearly associated with premature mortality, recent research suggests that short-term repeated ozone exposure also likely contributes to premature death. In 2008 the National Research Council (NRC) of the National Academy of Science (NAS) (NRC, 2008) issued a series of recommendations to the EPA regarding the quantification and valuation of ozone-related short-term mortality. Chief among these was that "...short-term exposure to ambient ozone is likely to contribute to premature deaths" and the committee recommended that "ozone-related mortality be included in future estimates of the health benefits of reducing ozone exposures..." The NAS also recommended that "...the greatest emphasis be placed on the multicity and NMMAPS studies without exclusion of the meta-analyses" (NRC, 2008).
In view of the findings of the NAS panel, we include used estimates of short-term ozone mortality from the Bell et al. (2004) NMMAPS analysis, the Schwartz (2005) multi-city study, the Huang and Bell (2005) multi-city study as well as effect estimates from the three meta-analyses (Bell et al., 2005; Levy et al., 2005; Ito et al., 2005).
Qualitative Assessment of Uncertainty and Other Analysis Limitations
Although we strive to incorporate as many quantitative assessments of uncertainty, there are several aspects for which we are only able to address qualitatively. These aspects are important factors to consider when evaluating the relative benefits of the attainment strategies for each of the alternative standards:
Above we present the estimates of the total monetized benefits, based on our interpretation of the best available scientific literature and methods and supported by the SAB-HES and the NAS (NRC, 2002). The benefits estimates are subject to a number of assumptions and uncertainties. For example, the key assumptions underlying the estimates for premature mortality, which typically account for at least 90% of the total monetized benefits, include the following:
      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 produced via transported precursors emitted from EGUs may differ significantly from direct PM2.5 released from diesel engines and other industrial sources, but the scientific evidence is not yet sufficient to allow differentiation of effect estimates by particle type.
      2. We assume that the health impact function for fine particles is linear down to the lowest air quality levels modeled in this analysis. Thus, the estimates include health benefits from reducing fine particles in areas with varied concentrations of PM2.5, including both regions that are in attainment with fine particle standard and those that do not meet the standard down to the lowest modeled concentrations.
      3. To characterize the uncertainty in the relationship between PM2.5 and premature mortality (which typically accounts for 85% to 95% of total monetized benefits), we include a set of twelve estimates based on results of the expert elicitation study in addition to our core estimates. Even these multiple characterizations omit the uncertainty in air quality estimates, baseline incidence rates, populations exposed and transferability of the effect estimate to diverse locations. As a result, the reported confidence intervals and range of estimates give an incomplete picture about the overall uncertainty in the PM2.5 estimates. This information should be interpreted within the context of the larger uncertainty surrounding the entire analysis. For more information on the uncertainties associated with PM2.5 benefits, please consult the PM2.5 NAAQS RIA (Table 5.5).
It is important to note that the monetized benefit-per-ton estimates used here reflect specific geographic patterns of emissions reductions and specific air quality and benefits modeling assumptions. For example, these estimates assume that emissions are reduced equally across all boilers and that the geographic distribution of boiler emissions will remain constant through the analysis year. As we did not model fuel subcategories, we assume that each fuel type is equally represented in the emissions and geographic distribution. Use of these $/ton values to estimate benefits associated with a specific fuel type (e.g., for reducing emissions from liquid fuel) may lead to higher or lower benefit estimates than if benefits were calculated based on direct air quality modeling of that fuel subcategory. Great care should be taken in applying these estimates to emission reductions occurring in any specific location, as these are all based on national emission reduction assumptions and therefore represent an average benefits-per-ton over the entire United States. The benefits- per-ton for variable emission reductions in specific locations may be very different from the estimates presented here. Despite our inability to fully characterize and quantify these relatively small uncertainties, we believe that, on net, the air quality impacts and associated monetized benefits are representative of the magnitude of benefits anticipated from these rules.
As previously described, we strive to monetize as many of the benefits anticipated from these rules as possible, but the monetized benefits estimated in this RIA inevitably only reflect the portion of benefits. Specifically, only the benefits attributable to the health impacts associated with exposure to ambient fine particles and ozone have been monetized in this analysis. Methodological and time limitations under the court-ordered schedule for this rule prevented EPA from quantifying or monetizing the benefits from several important benefit categories, including benefits from reducing toxic emissions, ecosystem effects, and visibility impairment. Data limitations include limited monitoring for HAPs, incomplete emissions inventories for HAPs, and limited photochemical air quality modeling for non-mercury HAPs. Resource limitations include limited staff and extramural funding in conjunction with a heavy regulatory workload, which led EPA to not model these endpoints. Methodological limitations include an absence of concentration-response functions for many HAP health effects, with issues such as exposure misclassification, small number of cases, confounding, and extrapolation of toxicological effects down to ambient levels. Despite our inability to monetize all of the benefit categories, the total combined monetized benefits still exceed the costs by a substantial margin.
This RIA does not include the type of detailed uncertainty assessment found in the PM NAAQS RIA (U.S. EPA, 2006). However, the results of the Monte Carlo analyses of the health and welfare benefits presented in Chapter 5 of the PM RIA can provide some evidence of the uncertainty surrounding the benefits results presented in this analysis.
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U.S. Environmental Protection Agency (U.S. EPA). 2010h. Regulatory Impact Analysis for the SO2 NAAQS. Office of Air Quality Planning and Standards, Research Triangle Park, NC. June. Available at < http://www.epa.gov/ttn/ecas/regdata/RIAs/fso2ria100602full.pdf>.
Viscusi, V.K., and J.E. Aldy. 2003. "The Value of a Statistical Life: A Critical Review of Market Estimates throughout the World." Journal of Risk and Uncertainty. 27(1): p. 5-76.

Comparison of Monetized Benefits and Costs
Boiler MACT
       Because we were unable to monetize the benefits associated with reducing HAPs, all monetized benefits reflect improvements in ambient PM2.5 and ozone concentrations. This results in an underestimate of the monetized benefits. Using a 3% discount rate, we estimate the total monetized benefits of the major source rule to be $22 billion to $54 billion in the implementation year (2014) (Table 8-1). Using a 7% discount rate, we estimate the total monetized benefits to be $20 billion to $49 billion. The annualized costs are $1.5 billion at a 7% interest rate. The net benefits are $21 billion to $53 billion at a 3% discount rate for the benefits and $19 million to $48 billion at a 7% discount rate. All estimates are in 2008$. The benefits from reducing other air pollutants have not been monetized in this analysis, including reducing 112,000 tons of carbon monoxide, 30,000 tons of HCl, 820 tons of HF, 2,800 pounds of mercury, 2,700 tons of other metals, and 23 grams of dioxins/furans (TEQ) each year.
Boiler Area Source Rule
Because we were unable to monetize the benefits associated with reducing HAPs, all monetized benefits reflect improvements in ambient PM2.5 and ozone concentrations. This results in an underestimate of the monetized benefits. Using a 3% discount rate, we estimate the total monetized benefits of the area source rule to be $210 million to $520 million in the implementation year (2014) (Table 8-2). Using a 7% discount rate, we estimate the total monetized benefits to be $190 million to $470 million. The annualized costs are $490 million at a 7% interest rate. The net benefits are −$280 million to $30 million at a 3% discount rate for the benefits and −$300 million to −$20 million at a 7% discount rate. All estimates are in 2008$. The benefits from reducing other air pollutants have not been monetized in this analysis, including reducing 1,100 tons of carbon monoxide, 340 tons of HCl, 8 tons of HF, 90 pounds of mercury, 320 tons of other metals, and less than 1 gram of dioxins/furans (TEQ) each year.
Table 8-1.	Summary of the Monetized Benefits, Social Costs, and Net Benefits for the Boiler MACT in 2014 (millions of 2008$)[a]

3% Discount Rate
7% Discount Rate
                                   Selected
Total Monetized Benefits[b]
                                    $22,000
                                      to
                                    $54,000
                                    $20,000
                                      to
                                    $49,000
Total Social Costs[c]
                                    $1,500
                                    $1,500
Net Benefits
                                    $20,500
                                      to
                                    $52,500
                                    $18,500
                                      to
                                    $47,500
Non-monetized Benefits
112,000 tons of carbon monoxide

30,000 tons of HCl

820 tons of HF

2,800 pounds of mercury

2,700 tons of other metals

23 grams of dioxins/furans (TEQ)

Health effects from SO2 exposure

Ecosystem effects

Visibility impairment
                                  Alternative
Total Monetized Benefits[b]
                                    $18,000
                                      to
                                    $43,000
                                    $16,000
                                      to
                                    $39,000
Total Social Costs[b]
                                    $1,900
                                    $1,900
Net Benefits
                                    $16,100
                                      to
                                    $41,100
                                    $14,100
                                      to
                                    $37,100
Non-monetized Benefits
112,000 tons of carbon monoxide

22,000 tons of HCl

620 tons of HF

2,400 pounds of mercury

2,600 tons of other metals

23 grams of dioxins/furans (TEQ)

Health effects from SO2 exposure

Ecosystem effects

Visibility impairment
[a]	All estimates are for the implementation year (2014), and are rounded to two significant figures. These results include units anticipated to come online and the lowest cost disposal assumption.
[b]	The total monetized benefits reflect the human health benefits associated with reducing exposure to PM2.5 through reductions of directly emitted PM2.5 and PM2.5 precursors such as SO2, as well as reducing exposure to ozone through reductions of VOCs. 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 Pope et al. (2002) to Laden et al. (2006). 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. These estimates include energy disbenefits associated with the increased emissions from additional energy usage valued at $22 million for the selected option and $37 million for the alternative option. Ozone benefits are valued at $3.6 to $15 million for both options.
[c]	The methodology used to estimate social costs for one year in the multimarket model using surplus changes results in the same social costs for both discount rates.
Table 8-2	Summary of the Monetized Benefits, Social Costs, and Net Benefits for the Boiler Area Source Rule in 2014 (millions of 2008$)[a]

3% Discount Rate
7% Discount Rate
                           Final MACT/GACT Approach
Total Monetized Benefits[b]
                                     $210
                                      to
                                     $520
                                     $190
                                      to
                                     $470
Total Social Costs[c]
                                     $490
                                     $490
Net Benefits
                                    −$280
                                      to
                                      $30
                                    −$300
                                      to
                                    −$20
Non-monetized Benefits
1,100 tons of carbon monoxide

340 tons of HCl

8 tons of HF

90 pounds of mercury

320 tons of other metals

<1 gram of dioxins/furans (TEQ)

Health effects from SO2 exposure

Ecosystem effects

Visibility impairment
                            Proposed MACT Approach
Total Monetized Benefits[b]
                                     $200
                                      to
                                     $490
                                     $180
                                      to
                                     $440
Total Social Costs[c]
                                     $850
                                     $850
Net Benefits
                                    −$650
                                      to
                                    −$360
                                    −$670
                                      to
                                    −$410
Non-monetized Benefits
1,100 tons of carbon monoxide

340 tons of HCl

8 tons of HF

90 pounds of mercury

320 tons of other metals

<1 gram of dioxins/furans (TEQ)

Health effects from SO2 exposure

Ecosystem effects

Visibility impairment
[a]	All estimates are for the implementation year (2014), and are rounded to two significant figures. These results include units anticipated to come online and the lowest cost disposal assumption.
[b]	The total monetized benefits reflect the human health benefits associated with reducing exposure to PM2.5 through reductions of directly emitted PM2.5 and PM2.5 precursors such as SO2. 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 Pope et al. (2002) to Laden et al. (2006). 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. These estimates include energy disbenefits associated with the increased emissions from additional energy usage valued at less than $1 million.
[c]	The methodology used to estimate social costs for one year in the multimarket model using surplus changes results in the same social costs for both discount rates.
Figures 8-1 and 8-2 show the full range of net benefits estimates (i.e., annual benefits minus annualized costs) quantified in terms of PM2.5 benefits for the implementation year (2014).

Figure 8-1.	Net Benefits for the Final Major and Area Source Boiler Rules at 3% Discount Rate[a]
[a]	Net benefits are quantified in terms of PM2.5 benefits for the implementation year (2014). This graph shows 14 benefits estimates combined with the cost estimate. 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. The monetized benefits incorporate the conversion from precursor emissions to ambient fine particles.

Figure 8-2.	Net Benefits for the Final Major and Area Source Boiler Rules at 7% Discount Rate[a]
[a]	Net benefits are quantified in terms of PM2.5 benefits for the implementation year (2014). This graph shows 14 benefits estimates combined with the cost estimate. 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. The monetized benefits incorporate the conversion from precursor emissions to ambient fine particles.
Section 8 References
Laden, F., J. Schwartz, F.E. Speizer, and D.W. Dockery. 2006. "Reduction in Fine Particulate Air Pollution and Mortality." American Journal of Respiratory and Critical Care Medicine 173:667-672.
Pope, C.A., III, R.T. Burnett, M.J. Thun, E.E. Calle, D. Krewski, K. Ito, and G.D. Thurston. 2002. "Lung Cancer, Cardiopulmonary Mortality, and Long-term Exposure to Fine Particulate Air Pollution." Journal of the American Medical Association 287:1132-1141.

Appendix A
OAQPS Multimarket Model to Assess the Economic
Impacts of Environmental
Regulation

A.1	Introduction
An economic impact analysis (EIA) provides information about a policy's effects (i.e., social costs); emphasis is also placed on how the costs are distributed among stakeholders (EPA, 2000). In addition, large-scale policies require additional analysis to better understand how costs are passed across the economy. Although several tools are available to estimate social costs, current EPA guidelines suggest that multimarket models "...are best used when potential economic impacts and equity effects on related markets might be considerable" and modeling using a computable general equilibrium model is not available or practical (EPA, 2000, p. 146). Other guides for environmental economists offer similar advice (Berck and Hoffmann, 2002; Just, Hueth, and Schmitz, 2004).
Multimarket models focus on "short-run" time horizons and measure a policy's near term or transition costs (EPA, 1999). Recent studies suggest short-run analyses can complement full dynamic general equilibrium analysis.
The multimarket model described in this appendix is a new addition to the Office of Air Quality Planning and Standards' (OAQPS's) economic model tool kit; it is designed to be used as a transparent tool that can respond quickly to requests about how stakeholders in 100 U.S. industries might respond to new environmental policy. Next, we provide an overview of the model, data, and parameters.
A.2	Multimarket Model
The multimarket model contains the following features:
Industry sectors and benchmark data set
100 industry sectors
a single benchmark year (2010)
estimates of industry carbon dioxide (CO2) emissions
estimates of industry employment
Economic behavior
industries respond to regulatory costs by changing production rates
market prices rise to reflect higher energy and other nonenergy material costs
customers respond to these price increases and consumption falls
Model scope
100 sectors are linked with each other based on their use of energy and other nonenergy materials. For example, the construction industry is linked with the petroleum, cement, and steel industries and is influenced by price changes that occur in each sector. The links allow EPA to account for indirect effects the regulation has on related markets.
 Links come from input-output information used from OAQPS's computable general equilibrium model (EMPAX)
production adjustments influence employment levels
international trade (imports/exports) behavior considered
Model time horizon ("short-run")
fixed production resources (e.g., capital) leads to an upward-sloping industry supply function
firms cannot alter input mixes; there is no substitution among intermediate production inputs
price of labor (i.e., wage) is fixed
investment and government expenditures are fixed
A.2.1	Industry Sectors and Benchmark Data Set
The multimarket model includes 100 industries. For the benchmark year, the model uses information from OAQPS's computable general equilibrium model's balanced social accounting matrix (SAM) and the following accounting identity holds:
	Output + Imports = Consumption + Investment + Government + Exports	(A.1)
If we abstract and treat each industry as a national market, the identity represents the prepolicy market-clearing condition, or benchmark "equilibrium"; supply equals demand in each market. In Table A-1, we identify the 100 industries for the multimarket model; Table A-2 provides the 2010 benchmark data set. Since the benchmark data are reported in value terms, we also use the common "Harberger convention" and choose units where are all prices are one in the benchmark equilibrium (Shoven and Whalley, 1995).
Table A-1.	Industry Sectors Included in Multimarket Model
Industry Label
Description
Representative NAICS[a]
Energy Industries

COL
Coal
2121
CRU
Crude Oil Extraction
211111 (exc. nat gas)
ELE
Electric Generation
2211
GAS
Natural Gas
211112 2212 4862
OIL
Refined Petroleum
324
Nonmanufacturing

AGR
Agricultural
11
MIN
Mining
21 less others
CNS
Construction
23
Manufactured Goods

Food, beverages, and textiles

ANM
Animal Foods
3111
GRN
Grain Milling
3112
SGR
Sugar
3113
FRU
Fruits and Vegetables
3114
MIL
Dairy Products
3115
MEA
Meat Products
3116
SEA
Seafood
3117
BAK
Baked Goods
3118
OFD
Other Food Products
3119
BEV
Beverages and Tobacco
312
TEX
Textile Mills
313
TPM
Textile Product Mills
314
WAP
Wearing Apparel
315
LEA
Leather
316
Lumber, paper, and printing

SAW
Sawmills
3211
PLY
Plywood and Veneer
3212
LUM
Other Lumber
3219
PAP
Pulp and Paper Mills
3221
CPP
Converted Paper Products
3222
PRN
Printing
323
Chemicals

CHM
Chemicals and Gases
3251
RSN
Resins
3252
FRT
Fertilizer
3253
MED
Drugs and Medicine
3254
PAI
Paints and Adhesives
3255
SOP
Soap
3256
OCM
Other Chemicals
3259
(continued)
Table A-1.	Industry Sectors Included in Multimarket Model (continued)
Industry Label
Description
Representative NAICS[a]
Plastics and Rubber

PLS
Plastic
3261
RUB
Rubber
3262
Nonmetallic Minerals

CLY
Clay
3271
GLS
Glass
3272
CEM
Cement
3273
LIM
Lime and Gypsum
3274
ONM
Other Non-Metallic Minerals
3279
Primary Metals

I_S
Iron and Steel
3311 3312 33151
ALU
Aluminum
3313<PHONE_NUMBER>24
OPM
Other Primary Metals
3314<PHONE_NUMBER>25 331528
Fabricated Metals

FRG
Forging and Stamping
3321
CUT
Cutlery
3322
FMP
Fabricated Metals
3323
BOI
Boilers and Tanks
3324
HRD
Hardware
3325
WIR
Springs and Wires
3326
MSP
Machine Shops
3327
EGV
Engraving
3328
OFM
Other Fabricated Metals
3329
Machinery and Equipment

CEQ
Construction and Agricultural Equipment
3331
IEQ
Industrial Equipment
3332
SEQ
Service Industry Equipment
3333
HVC
HVAC Equipment
3334
MEQ
Metalworking Equipment
3335
EEQ
Engines
3336
GEQ
General Equipment
3339
Electronic Equipment

CPU
Computers
3341
CMQ
Communication Equipment
3342
TVQ
TV Equipment
3343
SMI
Semiconductor Equipment
3344
INS
Instruments
3345
MGT
Magnetic Recording Equipment
3346
LGT
Lighting
3351
APP
Appliances
3352
(continued)
Table A-1.	Industry Sectors Included in Multimarket Model (continued)
Industry Label
Description
Representative NAICS[a]
Electronic Equipment (continued)
ELQ
Electric Equipment
3353
OEQ
Other Electric Equipment
3359
Transportation Equipment

M_V
Motor Vehicles
3361
TKB
Truck Bodies
3362
MVP
Motor Vehicle Parts
3363
ARC
Aircraft
3364
R_R
Rail Cars
3365
SHP
Ships
3366
OTQ
Other Transport Equipment
3369
Other

FUR
Furniture
337
MSC
Miscellaneous Manufacturing
339
Services

Wholesale and Retail Trade

WHL
Wholesale Trade
42
RTL
Retail Trade
44 - 45
Transportation Services

ATP
Air Transportation
481
RTP
Railroad Transportation
482
WTP
Water Transportation
483
TTP
Freight Truck Transportation
484
PIP
Pipeline Transport
486
OTP
Other Transportation Services
485 487 488
Other Services

INF
Information
51
FIN
Finance and Insurance
52
REL
Real Estate
53
PFS
Professional Services
54
MNG
Management
55
ADM
Administrative Services
56
EDU
Education
61
HLT
Health Care
62
ART
Arts
71
ACM
Accommodations
72
OSV
Other Services
81
PUB
Public Services
92
[a]	NAICS = North American Industry Classification System. Industry assignments are based on data used in the EMPAX-modeling system, which relies on the commodity code system used in IMPLAN.
Table A-2.	2010 Benchmark Data Set (billion 2006$)
Industry Label
Industry Description
Output
Imports
Consumption
Investment and Government
Exports
ACM
Accommodations
$828
$6
$816
$17
$1
ADM
Administrative Services
$795
$37
$771
$61
                                 Less than $1
AGR
Agricultural
$314
$53
$333
$5
$29
ALU
Aluminum
$65
$17
$70
$4
$8
ANM
Animal Foods
$45
                                 Less than $1
$36
                                 Less than $1
$9
APP
Appliances
$25
$19
$35
$6
$3
ARC
Aircraft
$217
$60
$58
$120
$98
ART
Arts
$252
                                 Less than $1
$246
$3
$3
ATP
Air Transportation
$154
$28
$91
$32
$59
BAK
Baked Goods
$61
$3
$61
$2
                                 Less than $1
BEV
Beverages and Tobacco
$133
$54
$186
                                 Less than $1
$1
BOI
Boilers and Tanks
$27
$2
$19
$9
$2
CEM
Cement
$52
                                 Less than $1
$47
$3
$2
CEQ
Construction and Agricultural Equipment
$70
$24
$47
$33
$14
CHM
Chemicals and Gases
$284
$75
$300
$10
$49
CLY
Clay
$8
$4
$10
$1
$2
CMQ
Communication Equipment
$73
$40
$47
$56
$11
CNS
Construction
$983
$77
$594
$465
                                 Less than $1
COL
Coal
$44
$2
$42
                                 Less than $1
$4
CPP
Converted Paper Products
$52
$2
$43
$6
$6
CPU
Computers
$145
$76
$132
$52
$37
CRU
Crude Oil Extraction
$67
$189
$255
                                 Less than $1
                                 Less than $1
CUT
Cutlery
$11
$5
$9
$5
$2
EDU
Education
$970
                                 Less than $1
$257
$701
$13
EEQ
Engines
$36
$14
$30
$6
$13
EGV
Engraving
$21
                                 Less than $1
$9
$5
$7
ELE
Electric Generation
$317
                                 Less than $1
$287
$31
                                 Less than $1
ELQ
Electric Equipment
$33
$16
$23
$17
$10
FIN
Finance and Insurance
$2,015
$106
$1,972
$43
$106
FMP
Fabricated Metals
$66
$1
$58
$7
$2
FRG
Forging and Stamping
$20
                                 Less than $1
$17
$1
$2
FRT
Fertilizer
$42
$5
$33
$4
$10
(continued)
Table A-2.	2010 Benchmark Data Set (billion 2006$) (continued)
Industry Label
Industry Description
Output
Imports
Consumption
Investment and Government
Exports
FRU
Fruits and Vegetables
$74
$12
$76
$4
$5
FUR
Furniture
$66
$38
$84
$17
$2
GAS
Natural Gas
$139
$32
$160
$6
$6
GEQ
General Equipment
$54
$32
$47
$25
$14
GLS
Glass
$30
                                 Less than $1
$18
$2
$10
GRN
Grain Milling
$77
$9
$74
$2
$10
HLT
Health Care
$1,863
                                 Less than $1
$1,823
$20
$20
HRD
Hardware
$8
$4
$5
$4
$3
HVC
HVAC Equipment
$34
$9
$26
$10
$6
I_S
Iron and Steel
$125
$42
$143
$10
$13
IEQ
Industrial Equipment
$26
$14
$16
$14
$11
INF
Information
$1,305
$77
$1,217
$154
$11
INS
Instruments
$112
$44
$71
$65
$20
LEA
Leather
$4
$26
$29
                                 Less than $1
$1
LGT
Lighting
$12
$11
$16
$5
$1
LIM
Lime and Gypsum
$7
                                 Less than $1
$1
                                 Less than $1
$5
LUM
Other Lumber
$41
$2
$32
$9
$2
M_V
Motor Vehicles
$272
$190
$313
$106
$43
MEA
Meat Products
$174
$9
$169
$5
$10
MED
Drugs and Medicine
$258
$102
$316
$18
$27
MEQ
Metalworking Equipment
$24
$11
$16
$14
$4
MGT
Magnetic Recording Equipment
$15
$2
$13
$2
$2
MIL
Dairy Products
$87
$3
$84
$4
$2
MIN
Mining
$53
$2
$30
$15
$11
MNG
Management
$469
                                 Less than $1
$378
                                 Less than $1
$92
MSC
Miscellaneous Manufacturing
$178
$129
$189
$73
$46
MSP
Machine Shops
$38
$2
$32
$5
$2
MVP
Motor Vehicle Parts
$220
$75
$226
$17
$52
OCM
Other Chemicals
$45
$2
$23
$9
$15
OEQ
Other Electric Equipment
$31
$16
$28
$7
$11
OFD
Other Food Products
$92
$7
$90
$2
$7
OFM
Other Fabricated Metals
$56
$28
$50
$22
$12
(continued)
Table A-2.	2010 Benchmark Data Set (billion 2006$) (continued)
Industry Label
Industry Description
Output
Imports
Consumption
Investment and Government
Exports
OIL
Refined Petroleum
$415
$106
$462
$12
$47
ONM
Other Non-Metallic Minerals
$13
$5
$15
$1
$2
OPM
Other Primary Metals
$40
$27
$52
$2
$12
OSV
Other Services
$2,321
                                 Less than $1
$1,479
$598
$244
OTP
Other Transportation Services
$245
                                 Less than $1
$202
$22
$22
OTQ
Other Transport Equip
$23
$10
$14
$13
$5
PAI
Paints and Adhesives
$36
$1
$28
$3
$6
PAP
Pulp and Paper Mills
$131
$21
$133
$5
$14
PFS
Professional Services
$2,103
$84
$1,715
$461
$10
PIP
Pipeline Transport
$37
$83
$20
$98
$1
PLS
Plastic
$145
$14
$139
$4
$15
PLY
Plywood and Veneer
$19
$8
$25
$1
$1
PRN
Printing
$51
$1
$34
$11
$6
PUB
Public Services
$1,099
$22
$355
$766
                                 Less than $1
R_R
Rail Cars
$11
$2
$6
$6
$2
REL
Real Estate
$2,719
$2
$2,559
$131
$31
RSN
Resins
$107
$23
$98
$6
$26
RTL
Retail Trade
$1,440
$53
$1,412
$82
                                 Less than $1
RTP
Railroad Transportation
$70
                                 Less than $1
$42
$18
$11
RUB
Rubber
$38
$20
$36
$15
$6
SAW
Sawmills
$29
$9
$36
$1
$1
SEA
Seafood
$13
$3
$14
$1
$1
SEQ
Service Industry Equipment
$29
$23
$22
$24
$6
SGR
Sugar
$34
$6
$36
$2
$3
SHP
Ships
$36
$6
$13
$29
                                 Less than $1
SMI
Semiconductor Equipment
$141
$69
$157
$12
$41
SOP
Soap
$82
$5
$74
$3
$9
TEX
Textile Mills
$29
$9
$31
$1
$6
TKB
Truck Bodies
$58
$12
$34
$32
$5
TPM
Textile Product Mills
$27
$19
$37
$7
$2
TTP
Freight Truck Transportation
$301
                                 Less than $1
$211
$39
$51
(continued)
Table A-2.	2010 Benchmark Data Set (billion 2006$) (continued)
Industry Label
Industry Description
Output
Imports
Consumption
Investment and Government
Exports
TVQ
TV Equipment
$19
$37
$50
$3
$3
WAP
Wearing Apparel
$25
$94
$117
$1
                                 Less than $1
WHL
Wholesale Trade
$1,309
$22
$1,021
$172
$138
WIR
Springs and Wires
$5

$2
$1
$3
WTP
Water Transportation
$45

$14
$12
$19

A.2.1.2	Employment Data
The model includes employment forecasts for each of the 100 sectors. Employment estimates are based on data from three sources: the AEO 2009 estimates of employment (AEO supplemental Table 126 and indicators of Macroeconomic Activity), and Global Insights, Inc., and the Bureau of Labor Statistics (BLS) 2008 end-of year-employment (Current Employment Statistics -- CES [National]). Typically, 3-digit NAICS sectors' employment estimates are either directly reported in the updated AEO 2009 release or Global Insights For multimarket industries with finer NAICs detail, estimates were calculated by selecting a primary NAICS supersector estimate (AEO or Global Insights) and distributing total employment from the larger NAICS supersectors across more detailed NAICS sectors within the super-sector. The distributions were determined using observed 2008 BLS employment data. In the last step, In order to match aggregate U.S. employment numbers reported in the AEO 2009 release (140.1 million), a single adjustment factor was applied to all sectors that use Global Insights' supersector data. Table A-4 reports the baseline employment for each of the 100 sectors.
A.2.2	Economic Behavior
A.2.2.1	U.S. Supply
In a postpolicy scenario, industry responds to changes in the new market-clearing "net" price for the good or service sold:
	%Δ"net" price = %Δ market price − %Δ direct costs − %Δ indirect costs	(A.2)
The %Δ direct costs are approximated using the engineering cost analysis and baseline value of output. For example, a $1 billion increase in compliance costs for the electricity sector (ELE) would be represented in the model as follows:
	%Δ direct costs = $1/$317= 0.03%	(A.3)
As shown in Figure A-1, the cost change provides the industry with incentives to alter production rates at current market prices; market prices must rise to maintain the original prepolicy production levels (Q).
Table A-4.	2010 U.S. Employment Projections
Industry Label
Industry Description
Projected Employment (1,000)
ACM
Accommodations
11,239
ADM
Administrative Services
9,274
AGR
Agricultural
1,607
ALU
Aluminum
87
ANM
Animal Foods
45
APP
Appliances
33
ARC
Aircraft
449
ART
Arts
1,939
ATP
Air Transportation
506
BAK
Baked Goods
247
BEV
Beverages and Tobacco
92
BOI
Boilers and Tanks
67
CEM
Cement
164
CEQ
Construction and Agricultural Equipment
176
CHM
Chemicals and Gases
147
CLY
Clay
38
CMQ
Communication Equipment
73
CNS
Construction
7,446
COL
Coal
79
CPP
Converted Paper Products
306
CPU
Computers
104
CRU
Crude Oil Extraction
384
CUT
Cutlery
34
EDU
Education
2,892
EEQ
Engines
75
EGV
Engraving
100
ELE
Electric Generation
219
ELQ
Electric Equipment
72
FIN
Finance and Insurance
6,051
FMP
Fabricated Metals
285
FRG
Forging and Stamping
75
FRT
Fertilizer
35
FRU
Fruits and Vegetables
153
FUR
Furniture
327
(continued)
Table A-4.	2010 U.S. Employment Projections (continued)
Industry Label
Industry Description
Projected Employment (1,000)
GAS
Natural Gas
98
GEQ
General Equipment
198
GLS
Glass
71
GRN
Grain Milling
55
HLT
Health Care
15,190
HRD
Hardware
20
HVC
HVAC Equipment
109
I_S
Iron and Steel
205
IEQ
Industrial Equipment
88
INF
Information
2,939
INS
Instruments
250
LEA
Leather
3
LGT
Lighting
26
LIM
Lime and Gypsum
10
LUM
Other Lumber
216
M_V
Motor Vehicles
170
MEA
Meat Products
450
MED
Drugs and Medicine
279
MEQ
Metalworking Equipment
139
MGT
Magnetic Recording Equipment
20
MIL
Dairy Products
113
MIN
Mining
599
MNG
Management
1,732
MSC
Miscellaneous Manufacturing
180
MSP
Machine Shops
251
MVP
Motor Vehicle Parts
485
OCM
Other Chemicals
92
OEQ
Other Electric Equipment
63
OFD
Other Food Products
144
OFM
Other Fabricated Metals
196
OIL
Refined Petroleum
70
ONM
Other Non-metallic Minerals
61
OPM
Other Primary Metals
87
OSV
Other Services
5,271
OTP
Other Transportation Services
1,064
(continued)
Table A-4.	2010 U.S. Employment Projections (continued)
Industry Label
Industry Description
Projected Employment (1,000)
OTQ
Other Transport Equipment
36
PAI
Paints and Adhesives
60
PAP
Pulp and Paper Mills
121
PFS
Professional Services
18,989
PIP
Pipeline Transport
43
PLS
Plastic
473
PLY
Plywood and Veneer
74
PRN
Printing
248
PUB
Public Services
21,787
R_R
Rail Cars
25
REL
Real Estate
2,158
RSN
Resins
102
RTL
Retail Trade
15,283
RTP
Railroad Transportation
236
RUB
Rubber
117
SAW
Sawmills
84
SEA
Seafood
36
SEQ
Service Industry Equipment
77
SGR
Sugar
62
SHP
Ships
140
SMI
Semiconductor Equipment
245
SOP
Soap
104
TEX
Textile Mills
110
TKB
Truck Bodies
126
TPM
Textile Product Mills
32
TTP
Freight Truck Transportation
1,429
TVQ
TV Equipment
15
WAP
Wearing Apparel
153
WHL
Wholesale Trade
5,869
WIR
Springs and Wires
36
WTP
Water Transportation
67
Total

144,100

The multimarket model also simultaneously considers how the policy influences other important production costs (via changes in energy and other intermediate material prices). As a result, the multimarket model can provide additional information about how policy costs are transmitted through the economy. As shown in Figure A-2, the indirect cost change provides the industry with additional incentives to alter production rates at current market prices.
The %Δ indirect effects associated with each input are approximated using an input "use" ratio and the price change that occurs in the input market.
	%Δ indirect costs = input use ratio x %Δ input price	(A.4)
The social accounting matrix provides an internally consistent estimate of the use ratio and describes the dollar amount of an input that is required to produce a dollar of output. Higher ratios suggest strong links between industries, while lower ratios suggest weaker links. Given the short time horizon, we assume the input use ratio is fixed and cannot adjust their input mix; this is a standard assumption in public and commercial input-output (IO) and SAM multiplier models (Berck and Hoffmann, 2002). Morgenstern and colleagues (2004) and Ho and colleagues (2008) also use this assumption when examining near-term effects of environmental policy.

Figure A-1.	Direct Compliance Costs Reduce Production Rates at Benchmark Prices

Figure A-2.	Indirect Costs Further Reduce Production Rates at Benchmark Prices

Following guidance in the OAQPS economic resource manual (EPA, 1999), we use a general form for the U.S. industry supply function:
		(A.5)
where
      	=	with-policy supply quantity (g)
      	=	calibrated scale parameter for the supply price relationship
      	=	with-policy price for output (g)
      	=	direct compliance costs per unit of supply
      	=	input use ratio (g using input i)
      	=	with-policy input (i) price
      	=	benchmark input (i) price
      	=	price elasticity of supply for output (g)
The key supply parameter that controls the industry production adjustments is the price elasticity of supply (εg). To our knowledge, there is no existing empirical work that estimates short-run supply elasticities for all industry groups used in the multimarket model. As a result, we assume the U.S. supply elasticities are a function of econometrically estimated rest-of-world (ROW) export supply elasticities (see discussion in the next section). We report the values currently available in the model in Table A-5.
A.2.2.2	International Competition
International competition is captured by a single ROW supply function:
		(A.6)
where
      	=	with-policy supply quantity (g)
      c	=	calibrated scale parameter for the supply and price relationship
      	=	with-policy U.S. price for output (g)
      	=	price elasticity of supply of goods from the ROW to the United States (imports) (g)
The key supply parameter that controls the ROW supply adjustments is the price elasticity of supply (). We obtained these estimates for a variety of industry groups from a recently published article by Broda and colleagues (2008b).
A.2.2.3	Price Elasticity of Supply: Rest of World (ROW)
Broda and colleagues (2008a and 2008b) provide an empirical basis for the multimarket model supply elasticities. Broda et al. provide over 1,000 inverse elasticities that RTI organized to be comparable with the 100-sector model. The first step was to match the Harmonized Trade System (HS) elasticities estimated in the article to the appropriate NAICS codes. Many of the HS codes correspond with a detailed NAICS codes (5- and 6-digit level), while the multimarket sector industries typically correspond with more aggregated sectors (NAICS 2-, 3-, or 4-digit levels). To adapt these labels to our model, we combined the 5- and 6-digit NAICS under their 3- and 4-digit codes and calculated an average inverse elasticity value for codes that fell within the multimarket model's aggregate industrial sectors. This gives a crude way to account for the variety of products detailed in the original data set. We also restricted the elasticity sample to those that Broda et al. classify as "medium" and "low" categories; these categories tend to have lower elasticity values that are consistent with the multimarket model's modeling horizon (i.e., in the short run importers are likely to have less flexibility to respond to price changes and elasticities are low).
Table A-5.	Supply Elasticities
Industry Label
Industry Description
Rest of World (ROW)
U.S.
ACM
Accommodations
                                      0.7
                                      0.7
ADM
Administrative Services
                                      0.7
                                      0.7
AGR
Agricultural
                                      1.0
                                      1.0
ALU
Aluminum
                                      0.8
                                      0.5
ANM
Animal Foods
                                      1.1
                                      0.8
APP
Appliances
                                      0.9
                                      0.8
ARC
Aircraft
                                      0.9
                                      0.6
ART
Arts
                                      0.7
                                      0.7
ATP
Air Transportation
                                      0.7
                                      0.7
BAK
Baked Goods
                                      0.8
                                      0.7
BEV
Beverages and Tobacco
                                      2.9
                                      2.9
BOI
Boilers and Tanks
                                      1.1
                                      0.8
CEM
Cement
                                      0.9
                                      0.7
CEQ
Construction and Agricultural Equipment
                                      0.8
                                      0.6
CHM
Chemicals and Gases
                                      1.1
                                      0.8
CLY
Clay
                                      0.8
                                      0.6
CMQ
Communication Equipment
                                      2.5
                                      1.0
CNS
Construction
                                      0.7
                                      0.7
COL
Coal
                                      2.2
                                      2.2
CPP
Converted Paper Products
                                      0.9
                                      0.7
CPU
Computers
                                      1.0
                                      0.7
CRU
Crude Oil Extraction
                                      3.7
                                      3.7
CUT
Cutlery
                                      1.4
                                      1.1
EDU
Education
                                      0.7
                                      0.7
EEQ
Engines
                                      1.2
                                      1.0
EGV
Engraving
                                      1.1
                                      0.8
ELE
Electric Generation
                                      2.0
                                      2.0
ELQ
Electric Equipment
                                      0.8
                                      0.6
FIN
Finance and Insurance
                                      0.7
                                      0.7
FMP
Fabricated Metals
                                      1.2
                                      1.1
FRG
Forging and Stamping
                                      1.6
                                      1.5
(continued)
Table A-5.	Supply Elasticities (continued)
Industry Label
Industry Description
Rest of World (ROW)
U.S.
FRT
Fertilizer
                                      1.0
                                      0.7
FRU
Fruits and Vegetables
                                      1.0
                                      0.7
FUR
Furniture
                                      1.9
                                      1.9
GAS
Natural Gas
                                     12.2
                                     12.2
GEQ
General Equipment
                                      1.0
                                      0.7
GLS
Glass
                                      0.8
                                      0.6
GRN
Grain Milling
                                      1.7
                                      1.5
HLT
Health Care
                                      0.7
                                      0.7
HRD
Hardware
                                      1.1
                                      0.8
HVC
HVAC Equipment
                                      0.9
                                      0.6
I_S
Iron and Steel
                                      1.0
                                      0.6
IEQ
Industrial Equipment
                                      0.9
                                      0.6
INF
Information
                                      0.7
                                      0.7
INS
Instruments
                                      0.9
                                      0.6
LEA
Leather
                                      0.9
                                      0.7
LGT
Lighting
                                      1.1
                                      0.7
LIM
Lime and Gypsum
                                      0.9
                                      0.7
LUM
Other Lumber
                                      0.9
                                      0.7
M_V
Motor Vehicles
                                      1.3
                                      0.7
MEA
Meat Products
                                      1.2
                                      3.9
MED
Drugs and Medicine
                                      1.3
                                      1.0
MEQ
Metalworking Equipment
                                      0.7
                                      0.5
MGT
Magnetic Recording Equipment
                                      1.0
                                      0.7
MIL
Dairy Products
                                      1.1
                                      0.9
MIN
Mining
                                      2.2
                                      2.2
MNG
Management
                                      0.7
                                      0.7
MSC
Miscellaneous Manufacturing
                                      1.0
                                      0.8
MSP
Machine Shops
                                      1.1
                                      0.8
MVP
Motor Vehicle Parts
                                      0.9
                                      0.6
OCM
Other Chemicals
                                      1.1
                                      0.6
OEQ
Other Electric Equipment
                                      1.0
                                      0.7
OFD
Other Food Products
                                      1.1
                                      0.7
(continued)
Table A-5.	Supply Elasticities (continued)
Industry Label
Industry Description
Rest of World (ROW)
U.S.
OFM
Other Fabricated Metals
                                      0.9
                                      0.6
OIL
Refined Petroleum
                                      1.0
                                      0.7
ONM
Other Non-metallic Minerals
                                      1.5
                                      0.7
OPM
Other Primary Metals
                                      0.7
                                      0.5
OSV
Other Services
                                      0.7
                                      0.7
OTP
Other Transportation Services
                                      0.7
                                      0.7
OTQ
Other Transport Equipment
                                      1.0
                                      0.7
PAI
Paints and Adhesives
                                      1.0
                                      0.7
PAP
Pulp and Paper Mills
                                      1.1
                                      0.7
PFS
Professional Services
                                      0.7
                                      0.7
PIP
Pipeline Transport
                                      2.0
                                      2.0
PLS
Plastic
                                      1.0
                                      0.7
PLY
Plywood and Veneer
                                      1.3
                                      1.3
PRN
Printing
                                      1.0
                                      0.7
PUB
Public Services
                                      0.7
                                      0.7
R_R
Rail Cars
                                      1.8
                                      0.7
REL
Real Estate
                                      0.7
                                      0.7
RSN
Resins
                                      1.0
                                      0.7
RTL
Retail Trade
                                      0.7
                                      0.7
RTP
Railroad Transportation
                                      0.7
                                      0.7
RUB
Rubber
                                      1.3
                                      1.1
SAW
Sawmills
                                      0.8
                                      0.6
SEA
Seafood
                                      1.1
                                      0.8
SEQ
Service Industry Equipment
                                      0.8
                                      0.6
SGR
Sugar
                                      1.1
                                      0.8
SHP
Ships
                                      1.0
                                      0.7
SMI
Semiconductor Equipment
                                      1.2
                                      1.0
SOP
Soap
                                      0.8
                                      0.6
TEX
Textile Mills
                                      1.0
                                      0.7
TKB
Truck Bodies
                                      3.2
                                      3.1
TPM
Textile Product Mills
                                      0.8
                                      0.6
TTP
Freight Truck Transportation
                                      0.7
                                      0.7
TVQ
TV Equipment
                                      5.8
                                      5.4
(continued)
Table A-5.	Supply Elasticities (continued)
Industry Label
Industry Description
Rest of World (ROW)
U.S.
WAP
Wearing Apparel
                                      1.2
                                      0.8
WHL
Wholesale Trade
                                      0.7
                                      0.7
WIR
Springs and Wires
                                      1.9
                                      0.8
WTP
Water Transportation
                                      0.7
                                      0.7
Note:	RTI mapped Broda et al. data for their industry aggregation to the multimarket model's 100 industries. Domestic supply elasticities are typically assumed to be within one standard deviation of the sample of supply elasticities used for the ROW. In selected cases where this information is not available, the U.S. supply elasticity is set equal to the ROW.
Source:	Broda, C., N. Limao, and D. Weinstein. 2008a. "Export Supply Elasticities." http://faculty.chicagobooth.edu/christian.broda/website/research/unrestricted/TradeElasticities/TradeElasticities.html. Accessed September 2009.
Our ideal preference was to use an exact 3- or 4-digit match from the medium category if one was available. If the multimarket model had a 4-digit code for which there was no direct match, we aggregated up a level and applied the relevant 3-digit elasticity. If a multimarket code was not covered in the medium set of elasticities, we used the low elasticity category. This method was sufficient for mapping the majority of the sectors in the model. After applying our inverse elasticity values to the multimarket sectors, we calculated the inverse of the value to arrive at the actual supply elasticity. Since Broda et al.'s article focused on industrial production goods, some of the multimarket sectors were not covered in the elasticity data. These sectors included mainly service industries, transportation, and energy sources.
In order to fill these gaps, we turned to the source substitution elasticities from Purdue University's Global Trade Analysis Project (GTAP). Although the elasticities in the GTAP model are a different type of international trade elasticity and cannot be directly applied in the multimarket model (e.g., they are based on the Armington structure), the parameters provide us with some additional information about the relative trade elasticity differences between industry sectors. To use the GTAP information to develop assumptions about the multimarket model sectors with missing elasticities, we chose a base industrial sector (iron and steel) for which we had parameter value from Broda et al. Next, we developed industry-specific ratios for missing industries using the corresponding GTAP sector trade elasticities and the GTAP iron and steel sector. We multiplied the resulting ratio by the Broda et al. iron and steel parameter (1.0). For example, the GTAP trade elasticity for coal (6.10) is approximately 2.2 times the trade elasticity for iron and steel (2.95). As a result, the multimarket import supply elasticity for coal is computed as 2.2 (2.2 x 1.0).
A.2.2.4	Price Elasticity of Supply: United States
We also used Broda et al.'s elasticities to derive a set of domestic supply elasticities for the model. We have assumed that a product's domestic supply would be equal to or less elastic than other countries' supply of imports. When we aggregated and averaged the original elasticities to the 3- and 4-digit NAICS level for our foreign supply elasticities, we also calculated the standard deviation of each 3- and 4-digit NAICS sample. By adding the standard deviation to the corresponding foreign supply and then taking the inverse, we were able to calculate a domestic supply elasticity for each sector that was lower than its foreign counterpart while maintaining the structure of the original elasticities. For sectors in which no standard deviation was available, we used professional judgment to apply the closest available substitute from a similar industry. Without a comparable way of scaling our foreign elasticities for the sectors in which we used the GTAP elasticities, we elected to keep the domestic and foreign supply elasticities the same.
A.2.2.5	Demand
Uses for industry output are divided into three groups: investment/government use, domestic intermediate uses, and other final use (domestic and exports). Given the short time horizon, investment/government does not change. Intermediate use is determined by the input use ratios and the industry output decisions.
		(A.7)
      	=	with-policy input demand quantity (i)
      	=	input use ratio (g using input i)
      	=	with-policy output quantity (g)
Other final use does respond to market price changes. Following guidance in the OAQPS economic resource manual (EPA, 1999), we use a general form for the U.S. industry demand function:
		(A.8)
where
      	=	with-policy demand quantity (g)
      	=	calibrated scale parameter for the demand and price relationship
      	=	with-policy price for output (g)
      	=	price elasticity of demand (g)
The key parameter that controls consumption adjustments is the price elasticity of demand (ηg). To approximate the response, we use demand elasticities that were simulated with a general equilibrium model (Ho, Morgenstern, and Shih, 2008). Table A-6 reports the values currently available in the model.
A.2.2.6	Model Scope
The multimarket model includes 100 sectors covering energy, manufacturing, and service applications. Each sector's production technology requires the purchase of energy and other intermediate goods made by other sectors included in the model. Linking the sectors in this manner allows the model to trace direct and indirect policy effects across different sectors. Therefore, it is best used when potential economic impacts and equity effects on related markets might be important to stakeholders not directly affected by an environmental policy. However, the model can also be run in single-market partial equilibrium mode to support and provide insights for other types of environmental policies.
A.2.2.7	Model Time Horizon
The model is designed to address short-run and transitional effects associated with environmental policy. Production technologies are fixed; the model does not assess substitution among production inputs (labor, energy intermediates, and other intermediates) and assumes each investment cannot be changed during the time frame of the analysis. These issues are better addressed using other frameworks such as computable general equilibrium modeling. Similarly, government purchases from each sector do not adjust in response to changes in goods/service prices. Although, employment levels (number of jobs) adjust as production levels change, wages are assumed to be fixed.
Table A-6.	U.S. Demand Elasticities
Industry Label
Industry Description
Demand Elasticity
ηg
ACM
Accommodations
−0.7
ADM
Administrative Services
−0.7
AGR
Agricultural
−0.8
ALU
Aluminum
−1.0
ANM
Animal Foods
−0.6
APP
Appliances
−2.6
ARC
Aircraft
−2.5
ART
Arts
−0.7
ATP
Air Transportation
−0.8
BAK
Baked Goods
−0.6
BEV
Beverages and Tobacco
−0.6
BOI
Boilers and Tanks
−0.5
CEM
Cement
−0.8
CEQ
Construction and Agricultural Equipment
−1.7
CHM
Chemicals and Gases
−1.0
CLY
Clay
−0.8
CMQ
Communication Equipment
−2.6
CNS
Construction
−0.8
COL
Coal
−0.1
CPP
Converted Paper Products
−0.7
CPU
Computers
−2.6
CRU
Crude Oil Extraction
−0.3
CUT
Cutlery
−0.5
EDU
Education
−0.7
EEQ
Engines
−1.7
EGV
Engraving
−0.5
ELE
Electric Generation
−0.2
ELQ
Electric Equipment
−2.6
FIN
Finance and Insurance
−0.7
FMP
Fabricated Metals
−0.5
FRG
Forging and Stamping
−0.5
FRT
Fertilizer
−1.0
FRU
Fruits and Vegetables
−0.6
FUR
Furniture
−0.7
(continued)
Table A-6.	U.S. Demand Elasticities (continued)
Industry Label
Industry Description
Demand Elasticity
ηg
GAS
Natural Gas
−0.3
GEQ
General Equipment
−1.7
GLS
Glass
−0.8
GRN
Grain Milling
−0.6
HLT
Health Care
−0.7
HRD
Hardware
−0.5
HVC
HVAC Equipment
−1.7
I_S
Iron and Steel
−1.0
IEQ
Industrial Equipment
−1.7
INF
Information
−0.7
INS
Instruments
−2.6
LEA
Leather
−1.1
LGT
Lighting
−2.6
LIM
Lime and Gypsum
−0.8
LUM
Other Lumber
−0.7
M_V
Motor Vehicles
−2.5
MEA
Meat Products
−0.6
MED
Drugs and Medicine
−1.0
MEQ
Metalworking Equipment
−1.7
MGT
Magnetic Recording Equipment
−2.6
MIL
Dairy Products
−0.6
MIN
Mining
−0.6
MNG
Management
−0.7
MSC
Miscellaneous Manufacturing
−1.7
MSP
Machine Shops
−0.5
MVP
Motor Vehicle Parts
−2.5
OCM
Other Chemicals
−1.0
OEQ
Other Electric Equipment
−2.6
OFD
Other Food Products
−0.6
OFM
Other Fabricated Metals
−0.5
OIL
Refined Petroleum
−0.1
ONM
Other Non-metallic Minerals
−0.8
OPM
Other Primary Metals
−1.0
OSV
Other Services
−0.7
OTP
Other Transportation Services
−0.8
(continued)
Table A-6.	U.S. Demand Elasticities (continued)
Industry Label
Industry Description
Demand Elasticity
ηg
OTQ
Other Transport Equip
−2.5
PAI
Paints and Adhesives
−1.0
PAP
Pulp and Paper Mills
−0.7
PFS
Professional Services
−0.7
PIP
Pipeline Transport
−0.8
PLS
Plastic
−1.0
PLY
Plywood and Veneer
−0.7
PRN
Printing
−0.7
PUB
Public Services
−0.7
R_R
Rail Cars
−2.5
REL
Real Estate
−0.7
RSN
Resins
−1.0
RTL
Retail Trade
−0.7
RTP
Railroad Transportation
−0.8
RUB
Rubber
−1.0
SAW
Sawmills
−0.7
SEA
Seafood
−0.6
SEQ
Service Industry Equipment
−1.7
SGR
Sugar
−0.6
SHP
Ships
−2.5
SMI
Semiconductor Equipment
−2.6
SOP
Soap
−1.0
TEX
Textile Mills
−1.1
TKB
Truck Bodies
−2.5
TPM
Textile Product Mills
−1.1
TTP
Freight Truck Transportation
−0.8
TVQ
TV Equipment
−2.6
WAP
Wearing Apparel
−2.4
WHL
Wholesale Trade
−0.7
WIR
Springs and Wires
−0.5
WTP
Water Transportation
−0.8
Note: RTI assigned an elasticity using the most similar industry from Ho and colleagues' industry aggregation.
Source:	Ho, M. S, R. Morgenstern, and J. S. Shih. 2008. "Impact of Carbon Price Policies on US Industry." RFF Discussion Paper 08-37. Http://Www.Rff.Org/Publications/Pages/Publicationdetails.Aspx?. Publicationid=20680. Accessed August 2009. Table B.6.
A.3	Appendix A References
Berck, P., and S. Hoffmann. 2002. "Assessing the Employment Impacts of Environmental and Natural Resource Policy." Environmental and Resource Economics 22(1):133-156.
Broda, C., N. Limao, and D.E. Weinstein. 2008a. "Optimal Tariffs and Market Power: The Evidence." American Economic Review 98(5):2032-2065.
Broda, C., N. Limao, and D. Weinstein. 2008b. "Export Supply Elasticities." <http://faculty.
chicagobooth.edu/christian.broda/website/research/unrestricted/TradeElasticities/
TradeElasticities.html.>
Ho, M.S., R. Morgenstern, and J.S. Shih. 2008. "Impact of Carbon Price Policies on US Industry" (RFF Discussion Paper 08-37). <http://www.Rff.Org/Publications/Pages/Publicationdetails.Aspx?. Publicationid=20680>.
Just, R.E., D.L. Hueth, and A. Schmitz. 2004. The Welfare Economics of Public Policy. Northampton, MA: Edward Elgar.
Morgenstern, R.D., M. Ho, J.S. Shih, and X. Zhang. 2004. "The Near-Term Impacts of Carbon Mitigation Policies on Manufacturing Industries." Energy Policy 32(16): 1825-1841.
Shoven, J.B., and J. Whalley. 1995. Applying General Equilibrium. New York: Cambridge University Press.
U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards. 1999. OAQPS Economic Analysis Resource Document. http://www.epa.gov/ttn/ecas/analguid.html.
U.S. Environmental Protection Agency. 2000. "Guidelines for Preparing Economic Analyses" (EPA 240-R-00-003). <http://yosemite.epa.gov/ee/epa/eed.nsf/webpages/Guidelines.html/$file/cover.pdf>.

Appendix B
Detailed Economic Model Results by Sector

Table B-1.	Prices (Percentage Change from Benchmark): Industry Detail

                                     Major

                                     Area

                                Selected Option
                              Alternative Option

                                Selected Option
                              Alternative Option
Energy
0.0279%
0.0228%

−0.0001%
−0.0001%
Nonmanufacturing
0.0007%
0.0054%

0.0013%
0.0016%
Manufacturing

Food, beverages, and textiles
0.0106%
0.0118%

0.0016%
0.0030%