From: Culligan, Kevin [mailto:Culligan.Kevin@epa.gov] 

Sent: Monday, May 26, 2014 4:37 PM 

To: Higgins, Cortney 

Cc: Wayland, Robertj 

Subject: FW: EGU GHG Existing Source RIA Chapter 4 

For tomorrow's 10:00 benefits discussion. 

<< E.O. 12866 Chapter_4_Existing_EGU_Benefits_5.26.14.docx >> 
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CHAPTER 4: ESTIMATED CLIMATE BENEFITS AND HUMAN 

HEALTH CO-BENEFITS 

4.1 Introduction 

Implementing the proposed Emission Guidelines for Greenhouse Gas (GHG) Emissions 

from Existing Stationary Sources: Electric Utility Generating Units (EGUs) (hereafter, EGU 

GHG Existing Source Guidelines) is expected to reduce emissions of carbon dioxide (CO2) and 

have ancillary human health benefits (i.e., co-benefits) associated with lower ambient 

concentrations of criteria air pollutants. This chapter describes the methods used to estimate the 

monetized climate benefits and the monetized air pollution health co-benefits associated with 

reducing exposure to ambient fine particulate matter (PM2.5) and ozone by reducing emissions of 

precursor pollutants (i.e., sulfur dioxide (SO2), nitrogen dioxide (NO2), and directly emitted 

PM2.5). Data, resource, and methodological limitations prevent EPA from monetizing the 

benefits from several important co-benefit categories, including reducing direct exposure to SO2, 

NO2, and hazardous air pollutants (HAP), as well as ecosystem effects and visibility impairment. 

We qualitatively discuss these unquantified benefits in this chapter. 

This chapter provides estimates of the monetized climate benefits and air pollution health 

co-benefits associated with emission reductions for two options with two illustrative compliance 

scenarios across several analysis years and discount rates. The estimated benefits associated with 

these emission reductions are beyond those achieved by previous EPA rulemakings, including 

the Mercury and Air Toxics Standards (MATS). 

4.2 Estimated Climate Benefits from CO2

The primary goal of the proposed guidelines is to reduce emissions of CO2. In this section, 

we provide an overview of the public health and public welfare effects associated with climate 

change. We also provide information regarding the economic valuation of CO2 using the Social 

Cost of Carbon (SCC), a metric that estimates the monetary value of impacts associated with 

marginal changes in CO2 emissions in a given year. Table 4-1 summarizes the quantified and 

unquantified climate benefits in this analysis. 
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 Table 4-1. Climate Effects 

Benefits 

Category 

Specific Effect Effect Has Been 

Quantified 

Effect Has Been 

Monetized 

More 

Information 

Improved Environment 

Reduced 

climate effects 

Global climate impacts from CO2 —  SCC TSD 

Climate impacts from ozone and black 

carbon (directly emitted PM) 

— — Ozone ISA, PM 

ISA*

Other climate impacts (e.g., other GHGs 

such as methane, aerosols, other impacts) 

— — IPCC*

* We assess these co-benefits qualitatively because we do not have sufficient confidence in available data or 

methods. 

4.2.1 Public Health Effects from Climate Change 

Climate change threatens public health in a number of ways: direct temperature effects, the 

effect of higher atmospheric CO2 concentrations on characteristics of air quality, changes in 

vector-borne diseases, and changes in the severity and frequency of extreme weather events. This 

section summarizes the 2009 Endangerment Finding’s conclusions for each of these effects in 

turn.1

Heat is already the leading cause of weather-related deaths in the United States, and heat 

waves are associated with marked short-term increases in mortality and morbidity. It has already 

been observed that unusually hot days and heat waves are becoming more frequent, and that 

unusually cold days are becoming less frequent. In the future, severe heat waves are projected to 

further intensify in magnitude and duration over the portions of the United States where these 

events have already been observed. If this observed warming trend continues as projected, it is 

expected that heat-related mortality and morbidity will increase. Different segments of the 

population are sensitive for different reasons, with older adults, the chronically sick, the very 

young, city-dwellers, and those taking medications that disrupt thermoregulation being the most 

sensitive to hot temperatures. Other groups that have also been found to be sensitive to hot 

temperatures are the mentally ill, those lacking access to air conditioning, those working or 

playing outdoors, and socially isolated persons. With continued warming over time, these 

 

1

 For more information on the impacts of climate change see the 2009 Endangerment Finding. “Endangerment and 

Cause or Contribute Findings for Greenhouse Gases Under Section 202(a) of the Clean Air Act,” 74 Fed. Reg. 

66,496 (Dec. 15, 2009). http://www.epa.gov/climatechange/endangerment/ 

Comment [A1]: OMB comment: As noted 

separately, we want to discuss the language section 

and make sure we have consistent and agreed upon 

language, similar to what we’ve done with the PM 

benefits language. 

EPA Response: EPA is happy to discuss further. 

Please note that we have updated the parallel section 

in the preamble to include, for example, a brief 

discussion of peer-reviewed assessments published 

after the 2009 Endangerment Finding. We anticipate 

carrying these edits to the relevant section of RIA 

Section 4.2.1 (benefits chapter) 

Comment [A2]: OMB Comment: Citation? 

EPA Response: Please see paragraph above, which 

states that the entire section summarizes the 2009 

Endangerment Finding. 
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adverse effects would be expected to increase as serious heat events become more frequent, 

prolonged, and extreme. 

Conversely, increases in temperature are also expected to lead to some reduction in the risk 

of death, related to extreme cold. Evidence suggests that the net impact of changes in heat and 

cold mortality is likely to be adverse. The latest report from At the time of the Finding, the U.S. 

Global Change Research Program (USGCRP, 2009) referenceds a study which by MedinaRamon and Schwartz (2007) that analyzed daily mortality and weather data in 50 U.S. cities 

from 1989 to 2000. This report found that, on average, cold snaps in the United States increased 

death rates by 1.6 percent, while heat waves triggered a 5.7 percent increase in death rates. While 

a single study is not conclusive, this study finds that increases in heat-related mortality, due to 

global warming in the United States, are likely to be greater than decreases in cold-related 

mortality found that reductions in deaths related to the projected decline in extreme cold events 

were not expected to compensate for the projected increase in heat-related deaths: the most 

recent report (USGRP, 2014) has further confirmed that conclusion. The tendency for mortality 

rates to increase in winter occurs for reasons which are not strongly correlated with cold 

temperatures, such as influenza. Analyses of the effects of warmer winters on mortality must 

consider these relationships to avoid overestimating the effects of temperature increases during 

that season. 

Climate change is expected to increase regional ozone pollution, relative to ozone levels in 

the absence of climate change. Increased levels of ozone are associated with risks of respiratory 

illnesses and premature death. In addition to human health effects, tropospheric ozone has 

significant adverse effects on crop yields, pasture and forest growth, and the composition of plant 

and animal species populations, pasture and forest growth, and the composition of plant and 

animal species populations. 

Peer reviewed modeling studies discussed in EPA’s Interim Assessment (U.S. EPA, 2009a) 

show that climate change is likely to cause increases in summertime ozone concentrations over 

substantial regions of the country, especially on the highest ozone days and in the largest 

metropolitan areas with the worst ozone problems. This effect was not uniform across the 

country, with some areas projected to have little change or slight decreases, though the decreases 
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are expected to be less pronounced and occur in less populous regions than the increases. The 

key metric for regulating U.S. air quality is the maximum daily 8-hour average ozone 

concentration. For those regions where modeling projected climate-induced increases, the 

increase in 2050 was in the range of 2 to 8 ppb, averaged over the summer season. The increases 

were substantially greater than 2 to 8 ppb during the peak pollution episodes that tend to occur 

over a number of days each summer. 

In addition to direct temperature health effects and climate change induced air quality 

effects, there is also the potential for increased mortality rates, injuries, infectious diseases, and 

stress-related disorders resulting from more frequent extreme weather events. 

Climate change is expected to lead to an increase in the frequency of heavy precipitation 

events, which would be associated with increased risk of deaths and injuries, as well as 

infectious, respiratory and skin diseases. Floods are low-probability, high-impact events that can 

overwhelm physical infrastructure, human resilience, and social organization. Floods can result 

in health impacts such as deaths, injuries, infectious diseases, toxic contamination, and mental 

health problems. 

Climate change is expected to lead to increases tropical cyclone intensity (hurricanes and 

tropical storms). Increases in tropical cyclone intensity are linked to increases in the risk of 

deaths, injuries, waterborne and food borne diseases, as well as post-traumatic stress disorders. 

Storm surge is the leading cause of death in coastal storms, and the risk of death by drowning 

due to surge is expected to increase with rising sea levels and increased storm intensity as the 

result of climate change. Projected increases in flooding due to increased intensity of cyclonic 

events is also expected to lead to adverse public health impacts as noted above. 

According to the assessment literature, under changing climate and environmental 

conditions there will also likely be an increase in the spread of serial episodes of food and waterborne pathogens among susceptible populations, depending on the pathogens’ survival, 

persistence, habitat range and transmission. Food borne diseases show some relationship with 

temperature. The range of some zoonotic disease carriers, such as the Lyme disease-carrying 

tick, may increase with temperature. 
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Increases in atmospheric CO2 concentrations directly as well as changes in the climate are 

expected to stimulate plants to increase photosynthesis, biomass, water use efficiency, and 

reproductive effort resulting in greater growth and distribution of weeds, grasses, and trees. Such 

increases could lead to greater production, distribution, dispersion, and allergenicity of 

aeroallergens. These changes in aeroallergens and subsequent human exposures could affect the 

prevalence and severity of allergy symptoms. However, the scientific literature does not provide 

definitive data or conclusions on how climate change might impact aeroallergens and 

subsequently the prevalence of allergenic illnesses in the U.S. The IPCC concluded that pollens 

are likely to increase with elevated temperature and CO2 (IPCC, 2007). 

4.2.2 Public Welfare Effects from Climate Change 

As with public health, there are multiple pathways in which the greenhouse gas air 

pollution and resultant climate change affect climate-sensitive economic sectors and 

environmental media. These sectors include agriculture; forestry; water resources; sea level rise 

in coastal areas; energy, infrastructure, and settlements; and ecosystems and wildlife. Impacts 

from climate change will also occur outside of the United States. This section summarizes the 

2009 Endangerment Finding’s conclusions for each of these effects.2

As noted above, elevated CO2 concentrations and modest temperature increases may 

stimulate plants to increase photosynthesis, biomass, water use efficiency, and reproductive 

effort. These impacts and the potential for longer growing seasons may result in increased 

agricultural productivity. But, the same effects may also enhance pest and weed growth. Further 

temperature increases, possible changes to precipitation patterns and variability, and any 

increases in ground-level ozone induced by higher temperatures are expected to reduce 

agricultural productivity. A USGCRP report (2009) concluded that while for some crops such as 

grain and oilseed crops there may be, on net, a beneficial effect over the next couple decades, as 

temperature rises, these crops will begin to experience failure, especially if climate variability 

increases and precipitation lessens or becomes more variable. Changes in the intensity and 

frequency of extreme weather events, such as droughts and heavy storms, have the potential to 

 

2

 “Endangerment and Cause or Contribute Findings for Greenhouse Gases Under Section 202(a) of the Clean Air 

Act,” 74 Fed. Reg. 66,496 (Dec. 15, 2009) (“Endangerment Finding”). 
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negatively impact U.S. agriculture. Changes in precipitation patterns, in addition to increasing 

temperatures and longer growing seasons, can also change the demand for irrigation 

requirements and could potentially increase irrigation demand. 

With respect to livestock, higher temperatures will very likely reduce livestock production 

during the summer season in some areas, but these losses will very likely be partially offset by 

warmer temperatures during the winter season. The impact on livestock productivity due to 

increased variability in weather patterns, will likely be far greater than the effects associated with 

an absolute change in average climatic conditions. 

For the forestry sector, the effects are similar to the agriculture sector. There is the 

potential for beneficial effects due to elevated concentrations of carbon dioxide, modest 

temperature increases, and nitrogen deposition, but there is also the potential for adverse effects 

from higher temperature increases, changing precipitation patterns, increased populations and 

spread of insects and disease, and the potential for more frequent and severe extreme weather 

events. According to the science assessment reports upon which the Administrator relied for the 

2009 Endangerment Finding, climate change has very likely increased the size and number of 

wildfires, insect outbreaks, and increased tree mortality in the Interior West, the Southwest, and 

Alaska, and will continue to do so. 

If existing trends in precipitation continue, it is expected that forest productivity will likely 

decrease in the Interior West, Southwest, eastern portions of the Southeast and Alaska. Forest 

productivity will likely increase in the northeastern United States, Lake States, and in western 

portions of the Southeast. An increase in forecast drought events, as a result of climate change, is 

also likely to reduce forest productivity where such events occur. 

The connection of water resources to climate change is very important, given the 

increasing demand for adequate water supplies and services for agricultural, municipal, energy, 

and industrial uses, coupled with existing strains on this scarce resource in many parts of the 

country. According to the assessment literature, climate change has already altered, and will 

likely continue to alter the water cycle, affecting where, when, and how much water is available 

for all uses. With higher temperatures, the water-holding capacity of the atmosphere and 
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evaporation into the atmosphere increase, and this favors increased climate variability, with more 

intense precipitation and more droughts. 

Climate change is and will continue to cause the shrinking of the snowpack, induced by 

increasing temperatures. In the western United States, there is already well-documented evidence 

of a shrinking snowpack due to climate warming. Earlier meltings, with increased runoff in the 

winter and early spring, increase flood concerns and also result in substantially decreased 

summer flows. This pattern of reduced snowpack and changes to the flow patterns pose serious 

risks to major population regions, such as California, which rely on snowmelt-dominated 

watersheds for their water supply. Increased precipitation is expected to increase total water flow 

in some eastern areas leading to increased flood risk and potential water pollution. Despite the 

increased average flow, increased variability may still enable periods of drought. 

Climate change will likely further constrain over-allocated water resources in some regions 

of the United States, increasing competition among agricultural, municipal, industrial, and 

ecological uses. Increased incidence of extreme weather and floods may also overwhelm or 

damage water treatment and management systems, resulting in water quality problems. 

According to the assessment literature, sea level is rising along much of the U.S. coast and 

the rate of change will very likely increase in the future, exacerbating the impacts of progressive 

inundation, storm-surge flooding, and shoreline erosion. A large percentage of the U.S. 

population lives in these coastal areas. The most vulnerable areas are the Atlantic and Gulf 

Coasts, the Pacific Islands, and parts of Alaska. Cities such as New Orleans, Miami, and New 

York are particularly at risk, and could have difficulty coping with the sea level rise projected by 

the end of the century under a higher emissions scenario. Population growth and the rising value 

of infrastructure increases the vulnerability of coastal areas to climate variability and future 

climate change. Adverse impacts on islands present concerns for Hawaii and the U.S. territories. 

Reductions in Arctic sea ice increases extreme coastal erosion in Alaska, due to the increased 

exposure of the coastline to strong wave action. In the Great Lakes, where sea level rise is not a 

concern, both extremely high and low water levels, resulting from changes to the hydrological 

cycle, have been damaging and disruptive to shoreline communities. 
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Coastal wetland loss is being observed in the United States where these ecosystems are 

squeezed between natural and artificial landward boundaries and rising sea levels. Up to 21 

percent of the remaining coastal wetlands in the U.S. mid-Atlantic region are potentially at risk 

of inundation between 2000 and 2100. Stress will increase on coastal habitats through the 

interaction of climate change with development and pollution related to development. 

Although increases in mean sea level over the 21st century and beyond are projected to 

inundate unprotected, low-lying areas, the most devastating impacts are likely to be associated 

with storm surge. In combination, expected rates of sea level rise, projected storm intensity, 

wave height, and storm surge all suggest more severe coastal flooding and erosion hazards. 

Higher sea level provides an elevated base from which storm surges occur and diminishes the 

rate at which low-lying areas drain, increasing the risk of flooding from rainstorms. In New York 

City and Long Island, flooding from a combination of sea level rise and storm surge could be 

several meters deep. Projections suggest that the recurrence period of a 100-year flood event in 

this area might be reduced to 4–60 years by the 2080s. Additionally, some major urban centers in 

the U.S., such as areas of New Orleans are situated in low-lying flood plains, presenting 

increased risk from storm surges. 

With respect to infrastructure, climate change vulnerabilities of industry, settlement, and 

society are more related to changes in the intensity and frequency of extreme weather events than 

gradual changes in temperature. Extreme weather events could threaten U.S. energy 

infrastructure (transmission and distribution), transportation infrastructure (roads, bridges, 

airports and seaports), water infrastructure, and other built aspects of human settlements. 

Moreover, soil subsidence caused by the melting of permafrost in the Arctic region is a risk to 

gas and oil pipelines, electrical transmission towers, roads, and water systems. 

Within settlements experiencing climate change stressors, certain parts of the population 

may be especially vulnerable based on their circumstances. These include the poor, the elderly, 

the very young, those already in poor health, the disabled, those living alone, and/or indigenous 

populations dependent on one or a few resources. In Alaska, indigenous communities are likely 

to experience disruptive impacts, including shifts in the range or abundance of wild species 

crucial to their livelihoods and well-being. 
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Climate change is exerting major influences on natural environments and biodiversity, and 

these influences are generally expected to grow with increased warming. Observed changes in 

the life cycles of plants and animals include shifts in habitat ranges, timing of migration patterns, 

and changes in reproductive timing and behavior. 

The underlying assessment literature finds with high confidence that substantial changes in 

the structure and functioning of terrestrial ecosystems are very likely to occur with global 

warming greater than 2 to 3 °C above pre-industrial levels, with predominantly negative 

consequences for biodiversity and the provisioning of ecosystem goods and services. With global 

average temperature changes above 2 °C, many terrestrial, freshwater, and marine species 

(particularly endemic species), are at far greater risk of extinction than in the geological past. 

Climate change and ocean acidification will likely impair a wide range of planktonic and other 

marine calcifiers such as corals. Even without ocean acidification effects, increases in sea surface 

temperature of about 1 to 3 °C are projected to result in more frequent coral bleaching events and 

widespread coral mortality. In the Arctic, wildlife faces great challenges from the effects of 

climatic warming, as projected reductions in sea ice will drastically shrink marine habitat for 

polar bears, ice-inhabiting seals, and other animals. 

Some common forest types are projected to expand, others are projected to contract, and 

others, such as spruce-fir, are likely to disappear from the contiguous United States. Changes in 

plant species composition in response to climate change can increase ecosystem vulnerability to 

other disturbances, including wildfires and biological invasion. Disturbances such as wildfires 

and insect outbreaks are increasing in the United States and are likely to intensify with warmer 

winters, drier soils and longer growing seasons in the future. The areal extent of drought-limited 

ecosystems is projected to increase 11 percent per °C warming in the United States. In 

California, temperature increases greater than 2 °C may lead to conversion of shrubland into 

desert and grassland ecosystems and evergreen conifer forests into mixed deciduous forests. 

Greater intensity of extreme events may alter disturbance regimes in coastal ecosystems leading 

to changes in diversity and ecosystem functioning. Species inhabiting salt marshes, mangroves, 

and coral reefs are likely to be particularly vulnerable to these effects. 
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According to the USGCRP report of June 2009 and other sources, climate change impacts 

in certain regions of the world may exacerbate problems that raise humanitarian, trade, and 

national security issues for the United States.3

 The IPCC (2007) identifies the most vulnerable 

regions of the world as: 

1. Africa, especially the sub-Saharan region, because of current low adaptive capacity as 

well as climate change 

2. Small islands due to high exposure of population and infrastructure to risk of sea-level 

rise and increased storm surge 

3. Asian mega-deltas due to large populations and high exposure to sea level rise, storm 

surge, and river flooding 

4. The Arctic because of the effects of high rates of projected warming on natural 

systems 

Climate change has been described as a potential threat multiplier with regard to national 

security issues. While some of these international risks do not readily lend themselves to precise 

analyses or future projections, given the unavoidable global nature of the climate change 

problem it is appropriate and prudent to consider how impacts in other world regions may 

present risks to the U.S. population.

4.2.3 Climate Change Impacts in New Assessments 

A number of major peer-reviewed scientific assessments have been released since the 

administrative record concerning the Endangerment Finding closed following the EPA’s 2010 

Reconsideration Denial. These assessments include the IPCC’s 2012 “Special Report on 

Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation” 

(SREX) and 2013-14 Fifth Assessment Report (AR5), the USGCRP’s 2014 “Climate Change 

Impacts in the United States” (Climate Change Impacts), and the NRC’s 2010 “Ocean 

Acidification: A National Strategy to Meet the Challenges of a Changing Ocean” (Ocean 

Acidification), 2011 “Report on Climate Stabilization Targets: Emissions, Concentrations, and 

 

3

 ‘‘In an increasingly interdependent world, U.S. vulnerability to climate change is linked to the fates of other 

nations. For example, conflicts or mass migrations of people resulting from food scarcity and other resource 

limits, health impacts or environmental stresses in other parts of the world could threaten U.S. national 

security.’’ (Karl et al., 2009). 

Formatted: 03 Second-Level Subheading BOLD

Comment [A3]: EPA Response: This section 

was added in response to comments on the Preamble 

requesting more discussion of recent assessments. 

EPA would be happy to discuss this addition. 

Note that we continue to rely most heavily on the 

Endangerment Finding itself, as it is well-vetted and 

Court-tested and is a key part of the justification for 

the regulation of GHGs, and as the conclusions of 

the Finding have generally been confirmed by 

subsequent assessments. 

Formatted: Default Paragraph Font
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Impacts over Decades to Millennia” (Climate Stabilization Targets), 2011 ““National Security 

Implications for U.S. Naval Forces” (National Security Implications), 2011 “Understanding 

Earth’s Deep Past: Lessons for Our Climate Future” (Understanding Earth’s Deep Past), 2012 

“Sea Level Rise for the Coasts of California, Oregon, and Washington: Past, Present, and 

Future”, 2012 “Climate and Social Stress: Implications for Security Analysis” (Climate and 

Social Stress), and 2013 “Abrupt Impacts of Climate Change” (Abrupt Impacts) assessments. 

The EPA has reviewed these assessments and finds that in general, the improved 

understanding of the climate system they present strengthens the case that GHGs endanger 

public health and welfare. 

The IPCC report and three of the new NRC assessments provide estimates of projected 

global sea level rise that are larger than, and in some cases more than twice as large as, the rise 

estimated in a 2007 IPCC assessment of between 0.18 and 0.59 meters by the end of the century, 

relative to 1990. (It should be noted that in 2007, the IPCC stated that including poorly 

understood ice sheet processes could lead to an increase in the projections.) While these NRC 

and IPCC assessments continue to recognize and characterize the uncertainty inherent in 

accounting for ice sheet processes, these revised estimates strongly support and strengthen the 

existing finding that GHGs are reasonably anticipated to endanger public health and welfare. 

Other key findings of the recent assessments are described briefly below. 

According to the IPCC in the SREX, “A changing climate leads to changes in the 

frequency, intensity, spatial extent, duration, and timing of extreme weather and climate events, 

and can result in unprecedented extreme weather and climate events. ” The SREX documents 

observational evidence of changes in some weather and climate extremes that have occurred 

globally since 1950. The assessment also provides evidence regarding the cause of some of these 

changes to elevated concentrations of GHGs, including warming of extreme daily temperatures, 

intensified extreme precipitation events, and increases in extreme coastal high water levels due to 

rising sea level. The SREX projects further increases in some extreme weather and climate 

events during the 21st century. Combined with increasing vulnerability and exposure of 

populations and assets, changes in extreme weather and climate events have consequences for 
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disaster risk, with particular impacts on the water, agriculture and food security and health 

sectors. 

In the Climate Stabilization Targets assessment, the NRC states: 

Emissions of carbon dioxide from the burning of fossil fuels have ushered in a new epoch 

where human activities will largely determine the evolution of Earth’s climate. Because carbon 

dioxide in the atmosphere is long lived, it can effectively lock Earth and future generations into a 

range of impacts, some of which could become very severe. 

The assessment concludes that carbon dioxide emissions will alter the atmosphere’s 

composition and therefore the climate for thousands of years; and attempts to quantify the results 

of stabilizing GHG concentrations at different levels. The report also projects the occurrence of 

several specific climate change impacts, finding warming could lead to increases in heavy 

rainfall and decreases in crop yields and Arctic sea ice extent, along with other significant 

changes in precipitation and stream flow. For an increase in global average temperature of 1 to 2 

oC above pre-industrial levels, the assessment found that the area burnt by wildfires in western 

North America will likely more than double and coral bleaching and erosion will increase due 

both to warming and ocean acidification. An increase of 3 oC will lead to a sea level rise of 0.5 to 

1 meter by 2100. With an increase of 4 oC, the average summer in the United States would be as 

warm as the warmest summers of the past century. The assessment notes that although many 

important aspects of climate change are difficult to quantify, the risk of adverse impacts is likely 

to increase with increasing temperature, and the risk of surprises can be expected to increase with 

the duration and magnitude of the warming. 

The NRC assessment on Sea Level Rise projects a global sea level rise of 0.5 to 1.4 meters 

by 2100, which is sufficient to lead to rising relative sea level even in the northern states. 

Another assessment considers potential impacts of sea level rise and suggests that “the 

Department of the Navy should expect roughly 0.4 to 2 meters global average sea-level rise by 

2100. This assessment also recommends preparing for increased needs for humanitarian aid; 

responding to the effects of climate change in geopolitical hotspots, including possible mass 
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migrations; and addressing changing security needs in the Arctic as sea ice retreats. A third NRC 

assessment found that it would be “prudent for security analysts to expect climate surprises in the 

coming decade . . . and for them to become progressively more serious and more frequent 

thereafter[.]” 

Another NRC assessment finds that “the magnitude and rate of the present greenhouse gas 

increase place the climate system in what could be one of the most severe increases in radiative 

forcing of the global climate system in Earth history.” This assessment finds that CO2 

concentrations by the end of the century, without a reduction in emissions, are projected to 

increase to levels that Earth has not experienced for more than 30 million years. 

Similarly, an NRC assessment on ocean acidification finds that “[t]he chemistry of the 

ocean is changing at an unprecedented rate and magnitude due to anthropogenic carbon dioxide 

emissions; the rate of change exceeds any known to have occurred for at least the past hundreds 

of thousands of years.” The assessment notes that the full range of consequences is still 

unknown, but the risks “threaten coral reefs, fisheries, protected species, and other natural 

resources of value to society.” 

The most recent assessments to be released were the IPCC AR5 assessments between 

September 2013 and April 2014, the NRC Abrupt Impacts assessment in December of 2013, and 

the USGCRP Climate Impacts assessment in May of 2014. The NRC Abrupt Impacts report 

examines the potential for tipping points, thresholds beyond which major and rapid changes 

occur in the Earth’s climate system or other systems impacted by the climate. The Abrupt 

Impacts report did find less cause for concern than some previous assessments regarding some 

abrupt events within the next century such as disruption of the Atlantic Meridional Overturning 

Circulation (AMOC) and sudden releases of high-latitude methane from hydrates and 

permafrost , but found that the potential for abrupt changes in ecosystems, weather and climate 

extremes, and groundwater supplies critical for agriculture now seem more likely, severe, and 

imminent. The assessment found that some abrupt changes were already underway (Arctic sea 

ice retreat and increases in extinction risk due to the speed of climate change), but cautioned that 

even abrupt changes such as the AMOC disruption that are not expected in this century can have 

severe impacts when they happen. 
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The IPCC AR5 assessments also generally confirm and strengthen previous findings. For 

example, confidence in attributing recent warming to human causes has increased: the IPCC 

stated that it is extremely likely (>95% confidence) that human influences have been the 

dominant cause of recent warming. Moreover, the IPCC found that the last 30 years were likely 

(>66% confidence) the warmest 30 year period in the Northern Hemisphere of the past 1400 

years, that the rate of ice loss of worldwide glaciers and the Greenland and Antarctic ice sheets 

has likely increased, that there is medium confidence that the recent summer sea ice retreat in the 

Arctic is larger than has been in 1450 years, and that concentrations of carbon dioxide and 

several other of the major greenhouse gases are higher than they have been in at least 800,000 

years. Climate-change induced impacts have been observed in changing precipitation patterns, 

melting snow and ice, species migration, negative impacts on crops, increased heat and 

decreased cold mortality, and altered ranges for water-borne illnesses and disease vectors. 

Additional risks from future changes include death, injury, and disrupted livelihoods in coastal 

zones and regions vulnerable to inland flooding, food insecurity linked to warming, drought, and 

flooding, especially for poor populations, reduced access to drinking and irrigation water for 

those with minimal capital in semi-arid regions, and decreased biodiversity in marine 

ecosystems, especially in the Arctic and tropics, with implications for coastal livelihoods. The 

IPCC determined that continued emissions of greenhouse gases will cause future warming and 

changes in all components of the climate system, and that limiting climate change will require 

substantial and sustained reductions of greenhouse gases. 

Finally, the recently released USGCRP “Climate Change Impacts” assessment emphasizes 

that climate change is already happening now and it is happening in the United States. The 

assessment documents the increases in some extreme weather and climate events in recent 

decades, the damage and disruption to infrastructure and agriculture, and projects continued 

increase and more severe changes across a wide range of peoples, sectors, and ecosystems. 

The EPA plans to continue relying heavily on the major assessments by the USGCRP, the IPCC, 

and the NRC to summarize the state of climate science and to inform EPA actions related to 

climate change. Assessments from these bodies represent the current state of knowledge, 

comprehensively cover and synthesize thousands of individual studies to obtain the majority 

Formatted: Indent: First line: 0"
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4-15 

conclusions from the body of scientific literature and undergo a rigorous and exacting standard 

of review by the peer expert community and U.S. government.

4.2.43 Social Cost of Carbon 

We estimate the global social benefits of CO2 emission reductions expected from the 

proposed guidelines using the federal government’s SCC estimates presented in the 2013 

Technical Support Document: Technical Update of the Social Cost of Carbon for Regulatory 

Impact Analysis Under Executive Order 12866 (2013 SCC TSD).

4 We refer to these estimates, 

which were developed by the U.S. government, as “SCC estimates.” The SCC is a metric that 

estimates the monetary value of impacts associated with marginal changes in CO2 emissions in a 

given year. It includes a wide range of anticipated climate impacts, such as net changes in 

agricultural productivity and human health, property damage from increased flood risk, and 

changes in energy system costs, such as reduced costs for heating and increased costs for air 

conditioning. It is typically used to assess the avoided damages as a result of regulatory actions 

(i.e., benefits of rulemakings that have an incremental impact on cumulative global CO2

emissions). 

The federal SCC estimates of SCC used in this analysis were developed over many years, 

using the best science available, and with input from the public. The EPA and other federal 

agencies have considered the extensive public comments on ways to improve SCC estimation 

received via the notice and comment period that was part of numerous rulemakings since 2006. 

In addition, OMB’s Office of Information and Regulatory Affairs recently sought public 

 

4 Docket ID EPA-HQ-OAR-2013-0495, Technical Support Document: Technical Update of the Social Cost of 

Carbon for Regulatory Impact Analysis Under Executive Order 12866, Interagency Working Group on Social 

Cost of Carbon, with participation by Council of Economic Advisers, Council on Environmental Quality, 

Department of Agriculture, Department of Commerce, Department of Energy, Department of Transportation, 

Environmental Protection Agency, National Economic Council, Office of Energy and Climate Change, Office of 

Management and Budget, Office of Science and Technology Policy, and Department of Treasury (May 2013, 

Revised November 2013). Available at: 

http://www.whitehouse.gov/sites/default/files/omb/assets/inforeg/technical-update-social-cost-of-carbon-forregulator-impact-analysis.pdf.The federal government concluded that a global measure of the benefits from 

reducing U.S. GHG emissions should be the standard practice when conducting regulatory impact analysis in 

support of federal rule makings. See Technical Support Document: Technical Update of the Social Cost of 

Carbon for Regulatory Impact Analysis Under Executive Order 12866, Interagency Working Group on Social 

Cost of Carbon (2013) and Technical Support Document: Technical Update of the Social Cost of Carbon for 

Regulatory Impact Analysis Under Executive Order 12866, Interagency Working Group on Social Cost of 

Carbon (2010). 

Formatted: Font: Italic

Comment [A4]: OMB comment: Vis a vis 

footnote 59 See edits to exec summary – referring to 

this is the “federal government” rather than the TSD 

by name, in the way you’d refer to any E.O. 

guidance (e.g., A-4) is awkward. 

EPA Response: As noted in edits to the Executive 

Summary, EPA has deleted labels of “federal gov’t” 

and replaced with a complete definition referencing 

the TSD. 
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-16 

comment on the approach used to develop the SCC estimates. The comment period ended on 

February 26, 2014, and OMB is reviewing the comments received. 

An interagency process that included the EPA and other executive branch entities used 

three integrated assessment models (IAMs) to develop SCC estimates and selected four global 

values for use in regulatory analyses. The SCC estimates were first released in February 2010 

and updated in 2013 using new versions of each IAM. 5 

The SCC estimates represent global measures because of the distinctive nature of the 

climate change problem. The climate change problem is highly unusual in at least two respects. 

First, emissions of most GHGs contribute to damages around the world even when they are 

emitted in the United States. The SCC must therefore incorporate the full (global) damages 

caused by GHG emissions in order to address the global nature of the problem. Second, climate 

change presents a problem that the United States along cannot solve. The US now operates in a 

global, highly interconnected economy such that impacts on the other side of the world now 

affect our economy. Climate damages in other countries can affect U.S. companies. Climateexacerbated conflict can require military expenditures by the U.S. All of this means that the true 

cost of climate change to U.S. is much larger than impacts that simply occur in the U.S. Climate 

change presents a problem that the United States alone cannot solve. A global number is the 

economically appropriate reference point for collective actions to reduce climate change. 

A key objective in the development of the SCC estimates was to enable a consistent 

exploration of three integrated assessment models (DICE, FUND, and PAGE)6 while respecting 

the different approaches to quantifying damages taken by the key modelers in the field. The 

 

5

 Docket ID EPA-HQ-OAR-2009-0472-114577, Technical Support Document: Social Cost of Carbon for 

Regulatory Impact Analysis Under Executive Order 12866, Interagency Working Group on Social Cost of 

Carbon, with participation by Council of Economic Advisers, Council on Environmental Quality, Department of 

Agriculture, Department of Commerce, Department of Energy, Department of Transportation, Environmental 

Protection Agency, National Economic Council, Office of Energy and Climate Change, Office of Management 

and Budget, Office of Science and Technology Policy, and Department of Treasury (February 2010). Available at: 

http://www.whitehouse.gov/sites/default/files/omb/inforeg/for-agencies/Social-Cost-of-Carbon-for-RIA.pdf. See 

previous citation for 2013 SCC TSD. 

6

 The full models names are as follows: Dynamic Integrated Climate and Economy (DICE); Climate Framework 

for Uncertainty, Negotiation, and Distribution (FUND); and Policy Analysis of the Greenhouse Gas Effect 

(PAGE). 
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4-17 

selection of the three input parameters (equilibrium climate sensitivity, reference socioeconomic 

scenarios, discount rate) was based on an extensive review of the literature. Specifically, a 

probability distribution for climate sensitivity was specified as an input into all three models. In 

addition, the interagency group used a range of scenarios for the socio-economic parameters and 

a range of values for the discount rate. All other model features were left unchanged, relying on 

the model developers’ best estimates and judgments. In DICE, these parameters are handled 

deterministically and represented by fixed constants; in PAGE, most parameters are represented 

by probability distributions. FUND was also run in a mode in which parameters were treated 

probabilistically. The use of three models and these input parameters allowed for exploration of 

important uncertainties in the way climate damages are estimated, equilibrium climate 

sensitivity, reference socioeconomic and emission trajectories, and discount rate. As stated in 

the 2010 SCC TSD, however, key uncertainties remain as the existing models are imperfect and 

incomplete.The federal government first released the estimates in February 2010 and updated 

them in 2013 using new versions of each IAM. See tThe 2010 SCC Technical Support Document 

(SCC TSD) for provides a complete discussion of the methods used to develop the estimates and 

the key uncertainties, and the 2013 SCC TSD for presents and discusses the updated estimates.7,8 

Notably, the 2013 process did not revisit the 2010 interagency modeling decisions (e.g., 

with regard to the discount rate, reference case socioeconomic and emission scenarios or 

equilibrium climate sensitivity). Rather, improvements in the way damages are modeled are 

confined to those that have been incorporated into the latest versions of the models by the 

 

7

 Docket ID EPA-HQ-OAR-2009-0472-114577, Technical Support Document: Social Cost of Carbon for 

Regulatory Impact Analysis Under Executive Order 12866, Interagency Working Group on Social Cost of 

Carbon, with participation by Council of Economic Advisers, Council on Environmental Quality, Department of 

Agriculture, Department of Commerce, Department of Energy, Department of Transportation, Environmental 

Protection Agency, National Economic Council, Office of Energy and Climate Change, Office of Management 

and Budget, Office of Science and Technology Policy, and Department of Treasury (February 2010). Available 

at: http://www.whitehouse.gov/sites/default/files/omb/inforeg/for-agencies/Social-Cost-of-Carbon-for-RIA.pdf 

8

 Docket ID EPA-HQ-OAR-2013-0495, Technical Support Document: Technical Update of the Social Cost of 

Carbon for Regulatory Impact Analysis Under Executive Order 12866, Interagency Working Group on Social 

Cost of Carbon, with participation by Council of Economic Advisers, Council on Environmental Quality, 

Department of Agriculture, Department of Commerce, Department of Energy, Department of Transportation, 

Environmental Protection Agency, National Economic Council, Office of Energy and Climate Change, Office of 

Management and Budget, Office of Science and Technology Policy, and Department of Treasury (May 2013, 

Revised November 2013). Available at: 

http://www.whitehouse.gov/sites/default/files/omb/assets/inforeg/technical-update-social-cost-of-carbon-forregulator-impact-analysis.pdf. 
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-18 

developers themselves and used in peer-reviewed publications. The model updates that are 

relevant to the SCC estimates include: an explicit representation of sea level rise damages in the 

Dynamic Integrated Climate and Economy (DICE) and Policy Analysis of the Greenhouse Effect 

(PAGE) models; updated adaptation assumptions, revisions to ensure damages are constrained 

by GDP, updated regional scaling of damages, and a revised treatment of potentially abrupt shifts 

in climate damages in the PAGE model; an updated carbon cycle in the DICE model; and 

updated damage functions for sea level rise impacts, the agricultural sector, and reduced space 

heating requirements, as well as changes to the transient response of temperature to the buildup 

of GHG concentrations and the inclusion of indirect effects of methane emissions in the Climate 

Framework for Uncertainty, Negotiation, and Distribution (FUND) model. The 2013 SCC TSD 

provides complete details. 

When attempting to assess the incremental economic impacts of carbon dioxide emissions, 

the analyst faces a number of serious challenges. A report from the National Academies of 

Science (NRC, 2009) points out that any assessment will suffer from uncertainty, speculation, 

and lack of information about (1) future emissions of greenhouse gases, (2) the effects of past 

and future emissions on the climate system, (3) the impact of changes in climate on the physical 

and biological environment, and (4) the translation of these environmental impacts into 

economic damages.9

 As a result, any effort to quantify and monetize the harms associated with 

climate change will raise serious questions of science, economics, and ethics and should be 

viewed as provisional. 

The 2010 SCC TSD noted a number of limitations to the SCC analysis, including the 

incomplete way in which the integrated assessment models capture catastrophic and noncatastrophic impacts, their incomplete treatment of adaptation and technological change, 

uncertainty in the extrapolation of damages to high temperatures, and assumptions regarding risk 

aversion. Current integrated assessment models do not assign value to all of the important 

physical, ecological, and economic impacts of climate change recognized in the climate change 

literature due to a lack of precise information on the nature of damages and because the science 

 

9

 National Research Council (2009). Hidden Costs of Energy: Unpriced Consequences of Energy Production and 

Use. National Academies Press. See docket ID EPA-HQ-OAR-2009-0472-11486. 

Comment [A5]: OMB comment: See comments 

in exec summary re: needing to add language re: 

uncertainty/limitations. We can point you to the 

passages in the 2010 TSD that we think should be 

added here. 

EPA response: Yes, please let us know which text 

you are thinking of in particular. Also, we did not 

see comments about uncertainty/limitations in the 

RIA executive summary related to SCC. We 

responded to comments requesting info about public 

comments, global versus domestic, the 3 input 

assumptions, and nomenclature. We also updated 

this chapter with information about uncertainty. 

Please let us know if we missed something. 
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-19 

incorporated into these models understandably lags behind the most recent research. The limited 

amount of research linking climate impacts to economic damages makes the modeling exercise 

even more difficult. These individual limitations do not all work in the same direction in terms of 

their influence on the SCC estimates, though taken together they suggest that the SCC estimates 

are likely conservative. In particular, the IPCC Fourth Assessment Report (2007) concluded that 

“It is very likely that [SCC estimates] underestimate the damage costs because they cannot 

include many non-quantifiable impacts.” 

Nonetheless, these estimates and the discussion of their limitations represent the best 

available information about the social benefits of CO2 reductions to inform benefit-cost analysis. 

The new versions of the models used to estimate the values presented below offer some 

improvements in these areas, although further work remains warranted. Accordingly, the EPA 

and other agencies continue to engage in research on modeling and valuation of climate impacts 

with the goal to improve these estimates. Additional details are provided in the SCC TSDs. 

The four SCC estimates, updated in 2013, are as follows: $13, $46, $68, and $137 per 

metric ton of CO2 emissions in the year 2020 (2011$).10 The first three values are based on the 

average SCC from the three IAMs, at discount rates of 5, 3, and 2.5 percent, respectively. SCC 

estimates for several discount rates are included because the literature shows that the SCC is 

quite sensitive to assumptions about the discount rate, and because no consensus exists on the 

appropriate rate to use in an intergenerational context (where costs and benefits are incurred by 

different generations). The fourth value is the 95th percentile of the SCC from all three models at 

a 3 percent discount rate. It is included to represent higher-than-expected impacts from 

temperature change further out in the tails of the SCC distribution (representing less likely, but 

potentially catastrophic, outcomes). 

Table 4-2 presents the updated global SCC estimates for the years 2015 to 2050. In order 

to calculate the dollar value for emission reductions, the SCC estimate for each emissions year 

would be applied to changes in CO2 emissions for that year, and then discounted back to the 

 

10 The 2010 and 2013 TSDs present SCC in 2007$. The estimates were adjusted to 2011$ using GDP Implicit Price 

Deflator, http://www.gpo.gov/fdsys/pkg/ECONI-2013-02/pdf/ECONI-2013-02-Pg3.pdf. 
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4-20 

analysis year using the same discount rate used to estimate the SCC.11 The SCC increases over 

time because future emissions are expected to produce larger incremental damages as physical 

and economic systems become more stressed in response to greater climate change. Note that the 

interagency group estimated the growth rate of the SCC directly using the three integrated 

assessment models rather than assuming a constant annual growth rate. This helps to ensure that 

the estimates are internally consistent with other modeling assumptions. Tables 4-3 through 4-5 

report the incremental climate benefits estimated in three analysis years (2020, 2025, and 2030) 

for the two illustrative compliance scenarios (i.e., state and regional) for two options evaluated. 

 

 Table 4-2. Global Social Cost of CO2, 2015-2050 (in 2011$)* 

Year 

Discount Rate and Statistic 

5% Average 3% Average 2.5% Average 3% (95th percentile) 

2015 $12 $39 $61 $116

2020 $13 $46 $68 $137

2025 $15 $50 $74 $153

2030 $17 $55 $80 $170

2035 $20 $60 $85 $187

2040 $22 $65 $92 $204

2045 $26 $70 $98 $220

2050 $28 $76 $104 $235

* The SCC values vary depending on the year of CO2 emissions and are defined in real terms, i.e., adjusted for 

inflation using the GDP implicit price deflator. These SCC values are stated in $/metric ton. 

Table 4-3. Estimated Global Climate Benefits of CO2 Reductions for Proposed EGU GHG 

Existing Source Guidelines in 2020 (billions of 2011$)* 

Discount Rate and Statistic Option 1 – 

state 

Option 1 – 

regional 

Option 2 – 

state 

Option 2 - 

regional 

Million metric tonnes of CO2 reduced 383 371 295 283 

5% (average) $4.9 $4.7 $3.8 $3.6 

3% (average) $18 $17 $14 $13 

2.5% (average) $26 $25 $20 $19 

3% (95th percentile) $52 $51 $40 $39 

* The SCC values are dollar-year and emissions-year specific. SCC values represent only a partial accounting of 

climate impacts. 

 

11 This analysis considered the climate impacts of only CO2 emission change. As discussed below, the climate 

impacts of other pollutants were not calculated for the proposed guidelines. Furthermore, the U.S. Interagency 

Working Group on the Social Cost of Carbon has so far only considered estimates for the social cost of CO2. 

While CO2 is the dominant GHG emitted by the sector, we recognize the representative facilities within these 

comparisons may also have different emission rates for other climate forcers that will serve a minor role in 

determining the overall social cost of generation. 
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4-21 

Table 4-4. Estimated Global Climate Benefits of CO2 Reductions for Proposed EGU GHG 

Existing Source Guidelines in 2025 (billions of 2011$)* 

Discount Rate and Statistic Option 1 – 

state 

Option 1 - 

regional 

Option 2 – 

state 

Option 2 - 

regional 

Million metric tonnes of CO2 reduced 506 501 376 368 

5% (average) $7.6 $7.5 $5.6 $5.5 

3% (average) $25 $25 $19 $18 

2.5% (average) $37 $37 $28 $27 

3% (95th percentile) $77 $76 $57 $56 

* The SCC values are dollar-year and emissions-year specific. SCC values represent only a partial accounting of 

climate impacts. 

Table 4-5. Estimated Global Climate Benefits of CO2 Reductions for Proposed EGU GHG 

Existing Source Guidelines in 2030 (billions of 2011$)* 

Discount Rate and Statistic Option 1 – 

state 

Option 1 - 

regional 

Option 2 – 

state 

Option 2 - 

regional 

Million metric tonnes of CO2 reduced 555 545 373 353 

5% (average) $9.5 $9.3 $6.4 $6.0 

3% (average) $31 $30 $21 $20 

2.5% (average) $44 $44 $30 $28 

3% (95th percentile) $94 $92 $63 $60 

* The SCC values are dollar-year and emissions-year specific. SCC values represent only a partial accounting of 

climate impacts. 

It is important to note that the climate benefits presented above are associated with changes 

in CO2 emissions only. Implementing these guidelines, however, will have an impact on the 

emissions of other pollutants that wouldill affect the climate. Both predicting reductions in 

emissions and estimating tThe climate impacts of these other pollutants, however, can is be

complex. The climate impacts of these other pollutants, and have not been calculated for the 

proposed guidelines.12 

These other emissionpollutants potentially reduced as a result of these guidelines include 

other greenhouse gases (such as methane), aerosols and aerosol precursors such as black carbon, 

organic carbon, sulfur dioxide and nitrogen oxides, and ozone precursors such as nitrogen oxides 

and volatile organic carbon compounds. Changes in emissions of these pollutants (both increases 

and decreases) can could be a directly result from of changes in electricity generation, upstream 

fossil fuel extraction and transport, and/or downstream secondary market impacts. Reductions in 

black carbon or ozone precursors are projected to would lead to further cooling, but reductions in 

 

12 The federal government’s SCC estimates are designed to assess the climate benefits associated with changes in 

CO2 emissions only. 

Comment [A6]: OMB Edit: “Both predicting 

reductions in emissions and estimating climate 

impacts of these pollutants, however, is complex, 

and have not been calculated for the proposed 

guidelines. 

EPA response: Edited to retain accuracy; as 

discussed below, some of the upstream emissions 

changes were estimated. 
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4-22 

the other aerosol species and precursors are projected to would lead to warming. Therefore, 

changes in non-CO2 pollutants could potentially augment or offset the climate benefits calculated 

here. These pollutants can act in different ways and on different timescales than carbon dioxide. 

For example, aerosols reflect (and in the case of black carbon, absorb) incoming radiation, 

whereas greenhouse gases absorb outgoing infrared radiation. In addition, tThese aerosols are 

thought to can also affect climate indirectly by altering properties of clouds. Black carbon can 

also deposit on snow and ice, darkening these surfaces and accelerating melting. In terms of 

lifetime, while carbon dioxide emissions can increase concentrations in the atmosphere for 

hundreds or thousands of years, many of these other pollutants are short lived and remain in the 

atmosphere for short periods of time ranging from days to weeks and can therefore exhibit large 

spatial and temporal variability. 

While the EPA has not quantified the climate impacts of these other pollutants for the 

proposed guidelines, the Agency has analyzed the potential changes in upstream methane 

emissions from the natural gas and coal production sectors that may result from the compliance 

scenarios examined in this RIA in the appendix to Chapter 3. The EPA assessed whether the net 

change in upstream methane emissions from natural gas and coal production is likely to be 

positive or negative and also assessed the potential magnitude of changes relative to CO2

emissions reductions anticipated at power plants. This assessment included CO2 emissions from 

the flaring of methane, but did not evaluate potential changes in other combustion-related CO2

emissions, such as emissions associated with drilling, mining, processing, and transportation in 

the natural gas and coal production sectors. This analysis found that the net upstream CH4

emissions from natural gas systems and coal mines and CO2 emissions from flaring of methane 

will likely decrease under the proposed guidelines. Furthermore, the analysis suggests that the 

changes in upstream methane emissions are small relative to the changes in direct emissions 

from power plants. 

4.3 Estimated Human Health Co-Benefits 

In addition to CO2, implementing these proposed guidelines is expected to reduce 

emissions of SO2 and NOX, which are precursors to formation of ambient PM2.5, as well as 

Comment [A7]: OMB Edit: change “and 

accelerating” to “which may accelerate.” 

EPA Response: EPA’s climate science experts view 

this edit as an unnecessary caveat because darkening 

a snow surface accelerates melting. 

Comment [A8]: OMB edit: is projected to 

reduce...” 

EPA response: We did not accept this edit because 

we believe the current wording is more accurate. 
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-23 

directly emitted fine particles.13 Therefore, reducing these emissions would also reduce human 

exposure to ambient PM2.5 and the incidence of PM2.5-related health effects. In addition, in the 

presence of sunlight, NOX and VOCs can undergo a chemical reaction in the atmosphere to form 

ozone. Depending on localized concentrations of volatile organic compounds (VOCs), rReducing 

NOX emissions would also reduce human exposure to ozone and the incidence of ozone-related 

health effects. Although we do not have sufficient data to quantify these impacts in this analysis, 

reducing emissions of SO2 and NOx would also reduce ambient exposure to SO2 and NO2, 

respectively. In this section, we provide an overview of the monetized PM2.5 and ozone-related 

co-benefits estimated for the proposed guidelines. The estimated co-benefits associated with 

these emission reductions are beyond those achieved by previous EPA rulemakings, including 

MATS. A full description of the underlying data, studies, and assumptions is provided in the PM 

NAAQS RIA (U.S. EPA, 2012a) and Ozone NAAQS RIA (U.S. EPA, 2008b, 2010d). 

There are several important considerations in assessing the air pollution-related health cobenefits for a climate-focused rulemaking. First, these estimated health co-benefits do not 

account for any climate-related air quality changes (e.g., increased ambient ozone associated 

with higher temperatures) but rather changes in precursor emissions affected by this rulemaking. 

Excluding climate-related air quality changes may underestimate ozone-related health cobenefits. It is unclear how PM2.5-related health co-benefits would be impacted by excluding 

climate-related air quality changes since the science is unclear as to how climate change may 

affect PM2.5 exposure. Second, the estimated health co-benefits also do not consider temperature 

modification of PM2.5 and ozone risks (Roberts 2004; Ren 2006a, 2006b, 2008a, 2008b). Third, 

the estimated climate benefits reported in this RIA reflect global benefits, while the estimated 

health co-benefits are calculated for the contiguous U.S. only. Excluding temperature 

modification of air pollution risks and international air pollution-related health benefits implies 

that the quantified health co-benefits likely lead to underestimation. 

 

13 We estimate the health co-benefits associated with emission reductions of two categories of directly emitted 

particles: elemental carbon plus organic carbon (EC+OC) and crustal. Crustal emissions are composed of 

compounds associated with minerals and metals from the earth’s surface, including carbonates, silicates, iron, 

phosphates, copper, and zinc. Often, crustal material represents particles not classified as one of the other species 

(e.g., organic carbon, elemental carbon, nitrate, sulfate, chloride, etc.). 

Comment [A9]: OMB edit: Depending on local 

levels of VOCs, reducing NOx emissions tends to 

reduce human exposure to ozone, which is 

associated with ozone-related health effects. 

EPA response: We did not accept the edits to the 

second half of the sentence because we believe the 

current wording to be more accurate. Also, it is 

parallel to the preceding statement regarding the 

incidence of PM-related health effects. 
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4-24 

Implementing these guidelines may lead to reductions in ambient PM2.5 concentrations 

below the National Ambient Air Quality Standards (NAAQS) for PM and ozone in some areas 

and assist other areas with attaining these NAAQS. Because the NAAQS RIAs (U.S. EPA, 

2012a, 2008b, 2010d) also calculated PM and ozone benefits, there are important differences 

worth noting in the design and analytical objectives of each RIA. The NAAQS RIAs illustrate 

the potential costs and benefits of attaining a revised air quality standard nationwide based on an 

array of emission reduction strategies for different sources including known and unknown 

controls, incremental to implementation of existing regulations and controls needed to attain the 

current standards. In short, NAAQS RIAs hypothesize, but do not predict, the reduction 

strategies that States may choose to enact when implementing a revised NAAQS. The setting of 

a NAAQS does not directly result in costs or benefits, and as such, EPA’s NAAQS RIAs are 

merely illustrative and the estimated costs and benefits are not intended to be added to the costs 

and benefits of other regulations that result in specific costs of control and emission reductions. 

However, it is possible that some costs and benefits estimated in this RIA may account for the 

same air quality improvements as estimated in the illustrative NAAQS RIAs. 

Similar to NAAQS RIAs, the emission reduction scenarios estimated for the proposed 

guidelines are also illustrative. In contrast to NAAQS RIAs, all of the emission reductions for the 

illustrative compliance scenarios would occur in one well-characterized sector (i.e., the EGU 

sector). In general, EPA is more confident in the magnitude and location of the emission 

reductions for implementation rules, which typically require specific emission reductions in a 

specific sector. As such, emission reductions achieved under promulgated implementation rules 

will ultimately be reflected in the baseline of future NAAQS analyses, which would reduce the 

incremental costs and benefits associated with attaining revised future NAAQS. EPA does not 

re-issue illustrative RIAs outside of the rulemaking process that retroactively update the baseline 

to account for implementation rules promulgated after an RIA was completed. For more 

information on the relationship between illustrative analyses, such as for the NAAQS and this 

proposal, and implementation rules, please see Section 1.3 of the PM NAAQS RIA (U.S. EPA, 

2012a). 
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4-25 

4.3.1 Health Impact Assessment for PM2.5 and Ozone 

The Integrated Science Assessment for Particulate Matter (PM ISA) (U.S. EPA, 2009b) 

identified the human health effects associated with ambient PM2.5 exposure, which include 

premature morality and a variety of morbidity effects associated with acute and chronic 

exposures. Similarly, the Integrated Science Assessment for Ozone and Related Photochemical 

Oxidants (Ozone ISA) (U.S. EPA, 2013b) identified the human health effects associated with 

ambient ozone exposure, which include premature morality and a variety of morbidity effects 

associated with acute and chronic exposures. Table 4-6 identifies the quantified and unquantified 

co-benefit categories captured in EPA’s health co-benefits estimates for reduced exposure to 

ambient PM2.5 and ozone. Although the table below does not list unquantified health effects such 

as those associated with exposure to SO2, NO2, and mercury nor welfare effects such as 

acidification and nutrient enrichment, these effects are described in detail in Chapters 5 and 6 of 

the PM NAAQS RIA (U.S. EPA, 2012a) and summarized later in this chapter. It is important to 

emphasize that the list of unquantified benefit categories is not exhaustive, nor is quantification 

of each effect complete. 
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4-26 

 Table 4-6. Human Health Effects of Ambient PM2.5 and Ozone 

Category Specific Effect 

Effect Has 

Been 

Quantified 

Effect Has 

Been 

Monetized 

More 

Information 

Improved Human Health 

Reduced incidence of 

premature mortality 

from exposure to 

PM2.5

Adult premature mortality based on cohort study 

estimates and expert elicitation estimates (age >25 or 

age >30) 

  PM ISA 

Infant mortality (age <1)   PM ISA

Reduced incidence of 

morbidity from 

exposure to PM2.5

Non-fatal heart attacks (age > 18)   PM ISA

Hospital admissions—respiratory (all ages)   PM ISA

Hospital admissions—cardiovascular (age >20)   PM ISA

Emergency room visits for asthma (all ages)   PM ISA

Acute bronchitis (age 8-12)   PM ISA

Lower respiratory symptoms (age 7-14)   PM ISA

Upper respiratory symptoms (asthmatics age 9-11)   PM ISA

Asthma exacerbation (asthmatics age 6-18)   PM ISA

Lost work days (age 18-65)   PM ISA

Minor restricted-activity days (age 18-65)   PM ISA

Chronic Bronchitis (age >26) — — PM ISA

Emergency room visits for cardiovascular effects (all 

ages) — — PM ISA1

Strokes and cerebrovascular disease (age 50-79) — — PM ISA1

Other cardiovascular effects (e.g., other ages) — — PM ISA2

Other respiratory effects (e.g., pulmonary function, 

non-asthma ER visits, non-bronchitis chronic 

diseases, other ages and populations) 

— — PM ISA2

Reproductive and developmental effects (e.g., low 

birth weight, pre-term births, etc) — — PM ISA2,3

Cancer, mutagenicity, and genotoxicity effects — — PM ISA2,3

Reduced incidence of 

mortality from 

exposure to ozone 

Premature mortality based on short-term study 

estimates (all ages)   Ozone ISA 

Premature mortality based on long-term study 

estimates (age 30–99) — — Ozone ISA1

Reduced incidence of 

morbidity from 

exposure to ozone 

Hospital admissions—respiratory causes (age > 65)   Ozone ISA 

Hospital admissions—respiratory causes (age <2)   Ozone ISA 

Emergency department visits for asthma (all ages)   Ozone ISA 

Minor restricted-activity days (age 18–65)   Ozone ISA 

School absence days (age 5–17)   Ozone ISA 

Decreased outdoor worker productivity (age 18–65) — — Ozone ISA1

Other respiratory effects (e.g., premature aging of 

lungs) — — Ozone ISA2

Cardiovascular and nervous system effects — — Ozone ISA2

Reproductive and developmental effects — — Ozone ISA2,3

1

 We assess these co-benefits qualitatively due to data and resource limitations for this analysis, but we have 

quantified them in sensitivity analyses for other analyses. 

2

 We assess these co-benefits qualitatively because we do not have sufficient confidence in available data or 

methods. 

3 We assess these co-benefits qualitatively because current evidence is only suggestive of causality or there are other 

significant concerns over the strength of the association. 
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4-27 

We follow a “damage-function” approach in calculating benefits, which estimates changes 

in individual health endpoints (specific effects that can be associated with changes in air quality) 

and assigns values to those changes assuming independence of the values for those individual 

endpoints. Because EPA rarely has the time or resources to perform new research to measure 

directly, either health outcomes or their values for regulatory analyses, our estimates are based 

on the best available methods of benefits transfer, which is the science and art of adapting 

primary research from similar contexts to estimate benefits for the environmental quality change 

under analysis. We use a “benefit-per-ton” approach to estimate the PM2.5 and ozone co-benefits 

in this RIA. This section describes the underlying basis for the health and economic valuation 

estimates, and the subsequent section provides an overview of the benefit-per-ton estimates, 

which are described in detail in the appendix to this chapter. 

The health impact assessment (HIA) quantifies the changes in the incidence of adverse 

health impacts resulting from changes in human exposure to PM2.5 and ozone. We use the 

environmental Benefits Mapping and Analysis Program (BenMAP) (version 4.0.66) to 

systematize health impact analyses by applying a database of key input parameters, including 

population projections, health impact functions, and valuation functions (Abt Associates, 2012). 

For this assessment, the HIA is limited to those health effects that are directly linked to ambient 

PM2.5 and ozone concentrations. There may be other indirect health impacts associated with 

reducing emissions, such as occupational health exposures. Epidemiological studies generally 

provide estimates of the relative risks of a particular health effect for a given increment of air 

pollution (often per 10 μg/m3

 for PM2.5 or ppb for ozone). These relative risks can be used to 

develop risk coefficients that relate a unit reduction in PM2.5 to changes in the incidence of a 

health effect. We refer the reader to the PM NAAQS RIA (U.S. EPA, 2012a) and Ozone 

NAAQS RIA (U.S. EPA, 2008b, 2010d) for more information regarding the epidemiology 

studies and risk coefficients applied in this analysis, and we briefly elaborate on adult premature 

mortality below. The size of the mortality effect estimates from epidemiological studies, the 

serious nature of the effect itself, and the high monetary value ascribed to prolonging life make 

mortality risk reduction the most significant health endpoint quantified in this analysis.
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Considering a substantial body of published scientific literature and reflecting thousands of 

epidemiology, toxicology, and clinical studies, the PM ISA documents the association between 

elevated PM2.5 concentrations and adverse health effects, including increased premature mortality 

(U.S. EPA, 2009b). The PM ISA, which was twice reviewed by the Clean Air Scientific 

Advisory Committee of EPA’s Science Advisory Board (SAB-CASAC) (U.S. EPA-SAB, 2009b, 

2009c), concluded that there is a causal relationship between mortality and both long-term and 

short-term exposure to PM2.5 based on the entire body of scientific evidence. The PM ISA also 

concluded that the scientific literature consistently finds that a no-threshold log-linear model 

most adequately portrays the PM-mortality concentration-response relationship while 

recognizing potential uncertainty about the exact shape of the concentration-response function. 

In addition to adult mortality discussed in more detail below, we use effect coefficients from 

Woodruff et al. (1997) to estimate PM-related infant mortality. 

For adult PM-related mortality, we use the effect coefficients from the most recent 

epidemiology studies examining two large population cohorts: the American Cancer Society 

cohort (Krewski et al., 2009) and the Harvard Six Cities cohort (Lepeule et al., 2012). The PM 

ISA (U.S. EPA, 2009b) concluded that the ACS and Six Cities cohorts provide the strongest 

evidence of the association between long-term PM2.5 exposure and premature mortality with 

support from a number of additional cohort studies. The SAB’s Health Effects Subcommittee 

(SAB-HES) also supported using these two cohorts for analyses of the benefits of PM reductions 

(U.S. EPA-SAB, 2010a). As both the ACS and Six Cities cohort studies have inherent strengths 

and weaknesses, we present PM2.5 co-benefits estimates using relative risk estimates from both 

these cohorts (Krewski et al., 2009; Lepeule et al., 2012). 

As a characterization of uncertainty regarding the adult PM2.5-mortality relationship, EPA 

graphically presents the PM2.5 co-benefits derived from EPA’s expert elicitation study (Roman et 

al., 2008; IEc, 2006). The primary goal of the 2006 study was to elicit from a sample of health 

experts probabilistic distributions describing uncertainty in estimates of the reduction in 

mortality among the adult U.S. population resulting from reductions in ambient annual average 

PM2.5 concentrations. In that study, twelve experts provided independent opinions regarding the 

4.3.1.1 Mortality Concentration-Response Functions for PM2.5 Formatted: Style 04 Third-Level Subheading

BOLD + After: 12 pt1, Space After: 0 pt
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4-29 

PM2.5-mortality concentration-response function. Because the experts relied upon the ACS and 

Six Cities cohort studies to inform their concentration-response functions, the benefits estimates 

derived from the expert responses generally fall between results derived from these studies (see 

Figure 4-1). We do not combine the expert results in order to preserve the breadth and diversity 

of opinion on the expert panel. This presentation of the expert-derived results is generally 

consistent with SAB advice (U.S. EPA-SAB, 2008), which recommended that the EPA 

emphasize that “scientific differences existed only with respect to the magnitude of the effect of 

PM2.5 on mortality, not whether such an effect existed” and that the expert elicitation “supports 

the conclusion that the benefits of PM2.5 control are very likely to be substantial”. Although it is 

possible that newer scientific literature could revise the experts’ quantitative responses if elicited 

again, we believe that these general conclusions are unlikely to change. 

4.3.1.2 Mortality Concentration-Response Functions for Ozone 

In 2008, the National Academies of Science (NRC, 2008) issued a series of 

recommendations to the EPA regarding the quantification and valuation of ozone-related shortterm 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 

[National Morbidity, Mortality, and Air Pollution Study] studies without exclusion of the metaanalyses” (NRC, 2008). In view of the findings of the National Academies panel, we estimate the 

co-benefits of avoiding short-term ozone mortality using the Bell et al. (2004) NMMAPS 

analysis, the Schwartz (2005) multi-city study, the Huang et al. (2005) multi-city study as well as 

effect estimates from the three meta-analyses (Bell et al. (2005), Levy et al. (2005), and Ito et al. 

(2005)). These studies are consistent with the studies used in the Ozone NAAQS RIA (U.S. 

EPA, 2008b, 2010d).14 For simplicity, we report the ozone mortality estimates in this RIA as a 

range from Bell et al. (2004) to Levy et al. (2005) to represent the lowest and the highest co-

 

14 Since EPA received NAS advice, EPA published the Ozone ISA (U.S. EPA, 2013b) and the second draft Ozone 

Health Risk and Exposure Assessment (U.S. EPA, 2014a). Therefore, the ozone mortality studies applied in this 

analysis, while current at the time of the previous Ozone NAAQS RIAs, do not reflect the most updated 

literature available. The selection of ozone mortality studies used to estimate benefits in RIAs will be revisited in 

the forthcoming RIA accompanying the on-going review of the Ozone NAAQS. 
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benefits estimates based on these six ozone mortality studies. In addition, we graphically present 

estimated co-benefits derived from all six studies mentioned above as a characterization of 

uncertainty regarding the ozone -mortality relationship in Figure 4-1. 

4.3.2 Economic Valuation for Health Co-benefits 

After quantifying the change in adverse health impacts, we estimate the economic value of 

these avoided impacts. Reductions in ambient concentrations of air pollution generally lower the 

risk of future adverse health effects by a small amount for a large population. Therefore, the 

appropriate economic measure is willingness to pay (WTP) for changes in risk of a health effect. 

For some health effects, such as hospital admissions, WTP estimates are generally not available, 

so we use the cost of treating or mitigating the effect. These cost-of-illness (COI) estimates 

generally (although not necessarily in every case) understate the true value of reductions in risk 

of a health effect. They tend to reflect the direct expenditures related to treatment but not the 

value of avoided pain and suffering from the health effect. The unit values applied in this 

analysis are provided in Table 5-9 of the PM NAAQS RIA for each health endpoint (U.S. EPA, 

2012a). 

Avoided premature deaths account for 98 percent of monetized PM-related co-benefits and 

over 90 percent of monetized ozone-related co-benefits. The economics literature concerning the 

appropriate method for valuing reductions in premature mortality risk is still developing. The 

adoption of a value for the projected reduction in the risk of premature mortality is the subject of 

continuing discussion within the economics and public policy analysis community. Following 

the advice of the SAB’s Environmental Economics Advisory Committee (SAB-EEAC), the EPA 

currently uses the value of statistical life (VSL) approach in calculating estimates of mortality 

benefits, because we believe this calculation provides the most reasonable single estimate of an 

individual’s willingness to trade off money for reductions in mortality risk (U.S. EPA-SAB, 

2000). The VSL approach is a summary measure for the value of small changes in mortality risk 

experienced by a large number of people. 

EPA continues work to update its guidance on valuing mortality risk reductions, and the 

Agency consulted several times with the SAB-EEAC on this issue. Until updated guidance is 

available, the Agency determined that a single, peer-reviewed estimate applied consistently, best 
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4-31 

reflects the SAB-EEAC advice it has received. Therefore, EPA has decided to apply the VSL 

that was vetted and endorsed by the SAB in the Guidelines for Preparing Economic Analyses 

(U.S. EPA, 2000)15 while the Agency continues its efforts to update its guidance on this issue. 

This approach calculates a mean value across VSL estimates derived from 26 labor market and 

contingent valuation studies published between 1974 and 1991. The mean VSL across these 

studies is $6.3 million (2000$).16 We then adjust this VSL to account for the currency year and to 

account for income growth from 1990 to the analysis year. Specifically, the VSLs applied in this 

analysis in 2011$ after adjusting for income growth are $9.9 million for 2020 and $10.1 million 

for 2025 and 2030.17 

The Agency is committed to using scientifically sound, appropriately reviewed evidence in 

valuing mortality risk reductions and has made significant progress in responding to the SABEEAC’s specific recommendations. In the process, the Agency has identified a number of 

important issues to be considered in updating its mortality risk valuation estimates. These are 

detailed in a white paper, “Valuing Mortality Risk Reductions in Environmental Policy” (U.S. 

EPA, 2010c), which recently underwent review by the SAB-EEAC. A meeting with the SAB on 

this paper was held on March 14, 2011 and formal recommendations were transmitted on 

July 29, 2011 (U.S. EPA-SAB, 2011). EPA is taking SAB’s recommendations under advisement. 

In valuing PM2.5-related premature mortality, we discount the value of premature mortality 

occurring in future years using rates of 3 percent and 7 percent (OMB, 2003). We assume that 

there is a “cessation” lag between changes in PM exposures and the total realization of changes 

in health effects. Although the structure of the lag is uncertain, the EPA follows the advice of the 

SAB-HES to assume a segmented lag structure characterized by 30 percent of mortality 

reductions in the first year, 50 percent over years 2 to 5, and 20 percent over the years 6 to 20 

after the reduction in PM2.5 (U.S. EPA-SAB, 2004c). Changes in the cessation lag assumptions 

 

15 In the updated Guidelines for Preparing Economic Analyses (U.S. EPA, 2010e), EPA retained the VSL endorsed 

by the SAB with the understanding that further updates to the mortality risk valuation guidance would be 

forthcoming in the near future. 

16 In 1990$, this base VSL is $4.8 million. 

17 Income growth projections are only currently available in BenMAP through 2024, so both the 2025 and 2030 

estimates use income growth only through 2024 and are therefore likely underestimates. 
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do not change the total number of estimated deaths but rather the timing of those deaths. Because 

short-term ozone-related premature mortality occurs within the analysis year, the estimated 

ozone-related co-benefits are identical for all discount rates. 

4.3.3 Benefit-per-ton Estimates for PM2.5

We used a “benefit-per-ton” approach to estimate the PM2.5 co-benefits in this RIA. EPA 

has applied this approach in several previous RIAs (e.g., U.S. EPA, 2011b, 2011c, 2012b). These 

benefit-per-ton estimates provide the total monetized human health co-benefits (the sum of 

premature mortality and premature morbidity), of reducing one ton of PM2.5 (or PM2.5 precursor 

such as NOX or SO2) from a specified source. Specifically, in this analysis, we multiplied the 

estimates for the EGU sector by the corresponding emission reductions based on regional (i.e., 

East, West, and California) benefit-per-ton estimates. 

The method used to calculate the regional benefit-per-ton estimates is a slight modification 

of the national benefit-per-ton estimates described in the TSD: Estimating the Benefit per Ton of 

Reducing PM2.5 Precursors from 17 Sectors (U.S. EPA, 2013a). The national estimates were 

derived using the approach published in Fann et al. (2012), but they have since been updated to 

reflect the epidemiology studies and Census population data first applied in the final PM 

NAAQS RIA (U.S. EPA, 2012a). The approach in Fann et al. (2012) is similar to the work 

previously published by Fann et al. (2009), but the newer study includes improvements that 

provide more refined estimates of PM2.5-related health benefits for emissions reductions in the 

various sectors. Specifically, the air quality modeling data reflect industrial sectors that are more 

narrowly defined. In addition, the updated air quality modeling data reflects more recent 

emissions data (2005 rather than 2001) and has higher spatial resolution (12km rather than 36 km 

grid cells).18 For this rulemaking, to generate the regional benefit-per-ton estimates we simply 

aggregated the EGU impacts in BenMAP to the region (i.e., East, West, and California) rather 

than aggregating to the nation as was done in Fann et al. (2012). We then divided the regional 

 

18 Although the modeling underlying the benefit-per-ton estimates does not reflect emission reductions anticipated 

from MATS, the EGU contribution to ambient PM2.5 and ozone on a per-ton basis would be similar. (Fann, 

Fulcher, and Baker, 2013) Because the emission reductions in this RIA are calculated from an IPM base case 

that includes MATS (see Chapter 3), there is no double-counting concern with the resulting co-benefits 

estimates. 
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4-33 

benefits by the regional emissions rather than the national emissions. The appendix to this 

chapter provides additional detail regarding these calculations. 

As noted below in the characterization of uncertainty, all benefit-per-ton estimates have 

inherent limitations. Specifically, all benefit-per-ton estimates reflect the geographic distribution 

of the modeled sector emissions, which may not match the emission reductions anticipated by 

the proposed guidelines, and they may not reflect local variability in population density, 

meteorology, exposure, baseline health incidence rates, or other local factors for any specific 

location. In addition, these estimates reflect the regional average benefit-per-ton for each ambient 

PM2.5 precursor emitted from EGUs, which assumes a linear atmospheric response to emission 

reductions. The regional benefit-per-ton estimates, although less subject to these types of 

uncertainties than national estimates, still should be interpreted with caution. Even though we 

assume that all fine particles have equivalent health effects, the benefit-per-ton estimates vary 

between precursors depending on the location and magnitude of their impact on PM2.5 levels, 

which drive population exposure. 

4.3.4 Benefit-per-ton Estimates for Ozone 

Similar to PM2.5, we used a “benefit-per-ton” approach in this RIA to estimate the ozone 

co-benefits, which represent the total monetized human health co-benefits (the sum of premature 

mortality and premature morbidity) of reducing one ton of NOx (an ozone precursor). Also 

consistent with the PM2.5 estimates, we generated regional benefit-per-ton estimates for ozone for 

the EGU sector using the air quality modeling data described in Fann et al. (2012) and using the 

updated Census population data first applied in the final PM NAAQS RIA (U.S. EPA, 2012a). In 

contrast to the PM2.5 estimates, the ozone estimates are not based on changes to annual 

emissions. Instead, the regional estimates (i.e., East, West, and California) correspond to NOX

emissions from U.S. EGUs during the ozone-season (May to September). Because we estimate 

ozone health impacts from May to September only, this approach underestimates ozone cobenefits in areas with a longer ozone season such as southern California and Texas. These 

estimates assume that EGU-attributable ozone formation at the regional-level is due to NOx 

alone. Because EGUs emit little VOC relative to NOX emissions, it is unlikely that VOCs 

emitted by EGUs would contribute substantially to regional ozone formation. All benefit-per-ton 
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4-34 

estimates have inherent limitations and should be interpreted with caution. We provide more 

detailed information regarding the generation of these estimates in the appendix to this chapter. 

4.3.5 Estimated Health Co-Benefits Results 

Tables 4-7 through 4-9 provide the national and regional benefit-per-ton estimates for 

2020, 2025, and 2030. Tables 4-10 through 4-12 provide the emission reductions estimated to 

occur in three analysis years (2020, 2025, and 2030) for two illustrative compliance scenarios 

(i.e., state and regional) for two options by region (i.e., East, West, and California).19 Tables 4-13 

through 4-15 summarize the national monetized PM and ozone-related health co-benefits 

estimated to occur in three analysis years (2020, 2025, and 2030) for the options by precursor 

pollutant using discount rates of 3 percent and 7 percent. Tables 4-16 through 4-18 provide 

national summaries of the reductions in health incidences estimated for the options associated 

with these pollution reductions in 2020, 2025, and 2030. Figure 4-1 provides a visual 

representation of the range of estimated PM2.5 and ozone-related co-benefits using concentrationresponse functions from different studies and expert opinion for the options evaluated in 2020 as 

an illustrative analysis year. Figures 4-2 and 4-3 provide a breakdown of the monetized health 

co-benefits for each of the options evaluated in 2020 as an illustrative analysis year by precursor 

pollutant and region, respectively. 

 

19 See Chapter 3 of this RIA for more information regarding the expected emission reductions used to calculate the 

health co-benefits in this chapter. Chapter 3 also provides more information regarding the illustrative compliance 

scenarios. 
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4-35 

Table 4-7. Summary of National and Regional Benefit-per-ton Estimates for EGUs in 

2020 (2011$)* 

Pollutant Discount 

Rate National Regional 

East West California 

SO2

3% $38,000 to $86,000 $40,000 to $90,000 $7,800 to $18,000 $160,000 to $320,000 

7% $34,000 to $77,000 $36,000 to $82,000 $7,100 to $16,000 $140,000 to $320,000 

Directly emitted 

PM2.5 (EC+OC) 

3% $140,000 to $320,000 $140,000 to $320,000 $56,000 to $130,000 $280,000 to $570,000 

7% $130,000 to $290,000 $130,000 to $280,000 $50,000 to $110,000 $250,000 to $570,000 

Directly emitted 

PM2.5 (crustal) 

3% $18,000 to $40,000 $18,000 to $41,000 $11,000 to $25,000 $110,000 to $220,000 

7% $16,000 to $36,000 $16,000 to $37,000 $10,000 to $23,000 $95,000 to $220,000 

NOX (as PM2.5) 3% $5,600 to $13,000 $6,700 to $15,000 $1,200 to $2,600 $17,000 to $34,000 

7% $5,000 to $11,000 $6,000 to $14,000 $1,000 to $2,400 $15,000 to $34,000 

NOX (as Ozone) N/A $3,800 to $16,000 $4,600 to $19,000 $930 to $4,000 $7,400 to $31,000 

* The range of estimates reflects the range of epidemiology studies for avoided premature mortality for PM2.5 and 

ozone. All estimates are rounded to two significant figures. The monetized co-benefits do not include reduced 

health effects from direct exposure to NO2, SO2, ecosystem effects, or visibility impairment. All fine particles are 

assumed to have equivalent health effects, but the benefit per ton estimates vary depending on the location and 

magnitude of their impact on PM2.5 concentrations, which drive population exposure. The monetized co-benefits 

incorporate the conversion from precursor emissions to ambient fine particles and ozone. Benefit-per-ton 

estimates for ozone are based on ozone season NOX emissions. Ozone co-benefits occur in analysis year, so they 

are the same for all discount rates. Confidence intervals are unavailable for this analysis because of the benefitper-ton methodology. In general, the 95th percentile confidence interval for monetized PM2.5 benefits ranges from 

approximately -90 percent to +180 percent of the central estimates based on Krewski et al. (2009) and Lepeule et 

al. (2012). 

Table 4-8. Summary of National and Regional Benefit-per-Ton Estimates for EGUs in 

2025 (2011$)* 

Pollutant Discount 

Rate National Regional 

East West California 

SO2

3% $41,000 to $93,000 $44,000 to $98,000 $8,800 to $20,000 $180,000 to $410,000 

7% $37,000 to $84,000 $39,000 to $89,000 $8,000 to $18,000 $160,000 to $370,000 

Directly emitted 

PM2.5 (EC+OC) 

3% $150,000 to $350,000 $150,000 to $340,000 $64,000 to $140,000 $320,000 to $720,000 

7% $140,000 to $310,000 $140,000 to $310,000 $58,000 to $130,000 $290,000 to $650,000 

Directly emitted 

PM2.5 (crustal) 

3% $17,000 to $39,000 $18,000 to $40,000 $12,000 to $27,000 $43,000 to $96,000 

7% $15,000 to $35,000 $16,000 to $36,000 $11,000 to $24,000 $38,000 to $87,000 

NOX (as PM2.5) 3% $6,000 to $14,000 $7,200 to $16,000 $1,300 to $2,900 $19,000 to $42,000 

7% $5,400 to $12,000 $6,500 to $15,000 $1,200 to $2,600 $17,000 to $38,000 

NOX (as Ozone) N/A $4,900 to $21,000 $5,900 to $25,000 $1,200 to $5,400 $9,900 to $42,000 

* The range of estimates reflects the range of epidemiology studies for avoided premature mortality for PM2.5 and 

ozone. All estimates are rounded to two significant figures. The monetized co-benefits do not include reduced 

health effects from direct exposure to NO2, SO2, ecosystem effects, or visibility impairment. All fine particles are 

assumed to have equivalent health effects, but the benefit per ton estimates vary depending on the location and 

magnitude of their impact on PM2.5 concentrations, which drive population exposure. The monetized co-benefits 

incorporate the conversion from precursor emissions to ambient fine particles and ozone. Benefit-per-ton 

estimates for ozone are based on ozone season NOX emissions. Ozone co-benefits occur in analysis year, so they 

are the same for all discount rates. Confidence intervals are unavailable for this analysis because of the benefitper-ton methodology. In general, the 95th percentile confidence interval for monetized PM2.5 benefits ranges from 

approximately -90 percent to +180 percent of the central estimates based on Krewski et al. (2009) and Lepeule et 

al. (2012).
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Table 4-9. Summary of National and Regional Benefit-per-Ton Estimates for EGUs in 

2030 (2011$)* 

Pollutant Discount 

Rate National Regional 

East West California 

SO2

3% $44,000 to $100,000 $47,000 to $110,000 $9,800 to $22,000 $200,000 to $450,000 

7% $40,000 to $90,000 $42,000 to $95,000 $8,800 to $20,000 $180,000 to $410,000 

Directly emitted 

PM2.5 (EC+OC) 

3% $170,000 to $370,000 $160,000 to $370,000 $71,000 to $160,000 $360,000 to $800,000 

7% $150,000 to $340,000 $150,000 to $330,000 $64,000 to $150,000 $320,000 to $730,000 

Directly emitted 

PM2.5 (crustal) 

3% $18,000 to $42,000 $19,000 to $43,000 $13,000 to $30,000 $47,000 to $110,000 

7% $17,000 to $38,000 $17,000 to $38,000 $12,000 to $27,000 $43,000 to $96,000 

NOX (as PM2.5) 3% $6,400 to $14,000 $7,600 to $17,000 $1,400 to $3,200 $21,000 to $42,000 

7% $5,800 to $13,000 $6,900 to $16,000 $1,300 to $2,900 $19,000 to $47,000 

NOX (as Ozone) N/A $5,300 to $23,000 $6,300 to $27,000 $1,400 to $6,000 $11,000 to $47,000 

* The range of estimates reflects the range of epidemiology studies for avoided premature mortality for PM2.5 and 

ozone. All estimates are rounded to two significant figures. The monetized co-benefits do not include reduced 

health effects from direct exposure to NO2, SO2, ecosystem effects, or visibility impairment. All fine particles are 

assumed to have equivalent health effects, but the benefit per ton estimates vary depending on the location and 

magnitude of their impact on PM2.5 concentrations, which drive population exposure. The monetized co-benefits 

incorporate the conversion from precursor emissions to ambient fine particles and ozone. Benefit-per-ton 

estimates for ozone are based on ozone season NOX emissions. Ozone co-benefits occur in analysis year, so they 

are the same for all discount rates. Confidence intervals are unavailable for this analysis because of the benefitper-ton methodology. In general, the 95th percentile confidence interval for monetized PM2.5 benefits ranges from 

approximately -90 percent to +180 percent of the central estimates based on Krewski et al. (2009) and Lepeule et 

al. (2012). 
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Table 4-10. Emission Reductions of Criteria Pollutants for the Proposed EGU GHG 

Existing Source Guidelines in 2020 (thousands of short tons)*

Region SO2 All-year NOx Ozone-Season 

NOx 

Directly emitted 

PM2.5 (EC+OC)*

Directly emitted 

PM2.5 (crustal)*

Option 1 - State 

East 311 315 135 5 41

West 25 51 22 <1 4

California 0<1 1 1 N/A N/A

National Total 335 367 157 6 45

Option 1 - Regional 

East 279 305 130 5 41

West 10 32 13 0 3

California 2 8 3 N/A N/A

National Total 292 345 146 6 44

Option 2 - State 

East 247 240 101 4 35

West 20 40 18 <1 3

California 0<1 1 1 N/A N/A

National Total 267 281 119 5 38

Option 2 - Regional 

East 234 235 97 4 33

West 8 25 11 <1 2

California 2 8 3 N/A N/A

National Total 244 268 111 5 36

*Emissions of directly emitted particles not yet available. *All emissions shown in the table are rounded, so regional 

emission reductions may appear to not sum to national total. 

Formatted Table

Formatted Table

Formatted: Font: 10 pt
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Table 4-11. Emission Reductions of Criteria Pollutants for the Proposed EGU GHG 

Existing Source Guidelines in 2025 (thousands of short tons)*

Region SO2

All-year 

NOx 

Ozone-Season 

NOx 

Directly emitted 

PM2.5 (EC+OC)*

Directly emitted 

PM2.5 (crustal)*

Option 1 - State 

East 395 378 164 6 44

West 30 53 23 1 6

California 1 5 2 N/A N/A

National Total 425 436 190 6 49

Option 1 - Regional 

East 376 372 160 5 42

West 16 34 15 1 4

California 3 16 5 N/A N/A

National Total 395 421 180 6 46

Option 2 - State 

East 301 271 114 4 34

West 25 42 20 <1 4

California 1 4 2 N/A N/A

National Total 327 317 136 5 38

Option 2 - Regional 

East 281 270 113 4 32

West 13 24 11 <1 3

California 3 14 5 N/A N/A

National Total 297 309 129 4 34

*Emissions of directly emitted particles not yet available. *All emissions shown in the table are rounded, so regional 

emission reductions may appear to not sum to national total. 

 
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-39 

Table 4-12. Emission Reductions of Criteria Pollutants for the Proposed EGU GHG 

Existing Source Guidelines in 2030 (thousands of short tons)*

Region SO2 All-year NOx Ozone-Season 

NOx 

Directly emitted 

PM2.5 (EC+OC)* 

Directly emitted 

PM2.5 (crustal)* 

Option 1 - State 

East 441 376 163 5 39

West 30 52 24 1 5

California 0<1 0<1 0<1 N/A N/A

National Total 471 428 187 6 44

Option 1 - Regional 

East 406 366 158 5 39

West 16 33 15 <1 4

California 2 7 3 N/A N/A

National Total 424 407 176 5 42

Option 2 - State 

East 299 238 104 4 31

West 22 36 16 <1 3

California 0<1 0<1 0 N/A N/A

National Total 321 274 120 4 34

Option 2 - Regional 

East 275 227 98 4 29

West 11 20 9 <1 2

California 1 4 3 N/A N/A

National Total 287 252 110 4 31

*Emissions of directly emitted particles not yet available. *All emissions shown in the table are rounded, so regional 

emission reductions may appear to not sum to national total. 

Formatted: Font: 10 pt

Formatted: Normal

Formatted: Normal
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-40 

Table 4-13. Summary of Estimated Monetized Health Co-Benefits for the Proposed EGU 

GHG Existing Source Guidelines in 2020 (millions of 2011$) * 

Pollutant 3% Discount Rate 7% Discount Rate 

Option 1 - State 

SO2 $13,000 to $29,000 $11,000 to $26,000 

Directly emitted PM2.5 (EC+OC) $760 to $1,700 $690 to $1,600

Directly emitted PM2.5 (crustal) $790 to $1,800 $710 to $1,600

NOx (as PM2.5) $2,200 to $4,900 $2,000 to $4,400 

NOx (as Ozone) $640 to $2,700 $640 to $2,700 

Total $17,000$15,000 to $40,000$36,000 $15,000$14,000 to $36,000$33,000

Option 1 - Regional 

SO2 $12,000 to $26,000 $11,000 to $24,000 

Directly emitted PM2.5 (EC+OC) $750 to $1,700 $670 to $1,500

Directly emitted PM2.5 (crustal) $770 to $1,700 $690 to $1,600

NOx (as PM2.5) $2,200 to $5,000 $2,000 to $4,500 

NOx (as Ozone) $630 to $2,700 $630 to $2,700 

Total $16,000$15,000 to $37,000$34,000 $15,000$13,000 to $34,000$31,000

Option 2 - State 

SO2 $10,000 to $23,000 $9,100 to $21,000 

Directly emitted PM2.5 (EC+OC) $640 to $1,500 $580 to $1,300

Directly emitted PM2.5 (crustal) $660 to $1,500 $600 to $1,400

NOx (as PM2.5) $1,700 to $3,800 $1,500 to $3,400 

NOx (as Ozone) $480 to $2,100 $480 to $2,100 

Total $14,000$12,000 to $32,000$29,000 $12,000$11,000 to $29,000$26,000

Option 2 - Regional 

SO2 $9,800 to $22,000 $8,900 to $20,000 

Directly emitted PM2.5 (EC+OC) $610 to $1,400 $550 to $1,200

Directly emitted PM2.5 (crustal) $630 to $1,400 $570 to $1,300

NOx (as PM2.5) $1,700 to $3,900 $1,600 to $3,500 

NOx (as Ozone) $470 to $2,000 $470 to $2,000 

Total $13,000$12,000 to $31,000$28,000 $12,000$11,000 to $28,000$25,000

* All estimates are rounded to two significant figures so numbers may not sum down columns. The estimated 

monetized co-benefits do not include climate benefits or reduced health effects from direct exposure to NO2, SO2, 

ecosystem effects, or visibility impairment. All fine particles are assumed to have equivalent health effects, but the 

benefit-per-ton estimates vary depending on the location and magnitude of their impact on PM2.5 levels, which 

drive population exposure. The monetized co-benefits incorporate the conversion from precursor emissions to 

ambient fine particles and ozone. Co-benefits for PM2.5 precursors are based on regional benefit-per-ton estimates. 

Co-benefits for ozone are based on ozone season NOx emissions. Ozone co-benefits occur in analysis year, so they 

are the same for all discount rates. Confidence intervals are unavailable for this analysis because of the benefit-perton methodology. In general, the 95th percentile confidence interval for monetized PM2.5 benefits ranges from 

approximately -90 percent to +180 percent of the central estimates based on Krewski et al. (2009) and Lepeule et 

al. (2012). Emissions of directly emitted particles not yet available.
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-41 

Table 4-14. Summary of Estimated Monetized Health Co-Benefits for the Proposed EGU 

GHG Existing Source Guidelines in 2025 (millions of 2011$) * 

Pollutant 3% Discount Rate 7% Discount Rate 

Option 1 - State 

SO2 $18,000 to $40,000 $16,000 to $36,000 

Directly emitted PM2.5 (EC+OC) $900 to $2,000 $810 to $1,800

Directly emitted PM2.5 (crustal) $830 to $1,900 $750 to $1,700

NOx (as PM2.5) $2,900 to $6,500 $2,600 to $5,800 

NOx (as Ozone) $1,000 to $4,400 $1,000 to $4,400 

Total $23,000$22,0

00 to $54,000$51,000 $21,000$20,000 to $49,000$46,000

Option 1 - Regional 

SO2 $17,000 to $38,000 $15,000 to $35,000 

Directly emitted PM2.5 (EC+OC) $850 to $1,900 $760 to $1,700

Directly emitted PM2.5 (crustal) $780 to $1,800 $700 to $1,600

NOx (as PM2.5) $3,000 to $6,800 $2,700 to $6,100 

NOx (as Ozone) $1,000 to $4,300 $1,000 to $4,300 

Total $23,000$21,0

00 to $53,000$50,000 $21,000$19,000 to $48,000$45,000

Option 2 - State 

SO2 $14,000 to $30,000 $12,000 to $27,000 

Directly emitted PM2.5 (EC+OC) $690 to $1,600 $630 to $1,400

Directly emitted PM2.5 (crustal) $640 to $1,400 $580 to $1,300

NOx (as PM2.5) $2,100 to $4,700 $1,900 to $4,200 

NOx (as Ozone) $720 to $3,100 $720 to $3,100 

Total $18,000$16,0

00 to $41,000$38,000 $16,000$15,000 to $37,000$35,000

Option 2 - Regional 

SO2 $13,000 to $29,000 $12,000 to $26,000 

Directly emitted PM2.5 (EC+OC) $640 to $1,400 $580 to $1,300

Directly emitted PM2.5 (crustal) $590 to $1,300 $530 to $1,200

NOx (as PM2.5) $2,200 to $5,000 $2,000 to $4,500 

NOx (as Ozone) $730 to $3,100 $730 to $3,100 

Total $17,000$16,0

00 to $40,000$37,000 $16,000$14,000 to $36,000$34,000

* All estimates are rounded to two significant figures so numbers may not sum down columns. The estimated 

monetized co-benefits do not include climate benefits or reduced health effects from direct exposure to NO2, SO2, 

ecosystem effects, or visibility impairment. All fine particles are assumed to have equivalent health effects, but the 

benefit-per-ton estimates vary depending on the location and magnitude of their impact on PM2.5 levels, which 

drive population exposure. The monetized co-benefits incorporate the conversion from precursor emissions to 

ambient fine particles and ozone. Co-benefits for PM2.5 precursors are based on regional benefit-per-ton estimates. 

Co-benefits for ozone are based on ozone season NOx emissions. Ozone co-benefits occur in analysis year, so they 

are the same for all discount rates. Confidence intervals are unavailable for this analysis because of the benefit-perton methodology. In general, the 95th percentile confidence interval for monetized PM2.5 benefits ranges from 

approximately -90 percent to +180 percent of the central estimates based on Krewski et al. (2009) and Lepeule et 

al. (2012). Emissions of directly emitted particles not yet available.
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-42 

Table 4-15. Summary of Estimated Monetized Health Co-Benefits for the Proposed EGU 

GHG Existing Source Guidelines in 2030 (millions of 2011$) * 

Pollutant 3% Discount Rate 7% Discount Rate 

Option 1 - State 

SO2 $21,000 to $47,000 $19,000 to $43,000 

Directly emitted PM2.5 (EC+OC) $870 to $2,000 $780 to $1,800

Directly emitted PM2.5 (crustal) $800 to $1,800 $720 to $1,600

NOx (as PM2.5) $2,900 to $6,600 $2,600 to $6,000 

NOx (as Ozone) $1,100 to $4,600 $1,100 to $4,600 

Total $27,000$25,0

00 to $62,000$58,000 $24,000$23,000 to $57,000$53,000

Option 1 - Regional 

SO2 $20,000 to $44,000 $18,000 to $40,000 

Directly emitted PM2.5 (EC+OC) $840 to $1,900 $760 to $1,700

Directly emitted PM2.5 (crustal) $770 to $1,700 $700 to $1,600

NOx (as PM2.5) $3,000 to $6,700 $2,700 to $6,100 

NOx (as Ozone) $1,100 to $4,500 $1,100 to $4,500 

Total $25,000$24,0

00 to $59,000$55,000 $23,000$21,000 to $54,000$50,000

Option 2 - State 

SO2 $14,000 to $32,000 $13,000 to $29,000

Directly emitted PM2.5 (EC+OC) $680 to $1,500 $610 to $1,400

Directly emitted PM2.5 (crustal) $620 to $1,400 $560 to $1,300

NOx (as PM2.5) $1,900 to $4,200 $1,700 to $3,800

NOx (as Ozone) $680 to $2,900 $680 to $2,900

Total $18,000$17,0

00 to $42,000$39,000 $16,000$15,000 to $38,000$36,000

Option 2 - Regional 

SO2 $13,000 to $30,000 $12,000 to $27,000

Directly emitted PM2.5 (EC+OC) $630 to $1,400 $570 to $1,300

Directly emitted PM2.5 (crustal) $580 to $1,300 $520 to $1,200

NOx (as PM2.5) $1,800 to $4,200 $1,700 to $3,800

NOx (as Ozone) $660 to $2,800 $660 to $2,800

Total $17,000$16,0

00 to $40,000$37,000 $15,000$14,000 to $36,000$34,000

* All estimates are rounded to two significant figures so numbers may not sum down columns. The estimated 

monetized co-benefits do not include climate benefits or reduced health effects from direct exposure to NO2, SO2, 

ecosystem effects, or visibility impairment. All fine particles are assumed to have equivalent health effects, but the 

benefit-per-ton estimates vary depending on the location and magnitude of their impact on PM2.5 levels, which 

drive population exposure. The monetized co-benefits incorporate the conversion from precursor emissions to 

ambient fine particles and ozone. Co-benefits for PM2.5 precursors are based on regional benefit-per-ton estimates. 

Co-benefits for ozone are based on ozone season NOx emissions. Ozone co-benefits occur in analysis year, so they 

are the same for all discount rates. Confidence intervals are unavailable for this analysis because of the benefit-perton methodology. In general, the 95th percentile confidence interval for monetized PM2.5 benefits ranges from 

approximately -90 percent to +180 percent of the central estimates based on Krewski et al. (2009) and Lepeule et 

al. (2012). Emissions of directly emitted particles not yet available.
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-43 

Table 4-16. Summary of Avoided Health Incidences from PM2.5-Related and OzoneRelated Co-benefits for Proposed EGU GHG Existing Source Guidelines in 

2020* 

Option 1 - 

state 

Option 1 - 

regional 

Option 2 - 

state 

Option 2 - 

regional 

PM2.5-related Health Incidences 

Avoided Premature Mortality 

Krewski et al. (2009) (adult) 1,8001,600 1,7001,50

0 1,4001,300 1,4001,300

Lepeule et al. (2012) (adult) 4,1003,700 3,8003,40

0 3,2002,900 3,2002,900

Woodruff et al. (1997) (infant) 44 43 33 33

Avoided Morbidity 

Emergency department visits for asthma (all ages) 950860 890800 760680 740670

Acute bronchitis (age 8–12) 2,6002,400 2,5002,20

0 2,1001,900 2,0001,800

Lower respiratory symptoms (age 7–14) 33,00030,00

0

31,00028,

000

27,00024,00

0

26,00024,

000

Upper respiratory symptoms (asthmatics age 9–11) 48,00044,00

0

45,00041,

000

38,00035,00

0

38,00034,

000

Minor restricted-activity days (age 18–65) 1,300,0001,

200,000

1,200,000

1,100,000

1,000,00094

0,000

1,000,000

930,000

Lost work days (age 18–65) 220,000200,

000

210,00019

0,000

180,000160,

000

170,00016

0,000

Asthma exacerbation (age 6–18) 100,00098,0

00

95,00091,

000

82,00078,00

0

80,00076,

000

Hospital admissions—respiratory (all ages) 530480 490450 420380 410370

Hospital admissions—cardiovascular (age > 18) 650590 610550 520470 500460

Non-Fatal Heart Attacks (age >18) 

Peters et al. (2001) 2,1001,900 1,9001,70

0 1,6001,500 1,6001,400

Pooled estimate of 4 studies 220200 210190 180160 170160

Ozone-Related Health Incidences 

Avoided Premature Mortality 

Bell et al. (2004) (all ages) 36 35 27 27 

Levy et al. (2005) (all ages) 170 160 120 120 

Avoided Morbidity 

Hospital admissions—respiratory causes (ages > 65) 220 210 160 160 

Hospital admissions—respiratory causes (ages < 2) 100 98 76 74 

Emergency room visits for asthma (all ages) 120 110 89 87 

Minor restricted-activity days (ages 18-65) 210,000 210,000 160,000 160,000 

School absence days 72,00072,00

0

71,00071,

000 55,000 54,000 

* All estimates are rounded to whole numbers with two significant figures. Co-benefits for PM2.5 precursors are 

based on regional benefit-per-ton estimates for all precursors. Co-benefits for ozone are based on ozone season NOx 

emissions. Confidence intervals are unavailable for this analysis because of the benefit-per-ton methodology. In 

general, the 95th percentile confidence interval for the health impact function alone ranges from approximately ±30 

percent for mortality incidence based on Krewski et al. (2009) and ±46 percent based on Lepeule et al. (2012). 

Formatted Table
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-44 

Table 4-17. Summary of Avoided Health Incidences from PM2.5-Related and OzoneRelated Co-benefits for Proposed EGU GHG Existing Source Guidelines in 

2025* 

Option 1 - 

state 

Option 1 - 

regional 

Option 2 - 

state 

Option 2 - 

regional 

PM2.5-related Health Incidences 

Avoided Premature Mortality 

Krewski et al. (2009) (adult) 2,4003,200 2,3002,10

0

1,8002,50

0 1,8001,600

Lepeule et al. (2012) (adult) 5,4007,200 5,3004,80

0

4,1005,50

0 4,0003,700

Woodruff et al. (1997) (infant) 56 54 45 43

Avoided Morbidity 

Emergency department visits for asthma (all ages) 1,2001,600 1,2001,10

0 9301,200 900830

Acute bronchitis (age 8–12) 3,4004,800 3,3003,10

0

2,6003,70

0 2,5002,300

Lower respiratory symptoms (age 7–14) 43,00061,0

00

43,00039,

000

33,00047,

000 32,00030,000

Upper respiratory symptoms (asthmatics age 9–11) 63,00088,0

00

62,00057,

000

48,00068,

000 46,00043,000

Minor restricted-activity days (age 18–65) 1,700,0002,

400,000

1,600,000

1,500,000

1,300,000

1,800,000

1,200,0001,10

0,000

Lost work days (age 18–65) 280,000400

,000

280,00026

0,000

210,00031

0,000

210,000190,00

0

Asthma exacerbation (age 6–18) 130,000200

,000

130,00013

0,000

100,00016

0,000 100,00096,000

Hospital admissions—respiratory (all ages) 730920 710660 560710 540490

Hospital admissions—cardiovascular (age > 18) 8901,100 870800 680850 650600

Non-Fatal Heart Attacks (age >18) 

Peters et al. (2001) 2,8003,600 2,7002,50

0

2,1002,80

0 2,1001,900

Pooled estimate of 4 studies 310390 300280 230300 220210

Ozone-Related Health Incidences 

Avoided Premature Mortality 

Bell et al. (2004) (all ages) 93 92 65 66 

Levy et al. (2005) (all ages) 420 420 300 300 

Avoided Morbidity 

Hospital admissions—respiratory causes (ages > 65) 600 600 430 430 

Hospital admissions—respiratory causes (ages < 2) 250 240 180 180 

Emergency room visits for asthma (all ages) 290 290 210 210 

Minor restricted-activity days (ages 18-65) 520,000 520,000 370,000 370,000 

School absence days 180,000 180,000 130,000 130,000 

* All estimates are rounded to whole numbers with two significant figures. Co-benefits for PM2.5 precursors are 

based on regional benefit-per-ton estimates for all precursors. Co-benefits for ozone are based on ozone season NOx 

emissions. In general, the 95th percentile confidence interval for the health impact function alone ranges from 

approximately ±30 percent for mortality incidence based on Krewski et al. (2009) and ±46 percent based on Lepeule 

et al. (2012). 

Formatted Table
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-45 

Table 4-18. Summary of Avoided Health Incidences from PM2.5-Related and OzoneRelated Co-Benefits for Proposed EGU GHG Existing Source Guidelines in 

2030* 

Option 1 - 

state 

Option 1 - 

regional 

Option 2 - 

state 

Option 2 - 

regional 

PM2.5-related Health Incidences 

Avoided Premature Mortality 

Krewski et al. (2009) (adult) 2,7002,600 2,6002,40

0

1,9001,70

0 1,8001,600

Lepeule et al. (2012) (adult) 6,2005,800 5,9005,40

0

4,2003,90

0 4,0003,600

Woodruff et al. (1997) (infant) 55 54 33 43

Avoided Morbidity 

Emergency department visits for asthma (all ages) 1,4001,300 1,3001,20

0 930850 900800

Acute bronchitis (age 8–12) 3,7003,500 3,5003,30

0

2,5002,30

0 2,5002,200

Lower respiratory symptoms (age 7–14) 48,00045,00

0

45,00042,

000

33,00030,

000

32,00028,0

00

Upper respiratory symptoms (asthmatics age 9–11) 69,00064,00

0

66,00061,

000

47,00043,

000

46,00041,0

00

Minor restricted-activity days (age 18–65) 1,800,0001,

700,000

1,700,000

1,600,000

1,200,000

1,100,000

1,200,0001,

100,000

Lost work days (age 18–65) 310,000290,

000

290,00027

0,000

210,00019

0,000

210,000180

,000

Asthma exacerbation (age 6–18) 150,000150,

000

140,00014

0,000

100,00099

,000

100,00093,

000

Hospital admissions—respiratory (all ages) 870810 820760 590540 540510

Hospital admissions—cardiovascular (age > 18) 1,000970 980910 710650 650610

Non-Fatal Heart Attacks (age >18) 

Peters et al. (2001) 3,3003,100 3,1002,90

0

2,2002,10

0 2,1001,900

Pooled estimate of 4 studies 360330 340320 240220 220210

Ozone-Related Health Incidences 

Avoided Premature Mortality 

Bell et al. (2004) (all ages) 97 96 62 60 

Levy et al. (2005) (all ages) 440 430 280 270 

Avoided Morbidity 

Hospital admissions—respiratory causes (ages > 65) 670 660 430 410 

Hospital admissions—respiratory causes (ages < 2) 250 240 160 150 

Emergency room visits for asthma (all ages) 290 290 190 180 

Minor restricted-activity days (ages 18-65) 510,000 510,000 330,000 320,000 

School absence days 180,000 180,000 120,000 110,000 

* All estimates are rounded to whole numbers with two significant figures. Co-benefits for PM2.5 precursors are 

based on regional benefit-per-ton estimates for all precursors. Co-benefits for ozone are based on ozone season NOx 

emissions. In general, the 95th percentile confidence interval for the health impact function alone ranges from 

approximately ±30 percent for mortality incidence based on Krewski et al. (2009) and ±46 percent based on Lepeule 

et al. (2012). 

 

Formatted Table

Formatted Table
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-46 

PM2.5       Ozone
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-47 

Figure 4-1. Monetized Health Co-benefits for each Option (Regional Compliance) of the 

Proposed EGU GHG Existing Source Guidelines in 2020 * 

*The PM2.5 graphs show the estimated PM2.5 co-benefits at discoount rates of 3% and 7% using effect coefficients 

derived from the Krewski et al. (2009) study and the Lepeule et al. (2012) study, as well as 12 effect coefficients 

derived from EPA’s expert elicitation on PM mortality (Roman et al., 2008). The results shown are not the direct 

results from the studies or expert elicitation; rather, the estimates are based in part on the concentration-response 

functions provided in those studies. The ozone graphs show the estimated ozone co-benefits derived from six ozone 

mortality studies (i.e., Bell et al. (2004), Schwartz (2005), Huang et al. (2005), Bell et al. (2005), Levy et al. (2005), 

and Ito et al. (2005). Ozone co-benefits occur in the analysis year, so they are the same for all discount rates. These 

estimates do not include climate benefits. The monetized co-benefits do not include climate benefits or reduced 

health effects from direct exposure to NO2, SO2, ecosystem effects, or visibility impairment. Results would be 

similar if the state compliance scenario was shown.
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-48 

SO2 73%

Directly

emitted

PM2.5

(EC+OC) 4%

Directly

emitted

PM2.5

(Crustal)

5%

NOx (as

PM2.5)

14%

NOx (as

Ozone)

4%

Option 1 ‐ Regional

SO2 70%

Directly

emitted

PM2.5

(EC+OC) 5%

Directly

emitted

PM2.5

(Crustal)

5%

NOx (as

PM2.5)

13%

NOx (as

Ozone)

7%

Option 1 ‐ Regional

Low Health Co‐benefits    High Health Co‐benefits

Low Health Co‐benefits     High Health Co‐benefits  
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-49 

SO2 74%

Directly

emitted

PM2.5

(EC+OC)

5%

Directly

emitted

PM2.5

(Crustal)

5%

NOx (as

PM2.5)

13%

NOx (as

Ozone)

3%

Option 2 ‐ Regional

SO2 72%

Directly

emitted

PM2.5

(EC+OC) 4%

Directly

emitted

PM2.5

(Crustal)

5%

NOx (as

PM2.5)

13%

NOx (as

Ozone)

6%

Option 2 ‐ Regional

SO2 81%

Directly

emitted

PM2.5

(EC+OC)

0%

Directly

emitted

PM2.5

(Crustal) 0%

NOx (as

PM2.5)

15%

NOx (as

Ozone)

4%

Option 1 ‐ Regional

SO2 77%

Directly

emitted

PM2.5

(EC+OC)

0%

Directly

emitted

PM2.5

(Crustal)

0%

NOx (as

PM2.5)

15%

NOx (as

Ozone)

8%

Option 1 ‐ Regional

SO2 82%

Directly

emitted

PM2.5

(EC+OC)

0%

Directly

emitted

PM2.5

(Crustal) 0%

NOx (as

PM2.5)

14%

NOx (as

Ozone)

4%

Option 2 ‐ Regional

SO2 79%

Directly

emitted

PM2.5

(EC+OC)

0%

Directly

emitted

PM2.5

(Crustal)

0%

NOx (as

PM2.5)

14%

NOx (as

Ozone)

7%

Option 2 ‐ Regional

Formatted: 10 Caption Figure, Centered,

Space After: 6 pt, Keep with next
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-50 

Figure 4-2. Breakdown of Monetized Health Co-benefits by Precursor Pollutant at a 3% 

Discount Rate for each Option (Regional Compliance) for Proposed EGU GHG 

Existing Source Guidelines in 2020* 

* “Low Health Co-benefits” refers to the combined health co-benefits estimated using the Bell et al. (2004) 

mortality study for ozone with the Krewski et al. (2009) mortality study for PM2.5. “High Health Co-benefits” refers 

to the combined health co-benefits estimated using the Levy et al. (2005) mortality study for ozone with the Lepeule 

et al. (2012) mortality study for PM2.5. Emission reductions of directly emitted particles are not yet available. 

Results would be similar if the state compliance scenario was shown.

 
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East 95.6%

West

1.1%

California

3.3%

Option 1 ‐ Regional

East 95.5%

West 1.2%

California

3.3%

Option 1 ‐ Regional

East 95.5%

West

0.9%

California

3.6%

Option 1 ‐ Regional

East 95.5%

West 0.9%

California

3.6%

Option 1 ‐ Regional

Low Health Co‐benefits    High Health Co‐benefits
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Figure 4-3. Breakdown of Monetized Health Co-Benefits by Region at a 3% Discount Rate 

for each Option (Regional compliance) for Proposed EGU GHG Existing Source 

Guidelines in 2020* 

* “Low Health Co-benefits” refers to the combined health co-benefits estimated using the Bell et al. (2004) 

mortality study for ozone with the Krewski et al. (2009) mortality study for PM2.5. “High Health Co-benefits” refers 

to the combined health co-benefits estimated using the Levy et al. (2005) mortality study for ozone with the Lepeule 

et al. (2012) mortality study for PM2.5. Results would be similar if the state compliance scenario was shown.

4.3.6 Characterization of Uncertainty in the Estimated Health Co-benefits 

In any complex analysis using estimated parameters and inputs from numerous models, 

there are likely to be many sources of uncertainty. This analysis is no exception. This analysis 

includes many data sources as inputs, including emission inventories, air quality data from 

models (with their associated parameters and inputs), population data, population estimates, 

health effect estimates from epidemiology studies, economic data for monetizing co-benefits, 

and assumptions regarding the future state of the world (i.e., regulations, technology, and human 

East 95.1%

West

1.1%

California

3.8%

Option 2 ‐ Regional

East 95.0%

West 1.1%

California

3.9%

Option 2 ‐ Regional

East 94.9%

West

0.8%

California

4.2%

Option 2 ‐ Regional

East 94.8%

West 0.9%

California

4.3%

Option 2 ‐ Regional
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-53 

behavior). Each of these inputs may be uncertain and would affect the estimate of co-benefits. 

When the uncertainties from each stage of the analysis are compounded, even small uncertainties 

can have large effects on the total quantified benefits. Therefore, the estimates of co-benefits in 

each analysis year should be viewed as representative of the general magnitude of co-benefits of 

the illustrative compliance scenarios, rather than the actual co-benefits anticipated from 

implementing the proposed guidelines. 

This RIA does not include the type of detailed uncertainty assessment found in the PM 

NAAQS RIA (U.S. EPA, 2012a) or the Ozone NAAQS RIA (U.S. EPA, 2008b) because we lack 

the necessary air quality modeling input and/or monitoring data to run the benefits model. 

However, the results of the quantitative and qualitative uncertainty analyses presented in the PM 

NAAQS RIA and Ozone NAAQS RIAs can provide some information regarding the uncertainty 

inherent in the estimated co-benefits results presented in this analysis. For example, sensitivity 

analyses conducted for the PM NAAQS RIA indicate that alternate cessation lag assumptions 

could change the estimated PM2.5-related mortality co-benefits discounted at 3 percent by 

between 10 percent and –27 percent and that alternate income growth adjustments could change 

the PM2.5-related mortality co-benefits by between 33 percent and −14 percent. Although we 

generally do not calculate confidence intervals for benefit-per-ton estimates and they can provide 

an incomplete picture about the overall uncertainty in the benefits estimates, the PM NAAQS RIA 

can provide an indication of the random sampling error in the health impact and economic 

valuation functions using Monte Carlo methods. In general, the 95th percentile confidence interval 

for monetized PM2.5 benefits ranges from approximately -90 percent to +180 percent of the 

central estimates based on Krewski et al. (2009) and Lepeule et al. (2012). The 95th percentile 

confidence interval for the health impact function alone ranges from approximately ±30 percent for 

mortality incidence based on Krewski et al. (2009) and ±46 percent based on Lepeule et al. (2012). 

Unlike RIAs for which EPA conducts air quality modeling, we do not have information on 

the specific location of the air quality changes associated with the proposed guidelines. As such, 

it is not feasible to estimate the proportion of co-benefits occurring in different locations, such as 

designated nonattainment areas. Instead, we applied benefit-per-ton estimates, which reflect 

specific geographic patterns of emissions reductions and specific air quality and benefits 

modeling assumptions. For example, these estimates may not reflect local variability in 
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4-54 

population density, meteorology, exposure, baseline health incidence rates, or other local factors 

that might lead to an over-estimate or under-estimate of the actual co-benefits of controlling PM 

and ozone precursors. Use of these benefit-per-ton values to estimate co-benefits may lead to 

higher or lower benefit estimates than if co-benefits were calculated based on direct air quality 

modeling. Great care should be taken in applying these estimates to emission reductions 

occurring in any specific location, as these are all based on broad emission reduction scenarios 

and therefore represent average benefits-per-ton over the entire region. The benefit-per-ton for 

emission reductions in specific locations may be very different than the estimates presented here. 

To the extent that the geographic distribution of the emissions reductions achieved by 

implementing the proposed guidelines is different than the emissions in the sector modeling, the 

co-benefits may be underestimated or overestimated. For more information regarding the 

limitations of benefit-per-ton estimates derived from the sector modeling, see the TSD describing 

the calculation of the national benefit-per-ton estimates (U.S. EPA, 2013a) and Fann et al. 

(2012). In addition, the appendix to this chapter provides additional uncertainty information 

regarding the benefit-per-ton estimates applied in this RIA, including an evaluation of the 

similarities and differences in the spatial distribution of EGU emissions in the sector modeling 

and the IPM base case discussed in Chapter 3 of this RIA. 

Our estimate of the total monetized co-benefits is based on EPA’s interpretation of the best 

available scientific literature and methods and supported by the SAB-HES and the National 

Academies of Science (NRC, 2002). Below are key assumptions underlying the estimates for 

PM2.5-related premature mortality, which accounts for 98 percent of the monetized PM2.5 health 

co-benefits. 

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

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

because PM2.5 varies considerably in composition across sources, but the scientific 

evidence is not yet sufficient to allow differentiation of effect estimates by particle 

type. The PM ISA concluded that “many constituents of PM2.5 can be linked with 

multiple health effects, and the evidence is not yet sufficient to allow differentiation 

of those constituents or sources that are more closely related to specific outcomes” 

(U.S. EPA, 2009b). 

2. We assume that the health impact function for fine particles is log-linear without a 

threshold in this analysis. Thus, the estimates include health co-benefits from 
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4-55 

reducing fine particles in areas with varied concentrations of PM2.5, including both 

areas that do not meet the fine particle standard and those areas that are in attainment, 

down to the lowest modeled concentrations. 

3. We assume that there is a “cessation” lag between the change in PM exposures and 

the total realization of changes in mortality effects. Specifically, we assume that some 

of the incidences of premature mortality related to PM2.5 exposures occur in a 

distributed fashion over the 20 years following exposure based on the advice of the 

SAB-HES (U.S. EPA-SAB, 2004c), which affects the valuation of mortality cobenefits at different discount rates. 

In general, we are more confident in the magnitude of the risks we estimate from simulated 

PM2.5 concentrations that coincide with the bulk of the observed PM concentrations in the 

epidemiological studies that are used to estimate the benefits. Likewise, we are less confident in 

the risk we estimate from simulated PM2.5 concentrations that fall below the bulk of the observed 

data in these studies. Concentration benchmark analyses (e.g., lowest measured level [LML], 

one standard deviation below the mean of the air quality data in the study, etc.) allow readers to 

determine the portion of population exposed to annual mean PM2.5 levels at or above different 

concentrations, which provides some insight into the level of uncertainty in the estimated PM2.5 

mortality benefits. In this analysis, we apply two concentration benchmark approaches (LML and 

one standard deviation below the mean) that have been incorporated into recent RIAs and EPA’s 

Policy Assessment for Particulate Matter (U.S. EPA, 2011d). There are uncertainties inherent in 

identifying any particular point at which our confidence in reported associations becomes 

appreciably less, and the scientific evidence provides no clear dividing line. However, the EPA 

does not view these concentration benchmarks as a concentration threshold below which we 

would not quantify health co-benefits of air quality improvements.20 Rather, the co-benefits 

estimates reported in this RIA are the best estimates because they reflect the full range of air 

quality concentrations associated with the emission reduction strategies. The PM ISA concluded 

that the scientific evidence collectively is sufficient to conclude that the relationship between 

long-term PM2.5 exposures and mortality is causal and that overall the studies support the use of a 

no-threshold log-linear model to estimate PM-related long-term mortality (U.S. EPA, 2009b). 

 

20 For a summary of the scientific review statements regarding the lack of a threshold in the PM2.5-mortality 

relationship, see the TSD entitled Summary of Expert Opinions on the Existence of a Threshold in the 

Concentration-Response Function for PM2.5-related Mortality (U.S. EPA, 2010b). 
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

4-56 

For this analysis, policy-specific air quality data is not available. For this RIA, we are 

unable to estimate the percentage of premature mortality associated with the emission reductions 

at each PM2.5 concentration. However, we believe that it is still important to characterize the 

distribution of exposure to baseline concentrations. As a surrogate measure of mortality impacts, 

we provide the percentage of the population exposed at each PM2.5 level in the baseline of the 

source apportionment modeling used to calculate the benefit-per-ton estimates for this sector.21 It 

is important to note that baseline exposure is only one parameter in the health impact function, 

along with baseline incidence rates population and change in air quality. In other words, the 

percentage of the population exposed to air pollution below the LML is not the same as the 

percentage of the population experiencing health impacts as a result of a specific emission 

reduction policy. The most important aspect, which we are unable to quantify without rulespecific air quality modeling, is the shift in exposure anticipated by implementing the proposed 

guidelines. Therefore, caution is warranted when interpreting the LML assessment in this RIA 

because these results are not consistent with results from RIAs that had air quality modeling. 

Table 4-19 provides the percentage of the population exposed above and below two 

concentration benchmarks (i.e., LML and one standard deviation below the mean) in the 

modeled baseline for the sector modeling. Figure 4-4 shows a bar chart of the percentage of the 

population exposed to various air quality levels in the baseline, and Figure 4-5 shows a 

cumulative distribution function of the same data. Both figures identify the LML for each of the 

major cohort studies. 

 

21 As noted above, the modeling used to generate the benefit-per-ton estimates does not reflect emission reductions 

anticipated from MATS rule. Therefore, the baseline PM2.5 concentrations in the LML assessment are higher than 

would be expected if MATS was reflected. 
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Table 4-19. Population Exposure in the Baseline Modeling Above and Below Various 

Concentrations Benchmarks in the Underlying Epidemiology Studies * 

Epidemiology Study 

Below 1 Standard 

Deviation. 

Below AQ Mean 

At or Above 1 Standard 

Deviation Below AQ 

Mean 

Below LML At or Above LML 

Krewski et al. (2009) 89% 11% 7% 93% 

Lepeule et al. (2012) N/A N/A 23% 67% 

* One standard deviation below the mean is equivalent to the middle of the range between the 10th and 25th

percentile. For Krewski, the LML is 5.8 μg/m3

 and one standard deviation below the mean is 11.0 μg/m3. For 

Lepeule et al., the LML is 8 μg/m3

 and we do not have the data for one standard deviation below the mean. It is 

important to emphasize that although we have lower levels of confidence in levels below the LML for each study, 

the scientific evidence does not support the existence of a level below which health effects from exposure to PM2.5

do not occur. 

Figure 4-4 . Percentage of Adult Population (age 30+) by Annual Mean PM2.5 Exposure in 

the Baseline Sector Modeling 

Among the populations exposed to PM2.5 in the baseline:

93% are exposed to PM2.5 levels at or above the LML of the Krewski et al. (2009) study

67% are exposed to PM2.5 levels at or above the LML of the Lepeule et al. (2012) study

0%

5%

10%

15%

20%

25%

<1 1‐2 2‐3 3‐4 4‐5 5‐6 6‐7 7‐8 8‐9 9‐10 10‐11 11‐12 12‐13 13‐14 14‐15 15‐16 16‐17 17‐18 18‐19 19‐20

Percentage of PopulationExposed

Baseline Annual MeanPM2.5 Level (μg/m3)

LML of Lepeule et al.

(2012)study

LML of Krewski et

al. (2009) study
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4-58 

Figure 4-5 . Cumulative Distribution of Adult Population (age 30+) by Annual Mean PM2.5 

Exposure in the Baseline Sector Modeling 

4.4 Combined Climate Benefits and Health Co-benefits Estimates 

In this analysis, we were able to monetize the estimated co-benefits associated with the 

decreased emissions of CO2 and reduced exposure to PM2.5 and ozone, but we were unable to 

monetize the co-benefits associated with reducing exposure to mercury, hydrogen chloride, 

carbon monoxide, SO2, and NO2, as well as ecosystem effects and visibility impairment. 

Specifically, we estimated the combined climate benefits at discount rates of 5 percent, 3 

percent, 2.5 percent, and 3 percent (95th percentile) (as estimated by the interagency working 

group), and health co-benefits at discount rates of 3 percent and 7 percent (as recommended by 

EPA’s Guidelines for Preparing Economic Analyses (U.S. EPA, 2010e) and OMB’s Circular A4 [OMB, 2003]). 

Different discount rates are applied to SCC than to the health co-benefit estimates 

because CO2 emissions are long-lived and subsequent damages occur over many years. 

Among the populations exposed to PM2.5 in the baseline:

93% are exposed to PM2.5 levels at or above the LML of the Krewski et al. (2009) study

67% are exposed to PM2.5 levels at or above the LML of the Lepeule et al. (2012) study

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Cumulative percentage of populationexposed

Baseline AnnualMeanPM2.5 Level(μg/m3)

LML of Krewski et

al. (2009) study

LML of Lepeule et

al. (2012) study
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4-59 

Moreover, several rates are applied to SCC because the literature shows that it is sensitive to 

assumptions about discount rate and because no consensus exists on the appropriate rate to use in 

an intergenerational context. The SCC interagency group centered its attention on the 3 percent 

discount rate but emphasized the importance of considering all four SCC estimates.22 The EPA 

has evaluated the range of potential impacts by combining all SCC values with health co-benefits 

values at the 3 percent and 7 percent discount rates. To be consistent with concepts of 

intergenerational discounting, values for health benefits, which occur within a generation, would 

only be combined with SCC values using a lower discount rate (e.g., the 7 percent health benefit 

estimates would be combined with 5 percent or lower SCC values, but the 3 percent health 

benefit would not be combined with the 5 percent SCC value). While the 5 percent SCC and 3 

percent health benefit estimate falls within the range of values we analyze, this individual 

estimate should not be used independently in an analysis, as it represents a combination of 

discount rates that is unlikely to occur. Combining the 3 percent SCC values with the 3 percent 

health benefit values assumes that there is no difference in discount rates between 

intragenerational and intergenerational impacts. 

Tables 4-20 through 4-22 provide the combined climate and health benefits for each 

option evaluated for 2020, 2025, and 2030. Figure 4-6 shows the breakdown of the monetized 

benefits by pollutant for each option evaluated in 2020 as an illustrative analysis year using a 3 

percent discount rate. 

 

22 See the 2010 SCC TSD. Docket ID EPA-HQ-OAR-2009-0472-114577 or 

http://www.whitehouse.gov/sites/default/files/omb/inforeg/for-agencies/Social-Cost-of-Carbon-for-RIA.pdf for 

details. 
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Table 4-20. Combined Climate Benefits and Health Co-Benefits for Proposed EGU GHG 

Existing Source Guidelines in 2020 (billions of 2011$)* 

SCC Discount Rate Climate 

Benefits Only 

Climate Benefits and plus Health Co-Benefits 

(Discount Rate Applied to Health Co-Benefits) 

3% 7% 

Option 1 - state 383 million metric tonnes CO2

5% $4.9 $22$20 toto $45$41 $20$19 toto $41$38

3% $18 $35$33 toto $57$54 $33$32 toto $54$50

2.5% $26 $43$42 toto $66$62 $42$40 toto $62$59

3% (95th percentile) $52 $69$68 toto $92$89 $68$66 toto $88$85

Option 1 - regional 371 million metric tonnes CO2

5% $4.7 $21$19 toto $42$39 $19$18 toto $39$36

3% $17 $33$32 toto $54$51 $32$30 toto $51$48

2.5% $25 $41$40 toto $63$59 $40$38 toto $59$56

3% (95th percentile) $51 $67$65 toto $88$85 $65$64 toto $85$81

Option 2 - state 295 million metric tonnes CO2

5% $3.8 $17$16 toto $35$32 $16$15 toto $32$30

3% $14 $27$26 toto $45$42 $26$25 toto $42$40

2.5% $20 $34$32 toto $52$49 $32$31 toto $49$46

3% (95th percentile) $40 $54$53 toto $72$69 $53$51 toto $69$66

Option 2 - regional 283 million metric tonnes CO2

5% $3.6 $17$16 toto $34$32 $16$15 toto $32$29

3% $13 $26$25 toto $44$41 $25$24 toto $41$38

2.5% $19 $33$31 toto $50$47 $31$30 toto $47$45

3% (95th percentile) $39 $52$51 toto $70$67 $51$50 toto $67$64

*All estimates are rounded to two significant figures. Climate benefits are based on reductions in CO2 emissions. 

Co-benefits are based on regional benefit-per-ton estimates. Co-benefits for ozone are based on ozone season NOx 

emissions. Ozone co-benefits occur in analysis year, so they are the same for all discount rates. The health cobenefits reflect the sum of the PM2.5 and ozone co-benefits and reflect the range based on adult mortality functions 

(e.g., from Krewski et al. (2009) with Bell et al. (2004) to Lepeule et al. (2012) with Levy et al. (2005)). The 

monetized health co-benefits do not include reduced health effects from direct exposure to NO2, SO2, and HAP; 

ecosystem effects; or visibility impairment. Emission reductions of directly emitted particles are not yet available.
***E.O. 12866 Review – Draft – Do Not Cite, Quote or Release During Review***

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Table 4-21. Combined Climate Benefits and Health Co-Benefits for Proposed EGU GHG 

Existing Source Guidelines in 2025 (billions of 2011$)* 

SCC Discount Rate Climate 

Benefits Only 

Climate Benefits and plus Health Co-Benefits 

(Discount Rate Applied to Health Co-Benefits) 

3% 7% 

Option 1 - state 506 million metric tonnes CO2

5% $7.6 $31$29 toto $62$58 $29$27 toto $57$54

3% $25 $49$47 toto $80$76 $46$45 toto $75$71

2.5% $37 $61$59 toto $92$88 $58$57 toto $87$83

3% (95th percentile) $77 $100$99 toto $130$130 $98$97 toto $130$120

Option 1 - regional 501 million metric tonnes CO2

5% $7.5 $30$29 toto $61$57 $28$27 toto $56$53

3% $25 $48$46 toto $78$75 $46$44 toto $74$70

2.5% $37 $60$58 toto $90$86 $57$56 toto $85$82

3% (95th percentile) $76 $99$98 toto $130$130 $97$96 toto $120$120

Option 2 - state 376 million metric tonnes CO2

5% $5.6 $23$22 toto $47$44 $22$20 toto $43$40

3% $19 $36$35 toto $60$57 $35$34 toto $56$54

2.5% $28 $45$44 toto $69$66 $44$42 toto $65$62

3% (95th percentile) $57 $75$74 toto $98$95 $73$72 toto $95$92

Option 2 - regional 368 million metric tonnes CO2

5% $5.5 $23$21 toto $46$43 $21$20 toto $42$39

3% $18 $36$34 toto $59$56 $34$33 toto $55$52

2.5% $27 $44$43 toto $67$64 $43$42 toto $64$61

3% (95th percentile) $56 $73$72 toto $96$93 $72$71 toto $93$90

*All estimates are rounded to two significant figures. Climate benefits are based on reductions in CO2 emissions. 

Co-benefits are based on regional benefit-per-ton estimates. Co-benefits for ozone are based on ozone season NOx 

emissions. Ozone co-benefits occur in analysis year, so they are the same for all discount rates. The health cobenefits reflect the sum of the PM2.5 and ozone co-benefits and reflect the range based on adult mortality functions 

(e.g., from Krewski et al. (2009) with Bell et al. (2004) to Lepeule et al. (2012) with Levy et al. (2005)). It is 

important to note that the monetized health co-benefits do not include reduced health effects from direct exposure to 

NO2, SO2, and HAP; ecosystem effects; or visibility impairment. Emission reductions of directly emitted particles 

are not yet available.
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4-62 

Table 4-22. Combined Climate Benefits and Health Co-Benefits for Proposed EGU GHG 

Existing Source Guidelines in 2030 (billions of 2011$)* 

SCC Discount Rate Climate 

Benefits Only 

Climate Benefits and plus Health Co-Benefits 

(Discount Rate Applied to Health Co-Benefits) 

3% 7% 

Option 1 - state 555 million metric tonnes CO2

5% $9.5 $36$34 toto $72$68 $34$32 toto $66$63

3% $31 $57$56 toto $93$89 $55$53 toto $87$84

2.5% $44 $71$69 toto $110$100 $69$67 toto $100$98

3% (95th percentile) $94 $120$120 toto $160$150 $120$120 toto $150$150

Option 1 - regional 545 million metric tonnes CO2

5% $9.3 $35$32 toto $68$62 $32$30 toto $63$57

3% $30 $55$53 toto $89$83 $53$51 toto $84$78

2.5% $44 $69$67 toto $100$97 $66$65 toto $97$92

3% (95th percentile) $92 $120$120 toto $150$150 $120$110 toto $150$140

Option 2 - state 373 million metric tonnes CO2

5% $6.4 $24$33 toto $48$65 $23$31 toto $44$60

3% $21 $39$54 toto $63$86 $37$52 toto $59$81

2.5% $30 $48$67 toto $72$99 $46$65 toto $68$94

3% (95th percentile) $63 $81$120 toto $110$150 $80$110 toto $100$140

Option 2 - regional 353 million metric tonnes CO2

5% $6.0 $23$22 toto $46$43 $21$20 toto $42$40

3% $20 $37$35 toto $59$56 $35$34 toto $56$53

2.5% $28 $45$44 toto $68$65 $44$43 toto $64$62

3% (95th percentile) $60 $77$76 toto $100$97 $75$74 toto $96$93

*All estimates are rounded to two significant figures. Climate benefits are based on reductions in CO2 emissions. 

Co-benefits are based on regional benefit-per-ton estimates. Co-benefits for ozone are based on ozone season NOx 

emissions. Ozone co-benefits occur in analysis year, so they are the same for all discount rates. The health cobenefits reflect the sum of the PM2.5 and ozone co-benefits and reflect the range based on adult mortality functions 

(e.g., from Krewski et al. (2009) with Bell et al. (2004) to Lepeule et al. (2012) with Levy et al. (2005)). It is 

important to note that the monetized health co-benefits do not include reduced health effects from direct exposure to 

NO2, SO2, and HAP; ecosystem effects; or visibility impairment. Emission reductions of directly emitted particles 

are not yet available.
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4-63 

CO2 54%

SO2 37% Directly

emitted

PM2.5

(EC+OC) 0%

Directly

emitted

PM2.5

(Crustal) 0%

NOx (as

PM2.5)

7%

NOx (as

Ozone) 2%

Option 1 ‐ Regional

CO2 33%

SO2 52% Directly

emitted

PM2.5

(EC+OC) 0%

Directly

emitted

PM2.5

(Crustal) 0%

NOx (as

PM2.5)

10%

NOx (as

Ozone) 5%

Option 1 ‐ Regional

CO2 52%

SO2 35%

Directly

emitted

PM2.5

(EC+OC) 2%

Directly

emitted

PM2.5

(Crustal) 2%

NOx (as

PM2.5)

7%

NOx (as

Ozone) 2%

Option 1 ‐ Regional

CO2 31%

SO2 49%

Directly

emitted

PM2.5

(EC+OC) 3%

Directly

emitted

PM2.5

(Crustal) 3%

NOx (as

PM2.5)

9%

NOx (as

Ozone) 5%

Option 1 ‐ Regional

Low Health Co‐benefits     High Health Co‐benefits  
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4-64 

Figure 4-6. Breakdown of Combined Monetized Climate and Health Co-benefits of 

Proposed EGU GHG Existing Source Guidelines in 2020 by Option (Regional 

Compliance) and Pollutant (3% discount rate)* 

* “Low Health Co-benefits” refers to the combined health co-benefits estimated using the Bell et al. (2004) 

mortality study for ozone with the Krewski et al. (2009) mortality study for PM2.5. “High Health Co-benefits” refers 

to the combined health co-benefits estimated using the Levy et al. (2005) mortality study for ozone with the Lepeule 

et al. (2012) mortality study for PM2.5. Results would be similar if the state compliance scenario was shown.

4.5 Unquantified Co-benefits 

The monetized co-benefits estimated in this RIA only reflect a subset of co-benefits 

attributable to the health effect reductions associated with ambient fine particles and ozone. Data, 

time, and resource limitations prevented EPA from quantifying the impacts to, or monetizing the 

co-benefits from several important benefit categories, including co-benefits associated with 

exposure to several HAP (including mercury and hydrogen chloride) SO2 and NO2, as well as 

CO2 50%

SO2 37%

Directly

emitted

PM2.5

(EC+OC) 2%

Directly

emitted

PM2.5

(Crustal) 2%

NOx (as

PM2.5)

7%

NOx (as

Ozone) 2%

Option 2 ‐ Regional

CO2 30%

SO2 50%

Directly

emitted

PM2.5

(EC+OC) 3%

Directly

emitted

PM2.5

(Crustal) 3%

NOx (as

PM2.5) NOx (as 9% Ozone) 5%

Option 2 ‐ Regional

CO2 52%

SO2 39%

Directly

emitted

PM2.5

(EC+OC) 0%

Directly

emitted

PM2.5

(Crustal) 0%

NOx (as

PM2.5)

7%

NOx (as

Ozone) 2%

Option 2 ‐ Regional

CO2 32%

SO2 54%

Directly

emitted

PM2.5

(EC+OC) 0%

Directly

emitted

PM2.5

(Crustal) 0%

NOx (as

PM2.5)

9%

NOx (as

Ozone) 5%

Option 2 ‐ Regional
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4-65 

ecosystem effects, and visibility impairment due to the absence of air quality modeling data for 

these pollutants in this analysis. This does not imply that there are no co-benefits associated with 

these emission reductions. In this section, we provide a qualitative description of these benefits, 

which are listed in Table 4-23. 

Table 4-23. Unquantified Health and Welfare Co-benefits Categories 

Category Specific Effect 

Effect Has 

Been 

Quantified 

Effect Has 

Been 

Monetized 

More 

Information 

Improved Human Health 

Reduced incidence of 

morbidity from 

exposure to NO2

Asthma hospital admissions (all ages) — — NO2 ISA1

Chronic lung disease hospital admissions (age > 

65) — — NO2 ISA1

Respiratory emergency department visits (all ages) — — NO2 ISA1

Asthma exacerbation (asthmatics age 4–18) — — NO2 ISA1

Acute respiratory symptoms (age 7–14) — — NO2 ISA1

Premature mortality — — NO2 ISA1,2,3

Other respiratory effects (e.g., airway 

hyperresponsiveness and inflammation, lung 

function, other ages and populations) 

— — NO2 ISA2,3

Reduced incidence of 

morbidity from 

exposure to SO2

Respiratory hospital admissions (age > 65) — — SO2 ISA1

Asthma emergency department visits (all ages) — — SO2 ISA1

Asthma exacerbation (asthmatics age 4–12) — — SO2 ISA1

Acute respiratory symptoms (age 7–14) — — SO2 ISA1

Premature mortality — — SO2 ISA1,2,3

Other respiratory effects (e.g., airway 

hyperresponsiveness and inflammation, lung 

function, other ages and populations) 

— — SO2 ISA1,2

Reduced incidence of 

morbidity from 

exposure to CO 

Cardiovascular effects — — CO ISA 1,2

Respiratory effects — — CO ISA 1,2,3

Central nervous system effects — — CO ISA 1,2,3

Premature mortality — — CO ISA 1,2,3

Reduced incidence of 

morbidity from 

exposure to 

methylmercury 

Neurologic effects—IQ loss — — IRIS; NRC, 

20001

Other neurologic effects (e.g., developmental 

delays, memory, behavior) — — IRIS; NRC, 

20002

Cardiovascular effects — — IRIS; NRC, 

20002,3

Genotoxic, immunologic, and other toxic effects — — IRIS; NRC, 

20002,3

Reduced incidence of 

morbidity from exposure 

to HAP 

Effects associated with exposure to hydrogen 

chloride — — ATSDR, IRIS1,2

Improved Environment 

Table 4-12. Continued 

Reduced visibility 

impairment 

Visibility in Class 1 areas — — PM ISA1

Visibility in residential areas — — PM ISA1
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4-66 

Category Specific Effect 

Effect Has 

Been 

Quantified 

Effect Has 

Been 

Monetized 

More 

Information 

Reduced effects on 

materials 

Household soiling — — PM ISA1,2

Materials damage (e.g., corrosion, increased wear) — — PM ISA2

Reduced effects from 

PM deposition (metals 

and organics) 

Effects on Individual organisms and ecosystems — — PM ISA2

Reduced vegetation 

and ecosystem effects 

from exposure to ozone 

Visible foliar injury on vegetation — — Ozone ISA1

Reduced vegetation growth and reproduction — — Ozone ISA1

Yield and quality of commercial forest products 

and crops — — Ozone ISA1

Damage to urban ornamental plants — — Ozone ISA2

Carbon sequestration in terrestrial ecosystems — — Ozone ISA1

Recreational demand associated with forest 

aesthetics — — Ozone ISA2

Other non-use effects Ozone ISA2

Ecosystem functions (e.g., water cycling, 

biogeochemical cycles, net primary productivity, 

leaf-gas exchange, community composition) 

— — Ozone ISA2

Reduced effects from 

acid deposition 

Recreational fishing — — NOx SOx ISA1

Tree mortality and decline — — NOx SOx ISA2

Commercial fishing and forestry effects — — NOx SOx ISA2

Recreational demand in terrestrial and aquatic 

ecosystems — — NOx SOx ISA2

Other non-use effects NOx SOx ISA2

Ecosystem functions (e.g., biogeochemical cycles) — — NOx SOx ISA2

Reduced effects from 

nutrient enrichment 

Species composition and biodiversity in terrestrial 

and estuarine ecosystems — — NOx SOx ISA2

Coastal eutrophication — — NOx SOx ISA2

Recreational demand in terrestrial and estuarine 

ecosystems — — NOx SOx ISA2

Other non-use effects NOx SOx ISA2

Ecosystem functions (e.g., biogeochemical cycles, 

fire regulation) — — NOx SOx ISA2

Reduced vegetation 

effects from ambient 

exposure to SO2 and 

NOx

Injury to vegetation from SO2 exposure — — NOx SOx ISA2

Injury to vegetation from NOx exposure — — NOx SOx ISA2

Reduced ecosystem 

effects from exposure 

to methylmercury 

Effects on fish, birds, and mammals (e.g., 

reproductive effects) — — Mercury Study 

RTC2

Commercial, subsistence and recreational fishing — — Mercury Study 

RTC1

1 We assess these co-benefits qualitatively due to data and resource limitations for this RIA. 2

We assess these co-benefits qualitatively because we do not have sufficient confidence in available data or 

methods. 3 We assess these co-benefits qualitatively because current evidence is only suggestive of causality or there are other 

significant concerns over the strength of the association. 

4.5.1 HAP Co-benefits 

Due to methodology and resource limitations, we were unable to estimate the co-benefits 

associated with reducing emissions of the hazardous air pollutants in this analysis. EPA’s SAB-
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HES concluded that “the challenges for assessing progress in health improvement as a result of 

reductions in emissions of hazardous air pollutants (HAPs) are daunting...due to a lack of 

exposure-response functions, uncertainties in emissions inventories and background levels, the 

difficulty of extrapolating risk estimates to low doses and the challenges of tracking health 

progress for diseases, such as cancer, that have long latency periods” (U.S. EPA-SAB, 2008). In 

2009, EPA convened a workshop to address the inherent complexities, limitations, and 

uncertainties in current methods to quantify the benefits of reducing HAP. Recommendations 

from this workshop included identifying research priorities, focusing on susceptible and 

vulnerable populations, and improving dose-response relationships (Gwinn et al., 2011). 

Chapter 4 of the MATS RIA (U.S. EPA, 2011b) describes the health effects associated 

with HAP emitted by EGUs. Below we describe the health effects associated with the two HAP 

for which we were able to quantify emission reductions for the proposed guidelines: mercury and 

hydrogen chloride. Using the IPM modeling described in Chapter 3 of this RIA, wWe estimate 

that the illustrative compliance scenarios for the proposed guidelines would reduce mercury 

emissions by up to 2.1 tons and hydrogen chloride by up to 590 tons by 2030. These HAP 

emission reductions are beyond those achieved by MATS.

4.5.1.1 Mercury 

Mercury in the environment is transformed into a more toxic form, methylmercury 

(MeHg). Because Hg is a persistent pollutant, MeHg accumulates in the food chain, especially 

the tissue of fish. When people consume these fish, they consume MeHg. In 2000, the NAS 

Study was issued which provides a thorough review of the effects of MeHg on human health 

(NRC, 2000).23 Many of the peer-reviewed articles cited in this section are publications 

originally cited in the Mercury Study.24 In addition, EPA has conducted literature searches to 

obtain other related and more recent publications to complement the material summarized by the 

NRC in 2000. 

 

23 National Research Council (NRC). 2000. Toxicological Effects of Methylmercury. Washington, DC: National 

Academies Press. 

24 U.S. Environmental Protection Agency (U.S. EPA). 1997. Mercury Study Report to Congress, EPA–HQ–OAR–

2009–0234–3054. December. Available on the Internet at <http://www.epa.gov/hg/report.htm>. 

Comment [A10]: OMB comment: Can EPA 

better explain how these numbers were derived? 

We’d also like to discuss whether the same tons of 

NOx are being modeled in the indirect PM and 

ozone sections. 

EPA response: We clarified the origin of the HAP 

emission reductions in the text. In response to the 

second question, the source apportionment modeling 

captures the atmospheric transformation from NOx 

emissions into ambient PM2.5 and ozone. Because 

NOx forms both ambient PM2.5 and ozone, there is 

no double counting of benefits. We have 

conservatively limited the NOx emissions to the 

defined ozone season for estimating ozone cobenefits, and we use the all-year NOx emissions to 

estimate PM co-benefits. 
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In its review of the literature, the NAS found neurodevelopmental effects to be the most 

sensitive and best documented endpoints and appropriate for establishing a reference dose (RfD) 

(NRC, 2000); in particular NAS supported the use of results from neurobehavioral or 

neuropsychological tests. The NAS report noted that studies in animals reported sensory effects 

as well as effects on brain development and memory functions and support the conclusions based 

on epidemiology studies. The NAS noted that their recommended endpoints for an RfD are 

associated with the ability of children to learn and to succeed in school. They concluded the 

following: “The population at highest risk is the children of women who consumed large 

amounts of fish and seafood during pregnancy. The committee concludes that the risk to that 

population is likely to be sufficient to result in an increase in the number of children who have to 

struggle to keep up in school.” 

The NAS summarized data on cardiovascular effects available up to 2000. Based on these 

and other studies, the NRC concluded that “Although the data base is not as extensive for 

cardiovascular effects as it is for other end points (i.e., neurologic effects), the cardiovascular 

system appears to be a target for MeHg toxicity in humans and animals.” The 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.” 

Additional cardiovascular studies have been published since 2000. EPA did not develop a 

quantitative dose-response assessment for cardiovascular effects associated with MeHg 

exposures, as there is no consensus among scientists on the dose-response functions for these 

effects. In addition, there is inconsistency among available studies as to the association between 

MeHg exposure and various cardiovascular system effects. The pharmacokinetics of some of the 

exposure measures (such as toenail Hg levels) are not well understood. The studies have not yet 

received the review and scrutiny of the more well-established neurotoxicity data base. 

The Mercury Study noted that MeHg is not a potent mutagen but is capable of causing 

chromosomal damage in a number of experimental systems. The NAS concluded that evidence 

that human exposure to MeHg caused genetic damage is inconclusive; they note that some earlier 

studies showing chromosomal damage in lymphocytes may not have controlled sufficiently for 

potential confounders. One study of adults living in the Tapajós River region in Brazil (Amorim 
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4-69 

et al., 2000) reported a direct relationship between MeHg concentration in hair and DNA damage 

in lymphocytes, as well as effects on chromosomes.25 Long-term MeHg exposures in this 

population were believed to occur through consumption of fish, suggesting that genotoxic effects 

(largely chromosomal aberrations) may result from dietary, and chronic MeHg exposures similar 

to and above those seen in the Faroes and Seychelles populations. 

Although exposure to some forms of Hg can result in a decrease in immune activity or an 

autoimmune response (ATSDR, 1999), evidence for immunotoxic effects of MeHg is limited 

(NRC, 2000)26. 

Based on limited human and animal data, MeHg is classified as a “possible” human 

carcinogen by the International Agency for Research on Cancer (IARC, 1994)27 and in IRIS 

(U.S. EPA, 2002).28 The existing evidence supporting the possibility of carcinogenic effects in 

humans from low-dose chronic exposures is tenuous. Multiple human epidemiological studies 

have found no significant association between Hg exposure and overall cancer incidence, 

although a few studies have shown an association between Hg exposure and specific types of 

cancer incidence (e.g., acute leukemia and liver cancer) (NRC, 2000). 

There is also some evidence of reproductive and renal toxicity in humans from MeHg 

exposure. However, overall, human data regarding reproductive, renal, and hematological 

toxicity from MeHg are very limited and are based on either studies of the two high-dose 

poisoning episodes in Iraq and Japan or animal data, rather than epidemiological studies of 

chronic exposures at the levels of interest in this analysis. 

 

25 Amorim, M.I.M., D. Mergler, M.O. Bahia, H. Dubeau, D. Miranda, J. Lebel, R.R. Burbano, and M. Lucotte. 

2000. Cytogenetic damage related to low levels of methyl mercury contamination in the Brazilian Amazon. An. 

Acad. Bras. Ciênc. 72(4): 497-507. 

26 Agency for Toxic Substances and Disease Registry (ATSDR). 1999. Toxicological Profile for Mercury. U.S. 

Department of Health and Human Services, Public Health Service, Atlanta, GA. 

27 International Agency for Research on Cancer (IARC). 1994. IARC Monographs on the Evaluation of 

Carcinogenic Risks to Humans and their Supplements: Beryllium, Cadmium, Mercury, and Exposures in the 

Glass Manufacturing Industry. Vol. 58. Jalili, H.A., and A.H. Abbasi. 1961. Poisoning by ethyl mercury toluene 

sulphonanilide. Br. J. Indust. Med. 18(Oct.):303-308 (as cited in NRC, 2000). 

28 U.S. Environmental Protection Agency (EPA). 2002. Integrated Risk Information System (IRIS) on 

Methylmercury. National Center for Environmental Assessment. Office of Research and Development. Available 

at http://www.epa.gov/iris/subst/0073.htm. 
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4.5.1.2 Hydrogen Chloride 

Hydrogen chloride (HCl) is a corrosive gas that can cause irritation of 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.29 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. Exposure to HCl can 

lead to Reactive Airways 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.30

4.5.2 Additional NO2 Health Co-Benefits 

In addition to being a precursor to PM2.5 and ozone, NOx emissions are also associated 

with a variety of adverse health effects associated with direct exposure. Unfortunately, we were 

unable to estimate the health co-benefits associated with reduced NO2 exposure in this analysis. 

Therefore, this analysis only quantified and monetized the PM2.5 and ozone co-benefits 

associated with the reductions in NO2 emissions. 

Following a comprehensive review of health evidence from epidemiologic and laboratory 

studies, the Integrated Science Assessment for Oxides of Nitrogen —Health Criteria (NOx ISA) 

(U.S. EPA, 2008c) concluded that there is a likely causal relationship between respiratory health 

effects and short-term exposure to NO2. The NOx ISA concluded that the evidence “is sufficient 

to infer a likely causal relationship between short-term NO2 exposure and adverse effects on the 

respiratory system.” These epidemiologic and experimental studies encompass a number of 

endpoints including emergency department visits and hospitalizations, respiratory symptoms, 

 

29 Agency for Toxic Substances and Disease Registry (ATSDR). Medical Management Guidelines for Hydrogen 

Chloride. Atlanta, GA: U.S. Department of Health and Human Services. Available at 

http://www.atsdr.cdc.gov/mmg/mmg.asp?id=758&tid=147#bookmark02. 30 U.S. Environmental Protection Agency (U.S. EPA). 1995. “Integrated Risk Information System File of Hydrogen 

Chloride.” Washington, DC: Research and Development, National Center for Environmental Assessment. This 

material is available at http://www.epa.gov/iris/subst/0396.htm. 
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airway hyperresponsiveness, airway inflammation, and lung function. The NOx ISA also 

concluded that the relationship between short-term NO2 exposure and premature mortality was 

“suggestive but not sufficient to infer a causal relationship,” because it is difficult to attribute the 

mortality risk effects to NO2 alone. Although the NOx ISA stated that studies consistently 

reported a relationship between NO2 exposure and mortality, the effect was generally smaller 

than that for other pollutants such as PM. 

4.5.3 Additional SO2 Health Co-Benefits 

In addition to being a precursor to PM2.5, SO2 emissions are also associated with a variety 

of adverse health effects associated with direct exposure. Unfortunately, we were unable to 

estimate the health co-benefits associated with reduced SO2 in this analysis because we do not 

have air quality modeling data available. Therefore, this analysis only quantifies and monetizes 

the PM2.5 co-benefits associated with the reductions in SO2 emissions. 

Following an extensive evaluation of health evidence from epidemiologic and laboratory 

studies, the Integrated Science Assessment for Oxides of Sulfur —Health Criteria (SO2 ISA) 

concluded that there is a causal relationship between respiratory health effects and short-term 

exposure to SO2 (U.S. EPA, 2008a). The immediate effect of SO2 on the respiratory system in 

humans is bronchoconstriction. Asthmatics are more sensitive to the effects of SO2 likely 

resulting from preexisting inflammation associated with this disease. A clear concentrationresponse relationship has been demonstrated in laboratory studies following exposures to SO2 at 

concentrations between 20 and 100 ppb, both in terms of increasing severity of effect and 

percentage of asthmatics adversely affected. Based on our review of this information, we 

identified four short-term morbidity endpoints that the SO2 ISA identified as a “causal 

relationship”: asthma exacerbation, respiratory-related emergency department visits, and 

respiratory-related hospitalizations. The differing evidence and associated strength of the 

evidence for these different effects is described in detail in the SO2 ISA. The SO2 ISA also 

concluded that the relationship between short-term SO2 exposure and premature mortality was 

“suggestive of a causal relationship” because it is difficult to attribute the mortality risk effects to 

SO2 alone. Although the SO2 ISA stated that studies are generally consistent in reporting a 

relationship between SO2 exposure and mortality, there was a lack of robustness of the observed 
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associations to adjustment for pollutants. We did not quantify these co-benefits due to data 

constraints. 

4.5.4 Additional NO2 and SO2 Welfare Co-Benefits 

As described in the Integrated Science Assessment for Oxides of Nitrogen and Sulfur —

Ecological Criteria (NOx ISA) (U.S. EPA, 2008d), SO2 and NOx emissions also contribute to a 

variety of adverse welfare effects, including acidic deposition, visibility impairment, and nutrient 

enrichment. Deposition of nitrogen 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 U.S., 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). (U.S. EPA, 2008d) 

Deposition of nitrogen is also associated with aquatic and terrestrial nutrient enrichment. In 

estuarine waters, excess nutrient enrichment can lead to eutrophication. Eutrophication of 

estuaries can disrupt an important source of food production, particularly fish and shellfish 

production, and a variety of cultural ecosystem services, including water-based recreational and 

aesthetic services. Terrestrial nutrient enrichment is associated with changes in the types and 

number of species and biodiversity in terrestrial systems. Excessive nitrogen deposition upsets 

the balance between native and nonnative plants, changing the ability of an area to support 

biodiversity. When the composition of species changes, then fire frequency and intensity can 

also change, as nonnative grasses fuel more frequent and more intense wildfires. (U.S. EPA, 

2008d) 
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4.5.5 Ozone Welfare Co-Benefits 

Exposure to ozone has been associated with a wide array of vegetation and ecosystem 

effects in the published literature (U.S. EPA, 2013b). 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. 

4.5.6 Carbon Monoxide Co-Benefits 

CO in ambient air is formed primarily by the incomplete combustion of carbon-containing 

fuels and photochemical reactions in the atmosphere. The amount of CO emitted from these 

reactions, relative to carbon dioxide (CO2), is sensitive to conditions in the combustion zone, 

such as fuel oxygen content, burn temperature, or mixing time. Upon inhalation, CO diffuses 

through the respiratory system to the blood, which can cause hypoxia (reduced oxygen 

availability). Carbon monoxide can elicit a broad range of effects in multiple tissues and organ 

systems that are dependent upon concentration and duration of exposure. The Integrated Science 

Assessment for Carbon Monoxide (U.S. EPA, 2010a) concluded that short-term exposure to CO 

is “likely to have a causal relationship” with cardiovascular morbidity, particularly in individuals 

with coronary heart disease. Epidemiologic studies associate short-term CO exposure with 

increased risk of emergency department visits and hospital admissions. Coronary heart disease 

includes those who have angina pectoris (cardiac chest pain), as well as those who have 

experienced a heart attack. Other subpopulations potentially at risk include individuals with 

diseases such as chronic obstructive pulmonary disease (COPD), anemia, or diabetes, and 

individuals in very early or late life stages, such as older adults or the developing young. The 

evidence is suggestive of a causal relationship between short-term exposure to CO and 

respiratory morbidity and mortality. The evidence is also suggestive of a causal relationship for 

birth outcomes and developmental effects following long-term exposure to CO, and for central 

nervous system effects linked to short- and long-term exposure to CO. 
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4.5.7 Visibility Impairment Co-Benefits 

Reducing secondary formation of PM2.5 would improve levels visibility in the U.S. because 

suspended particles and gases degrade visibility by scattering and absorbing light (U.S. EPA, 

2009b). Fine particles with significant light-extinction efficiencies include sulfates, nitrates, 

organic carbon, elemental carbon, and soil (Sisler, 1996). Visibility has direct significance to 

people’s enjoyment of daily activities and their overall sense of wellbeing. Good visibility 

increases the quality of life where individuals live and work, and where they engage in 

recreational activities. Particulate sulfate is the dominant source of regional haze in the eastern 

U.S. and particulate nitrate is an important contributor to light extinction in California and the 

upper Midwestern U.S., particularly during winter (U.S. EPA, 2009b). Previous analyses (U.S. 

EPA, 2011a) show that visibility co-benefits can be a significant welfare benefit category. 

Without air quality modeling, we are unable to estimate visibility related benefits, and we are 

also unable to determine whether the emission reductions associated with the proposed 

guidelines would be likely to have a significant impact on visibility in urban areas or Class I 

areas. 

 
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4.6 References 

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Industrial Economics, Incorporated (IEc). 2009. Section 812 Prospective Study of the Benefits 

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Levy, J.I.; S.M. Chemerynski and J.A. Sarnat. 2005. “Ozone Exposure and Mortality: An 

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