Acrolein is extremely acrid and irritating to humans when inhaled, with acute exposure resulting in upper respiratory tract irritation, mucus hypersecretion and congestion.  The intense irritancy of this carbonyl has been demonstrated during controlled tests in human subjects, who suffer intolerable eye and nasal mucosal sensory reactions within minutes of exposure.  These data and additional studies regarding acute effects of human exposure to acrolein are summarized in EPA's 2003 IRIS Human Health Assessment for acrolein.  Studies in humans indicate that levels as low as 0.09 ppm (0.21 mg/m[3]) for five minutes may elicit subjective complaints of eye irritation with increasing concentrations leading to more extensive eye, nose and respiratory symptoms.  Acute exposures in animal studies report bronchial hyper-responsiveness.  Based on animal data (more pronounced respiratory irritancy in mice with allergic airway disease in comparison to non-diseased mice) and demonstration of similar effects in humans (e.g., reduction in respiratory rate), individuals with compromised respiratory function (e.g., emphysema, asthma) are expected to be at increased risk of developing adverse responses to strong respiratory irritants such as acrolein.  EPA does not currently have an acute reference concentration for acrolein.  The available health effect reference values for acrolein have been summarized by EPA and include an ATSDR MRL for acute exposure to acrolein of 7 ug/m[3] for 1-14 days exposure; and Reference Exposure Level (REL) values from the California Office of Environmental Health Hazard Assessment (OEHHA) for one-hour and 8-hour exposures of 2.5 ug/m[3] and 0.7 ug/m[3], respectively.
    Polycyclic Organic Matter
The term polycyclic organic matter (POM) defines a broad class of compounds that includes the polycyclic aromatic hydrocarbon compounds (PAHs).  One of these compounds, naphthalene, is discussed separately below.  POM compounds are formed primarily from combustion and are present in the atmosphere in gas and particulate form.  Cancer is the major concern from exposure to POM.  Epidemiologic studies have reported an increase in lung cancer in humans exposed to diesel exhaust, coke oven emissions, roofing tar emissions, and cigarette smoke; all of these mixtures contain POM compounds.[,]  Animal studies have reported respiratory tract tumors from inhalation exposure to benzo[a]pyrene and alimentary tract and liver tumors from oral exposure to benzo[a]pyrene.  In 1997 EPA classified seven PAHs (benzo[a]pyrene, benz[a]anthracene, chrysene, benzo[b]fluoranthene, benzo[k]fluoranthene, dibenz[a,h]anthracene, and indeno[1,2,3-cd]pyrene) as Group B2, probable human carcinogens.  Since that time, studies have found that maternal exposures to PAHs in a population of pregnant women were associated with several adverse birth outcomes, including low birth weight and reduced length at birth, as well as impaired cognitive development in preschool children (3 years of age).[,]  These and similar studies are being evaluated as a part of the ongoing IRIS assessment of health effects associated with exposure to benzo[a]pyrene.
    Naphthalene
Naphthalene is found in small quantities in gasoline and diesel fuels.  Naphthalene emissions have been measured in larger quantities in both gasoline and diesel exhaust compared with evaporative emissions from mobile sources, indicating it is primarily a product of combustion.  Acute (short-term) exposure of humans to naphthalene by inhalation, ingestion, or dermal contact is associated with hemolytic anemia and damage to the liver and the nervous system.  Chronic (long term) exposure of workers and rodents to naphthalene has been reported to cause cataracts and retinal damage.  EPA released an external review draft of a reassessment of the inhalation carcinogenicity of naphthalene based on a number of recent animal carcinogenicity studies.  The draft reassessment completed external peer review.  Based on external peer review comments received, a revised draft assessment that considers all routes of exposure, as well as cancer and noncancer effects, is under development.  The external review draft does not represent official agency opinion and was released solely for the purposes of external peer review and public comment.  The National Toxicology Program listed naphthalene as "reasonably anticipated to be a human carcinogen" in 2004 on the basis of bioassays reporting clear evidence of carcinogenicity in rats and some evidence of carcinogenicity in mice.  California EPA has released a new risk assessment for naphthalene, and the IARC has reevaluated naphthalene and re-classified it as Group 2B: possibly carcinogenic to humans.
Naphthalene also causes a number of chronic non-cancer effects in animals, including abnormal cell changes and growth in respiratory and nasal tissues.  The current EPA IRIS assessment includes noncancer data on hyperplasia and metaplasia in nasal tissue that form the basis of the inhalation RfC of 3 ug/m[3].  The ATSDR MRL for acute exposure to naphthalene is 0.6 mg/kg/day.
    Other Air Toxics
In addition to the compounds described above, other compounds in gaseous hydrocarbon and PM emissions from motor vehicles will be affected by this action.  Mobile source air toxic compounds that will potentially be impacted include ethylbenzene, propionaldehyde, toluene, and xylene.  Information regarding the health effects of these compounds can be found in EPA's IRIS database.
Exposure and Health Effects Associated with Traffic
Locations in close proximity to major roadways generally have elevated concentrations of many air pollutants emitted from motor vehicles.  Hundreds of such studies have been published in peer-reviewed journals, concluding that concentrations of CO, NO, NO2, benzene, aldehydes, particulate matter, black carbon, and many other compounds are elevated in ambient air within approximately 300-600 meters (about 1,000-2,000 feet) of major roadways.  Highest concentrations of most pollutants emitted directly by motor vehicles are found at locations within 50 meters (about 165 feet) of the edge of a roadway's traffic lanes.
A recent large-scale review of air quality measurements in vicinity of major roadways between 1978 and 2008 concluded that the pollutants with the steepest concentration gradients in vicinities of roadways were CO, ultrafine particles, metals, elemental carbon (EC), NO, NOX, and several VOCs.  These pollutants showed a large reduction in concentrations within 100 meters downwind of the roadway.  Pollutants that showed more gradual reductions with distance from roadways included benzene, NO2, PM2.5, and PM10.  In the review article, results varied based on the method of statistical analysis used to determine the trend.
For pollutants with relatively high background concentrations relative to near-road concentrations, detecting concentration gradients can be difficult.  For example, many aldehydes have high background concentrations as a result of photochemical breakdown of precursors from many different organic compounds.  This can make detection of gradients around roadways and other primary emission sources difficult.  However, several studies have measured aldehydes in multiple weather conditions, and found higher concentrations of many carbonyls downwind of roadways.[,]  These findings suggest a substantial roadway source of these carbonyls.
In the past 15 years, many studies have been published with results reporting that populations who live, work, or go to school near high-traffic roadways experience higher rates of numerous adverse health effects, compared to populations far away from major roads.  In addition, numerous studies have found adverse health effects associated with spending time in traffic, such as commuting or walking along high-traffic roadways.[,][,][,]  The health outcomes with the strongest evidence linking them with traffic-associated air pollutants are respiratory effects, particularly in asthmatic children, and cardiovascular effects.
Numerous reviews of this body of health literature have been published as well.  In 2010, an expert panel of the Health Effects Institute (HEI) published a review of hundreds of exposure, epidemiology, and toxicology studies.  The panel rated how the evidence for each type of health outcome supported a conclusion of a causal association with traffic-associated air pollution as either "sufficient," "suggestive but not sufficient," or "inadequate and insufficient."  The panel categorized evidence of a causal association for exacerbation of childhood asthma as "sufficient."  The panel categorized evidence of a causal association for new onset asthma as between "sufficient" and as "suggestive but not sufficient."  "Suggestive of a causal association" was how the panel categorized evidence linking traffic-associated air pollutants with exacerbation of adult respiratory symptoms and lung function decrement.  It categorized as "inadequate and insufficient" evidence of a causal relationship between traffic-related air pollution and health care utilization for respiratory problems, new onset adult asthma, chronic obstructive pulmonary disease (COPD), nonasthmatic respiratory allergy, and cancer in adults and children.  Other literature reviews have been published with conclusions generally similar to the HEI panel's.[,][,][,]  However, researchers from the U.S. Centers for Disease Control and Prevention (CDC) recently published a systematic review and meta-analysis of studies evaluating the risk of childhood leukemia associated with traffic exposure, and reported positive associations between "postnatal" proximity to traffic and leukemia risks, but no such association for "prenatal" exposures.
Health outcomes with few publications suggest the possibility of other effects still lacking sufficient evidence to draw definitive conclusions.  Among these outcomes with a small number of positive studies are neurological impacts (e.g., autism and reduced cognitive function) and reproductive outcomes (e.g., preterm birth, low birth weight).[,][,][,]
In addition to health outcomes, particularly cardiopulmonary effects, conclusions of numerous studies suggest mechanisms by which traffic-related air pollution affects health.  Numerous studies indicate that near-roadway exposures may increase systemic inflammation, affecting organ systems, including blood vessels and lungs.[,][,][,]  Long-term exposures in near-road environments have been associated with inflammation-associated conditions, such as atherosclerosis and asthma.[,][,]
Several studies suggest that some factors may increase susceptibility to the effects of traffic-associated air pollution.  Several studies have found stronger respiratory associations in children experiencing chronic social stress, such as in violent neighborhoods or in homes with high family stress.[,][,]
The risks associated with residence, workplace, or schools near major roads are of potentially high public health significance due to the large population in such locations.  According to the 2009 American Housing Survey, over 22 million homes (17.0 percent of all U.S. housing units) were located within 300 feet of an airport, railroad, or highway with four or more lanes.  This corresponds to a population of more than 50 million U.S. residents in close proximity to high-traffic roadways or other transportation sources.  Based on 2010 Census data, a 2013 publication estimated that 19 percent of the U.S. population (over 59 million people) lived within 500 meters of roads with at least 25,000 annual average daily traffic (AADT), while about 3.2 percent of the population lived within 100 meters (about 300 feet) of such roads.  Another 2013 study estimated that 3.7 percent of the U.S. population (about 11.3 million people) lived within 150 meters (about 500 feet) of interstate highways, or other freeways and expressways.  As discussed in Section VIII. B. (9), on average, populations near major roads have higher fractions of minority residents and lower socioeconomic status.  Furthermore, on average, Americans spend more than an hour traveling each day, bringing nearly all residents into a high-exposure microenvironment for part of the day.
In light of these concerns, EPA has required and is working with states to ensure that air quality monitors be placed near high-traffic roadways for determining NAAQS compliance for CO, NO2, and PM2.5 (in addition to those existing monitors located in neighborhoods and other locations farther away from pollution sources).  Near-roadway monitors for NO2 begin operation between 2014 and 2017 in Core Based Statistical Areas (CBSAs) with population of at least 500,000.  Monitors for CO and PM2.5 begin operation between 2015 and 2017.  These monitors will further our understanding of exposure in these locations.
EPA and DOT continue to research near-road air quality, including the types of pollutants found in high concentrations near major roads and health problems associated with the mixture of pollutants near roads.
Environmental Justice
 Environmental justice (EJ) is a principle asserting that all people deserve fair treatment and meaningful involvement with respect to environmental laws, regulations, and policies.  EPA seeks to provide the same degree of protection from environmental health hazards for all people.  DOT shares this goal and is informed about the potential environmental impacts of its rulemakings through its NEPA process (see NHTSA's DEIS).  As referenced below, numerous studies have found that some environmental hazards are more prevalent in areas where racial/ethnic minorities and people with low socioeconomic status (SES), represent a higher fraction of the population compared with the general population.
 As discussed in Section VIII. B. (8) of this document and NHTSA's DEIS, concentrations of many air pollutants are elevated near high-traffic roadways.  If minority populations and low-income populations disproportionately live near such roads, then an issue of EJ may be present.  We reviewed existing scholarly literature examining the potential for disproportionate exposure among minorities and people with low SES and we conducted our own evaluation of two national datasets: the U.S. Census Bureau's American Housing Survey for calendar year 2009 and the U.S. Department of Education's database of school locations.
 Publications that address EJ issues generally report that populations living near major roadways (and other types of transportation infrastructure) tend to be composed of larger fractions of nonwhite residents.  People living in neighborhoods near such sources of air pollution also tend to be lower in income than people living elsewhere.  Numerous studies evaluating the demographics and socioeconomic status of populations or schools near roadways have found that they include a greater percentage of minority residents, as well as lower SES (indicated by variables such as median household income).  Locations in these studies include Los Angeles, CA; Seattle, WA; Wayne County, MI; Orange County, FL; and the State of California [,][,][,][,][,]  Such disparities may be due to multiple factors.
 People with low SES often live in neighborhoods with multiple stressors and health risk factors, including reduced health insurance coverage rates, higher smoking and drug use rates, limited access to fresh food, visible neighborhood violence, and elevated rates of obesity and some diseases such as asthma, diabetes, and ischemic heart disease.  Although questions remain, several studies find stronger associations between air pollution and health in locations with such chronic neighborhood stress, suggesting that populations in these areas may be more susceptible to the effects of air pollution.[,][,][,]  Household-level stressors such as parental smoking and relationship stress also may increase susceptibility to the adverse effects of air pollution.[,]
 More recently, three publications report nationwide analyses that compare the demographic patterns of people who do or do not live near major roadways.[,][,]  All three of these studies found that people living near major roadways are more likely to be minorities or low in SES.  They also found that the outcomes of their analyses varied between regions within the U.S.  However, only one such study looked at whether such conclusions were confounded by living in a location with higher population density and how demographics differ between locations nationwide.  In general, it found that higher density areas have higher proportions of low income and minority residents.
 We analyzed two national databases that allowed us to evaluate whether homes and schools were located near a major road and whether disparities in exposure may be occurring in these environments.  The American Housing Survey (AHS) includes descriptive statistics of over 70,000 housing units across the nation.  The study survey is conducted every two years by the U.S. Census Bureau.  The second database we analyzed was the U.S. Department of Education's Common Core of Data, which includes enrollment and location information for schools across the U.S.
 In analyzing the 2009 AHS, we focused on whether or not a housing unit was located within 300 feet of "4-or-more lane highway, railroad, or airport."  We analyzed whether there were differences between households in such locations compared with those in locations farther from these transportation facilities.  We included other variables, such as land use category, region of country, and housing type.  We found that homes with a nonwhite householder were 22-34 percent more likely to be located within 300 feet of these large transportation facilities than homes with white householders.  Homes with a Hispanic householder were 17-33 percent more likely to be located within 300 feet of these large transportation facilities than homes with non-Hispanic householders.  Households near large transportation facilities were, on average, lower in income and educational attainment, more likely to be a rental property and located in an urban area compared with households more distant from transportation facilities.
 In examining schools near major roadways, we examined the Common Core of Data (CCD) from the U.S. Department of Education, which includes information on all public elementary and secondary schools and school districts nationwide.  To determine school proximities to major roadways, we used a geographic information system (GIS) to map each school and roadways based on the U.S. Census's TIGER roadway file.  We found that minority students were overrepresented at schools within 200 meters of the largest roadways, and that schools within 200 meters of the largest roadways also had higher than expected numbers of students eligible for free or reduced-price lunches.  For example, Black students represent 22 percent of students at schools located within 200 meters of a primary road, whereas Black students represent 17 percent of students in all U.S. schools.  Hispanic students represent 30 percent of students at schools located within 200 meters of a primary road, whereas Hispanic students represent 22 percent of students in all U.S. schools.
Overall, there is substantial evidence that people who live or attend school near major roadways are more likely to be of a minority race, Hispanic ethnicity, and/or low SES.  The emission reductions from this proposed rule would likely result in widespread air quality improvements, but the impact on pollution levels in close proximity to roadways would be most direct.  Thus, this proposed rule would likely help in mitigating the disparity in racial, ethnic, and economically-based exposures.

         Environmental Effects of Non-GHG Pollutants
Visibility
Visibility can be defined as the degree to which the atmosphere is transparent to visible light.  Visibility impairment is caused by light scattering and absorption by suspended particles and gases.  Visibility is important because it has direct significance to people's enjoyment of daily activities in all parts of the country.  Individuals value good visibility for the well-being it provides them directly, where they live and work, and in places where they enjoy recreational opportunities.  Visibility is also highly valued in significant natural areas, such as national parks and wilderness areas, and special emphasis is given to protecting visibility in these areas.  For more information on visibility see the final 2009 PM ISA.
EPA is working to address visibility impairment.  Reductions in air pollution from implementation of various programs associated with the Clean Air Act Amendments of 1990 (CAAA) provisions have resulted in substantial improvements in visibility, and will continue to do so in the future. Because trends in haze are closely associated with trends in particulate sulfate and nitrate due to the simple relationship between their concentration and light extinction, visibility trends have improved as emissions of SO2 and NOx have decreased over time due to air pollution regulations such as the Acid Rain Program.
In the Clean Air Act Amendments of 1977, Congress recognized visibility's value to society by establishing a national goal to protect national parks and wilderness areas from visibility impairment caused by manmade pollution.  In 1999, EPA finalized the regional haze program to protect the visibility in Mandatory Class I Federal areas.  There are 156 national parks, forests and wilderness areas categorized as Mandatory Class I Federal areas.  These areas are defined in CAA Section 162 as those national parks exceeding 6,000 acres, wilderness areas and memorial parks exceeding 5,000 acres, and all international parks which were in existence on August 7, 1977.
EPA has also concluded that PM2.5 causes adverse effects on visibility in other areas that are not protected by the Regional Haze Rule, depending on PM2.5 concentrations and other factors such as dry chemical composition and relative humidity (i.e., an indicator of the water composition of the particles).  EPA revised the PM2.5 standards in December 2012 and established a target level of protection that is expected to be met through attainment of the existing secondary standards for PM2.5.
Plant and Ecosystem Effects of Ozone
The welfare effects of ozone can be observed across a variety of scales, i.e. subcellular, cellular, leaf, whole plant, population and ecosystem.  Ozone effects that begin at small spatial scales, such as the leaf of an individual plant, when they occur at sufficient magnitudes (or to a sufficient degree) can result in effects being propagated along a continuum to larger and larger spatial scales.  For example, effects at the individual plant level, such as altered rates of leaf gas exchange, growth and reproduction can, when widespread, result in broad changes in ecosystems, such as productivity, carbon storage, water cycling, nutrient cycling, and community composition.
Ozone can produce both acute and chronic injury in sensitive species depending on the concentration level and the duration of the exposure.  In those sensitive species, effects from repeated exposure to ozone throughout the growing season of the plant tend to accumulate, so that even low concentrations experienced for a longer duration have the potential to create chronic stress on vegetation.  Ozone damage to sensitive species includes impaired photosynthesis and visible injury to leaves.  The impairment of photosynthesis, the process by which the plant makes carbohydrates (its source of energy and food), can lead to reduced crop yields, timber production, and plant productivity and growth.  Impaired photosynthesis can also lead to a reduction in root growth and carbohydrate storage below ground, resulting in other, more subtle plant and ecosystems impacts.  These latter impacts include increased susceptibility of plants to insect attack, disease, harsh weather, interspecies competition and overall decreased plant vigor.  The adverse effects of ozone on areas with sensitive species could potentially lead to species shifts and loss from the affected ecosystems, resulting in a loss or reduction in associated ecosystem goods and services.  Additionally, visible ozone injury to leaves can result in a loss of aesthetic value in areas of special scenic significance like national parks and wilderness areas and reduced use of sensitive ornamentals in landscaping.
The Integrated Science Assessment (ISA) for Ozone presents more detailed information on how ozone effects vegetation and ecosystems.  The ISA concludes that ambient concentrations of ozone are associated with a number of adverse welfare effects and characterizes the weight of evidence for different effects associated with ozone.  The ISA concludes that visible foliar injury effects on vegetation, reduced vegetation growth, reduced productivity in terrestrial ecosystems, reduced yield and quality of agricultural crops, and alteration of below-ground biogeochemical cycles are causally associated with exposure to ozone.  It also concludes that reduced carbon sequestration in terrestrial ecosystems, alteration of terrestrial ecosystem water cycling, and alteration of terrestrial community composition are likely to be causally associated with exposure to ozone.
Atmospheric Deposition
Wet and dry deposition of ambient particulate matter delivers a complex mixture of metals (e.g., mercury, zinc, lead, nickel, aluminum, cadmium), organic compounds (e.g., polycyclic organic matter, dioxins, furans) and inorganic compounds (e.g., nitrate, sulfate) to terrestrial and aquatic ecosystems.  The chemical form of the compounds deposited depends on a variety of factors including ambient conditions (e.g., temperature, humidity, oxidant levels) and the sources of the material.  Chemical and physical transformations of the compounds occur in the atmosphere as well as the media onto which they deposit.  These transformations in turn influence the fate, bioavailability and potential toxicity of these compounds.
Adverse impacts to human health and the environment can occur when particulate matter is deposited to soils, water, and biota.  Deposition of heavy metals or other toxics may lead to the human ingestion of contaminated fish, impairment of drinking water, damage to terrestrial, freshwater and marine ecosystem components, and limits to recreational uses.  Atmospheric deposition has been identified as a key component of the environmental and human health hazard posed by several pollutants including mercury, dioxin and PCBs.
The ecological effects of acidifying deposition and nutrient enrichment are detailed in the Integrated Science Assessment for Oxides of Nitrogen and Sulfur-Ecological Criteria. Atmospheric deposition of nitrogen and sulfur contributes to acidification, altering biogeochemistry and affecting animal and plant life in terrestrial and aquatic ecosystems across the United States.  The sensitivity of terrestrial and aquatic ecosystems to acidification from nitrogen and sulfur deposition is predominantly governed by geology.  Prolonged exposure to excess nitrogen and sulfur deposition in sensitive areas acidifies lakes, rivers and soils.  Increased acidity in surface waters creates inhospitable conditions for biota and affects the abundance and biodiversity of fishes, zooplankton and macroinvertebrates and ecosystem function.  Over time, acidifying deposition also removes essential nutrients from forest soils, depleting the capacity of soils to neutralize future acid loadings and negatively affecting forest sustainability.  Major effects in forests include a decline in sensitive tree species, such as red spruce (Picea rubens) and sugar maple (Acer saccharum).  In addition to the role nitrogen deposition plays in acidification, nitrogen deposition also leads to nutrient enrichment and altered biogeochemical cycling.  In aquatic systems increased nitrogen can alter species assemblages and cause eutrophication.  In terrestrial systems nitrogen loading can lead to loss of nitrogen sensitive lichen species, decreased biodiversity of grasslands, meadows and other sensitive habitats, and increased potential for invasive species.  For a broader explanation of the topics treated here, refer to the description in Chapter <IP_ADDRESS> of the RIA.
Building materials including metals, stones, cements, and paints undergo natural weathering processes from exposure to environmental elements (e.g., wind, moisture, temperature fluctuations, sunlight, etc.).  Pollution can worsen and accelerate these effects. Deposition of PM is associated with both physical damage (materials damage effects) and impaired aesthetic qualities (soiling effects).  Wet and dry deposition of PM can physically affect materials, adding to the effects of natural weathering processes, by potentially promoting or accelerating the corrosion of metals, by degrading paints and by deteriorating building materials such as stone, concrete and marble. The effects of PM are exacerbated by the presence of acidic gases and can be additive or synergistic due to the complex mixture of pollutants in the air and surface characteristics of the material. Acidic deposition has been shown to have an effect on materials including zinc/galvanized steel and other metal, carbonate stone (as monuments and building facings), and surface coatings (paints). The effects on historic buildings and outdoor works of art are of particular concern because of the uniqueness and irreplaceability of many of these objects.
Environmental Effects of Air Toxics
Emissions from producing, transporting and combusting fuel contribute to ambient levels of pollutants that contribute to adverse effects on vegetation.  Volatile organic compounds, some of which are considered air toxics, have long been suspected to play a role in vegetation damage.  In laboratory experiments, a wide range of tolerance to VOCs has been observed.  Decreases in harvested seed pod weight have been reported for the more sensitive plants, and some studies have reported effects on seed germination, flowering and fruit ripening.  Effects of individual VOCs or their role in conjunction with other stressors (e.g., acidification, drought, temperature extremes) have not been well studied.  In a recent study of a mixture of VOCs including ethanol and toluene on herbaceous plants, significant effects on seed production, leaf water content and photosynthetic efficiency were reported for some plant species.
Research suggests an adverse impact of vehicle exhaust on plants, which has in some cases been attributed to aromatic compounds and in other cases to nitrogen oxides.[,],
         Air Quality Impacts of Non-GHG Pollutants
Current Concentrations of Non-GHG Pollutants
Nationally, levels of PM2.5, ozone, NOX, SOX, CO and air toxics are declining.  However, as of July 2, 2014 approximately 147 million people lived in counties designated nonattainment for one or more of the NAAQS, and this figure does not include the people living in areas with a risk of exceeding the NAAQS in the future.  Further, the majority of Americans continue to be exposed to ambient concentrations of air toxics at levels which have the potential to cause adverse health effects.  In addition, populations who live, work, or attend school near major roads experience elevated exposure concentrations to a wide range of air pollutants.

Impacts of Proposed Standards on Future Ambient Concentrations of Non-GHG Pollutants
Full-scale photochemical air quality modeling is necessary to accurately project levels of criteria pollutants and air toxics.  For the final rulemaking, national-scale air quality modeling analyses will be performed to analyze the impacts of the standards on PM2.5, ozone, NO2, and selected air toxics (i.e., benzene, formaldehyde, acetaldehyde, naphthalene, acrolein and 1,3-butadiene).  The length of time needed to prepare the necessary emissions inventories, in addition to the processing time associated with the modeling itself, has precluded us from performing air quality modeling for this proposal.
Section VIII.A of the preamble presents projections of the changes in criteria pollutant and air toxics emissions due to the proposed vehicle standards; the basis for those estimates is set out in Chapter 5 of the draft RIA.  NHTSA also provides its projections in Chapter 4 of its DEIS.  The atmospheric chemistry related to ambient concentrations of PM2.5, ozone and air toxics is very complex, and making predictions based solely on emissions changes is extremely difficult.  However, based on the magnitude of the emissions changes predicted to result from the proposed standards, the agencies expect that there will be improvements in ambient air quality, pending more comprehensive analyses for the final rulemaking.
For the final rulemaking national-scale air quality modeling analyses will be performed to estimate future year ambient ozone, NO2, and PM2.5 concentrations, air toxics concentrations, visibility levels and nitrogen and sulfur deposition levels for 2040.  The agencies intend to use a 2011-based Community Multi-scale Air Quality (CMAQ) modeling platform as the tool for the air quality modeling.  The CMAQ modeling system is a comprehensive three-dimensional grid-based Eulerian air quality model designed to estimate the formation and fate of oxidant precursors, primary and secondary PM concentrations and deposition, and air toxics, over regional and urban spatial scales (e.g., over the contiguous United States).[,][,][,]  The CMAQ model is a well-known and well-established tool and is commonly used by EPA for regulatory analyses, by States in developing attainment demonstrations for their State Implementation Plans, and in numerous other national and international applications.[,][,][,]  The CMAQ model version 5.0 was most recently peer-reviewed in September of 2011 for the U.S. EPA.  CMAQ includes numerous science modules that simulate the emission, production, decay, deposition and transport of organic and inorganic gas-phase and particle-phase pollutants in the atmosphere.  This 2011 multi-pollutant modeling platform used the most recent multi-pollutant CMAQ code available at the time of air quality modeling (CMAQ version 5.0.2; multipollutant version
  CMAQ v5.0.2 reflects updates to version 5.0 to improve the underlying science algorithms as well as include new diagnostic/scientific modules which are detailed at http://www.cmascenter.org.[,][,]

Economic and Other Impacts
This section presents the costs, benefits and other economic impacts of the proposed Phase 2 standards.  It is important to note that NHTSA's proposed fuel consumption standards and EPA's proposed GHG standards would both be in effect, and each would lead to average fuel efficiency increases and GHG emission reductions.
The net benefits of the proposed Phase 2 standards consist of the effects of the program on:
the vehicle program costs (costs of complying with the vehicle CO2 and fuel consumption standards),
fuel expenditure changes associated with reduced fuel usage resulting from more efficient vehicles and increased fuel usage associated with the "rebound" effect, both of which result from the program,
the economic value of reductions in GHGs,
the economic value of reductions in non-GHG pollutants,
costs associated with increases in noise, congestion, and accidents resulting from increased vehicle use,
benefits of increased vehicle use associated with the "rebound" effect,
the economic value of improvements in U.S. energy security impacts.
The benefits and costs of this rule are analyzed using 3 percent and 7 percent discount rates, consistent with current OMB guidance.  These rates are intended to represent consumers' preference for current over future consumption (3 percent), and the real rate of return on private investment (7 percent) which indicates the opportunity cost of capital. However, neither of these rates necessarily represents the discount rate that individual decision-makers use.
The program may also have other economic effects that are not included here.  The agencies seek comment on whether any costs or benefits are omitted from this analysis, so that they can be explicitly recognized in the final rules.  In particular, as discussed in Sections III through VI of this preamble and in Chapter 2 of the draft RIA, the technology cost estimates developed here take into account the costs to hold other vehicle attributes, such as size and performance, constant.  With these assumptions, and because welfare losses represent monetary estimates of how much buyers would have to be compensated to be made as well off as they would have been in the absence of this regulation, price increases for new vehicles measure the welfare losses to the vehicle buyers.  If the full technology cost gets passed along to the buyer as an increase in price, the technology cost thus measures the primary welfare loss of the standards, including impacts on buyers.  Increasing fuel efficiency would have to lead to other changes in the vehicles that buyers find undesirable for there to be additional welfare losses that are not included in the technology costs.
As the 2012-2016 and 2017-2025 light-duty GHG/CAFE rules discussed, if other vehicle attributes are not held constant, then the technology cost estimates do not capture the losses to vehicle buyers associated with these changes.  The light-duty rules also discussed other potential issues that could affect the calculation of the welfare impacts of these types of changes, such as aspects of buyers' behavior that might affect the demand for technology investments, uncertainty in buyers' investment horizons, and the rate at which truck owner's trade off higher vehicle purchase price against future fuel savings.  The agencies seek comments, including supporting data and quantitative analyses, of any additional impacts of the proposed standards on vehicle attributes and performance, or other potential aspects that could positively or negatively affect the welfare implications of this proposed rulemaking.
Where possible, we identify the uncertain aspects of these economic impacts and attempt to quantify them (e.g., sensitivity ranges associated with quantified and monetized GHG impacts; range of dollar-per-ton values to monetize non-GHG health benefits; uncertainty with respect to learning and markups).  For HD pickups and vans, the agencies explicitly analyzed the uncertainty surrounding its estimates of the economic impacts from requiring higher fuel efficiency in Preamble Section VI.  The agencies have also examined the sensitivity of oil prices on fuel expenditures; results of this sensitivity analysis can be found in Chapter 8 of the RIA.  NHTSA's draft EIS also characterizes the uncertainty in economic impacts associated with the HD national program.  For other impacts, however, there is inadequate information to inform a thorough, quantitative assessment of uncertainty.  EPA and NHTSA continue to work toward developing a comprehensive strategy for characterizing the aggregate impact of uncertainty in key elements of its analyses and we will continue to work to refine these uncertainty analyses in the future as time and resources permit.  The agencies seek comments on the methods and assumptions used to quantify uncertainty in this analysis, as well as comments on methods and data that might inform relevant uncertainty analyses not quantified in this analysis.
This and other sections of the preamble address Section 317 of the Clean Air Act on economic analysis.  Section IX.L addresses Section 321 of the Clean Air Act on employment analysis.  The total monetized benefits and costs of the program are summarized in Section IX.K for the preferred alternative and in Section X for all alternatives.
Conceptual Framework
The HD Phase 2 proposed standards are motivated by both the 2007 Energy Independence and Security Act requirement that NHTSA establish fuel efficiency standards for medium- and heavy-duty vehicles and the Clean Air Act requirement that EPA determine whether GHG emissions endanger health and welfare, and if so adopt measures that will reduce them.  NHTSA's statutory mandate is intended to further the agency's long-standing goals of reducing U.S. consumption and imports of petroleum energy to improve the nation's energy security, and the Clean Air Act requires EPA to reduce the threat to health and welfare posed by climate change.
From an economics perspective, government actions to improve our nation's energy security and to protect our nation from the potential threats of climate change address "externalities," or economic consequences of decisions by individuals and businesses that extend beyond those who make these decisions.  For example, users of transportation fuels increase the entire U.S. economy's risk of having to make costly adjustments due to rapid increases in oil prices, but these users generally do not consider such costs when they decide to consume more fuel.
Similarly, consuming transportation fuel also increases emissions of greenhouse gases and other more localized air pollutants that occur when fuel is refined, distributed, and consumed. Some of these emissions increase the likelihood and severity of potential climate-related economic damages, and others cause economic damages by adversely affecting human health.  The need to address these external costs and other adverse effects provides a well-established economic rationale that supports the statutory direction given to government agencies to establish regulatory programs that reduce the magnitude of these adverse effects at reasonable costs.
The proposed Phase 2 standards would require manufacturers of new heavy-duty vehicles, including trailers (HDVs), to improve the fuel efficiency of the products that they produce.  As HDV users purchase and operate these new vehicles, they would consume significantly less fuel, in turn reducing U.S. petroleum consumption and imports as well as emissions of GHGs and other air pollutants.  Thus as a consequence of the agencies' efforts to meet our statutory obligations to improve U.S. energy security and reduce the threat of climate damages, the proposed fuel efficiency and GHG emission standards would also reduce HDV operators' outlays for fuel purchases.  These fuel savings are one measure of the proposed rule's effectiveness in promoting NHTSA's statutory goal of conserving energy.  Although these savings are not the agencies' primary motivation for adopting higher fuel efficiency standards, these substantial fuel savings represent significant additional economic benefits of this proposal.
Potential savings in fuel costs would appear to offer HDV buyers strong incentives to pay higher prices for vehicles that feature technology or equipment that reduces fuel consumption.  These potential savings would also appear to offer HDV manufacturers similarly strong incentives to produce more fuel-efficient vehicles.  Economic theory suggests that interactions between vehicle buyers and sellers in a normally-functioning competitive market would lead HDV manufacturers to incorporate all technologies that contribute to lower net costs into the vehicles they offer, and buyers to purchase them willingly.  Nevertheless, many readily available technologies that appear to offer cost-effective increases in HDV fuel efficiency (when evaluated over their expected lifetimes using conventional discount rates) have not been widely adopted, despite their potential to repay buyers' initial investments rapidly.
This economic situation is commonly known as the "energy efficiency gap" or "energy paradox."  This situation is perhaps more challenging to understand with respect to the heavy-duty sector versus the light-duty vehicle sector. Unlike light-duty vehicles  -  which are purchased and used mainly by individuals and households  -  the vast majority of HDVs are purchased and operated by profit-seeking businesses for which fuel costs represent a substantial operating expense. Nevertheless, on the basis of evidence reviewed below, the agencies believe that a significant number of fuel efficiency improving technologies would remain far less widely adopted in the absence of these proposed standards.
Economic research offers several possible explanations for why the prospect of these apparent savings might not lead HDV manufacturers and buyers to adopt technologies that would be expected to reduce HDV operating costs.  Some of these explanations involve failures of the HDV market for reasons other than the externalities caused by producing and consuming fuel.  These include situations where information about the performance of fuel economy technologies is incomplete, costly to obtain, or available only to one party to a transaction (or "asymmetrical"), as well as behavioral rigidities in either the HDV manufacturing or HDV-operating industries, such as standardized or inflexibly administered operating procedures, or requirements of other regulations on HDVs. Other explanations for the limited use of apparently cost-effective technologies that do not involve market failures include HDV operators' concerns about the performance, reliability, or maintenance requirements of new technology under the demands of everyday use, uncertainty about the fuel savings they will actually realize, and questions about possible effects on carrying capacity or other aspects of HDVs' utility.
In the HD Phase 1 rulemaking (which, in contrast to these proposed standards, did not apply to trailers), the agencies raised five hypotheses that might explain this energy efficiency gap or paradox:
         *          Imperfect information in the new vehicle market: information available to prospective buyers about the effectiveness of some fuel-saving technologies for new vehicles may be inadequate or unreliable.  If reliable information on their effectiveness in reducing fuel consumption is unavailable or difficult to obtain, HDV buyers will understandably be reluctant to pay higher prices to purchase vehicles equipped with unproven technologies.
         *          Imperfect information in the resale market: buyers in the used vehicle market may not be willing to pay adequate premiums for more fuel efficient vehicles when they are offered for resale to ensure that buyers of new vehicles can recover the remaining value of their original investment in higher fuel efficiency.  The prospect of an inadequate return on their original owners' investments in higher fuel efficiency may contribute to the short payback periods that buyers of new vehicles appear to demand.
         *          Principal-agent problems causing split incentives: an HDV buyer may not be directly responsible for its future fuel costs, or the individual who will be responsible for fuel costs may not participate in the HDV purchase decision.  In these cases, the signal to invest in higher fuel efficiency normally provided by savings in fuel costs may not be transmitted effectively to HDV buyers, and the incentives of HDV buyers and fuel buyers will diverge, or be "split."  The trailers towed by heavy-duty tractors, which are typically not supplied by the tractor manufacturer or seller, present an obvious potential situation of split incentives that was not addressed in the HD Phase 1 rulemaking, but it may apply in this rulemaking.  If there is inadequate pass-through of price signals from trailer users to their buyers, then low adoption of fuel-saving technologies may result.
         *          Uncertainty about future fuel cost savings: HDV buyers may be uncertain about future fuel prices, or about maintenance costs and reliability of some fuel efficiency technologies.  Buyers may react to this uncertainty by implicitly discounting potential future savings at rates above standard discount rates.  In contrast, the costs of fuel-saving or maintenance-reducing technologies are immediate and thus not subject to discounting.  In this situation, potential variability about buyers' expected returns on capital investments to achieve higher fuel efficiency may shorten the payback period  -  the time required to repay those investments  -  they demand in order to make them.
         *          Adjustment and transactions costs: potential resistance to new technologies  -  stemming, for example, from drivers' reluctance or slowness to adjust to changes in the way vehicles operate  -  may slow or inhibit new technology adoption.  If a conservative approach to new technologies leads HDV buyers to adopt them slowly, then successful new technologies would be adopted over time without market intervention, but only with potentially significant delays in achieving the fuel saving, environmental, and energy security benefits they offer.  There also may be costs associated with training drivers to realize potential fuel savings enabled by new technologies, or with accelerating fleet operators' scheduled fleet turnover and replacement to hasten their acquisition of vehicles equipped with these technologies.
Some of these explanations imply failures in the private market for fuel-saving technology beyond the externalities caused by producing and consuming fuel, while others suggest that complications in valuing or adapting to technologies that reduce fuel consumption may partly explain buyers' hesitance to purchase more fuel-efficient vehicles.  In either case, adopting this proposed rule would provide regulatory certainty and generate important economic benefits in addition to reducing externalities.
Since the HD Phase 1 rulemaking, new research has provided further insight into potential barriers to adoption of fuel-saving technologies.  Several studies utilized focus groups and interviews involving small numbers of participants, who were people with time and inclination to join such studies, rather than selected at random.  As a result, the information from these groups is not necessarily representative of the industry as a whole.  While these studies cannot provide conclusive evidence about how all HDV buyers make their decisions, they do describe issues that arise for those that participated.
One common theme that emerges from these studies is the inability of HDV buyers to obtain reliable information about the fuel savings, reliability, and maintenance costs of technologies that improve fuel efficiency.  In many product markets, such as consumer electronics, credible reviews and tests of product performance are readily available to potential buyers.  In the trucking industry, however, the performance of fuel-saving technology is likely to depend on many firm-specific attributes, including the intensity of HDV use, the typical distance and routing of HDV trips, driver characteristics, road conditions, regional geography and traffic patterns.
As a result, businesses that operate HDVs have strong preferences for testing fuel-saving technologies "in-house" because they are concerned that their patterns of vehicle use may lead to different results from those reported in published information.  Businesses with less capability to do in-house testing often seek information from peers, yet often remain skeptical of its applicability due to differences in the nature of their operations.  One source of imperfect information is the lack of availability of certain technologies from preferred suppliers.  HDV buyers often prefer to have technology or equipment installed by their favored original equipment manufacturers.  However, some technologies may not be available through these preferred sources, or may be available only as after-market installations from third parties (Aarnink et al. 2012, Roeth et al. 2013).
Although these studies appear to show that information in the new HDV market is often limited or viewed as unreliable, the evidence for imperfect information in the market for used HDVs is mixed.  On the one hand, some studies noted that fuel-saving technology is often not valued or demanded in the used vehicle market, because of imperfect information about its benefits, or greater mistrust of its performance among buyers in the used vehicle market than among buyers of new vehicles.  The lack of demand might also be due to the intended use of the used HDV, which may not require or reward the presence of certain fuel-saving technologies.  In other cases, however, fuel-saving technology can lead to a premium in the used market, as for instance to meet the more stringent requirements for HDVs operating in California.
All of the recent research identifies split incentives, or principal-agent problems, as a potential barrier to technology adoption.  These occur when those responsible for investment decisions are different from the main beneficiaries of the technology.  For instance, businesses that own and lease trailers to HDV operators may not have an incentive to invest in trailer-specific fuel-saving technology, since they do not collect the savings from the lower fuel costs that result.  Vernon and Meier (2012) estimate that 23 percent of trailers may be exposed to this kind of principal-agent problem, although they do not quantify its financial significance.
Split incentives can also exist when the HDV driver is not responsible for paying fuel costs.  Some technologies require additional effort, training, or changes in driving behavior to achieve their promised fuel savings; drivers who do not pay for fuel may be reluctant to undertake those changes, thus reducing the fuel-saving benefits from the perspective of the individual or company paying for the fuel.  For instance, drivers might not consistently deploy boat-tails equipped on trailers to improve vehicle aerodynamics.  Vernon and Meier also calculate that 91 percent of HDV fuel use is subject to this form of principal-agent problem, although they do not estimate how much it might reduce fuel savings to those who are paying for the fuel.
The studies based on focus groups and interviews (Klemick et al. 2013, Aarnink et al. 2012, Roeth et al. 2013) provide mixed evidence on the severity of the split-incentive problem.  Focus groups often do identify diverging incentives between drivers and the decision-makers responsible for purchasing vehicles, and economics literature recognizes that this split incentive can be a barrier to adopting new technology.  Aarnink et al. (2012) and Roeth et al. (2013) cite examples of split incentives involving trailers and fuel surcharges, although the latter also cites other examples where these same issues do not lead to split incentives.
In an effort to minimize problems that can arise form split incentives, many businesses that operate HDVs also train drivers in the use of specific technologies or to modify their driving behavior in order to improve fuel efficiency, while some also offer financial incentives to their drivers to conserve fuel.  All of these options can help to reduce the split incentive problem, although they may not be effective where it arises from different ownership of combination tractors and trailers.
Uncertainty about future costs for fuel and maintenance, or about the reliability of new technology, also appears to be a significant obstacle that can slow the adoption of fuel-saving technologies.  These examples illustrate the problem of uncertain or unreliable information about the actual performance of fuel efficiency technology discussed above.  In addition, businesses that operate HDVs may be concerned about how reliable new technologies will prove to be on the road, and whether significant additional maintenance costs or equipment malfunctions that result in costly downtime could occur.  Roeth et al. (2013) and Klemick et al. (2013) both document the short payback periods that HDV buyers require on their investments -- usually about 2 years -- which may be partly attributable to these uncertainties.
These studies also provide some support for the view that adjustment and transactions costs may impede HDV buyers from investing in higher fuel efficiency.  As discussed above, several studies note that HDV buyers are less likely to select new technology when it is not available from their preferred manufacturers. Some technologies are only available as after-market additions, which can add other costs to adopting them.
Some studies also cite driver acceptance of new equipment or technologies as a barrier to their adoption. HDV driver turnover is high in the U.S., and businesses that operate HDVs are concerned about retaining their best drivers. Therefore, they may avoid technologies that require significant new training or adjustments in driver behavior.  For some technologies that can be used to meet the proposed standards, such as automatic tire inflation systems, training costs are likely to be minimal.  Other technologies such as stop-start systems, however, may require drivers to adjust their expectations about vehicle operation, and it is difficult for the agencies to anticipate how drivers will respond to such changes.
In addition to these factors, the studies considered other possible explanations for HDV buyers' apparent reluctance or slowness to invest in fuel-saving equipment or technology.  Financial constraints  -  access to lending sources willing to finance purchases of more expensive vehicles  -  do not appear to be a problem for the medium- and large-sized businesses participating in Klemick et al.'s (2013) study.  However, Roeth et al. (2013) noted that access to capital can be a significant challenge to smaller or independent businesses, and that price is always a concern to buyers.  In general, businesses that operate HDVs face a range of competing uses for available capital other than investing in fuel-saving technologies, and may assign higher priority to these other uses, even when investing in higher fuel efficiency HDVs appears to promise adequate financial returns.
Other potentially important barriers to the adoption of measures that improve fuel efficiency may arise from "network externalities," where the benefits to new users of a technology depend on how many others have already adopted it.  One example where network externalities seem likely to arise is the market for natural gas-fueled HDVs: the limited availability of refueling stations may reduce potential buyers' willingness to purchase natural gas-fueled HDVs, while the small number of such HDVs in-use does not provide sufficient economic incentive to construct more natural gas refueling stations.
Some businesses that operate HDVs may also be concerned about the difficulty in locating repair facilities or replacement parts, such as single-wide tires, wherever their vehicles operate.  When a technology has been widely adopted, then it is likely to be serviceable even in remote or rural places, but until it becomes widely available, its early adopters may face difficulties with repairs or replacements.   By accelerating the widespread adoption of these technologies, the proposed standards may assist in overcoming these difficulties.
As discussed previously, the lack of availability of fuel-saving technologies from preferred manufactures can also be a significant barrier to adoption (Roeth et al. 2013).  Manufacturers may be hesitant to offer technologies for which there is not strong demand, especially if the technologies require significant research and development expenses and other costs of bringing the technology to a market of uncertain demand.
Roeth et al. (2013) also noted that it can take years, and sometimes as much as a decade, for a specific technology to become available from all manufacturers.  Many manufacturers prefer to observe the market and follow other manufacturers rather than be the first to market with a specific technology.  The "first-mover disadvantage" has been recognized in other research where the "first-mover" pays a higher proportion of the costs of developing technology, but loses the long-term advantage when other businesses follow quickly.  In this way, there may be barriers to innovation on the supply side that result in lower adoption rates of fuel-efficiency technology than would be optimal.
In summary, the agencies recognize that businesses that operate HDVs are under competitive pressure to reduce operating costs, which should compel HDV buyers to identify and rapidly adopt cost-effective fuel-saving technologies.  Outlays for labor and fuel generally constitute the two largest shares of HDV operating costs, depending on the price of fuel, distance traveled, type of HDV, and commodity transported (if any), so businesses that operate HDVs face strong incentives to reduce these costs.[,]
However, the short payback periods that buyers of new HDVs appear to require suggest that some combination of uncertainty about future cost savings, transactions costs, and imperfectly functioning markets impedes this process.  Markets for both new and used HDVs may face these problems, although it is difficult to assess empirically the degree to which they actually do.  Even if the benefits from widespread adoption of fuel-saving technologies exceed their costs, their use may remain limited or spread slowly because their early adopters bear a disproportionate share of those costs.  In this case, the proposed standards may help to overcome such barriers by ensuring that these measures would be widely adopted.
Providing information about fuel-saving technologies, offering incentives for their adoption, and sharing HDV operators' real-world experiences with their performance through voluntary programs such as EPA's SmartWay Transport Partnership should assist in the adoption of new cost-saving technologies.  Nevertheless, other barriers that impede the diffusion of new technologies are likely to remain.  Buyers who are willing to experiment with new technologies expect to find cost savings, but those savings may be difficult to verify or replicate.  As noted previously, because benefits from employing these technologies are likely to vary with the characteristics of individual routes and traffic patterns, buyers of new HDVs may find it difficult to identify or verify the effects of fuel-saving technologies in their operations.  Risk-averse buyers may also avoid new technologies out of concerns over the possibility of inadequate returns on their investments, or with other possible adverse impacts.
Some HDV manufacturers may delay in investing in the development and production of new technologies, instead waiting for other manufacturers to bear the risks of those investments first.  Competitive pressures in the HDV freight transport industry can provide a strong incentive to reduce fuel consumption and improve environmental performance.  However, not every HDV operator has the requisite ability or interest to access and utilize the technical information, or the resources necessary, to evaluate this information within the context of his or her own operations.
As discussed previously, whether the technologies available to improve HDVs' fuel efficiency would be adopted widely in the absence of the program is challenging to assess.  To the extent that these technologies would be adopted in its absence, neither their costs nor their benefits would be attributed to the program.  To account for this possibility, the agencies analyzed the proposed standards and the regulatory alternatives against two reference cases, or baselines, as described in Section X.
The first case uses a baseline that projects some improvement in fuel efficiency for new trailers, but no improvement in fuel efficiency for other vehicle segments in the absence of new Phase 2 standards.  This first case is referred to as the less dynamic baseline, or Alternative 1a.  The second case uses a baseline that projects some improvement in vehicle fuel efficiency for tractors, trailers, pickup trucks, and vans but not for vocational vehicles.  This second case is referred to as the more dynamic baseline, or Alternative 1b.
The agencies will continue to explore reasons for the slow adoption of readily available and apparently cost-effective technologies for improving fuel efficiency.  We also seek comments on our hypotheses about its causes, as well as data or other information that can inform our understanding of why this situation seems to persist.

Vehicle-Related Costs Associated with the Program
Technology Cost Methodology
Direct Manufacturing Costs
The direct manufacturing costs (DMCs) used throughout this analysis are derived from several sources.  Many of the tractor, vocational and trailer DMCs can be sourced to the Phase 1 rule which, in turn, were sourced largely from a contracted study by ICF International for EPA. We have updated those costs by converting them to 2012 dollars, as described in Section IX.B.1.e below, and by continuing the learning effects described in the Phase 1 rule and in Section IX.B.1.c below.  The new tractor, vocational and trailer costs can be sourced to a more recent study conducted by Southwest Research Institute (SwRI) under contract to NHTSA.  The cost methodology used by SwRI was to estimate retail costs and work backward from there to derive a DMC for each technology.  The agencies did not agree with the approach used by SwRI to move from retail cost to DMC as the approach was to simply divide retail costs by 2 and use the result as a DMC. Our research, discussed below, suggests that a divisor of 2 is too high. Therefore, where we have used a SwRI derived retail estimate, we have divided by our researched markups to arrive at many of the DMCs used in this analysis.  In this way, the agencies have used an approach consistent with past GHG/CAFE/fuel consumption rules by dividing estimated retail prices by our estimated retail price equivalent (RPE) markups to derive an appropriate DMC for each technology.  We describe our RPEs in Section IX.B.1.b, below.
For HD pickups and vans, we have relied primarily on the Phase 1 rule and the recent light-duty 2017-2025 model year rule since most technologies expected on these vehicles are, in effect, the same as those used on light-duty pickups.  Many of those technology DMCs are based on cost teardown studies which the agencies consider to be the most robust method of cost estimation.  However, because most of the HD versions of those technologies are expected to be more costly than their light-duty counterparts, we have scaled upward most of the light-duty DMCs for this analysis.  We have also used some costs developed under contract to NHTSA by SwRI.
Importantly, in our methodology, all technologies are treated as being sourced from a supplier rather than being developed and produced in-house.  As a result, some portion of the total indirect costs of making a technology or system -- those costs incurred by the supplier for research, development, transportation, marketing etc. -- are contained in the sales price to the engine and/or vehicle/trailer manufacturer (i.e., the original equipment manufacturer (OEM)). That sale price paid by the OEM to the supplier is the DMC we estimate.
We present the details -- sources, DMC values, scaling from light-duty values, markups, learning effects, adoption rates -- behind all our costs in Chapter 2 of the draft RIA.
Indirect Costs
To produce a unit of output, engine and truck manufacturers incur direct and indirect costs.  Direct costs include cost of materials and labor costs.  Indirect costs are all the costs associated with producing the unit of output that are not direct costs  -  for example, they may be related to production (such as research and development [R&D]), corporate operations (such as salaries, pensions, and health care costs for corporate staff), or selling (such as transportation, dealer support, and marketing).  Indirect costs are generally recovered by allocating a share of the costs to each unit of good sold.  Although it is possible to account for direct costs allocated to each unit of good sold, it is more challenging to account for indirect costs allocated to a unit of goods sold.  To make a cost analysis process more feasible, markup factors, which relate total indirect costs to total direct costs, have been developed.  These factors are often referred to as retail price equivalent (RPE) multipliers.
While the agencies have traditionally used RPE multipliers to estimate indirect costs, in recent GHG/CAFE/fuel consumption rules RPEs have been replaced in the primary analysis with indirect cost multipliers (ICMs).  ICMs differ from RPEs in that they attempt to estimate not all indirect costs incurred to bring a product to point of sale, but only those indirect costs that change as a result of a government action or regulatory requirement.  As such, some indirect costs, notably health and retirement benefits of retired employees, among other indirect costs, would not be expected to change due to a government action and, therefore, the portion of the RPE that covered those costs does not change.
Further, the ICM is not a "one-size-fits-all" markup as is the traditional RPE.  With ICMs, higher complexity technologies like hybridization or moving from a manual to automatic transmission may require higher indirect costs -- more research and development, more integration work, etc. -- suggesting a higher markup. Conversely, lower complexity technologies like reducing friction or adding passive aero features may require fewer indirect costs thereby suggesting a lower markup.
Notably, ICMs are also not a simple multiplier as are traditional RPEs.  The ICM is broken into two parts -- warranty related and non-warranty related costs. The warranty related portion of the ICM is relatively small while the non-warranty portion represents typically over 95 percent of indirect costs.  These two portions are applied to different DMC values to arrive at total costs (TC).  The warranty portion of the markup is applied to a DMC that decreases year-over-year due to learning effects (described below in Section IX.B.1.c).   As learning effects decrease the DMC with production volumes, it makes sense that warranty costs would decrease since those parts replaced under warranty should be less costly. In contrast, the non-warranty portion of the markup is applied to a static DMC year-over-year resulting in static indirect costs. This is logical since the production plants and transportation networks and general overhead required to build parts, market them, deliver them and integrate them into vehicles do not necessarily decrease in cost year-over-year.  Because the warranty and non-warranty portions of the ICM are applied differently, one cannot compare the markup itself to the RPE to determine which markup would result in higher indirect cost estimates, at least in the time periods typically considered in our rules (four to ten years).
The agencies are concerned that some potential costs associated with this rulemaking may not be adequately captured by our ICMs.  ICMs are estimated based on a few specific technologies and these technologies may not be representative of the changes actually made to meet the proposed requirements.  Specifically, we may not have adequately estimated the costs for accelerated R&D or potential reliability issues with advanced technologies required by Alternative 4.  There is a great deal of uncertainty regarding these costs, and this makes estimates for this alternative of particular concern. We request comment on that aspect of our estimates.
We provide more details on our ICM approach and the markups used for each technology in Chapter 2.12 of the draft RIA.
Learning Effects on Direct and Indirect Costs
For some of the technologies considered in this analysis, manufacturer learning effects would be expected to play a role in the actual end costs.  The "learning curve" or "experience curve" describes the reduction in unit production costs as a function of accumulated production volume.  In theory, the cost behavior it describes applies to cumulative production volume measured at the level of an individual manufacturer, although it is often assumed -- as both agencies have done in past regulatory analyses -- to apply at the industry-wide level, particularly in industries that utilize many common technologies and component supply sources.  Both agencies believe there are indeed many factors that cause costs to decrease over time.  Research in the costs of manufacturing has consistently shown that, as manufacturers gain experience in production, they are able to apply innovations to simplify machining and assembly operations, use lower cost materials, and reduce the number or complexity of component parts.  All of these factors allow manufacturers to lower the per-unit cost of production (i.e., the manufacturing learning curve).
In this analysis, the agencies are using the same approach to learning as done in past GHG/CAFE/fuel consumption rules. In short, learning effects result in rapid cost reductions in the early years following introduction of a new technology.  The agencies have estimated those cost reductions as resulting in 20 percent lower costs for every doubling of production volume. As production volumes increase, learning rates continue at the same pace but flatten asymptotically due to the nature of the persistent doubling of production required to realize that cost reduction.  As such, the cost reductions flatten out as production volumes continue to increase.  Consistent with the Phase 1 rule, we refer to these two distinct portions of the "learning cost reduction curve" or "learning curve" as the steeper and flatter portions of the curve.  On that steep portion of the curve, costs are estimated to decrease by 20 percent for each double of production or, by proxy, in the third and then fifth year of production following introduction.  On the flat portion of the curve, costs are estimated to decrease by 3 percent per year for 5 years, then 2 percent per year for 5 years, then 1 percent per year for 5 years.  Also consistent with the Phase 1 rule, the majority of the technologies we expect would be adopted are considered to be on the flat portion of the learning curve meaning that the 20 percent cost reductions are rarely applied.  The agencies request comment on this approach to estimating these effects, and request that commenters provide data and forward-looking information to support any alternative methods or specific estimates.
We provide more details on the concept of learning-by-doing and the learning effects applied in this analysis in Chapter 2 of the draft RIA.
Technology Adoption Rates and Developing Package Costs
Determining the stringency of the proposed standards involves a balancing of relevant factors  -  chiefly technology feasibility and effectiveness, costs, and lead time.  For vocational vehicles, tractors and trailers, the agencies have projected a technology path to achieve the proposed standards reflecting an application rate of those technologies the agencies consider to be available at reasonable cost in the lead times provided.  The agencies do not expect each of the technologies for which costs have been developed to be employed by all trucks and trailers across the board.  Further, many of today's vehicles are already equipped with some of the technologies and/or are expected to adopt them by MY2018 to comply with the HD Phase 1 standards. Estimated adoption rates in both the reference and control cases are necessary for each vehicle/trailer category.  The adoption rates for most technologies are zero in the reference case; however, for some technologies -- notably aero and tire technologies -- the adoption rate is not zero in the reference case.  These reference and control case adoption rates are then applied to the technology costs with the result being a package cost for each vehicle/trailer category.
For HD pickups and vans, the CAFE model determines the technology adoption rates that most cost effectively meet the standards being proposed. Similar to vocational vehicles, tractors and trailers, package costs are rarely if ever a simple sum of all the technology costs since each technology would be expected to be adopted at different rates.  The methods for estimating technology adoption rates and resultant costs (and other impacts) for HD pickups and vans are discussed above in Section 6.
We provide details of expected adoption rates in Chapter 2 of the draft RIA.  We present package costs both in Sections III through VI of this preamble and in more detail in Chapter 2 of the draft RIA.
Conversion of Technology Costs to 2012 US Dollars
As noted above in Section IX.B.1, the agencies are using technology costs from many different sources.  These sources, having been published in different years, present costs in different year dollars (i.e., 2009 dollars or 2010 dollars). For this analysis, the agencies sought to have all costs in terms of 2012 dollars to be consistent with the dollars used by AEO in its 2014 Annual Energy Outlook.  The agencies have used the GDP Implicit Price Deflator for Gross Domestic Product as the converter, with the actual factors used as shown in Table IX-1 .
Table IX-1 Implicit Price Deflators and Conversion Factors for Conversion to 2012$

                                     2006
                                     2007
                                     2008
                                     2009
                                     2010
                                     2011
                                     2012
                                     2013
Price index for GDP
                                                                         94.818
                                                                         97.335
                                                                         99.236
                                                                            100
                                                                        101.211
                                                                        103.199
                                                                        105.002
                                                                        106.588
Factor applied for 2012$
                                                                          1.107
                                                                          1.079
                                                                          1.058
                                                                          1.050
                                                                          1.037
                                                                          1.017
                                                                          1.000
                                                                          0.985

Compliance Program Costs
The agencies have also estimated additional and/or new compliance costs associated with the proposed standards.  Normally, compliance program costs would be considered part of the indirect costs and, therefore, would be accounted for via the markup applied to direct manufacturing costs.  However, since the agencies are proposing new compliance elements that were not present during development of the indirect cost markups used in this analysis, additional compliance program costs are being accounted for via a separate "line-item."  New research and development costs (see below) are being handled in the same way.
The new compliance program elements included in this proposal are new powertrain testing within the vocational vehicle program, and an all-new compliance program where none has existed to date within the trailer program.  Note that for HD pickups and vans, HD engines, vocational vehicles and tractors, the Phase 1 rule included analogous compliance program costs meant to account for costs incurred in the all-new compliance program placed on the regulated firms by that rule.  Compliance program costs cover costs associated with any necessary compliance testing and reporting to the agencies and differ somewhat by alternative since, for example, more manufacturers are expected to conduct powertrain testing under alternative 4 than under alternative 3, etc.  The details behind the estimated compliance program costs are provided in Chapter 7 of the draft RIA.
Research and Development Costs
Much like the compliance program costs described above, we have estimated additional HDD engine, vocational vehicle and tractor R&D associated with the proposed standards that is not accounted for via the indirect cost markups used for these segments.  Much like the Phase 1 rule, EPA is estimating these additional R&D costs will occur over a 4-year timeframe as the proposed standards come into force and industry works on means to comply.  After that period, the additional R&D costs go to $0 as R&D expenditures return to their normal levels and R&D costs are accounted for via the ICMs -- and the RPEs behind them -- used for these segments. Note that, due to the accelerated implementation of some technologies, alternative 4 has higher R&D costs than does alternative 3.  The details behind the estimated R&D costs are provided in Chapter 7 of the draft RIA.
Summary of Costs of the Proposed Vehicle Programs
The agencies have estimated the costs of the proposed vehicle standards on an annual basis for the years 2018 through 2050, and have also estimated costs for the full model year lifetimes of MY2018 through MY2029 vehicles.  Table IX-2 shows the annual costs of the proposed standards along with net present values using both 3 percent and 7 percent discount rates. Table IX-3 shows the discounted model year lifetime costs of the proposed standards at both 3 percent and 7 percent discount rates along with sums across applicable model years.
Table IX-2 Annual Costs of the Preferred Alternative and Net Present Values at 3% and 7% Discount Rates using Method B and Relative to the Less Dynamic Baseline
($Millions of 2012$)
                                   Calendar
                                     Year
                                      New
                                  Technology
                                  Compliance
                                    R&D
                                      Sum
                                     2018
                                                                           $116
                                                                             $0
                                                                             $0
                                                                           $116
                                     2019
                                                                           $113
                                                                             $0
                                                                             $0
                                                                           $113
                                     2020
                                                                           $112
                                                                             $0
                                                                             $0
                                                                           $112
                                     2021
                                                                         $2,173
                                                                            $18
                                                                           $240
                                                                         $2,432
                                     2022
                                                                         $2,161
                                                                             $6
                                                                           $240
                                                                         $2,407
                                     2023
                                                                         $2,224
                                                                             $6
                                                                           $240
                                                                         $2,470
                                     2024
                                                                         $3,455
                                                                             $6
                                                                           $240
                                                                         $3,701
                                     2025
                                                                         $3,647
                                                                             $6
                                                                             $0
                                                                         $3,653
                                     2026
                                                                         $3,736
                                                                             $6
                                                                             $0
                                                                         $3,742
                                     2027
                                                                         $5,309
                                                                             $6
                                                                             $0
                                                                         $5,315
                                     2028
                                                                         $5,334
                                                                             $6
                                                                             $0
                                                                         $5,340
                                     2029
                                                                         $5,376
                                                                             $6
                                                                             $0
                                                                         $5,381
                                     2030
                                                                         $5,399
                                                                             $6
                                                                             $0
                                                                         $5,405
                                     2035
                                                                         $5,856
                                                                             $6
                                                                             $0
                                                                         $5,862
                                     2040
                                                                         $6,316
                                                                             $6
                                                                             $0
                                                                         $6,322
                                     2050
                                                                         $6,987
                                                                             $6
                                                                             $0
                                                                         $6,992
                                    NPV, 3%
                                                                        $85,926
                                                                           $104
                                                                           $759
                                                                        $86,789
                                    NPV, 7%
                                                                        $40,516
                                                                            $56
                                                                           $561
                                                                        $41,133

Table IX-3 Discounted MY Lifetime Costs of the Preferred Alternative using Method B and Relative to the Less Dynamic Baseline ($Millions of 2012$)
                                     Model
                                     Year
                               Discounted at 3%
                               Discounted at 7%

                                      New
                                  Technology
                                  Compliance
                                    R&D
                                      Sum
                                      New
                                  Technology
                                  Compliance
                                    R&D
                                      Sum
                                     2018
                                                                           $104
                                                                             $0
                                                                             $0
                                                                           $104
                                                                            $91
                                                                             $0
                                                                             $0
                                                                            $91
                                     2019
                                                                            $99
                                                                             $0
                                                                             $0
                                                                            $99
                                                                            $84
                                                                             $0
                                                                             $0
                                                                            $84
                                     2020
                                                                            $95
                                                                             $0
                                                                             $0
                                                                            $95
                                                                            $77
                                                                             $0
                                                                             $0
                                                                            $77
                                     2021
                                                                         $1,794
                                                                            $15
                                                                           $198
                                                                         $2,007
                                                                         $1,401
                                                                            $12
                                                                           $155
                                                                         $1,567
                                     2022
                                                                         $1,731
                                                                             $5
                                                                           $193
                                                                         $1,928
                                                                         $1,302
                                                                             $3
                                                                           $145
                                                                         $1,450
                                     2023
                                                                         $1,730
                                                                             $4
                                                                           $187
                                                                         $1,921
                                                                         $1,252
                                                                             $3
                                                                           $135
                                                                         $1,390
                                     2024
                                                                         $2,610
                                                                             $4
                                                                           $181
                                                                         $2,795
                                                                         $1,818
                                                                             $3
                                                                           $126
                                                                         $1,947
                                     2025
                                                                         $2,674
                                                                             $4
                                                                             $0
                                                                         $2,678
                                                                         $1,793
                                                                             $3
                                                                             $0
                                                                         $1,796
                                     2026
                                                                         $2,660
                                                                             $4
                                                                             $0
                                                                         $2,664
                                                                         $1,717
                                                                             $3
                                                                             $0
                                                                         $1,719
                                     2027
                                                                         $3,670
                                                                             $4
                                                                             $0
                                                                         $3,673
                                                                         $2,280
                                                                             $2
                                                                             $0
                                                                         $2,283
                                     2028
                                                                         $3,580
                                                                             $4
                                                                             $0
                                                                         $3,583
                                                                         $2,141
                                                                             $2
                                                                             $0
                                                                         $2,143
                                     2029
                                                                         $3,502
                                                                             $4
                                                                             $0
                                                                         $3,506
                                                                         $2,017
                                                                             $2
                                                                             $0
                                                                         $2,019
                                      Sum
                                                                        $24,248
                                                                            $48
                                                                           $759
                                                                        $25,055
                                                                        $15,973
                                                                            $33
                                                                           $561
                                                                        $16,568
New technology costs begin in MY2018 as trailers begin to add new technology. Compliance costs begin with the new standards with capital cost expenditure in that year for building and upgrading test facilities to conduct the proposed powertrain testing in the vocational program.  Research and development costs begin in 2021 and last for 4 years as engine, tractor and vocational vehicle manufacturers conduct research and development testing to integrate new technologies into their engines and vehicles.
Changes in Fuel Consumption and Expenditures
Changes in Fuel Consumption
The new GHG and fuel consumption standards would result in significant improvements in the fuel efficiency of affected vehicles, and drivers of those vehicles would see corresponding savings associated with reduced fuel expenditures.  The agencies have estimated the impacts on fuel consumption for the proposed standards.  Details behind how these changes in fuel consumption were calculated are presented in Section VII of this preamble and in Chapter 5 of the draft RIA.  The total number of miles that vehicles are driven each year is different under the regulatory alternatives than in the reference case due to the "rebound effect" (discussed below in Section IX.E), so the changes in fuel consumption associated with each alternative are not strictly proportional to differences in the fuel economy levels they require.
The expected annual impacts on fuel consumption are shown in Table IX-4. Table IX-5 shows the MY lifetime changes in fuel consumption.  The gallons shown in these tables as reductions in fuel consumption reflect reductions due to the proposed standards and include any increased consumption resulting from the rebound effect (discussed below in Section IX.E).
Table IX-4 Annual Fuel Consumption Reductions due to the Preferred Alternative using Method B and Relative to the Less Dynamic Baseline (Millions of gallons)
                                   Calendar
                                     Year
                                   Gasoline
                                    Diesel

                                   Reference
                                     Case
                                     Fuel
                                  Consumption
                                   Reduction
                                       %
                                   Reduction
                                   Reference
                                     Case
                                     Fuel
                                  Consumption
                                   Reduction
                                       %
                                   Reduction
                                     2018
                                                                          6,781
                                                                              0
                                                                             0%
                                                                         45,999
                                                                             74
                                                                             0%
                                     2019
                                                                          6,799
                                                                              0
                                                                             0%
                                                                         46,362
                                                                            150
                                                                             0%
                                     2020
                                                                          6,832
                                                                              0
                                                                             0%
                                                                         46,768
                                                                            227
                                                                             0%
                                     2021
                                                                          6,884
                                                                             10
                                                                             0%
                                                                         47,236
                                                                            523
                                                                             1%
                                     2022
                                                                          6,944
                                                                             29
                                                                             0%
                                                                         47,761
                                                                            894
                                                                             2%
                                     2023
                                                                          7,005
                                                                             57
                                                                             1%
                                                                         48,309
                                                                          1,276
                                                                             3%
                                     2024
                                                                          7,054
                                                                             99
                                                                             1%
                                                                         48,807
                                                                          1,895
                                                                             4%
                                     2025
                                                                          7,113
                                                                            151
                                                                             2%
                                                                         49,400
                                                                          2,523
                                                                             5%
                                     2026
                                                                          7,169
                                                                            210
                                                                             3%
                                                                         49,967
                                                                          3,152
                                                                             6%
                                     2027
                                                                          7,221
                                                                            291
                                                                             4%
                                                                         50,420
                                                                          3,890
                                                                             8%
                                     2028
                                                                          7,273
                                                                            369
                                                                             5%
                                                                         50,821
                                                                          4,600
                                                                             9%
                                     2029
                                                                          7,332
                                                                            445
                                                                             6%
                                                                         51,262
                                                                          5,278
                                                                            10%
                                     2030
                                                                          7,396
                                                                            516
                                                                             7%
                                                                         51,792
                                                                          5,924
                                                                            11%
                                     2035
                                                                          7,732
                                                                            801
                                                                            10%
                                                                         54,602
                                                                          8,517
                                                                            16%
                                     2040
                                                                          8,075
                                                                            968
                                                                            12%
                                                                         58,082
                                                                         10,209
                                                                            18%
                                     2050
                                                                          8,806
                                                                          1,127
                                                                            13%
                                                                         65,937
                                                                         12,310
                                                                            19%

Table IX-5 Model Year Lifetime Fuel Consumption Reductions due to the Preferred Alternative using Method B and Relative to the Less Dynamic Baseline (Millions of gallons)
                                     Model
                                     Year
                                   Gasoline
                                    Diesel

                                   Reference
                                     Fuel
                                  Consumption
                                   Reduction
                                       %
                                   Reduction
                                   Reference
                                     Fuel
                                  Consumption
                                   Reduction
                                       %
                                   Reduction
                                     2018
                                                                              0
                                                                              0
                                                                             0%
                                                                         33,384
                                                                            754
                                                                             2%
                                     2019
                                                                              0
                                                                              0
                                                                             0%
                                                                         33,922
                                                                            745
                                                                             2%
                                     2020
                                                                              0
                                                                              0
                                                                             0%
                                                                         34,575
                                                                            738
                                                                             2%
                                     2021
                                                                          7,128
                                                                            113
                                                                             2%
                                                                         47,792
                                                                          4,424
                                                                             9%
                                     2022
                                                                          7,118
                                                                            216
                                                                             3%
                                                                         48,112
                                                                          4,568
                                                                             9%
                                     2023
                                                                          7,106
                                                                            317
                                                                             4%
                                                                         48,366
                                                                          4,703
                                                                            10%
                                     2024
                                                                          7,225
                                                                            493
                                                                             7%
                                                                         49,577
                                                                          7,628
                                                                            15%
                                     2025
                                                                          7,376
                                                                            602
                                                                             8%
                                                                         51,050
                                                                          7,967
                                                                            16%
                                     2026
                                                                          7,535
                                                                            714
                                                                             9%
                                                                         52,420
                                                                          8,289
                                                                            16%
                                     2027
                                                                          7,628
                                                                            982
                                                                            13%
                                                                         53,532
                                                                          9,984
                                                                            19%
                                     2028
                                                                          7,711
                                                                            992
                                                                            13%
                                                                         54,524
                                                                         10,181
                                                                            19%
                                     2029
                                                                          7,769
                                                                            999
                                                                            13%
                                                                         55,421
                                                                         10,360
                                                                            19%
                                      Sum
                                                                         66,596
                                                                          5,430
                                                                             8%
                                                                        562,673
                                                                         70,342
                                                                            13%

Fuel Savings
We have also estimated the changes in fuel expenditures, or the fuel savings, using fuel prices estimated in the Energy and Information Administration's 2014 Annual Energy Outlook.  As the AEO fuel price projections go through 2040 and not beyond, fuel prices beyond 2040 were set equal to the 2040 values.  These estimates do not account for the significant uncertainty in future fuel prices; the monetized fuel savings would be understated if actual fuel prices are higher (or overstated if fuel prices are lower) than estimated.  The Annual Energy Outlook (AEO) is a standard reference used by NHTSA and EPA and many other government agencies to estimate the projected price of fuel.  This has been done using both the pre-tax and post-tax fuel prices.  Since the post-tax fuel prices are the prices paid at fuel pumps, the fuel savings calculated using these prices represent the changes fuel purchasers would see.  The pre-tax fuel savings measure the value to society of the resources saved when less fuel is refined and consumed.  Assuming no change in fuel tax rates, the difference between these two columns represents the reduction in fuel tax revenues that would be received by state and federal governments, or about $240 million in 2021 and $5.2 billion by 2050 as shown in Table IX-6 where annual changes in monetized fuel savings are shown along with net present values using 3 percent and 7 percent discount rates. Table IX-7 and Table IX-8 show the discounted model year lifetime fuel savings using 3 percent and 7 percent discount rates, respectively.
Table IX-6 Annual Fuel Savings and Net Present Values at 3% and 7% Discount Rates using Method B for the Preferred Alternative and Relative to the Less Dynamic Baseline ($Millions of 2012$)
                                   Calendar
                                     Year
                             Fuel Savings - Retail
                           Fuel Savings  -  Untaxed
                                   Change in
                                    Transfer

                                   Gasoline
                                    Diesel
                                      Sum
                                   Gasoline
                                    Diesel
                                      Sum

                                     2018
                                                                             $0
                                                                           $261
                                                                           $261
                                                                             $0
                                                                           $227
                                                                           $227
                                                                            $34
                                     2019
                                                                             $0
                                                                           $540
                                                                           $540
                                                                             $0
                                                                           $472
                                                                           $472
                                                                            $68
                                     2020
                                                                             $0
                                                                           $834
                                                                           $834
                                                                             $0
                                                                           $731
                                                                           $731
                                                                           $103
                                     2021
                                                                            $31
                                                                         $1,958
                                                                         $1,989
                                                                            $27
                                                                         $1,723
                                                                         $1,750
                                                                           $239
                                     2022
                                                                            $92
                                                                         $3,413
                                                                         $3,505
                                                                            $80
                                                                         $3,015
                                                                         $3,095
                                                                           $410
                                     2023
                                                                           $183
                                                                         $4,936
                                                                         $5,119
                                                                           $160
                                                                         $4,372
                                                                         $4,532
                                                                           $587
                                     2024
                                                                           $324
                                                                         $7,426
                                                                         $7,750
                                                                           $285
                                                                         $6,594
                                                                         $6,879
                                                                           $871
                                     2025
                                                                           $496
                                                                        $10,035
                                                                        $10,531
                                                                           $436
                                                                         $8,937
                                                                         $9,372
                                                                         $1,158
                                     2026
                                                                           $695
                                                                        $12,683
                                                                        $13,378
                                                                           $613
                                                                        $11,321
                                                                        $11,934
                                                                         $1,445
                                     2027
                                                                           $976
                                                                        $15,883
                                                                        $16,859
                                                                           $861
                                                                        $14,215
                                                                        $15,076
                                                                         $1,782
                                     2028
                                                                         $1,243
                                                                        $18,938
                                                                        $20,181
                                                                         $1,099
                                                                        $16,980
                                                                        $18,079
                                                                         $2,102
                                     2029
                                                                         $1,511
                                                                        $21,974
                                                                        $23,485
                                                                         $1,338
                                                                        $19,745
                                                                        $21,083
                                                                         $2,402
                                     2030
                                                                         $1,770
                                                                        $24,905
                                                                        $26,675
                                                                         $1,571
                                                                        $22,422
                                                                        $23,993