\398\ Turtletaub, K.W. and Mani, C. (2003) Benzene metabolism in
rodents at doses relevant to human exposure from Urban Air. Research
Reports Health Effect Inst. Report No.113. Docket EPA-HQ-OAR-2010-
0162.
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(c) 1,3-Butadiene
    EPA has characterized 1,3-butadiene as carcinogenic to humans by
inhalation.399 400 The IARC has determined that 1,3-
butadiene is a human carcinogen and the U.S. DHHS has characterized
1,3-butadiene as a known human carcinogen.401 402 There are
numerous studies consistently demonstrating that 1,3-butadiene is
metabolized into genotoxic metabolites by experimental animals and
humans. The specific mechanisms of 1,3-butadiene-induced carcinogenesis
are unknown; however, the scientific evidence strongly suggests that
the carcinogenic effects are mediated by genotoxic metabolites. Animal
data suggest that females may be more sensitive than males for cancer
effects associated with 1,3-butadiene exposure; there are insufficient
data in humans from which to draw conclusions about sensitive
subpopulations. 1,3-butadiene also causes a variety of reproductive and
developmental effects in mice; no human data on these effects are
available. The most sensitive effect was ovarian atrophy observed in a
lifetime bioassay of female mice.\403\
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    \399\ U.S. EPA (2002) Health Assessment of 1,3-Butadiene. Office
of Research and Development, National Center for Environmental
Assessment, Washington Office, Washington, DC. Report No. EPA600-P-
98-001F. This document is available electronically at http://www.epa.gov/iris/supdocs/buta-sup.pdf. Docket EPA-HQ-OAR-2010-0162.
    \400\ U.S. EPA (2002) Full IRIS Summary for 1,3-butadiene (CASRN
106-99-0). Environmental Protection Agency, Integrated Risk
Information System (IRIS), Research and Development, National Center
for Environmental Assessment, Washington, DC http://www.epa.gov/iris/subst/0139.htm. Docket EPA-HQ-OAR-2010-0162.
    \401\ International Agency for Research on Cancer (1999)
Monographs on the evaluation of carcinogenic risk of chemicals to
humans, Volume 71, Re-evaluation of some organic chemicals,
hydrazine and hydrogen peroxide and Volume 97 (in preparation),
World Health Organization, Lyon, France. Docket EPA-HQ-OAR-2010-
0162.
    \402\ U.S. Department of Health and Human Services (2005)
National Toxicology Program 11th Report on Carcinogens available at:
ntp.niehs.nih.gov/index.cfm?objectid=32BA9724-F1F6-975E-7FCE50709CB4C932. Docket EPA-HQ-OAR-2010-0162
    \403\ Bevan, C.; Stadler, J.C.; Elliot, G.S.; et al. (1996)
Subchronic toxicity of 4-vinylcyclohexene in rats and mice by
inhalation. Fundam. Appl. Toxicol. 32:1-10. Docket EPA-HQ-OAR-2010-
0162.
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(d) Formaldehyde
    Since 1987, EPA has classified formaldehyde as a probable human
carcinogen based on evidence in humans and in rats, mice, hamsters, and
monkeys.\404\ EPA is currently reviewing recently published
epidemiological data. For instance, research conducted by the National
Cancer Institute found an increased risk of nasopharyngeal cancer and
lymphohematopoietic malignancies such as leukemia among workers exposed
to formaldehyde.405 406 In an analysis of the
lymphohematopoietic cancer mortality from an extended follow-up of
these workers, the National Cancer Institute confirmed an association
between lymphohematopoietic cancer risk and peak exposures.\407\ A
recent National Institute of Occupational Safety and Health study of
garment workers also found increased risk of death due to leukemia
among workers exposed to formaldehyde.\408\ Extended follow-up of a
cohort of British chemical workers did not find evidence of an increase
in nasopharyngeal or lymphohematopoietic cancers, but a continuing
statistically significant excess in lung cancers was reported.\409\
Recently, the IARC re-classified formaldehyde as a human carcinogen
(Group 1).\410\
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    \404\ U.S. EPA (1987) Assessment of Health Risks to Garment
Workers and Certain Home Residents from Exposure to Formaldehyde,
Office of Pesticides and Toxic Substances, April 1987. Docket EPA-
HQ-OAR-2010-0162.
    \405\ Hauptmann, M.; Lubin, J. H.; Stewart, P. A.; Hayes, R. B.;
Blair, A. 2003. Mortality from lymphohematopoetic malignancies among
workers in formaldehyde industries. Journal of the National Cancer
Institute 95: 1615-1623. Docket EPA-HQ-OAR-2010-0162.
    \406\ Hauptmann, M.; Lubin, J. H.; Stewart, P. A.; Hayes, R. B.;
Blair, A. 2004. Mortality from solid cancers among workers in
formaldehyde industries. American Journal of Epidemiology 159: 1117-
1130. Docket EPA-HQ-OAR-2010-0162.
    \407\ Beane Freeman, L. E.; Blair, A.; Lubin, J. H.; Stewart, P.
A.; Hayes, R. B.; Hoover, R. N.; Hauptmann, M. 2009. Mortality from
lymphohematopoietic malignancies among workers in formaldehyde
industries: The National Cancer Institute cohort. J. National Cancer
Inst. 101: 751-761. Docket EPA-HQ-OAR-2010-0162.
    \408\ Pinkerton, L. E. 2004. Mortality among a cohort of garment
workers exposed to formaldehyde: an update. Occup. Environ. Med. 61:
193-200. Docket EPA-HQ-OAR-2010-0162.
    \409\ Coggon, D, EC Harris, J Poole, KT Palmer. 2003. Extended
follow-up of a cohort of British chemical workers exposed to
formaldehyde. J National Cancer Inst. 95:1608-1615. Docket EPA-HQ-
OAR-2010-0162.
    \410\ International Agency for Research on Cancer. 2006.
Formaldehyde, 2-Butoxyethanol and 1-tert-Butoxypropan-2-ol. Volume
88. (in preparation), World Health Organization, Lyon, France.
Docket EPA-HQ-OAR-2010-0162
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    Formaldehyde exposure also causes a range of noncancer health
effects, including irritation of the eyes (burning and watering of the
eyes), nose and throat. Effects from repeated exposure in humans
include respiratory tract irritation, chronic bronchitis and nasal
epithelial lesions such as metaplasia and loss of cilia. Animal studies
suggest that formaldehyde may also cause airway inflammation--including
eosinophil infiltration into the airways. There are several studies
that suggest that formaldehyde may increase the risk of asthma--
particularly in the young.411 412
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    \411\ Agency for Toxic Substances and Disease Registry (ATSDR).
1999. Toxicological profile for Formaldehyde. Atlanta, GA: U.S.
Department of Health and Human Services, Public Health Service.
http://www.atsdr.cdc.gov/toxprofiles/tp111.html Docket EPA-HQ-OAR-
2010-0162.
    \412\ WHO (2002) Concise International Chemical Assessment
Document 40: Formaldehyde. Published under the joint sponsorship of
the United Nations Environment Programme, the International Labour
Organization, and the World Health Organization, and produced within
the framework of the Inter-Organization Programme for the Sound
Management of Chemicals. Geneva. Docket EPA-HQ-OAR-2010-0162.
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(e) Acetaldehyde
    Acetaldehyde is classified in EPA's IRIS database as a probable
human carcinogen, based on nasal tumors in rats, and is considered
toxic by the inhalation, oral, and intravenous

routes.\413\ Acetaldehyde is reasonably anticipated to be a human
carcinogen by the U.S. DHHS in the 11th Report on Carcinogens and is
classified as possibly carcinogenic to humans (Group 2B) by the
IARC.414 415 EPA is currently conducting a reassessment of
cancer risk from inhalation exposure to acetaldehyde.
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    \413\ U.S. EPA. 1991. Integrated Risk Information System File of
Acetaldehyde. Research and Development, National Center for
Environmental Assessment, Washington, DC. Available at http://www.epa.gov/iris/subst/0290.htm. Docket EPA-HQ-OAR-2010-0162.
    \414\ U.S. Department of Health and Human Services National
Toxicology Program 11th Report on Carcinogens available at:
ntp.niehs.nih.gov/index.cfm?objectid=32BA9724-F1F6-975E-7FCE50709CB4C932. Docket EPA-HQ-OAR-2010-0162.
    \415\ International Agency for Research on Cancer. 1999. Re-
evaluation of some organic chemicals, hydrazine, and hydrogen
peroxide. IARC Monographs on the Evaluation of Carcinogenic Risk of
Chemical to Humans, Vol 71. Lyon, France. Docket EPA-HQ-OAR-2010-
0162.
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    The primary noncancer effects of exposure to acetaldehyde vapors
include irritation of the eyes, skin, and respiratory tract.\416\ In
short-term (4 week) rat studies, degeneration of olfactory epithelium
was observed at various concentration levels of acetaldehyde
exposure.417 418 Data from these studies were used by EPA to
develop an inhalation reference concentration. Some asthmatics have
been shown to be a sensitive subpopulation to decrements in functional
expiratory volume (FEV1 test) and bronchoconstriction upon acetaldehyde
inhalation.\419\ The agency is currently conducting a reassessment of
the health hazards from inhalation exposure to acetaldehyde.
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    \416\ See Integrated Risk Information System File of
Acetaldehyde, Note 413, above.
    \417\ Appleman, L. M., R. A. Woutersen, V. J. Feron, R. N.
Hooftman, and W. R. F. Notten. 1986. Effects of the variable versus
fixed exposure levels on the toxicity of acetaldehyde in rats. J.
Appl. Toxicol. 6: 331-336. Docket EPA-HQ-OAR-2010-0162.
    \418\ Appleman, L.M., R.A. Woutersen, and V.J. Feron. 1982.
Inhalation toxicity of acetaldehyde in rats. I. Acute and subacute
studies. Toxicology. 23: 293-297. Docket EPA-HQ-OAR-2010-0162.
    \419\ Myou, S.; Fujimura, M.; Nishi K.; Ohka, T.; and Matsuda,
T. 1993. Aerosolized acetaldehyde induces histamine-mediated
bronchoconstriction in asthmatics. Am. Rev. Respir.Dis.148(4 Pt 1):
940-3. Docket EPA-HQ-OAR-2010-0162.
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(f) Acrolein
    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.\420\ 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.\421\ Evidence available from 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.\422\
Lesions to the lungs and upper respiratory tract of rats, rabbits, and
hamsters have been observed after subchronic exposure to acrolein.\423\
Acute exposure effects in animal studies report bronchial hyper-
responsiveness.\424\ In a recent study, the acute respiratory irritant
effects of exposure to 1.1 ppm acrolein were more pronounced in mice
with allergic airway disease by comparison to non-diseased mice which
also showed decreases in respiratory rate.\425\ Based on these animal
data 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.
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    \420\ U.S. EPA (U.S. Environmental Protection Agency). (2003)
Toxicological review of acrolein in support of summary information
on Integrated Risk Information System (IRIS) National Center for
Environmental Assessment, Washington, DC. EPA/635/R-03/003. p. 10.
Available online at: http://www.epa.gov/ncea/iris/toxreviews/0364tr.pdf. Docket EPA-HQ-OAR-2010-0162.
    \421\ See U.S. EPA 2003 Toxicological review of acrolein, Note
420, above.
    \422\ See U.S. EPA 2003 Toxicological review of acrolein, Note
420, at p. 11.
    \423\ Integrated Risk Information System File of Acrolein.
Office of Research and Development, National Center for
Environmental Assessment, Washington, DC. This material is available
at http://www.epa.gov/iris/subst/0364.htm Docket EPA-HQ-OAR-2010-
0162.
    \424\ See U.S. 2003 Toxicological review of acrolein, Note 420,
at p. 15.
    \425\ Morris JB, Symanowicz PT, Olsen JE, et al. 2003. Immediate
sensory nerve-mediated respiratory responses to irritants in healthy
and allergic airway-diseased mice. J Appl Physiol 94(4):1563-1571.
Docket EPA-HQ-OAR-2010-0162.
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    EPA determined in 2003 that the human carcinogenic potential of
acrolein could not be determined because the available data were
inadequate. No information was available on the carcinogenic effects of
acrolein in humans and the animal data provided inadequate evidence of
carcinogenicity.\426\ The IARC determined in 1995 that acrolein was not
classifiable as to its carcinogenicity in humans.\427\
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    \426\ U.S. EPA. 2003. Integrated Risk Information System File of
Acrolein. Research and Development, National Center for
Environmental Assessment, Washington, DC. This material is available
at http://www.epa.gov/iris/subst/0364.htm Docket EPA-HQ-OAR-2010-
0162.
    \427\ International Agency for Research on Cancer. 1995.
Monographs on the evaluation of carcinogenic risk of chemicals to
humans, Volume 63. Dry cleaning, some chlorinated solvents and other
industrial chemicals, World Health Organization, Lyon, France.
Docket EPA-HQ-OAR-2010-0162.
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(g) 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.\428,429\ Animal studies have
reported respiratory tract tumors from inhalation exposure to
benzo[a]pyrene and alimentary tract and liver tumors from oral exposure
to benzo[a]pyrene. EPA has classified seven PAHs (benzo[a]pyrene,
benz[a]anthracene, chrysene, benzo[b]fluoranthene,
benzo[k]fluoranthene, dibenz[a,h]anthracene, and indeno[1,2,3-
cd]pyrene) as Group B2, probable human carcinogens.\430\ Recent studies
have found that maternal exposures to PAHs in a population of pregnant
women were associated with several adverse birth outcomes, including
low birth weight and reduced length at birth, as well as impaired
cognitive development in preschool children (3 years of
age).\431,432\EPA has not yet evaluated these recent studies.
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    \428\ Agency for Toxic Substances and Disease Registry (ATSDR).
1995. Toxicological profile for Polycyclic Aromatic Hydrocarbons
(PAHs). Atlanta, GA: U.S. Department of Health and Human Services,
Public Health Service. Available electronically at http://www.atsdr.cdc.gov/ToxProfiles/TP.asp?id=122&tid=25.
    \429\ U.S. EPA (2002). Health Assessment Document for Diesel
Engine Exhaust. EPA/600/8-90/057F Office of Research and
Development, Washington DC. http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=29060. Docket EPA-HQ-OAR-2010-0162.
    \430\ U.S. EPA (1997). Integrated Risk Information System File
of indeno(1,2,3-cd)pyrene. Research and Development, National Center
for Environmental Assessment, Washington, DC. This material is
available electronically at http://www.epa.gov/ncea/iris/subst/0457.htm.
    \431\ Perera, F.P.; Rauh, V.; Tsai, W-Y.; et al. (2002) Effect
of transplacental exposure to environmental pollutants on birth
outcomes in a multiethnic population. Environ Health Perspect. 111:
201-205.
    \432\ Perera, F.P.; Rauh, V.; Whyatt, R.M.; Tsai, W.Y.; Tang,
D.; Diaz, D.; Hoepner, L.; Barr, D.; Tu, Y.H.; Camann, D.; Kinney,
P. (2006) Effect of prenatal exposure to airborne polycyclic
aromatic hydrocarbons on neurodevelopment in the first 3 years of
life among inner-city children. Environ Health Perspect 114: 1287-
1292.

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(h) 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. EPA released an external review draft of a reassessment of
the inhalation carcinogenicity of naphthalene based on a number of
recent animal carcinogenicity studies.\433\ The draft reassessment
completed external peer review.\434\ Based on external peer review
comments received, additional analyses are being undertaken. This
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.\435\ 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.\436\ Naphthalene also causes a number of
chronic non-cancer effects in animals, including abnormal cell changes
and growth in respiratory and nasal tissues.\437\
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    \433\ U. S. EPA. 2004. Toxicological Review of Naphthalene
(Reassessment of the Inhalation Cancer Risk), Environmental
Protection Agency, Integrated Risk Information System, Research and
Development, National Center for Environmental Assessment,
Washington, DC. This material is available electronically at http://www.epa.gov/iris/subst/0436.htm. Docket EPA-HQ-OAR-2010-0162.
    \434\ Oak Ridge Institute for Science and Education. (2004).
External Peer Review for the IRIS Reassessment of the Inhalation
Carcinogenicity of Naphthalene. August 2004. http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=84403 Docket EPA-HQ-OAR-2010-0162.
    \435\ National Toxicology Program (NTP). (2004). 11th Report on
Carcinogens. Public Health Service, U.S. Department of Health and
Human Services, Research Triangle Park, NC. Available from: http://ntp-server.niehs.nih.gov. Docket EPA-HQ-OAR-2010-0162.
    \436\ International Agency for Research on Cancer. (2002).
Monographs on the Evaluation of the Carcinogenic Risk of Chemicals
for Humans. Vol. 82. Lyon, France. Docket EPA-HQ-OAR-2010-0162.
    \437\ U. S. EPA. 1998. Toxicological Review of Naphthalene,
Environmental Protection Agency, Integrated Risk Information System,
Research and Development, National Center for Environmental
Assessment, Washington, DC. This material is available
electronically at http://www.epa.gov/iris/subst/0436.htm Docket EPA-
HQ-OAR-2010-0162.
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(i) Other Air Toxics
    In addition to the compounds described above, other compounds in
gaseous hydrocarbon and PM emissions from heavy-duty vehicles will be
affected by this final action. Mobile source air toxic compounds that
would 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.\438\
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    \438\ U.S. EPA Integrated Risk Information System (IRIS)
database is available at: http://www.epa.gov/iris.
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(j) Exposure and Health Effects Associated with Traffic
    Populations who live, work, or attend school near major roads
experience elevated exposure concentrations to a wide range of air
pollutants, as well as higher risks for a number of adverse health
effects. While the previous sections of this preamble have focused on
the health effects associated with individual criteria pollutants or
air toxics, this section discusses the mixture of different exposures
near major roadways, rather than the effects of any single pollutant.
As such, this section emphasizes traffic-related air pollution, in
general, as the relevant indicator of exposure rather than any
particular pollutant.
    Concentrations of many traffic-generated air pollutants are
elevated for up to 300-500 meters downwind of roads with high traffic
volumes.\439\ Numerous sources on roads contribute to elevated roadside
concentrations, including exhaust and evaporative emissions, and
resuspension of road dust and tire and brake wear. Concentrations of
several criteria and hazardous air pollutants are elevated near major
roads. Furthermore, different semi-volatile organic compounds and
chemical components of particulate matter, including elemental carbon,
organic material, and trace metals, have been reported at higher
concentrations near major roads.
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    \439\ Zhou, Y.; Levy, J.I. (2007) Factors influencing the
spatial extent of mobile source air pollution impacts: A meta-
analysis. BMC Public Health 7:89. doi:10.1186/1471-2458-7-89 Docket
EPA-HQ-OAR-2010-0162.
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    Populations near major roads experience greater risk of certain
adverse health effects. The Health Effects Institute published a report
on the health effects of traffic-related air pollution.\440\ It
concluded that evidence is ``sufficient to infer the presence of a
causal association'' between traffic exposure and exacerbation of
childhood asthma symptoms. The HEI report also concludes that the
evidence is either ``sufficient'' or ``suggestive but not sufficient''
for a causal association between traffic exposure and new childhood
asthma cases. A review of asthma studies by Salam et al. (2008) reaches
similar conclusions.\441\ The HEI report also concludes that there is
``suggestive'' evidence for pulmonary function deficits associated with
traffic exposure, but concluded that there is ``inadequate and
insufficient'' evidence for causal associations with respiratory health
care utilization, adult-onset asthma, chronic obstructive pulmonary
disease symptoms, and allergy. A review by Holguin (2008) notes that
the effects of traffic on asthma may be modified by nutrition status,
medication use, and genetic factors.\442\
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    \440\ HEI Panel on the Health Effects of Air Pollution. (2010)
Traffic-related air pollution: a critical review of the literature
on emissions, exposure, and health effects. [Online at http://www.healtheffects.org] Docket EPA-HQ-OAR-2010-0162.
    \441\ Salam, M.T.; Islam, T.; Gilliland, F.D. (2008) Recent
evidence for adverse effects of residential proximity to traffic
sources on asthma. Current Opin Pulm Med 14: 3-8. Docket EPA-HQ-OAR-
2010-0162.
    \442\ Holguin, F. (2008) Traffic, outdoor air pollution, and
asthma. Immunol Allergy Clinics North Am 28: 577-588. Docket EPA-HQ-
OAR-2010-0162.
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    The HEI report also concludes that evidence is ``suggestive'' of a
causal association between traffic exposure and all-cause and
cardiovascular mortality. There is also evidence of an association
between traffic-related air pollutants and cardiovascular effects such
as changes in heart rhythm, heart attack, and cardiovascular disease.
The HEI report characterizes this evidence as ``suggestive'' of a
causal association, and an independent epidemiological literature
review by Adar and Kaufman (2007) concludes that there is ``consistent
evidence'' linking traffic-related pollution and adverse cardiovascular
health outcomes.\443\
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    \443\ Adar, S.D.; Kaufman, J.D. (2007) Cardiovascular disease
and air pollutants: evaluating and improving epidemiological data
implicating traffic exposure. Inhal Toxicol 19: 135-149. Docket EPA-
HQ-OAR-2010-0162.
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    Some studies have reported associations between traffic exposure
and other health effects, such as birth outcomes (e.g., low birth
weight) and childhood cancer. The HEI report concludes that there is
currently ``inadequate and insufficient'' evidence for a causal
association between these effects and traffic exposure. A review by
Raaschou-Nielsen and Reynolds (2006) concluded that evidence of an
association between childhood cancer and traffic-related air pollutants
is weak,

but noted the inability to draw firm conclusions based on limited
evidence.\444\
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    \444\ Raaschou-Nielsen, O.; Reynolds, P. (2006) Air pollution
and childhood cancer: a review of the epidemiological literature.
Int J Cancer 118: 2920-2929. Docket EPA-HQ-OAR-2010-0162.
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    There is a large population in the United States living in close
proximity of major roads. According to the Census Bureau's American
Housing Survey for 2007, approximately 20 million residences in the
United States, 15.6 percent of all homes, are located within 300 feet
(91 m) of a highway with 4+ lanes, a railroad, or an airport.\445\
Therefore, at current population of approximately 309 million, assuming
that population and housing are similarly distributed, there are over
48 million people in the United States living near such sources. The
HEI report also notes that in two North American cities, Los Angeles
and Toronto, over 40 percent of each city's population live within 500
meters of a highway or 100 meters of a major road. It also notes that
about 33 percent of each city's population resides within 50 meters of
major roads. Together, the evidence suggests that a large U.S.
population lives in areas with elevated traffic-related air pollution.
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    \445\ U.S. Census Bureau (2008) American Housing Survey for the
United States in 2007. Series H-150 (National Data), Table 1A-7.
[Accessed at http://www.census.gov/hhes/www/housing/ahs/ahs07/ahs07.html on January 22, 2009] Docket EPA-HQ-OAR-2010-0162.
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    People living near roads are often socioeconomically disadvantaged.
According to the 2007 American Housing Survey, a renter-occupied
property is over twice as likely as an owner-occupied property to be
located near a highway with 4+ lanes, railroad or airport. In the same
survey, the median household income of rental housing occupants was
less than half that of owner-occupants ($28,921/$59,886). Numerous
studies in individual urban areas report higher levels of traffic-
related air pollutants in areas with high minority or poor
populations.446 447 448
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    \446\ Lena, T.S.; Ochieng, V.; Carter, M.; Holgu[iacute]n-Veras,
J.; Kinney, P.L. (2002) Elemental carbon and PM2.5 levels
in an urban community heavily impacted by truck traffic. Environ
Health Perspect 110: 1009-1015. Docket EPA-HQ-OAR-2010-0162.
    \447\ Wier, M.; Sciammas, C.; Seto, E.; Bhatia, R.; Rivard, T.
(2009) Health, traffic, and environmental justice: collaborative
research and community action in San Francisco, California. Am J
Public Health 99: S499-S504. Docket EPA-HQ-OAR-2010-0162.
    \448\ Forkenbrock, D.J. and L.A. Schweitzer, Environmental
Justice and Transportation Investment Policy. Iowa City: University
of Iowa, 1997. Docket EPA-HQ-OAR-2010-0162.
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    Students may also be exposed in situations where schools are
located near major roads. In a study of nine metropolitan areas across
the United States, Appatova et al. (2008) found that on average greater
than 33 percent of schools were located within 400 m of an Interstate,
U.S., or state highway, while 12 percent were located within 100
m.\449\ The study also found that among the metropolitan areas studied,
schools in the Eastern United States were more often sited near major
roadways than schools in the Western United States.
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    \449\ Appatova, A.S.; Ryan, P.H.; LeMasters, G.K.; Grinshpun,
S.A. (2008) Proximal exposure of public schools and students to
major roadways: a nationwide U.S. survey. J Environ Plan Mgmt Docket
EPA-HQ-OAR-2010-0162.
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    Demographic studies of students in schools near major roadways
suggest that this population is more likely than the general student
population to be of non-white race or Hispanic ethnicity, and more
often live in low socioeconomic status locations.450 451 452
There is some inconsistency in the evidence, which may be due to
different local development patterns and measures of traffic and
geographic scale used in the studies.\449\
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    \450\ Green, R.S.; Smorodinsky, S.; Kim, J.J.; McLaughlin, R.;
Ostro, B. (2004) Proximity of California public schools to busy
roads. Environ Health Perspect 112: 61-66. Docket EPA-HQ-OAR-2010-
0162.
    \451\ Houston, D.; Ong, P.; Wu, J.; Winer, A. (2006) Proximity
of licensed child care facilities to near-roadway vehicle pollution.
Am J Public Health 96: 1611-1617. Docket EPA-HQ-OAR-2010-0162.
    \452\ Wu, Y.; Batterman, S. (2006) Proximity of schools in
Detroit, Michigan to automobile and truck traffic. J Exposure Sci
Environ Epidemiol 16: 457-470. Docket EPA-HQ-OAR-2010-0162.
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C. Environmental Effects of Non-GHG Pollutants

    In this section we discuss some of the environmental effects of PM
and its precursors such as visibility impairment, atmospheric
deposition, and materials damage and soiling, as well as environmental
effects associated with the presence of ozone in the ambient air, such
as impacts on plants, including trees, agronomic crops and urban
ornamentals, and environmental effects associated with air toxics.
(1) Visibility
    Visibility can be defined as the degree to which the atmosphere is
transparent to visible light.\453\ 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.\454\
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    \453\ National Research Council, 1993. Protecting Visibility in
National Parks and Wilderness Areas. National Academy of Sciences
Committee on Haze in National Parks and Wilderness Areas. National
Academy Press, Washington, DC. Docket EPA-HQ-OAR-2010-0162. This
book can be viewed on the National Academy Press Web site at http://www.nap.edu/books/0309048443/html/.
    \454\ See U.S. EPA 2009 Final PM ISA, Note 355.
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    EPA is pursuing a two-part strategy to address visibility
impairment. First, EPA developed the regional haze program (64 FR
35714) which was put in place in July 1999 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 (62
FR 38680-38681, July 18, 1997). 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. Second, EPA has 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 that control their
visibility impact effectiveness such as dry chemical composition and
relative humidity (i.e., an indicator of the water composition of the
particles), and has set secondary PM2.5 standards to address
these areas. The existing annual primary and secondary PM2.5
standards have been remanded by the DC Circuit (see American Farm
Bureau v. EPA, 559 F. 3d 512 (DC Cir. 2009) and are being addressed in
the currently ongoing PM NAAQS review.
(2) Plant and Ecosystem Effects of Ozone
    Elevated ozone levels contribute to environmental effects, with
impacts to plants and ecosystems being of most concern. Ozone can
produce both acute and chronic injury in sensitive species depending on
the concentration level and the duration of the exposure. Ozone effects
also tend to accumulate over the growing season of the plant, so that
even low concentrations experienced for a longer duration have the
potential to create chronic stress on vegetation. Ozone damage to
plants includes visible injury to leaves and impaired photosynthesis,
both of which can lead to reduced plant growth and reproduction,
resulting in reduced crop yields, forestry production, and use of

sensitive ornamentals in landscaping. In addition, the impairment of
photosynthesis, the process by which the plant makes carbohydrates (its
source of energy and food), can lead to a subsequent 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 forest
and other natural vegetation can potentially lead to species shifts and
loss from the affected ecosystems, resulting in a loss or reduction in
associated ecosystem goods and services. Lastly, 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. The final
2006 Ozone Air Quality Criteria Document presents more detailed
information on ozone effects on vegetation and ecosystems.
(3) 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. 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.\455\
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    \455\ U.S. EPA (2000) Deposition of Air Pollutants to the Great
Waters: Third Report to Congress. Office of Air Quality Planning and
Standards. EPA-453/R-00-0005. Docket EPA-HQ-OAR-2010-0162.
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    Adverse impacts on water quality can occur when atmospheric
contaminants deposit to the water surface or when material deposited on
the land enters a waterbody through runoff. Potential impacts of
atmospheric deposition to waterbodies include those related to both
nutrient and toxic inputs. Adverse effects to human health and welfare
can occur from the addition of excess nitrogen via atmospheric
deposition. The nitrogen-nutrient enrichment contributes to toxic algae
blooms and zones of depleted oxygen, which can lead to fish kills,
frequently in coastal waters. Deposition of heavy metals or other
toxics may lead to the human ingestion of contaminated fish, impairment
of drinking water, damage to the marine ecology, and limits to
recreational uses. Several studies have been conducted in U.S. coastal
waters and in the Great Lakes Region in which the role of ambient PM
deposition and runoff is investigated.456 457 458 459 460
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    \456\ U.S. EPA (2004) National Coastal Condition Report II.
Office of Research and Development/Office of Water. EPA-620/R-03/
002. Docket EPA-HQ-OAR-2010-0162.
    \457\ Gao, Y., E.D. Nelson, M.P. Field, et al. 2002.
Characterization of atmospheric trace elements on PM2.5
particulate matter over the New York-New Jersey harbor estuary.
Atmos. Environ. 36: 1077-1086. Docket EPA-HQ-OAR-2010-0162.
    \458\ Kim, G., N. Hussain, J.R. Scudlark, and T.M. Church. 2000.
Factors influencing the atmospheric depositional fluxes of stable
Pb, 210Pb, and 7Be into Chesapeake Bay. J. Atmos. Chem. 36: 65-79.
Docket EPA-HQ-OAR-2010-0162.
    \459\ Lu, R., R.P. Turco, K. Stolzenbach, et al. 2003. Dry
deposition of airborne trace metals on the Los Angeles Basin and
adjacent coastal waters. J. Geophys. Res. 108(D2, 4074): AAC 11-1 to
11-24. Docket EPA-HQ-OAR-2010-0162.
    \460\ Marvin, C.H., M.N. Charlton, E.J. Reiner, et al. 2002.
Surficial sediment contamination in Lakes Erie and Ontario: A
comparative analysis. J. Great Lakes Res. 28(3): 437-450. Docket
EPA-HQ-OAR-2010-0162.
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    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 nutritional value of preferred prey species,
threatening biodiversity 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 include a
decline in sensitive forest tree species, such as red spruce (Picea
rubens) and sugar maple (Acer saccharum), and a loss of biodiversity of
fishes, zooplankton, and macro invertebrates.
    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
Section 7.1.2 of the RIA.
    Adverse impacts on soil chemistry and plant life have been observed
for areas heavily influenced by atmospheric deposition of nutrients,
metals and acid species, resulting in species shifts, loss of
biodiversity, forest decline and damage to forest productivity.
Potential impacts also include adverse effects to human health through
ingestion of contaminated vegetation or livestock (as in the case for
dioxin deposition), reduction in crop yield, and limited use of land
due to contamination.
    Atmospheric deposition of pollutants can reduce the aesthetic
appeal of buildings and culturally important articles through soiling,
and can contribute directly (or in conjunction with other pollutants)
to structural damage by means of corrosion or erosion. Atmospheric
deposition may affect materials principally by promoting and
accelerating the corrosion of metals, by degrading paints, and by
deteriorating building materials such as concrete and limestone.
Particles contribute to these effects because of their electrolytic,
hygroscopic, and acidic properties, and their ability to adsorb
corrosive gases (principally sulfur dioxide).
(4) 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.\461\ In laboratory experiments, a wide range of
tolerance to VOCs has been observed.\462\ 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.\463\
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    \461\ U.S. EPA. 1991. Effects of organic chemicals in the
atmosphere on terrestrial plants. EPA/600/3-91/001. Docket EPA-HQ-
OAR-2010-0162.
    \462\ Cape JN, ID Leith, J Binnie, J Content, M Donkin, M
Skewes, DN Price AR Brown, AD Sharpe. 2003. Effects of VOCs on
herbaceous plants in an open-top chamber experiment. Environ.
Pollut. 124:341-343. Docket EPA-HQ-OAR-2010-0162.
    \463\ Cape JN, ID Leith, J Binnie, J Content, M Donkin, M
Skewes, DN Price AR Brown, AD Sharpe. 2003. Effects of VOCs on
herbaceous plants in an open-top chamber experiment. Environ.
Pollut. 124:341-343. Docket EPA-HQ-OAR-2010-0162.
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    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.464 465 466 The impacts of
VOCs on plant reproduction may have long-term implications for
biodiversity and survival of native species near major roadways. Most
of the studies of the impacts of VOCs on vegetation have focused on
short-term exposure and few studies have focused on long-term effects
of VOCs on vegetation and the potential for metabolites of these
compounds to affect herbivores or insects.
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    \464\ Viskari E-L. 2000. Epicuticular wax of Norway spruce
needles as indicator of traffic pollutant deposition. Water, Air,
and Soil Pollut. 121:327-337. Docket EPA-HQ-OAR-2010-0162.
    \465\ Ugrekhelidze D, F Korte, G Kvesitadze. 1997. Uptake and
transformation of benzene and toluene by plant leaves. Ecotox.
Environ. Safety 37:24-29. Docket EPA-HQ-OAR-2010-0162.
    \466\ Kammerbauer H, H Selinger, R Rommelt, A Ziegler-Jons, D
Knoppik, B Hock. 1987. Toxic components of motor vehicle emissions
for the spruce Picea abies. Environ. Pollut. 48:235-243. Docket EPA-
HQ-OAR-2010-0162.
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D. Air Quality Impacts of Non-GHG Pollutants

    Air quality modeling was performed to assess the impact of the
heavy-duty vehicle standards on criteria and air toxic pollutants. In
this section, we present information on current modeled levels of
pollution as well as projections for 2030, with respect to ambient
PM2.5, ozone, selected air toxics, visibility levels and
nitrogen and sulfur deposition. The results are discussed in more
detail in Section 8.2 of the RIA.
    We used the Community Multi-scale Air Quality (CMAQ) photochemical
model, version 4.7.1, for our analysis. This version of CMAQ includes a
number of improvements to previous versions of the model. These
improvements are discussed in Section 8.2.2 of the RIA.
(1) Ozone
(a) Current Levels
    8-hour ozone concentrations exceeding the level of the ozone NAAQS
occur in many parts of the country. In 2008, the EPA amended the ozone
NAAQS (73 FR 16436, March 27, 2008). The final 2008 ozone NAAQS rule
set forth revisions to the previous 1997 NAAQS for ozone to provide
increased protection of public health and welfare. On January 6, 2010,
EPA proposed to reconsider the 2008 ozone NAAQS to ensure that they are
requisite to protect public health with an ample margin of safety, and
requisite to protect public welfare (75 FR 2938, January 19, 2010). EPA
intends to complete the reconsideration by July 31, 2011. If, as a
result of the reconsideration, EPA promulgates different ozone
standards, the new 2011 ozone standards would replace the 2008 ozone
standards and the requirement to designate areas for the replaced 2008
standards would no longer apply.
    As of April 21, 2011 there are 44 areas designated as nonattainment
for the 1997 8-hour ozone NAAQS, comprising 242 full or partial
counties with a total population of over 118 million people. These
numbers do not include the people living in areas where there is a
future risk of failing to maintain or attain the 1997 8-hour ozone
NAAQS. The numbers above likely underestimate the number of counties
that are not meeting the ozone NAAQS because the nonattainment areas
associated with the more stringent 2008 8-hour ozone NAAQS have not yet
been designated. Table VII-7 provides an estimate, based on 2006-08 air
quality data, of the counties with design values greater than the 2008
8-hour ozone NAAQS of 0.075 ppm.

  Table VII-7--Counties With Design Values Greater Than the Ozone NAAQS
------------------------------------------------------------------------
                                             Number of
                Standard                     counties     Population \a\
------------------------------------------------------------------------
1997 Ozone Standard: counties within the             266     122,343,799
 54 areas currently designated as
 nonattainment (as of 1/6/10)...........
2008 Ozone Standard: additional counties             156      36,678,478
 that would not meet the 2008 NAAQS
 (based on 2006-2008 air quality data)
 \b\....................................
    Total...............................             422     159,022,277
------------------------------------------------------------------------
Notes:
\a\ Population numbers are from 2000 census data.
\b\ Area designations for the 2008 ozone NAAQS have not yet been made.
  Nonattainment for the 2008 Ozone NAAQS would be based on three years
  of air quality data from later years. Also, the county numbers in this
  row include only the counties with monitors violating the 2008 Ozone
  NAAQS. The numbers in this table may be an underestimate of the number
  of counties and populations that will eventually be included in areas
  with multiple counties designated nonattainment.

(b) Projected Levels Without This Final Action
    States with 8-hour ozone nonattainment areas are required to take
action to bring those areas into compliance in the future. Based on the
final rule designating and classifying 8-hour ozone nonattainment areas
for the 1997 standard (69 FR 23951, April 30, 2004), most 8-hour ozone
nonattainment areas will be required to attain the ozone NAAQS in the
2007 to 2013 time frame and then maintain the NAAQS thereafter. As
noted, EPA is reconsidering the 2008 ozone NAAQS. If EPA promulgates
different ozone NAAQS in 2011 as a result of the reconsideration, these
standards would replace the 2008 ozone NAAQS and there would no longer
be a requirement to designate areas for the 2008 NAAQS. Attainment
dates for any 2011 ozone NAAQS would range from 3 to 20 years from
designation, depending on the area's classification.
    EPA has already adopted many emission control programs that are
expected to reduce ambient ozone levels and assist in reducing the
number of areas that fail to achieve the ozone NAAQS. Even so, our air
quality modeling projects that in 2030, with all current controls but
excluding the impacts of the heavy-duty standards, up to 10 counties
with a population of over 30 million may not attain the 2008 ozone
standard of 0.075 ppm (75 ppb). These numbers do not account for those

areas that are close to (e.g., within 10 percent of) the 2008 ozone
standard. These areas, although not violating the standards, will also
be impacted by changes in ozone as they work to ensure long-term
maintenance of the ozone NAAQS.
(c) Projected Levels With This Final Action
    Our modeling indicates ozone design value concentrations will
decrease in many areas of the country due to this action. The decreases
in ozone design values are likely due to projected tailpipe reductions
in NOX and projected upstream emissions decreases in
NOX and VOCs from reduced gasoline production. The majority
of the ozone design value decreases are less than 1 ppb. The maximum
projected decrease in an 8-hour ozone design value is 1.57 ppb in
Jefferson County, Tennessee. On a population-weighted basis, the
average modeled 8-hour ozone design values are projected to decrease by
0.39 ppb in 2030 and the design values for those counties that are
projected to be above the 2008 ozone standard in 2030 will see
population-weighted decreases of 0.16 ppb due to the heavy-duty
standards.
(2) Particulate Matter
(a) Current Levels
    PM2.5 concentrations exceeding the level of the
PM2.5 NAAQS occur in many parts of the country. In 2005, EPA
designated 39 nonattainment areas for the 1997 PM2.5 NAAQS
(70 FR 943, January 5, 2005). These areas are composed of 208 full or
partial counties with a total population exceeding 88 million. The 1997
PM2.5 NAAQS was revised in 2006 and the 2006 24-hour
PM2.5 NAAQS became effective on December 18, 2006. On
October 8, 2009, the EPA issued final nonattainment area designations
for the 2006 24-hour PM2.5 NAAQS (74 FR 58688, November 13,
2009). These designations include 32 areas composed of 121 full or
partial counties with a population of over 70 million. In total, there
are 54 PM2.5 nonattainment areas composed of 243 counties
with a population of almost 102 million people.
(b) Projected Levels Without This Final Action
    States with PM2.5 nonattainment areas are required to
take action to bring those areas into compliance in the future. Areas
designated as not attaining the 1997 PM2.5 NAAQS will need
to attain the 1997 standards in the 2010 to 2015 time frame, and then
maintain them thereafter. The 2006 24-hour PM2.5
nonattainment areas will be required to attain the 2006 24-hour
PM2.5 NAAQS in the 2014 to 2019 time frame and then be
required to maintain the 2006 24-hour PM2.5 NAAQS
thereafter. The heavy-duty standards finalized in this action become
effective in 2012 and therefore may be useful to states in attaining or
maintaining the PM2.5 NAAQS.
    EPA has already adopted many emission control programs that are
expected to reduce ambient PM2.5 levels and which will
assist in reducing the number of areas that fail to achieve the
PM2.5 NAAQS. Even so, our air quality modeling projects that
in 2030, with all current controls but excluding the impacts of the
heavy-duty standards adopted here, at least 4 counties with a
population of almost 7 million may not attain the 1997 annual
PM2.5 standard of 15 [micro]g/m\3\ and 22 counties with a
population of over 33 million may not attain the 2006 24-hour
PM2.5 standard of 35 [micro]g/m\3\. These numbers do not
account for those areas that are close to (e.g., within 10 percent of)
the PM2.5 standards. These areas, although not violating the
standards, will also benefit from any reductions in PM2.5
ensuring long-term maintenance of the PM2.5 NAAQS.
(c) Projected Levels With This Final Action
    Air quality modeling performed for this final action shows that in
2030 the majority of the modeled counties will see decreases of less
than 0.01 [micro]g/m\3\ in their annual PM2.5 design values.
The decreases in annual PM2.5 design values that we see in
some counties are likely due to emission reductions related to lower
fuel production at existing oil refineries and/or reductions in
PM2.5 precursor emissions (NOX, SOX,
and VOCs) due to improvements in road load. The maximum projected
decrease in an annual PM2.5 design value is 0.03 [micro]g/
m\3\ in Allen County, Indiana and Canyon County, Idaho. On a
population-weighted basis, the average modeled 2030 annual
PM2.5 design value is projected to decrease by 0.01
[micro]g/m\3\ due to this final action.
    In addition to looking at annual PM2.5 design values, we
also modeled the impact of the standards on 24-hour PM2.5
design values. Air quality modeling performed for this final action
shows that in 2030 the majority of the modeled counties will see
changes of between -0.05 [micro]g/m\3\ and 0 [micro]g/m\3\ in their 24-
hour PM2.5 design values. The decreases in annual
PM2.5 design values that we see in some counties are likely
due to emission reductions related to lower fuel production at existing
oil refineries and/or reductions in PM2.5 precursor
emissions (NOX, SOX, and VOCs) due to
improvements in road load. The maximum projected decrease in a 24-hour
PM2.5 design value is 0.27 [micro]g/m\3\ in Canyon County,
ID. There are also some counties that are projected to see increases of
less than 0.1 [micro]g/m\3\ in their 24-hour PM2.5 design
values. These small increases in 24-hour PM2.5 design values
are likely related to downstream emission increases from APUs. On a
population-weighted basis, the average modeled 2030 24-hour
PM2.5 design value is projected to decrease by 0.03
[micro]g/m\3\ due to this final action. Those counties that are
projected to be above the 24-hour PM2.5 standard in 2030
will see slightly smaller population-weighted decreases of 0.01
[micro]g/m\3\ in their design values due to this final action.
(3) Air Toxics
(a) Current Levels
    The majority of Americans continue to be exposed to ambient
concentrations of air toxics at levels which have the potential to
cause adverse health effects.\467\ The levels of air toxics to which
people are exposed vary depending on where people live and work and the
kinds of activities in which they engage, as discussed in detail in
U.S. EPA's most recent Mobile Source Air Toxics Rule.\468\ According to
the National Air Toxic Assessment (NATA) for 2005,\469\ mobile sources
were responsible for 43 percent of outdoor toxic emissions and over 50
percent of the cancer risk and noncancer hazard. Benzene is the largest
contributor to cancer risk of all 124 pollutants quantitatively
assessed in the 2002 NATA and mobile sources were responsible for 59
percent of benzene emissions in 2002. Over the years, EPA has
implemented a number of mobile source and fuel controls resulting in
VOC reductions, which also reduce benzene and other air toxic
emissions.
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    \467\ U.S. Environmental Protection Agency (2007). Control of
Hazardous Air Pollutants from Mobile Sources; Final Rule. 72 FR
8434, February 26, 2007.
    \468\ U.S. Environmental Protection Agency (2007). Control of
Hazardous Air Pollutants from Mobile Sources; Final Rule. 72 FR
8434, February 26, 2007.
    \469\ U.S. EPA. (2011) 2005 National-Scale Air Toxics
Assessment. http://www.epa.gov/ttn/atw/nata2005/. Docket EPA-HQ-OAR-
2010-0162.
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(b) Projected Levels
    Our modeling indicates that the heavy-duty standards have
relatively little impact on national average ambient concentrations of
the modeled air toxics. Additional detail on the air toxics results can
be found in Section <IP_ADDRESS> of the RIA.

(4) Nitrogen and Sulfur Deposition
(a) Current Levels
    Over the past two decades, the EPA has undertaken numerous efforts
to reduce nitrogen and sulfur deposition across the U.S. Analyses of
long-term monitoring data for the U.S. show that deposition of both
nitrogen and sulfur compounds has decreased over the last 17 years
although many areas continue to be negatively impacted by deposition.
Deposition of inorganic nitrogen and sulfur species routinely measured
in the U.S. between 2005 and 2007 were as high as 9.6 kilograms of
nitrogen per hectare (kg N/ha) averaged over three years and 20.8
kilograms of sulfur per hectare (kg S/ha) averaged over three
years.\470\ The data show that reductions were more substantial for
sulfur compounds than for nitrogen compounds. These numbers are
generated by the U.S. national monitoring network and they likely
underestimate nitrogen deposition because neither ammonia nor organic
nitrogen is measured. In the eastern U.S., where data are most
abundant, total sulfur deposition decreased by about 44 percent between
1990 and 2007, while total nitrogen deposition decreased by 25 percent
over the same timeframe.\471\
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    \470\ U.S. EPA. U.S. EPA's Report on the Environment (Final
Report). U.S. Environmental Protection Agency, Washington, DC, EPA/
600/R-07/045F (NTIS PB2008-112484). Docket EPA-HQ-OAR-2010-0162.
Updated data available online at: http://cfpub.epa.gov/eroe/index.cfm?fuseaction=detail.viewInd&ch=46&subtop=341&lv=list.listByChapter&r=201744.
    \471\ U.S. EPA. U.S. EPA's 2008 Report on the Environment (Final
Report). U.S. Environmental Protection Agency, Washington, DC, EPA/
600/R-07/045F (NTIS PB2008-112484). Docket EPA-HQ-OAR-2010-0162.
Updated data available online at: http://cfpub.epa.gov/eroe/index.cfm?fuseaction=detail.viewInd&ch=46&subtop=341&lv=list.listByChapter&r=201744.
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(b) Projected Levels
    Our air quality modeling projects decreases in nitrogen deposition,
especially in the Midwest, as a result of the heavy-duty standards
required by this final action. The heavy-duty standards will result in
annual percent decreases of 0.5 percent to more than 2 percent in some
cities in the Midwest, Phoenix, Albuquerque, and some areas in Texas.
The remainder of the country will see only minimal changes in nitrogen
deposition, ranging from decreases of less than 0.5 percent to
increases of less than 0.5 percent. For a map of 2030 nitrogen
deposition impacts and additional information on these impacts, see
Section <IP_ADDRESS> of the RIA. The impacts of the heavy-duty standards on
sulfur deposition are minimal, ranging from decreases of up to 0.5
percent to increases of up to 0.5 percent.
(5) Visibility
(a) Current Levels
    As mentioned in Section VII.D(1)(a), millions of people live in
nonattainment areas for the PM2.5 NAAQS. These populations,
as well as large numbers of individuals who travel to these areas, are
likely to experience visibility impairment. In addition, while
visibility trends have improved in mandatory class I federal areas, the
most recent data show that these areas continue to suffer from
visibility impairment. In summary, visibility impairment is experienced
throughout the U.S., in multi-state regions, urban areas, and remote
mandatory class I federal areas.
(b) Projected Levels
    Air quality modeling conducted for this final action was used to
project visibility conditions in 138 mandatory class I federal areas
across the U.S. in 2030. The results show that all the modeled areas
will continue to have annual average deciview levels above background
in 2030.\472\ The results also indicate that the majority of the
modeled mandatory class I federal areas will see very little change in
their visibility, but some mandatory class I federal areas will see
improvements in visibility due to the heavy-duty standards and a few
mandatory class I federal areas will see visibility decreases. The
average visibility at all modeled mandatory class I federal areas on
the 20 percent worst days is projected to improve by 0.01 deciviews, or
0.06 percent, in 2030. Section <IP_ADDRESS> of the RIA contains more detail
on the visibility portion of the air quality modeling.
---------------------------------------------------------------------------

    \472\ The level of visibility impairment in an area is based on
the light-extinction coefficient and a unitless visibility index,
called a ``deciview,'' which is used in the valuation of visibility.
The deciview metric provides a scale for perceived visual changes
over the entire range of conditions, from clear to hazy. Under many
scenic conditions, the average person can generally perceive a
change of one deciview. The higher the deciview value, the worse the
visibility. Thus, an improvement in visibility is a decrease in
deciview value.
---------------------------------------------------------------------------

VIII. What are the agencies' estimated cost, economic, and other
impacts of the final program?

    In this section, we present the costs and impacts of the final HD
National Program. It is important to note that NHTSA's final fuel
consumption standards and EPA's final GHG emissions standards will both
be in effect, and each will lead to average fuel efficiency increases
and GHG emission reductions. The two agencies' final standards comprise
the HD National Program.
    The net benefits of the final HD National Program consist of the
effects of the program on:

 The vehicle program costs (costs of complying with the vehicle
CO2 standards),
 Fuel savings associated with reduced fuel usage resulting from
the program,
 Reductions in greenhouse gas emissions,
 The reductions in other (non-GHG) pollutants,
 Costs associated with increases in noise, congestion, and
accidents resulting from increased vehicle use,
 Improvements in U.S. energy security impacts,
 Benefits associated with increased vehicle use due to the
``rebound'' effect.

    We also present the cost-effectiveness of the standards, or the
cost per ton of emissions reduced. Where possible, we identify the
uncertain aspects of these economic impacts and attempt to quantify
them when and if possible (e.g., sensitivity ranges associated with
quantified and monetized GHG impacts; probabilistic uncertainty
associated with non-GHG health benefits). For some impacts, however,
there is a lack of adequate information to inform a probabilistic
assessment of uncertainty. EPA continues 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 program may have other effects that are not included here. The
agencies sought comment on whether any costs or benefits were omitted
from this analysis, so that they could be explicitly recognized in the
final rules. In particular, as discussed in Section III and in Chapter
2 of the RIA, the technology cost estimates developed here take into
account the costs to hold other vehicle attributes, such as size and
performance, constant. In addition, the analysis assumes that the full
technology costs are passed along to vehicle buyers. With these
assumptions, because welfare losses are monetary estimates of how much
buyers would have to be compensated to be made as well off as in the
absence of the change,\473\ the price increase measures

the loss to the buyer.\474\ Assuming that the full technology cost gets
passed along to the buyer as an increase in price, the technology cost
thus measures the welfare loss to the buyer. Increasing fuel efficiency
would have to lead to other changes in the vehicles that buyers find
undesirable for there to be additional losses not included in the
technology costs.
---------------------------------------------------------------------------

    \473\ This approach describes the economic concept of
compensating variation, a payment of money after a change that would
make a consumer as well off after the change as before it. A related
concept, equivalent variation, estimates the income change that
would be an alternative to the change taking place. The difference
between them is whether the consumer's point of reference is her
welfare before the change (compensating variation) or after the
change (equivalent variation). In practice, these two measures are
typically very close together.
    \474\ Indeed, it is likely to be an overestimate of the loss to
the buyer, because the buyer has choices other than buying the same
vehicle with a higher price; she could choose a different vehicle,
or decide not to buy a new vehicle. The buyer would choose one of
those options only if the alternative involves less loss than paying
the higher price. Thus, the increase in price that the buyer faces
would be the upper bound of loss of consumer welfare, unless there
are other changes to the vehicle due to the fuel economy
improvements that make the vehicle less desirable to buyers.
---------------------------------------------------------------------------

    The agencies sought comments, including supporting data and
quantitative analyses, of any additional impacts of the final standards
on vehicle attributes and performance, and other potential aspects that
could positively or negatively affect the welfare implications of this
final rulemaking, not addressed in this analysis.
    The comments received by the agencies did not provide any clear
insights into this question. Some comments noted the diversity of the
trucking industry and expressed a request that the program continue the
great variety of options for the industry, because of the variation in
needs for different customers. Additional comments noted that the
separate engine and vehicle programs support the maintenance of variety
and current market structure. Though a few commenters raised concerns,
no information was offered to indicate that choice will in fact be
limited by the program, or that other vehicle attributes are adversely
affected.
    The total monetized benefits (excluding fuel savings) under the
program are projected to be $4.3 to $11.1 billion in 2030, depending on
the value used for the social cost of carbon. These benefits are
summarized below in Table 0-31. The costs of the program in 2030,
presented in Table 0-29 are estimated to be approximately $2.2 billion
for new engine and truck technology. The program is also estimated to
provide $20.6 billion in savings realized by trucking operations
through fewer fuel expenditures (calculated using pre-tax fuel prices),
as shown in Table 0-30. The present value of the total monetized
benefits (excluding fuel savings) under the program is expected to
range from $48.7 billion to $180.1 billion with a 3 percent discount
rate; with a 7 percent discount rate, the total monetized benefits are
expected to range from $24.3 billion to $155.7 billion. These values,
summarized in Table 0-31, depend on the value used for the social cost
of carbon. The present value of costs of the program for new engine and
truck technology, in Table 0-32, are expected to be $47.4 billion using
a 3 percent discount rate, and $24.7 billion with a 7 percent discount
rate. The present value of fuel savings (calculated using pre-tax fuel
prices) is estimated at $375.3 billion with a 3 percent discount rate,
and $166.5 billion with a 7 percent discount rate, as shown in Table 0-
32. Total net present benefits (in Table 0-32) are thus expected to
range from $376.6 billion to $508 billion with a 3 percent discount
rate, and $166.1 billion to $297.5 billion with a 7 percent discount
rate.
    The estimates developed here are measured against a baseline fuel
efficiency associated with MY 2010 vehicles. The agencies also
considered an alternate baseline associated with AEO 2011 projections,
which is further discussed in Section IX. All calculations presented in
Section VIII use the constant 2010 vehicle baseline. The extent to
which fuel efficiency improvements may have occurred in the absence of
the rules affects the net benefits associated with the program. If
trucks were to install technologies to achieve the fuel savings and
reduced GHG emissions in the absence of this program, then both the
costs and benefits of these fuel savings could be attributed to market
forces, not the rules. As a baseline for estimates of the extent of
fuel-saving technologies that might have been adopted in the absence of
the program, the proposal used the level of these technologies in MY
2010 vehicles. We sought comment on whether the agencies should use an
alternative baseline based on data provided by commenters to estimate
the degree to which the technologies discussed in the proposal would
have been adopted in the absence of these rules. No comments were
received on this issue. One comment cites the EPA draft RIA as noting a
historic 1 percent per year improvement in fuel efficiency, and argues
that the rules are therefore not needed; the actual figure in the draft
RIA, however, was a 0.09 percent per year improvement.
    EPA has undertaken an analysis of the economy-wide impacts of the
final heavy-duty truck fuel efficiency and GHG standards as an
exploratory exercise that EPA believes could provide additional
insights into the potential impacts of the program.\475\ These results
were not a factor regarding the appropriateness of the final standards.
It is important to note that the results of this modeling exercise are
dependent on the assumptions associated with how manufacturers would
make fuel efficiency improvements and how trucking operations would
respond to increases in higher vehicle costs and improved vehicle fuel
efficiency as a result of the final program.
---------------------------------------------------------------------------

    \475\ See Memorandum to Docket, ``Economy-Wide Impacts of Heavy-
Duty Truck Greenhouse Gas Emissions and Fuel Efficiency Standards'',
May 20, 2011. Docket EPA-HQ-OAR-2010-0162.
---------------------------------------------------------------------------

    Further information on these and other aspects of the economic
impacts of our rules are summarized in the following sections and are
presented in more detail in the RIA for this final rulemaking.

A. Conceptual Framework for Evaluating Impacts

    This regulation is motivated primarily by the goals of reducing
emissions of greenhouse gases and promoting U.S. energy security by
reducing consumption and imports of petroleum-based fuels. These
motivations involve classic externalities, meaning that private
decisions do not incorporate all of the costs associated with these
problems; these costs are not borne completely by the households or
businesses whose actions are responsible for them. In the absence of
some mechanism to ``internalize'' these costs--that is, to transfer
their burden to individuals or firms whose decisions impose them--
individuals and firms will consume more petroleum-based fuels than is
socially optimal. Externalities are a classic motivation for government
intervention in markets. These externalities, as well as effects due to
changes in emissions of other pollutants and other impacts, are
discussed in Sections VIII.H--VIII.K.
    In some cases, these classic externalities are by themselves enough
to justify the costs of imposing fuel efficiency standards. For some
discount rates and some projected social costs of carbon, however, the
reductions in these external costs are less than the costs of new fuel
saving technologies needed to meet the standards. (See Tables 9-24 and
9-25 in the RIA.) Nevertheless, this regulation reduces trucking
companies' fuel costs; according to our estimates, these savings in
fuel costs are by themselves sufficient to pay for the

technologies over periods of time considerably shorter than vehicles'
expected lifetimes under the assumptions used for this analysis (e.g.,
AEO 2011 projected fuel prices). If these estimates are correct, then
the entire value of the reductions in external costs represents
additional net benefits of the program, beyond those resulting from the
fact that the value of fuel savings exceeds the costs of technologies
necessary to achieve them.
    It is often asserted that there are cost-effective fuel-saving
technologies that markets do not take advantage of. This is commonly
known as the ``energy gap'' or ``energy paradox.'' Standard economic
theory suggests that in normally functioning competitive markets,
interactions between vehicle buyers and producers would lead producers
to incorporate all cost-effective technology into the vehicles that
they offer, without government intervention. Unlike in the light-duty
vehicle market, the vast majority of vehicles in the medium- and heavy-
duty truck market are purchased and operated by businesses with narrow
profit margins, and for which fuel costs represent a substantial
operating expense.
    Even in the presence of uncertainty and imperfect information--
conditions that hold to some degree in every market--we generally
expect firms to attempt to minimize their costs in an effort to survive
in a competitive marketplace, and therefore to make decisions that are
in the best interest of the company and its owners and/or shareholders.
In this case, the benefits of the rules would be due exclusively to
reducing the economic costs of externalities resulting from fuel
production and consumption. However, as discussed below in Section
VIII.E, the agencies have estimated that the application of fuel-saving
technologies in response to the final standards would, on average,
yield significant private returns to truck owners (see Tables VIII-9
through VIII-11, below). The agencies have also estimated that the
application of these technologies would be significantly lower in the
absence of the final standards (i.e., under the ``no action''
regulatory alternative), meaning that truck buyers and operators ignore
opportunities to make investments in higher fuel efficiency that appear
to offer significant cost savings.
    As discussed in the NPRM, there are several possible explanations
in the economics literature for why trucking companies do not adopt
technologies that would be expected to increase their profits: there
could be a classic market failure in the trucking industry--market
power, externalities, or asymmetric or incomplete (i.e., missing
market) information; there could be institutional or behavioral
rigidities in the industry (union rules, standard operating procedures,
statutory requirements, loss aversion, etc.), whereby participants
collectively do not minimize costs; or the engineering estimates of
fuel savings and costs for these technologies might overstate their
benefits or understate their costs in real-world applications. See 75
FR at 74303-307.
    To try to understand why trucking companies have not adopted these
seemingly cost-effective fuel-saving technologies, the agencies
surveyed published literature about the energy paradox, and held
discussions with numerous truck market participants. The proposal
discussed five categories of possible explanations derived from these
sources. Collectively, these five hypotheses may explain the apparent
inconsistency between the engineering analysis, which finds a number of
cost-effective methods of improving fuel efficiency, and the
observation that many of these technologies are not widely adopted.
    These hypotheses include imperfect information in the original and
resale markets, split incentives, uncertainty about future fuel prices,
and adjustment and transactions costs. As the discussion indicated,
some of these explanations suggest failures in the private market for
fuel-saving technology in addition to the externalities caused by
producing and consuming fuel that are the primary motivation for the
rules. Other explanations suggest market-based behaviors that may imply
additional costs of regulating truck fuel efficiency that are not
accounted for in this analysis. As noted above, an additional
explanation--adverse effects on other vehicle attributes--did not
elicit supporting information in the public comments. Anecdotal
evidence from various segments of the trucking industry suggests that
many of the hypotheses discussed here may play a role in explaining the
puzzle of why truck purchasers appear to under-invest in fuel
efficiency, although different explanations may apply to different
segments, or even different companies. The published literature does
not appear to include empirical analysis or data related to this
question.
    The agencies invited comment on these explanations, and on any data
or information that could be used to investigate the role of any or all
of these five hypotheses in explaining this energy paradox as it
applies specifically to trucks. Some comments expressed dissatisfaction
about the explanations presented; they argued that these arguments were
not sufficient to explain the phenomenon. These comments argued that
the truck owners and operators are better judges of the appropriate
amount of fuel efficiency than are government agencies; they choose not
to invest because of warranted skepticism about these technologies. The
agencies also requested comment and information regarding any other
hypotheses that could explain the appearance that cost-effective fuel-
saving technologies have not been widely incorporated into trucks. The
following discussion summarizes the fuller discussion provided in the
NPRM and includes discussion of the comments received.
(1) Information Issues in the Original Sale Markets
    One potential hypothesis for why the trucking industry does not
adopt what appear to be inexpensive fuel saving technologies is that
there is inadequate or unreliable information available about the
effectiveness of many fuel-saving technologies for new vehicles. If
reliable information on the effectiveness of many new technologies is
absent, truck buyers will understandably be reluctant to spend
additional money to purchase vehicles equipped with unproven
technologies.
    This lack of information can manifest itself in multiple ways. For
instance, the problem may arise purely because collecting reliable
information on technologies is costly (also see Section VIII.A.5 below
on transaction costs). Moreover, information has aspects of a public
good, in that no single firm has the incentive to do the costly
experimentation to determine whether or not particular technologies are
cost-effective, while all firms benefit from the knowledge that would
be gained from that experimentation. Similarly, if multiple firms must
conduct the same tests to get the same information, costs could be
reduced by some form of coordination of information gathering.
    While its effect on information is indirect, we expect the
requirement for the use of new technologies included in this program
will circumvent these information issues, resulting in their adoption,
thus providing more readily available information about their benefits.
The agencies appreciate, however, that the diversity of truck uses,
driving situations, and driver behavior will lead to variation in the
fuel savings that individual trucks or fleets experience from using
specific technologies.

    One commenter noted that the SmartWay program targets combination
tractor owners and thus should have the largest impact on that sector,
rather than vocational or medium-duty trucks. However, the gap between
actual investment in fuel efficiency and the agencies' estimates of
optimal investment is largest for combination tractors. Some of the
difference in magnitude is likely to be due to the higher vehicle miles
traveled for combination tractors compared to medium-duty and
vocational vehicles: more driving means more fuel savings.
Additionally, not even a majority of semi-trucks are owned by
participants in SmartWay; non-participants are unlikely to get all the
benefits of participants. Other explanations, noted below, are also
likely to play a role. This observation may also suggest some
limitations of improved information provision as a means of addressing
the ``efficiency gap.''
(2) Information Issues in the Resale Market
    In addition to issues in the new vehicle market, a second
hypothesis for why trucking companies may not adopt what appear to be
cost-effective technologies to save fuel is that the resale market may
not adequately reward the addition of fuel-saving technology to
vehicles to ensure their original purchase by new truck buyers. This
inadequate payback for users beyond the original owner may contribute
to the short payback period that new purchasers appear to expect.\476\
The agencies requested data and information on the extent to which
costs of fuel saving equipment can be recovered in the resale truck
market. No data were received. One reviewer disputed this theory on the
basis that people are willing to pay more for better vehicles, new or
used. It is not clear, however, whether buyers of used vehicles can
tell which are the better vehicles.\477\
---------------------------------------------------------------------------

    \476\ See NAS 2010, Note 197, at p. 188.
    \477\ Akerlof, George A. ``The Market for `Lemons' Quality
Uncertainty and the Market Mechanism,'' Quarterly Journal of
Economics 84(3) (1970): 488-500 points out that asymmetric
information--the seller has better information than the buyer--can
potentially lead to complete failure of a market, even when both
buyers and sellers would benefit from trade.
---------------------------------------------------------------------------

    Some of this unwillingness to pay for fuel-saving technology may be
due to the extension of the information problems in the new vehicle
market into resale markets. Buyers in the resale market have no more
reason to trust information on fuel-saving technologies than buyers in
the original market. Because actual fuel efficiency of trucks on the
road depends on many factors, including geography and driving styles or
habits, even objective sources such as logs of truck performance for
used vehicles may not provide reliable information about the fuel
efficiency that potential purchasers of used trucks will experience.
    A related possibility is that vehicles will be used for different
purposes by their second owners than those for which they were
originally designed, and the fuel-saving technology is therefore of
less value.
    It is possible, though, that the fuel savings experienced by the
secondary purchasers may not match those experienced by their original
owners if the optimal secondary new use of the vehicle does not earn as
many benefits from the technologies. One commenter asks whether the
fuel-saving technology is unvalued because it is unproven or overrated.
In that case, the premium for fuel-saving technology in the secondary
market should accurately reflect its value to potential buyers
participating in that market, even if it is lower than its value in the
original market, and the market has not failed. Because the information
necessary to optimize use in the secondary market may not be readily
available or reliable, however, buyers in the resale market may have
less ability than purchasers of new vehicles to identify and gain the
advantages of new fuel-saving technologies, and may thus be even less
likely to pay a premium for them.
    For these reasons, purchasers' willingness to pay for fuel
efficiency technologies may be even lower in the resale market than in
the original equipment market. Even when fuel-saving technologies will
provide benefits in the resale markets, purchasers of used vehicles may
not be willing to compensate their original owners fully for their
remaining value. As a result, the purchasers of original equipment may
expect the resale market to provide inadequate appropriate compensation
for the new technologies, even when those technologies would reduce
costs for the new buyers. This information issue may partially explain
what appears to be the very short payback periods required for new
technologies in the new vehicle market.
(3) Split Incentives in the Medium- and Heavy-Duty Truck Industry
    A third hypothesis explaining the energy paradox as applied to
trucking involves split incentives. When markets work effectively,
signals provided by transactions in one market are quickly transmitted
to related markets and influence the decisions of buyers and sellers in
those related markets. For instance, in a well-functioning market
system, changes in the expected future price of fuel should be
transmitted rapidly to those who purchase trucks, who will then
reevaluate the amount of fuel-saving technology to purchase for new
vehicles. If for some reason a truck purchaser will not be directly
responsible for future fuel costs, or the individual who will be
responsible for fuel costs does not decide which truck characteristics
to purchase, then those price signals may not be transmitted
effectively, and incentives can be described as ``split.''
    One place where such a split may occur is between the owners and
operators of trucks. Because they are generally responsible for
purchasing fuel, truck operators have strong incentives to economize on
its use, and are thus likely to support the use of fuel-saving
technology. However, the owners of trucks or trailers are often
different from operators, and may be more concerned about their
longevity or maintenance costs than about their fuel efficiency, when
purchasing vehicles. As a result, capital investments by truck owners
may be channeled into equipment that improves vehicles' durability or
reduces their maintenance costs, rather than into fuel-saving
technology. If operators can choose freely among the trucks they drive,
competition among truck owners to employ operators would encourage
owners to invest in fuel-saving technology. However, if truck owners
have more ability to choose among operators, then market signals for
improved fuel savings that would normally be transmitted to truck
owners may be muted. Truck fleets that rent their vehicles may provide
an example: renters may observe the cost of renting the truck, but not
its fuel efficiency; if so, then the purchasers will aim for vehicles
with lower costs, to lower the cost of the rental. It might be possible
to test this theory by comparing the fuel efficiency of trucks by
owner-operators with those that are leased by operators. The agencies
have not had the data to conduct such a test.
    One commenter noted that there are always tradeoffs in an
investment decision: a purchaser may prefer to invest in other vehicle
attributes than fuel efficiency. In an efficient market, however, a
purchaser should invest in fuel-saving technology as long as the
increase in fuel-saving technology costs less than the expected fuel
savings. This result should hold regardless of the level of investment
in other attributes, unless there are constraints on a

purchaser's access to investment capital. The agencies believe that
truck fleets do have an incentive to make investments in fuel
efficiency, and that this assumption is reflected in the regulatory
analysis. The agencies also believe, however, that sufficient evidence
suggests that truck fleets are not availing themselves of all the
opportunities for efficiency improvements.
    In addition, the NAS report notes that split incentives can arise
between tractor and trailer operators.\478\ Trailers affect the fuel
efficiency of shipping, but trailer owners do not face strong
incentives to coordinate with truck owners. EPA and NHTSA are not
regulating trailers in this action.
---------------------------------------------------------------------------

    \478\ See NAS 2010, Note 197, at p. 182.
---------------------------------------------------------------------------

    By itself, information provision may be inadequate to address the
potential underinvestment in fuel efficiency resulting from such split
incentives. In this setting, regulation may contribute to fuel savings
that otherwise may be difficult to achieve.
(4) Uncertainty About Future Cost Savings
    Another hypothesis for the lack of adoption of seemingly fuel
saving technologies may be uncertainty about future fuel prices or
truck maintenance costs. When purchasers have less than perfect
foresight about future operating expenses, they may implicitly discount
future savings in those costs due to uncertainty about potential
returns from investments that reduce future costs. In contrast, the
immediate costs of the fuel-saving or maintenance-reducing technologies
are certain and immediate, and thus not subject to discounting. In this
situation, both the expected return on capital investments in higher
fuel efficiency and potential variance about its expected rate may play
a role in a firm's calculation of its payback period on such
investments.
    In the context of energy efficiency investments for the home,
Metcalf and Rosenthal (1995) and Metcalf and Hassett (1995) observe
that households weigh known, up-front costs that are essentially
irreversible against an unknown stream of future fuel savings.\479\
Notably, in this situation, requiring households to adopt technologies
more quickly may make them worse off by imposing additional risk on
them.
---------------------------------------------------------------------------

    \479\ Metcalf, G., and D. Rosenthal (1995). ``The `New' View of
Investment Decisions and Public Policy Analysis: An Application to
Green Lights and Cold Refrigerators,'' Journal of Policy Analysis
and Management 14: 517-531. Hassett and Metcalf (1995). ``Energy Tax
Credits and Residential Conservation Investment: Evidence from Panel
Data'' Journal of Public Economics 57 (1995): 201-217. Metcalf, G.,
and K. Hassett (1999). ``Measuring the Energy Savings from Home
Improvement Investments: Evidence from Monthly Billing Data.'' The
Review of Economics and Statistics 81(3): 516-528.
---------------------------------------------------------------------------

    Greene et al (2009) also finds support for this explanation in the
context of light-duty fuel economy decisions: a loss-averse consumer's
expected net present value of increasing the fuel economy of a
passenger car can be very close to zero, even if a risk-neutral
expected value calculation shows that its buyer can expect significant
net benefits from purchasing a more fuel-efficient car.\480\ Supporting
this hypothesis is a finding by Dasgupta et al. (2007) that consumers
are more likely to lease than buy a vehicle with higher maintenance
costs because it provides them with the option to return it before
those costs become too high.\481\ However, the agencies know of no
studies that have estimated the impact of uncertainty on perceived
future savings for medium- and heavy-duty vehicles.
---------------------------------------------------------------------------

    \480\ Greene, D., J. German, and M. Delucchi (2009). ``Fuel
Economy: The Case for Market Failure'' in Reducing Climate Impacts
in the Transportation Sector, Sperling, D., and J. Cannon, eds.
Springer Science.
    \481\ Dasgupta, S., S. Siddarth, and J. Silva-Risso (2007). ``To
Lease or to Buy? A Structural Model of a Consumer's Vehicle and
Contract Choice Decisions.'' Journal of Marketing Research 44: 490-
502.
---------------------------------------------------------------------------

    Purchasers' uncertainty about future fuel prices implies that
mandating improvements in fuel efficiency can reduce the expected
utility associated with truck purchases. This is because adopting such
regulation requires purchasers to assume a greater level of risk than
they would in its absence, even if the future fuel savings predicted by
a risk-neutral calculation actually materialize. One commenter
expressed support for this argument. Thus the mere existence of
uncertainty about future savings in fuel costs does not by itself
assure that regulations requiring improved fuel efficiency will
necessarily provide economic benefits for truck purchasers and
operators. On the other hand, because risk aversion reduces expected
returns for businesses, competitive pressures can reduce risk aversion:
risk-neutral companies can make higher average profits over time. Thus,
significant risk aversion is unlikely to survive competitive pressures.
(5) Adjustment and Transactions Costs
    Another hypothesis is that transactions costs of changing to new
technologies (how easily drivers will adapt to the changes, e.g.) may
slow or prevent their adoption. Because of the diversity in the
trucking industry, truck owners and fleets may like to see how a new
technology works in the field, when applied to their specific
operations, before they adopt it. One commenter expressed support for
this argument. If a conservative approach to new technologies leads
truck buyers to adopt new technologies slowly, then successful new
technologies are likely to be adopted over time without market
intervention, but with potentially significant delays in achieving fuel
saving, environment, and energy security benefits.
    In addition, there may be costs associated with training drivers to
realize the 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 new fuel-saving
technologies. Here, again, there may be no market failure; requiring
the widespread use of these technologies may impose adjustment and
transactions costs not included in this analysis. As in the discussion
of the role of risk, these adjustment and transactions costs are
typically immediate and undiscounted, while their benefits are future
and uncertain; risk or loss aversion may further discourage companies
from adopting new technologies.
    To the extent that there may be transactions costs associated with
the new technologies, then regulation gives all new truck purchasers a
level playing field, because it will require all of them to adjust on
approximately the same time schedule. If experience with the new
technologies serves to reduce uncertainty and risk, the industry as a
whole may become more accepting of new technologies. This could
increase demand for future new technologies and induce additional
benefits in the legacy fleet through complementary efforts such as
SmartWay.
(6) Additional Hypotheses
    In the public comments, two additional ideas were raised for the
lack of adoption of what appears to be cost-effective fuel-saving
technology. The first suggestion is that tighter diesel emissions
standards caused engine manufacturers to invest heavily (both
financially and with personnel) in emissions reduction technologies,
and hence, were unable to invest in fuel efficiency technologies. A
second suggestion is that a truck may be a ``positional good''--that
is, a good whose value depends on how it compares to the goods owned by
others. If trucks confer status on their owners or operators, and if
that status depends on easily observable characteristics,

then owners may invest disproportionately in status-granting
characteristics rather than less visible characteristics, such as fuel
efficiency. Because status depends on comparisons to others, an ``arms
race'' may develop in which all parties spend additional money on
visible characteristics but may not manage to make themselves better
off. In this case, regulation may improve welfare: by increasing the
requirements for non-positional fuel efficiency, regulation could
reduce expenditures made purely for competition rather than actual
increase in welfare. In a competitive business, cost reduction provides
a major opportunity cost to investing in status rather than in fuel-
saving technology; thus, this argument may play less of a role in the
heavy-duty market than in the consumer market for vehicles.
    Both these hypotheses leave open the question, though, why
additional investments were not made in fuel efficiency if they would
provide rapid payback. Truck purchasers should, in principle, be
willing to buy additional fuel-saving technology as long as it is cost-
effective, regardless of other vehicle attributes. Limited access to
capital, if it is a problem in this sector, might provide some reason
for the ``crowding out'' of the purchase of fuel-saving technology. The
agencies received no evidence indicating that constrained access to
capital might explain the efficiency gap in this market.
(7) Summary
    On the one hand, commercial vehicle operators are under competitive
pressure to reduce operating costs, and thus their purchasers would be
expected to pursue and rapidly adopt cost-effective fuel-saving
technologies. On the other hand, the short payback period required by
buyers of new trucks is a symptom that suggests some combination of
uncertainty about future cost savings, transactions costs, and
imperfectly functioning markets. In addition, widespread use of
tractor-trailer combinations introduces the possibility that owners of
trailers may have weaker incentives than truck owners or operators to
adopt fuel-saving technology for their trailers. The market for medium-
and heavy-duty trucks may face these problems, both in the new vehicle
market and in the resale market.
    Provision of information about fuel-saving technologies through
voluntary programs such as SmartWay will assist in the adoption of new
cost-saving technologies, but diffusion of new technologies can still
be obstructed. Those who are willing to experiment with new
technologies expect to find cost savings, but those may be difficult to
prove. As noted above, because individual results of new technologies
vary, new truck purchasers may find it difficult to identify or verify
the effects of fuel-saving technologies. Those who are risk-averse are
likely to avoid new technologies out of concerns over the possibility
of inadequate returns on the investment, or with other adverse impacts.
Competitive pressures in the freight transport industry can provide a
strong incentive to reduce fuel consumption and improve environmental
performance. However, not every driver or trucking fleet operating
today has the requisite ability or interest to access the technical
information, some of which is already provided by SmartWay, nor the
resources necessary to evaluate this information within the context of
his or her own freight operation.
    It is unclear, as discussed above, whether some or many of the
technologies would be adopted in the absence of the program. To the
extent that they would have been adopted, the costs and the benefits
attributed to those technologies may not in fact be due to the program
and may therefore be overstated. Both baselines used project
substantially less adoption than the agencies consider to be cost-
effective. The agencies will continue to explore reasons for this slow
adoption of cost-effective technologies.

B. Costs Associated With the Final Program

    In this section, the agencies present the estimated costs
associated with the final program. The presentation here summarizes the
costs associated with new technology expected to be added to meet the
new GHG and fuel consumption standards. The analysis summarized here
provides the estimate of incremental costs on a per truck basis and on
an annual total basis.
    The presentation here summarizes the best estimate by EPA and NHTSA
staff as to the technology mix expected to be employed for compliance.
For details behind the cost estimates associated with individual
technologies, the reader is directed to Section III of this preamble
and to Chapter 2 of the RIA.
    With respect to the cost estimates presented here, the agencies
note that, because these estimates relate to technologies which are in
most cases already available, these cost estimates are technically
robust.
(1) Costs per Truck
    For the heavy-duty pickup trucks and vans, the agencies have used a
methodology consistent with that used for our recent light-duty joint
rulemaking since most of the technologies expected for heavy-duty
pickup trucks and vans is consistent with that expected for the larger
light-duty trucks. The cost estimates presented in the recent light-
duty joint rulemaking were then scaled upward to account for the larger
weight, towing capacity, and work demands of the trucks in these
heavier classes. For details on that scaling process and the resultant
costs for individual technologies, the reader is directed to Section
III of this preamble and to Chapter 2 of the RIA. Note also that all
cost estimates have been updated to 2009 dollars for this analysis
while the heavy-duty GHG emissions and fuel efficiency proposal was
presented in 2008 dollars and the light-duty rule was presented in 2007
dollars.
    For the loose heavy-duty gasoline engines, we have generally used
engine-related costs from the heavy-duty pickup truck and van estimates
since the loose heavy-duty gasoline engines are essentially the same
engines as those sold into the heavy-duty pickup truck and van market.
    For heavy-duty diesel engines, the agencies have estimated costs
using a different methodology than that employed in the recent light-
duty joint rulemaking. In the light-duty 2012-2016 MY vehicle rule, the
fixed costs were included in the hardware costs via an indirect cost
multiplier. As such, the hardware costs presented in that analysis, and
in the cost estimates for Class 2b and 3 trucks, included both the
actual hardware and the associated fixed costs. For this analysis, some
of the fixed costs are estimated separately for HD diesel engines and
are presented separately from the hardware costs. For details, the
reader is directed to Chapter 2 of the RIA. Importantly, both
methodologies after the figures are totaled account for all the costs
associated with the program. As noted above, all costs are presented in
2009 dollars.
    The estimates of vehicle compliance costs cover the years leading
up to--2012 and 2013--and including implementation of the program--2014
through 2018. Also presented are costs for the years following
implementation to shed light on the long term (2022 and later) cost
impacts of the program. The year 2022 was chosen here consistent with
the light-duty 2012-2016 MY vehicle rule. That year was considered long
term in that analysis because the short-term and long-term markup
factors described shortly below are applied in five year increments
with the 2012 through 2016 implementation span and

the 2017 through 2021 span both representing the short-term. Since many
of the costs used in this analysis are based on costs in the light-duty
rule analysis, consistency with that analysis seems appropriate.
    Some of the individual technology cost estimates are presented in
brief in Section III, and account for both the direct and indirect
costs incurred in the manufacturing and dealer industries (for a
complete presentation of technology costs, please refer to Chapter 2 of
the RIA). To account for the indirect costs on Class 2b and 3 pickup
trucks and vans, the agencies have applied an ICM factor to all of the
direct costs to arrive at the estimated technology cost. The ICM factor
used was 1.24 in the short-term (2014 through 2021) to account for
differences in the levels of R&D, tooling, and other indirect costs
that will be incurred. Once the program has been fully implemented,
some of the indirect costs will no longer be attributable to these
standards and, as such, a lower ICM factor is applied to direct costs
in 2022 and later. The agencies have also applied ICM factors to Class
4 through 8 trucks and to heavy-duty diesel engine technologies. Markup
factors in these categories range from 1.15 to 1.30 in the short term
(2014 through 2021) depending on the complexity of the given
technology. We have modified the manner in which ICMs are applied in
that they are no longer applied as a simple multiplicative factor on
top of the direct manufacturing costs. Instead, we have broken out the
warranty cost portion of the ICM and apply it in a multiplicative
manner then add the non-warranty cost portion of the ICM to that. The
latter portion, that for non-warranty costs, is determined for a given
year and held constant rather than decreasing year-over-year. This new
approach, which responds to criticisms from some that the
multiplicative approach used in the past essentially double counts
learning effects, is discussed in Section VIII.C and is detailed in
chapter 2 of the RIA. Note that, for the HD diesel engines, the
agencies have applied the ICMs to ensure that our estimates are
conservative since we have estimated fixed costs separately for
technologies applied to these categories--effectively making the use of
markups a double counting of indirect costs. For the details on the
background and the concept behind our use of ICMs to calculate indirect
costs, please refer to the report that has been placed in the docket
for this final action.\482\
---------------------------------------------------------------------------

    \482\ RTI International. Heavy-duty Truck Retail Price
Equivalent and Indirect Cost Multipliers. July 2010.
---------------------------------------------------------------------------

    The agencies have also considered the impacts of manufacturer
learning on the technology cost estimates by reflecting the phenomenon
of volume-based learning curve cost reductions in our modeling using
two algorithms depending on where in the learning cycle (i.e., on what
portion of the learning curve) we consider a technology to be--
``steep'' portion of the curve for newer technologies and ``flat''
portion of the curve for mature technologies. The observed phenomenon
in the economic literature which supports manufacturer learning cost
reductions are based on reductions in costs as production volumes
increase, and the economic literature suggests these cost reductions
occur indefinitely, though the absolute magnitude of the cost
reductions decrease as production volumes increase (with the highest
absolute cost reduction occurring with the first doubling of
production). The agencies use the terminology ``steep'' and ``flat''
portion of the curve to distinguish among newer technologies and more
mature technologies, respectively, and how learning cost reductions are
applied in cost analyses. The steep learning algorithm applies for the
early, steep portion of the learning curve and is estimated to result
in 20 percent lower costs after two full years of implementation (i.e.,
a 2016 MY cost would be 20 percent lower than the 2014 and 2015 model
year costs for a new technology being implemented in 2014). The flat
learning algorithm applies for the flatter portion of the learning
curve and is estimated to result in 3 percent lower costs in each of
the five years following first introduction of a mature technology
added in response to this final action. Once two steep learning steps
have occurred (for technologies having steep learning applied), flat
learning would begin. For technologies to which flat learning is
applied, learning would begin in year 2 at 3 percent per year for 5
years. Beyond 5 years of flat learning at 3 percent per year, 5 years
of flat learning at 2 percent per year, then 5 at 1 percent per year
become effective.
    Learning impacts have been considered on most but not all of the
technologies expected to be used because some of the expected
technologies are already used rather widely in the industry and,
presumably, learning impacts have already occurred. The agencies have
applied the steep learning algorithm for only a handful of technologies
considered to be new or emerging technologies such as energy recovery
systems and thermal storage units which might one day be used on big
trucks. For most technologies, the agencies have considered them to be
more established and, hence, the agencies have applied the lower flat
learning algorithm. For more discussion of the learning approach and
the technologies to which each type of learning has been applied the
reader is directed to chapter 2 of the RIA.
    The technology cost estimates discussed in Section III and detailed
in Chapter 2 of the RIA are used to build up technology package cost
estimates. For each engine and truck class, a single package for each
was developed capable of complying with the final standards and the
costs for each package was generated. The technology packages and
package costs are discussed in more detail in Chapter 2 of the RIA. The
compliance cost estimates take into account all credits and trading
programs and include costs associated with air conditioning controls.
Table VIII-1 presents the average incremental costs per truck for this
final action. For HD pickup trucks and vans (Class 2b and 3), costs
increase as the standards become more stringent in 2014 through 2018.
Following 2018, costs then decrease going forward as learning effects
result in decreased costs for individual technologies. By 2022, the
long term ICMs take effect and costs decrease yet again. For vocational
vehicles, cost trends are more difficult to discern as diesel engines
begin adding technology in 2014, gasoline engines begin adding
technology in 2016, and the trucks themselves begin adding technology
in 2014. With learning effects the costs, in general, decrease each
year except for the heavy-duty gasoline engine changes in 2016. Long
term ICMs take effect in 2022 to provide more cost reductions.

For combination tractors, costs generally decrease each year due to
learning effects with the exception of 2017 when the engines placed in
sleeper cab tractors add turbo compounding. Following that, learning
impacts result in cost reductions and the long term ICMs take effect in
2022 for further cost reductions. By 2030 and later, cost-per-truck
estimates remain constant for all classes. Regarding the long term ICMs
taking effect in 2022, the agencies consider this the point at which
some indirect costs decrease or are no longer considered attributable
to the program (e.g., warranty costs go down). Costs per truck remain
essentially constant thereafter.

                                     Table VIII-1--Estimated Cost per Truck
                                                 [2009 dollars]
----------------------------------------------------------------------------------------------------------------
                                                              HD Pickups &
                                                                  vans           Vocational        Combination
----------------------------------------------------------------------------------------------------------------
2014......................................................              $165              $329            $6,019
2015......................................................               215               320             5,871
2016......................................................               422               397             5,677
2017......................................................               631               387             6,413
2018......................................................             1,048               378             6,215
2020......................................................               985               366             6,004
2030......................................................               977               311             5,075
2040......................................................               977               305             5,075
2050......................................................               977               304             5,075
----------------------------------------------------------------------------------------------------------------

    These costs would, presumably, have some impact on new truck
prices, although the agencies make no attempt at determining what the
impact of increased costs would be on new truck prices. Nonetheless, on
a percentage basis, the costs shown in Table VIII-1 for 2018 MY trucks
(when all final requirements are fully implemented) would be roughly
three percent for a typical HD pickup truck or van, less than one
percent for a typical vocational vehicle, and roughly six percent for a
typical combination truck/tractor using new truck prices of $40,000,
$100,000 and $100,000, respectively. The costs would represent lower or
higher percentages of new truck prices for new trucks with higher or
lower prices, respectively. Given the wide range of new truck prices in
these categories--a Class 4 vocational work truck might be $40,000 when
new while a Class 8 refuse truck (i.e., a large vocational vehicle)
might be as much as $200,000 when new--it is very difficult to reflect
incremental costs as percentages of new truck prices for all trucks.
What is presented here is the average cost (Table VIII-1) compared with
typical new truck prices.
    As noted above, the fixed costs were estimated separately from the
hardware costs for HD diesel engines that are placed in vocational
vehicles and combination tractors. Those fixed costs are not included
in Table VIII-1. The agencies have estimated the R&D costs at $6.8
million per manufacturer per year for five years and the new test cell
costs (to accommodate measurement of N2O emissions) at
$63,087 per manufacturer. The test cell costs of N2O
emissions measurement has been adjusted for the final rulemaking to
reflect comments which stated approximately 75 percent of manufacturers
would be required to update existing equipment while the other 25
percent would require new equipment. These costs apply individually for
LHD, MHD and HHD engines. Given the 14 manufacturers impacted by the
final standards, 11 of which are estimated to sell both MHD and HHD
engines and 3 of which are estimated to sell LHD engines, we have
estimated a five year annual R&D cost of $170.3 million dollars (2 x 11
x $6.8 million plus 3 x $7.75 million for each year 2012-2016) and a
one-time test cell cost of $1.6 million dollars (2 x 11 x $63,087 plus
3 x $63,087 in 2013). Estimating annual sales of HD diesel engines at
roughly 600,000 units results in roughly $284 per engine per year for
five years beginning in 2012 and ending in 2016. Again, these costs are
not reflected in Table VIII-1, but are included in Table VIII-2 as
``Other Engineering Costs.''
    The certification and compliance program costs, for all engine and
truck types, are estimated at $6.5 million in the first year dropping
to $2.3 million in each year thereafter and continuing indefinitely.
These costs are detailed in the ``Draft Supporting Statement for
Information Collection Request'' which is contained in the docket for
this final action.\483\ The costs are higher in the first year due to
capital expenses required to comply with new reporting burdens
(facility upgrade costs are included in engineering costs as described
above). Estimating annual sales of heavy-duty trucks at roughly 1.5
million units would result in just over $4 per engine/truck in the
first year and less than $2 per engine/truck per year thereafter. These
costs are not reflected in Table VIII-1, but are included in Table
VIII-2 below as ``Compliance Program'' costs.
---------------------------------------------------------------------------

    \483\ ``Draft Supporting Statement for Information Collection
Request,'' Control of Greenhouse Gas Emissions from New Motor
Vehicles: Proposed Heavy-Duty Engine and Vehicle Standards, EPA ICR
Tracking Number 2394.01.
---------------------------------------------------------------------------

(2) Annual Costs of the HD National Program
    The costs presented here represent the incremental costs for newly
added technology to comply with the program. Together with the
projected increases in truck sales, the increases in per-truck average
costs shown in Table VIII-1, above result in the total annual costs
presented in Table VIII-2 below. Note that the costs presented in Table
VIII-2 do not include the savings that will occur as a result of the
improvements to fuel consumption. Those impacts are presented in
Section 0. Note also that the costs presented here represent costs
estimated to occur presuming that the final standards will continue in
perpetuity. Any changes to the final standards would be considered as
part of a future rulemaking. In other words, the final standards do not
apply only to 2014-2018 model year trucks--they do, in fact, apply to
all 2014 and later model year trucks. We present more detail regarding
the 2014-2018 model year trucks in Sections VIII.L, where we summarize
all monetized costs and benefits.

                                                 Table VIII-2--Annual Costs Associated With the Program
                                                                   [$Millions, 2009$]
--------------------------------------------------------------------------------------------------------------------------------------------------------
                                                                                Other
                          Year                             HD Pickup and    Vocational      Combination     engineering     Compliance     Annual costs
                                                               vans          vehicles        tractors          costs       program costs
--------------------------------------------------------------------------------------------------------------------------------------------------------
2012 \a\................................................              $0              $0              $0            $170              $0            $170
2013....................................................               0               0               0             172               0             172
2014....................................................             130             185           1,078             170             6.5           1,569
2015....................................................             157             170             922             170             2.3           1,422
2016....................................................             300             202             820             170             2.3           1,495
2017....................................................             447             198             951               0             2.3           1,598
2018....................................................             751             201           1,000               0             2.3           1,955
2020....................................................             754             202           1,001               0             2.3           1,959
2030....................................................             918             216           1,076               0             2.3           2,212
2040....................................................           1,024             281           1,372               0             2.3           2,679
2050....................................................           1,156             354           1,777               0             2.3           3,290
NPV, 3%.................................................          17,070           4,950          24,487             793              52          47,352
NPV, 7%.................................................           8,467           2,588          12,855             724              30          24,665
--------------------------------------------------------------------------------------------------------------------------------------------------------
Note:
\a\ As explained in the text, ``Other Engineering Costs'' are estimated for years 2012 through 2016. These costs represent facility related costs and
  engineering development costs, much of which will have to begin prior to implementation of the new standards.

C. Indirect Cost Multipliers

(1) Markup Factors To Estimate Indirect Costs
    For all segments in this analysis, indirect costs are estimated by
applying indirect cost multipliers (ICM) to direct cost estimates. ICMs
were calculated by EPA as a basis for estimating the impact on indirect
costs of individual vehicle technology changes that would result from
regulatory actions. Separate ICMs were derived for low, medium, and
high complexity technologies, thus enabling estimates of indirect costs
that reflect the variation in research, overhead, and other indirect
costs that can occur among different technologies. ICMs were also
applied in the light-duty rule.
    Prior to developing the ICM methodology, EPA and NHTSA both applied
a retail price equivalent (RPE) factor to estimate indirect costs. RPEs
are estimated by dividing the total revenue of a manufacturer by the
direct manufacturing costs. As such, it includes all forms of indirect
costs for a manufacturer and assumes that the ratio applies equally for
all technologies. ICMs are based on RPE estimates that are then
modified to reflect only those elements of indirect costs that would be
expected to change in response to a regulatory-induced technology
change. For example, warranty costs would be reflected in both RPE and
ICM estimates, while marketing costs might only be reflected in an RPE
estimate but not an ICM estimate for a particular technology, if the
new regulatory-induced technology change is not one expected to be
marketed to consumers. Because ICMs calculated by EPA are for
individual technologies, many of which are small in scale, they often
reflect a subset of RPE costs; as a result, the RPE is typically higher
than an ICM. This is not always the case, as ICM estimates for complex
technologies may reflect higher than average indirect costs, with the
resulting ICM larger than the averaged RPE for the industry.
    There is some level of uncertainty surrounding both the ICM and RPE
markup factors. The ICM estimates used in this final action group all
technologies into three broad categories and treat them as if
individual technologies within each of the three categories (low,
medium, and high complexity) will have the same ratio of indirect costs
to direct costs. This simplification means it is likely that the direct
cost for some technologies within a category will be higher and some
lower than the estimate for the category in general. More importantly,
the ICM estimates have not been validated through a direct accounting
of actual indirect costs for individual technologies. Rather, the ICM
estimates were developed using adjustment factors developed in two
separate occasions: the first, a consensus process, was reported in the
RTI report; the second, a modified Delphi method, was conducted
separately and reported in an EPA memo.\484\ Both these panels were
composed of EPA staff members with previous background in the
automobile industry; the memberships of the two panels overlapped but
were not the same.\485\ The panels evaluated each element of the
industry's RPE estimates and estimated the degree to which those
elements would be expected to change in proportion to changes in direct
manufacturing costs. The method and estimates in the RTI report were
peer reviewed by three industry experts and subsequently by reviewers
for the International Journal of Production Economics.\486\ RPEs
themselves are inherently difficult to estimate because the accounting
statements of manufacturers do not neatly categorize all cost elements
as either direct or indirect costs. Hence, each researcher developing
an RPE estimate must apply a certain amount of judgment to the
allocation of the costs. Moreover, RPEs for heavy- and medium-duty
trucks and for engine manufacturers are not as well studied as they are
for the light-duty automobile industry. Since empirical estimates of
ICMs are ultimately derived from the same data used to measure RPEs,
this affects both measures. However, the value of RPE has not been
measured for specific technologies, or for groups of specific
technologies. Thus applying a single average RPE to any given
technology by definition overstates costs for very simple technologies,