minimum absolute deviation (MAD) regression without sales weighting on
a technology-adjusted car and light truck fleet to fit a linear
equation. This equation was used as a starting point to develop
mathematical functions defining the standards as discussed above. The
agencies then identified footprints at which to apply minimum and
maximum values (rather than letting the standards extend without limit)
and transposed these constrained/piecewise linear functions vertically
(i.e., on a gpm or CO2 basis, uniformly downward) to produce
the fleetwide fuel economy and CO2 emission levels for cars
and light trucks described in the final rule.\116\
---------------------------------------------------------------------------

    \115\ 75 FR at 25362.
    \116\ See generally 74 FR at 49491-96; 75 FR at 25357-62.
---------------------------------------------------------------------------

4. How have the agencies changed the mathematical functions for the
proposed MYs 2017-2025 standards, and why?
    By requiring NHTSA to set CAFE standards that are attribute-based
and defined by a mathematical function, Congress appears to have wanted
the post-EISA standards to be data-driven--a mathematical function
defining the standards, in order to be ``attribute-based,'' should
reflect the observed relationship in the data between the

attribute chosen and fuel economy.\117\ EPA is also proposing to set
attribute-based CO2 standards defined by similar
mathematical functions, for the reasonable technical and policy grounds
discussed below and in section II of the preamble to the proposed rule,
and which supports a harmonization with the CAFE standards.
---------------------------------------------------------------------------

    \117\ A mathematical function can be defined, of course, that
has nothing to do with the relationship between fuel economy and the
chosen attribute--the most basic example is an industry-wide
standard defined as the mathematical function average required fuel
economy = X, where X is the single mpg level set by the agency. Yet
a standard that is simply defined as a mathematical function that is
not tied to the attribute(s) would not meet the requirement of EISA.
---------------------------------------------------------------------------

    The relationship between fuel economy (and GHG emissions) and
footprint, though directionally clear (i.e., fuel economy tends to
decrease and CO2 emissions tend to increase with increasing
footprint), is theoretically vague and quantitatively uncertain; in
other words, not so precise as to a priori yield only a single possible
curve.\118\ There is thus a range of legitimate options open to the
agencies in developing curve shapes. The agencies may of course
consider statutory objectives in choosing among the many reasonable
alternatives. For example, curve shapes that might have some
theoretical basis could lead to perverse outcomes contrary to the
intent of the statutes to conserve energy and protect human health and
the environment.\119\ Thus, the decision of how to set the target
curves cannot always be just about most ``clearly'' using a
mathematical function to define the relationship between fuel economy
and the attribute; it often has to have a normative aspect, where the
agencies adjust the function that would define the relationship in
order to avoid perverse results, improve equity of burden across
manufacturers, preserve consumer choice, etc. This is true both for the
decisions that guide the mathematical function defining the sloped
portion of the target curves, and for the separate decisions that guide
the agencies' choice of ``cutpoints'' (if any) that define the fuel
economy/CO2 levels and footprints at each end of the curves
where the curves become flat. Data informs these decisions, but how the
agencies define and interpret the relevant data, and then the choice of
methodology for fitting a curve to the data, must include a
consideration of both technical data and policy goals.
---------------------------------------------------------------------------

    \118\ In fact, numerous manufacturers have confidentially shared
with the agencies what they describe as ``physics based'' curves,
with each OEM showing significantly different shapes, and footprint
relationships. The sheer variety of curves shown to the agencies
further confirm the lack of an underlying principle of ``fundamental
physics'' driving the relationship between CO2 emission
or fuel consumption and footprint, and the lack of an underlying
principle to dictate any outcome of the agencies' establishment of
footprint-based standards.
    \119\ For example, if the agencies set weight-based standards
defined by a steep function, the standards might encourage
manufacturers to keep adding weight to their vehicles to obtain less
stringent targets.
---------------------------------------------------------------------------

    The next sections examine the policy concerns that the agencies
considered in developing the proposed target curves that define the
proposed MYs 2017-2025 CAFE and CO2 standards, new technical
work (expanding on similar analyses performed by NHTSA when the agency
proposed MY 2011-2015 standards, and by both agencies during
consideration of options for MY 2012-2016 CAFE and GHG standards) that
was completed in the process of reexamining potential mathematical
functions, how the agencies have defined the data, and how the agencies
explored statistical curve-fitting methodologies in order to arrive at
proposed curves.
5. What are the agencies proposing for the MYs 2017-2025 curves?
    The proposed mathematical functions for the proposed MYs 2017-2025
standards are somewhat changed from the functions for the MYs 2012-2016
standards, in response to comments received from stakeholders and in
order to address technical concerns and policy goals that the agencies
judge more significant in this 9-year rulemaking than in the prior one,
which only included 5 years. This section discusses the methodology the
agencies selected as, at this time, best addressing those technical
concerns and policy goals, given the various technical inputs to the
agencies' current analyses. Below the agencies discuss how the agencies
determined the cutpoints and the flat portions of the MYs 2017-2025
target curves. We also note that both of these sections address only
how the target curves were fit to fuel consumption and CO2
emission values determined using the city and highway test procedures,
and that in determining respective regulatory alternatives, the
agencies made further adjustments to the resultant curves in order to
account for adjustments for improvements to mobile air conditioners.
    Thus, recognizing that there are many reasonable statistical
methods for fitting curves to data points that define vehicles in terms
of footprint and fuel economy, the agencies have chosen for this
proposed rule to fit curves using an ordinary least-squares
formulation, on sales-weighted data, using a fleet that has had
technology applied, and after adjusting the data for the effects of
weight-to-footprint, as described below. This represents a departure
from the statistical approach for fitting the curves in MYs 2012-2016,
as explained in the next section. The agencies considered a wide
variety of reasonable statistical methods in order to better understand
the range of uncertainty regarding the relationship between fuel
consumption (the inverse of fuel economy), CO2 emission
rates, and footprint, thereby providing a range within which decisions
about standards would be potentially supportable.
a. What concerns were the agencies looking to address that led them to
change from the approach used for the MYs 2012-2016 curves?
    During the year and a half since the MYs 2012-2016 final rule was
issued, NHTSA and EPA have received a number of comments from
stakeholders on how curves should be fitted to the passenger car and
light truck fleets. Some limited-line manufacturers have argued that
curves should generally be flatter in order to avoid discouraging small
vehicles, because steeper curves tend to result in more stringent
targets for smaller vehicles. Most full-line manufacturers have argued
that a passenger car curve similar in slope to the MY 2016 passenger
car curve would be appropriate for future model years, but that the
light truck curve should be revised to be less difficult for
manufacturers selling the largest full-size pickup trucks. These
manufacturers argued that the MY 2016 light truck curve was not
``physics-based,'' and that in order for future tightening of standards
to be feasible for full-line manufacturers, the truck curve for later
model years should be steeper and extended further (i.e., made less
stringent) into the larger footprints. The agencies do not agree that
the MY 2016 light truck curve was somehow deficient in lacking a
``physics basis,'' or that it was somehow overly stringent for
manufacturers selling large pickups--manufacturers making these
arguments presented no ``physics-based'' model to explain how fuel
economy should depend on footprint.\120\ The same manufacturers
indicated that they believed that the light truck standard should be
somewhat steeper after MY 2016, primarily because, after more than ten
years of progressive increases in the stringency of applicable CAFE
standards, large pickups would be less capable of achieving further

improvements without compromising load carrying and towing capacity.
---------------------------------------------------------------------------

    \120\ See footnote 118.
---------------------------------------------------------------------------

    In developing the curve shapes for this proposed rule, the agencies
were aware of the current and prior technical concerns raised by OEMs
concerning the effects of the stringency on individual manufacturers
and their ability to meet the standards with available technologies,
while producing vehicles at a cost that allowed them to recover the
additional costs of the technologies being applied. Although we
continue to believe that the methodology for fitting curves for the
MY2012-2016 standards was technically sound, we recognize
manufacturers' technical concerns regarding their abilities to comply
with a similarly shallow curve after MY2016 given the anticipated mix
of light trucks in MYs 2017-2025. As in the MYs 2012-2016 rules, the
agencies considered these concerns in the analysis of potential curve
shapes. The agencies also considered safety concerns which could be
raised by curve shapes creating an incentive for vehicle downsizing, as
well as the potential loss to consumer welfare should vehicle upsizing
be unduly disincentivized. In addition, the agencies sought to improve
the balance of compliance burdens among manufacturers. Among the
technical concerns and resultant policy trade-offs the agencies
considered were the following:
     Flatter standards (i.e., curves) increase the risk that
both the weight and size of vehicles will be reduced, compromising
highway safety.
     Flatter standards potentially impact the utility of
vehicles by providing an incentive for vehicle downsizing.
     Steeper footprint-based standards may incentivize vehicle
upsizing, thus increasing the risk that fuel economy and greenhouse gas
reduction benefits will be less than expected.
     Given the same industry-wide average required fuel economy
or CO2 standard, flatter standards tend to place greater
compliance burdens on full-line manufacturers.
     Given the same industry-wide average required fuel economy
or CO2 standard, steeper standards tend to place greater
compliance burdens on limited-line manufacturers (depending of course,
on which vehicles are being produced).
     If cutpoints are adopted, given the same industry-wide
average required fuel economy, moving small-vehicle cutpoints to the
left (i.e., up in terms of fuel economy, down in terms of
CO2 emissions) discourages the introduction of small
vehicles, and reduces the incentive to downsize small vehicles in ways
that would compromise highway safety.
     If cutpoints are adopted, given the same industry-wide
average required fuel economy, moving large-vehicle cutpoints to the
right (i.e., down in terms of fuel economy, up in terms of
CO2 emissions) better accommodates the unique design
requirements of larger vehicles--especially large pickups--and extends
the size range over which downsizing is discouraged.
    All of these were policy goals that required trade-offs, and in
determining the curves they also required balance against the comments
from the OEMs discussed in the introduction to this section.
Ultimately, the agencies do not agree that the MY 2017 target curves
for this proposal, on a relative basis, should be made significantly
flatter than the MY 2016 curve,\121\ as we believe that this would undo
some of the safety-related incentives and balancing of compliance
burdens among manufacturers--effects that attribute-based standards are
intended to provide.
---------------------------------------------------------------------------

    \121\ While ``significantly'' flatter is subjective, the year
over year change in curve shapes is discussed in greater detail in
Section 0 and Chapter 2 of the joint TSD.
---------------------------------------------------------------------------

    Nonetheless, the agencies recognize full-line OEM concerns and have
tentatively concluded that further increases in the stringency of the
light truck standards will be more feasible if the light truck curve is
made steeper than the MY 2016 truck curve and the right (large
footprint) cut-point is extended over time to larger footprints. This
conclusion is supported by the agencies' technical analyses of
regulatory alternatives defined using the curves developed in the
manner described below.
b. What methodologies and data did the agencies consider in developing
the 2017-2025 curves?
    In considering how to address the various policy concerns discussed
in the previous sections, the agencies revisited the data and performed
a number of analyses using different combinations of the various
statistical methods, weighting schemes, adjustments to the data and the
addition of technologies to make the fleets less technologically
heterogeneous. As discussed above, in the agencies' judgment, there is
no single ``correct'' way to estimate the relationship between
CO2 or fuel consumption and footprint--rather, each
statistical result is based on the underlying assumptions about the
particular functional form, weightings and error structures embodied in
the representational approach. These assumptions are the subject of the
following discussion. This process of performing many analyses using
combinations of statistical methods generates many possible outcomes,
each embodying different potentially reasonable combinations of
assumptions and each thus reflective of the data as viewed through a
particular lens. The choice of a standard developed by a given
combination of these statistical methods is consequently a decision
based upon the agencies' determination of how, given the policy
objectives for this rulemaking and the agencies' MY 2008-based forecast
of the market through MY 2025, to appropriately reflect the current
understanding of the evolution of automotive technology and costs, the
future prospects for the vehicle market, and thereby establish curves
(i.e., standards) for cars and light trucks.
c. What information did the agencies use to estimate a relationship
between fuel economy, CO2 and footprint?
    For each fleet, the agencies began with the MY 2008-based market
forecast developed to support this proposal (i.e., the baseline fleet),
with vehicles' fuel economy levels and technological characteristics at
MY 2008 levels.\122\ The development, scope, and content of this market
forecast is discussed in detail in Chapter 1 of the joint Technical
Support Document supporting this rulemaking.
---------------------------------------------------------------------------

    \122\ While the agencies jointly conducted this analysis, the
coefficients ultimately used in the slope setting analysis are from
the CAFE model.
---------------------------------------------------------------------------

d. What adjustments did the agencies evaluate?
    The agencies believe one possible approach is to fit curves to the
minimally adjusted data shown above (the approach still includes sales
mix adjustments, which influence results of sales-weighted
regressions), much as DOT did when it first began evaluating potential
attribute-based standards in 2003.\123\ However, the agencies have
found, as in prior rulemakings, that the data are so widely spread
(i.e., when graphed, they fall in a loose ``cloud'' rather than tightly
around an obvious line) that they indicate a relationship between
footprint and CO2 and fuel consumption that is real but not
particularly strong. Therefore, as discussed below, the agencies also
explored possible adjustments that could help to explain and/or reduce
the ambiguity of this relationship, or could help to produce policy
outcomes the agencies judged to be more desirable.
---------------------------------------------------------------------------

    \123\ 68 FR 74920-74926.

---------------------------------------------------------------------------

i. Adjustment to reflect differences in technology
    As in prior rulemakings, the agencies consider technology
differences between vehicle models to be a significant factor producing
uncertainty regarding the relationship between CO2/fuel
consumption and footprint. Noting that attribute-based standards are
intended to encourage the application of additional technology to
improve fuel efficiency and reduce CO2 emissions, the
agencies, in addition to considering approaches based on the unadjusted
engineering characteristics of MY 2008 vehicle models, therefore also
considered approaches in which, as for previous rulemakings, technology
is added to vehicles for purposes of the curve fitting analysis in
order to produce fleets that are less varied in technology content.
    The agencies adjusted the baseline fleet for technology by adding
all technologies considered, except for the most advanced high-BMEP
(brake mean effective pressure) gasoline engines, diesel engines,
strong HEVs, PHEVs, EVs, and FCVs. The agencies included 15 percent
mass reduction on all vehicles.
ii. Adjustments reflecting differences in performance and ``density''
    For the reasons discussed above regarding revisiting the shapes of
the curves, the agencies considered adjustments for other differences
between vehicle models (i.e., inflating or deflating the fuel economy
of each vehicle model based on the extent to which one of the vehicle's
attributes, such as power, is higher or lower than average).
Previously, NHTSA had rejected such adjustments because they imply that
a multi-attribute standard may be necessary, and the agencies judged
multi-attribute standard to be more subject to gaming than a footprint-
only standard.124 125 Having considered this issue again for
purposes of this rulemaking, NHTSA and EPA conclude the need to
accommodate in the target curves the challenges faced by manufacturers
of large pickups currently outweighs these prior concerns. Therefore,
the agencies also evaluated curve fitting approaches through which fuel
consumption and CO2 levels were adjusted with respect to
weight-to-footprint alone, and in combination with power-to-weight.
While the agencies examined these adjustments for purposes of fitting
curves, the agencies are not proposing a multi-attribute standard; the
proposed fuel economy and CO2 targets for each vehicle are
still functions of footprint alone. No adjustment would be used in the
compliance process.
---------------------------------------------------------------------------

    \124\ For example, in comments on NHTSA's 2008 NPRM regarding MY
2011-2015 CAFE standards, Porsche recommended that standards be
defined in terms of a ``Summed Weighted Attribute'', wherein the
fuel economy target would calculated as follows: target = f(SWA),
where target is the fuel economy target applicable to a given
vehicle model and SWA = footprint + torque 1/1.5 + weight
1/2.5. (NHTSA-2008-0089-0174). While the standards the
agencies are proposing for MY 2017-2025 are not multi-attributes,
that is the target is only a function of footprint, we are proposing
curve shapes that were developed considering more than one
attribute.
    \125\ 74 FR 14359.
---------------------------------------------------------------------------

    The agencies also examined some differences between the technology-
adjusted car and truck fleets in order to better understand the
relationship between footprint and CO2/fuel consumption in
the agencies' MY 2008 based forecast. The agencies investigated the
relationship between HP/WT and footprint in the agencies' MY2008-based
market forecast. On a sales weighted basis, cars tend to become
proportionally more powerful as they get larger. In contrast, there is
a minimally positive relationship between HP/WT and footprint for light
trucks, indicating that light trucks become only slightly more powerful
as they get larger.
    This analysis, presented in chapter <IP_ADDRESS> of the agencies' joint
TSD, indicated that vehicle performance (power-to-weight ratio) and
``density'' (curb weight divided by footprint) are both correlated to
fuel consumption (and CO2 emission rate), and that these
vehicle attributes are also both related to vehicle footprint. Based on
these relationships, the agencies explored adjusting the fuel economy
and CO2 emission rates of individual vehicle models based on
deviations from ``expected'' performance or weight/footprint at a given
footprint; the agencies inflated fuel economy levels of vehicle models
with higher performance and/or weight/footprint than the average of the
fleet would indicate at that footprint, and deflated fuel economy
levels with lower performance and/or weight. Previously, NHTSA had
rejected such adjustments because they imply that a multi-attribute
standard may be necessary, and the agency judged multi-attribute
standard to be more subject to gaming than a footprint-only
standard.126 127 While the agencies considered this
technique for purposes of fitting curves, the agencies are not
proposing a multi-attribute standard, as the proposed fuel economy and
CO2 targets for each vehicle are still functions of
footprint alone. No adjustment would be used in the compliance process.
---------------------------------------------------------------------------

    \126\ For example, in comments on NHTSA's 2008 NPRM regarding MY
2011-2015 CAFE standards, Porsche recommended that standards be
defined in terms of a ``Summed Weighted Attribute'', wherein the
fuel economy target would calculated as follows: target = f(SWA),
where target is the fuel economy target applicable to a given
vehicle model and SWA = footprint + torque 1/1.5 + weight
1/2.5. (NHTSA-2008-0089-0174). While the standards the
agencies are proposing for MY 2017-2025 are not multi-attribute
standards, that is the target is only a function of footprint, we
are proposing curve shapes that were developed considering more than
one attribute.
    \127\ 74 FR 14359.
---------------------------------------------------------------------------

    The agencies seek comment on the appropriateness of the adjustments
as described in Chapter 2 of the joint TSD, particularly regarding
whether these adjustments suggest that standards should be defined in
terms of other attributes in addition to footprint, and whether they
may encourage changes other than encouraging the application of
technology to improve fuel economy and reduce CO2 emissions.
The agencies also seek comment regarding whether these adjustments
effectively ``lock in'' through MY 2025 relationships that were
observed in MY 2008.
e. What statistical methods did the agencies evaluate?
    The above approaches resulted in three data sets each for (a)
vehicles without added technology and (b) vehicles with technology
added to reduce technology differences, any of which may provide a
reasonable basis for fitting mathematical functions upon which to base
the slope of the standard curves: (1) Vehicles without any further
adjustments; (2) vehicles with adjustments reflecting differences in
``density'' (weight/footprint); and (3) vehicles with adjustments
reflecting differences in ``density,'' and adjustments reflecting
differences in performance (power/weight). Using these data sets, the
agencies tested a range of regression methodologies, each judged to be
possibly reasonable for application to at least some of these data
sets.
i. Regression Approach
    In the MYs 2012-2016 final rules, the agencies employed a robust
regression approach (minimum absolute deviation, or MAD), rather than
an ordinary least squares (OLS) regression.\128\ MAD is generally
applied to mitigate the effect of outliers in a dataset, and thus was
employed in that rulemaking as part of our interest in attempting to
best represent the underlying technology. NHTSA had used OLS in early
development of attribute-based CAFE

standards, but NHTSA (and then NHTSA and EPA) subsequently chose MAD
instead of OLS for both the MY 2011 and the MYs 2012-2016 rulemakings.
These decisions on regression technique were made both because OLS
gives additional emphasis to outliers \129\ and because the MAD
approach helped achieve the agencies' policy goals with regard to curve
slope in those rulemakings.\130\ In the interest of taking a fresh look
at appropriate regression methodologies as promised in the 2012-2016
light duty rulemaking, in developing this proposal, the agencies gave
full consideration to both OLS and MAD. The OLS representation, as
described, uses squared errors, while MAD employs absolute errors and
thus weights outliers less.
---------------------------------------------------------------------------

    \128\ See 75 FR at 25359.
    \129\ Id. at 25362-63.
    \130\ Id. at 25363.
---------------------------------------------------------------------------

    As noted, one of the reasons stated for choosing MAD over least
square regression in the MYs 2012-2016 rulemaking was that MAD reduced
the weight placed on outliers in the data. However, the agencies have
further considered whether it is appropriate to classify these vehicles
as outliers. Unlike in traditional datasets, these vehicles'
performance is not mischaracterized due to errors in their measurement,
a common reason for outlier classification. Being certification data,
the chances of large measurement errors should be near zero,
particularly towards high CO2 or fuel consumption. Thus,
they can only be outliers in the sense that the vehicle designs are
unlike those of other vehicles. These outlier vehicles may include
performance vehicles, vehicles with high ground clearance, 4WD, or boxy
designs. Given that these are equally legitimate on-road vehicle
designs, the agencies concluded that it would appropriate to reconsider
the treatment of these vehicles in the regression techniques.
    Based on these considerations as well as the adjustments discussed
above, the agencies concluded it was not meaningful to run MAD
regressions on gpm data that had already been adjusted in the manner
described above. Normalizing already reduced the variation in the data,
and brought outliers towards average values. This was the intended
effect, so the agencies deemed it unnecessary to apply an additional
remedy to resolve an issue that had already been addressed, but we seek
comment on the use of robust regression techniques under such
circumstances.
ii. Sales Weighting
    Likewise, the agencies reconsidered employing sales-weighting to
represent the data. As explained below, the decision to sales weight or
not is ultimately based upon a choice about how to represent the data,
and not by an underlying statistical concern. Sales weighting is used
if the decision is made to treat each (mass produced) unit sold as a
unique physical observation. Doing so thereby changes the extent to
which different vehicle model types are emphasized as compared to a
non-sales weighted regression. For example, while total General Motors
Silverado (332,000) and Ford F-150 (322,000) sales differ by less than
10,000 in MY 2021 market forecast, 62 F-150s models and 38 Silverado
models are reported in the agencies baselines. Without sales-weighting,
the F-150 models, because there are more of them, are given 63 percent
more weight in the regression despite comprising a similar portion of
the marketplace and a relatively homogenous set of vehicle
technologies.
    The agencies did not use sales weighting in the 2012-2016
rulemaking analysis of the curve shapes. A decision to not perform
sales weighting reflects judgment that each vehicle model provides an
equal amount of information concerning the underlying relationship
between footprint and fuel economy. Sales-weighted regression gives the
highest sales vehicle model types vastly more emphasis than the lowest-
sales vehicle model types thus driving the regression toward the sales-
weighted fleet norm. For unweighted regression, vehicle sales do not
matter. The agencies note that the light truck market forecast shows MY
2025 sales of 218,000 units for Toyota's 2WD Sienna, and shows 66 model
configurations with MY 2025 sales of fewer than 100 units. Similarly,
the agencies' market forecast shows MY 2025 sales of 267,000 for the
Toyota Prius, and shows 40 model configurations with MY2025 sales of
fewer than 100 units. Sales-weighted analysis would give the Toyota
Sienna and Prius more than a thousand times the consideration of many
vehicle model configurations. Sales-weighted analysis would, therefore,
cause a large number of vehicle model configurations to be virtually
ignored in the regressions.\131\
---------------------------------------------------------------------------

    \131\ 75 FR at 25362 and n. 64.
---------------------------------------------------------------------------

    However, the agencies did note in the MYs 2012-2016 final rules
that, ``sales weighted regression would allow the difference between
other vehicle attributes to be reflected in the analysis, and also
would reflect consumer demand.'' \132\ In reexamining the sales-
weighting for this analysis, the agencies note that there are low-
volume model types account for many of the passenger car model types
(50 percent of passenger car model types account for 3.3 percent of
sales), and it is unclear whether the engineering characteristics of
these model types should equally determine the standard for the
remainder of the market.
---------------------------------------------------------------------------

    \132\ 75 FR at 25632/3.
---------------------------------------------------------------------------

    In the interest of taking a fresh look at appropriate methodologies
as promised in the last final rule, in developing this proposal, the
agencies gave full consideration to both sales-weighted and unweighted
regressions.
iii. Analyses Performed
    We performed regressions describing the relationship between a
vehicle's CO2/fuel consumption and its footprint, in terms
of various combinations of factors: initial (raw) fleets with no
technology, versus after technology is applied; sales-weighted versus
non-sales weighted; and with and without two sets of normalizing
factors applied to the observations. The agencies excluded diesels and
dedicated AFVs because the agencies anticipate that advanced gasoline-
fueled vehicles are likely to be dominant through MY 2025, based both
on our own assessment of potential standards (see Sections III and IV
below) as well as our discussions with large number of automotive
companies and suppliers.
    Thus, the basic OLS regression on the initial data (with no
technology applied) and no sales-weighting represents one perspective
on the relation between footprint and fuel economy. Adding sales
weighting changes the interpretation to include the influence of sales
volumes, and thus steps away from representing vehicle technology
alone. Likewise, MAD is an attempt to reduce the impact of outliers,
but reducing the impact of outliers might perhaps be less
representative of technical relationships between the variables,
although that relationship may change over time in reality. Each
combination of methods and data reflects a perspective, and the
regression results simply reflect that perspective in a simple
quantifiable manner, expressed as the coefficients determining the line
through the average (for OLS) or the median (for MAD) of the data. It
is left to policy makers to determine an appropriate perspective and to
interpret the consequences of the various alternatives.
    We invite comments on the application of the weights as described

above, and the implications for interpreting the relationship between
fuel efficiency (or CO2) and footprint.
f. What results did the agencies obtain, which methodology did the
agencies choose for this proposal, and why is it reasonable?
    Both agencies analyzed the same statistical approaches. For
regressions against data including technology normalization, NHTSA used
the CAFE modeling system, and EPA used EPA's OMEGA model. The agencies
obtained similar regression results, and have based today's joint
proposal on those obtained by NHTSA. The draft Joint TSD Chapter 2
contains a large set of illustrative of figures which show the range of
curves determined by the possible combinations of regression technique,
with and without sales weighting, with and without the application of
technology, and with various adjustments to the gpm variable prior to
running a regression.
    The choice among the alternatives presented in the draft Joint TSD
Chapter 2 was to use the OLS formulation, on sales-weighted data, using
a fleet that has had technology applied, and after adjusting the data
for the effect of weight-to-footprint, as described above. The agencies
believe that this represents a technically reasonable approach for
purposes of developing target curves to define the proposed standards,
and that it represents a reasonable trade-off among various
considerations balancing statistical, technical, and policy matters,
which include the statistical representativeness of the curves
considered and the steepness of the curve chosen. The agencies judge
the application of technology prior to curve fitting to provide a
reasonable means--one consistent with the rule's objective of
encouraging manufacturers to add technology in order to increase fuel
economy--of reducing variation in the data and thereby helping to
estimate a relationship between fuel consumption/CO2 and
footprint.
    Similarly, for the agencies' current MY 2008-based market-forecast
and the agencies' current estimates of future technology effectiveness,
the inclusion of the weight-to-footprint data adjustment prior to
running the regression also helps to improve the fit of the curves by
reducing the variation in the data, and the agencies believe that the
benefits of this adjustment for this proposed rule likely outweigh the
potential that resultant curves might somehow encourage reduced load
carrying capability or vehicle performance (note that the we are not
suggesting that we believe these adjustments will reduce load carrying
capability or vehicle performance). In addition to reducing the
variability, the truck curve is also steepened, and the car curve
flattened compared to curves fitted to sales weighted data that do not
include these normalizations. The agencies agree with manufacturers of
full-size pick-up trucks that in order to maintain towing and hauling
utility, the engines on pick-up trucks must be more powerful, than
their low ``density'' nature would statistically suggest based on the
agencies' current MY2008-based market forecast and the agencies'
current estimates of the effectiveness of different fuel-saving
technologies. Therefore, it may be more equitable (i.e., in terms of
relative compliance challenges faced by different light truck
manufacturers) to adjust the slope of the curve defining fuel economy
and CO2 targets.
    As described above, however, other approaches are also technically
reasonable, and also represent a way of expressing the underlying
relationships. The agencies plan to revisit the analysis for the final
rule, after updating the underlying market forecast and estimates of
technology effectiveness, and based on relevant public comments
received. In addition, the agencies intend to update the technology
cost estimates, which could alter the NPRM analysis results and
consequently alter the balance of the trade-offs being weighed to
determine the final curves.
g. Implications of the proposed slope compared to MY 2012-2016
    The proposed slope has several implications relative to the MY 2016
curves, with the majority of changes on the truck curve. With the
agencies' current MY2008-based market forecast and the agencies'
current estimates of technology effectiveness, the combination of sales
weighting and WT/FP normalization produced a car curve slope similar to
that finalized in the MY 2012-2016 final rulemaking (4.7 g/mile in MY
2016, vs. 4.5 g/mile proposed in MY 2017). By contrast, the truck curve
is steeper in MY 2017 than in MY 2016 (4.0 g/mile in MY 2016 vs. 4.9 g/
mile in MY 2017). As discussed previously, a steeper slope relaxes the
stringency of targets for larger vehicles relative to those for smaller
vehicles, thereby shifting relative compliance burdens among
manufacturers based on their respective product mix.
6. Once the agencies determined the appropriate slope for the sloped
part, how did the agencies determine the rest of the mathematical
function?
    The agencies continue to believe that without a limit at the
smallest footprints, the function--whether logistic or linear--can
reach values that would be unfairly burdensome for a manufacturer that
elects to focus on the market for small vehicles; depending on the
underlying data, an unconstrained form could result in stringency
levels that are technologically infeasible and/or economically
impracticable for those manufacturers that may elect to focus on the
smallest vehicles. On the other side of the function, without a limit
at the largest footprints, the function may provide no floor on
required fuel economy. Also, the safety considerations that support the
provision of a disincentive for downsizing as a compliance strategy
apply weakly, if at all, to the very largest vehicles. Limiting the
function's value for the largest vehicles thus leads to a function with
an inherent absolute minimum level of performance, while remaining
consistent with safety considerations.
    Just as for slope, in determining the appropriate footprint and
fuel economy values for the ``cutpoints,'' the places along the curve
where the sloped portion becomes flat, the agencies took a fresh look
for purposes of this proposal, taking into account the updated market
forecast and new assumptions about the availability of technologies.
The next two sections discuss the agencies' approach to cutpoints for
the passenger car and light truck curves separately, as the policy
considerations for each vary somewhat.
a. Cutpoints for PC curve
    The passenger car fleet upon which the agencies have based the
target curves for MYs 2017-2025 is derived from MY 2008 data, as
discussed above. In MY 2008, passenger car footprints ranged from 36.7
square feet, the Lotus Exige 5, to 69.3 square feet, the Daimler
Maybach 62. In that fleet, several manufacturers offer small, sporty
coupes below 41 square feet, such as the BMW Z4 and Mini, Honda S2000,
Mazda MX-5 Miata, Porsche Carrera and 911, and Volkswagen New Beetle.
Because such vehicles represent a small portion (less than 10 percent)
of the passenger car market, yet often have performance, utility, and/
or structural characteristics that could make it technologically
infeasible and/or economically impracticable for manufacturers focusing
on such

vehicles to achieve the very challenging average requirements that
could apply in the absence of a constraint, EPA and NHTSA are again
proposing to cut off the sloped portion of the passenger car function
at 41 square feet, consistent with the MYs 2012-2016 rulemaking. The
agencies recognize that for manufacturers who make small vehicles in
this size range, putting the cutpoint at 41 square feet creates some
incentive to downsize (i.e., further reduce the size, and/or increase
the production of models currently smaller than 41 square feet) to make
it easier to meet the target. Putting the cutpoint here may also create
the incentive for manufacturers who do not currently offer such models
to do so in the future. However, at the same time, the agencies believe
that there is a limit to the market for cars smaller than 41 square
feet--most consumers likely have some minimum expectation about
interior volume, among other things. The agencies thus believe that the
number of consumers who will want vehicles smaller than 41 square feet
(regardless of how they are priced) is small, and that the incentive to
downsize to less than 41 square feet in response to this proposal, if
present, will be at best minimal. On the other hand, the agencies note
that some manufacturers are introducing mini cars not reflected in the
agencies MY 2008-based market forecast, such as the Fiat 500, to the
U.S. market, and that the footprint at which the curve is limited may
affect the incentive for manufacturers to do so.
    Above 56 square feet, the only passenger car models present in the
MY 2008 fleet were four luxury vehicles with extremely low sales
volumes--the Bentley Arnage and three versions of the Rolls Royce
Phantom. As in the MYs 2012-2016 rulemaking, NHTSA and EPA therefore
are proposing again to cut off the sloped portion of the passenger car
function at 56 square feet.
    While meeting with manufacturers prior to issuing the proposal, the
agencies received comments from some manufacturers that, combined with
slope and overall stringency, using 41 square feet as the footprint at
which to cap the target for small cars would result in unduly
challenging targets for small cars. The agencies do not agree. No
specific vehicle need meet its target (because standards apply to fleet
average performance), and maintaining a sloped function toward the
smaller end of the passenger car market is important to discourage
unsafe downsizing, the agencies are thus proposing to again ``cut off''
the passenger car curve at 41 square feet, notwithstanding these
comments.
    The agencies seek comment on setting cutpoints for the MYs 2017-
2025 passenger car curves at 41 square feet and 56 square feet.
b. Cutpoints for LT curve
    The light truck fleet upon which the agencies have based the target
curves for MYs 2017-2025, like the passenger car fleet, is derived from
MY 2008 data, as discussed in Section 2.4 above. In MY 2008, light
truck footprints ranged from 41.0 square feet, the Jeep Wrangler, to
77.5 square feet, the Toyota Tundra. For consistency with the curve for
passenger cars, the agencies are proposing to cut off the sloped
portion of the light truck function at the same footprint, 41 square
feet, although we recognize that no light trucks are currently offered
below 41 square feet. With regard to the upper cutpoint, the agencies
heard from a number of manufacturers during the discussions leading up
to this proposal that the location of the cutpoint in the MYs 2012-2016
rules, 66 square feet, meant that the same standard applied to all
light trucks with footprints of 66 square feet or greater, and that in
fact the targets for the largest light trucks in the later years of
that rulemaking were extremely challenging. Those manufacturers
requested that the agencies extend the cutpoint to a larger footprint,
to reduce targets for the largest light trucks which represent a
significant percentage of those manufacturers light truck sales. At the
same time, in re-examining the light truck fleet data, the agencies
concluded that aggregating pickup truck models in the MYs 2012-2016
rule had led the agencies to underestimate the impact of the different
pickup truck model configurations above 66 square feet on
manufacturers' fleet average fuel economy and CO2 levels (as
discussed immediately below). In disaggregating the pickup truck model
data, the impact of setting the cutpoint at 66 square feet after model
year 2016 became clearer to the agencies.
    In the agencies' view, there is legitimate basis for these
comments. The agencies' market forecast includes about 24 vehicle
configurations above 74 square feet with a total volume of about 50,000
vehicles or less during any MY in the 2017-2025 time frame. While a
relatively small portion of the overall truck fleet, for some
manufacturers, these vehicles are non-trivial portion of sales. As
noted above, the very largest light trucks have significant load-
carrying and towing capabilities that make it particularly challenging
for manufacturers to add fuel economy-improving/CO2-reducing
technologies in a way that maintains the full functionality of those
capabilities.
    Considering manufacturer CBI and our estimates of the impact of the
66 square foot cutpoint for future model years, the agencies have
initially determined to adopt curves that transition to a different cut
point. While noting that no specific vehicle need meet its target
(because standards apply to fleet average performance), we believe that
the information provided to us by manufacturers and our own analysis
supports the gradual extension of the cutpoint for large light trucks
in this proposal from 66 square feet in MY 2016 out to a larger
footprint square feet before MY 2025.

[GRAPHIC] [TIFF OMITTED] TP01DE11.034

    The agencies are proposing to phase in the higher cutpoint for the
truck curve in order to avoid any backsliding from the MY 2016
standard. A target that is feasible in one model year should never
become less feasible in a subsequent model year--manufacturers should
have no reason to remove fuel economy-improving/CO2-reducing
technology from a vehicle once it has been applied. Put another way,
the agencies are proposing to not allow ``curve crossing'' from one
model year to the next. In proposing MYs 2011-2015 CAFE standards and
promulgating MY 2011 standards, NHTSA proposed and requested comment on
avoiding curve crossing, as an ``anti-backsliding measure.'' \133\ The
MY 2016 2 cycle test curves are therefore a floor for the MYs 2017-2025
curves. For passenger cars, which have minimal change in slope from the
MY 2012-2016 rulemakings and no change in cut points, there are no
curve crossing issues in the proposed standards.
---------------------------------------------------------------------------

    \133\ 74 Fed. Reg. at 14370 (Mar. 30, 2009).
---------------------------------------------------------------------------

    The minimum stringency determination was done using the two cycle
curves. Stringency adjustments for air conditioning and other credits
were calculated after curves that did not cross were determined in two
cycle space. The year over year increase in these adjustments cause
neither the GHG nor CAFE curves (with A/C) to contact the 2016 curves
when charted.
7. Once the agencies determined the complete mathematical function
shape, how did the agencies adjust the curves to develop the proposed
standards and regulatory alternatives?
    The curves discussed above all reflect the addition of technology
to individual vehicle models to reduce technology differences between
vehicle models before fitting curves. This application of technology
was conducted not to directly determine the proposed standards, but
rather for purposes of technology adjustments, and set aside
considerations regarding potential rates of application (i.e., phase-in
caps), and considerations regarding economic implications of applying
specific technologies to specific vehicle models. The following
sections describe further adjustments to the curves discussed above,
that affect both the shape of the curve, and the location of the curve,
that helped the agencies determine curves that defined the proposed
standards.
a. Adjusting for Year over Year Stringency
    As in the MYs 2012-2016 rules, the agencies developed curves
defining regulatory alternatives for consideration by ``shifting''
these curves. For the MYs 2012-2016 rules, the agencies did so on an
absolute basis, offsetting the fitted curve by the same value (in gpm
or g/mi) at all footprints. In developing this proposal, the agencies
have reconsidered the use of this approach, and have concluded that
after MY 2016, curves should be offset on a relative basis--that is, by
adjusting the entire gpm-based curve (and, equivalently, the
CO2 curve) by the same percentage rather than the same
absolute value. The agencies' estimates of the effectiveness of these
technologies are all expressed in relative terms--that is, each
technology (with the exception of A/C) is estimated to reduce fuel
consumption (the inverse of fuel economy) and CO2 emissions
by a specific percentage of

fuel consumption without the technology. It is, therefore, more
consistent with the agencies' estimates of technology effectiveness to
develop the proposed standards and regulatory alternatives by applying
a proportional offset to curves expressing fuel consumption or
emissions as a function of footprint. In addition, extended
indefinitely (and without other compensating adjustments), an absolute
offset would eventually (i.e., at very high average stringencies)
produce negative (gpm or g/mi) targets. Relative offsets avoid this
potential outcome. Relative offsets do cause curves to become, on a
fuel consumption and CO2 basis, flatter at greater average
stringencies; however, as discussed above, this outcome remains
consistent with the agencies' estimates of technology effectiveness. In
other words, given a relative decrease in average required fuel
consumption or CO2 emissions, a curve that is flatter by the
same relative amount should be equally challenging in terms of the
potential to achieve compliance through the addition of fuel-saving
technology.
    On this basis, and considering that the ``flattening'' occurs
gradually for the regulatory alternatives the agencies have evaluated,
the agencies tentatively conclude that this approach to offsetting the
curves to develop year-by-year regulatory alternatives neither re-
creates a situation in which manufacturers are likely to respond to
standards in ways that compromise highway safety, nor undoes the
attribute-based standard's more equitable balancing of compliance
burdens among disparate manufacturers. The agencies invite comment on
these conclusions, and on any other means that might avoid the
potential outcomes--in particular, negative fuel consumption and
CO2 targets--discussed above.
b. Adjusting for anticipated improvements to mobile air conditioning
systems
    The fuel economy values in the agencies' market forecast are based
on the 2-cycle (i.e., city and highway) fuel economy test and
calculation procedures that do not reflect potential improvements in
air conditioning system efficiency, refrigerant leakage, or refrigerant
Global Warming Potential (GWP). Recognizing that there are significant
and cost effective potential air conditioning system improvements
available in the rulemaking timeframe (discussed in detail in Chapter 5
of the draft joint TSD), the agencies are increasing the stringency of
the target curves based on the agencies' assessment of the capability
of manufacturers to implement these changes. For the proposed CAFE
standards and alternatives, an offset is included based on air
conditioning system efficiency improvements, as these improvements are
the only improvements that effect vehicle fuel economy. For the
proposed GHG standards and alternatives, a stringency increase is
included based on air conditioning system efficiency, leakage and
refrigerant improvements. As discussed above in Chapter 5 of the join
TSD, the air conditioning system improvements affect a vehicle's fuel
efficiency or CO2 emissions performance as an additive
stringency increase, as compared to other fuel efficiency improving
technologies which are multiplicative. Therefore, in adjusting target
curves for improvements in the air conditioning system performance, the
agencies are adjusting the target curves by additive stringency
increases (or vertical shifts) in the curves.
    For the GHG target curves, the offset for air conditioning system
performance is being handled in the same manner as for the MY 2012-2016
rules. For the CAFE target curves, NHTSA for the first time is
proposing to account for potential improvements in air conditioning
system performance. Using this methodology, the agencies first use a
multiplicative stringency adjustment for the sloped portion of the
curves to reflect the effectiveness on technologies other than air
conditioning system technologies, creating a series of curve shapes
that are ``fanned'' based on two-cycle performance. Then the curves are
offset vertically by the air conditioning improvement by an equal
amount at every point.

D. Joint Vehicle Technology Assumptions

    For the past four to five years, the agencies have been working
together closely to follow the development of fuel consumption and GHG
reducing technologies. Two major analyses have been published jointly
by EPA and NHTSA: The Technical Support Document to support the MYs
2012-2016 final rule and the 2010 Technical Analysis Report (which
supported the 2010 Notice of Intent). The latter of these analyses was
also done in conjunction with CARB. Both of these analyses have both
been published within the past 18 months. As a result, much of the work
is still relevant and we continue to rely heavily on these references.
However, some technologies--and what we know about them--are changing
so rapidly that the analysis supporting this proposal contains a
considerable amount of new work on technologies included in this rule,
some of which were included in prior rulemakings, and others that were
not.
    Notably, we have updated our battery costing methodology
significantly since the MYs 2012-2016 final rule and even relative to
the 2010 TAR. We are now using a peer reviewed model developed by
Argonne National Laboratory for the Department of Energy which provides
us with more rigorous estimates for battery costs and allows us to
estimate future costs specific to hybrids, plug-in hybrids and electric
vehicles all of which have different battery design characteristics.
    We also have new cost data from more recently completed tear down
and other cost studies by FEV which were not available in either the
MYs 2012-2016 final rule or the 2010 TAR. These new studies analyzed a
8-speed automatic transmission replacing 6-speed automatic
transmission, a 8-speed dual clutch transmission replacing 6-speed dual
clutch transmission, a power-split hybrid powertrain with an I4 engine
replacing a conventional engine powertrain with V6 engine, a mild
hybrid with stop-start technology and an I4 engine replacing a
conventional I4 engine, and the Fiat Multi-Air engine technology. We
discuss the new tear down studies in Section II.D.2 of this preamble.
Based on this, we have updated some of the FEV-developed costs relative
to what we used in the 2012-2016 final rule, although these costs are
consistent with those used in the 2010 TAR. Furthermore, we have
completely re-worked our estimated costs associated with mass reduction
relative to both the MYs 2012-2016 final rule and the 2010 TAR.
    As would be expected given that some of our cost estimates were
developed several years ago, we have also updated all of our base
direct manufacturing costs to put them in terms of more recent dollars
(2009 dollars for this proposal). We have also updated our methodology
for calculating indirect costs associated with new technologies since
both the MYs 2012-2016 final rule and the TAR. We continue to use the
indirect cost multiplier (ICM) approach used in those analyses, but
have made important changes to the calculation methodology--changes
done in response to ongoing staff evaluation and public input.
    Lastly, we have updated many of the technologies' effectiveness
estimates largely based on new vehicle simulation work conducted by
Ricardo Engineering. This simulation work provides the effectiveness
estimates for

a number of the technologies most heavily relied on in the agencies'
analysis of potential standards for MYs 2017-2025.
    The agencies have also reviewed the findings and recommendations in
the updated NAS report ``Assessment of Fuel Economy Technologies for
Light-Duty Vehicles'' that was completed after the MYs 2012-2016 final
rule was issued,\134\ and NHTSA has performed a sensitivity analysis
(contained in its PRIA) to examine the impact of using some of the NAS
cost and effectiveness estimates on the proposed standards.
---------------------------------------------------------------------------

    \134\ ``Assessment of Fuel Economy Technologies for Light-Duty
Vehicles,'' National Research Council of the National Academies,
June 2010.
---------------------------------------------------------------------------

    Each of these changes is discussed briefly in the remainder of this
section and in much greater detail in Chapter 3 of the draft joint TSD.
First we provide a brief summary of the technologies we have considered
in this proposal before highlighting the above-mentioned items that are
new for this proposal. We request comment on all aspects of our
analysis as discussed here and detailed in the draft joint TSD.
1. What technologies did the Agencies Consider?
    For this proposal, the agencies project that manufacturers can add
a variety of technologies to each of their vehicle models and or
platforms in order to improve the vehicles' fuel economy and GHG
performance. In order to analyze a variety of regulatory alternative
scenarios, it is essential to have a thorough understanding of the
technologies available to the manufacturers. This analysis includes an
assessment of the cost, effectiveness, availability, development time,
and manufacturability of various technologies within the normal
redesign and refresh periods of a vehicle line (or in the design of a
new vehicle). As we describe in the draft Joint TSD, when a technology
can be applied can affect the cost as well as the technology
penetration rates (or phase-in caps) that are projected in the
analysis.
    The agencies considered dozens of vehicle technologies that
manufacturers could use to improve the fuel economy and reduce
CO2 emissions of their vehicles during the MYs 2017-2025
timeframe. Many of the technologies considered are available today, are
well known, and could be incorporated into vehicles once product
development decisions are made. These are ``near-term'' technologies
and are identical or very similar to those anticipated in the agencies'
analyses of compliance strategies for the MYs 2012-2016 final rule. For
this rulemaking, given its time frame, other technologies are also
considered that are not currently in production, but that are beyond
the initial research phase, and are under development and expected to
be in production in the next 5-10 years. Examples of these technologies
are downsized and turbocharged engines operating at combustion
pressures even higher than today's turbocharged engines, and an
emerging hybrid architecture combined with an 8 speed dual clutch
transmission, a combination that is not available today. These are
technologies which the agencies believe can, for the most part, be
applied both to cars and trucks, and which are expected to achieve
significant improvements in fuel economy and reductions in
CO2 emissions at reasonable costs in the MYs 2017 to 2025
timeframe. The agencies did not consider technologies that are
currently in an initial stage of research because of the uncertainty
involved in the availability and feasibility of implementing these
technologies with significant penetration rates for this analysis. The
agencies recognize that due to the relatively long time frame between
the date of this proposal and 2025, it is very possible that new and
innovative technologies will make their way into the fleet, perhaps
even in significant numbers, that we have not considered in this
analysis. We expect to reconsider such technologies as part of the mid-
term evaluation, as appropriate, and possibly could be used to generate
credits under a number of the proposed flexibility and incentive
programs provided in the proposed rules.
    The technologies considered can be grouped into four broad
categories: Engine technologies; transmission technologies; vehicle
technologies (such as mass reduction, tires and aerodynamic
treatments); and electrification technologies (including hybridization
and changing to full electric drive).\135\ The specific technologies
within each broad group are discussed below. The list of technologies
presented below is nearly identical to that presented in both the MYs
2012-2016 final rule and the 2010 TAR, with the following new
technologies added to the list since the last final rule: The P2
hybrid, a newly emerging hybridization technology that was also
considered in the 2010 TAR; continued improvements in gasoline engines,
with greater efficiencies and downsizing; continued significant
efficiency improvements in transmissions; and ongoing levels of
improvement to some of the seemingly more basic technologies such as
lower rolling resistance tires and aerodynamic treatments, which are
among the most cost effective technologies available for reducing fuel
consumption and GHGs. Not included in the list below are technologies
specific to air conditioning system improvements and off-cycle
controls, which are presented in Section II.F of this NPRM and in
Chapter 5 of the draft Joint TSD.
---------------------------------------------------------------------------

    \135\ NHTSA's analysis considers these technologies in five
groups rather than four--hybridization is one category, and
``electrification/accessories'' is another.
---------------------------------------------------------------------------

a. Types of Engine Technologies Considered
    Low-friction lubricants including low viscosity and advanced low
friction lubricant oils are now available with improved performance. If
manufacturers choose to make use of these lubricants, they may need to
make engine changes and conduct durability testing to accommodate the
lubricants. The costs in our analysis consider these engine changes and
testing requirements. This level of low friction lubricants is expected
to exceed 85 percent penetration by the 2017 MY.
    Reduction of engine friction losses can be achieved through low-
tension piston rings, roller cam followers, improved material coatings,
more optimal thermal management, piston surface treatments, and other
improvements in the design of engine components and subsystems that
improve efficient engine operation. This level of engine friction
reduction is expected to exceed 85 percent penetration by the 2017 MY.
    Advanced Low Friction Lubricant and Second Level of Engine Friction
Reduction are new for this analysis. As technologies advance between
now and the rulemaking timeframe, there will be further development in
low friction lubricants and engine friction reductions. The agencies
grouped the development in these two areas into a single technology and
applied them for MY 2017 and beyond.
    Cylinder deactivation disables the intake and exhaust valves and
prevents fuel injection into some cylinders during light-load
operation. The engine runs temporarily as though it were a smaller
engine which substantially reduces pumping losses.
    Variable valve timing alters the timing of the intake valves,
exhaust valves, or both, primarily to reduce pumping losses, increase
specific power, and control residual gases.
    Discrete variable valve lift increases efficiency by optimizing air
flow over a broader range of engine operation which

reduces pumping losses. This is accomplished by controlled switching
between two or more cam profile lobe heights.
    Continuous variable valve lift is an electromechanical or
electrohydraulic system in which valve timing is changed as lift height
is controlled. This yields a wide range of performance optimization and
volumetric efficiency, including enabling the engine to be valve
throttled.
    Stoichiometric gasoline direct-injection technology injects fuel at
high pressure directly into the combustion chamber to improve cooling
of the air/fuel charge as well as combustion quality within the
cylinder, which allows for higher compression ratios and increased
thermodynamic efficiency.
    Turbo charging and downsizing increases the available airflow and
specific power level, allowing a reduced engine size while maintaining
performance. Engines of this type use gasoline direct injection (GDI)
and dual cam phasing. This reduces pumping losses at lighter loads in
comparison to a larger engine. We continue to include an 18 bar brake
mean effective pressure (BMEP) technology (as in the MYs 2012-2016
final rule) and are also including both 24 bar BMEP and 27 bar BMEP
technologies. The 24 bar BMEP technology would use a single-stage,
variable geometry turbocharger which would provide a higher intake
boost pressure available across a broader range of engine operation
than conventional 18 bar BMEP engines. The 27 bar BMEP technology
requires additional boost and thus would use a two-stage turbocharger
necessitating use of cooled exhaust gas recirculation (EGR) as
described below. The 18 bar BMEP technology is applied with 33 percent
engine downsizing, 24 bar BMEP is applied with 50 percent engine
downsizing, and 27 bar BMEP is applied with 56 percent engine
downsizing.
    Cooled exhaust-gas recirculation (EGR) reduces the incidence of
knocking combustion with additional charge dilution and obviates the
need for fuel enrichment at high engine power. This allows for higher
boost pressure and/or compression ratio and further reduction in engine
displacement and both pumping and friction losses while maintaining
performance. Engines of this type use GDI and both dual cam phasing and
discrete variable valve lift. The EGR systems considered in this
assessment would use a dual-loop system with both high and low pressure
EGR loops and dual EGR coolers. For this proposal, cooled EGR is
considered to be a technology that can be added to a 24 bar BMEP engine
and is an enabling technology for 27 bar BMEP engines.
    Diesel engines have several characteristics that give superior fuel
efficiency, including reduced pumping losses due to lack of (or greatly
reduced) throttling, high pressure direct injection of fuel, a
combustion cycle that operates at a higher compression ratio, and a
very lean air/fuel mixture relative to an equivalent-performance
gasoline engine. This technology requires additional enablers, such as
a NOx adsorption catalyst system or a urea/ammonia selective
catalytic reduction system for control of NOx emissions
during lean (excess air) operation.
b. Types of Transmission Technologies Considered
    Improved automatic transmission controls optimize the shift
schedule to maximize fuel efficiency under wide ranging conditions and
minimizes losses associated with torque converter slip through lock-up
or modulation. The first level of controls is expected to exceed 85
percent penetration by the 2017 MY.
    Shift optimization is a strategy whereby the engine and/or
transmission controller(s) emulates a CVT by continuously evaluating
all possible gear options that would provide the necessary tractive
power and select the best gear ratio that lets the engine run in the
most efficient operating zone.
    Six-, seven-, and eight-speed automatic transmissions are optimized
by changing the gear ratio span to enable the engine to operate in a
more efficient operating range over a broader range of vehicle
operating conditions. While a six speed transmission application was
most prevalent for the MYs 2012-2016 final rule, eight speed
transmissions are expected to be readily available and applied in the
MYs 2017 through 2025 timeframe.
    Dual clutch or automated shift manual transmissions are similar to
manual transmissions, but the vehicle controls shifting and launch
functions. A dual-clutch automated shift manual transmission (DCT) uses
separate clutches for even-numbered and odd-numbered gears, so the next
expected gear is pre-selected, which allows for faster and smoother
shifting. The 2012-2016 final rule limited DCT applications to a
maximum of 6-speeds. For this proposal we have considered both 6-speed
and 8-speed DCT transmissions.
    Continuously variable transmission commonly uses V-shaped pulleys
connected by a metal belt rather than gears to provide ratios for
operation. Unlike manual and automatic transmissions with fixed
transmission ratios, continuously variable transmissions can provide
fully variable and an infinite number of transmission ratios that
enable the engine to operate in a more efficient operating range over a
broader range of vehicle operating conditions. The CVT is maintained
for existing baseline vehicles and not considered for future vehicles
in this proposal due to the availability of more cost effective
transmission technologies.
    Manual 6-speed transmission offers an additional gear ratio, often
with a higher overdrive gear ratio, than a 5-speed manual transmission.
    High Efficiency Gearbox (automatic, DCT or manual)--continuous
improvement in seals, bearings and clutches, super finishing of gearbox
parts, and development in the area of lubrication, all aimed at
reducing frictional and other parasitic load in the system for an
automatic or DCT type transmission.
c. Types of Vehicle Technologies Considered
    Lower-rolling-resistance tires have characteristics that reduce
frictional losses associated with the energy dissipated mainly in the
deformation of the tires under load, thereby improving fuel economy and
reducing CO2 emissions. New for this proposal (and also
marking an advance over low rolling resistance tires considered during
the heavy duty greenhouse gas rulemaking, see 76 FR at 57207, 57229) is
a second level of lower rolling resistance tires that reduce frictional
losses even further. The first level of low rolling resistance tires
will have 10 percent rolling resistance reduction while the 2nd level
would have 20 percent rolling resistance reduction compared to 2008
baseline vehicle. The first level of lower rolling resistance tires is
expected to exceed 85 percent penetration by the 2017 MY.
    Low-drag brakes reduce the sliding friction of disc brake pads on
rotors when the brakes are not engaged because the brake pads are
pulled away from the rotors.
    Front or secondary axle disconnect for four-wheel drive systems
provides a torque distribution disconnect between front and rear axles
when torque is not required for the non-driving axle. This results in
the reduction of associated parasitic energy losses.
    Aerodynamic drag reduction can be achieved via two approaches,
either reducing the drag coefficients or reducing vehicle frontal area.
To reduce the drag coefficient, skirts, air dams, underbody covers, and
more aerodynamic side view mirrors can be

applied. In addition to the standard aerodynamic treatments, the
agencies have included a second level of aerodynamic technologies which
could include active grill shutters, rear visors, and larger under body
panels. The first level of aero dynamic drag improvement is estimated
to reduce aerodynamic drag by 10 percent relative to the baseline 2008
vehicle while the second level would reduce aero dynamic drag by 20
percent relative to 2008 baseline vehicles. The second level of
aerodynamic technologies was not considered in the MYs 2012-2016 final
rule.
    Mass Reduction can be achieved in many ways, such as material
substitution, design optimization, part consolidation, improving
manufacturing process, etc. The agencies applied mass reduction of up
to 20 percent relative to MY 2008 levels in this NPRM compared to only
10 percent in 2012-2016 final rule. The agencies also determined
effectiveness values for hybrid, plug-in and electric vehicles based on
net mass reduction, or the delta between the applied mass reduction
(capped at 20 percent) and the added mass of electrification
components. In assessing compliance strategies and in structuring the
standards, the agencies only considered amounts of vehicle mass
reduction that would result in what we estimated to be no adverse
effect on overall fleet safety. The agencies have an extensive
discussion of mass reduction technologies as well as the cost of mass
reduction in chapter 3 of the draft joint TSD.
d. Types of Electrification/Accessory and Hybrid Technologies
Considered
    Electric power steering (EPS)/Electro-hydraulic power steering
(EHPS) is an electrically-assisted steering system that has advantages
over traditional hydraulic power steering because it replaces a
continuously operated hydraulic pump, thereby reducing parasitic losses
from the accessory drive. Manufacturers have informed the agencies that
full EPS systems are being developed for all light-duty vehicles,
including large trucks. However, the agencies have applied the EHPS
technology to large trucks and the EPS technology to all other light-
duty vehicles.
    Improved accessories (IACC) may include high efficiency
alternators, electrically driven (i.e., on-demand) water pumps and
cooling fans. This excludes other electrical accessories such as
electric oil pumps and electrically driven air conditioner compressors.
New for this proposal is a second level of IACC (IACC2) which consists
of the IACC technologies and the addition of a mild regeneration
strategy and a higher efficiency alternator. The first level of IACC
improvements is expected to be at more than 85 percent penetration by
the 2017MY.
    12-volt Stop-Start, sometimes referred to as idle-stop or 12-volt
micro hybrid is the most basic hybrid system that facilitates idle-stop
capability. These systems typically incorporate an enhanced performance
battery and other features such as electric transmission and cooling
pumps to maintain vehicle systems during idle-stop.
    Higher Voltage Stop-Start/Belt Integrated Starter Generator (BISG)
sometimes referred to as a mild hybrid, provides idle-stop capability
and uses a higher voltage battery with increased energy capacity over
typical automotive batteries. The higher system voltage allows the use
of a smaller, more powerful electric motor. This system replaces a
standard alternator with an enhanced power, higher voltage, higher
efficiency starter-alternator, that is belt driven and that can recover
braking energy while the vehicle slows down (regenerative braking).
This mild hybrid technology is not included by either agency as an
enabling technology in the analysis supporting this proposal, although
some automakers have expressed interest in possibly using the
technology during the rulemaking time frame. EPA and NHTSA are
providing incentives to encourage this and similar hybrid technologies
on pick-up trucks in particular, as described in Section II.F, and the
agencies are in the process of including this technology for the final
rule analysis as we expand our understanding of the associated costs
and limitations.
    Integrated Motor Assist (IMA)/Crank integrated starter generator
(CISG) provides idle-stop capability and uses a high voltage battery
with increased energy capacity over typical automotive batteries. The
higher system voltage allows the use of a smaller, more powerful
electric motor and reduces the weight of the wiring harness. This
system replaces a standard alternator with an enhanced power, higher
voltage, higher efficiency starter-alternator that is crankshaft
mounted and can recover braking energy while the vehicle slows down
(regenerative braking). The IMA technology is not included by either
agency as an enabling technology in the analysis supporting this
proposal, although it is included as a baseline technology because it
exists in our 2008 baseline fleet.
    P2 Hybrid is a newly emerging hybrid technology that uses a
transmission integrated electric motor placed between the engine and a
gearbox or CVT, much like the IMA system described above except with a
wet or dry separation clutch which is used to decouple the motor/
transmission from the engine. In addition, a P2 hybrid would typically
be equipped with a larger electric machine. Disengaging the clutch
allows all-electric operation and more efficient brake-energy recovery.
Engaging the clutch allows efficient coupling of the engine and
electric motor and, when combined with a DCT transmission, reduces
gear-train losses relative to power-split or 2-mode hybrid systems.
    2-Mode Hybrid is a hybrid electric drive system that uses an
adaptation of a conventional stepped-ratio automatic transmission by
replacing some of the transmission clutches with two electric motors
that control the ratio of engine speed to vehicle speed, while clutches
allow the motors to be bypassed. This improves both the transmission
torque capacity for heavy-duty applications and reduces fuel
consumption and CO2 emissions at highway speeds relative to
other types of hybrid electric drive systems. The 2-mode hybrid
technology is not included by either agency as an enabling technology
in the analysis supporting this proposal, although it is included as a
baseline technology because it exists in our 2008 baseline fleet.
    Power-split Hybrid is a hybrid electric drive system that replaces
the traditional transmission with a single planetary gearset and a
motor/generator. This motor/generator uses the engine to either charge
the battery or supply additional power to the drive motor. A second,
more powerful motor/generator is permanently connected to the vehicle's
final drive and always turns with the wheels. The planetary gear splits
engine power between the first motor/generator and the drive motor to
either charge the battery or supply power to the wheels. The power-
split hybrid technology is not included by either agency as an enabling
technology in the analysis supporting this proposal, (the agencies
evaluate the P2 hybrid technology discussed above where power-split
hybrids might otherwise have been appropriate) although it is included
as a baseline technology because it exists in our 2008 baseline fleet.
    Plug-in hybrid electric vehicles (PHEV) are hybrid electric
vehicles with the means to charge their battery packs from an outside
source of electricity (usually the electric grid). These

vehicles have larger battery packs with more energy storage and a
greater capability to be discharged than other hybrid electric
vehicles. They also use a control system that allows the battery pack
to be substantially depleted under electric-only or blended mechanical/
electric operation and batteries that can be cycled in charge
sustaining operation at a lower state of charge than is typical of
other hybrid electric vehicles. These vehicles are sometimes referred
to as Range Extended Electric Vehicles (REEV). In this MYs 2017-2025
analysis, PHEVs with several all-electric ranges--both a 20 mile and a
40 mile all-electric range--have been included as potential
technologies.
    Electric vehicles (EV) are equipped with all-electric drive and
with systems powered by energy-optimized batteries charged primarily
from grid electricity. EVs with several ranges--75 mile, 100 mile and
150 mile range--have been included as potential technologies.
e. Technologies Considered but Deemed ``Not Ready'' in the MYs 2017-
2025 Timeframe
    Fuel cell electric vehicles (FCEVs) utilize a full electric drive
platform but consume electricity generated by an on-board fuel cell and
hydrogen fuel. Fuel cells are electro-chemical devices that directly
convert reactants (hydrogen and oxygen via air) into electricity, with
the potential of achieving more than twice the efficiency of
conventional internal combustion engines. High pressure gaseous
hydrogen storage tanks are used by most automakers for FCEVs that are
currently under development. The high pressure tanks are similar to
those used for compressed gas storage in more than 10 million CNG
vehicles worldwide, except that they are designed to operate at a
higher pressure (350 bar or 700 bar vs. 250 bar for CNG). While we
expect there will be some limited introduction of FCEVs into the market
place in the time frame of this rule, we expect this introduction to be
relatively small, and thus FCEVs are not considered in the modeling
analysis conducted for this proposal.
    There are a number of other technologies that the agencies have not
considered in their analysis, but may be considered for the final rule.
These include HCCI, ``multi-air'', and camless valve actuation, and
other advanced engines currently under development.
2. How did the agencies determine the costs of each of these
technologies?
    As noted in the introduction to this section, most of the direct
cost estimates for technologies carried over from the MYs 2012-2016
final rule and subsequently used in this proposal are fundamentally
unchanged since the MYs 2012-2016 final rule analysis and/or the 2010
TAR. We say ``fundamentally'' unchanged since the basis of the direct
manufacturing cost estimates have not changed; however, the costs have
been updated to more recent dollars, the learning effects have resulted
in further cost reductions for some technologies, the indirect costs
are calculated using a modified methodology and the impact of long-term
ICMs is now present during the rulemaking timeframe. Besides these
changes, there are also some other notable changes to the costs used in
previous analyses. We highlight these changes in Section II.D.2.a,
below. We highlight the changes to the indirect cost methodology and
adjustments to more recent dollars in Sections II.D.2.b and c. Lastly,
we present some updated terminology used for our approach to estimating
learning effects in an effort to eliminate confusion with our past
terminology. This is discussed in Section II.D.2.d, below.
    The agencies note that the technology costs included in this
proposal take into account only those associated with the initial build
of the vehicle. Although comments were received to the MYs 2012-2016
rulemaking that suggested there could be additional maintenance
required with some new technologies (e.g., turbocharging, hybrids,
etc.), and that additional maintenance costs could occur as a result,
the agencies believe that it is equally possible that maintenance costs
could decrease for some vehicles, especially when considering full
electric vehicles (which lack routine engine maintenance) or the
replacement of automatic transmissions with simpler dual-clutch
transmissions. The agencies request comment on the possible maintenance
cost impacts associated with this proposal, reminding potential
commenters that increased warranty costs are already considered as part
of the ICMs.
a. Direct Manufacturing Costs (DMC)
    For direct manufacturing costs (DMC) related to turbocharging,
downsizing, gasoline direct injection, transmissions, as well as non-
battery-related costs on hybrid, plug-in hybrid and electric vehicles,
the agencies have relied on costs derived from teardown studies. For
battery related DMC for HEVs, PHEVs and EVs, the agencies have relied
on the BatPaC model developed by Argonne National Laboratory for the
Department of Energy. For mass reduction DMC, the agencies have relied
on several studies as described in detail in the draft Joint TSD. We
discuss each of these briefly here and in more detail in the draft
joint TSD. For the majority of the other technologies considered in
this proposal and described above, the agencies have relied on the
2012-2016 final rule and sources described there for estimates of DMC.
i. Costs from Tear-down Studies
    As a general matter, the agencies believe that the best method to
derive technology cost estimates is to conduct studies involving tear-
down and analysis of actual vehicle components. A ``tear-down''
involves breaking down a technology into its fundamental parts and
manufacturing processes by completely disassembling actual vehicles and
vehicle subsystems and precisely determining what is required for its
production. The result of the tear-down is a ``bill of materials'' for
each and every part of the relevant vehicle systems. This tear-down
method of costing technologies is often used by manufacturers to
benchmark their products against competitive products. Historically,
vehicle and vehicle component tear-down has not been done on a large
scale by researchers and regulators due to the expense required for
such studies. While tear-down studies are highly accurate at costing
technologies for the year in which the study is intended, their
accuracy, like that of all cost projections, may diminish over time as
costs are extrapolated further into the future because of uncertainties
in predicting commodities (and raw material) prices, labor rates, and
manufacturing practices. The projected costs may be higher or lower
than predicted.
    Over the past several years, EPA has contracted with FEV, Inc. and
its subcontractor Munro & Associates, to conduct tear-down cost studies
for a number of key technologies evaluated by the agencies in assessing
the feasibility of future GHG and CAFE standards. The analysis
methodology included procedures to scale the tear-down results to
smaller and larger vehicles, and also to different technology
configurations. FEV's methodology was documented in a report published
as part of the MY 2012-2016 rulemaking, detailing the costing of the
first tear-down conducted in this work (1 in the below
list).\136\ This report was peer reviewed by experts in the industry
and revised by FEV in response to the peer review

comments.\137\ Subsequent tear-down studies (2-5 in the below
list) were documented in follow-up FEV reports made available in the
public docket for the MY 2012-2016 rulemaking.\138\
---------------------------------------------------------------------------

    \136\ U.S. EPA, ``Light-Duty Technology Cost Analysis Pilot
Study,'' Contract No. EP-C-07-069, Work Assignment 1-3, December
2009, EPA-420-R-09-020, Docket EPA-HQ-OAR-2009-0472-11282.
    \137\ FEV pilot study response to peer review document November
6, 2009, is at EPA-HQ-OAR-2009-0472-11285.
    \138\ U.S. EPA, ``Light-duty Technology Cost Analysis--Report on
Additional Case Studies,'' EPA-HQ-OAR-2009-0472-11604.
---------------------------------------------------------------------------

    Since then, FEV's work under this contract work assignment has
continued. Additional cost studies have been completed and are
available for public review.\139\ The most extensive study, performed
after the MY 2012-2016 Final Rule, involved whole-vehicle tear-downs of
a 2010 Ford Fusion powersplit hybrid and a conventional 2010 Ford
Fusion. (The latter served as a baseline vehicle for comparison.) In
addition to providing powersplit HEV costs, the results for individual
components in these vehicles were subsequently used by FEV/Munro to
cost another hybrid technology, the P2 hybrid, which employs similar
hardware. This approach to costing P2 hybrids was undertaken because P2
HEVs were not yet in volume production at the time of hardware
procurement for tear-down. Finally, an automotive lithium-polymer
battery was torn down and costed to provide supplemental battery
costing information to that associated with the NiMH battery in the
Fusion. This HEV cost work, including the extension of results to P2
HEVs, has been extensively documented in a new report prepared by
FEV.\140\ Because of the complexity and comprehensive scope of this HEV
analysis, EPA commissioned a separate peer review focused exclusively
on it. Reviewer comments generally supported FEV's methodology and
results, while including a number of suggestions for improvement many
of which were subsequently incorporated into FEV's analysis and final
report. The peer review comments and responses are available in the
rulemaking docket.141 142
---------------------------------------------------------------------------

    \139\ FEV, Inc., ``Light-Duty Technology Cost Analysis, Report
on Additional Transmission, Mild Hybrid, and Valvetrain Technology
Case Studies'', November 2011.
    \140\ FEV, Inc., ``Light-Duty Technology Cost Analysis, Power-
Split and P2 HEV Case Studies'', EPA-420-R-11-015, November 2011.
    \141\ ICF, ``Peer Review of FEV Inc. Report Light Duty
Technology Cost Analysis, Power-Split and P2 Hybrid Electric Vehicle
Case Studies'', EPA-420-R-11-016, November 2011.
    \142\ FEV and EPA, ``FEV Inc. Report `Light Duty Technology Cost
Analysis, Power-Split and P2 Hybrid Electric Vehicle Case Studies',
Peer Review Report--Response to Comments Document'', EPA-420-R-11-
017, November 2011.
---------------------------------------------------------------------------

    Over the course of this work assignment, teardown-based studies
have been performed thus far on the technologies listed below. These
completed studies provide a thorough evaluation of the new
technologies' costs relative to their baseline (or replaced)
technologies.
    1. Stoichiometric gasoline direct injection (SGDI) and
turbocharging with engine downsizing (T-DS) on a DOHC (dual overhead
cam) I4 engine, replacing a conventional DOHC I4 engine.
    2. SGDI and T-DS on a SOHC (single overhead cam) on a V6 engine,
replacing a conventional 3-valve/cylinder SOHC V8 engine.
    3. SGDI and T-DS on a DOHC I4 engine, replacing a DOHC V6 engine.
    4. 6-speed automatic transmission (AT), replacing a 5-speed AT.
    5. 6-speed wet dual clutch transmission (DCT) replacing a 6-speed
AT.
    6. 8-speed AT replacing a 6-speed AT.
    7. 8-speed DCT replacing a 6-speed DCT.
    8. Power-split hybrid (Ford Fusion with I4 engine) compared to a
conventional vehicle (Ford Fusion with V6). The results from this tear-
down were extended to address P2 hybrids. In addition, costs from
individual components in this tear-down study were used by the agencies
in developing cost estimates for PHEVs and EVs.
    9. Mild hybrid with stop-start technology (Saturn Vue with I4
engine), replacing a conventional I4 engine. (Although results from
this cost study are included in the rulemaking docket, they were not
used by the agencies in this rulemaking's technical analyses.)
    10. Fiat Multi-Air engine technology. (Although results from this
cost study are included in the rulemaking docket, they were not used by
the agencies in this rulemaking's technical analyses.)
    Items 6 through 10 in the list above are new since the 2012-2016
final rule.
    In addition, FEV and EPA extrapolated the engine downsizing costs
for the following scenarios that were based on the above study cases:
    1. Downsizing a SOHC 2 valve/cylinder V8 engine to a DOHC V6.
    2. Downsizing a DOHC V8 to a DOHC V6.
    3. Downsizing a SOHC V6 engine to a DOHC 4 cylinder engine.
    4. Downsizing a DOHC 4 cylinder engine to a DOHC 3 cylinder engine.
    The agencies have relied on the findings of FEV for estimating the
cost of the technologies covered by the tear-down studies.
ii. Costs of HEV, EV & PHEV
    The agencies have also reevaluated the costs for HEVs, PHEVs, and
EVs since both the 2012-2016 final rule and the 2010 TAR. First,
electrified vehicle technologies are developing rapidly and the
agencies sought to capture results from the most recent analysis.
Second, the 2012-2016 rule employed a single $/kWhr estimate and did
not consider the specific vehicle and technology application for the
battery when we estimated the cost of the battery. Specifically,
batteries used in HEVs (high power density applications) versus EVs
(high energy density applications) need to be considered appropriately
to reflect the design differences, the chemical material usage
differences and differences in $/kWhr as the power to energy ratio of
the battery changes for different applications.
    To address these issues for this proposal, the agencies have done
two things. First, EPA has developed a spreadsheet tool that was used
to size the motor and battery based on the different road load of
various vehicle classes. Second, the agencies have used a battery cost
model developed by Argonne National Laboratory (ANL) for the Vehicle
Technologies Program of the U.S. Department of Energy (DOE) Office of
Energy Efficiency and Renewable Energy.\143\ The model developed by ANL
allows users to estimate unique battery pack costs using user
customized input sets for different hybridization applications, such as
strong hybrid, PHEV and EV. The DOE has established long term industry
goals and targets for advanced battery systems as it does for many
energy efficient technologies. ANL was funded by DOE to provide an
independent assessment of Li-ion battery costs because of ANL's
expertise in the field as one of the primary DOE National Laboratories
responsible for basic and applied battery energy storage technologies
for future HEV, PHEV and EV applications. Since publication of the 2010
TAR, ANL's battery cost model has been peer-reviewed and ANL has
updated the model and documentation to incorporate suggestions from
peer-reviewers, such as including a battery management system, a
battery disconnect unit, a thermal management system, etc.\144\ In this
proposal, NHTSA and EPA have used the recently revised version of this
updated model.
---------------------------------------------------------------------------

    \143\ ANL BatPac model Docket number EPA-HQ-OAR-2010-0799.
    \144\ Nelson, P.A., Santinit, D.J., Barnes, J. ``Factors
Determining the Manufacturing Costs of Lithium-Ion Batteries for
PHEVs,'' 24th World Battery, Hybrid and Fuel Cell Electric Vehicle
Symposium and Exposition EVS-24, Stavenger, Norway, May 13-16, 2009
(www.evs24.org).
---------------------------------------------------------------------------

    The agencies are using the ANL model as the basis for estimating
large-

format lithium-ion batteries for this assessment for the following
reasons. The model was developed by scientists at ANL who have
significant experience in this area. The model uses a bill of materials
methodology for developing cost estimates. The ANL model appropriately
considers the vehicle application's power and energy requirements,
which are two of the fundamental parameters when designing a lithium-
ion battery for an HEV, PHEV, or EV. The ANL model can estimate
production costs based on user defined inputs for a range of production
volumes. The ANL model's cost estimates, while generally lower than the
estimates we received from the OEMs, are consistent with some of the
supplier cost estimates that EPA received from large-format lithium-ion
battery pack manufacturers. This includes data which was received from
on-site visits done by the EPA in the 2008-2011 time frame. Finally,
the ANL model has been described and presented in the public domain and
does not rely upon confidential business information (which could not
be reviewed by the public).
    The potential for future reductions in battery cost and
improvements in battery performance relative to current batteries will
play a major role in determining the overall cost and performance of
future PHEVs and EVs. The U.S. Department of Energy manages major
battery-related R&D programs and partnerships, and has done so for many
years, including the ANL model utilized in this report. DOE has
reviewed the battery cost projections underlying this proposal and
supports the use of the ANL model for the purposes of this rulemaking.
    We have also estimated cost associated with in-home chargers and
installation of in-home chargers expected to be necessary for PHEVs and
EVs. Charger costs are covered in more detail in chapter 3 of the draft
Joint TSD.
iii. Mass Reduction Costs
    The agencies have revised the costs for mass reduction from the MYs
2012-2016 rule and the 2010 Technical Assessment Report. For this
proposal, the agencies are relying on a wide assortment of sources from
the literature as well as data provided from a number of OEMs. Based on
this review, the agencies have estimated a new cost curve such that the
costs increase as the levels of mass reduction increase. For the final
rule the agencies will consider any new studies that become available,
including two studies that the agencies are sponsoring and expect will
be completed in time to inform the final rule. These studies are
discussed in TSD chapter 3.
b. Indirect Costs (IC)
i. Markup Factors to Estimate Indirect Costs
    For this analysis, indirect costs are estimated by applying
indirect cost multipliers (ICM) to direct cost estimates. ICMs were
derived 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 MYs 2012-2016 rulemaking.
    Prior to developing the ICM methodology,\145\ 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,
for low complexity technologies, the RPE is typically higher than the
ICM. This is not always the case, as ICM estimates for particularly
complex technologies, specifically hybrid technologies (for near term
ICMs), and plug-in hybrid battery and full electric vehicle
technologies (for near term and long term ICMs), reflect higher than
average indirect costs, with the resulting ICMs for those technologies
equaling or exceeding the averaged RPE for the industry.
---------------------------------------------------------------------------

    \145\ The ICM methodology was developed by RTI International,
under contract to EPA. The results of the RTI report were published
in Alex Rogozhin, Michael Gallaher, Gloria Helfand, and Walter
McManus, ``Using Indirect Cost Multipliers to Estimate the Total
Cost of Adding New Technology in the Automobile Industry.''
International Journal of Production Economics 124 (2010): 360-368.
---------------------------------------------------------------------------

    There is some level of uncertainty surrounding both the ICM and RPE
markup factors. The ICM estimates used in this proposed action group
all technologies into four broad categories and treat them as if
individual technologies within each of the categories (``low'',
``medium'', ``high1'' and ``high2'' 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.\146\ 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 identical.\147\ 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. 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. 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, or understates them for advanced
technologies.
---------------------------------------------------------------------------

    \146\ Helfand, Gloria, and Sherwood, Todd. ``Documentation of
the Development of Indirect Cost Multipliers for Three Automotive
Technologies.'' Memorandum, Assessment and Standards Division,
Office of Transportation and Air Quality, U.S. Environmental
Protection Agency, August 2009.
    \147\ NHTSA staff participated in the development of the process
for the second, modified Delphi panel, and reviewed the results as
they were developed, but did not serve on the panel.
---------------------------------------------------------------------------

    In every recent GHG and fuel economy rulemaking proposal, we have
requested comment on our ICM factors and whether it is most appropriate
to use ICMs or RPEs. We have generally received little to no comment on
the issue specifically, other than basic comments that the ICM values
are too low. In addition, in the June 2010 NAS report, NAS noted that
the under the initial ICMs, no technology would be assumed to have
indirect costs as high as the average RPE. NRC found that ``RPE factors
certainly do vary depending on the complexity of the task of
integrating a component into a vehicle system, the extent of the
required changes to other components, the novelty of the technology,
and other factors. However, until empirical data derived by means of
rigorous estimation methods are available, the committee prefers to use
average markup factors.'' \148\ The committee also stated that ``The
EPA (Rogozhin et al., 2009), however, has taken the first steps in
attempting to analyze this problem in a way that could lead to a
practical method of estimating technology-specific markup factors''
where ``this problem'' spoke to the issue of estimating technology-
specific markup factors and indirect cost multipliers.\149\
---------------------------------------------------------------------------

    \148\ NRC, Finding 3-2 at page 3-23.
    \149\ NRC at page 3-19.
---------------------------------------------------------------------------

    The agencies note that, since the committee completed their work,
EPA has published its work in the Journal of Production Economics \150\
and has also published a memorandum furthering the development of
ICMs,\151\ neither of which the committee had at their disposal.
Further, having published two final rulemakings--the 2012-2016 light-
duty rule (see 75 FR 25324) and the more recent heavy-duty GHG rule
(see 76 FR 57106)--as well as the 2010 TAR where ICMs served as the
basis for all or most of the indirect costs, EPA believes that ICMs are
indeed fully developed for regulatory purposes. As thinking has
matured, we have adjusted our ICM factors such that they are slightly
higher and, importantly, we have changed the way in which the factors
are applied.
---------------------------------------------------------------------------

    \150\ Alex Rogozhin, Michael Gallaher, Gloria Helfand, and
Walter McManus, ``Using Indirect Cost Multipliers to Estimate the
Total Cost of Adding New Technology in the Automobile Industry.''
International Journal of Production Economics 124 (2010): 360-368.
    \151\ Helfand, Gloria, and Sherwood, Todd. ``Documentation of
the Development of Indirect Cost Multipliers for Three Automotive
Technologies.'' Memorandum, Assessment and Standards Division,
Office of Transportation and Air Quality, U.S. Environmental
Protection Agency, August 2009.
---------------------------------------------------------------------------

    The first change--increased ICM factors--has been done as a result
of further thought among EPA and NHTSA that the ICM factors presented
in the original RTI report for low and medium complexity technologies
should no longer be used and that we should rely solely on the
modified-Delphi values for these complexity levels. For that reason, we
have eliminated the averaging of original RTI values with modified-
Delphi values and instead are relying solely on the modified-Delphi
values for low and medium complexity technologies. The second change--
the way the factors are applied--results in the warranty portion of the
indirect costs being applied as a multiplicative factor (thereby
decreasing going forward as direct manufacturing costs decrease due to
learning), and the remainder of the indirect costs being applied as an
additive factor (thereby remaining constant year-over-year and not
being reduced due to learning). This second change has a comparatively
large impact on the resultant technology costs and, we believe, more
appropriately estimates costs over time. In addition to these changes,
a secondary-level change was also made as part of this ICM
recalculation to ICMs. That change was to revise upward the RPE level
reported in the original RTI report from an original value of 1.46 to
1.5, to reflect the long term average RPE. The original RTI study was
based on 2008 data. However, an analysis of historical RPE data
indicates that, although there is year to year variation, the average
RPE has remained roughly constant at 1.5. ICMs will be applied to
future years' data and, therefore, NHTSA and EPA staffs believe that it
would be appropriate to base ICMs on the historical average rather than
a single year's result. Therefore, ICMs have been adjusted to reflect
this average level. These changes to the ICMs and the methodology are
described in greater detail in Chapter 3 of the draft Joint TSD.
ii. Stranded Capital
    Because the production of automotive components is capital-
intensive, it is possible for substantial capital investments in
manufacturing equipment and facilities to become ``stranded'' (where
their value is lost, or diminished). This would occur when the capital
is rendered useless (or less useful) by some factor that forces a major
change in vehicle design, plant operations, or manufacturer's product
mix, such as a shift in consumer demand for certain vehicle types. It
can also be caused by new standards that phase-in at a rate too rapid
to accommodate planned replacement or redisposition of existing capital
to other activities. The lost value of capital equipment is then
amortized in some way over production of the new technology components.
    It is difficult to quantify accurately any capital stranding
associated with new technology phase-ins under the proposed standards
because of the iterative dynamic involved--that is, the new technology
phase-in rate strongly affects the potential for additional cost due to
stranded capital, but that additional cost in turn affects the degree
and rate of phase-in for other individual competing technologies. In
addition, such an analysis is very company-, factory-, and
manufacturing process-specific, particularly in regard to finding
alternative uses for equipment and facilities. Nevertheless, in order
to account for the possibility of stranded capital costs, the agencies
asked FEV to perform a separate bounding analysis of potential stranded
capital costs associated with rapid phase-in of technologies due to new
standards, using data from FEV's primary teardown-based cost
analyses.\152\
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    \152\ FEV, Inc., ``Potential Stranded Capital Analysis on EPA
Light-Duty Technology Cost Analysis'', Contract No. EP-C-07-069 Work
Assignment 3-3. November 2011.
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    The assumptions made in FEV's stranded capital analysis with
potential for major impacts on results are:
     All manufacturing equipment was bought brand new when the
old technology started production (no carryover of equipment used to
make the previous components that the old technology itself replaced).
     10-year normal production runs: Manufacturing equipment
used to make old technology components is straight-line depreciated
over a 10-year life.
     Factory managers do not optimize capital equipment phase-
outs (that is, they are assumed to routinely repair and replace
equipment without regard to whether or not it will soon be scrapped due
to adoption of new vehicle technology).
     Estimated stranded capital is amortized over 5 years of
annual production at 450,000 units (of the new technology components).
This annual production is identical to that assumed in FEV's primary
teardown-based cost analyses. The 5-year recovery period is chosen to
help ensure a conservative analysis; the actual recovery would of
course vary greatly with market conditions.

    The stranded capital analysis was performed for three transmission
technology scenarios, two engine technology scenarios, and one hybrid
technology scenario. The methodology used by EPA in applying the
results to the technology costs is described in Chapter 3.8.7 and
Chapter 5.1 of EPA's draft RIA. The methodology used by NHTSA in
applying the results to the technology costs is described in NHTSA's
preliminary RIA section V.
c. Cost Adjustment to 2009 Dollars
    This simple change is to update any costs presented in earlier
analyses to 2009 dollars using the GDP price deflator as reported by
the Bureau of Economic Analysis on January 27, 2011. The factors used
to update costs from 2007 and 2008 dollars to 2009 dollars are shown
below. For the final rule, we are considering moving to 2010 dollars
but, for this analysis, given the timing of conducting modeling runs
and developing inputs to those runs, the factors for converting to 2010
dollars were not yet available.
[GRAPHIC] [TIFF OMITTED] TP01DE11.035

d. Cost Effects Due to Learning
    For many of the technologies considered in this rulemaking, the
agencies expect that the industry should be able to realize reductions
in their costs over time as a result of ``learning effects,'' that is,
the fact that as manufacturers gain experience in production, they are
able to reduce the cost of production in a variety of ways. The
agencies continue to apply learning effects in the same way as we did
in both the MYs 2012-2016 final rule and in the 2010 TAR. However, we
have employed some new terminology in an effort to eliminate some
confusion that existed with our old terminology. This new terminology
was described in the recent heavy-duty GHG final rule (see 76 FR
57320). Our old terminology suggested we were accounting for two
completely different learning effects--one based on volume production
and the other based on time. This was not the case since, in fact, we
were actually relying on just one learning phenomenon, that being the
learning-by-doing phenomenon that results from cumulative production
volumes.
    As a result, 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 more 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 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.
    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, quantifiable 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
PHEV and EV batteries which are experiencing heavy development and,
presumably, rapid cost declines in coming years. 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 3
of the draft Joint TSD. Note that, since the agencies had to project
how learning will occur with new technologies over a long period of
time, we request comments on the assumptions of learning costs and
methodology. In particular, we are interested in input on the
assumptions for advanced 27-bar BMEP cooled exhaust gas recirculation
(EGR) engines, which are currently still in the experimental stage and
not expected to be available in volume production until 2017. For our
analysis, we have based estimates of the costs of this engine on
current (or soon to be current) production technologies (e.g., gasoline
direct injection fuel systems, engine downsizing, cooled EGR, 18-bar
BMEP capable turbochargers), and assumed that, since learning (and the
associated cost reductions) begins in 2012 for them that it also does
for the similar technologies used in 27-bar BMEP engines. We seek
comment on the appropriateness of this assumption.\153\
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    \153\ EPA notes that our modeling projections for the proposed
CO2 standards show a technology penetration rate of 2% in
the 2021MY and 5% in the 2025MY for 27-bar BMEP engines and, thus,
our cost estimates are not heavily reliant on this technology.
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3. How did the agencies determine the effectiveness of each of these
technologies?
    In 2007 EPA conducted a detailed vehicle simulation project to
quantify the effectiveness of a multitude of technologies for the MYs
2012-2016

rule (as well as the 2010 NOI). This technical work was conducted by
the global engineering consulting firm, Ricardo, Inc. and was peer
reviewed and then published in 2008. For this current rule, EPA has
conducted another peer reviewed study with Ricardo to broaden the scope
of the original project in order to expand the range of vehicle classes
and technologies considered, consistent with a longer-term outlook
through model years MYs 2017-2025. The extent of the project was vast,
including hundreds of thousands of vehicle simulation runs. The results
were, in turn, employed to calibrate and update EPA's lumped parameter
model, which is used to quantify the synergies and dis-synergies
associated with combining technologies together for the purposes of
generating inputs for the agencies respective OMEGA and CAFE modeling.
    Additionally, there were a number of technologies that Ricardo did
not model explicitly. For these, the agencies relied on a variety of
sources in the literature. A few of the values are identical to those
presented in the MYs 2012-2016 final rule, while others were updated
based on the newer version of the lumped parameter model. More details
on the Ricardo simulation, lumped parameter model, as well as the
effectiveness for supplemental technologies are described in Chapter 3
of the draft Joint TSD.
    The agencies note that the effectiveness values estimated for the
technologies considered in the modeling analyses may represent average
values, and do not reflect the virtually unlimited spectrum of possible
values that could result from adding the technology to different
vehicles. For example, while the agencies have estimated an
effectiveness of 0.6 to 0.8 percent, depending on the vehicle subclass
for low friction lubricants, each vehicle could have a unique
effectiveness estimate depending on the baseline vehicle's oil
viscosity rating. Similarly, the reduction in rolling resistance (and
thus the improvement in fuel economy and the reduction in
CO2 emissions) due to the application of low rolling
resistance tires depends not only on the unique characteristics of the
tires originally on the vehicle, but on the unique characteristics of
the tires being applied, characteristics which must be balanced between
fuel efficiency, safety, and performance. Aerodynamic drag reduction is
much the same--it can improve fuel economy and reduce CO2
emissions, but it is also highly dependent on vehicle-specific
functional objectives. For purposes of the proposal, NHTSA and EPA
believe that employing average values for technology effectiveness
estimates, as adjusted depending on vehicle subclass, is an appropriate
way of recognizing the potential variation in the specific benefits
that individual manufacturers (and individual vehicles) might obtain
from adding a fuel-saving technology.

E. Joint Economic and Other Assumptions

    The agencies' analysis of CAFE and GHG standards for the model
years covered by this proposed rulemaking rely on a range of forecast
information, estimates of economic variables, and input parameters.
This section briefly describes the agencies' proposed estimates of each
of these values. These values play a significant role in assessing the
benefits of both CAFE and GHG standards.
    In reviewing these variables and the agencies' estimates of their
values for purposes of this NPRM, NHTSA and EPA reconsidered comments
that the agencies previously received on both the Interim Joint TAR and
during the MYs 2012-2016 light duty vehicle rulemaking and also
reviewed newly available literature. As a consequence, for today's
proposal, the agencies are proposing to update some economic
assumptions and parameter estimates, while retaining a majority of
values consistent with the Interim Joint TAR and the MYs 2012-2016
final rule. To review the parameters and assumptions the agencies used
in the 2012-2016 final rule, please refer to 75 FR 25378 and Chapter 4
of the Joint Technical Support Document that accompanied the final
rule.\154\ The proposed values summarized below are discussed in
greater detail in Chapter 4 of the joint TSD that accompanies this
proposal and elsewhere in the preamble and respective RIAs. The
agencies seek comment on all of the assumptions discussed below.
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    \154\ See http://www.epa.gov/otaq/climate/regulations/420r10901.pdf.
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     Costs of fuel economy-improving technologies--These inputs
are discussed in summary form above and in more detail in the agencies'
respective sections of this preamble, in Chapter 3 of the draft joint
TSD, and in the agencies' respective RIAs. The technology direct
manufacturing cost estimates used in this analysis are intended to
represent manufacturers' direct costs for high-volume production of
vehicles with these technologies in the year for which we state the
cost is considered ``valid.'' Technology direct manufacturing cost
estimates are fundamentally unchanged from those employed by the
agencies in the 2012-2016 final rule, the heavy-duty truck rule (to the
extent relevant), and TAR for most technologies, although revised costs
are used for batteries, mass reduction, transmissions, and a few other
technologies. Indirect costs are accounted for by applying near-term
indirect cost multipliers ranging from 1.24 to 1.77 to the estimates of
vehicle manufacturers' direct costs for producing or acquiring each
technology, depending on the complexity of the technology and the time
frame over which costs are estimated. These values are reduced to 1.19
to 1.50 over the long run as some aspects of indirect costs decline.
Indirect cost markup factors have been revised from previous
rulemakings and the Interim Joint TAR to reflect the agencies current
thinking regarding a number of issues. These changes are discussed in
detail in Section II.D.2 of this preamble and in Chapter 3 of the draft
joint TSD. Details of the agencies' technology cost assumptions and how
they were derived can be found in Chapter 3 of the draft joint TSD.
     Potential opportunity costs of improved fuel economy--This
issue addresses the possibility that achieving the fuel economy
improvements required by alternative CAFE or GHG standards would
require manufacturers to compromise the performance, carrying capacity,
safety, or comfort of their vehicle models. If it did so, the resulting
sacrifice in the value of these attributes to consumers would represent
an additional cost of achieving the required improvements, and thus of
manufacturers' compliance with stricter standards. Currently the
agencies project that these vehicle attributes will not change as a
result of this rule. Section II.C above and Chapter 2 of the draft
joint TSD describes how the agency carefully selected an attribute-
based standard to minimize manufacturers' incentive to reduce vehicle
capabilities. While manufacturers may choose to do this for other
reasons, the agencies continue to believe that the rule itself will not
result in such changes. Additionally, EPA and NHTSA have sought to
include the cost of maintaining these attributes as part of the cost
estimates for technologies that are included in the cost analysis for
the proposal. For example, downsized engines are assumed to be
turbocharged, so that they provide the same performance and utility
even though they are smaller.\155\ Nonetheless, it is

possible that in some cases, the technology cost estimates may not
include adequate allowance for the necessary efforts by manufacturers
to maintain vehicle acceleration performance, payload, or utility while
improving fuel economy and reducing GHG emissions. As described in
Section III.D.3 and Section IV.G, there are two possible exceptions in
cases where some vehicle types are converted to hybrid or full electric
vehicles (EVs), but, in such cases, we believe that sufficient options
would exist for consumers concerned about the possible loss of utility
(e.g., they would purchase the non-hybridized version of the vehicle or
not buy an EV) that welfare loss should not necessarily be assumed.
Although consumer vehicle demand models can measure these effects, past
analyses using such models have not produced consistent estimates of
buyers' willingness-to-pay for higher fuel economy, and it is difficult
to decide whether one data source, model specification, or estimation
procedure is clearly preferred over another. Thus, the agencies seek
comment on how to estimate explicitly the changes in vehicle buyers'
choices and welfare from the combination of higher prices for new
vehicle models, increases in their fuel economy, and any accompanying
changes in vehicle attributes such as performance, passenger- and
cargo-carrying capacity, or other dimensions of utility.
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    \155\ The agencies do not believe that adding fuel-saving
technology should preclude future improvements in performance,
safety, or other attributes, though it is possible that the costs of
these additions may be affected by the presence of fuel-saving
technology.
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     The on-road fuel economy ``gap''--Actual fuel economy
levels achieved by light-duty vehicles in on-road driving fall somewhat
short of their levels measured under the laboratory test conditions
used by EPA to establish compliance with the proposed CAFE and GHG
standards. The modeling approach in this proposal follows the 2012-2016
final rule and the Interim Joint TAR. In calculating benefits of the
program, the agencies estimate that actual on-road fuel economy
attained by light-duty vehicles that operate on liquid fuels will be 20