values is that they are ``incremental'' in nature, meaning that the
estimates are ``referenced'' to some prior technology state in the
decision tree in which the applied technology is represented, typically
the preceding technology. Therefore, when considering values shown in
the input sheet, the reader must understand that in all but a few cases
they cannot fully deduce the accumulated or ``NET'' cost and
effectiveness, referenced back to the base condition (i.e., start of
the decision tree), without performing a more detailed analysis. The
method for conducting this analysis, and a brief example of how it is
done, is discussed in the Decision Tree section below. For the final
rule, to help readers better understand Volpe model net or accumulated
costs and fuel consumption reductions, NHTSA has published net values
to key technology locations on the decision trees (e.g., to diesel
engine conversion, or a strong hybrid). See the Tables showing
Approximate Net Technology Costs and Approximate Net Technology
Effectiveness, located in Section IV.E below. The tables have been
produced for each of the four vehicle subclasses in the passenger car,
performance passenger car, and light truck vehicle groups.
    The incremental costs of some technologies are dependent on certain
factors specific to the vehicle to which they are applied. For
instance, when the Material Substitution technology is applied, the
cost of application is based on a cost per unit weight reduction, in
dollars per pound, since the weight removed is a percentage of the curb
weight of the vehicle (which differs from one vehicle to the next).
Similarly, some engine technologies need to be calculated on a cost per
cylinder basis, or a cost per configuration basis (i.e., a cost per
bank basis, so that a V-configured engine would cost twice as much as
an in-line, single bank engine). For each technology, the input sheet
also contains a Cost Basis variable which indicates whether the costs
need to be adjusted in this manner. This functionality, some of which
is new for the final rule, allows NHTSA to estimate more accurately the
costs of technology application, since in the NPRM the vehicles in a
subclass were assumed to have common cylinder counts and configurations
(thus the costs were underestimated for some vehicles and overestimated
for others).
    Lastly for the technology input file, the term ``synergy'' as it
applies to the Volpe modeling process refers to the condition that
occurs when two or more technologies are applied to a vehicle and their
effects interact with each other, resulting in a different net effect
than the combination of the individual technologies. The term synergy
usually connotes a positive interaction (e.g., 1 + 1 is more than 2),
but as used here it also includes negative interactions (e.g., 1 + 1 is
less than 2). Synergies are discussed in greater detail below in
Section IV.C.7, and the values for the synergy factors NHTSA used in
the final rule are stored in the technology input file.
    In some cases more than one decision tree path can lead to a
subsequently applied technology. For example, the power split hybrid
technology can be reached from one of two prior transmission
technologies (CVT or DCTAM). Accordingly the incremental cost and
effectiveness for applying the technology may vary depending on the
path and the modifications made in the prior technology. To ensure
accurate tracking of net costs and effectiveness, the Volpe model
utilizes path correction factors, as discussed further in the decision
tree discussion below. This functionality is an improvement to the
final rule, and the specific factors used are stored in the technology
input sheets. A copy of the final rule input sheets, titled ``2011-
2015--LV--CAFE--FinalRuleInputSheets20081019.pdf,'' can be obtained
from the final rule docket.
    One additional concept to understand about how the Volpe model
functions is called an ``engineering constraint,'' a programmatic
method of controlling technology application that is independent of
those discussed above. NHTSA has determined that some technologies are
only suitable or

unsuitable when certain vehicle, engine, or transmission conditions
exist. For example, secondary axle disconnect is only suitable for 4WD
vehicles, and cylinder deactivation is unsuitable for any engine with
fewer than 6 cylinders, while material substitution is only available
for vehicles with curb weights greater than 5,000 pounds. Additionally,
in response to comments received, an engineering constraint was added
for purposes of the final rule to prevent the cylinder deactivation
technology from being applied to vehicles equipped with manual
transmissions, due primarily to driveability and NVH concerns
documented by the commenter. Where appropriate and required, NHTSA has
utilized engineering constraints to ensure accurate application of the
fuel saving technologies.
3. Technology Application Decision Trees
    Several changes were made to the Volpe model between the analysis
reported in the NPRM and the final rule. This section will discuss two
of those changes: First, the updates to the set of technologies; and
second, the updates to the logical sequence for progressing through
these technologies, which NHTSA describes as ``decision trees.''
    As discussed above, the set of technologies considered by the
agency has evolved since the NPRM. The set of technologies now included
in the Volpe model is shown below in Table IV-1, with abbreviations
used by the model to refer to each technology in the interest of
brevity. Section IV.D below explains each technology in much greater
detail, including definitions and cost and effectiveness values.

[GRAPHIC] [TIFF OMITTED] TR30MR09.015

    As in the NPRM, each technology is assigned to one of the five
following categories based on the system it affects or impacts: engine,
transmission, electrification/accessory, hybrid or vehicle. Each of
these categories has its own decision tree that the Volpe model uses to
apply technologies sequentially during the compliance analysis. The
decision trees were designed and configured to allow the Volpe model to
apply technologies in a cost-effective, logical order that also
considers ease of implementation. For example, effective software or
control logic changes are implemented before replacing a component or
system with a completely redesigned one, which is typically a much more
expensive option.
    Each technology within the decision trees has an incremental cost
and an incremental effectiveness estimate associated with it, and the
estimates are specific to a particular vehicle subclass (see the tables
provided below in Section IV.D). Each technology's

incremental estimate takes into account its position in the decision
tree path. If a technology is located further down the decision tree,
the estimates for the costs and effectiveness values attributed to that
technology are influenced by the incremental estimates of costs and
effectiveness values for prior technology applications. In essence,
this approach accounts for ``in-path'' effectiveness synergies and cost
effects that occur between the technologies in the same path. When
comparing cost and effectiveness estimates from various sources and
those provided by commenters, it is vital that the estimates are
evaluated in the proper context, especially as concerns their likely
position in the decision trees and other technologies that may be
present or missing. Not all estimates provided by commenters can be
considered an ``apples-to-apples'' comparison with those used by the
Volpe model, since in some cases the order of application, or included
technology content, is inconsistent with that assumed in the decision
tree.
    For the final rule, significant revisions have been made to the
sequence of technology applications within the decision trees, and in
some cases the paths themselves have been modified and additional paths
have been added. The additional paths allow for a more accurate
application of technology, insofar as the model now considers the
existing configuration of the vehicle when applying technology. In this
analysis, single overhead camshaft (SOHC), dual overhead camshaft
(DOHC) and overhead valve (OHV) configured engines now have separate
paths that allow for unique path-dependent versions of certain engine
technologies. Thus, the cylinder deactivation technology (DEAC) now
consists of three unique versions that depend on whether the engine
being evaluated is an SOHC, DOHC or OHV design; these technologies are
designated by the abbreviations DEACS, DEACD and DEACO, respectively,
to designate which engine path they are located on. Similarly the last
letter for the Coupled Cam Phasing (CCP) and Discrete Variable Valve
Lift (DVVL) abbreviations are used to identify which path the
technology is applicable to.
    Use of separate valvetrain paths and unique path-dependent
technology variations also ensures that the incremental cost and
effectiveness estimates properly account for technology effects so as
not to ``double-count.'' For example, in the SOHC path, the incremental
effectiveness estimate for DVVLS assumes that some pumping loss
reductions have already been accomplished by the preceding technology,
CCPS, which reduces or diminishes the effectiveness estimate for DVVLS
because part of the efficiency gain associated with the reduction of
the pumping loss mechanism has already occurred. Commenters pointed out
several instances in the NPRM where double-counting appeared to have
occurred, and the accounting approach used in the final rule resolves
these concerns.
    In reviewing NPRM comments, NHTSA noted several questions regarding
the retention of previously applied technologies when more advanced
technologies (i.e., those further down the decision tree) were applied.
In response, NHTSA has clarified the final rule discussions on this
issue. In both the NPRM and final rule, as appropriate and feasible,
previously-applied technologies are retained in combination with the
new technology being applied, but this is not always the case. For
instance, one exception to this would be the application of diesel
technology, where the entire engine is assumed to be replaced, so
gasoline engine technologies cannot carry over. This exception for
diesels, along with a few other technologies, is documented below in
the detailed discussion of changes to each decision tree and
corresponding technologies.
    As the Volpe model steps through the decision trees and applies
technologies, it accumulates total or ``NET'' cost and effectiveness
values. Net costs are accumulated using an additive approach while net
effectiveness estimates are accumulated multiplicatively. To help
readers better understand the accumulation process, and in response to
comments expressing confusion on this subject, the following examples
demonstrate how the Volpe model calculates net values.
    Accumulation of net cost is explained first as this is the simpler
process. This example uses the Electrification/Accessory decision tree
sequentially applying the EPS, IACC, MHEV, HVIA and ISG technologies to
a subcompact vehicle using the cost and effectiveness estimates from
its input sheet. As seen in Table IV-2 below, the input sheet cost
estimates have a lower and upper value which may be the same or a
different value (i.e., a single value or a range) as shown in columns
two and three. The Volpe model first averages the values (column 4),
and then sums the average values to calculate the net cost of applying
each technology (column 5). Accordingly, the net cost to apply the MHEV
technology for example would be ($112.50 + $192.00 + $372.00 =
$676.50). Net costs are calculated in a similar manner for all the
decision trees.
[GRAPHIC] [TIFF OMITTED] TR30MR09.016

    The same decision tree, technologies, and vehicle are used for the
example demonstrating the model's net effectiveness calculation. Table
IV-3 below shows average incremental effectiveness estimates in column
two; this value is calculated in the same manner as the cost estimates
above (average of lower and upper value taken from the input sheet). To
calculate the change in fuel consumption due to application of the EPS
technology with incremental effectiveness of 1.5 percent (or 0.015 in
decimal form, column 3), when applied multiplicatively, means that the
vehicle's current fuel consumption `X' would be reduced by a factor of
(1-0.015) = 0.985,\117\ or mathematically 0.985*X. To represent the
changed fuel consumption in the normal fashion (as a percentage
change), this value is subtracted from 1 (or 100%) to show the net
effectiveness in column 5.
---------------------------------------------------------------------------

    \117\ A decrease in fuel consumption (FC) means the fuel economy
(FE) will be increased since fuel consumption and economy are
related by the equation FC = 1/FE.
---------------------------------------------------------------------------

    As the IACC technology is applied, the vehicle's fuel consumption
is already reduced to 0.985 of its original value. Therefore the
reduction for an additional incremental 1.5 percent results in a new
fuel consumption value of 0.9702, or a net 2.98 percent effectiveness,
as shown in the table. Net effectiveness is calculated in a similar
manner for the all decision trees. It should be noted that all
incremental effectiveness estimates were derived with this
multiplicative approach in mind; calculating the net effectiveness
using an additive approach will yield a different and incorrect net
effectiveness.
[GRAPHIC] [TIFF OMITTED] TR30MR09.017

    To improve the accuracy of accumulating net cost and effectiveness
estimates for the final rule, ``path-dependent corrections'' were
employed. The NPRM analysis had the potential to either overestimate or
underestimate net cost and effectiveness depending on which decision
tree path the Volpe model followed when applying the technologies. For
example, if in the NPRM analysis a diesel technology was applied to a
vehicle that followed the OHV path, the net cost and effectiveness
could be different from the net estimates for a vehicle that followed
the OHC path even though the intention was to have the same net cost
and effectiveness. In order to correct this issue, the final rule
analysis has added path-dependent correction tables to the input
sheets. The model uses these tables to correct net cost and
effectiveness estimate differences that occur when multiple paths lead
into a single technology that is intended to have the same net cost and
effectiveness no matter which path was followed.\118\ Path-dependent
corrections were used when applying cylinder deactivation (on the DOHC
path), turbocharging and downsizing, diesel and strong hybrids. This is
essentially an accounting issue and the path-dependent corrections are
meant to remedy the accuracy issues reported in the NPRM comment
responses.
---------------------------------------------------------------------------

    \118\ The correction tables are used for path deviations within
the same decision tree. However, there is one exception to this
rule, specifically that the tables are used to keep the model from
double-counting cost and effectiveness estimates when both the CBRST
and MHEV are applied to the same vehicle. Both technologies try to
accomplish the same goal of reducing fuel consumption, by limiting
idle time, but through different means. If either of these
technologies exists on a vehicle and the Volpe model applies the
other, the correction tables are used to remove the cost and
effectiveness estimates for CBRST, thus ensuring that double-
counting does not occur.
---------------------------------------------------------------------------

    The following paragraphs explain, in greater detail, the revisions
to the decision trees and technologies from the NPRM to the final rule.
Revisions were made in response to comments received and pursuant to
NHTSA's analysis, and were made to improve the accuracy of the Volpe
compliance analysis, or to correct other concerns from the NPRM
analysis.
Engine Technology Decision Tree
    Figure IV-1 below shows the final rule decision tree for the engine
technology category. For the final rule, NHTSA removed camless valve
actuation (CVA), lean-burn GDI (LBDI), and homogenous charge
compression ignition (HCCI) from the decision trees because these
technologies were determined to be still in the research phase of
development. NHTSA did not receive any new information or comments that
suggested these technologies are under development, so NHTSA removed
them from the decision trees. At the top of the engine decision tree
Low Friction Lubricants (LUB) and Engine Friction Reduction (EFR)
technologies are retained as utilized in the NPRM.
    As stated above, SOHC, DOHC and OHV engines have separate paths,
whereas as the NPRM only made the distinction between OHC and OHV
engines. The separation of SOHC and DOHC engines allowed the model to
more accurately apply unique path-dependent valvetrain technologies
including variations of Variable Valve Timing (VVT), Variable Valve
Lift (VVL) and cylinder deactivation that are tailored to either SOHC
or DOHC engines. This separation also allowed for a more accurate
method of accounting for net cost and effectiveness

compared to the NPRM. For both the SOHC and DOHC paths, VVL
technologies were moved upstream of cylinder deactivation in response
to comments from the Alliance, additional confidential manufacturer
comments and submitted product plan trends, and NHTSA's analysis.
Confidential comments stated that applying cylinder deactivation to an
OHC engine is more complex and expensive than applying it to an OHV
engine. The Alliance additionally stated that cylinder deactivation is
very application-dependent, and is more effective when applied to
vehicles with high power-to-weight ratios. Taking in account the
application-specific nature of cylinder deactivation and the fact the
VVL technologies are more suitable to a broader range of applications,
NHTSA moved VVL technologies ``upstream'' of cylinder deactivation on
the SOHC and DOHC to more accurately represent how a manufacturer might
apply these technologies.
BILLING CODE 4910-59-P

[GRAPHIC] [TIFF OMITTED] TR30MR09.018

BILLING CODE 4910-59-C
    On the OHV path, the ordering of cylinder deactivation (DEACO) then
Coupled Cam Phasing (CCPO), which is opposite the order of the SOHC and
DOHC paths, was retained as defined in the NPRM. This ordering depicts
most accurately how manufacturers would actually implement these
technologies and was reflected in the submitted product plans for OHV
engines, which are largely used on trucks with high power-to-weight
ratios. After the application of CCPO on the OHV decision tree, the
model chooses between Discrete Variable Valve Lift (DVVLO) and the
conversion to a dual overhead camshaft engine (CDOHC). This conversion
now includes Dual Cam Phasing (DCP) instead of Continuously Variable
Valve Lift (CVVL) because it is assumed that DCP, with its higher
application rates, would more likely be

applied than CVVL, with its lower application rates.
    At this stage, and similar to the NPRM, the decision tree paths all
converge into Stoichiometric Gasoline Direct Injection (SGDI). All
previously applied technologies are retained with the assumption that
SGDI is applied in addition to the pre-existing engine technologies.
After SGDI, a newly defined technology, Combustion Restart (CBRST), has
been added.
    The ``branch point'' after CBRST has been limited to two paths
instead of the three paths in NPRM. This is due to the removal of HCCI
from the final rule decision trees. The final rule engine decision tree
allowed the model to apply either Turbocharging and Downsizing (TRBDS)
or the conversion to diesel (DSLC). TRBDS is considered to be a
completely new engine that has been converted to DOHC, if not already
converted, with only LUB, EFR, DCP, SGDI and CBRST applied.
    The conversion to diesel is also considered to be a completely new
engine that replaces the gasoline engine (although it carries over the
LUB and EFR technologies). If the model chooses to follow the TRBDS
path, the next technology that can be applied is another newly-added
technology, EGR Boost (EGRB). After EGRB, the model is allowed to then
convert the engine to diesel (DSLT). It should be noted that the path-
dependent variations of diesel, (DSLC) and (DSLT), result in the exact
same technology. The net cost and effectiveness estimates are the same
for both but DSLT's incremental cost and effectiveness estimates are
slightly lower to account for the TRBDS and EGRB technologies that have
already been applied.
Electrification/Accessory Technology Decision Tree
    This path, shown in Figure IV-2, was named simply ``Accessory
Technology'' in the NPRM. Electric Power Steering (EPS) is now the
first technology in this decision tree, since it is a primary enabler
for both mild and strong hybrids. Improved Accessories (IACC) has been
redefined to include only an intelligent cooling system and follows EPS
(in the NPRM, IACC was the first technology in the tree). The 42-volt
Electrical System (42V) technology has been removed because it is no
longer viewed as the voltage of choice by manufactures and is being
replaced by higher voltage systems. Micro-Hybrid (MHEV), which follows
IACC, has been added as a 12-volt stop/start system to replace
Integrated Starter/Generator with Idle-Off (ISGO), which was on the
``Transmission/Hybrid Technology'' decision tree in the NPRM. Higher
Voltage/Improved Alternator (HVIA), a higher efficiency alternator that
can incorporate higher voltages (greater than 42V) follows MHEV.
Integrated Starter Generator Hybrid (ISG) replaced IMA/ISAD/BSG Hybrid
(which was also on the Transmission/Hybrid Technology decision tree in
the NPRM) as a higher voltage hybrid system with limited regenerative
capability. ISG takes into account all the previously applied
Electrification/Accessory technologies and is the final step necessary
in order to convert the vehicle to a (full) strong hybrid. All
Electrification/Accessory technologies can be applied to both automatic
and manual transmission vehicles.
Transmission Technology Decision Tree
    This decision tree, shown in Figure IV-2, contains two paths: one
for automatic transmissions and one for manual transmissions. On the
automatic path, the Aggressive Shift Logic (ASL) and Early Torque
Converter Lockup (TORQ) technologies from the NPRM have been combined
into an Improved Auto Trans Controls/Externals (IATC) technology, as
both these technologies typically include only software or calibration-
related transmission modifications. This technology was moved to the
top of the decision tree since it was deemed to be easier and less
expensive to implement than a major redesign of the existing
transmission. The 5-Speed Automatic Transmission (5SP) technology from
the NPRM has been deleted due to several factors. First, the updated
decision tree logic seeks to optimize the current hardware as an
initial step, instead of applying an expensive redesign technology.
Second, NHTSA determined an industry trend of 4-speed automatics going
directly to 6-speed automatics, as reflected in the submitted product
plans. And finally, confidential manufacturer comments indicated that
in some cases 5-speed transmissions offered little or no fuel economy
improvement over 4-speed transmissions (primarily due to higher
internal mechanical and hydraulic losses, and increased rotating mass),
making the technology less attractive from a cost and effectiveness
perspective. In the final rule, both 4-speed and 5-speed automatic
transmissions get the IATC technology applied first, before progressing
through the rest of the transmission decision tree.
    After IATC the decision tree splits into a ``Unibody only'' and
``Unibody or Ladder Frame'' paths, which is identical to the NRPM
version of the decision tree. Both of these paths represent a
conversion to new and fully optimized designs. The Unibody only path
contains the Continuously Variable Transmission (CVT) technology, while
the Unibody or Ladder Frame path has the 6-Speed Automatic Transmission
(6SP) technology being replaced by 6/7/8-Speed Automatic Transmission
with Improved Internals (NAUTO). The NAUTO technology represents a new
generation of automatics with lower internal losses from gears and
hydraulic systems.
    The NPRM technology ``Automated Manual Transmission (AMT)'' has
been renamed Dual Clutch Transmission/Automated Manual Transmission
(DCTAM) to more accurately reflect the true intent of this technology
to be a Dual Clutch Transmission (DCT). The NPRM's use of the
abbreviation ``AMT'' was confusing to many commenters, including the
Alliance, BorgWarner, Chrysler, Ford and General Motors, and appeared
to indicate that the NPRM analysis applied true automated manual
transmissions, which exhibit a torque interrupt characteristic that
many in the industry feel will not be customer acceptable. DCT does not
have the torque interrupt concern. The technology DCTAM for the final
rule assumes the use of a DCT type transmission only.
    The manual transmission path only has one technology application,
like the NPRM. However, the technology being applied has been defined
as conversion to a 6-Speed Manual with Improved Internals (6MAN)
instead of a conversion to a 6/7/8-Speed Manual Transmission as defined
in the NRPM. Extremely limited use of manual transmissions with more
than 6 speeds is indicated in the updated product plans, so NHTSA
believes this is a more accurate option for replacing a 4 or 5-speed
manual transmission.
Hybrid Technology Decision Tree
    The strong hybrid options, 2-Mode (2MHEV) and Power Split (PSHEV),
are no longer sequential as defined in the NPRM's Transmission/Hybrid
decision tree. For the final rule, the model only applies strong hybrid
technologies when both the Electrification/Accessory and Transmission
(automatic transmissions only) technologies have been fully added to
the vehicle, as seen in Figure IV-2. The final rule analysis and logic
ensures that the model does not double-count the cost and effectiveness
estimates for previously applied technologies that are included (e.g.,
EPS) or replaced (e.g., transmission) by strong hybrid systems, which
is responsive to General Motors' comment

[[Continued on page 14245]]

]

[[pp. 14245-14294]] Average Fuel Economy Standards Passenger Cars and Light Trucks
Model Year 2011

[[Continued from page 14244]]

stating that the NPRM analysis had the potential to double-count
effectiveness estimates when applying strong hybrids. For the final
rule analysis, when the Volpe model applies strong hybrids it now takes
into account that some of the fuel consumption reductions have already
been accounted for when technologies like EPS or IACC have been
previously applied. Once all the Electrification/Accessory and
Transmission technologies have been applied, the model is allowed to
choose between the application of 2MHEV, PSHEV and the newly added
Plug-in Hybrid Vehicle (PHEV). The NPRM decision tree required the
Volpe model to step through 2MHEV in order to apply PSHEV. This updated
final rule decision tree is a more realistic representation of how
manufacturers might apply strong hybrids, and allows the Volpe model to
choose the strong hybrid that is most appropriate for each vehicle
based on its vehicle subclass or the most cost-effective technology
application. The PHEV technology was added to the decision tree in the
final rule based upon information in the public domain and submitted
product plans showing that limited quantities of these vehicles will be
available from some manufacturers in this timeframe.
[GRAPHIC] [TIFF OMITTED] TR30MR09.019

Vehicle Technology Decision Tree
    Material Substitution (MS1), (MS2), and (MS5) are now located on
dedicated material substitution path in the Vehicle Technology Decision
Tree, shown in Figure IV-3. Low Rolling Resistance Tires (ROLL), Low
Drag Brakes (LDB) and Secondary Axle Disconnect (SAX) now reside as a
separate path, due to the relocation of material substitution
technologies. Secondary Axle Disconnect has been redefined for the
final rule to apply to 4WD vehicles only to more accurately reflect
feasible applications of this technology. Aerodynamic Drag Reduction
(AERO) remains a separate tree, and is now a 10 percent reduction for
both car and truck classes (excluding performance cars, which are
exempt).

[GRAPHIC] [TIFF OMITTED] TR30MR09.020

4. Division of Vehicles Into Subclasses Based on Technology
Applicability, Cost and Effectiveness
    In assessing the feasibility of technologies under consideration,
the agency evaluated whether each of these technologies could be
implemented on all types and sizes of vehicles and whether some
differentiation is necessary with respect to the potential to apply
certain technologies to certain types and sizes of vehicles, and with
respect to the cost incurred and fuel consumption achieved when doing
so. The 2002 NAS Report differentiated technology application using ten
vehicle classes (4 cars classes and 6 truck classes, including
subcompact cars, compact cars, midsize cars, large cars, small SUVs,
midsize SUVs, large SUVs, small pickups, large pickups, and minivans),
but did not determine how cost and effectiveness values differ from
``class'' to ``class.'' NAS's purpose in separating vehicles into these
``classes'' was to create groups of ``like'' vehicles, i.e., vehicles
similar in size, powertrain configuration, weight, and consumer use,
and for which similar technologies are applicable. This vehicle
differentiation is done solely for the purpose of applying technologies
to vehicles and assessing their incremental costs and effectiveness,
and should not be confused with, the regulatory classifications
pursuant to 49 CFR part 523 discussed in Chapter XI.
    The Volpe model, which NHTSA has used to perform analysis
supporting today's notice, divides the vehicle fleet into subclasses
based on model inputs, and applies subclass-specific estimates, also
from model inputs, of the applicability, cost, and effectiveness of
each fuel-saving technology. Therefore, the model's estimates of the
cost to improve the fuel economy of each vehicle model depend upon the
subclass to which the vehicle model is assigned.
    In its MY 2005-2007 and MY 2008-2011 light truck CAFE standards as
well as NPRM, NHTSA performed analysis using the same vehicle classes
defined by NAS in its 2002 Report. In its 2008 NPRM for MY 2011-2015,
NHTSA included some differentiation in cost and effectiveness numbers
between the various classes to account for differences in technology
costs and effectiveness that are observed when technologies are applied
on to different classes and subclasses of vehicles. The agency found it
important to make that differentiation because the agency estimated
that, for example, engine turbocharging and downsizing would have
different implications for large vehicles than for smaller vehicles.
For the final rule, NHTSA, working with Ricardo, increased the accuracy
of its technology assumptions by reexaming the subclasses developed for
the purpose of modeling technology application and by providing more
differentiation in the costs and effectiveness values by vehicle
subclass.
    In the request for comments accompanying the NPRM, NHTSA asked
manufacturers to identify the style of each vehicles model they submit
in their product plans from eight possible groupings (convertible,
coupe, hatchback, pickup, sedan, sport utility, van, or wagon) or
sixteen possible market segments (cargo van, compact car, large car,
large pickup, large station wagon, midsize car, midsize station wagon,
mini-compact, minivan, passenger van, small pickup, small station
wagon, special purpose, sport utility truck, subcompact car, and two-
seat car). NHTSA also requested that manufacturers identify many
specific characteristics relevant to each vehicle model, such as the
number of cylinders of the vehicle's engine and other engine,
transmission and vehicle characteristics. This information was
evaluated by NHTSA staff, entered in NHTSA's market data file, and used
by NHTSA to assess how to divide the vehicles into subclasses for
purposes of differentiating the applicability, effectiveness, and cost
of available technologies.
    In response to the NPRM, the Alliance commented that NHTSA's
classification approach is not robust enough. With regard to subclasses
of cars, the Alliance stated that NHTSA did not distinguish high-
performance and sports cars which cannot accommodate certain
technologies without changing the purpose and configuration of the
vehicle. With regard to subclasses of trucks, the Alliance argued that
SUVs were not adequately distinguished by size. The Alliance further
stated the classification used by Sierra Research in

its report to distinguish groups of like vehicles for technology
application purposes was more realistic and representative of
differences in market segments than NHTSA's classification. The
Alliance suggested that NHTSA consider the classes identified by Sierra
Research in the final rule.
    NHTSA is not adopting Sierra's approach to classification for the
following reasons. First, Sierra's classification scheme is too
dependent on vehicle characteristics for which NHTSA often did not
receive complete information from manufacturers. For example, although
NHTSA requested that manufacturers provide estimates of the aerodynamic
drag coefficient of each vehicle model planned for MY2011-2015, the
agency received no estimates for many vehicles. NHTSA believes
manufacturers are too far from production on many vehicles to
confidently provide such estimates. Second, Sierra's classification
scheme is, for NHTSA's purposes, excessively fine-grained. Sierra's
analysis relied on 25 subclasses in total, 13 for cars and 12 for
trucks. While their report provided tables comparing their classes to
those of NHTSA's and cited product examples for each class, it did not
provide a reason for why this detailed differentiation would
significantly improve the outcome. NHTSA's review of the Sierra report
did not reveal many differences in technology-application between these
subclasses. In addition, the agency does not believe that the effort
required by the agency to create a more detailed yet more complex
modeling structure based on 25 subclasses would result in significant
improvement in the accuracy of the results. Sierra may have found this
additional differentiation important for the full vehicle simulation
approach that the Alliance claimed should be used throughout NHTSA's
analysis. However, as discussed below, NHTSA has concluded that this
approach is neither necessary nor practical for CAFE analysis.
    The agency agrees with the Alliance, however, that some refinement
in the classification approach used by NHTSA in the NPRM is merited in
order to ensure the practicability of technologies being added. The
agency also believes that the limited differentiation in costs and
effectiveness values by vehicle class needs to be expanded in order to
better account for fuel savings and costs.
    For the final rule, NHTSA first reexamined the Volpe model
technology output files from the NPRM to identify where and why
technologies may have been inappropriately applied by the model. Where
this reexamination revealed logical errors, the Volpe model was revised
accordingly. However, the review revealed that most of the observed
inaccuracies resulted from the manner in which vehicles were assigned
to subclasses for the purpose of technology applications. NHTSA also
reviewed the confidential vehicle level information received from
manufacturers, how manufacturers classified their vehicles by style or
market segment groupings requested by NHTSA and the specific engine,
transmission and other vehicle characteristics identified by the
manufacturers for each vehicle model. This conclusion was among those
that led NHTSA to assign more staff to perform quality control when
reviewing and integrating manufacturers' product plans.
    In order to improve the accuracy of technology application
modeling, NHTSA examined at the car and truck segments separately.
First, for the car segment, NHTSA plotted the footprint distribution of
vehicles in the product plans and divided that distribution into four
equivalent footprint range segments. The footprint ranges were named
Subcompact, Compact, Midsize, and Large classes in ascending order.
Cars were then assigned to one of these classes based on their specific
footprint size. Vehicles in each range were then manually reviewed by
NHTSA staff to evaluate and confirm that they represented a fairly
reasonable homogeneity of size, weight, powertrains, consumer use, etc.
However, as the Alliance pointed out, some vehicles in each group were
sports or high-performance models. Since different technologies and
cost and effectiveness estimates are appropriate for these vehicles,
NHTSA created a performance subclass within each car class to maximize
the accuracy of technology application. To determine which cars would
be assigned to the performance subclasses, NHTSA graphed (in ascending
rank order) the power-to-weight ratio for each vehicle in a class. An
example of the Compact subclass plot is shown below. The subpopulation
was then manually reviewed by NHTSA staff to determine an appropriate
transition point between ``performance'' and ``non-performance'' models
within each class.
[GRAPHIC] [TIFF OMITTED] TR30MR09.021

    A total of eight classes (including performance subclasses) were
identified for the car segment: Subcompact, Subcompact Performance,
Compact, Compact Performance, Midsize, Midsize Performance, Large,
Large Performance. In total, the number of cars that were ultimately
assigned to a performance subclass was less than 10 percent. The table
below shows the difference in the classification between the NPRM and
Final Rule and provides examples of the types of vehicles assigned to
each.
[GRAPHIC] [TIFF OMITTED] TR30MR09.074

[GRAPHIC] [TIFF OMITTED] TR30MR09.022

    For light trucks, in reviewing the updated manufacturer product
plans and in reconsidering how to divide trucks into classes and
subclasses based on technology applicability, NHTSA found less of a
distinction between SUVs and pickup trucks than appeared to exist in
earlier rulemakings. Manufacturers appear to be planning fewer ladder-
frame and more unibody pickups, and many pickups will share common
powertrains with SUVs. Consequently, NHTSA condensed the classes
available to trucks, such that SUVs and pickups are no longer divided.
Recognizing structural differences between various types of ``Vans,''
NHTSA revisited how it assigned the different types of ``Vans.''
Instead of merging minivans, cargo vans, utility and multi-passenger
type vans under the same class, as it did for the NPRM and in previous
rules, NHTSA formed a separate minivan class, because minivans (e.g.,
the Honda Odyssey) are expected to remain closer in terms of structural
and other engineering characteristics than vans (e.g., Ford's E-
Series--also known as Econoline--vans) intended for more passengers
and/or heavier cargo.
    The remaining vehicles (other vans, pickups, and SUVs) were then
segregated into three footprint ranges and assigned a class of Small
Truck/SUV, Midsize Truck/SUV, and Large Truck/SUV based on their
footprints. NHTSA staff then manually reviewed each population for
inconsistent vehicles based on engine cylinder count, weight (curb and/
or gross), or intended usage, since these are important considerations
for technology application, and reassigned vehicles to classes as
appropriate. This system produced four truck segment classes--minivans
and small, medium, and large SUVs/Pickups/Vans. The table below shows
the difference in the classification between the NPRM and Final Rule.

[GRAPHIC] [TIFF OMITTED] TR30MR09.023

    Based on a close review of detailed output from the Volpe model,
NHTSA has concluded that its revised classification for purposes of
technology applicability substantially improves the overall accuracy of
the results as compared to the system employed in the NPRM. The new
method uses footprint as a first indicator for both the car and truck
segments, and all are then manually reviewed for the types of
technologies applicable to them and revised by NHTSA to ensure that
they have been properly assigned. The addition of the performance
subclasses in the car segment and the condensing of classes in the
truck segment further refine the system. The new method increases the
accuracy of technology application without overly complicating the
Volpe modeling process, and the revisions address comments received in
response to the NPRM.
5. How did NHTSA develop technology cost and effectiveness estimates
for the final rule?
    In the NPRM, NHTSA employed technology cost and effectiveness
estimates developed in consultation with EPA. They represented NHTSA
and EPA staff's best assessment of the costs for each technology
considered based on the available public and confidential information
and data sources that the agencies had back in 2007 when the rulemaking
was initiated. EPA also published a report and submitted it to the NRC
committee on fuel economy of light-duty vehicles.\119\
---------------------------------------------------------------------------

    \119\ EPA Staff Technical Report: Cost and Effectiveness
Estimates of Technologies Used to Reduce Light-Duty Vehicle Carbon
Dioxide Emissions. EPA420-R-08-008, March 2008.
---------------------------------------------------------------------------

    Public comments on the NPRM's technology cost estimates generally
fell into four categories: (1) That costs are underestimated because
NHTSA did not account for all changes/costs required to apply a
technology or because although NHTSA correctly identified all the
changes required, it did not cost those changes appropriately; (2) that
costs are underestimated because the Retail Price Equivalent (RPE)
factors have been applied incorrectly to technologies; (3) that costs
are either over- or underestimated because learning curves have been
applied incorrectly to technologies; and (4) that cost assumptions are
overly simplified as applied to the full range of fleet vehicles and do
not properly account for the differences in cost impacts across vehicle
and engine types (e.g., technologies applied to a sub-compact car will
be unique to those same technologies applied to a large SUV). Many
commenters also stated that they found it difficult to understand how
NHTSA and EPA had derived the cost estimates. In addition to commenting
on NHTSA's methodology, many commenters, particularly manufacturers,
also submitted their own cost estimates for each technology and
requested that NHTSA consider them for the final rule.
    As explained above, NHTSA contracted with Ricardo to aid the agency
in analyzing the comments on the technology assumptions used in the
NPRM, and relied considerably on Ricardo's expertise in developing the
final technology cost and effectiveness estimates based on that
analysis. For every technology included in NHTSA's analysis of
technology costs and effectiveness, Ricardo and NHTSA engineers
reviewed the comments thoroughly and exercised their expertise in
assessing the merits of the comments, and in resolving the differences
and determining which estimates should be used for the final rule.
    For each technology, NHTSA relied on Ricardo's experience with
``bill of materials'' (BOM) costing. Some commenters criticized NHTSA
for not using a BOM as the basis for its cost analysis. The 2008 Martec
report,\120\ which updated the Martec report on which the 2004 NESCCAF
study was based, was submitted by auto industry commenters to NHTSA's
NPRM docket for the agency's consideration. This report provides cost
estimates developed on a ``bill of materials'' basis and methodology.
NHTSA, with Ricardo's assistance, reviewed the ``bill of materials''
methodology in the Martec report and found it to be, compared to the
methodology used in the NPRM, a more defensible and transparent basis
for evaluating the costs of applicable technologies.
---------------------------------------------------------------------------

    \120\ Martec, ``Variable Costs of Fuel Economy Technologies,''
June 1, 2008.
---------------------------------------------------------------------------

    A bill of materials in a general sense is a list of components that
make up a system--in this case, an item of fuel economy-improving
technology. In

order to determine what a system costs, one of the first steps is to
determine its components and what they cost. In cases in which it was
not practicable for the agency and Ricardo to estimate the cost of each
component on a BOM basis because there was a shift to a more advanced
technology and or because of difficulty in accounting for the sum of
costs of all added components less the sum of costs of all deleted
components (e.g., in the transition from a gas engine to a diesel
engine), incremental costs were estimated to be those of the entire new
technology platform (in this example, the diesel engine) less those of
the entire old technology platform (in this example, the gas engine).
This ``net difference'' process was only used where developing a
ground-up description of all component changes necessitated by the
incremental technology was deemed to be impracticable.
    With that framework in mind, Ricardo and NHTSA engineers proceeded
with reviewing cost information for each major component of each
technology. They compared the multiple sources available in the docket
and assessed their validity. While NHTSA and Ricardo engineers relied
considerably on the 2008 Martec Report for costing contents of some
technologies, they did not do so for all. When relevant publicly
available information and data sets, including the 2008 Martec report,
were determined to be incomplete or non-existent, NHTSA looked to prior
published data, including the NPRM, or to values provided to NHTSA by
commenters familiar with the material costs of the described
technologies.
    Generally, whenever cost information for a technology component
existed in a non-confidential and publicly available report submitted
to the NPRM docket and that information agreed with Ricardo's
independent review of cost estimates based on Ricardo's historical
institutional knowledge, Ricardo and NHTSA cited that information.
Ricardo and NHTSA were able to take that approach frequently, as is
evident in the explanation of the cost figures of each technology. When
that approach was not possible, but there was confidential manufacturer
data that had been submitted to NHTSA in response to the NPRM, and
those costs were consistent with Ricardo's independently-reviewed cost
estimates, NHTSA and Ricardo cited those data. When multiple
confidential data sources differed greatly and conflicted with the
Martec valuation or when the technical assumptions described by NHTSA
for purposes of this rulemaking did not match exactly with the content
costed by either Martec or other commenters, NHTSA and Ricardo
engineers used component-level data to build up a partial cost,
substituting Ricardo's institutional knowledge for the remaining gaps
in component level data.
    Occasionally, NHTSA and Ricardo found that some cost information
submitted by the public was either not very clearly described or
revealed a lack of knowledge on the part of the commenter about NHTSA's
methodology. In those cases, and in cases for which no cost data
(either public or confidential) was available, NHTSA worked with
Ricardo either to confirm the estimates it used in the NPRM, or to
revise and update them.
    In several cases, values described in the NPRM were simply adjusted
from 2006 dollars to 2007 dollars, using a ratio of GDP values for the
associated calendar years.\121\ In many instances, an RPE factor of 1.5
was determined to have been omitted from the cost estimates provided in
the NPRM, so NHTSA applied the multiplier where necessary to calculate
the price to the consumer.
---------------------------------------------------------------------------

    \121\ NHTSA examined the use of the CPI multiplier instead of
GDP for adjusting these dollar values, but found the difference to
be exceedingly small--only $0.14 over $100.
---------------------------------------------------------------------------

    Finally, in response to comments stating that cost estimates for
individual technologies should be varied, based on the type and size of
vehicle to which they are applied, NHTSA worked with Ricardo to account
for that. Additionally, application of some technologies might be more
or less expensive, depending on content (e.g., with or without a noise
attenuation package), for particular vehicles. In these cases, NHTSA
and Ricardo described a range of costs for this technology, and
referred to sources that indicate the appropriate boundaries of that
range.
    The agency notes that several technologies considered in the final
rule have been updated with substantially different cost estimates
relative to those costs described in the NPRM. For example, RPE
estimates for turbocharging and downsizing (TRBDS), diesel technologies
(DSLT) and hybrid technologies (like ISG) are much higher than the
costs cited in the NPRM for those technologies. This is due in large
part to the updated cost estimates of the 2008 Martec Report and
others, referenced in the final rule, which reflect the dramatic rise
of global costs for raw materials associated with the above
technologies since the 2004 Martec report and other prior referenced
cost estimates were conducted. The NPRM costs were not updated to
reflect that rise in commodities prices. As described in the 2008
Martec Report, advanced battery technologies with substantial copper,
nickel or lithium content, and engine technologies employing high
temperature steels or catalysts with considerable platinum group metals
usage, have experienced tremendous inflation of raw material prices
since the cost studies referenced in the NPRM were conducted. As of the
time the sources were developed, prices of nickel, platinum, lithium,
copper, dysprosium and rhodium had demonstrated cost inflation
amounting to between 300 and 750 percent of global prices at the time
of the original NESCCAF study \122\ and this is reflected in the higher
costs described in the 2008 Martec report, and thus in the final rule.
NHTSA is aware that commodity prices, like those for steel and platinum
group metals described above, have dropped over the last several
months. However, there is little information in the record to determine
how prices of components used in MY 2011 could be impacted by the
prices of metals and other commodities over the last few years. It is
not clear whether the prices of components built and used in MY 2011
are more likely to reflect the high price of commodities in the years
prior to 2008, the current low prices of commodities, the prices of
commodities closer to MY 2011, or some mixture of these. The agency
notes, though, as mentioned above, that manufacturers' product plans
were submitted along with manufacturers' indications that these plans
were generally informed by expectations that relatively high commodity
prices would prevail in the future. Therefore, in the expectation that
economic conditions will improve by MY 2011, the agency relies on the
commodity prices reflected in, for example, the 2008 Martec report.
However, the agency further notes that these decisions are limited to
the MY 2011 rulemaking. We intend to monitor commodity prices carefully
and will adjust affected technology costs as appropriate in future
rulemakings.
---------------------------------------------------------------------------

    \122\ 2008 Martec report, at 13-20.
---------------------------------------------------------------------------

    Some commenters referenced the price differential between vehicles
with advanced technologies and more standard versions as evidence of
those advanced technologies' costs, and argued that NHTSA should
consider these price differentials in its cost estimation process. In
response, NHTSA believes that the ``bottom-up, material cost based''
cost estimation methodology employed for the final rule is preferable
to estimating costs based

on manufacturer price differentials between versions of vehicle models.
Wherever possible, technologies were costed based on the estimation of
variable material cost impacts to vehicle manufacturers at a fixed
point in time (in 2007 dollar terms) for a prescribed set of component
changes anticipated to be required in implementing the technology on a
particular platform (e.g., wastegate turbo, increased high nickel
alloyed exhaust manifolds, air charge cooler, etc. for TRBDS). The
content assumptions are modified or scaled to account for differences
across the range of vehicle sizes and functional requirements and
associated material cost impacts are adjusted to account for the
revised content. The material cost impacts to the vehicle manufacturers
are then summed and converted to retail price equivalent impacts by
multiplying by 1.5 to account for fixed costs and other overheads
incurred in the implementation of new vehicle technologies but not
contained in the variable material price impacts to the manufacturers.
    In employing this methodology, NHTSA relied on information provided
to NHTSA by the suppliers and vehicle manufacturers themselves. Though
this estimation process relies on often confidential data and employs a
simplifying assumption in relating all variable material costs to
retail impacts through the use of a consistent 1.5 RPE, the methodology
is preferable to a ``top-down, retail price based'' methodology as
might be used by comparing retail price differences of vehicles with
different technologies. The ``bottom-up'' approach offers the benefits
of providing a consistent and reasonable assessment of true, total
costs for all technologies independent of geographic, or strategic
pricing policies by vehicle manufacturers that could result in selling
products at sub-standard or even negative margins. For many vehicle
manufacturers, contribution to corporate profit varies dramatically
across vehicle segment. Given that vehicle pricing is often decoupled
from true costs and will vary with sales cycle, product maturity,
geography, vehicle class, and marque, a ``top-down'' approach, while
offering improved data transparency, is inherently limited in providing
a consistent means of cost estimation. As such, NHTSA has adopted the
described ``bottom-up'' cost estimation approach and has attempted to
mitigate transparency issues with a reliance on Martec 2008 (where in
agreement with other provided cost data), because it provides a
detailed description of the costed content. Fundamentally, NHTSA
believes that a ``bottom-up'' cost estimation methodology with a common
RPE adjustment factor offers an intuitive, consistent process across
all technologies, whether mature or otherwise, that avoids the pitfalls
of reliance on significantly more variable and volatile pricing
policies.
    Regarding estimates for technology effectiveness, NHTSA, working
with Ricardo, also reexamined its NPRM estimates and those in the EPA
Staff Technical Report,\123\ which largely mirrored NHTSA's NPRM
estimates. We compared these estimates to estimates provided in
comments, reports and confidential data received in response to our
NPRM. Comments on the NPRM's effectiveness estimates generally fell
into three categories: (1) That NHTSA did not account sufficiently for
fuel economy or performance impacts because it used the Volpe model
approach rather than full vehicle simulation; (2) that the synergy
values used did not properly account for technology interactions; and
(3) that NHTSA made errors when using estimates provided by
manufacturers. In addition to commenting on NHTSA's methodology, many
commenters, particularly manufacturers, also submitted their own fuel
consumption reduction estimates for each technology and requested that
NHTSA consider them for the final rule. NHTSA addresses comments
relating to vehicle simulation in Section IV.C.8 and synergies in
Section IV.C.7, but the section below describes NHTSA's process for
developing effectiveness estimates for the final rule, which addresses
the comments regarding NHTSA's use of estimates submitted by
manufacturers.
---------------------------------------------------------------------------

    \123\ EPA Staff Technical Report: Cost and Effectiveness
Estimates of Technologies Used to Reduce Light-Duty Vehicle Carbon
Dioxide Emissions. EPA420-R-08-008, March 2008.
---------------------------------------------------------------------------

    For each technology, NHTSA also relied on Ricardo's experience with
``bill of materials'' (BOM) technology descriptions. Some commenters
argued that the same BOM used as the basis for the cost analysis could
and should be used to define the technologies being studied for
effectiveness. In fact, Ricardo's methodology for cost and
effectiveness estimates for this rule was to define a vehicle class-
specific BOM or BOMs, depending upon the number of variants possible
within a class and within a decision tree. These BOMs were defined for
the baseline configuration for each class and then for each incremental
step in the decision tree. Use of a consistently-defined BOM is very
important to estimating the impacts of technologies accurately, as it
helps to ensure that technologies are not applied to baseline vehicles
that already contain the technology (with the exception of items that
are not well-defined such as aerodynamic drag reduction, reduced
rolling resistance tires, weight reduction, and engine friction
reduction.)
    In defining these BOMs, Ricardo relied on its experience working
with industry over many years and its recent experience preparing the
December 2007 study for EPA. Ricardo built on its vehicle simulation
work for EPA to help NHTSA evaluate appropriate effectiveness values
for individual fuel-saving technologies. In considering the comments,
NHTSA and Ricardo evaluated the 10 ``vehicle subclasses'' used in the
NPRM for applicability of technologies and determined that the cost and
effectiveness estimates could be more accurate by revising the
``vehicle subclasses'' as described above so that they better
represented the parameters of the vehicles they included. This, in
turn, enabled NHTSA and Ricardo to distinguish more clearly the
differences in fuel consumption reduction occurring when a technology
is added to different vehicles.
    Then, with the BOM framework applied to more precisely-defined
vehicle subclasses, NHTSA and Ricardo engineers reviewed effectiveness
information from multiple sources for each technology. Together, they
compared the multiple sources available in the docket and assessed
their validity, taking care to ensure that common BOM definitions and
other vehicle attributes such as performance, refinement, and
drivability were not compromised.
    Generally, whenever relevant effectiveness information for a
technology component existed in a non-confidential and publicly-
available report submitted to the NPRM docket, and that information
agreed with Ricardo's independent review of estimates based on
Ricardo's historical institutional knowledge, NHTSA and Ricardo cited
that information. NHTSA and Ricardo were able to take that approach
frequently, as is evident in the explanation of the effectiveness for
each technology. When that approach was not possible, but there was
confidential manufacturer data that had been submitted to NHTSA in
response to the NPRM, and those values were consistent with Ricardo's
independently-reviewed estimates, NHTSA and Ricardo cited those data.
When multiple confidential data sources differed greatly or when the
technical assumptions described by NHTSA for purposes of this
rulemaking

did not match the content included in Ricardo's study for EPA or in
other comments, NHTSA and Ricardo engineers relied on Ricardo's
experience and an understanding of the maximum theoretical losses that
could be eliminated by particular technologies to build up an
effectiveness estimate, substituting Ricardo's institutional knowledge
for the remaining gaps in data.
    Occasionally, NHTSA and Ricardo found that some fuel consumption
reduction information submitted by the public was either not very
clearly described or revealed a lack of knowledge on the part of the
commenter about NHTSA's methodology. In those cases, and in cases for
which no effectiveness data (either public or confidential) was
available, NHTSA worked with Ricardo either to confirm the estimates it
used in the NPRM, or to revise and enhance them. In other cases, the
commenters appeared unsure how to evaluate the data from the NPRM, and
so NHTSA and Ricardo provided more detailed explanations on the process
used or the components involved.
    In response to comments stating that estimates for individual
technologies should be varied based on the type and size of vehicle to
which they are applied, NHTSA worked with Ricardo to account for those
differences mostly through the refined vehicle subclass definitions.
However, even after making these adjustments, there are still some
classes that require spanning different engine architectures and
performance thresholds. Just as the application of some technologies
might be more or less expensive, depending on content (e.g., with or
without a noise attenuation package), particular vehicle technologies
may have more or less impact between classes where maintaining
equivalent performance led to a reduced effectiveness. In these cases,
NHTSA and Ricardo described a range of effectiveness values for this
technology, and referred to sources that indicate the appropriate
boundaries of that range.
    With Ricardo's assistance, the technology cost and effectiveness
estimates for the final rule were developed consistently, using this
systematic approach. While NHTSA still believes that the ideal
estimates for the final rule would be those that have been through a
peer-reviewed process such as that used for the 2002 NAS Report, and
will continue to work with NAS, as required by EISA, to update the
technology cost and effectiveness estimates for subsequent CAFE
rulemakings, this approach, combined with the BOM methodology for cost
and effectiveness, expanded number and types of vehicle subclasses and
the changes to the synergistic effects described below, not only help
to address the concerns raised by commenters, but also represent a
considerable improvement in terms of accuracy and transparency over the
approach used to develop the cost and effectiveness estimates in the
NPRM.
6. Learning Curves
    As explained in the NPRM, historically NHTSA did not explicitly
account for the cost reductions a manufacturer might realize through
learning achieved from experience in actually applying a technology.
However, based on its work with EPA, in the NPRM NHTSA employed a
learning factor for certain newer, emerging technologies. The
``learning curve'' describes the reduction in unit incremental
production costs as a function of accumulated production volume and
small redesigns that reduce costs. The NPRM implemented technology
learning curves by using three parameters: (1) The initial production
volume that must be reached before cost reductions begin to be realized
(referred to as ``threshold volume''); (2) the percent reduction in
average unit cost that results from each successive doubling of
cumulative production volume (usually referred to as the ``learning
rate''); and (3) the initial cost of the technology. The majority of
technologies considered in the NPRM did not have learning cost
reductions applied to them.
    NHTSA assumed that learning-based reductions in technology costs
occur at the point that a manufacturer applies the given technology to
the first 25,000 cars or trucks, and are repeated a second time as it
produces another 25,000 cars or trucks for the second learning
step.\124\ NHTSA explained that the volumes chosen represented the
agency's best estimate for where learning would occur, and that they
were better suited to NHTSA's analysis than using a single number for
the learning curve factor, because each manufacturer would implement
technologies at its own pace in the rule, rather than assuming that all
manufacturers implement identical technology at the same time.
---------------------------------------------------------------------------

    \124\ NHTSA treated car and truck volumes separately for
determining those sales volumes.
---------------------------------------------------------------------------

    NHTSA further assumed that after having produced 25,000 cars or
trucks with a specific part or system, sufficient learning will have
taken place such that costs will be lower by 20 percent for some
technologies and 10 percent for others. For those technologies, NHTSA
additionally assumed that another cost reduction would be realized
after another 25,000 units. If a technology was already in widespread
use (e.g., on the order of several million units per year) or expected
to be so by the MY 2011-2012 time frame, NHTSA assumed that the
technology was ``learned out,'' and that no more cost reductions were
available for additional volume increases. If a technology was not
estimated to be available until later in the rulemaking period at that
time, like MY 2014-2015, NHTSA did not apply learning for those
technologies until those model years. Most of the technologies for
which learning was applied after MY 2014 were adopted from the 2004
NESCCAF study, which was completed by Martec. Whenever source data,
like the 2004 NESCCAF study, indicated that manufacturer cost reduction
from future learning would occur, NHTSA took that information into
account.
    Comments received regarding NHTSA's approach to technology cost
reductions due to manufacturer learning generally disagreed with the
agency's method. The Alliance, AIAM, Honda, GM, and Chrysler all
commented that NHTSA had substantially overestimated, and essentially
``double-counted,'' learning effects by applying learning reductions to
component costs, specifically Martec estimates, which were already at
high volume. The Alliance submitted the 2008 Martec Report, which
stated that NHTSA had ``misstated'' Martec's approach to cost
reductions due to learning in the NPRM. As Martec explained,

    Martec did not ask suppliers to quote prices that would be valid
for three years, and Martec did not receive cost reductions from
suppliers for some components in years two and three. Rather,
industry respondents were asked to establish mature component
pricing on a forward basis given the following conditions: At least
three (3) manufacturers demanding 500,000 units per year and at
least three (3) globally-capable suppliers available to supply the
needs of each manufacturer.
    In no case did Martec ask industry respondents to provide low
volume, launch or transition costs for fuel consumption/
CO2 reducing technologies. Martec specifically designed
the economic parameters in order to capture the effects of learning
which is a reality in the low margin, high capital cost, high
volume, highly competitive global automotive industry. Applying
additional reductions attributable to ``learning'' based on 25,000
unit improvements in cumulative volume after production launch (as
described on pages 118-125 of the NHTSA NPRM) on top of Martec's
mature costs is an error. Martec's costs are based on 1.5-2.0
equivalent modules of powertrain capacity (500,000 units/year) so
25,000 unit

incremental changes in cumulative production, as defined by NHTSA,
will have no effect on costs.

    The 2008 Martec Report also stated that current industry practice
consists of using competitive bidding based on long-term, high-volume
contracts that are negotiated before technology implementation
decisions are made. Martec stated that this practice considers the
effects of volume, learning, and capital depreciation. Martec also
indicated that most of the technologies evaluated in the study are in
high volume production in the global automotive industry today, and
thus this forms a solid basis from which to estimate future costs.
    Honda also commented on NHTSA's 25,000 unit (per manufacturer per
year) volume threshold stating that, in their experience, costs were
only likely to decrease due to learning at volumes exceeding about
300,000 units per year per manufacturer. GM agreed, stating that
suppliers do not respond to, change processes, or change contract terms
for relatively small volume changes like NHTSA's 25,000 unit increment,
thus volume changes of this magnitude have no effect on component
pricing. GM also commented that its learning cycles are based on time,
not volume, and agreed with Martec's assessment that contracts with
suppliers typically specify volumes and costs over a period, which are
usually equal to a product life cycle, a 4- to 5-year period.
    Ford commented that base costs in the automotive industry are
determined by a target setting process, where manufacturers develop
pricing with suppliers for a set period, and manufacturers receive cost
reductions from the suppliers due to learning as time passes,
apparently at a set amount year over year for several years. Ford also
commented that NHTSA's approach to learning curves had not accounted
for current economic factors, like increases in commodity and energy
prices, and cited the example of costs of batteries for hybrids and
PHEVs which Ford stated ``are not likely to depend solely on experience
learned, but, to a large extent, on the additional energy and material
costs they incur relative to the vehicles without the new technology.''
Ford commented that NHTSA should account for these costs, and the
factor of declining vehicle sales, in its learning curve approach.
    BorgWarner, a components supplier, commented that learning-related
costs savings are valid for technologies that ``start at low volume''
(commenter's emphasis). BorgWarner argued, however, that NHTSA's
assumed learning curve would not apply to the technologies it supplies
to manufacturers,\125\ since these components are well-developed and in
high volume use already, and are thus already ``learned out.''
BorgWarner further commented that an increase in demand could in fact
lead to higher prices if demand for raw materials exceeded supply.
---------------------------------------------------------------------------

    \125\ BorgWarner manufacturers and supplies turbochargers, dual
clutch transmissions, variable valve timing systems, diesel engine
components (EGR and starting), aggressive shift logic and early
torque convertor lockup systems.
---------------------------------------------------------------------------

    UCS, in contrast, commented that NHTSA had not accounted for enough
cost reductions due to learning. UCS stated that NHTSA should have
provided ``source data'' for manufacturer-specific learning curves, and
argued that NHTSA's approach was ``fundamentally flawed'' for two
primary reasons: First, because NHTSA had not considered the fact that
manufacturers engage in joint ventures to develop new technologies, and
second, because manufacturers may also learn from one another ``through
the standard practice of tearing down competitors' products.'' UCS
argued that NHTSA's learning-based cost reductions should account for
these methods of learning. UCS further stated that NHTSA should not
``treat[] car and truck sales volumes separately when estimating
learning curves'' because there may be much overlap in terms of
technology application, especially for vehicles like crossovers which
may be either cars or trucks. UCS concluded that NHTSA should use EPA's
suggested learning factor of 20 percent, citing EPA's Staff Technical
Report.
    Public Citizen agreed that NHTSA should account for economies of
scale, but argued that NHTSA should not have relied on initial cost
estimates from industry, which the commenter stated were ``often
overestimated.'' Public Citizen cited a 1997 briefing paper by the
Economic Policy Institute in support of this point, and argued that
compliance cost estimates were often much lower than actual costs.
Public Citizen concluded that NHTSA's use of learning curve factors
``impedes transparency'' in NHTSA's analysis.
    Agency response: Based on the comments received and on its work
with Ricardo, NHTSA has revised its approach to accounting for
technology cost reductions due to manufacturer learning. The method of
learning used in the NPRM has been retained, but the threshold volume
has been revised and is now calculated on an industry-wide production
basis. However, learning of this type, which NHTSA now refers to as
``volume-based'' learning, is not applicable to any technologies for MY
2011. Additionally, NHTSA has adopted a fixed rate, year-over-year
(YOY) cost reduction for widely-available, high-volume, mature
technologies, in response to comments from Ford and others. NHTSA
refers to this type cost reduction as ``time-based'' learning. For each
technology, if learning is applicable, only one type of learning would
be applied, either volume-based or time-based (i.e., the types are
independent of each other). These revisions are discussed below.
    For volume-based learning, NHTSA considered comments from UCS and
decided to revise the method used to calculate the threshold volume
from a per-manufacturer to an industry-wide production volume basis.
NHTSA agreed with UCS' comment that cars and trucks may share common
components--this is true across many makes and models which share
common engines, transmissions, accessory systems, and mild or strong
hybrid systems, all of which can potentially utilize the technologies
under consideration. These systems are often manufactured by suppliers
who contract with multiple OEMs, all of whom benefit (in the form of
cost reductions for the technology) from the supplier's learning. The
2008 Martec Report and the BorgWarner comments additionally both
indicated that when manufacturers demand components in high volumes,
suppliers are able to pass on learning-based savings to all
manufacturers with whom they contract. Thus, it made sense to NHTSA to
revise its method of determining whether the threshold volume has been
achieved from an annual per-manufacturer to an annual industry-wide
production volume basis.
    NHTSA also changed the threshold volume for volume-based learning
from 25,000 to 300,000 units. The 2008 Martec Report and comments from
multiple manufacturers indicated that 25,000 units was far too small a
production volume to affect component costs. In response, NHTSA began
with the Martec estimate that technologies were fully learned-out at
1.5 million units of production (which met the production needs of
three manufacturers, according to that report). NHTSA then applied two
cycles of learning in a reverse direction to determine what the proper
threshold volume would be for these conditions. One cycle would be
applied at 750,000 units (1.5 million divided by 2, which would
represent the second volume doubling) and one at 375,000 units (750,000
divided by 2, which would represent the first volume doubling).

NHTSA thus estimated that the Martec analysis would suggest a threshold
volume of 375,000 units. However, the agency notes that Martec stated
that it chose the 1.5 million units number specifically because Martec
knew it was well beyond the point where learning is a factor, which
means that 1.5 million was beyond the cusp of the learning threshold.
NHTSA therefore concluded that 375,000 units should represent the upper
bound for the threshold volume for Martec's analysis.
    Having determined this, NHTSA sought to establish a lower bound for
the threshold volume. The 2008 Martec report indicated that production
efficiencies are maximized at 250,000-350,000 units (which averages to
300,000 units), and that manufacturers consequently target this range
when planning and developing manufacturing operations. Honda also cited
this production volume. Thus, for three manufacturers, the annual
volume requirement would be 900,000 units.\126\ NHTSA concluded this
could also represent high volume where learned costs could be
available, and considered it as a lower bound estimate. With the upper
and lower values established, and given that Martec specifically
indicated that 1.5 million did not represent the cusp of the learning
threshold, NHTSA chose the mid-point of 1.2 million units as the best
estimate of annual industry volumes where learned costs would be
experienced. For proper forward learning, this would mean the first
learning cycle would occur at 300,000 and the second at 600,000.
Accordingly NHTSA has established the threshold volume for the final
rule at 300,000 industry units per year.
---------------------------------------------------------------------------

    \126\ An industry volume of 900,000 would imply a threshold
volume of 225,000 units according to NHTSA's analysis. This is still
nine times the value used at the NPRM.
---------------------------------------------------------------------------

    Having established the threshold volume, NHTSA next considered
which technologies to apply volume learning to. Comments confirmed that
NHTSA had been correct in the NPRM to assume that learning would be
applicable to low-volume, emerging technologies that could benefit from
economies of scale, so NHTSA consulted confidential product plans to
determine the volumes of technologies to be applied by manufacturers
during the rulemaking period. If the product plans indicated that the
technologies would be in high-volume use (i.e., above 600,000 units
produced annually for cars and trucks by all manufacturers) at the
beginning of its first year of availability, then volume-based learning
was not considered applicable, since at this volume the technology
would be available at learned cost. If the volume was below 600,000
units annually, then NHTSA also looked at the Volpe model's application
of the technology. If the model applied more than 600,000 units within
the first year of availability, NHTSA did not apply volume-based
learning. If neither manufacturers nor the model applied more than
600,000 units within the first year, then volume learning was applied
to the technology.
    Based on this analysis, NHTSA determined that volume-based learning
would be applicable to three technologies for purposes of the final
rule: integrated starter generator, 2-mode hybrid, and plug-in hybrid.
For these three technologies, and where the agency's initial cost
estimates reflected full learning, NHTSA reverse-learned the cost by
dividing the estimate by the learning rate twice to properly offset the
learned cost estimate. NHTSA used a 20 percent learning rate in the
NPRM for these technologies, and concluded that that rate was still
applicable for the final rule. This learning rate was validated using
manufacturer-submitted current and forecast cost data for advanced-
battery hybrid vehicle technology, and accepted industry forecasts for
U.S. sales volumes of these same vehicles. This limited study indicated
that cost efficiencies were approximately 20 percent for a doubling of
U.S. market annual sales of a particular advanced battery technology,
and the learning rate was thus used as a proxy for other advanced
vehicle technologies.
    Commenters also indicated that learning-related cost reductions
could occur not only as a result of production volume changes, but also
as a function of time. For example, Ford stated that technology cost
reductions were negotiated as part of the contractual agreement to
purchase components from suppliers, a target-setting process which Ford
described as common in the automotive industry. In this arrangement
suppliers agree to reduce costs on a fixed percentage year over year
according to negotiated terms. GM described a cost reduction process
that occurs over the course of a product life cycle, typically no less
than 4-5 years, where costs are reduced as production experience
increases. GM stated that its cost reductions included engineering,
manufacturing, investment, and material costs, and were also defined
through supplier contracts that anticipate volume and costs over the
whole period. The components involved are assumed to be high volume,
mature technologies being used in current vehicle production. These are
the types of components that would typically be subject to ``cost-
down'' \127\ efforts that target savings through small, incremental
design, manufacturing, assembly, and material changes on a recurring or
periodic basis.
---------------------------------------------------------------------------

    \127\ Cost-down efforts are a common practice in competitive
manufacturing environments like the automotive industry.
---------------------------------------------------------------------------

    In response to these comments, NHTSA has adopted this approach as
an additional type of learning related cost reduction, referring to it
as ``time-based'' learning. For purposes of the final rule, time-based
learning is applied to high-volume, mature technologies likely to be
purchased by OEMs on a long-term contractual basis. This would include
most of the fuel-saving technologies under consideration, except those
where volume-based learning is applied, or those where components might
consist of commodity materials, such as oil or rubber, where pricing
fluctuations prevent long-term or fixed value contracts. NHTSA has used
a 3 percent reduction rate for time-based learning, based on
confidential manufacturer information and NHTSA's understanding of
current industry practice. Thus, if time-based learning is deemed
applicable, then in year two of a technology's application, and in each
subsequent year (if any), the initial cost is reduced by 3 percent.
This approach is responsive to comments about compliance costs
estimation, and improves the accuracy of projecting future costs
compared to the NPRM.
    With regard to the comments from UCS, NHTSA recognizes that joint-
venture collaboration and competitor tear-downs are methods used by
manufacturers for designing and developing new products and components,
but notes that these methods are used prior to the manufacturing stage,
and thus are not considered manufacturing costs. NHTSA has received no
specific manufacturer learning curve-related data, and thus has no
``source data'' to disclose. NHTSA continues to use a 20 percent
learning factor for volume-based learning, which is consistent with
EPA's learning factor recommended by UCS for NHTSA's use.
    With regard to the comments from Public Citizen, although NHTSA
reviewed the paper cited by the commenter, the agency found its
analysis largely irrelevant to NHTSA's estimation of cost reduction
factors due to automobile manufacturer learning, and thus declines to
adopt its findings.
    Table IV-4 below shows the applicability and type of learning
applied in the final rule.
BILLING CODE 4910-59-P

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BILLING CODE 4910-59-C
7. Technology Synergies
    When two or more technologies are added to a particular vehicle
model to improve its fuel efficiency, the resultant fuel consumption
reduction may sometimes be higher or lower than the product of the
individual effectiveness values for those items.\128\ This may

occur because one or more technologies applied to the same vehicle
partially address the same source or sources of engine, drivetrain or
vehicle losses. Alternately, this effect may be seen when one
technology shifts the engine operating points, and therefore increases
or reduces the fuel consumption reduction achieved by another
technology or set of technologies. The difference between the observed
fuel consumption reduction associated with a set of technologies and
the product of the individual effectiveness values in that set is
referred to for purposes of this rulemaking as a ``synergy.'' Synergies
may be positive (increased fuel consumption reduction compared to the
product of the individual effects) or negative (decreased fuel
consumption reduction).
---------------------------------------------------------------------------

    \128\ More specifically, the products of the differences between
one and the technology-specific levels of effectiveness in reducing
fuel consumption. For example, not accounting for interactions, if
technologies A and B are estimated to reduce fuel consumption by 10%
(i.e., 0.1) and 20% (i.e., 0.2) respectively, the ``product of the
individual effectiveness values'' would be 1-0.1 times 1-0.2, or 0.9
times 0.8, which equals 0.72, corresponding to a combined
effectiveness of 28% rather than the 30% obtained by adding 10% to
20%. The ``synergy factors'' discussed in this section further
adjust these multiplicatively combined effectiveness values.
---------------------------------------------------------------------------

    For the NPRM, the Volpe model was modified to estimate the
interactions of technologies using estimates of incremental synergies
associated with a number of technology pairs identified by NHTSA. The
use of discrete technology pair incremental synergies is similar to
that in DOE's National Energy Modeling System (NEMS).\129\ Inputs to
the Volpe model incorporate NEMS-identified pairs, as well as
additional pairs for the final rule from the set of technologies
considered in the Volpe model. However, to maintain an approach that
was consistent with the technology sequencing developed by NHTSA, new
incremental synergy estimates for all pairs were obtained from a first-
order ``lumped parameter'' analysis tool created by EPA.\130\
---------------------------------------------------------------------------

    \129\ U.S. Department of Energy, Energy Information
Administration, Transportation Sector Module of the National Energy
Modeling System: Model Documentation 2007, May 2007, Washington, DC,
DOE/EIAM070(2007), at 29-30. Available at http://tonto.eia.doe.gov/
ftproot/modeldoc/m070(2007).pdf (last accessed Oct. 24, 2008).
    \130\ EPA Staff Technical Report: Cost and Effectiveness
Estimates of Technologies Used to Reduce Light-duty Vehicle Carbon
Dioxide Emissions; EPA420-R-08-008, March 2008.
---------------------------------------------------------------------------

    The lumped parameter tool is a spreadsheet model that represents
energy consumption in terms of average performance over the fuel
economy test procedure, rather than explicitly analyzing specific drive
cycles. The tool begins with an apportionment of fuel consumption
across several loss mechanisms and accounts for the average extent to
which different technologies affect these loss mechanisms using
estimates of engine, drivetrain and vehicle characteristics that are
averaged over the EPA fuel economy drive cycle. Results of this
analysis were generally consistent with those of full-scale vehicle
simulation modeling performed by Ricardo, Inc. However, regardless of a
generally consistent set of results for the vehicle class and set of
technologies studied, the lumped parameter tool is not a full vehicle
simulation and cannot replicate the physics of such a simulation.
    Many comments were received that stated this and pointed to errors
in the synergies listed in the NPRM being in some cases inaccurate or
even directionally incorrect. NHTSA recognizes that the estimated
synergies applied for the NPRM were not all correct, and has
reevaluated all estimated synergies applied in the analysis supporting
today's final rule. In response to commenters calling for NHTSA to use
full vehicle simulation, either in the first instance or as a check on
the synergy factors that NHTSA developed, the agency has concluded that
the vehicle simulation analyses conducted previously by Ricardo provide
a sufficient point of reference, especially considering the time
constraints for establishing the final rule. NHTSA did, however,
improve the predictive capability of the lumped parameter tool.
    The lumped parameter tool was first updated with the new list of
technologies and their associated effectiveness values. Second, NHTSA
conducted a more rigorous qualitative analysis of the technologies for
which a competition for losses would be expected, which led to a much
larger list of synergy pairings than was present in the NRPM. The types
of losses that were analyzed were tractive effort, transmission/
drivetrain, engine mechanical friction, engine pumping, engine
indicated (combustion) efficiency and accessory (see Table IV-5). As
can be seen from Table IV-5, engine mechanical friction, pumping and
accessory losses are improved by various technologies from engine,
transmission, electrification and hybrid decision trees and must be
accounted for within the model with a synergy value. The updated lumped
parameter model was then re-run to develop new synergy estimates for
the expanded list of pairings. That list is shown in Tables IV-6a-d.
The agency notes that synergies that occur within a decision tree are
already addressed within the incremental values assigned and therefore
do not require a synergy pair to address. For example, all engine
technologies take into account incremental synergy factors of preceding
engine technologies, and all transmission technologies take into
account incremental synergy factors of preceding transmission
technologies. These factors are expressed in the fuel consumption
improvement factors in the input files used by the Volpe model.
    For applying incremental synergy factors in separate path
technologies, the Volpe model uses an input table (see Tables IV-6a-d)
which lists technology pairings and incremental synergy factors
associated with those pairings, most of which are between engine
technologies and transmission/electrification/hybrid technologies. When
a technology is applied to a vehicle by the Volpe model, all instances
of that technology in the incremental synergy table which match
technologies already applied to the vehicle (either pre-existing or
previously applied by the Volpe model) are summed and applied to the
fuel consumption improvement factor of the technology being applied.
Synergies for the strong hybrid technology fuel consumption reductions
are included in the incremental value for the specific hybrid
technology block since the model applies technologies in the order of
the most effectiveness for least cost and also applies all available
electrification and transmission technologies before applying strong
hybrid technologies.
    As another possible alternative to using synergy factors, NHTSA has
also considered modifying the Volpe model to apply inputs--for each
vehicle model--specifying the share of total fuel consumption
attributable to each of several energy loss mechanisms. The agency has
determined that this approach, discussed in greater detail below,
cannot be implemented at this time because the requisite information is
not available.

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[GRAPHIC] [TIFF OMITTED] TR30MR09.029

BILLING CODE 4910-59-C
8. How does NHTSA use full vehicle simulation?
    For regulatory purposes, the fuel economy of any given vehicle is
determined by placing the vehicle on a chassis dynamometer (akin to a
large treadmill that puts the vehicle's wheels in contact with one or
more rollers, rather than with a belt stretched between rollers) in a
controlled

environment, driving the vehicle over a specific driving cycle (in
which driving speed is specified for each second of operation),
measuring the amount of carbon dioxide emitted from the vehicle's
tailpipe, and calculating fuel consumption based on the density and
carbon content of the fuel.
    One means of determining the effectiveness of a given technology as
applied to a given vehicle model would be to measure the vehicle's fuel
economy on a chassis dynamometer, install the new technology, and then
re-measure the vehicle's fuel economy. However, most technologies
cannot simply be ``swapped out,'' and even for those that can, simply
doing so without additional engineering work may change other vehicle
characteristics (e.g., ride, handling, performance, etc.), producing an
``apples to oranges'' comparison.
    Some technologies can also be more narrowly characterized through
bench or engine dynamometer (i.e., in which the engine drives a
generator that is, in turn, used to apply a controlled load to the
engine) testing. For example, engine dynamometer testing could be used
to evaluate the brake-specific fuel consumption (e.g., grams per
kilowatt-hour) of a given engine before and after replacing the engine
oil with a less viscous oil. However, such testing does not provide a
direct measure of overall vehicle fuel economy or changes in overall
vehicle fuel economy.
    For a vehicle that does not yet exist, as in NHTSA's analysis of
CAFE standards applicable to future model years, even physical testing
can provide only an estimate of the vehicle's eventual fuel economy.
Among the alternatives to physical testing, automotive engineers
involved in vehicle design make use of computer-based analysis tools,
including a powerful class of tools commonly referred to as ``full
vehicle simulation.'' Given highly detailed inputs regarding vehicle
engineering characteristics, full vehicle simulation provides a means
of estimating vehicle fuel consumption over a given drive cycle, based
on the explicit representation of the physical laws governing vehicle
propulsion and dynamics. Some vehicle simulation tools also incorporate
combustion simulation tools that represent the combustion cycle in
terms of governing physical and chemical processes. Although these
tools are computationally intensive and required a great deal of input
data, they provide engineers involved in vehicle development and design
with an alternative that can be considerably faster and less expensive
than physical experimentation and testing.
    Properly executed, methods such as physical testing and full
vehicle simulation can provide reasonably (though not absolutely)
certain estimates of the vehicle fuel economy of specific vehicles to
be produced in the future. However, when analyzing potential CAFE
standards, NHTSA is not actually designing specific vehicles. The
agency is considering implications of new standards that will apply to
the average performance of manufacturers' entire production lines. For
this type of analysis, precision in the estimation of the fuel economy
of individual vehicle models is not essential; although it is important
that the agency avoid systematic upward or downward bias, uncertainty
at the level of individual models is mitigated by the fact that
compliance with CAFE standards is based on average fleet performance.
    As discussed above, the Volpe Model, which the agency has used to
perform the analysis supporting today's final rule, applies an
incrementally multiplicative approach to estimating the fuel savings
achieved through the progressive addition of fuel-saving technologies.
NAS' use of the same approach in its 2002 report was, at the time and
henceforth, criticized by a small number of observers as being prone to
systematic overestimation of available fuel savings. This assertion was
based on the fact that, among the technologies present on any given
vehicle, more than one may address the same energy loss mechanism
(notably, pumping losses on throttled engines). Once all energy losses
of a given type are eliminated, even theoretical improvements
attributable to that loss mechanism are no longer available.
    The most direct critique of NAS' methods appeared in a 2002 SAE
paper by four General Motors researchers (Patton, et al.), who compared
some of NAS' calculations to fuel consumption estimates obtained
through vehicle testing and simulation, and concluded that, as
increasing numbers of technologies were applied, NAS' estimates became
increasingly subject to overestimation of available fuel consumption
reductions.\131\
---------------------------------------------------------------------------

    \131\ Patton, K.J., et al., General Motors Corporation,
``Aggregating Technologies for Reduced Fuel Consumption: A Review of
the Technical Content in the 2002 National Research Council Report
on CAFE'', 2002-01-0628, Society of Automotive Engineers, Inc.,
2002.
---------------------------------------------------------------------------

    In response to such concerns, which had also been raised as the NAS
committee performed its analysis, the NAS report concluded that vehicle
simulation performed for the committee indicated that the report's
incremental fuel savings estimates were ``quite reasonable'' for the
less aggressive two of the three product development paths it
evaluated. The report did, however, conclude that uncertainty increased
with consideration of more technologies, especially under the more
aggressive ``path 3'' evaluated by the committee. The report did not,
however, mention any directional bias to this uncertainty.\132\
---------------------------------------------------------------------------

    \132\ NRC (2002), op. cit., p. 151.
---------------------------------------------------------------------------

    Notwithstanding this prior response to concerns about the possible
overestimation of available fuel savings, and considering that analyses
supporting the development of the NPRM, the Volpe model applies
``synergy factors'' that adjust fuel savings calculations when some
pairs of technologies are applied to the same vehicle, as discussed
above in Section IV.C.7. These factors reduce uncertainty and the
potential for positive or negative biases in the Volpe model's
estimates of the effects of technologies.
    As an alternative to estimating fuel consumption through
incremental multiplication and the application of ``synergy'' factors
to address technology interactions, NHTSA considered basing its
analysis of fuel economy standards on full vehicle simulation at every
step. However, considering the nature of CAFE analysis (in particular,
the analysis of fleets projected to be sold in the future by each
manufacturer), as well as the quantity and availability of information
required to perform vehicle simulation, the agency explained that it
believed detailed simulation when analyzing the entire fleet of future
vehicles is neither necessary nor feasible. Still, when estimating
synergies between technologies, the agency did make use of vehicle
simulation studies, as discussed above. The agency has also done so
when re-estimating synergies before performing the analysis supporting
today's final rule.
    NHTSA also considered estimating changes in fuel consumption by
explicitly accounting for each of several energy loss mechanisms--that
is, physical mechanisms to which the consumption of (chemical) energy
in fuel may be attributed. This approach would be similar to that
proposed in 2002 by Patton et al. The agency invited comment on this
approach, requested that manufacturers submit product plans
disaggregating fuel consumption into each of nine loss mechanisms, and
sought estimates of the extent to which fuel-saving technologies affect
each of these loss mechanisms.

    In response to the NPRM, the Alliance presented a detailed analysis
by Sierra Research, which used a modified version of VEHSIM (a vehicle
simulation tool) to estimate the fuel consumption resulting from the
application of various vehicle technologies to 25 vehicle categories
intended to represent the fleet. The Alliance commented that this
simulation-based approach is more accurate than that applied by NHTSA,
and indicated that Sierra's ability to perform this analysis
demonstrates that NHTSA should be able to do the same.
    General Motors also raised questions regarding the multiplicative
approach to fuel consumption estimation NHTSA has implemented using the
Volpe model. GM indicated that the Volpe model should be enhanced with
modifications to ``take into account the basic physics of vehicles.''
\133\ Although GM's comments did not explicitly mention vehicle
simulation, GM did express full support for the Alliance's comments.
---------------------------------------------------------------------------

    \133\ GM comments at 2, Docket No. NHTSA-2008-0089-0162.
---------------------------------------------------------------------------

    The California Air Resources Board (CARB) presented comparisons of
different simulation studies, commenting that these demonstrate that
the VEHSIM model used by Sierra Research ``cannot accurately simulate
vehicles that use advanced technologies such as variable valve timing
and lift and advanced transmissions.'' \134\ CARB also questioned
Sierra Research's simulation capabilities and suggested that, in
support of actual product development, manufacturers neither contract
with Sierra Research for such services nor make use of VEHSIM. CARB
further commented that both AVL (which performed simulation studies for
CARB's evaluation of potential greenhouse gas standards) and Ricardo
(which has recently performed simulation studies and related analysis
for both EPA and NHTSA) provide such services to manufacturers.\135\
---------------------------------------------------------------------------

    \134\ CARB comments at 5, Docket No. NHTSA-2008-0089-0173. In
developing potential greenhouse gas (GHG) emissions standards for
light vehicles, CARB made significant use of vehicle simulation
results presented in ``Reducing Greenhouse Gas Emissions from Light-
Duty Motor Vehicles'', which was published in 2004 by the Northeast
States Center for a Clean Air Future (NESCCAF). As NHTSA discussed
in the NPRM, CARB's and NESCCAF's approach, which effectively
reduces each manufacturer's fleet to five ``representative''
vehicles and two average vehicle weights, is too limited for
purposes of CAFE analysis.
    \135\ California Air Resources Board, ``Air Resources Board
Staff Comments on Sierra and Martec NRC Presentations'', p. 2.
---------------------------------------------------------------------------

    However, the Alliance and GM have criticized technical aspects of
the AVL and Ricardo vehicle simulation studies mentioned by CARB.
Regarding the AVL vehicle simulations CARB utilized, GM raised concerns
that, among other things, some of AVL's simulations assumed the use of
premium-grade gasoline, and some effectively assume vehicle performance
and utility would be compromised.\136\ Similarly, the Alliance raised
concerns that some of the simulations performed by Ricardo for EPA
assumed the use of premium fuel, and that many of the simulations
assumed vehicle performance would be reduced.\137\ The Alliance also
indicated that the five vehicles analyzed by Ricardo for EPA were not
representative of all vehicles in the fleet, leading to overstatement
of the degree of improvement potentially available to vehicles that
already use technologies not present in the vehicles examined by EPA.
The Alliance further argued that the report did not reveal sufficient
detail regarding important simulation details (related, e.g., to
cylinder deactivation), that it failed to account for some parasitic
and accessory loads, and that EPA directed Ricardo to unrealistically
assume universal improvements in aerodynamics, tire efficiency, and
powertrain friction.\138\
---------------------------------------------------------------------------

    \136\ Testimony of Kenneth Patton (GM); Testimony of Kevin
McMahon (Martec); Plaintiffs' Proposed Findings of Fact, June 15,
2007, pp. 103 -113.
    \137\ Alliance of Automobile Manufacturers, ``Detailed Technical
Comments on Ricardo `Study of Potential Effectiveness of Carbon
Dioxide Reducing Vehicle Technologies' Report'', March 6, 2008.
    \138\ For the reader's reference, Ricardo's study for EPA was
based on specific EPA-defined requirements, such as performing full
vehicle simulations of 26 different technology packages on the EPA-
specified 5 baseline vehicles. Thus, to the extent that Ricardo's
numbers do not reflect specific differences in technology
effectiveness by vehicle model, in conducting the analysis for
NHTSA's final rule, NHTSA and Ricardo drew on Ricardo's knowledge to
develop incremental benefits based in part on Ricardo's simulation
work. Ricardo also noted differences between its report for EPA and
the EPA Staff Technical Report in terms of the incremental benefits
for individual technologies developed by EPA based on Ricardo's
simulation.
---------------------------------------------------------------------------

    Although submitted after the close of the comment period specified
in the NPRM, comments by several state Attorneys General and other
state and local official questioned the need and merits of full vehicle
simulation within the context of CAFE analysis, stating that

    Computer simulation models such as VEHSIM are not practical
except perhaps during vehicle development to determine the
performance of specific vehicle models where all vehicle engineering
parameters are known and can be accounted for in the inputs to the
model. Such an exercise is extremely data intensive, and extending
it to the entire fleet makes it subject to multiple errors unless
the specific parameters for each vehicle model are known and
accounted for in the model inputs.\139\
---------------------------------------------------------------------------

    \139\ Attorneys General of the States of California, Arizona,
Connecticut, Illinois, Maryland, Massachusetts, New Jersey, New
Mexico, Oregon, and Vermont, the Executive Officer of the California
Air Resources Board, the Commissioner of the New Jersey Department
of Environmental Protection, the Secretary of the New Mexico
Environment Department, the Secretary of the Commonwealth of
Pennsylvania Department of Environmental Protection, and the
Corporation Counsel of the City of New York, Supplemental Comments
Regarding Alliance of Automobile Manufacturers Comments, Docket No.
NHTSA-2008-0089-0495, October 8, 2008, p. 3.

    Considering the comments summarized above, the analyses to which
they refer, and the nature of the analysis the agency performs when
evaluating potential CAFE standards, NHTSA has concluded that full
vehicle simulation, though useful to manufacturers' own product
development efforts, remains neither necessary nor feasible for the MY
2011 CAFE analysis. NHTSA's basis for this conclusion is as follows:
    Full vehicle simulation involves estimating the fuel consumption
(and, typically, emissions) of a specific vehicle over a specific
driving cycle. Many engineering characteristics of the vehicle must be
specified, including, but not limited to weight, rolling resistance,
tire radius, aerodynamic drag coefficient, frontal area, engine
maps\140\ and detailed transmission characteristics (gear ratios, shift
logic, etc.), other drivetrain characteristics, and accessory loads.
Additional engine test data would also be required in order to update
engine maps when evaluating the application of advanced engine
technologies. Driving cycles--vehicle speeds over time--are specified
on a second-by-second (or more finely-grained) basis. Using full
vehicle simulation to estimate average fuel consumption under the test
procedures relevant to CAFE involves many simulations to capture all
the potential combinations of technologies that could be used.
---------------------------------------------------------------------------

    \140\ An engine map specifies the engine's efficiency under many
different operating conditions, each of which is defined in terms of
rotational speed (i.e., revolutions per minute, or RPM) and load
(i.e., torque).
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    Given all of the requisite data representing a specific vehicle,
full vehicle simulation can provide a powerful means of estimating
vehicle performance while accounting for interactions between various
vehicle components and systems. Full simulation can also provide a
means of estimating vehicle performance under driving conditions not
represented by the fuel economy test procedures. For

an engineer involved in the design of a specific vehicle or vehicle
component or system, or a manufacturer making specific decisions
regarding the fleet of vehicles it will produce, vehicle simulation can
be a powerful tool. However, even the most detailed simulation
involving full combustion cycle simulation is not the ``gold standard''
for product design. Chrysler, for example, has portrayed simulation as
one of several tools in its CAFE planning process, which also involves
physical testing (i.e., bench testing, chassis dynamometer testing) of
actual components and assembled vehicles.\141\
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    \141\ Fodale, F., Chrysler LLC, ``Fuel Economy/Fuels--Presented
to NRC Committee on Fuel Economy of Light-Duty Vehicles'', November
27, 2007.
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    In purpose and corresponding requirements, NHTSA's evaluation of
regulatory options is fundamentally different from the type of product
planning and development that a manufacturer conducts. A manufacturer
must make specific decisions regarding every component that will be
installed in every vehicle it plans to produce, and it must ultimately
decide how many of each vehicle it will produce. Although manufacturers
have some ability to make ``mid-course adjustments,'' that ability is
limited by a range of factors, such as contracts and tooling
investments. By comparison, NHTSA attempts only to estimate how a given
manufacturer might attempt to comply with a potential CAFE standard;
given the range of options available to each manufacturer, NHTSA has
little hope of predicting specifically what a given manufacturer will
do. CAFE standards require average levels of performance, not specific
technology outcomes. Therefore, while it is important that NHTSA avoid
systematic bias when estimating the potential to increase the fuel
economy of specific vehicle models, it is not important that the
agency's estimates precisely forecast results for every future vehicle.
    Furthermore, NHTSA evaluates the impact of CAFE standards on all
manufacturers, based on a forecast of specific vehicle models each
manufacturer will produce for sale in the U.S. in the future. An
analysis for MY 2011 can involve thousands of unique vehicle models,
hundreds of unique engines, and hundreds of unique transmissions.
Model-by-model representation, as used in the analysis for this final
rule, allows the agency to, among other things, account for
technologies expected to be present on each vehicle under ``business as
usual'' conditions, thereby avoiding errors regarding the potential to
add further technologies.
    Because of the intense informational and computational
requirements, industry-wide studies that rely on vehicle simulation
reduce the fleet to a limited number of ``representative'' vehicles.
This reduction limits the ability to account for technological and
other heterogeneity of the fleet, virtually ensuring the overestimation
of improvements available to some vehicles (e.g., vehicles that begin
with a great deal of technology) and some manufacturers (e.g.,
manufacturers that sell many high-technology vehicles). AVL's analysis
for NESCCAF and Ricardo's analysis for EPA, each of which considered
only five vehicle models, are both, therefore, of severely limited use
for the kind of fleetwide analysis used in this final rule, although
both provide useful information regarding the range of fuel savings
achieved by specific technologies and ``packages'' of technologies.
    The analysis conducted by Sierra Research for the Alliance
considers a significantly greater number (25) of ``representative''
vehicles, drawing important distinctions between similarly-sized cars
based on performance. Sierra was able to do so in part because it
analyzed historical vehicles. For example, Sierra indicates that model
year 1998 engines were used to supply VEHSIM with baseline, ``blended''
engine maps applied universally (rather than specific maps for each
manufacturer and vehicle model) for vehicle model years out to 2020.
Considering that, even without increases in CAFE standards, many
vehicles produced for sale in the U.S. during the time period
considered in a CAFE rulemaking are likely to have technologies such as
VVLT and cylinder deactivation, NHTSA doubts ``blended'' 1998 engines
are as representative as implied by Sierra's analysis.
    Although NHTSA could, in principle, integrate full vehicle
simulation of every vehicle model into its analysis of the future
fleet, the agency expects that manufacturers would be unable to provide
much of the required information for future vehicles. Even if
manufacturers were to provide such information, using full vehicle
simulation to estimate the effect of further technological improvements
to future vehicles would involve uncertain detailed estimates, such as
valve timing, cylinder deactivation operating conditions, transmission
shift points, and hybrid vehicle energy management strategies for each
specific vehicle, engine, and transmission combination. Even setting
aside the vast increases in computational demands that would accompany
the use of full vehicle simulation in model-by-model analysis of the
entire fleet, the agency remains convinced that the availability of
underlying information and data would be too limited for this approach
to be practical.
    As a third alternative, one that might be more explicitly
``physics-based'' than the use of synergy factors and vastly more
practical than full vehicle simulation, NHTSA requested comment on the
use of partitioned fuel consumption accounting. Aside from GM's
nonspecific recommendation that the Volpe model be modified to account
for the ``basic physics of vehicles,'' NHTSA did not receive comments
regarding the relative merits of partitioning fuel consumption into
several energy loss mechanisms for purposes of estimating the effects
of fuel-saving technologies, even though the concept is similar to that
proposed by Patton, et al. in 2002.\142\ Some manufacturers provided
some of the information that would have been necessary for the
implementation of this approach. However, as a group, manufacturers
that submitted product plan information to the agency provided far too
little disaggregated fuel consumption information to support the
development of this approach. Although NHTSA continues to believe that
partitioning fuel consumption into various loss mechanisms could
provide a practical and sound basis for future analysis, the
information required to support this approach is not available at this
time.
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    \142\ Patton, et al., present an energy balance calculation that
disaggregates fuel consumption into six energy loss categories,
indicating that ``an accounting of the effects of individual
technologies on energy losses within these categories provides a
practical, physically-based means to evaluate and compare the fuel
consumption effects of the various technologies.'' (Patton, et al.,
(2002), op. cit., p. 11.)
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    In conclusion, NHTSA observes that with respect to the CAFE
analysis prepared for this final rule, full vehicle simulation could
theoretically be used at three different levels. First, full vehicle
simulation could be used only to provide specific estimates, that,
combined with other data (e.g., from bench testing) would provide a
basis for estimates of the effectiveness of specific individual
technologies. While NHTSA will continue considering this type of
analysis, the agency anticipates that it will continue to be feasible
and informative to make somewhat greater use of full vehicle
simulation. Second, full vehicle simulation could be fully integrated
into NHTSA's model-by-model analysis of the entire fleet to be

projected to be produced in future model years. NHTSA expects, however,
that this level of integration will remain infeasible considering the
size and complexity of the fleet. Also, considering the forward-looking
nature of NHTSA's analysis, and the amount of information required to
perform full vehicle simulation, NHTSA anticipates that this level of
integration would involve misleadingly precise estimates of fuel
consumption, even for MY 2011. Finally, full vehicle simulation can be
used to develop less complex representations of interactions between
technologies (such as was done using the lumped parameter model to
develop the synergies for the final rule), and to perform reference
points to which vehicle-specific estimates may be compared. NHTSA views
this as a practical and productive potential use of full vehicle
simulation, and will consider following this approach in the future.
NHTSA has contracted with NAS to, among other things, evaluate the
potential use of full vehicle simulation and other fuel consumption
estimation methodologies. Nevertheless, in addition to considering
further modifications to the Volpe model, NHTSA will continue to
consider other methods for evaluating the cost and effect of adding
technology to manufacturers' fleets.
9. Refresh and Redesign Schedule
    In addition to, and as discussed below, developing analytical
methods that address limitations on overall rates at which new
technologies can be expected to feasibly penetrate manufacturers'
fleets, the agency has also developed methods to address the feasible
scheduling of changes to specific vehicle models. In the Volpe model,
which the agency has used to support the current rulemaking, these
scheduling-related methods were first applied in 2003, in response to
concerns that an early version of the model would sometimes add and
then subsequently remove some technologies.\143\ By 2006, these methods
were integrated into a new version of the model, one which explicitly
``carried forward'' technologies added to one vehicle model to
succeeding vehicle models in the next model year, and which timed the
application of many technologies to coincide with the redesign or
freshening of any given vehicle model.\144\
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    \143\ 68 FR 16874 (Apr. 7, 2003).
    \144\ 71 FR 17582 (Apr. 6, 2006).
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    Even within the context of the phase-in caps discussed below, NHTSA
considers these model-by-model scheduling constraints necessary in
order to produce an analysis that reasonably accounts for the need for
a period of stability following the redesign of any given vehicle
model. If engineering, tooling, testing, and other redesign-related
resources were free, every vehicle model could be redesigned every
year. In reality, however, every vehicle redesign consumes resources
simply to address the redesign. Phase-in caps, which are applied at the
level of manufacturer's entire fleet, do not constrain the scheduling
of changes to any particular vehicle model. Conversely, scheduling
constraints to address vehicle freshening and redesign do not
necessarily yield realistic overall penetration rates (e.g., for strong
hybrids).
    In the automobile industry there are two terms that describe when
changes to vehicles occur: redesign and refresh (i.e., freshening).
Vehicle redesign usually encompasses changes to a vehicle's appearance,
shape, dimensions, and powertrain, and is traditionally associated with
the introduction of ``new'' vehicles into the market, which is often
characterized as the next generation of a vehicle. In contrast, vehicle
refresh usually encompasses only changes to a vehicle's appearance, and
may include an upgraded powertrain. Refresh is traditionally associated
with mid-cycle cosmetic changes to a vehicle, within its current
generation, to make it appear ``fresh.'' Vehicle refresh traditionally
occurs no earlier than two years after a vehicle redesign or at least
two years before a scheduled redesign. In the NPRM, NHTSA tied the
application of the majority of the technologies to a vehicle's refresh/
redesign cycle, because their application was significant enough that
it could involve substantial engineering, testing, and calibration
work.
    NHTSA based the redesign and refresh schedules used in the NPRM as
inputs to the Volpe model on a combination of manufacturers'
confidential product plans and NHTSA's engineering judgment. In most
instances, NHTSA reviewed manufacturers' planned redesign and refresh
schedules and used them in the same manner it did in past rulemakings.
However, in NHTSA's judgment, manufacturers' planned redesign and
refresh schedules for some vehicle models were unrealistically slow
considering overall market trends. In these cases, the agency re-
estimated redesign and refresh schedules more consistent with the
agency's expectations, as discussed below. Also, if companies did not
provide product plan data, NHTSA used publicly available data about
vehicle redesigns to project the redesign and refresh schedules for the
vehicles produced by these companies.\145\
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    \145\ Sources included, but were not limited to manufacturers'
web sites, industry trade publications (e.g., Automotive News), and
commercial data sources (e.g., Wards Automotive, etc.).
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    Unless a manufacturer submitted plans for a more rapid redesign and
refresh schedule, NHTSA assumed that passenger cars would normally be
redesigned every 5 years, based on the trend over the last 10-15 years
showing that passenger cars are typically redesigned every 5 years.
These trends were reflected in the manufacturer product plans that
NHTSA used in the NPRM analysis, and were also confirmed by many
automakers in meetings held with NHTSA to discuss various general
issues regarding the rulemaking.
    NHTSA explained that it believes that the vehicle design process
has progressed and improved rapidly over the last decade and that these
improvements have made it possible for some manufacturers to shorten
the design process for some vehicles in order to introduce vehicles
more frequently in response to competitive market forces. Although
manufacturers have likely already taken advantage of most available
improvements, according to public and confidential data available to
NHTSA, almost all passenger cars will be on a 5-year redesign cycle by
the end of the decade, with the exception being some high performance
vehicles and vehicles with specific market niches.
    NHTSA also stated in the NPRM that light trucks are currently
redesigned every 5 to 7 years, with some vehicles (like full-size vans)
having longer redesign periods. In the most competitive SUV and
crossover vehicle segments, the redesign cycle currently averages
slightly above 5 years. NHTSA explained that it is expected that the
light truck redesign schedule will be shortened in the future due to
competitive market forces Thus, for almost all light trucks scheduled
for a redesign in model year 2014 and later, NHTSA projected a 5-year
redesign cycle. Exceptions were made for high performance vehicles and
other vehicles that traditionally had longer than average design
cycles. For those vehicles, NHTSA attempted to preserve their
historical redesign cycle rates.
    NHTSA discussed these assumptions with several manufacturers at the
NPRM stage, before the current economic crisis. Two manufacturers
indicated at

that time that their vehicle redesign cycles take at least five years
for cars and 6 years and longer for trucks because they rely on those
later years to earn a profit on the vehicles. They argued that they
would not be able to sustain their business if forced by CAFE standards
to a shorter redesign cycle. The agency recognizes that some
manufacturers are severely stressed in the current economic
environment, and that some manufacturers may be hoping to delay planned
vehicle redesigns in order to conserve financial resources. However,
consistent with its forecast of the overall size of the light vehicle
market from MY 2011 on, the agency currently expects that the
industry's status will improve, and that manufacturers will typically
redesign both car and truck models every 5 years in order to compete in
that market.
    NHTSA received relatively few comments regarding its refresh/
redesign schedule assumptions. UCS commented that redesign schedules
should be shortened to 3 years, based on recent public statements by
Ford that they intended to move to that cycle, and based on other
recent manufacturer behavior.
    Although NHTSA agrees with UCS that remarks by one Ford official at
a January 2008 conference suggest that that company was then hoping to
accelerate its vehicle ``cycle time'' to 3 years, the agency questions
the context, intended meaning and scope, and representation of those