weights in the 2016 Puckett and Kindelberger report: 3,197 pounds
for cars, 4,947 pounds for truck-based LTVs.
[GRAPHIC] [TIFF OMITTED] TP24AU18.068

    New results are directionally the same as in 2012; in the 2016
analysis, the estimate for lighter LTVs was of opposite sign (but small
magnitude). Consistent with the 2012 Kahane and 2016 Puckett and
Kindelberger reports, mass reductions in lighter cars are estimated to
lead to increases in fatalities, and mass reductions in heavier LTVs
are estimated to lead to decreases in fatalities. However, NHTSA does
not consider this conclusion to be definitive because of the relatively
wide confidence bounds of the estimates. The estimated mass effects are
similar among analyses for both classes of passenger cars; for all
reports, the estimate for lighter passenger cars is statistically
significant at the 85-percent confidence level, while the estimate for
heavier passenger cars is insignificant.
    The estimated mass effect for heavier truck-based LTVs is stronger
in this analysis and in the 2016 Puckett and Kindelberger report than
in the 2012 Kahane report; both estimates are statistically significant
at the 85-percent confidence level, unlike the corresponding
insignificant estimate in the 2012 Kahane report. The estimated mass
effect for lighter truck-based LTVs is insignificant and positive in
this analysis and the 2012 Kahane report, while the corresponding
estimate in the 2016 Puckett and Kindelberger report was insignificant
and negative.
    Vehicle mass continued an historical upward trend across the MYs in
the newest databases. The average (VMT-weighted) masses of passenger
cars and CUVs both increased by approximately three percent from MYs
2004 to 2011 (3,184 pounds to 3,289 pounds for passenger cars, and
3,821 pounds to 3,924 pounds for CUVs). Over the same period, the
average mass of minivans increased by six percent (from 4,204 pounds to
4,462 pounds), and the average mass of LTVs increased by 10% (from
4,819 pounds to 5,311 pounds).

Historical reasons for mass increases within vehicle classes include:
Manufacturers discontinuing lighter models; manufacturers re-designing
models to be heavier and larger; and shifting consumer preferences with
respect to cabin size and overall vehicle size.
    The principal difference between heavier vehicles, especially
truck-based LTVs, and lighter vehicles, especially passenger cars, is
mass reduction has a different effect in collisions with another car or
LTV. When two vehicles of unequal mass collide, the change in velocity
(delta V) is greater in the lighter vehicle. Through conservation of
momentum, the degree to which the delta V in the lighter vehicle is
greater than in the heavier vehicle is proportional to the ratio of
mass in the heavier vehicle to mass in the lighter vehicle:
[GRAPHIC] [TIFF OMITTED] TP24AU18.069

    Because fatality risk is a positive function of delta V, the
fatality risk in the lighter vehicle in two-vehicle collisions is also
higher. Removing some mass from the heavy vehicle reduces delta V in
the lighter vehicle, where fatality risk is higher, resulting in a
large benefit, offset by a small penalty because delta V increases in
the heavy vehicle where fatality risk is low--adding up to a net
societal benefit. Removing some mass from the lighter vehicle results
in a large penalty offset by a small benefit--adding up to net harm.
    These considerations drive the overall result: Mass reduction is
associated with an increase in fatality risk in lighter cars, a
decrease in fatality risk in heavier LTVs, CUVs, and minivans, and has
smaller effects in the intermediate groups. Mass reduction may also be
harmful in a crash with a movable object such as a small tree, which
may break if hit by a high mass vehicle resulting in a lower delta V
than may occur if hit by a lower mass vehicle which does not break the
tree and therefore has a higher delta V. However, in some types of
crashes not involving collisions between cars and LTVs, especially
first-event rollovers and impacts with fixed objects, mass reduction
may not be harmful and may be beneficial. To the extent lighter
vehicles may respond more quickly to braking and steering, or may be
more stable because their center of gravity is lower, they may more
successfully avoid crashes or reduce the severity of crashes.
    Farmer, Green, and Lie, who reviewed the 2010 Kahane report, again
peer-reviewed the 2011 Kahane report.\307\ In preparing his 2012 report
(along with the 2016 Puckett and Kindelberger report and Draft TAR),
Kahane also took into account Wenzel's \308\ assessment of the
preliminary report and its peer reviews, DRI's analyses published early
in 2012, and public comments such as the International Council on Clean
Transportation's comments submitted on NHTSA and EPA's 2010 notice of
joint rulemaking.\309\ These comments prompted supplementary analyses,
especially sensitivity tests, discussed at the end of this section.
---------------------------------------------------------------------------

    \307\ Items 0035 (Lie), 0036 (Farmer) and 0037 (Green) in Docket
No. NHTSA-2010-0152.
    \308\ Wenzel, T. An Analysis of the Relationship Between
Casualty Risk Per Crash and Vehicle Mass and Footprint for Model
Year 2000-2007 Light Duty Vehicles, Lawrence Berkeley National
Laboratory (Dec. 2011), available at http://eta-publications.lbl.gov/sites/default/files/lbnl-5695e.pdf; Tom Wenzel,
Lawrence Berkeley National Laboratory -Assessment of NHTSA Report
Relationships Btw Fatality Risk Mass and Footprint in MY 2000-2007
PC and LTV,'' Docket NHTSA-2010-0131-0315; and a peer review of
Wenzel's reports--Peer Review of LBNL Statistical Analysis of the
Effect of Vehicle Mass & Footprint Reduction on Safety (LBNL Phase 1
and 2 Reports), prepared for U.S. EPA (Feb. 2012), available at
Docket ID NHTSA-2010-0131-0328.
    \309\ Comment by International Council on Clean Transportation,
Docket ID NHTSA-2010-0131-0258.
---------------------------------------------------------------------------

    The regression results are best suited to predict the effect of a
small change in mass, leaving all other factors, including footprint,
the same. With each additional change from the current environment
(e.g., the scale of mass change, presence and prevalence of safety
features, demographic characteristics), the model may become less
accurate. It is recognized that the light-duty vehicle fleet in the MY
2021-2026 timeframe will be different from the MY 20042011 fleet
analyzed here.
    Nevertheless, one consideration provides some basis for confidence
in applying regression results to estimate effects of relatively large
mass reductions or mass reductions over longer periods. This is NHTSA's
sixth evaluation of effects of mass reduction and/or downsizing,\310\
comprising

databases ranging from MYs 1985 to 2011.
---------------------------------------------------------------------------

    \310\ As outlined throughout this section, NHTSA's six related
studies include the new analysis supporting this rulemaking, and:
Kahane, C. J. Vehicle Weight, Fatality Risk and Crash Compatibility
of Model Year 1991-99 Passenger Cars and Light Trucks, National
Highway Traffic Safety Administration (Oct. 2003), available at
https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/809662;
Kahane, C. J. Relationships Between Fatality Risk, Mass, and
Footprint in Model Year 1991-1999 and Other Passenger Cars and LTVs
(Mar. 24, 2010), in Final Regulatory Impact Analysis: Corporate
Average Fuel Economy for MY 2012-MY 2016 Passenger Cars and Light
Trucks, National Highway Traffic Safety Administration (Mar. 2010)
at 464-542; Kahane, C. J. Relationships Between Fatality Risk, Mass,
and Footprint in Model Year 2000-2007 Passenger Cars and LTVs--
Preliminary Report, National Highway Traffic Safety Administration
(Nov. 2011), available at Docket ID NHTSA-2010-0152- 0023); Kahane,
C. J. Relationships Between Fatality Risk, Mass, and Footprint in
Model Year 2000-2007 Passenger Cars and LTVs: Final Report, NHTSA
Technical Report. Washington, DC: NHTSA, Report No. DOT-HS-811-665;
and Puckett, S. M., & Kindelberger, J. C. Relationships between
Fatality Risk, Mass, and Footprint in Model Year 2003-2010 Passenger
Cars and LTVs--Preliminary Report, National Highway Traffic Safety
Administration (June 2016), available at https://www.nhtsa.gov/sites/nhtsa.dot.gov/files/2016-prelim-relationship-fatalityrisk-mass-footprint-2003-10.pdf.
---------------------------------------------------------------------------

    Results of the six studies are not identical, but they have been
consistent to a point. During this time period, many makes and models
have increased substantially in mass, sometimes as much as 30-40%.\311\
If the statistical analysis has, over the past years, been able to
accommodate mass increases of this magnitude, perhaps it will also
succeed in modeling effects of mass reductions of approximately 10-20%,
should they occur in the future.
---------------------------------------------------------------------------

    \311\ For example, one of the most popular models of small 4-
door sedans increased in curb weight from 1,939 pounds in MY 1985 to
2,766 pounds in MY 2007, a 43% increase. A high-sales mid-size sedan
grew from 2,385 to 3,354 pounds (41%); a best-selling pickup truck
from 3,390 to 4,742 pounds (40%) in the basic model with two-door
cab and rear-wheel drive; and a popular minivan from 2,940 to 3,862
pounds (31%).
---------------------------------------------------------------------------

(d) Calculation of MY 2021-2026 Safety Impact
    Neither CAFE standards nor this analysis mandate mass reduction, or
mandate mass reduction occur in any specific manner. However, mass
reduction is one of the technology applications available to
manufacturers, and thus a degree of mass reduction is allowed within
the CAFE model to: (1) Determine capabilities of manufacturers; and (2)
to predict cost and fuel consumption effects of improved CAFE
standards.
    The agency utilized the relationships between weight and safety
from the new NHTSA analysis, expressed as percentage increases in
fatalities per 100-pound weight reduction, and examined the weight
impacts assumed in this CAFE analysis. The effects of mass reduction on
safety were estimated relative to estimated baseline levels of safety
across vehicle classes and model years. To identify baseline levels of
safety, the agency examined effects of identifiable safety trends over
lifetimes of vehicles produced in each model year. The projected
effectiveness of existing and forthcoming safety technologies and
expected on-road fleet penetration of safety technologies were
incorporated into observed trends in fatality rates to estimate
baseline fatality rates in future years across vehicle classes and
model years.
    The agency assumed safety trends will result in a reduction in the
target population of fatalities from which the vehicle mass impacts are
derived. Table II-47 through Table II-52 show results of NHTSA's
vehicle mass-size-safety analysis over the cumulative lifetime of MY
1977-2029 vehicles, for both the CAFE and GHG programs, based on the MY
2016 baseline fleet, accounting for the projected safety baselines. The
reported fatality impacts are undiscounted, but the monetized safety
impacts are discounted at three-percent and seven-percent discount
rates. The reported fatality impacts are estimated increases or
decreases in fatalities over the lifetime of the model year fleet. A
positive number means that fatalities are projected to increase; a
negative number (in parentheses) means that fatalities are projected to
decrease.
    Results are driven extensively by the degree to which mass is
reduced in relatively light passenger cars and in relatively heavy
vehicles because their coefficients in the logistic regression analysis
have the most significant values. We assume any impact on fatalities
will occur over the lifetime of the vehicle, and the chance of a
fatality occurring in any particular year is directly related to the
weighted vehicle miles traveled in that year.

[GRAPHIC] [TIFF OMITTED] TP24AU18.070

[GRAPHIC] [TIFF OMITTED] TP24AU18.071

[GRAPHIC] [TIFF OMITTED] TP24AU18.072

[GRAPHIC] [TIFF OMITTED] TP24AU18.073

[GRAPHIC] [TIFF OMITTED] TP24AU18.074

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    For all light-duty vehicles, mass changes are estimated to lead to
a decrease in fatalities over the cumulative lifetime of MY 1977-2029
vehicles in all alternatives evaluated. The effects of mass changes on
fatalities

range from a combined decrease (relative to the augural standards, the
baseline) of 12 fatalities for Alternative #7 to a combined decrease of
173 fatalities for Alternative #4. The difference in results by
alternative depends upon how much weight reduction is used in that
alternative and the types and sizes of vehicles to which the weight
reduction applies. The decreases in fatalities are driven by impacts
within passenger cars (decreases of between 17 and 281 fatalities) and
are offset by impacts within light trucks (increases of between 6 and
120 fatalities).
    Additionally, social effects of increasing fatalities can be
monetized using NHTSA's estimated comprehensive cost per life of
$9,900,000 in 2016 dollars. This consists of a value of a statistical
life of $9.6 million in 2015 dollars plus external economic costs
associated with fatalities such as medical care, insurance
administration costs and legal costs, updated for inflation to 2016
dollars.
    Typically, NHTSA would also estimate the effect on injuries and add
that to social costs of fatalities, but in this case NHTSA does not
have a model estimating the effect of vehicle mass on injuries. Blincoe
et al. estimates that fatalities account for 39.5% of total
comprehensive costs due to injury.\312\ If vehicle mass impacts non-
fatal injuries proportionally to its impact on fatalities, then total
costs would be approximately 2.53 (\1/0\.395) times the value of
fatalities alone or around $25.07 million per fatality. NHTSA has
selected this value as representative of the relationship between
fatality costs and injury costs because this approach is internally
consistent among NHTSA studies.
---------------------------------------------------------------------------

    \312\ Blincoe, L. et al., The Economic and Social Impact of
Motor Vehicle Crashes, 2010 (Revised), National Highway Traffic
Safety Administration (May 2015), available at https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/812013. The
estimate of 39.5% (see Table 1-8) is equal to the estimated value of
MAIS6 (fatal) injuries in vehicle incidents divided by the estimated
value of MAIS0-MAIS6 (non-fatal and fatal) injuries in vehicle
incidents.
---------------------------------------------------------------------------

    Changes in vehicle mass are estimated to decrease social safety
costs over the lifetime of the nine model years by between $176 million
(for Alternative #7) and $2.7 billion (for Alternative #4) relative to
the augural standards at a three-percent discount rate and by between
$97 million and $1.6 billion at a seven-percent discount rate. The
estimated decreases in social safety costs are driven by estimated
decreases in costs associated with passenger cars, ranging from $264
million (for Alternative #7) to $4.4 billion (for Alternative #1)
relative to the Augural standards at a three-percent discount rate and
by between $146 million and $2.5 billion at a seven-percent discount
rate. The estimated decreases in costs associated with passenger cars
are offset by estimated increases in costs associated with light
trucks, ranging from $88 million (for Alternative #7) to $2.0 billion
(for Alternative #1) relative to the Augural standards at a three-
percent discount rate and by between $49 million and $1.3 billion at a
seven-percent discount rate.
    Table II-53 through Table II-55 presents average annual estimated
safety effects of vehicle mass changes, for CYs 2035-2045:

[GRAPHIC] [TIFF OMITTED] TP24AU18.076

[GRAPHIC] [TIFF OMITTED] TP24AU18.077

[GRAPHIC] [TIFF OMITTED] TP24AU18.078

[GRAPHIC] [TIFF OMITTED] TP24AU18.079

[GRAPHIC] [TIFF OMITTED] TP24AU18.080

[GRAPHIC] [TIFF OMITTED] TP24AU18.081

    For all light-duty vehicles, mass changes are estimated to lead to
an average annual decrease in fatalities in all alternatives evaluated
for CYs 2035-2045. The effects of mass changes on fatalities range from
a combined

decrease (relative to the Augural standards) of 1 fatality per year for
Alternative #7 to a combined increase of 22 fatalities per year for
Alternative #1. The difference in the results by alternative depends
upon how much weight reduction is used in that alternative and the
types and sizes of vehicles to which the weight reduction applies. The
decreases in fatalities are generally driven by impacts within
passenger cars (decreases of between 1 and 33 fatalities per year
relative to the Augural standards) and are generally offset by impacts
within light trucks (increases of between 1 and 12 fatalities per
year).
    Changes in vehicle mass are estimated to decrease average annual
social safety costs in CY 2035-2045 by between $2 million (for
Alternative #7) and $271 million (for Alternative #1) relative to the
Augural standards at a three-percent discount rate and by between $1
million and $111 million at a seven-percent discount rate. The
estimated decreases in social safety costs are generally driven by
estimated decreases in costs associated with passenger cars, decreasing
between $13 million (for Alternative #7) and $424 million (for
Alternative #1) relative to the Augural standards at a three-percent
discount rate and decreasing between $5 million and $175 million at a
seven-percent discount rate. The estimated decreases in costs
associated with passenger cars are generally offset by estimated
increases in costs associated with light trucks, decreasing between $11
million (for Alternative #7) and $153 million (for Alternative #1)
relative to the Augural standards at a three-percent discount rate and
decreasing between $5 million and $64 million at a seven-percent
discount rate.
    To help illuminate effects at the model year level, Table II-59
presents the lifetime fatality impacts associated with vehicle mass
changes for passenger cars, light trucks, and all light-duty vehicles
by model year under Alternative #1, relative to the Augural standards
for the CAFE Program. Table II-59 presents an analogous table for the
GHG Program.

[GRAPHIC] [TIFF OMITTED] TP24AU18.082

    Under Alternative #1, passenger car fatalities associated with mass
changes are estimated to decrease generally from MY 2017 (decrease of
three fatalities) through MY 2029 (decrease of 36 fatalities), peaking
in MY 2025 (37 fatalities). Corresponding estimates of light truck
fatalities associated with mass changes are generally positive, ranging
from a decrease of one fatality in MYs 2017 and 2018 to an increase of
14 fatalities in MYs 2026 through 2029. Altogether, light-duty vehicle
fatality reductions associated with mass changes under Alternative #1
are

estimated to be concentrated among MY 2023 through MY 2029 vehicles
(146 out of 165, or 91% of net fatalities mitigated).
    Table II-61 and Table II-62 present estimates of monetized lifetime
social safety costs associated with mass changes by model year at
three-percent and seven-percent discount rates, respectively for the
CAFE Program. Table II-63 and Table II-64 show comparable tables from
the perspective of the GHG Program.

[GRAPHIC] [TIFF OMITTED] TP24AU18.083

[GRAPHIC] [TIFF OMITTED] TP24AU18.084

    Lifetime social safety costs are estimated to decrease generally by
model year, with decreases associated with passenger cars generally
offset partially by increases associated with light trucks. At a three-
percent discount

rate, decreases in lifetime social safety costs related to passenger
cars are estimated to range from $13 million for existing (MY 1977
through MY 2016) cars, to $230 million for MY 2025 cars. The
corresponding estimates at a seven-percent discount rate range from $7
million to $136 million. At a three-percent discount rate, impacts on
lifetime social safety costs related to light trucks are estimated to
range from a decrease of $5 million for MY 2017 light trucks to an
increase of $96 million for MY 2022 light trucks. The corresponding
estimates at a seven-percent discount rate range from $3 million to $65
million.
    Consistent with the analysis of fatality impacts by model year in
Table II-61, decreases in lifetime social safety costs associated with
mass changes are generally concentrated in MY 2023 through MY 2029
light-duty vehicles under Alternative #1. At a three-percent discount
rate, 93% of the reduction in total lifetime costs ($872 million out of
$937 million) is attributed to MY 2023 through MY 2029 light-duty
vehicles; at a seven-percent discount rate, 97% of the reduction in
total lifetime costs ($486 million out of $501 million) is attributed
to MY 2023 through MY 2029 light-duty vehicles.
(e) Sensitivity Analyses
    Table II-65 shows the principal findings and includes sampling-
error confidence bounds for the five parameters used in the CAFE model.
The confidence bounds represent the statistical uncertainty that is a
consequence of having less than a census of data. NHTSA's 2011, 2012,
and 2016 reports acknowledged another source of uncertainty: The
baseline statistical model can be varied by choosing different control
variables or redefining the vehicle classes or crash types, which for
example, could produce different point estimates.
    Beginning with the 2012 Kahane report, NHTSA has provided results
of 11 plausible alternative models that serve as sensitivity tests of
the baseline model. Each alternative model was tested or proposed by:
Farmer (IIHS) or Green (UMTRI) in their peer reviews; Van Auken (DRI)
in his public comments; or Wenzel in his parallel research for DOE. The
2012 Kahane and 2016 Puckett and Kindelberger reports provide further
discussion of the models and the rationales behind them.
    Alternative models use NHTSA's databases and regression-analysis
approach but differ from the baseline model in one or more explanatory
variables, assumptions, or data restrictions. NHTSA applied the 11
techniques to the latest databases to generate alternative CAFE model
coefficients. The range of estimates produced by the sensitivity tests
offers insight to the uncertainty inherent in the formulation of the
models, subject to the caveat these 11 tests are, of course, not an
exhaustive list of conceivable alternatives.
    The baseline and alternative results follow, ordered from the
lowest to the highest estimated increase in societal risk per 100-pound
reduction for cars weighing less than 3,201 pounds:
[GRAPHIC] [TIFF OMITTED] TP24AU18.085

    The sensitivity tests illustrate both the fragility and the
robustness of baseline estimates. On the one hand, the variation among
NHTSA's coefficients is quite large relative to the baseline estimate:
In the preceding example of cars < 3,201 pounds, the estimated
coefficients range from almost zero to almost double the baseline
estimate. This result underscores the key relationship that the
societal effect of mass reduction is small and, as Wenzel has said, it
``is overwhelmed by other known vehicle, driver, and crash factors.''
\313\ In other words, varying how to model some of these other vehicle,
driver, and crash factors, which is exactly what sensitivity tests do,
can appreciably change the estimate of the societal effect of mass
reduction.
---------------------------------------------------------------------------

    \313\ Wenzel, T. Assessment of NHTSA's Report ``Relationships
Between Fatality Risk, Mass, and Footprint in Model Year 2000-2007
Passenger Cars and LTVs,'' Lawrence Berkeley National Laboratory at
iv (Nov. 2011), available at Docket ID NHTSA-2010-0152-0026.
---------------------------------------------------------------------------

    On the other hand, variations are not particularly large in
absolute terms. The ranges of alternative estimates are generally in
line with the sampling-error confidence bounds for the baseline
estimates. Generally, in alternative models as in the baseline models,
mass reduction tends to be relatively more harmful in the lighter
vehicles and more beneficial in the heavier vehicles, just as they are
in the central analysis. In all models, the point estimate of NHTSA's
coefficient is positive for the lightest vehicle class, cars < 3,201
pounds. In nine out of 11 models, the point estimate is negative for
CUVs and minivans, and in eight out of 11 models the point estimate is
negative for LTVs >= 5,014 pounds.
(f) Fleet Simulation Model
    NHTSA has traditionally used real world crash data as the basis for
projecting the future safety implications for regulatory changes.
However, because lightweight vehicle designs are introducing
fundamental changes to the structure of the vehicle, there is some
concern that historical safety trends may not apply. To address this
concern, NHTSA developed an approach to utilize lightweight vehicle
designs to evaluate safety in a subset of real-world representative
crashes. The methodology focused on frontal crashes because of the
availability of existing vehicle and occupant restraint models.
Representative crashes were simulated between baseline and lightweight
vehicles against a range of vehicles and roadside objects using two
different size belted driver occupants (adult male and small female)
only. No passenger(s) or unbelted driver occupants were considered in
this fleet simulation. The occupant injury risk from each simulation
was calculated and summed to obtain combined occupant injury risk. The
combined occupant injury risk was weighted according to the frequency
of real world occurrences to develop overall societal risk for baseline
and light-weighted vehicles. Note: The generic restraint system
developed and used in the baseline occupant simulations was also used
in the light-weighted vehicle occupant simulations as the purpose of
this fleet simulation was to understand changes in societal injury
risks because of mass reduction for different classes of vehicles in
frontal crashes. No modifications to the restraint systems were made
for light-weighted vehicle occupant simulations. Any modifications to
restraint systems to improve occupant injury risks or societal injury
risks in the light-weighted vehicle would have conflated results
without identifying effects of mass reduction only. The following
sections provide an overview of the fleet simulation study:
    NHTSA contracted with George Washington University to develop a
fleet simulation model \314\ to study the impact and relationship of
light-weighted vehicle design with injuries and fatalities. In this
study, there were eight vehicles as follows:
---------------------------------------------------------------------------

    \314\ Samaha, R. R. et al., Methodology for Evaluating Fleet
Protection of New Vehicle Designs: Application to Lightweight
Vehicle Designs, National Highway Traffic Safety Administration
(Aug. 2014), available at https://www.nhtsa.gov/crashworthiness/vehicle-aggressivity-and-fleet-compatibility-research (accessed by
clicking on the .zip file for DOT HS 812 051).
---------------------------------------------------------------------------

     2001 model year Ford Taurus finite element model baseline
and two simple design variants included a 25% lighter vehicle while
maintaining the same vehicle front end stiffness and 25% overall
stiffer vehicle while maintaining the same overall vehicle mass.\315\
---------------------------------------------------------------------------

    \315\ Samaha, R. R. et al., Methodology for Evaluating Fleet
Protection of New Vehicle Designs: Application to Lightweight
Vehicle Designs, appendices, National Highway Traffic Safety
Administration (Aug. 2014), available at https://www.nhtsa.gov/crashworthiness/vehicle-aggressivity-and-fleet-compatibility-research (accessed by clicking on the .zip file for DOT HS 812 051
[appendices are Part 2]).
---------------------------------------------------------------------------

     2011 model year Honda Accord finite element baseline
vehicle and its 20% light-weight vehicle designed by Electricore. (This
mass reduction study was sponsored by NHTSA).\316\
---------------------------------------------------------------------------

    \316\ Singh, H. et al., Update to future midsize lightweight
vehicle findings in response to manufacturer review and IIHS small-
overlap testing, National Highway Traffic Safety Administration
(Feb. 2016), available at https://www.nhtsa.gov/sites/nhtsa.dot.gov/files/812237_lightweightvehiclereport.pdf.
---------------------------------------------------------------------------

     2009/2010 model year Toyota Venza finite element baseline
vehicle and two design variants included a 20% light-weight vehicle
model (2010 Venza) (Low option mass reduction vehicle funded by EPA and
International Council on Clean Transportation (ICCT)) and a 35% light-
weight vehicle (2009 Venza) (High option mass reduction vehicle funded
by California Air Resources Board).\317\
---------------------------------------------------------------------------

    \317\ Light-Duty Vehicle Mass Reduction and Cost Analysis --
Midsize Crossover Utility Vehicle, U.S. EPA (Aug. 2012), https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryID=230748.
---------------------------------------------------------------------------

    Light weight vehicles were designed to have similar vehicle crash
pulses as baseline vehicles. More than 440 vehicle crash simulations
were conducted for the range of crash speeds and crash configurations
to generate crash pulse and intrusion data points. The crash pulse data
and intrusion data points will be used as inputs in the occupant
simulation models.
    For vehicle to vehicle impact simulations, four finite element
models were chosen to represent the fleet. The partner vehicle models
were selected to represent a range of vehicle types and weights. It was
assumed vehicle models would reflect the crash response for all
vehicles of the same type, e.g. mid-size car. Only the safety or injury
risk for the driver in the target vehicle and in the partner vehicle
were evaluated in this study.
    As noted, vehicle simulations generated vehicle deformations and
acceleration responses utilized to drive occupant restraint simulations
and predict the risk of injury to the head, neck, chest, and lower
extremities. In all, more than 1,520 occupant restraint simulations
were conducted to evaluate the risk of injury for mid-size male and
small female drivers.
    The computed societal injury risk (SIR) for a target vehicle v in
frontal crashes is an aggregate of individual serious crash injury
risks weighted by real-world frequency of occurrence (v) of a frontal
crash incident. A crash incident corresponds to a crash with different
partners (Npartner) at a given impact speed (Pspeed), for a given
driver occupant size (Loccsize), in the target or partner vehicle (T/
P), in a given crash configuration (Mconfig), and in a single- or two-
vehicle crash (Kevent). CIR (v) represents the combined injury risk (by
body region) in a single crash incident. (v) designates the weighting
factor, i.e., percent of occurrence, derived from National Automotive
Sampling System Crashworthiness Data System (NASS CDS) for the crash
incident. A driver age group of 16 to 50

years old was chosen to provide a population with a similar, i.e., more
consistent, injury tolerance.
    The fleet simulation was performed using the best available
engineering models, with base vehicle restraint and airbag settings, to
estimate societal risks of future lightweight vehicles. The range of
the predicted risks for the baseline vehicles is from 1.25% to 1.56%,
with an average of 1.39%, for the NASS frontal crashes that were
simulated. The change in driver injury risk between the baseline and
light-weighted vehicles will provide insight into the estimate of
modification needed in the restraint and airbag systems of lightweight
vehicles. If the difference extends beyond the expected baseline
vehicle restraint and airbag capability, then adjustments to the
structural designs would be needed. Results from the fleet simulation
study show the trend of increased societal injury risk for light-
weighted vehicle designs, as compared to their baselines, occurs for
both single vehicle and two-vehicle crashes. Results are listed in
Table II-66.
    In general, the societal injury risk in the frontal crash
simulation associated with the small size driver is elevated when
compared to that of the mid-size driver. However, both occupant sizes
had reasonable injury risk in the simulated impact configurations
representative of the regulatory and consumer information testing.
NHTSA examined three methods for combining injuries with different body
regions. One observation was the baseline mid-size CUV model was more
sensitive to leg injuries.
[GRAPHIC] [TIFF OMITTED] TP24AU18.086

    This study only looked at lightweight designs for a midsize sedan
and a mid-size CUV and did not examine safety implications for heavier
vehicles. The study was also limited to only frontal crash
configurations and considered just mid-size CUVs whereas the
statistical regression model considered all CUVs and all crash modes.
    The change in the safety risk from the MY 2010 fleet simulation
study was directionally consistent with results for passenger cars from
NHTSA 2012 regression analysis study,\318\ which covered data for MY
2000-MY 2007. The NHTSA 2012 regression analysis study was updated in
2016 to reflect newer MY 2003 to MY 2010. Comparing the fleet
simulation societal risk to the 2016 update of the NHTSA 2012
regression analysis and the updated analysis used in this NPRM, the
risk assessment from the fleet simulation is similarly directionally
consistent with the passenger car risk assessment from the regression
analysis. As noted, fleet simulations were performed only in frontal
crash mode and did not consider other crash modes including rollover
crashes.\319\
---------------------------------------------------------------------------

    \318\ The 2012 Kahane study considered only fatalities, whereas,
the fleet simulation study considered severe (AIS 3+) injuries and
fatalities (DOT HS 811 665).
    \319\ The risk assessment for CUV in the regression model
combined CUVs and minivans in all crash modes and included belted
and unbelted occupants.
---------------------------------------------------------------------------

    This fleet simulation study does not provide information that can
be used to modify coefficients derived for the NPRM regression analysis
because of the restricted types of crashes \320\ and vehicle designs.
As explained earlier, the fleet simulation study assumed restraint
equipment to be as in the baseline model, in which restraints/airbags
are not redesigned to be optimal with light-weighting.
---------------------------------------------------------------------------

    \320\ The fleet simulation considered only frontal crashes.
---------------------------------------------------------------------------

2. Impact of Vehicle Scrappage and Sales Response on Fatalities
    Previous versions of the CAFE model, and the accompanying
regulatory analyses relying on it, did not carry a representation of
the full on-road vehicle population, only those vehicles from model
years regulated under proposed (or final) standards. The omission of an
on-road fleet implicitly assumed the population of vehicles registered
at the time a set of CAFE standards is promulgated is not affected by
those standards. However, there are several mechanisms by which CAFE
standards can affect the existing vehicle

population. The most significant of these is deferred retirement of
older vehicles. CAFE standards force manufacturers to apply fuel saving
technologies to offered vehicles and then pass along the cost of those
technologies (to the extent possible) to buyers of new vehicles. These
price increases affect the length of loan terms and the desired length
of ownership for new vehicle buyers and can discourage some buyers on
the margin from buying a new vehicle in a given year. To the extent new
vehicle purchases offset pending vehicle retirements, delaying new
purchases in favor of continuing to use an aging vehicle affects the
overall safety of the on-road fleet even if the vehicle whose
retirement was delayed was not directly subject to a binding CAFE
standard in the model year during its production.
    The sales response in the CAFE model acts to modify new vehicle
sales in two ways:
    1. Changes in new vehicle prices either increase or decrease total
sales (passenger cars and light trucks combined) each year in the
context of forecasted macroeconomic conditions.
    2. Changes in new vehicle attributes and fuel prices influence the
share of new vehicles sold that are light trucks, and therefore also
passenger cars.
    These two responses change the total number of new vehicles sold in
each model year across regulatory alternatives and the relative
proportion of new vehicles that are passenger cars and light trucks.
This response has two effects on safety. The first response slows the
rate at which new vehicles, and their associated safety improvements,
enter the on-road population. The second response influences the mix of
vehicles on the road--with more stringent CAFE standards leading to a
higher share of light trucks sold in the new vehicle market, assuming
all else is equal. Light trucks have higher rates of fatal crashes when
interacting with passenger cars and, as earlier sections discussed,
different directional responses to mass reduction technology based on
the existing mass and body style of the vehicle.
    The sales response and scrappage response influence safety outcomes
through the same basic mechanism, fleet turnover. In the case of the
scrappage response, delaying fleet turnover keeps drivers in older
vehicles likely to be less safe than newer model year vehicles that
could replace them. Similarly, delaying the sale of new vehicles can
force households to keep older vehicles in use longer, reallocate VMT
within their household fleet, and generally meet travel demand through
the use of older, less safe vehicles. As an illustration, if we
simplify by ignoring that the share of new vehicles that are passenger
cars changes with the stringency of the alternatives, simply changing
the number of new vehicles between scenarios affects the mileage
accumulation of the fleet and therefore all fleet level effects.
Reducing the number of new vehicles sold, relative to a baseline
forecasted value, reduces the size of the registered vehicle fleet that
is able to service the underlying demand for travel.
    Consider a simple example where we show sales effects operating on
a micro-scale for a single household whose choices of whether to
purchase a new vehicle is affected by vehicle price. A household starts
with three vehicles, aged three, five, and eight years old. In a
scenario with no CAFE standards and therefore no related changes in
vehicle sales prices, the household buys a new car and scraps the
eight-year old car; the other two cars in the fleet each get a year
older. In a scenario where CAFE standards become more stringent causing
vehicle sales prices to increase, this household chooses to delay
buying a new car and each of their three existing cars gets a year
older. In both cases, all three vehicles (including the new car in the
first scenario, and the year-year-old car in the second scenario) have
to serve the family's travel demand.
    The scrappage effect is visible in the household's vehicle fleet as
it moves from the first scenario to the second scenario with changes in
CAFE standards. In the second scenario, the nine-year-old car remains
in the household's fleet to service demand for travel, when it would
otherwise have been retired. While the scrappage effect can be
symmetrical to the sales effect, it need not be. The ``new car'' in the
scenario without CAFE standards could be a new vehicle from the current
model year or a used car that is of a newer vintage than the 8-year-old
vehicle it replaces. The latter instance is an effect of scrappage
decisions that do not directly affect new vehicle sales. Eventually,
new vehicles transition to the used car market, but that on average
take several years, and the shift is slow. At the household level, the
scrappage decision occurs in a single year, each year, for every
vehicle in the fleet. To the extent CAFE standards affect new vehicle
prices and fuel economies, relative to vehicles already owned,
scrappage could accelerate or decelerate depending upon the direction
(and magnitude) of the changes.
3. Safety Model
    The analysis supporting the CAFE rule for MYs 2017 and beyond did
not account for differences in exposure or inherent safety risk as
vehicles aged throughout their useful lives. However, the relationship
between vehicle age and fatality risk is an important one. In a 2013
Research Note,\321\ NHTSA's National Center for Statistics and Analysis
concluded a driver of a vehicle that is four to seven years old is 10%
more likely to be killed in a crash than the driver of a vehicle zero
to three years old, accounting for the other factors related to the
crash. This trend continued for older vehicles more generally, with a
driver of a vehicle 18 years or older being 71% more likely to be
killed in a crash than a driver in a new vehicle. While there are more
registered vehicles that are zero to three years old than there are 20
years or older (nearly three times as many) because most of the
vehicles in earlier vintages are retired sooner, the average age of
vehicles in the United States is 11.6 years old and has risen
significantly in the past decade.\322\ This relationship reflects a
general trend visible in the Fatality Analysis Reporting System (FARS)
when looking at a series of calendar years: Newer vintages are safer
than older vintages, over time, at each age. This is likely because of
advancements in safety technology, like side-impact airbags, electronic
stability control, and (more recently) sophisticated crash avoidance
systems starting to work their way into the vehicle population. In
fact, the 2013 Research Note indicated that the percentage of occupants
fatally injured in fatal crashes increased with vehicle age: From 27%
for vehicles three or fewer years old, to 41% for vehicles 12-14 years
old, to 50% for vehicles 18 or more years old.
---------------------------------------------------------------------------

    \321\ National Center for Statistics and Analysis, How Vehicle
Age and Model Year Relate to Driver Injury Severity in Fatal
Crashes, National Highway Traffic Safety Administration (Aug. 2013),
available at https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/811825.
    \322\ Based on data acquired from Ward's Automotive.
---------------------------------------------------------------------------

    With an integrated fleet model now part of the analytical framework
for CAFE analysis, any effects on fleet turnover (either from delayed
vehicle retirement or deferred sales of new vehicles) will affect the
distribution of both ages and model years present in the on-road fleet.
Because each of these vintages carries with it inherent rates of fatal
crashes, and newer vintages are generally safer than older ones,
changing that distribution will change

the total number of on-road fatalities under each regulatory
alternative.
    To estimate the empirical relationship between vehicle age, model
year vintage, and fatalities, DOT conducted a statistical analysis
linking data from the FARS database, a time series of Polk registration
data to represent the on-road vehicle population, and assumed per-
vehicle mileage accumulation rates (the derivation of which is
discussed in detail in PRIA Chapter 11). These data were used to
construct per-mile fatality rates that varied by vehicle vintage,
accounting for the influence of vehicle age. However, unlike the NCSA
study referenced above, any attempt to account for this relationship in
the CAFE analysis faces two challenges. The first challenge is the CAFE
model lacks the internal structure to account for other factors related
to observed fatal crashes--for example, vehicle speed, seat belt use,
drug use, or age of involved drivers or passengers. Vehicle
interactions are simply not modeled at this level; the safety analysis
in the CAFE model is statistical, using aggregate values to represent
the totality of fleet interactions over time. The second challenge is
perhaps the more significant of the two: The CAFE analysis is
inherently forward-looking. To implement a statistical model analogous
to the one developed by NCSA, the CAFE model would require forecasts of
all factors considered in the NCSA model--about vehicle speeds in
crashes, driver behavior, driver and passenger ages, vehicle vintages,
and so on. In particular, the model would require distributions (joint
distributions, in most cases) of these factors over a period of time
spanning decades. Any such forecasts would be highly uncertain and
would be likely to assume a continuation of current conditions.
    Instead of trying to replicate the NCSA work at a similar level of
detail, DOT conducted a simpler statistical analysis to separate the
safety impact of the two factors the CAFE model explicitly accounts
for: The distribution of vehicle ages in the fleet and the number of
miles driven by those vehicles at each age. To accomplish this, DOT
used data from the FARS database at a lower level of resolution; rather
than looking at each crash and the specific factors that contributed to
its occurrence, staff looked at the total number of fatal crashes
involving light-duty vehicles over time with a focus on the influence
of vehicle age and vehicle vintage. When considering the number of
fatalities relative to the number of registered vehicles for a given
model year (without regard to the passenger car/light-truck
distinction, which has evolved over time and can create inconsistent
comparisons), a somewhat noisy pattern develops. Using data from
calendar year 1996 through 2015, some consistent stories develop. The
points in Figure II-4 represent the number of fatalities per registered
vehicle with darker circles associated with increasingly current
calendar years.
[GRAPHIC] [TIFF OMITTED] TP24AU18.087

    As shown in Figure II-4, fatalities per registered vehicle have
generally declined over time across all vehicle ages (the darker points
representing newer vintages being closer to the x-axis) and, across
most recent calendar years, fatality rates (per registered vehicle)
start out at a low point, rise through age 15 or so, then decline
through age 30 (at which point little of the initial model year cohort
is still registered). While this pattern is evident in the registration
data, it is magnified by imposing a mileage accumulation schedule on
the registered population and examining fatalities per billion miles of
VMT.
    The mileage accumulation schedule used in this analysis was
developed using odometer readings of vehicles aged 0-15 years in
calendar year 2015.

The years spanned by the FARS database cover all model years from
calendar year 1996 through 2015. Given that there is a significant
number of years between the older vehicles in the 1996 CY data and the
most recent model years in the odometer data the informed the mileage
accumulation schedules, staff applied an elasticity of -0.20 to the
change in the average cost per mile of vehicles over their lives. While
the older vehicles had lower fuel economies, which would be associated
with higher per-mile driving costs, they also (mostly) faced lower fuel
prices. This adjustment increased the mileage accumulation for older
vehicles, but not by large amounts. Because the CAFE model uses the
mileage accumulation schedule and applies it to all vehicles in the
fleet, it is necessary to use the same schedule to estimate per-mile
fatality rates in the statistical analysis--even if the schedule is
based on vehicles that look different than the oldest vehicles in the
FARS dataset.
    When the per-vehicle fatality rates are converted into per-mile
fatality rates, the pattern observed in the registration comparison
becomes clearer. As Figure II-5 shows, the trend present in the
fatality data on a per-registration basis is even clearer on a per-mile
basis: Newer vintages are safer than older vintages, at each age, over
time.
[GRAPHIC] [TIFF OMITTED] TP24AU18.088

    The shape of the curve in Figure II-5 suggests a polynomial
relationship between fatality rate and vehicle age, so DOT's
statistical model is based on that structure.
    The final model is a weighted quartic polynomial regression (by
number of registered vehicles) on vehicle age with fixed effects for
the model years present in the dataset: \323\
---------------------------------------------------------------------------

    \323\ Note: The dataset included MY 1975, but that fixed effect
is excluded from the set. The constant term acts as the fixed effect
for 1975 and all others are relative to that one.
[GRAPHIC] [TIFF OMITTED] TP24AU18.089

    The coefficient estimates and model summary are in Table II-67.

[GRAPHIC] [TIFF OMITTED] TP24AU18.090

[GRAPHIC] [TIFF OMITTED] TP24AU18.091

    This function is now embedded in the CAFE model, so the combination
of VMT per vehicle and the distribution of ages and model years present
in the on-road fleet determine the number of fatalities in a given
calendar year. The model reproduces the observed fatalities of a given
model year, at each age, reasonably well with more recent model years
(to which the VMT schedule is a better match) estimated with smaller
errors.
    While the final specification was not the only one considered, the
fact this model was intended to live inside the CAFE model to
dynamically estimate fatalities for a dynamically changing on-road
vehicle population was a constraining factor.

(a) Predicting Future Safety Trends

    The base model predicts a net increase in fatalities due primarily
to slower adoption of safer vehicles and added driving because of less
costly vehicle operating costs. In earlier calendar years, the
improvement in safety of the on-road fleet produces a net reduction in
fatalities, but from the mid-2020s forward, the baseline model predicts
no further increase in safety, and the added risk from more VMT and
older vehicles produces a net increase in fatalities. This model thus
reflects a conservative limitation; it implicitly assumes the trend
toward increasingly safe vehicles that has been apparent for the past 3
decades will flatten in mid-2020s. The agency does not assert this is
the most likely case. In fact, the development of advanced crash
avoidance technologies in recent years indicates some level of safety
improvement is almost certain to occur. The difficulty is for most of
these technologies, their effectiveness against fatalities and the pace
of their adoption are highly uncertain. Moreover, autonomous vehicles
offer the possibility of significantly reducing or eventually even
eliminating the effect of human error in crash causation, a
contributing factor in roughly 94% of all crashes. This conservative
assumption may cause the NPRM to understate the beneficial effect of
proposed standards on improving (reducing) the number of fatalities.
    Advanced technologies that are currently deployed or in development
include:
    Forward Collision Warning (FCW) systems are intended to passively
assist the driver in avoiding or mitigating the impact of rear-end
collisions (i.e., a vehicle striking the rear portion of a vehicle
traveling in the same direction directly in front of it). FCW uses
forward-looking vehicle detection capability, such as RADAR, LIDAR
(laser), camera, etc., to detect other vehicles ahead and use the
information from these sensors to warn the driver and to prevent
crashes. FCW systems provide an audible, visual, or haptic warning, or
any combination thereof, to alert the driver of an FCW-equipped vehicle
of a potential collision with another vehicle or vehicles in the
anticipated forward pathway of the vehicle.
    Crash Imminent Braking (CIB) systems are intended to actively
assist the driver by mitigating the impact of rear-end collisions.
These safety systems have forward-looking vehicle detection capability
provided by sensing technologies such as RADAR, LIDAR, video camera,
etc. CIB systems mitigate crash severity by automatically applying the
vehicle's brakes shortly before the expected impact (i.e., without
requiring the driver to apply force to the brake pedal).
    Dynamic Brake Support (DBS) is a technology that actively increases
the amount of braking provided to the driver during a rear-end crash
avoidance maneuver. If the driver has applied force to the brake pedal,
DBS uses forward-looking sensor data provided by technologies such as
RADAR, LIDAR, video cameras, etc. to assess the potential for a rear-
end crash. Should DBS ascertain a crash is likely (i.e., the sensor
data indicate the driver has not applied enough braking to avoid the
crash), DBS automatically intervenes. Although the manner in which DBS
has been implemented differs among vehicle manufacturers, the objective
of the interventions is largely the same: To supplement the driver's
commanded brake input by increasing the output of the foundation brake
system. In some situations, the increased braking provided by DBS may
allow the driver to avoid a crash. In other cases, DBS interventions
mitigate crash severity.
    Pedestrian AEB (PAEB) systems provide automatic braking for
vehicles when pedestrians are in the forward path of travel and the
driver has taken insufficient action to avoid an imminent crash. Like
CIB, PAEB safety systems use information from forward-looking sensors
to automatically apply or supplement the brakes in certain driving

situations in which the system determines a pedestrian is in imminent
danger of being hit by the vehicle. Many PAEB systems use the same
sensors and technologies used by CIB and DBS.
    Rear Automatic Braking feature means installed vehicle equipment
that has the ability to sense the presence of objects behind a
reversing vehicle, alert the driver of the presence of the object(s)
via auditory and visual alerts, and automatically engage the available
braking system(s) to stop the vehicle.
    Semi-automatic Headlamp Beam Switching device provides either
automatic or manual control of headlamp beam switching at the option of
the driver. When the control is automatic, headlamps switch from the
upper beam to the lower beam when illuminated by headlamps on an
approaching vehicle and switch back to the upper beam when the road
ahead is dark. When the control is manual, the driver may obtain either
beam manually regardless of the conditions ahead of the vehicle.
    Rear Turn Signal Lamp Color Turn signal lamps are the signaling
element of a turn signal system, which indicates the intention to turn
or change direction by giving a flashing light on the side toward which
the turn will be made. FMVSS No. 108 permits a rear turn signal lamp
color of amber or red.
    Lane Departure Warning (LDW) system is a driver assistance system
that monitors lane markings on the road and alerts the driver when
their vehicle is about to drift beyond a delineated edge line of their
current travel lane.
    Blind Spot Detection (BSD) systems uses digital camera imaging
technology or radar sensor technology to detect one or more vehicles in
either of the adjacent lanes that may not be apparent to the driver.
The system warns the driver of an approaching vehicle's presence to
help facilitate safe lane changes.
    These technologies are either under development or are currently
being offered, typically in luxury vehicles, as either optional or
standard equipment.
    To estimate baseline fatality rates in future years, NHTSA examined
predicted results from a previous NCSA study \324\ that measured the
effect of known safety regulations on fatality rates. This study relied
on statistical evaluations of the effectiveness of motor vehicle safety
technologies based on real world performance in the on-road vehicle
fleet to determine the effectiveness of each safety technology. These
effectiveness rates were applied to existing fatality target
populations and adjusted for current technology penetration in the on-
road fleet, taking into account the retirement of existing vehicles and
the pace of future penetration required to meet statutory compliance
requirements, as well as adjustments for overlapping target
populations. Based on these factors, as well as assumptions regarding
future VMT, the study predicted future fatality levels and rates.
Because the safety impact in the CAFE model independently predicts
future VMT, we removed the VMT growth rate from the NCSA study and
developed a prediction of vehicle fatality trends based only on the
penetration pace of new safety technologies into the on-road fleet.
These data were then normalized into relative safety factors with CY
2015 as the baseline (to match the baseline fatality year used in this
CAFE analysis). These factors were then converted into equivalent
fatality rates/100 million VMT by anchoring them to the 2015 fatality
rate/100 million VMT published by NHTSA. Figure II-6 below illustrates
the modelling output and projected fatality trend from the analysis of
the NCSA study, prior to adjustment to fatality rates/100 million VMT.
---------------------------------------------------------------------------

    \324\ Blincoe, L. & Shankar, U. The Impact of Safety Standards
and Behavioral Trends on Motor Vehicle Fatality Rates, National
Highway Traffic Safety Administration (Jan. 2007), available at
https://www.nhtsa.gov/sites/nhtsa.dot.gov/files/documents/810777v3.pdf.
[GRAPHIC] [TIFF OMITTED] TP24AU18.092

    This model was based on inputs representing the impact of
technology improvement through CY 2020. Projecting this trend beyond
2020 can be justified based on the continued transformation of the on-
road fleet to 100% inclusion of the known safety technologies. Based on
projections in the NCSA study, significant further technology
penetration can be expected in the on-road fleet for side impact
improvements (FMVSSS 214), electronic stability control (FMVSS 126),
upper interior head impact protection (FMVSS 301), tire pressure
monitoring systems (FMVSS 138), ejection mitigation (FMVSS 226), and
heavy truck stopping distance improvements (FMVSS 121). These
technologies were estimated to be installed in only 40-70% of the on-
road fleet as of CY 2020, implying further safety improvement well
beyond the 2020 calendar year.
    The NCSA study focused on projections to reflect known technology
adaptation requirements, but it was conducted prior to the 2008
recession, which disrupted the economy and changed travel patterns
throughout the country. Thus, while the relative trends it predicts
seem reasonable, they cannot account for the real-world disruption and
recovery that occurred in the 2008-2015 timeframe. In addition, the
NCSA study did not attempt to adjust for safety impacts that may have
resulted from changes in the vehicle sales mix (vehicle types and sizes
creating different interactions in crashes), in commuting patterns, or
in shopping or socializing habits associated with internet access and
use. To address this, NHTSA also examined the actual change in the
fatality rate as measured by fatality counts and VMT estimates. Figure
II-7 below illustrates the actual fatality rates measured from 2000
through 2016 and the modeled fatality rate trend based on these
historical data.
[GRAPHIC] [TIFF OMITTED] TP24AU18.093

    The effect of the recession and subsequent recovery can be seen in
chaotic shift in the fatality rate trend starting in 2008. The
generally gradual decline that had been occurring over the previous
decade was interrupted by a slowdown in the rate of change followed by
subsequent upward and downward shifts. More recently, the rate has
begun to increase. These shifts reflect some combination of factors not
captured in the NCSA analysis mentioned above. The significance of this
is that although there was a steady increase in the penetration of
safety technologies into the on-road fleet between 2008 and 2015, other
unknown factors offset their positive influence and eventually reversed
the trend in vehicle safety rates. Because of the upward shift over the
2014-2015 period, this model, which does not reflect technology trend
savings after 2015, will predict an upward shift of fatality rates
after 2020.
    Predicting future safety trends has significant uncertainty.
Although further safety improvements are expected because of advanced
safety technologies such as automatic braking and eventually, fully
automated vehicles, the pace of development and extent of consumer
acceptance of these improvements is uncertain. Thus, two imperfect
models exist for predicting future safety trends. The NCSA model
reflects the expected trend from required technologies and indicates
continued improvement well beyond the 2020 timeframe, which is when the
historical fatality rate based model breaks down. By contrast, the
historical fatality rate model reflects shifts in safety not captured
by the NCSA model, but gives arguably implausible results after 2020.
It essentially represents a scenario in which economic, market, or
behavioral factors minimize or offset much of the potential impact of
future safety technology.
    For the NPRM, the analysis examines a scenario projecting safety
improvements beyond 2015 using a simple average of the NCSA and
historical fatality rate models, accepting each as an illustration of
different and conflicting possible future scenarios. As

both models eventually curve up because of their quadratic form, each
models' results are flattened at the point where they begin to trend
upward. This occurs in 2045 for the NCSA model and in 2021 for the
historical model. The results are shown in Figure II-8 below. The
results indicate roughly a 19% reduction in fatality rates between 2015
and 2050. This is a slower pace than what has historically occurred
over the past several decades, but the biggest influence on historical
rates was significant improvement in safety belt use, which was below
10% in 1960 and had risen to roughly 70% by 2000, and is now more than
90%. Because belt use is now above 90%, further such improvements are
unlikely unless they come from new technologies.
[GRAPHIC] [TIFF OMITTED] TP24AU18.094

    A difficulty with these trend models is they are based on calendar
year predictions, which are derived from the full on-road vehicle fleet
rather than the model year fleet, which is the basis for calculations
in the CAFE model. As such they are useful primarily as indicators that
vehicle safety has steadily improved over the past several decades, and
given the advanced safety technologies under current development, we
would expect some continuation of improvement in MY vehicle safety over
the near and mid-term future. To account for this, NHTSA approximated a
model year safety trend continuing through about 2035 (Figure II-9).
For this trend the agency used actual data from FARS to calculate the
change in fatality rates through 2007. The recession, which struck our
economy in 2008, distorted normal behavioral patterns and affected both
VMT and the mix of drivers and type of driving to an extent we do not
believe the recession era gives an accurate picture of the safety
trends inherent in the vehicles themselves. Therefore, beginning in
2008, NHTSA approximated a trend for safety improvement through about
MY 2035 to reflect the continued effect of improved safety technologies
such as advanced automatic braking, which manufacturers have announced
will be in all new vehicles by MY 2022. The agency recognize this is
only an estimate, and actual MY trends could be above or below this
line. NHTSA examined alternate trends in a sensitivity analysis and
request comments on the best way to address future safety trends.
    NHTSA also notes although we project vehicles will continue to
become safer going forward to about 2035, we do not have corresponding
cost information for technologies enabling this improvement. In a
standard elasticity model, sales impacts are a function of the percent
change in vehicle price. Hypothetically, increasing the base price for
added safety technologies would decrease the impact of higher prices
due to impacts of CAFE standards on vehicle sales. The percentage
change in baseline price would decrease, which would mean a lower
elasticity effect, which would mean a lower impact on sales. NHTSA will
consider possible ways to address this issue before the final rule, and
we request comments on the need and/or practicability for such an
adjustment, as well as any data and other relevant information that
could support such an analysis of these costs, as well as the future
pace of technological adoption within the vehicle fleet.

[GRAPHIC] [TIFF OMITTED] TP24AU18.095

(b) Adjusting for Behavioral Impacts
    The influence of delayed purchases of new vehicles is estimated to
have the most significant effect on safety imposed by CAFE standards.
Because of a combination of safety regulations and voluntary safety
improvements, passenger vehicles have become safer over time. Compared
to prior decades, fatality rates have declined significantly because of
technological improvements, as well as behavioral shifts, such as
increased seat belt use. As these safer vehicles replace older less
safe vehicles in the fleet, the on-road fleet is replaced with vehicles
reflecting the improved fatality rates of newer, safer vehicles.
However, fatality rates associated with different model year vehicles
are influenced by the vehicle itself and by driver behavior. Over time,
used vehicles are purchased by drivers in different demographic
circumstances who also tend to have different behavioral
characteristics. Drivers of older vehicles, on average, tend to have
lower belt use rates, are more likely to drive inebriated, and are more
likely to drive over the speed limit. Additionally, older vehicles are
more likely to be driven on rural roadways, which typically have higher
speeds and produce more serious crashes. These relationships are
illustrated graphically in Chapter 11 of the PRIA accompanying this
proposed rule.
    The behavior being modelled and ascribed to CAFE involves decisions
by drivers who are contemplating buying a new vehicle, and the purchase
of a newer vehicle will not in itself cause those drivers to suddenly
stop wearing seat belts, speed, drive under the influence, or shift
driving to different land use areas. The goal of this analysis is to
measure the effect of different vehicle designs that change by model
year. The modelling process for estimating safety essentially involves
substituting fatality rates of older MY vehicles for improved rates
that would have been experienced with a newer vehicle. Therefore, it is
important to control for behavioral aspects associated with vehicle age
so only vehicle design differences are reflected in the estimate of
safety impacts. To address this, the CAFE safety model was run to
control for vehicle age. That is, it does not reflect a decision to
replace an older model year vehicle that is, for example, 10 years old
with a new vehicle. Rather, it reflects the difference in the average
fatality rate of each model year across its entire lifespan. This will
account for most of the difference because of vehicle age, but it may
still reflect a bias caused by the upward trend in societal seat belt
use over time. Because of this secular trend, each subsequent model
year's useful life will occur under increasingly higher average seat
belt use rates. This could cause some level of behavioral safety
improvement to be ascribed to the model year instead of the driver
cohort. However, it is difficult to separate this effect from the belt
use impacts of changing driver cohorts as vehicles age.
    Glassbrenner (2012) analyzed the effect of improved safety in newer
vehicles for model years 2001 through 2008. She developed several
statistical regression models that specifically controlled for most
behavioral factors to isolate model year vehicle characteristics.
However, her study did not specifically report the change in MY
fatality rates--rather, she reported total fatalities that could have
been saved in a baseline year (2008) had all vehicles in the on-road
fleet had the same safety features as the MY 2001 through MY 2008
vehicles. This study potentially provides a basis for comparison with
results of the CAFE safety estimates. To make this comparison, the CY
2008 passenger car and light truck fatalities total from FARS were
modified by subtracting the values found in Figure II-9 of her study.
This gives a stream of comparable hypothetical CY 2008 fatality totals
under progressively less safe model year designs. Results indicated
that had the 2008 on-road fleet been equipped with MY 2008 safety
equipment and vehicle characteristics, total fatalities would have been
reduced by 25% compared to vehicles that were actually on the road in
2008. Similar results were calculated for each model years' vehicle
characteristics back to 2001.
    For comparison, predicted MY fatality rates were derived from the
CAFE safety model and applied to the CY 2008 VMT calculated by that
model. This gives an estimate of CY 2008 fatalities under each model
years' fatality rate, which, when compared to the predicted CY fatality
total, gives a trendline

comparable to the Glassbrenner trendline illustrating the change in MY
fatality rates. Both models are sensitive to the initial 2008 baseline
fatality total, and because the predicted CAFE total is somewhat lower
than the actual total, the agency ran a third trendline to examine the
influence of this difference. Results are shown in Figure II-10.
    Using the corrected fatality count, but retaining the predicted VMT
changes the initial 2018 CY fatality rate to 12.62 (instead of 12.15)
and produces the result shown in Figure II-10. The CAFE model trendline
shifts up, which narrows the difference in early years but expands it
in later years. However, VMT and fatalities are linked in the CAFE
model, so the actual level of the MY safety predicted by the CAFE curve
has uncertainty. Perhaps the most meaningful result from this
comparison is the difference in slopes; the CAFE model predicts more
rapid change through 2006, but in the last few years change decreases.
This might reflect the trend in societal belt use, which rose steadily
through 2005 and levelled off. Later model years' fatality rates would
benefit from this trend while earlier model years would suffer. This
seems consistent with our using lifetime MY fatality rates to reflect
MY change rather than first year MY fatality rates (although even first
year rates would reflect this bias, but not as much).
[GRAPHIC] [TIFF OMITTED] TP24AU18.096

    To provide another perspective on safety impacts, NHTSA accessed
data from a comprehensive study of the effects of safety technologies
on motor vehicle fatalities. Kahane (2015) \325\ examined all safety
effects of vehicle safety technologies from 1960 through 2012 and found
these technologies saved more than 600,000 lives during that time span.
Kahane is currently working under contract for NHTSA to update this
study through 2016. At NHTSA's request, Kahane accessed his database to
provide a measure of relative MY vehicle design safety by controlling
for seat belt use. The result was a MY safety index illustrating the
progress in vehicle safety by model year which isolates vehicle design
from the primary behavioral impact--seat belt usage. We normalized
Kahane's index to MY 1975 and did the same to the ``fixed effects'' we
are currently using from our safety model to compare the trends in MY
safety from the two methods. Results are shown in Figure II-11.
---------------------------------------------------------------------------

    \325\ Kahane, C.J. Lives Saved by Safety Standards and
Associated Vehicle Safety Technologies, 1960-2012--Passenger Cars
and LTVs--with Reviews of 26 FMVSS and the Effectiveness of their
Associated Safety Technologies in Reducing Fatalities, Injuries, and
Crashes, National Highway Traffic Safety Administration (Jan. 2015),
available at https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/812069.

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

[GRAPHIC] [TIFF OMITTED] TP24AU18.097

    From Figure II-11 both approaches show similar long-term downward
trends, but this model shows a steeper slope than Kahane's model. The
two models involve completely different approaches, so some difference
is to be expected. However, it is also possible this reflects different
methods used to isolate vehicle design safety from behavioral impacts.
As discussed previously, NHTSA addressed this issue by removing vehicle
age impacts from its model, whereas Kahane's model does it by
controlling for belt use. As noted previously, aside from the age
impact on belt use associated with the different demographics driving
older vehicles, there is a secular trend toward more belt use
reflecting the increase in societal awareness of belt use importance
over time. This trend is illustrated in Figure II-12 below.\326\
NHTSA's current approach removes the age trend in belt use, but it's
not clear whether it accounts for the full impacts of the secular trend
as well. If not, some portion of the gap between the two trendlines
could reflect behavioral impacts rather than vehicle design.
---------------------------------------------------------------------------

    \326\ Note: The drop occurring in 1994 reflects a shift in the
basis for determining belt use rates. Effective in 1994, data were
reported from the National Occupant Protection Survey (NOPUS). Prior
to this, a conglomeration of state studies provided the basis. It is
likely the pre-NOPUS surveys produced inflated results, especially
in the 1991-1993 period.
---------------------------------------------------------------------------

    These models (NHTSA, Glassbrenner, and Kahane) involve differing
approaches and assumptions contributing to uncertainty, and given this,
their differences are not surprising. It is encouraging they show
similar directional trends, reinforcing the basic concept we are
measuring. NHTSA recognizes predicting future fatality impacts, as well
as sales impacts that cause them, is a difficult and imprecise task.
NHTSA will continue to investigate this issue, and we seek comment on
these estimates as well as alternate methods for predicting the safety
effects associated with delayed new vehicle purchases.

[GRAPHIC] [TIFF OMITTED] TP24AU18.098

4. Impact of Rebound Effect on Fatalities
    Based on historical data, it is possible to calculate a baseline
fatality rate for vehicles of any model year vintage. By simply taking
the total number of vehicles involved in fatal accidents over all ages
for a model year and dividing by the cumulative VMT over the useful
life of every vehicle produced in that model year, one arrives at a
baseline hazard rate denominated in fatalities per billion miles. The
fatalities associated with vehicles produced in that model year are
then proportional to the cumulative lifetime VMT, where total
fatalities equal the product of the baseline hazard rate and VMT. A
more comprehensive discussion of the rebound effect and the basis for
calculating its impact on mileage and risk is in Chapter 8 of the PRIA
accompanying this proposed rule.
5. Adjustment for Non-Fatal Crashes
    Fatalities estimated to be caused by various alternative CAFE
standards are valued as a societal cost within the CAFE models' cost/
benefit accounting. Their value is based on the comprehensive value of
a fatality derived from data in Blincoe et al. (2015), adjusted to 2016
economics and updated to reflect the official DOT guidance on the value
of a statistical life in 2016. This gives a societal value of $9.9
million for each fatality. The CAFE safety model estimates effects on
traffic fatalities but does not address corresponding effects on non-
fatal injuries and property damage that would result from the same
factors influencing fatalities. To address this, we developed an
adjustment factor that would account for these crashes.
    Development of this factor is based on the assumption nonfatal
crashes will be affected by CAFE standards in proportion to their
nationwide incidence and severity. That is, NHTSA assumes the same
injury profile, the relative number of cases of each injury severity
level, that occur nationwide, will be increased or decreased because of
CAFE. The agency recognizes this may not be the case, but the agency
does not have data to support individual estimates across injury
severities. There are reasons why this may not be true. For example,
because older model year vehicles are generally less safe than newer
vehicles, fatalities may make up a larger portion of the total injury
picture than they do for newer vehicles. This would imply lower ratios
across the non-fatal injury and PDO profile and would imply our
adjustment may overstate total societal impacts. NHTSA requests
comments on this assumption and alternative methods to estimate injury
impacts.
    The adjustment factor is derived from Tables 1-8 and I-3 in Blincoe
et al. (2015). Incidence in Table I-3 reflects the Abbreviated Injury
Scale (AIS), which ranks nonfatal injury severity based on an ascending
5 level scale with the most severe injuries ranked as level 5. More
information on the basis for these classifications is available from
the Association for the Advancement of Automotive Medicine at https://www.aaam.org/abbreviated-injury-scale-ais/.
    Table 1-3 in Blincoe lists injured persons with their highest
(maximum) injury determining the AIS level (MAIS). This scale is
represented in terms of MAIS level, or maximum abbreviated injury
scale. MAIS0 refers to uninjured occupants in injury vehicles, MAIS1
are generally considered minor injuries, MAIS2 moderate injuries, MAIS3
serious injuries, MAIS4 severe injuries, and MAIS5 critical injuries.
PDO refers to property damage only crashes, and counts for PDOs refer
to vehicles in which no one was injured. From Table II-68, ratios of
injury incidence/fatality are derived for each injury severity level as
follows:

[GRAPHIC] [TIFF OMITTED] TP24AU18.099

    For each fatality that occurs nationwide in traffic crashes, there
are 561 vehicles involved in PDOs, 139 uninjured occupants in injury
vehicles, 105 minor injuries, 10 moderate injuries, 3 serious injuries,
and fractional numbers of the most serious categories which include
severe and critical nonfatal injuries. For each fatality ascribed to
CAFE it is assumed there will be nonfatal crashes in these same ratios.
    Property damage costs associated with delayed new vehicle purchases
must be treated differently because crashes that subsequently occur
damage older used vehicles instead of newer vehicles. Used vehicles are
worth less and will cost less to repair, if they are repaired at all.
The consumer's property damage loss is thus reduced by longer retention
of these vehicles. To estimate this loss, average new and used vehicle
prices were compared. New vehicle transaction prices were estimated
from a study published by Kelley Blue Book.\327\ Based on these data,
the average new vehicle transaction price in January 2017 was $34,968.
Used vehicle transaction prices were obtained from Edmonds Used Vehicle
Market Report published in February of 2017.\328\ Edmonds data indicate
the average used vehicle transaction price was $19,189 in 2016. There
is a minor timing discrepancy in these data because the new vehicle
data represent January 2017, and the used vehicle price is for the
average over 2016. NHTSA was unable to locate exact matching data at
this time, but the agency believes the difference will be minor.
---------------------------------------------------------------------------

    \327\ Press Release, Kelley Blue Book, New-Car Transaction
Prices Remain High, Up More Than 3 Percent Year-Over-Year in January
2017, According to Kelley Blue Book (Feb. 1, 2017), https://mediaroom.kbb.com/2017-02-01-New-Car-Transaction-Prices-Remain-High-Up-More-Than-3-Percent-Year-Over-Year-In-January-2017-According-To-Kelley-Blue-Book.
    \328\ Edmunds Used Vehicle Market Report, Edmunds (Feb. 2017),
https://dealers.edmunds.com/static/assets/articles/2017_Feb_Used_Market_Report.pdf.
---------------------------------------------------------------------------

    Based on these data, new vehicles are on average worth 82% more
than used vehicles. To estimate the effect of higher property damage
costs for newer vehicles on crashes, the per unit property damage costs
from Table I-9 in Blincoe et al. (2015) were multiplied by this factor.
Results are illustrated in Table II-69.
[GRAPHIC] [TIFF OMITTED] TP24AU18.100

    The total property damage cost reduction was then calculated as a
function of the number of fatalities reduced or increased by CAFE as
follows:
[GRAPHIC] [TIFF OMITTED] TP24AU18.145

Where:
S = total property damage savings from retaining used vehicles
longer
F = change in fatalities estimated for CAFE due to retaining used
vehicles
r = ratio of nonfatal injuries or PDO vehicles to fatalities (F)
p = value of property damage prevented by retaining older vehicle

n = the 8 injury severity categories

    The number of fatalities ascribed to CAFE because of older vehicle
retention was multiplied by the unit cost per fatality from Table I-9
in Blincoe et al. (2015) to determine the societal impact accounted for
by these fatalities.\329\ From Table I-8 in Blincoe et al. (2015),
NHTSA subtracted property damage costs from all injury severity levels
and recalculated the total comprehensive value of societal losses from
crashes. The agency then divided the portion of these crashes because
of fatalities by the resulting total to estimate the portion of crashes
excluding property damage that are accounted for by fatalities. Results
indicate fatalities accounted for approximately 40% of all societal
costs exclusive of property damage. NHTSA then divided the total cost
of the added fatalities by 0.4 to estimate the total cost of all
crashes prevented exclusive of the savings in property damage. After
subtracting the total savings in property damage from this value, we
divided the fatality cost by it to estimate that overall, fatalities
account for 43% of the total costs that would result from older vehicle
retention.
---------------------------------------------------------------------------

    \329\ Note: These calculations used the original values in the
Blincoe et all (2015) tables without adjusting for economics. These
calculations produce ratios and are thus not sensitive to
adjustments for inflation.
---------------------------------------------------------------------------

    For the fatalities that occur because of mass effects or to the
rebound effect, the calculation was more direct, a simple application
of the ratio of the portion of costs produced by fatalities. In this
case, there is no need to adjust for property damage because all
impacts were derived from the mix of vehicles in the on-road fleet.
Again, from Table I-8 in Blincoe et al (2015), we derive this ratio
based on all cost factors including property damage to be .36. These
calculations are summarized as follows:
[GRAPHIC] [TIFF OMITTED] TP24AU18.101

Where:

SV = Value of societal Impacts of all crashes
F = change in fatalities estimated for CAFE due to retaining used
vehicles
v = Comprehensive societal value of preventing 1 fatality
x = Percent of total societal loss from crashes excluding property
damage accounted for by fatalities
S = total property damage savings from retaining used vehicles
longer
M = change in fatalities due to changes in vehicle mass to meet CAFE
standards
c = Percent of total societal loss from all cost factors in all
crashes accounted for by fatalities

    For purposes of application in the CAFE model, these two factors
were combined based on the relative contribution to total fatalities of
different factors. As noted, although a safety impact from the rebound
effect is calculated, these impacts are considered to be freely chosen
rather than imposed by CAFE and imply personal benefits at least equal
to the sum of their added costs and safety consequences. The impacts of
this nonfatal crash adjustment affect costs and benefits equally. When
considering safety impacts actually imposed by CAFE standards, only
those from mass changes and vehicle purchase delays are considered.
NHTSA has two different factors depending on which metric is
considered. The agency created these factors by weighting components by
the relative contribution to changes in fatalities associated with each
component. This process and results are shown in Table II-70. Note: For
the NPRM, NHTSA applied the average weighted factor to all fatalities.
This will tend to slightly overstate costs because of sales and
scrappage and understate costs associated with mass and rebound. The
agency will consider ways to adjust this minor discrepancy for the
final rule.
[GRAPHIC] [TIFF OMITTED] TP24AU18.102

    Table II-71, Table II-72, Table II-73, and Table II-74 summarize
the safety effects of CAFE standards across the various alternatives
under the 3% and 7% discount rates. As noted in Section II.F.5,
societal impacts are valued using a $9.9 million value per statistical
life (VSL). Fatalities in these tables are undiscounted; only the
monetized societal impact is discounted.

[GRAPHIC] [TIFF OMITTED] TP24AU18.103

[GRAPHIC] [TIFF OMITTED] TP24AU18.104

[GRAPHIC] [TIFF OMITTED] TP24AU18.105

[GRAPHIC] [TIFF OMITTED] TP24AU18.106

[GRAPHIC] [TIFF OMITTED] TP24AU18.107

    Table II-75 through Table II-78 summarize the safety effects of GHG
standards across the various alternatives under the 3% and 7% discount
rates. As noted in Section II.F.5, societal impacts are valued using a
$9.9 million value per statistical life (VSL). Fatalities in these
tables are undiscounted; only the monetized societal impact is
discounted.

[GRAPHIC] [TIFF OMITTED] TP24AU18.108

[GRAPHIC] [TIFF OMITTED] TP24AU18.109

[GRAPHIC] [TIFF OMITTED] TP24AU18.110

[GRAPHIC] [TIFF OMITTED] TP24AU18.111

[GRAPHIC] [TIFF OMITTED] TP24AU18.112

    While NHTSA notes the value of rebound effect fatalities, as well
as total fatalities from all causes, the agency does not add rebound
effects to the other CAFE-related impacts because rebound-related
fatalities and injuries result from risk that is freely chosen and
offset by societal valuations that at a minimum exceed the aggregate
value of safety consequences plus added vehicle operating and
maintenance costs.\330\ These costs implicitly involve a cost and a
benefit that are offsetting. The relevant safety impacts attributable
to CAFE are highlighted in bold in the above tables.
---------------------------------------------------------------------------

    \330\ It would also include some level of consumer surplus,
which we have estimated using the standard triangular function. This
is discussed in Chapter 8.5.1 of the PRIA.
---------------------------------------------------------------------------

G. How the Model Analyzes Different Potential CAFE and CO2 Standards

1. Specification of No-Action and Other Regulatory Alternatives
(a) Mathematical Functions Defining Passenger Car and Light Trucks
Standards for Each Model Year During 2016-2032
    In the U.S. market, the stringency of CAFE and CO2
standards can influence the design of new vehicles offered for sale by
requiring manufacturers to produce increasingly fuel efficient vehicles
in order to meet program

requirements. This is also true in the CAFE model simulation, where the
standards can be defined with a great deal of flexibility to examine
the impact of different program specifications on the auto industry.
Standards are defined for each model year and can represent different
slopes that relate fuel economy to footprint, different regions of flat
slopes, and different rates of increase for each of three regulatory
classes covered by the CAFE program (domestic passenger cars, imported
passenger cars, and light trucks).
    The CAFE model takes, as inputs, the coefficients of the
mathematical functions described in Sections III and IV. It uses these
coefficients and the function to which they belong to define the target
for each vehicle in the fleet, then computes the standard using the
harmonic average of the targets for each manufacturer and fleet. The
model also allows the user to define the extent and duration of various
compliance flexibilities (e.g., limits on the amount of credit that a
manufacturer may claim related to air conditioning efficiency
improvements or off-cycle fuel economy adjustments) as well as limits
on the number of years that CAFE credits may be carried forward or the
amount that may be transferred between a manufacturer's fleets.
(b) Off-Cycle and A/C Efficiency Adjustments Anticipated for Each Model
Year
    Another aspect of credit accounting is partially implemented in the
CAFE model at this point--those related to the application of off-cycle
and A/C efficiency adjustments, which manufacturers earn by taking
actions such as special window glazing or using reflective paints that
provide fuel economy improvements in real-world operation but do not
produce measurable improvements in fuel consumption on the 2-cycle
test.
    NHTSA's inclusion of off-cycle and A/C efficiency adjustments began
in MY 2017, while EPA has collected several years' worth of submissions
from manufacturers about off-cycle and A/C efficiency technology
deployment. Currently, the level of deployment can vary considerably by
manufacturer with several claiming extensive Fuel Consumption
Improvement Values (FCIV) for off-cycle and A/C efficiency technologies
and others almost none. The analysis of alternatives presented here
does not attempt to project how future off-cycle and A/C efficiency
technology use will evolve or speculate about the potential
proliferation of FCIV proposals submitted to the agencies. Rather, this
analysis uses the off-cycle credits submitted by each manufacturer for
MY 2017 compliance and carries these forward to future years with a few
exceptions. Several of the technologies described in Section II.D are
associated with A/C efficiency and off-cycle FCIVs. In particular,
stop-start systems, integrated starter generators, and full hybrids are
assumed to generate off-cycle adjustments when applied to vehicles to
improve their fuel economy. Similarly, higher levels of aerodynamic
improvements are assumed to include active grille shutters on the
vehicle, which also qualify for off-cycle FCIVs.
    The analysis assumes that any off-cycle FCIVs that are associated
with actions outside of the technologies discussed in Section II.D
(either chosen from the pre-approved ``pick list,'' or granted in
response to individual manufacturer petitions) remain at the levels
claimed by manufacturers in MY 2017. Any additional A/C efficiency and
off-cycle adjustments that accrue as the result of explicit technology
application are calculated dynamically in each model year for each
alternative. The off-cycle FCIVs for each manufacturer and fleet,
denominated in grams CO2 per mile,\331\ are provided in
Table II-79.
---------------------------------------------------------------------------

    \331\ For the purpose of estimating their contribution to CAFE
compliance, the grams CO2/mile values in Table II-79 are
converted to gallons/mile and applied to a manufacturer's 2-cycle
CAFE performance. When calculating compliance with EPA's GHG
program, there is no conversion necessary (as standards are also
denominated in grams/mile).

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

[GRAPHIC] [TIFF OMITTED] TP24AU18.113

    The model currently accounts for any off-cycle adjustments
associated with technologies that are included in the set of fuel-
saving technologies explicitly simulated as part of this proposal (for
example, start-stop systems that reduce fuel consumption during idle or
active grille shutters that improve aerodynamic drag at highway speeds)
and accumulates these adjustments up to the 10 g/mi cap. As a practical
matter, most of the adjustments for which manufacturers are claiming
off-cycle FCIV exist outside of the technology tree, so the cap is
rarely reached during compliance simulation. If those FCIVs become a
more important compliance mechanism, it may be necessary to model their
application explicitly. However, doing so will require data on which
vehicle models already possess these improvements as well as the cost
and expected value of applying them to other models in the future.
Comment is sought on both the data requirements and strategic decisions
associated with manufacturers' use of A/C efficiency and off-cycle
technologies to improve CAFE and CO2 compliance.
(c) Civil Penalty Rate and OEMs' Anticipated Willingness To Treat Civil
Penalties as a Program Flexibility
    Throughout the history of the CAFE program, some manufacturers have
consistently achieved fuel economy levels below their standard. As in
previous versions of the CAFE model, the current version allows the
user to specify inputs identifying such manufacturers and to consider
their compliance decisions as if they are willing to pay civil
penalties for non-compliance with the CAFE program. The assumed civil
penalty rate in the current analysis is $5.50 per 1/10 of a mile per
gallon, per vehicle sold.
    It is worth noting that treating a manufacturer as if they are
willing to pay civil penalties does not necessarily mean that it is
expected to pay penalties in reality. It merely implies that the
manufacturer will only apply fuel economy technology up to a point, and
then stop, regardless of whether or not its corporate average fuel
economy is above its standard. In practice, we expect that many of
these manufacturers will continue to be active in the credit market,
using trades with other manufacturers to transfer credits into specific
fleets that are challenged in any given year, rather than paying
penalties to resolve CAFE deficits. The CAFE model calculates the
amount of penalties paid by each manufacturer, but it does not simulate
trades between manufacturers. In practice, some (possibly most) of the
total estimated penalties may be a transfer from one OEM to another.
    While the Energy Policy and Conservation Act (EPCA), as amended in
2007 by the Energy Independence and Security Act, prescribes these
specific civil penalty provisions for CAFE standards, the Clean Air Act
(CAA) does not contain similar provisions. Rather, the CAA's provisions
regarding noncompliance constitute a de facto prohibition against
selling vehicles failing to comply with emissions standards. Therefore,
inputs regarding civil penalties--including inputs regarding
manufacturers' potential willingness to treat civil penalty payment as
an economic choice--apply only to simulation of CAFE standards.
(d) Treatment of Credit Provisions for ``Standard Setting'' and
``Unconstrained'' Analyses
    NHTSA may not consider the application of CAFE credits toward
compliance with new standards when establishing the standards
themselves.\332\ As such, this analysis considers 2020 to be the last
model year in which carried-forward or transferred credits can be
applied for the CAFE program. Beginning in model year 2021,

today's ``standard setting'' analysis is conducted assuming each fleet
must comply with the CAFE standard separately in every model year.
---------------------------------------------------------------------------

    \332\ 49 U.S.C. 32902(h) (2007).
---------------------------------------------------------------------------

    The ``unconstrained'' perspective acknowledges that these
flexibilities exist as part of the program and, while not considered in
NHTSA's decision of the preferred alternative, are important to
consider when attempting to estimate the real impact of any
alternative. Under the ``unconstrained'' perspective, credits may be
earned, transferred, and applied to deficits in the CAFE program
throughout the full range of model years in the analysis. The Draft
Environmental Impact Analysis (DEIS) accompanying today's NPRM presents
results of ``unconstrained'' modeling. Also, because the CAA provides
no direction regarding consideration of any CO2 credit
provisions, today's analysis includes simulation of carried-forward and
transferred CO2 credits in all model years.
(e) Treatment of AFVs for ``Standard Setting'' and ``Unconstrained''
Analyses
    NHTSA is also prohibited from considering the possibility that a
manufacturer might produce alternatively fueled vehicles as a
compliance mechanism,\333\ taking advantage of credit provisions
related to AFVs that significantly increase their fuel economy for CAFE
compliance purposes. Under the ``standard setting'' perspective, these
technologies (pure battery electric vehicles and fuel cell vehicles
\334\) are not available in the compliance simulation to improve fuel
economy. Under the ``unconstrained'' perspective, such as is documented
in the DEIS, the CAFE model considers these technologies in the context
of all other available technologies and may apply them if they
represent cost-effective compliance pathways. However, under both
perspectives, the analysis continues to include dedicated AFVs that
already exist in the MY 2016 fleet (and their projected future volumes)
in CAFE calculations. Also, because the CAA provides no direction
regarding consideration of alternative fuels, today's analysis includes
simulation of the potential that some manufacturers might introduce new
AFVs in response to CO2 standards. To fully represent the
compliance benefit from such a response, NHTSA modified the CAFE model
to include the specific provisions related to AFVs under the
CO2 standards. In particular, the CAFE model now carries a
full representation of the production multipliers related to electric
vehicles, fuel cell vehicles, plug-in hybrids, and CNG vehicles, all of
which vary by year through MY 2021.
---------------------------------------------------------------------------

    \333\ Id.
    \334\ Dedicated compressed natural gas (CNG) vehicles should
also be excluded in this perspective but are not considered as a
compliance strategy under any perspective in this analysis.
---------------------------------------------------------------------------

2. Simulation of Manufacturers' [and Buyers'] Potential Responses to
Each Alternative
    The CAFE model provides a way of estimating how manufacturers could
attempt to comply with a given CAFE standard by adding technology to
fleets that the agencies anticipate they will produce in future model
years. This exercise constitutes a simulation of manufacturers'
decisions regarding compliance with CAFE or CO2 standards.
    This compliance simulation begins with the following inputs: (a)
The analysis fleet of vehicles from model year 2016 discussed above in
Section II.B, (b) fuel economy improving technology estimates discussed
above in Section II.D, (c) economic inputs discussed above in Section
II.E, and (d) inputs defining baseline and potential new CAFE
standards. For each manufacturer, the model applies technologies in
both a logical sequence and a cost-minimizing strategy in order to
identify a set of technologies the manufacturer could apply in response
to new CAFE or CO2 standards. The model applies technologies
to each of the projected individual vehicles in a manufacturer's fleet,
considering the combined effect of regulatory and market incentives
while attempting to account for manufacturers' production constraints.
Depending on how the model is exercised, it will apply technology until
one of the following occurs:

    (1) The manufacturer's fleet achieves compliance \335\ with the
applicable standard and continuing to add technology in the current
model year would be attractive neither in terms of stand-alone
(i.e., absent regulatory need) cost-effectiveness nor in terms of
facilitating compliance in future model years;
---------------------------------------------------------------------------

    \335\ When determining whether compliance has been achieved in
the CAFE program, existing CAFE credits that may be carried over
from prior model years or transferred between fleets are also used
to determine compliance status. For purposes of determining the
effect of maximum feasible CAFE standards, NHTSA cannot consider
these mechanisms for years being considered (though does so for
model years that are already final) and exercises the CAFE model
without enabling these options.
---------------------------------------------------------------------------

    (2) The manufacturer ``exhausts'' available technologies; \336\
or
---------------------------------------------------------------------------

    \336\ In a given model year, it is possible that production
constraints cause a manufacturer to ``run out'' of available
technology before achieving compliance with standards. This can
occur when: (a) An insufficient volume of vehicles are expected to
be redesigned, (b) vehicles have moved to the ends of each
(relevant) technology pathway, after which no additional options
exist, or (c) engineering aspects of available vehicles make
available technology inapplicable (e.g., secondary axle disconnect
cannot be applied to two-wheel drive vehicles).
---------------------------------------------------------------------------

    (3) For manufacturers assumed to be willing to pay civil
penalties (in the CAFE program), the manufacturer reaches the point
at which doing so would be more cost-effective (from the
manufacturer's perspective) than adding further technology.

    The model accounts explicitly for each model year, applying
technologies when vehicles are scheduled to be redesigned or freshened
and carrying forward technologies between model years once they are
applied (until, if applicable, they are superseded by other
technologies). The model then uses these simulated manufacturer fleets
to generate both a representation of the U.S. auto industry and to
modify a representation of the entire light-duty registered vehicle
population. From these fleets, the model estimates changes in physical
quantities (gallons of fuel, pollutant emissions, traffic fatalities,
etc.) and calculates the relative costs and benefits of regulatory
alternatives under consideration.
    The CAFE model accounts explicitly for each model year, in turn,
because manufacturers actually ``carry forward'' most technologies
between model years, tending to concentrate the application of new
technology to vehicle redesigns or mid-cycle ``freshenings,'' and
design cycles vary widely among manufacturers and specific products.
Comments by manufacturers and model peer reviewers strongly support
explicit year-by-year simulation. Year-by-year accounting also enables
accounting for credit banking (i.e., carry-forward), as discussed
above, and at least four environmental organizations recently submitted
comments urging the agencies to consider such credits, citing NHTSA's
2016 results showing impacts of carried-forward credits.\337\ Moreover,
EPCA/EISA requires that NHTSA make a year-by-year determination of the
appropriate level of stringency and then set the standard at that
level, while ensuring ratable increases in average fuel economy through
MY 2020. The multi-year planning capability, (optional) simulation of
``market-driven overcompliance,'' and EPCA credit mechanisms (again,
for purposes of modeling the CAFE program) increase the model's ability
to simulate manufacturers' real-world behavior, accounting for the fact
that

manufacturers will seek out compliance paths for several model years at
a time, while accommodating the year-by-year requirement. This same
multi-year planning structure is used to simulate responses to
standards defined in grams CO2/mile, and utilizing the set
of specific credit provisions defined under EPA's program.
---------------------------------------------------------------------------

    \337\ Comment by Environmental Law & Policy Center, Natural
Resources Defense Council (NRDC), Public Citizen, and Sierra Club,
Docket ID EPA-HQ-OAR-2015-0827-9826, at 28-29.
---------------------------------------------------------------------------

(a) Representation of Manufacturers' Production Constraints
    After the light-duty rulemaking analysis accompanying the 2012
final rule that finalized NHTSA's standards through MY 2021, NHTSA
began work on changes to the CAFE model with the intention of better
reflecting constraints of product planning and cadence for which
previous analyses did not account.
(b) Product Cadence
    Past comments on the CAFE model have stressed the importance of
product cadence--i.e., the development and periodic redesign and
freshening of vehicles--in terms of involving technical, financial, and
other practical constraints on applying new technologies, and DOT has
steadily made changes to both the CAFE model and its inputs with a view
toward accounting for these considerations. For example, early versions
of the model added explicit ``carrying forward'' of applied
technologies between model years, subsequent versions applied
assumptions that most technologies will be applied when vehicles are
freshened or redesigned, and more recent versions applied assumptions
that manufacturers would sometimes apply technology earlier than
``necessary'' in order to facilitate compliance with standards in
ensuing model years. Thus, for example, if a manufacturer is expected
to redesign many of its products in model years 2018 and 2023, and the
standard's stringency increases significantly in model year 2021, the
CAFE model will estimate the potential that the manufacturer will add
more technology than necessary for compliance in MY 2018, in order to
carry those product changes forward through the next redesign and
contribute to compliance with the MY 2021 standard. This explicit
simulation of multiyear planning plays an important role in determining
year-by-year analytical results.
    As in previous iterations of CAFE rulemaking analysis, the
simulation of compliance actions that manufacturers might take is
constrained by the pace at which new technologies can be applied in the
new vehicle market. Operating at the Make/Model level (e.g., Toyota
Camry) allows the CAFE model to explicitly account for the fact that
individual vehicle models undergo significant redesigns relatively
infrequently. Many popular vehicle models are only redesigned every six
years or so, with some larger/legacy platforms (the old Ford Econoline
Vans, for example) stretching more than a decade between significant
redesigns. Engines, which are often shared among many different models
and platforms for a single manufacturer, can last even longer--eight to
ten years in most cases.
    While these characterizations of product cadence are important to
any evaluation of the impacts of CAFE or CO2 standards, they
are not known with certainty--even by the manufacturers themselves over
time horizons as long as those covered by this analysis. However, lack
of certainty about redesign schedules is not license to ignore them.
Indeed, when manufacturers meet with the agencies to discuss
manufacturers' plans vis-[agrave]-vis CAFE and CO2
requirements, manufacturers typically present specific and detailed
year-by-year information that explicitly accounts for anticipated
redesigns. Such year-by-year analysis is also essential to
manufacturers' plans to make use of provisions (for CAFE, statutory and
specific) allowing credits to be carried forward to future model years,
carried back from future model years, transferred between regulated
fleets, and traded with other manufacturers. Manufacturers are never
certain about future plans, but they spend considerable effort
developing, continually adjusting, and implementing them.
    For every model that appears in the MY 2016 analysis fleet, the
model years have been estimated in which future redesigns (and less
significant ``freshenings,'' which offer manufacturers the opportunity
to make less significant changes to models) will occur. These appear in
the market data file for each model variant. Mid-cycle freshenings
provide additional opportunities to add some technologies in years
where smaller shares of a manufacturer's portfolio is scheduled to be
redesigned. In addition, the analysis accounts for multiyear planning--
that is, the potential that manufacturers may apply ``extra''
technology in an early model year with many planned redesigns in order
to carry technology forward to facilitate compliance in a later model
year with fewer planned redesigns. Further, the analysis accounts for
the potential that manufacturers could earn CAFE and/or CO2
credits in some model years and use those credits in later model years,
thereby providing another compliance option in years with few planned
redesigns. Finally, it should be noted that today's analysis does not
account for future new products (or discontinued products)--past trends
suggest that some years in which an OEM had few redesigns may have been
years when that OEM introduced significant new products. Such changes
in product offerings can obviously be important to manufacturers'
compliance positions but cannot be systematically and transparently
accounted for with a fleet forecast extrapolated forward 10 or more
years from a largely-known fleet. While manufacturers' actual plans
reflect intentions to discontinue some products and introduce others,
those plans are considered CBI. Further research would be required in
order to determine whether and, if so, how it would be practicable to
simulate such decisions, especially without relying on CBI.
    Additionally, each technology considered for application by the
CAFE model is assigned to either a ``refresh'' or ``redesign'' cadence
that dictates when it can be applied to a vehicle. Technologies that
are assigned to ``refresh/redesign'' can be applied at either a refresh
or redesign, while technologies that are assigned to ``redesign'' can
only be applied during a significant vehicle redesign. Table II-80 and
Table II-81 show the technologies available to manufacturers in the
compliance simulation, the level at which they are applied (described
in greater detail in the CAFE model documentation), whether they are
available outside of a vehicle redesign, and a short description of
each. A brief examination of the tables shows that most technologies
are only assumed to be available during a vehicle redesign--and nearly
all engine improvements are assumed to be available only during
redesign. In a departure from past CAFE analyses, all transmission
improvements are assumed to be available during refresh as well as
redesign. While there are past and recent examples of mid-cycle product
changes, it seems reasonable to expect that manufacturers will tend to
attempt to keep engineering and other costs down by applying most major
changes mainly during vehicle redesigns and some mostly modest changes
during product freshenings. As mentioned below, comment is sought on
the approach to account for product cadence.
(c) Component Sharing and Inheritance (Engines, Transmissions, and
Platforms)
    In practice, manufacturers are limited in the number of engines and
transmissions that they produce.

Typically, a manufacturer produces a number of engines--perhaps six or
eight engines for a large manufacturer--and tunes them for slight
variants in output for a variety of car and truck applications.
Manufacturers limit complexity in their engine portfolio for much the
same reason as they limit complexity in vehicle variants: They face
engineering manpower limitations, and supplier, production, and service
costs that scale with the number of parts produced.
    In previous analyses that used the CAFE model (with the exception
of the 2016 Draft TAR), engines and transmissions in individual vehicle
models were allowed relative freedom in technology application,
potentially leading to solutions that would, if followed, create many
more unique engines and transmissions than exist in the analysis fleet
(or in the market) for a given model year. This multiplicity likely
failed to sufficiently account for costs associated with such increased
complexity in the product portfolio and may have represented an
unrealistic diffusion of products for manufacturers that are
consolidating global production to increasingly smaller numbers of
shared engines and platforms.\338\ The lack of a constraint in this
area allowed the model to apply different levels of technology to the
engine in each vehicle in which it was present at the time that vehicle
was redesigned or refreshed, independent of what was done to other
vehicles using a previously identical engine.
---------------------------------------------------------------------------

    \338\ 2015 NAS Report, at pg. 258-259.
---------------------------------------------------------------------------

    One peer reviewer of the CAFE model recently commented, ``The
integration of inheritance and sharing of engines, transmissions, and
platforms across a manufacturer's light duty fleet and separately
across its light duty truck fleet is standard practice within the
industry.'' In the current version of the CAFE model, engines and
transmissions that are shared between vehicles must apply the same
levels of technology, in all technologies, dictated by engine or
transmission inheritance. This forced adoption is referred to as
``engine inheritance'' in the model documentation. In practice, the
model first chooses an ``engine leader'' among vehicles sharing the
same engine--the vehicle with the highest sales in MY 2016. If there is
a tie, the vehicle with the highest average MSRP is chosen,
representing the idea that manufacturers will choose to pilot the
newest technology on premium vehicles if possible. The model applies
the same logic with respect to the application of transmission changes.
After the model modifies the engine on the ``engine leader'' (or
``transmission leader''), the changes to that engine propagate through
to the other vehicles that share that engine (or transmission) in
subsequent years as those vehicles are redesigned. The CAFE model has
been modified to provide additional flexibility vis-[agrave]-vis
product cadence. In a recent public comment, NRDC noted:

    EPA and NHTSA currently constrain their model to apply
significant fuel-efficient technologies mainly during a product-
redesign as opposed to product-refresh (or mid-cycle). This was
identified as one of the most sensitive assumptions affecting
overall program costs by NHTSA in the TAR. By constraining the
model, the agencies have likely under-estimated the ability of auto
manufacturers to incorporate some technologies during their product
refreshes. This is particularly true regarding the critical
powertrain technologies which are undergoing continuous improvement.
The agency should account for these trends and incorporate greater
flexibility for automakers--within their models--to incorporate more
mid-cycle enhancements.\339\
---------------------------------------------------------------------------

    \339\ Comment by Environmental Law & Policy Center, Natural
Resources Defense Council (NRDC), Public Citizen, and Sierra Club,
Docket ID EPA-HQ-OAR-2015-0827-9826, at 32.

    While engine redesigns are only applied to the engine leader when
it is redesigned in the model, followers may now inherit upgraded
engines (that they share with the leader) at either refresh or
redesign. All transmission changes, whether upgrades to the ``leader''
or inheritance to ``followers'' can occur at refresh as well as
redesign. This provides additional opportunities for technology
diffusion within manufacturers' product portfolios.
    While ``follower'' vehicles are awaiting redesign (or, for
transmissions, refreshing as applicable), they carry a legacy version
of the shared engine or transmission. As one peer reviewer recently
stated, ``Most of the time a manufacturer will convert only a single
plant within a model year. Thus both the `old' and `new' variant of the
engine (or transmission) will produced for a finite number of years.''
\340\ The CAFE model currently carries no additional cost associated
with producing both earlier revisions of an engine and the updated
version simultaneously. Further research would be needed to determine
whether sufficient data is likely to be available to explicitly specify
and apply additional costs involved with continuing to produce an
existing engine or transmission for some vehicles that have not yet
progressed to a newer version of that engine or transmission. Comment
is sought on possible data sources and approaches that could be used to
represent any additional costs associated with phased introduction of
new engines or transmissions.
---------------------------------------------------------------------------

    \340\ CAFE Model Peer Review, p. 19.
---------------------------------------------------------------------------

    There are some logical consequences of this approach, the first of
which is that forcing engine and transmission changes to propagate
through to other vehicles in this way effectively dictates the pace at
which new technology can be applied and limits the total number of
unique engines that the model simulates. In the past, NHTSA used
``phase-in caps'' (see discussion below) to limit the amount of
technology that can be applied to any vehicle in a given year. However,
by explicitly tying the engine changes to a specific vehicle's product
cadence, rather than letting the timing of changes vary across all the
vehicles that share an engine, the model ensures that an engine is only
changed when its leader is redesigned (at most). Given that most
vehicle redesign cycles are five to eight years, this approach still
represents shorter average lives than most engines in the market, which
tend to be in production for eight to ten years or more. It is also the
case that vehicles which share an engine in the analysis fleet (MY
2016, for this analysis) are assumed to share that same engine
throughout the analysis--unless one or both of them are converted to
power-split hybrids (or farther) on the electrification path. In the
market, this is not true--since a manufacturer will choose an engine
from among the engines it produces to fulfill the efficiency and power
demands of a vehicle model upon redesign. That engine need not be from
the same family of engines as the prior version of that vehicle. This
is a simplifying assumption in the model. While the model already
accommodates detailed inputs regarding redesign schedules for specific
vehicles and commercial information sources are available to inform
these inputs, further research would be needed to determine whether
design schedules for specific engines and transmissions can practicably
be simulated.
    The CAFE model has implemented a similar structure to address
shared vehicle platforms. The term ``platform'' is used loosely in
industry but generally refers to a common structure shared by a group
of vehicle variants. The degree of commonality varies with some
platform variants exhibiting traditional ``badge engineering'' where
two products are differentiated by little more than insignias, while
other platforms may be used to produce a broad suite of vehicles that
bear little outer resemblance to one another.

    Given the degree of commonality between variants of a single
platform, manufacturers do not have complete freedom to apply
technology to a vehicle: While some technologies (e.g. low rolling
resistance tires) are very nearly ``bolt-on'' technologies, others
involve substantial changes to the structure and design of the vehicle,
and therefore necessarily are constant among vehicles that share a
common platform. NHTSA has, therefore, modified the CAFE model such
that all mass reduction technologies are forced to be constant among
variants of a platform.
    Within the analysis fleet, each vehicle is associated with a
specific platform. Similar to the application of engine and
transmission technologies, the CAFE model defines a platform ``leader''
as the vehicle variant of a given platform that has the highest level
of observed mass reduction present in the analysis fleet. If there is a
tie, the CAFE model begins mass reduction technology on the vehicle
with the highest sales in model year 2016. If there remains a tie, the
model begins by choosing the vehicle with the highest MSRP in MY 2016.
As the model applies technologies, it effectively levels up all
variants on a platform to the highest level of mass reduction
technology on the platform. So, if the platform leader is already at
MR3 in MY 2016, and a ``follower'' starts at MR0 in MY 2016, the
follower will get MR3 at its next redesign (unless the leader is
redesigned again before that time, and further increases the MR level
associated with that platform, then the follower would receive the new
MR level).
    In the 2015 NPRM proposing new fuel consumption and GHG standards
for heavy-duty pickups and vans, NHTSA specifically requested comment
on the general use of shared engines, transmissions, and platforms
within CAFE rulemakings. While no commenter responded to this specific
request, comments from some environmental organizations cited examples
of technology sharing between light- and heavy-duty products. NHTSA has
continued to refine its implementation of an approach accounting for
shared engines, transmissions, and platforms, and again seeks comment
on the approach, recommendations regarding any other approaches, and
any information that would facilitate implementation of the agency's
current approach or any alternative approaches.
(d) Phase-In Caps
    The CAFE model retains the ability to use phase-in caps (specified
in model inputs) as proxies for a variety of practical restrictions on
technology application, including the improvements described above.
Unlike vehicle-specific restrictions related to redesign, refreshes or
platforms/engines, phase-in caps constrain technology application at
the vehicle manufacturer level for a given model year. Introduced in
the 2006 version of the CAFE model, they were intended to reflect a
manufacturer's overall resource capacity available for implementing new
technologies (such as engineering research and development personnel
and financial resources), thereby ensuring that resource capacity is
accounted for in the modeling process.
    Compared to prior analyses of light-duty standards, these model
changes result in some changes in the broad characteristics of the
model's application of technology to manufacturers' fleets. Since the
use of phase-in caps has been de-emphasized and manufacturer technology
deployment remains tied strongly to estimated product redesign and
freshening schedules, technology penetration rates may jump more
quickly as manufacturers apply technology to high-volume products in
their portfolio. As a result, the model will ignore a phase-in cap to
apply inherited technology to vehicles on shared engines,
transmissions, and platforms.
    In previous CAFE rulemakings, redesign/refresh schedules and phase-
in caps were the primary mechanisms to reflect an OEM's limited pool of
available resources during the rulemaking time frame and the years
preceding it, especially in years where many models may be scheduled
for refresh or redesign. The newly-introduced representation of
platform-, engine-, and transmission-related considerations discussed
above augment the model's preexisting representation of redesign cycles
and eliminate the need to rely on phase-in caps. By design,
restrictions that enforce commonality of mass reduction on variants of
a platform, and those that enforce engine and transmission inheritance,
will result in fewer vehicle-technology combinations in a
manufacturer's future modeled fleet. The integration of shared
components and product cadence as a mechanism to control the pace of
technology application also more accurately represents each
manufacturer's unique position in the market and its existing
technology footprint, rather than a technology-specific phase-in cap
that is uniformly applied to all manufacturers in a given year. Comment
is sought regarding this shift away from relying on phase-in caps and,
if greater reliance on phase-in caps is recommended, what approach and
information can be used to define and apply these caps.
(e) Interactions Between Regulatory Classes
    Like earlier versions, the current CAFE model provides the
capability for integrated analysis spanning different regulatory
classes, accounting both for standards that apply separately to
different classes and for interactions between regulatory classes.