types of risk on the relative risk by vehicle type, he cautioned that,
when analyzing casualty risk per crash, analysts should control for
driver age and gender, crash location (urban vs. rural), and the state
in which the crash occurred (to account for crash reporting biases).
    Several participants pointed out that analyses must also control
for individual technologies with significant safety effects (e.g.,
Electronic Stability Control, airbags). It was not always conclusive
whether a specialty vehicle group (e.g., sports cars, two-door cars,
early crossover SUVs) were outliers that confound the trend or unique
datasets that isolate specific vehicle characteristics. Unfortunately,
specialty vehicle groups are usually adopted by specific driver groups,
often with outlying vehicle usage or driver behavior patterns. Green,
who conducted an independent review of 18 previous statistical
analyses, suggested that evaluating residuals will give an indication
of whether or not a data subset can be legitimately removed without
inappropriately affecting the analytical results.
    It was recognized that the physics of a two-vehicle crash require
that the lighter vehicle experience a greater change in velocity,
which, all else being equal, often leads to disproportionately more
injury risk. Lund noted persistent historical trends that, in any time
period, occupants of the smallest and lightest vehicles had, on
average, fatality rates approximately twice those of occupants of the
largest and heaviest vehicles, but also predicted that ``the sky will
not fall'' as the fleet downsizes, insofar as we will not see an
increase in absolute injury risk because smaller cars will become
increasingly protective of their occupants. Padmanaban also noted in
her research of the historical trends that mass ratio and vehicle
stiffness are significant predictors with mass ratio consistently the
dominant parameter when correlating harm. Reducing the mass of any
vehicle may have competing societal effects as it increases the injury
risk in the lightened vehicle and decreases them in the partner
vehicle.
    The separation of key parameters was also discussed as a challenge
to the analyses, as vehicle size has historically been highly
correlated with vehicle mass. Presenters had varying approaches for
dealing with the potential multicollinearity between these two
variables. Van Auken of DRI stated that there was disagreement on what
value of Variance Inflation Factor (VIF, a measure of
multicollinearity) that would call results into question, and suggested
that a large value of VIF for curb weight might imply ``perhaps the
effect of weight is too small in comparison to other factors.'' Green,
of UMTRI, stated that highly correlated variables may not be
appropriate for use in a predictive model and that ``match[ing] on
footprint'' (i.e., conducting multiple analyses for data subsets with
similar footprint values) may be the most effective way to resolve the
issue.
    There was no consensus on whether smaller, lighter vehicles
maneuver better, and thus avoid more crashes, than larger, heavier
vehicles. German noted that lighter vehicles should have improved
handling and braking characteristics and ``may be more likely to avoid
collisions.'' Lund presented crash involvement data that implied that,
among vehicles of similar function and use rates, crash risk does not
go down for more ``nimble'' vehicles. Several presenters noted the
difficulties of projecting past data into the future as new
technologies will be used that were not available when the data were
collected. The advances in technology through the decades have
dramatically improved safety for all weight and size classes. A video
of IIHS's 50th anniversary crash test of a 1959 Chevrolet Bel Air and
2009 Chevrolet Malibu graphically demonstrated that stark differences
in design and technology can possibly mask the discrete mass effects,
while videos of compatibility crash tests between smaller, lighter
vehicles and contemporary larger, heavier vehicles graphically showed
the significance of vehicle mass and size.
    Kahane presented results from his 2010 report \330\ that found that
a scenario which took some mass out of heavier vehicles but little or
no mass out of the lightest vehicles did not impact safety in absolute
terms. Kahane noted that if the analyses were able to consider the mass
of both vehicles in a two-vehicle crash, the results may be more
indicative of future crashes. There is apparent consistency with other
presentations (e.g., Padmanaban, Nusholtz) that reducing the overall
ranges of masses and mass ratios seems to reduce overall societal harm.
That is, the effect of mass reduction exclusively does not appear to be
a ``zero sum game'' in which any increase in harm to occupants of the
lightened vehicle is precisely offset by a decrease in harm to the
occupants of the partner vehicle. If the mass of the heavier vehicle is
reduced by a larger percentage than that of its lighter crash partner,
the changes in velocity from the collision are more nearly equal and
the injuries suffered in the lighter vehicle are likely to be reduced
more than the injuries in the heavier vehicle are increased.
Alternatively, a fixed absolute mass reduction (say, 100 pounds) in all
vehicles could increase societal harm whereas a fixed percentage mass
reduction is more likely to be neutral.
---------------------------------------------------------------------------

    \330\ Kahane, C. J. (2010). ``Relationships Between Fatality
Risk, Mass, and Footprint in Model Year 1991-1999 and Other
Passenger Cars and LTVs,'' Final Regulatory Impact Analysis:
Corporate Average Fuel Economy for MY 2012-MY 2016 Passenger Cars
and Light Trucks. Washington, DC: National Highway Traffic Safety
Administration, pp. 464-542, available at http://www.nhtsa.gov/staticfiles/rulemaking/pdf/cafe/CAFE_2012-2016_FRIA_04012010.pdf.
---------------------------------------------------------------------------

    Padmanaban described a series of studies conducted in recent years.
She included numerous vehicle parameters including bumper height and
several measures of vehicle size and stiffness and also commented on
previous analyses that using weight and wheelbase together in a
logistic regression model distorts the estimates, resulting in high
variance inflation factors with wrong signs and magnitudes in the
results. Her results consistently showed that the ratio between the
masses of two vehicles involved in a two-vehicle crash was a more
important parameter than variables describing vehicle geometry or
stiffness. Her ultimate conclusion was that removing mass (e.g., 100
lbs.) from all passenger cars would cause an overall increase in
fatalities in truck-to-car crashes while removing the same amount from
light trucks would cause an overall decrease in fatalities.
c. Report by Green et al., UMTRI--``Independent Review: Statistical
Analyses of Relationship Between Vehicle Curb Weight, Track Width,
Wheelbase and Fatality Rates,'' April 2011
    As explained above, NHTSA contracted with the University of
Michigan Transportation Research Institute (UMTRI) to conduct an
independent review \331\ of a set of

statistical analyses of relationships between vehicle curb weight, the
footprint variables (track width, wheelbase) and fatality rates from
vehicle crashes. The purpose of this review was to examine analysis
methods, data sources, and assumptions of the statistical studies, with
the objective of identifying the reasons for any differences in
results. Another objective was to examine the suitability of the
various methods for estimating the fatality risks of future vehicles.
---------------------------------------------------------------------------

    \331\ The review is independent in the sense that it was
conducted by an outside third party without any interest in the
reported outcome.
---------------------------------------------------------------------------

    UMTRI reviewed a set of papers, reports, and manuscripts provided
by NHTSA (listed in Appendix A of UMTRI's report, which is available in
the docket to this rulemaking) that examined the statistical
relationships between fatality or casualty rates and vehicle properties
such as curb weight, track width, wheelbase and other variables.
    It is difficult to summarize a study of that length and complexity
for purposes of this discussion, but fundamentally, the UMTRI team
concluded the following:
     Differences in data may have complicated comparisons of
earlier analyses, but if the methodology is robust, and the methods
were applied in a similar way, small changes in data should not lead to
different conclusions. The main conclusions and findings should be
reproducible. The database created by Kahane appears to be an
impressive collection of files from appropriate sources and the best
ones available for answering the research questions considered in this
study.
     In statistical analysis simpler models generally lead to
improved inference, assuming the data and model assumptions are
appropriate. In that regard, the disaggregate logistic regression model
used by NHTSA in the 2003 report \332\ seems to be the most appropriate
model, and valid for the analysis in the context that it was used:
finding general associations between fatality risk and mass--and the
general directions of the reported associations are correct.
---------------------------------------------------------------------------

    \332\ Kahane, C. J. (2003). Vehicle Weight, Fatality Risk and
Crash Compatibility of Model Year 1991-99 Passenger Cars and Light
Trucks, NHTSA Technical Report. DOT HS 809 662. Washington, DC:
National Highway Traffic Safety Administration, http://www-nrd.nhtsa.dot.gov/Pubs/809662.PDF.
---------------------------------------------------------------------------

     The two-stage logistic regression model in combination
with the two-step aggregate regression used by DRI seems to be more
complicated than is necessary based on the data being analyzed, and
summing regression coefficients from two separate models to arrive at
conclusions about the effects of reductions in weight or size on
fatality risk seems to add unneeded complexity to the problem.
     One of the biggest issues regarding the various
statistical analyses is the historical correlation between curb weight,
wheelbase, and track width. Including three variables that are highly
correlated in the same model can have adverse effects on the fit of the
model, especially with respect to the parameter estimates, as discussed
by Kahane. UMTRI makes no conclusions about multicollinearity, other
than to say that inferences made in the presence of multicollinearity
should be judged with great caution. At the NHTSA workshop on size,
safety and mass, Paul Green suggested that a matched analysis, in which
regressions are run on the relationship between mass reduction and risk
separately for vehicles of similar footprint, could be undertaken to
reduce the effect of multicollinearity between vehicle mass and size.
Kahane has combined wheelbase and track width into one variable
(footprint) to compare with curb weight. NHTSA believes that the 2012
Kahane analysis has done all it can to lessen concerns about
multicollinearity, but a concern still exists.

 In considering other studies provided by NHTSA for evaluation
by the UMTRI team:
     Papers by Wenzel, and Wenzel and Ross, addressing
associations between fatality risk per vehicle registration-year,
weight, and size by vehicle model contribute to understanding some of
the relationships between risk, weight, and size. However, least
squares linear regression models, without modification, are not
exposure-based risk models and inferences drawn from these models tend
to be weak since they do not account for additional differences in
vehicles, drivers, or crash conditions that could explain the variance
in risk by vehicle model.
     A 2009 J.P. Research paper focused on the difficulties
associated with separating out the contributions of weight and size
variables when analyzing fatality risk properly recognized the problem
arising from multicollinearity and included a clear explanation of why
societal fatality risk in two-vehicle crashes is expected to increase
with increasing mass ratio. UMTRI concluded that the increases in
fatality risk associated with a 100-pound reduction in weight allowing
footprint to vary with weight as estimated by Kahane and JP Research,
are broadly more convincing than the 6.7 percent reduction in fatality
risk associated with mass reduction while holding footprint constant,
as reported by DRI.
     A paper by Nusholtz et al. focused on the question of
whether vehicle size can reasonably be the dominant vehicle factor for
fatality risk, and finding that changing the mean mass of the vehicle
population (leaving variability unchanged) has a stronger influence on
fatality risk than corresponding (feasible) changes in mean vehicle
dimensions, concluded unequivocally that reducing vehicle mass while
maintaining constant vehicle dimensions will increase fatality risk.
UMTRI concluded that if one accepts the methodology, this conclusion is
robust against realistic changes that may be made in the force vs.
deflection characteristics of the impacting vehicles.
     Two papers by Robertson, one a commentary paper and the
other a peer-reviewed journal article, were reviewed. The commentary
paper did not fit separate models according to crash type, and included
passenger cars, vans, and SUVs in the same model. UMTRI concluded that
some of the claims in the commentary paper appear to be overstated, and
intermediate results and more documentation would help the reader
determine if these claims are valid. The second paper focused largely
on the effects of electronic stability control (ESC), but generally
followed on from the first paper except that fuel economy is used as a
surrogate for curb weight.
    The UMTRI study provided a number of useful suggestions that Kahane
considered in updating his 2011 analysis, and that have been
incorporated into the safety effects estimates for the current
rulemaking.
d. Two Reports by Dr. Charles Kahane, NHTSA titled ``Relationships
Between Fatality Risk, Mass, and Footprint in Model Year 2000-2007
Passenger Cars and LTVs'': Preliminary Report, November 2011 and Final
Report, August 2012
    The relationship between a vehicle's mass, size, and fatality risk
is complex, and varies in different types of crashes. NHTSA, along with
others, has been examining this relationship for over a decade. The
safety chapter of NHTSA's April 2010 final regulatory impact analysis
(FRIA) of CAFE standards for

MYs 2012-2016 passenger cars and light trucks included a statistical
analysis of relationships between fatality risk, mass, and footprint in
MY 1991-1999 passenger cars and LTVs (light trucks and vans), based on
calendar year (CY) 1995-2000 crash and vehicle-registration data.\333\
The 2010 analysis used the same data as the 2003 analysis, but included
vehicle mass and footprint in the same regression model.
---------------------------------------------------------------------------

    \333\ Kahane (2010).
---------------------------------------------------------------------------

    The principal findings of NHTSA's 2010 analysis were that mass
reduction in lighter cars, even while holding footprint constant, would
significantly increase societal fatality risk, whereas mass reduction
in the heavier LTVs would significantly reduce net societal fatality
risk, because it would reduce the fatality risk of occupants in lighter
vehicles which collide with the heavier LTVs. NHTSA concluded that, as
a result, any reasonable combination of mass reductions while holding
footprint constant in MYs 2012-2016 vehicles--concentrated, at least to
some extent, in the heavier LTVs and limited in the lighter cars--would
likely be approximately safety-neutral; it would not significantly
increase fatalities and might well decrease them.
    NHTSA's 2010 report partially agreed and partially disagreed with
analyses published during 2003-2005 by Dynamic Research, Inc. (DRI).
NHTSA and DRI both found a significant protective effect for footprint,
and that reducing mass and footprint together (downsizing) on smaller
vehicles was harmful. DRI's analyses estimated a significant overall
reduction in fatalities from mass reduction in all light-duty vehicles
if wheelbase and track width were maintained, whereas NHTSA's report
showed overall fatality reductions only in the heavier LTVs, and
benefits only in some types of crashes for other vehicle types. Much of
NHTSA's 2010 report, as well as recent work by DRI, involved
sensitivity tests on the databases and models, which generated a range
of estimates somewhere between the initial DRI and NHTSA results.\334\
---------------------------------------------------------------------------

    \334\ Van Auken, R. M., and Zellner, J. W. (2003). A Further
Assessment of the Effects of Vehicle Weight and Size Parameters on
Fatality Risk in Model Year 1985-98 Passenger Cars and 1986-97 Light
Trucks. Report No. DRI-TR-03-01. Torrance, CA: Dynamic Research,
Inc.; Van Auken, R. M., and Zellner, J. W. (2005a). An Assessment of
the Effects of Vehicle Weight and Size on Fatality Risk in 1985 to
1998 Model Year Passenger Cars and 1985 to 1997 Model Year Light
Trucks and Vans. Paper No. 2005-01-1354. Warrendale, PA: Society of
Automotive Engineers; Van Auken, R. M., and Zellner, J. W. (2005b).
Supplemental Results on the Independent Effects of Curb Weight,
Wheelbase, and Track on Fatality Risk in 1985-1998 Model Year
Passenger Cars and 1986-97 Model Year LTVs. Report No. DRI-TR-05-01.
Torrance, CA: Dynamic Research, Inc.; Van Auken, R.M., and Zellner,
J. W. (2011).2012a). Updated Analysis of the Effects of Passenger
Vehicle Size and Weight on Safety, Phase I. Report No. DRI-TR-11-01.
(Docket No. NHTSA-2010-0152-0030). Torrance, CA: Dynamic Research,
Inc.
---------------------------------------------------------------------------

    In April 2010, NHTSA, working closely with EPA and the Department
of Energy (DOE), commenced a new statistical analysis of the
relationships between fatality rates, mass and footprint, updating the
crash and exposure databases to the latest available model years,
refining the methodology in response to peer reviews of the 2010 report
and taking into account changes in vehicle technologies. The previous
databases of MYs 1991-1999 vehicles in CYs 1995-2000 crashes had become
outdated as new safety technologies, vehicle designs and materials were
introduced. The new databases are comprised of MYs 2000-2007 vehicles
in CY 2002-2008 crashes with the most up-to-date possible data, given
the processing lead time for crash data and the need for enough crash
cases to permit statistically meaningful analyses. NHTSA made the first
version of the new databases available to the public in May 2011 and an
updated version in April 2012,\335\ enabling other researchers to
analyze the same data and hopefully minimizing discrepancies in the
results due to inconsistencies across the data used.\336\
---------------------------------------------------------------------------

    \335\ http://www.nhtsa.gov/fuel-economy.
    \336\ 75 FR 25324 (May 7, 2010); the discussion of planned
statistical analyses is on pp. 25395-25396.
---------------------------------------------------------------------------

    One way to estimate these effects is the use of statistical
analyses of societal fatality rates per vehicle miles traveled (VMT),
by vehicles' mass and footprint, for the current on-road vehicle fleet.
The basic analytical method used for the 2011-2012 NHTSA reports is the
same as in NHTSA's 2010 report: cross-sectional analyses of the effect
of mass and footprint reductions on the societal fatality rate per
billion vehicle miles of travel (VMT), while controlling for driver age
and gender, vehicle type, vehicle safety features, crash times and
locations, and other factors. Separate logistic regression models are
run for three types of vehicles and nine types of crashes. Societal
fatality rates include occupants of all vehicles in the crash, as well
as non-occupants, such as pedestrians and cyclists. NHTSA's 2011-2012
reports\337\ analyze MYs 2000-2007 cars and LTVs in CYs 2002-2008
crashes. Fatality rates were derived from FARS data, 13 State crash
files, and registration and mileage data from R.L. Polk.
---------------------------------------------------------------------------

    \337\ Kahane, C. J. (2011). ``Relationships Between Fatality
Risk, Mass, and Footprint in Model Year 2000-2007 Passenger Cars and
LTVs--Preliminary Report,'' is available in the NHTSA docket, NHTSA-
2010-0152 as item no. 0023. Kahane, C. J. (2012). ``Relationships
Between Fatality Risk, Mass, and Footprint in Model Year 2000-2007
Passenger Cars and LTVs--Final Report,'' is also in that docket. You
can access the docket at http://www.regulations.gov/#!home by typing
``NHTSA-2010-0152'' where it says ``enter keyword or ID'' and then
clicking on ``Search.''
---------------------------------------------------------------------------

    The most noticeable change in MYs 2000-2007 vehicles from MYs 1991-
1999 has been the increase in crossover utility vehicles (CUV), which
are SUVs of unibody construction, sometimes built upon a platform
shared with passenger cars. CUVs have blurred the distinction between
cars and trucks. The new analyses treat CUVs and minivans as a separate
vehicle class, because they differ in some respects from pickup-truck-
based LTVs and in other respects from passenger cars. In the 2010
report, the many different types of LTVs were combined into a single
analysis. NHTSA believes that this may have made the analyses too
complex and might have contributed to some of the uncertainty in the
results.
    The new database has more accurate VMT estimates than NHTSA's
earlier databases, derived from a file of odometer readings by make,
model, and model year recently developed by R.L. Polk and purchased by
NHTSA.\338\ For the 2011-2012 reports, the relative distribution of
crash types has been changed to reflect the projected distribution of
crashes during the period from 2017 to 2025, based on the estimated
effectiveness of electronic stability control (ESC) in reducing the
number of fatalities in rollover crashes and crashes with a stationary
object. The annual target population of fatalities or the annual
fatality distribution baseline \339\ was not decreased in the period
between 2017 and 2025 for the safety statistics analysis, but is taken
into account later in the Volpe model analysis, since all light-duty
vehicles manufactured on or after September 1, 2011 are required to be
equipped with ESC.\340\
---------------------------------------------------------------------------

    \338\ In the 1991-1999 data base, VMT was estimated only by
vehicle class, based on NASS CDS data.
    \339\ MY 2004-2007 vehicles with fatal crashes occurred in CY
2004-2008 are selected as the annual fatality distribution baseline
in the Kahane analysis.
    \340\ In the Volpe model, NHTSA assumed that the safety trend
would result in 12.6 percent reduction between 2007 and 2020 due to
the combination of ESC, new safety standard, and behavior changes
anticipated.
---------------------------------------------------------------------------

    For the 2011-2012 reports, vehicles are now grouped into five
classes rather than four: passenger cars (including both 2-door and 4-
door cars) are split in half by median weight; CUVs and minivans; and
truck-based LTVs, which

are also split in half by median weight of the model year 2000-2007
vehicles. Table II-24 presents the 2011 preliminary report's estimated
percent increase in U.S. societal fatality risk per ten billion VMT for
each 100-pound reduction in vehicle mass, while holding footprint
constant, for each of the five classes of vehicles.

 Table II-24--Results of 2011 NHTSA Preliminary Report: Fatality Increase (%) per 100-Pound Mass Reduction While
                                           Holding Footprint Constant
----------------------------------------------------------------------------------------------------------------
                                                     Fatality increase (%) per 100-pound mass reduction while
                                                                    holding footprint constant
           MY 2000-2007  CY 2002-2008           ----------------------------------------------------------------
                                                   Point estimate               95% confidence bounds
----------------------------------------------------------------------------------------------------------------
Cars < 3,106 pounds............................               1.44  +.29 to +2.59
Cars >= 3,106 pounds...........................                .47  -.58 to +1.52
CUVs and minivans..............................               -.46  -1.75 to +.83
Truck-based LTVs < 4,594 pounds................                .52  -.43 to +1.46
Truck-based LTVs >= 4,594 pounds...............               -.39  -1.06 to +.27
----------------------------------------------------------------------------------------------------------------

    Charles Farmer, Paul E. Green, and Anders Lie, who reviewed NHTSA's
2010 report, again peer-reviewed the 2011 preliminary report.\341\ In
preparing its 2012 final report, NHTSA also took into account Wenzel's
assessment of the preliminary report and its peer reviews, DRI's
analyses published early in 2012, and public comments such as those by
ICCT.\342\ These comments prompted supplementary analyses, especially
sensitivity tests, discussed below. However, the basic analysis of the
2012 final report is almost unchanged from the 2011 preliminary report,
differing only in the addition of some crash data that became available
in the interim and a minor change in the formula for estimating annual
VMT. Table II-25 presents the 2012 final report's estimated percent
increase in U.S. societal fatality risk per ten billion VMT for each
100-pound reduction in vehicle mass, while holding footprint constant,
for each of the five classes of vehicles.
---------------------------------------------------------------------------

    \341\ Items 0035 (Lie), 0036 (Farmer) and 0037 (Green) in Docket
No. NHTSA-2010-0152.
    \342\ Item 0258 in Docket No. NHTSA-2010-0131.

    Table II-25--Results of 2012 NHTSA Final Report: Fatality Increase (%) per 100-Pound Mass Reduction While
                                           Holding Footprint Constant
----------------------------------------------------------------------------------------------------------------
                                                     Fatality increase (%) per 100-pound mass reduction While
                                                                    holding footprint constant
           MY 2000-2007 CY 2002-2008            ----------------------------------------------------------------
                                                   Point estimate               95% confidence bounds
----------------------------------------------------------------------------------------------------------------
Cars < 3,106 pounds............................               1.56  +.39 to +2.73
Cars >= 3,106 pounds...........................                .51  -.59 to +1.60
CUVs and minivans..............................               -.37  -1.55 to +.81
Truck-based LTVs < 4,594 pounds................                .52  -.45 to +1.48
Truck-based LTVs >= 4,594 pounds...............               -.34  -.97 to +.30
----------------------------------------------------------------------------------------------------------------

    Only the 1.56 percent risk increase in the lighter-than-average
cars is statistically significant. There are nonsignificant increases
in the heavier-than-average cars and the lighter-than-average truck-
based LTVs, and non-significant societal benefits for mass reduction in
CUVs, minivans, and the heavier-than-average truck-based LTVs. The
report concludes that judicious combinations of mass reductions that
maintain footprint and are proportionately higher in the heavier
vehicles are likely to be safety-neutral--i.e., they are unlikely to
have a societal effect large enough to be detected by statistical
analyses of crash data. The primarily non-significant results are not
due to a paucity of data, but because the societal effect of mass
reduction while maintaining footprint, if any, is small.
    MY 2000-2007 vehicles of all types are heavier and larger than
their MY 1991-1999 counterparts. The average mass of passenger cars
increased by 5 percent from 2000 to 2007 and the average mass of pickup
trucks increased by 19 percent. Other types of vehicles became heavier,
on the average, by amounts within this range. There are several reasons
for these increases: During this time, some of the lighter make-models
were discontinued; many models were redesigned to be heavier and
larger; and consumers more often selected stretched versions such as
crew cabs in their new-vehicle purchases.
    It is interesting to compare the new results to NHTSA's 2010
analysis of MY 1991-1999 vehicles in CY 1995-2000, especially the new
point estimate to the ``actual regression result scenario'' in the 2010
report:

  Table II-26--2010 Report: MY 1991-1999, CY 1995-2000 Fatality Increase (%) per 100-Pound Mass Reduction While
                                           Holding Footprint Constant
----------------------------------------------------------------------------------------------------------------
                                          Actual regression
                                           result scenario      Upper-estimate scenario  Lower-estimate scenario
----------------------------------------------------------------------------------------------------------------
Cars < 2,950 pounds..................                     2.21                     2.21                     1.02

Cars >= 2,950 pounds.................                     0.90                     0.90                     0.44
LTVs < 3,870 pounds..................                     0.17                     0.55                     0.41
LTVs >= 3,870 pounds.................                    -1.90                    -0.62                    -0.73
----------------------------------------------------------------------------------------------------------------

  Table II-27--Fatality Increase (%) per 100-Pound Mass Reduction While
                       Holding Footprint Constant
------------------------------------------------------------------------
                                                     NHTSA       NHTSA
                                                    (2010)      (2012)
                                                   (percent)   (percent)
------------------------------------------------------------------------
Lighter cars....................................        2.21        1.56
Heavier cars....................................        0.90        0.51
Lighter LTVs....................................       0.17*        0.52
Heavier LTVs....................................      -1.90*       -0.34
CUV/minivan.....................................  ..........       -0.37
------------------------------------------------------------------------
* Includes CUV/minivan

    The new results are directionally similar to the 2010 results:
Fatality increase in the lighter cars, safety benefit in the heavier
LTVs. But the effects may have become weaker at both ends. (NHTSA does
not consider this conclusion to be definitive because of the relatively
wide confidence bounds of the estimates.) The fatality increase in the
lighter cars tapered off from 2.21 percent to 1.56 percent while the
societal fatality-reduction benefit of mass reduction in the heaviest
LTVs diminished from 1.90 percent to 0.34 percent and is no longer
statistically significant.
    The agencies believe that the changes may be due to a combination
of the characteristics of newer vehicles and revisions to the analysis.
NHTSA believes, above all, that several light, small car models with
poor safety performance were discontinued by 2000 or during MYs 2000-
2007. Also, the tendency of light, small vehicles to be driven in a
manner that results in high crash rates is not as strong as it used to
be.\343\ Both agencies believe that at the other end of the weight/size
spectrum, blocker beams and other voluntary compatibility improvements
in LTVs, as well as compatibility-related self-protection improvements
to cars, have made the heavier LTVs less aggressive in collisions with
lighter vehicles (although the effect of mass disparity remains). This
report's analysis of CUVs and minivans as a separate class of vehicles
may have relieved some inaccuracies in the 2010 regression results for
LTVs. Interestingly, the new actual-regression results are quite close
to the previous report's ``lower-estimate scenario,'' which was an
attempt to adjust for supposed inaccuracies in some regressions and for
a seemingly excessive trend toward higher crash rates in smaller and
lighter cars.
---------------------------------------------------------------------------

    \343\ Kahane (2012), pp. 30-36.
---------------------------------------------------------------------------

    The principal difference between the heavier vehicles, especially
truck-based LTVs, and the lighter vehicles, especially passenger cars,
is that mass reduction has a different effect depending on whether the
crash partner is another car or LTV (34 percent of fatalities occurred
in crashes involving two light-duty vehicles, and another 6 percent
occurred in crashes involving a light-duty vehicle and a heavy-duty
vehicle) When two vehicles of unequal mass collide, the delta V is
higher in the lighter vehicle, in the same proportion as the mass
ratio. As a result, the fatality risk 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: Fatality increase in the lighter cars, reduction in the
heavier LTVs, and little effect in the intermediate groups. However, in
some types of crashes, especially first-event rollovers and impacts
with fixed objects (which, combined, accounted for 23 percent of
fatalities), mass reduction is usually not harmful and often
beneficial, because the lighter vehicles respond more quickly to
braking and steering. Offsetting this beneficial, is the continuing
historical tendency of lighter and smaller vehicles to be driven less
well--although it continues to be unknown why that is so, and to what
extent, if any, the lightness or smallness of the vehicle contributes
to people driving it less safely.\344\
---------------------------------------------------------------------------

    \344\ Ibid., pp. 27-30.
---------------------------------------------------------------------------

    The estimates in Table II-25 of the model are formulated for each
100-pound reduction in mass; in other words, if risk increases by 1
percent for 100 pounds reduction in mass, it would increase by 2
percent for a 200-pound reduction, and 3 percent for a 300-pound
reduction (more exactly, 2.01 percent and 3.03 percent, because the
effects work like compound interest). Confidence bounds around the
point estimates will grow wider by the same proportions.
    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, the
model may become somewhat less accurate and it is difficult to assess
the sensitivity to additional mass reduction greater than 100 pounds.
The agencies recognize that the light-duty vehicle fleet in the MYs
2017-2025 timeframe will be different from the MYs 2000-2007 fleet
analyzed for this study. Nevertheless, one consideration provides some
basis for confidence in applying the regression results to estimate the
effects of mass reductions larger than 100 pounds or over longer time
periods. This is NHTSA's fourth evaluation of the effects of mass
reduction and/or downsizing, comprising databases ranging from MYs 1985
to 2007. The results of the four studies are not identical, but they
have been consistent up to a point. During this time period, many makes
and models have increased substantially in mass, sometimes as much as
30-40 percent.\345\ 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 the effects of mass reductions
on the order of 10-20 percent, if they occur in the future.
---------------------------------------------------------------------------

    \345\ 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 percent 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 2-door cab and rear-wheel drive; and a popular minivan from
2,940 to 3,862 pounds (31%).
---------------------------------------------------------------------------

    NHTSA's 2011 preliminary report acknowledged another source of
uncertainty, namely that the baseline statistical model can be varied
by choosing different control variables or redefining the vehicle
classes or crash types, for example. Alternative models produce
different point estimates.

NHTSA believed it was premature to address that in the preliminary
report. ``The potential for variation will perhaps be better understood
after the public and other agencies have had an opportunity to work
with the new database.'' \346\ Indeed, the principal comments on the
2011 preliminary report were suggestions or demonstrations of other
ways to analyze NHTSA's database, especially by Farmer and Green in
their peer reviews, Van Auken (DRI) in his most recent analyses, and
Wenzel in his assessment of NHTSA's report. The analyses and findings
of Wenzel's and Van Auken's reports are summarized in Sections
II.G.3.e, II.G.3.f, and II.G.3.g, below. These reports, among other
analyses, define and run specific alternative regression models to
analyze NHTSA's 2011 or 2012 databases.\347\
---------------------------------------------------------------------------

    \346\ Kahane (2011), p. 81.
    \347\ Wenzel (2012a), Van Auken and Zellner (2012b, 2012c,
2012d).
---------------------------------------------------------------------------

    From these suggestions and demonstrations, NHTSA garnered 11 more
or less plausible alternative techniques that could be construed as
sensitivity tests of the baseline model.\348\ The models use NHTSA's
databases and regression-analysis approach, but differ from the
baseline model in one or more terms or assumptions. All of them try to
control for fundamentally the same driver, vehicle, and crash factors,
but differ in how they define these factors or how much detail or
emphasis they provide for some of them. NHTSA applied the 11 techniques
to the latest databases to generate alternative estimates of the
societal effect of 100-pound mass reductions in the five classes of
vehicles. The range of estimates produced by the sensitivity tests
gives an idea of the uncertainty inherent in the formulation of the
models, subject to the caveat that these 11 tests are, of course, not
an exhaustive list of conceivable alternatives. Below are the baseline
and alternative results, ordered from the lowest to the highest
estimated increase in societal risk for cars weighing less than 3,106
pounds:
---------------------------------------------------------------------------

    \348\ See Kahane (2012), pp. 14-16 and 109-128 for a further
discussion of the alternative models and the rationales behind them.

   Table II-28--Societal Fatality Increase (%) per 100-Pound Mass Reduction While Holding Footprint * Constant
----------------------------------------------------------------------------------------------------------------
                                                                      CUVs &       LTVs [dagger]   LTVs [dagger]
                                   Cars < 3,106    Cars >= 3,106     minivans         < 4,594        >= 4,594
----------------------------------------------------------------------------------------------------------------
Baseline estimate...............            1.56             .51           - .37             .52           - .34
95% confidence bounds (sampling
 error):
    Lower.......................             .39           - .59          - 1.55           - .45           - .97
    Upper.......................            2.73            1.60             .81            1.48             .30
----------------------------------------------------------------------------------------------------------------
                                              11 Alternative Models
----------------------------------------------------------------------------------------------------------------
1. Track width/wheelbase w.                  .25           - .89           - .13           - .09           - .97
 stopped veh data...............
2. With stopped -vehicle State               .97           - .62           - .33             .35           - .80
 data...........................
3. By track width & wheelbase...             .97             .24           - .24           - .07           - .58
4. W/O CY control variables.....            1.53             .43             .04            1.20             .30
5. CUVs/minivans weighted by                1.56             .51             .53             .52           - .35
 2010 sales.....................
6. W/O non - significant control            1.64             .68           - .46             .35           - .54
 variables......................
7. Incl. muscle/police/AWD cars/            1.81             .49           - .37             .49           - .76
 big vans.......................
8. Control for vehicle                      1.91             .75            1.64             .68           - .13
 manufacturer...................
9. Control for veh manufacturer/            2.07            1.82            1.31             .66           - .13
 nameplate......................
10. Limited to drivers with                 2.32            1.06           - .19             .86           - .58
 BAC=0..........................
11. Limited to good drivers                 3.00            1.62            -.00            1.09           - .30
 [Dagger].......................
----------------------------------------------------------------------------------------------------------------
* While holding track width and wheelbase constant in alternative model nos. 1 and 3.
[dagger] Excluding CUVs and minivans.
[Dagger] Blood alcohol content = 0, no drugs, valid license, at most 1 crash and 1 violation during the past 3
  years.

    For example, in cars weighing less than 3,106 pounds, the baseline
estimate associates 100minus;pound mass reduction, while holding
footprint constant, with a 1.56 percent increase in societal fatality
risk. The corresponding estimates for the 11 sensitivity tests range
from a 0.25 to a 3.00 percent increase. The sensitivity tests
illustrate both the fragility and the robustness of the baseline
estimate. On the one hand, the variation among the alternative
estimates is quite large relative to the baseline estimate: In the
preceding example of cars < 3,106 pounds, from almost zero to almost
double the baseline. In fact, the difference in estimates is a
reflection of the small statistical effect that mass reduction has on
societal risk, relative to other factors. Thus, sensitivity tests which
vary vehicle, driver, and crash factors can appreciably change the
estimate of the effect of mass reduction on societal risk in relative
terms.
    On the other hand, the variations are not all that large in
absolute terms. The ranges of the alternative estimates, at least these
alternatives, are about as wide as the sampling-error confidence bounds
for the baseline estimates. As a general rule, in the 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. Thus, in all models, the estimated effect of mass
reduction is a societal fatality increase (not necessarily a
statistically significant increase) for cars < 3,106 pounds, and in all
models except one, a societal fatality reduction for LTVs >= 4,594
pounds. None of these models suggest mass reduction in small cars would
be beneficial. All suggest mass reduction in heavy LTVs would be
beneficial or, at least, close to neutral. In general, any judicious
combination of mass reductions that maintain footprint and are
proportionately higher in the heavier vehicles is unlikely to have a
societal effect large enough to be detected by statistical analyses of
crash data. NHTSA has conducted a sensitivity analysis to estimate the
fatality impact of the alternative models using the coefficients for
these 11 test

cases. The results for these sensitivity runs can be found in Table IX-
6 of NHTSA's FRIA.
    Four additional comments on NHTSA's 2011 report are addressed in
the 2012 report. ICCT noted that DRI's latest analyses are two-stage
analyses that subdivide the effect of mass reduction into a fatalities-
per-crash component (called ``effect on crashworthiness'') and a
crashes-per-VMT component (called ``effect on crash avoidance''). ICCT
believes it counterintuitive that DRI's two-stage analysis using the
same independent variables as NHTSA's basic model shows mass reduction
harms ``crash avoidance''; thus, ICCT prefers DRI's alternative models
(using different independent variables) that do not show mass reduction
harming crash avoidance. NHTSA's response is that DRI's estimates of
separate fatalities-per-crash and crashes-per-VMT components appear to
be valid, but, in NHTSA's opinion, these components do not necessarily
correspond to the intuitive concepts of ``crashworthiness'' and ``crash
avoidance.'' Specifically, the fatalities-per-crash component is
affected not only by the crashworthiness of the vehicles, but also by
how severe their crashes are: a crash-avoidance issue. Farmer
recommended that, in the analyses of crashes between two light
vehicles, NHTSA estimate the effect of mass reduction in the case
vehicle separately for the occupants of that vehicle and for the
occupants of the other vehicle. The analysis shows that mass reduction
consistently and substantially increases risk for the vehicle's own
occupants and substantially lowers it for the occupants of the partner
vehicle. Several commenters suggested that NHTSA consider logistic
ridge regression as a tool for addressing multicollinearity; NHTSA was
unable to acquire software for logistic ridge regression now, but will
attempt to acquire it for future analyses. Lie requested--and NHTSA
added--a comparison of the estimated safety effects of mass reduction
to the effects of safety technologies and the differences in risk
between vehicles with good and poor test ratings.
e. Report by Tom Wenzel, LBNL, ``An Assessment of NHTSA's Report
`Relationships Between Fatality Risk, Mass, and Footprint in Model Year
2000-2007 Passenger Cars and LTVs' '', 2011
    DOE contracted with Tom Wenzel of Lawrence Berkeley National
Laboratory to conduct an assessment of NHTSA's updated 2011 study of
the effect of mass and footprint reductions on U.S. fatality risk per
vehicle miles traveled (LBNL Phase 1 report), and to provide an
analysis of the effect of mass and footprint reduction on casualty risk
per police-reported crash, using independent data from thirteen states
(LBNL Phase 2 report). Both reports have been reviewed by NHTSA, EPA,
and DOE staff, as well as by a panel of reviewers.\349\ The final
versions of the reports reflect responses to comments made in the
formal review process, as well as changes made to the VMT weights
developed by NHTSA for the final rule, and inclusion of 2008 data for
six states that were not available for the analyses in the draft final
versions included in the NPRM docket.
---------------------------------------------------------------------------

    \349\ EPA sponsored the peer review of the LBNL Phase 1 and 2
Reports.
---------------------------------------------------------------------------

    The LBNL Phase 1 report replicates Kahane's analysis for NHTSA,
using the same data and methods, and in many cases using the same SAS
programs, in order to confirm NHTSA's results. The LBNL report confirms
NHTSA's 2012 finding that mass reduction is associated with a
statistically significant 1.55% increase in fatality risk per vehicle
miles travelled (VMT) for cars weighing less than 3,106 pounds; for
other vehicle types, mass reduction is associated with a smaller
increase, or even a small decrease, in risk. Wenzel tested the
sensitivity of these estimates to changes in the measure of risk and
the control variables and data used in the regression models. Wenzel
also concluded that there is a wide range in fatality risk by vehicle
model for models that have comparable mass or footprint, even after
accounting for differences in drivers' age and gender, safety features
installed, and crash times and locations. This section summarizes the
results of the Wenzel assessment of the most recent NHTSA analysis.
    The LBNL Phase 1 report notes that many of the control variables
NHTSA includes in its logistic regressions are statistically
significant, and have a much larger estimated effect on fatality risk
than vehicle mass. For example, installing torso side airbags,
electronic stability control, or an automated braking system in a car
is estimated to reduce fatality risk by about 10%; cars driven by men
are estimated to have a 40% higher fatality risk than cars driven by
women; and cars driven at night, on rural roads, or on roads with a
speed limit higher than 55 mph are estimated to have a fatality risk
over 100 times higher than cars driven during the daytime on low-speed
non-rural roads. While the estimated effect of mass reduction may
result in a statistically-significant increase in risk in certain
cases, the increase is small and is overwhelmed by other known vehicle,
driver, and crash factors.
    NHTSA notes these findings are additional evidence that estimating
the effect of mass reduction is a complex statistical problem, given
the presence of other factors that have large effects. The findings do
not propose future technologies that could neutralize the potentially
deleterious effects of mass reduction. Indeed, the preceding examples
are limited to technologies emerging in the 2002-2008 timeframe of the
crash database but that will be in all model year 2017-2025 vehicles
(side airbags, electronic stability control) or factors that are simply
unchangeable circumstances in the crash environment outside the control
of CAFE or other vehicle regulations (for example, that about half of
the drivers are males and that much driving is at night or on rural
roads).
    Sensitivity tests: LBNL tested the sensitivity of the NHTSA
estimates of the relationship between vehicle weight and risk using 19
different regression analyses that changed the measure of risk, the
control variables used, or the data used in the regression models.

Table II-29--Societal Fatality Increase (%) per 100-Pound Mass Reduction While Holding Footprint * Constant From
                                                  Wenzel Study
----------------------------------------------------------------------------------------------------------------
                                                                                LTVS[dagger]
                                   Cars  < 3,106  Cars  >= 3,106      CUVS &       LTVS[dagger]      >= 4,594
                                                                     minivans         < 4,594
----------------------------------------------------------------------------------------------------------------
Baseline estimate...............            1.55            0.51           -0.38            0.52           -0.34
----------------------------------------------------------------------------------------------------------------

                                              19 Alternative Models
----------------------------------------------------------------------------------------------------------------
1. Weighted by current                      1.27            0.37           -0.70            0.42           -0.36
 distribution of fatalities.....
2. Single regression model for              1.26            0.35           -0.74            0.41           -0.42
 all crash types................
3. Excluding footprint (allowing            2.74            1.95            0.60            0.47           -0.39
 footprint to vary with mass)...
4. Fatal crashes per VMT........            1.95            0.89           -0.47            0.54           -0.42
5. Fatalities per induced                  -0.22           -1.45           -0.84           -1.13           -0.76
 exposure crash.................
6. Fatalities per registered                0.93            2.40           -0.40           -0.09           -0.76
 vehicle-year...................
7. Accounting for vehicle                   1.90            0.75            1.62            0.59           -0.11
 manufacturer...................
8. Accounting for vehicle                   2.04            1.80            1.28            0.57           -0.11
 manufacturer plus five luxury
 brands.........................
9. Accounting for initial                   1.42            0.84           -0.92            0.45           -0.52
 vehicle purchase price.........
10. Excluding CY variables......            1.52            0.43            0.03            1.20            0.30
11. Excluding crashes with                  1.88            0.88           -0.16            0.78           -0.35
 alcohol/drugs..................
12. Excluding crashes with                  2.32            1.19           -0.01            1.01           -0.11
 alcohol/drugs or bad drivers...
13. Accounting for median                   1.20            0.16           -0.44            0.68           -0.30
 household income...............
14. Including sports, squad, AWD            1.79            0.49           -0.38            0.49           -0.77
 cars and fullsize vans.........
15. Stopped instead of non-                 0.97           -0.63           -0.33            0.35           -0.80
 culpable vehicles for induced
 exposure.......................
16. Including track width and               0.95            0.24           -0.25           -0.07           -0.58
 wheelbase instead of footprint.
17. Using stopped vehicles and              0.26           -0.90           -0.14           -0.10           -0.97
 track width/wheelbase..........
18. Reweighting CUVs and                    1.55            0.51            0.55            0.52           -0.34
 minivans by 2010 sales.........
19. Excluding non-significant               1.63            0.69           -0.46            0.35           -0.54
 control variables..............
----------------------------------------------------------------------------------------------------------------
* While holding track width and wheelbase constant in alternative model nos. 1 and 3.
[dagger] Excluding CUVs and minivans.

    For all five vehicle types, the range in estimates from the
nineteen alternative models spanned zero, with the individual estimated
effects of a 100-pound mass reduction in Table II-28 ranging from a
1.45 percent fatality reduction (cars >= 3,106 pounds, alternative 5)
up to an increase in risk of 2.74 percent (cars < 3,106 pounds,
alternative 3). Nevertheless, for cars weighing less than 3,106 pounds,
only one of the 19 alternative regressions estimated a reduction rather
than an increase in U.S. fatality risk: Alternative 5, where risk was
defined as fatalities per induced exposure crash (rather than
fatalities per VMT). Whereas for LTVs >= 4,594 pounds, only one of the
19 alternatives estimated an increase in fatality risk, namely the
model without CY variables (alternative 10).
    NHTSA notes that all of these models suggest mass reduction in
small cars would be harmful or, at best, close to neutral; all suggest
mass reduction in heavy LTVs would be beneficial or, at worst, close to
neutral. The range on these 19 sensitivity tests is similar to the
range in the 11 tests included in the Kahane write-up.
    Multicollinearity issues (from LBNL study): Using two or more
variables that are strongly correlated in the same regression model
(referred to as multicollinearity) can lead to inaccurate results.
However, the correlation between vehicle mass and footprint may not be
strong enough to cause serious concern. The Pearson correlation
coefficient r between vehicle mass and footprint ranges from 0.90 for
four-door sedans and SUVs, to just under 0.50 for minivans. The
variance inflation factor (VIF) is a more formal measure of
multicollinearity of variables included in a regression model. Allison
\350\ ``begins to get concerned'' with VIF values greater than 2.5,
while Menard \351\ suggests that a VIF greater than 5 is a ``cause for
concern'', while a VIF greater than 10 ``almost certainly indicates a
serious collinearity problem''; however, O'Brien \352\ suggests that
``values of VIF of 10, 20, 40 or even higher do not, by themselves,
discount the results of regression analyses.'' When both weight and
footprint are included in the regression models, the VIF associated
with weight exceeds 5 for four-door cars, small pickups, SUVs, and
CUVs, and exceeds 2.5 for two-door cars and large pickups; the VIF
associated with weight is only 2.1 for minivans. NHTSA included several
analyses to address possible effects of the near-multicollinearity
between mass and footprint.
---------------------------------------------------------------------------

    \350\ Allison, P.D.. Logistic Regression Using SAS, Theory and
Application. SAS Institute Inc., Cary NC, 1999.
    \351\ Menard, S. Applied Logistic Regression Analysis, Second
Edition. Sage Publications, Thousand Oaks, CA 2002.
    \352\ O'Brien, R.M. ``A Caution Regarding Rules of Thumb for
Variance Inflation Factors,'' Quality and Quantity, (41) 673-690,
2007.
---------------------------------------------------------------------------

    First, NHTSA ran a sensitivity case where footprint is not held
constant, but rather allowed to vary as mass varies (i.e., NHTSA ran a
regression model which includes mass but not footprint.\353\ If the
multicollinearity was so great that including both variables in the
same model gave misleading results, removing footprint from the model
would give much different results than keeping it in the model. NHTSA's
sensitivity test estimates that when footprint is allowed to vary with
mass, the effect of mass reduction on risk increases from 1.55% to
2.74% for cars weighing less than 3,106 pounds, from a non-significant
0.51% to a statistically-significant 1.95% for cars weighing more than
3,106 pounds, and from a non-significant 0.38% decrease to a
statistically-significant 0.60% increase in risk for CUVs and minivans;
however, the effect of mass reduction on light trucks is unchanged.
---------------------------------------------------------------------------

    \353\ Kahane (2012), pp. 93-94.
---------------------------------------------------------------------------

    Second, NHTSA conducted a stratification analysis of the effect of
mass reduction on risk by dividing vehicles into deciles based on their
footprint, and running a separate regression model for each vehicle and
crash type, for each footprint decile (3 vehicle types times 9 crash
types times

10 deciles equals 270 regressions).\354\ This analysis estimates the
effect of mass reduction on risk separately for vehicles with similar
footprint. The analysis indicates that reducing vehicle mass does not
consistently increase risk across all footprint deciles for any
combination of vehicle type and crash type. Risk increases with
decreasing mass in a majority of footprint deciles for 12 of the 27
crash and vehicle combinations, but few of these increases are
statistically significant. On the other hand, risk decreases with
decreasing mass in a majority of footprint deciles for 5 of the 27
crash and vehicle combinations; in some cases these risk reductions are
large and statistically significant.\355\ If reducing vehicle mass
while maintaining footprint inherently leads to an increase in risk,
the coefficients on mass reduction should be more consistently
positive, and with a larger R\2\, across the 27 vehicle/crash
combinations, than shown in the analysis. These findings are consistent
with the conclusion of the basic regression analyses; namely, that the
effect of mass reduction while holding footprint constant, if any, is
small.
---------------------------------------------------------------------------

    \354\ Ibid., pp. 73-78.
    \355\ And in 10 of the 27 crash and vehicle combinations, risk
increased in 5 deciles and decreased in 5 deciles with decreasing
vehicle mass.
---------------------------------------------------------------------------

    One limitation of using logistic regression to estimate the effect
of mass reduction on risk is that a standard statistic to measure the
extent to which the variables in the model explain the range in risk,
equivalent to the R\2\ statistic in a linear regression model, does not
exist. (SAS does generate a pseudo-R\2\ value for logistic regression
models; in almost all of the NHTSA regression models this value is less
than 0.10). For this reason LBNL conducted an analysis of risk versus
mass by vehicle model. LBNL used the results of the NHTSA logistic
regression model to predict the number of fatalities expected after
accounting for all vehicle, driver, and crash variables included in the
NHTSA regression model except for vehicle weight and footprint. LBNL
then plotted expected fatality risk per VMT by vehicle model against
the mass of each model, and analyzed the change in risk as mass
increases, as well as how much of the change in risk was explained by
all of the variables included in the model.
    The analysis indicates that, after accounting for all the control
variables except vehicle mass and footprint, risk does decrease as mass
increases; however, risk and mass are not strongly correlated, with the
R\2\ ranging from 0.32 for CUVs to less than 0.13 for all other vehicle
types (as shown in Figure II-2). This means that, on average, risk
decreases as mass increases, but the variation in risk among individual
vehicle models is stronger than the trend in risk from light to heavy
vehicles. For full-size (i.e. \3/4\- and 1-ton) pickups, societal risk
increases as mass increases, with an R\2\ of 0.45; this is consistent
with NHTSA's basic regression results for light trucks weighing more
than 4,594 pounds, with societal risk decreasing as mass decreases.
LBNL also examined the relationship between vehicle mass and residual
risk, that is, the remaining unexplained risk after accounting for all
other vehicle, driver and crash variables, and found similarly poor
correlations. This implies that the remaining factors not included in
the regression model that account for the observed range in risk by
vehicle model also are not correlated with mass. (LBNL found similar
results when the analysis compared risk to vehicle footprint.)
    Figure II-2 indicates that some vehicles on the road today have the
same, or lower, fatality risk than models that weigh substantially
more, and are substantially larger in terms of footprint. After
accounting for differences in driver age and gender, safety features
installed, and crash times and locations, there are numerous examples
of different models with similar weight and footprint yet widely
varying fatality risk. The variation of fatality risk among individual
models may reflect differences in vehicle design, differences in the
drivers who choose such vehicles (beyond what can be explained by
demographic variables such as age and gender), and statistical
variation of fatality rates based on limited data for individual
models.
    The figure shows that when the data are aggregated at the make-
model level, the combination of differences in vehicle design, vehicle
selection, and statistical variations has more influence than mass on
fatality rates. The figure perhaps also suggests that, to the extent
these variations in fatality rates are due to differences in vehicle
design rather than vehicle selection or statistical variations, there
is potential for lowering fatality rates through improved vehicle
design. This is consistent with NHTSA's opinion that some of the
changes in its regression results between the 2003 study and the 2011
study are due to the redesign or removal of certain smaller and lighter
models of poor design.

[GRAPHIC] [TIFF OMITTED] TR15OC12.009

f. Report by Tom Wenzel, LBNL, ``An Analysis of the Relationship
Between Casualty Risk per Crash and Vehicle Mass and Footprint for
Model Year 2000-2007 Light-Duty Vehicles'', 2012 (LBNL Phase 2 Report)
    LBNL compared the logistic regression results of NHTSA's analysis
of U.S. fatality risk per VMT, replicated in the LBNL Phase 1 report,
with an independent analysis of 13-state fatality risk and casualty
risk per crash (LBNL Phase 2 report). The LBNL Phase 2 analysis differs
from the NHTSA analysis in two respects: first, it analyzes risk per
crash, using data on all police-reported crashes from thirteen states,
rather than risk per estimated VMT; and second, it analyzes casualty
(fatality plus serious injury) risk, as opposed to just fatality risk.
There are several good reasons to investigate the effect of mass and
footprint reduction on casualty risk per crash. First, risk per VMT
includes two components that influence whether a person is killed or
seriously injured in a crash: how well a vehicle can be (based on its
handling, acceleration, and braking capabilities), or actually is,
driven to avoid being involved in a serious crash (crash avoidance),
and, once a serious crash has occurred, how well a vehicle protects its
occupants from fatality or serious injury (crashworthiness) as well as
the occupants of any crash partner (compatibility). By encompassing
both of these aspects of vehicle design, risk per VMT gives a complete
picture of how vehicle design can promote, or reduce, road user safety.
On the other hand, risk per crash isolates the second of these two
safety effects, crashworthiness/compatibility, by examining the
relationship between mass or footprint and how well a vehicle protects
its occupants and others once a crash occurs.
    Second, estimating risk on a per crash basis only requires using
data on police-reported crashes from states, and does not require
combining them with data from other sources, such as vehicle
registration data and VMT information, as in NHTSA's 2012 analysis.
Only 16 states currently record the vehicle identification number of
vehicles involved in police-reported crashes, which is necessary to
determine vehicle characteristics, and only 13 states also report the
posted speed limit of the roadway on which the crash occurred. Given
the limited number of fatality cases in 13 States, extending the
analysis to casualties (fatalities plus serious/incapacitating
injuries; i.e., level ``K'' and ``A'' injuries in police reports, a
substantially larger number of cases than fatalities alone) reduces the
statistical uncertainty of the results. Finally, a serious
incapacitating injury can be just as traumatic to the victim and his or
her family, and costly from an economic perspective, as a fatality.
Limiting the analysis to the risk of fatality, which is a relatively
rare event, ignores the effect vehicle design may have on reducing the
large number of incapacitating injuries that occur each year on the
nation's roadways. All risks in the report are societal risk, including
fatalities and serious injuries in the case vehicle and any crash
partners, and include not only driver but passenger casualties as well
as non-occupant casualties such as pedestrians.
    NHTSA notes that casualty severity is identified by public safety
officers at the crash scene prior to examination by medical
professionals, and therefore reported casualty severity will inherently
have a degree of subjectivity.\356\
---------------------------------------------------------------------------

    \356\ NHTSA notes that police-reported ``A'' injuries do not
necessarily correspond to life-threatening or seriously disabling
injuries as defined by medical professionals. In 2000-2008 CDS data,
59% of the injuries that were coded ``A'' injuries were in fact
medically minor (AIS 0-1), while 39% of serious (AIS 3) and 27% of
life-threatening (AIS 4-5) injuries are not coded ``A.'' NHTSA does
not include serious casualties in its analysis of the effects of
vehicle mass and size on societal safety because of these
inaccuracies.

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

    The LBNL Phase 2 report estimates that mass reduction increases
crash frequency (columns B and E) in all five vehicle types, with
larger estimated increases in lighter-than-average cars and light-duty
trucks. As a result, mass reduction is estimated to have a more
beneficial effect on casualty risk per crash (column F) than on
casualty risk per VMT (column G), and on fatality risk per crash
(column C) than on fatality risk per VMT (column D). Mass reduction is
associated with decreases in casualty risk per crash (column F) in all
vehicles except cars weighing less than 3,106 pounds; in two of the
four cases these estimated reductions are statistically significant,
albeit small. For cars and light trucks, lower mass is associated with
a more beneficial effect on fatality risk per crash (column C) than on
casualty risk per crash (column F); for CUVs/minivans we estimate the
opposite: lower mass is associated with a more beneficial effect on
casualty risk than fatality risk per crash.

 Table II-30--Estimated Effect of Mass or Footprint Reduction on two Components of 13-State Fatality and Casualty Risk per VMT: Crash Frequency (Crashes
                                               per VMT) and Crashworthiness/Compatibility (Risk per Crash)
--------------------------------------------------------------------------------------------------------------------------------------------------------
                                                                 A. NHTSA
                                                                   U.S.     B. 13-state  C. 13-state  D. 13-state  E. 13-state  F. 13-state  G. 13-state
              Variable                   Case vehicle type      fatalities  crashes per   fatalities   fatalities  crashes per   casualties   casualties
                                                                 per VMT        VMT       per crash     per VMT        VMT       per crash     per VMT
                                                                (percent)    (percent)    (percent)    (percent)    (percent)    (percent)    (percent)
--------------------------------------------------------------------------------------------------------------------------------------------------------
Mass Reduction......................  Cars < 3106 lbs........       1.55 *         2.00        -0.54         1.42         2.00         0.09         1.86
                                      Cars > 3106 lbs........         0.51         1.50        -2.39        -1.07         1.50        -0.77         0.73
                                      LTs < 4594 lbs.........         0.52         1.44        -1.61        -0.13         1.44        -0.11         1.55
                                      LTs > 4594 lbs.........        -0.34         0.94        -1.25        -0.34         0.94        -0.62        -0.04
                                      CUV/minivan............        -0.38         0.95         0.98         1.60         0.95        -0.16         0.10
Footprint Reduction.................  Cars...................         1.87         0.64         0.92         2.11         0.64         0.23         1.54
                                      LTs....................        -0.07         1.04         0.48         1.64         1.04        -0.25         0.94
                                      CUV/minivan............         1.72        -0.55        -1.67        -1.24        -0.55         0.56         1.54
--------------------------------------------------------------------------------------------------------------------------------------------------------
* Based on NHTSA's estimation of uncertainty using a jack-knife method, only mass reduction in cars less than 3,106 pounds has a statistically
  significant effect on U.S. fatality risk.
Estimates that are statistically significant at the 95% level are shown in italics.

    It is unclear why lower vehicle mass is associated with higher
crash frequency, but lower risk per crash, in the regression models. It
is possible that including variables that more accurately account for
important differences among vehicles and driver behavior would reverse
this relationship. For example, adding vehicle purchase price as a
control variable reduces the estimated increase in crash frequency as
vehicle mass decreases, for all five vehicle types; in the case of cars
weighing more than 3,106 pounds, controlling for purchase price even
reverses the sign of the relationship: mass reduction is estimated to
slightly decrease crash frequency.\357\ It also appears that, in model
year 2000-2007 vehicles, the effect of mass reduction on casualties per
crash is simply very small, if any (estimated effects in Table II-30,
column F are under 1% per 100-pound reduction in all five vehicle
groups).
---------------------------------------------------------------------------

    \357\ Wenzel (2012b), pp. 59-60, especially Figure 4-10.
---------------------------------------------------------------------------

    The association of mass reduction with 13-state casualty risk per
VMT (column G) is quite consistent with that NHTSA estimated for U.S.
fatality risk per VMT in its 2012 report (column A), although LBNL
estimated the effects on casualty risk to be more detrimental than the
effects on fatality risk, for all vehicle types. In contrast with
NHTSA's estimates of U.S. fatality risk per VMT (column A), mass
reduction is estimated to reduce casualty risk per crash (column F) for
four of the five vehicle types, with two of these four reductions
estimated to be statistically significant. Mass reduction is associated
with a small but insignificant increase in casualty risk per crash for
cars weighing less than 3,106 pounds.
    As in the LBNL Phase 1 study, replicating NHTSA methodology, many
of the control variables included in the logistic regressions are
statistically significant, and have a large effect on fatality or
casualty risk per crash, in some cases one to two orders of magnitude
larger than those estimated for mass or footprint reduction. However,
the estimated effect of these variables on risk per crash is not as
large as their estimated effect on fatality risk per VMT. LBNL
concludes that the estimated effect of mass reduction on casualty risk
per crash is small and is overwhelmed by other known vehicle, driver,
and crash factors.
    NHTSA notes that to estimate the effect of mass reduction on safety
requires careful examination of how to model the covariant effects of
vehicle, driver, and crash factors.
    LBNL states that regarding the control variables, there are several
results that, at first glance, would not be expected: side airbags in
light trucks and CUVs/minivans are estimated to reduce crash frequency;
ESC and ABS, crash avoidance technologies, are estimated to reduce risk
once a crash has occurred; and AWD and brand new vehicles are estimated
to increase risk once a crash has occurred. In addition, male drivers
are estimated to have essentially no effect on crash frequency, but are
associated with a statistically significant increase in fatality risk
once a crash occurs. And driving at night, on high-speed or rural
roads, are associated with higher increases in risk per crash than on
crash frequency. A possible explanation for these unexpected results is
that important control variables are not being included in the
regression models. For example, crashes involving male drivers, in
vehicles equipped with AWD, or that occur at night on rural or high-
speed roads, may not be more frequent but rather more severe than other
crashes, and thus lead to greater fatality or casualty risk. And
drivers who select vehicles with certain safety features may tend to
drive more carefully, resulting in vehicle safety features designed to
improve

crashworthiness or compatibility, such as side airbags, being also
associated with lower crash frequency.
    As with NHTSA's analysis of fatality risk per VMT, lower mass is
not consistently associated with increased casualty risk per crash
across all footprint deciles for any combination of vehicle type and
crash type. Lower mass is associated with increased casualty risk per
crash in a majority of footprint deciles for 9 of the 27 crash and
vehicle combinations, but few of these increases are statistically
significant. On the other hand, lower mass is associated with decreased
risk in a majority of footprint deciles for 12 of the 27 crash and
vehicle combinations.
    The correlation between mass and the casualty risk per crash by
vehicle model is very low, after accounting for all of the control
variables in the logistic regression model except for vehicle mass and
footprint. Furthermore, when casualty rates are aggregated at the make-
model level, there is no significant correlation between the residual,
unexplained risk and vehicle weight. Even after accounting for many
vehicle, driver, and crash factors, the variation in casualty risk per
crash by vehicle model is quite large and unrelated to vehicle weight.
That parallels the LBNL Phase 1 report, which found similar variation
in fatality rates per VMT at the make-model level. The variations among
individual models may reflect differences in vehicle design,
differences in the drivers who choose such vehicles, and statistical
variation due to the limited data for individual models. To the extent
the variations are due to differences in vehicle design rather than
vehicle selection or statistical variations, there is potential for
lowering fatality or casualty rates through improved vehicle design. To
the extent that the variations are due to differences in what drivers
choose what vehicles, it is possible that including variables that
account for these factors in the regression models would change the
estimated relationship between mass or footprint and risk.
    NHTSA notes that the statistical variation due to the limited data
for individual models is an additional source of uncertainty inherent
in the technique of aggregating the data by make and model, a technique
whose primary goal is not the estimation of the effect of mass
reduction on safety.
g. Reports by Van Auken & Zellner, DRI--``Updated Analysis of the
Effects of Passenger Vehicle Size and Weight on Safety,'' 2012
    The International Council on Clean Transportation (ICCT), the
Energy Foundation, and American Honda Motor Co. contracted Mike Van
Auken and John Zellner of Dynamic Research Institute (DRI) to conduct a
study to update the analysis of the effects of passenger vehicle size
and weight on safety, based on the newly released NHTSA 2011 database.
As noted earlier, DRI reports its study in three parts: Phase I,\358\
II,\359\ and Supplement.\360\ This study was not complete in time for
the NPRM, but was finished in time to be submitted to the docket as
part of ICCT's public comments. The study has not yet been peer
reviewed.
---------------------------------------------------------------------------

    \358\ Van Auken and Zellner (2012a).
    \359\ Van Auken and Zellner (2012b), Van Auken and Zellner
(2012c).
    \360\ Van Auken and Zellner (2012d).
---------------------------------------------------------------------------

    Phase I, which analyzed CY 1995-2000 fatalities in MY 1991-1999
vehicles to replicate the NHTSA 2003 and 2010 studies, has already been
discussed and responded to above. The purpose of Phase II was to extend
and refined the analytical methods used by DRI in the Phase I of this
program to the more recent model year and calendar year data used in
the Kahane (2011) analysis, in order to confirm the Kahane (2011)
results and to estimate the effects of vehicle weight and size
reduction on fatalities per 100 reported crash involvements and
reported crash involvements per VMT (which DRI calls, respectively,
``effect on crashworthiness/crash compatibility'' and ``effect on crash
avoidance'').
    The Phase II study was accomplished by updating the regression
analysis tools to use the newer databases for 2000 through 2007 model
year light passenger vehicles in the 2002 through 2008 calendar years.
The fatal and induced exposure databases were compiled by NHTSA from
the U.S. DOT FARS database and accident data files from 13 U.S. States.
In addition, police reported accident data files were obtained from 10
states. These 10 states were a subset of the 13 induced-exposure data
states which NHTSA used. Data for the other three states were not
available to non-government researchers at the time of this analysis.
    The main results of the DRI Phase II analyses are as follows:
     The DRI one-stage analysis was able to reproduce NHTSA's
baseline results very closely. However, in these analyses, DRI, like
NHTSA, defines the induced-exposure cases to be the non-culpable
vehicles involved in two-vehicle crashes. Later, in its supplemental
report, DRI considers limiting the induced-exposure cases to stopped
vehicles.
     The DRI two-stage analysis was able to replicate the DRI
and NHTSA one stage results.
     The DRI Phase II two-stage results, which used more recent
data were directionally similar to the DRI Phase I two-stage results.
They showed an increase in reported crash involvements per VMT for
lighter and smaller vehicles, but reductions of fatalities per 100
reported crash involvements. The DRI results for crash avoidance are
also similar to those of Wenzel Phase 2 (2011b).
     The two-stage results for passenger cars weighing less
than 3,106 pounds indicated that the increase in fatalities attributed
to mass reduction was due to an increase in the number of crashes per
exposure, more than offsetting a reduction in the number of fatalities
per crash. The underlying reasons for these offsetting effects are
unknown at this time, but could involve driver, vehicle, environment or
accident factors that have not been controlled for in the current
analyses. These results are similar to those obtained in Wenzel Phase 2
(2011b).
    The overall results from DRI Phase II indicated very close
agreement between the DRI and NHTSA one-stage results using the same
methods and data. The results also indicate that the DRI one-stage and
two-stage results are similar but have some differences due to the
number of stages in the regression analysis. It may be possible to
reduce these differences in the future by updating the state accident
data for the 2008 calendar year, and adding ``internal control
variables.''
    The DRI Supplemental report discusses in further detail two
previous key assumptions that were used in the Kahane (2011), Wenzel
(2011b), and DRI (2012b) reports, and describes two alternative
assumptions. The previous key assumptions were that the effects of
vehicle weight and size can be best modeled by curb weight and
footprint; and that the crash exposure is best represented by non-
culpable vehicle induced-exposure data. The alternative assumptions are
that the weight and size can be best modeled by curb weight, wheelbase,
and track width; and that the crash exposure is best represented by
stopped-vehicle induced-exposure data (because non-culpable vehicle
data may underrepresent vehicles and drivers that are better at
avoiding crashes, even if they would have been non-culpable in those
crashes). Some of the potential advantages and disadvantages of the
previous assumptions and these alternative assumptions are described in
the DRI supplemental report.

    The results in the DRI Supplemental report indicate a range of
estimates for the effects of a 100 pound curb mass reduction based on
the type of induced-exposure data that is used and the candidate
vehicle weight and size model. These results indicate:
     The estimated effects of mass reduction on fatalities are
not statistically significant for any vehicle category, if the
wheelbase and track model is used with the non-culpable vehicle
induced-exposure data. (This assumes the width of confidence bounds is
similar to those seen in the Kahane (2011) analyses.)
     The estimated effects of mass reduction on fatalities
either result in a statistically significant decrease in fatalities
(for truck-based LTVs weighing 4,594 lbs or more), or are not
statistically significant (for all other vehicle categories), if the
stopped-vehicle induced-exposure data is used (irrespective of the two
candidate size models, e.g., the footprint model, or the wheelbase and
track width model).
     The estimated effect of curb mass reduction for passenger
cars weighing less than 3,106 pounds is a statistically significant
increase in fatalities (when compared to the jackknife based confidence
intervals) only if the curb weight and footprint model is used with the
non-culpable vehicle induced-exposure data.
     All other estimated effects of mass reduction on
fatalities are not statistically significant when compared to the
jackknife based confidence intervals.
    In addition, the variance inflation factors are approximately the
same when modeling the independent effects of curb weight, wheelbase
and track width as when modeling curb weight and footprint, which
suggests there is no adverse effect for modeling with track width and
wheelbase in the context of potential overparameterization and
excessive multicollinearity. In addition, wheelbase and track width
would be expected to have separate, different, physics-based effects on
vehicle crash avoidance and crashworthiness/compatibility, which
effects are confounded when they are combined into a single variable,
footprint.
    DRI further recommended that the final version of the Kahane (2011)
report include models based on curb weight, wheelbase and track width;
and also include results based on non-culpable stopped-vehicle induced-
exposure data as well as non-culpable vehicle induced-exposure. DRI
concludes that the latter could be addressed by averaging the estimates
from both the stopped-vehicle induce-exposure and the non-culpable
vehicle induced-exposure, and incorporate the range of estimates into
the reported uncertainty in the results (i.e., confidence intervals).
    DRI also recommended that NHTSA provide the following additional
variables in the current publicly available induced-exposure dataset so
that other researchers can reproduce the sensitivity to the induced-
exposure definition:
     An additional variable indicating whether each induced-
exposure vehicle was moving or stopped at the time of the initial
impact. This variable could then be used to derive a non-culpable
stopped-vehicle induced-exposure dataset from the non-culpable vehicle
induced-exposure dataset.
     Add accident case identifiers to the induced-exposure
dataset that are suitable for linking to the original state accident
data files, but do not otherwise disclose any private information. This
would assist researchers with access to the original accident data in
better understanding the induced-exposure data.
    As noted in the preceding discussion of the Kahane (2012) and
Wenzel (2012a) reports, NHTSA and LBNL have added models based on track
width and wheelbase and/or stopped-vehicle induced exposure to the
report. Table II-28 (test nos. 1, 2, and 3) and Table II-29 (tests nos.
15, 16, and 17) show results for those models. NHTSA has also made
available to the public an induced-exposure database limited to stopped
vehicles.
h. DOT Summary and Response to Recent Statistical Studies
    The preceding sections reviewed three groups of reports issued in
2012 that estimated the effect of mass reduction on societal fatality
or casualty risk, based on statistical analyses of crash and exposure
data for model year 2000-2007 vehicles: NHTSA/Kahane's report and LBNL/
Wenzel's Phase 1 report analyze fatality rates per VMT. DRI/Van Auken's
reports likewise estimate the overall effect of mass reduction on
fatalities per VMT, but they also provide separate sub-estimates of the
effect on fatalities per 100 reported crash involvements and on
reported crash involvements per VMT (which Van Auken calls ``effect on
crashworthiness/compatibility'' and ``effect on crash avoidance'').
Wenzel's Phase 2 report analyzes casualty rates per VMT, including sub-
estimates of the effects on casualties per 100 crash involvements and
crashes per VMT. ``Casualties'' include fatalities and the highest
police-reported level of nonfatal injury (usually called level ``A'').
    For the final regulatory analysis, like the preliminary analysis,
NHTSA and EPA rely on the coefficients in the NHTSA/Kahane study for
estimating the potential safety effects of the CAFE and GHG standards
for MYs 2017-2025. NHTSA takes this opportunity to summarize and
compare the reports and also explain why we continue to rely on the
results of our own study in projecting safety effects.
    The important common feature of these 2012 reports is that they all
support the same principal conclusions--in NHTSA's words:
     The societal effect of mass reduction while maintaining
footprint, if any, is small.\361\
---------------------------------------------------------------------------

    \361\ Kahane (2012), p. 1.
---------------------------------------------------------------------------

     Any judicious combination of mass reductions that maintain
footprint and are proportionately higher in the heavier vehicles is
[likely to be safety-neutral--i.e., it is] unlikely to have a societal
effect large enough to be detected by statistical analyses of crash
data.\362\
---------------------------------------------------------------------------

    \362\ Ibid., p. 16.
---------------------------------------------------------------------------

    This greatly contrasts with the disagreement in 2004-2005, based on
earlier fatality databases, when DRI estimated a decrease of 1,518
fatalities per 100-pound mass reduction in all vehicles while
maintaining wheelbase and track width \363\ while NHTSA estimated a
1,118-fatality increase for downsizing all vehicles by 100 pounds (with
commensurate reductions in wheelbase and track width).\364\ In
comparison, the estimates from 11 sensitivity tests using the current
database only range from a 211-fatality reduction to an increase of
486, only 25 percent of the earlier range, and basically down to the
level of statistical uncertainty typically inherent in this type of
analysis.\365\ NHTSA believes two or possibly three conditions may have
contributed to the extensive convergence of the results. One is the
extensive dialogue and cooperation among researchers, including the
agreement to use NHTSA's database and discussions that led to
consistent definitions of control variables or shared analysis
techniques. The second is the real change in the new-vehicle fleet and
perhaps also in driving patterns over the

past decade, which appears to have attenuated some of the stronger
effects of mass reduction and footprint reduction. A third possible
factor is that multicollinearity may somehow have become less of an
issue with the new database and with the new technique of treating CUVs
and minivans as a separate class of vehicles.
---------------------------------------------------------------------------

    \363\ Van Auken and Zellner (2005b), sum of 836 for passenger
cars (Table 2, p. 27) and 682 for LTVs (Table 5, p. 36).
    \364\ Kahane, C.J. (2003), Vehicle Weight, Fatality Risk and
Crash Compatibility of Model Year 1991-99 Passenger Cars and Light
Trucks, NHTSA Technical Report. DOT HS 809 662. Washington, DC:
National Highway Traffic Safety Administration, http://www-nrd.nhtsa.dot.gov/Pubs/809662.PDF. sum of 71 and 234 on p. ix, 216
and 597 on p. xi.
    \365\ Kahane (2012), p. 113, scenario 3 in Table 4-2.
---------------------------------------------------------------------------

    Even though the studies now agree more than they disagree, there
are still qualitative differences among the results. The baseline NHTSA
findings indicate a statistically significant fatality increase for
mass reduction in cars weighing less than 3,106 pounds. The NHTSA
results do not encourage mass reduction in the lightest cars, at least
for the foreseeable future, as long as so many heavy cars and LTVs
remain on the road. But DRI's two analyses substituting track width and
wheelbase for footprint or stopped-vehicle induced exposure for non-
culpable vehicles each reduce the estimate fatality-increasing effect
of mass reduction in lighter-than-average cars to a statistically non-
significant level, while the simultaneous application of both
techniques reduces the effect close to zero.
    DRI suggests that track width and wheelbase have more intuitive
relationships with crash and fatality risk than footprint and do not
aggravate multicollinearity issues, as evidenced by variance inflation
factors; and that stopped-vehicle induced-exposure data may be
preferable because non-culpable vehicle data may underrepresent
vehicles and drivers that are good at avoiding crashes. NHTSA finds
DRI's argument plausible and has now included both techniques among the
sensitivity tests in its 2012 report. But these sensitivity tests have
not replaced NHTSA's baseline analysis. In the regressions for cars and
LTVs, wheelbase often did not have the expected relationships with risk
and added little information (In the regressions for CUVs and minivans,
it was track width that had little relationship with risk). Limiting
the induced-exposure data to stopped vehicles is a technique that
earlier peer reviewers criticized, eliminates 75 percent of the
induced-exposure cases (even more on high-speed roads), and may
underrepresent older drivers. Furthermore, Table II-28 shows that some
of the other sensitivity tests increase the fatality-increasing effect
of mass reduction in light cars to about the same extent that these
techniques diminish it. On the whole, NHTSA does not now see adequate
justification for mass reduction in light cars, but additional analysis
may be considered as the vehicle fleet changes.\366\
---------------------------------------------------------------------------

    \366\ Ibid., pp. 115-119.
---------------------------------------------------------------------------

    Another analysis strategy of DRI and also of Wenzel's Phase 2
report is to obtain separate estimates of the effect of mass reduction
on fatalities [or casualties] per reported crash and reported crashes
per VMT, as well as the composite estimate of its effect on fatalities
per VMT. Van Auken and Wenzel both call the first estimate the ``effect
on crashworthiness/compatibility'' and the second, the ``effect on
crash avoidance.'' NHTSA believes the separate estimates are
computationally valid, but these names are inaccurate characterizations
that can lead to misunderstandings. For example, ICCT argues that the
relationship between mass reduction and crash avoidance observed in the
DRI and LBNL Phase 2 studies (i.e., that crash frequency increases as
mass decreases) is counterintuitive.\367\ NHTSA believes the metric of
fatalities per reported crash takes into account not just
crashworthiness but also certain important aspects of crash avoidance,
namely the severity of a crash. In addition, it could be influenced by
how often crashes are reported or not reported, which varies greatly
from State to State and depending on local circumstances. As Wenzel
notes, these analyses produced unexpected results, such as a reduction
in crash frequency with side air bags, or an increase in fatalities per
crash when the driver is male (when, in fact, males are less vulnerable
than females, given the same physical insult \368\) or when it is
nighttime. The fatality rates are higher for male drivers and at night
because the crashes are more severe, not primarily because of
crashworthiness issues. By the same token, the effect of mass reduction
on fatalities or casualties per crash need not be purely an effect on
``crashworthiness and compatibility'' but may also comprise some
aspects of crash avoidance.
---------------------------------------------------------------------------

    \367\ Docket No. NHTSA-2010-0131-0258, p. 10.
    \368\ Evans, L. (1991). Traffic Safety and the Driver. New York:
Van Nostrand Reinhold, pp. 22-28.
---------------------------------------------------------------------------

    Wenzel's Phase 1 and Phase 2 reports show that when fatality or
casualty rates are aggregated at the make-model level, differences
between the models ``overwhelm'' the effect of mass. Likewise, in the
basic regression analyses, the effects of many control variables are
much stronger than the effect of mass. NHTSA does not dispute the
validity of these analyses or disagree with the findings, but they must
not be misinterpreted. Specifically, it would be wrong to conclude that
the effect of mass reduction should not be estimated at all because
other ambient effects are considerably stronger. Researchers must often
measure a weak effect in the presence of strong effects--for example:
Studying the light from faraway galaxies despite the presence of much
stronger light from nearby stars; evaluating a dietary additive based
on a sample of test subjects who vary greatly in age, weight, and
eating habits. Furthermore, the technique of aggregating the rates by
make-model, while useful for graphically depicting the effect of mass
relative to other factors, is no substitute for regression analyses on
the full database in terms of directly estimating the effects of mass
reduction on safety; at best, the analysis aggregated by make-model can
indirectly generate less precise estimates of these effects. NHTSA
believes the sensitivity tests in Table II-28 and Table II-29 are
useful for addressing the effects of other factors, since most of these
tests consist of alternative ways to quantify those factors. The tests
showed two consistent trends: almost all (18 of Wenzel's 19 and all 11
of Kahane's) estimated a fatality increase for mass reduction in cars
weighing less than 3,106 pounds and almost all (18 of Wenzel's and 10
of Kahane's) estimated a societal benefit if mass is reduced in the
LTVs weighing 4,594 pounds or more.
    Wenzel's Phase 2 report on casualty risk introduces one more source
of data-driven uncertainty. To achieve adequate sample size, it must
rely on the injury data in State crash files, specifically the highest
reported level of nonfatal injury, usually called level ``A.'' But the
coding of injury in police-reported crash databases is usually not
based on medical records. ``A'' injuries do not necessarily correspond
to life-threatening or seriously disabling injuries as defined by
medical professionals. In 2000-2008 National Automotive Sampling System
data, 59% of ``A'' injuries were in fact medically minor (levels 0 or 1
on the Abbreviated Injury Scale, based on subsequently retrieved
medical records), while 39% of the serious (AIS 3) and 27% of life-
threatening (AIS 4-5) injuries were not coded ``A.'' Despite this,
Wenzel's composite results for casualties per VMT show about the same
effects for mass reduction as Kahane's analyses of fatalities per VMT--
e.g., in the lighter cars, the estimated effect of a 100-pound mass
reduction is slightly more detrimental for casualties per VMT (1.86%
increase\369\) than for fatalities

(1.56% increase \370\). NHTSA concurs with analyzing casualties per
VMT, but, given that so many of the ``A'' injuries are minor while
quite a few disabling injuries are not ``A,'' does not believe the
results are as critical as the fatality analyses.
---------------------------------------------------------------------------

    \369\ Wenzel (2012b), p. v, Table ES.1, column G.
    \370\ Kahane (2012), p. 12.
---------------------------------------------------------------------------

    i. Based on this information, what do the Agencies consider to be
the current state of statistical research on vehicle mass and safety?
    The agencies believe that statistical analysis of historical crash
data continues to be an informative and important tool in assessing the
potential safety impacts of the proposed standards. The effect of mass
reduction while maintaining footprint is a complicated topic and there
are open questions whether future vehicle designs will reduce the
historical correlation between weight and size. It is important to note
that while the updated database represents more current vehicles with
technologies more representative of vehicles on the road today, that
database cannot fully represent what vehicles will be on the road in
the MYs 2017-2025 timeframe. The vehicles manufactured in the 2000-2007
timeframe were not subject to footprint-based fuel economy standards.
As explained earlier, the agencies expect that the attribute-based
standards will likely facilitate the design of vehicles such that
manufacturers may reduce mass while maintaining footprint. Therefore,
it is possible that the analysis for MYs 2000-2007 vehicles may not be
fully representative of the vehicles that will be on the road in 2017
and beyond.
    We recognize that statistical analysis of historical crash data may
not be the only way to think about the future relationship between
vehicle mass and safety. However, we recognize that other assessment
methods are also subject to uncertainties, which makes statistical
analysis of historical data an important starting point if employed
mindfully and recognized for how it can be useful and what its
limitations may be.
    NHTSA funded an independent review of statistical studies and held
a mass-safety workshop in February 2011 in order to help the agencies
sort through the ongoing debates over how statistical analysis of the
historical relationship between mass and safety should be interpreted.
Previously, the agencies have assumed that differences in results were
due in part to inconsistent databases. By creating the updated common
database and making it publicly available, we are hopeful that this
aspect of the problem has been resolved. Moreover, the independent
review of 18 statistical reports by UMTRI suggested that differences in
data were probably less significant than the agencies may have thought.
UMTRI stated that statistical analyses of historical crash data should
be examined more closely for potential multicollinearity issues that
exist in some of the current analyses. The agencies will continue to
monitor issues with multicollinearity in our analyses, and hope that
outside researchers will do the same. And finally, based on the
findings of the independent review, the agencies continue to be
confident that Kahane's analysis is one of the best for the purpose of
analyzing potential safety effects of future CAFE and GHG standards.
UMTRI concluded that Kahane's approach is valid, and Kahane has
continued and refined that approach for the current analysis. The NHTSA
2012 statistical fatality report finds directionally similar but fewer
statistically significant relationships between vehicle mass, size, and
footprint, as discussed above. Based on these findings, the agencies
believe that in the future, fatalities due to mass reduction will be
best reduced if mass reduction is concentrated in the heaviest
vehicles. NHTSA considers part of the reason that more recent
historical data shows a dampened effect in the relationship between
mass reduction and safety is that all vehicles, including traditionally
lighter ones, grew heavier during that timeframe (2000s). As lighter
vehicles might become more prevalent in the fleet again over the next
decade, it is possible that the trend could strengthen again. On the
other hand, extensive use of new lightweight materials and optimized
vehicle design may weaken the relationship. As the Alliance mentioned
in its comments noted above, future updated analyses will be necessary
to determine how the effect of mass reduction on safety changes over
time.
    Both agencies agree that there are several identifiable safety
trends already in place or expected to occur in the foreseeable future
that are not accounted for in the study, since they were not in effect
at the time that the vehicles in question were manufactured. For
example, there are two important new safety standards that have already
been issued and have been phasing in after MY 2008. FMVSS No. 126 (49
CFR Sec.  571.126) requires electronic stability control in all new
vehicles by MY 2012, and the upgrade to FMVSS No. 214 (Side Impact
Protection, 49 CFR Sec.  571.214) will likely result in all new
vehicles being equipped with head-curtain air bags by MY 2014.
Additionally, based on historical trends, we anticipate continued
improvements in driver (and passenger) behavior, such as higher safety
belt use rates. All of these may tend to reduce the absolute number of
fatalities. Moreover, as crash avoidance technology improves, future
statistical analysis of historical data may be complicated by a lower
number of crashes. In summary, the agencies have relied on the
coefficients in the Kahane 2012 study for estimating the potential
safety effects of the CAFE and GHG standards for MYs 2017-2025, based
on our assumptions regarding the amount of mass reduction that could be
used to meet the standards in a cost-effective way without adversely
affecting safety. Section II.G.5.a below discusses the methodology used
by the agencies in more detail. While the results of the safety effects
analysis are less statistically significant than the results in the MYs
2012-2016 final rule, the agencies still believe that any statistically
significant results warrant careful consideration of the assumptions
about appropriate levels of mass reduction, and have acted accordingly
in developing the final standards.
4. How do the Agencies think technological solutions might affect the
safety estimates indicated by the statistical analysis?
    As mass reduction becomes a more important technology option for
manufacturers in meeting future CAFE and GHG standards, manufacturers
will invest more and more resources in developing increasingly
lightweight vehicle designs that meet their needs for manufacturability
and the public's need for vehicles that are also safe, useful,
affordable, and enjoyable to drive. There are many different ways to
reduce mass, as discussed in Chapter 3 of this TSD and in Sections II,
III, and IV of the preamble, and a considerable amount of information
is available today on lightweight vehicle designs currently in
production and that may be able to be put into production in the
rulemaking timeframe. Discussion of lightweight material designs from
NHTSA's workshop is presented below.
    Besides ``lightweighting'' technologies themselves, though, there
are a number of considerations when attempting to evaluate how future
technological developments might affect the safety estimates indicated
by the historical statistical analysis. As discussed in the first part
of this section, for example, careful changes in design and/or
materials used might mitigate some of the potential increased risk from
mass reduction for vehicle self-protection,

through improved distribution of crash pulse energy, etc. At the same
time, these lightweighting techniques can sometimes lead to other
problems, such as increased crash forces on vehicle occupants that have
to be mitigated, or greater aggressivity against other vehicles in
crashes. Manufacturers may develop new and better restraints--air bags,
seat belts, etc.--to protect occupants in lighter vehicles in crashes,
but NHTSA's current safety standards for restraint systems are designed
based on the current fleet, not the yet-unknown future fleet. The
agency will need to monitor trends in the crash data to see whether
changes to the safety standards (or new safety standards) become
advisable. Manufacturers are also increasingly investigating a variety
of crash avoidance technologies--ABS, electronic stability control
(ESC), lane departure warnings, vehicle-to-vehicle (V2V)
communications--that, as they become more prevalent in the fleet, are
expected to reduce the number of overall crashes, and thus crash
fatalities. Until these technologies are present in the fleet in
greater numbers, however, it will be difficult to assess whether they
can mitigate the observed relationship between vehicle mass and safety
in the historical data.
    Along with the California Air Resources Board (CARB), the agencies
have completed several technical/engineering projects described below
to estimate the maximum potential for advanced materials and improved
designs to reduce mass in the MY 2017-2021 timeframe, while continuing
to meet safety regulations and maintain functionality and affordability
of vehicles. Another NHTSA-sponsored study will estimate the effects of
these design changes on overall fleet safety. The detailed discussions
about these studies can be found in the Joint TSD section <IP_ADDRESS>.
    A. NHTSA awarded a contract in December 2010 to Electricore, with
EDAG and George Washington University (GWU) as subcontractors, to study
the maximum feasible amount of mass reduction of a mid-size car--
specifically, a Honda Accord--while maintaining the functionality of
the baseline vehicle. The project team was charged to maximize the
amount of mass reduction with the technologies that are considered
feasible for 200,000 units per year production volume during the time
frame of this rulemaking while maintaining the retail price in parity
(within 10% variation) with the baseline vehicle. When
selecting materials, technologies and manufacturing processes, the
Electricore/EDAG/GWU team utilized, to the extent possible, only those
materials, technologies and design which are currently used or planned
to be introduced in the near term (MY 2012-2015) on low-volume
production vehicles. This approach, commonly used in the automotive
industry, is employed by the team to make sure that the technologies
used in the study will be feasible for mass production for the time
frame of this rulemaking. The Electricore/EDAG/GWU team took a ``clean
sheet of paper'' approach and adopted collaborative design, engineering
and CAE process with built-in feedback loops to incorporate results and
outcomes from each of the design steps into the overall vehicle design
and analysis. The team tore down and benchmarked 2011 Honda Accord and
then undertook a series of baseline design selections, new material
selections, new technology selections and overall vehicle design
optimization. Vehicle performance, safety simulation and cost analyses
were run in parallel to the design and engineering effort to help
ensure that the design decisions are made in-line with the established
project constrains.
    While the project team worked within the constraint of maintaining
the baseline Honda Accord's exterior size and shape, the body structure
was first redesigned using topology optimization with six load cases,
including bending stiffness, torsion stiffness, IIHS frontal impact,
IIHS side impact, FMVSS pole impact, FMVSS rear impact and FMVSS roof
crush cases. The load paths from topology optimization were analyzed
and interpreted by technical experts and the results were then fed into
low fidelity 3G (Gauge, Grade and Geometry) optimization programs to
further optimize for material properties, material thicknesses and
cross-sectional shapes while trying to achieve the maximum amount of
mass reduction. The project team carefully reviewed the optimization
results and built detailed CAD/CAE models for the body structure,
closures, bumpers, suspension, and instrumentation panel. The vehicle
designs were also carefully reviewed to ensure that they can be
manufactured at high volume production rates,
    Multiple materials were used for this study. The body structure was
redesigned using a significant amount of high strength steel. The
closures and suspension were designed using a significant amount of
aluminum. Magnesium was used for the instrument panel cross-car beam. A
limited amount of composite material was used for the seat structure.
    Safety performance of the light-weighted design was compared to the
safety rating of the baseline MY2011 Honda Accord for seven consumer
information and federal safety crash tests using LS-DYNA.\371\ These
seven tests are the NCAP frontal test, NCAP lateral MDB test, NCAP
lateral pole test, IIHS roof crush, IIHS lateral MDB, IIHS front offset
test, and FMVSS No. 301 rear impact tests. These crash simulation
analyses did not include use of a dummy model. Therefore only the crash
pulse and intrusion were compared with the baseline vehicle test
results. The vehicle achieved equivalent safety performance in all
seven self-protection tests comparing to MY 2011 Honda Accord with no
damage to the fuel tank. Vehicle handling is evaluated using MSC/ADAMS
\372\ modeling on five maneuvers, fish-hook test, double lane change
maneuver, pothole test, 0.7G constant radius turn test and 0.8G forward
braking test. The results from the fish-hook test show that the light-
weighted vehicle can achieve a five-star rating for rollover, same as
baseline vehicle. The double lane change maneuver tests show that the
chosen suspension geometry and vehicle parameter of the light-weighted
design are within acceptable range for safe high speed maneuvers.
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    \371\ LS-DYNA is a software developed by Livermore Software
Technologies Corporation used widely by industry and researchers to
perform highly non-linear transient finite element analysis.
    \372\ MSC/ADAMS: Macneal-Schwendler Corporation/Automatic
Dynamic Analysis of Mechanical Systems.
---------------------------------------------------------------------------

    Overall the complete light weight vehicle achieved a total weight
savings of 22 percent (332kg) relative to the baseline vehicle (1480
kg). The study has been peer reviewed by three technical experts from
the industry, academia and a DOE national lab. The project team
addressed the peer review comments in the report and also composed a
response to peer review comment document. The final report, CAE model
and cost model are published in docket NHTSA-2010-0131 and can also be
found on NHTSA's Web site.\373\ The peer review comments with responses
to peer review comments can also be found at the same docket and Web
site.
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    \373\ Final report, CAE model and cost model for NHTSA's light
weighting study can be found at NHTSA's Web site: http://www.nhtsa.gov/fuel-economy.
---------------------------------------------------------------------------

    B. EPA, along with ICCT, funded a contract with FEV, with
subcontractors EDAG (CAE modeling) and Munro & Associates, Inc.
(component technology research) to study the feasibility, safety and
cost of 20% mass reduction on a 2017-2020 production ready mid-size

CUV (crossover utility vehicle) specifically, a Toyota Venza while
trying to achieve the same or lower cost. The EPA report is entitled
``Light-Duty Vehicle Mass-Reduction and Cost Analysis--Midsize
Crossover Utility Vehicle''. \374\ This study is a Phase 2 study of the
low development design in the 2010 Lotus Engineering study ``An
Assessment of Mass Reduction Opportunities for a 2017-2020 Model Year
Vehicle Program'',\375\ herein described as ``Phase 1''.
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    \374\ FEV, ``Light-Duty Vehicle Mass-Reduction and Cost
Analysis--Midsize Crossover Utility Vehicle''. July 2012, EPA
Docket: EPA-HQ-OAR-2010-0799.
    \375\ Systems Research and Application Corporation, ``Peer
Review of Demonstrating the Safety and Crashworthiness of a 2020
Model-Year, Mass-Reduced Crossover Vehicle (Lotus Phase 2 Report)'',
February 2012, EPA docket: EPA-HQ-OAR-2010-0799.
---------------------------------------------------------------------------

    The original 2009/2010 Phase 1 effort by Lotus Engineering was
funded by Energy Foundation and ICCT to generate a technical paper
which would identify potential mass reduction opportunities for a
selected vehicle representing the crossover utility segment, a 2009
Toyota Venza. Lotus examined mass reduction for two scenarios--a low
development (20% MR and 2017 production with technology readiness of
2014) and high development (40% MR and 2020 production with technology
readiness of 2017). Lotus disassembled a 2009 Toyota Venza and created
a bill of materials (BOM) with all components. Lotus then investigated
emerging/current technologies and opportunities for mass reduction. The
report included the BOM for full vehicle, systems, sub-systems and
components as well as recommendations for next steps. The potential
mass reduction for the low development design includes material changes
to portions of the body in white (underfloor and body, roof, body side,
etc.), seats, console, trim, brakes, etc. The Phase 1 project achieved
19% (without the powertrain), 246 kg, at 99% of original cost at full
phase-in after peer review comments taken into
consideration.376,377 This was calculated to be -$0.45/kg
utilizing information from Lotus.
---------------------------------------------------------------------------

    \376\ The original powertrain was changed to a hybrid
configuration.
    \377\ Cost estimates were given in percentages--no actual cost
analysis was presented for it was outside the scope of the study,
though costs were estimated by the agency based on the report.
---------------------------------------------------------------------------

    The peer reviewed Lotus Phase 1 study created a good foundation for
the next step of analyses of CAE modeling for safety evaluations and
in-depth costing (these steps were not within the scope of the Phase 1
study) as noted by the peer reviewer recommendations.\378\
---------------------------------------------------------------------------

    \378\ RTI International,``Peer Review of Lotus Engineering
Vehicle Mass Reduction Study'' EPA-HQ-OAR-2010-0799-0710, November
2010.
---------------------------------------------------------------------------

    Similar to Lotus Phase 1 study, the EPA Phase 2 study begins with
vehicle tear down and BOM development. FEV and its subcontractors tore
down a MY 2010 Toyota Venza in order to create a BOM as well as
understand the production methods for each component. Approximately 140
coupons from the BIW were analyzed in order to understand the full
material composition of the baseline vehicle. A baseline CAE model was
created based on the findings of the vehicle teardown and analysis. The
model's results for static bending, static torsion, and modal frequency
simulations (NVH) were obtained and compared to actual results from a
Toyota Venza vehicle. After confirming that the results were within
acceptable limits, this model was then modified to create light-
weighted vehicle models. EDAG reviewed the Lotus Phase 1 low
development BIW ideas and found redesign was needed to achieve the full
set of acceptable NVH characteristics. EDAG utilized a commercially
available computerized optimization tool called HEEDS MDO to build the
optimization model. The model consisted of 484 design variables, 7 load
cases (2 NVH + 5 crash), and 1 cost evaluation. The outcome of EDAG's
lightweight design optimization included the optimized vehicle assembly
and incorporated the following while maintaining the original BIW
design: Optimized gauge and material grades for body structure parts,
laser welded assembly at shock towers, rocker, roof rail, and rear
structure subassemblies, aluminum material for front bumper, hood, and
tailgate parts, TRBs on B-pillar, A-pillar, roof rail, and seat cross
member parts, design change on front rail side members. EDAG achieved
13% mass reduction in the BIW including closure. If aluminum doors were
included then an additional decrease of 28 kg could be achieved for a
total of 18% mass reduction from the body structure. All other systems
within the vehicle were examined for mass reduction, including the
powertrain (engine, transmission, fuel tank, exhaust, etc.). FEV and
Munro incorporated the Lotus Phase 1 low development concepts into
their own idea matrix. Each component and sub-system chosen for mass
reduction was scaled to the dimensions of the baseline vehicle, trying
to maximize the amount of mass reduction with cost effective
technologies and techniques that are considered feasible and
manufacturable in high volumes in MY2017. FEV included a full
discussion of the chosen mass reduction options for each component and
subsystem.
    Safety performance of the baseline and light-weighted designs
(Lotus Phase 1 low development and the final EPA Phase 2 design) were
evaluated by EDAG through their constructed detailed CAD/CAE vehicle
models. Five federal safety crash tests were performed, including FMVSS
flat frontal crash, side impact, rear impact and roof crush (using IIHS
resistance requirements) as well as Euro NCAP/IIHS offset frontal
crash. Criteria including the crash pulse, intrusion and visual crash
information were evaluated to compare the results of the light weighted
models to the results of the baseline model. The light weighted vehicle
achieved equivalent safety performance in all tests to the baseline
model with no damage to the fuel tank. In addition, CAE was used to
evaluate the BIW vibration modes in torsion, lateral bending, rear end
match boxing, and rear end vertical bending, and also to evaluate the
BIW stiffness in bending and torsion.
    The Phase 2 study 2010 Toyota Venza light weight vehicle achieved,
with powertrain, a total weight savings of 18 percent (312 kg) relative
to the baseline vehicle (1710 kg) at -$0.43/kg, and the cost figure is
near zero at 20 percent. The study report and models have been peer
reviewed by four technical experts from a material association,
academia, DOE, and a National Laboratory. The peer review comments for
this study were generally complimentary, and concurred with the ideas
and methodology of the study. A few of the comments required further
investigation, which were completed for the final report. The project
team addressed the peer review comments in the report and also composed
a response to peer review comment document. Changes to the BIW CAE
models resulted in minimal differences. The final report is published
in EPA's docket EPA-HQ-OAR-2010-0799 and the CAE LS DYNA model files
and overview cost model files are found on EPA's Web site http://www.epa.gov/otaq/climate/publications.htm#vehicletechnologies. The peer
review comments with responses to peer review comments can also be
found at the same docket and Web site.
    C. The California Air Resources Board (CARB) funded a study with
Lotus Engineering to further develop the high development design from
Lotus' 2010 Toyota Venza work (``Phase 1''). The CARB-sponsored Lotus
``Phase 2'' study

provides the updated design, crash simulation results, detailed
costing, and analysis of the manufacturing feasibility of the BIW and
closures. Based on the safety validation work, Lotus strengthened the
design with a more aluminum-intensive BIW (with less magnesium). In
addition to the increased use of advanced materials, the new design by
Lotus included a number of instances in which multiple parts were
integrated, resulting in a reduction in the number of manufactured
parts in the lightweight BIW. The Phase 2 study reports that the number
of parts in the BIW was reduced from 419 to 169. The BIW was analyzed
for torsional stiffness and crash test safety with Computer-Aided
Engineering (CAE). The new design's torsional stiffness was 32.9 kNm/
deg, which is higher than the baseline vehicle and comparable to more
performance-oriented models. The research supported the conclusion that
the lightweight vehicle design could pass standard FMVSS 208 frontal
impact, FMVSS 210 seatbelt anchorages, FMVSS child restraint anchorage,
FMVSS 214 side impact and side pole, FMVSS 216 roof crush (with 3xcurb