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TABLE 

### IV.C.1–6a—C URRENT 

### B ASELINE 

### AVERAGE 

### MY 2011 V EHICLE 

### P OWER



### - TO 

### -W EIGHT 

### RATIO

### —Continued

[hp/lb]

Manufacturer PC LT Avg.

Industry Average ................

............................................................................................... ............... 0.056 0.056 0.056



### TABLE 

### IV.C.1–6b—MY 2011 F INAL 

### R ULE 

### AVERAGE 

### P LANNED 

### MY 2011 V EHICLE 

### P OWER



### - TO 

### -W EIGHT 

### RATIO

[hp/lb]

PC LT Avg.

Manufacturer 1 .................................................................................................................

........................ Manufacturer 2 .................................................................................................................

........................ Manufacturer 3 .................................................................................................................

........................ Manufacturer 4 .................................................................................................................

........................ Manufacturer 5 .................................................................................................................

........................ Manufacturer 6 .................................................................................................................

........................ Manufacturer 7 .................................................................................................................

........................ 0.065

0.061

0.053

0.060

0.060

0.063

0.053 0.058

0.065

0.059

0.058

0.057

0.065

0.055 0.060

0.062

0.056

0.059

0.059

0.065

0.053

Industry Average ...............................................................................................................

............... 0.060 0.059 0.060

As discussed above, the agencies’
market forecast for MY 2012–2016 holds the performance and other
characteristics of individual vehicle models constant, adjusting the size and composition of the fleet from one model year to the next.

Refresh and redesign schedules (for application in NHTSA’s modeling):

Expected model years in which each vehicle model will be redesigned or freshened constitute another important aspect of NHTSA’s market forecast. As discussed in Section IV.C.2.c below, NHTSA’s analysis supporting the current rulemaking times the addition of nearly all technologies to coincide with either a vehicle redesign or a vehicle freshening. Product plans submitted to NHTSA preceding the MY 2011 final
rule contained manufacturers’ estimates of vehicle redesign and freshening
schedules and NHTSA’s estimates of the timing of the five-year redesign cycle and the two- to three-year refresh cycle
were made with reference to those plans. In the current baseline, in contrast, estimates of the timing of the refresh and redesign cycles were based
on historical dates—i.e., counting forward from known redesigns occurring in or prior to MY 2008 for each vehicle in the fleet and assigning refresh and redesign years accordingly. After applying these estimates, the shares of manufacturers’ passenger car and light truck estimated to be redesigned in MY 2011 were as

summarized below for the current

baseline and the MY 2011 final rule.

Table IV.C.1–7 below shows the percentages of each manufacturer’s fleets expected to be redesigned in MY

2011 for the current baseline. Table IV.C.1–8 presents corresponding

estimates from the market forecast used by NHTSA in the analysis supporting the MY 2011 final rule (again, to protect confidential information, manufacturers are not identified by name).



### TABLE 

### IV.C.1–7—C URRENT 

### B ASELINE 

### , S HARE OF 

### F LEET 

### R EDESIGNED IN 

### MY 2011

Manufacturer PC LT Avg.

BMW ............................................................................................................................

............................ Chrysler .......................................................................................................................

............................. Ford ...........................................................................................................................

............................... Subaru .........................................................................................................................

............................ General Motors .................................................................................................................

....................... Honda ..........................................................................................................................

............................ Hyundai ........................................................................................................................

............................ Tata ...........................................................................................................................

............................... Kia ............................................................................................................................

................................ Mazda ..........................................................................................................................

............................ Daimler ........................................................................................................................

............................. Mitsubishi .....................................................................................................................

............................ Nissan .........................................................................................................................

............................. Porsche ........................................................................................................................

............................ Suzuki .........................................................................................................................

............................. Toyota .........................................................................................................................

............................. Volkswagen .....................................................................................................................

......................... 32%

0%

12%

0%

20%

31%

20%

28%

35%

0%

0%

0%

4%

0%

8%

4%

23% 40%

11%

7%

51%

2%

33%

0%

100%

87%

0%

0%

56%

18%

100%

21%

24%

0% 34%

8%

10%

22%

11%

32%

16%

73%

48%

0%

0%

7%

9%

41%

11%

12%

18%

Industry Average ...............................................................................................................

............... 15% 17% 15%



### TABLE 

### IV.C.1–8—MY 2011 F INAL 

### R ULE 

### , S HARE OF 

### F LEET 

### R EDESIGNED IN 

### MY 2011

PC (percent) LT (percent) Avg. (percent)

Manufacturer 1 .................................................................................................................

........................ 19 0 11VerDate Nov<24>2008 23:31 Sep 25, 2009 Jkt 217001 PO 00000 Frm 00202 Fmt 4701 Sfmt 4702 E:\FR\FM\28SEP2.SGM 28SEP2mstockstill on DSKH9S0YB1PROD with PROPOSALS 

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TABLE 

### IV.C.1–8—MY 2011 F INAL 

### R ULE 

### , S HARE OF 

### F LEET 

### R EDESIGNED IN 

### MY 2011—Continued

PC (percent) LT (percent) Avg. (percent)

Manufacturer 2 .................................................................................................................

........................ Manufacturer 3 .................................................................................................................

........................ Manufacturer 4 .................................................................................................................

........................ Manufacturer 5 .................................................................................................................

........................ Manufacturer 6 .................................................................................................................

........................ Manufacturer 7 .................................................................................................................

........................ 34

5

7

19

34

27 27

0

0

0

28

28 29

3

5

11

33

28

Overall ........................................................................................................................

...................... 20 9 15

We continue, therefore, to estimate that manufacturers’ redesigns will not be uniformly distributed across model years. This is in keeping with standard industry practices, and reflects what manufacturers actually do—NHTSA has
observed that manufacturers in fact do redesign more vehicles in some years
than in others. NHTSA staff have closely examined manufacturers’ planned redesign schedules, contacting
some manufacturers for clarification of some plans, and confirmed that these plans remain unevenly distributed over time. For example, although Table 8 shows that NHTSA expects Company 2 to redesign 34 percent of its passenger car models in MY 2011, current information indicates that this company will then redesign only (a different) 10 percent of its passenger cars in MY 2012. Similarly, although Table 8 shows that NHTSA expects four of the largest seven light truck manufacturers to redesign virtually no light truck models in MY 2011, current information also
indicates that these four manufacturers will redesign 21–49 percent of their light trucks in MY 2012.

e. How Does Manufacturer Product Plan
Data Factor Into the Baseline Used in This Proposal?

As discussed in Section II.B.4 above, while the agencies received updated product plans in Spring 2009 in response to NHTSA’s request, the baseline data used in this proposal is not informed by these product plans, because they contain confidential business information the agencies are legally required to protect from disclosure, and because the agencies have concluded that, for purposes of this NPRM, a transparent baseline is preferable.

However, as also discussed above, NHTSA has conducted a separate analysis that does make use of these product plans, contained in NHTSA’s PRIA. NHTSA performed this separate analysis for purposes of comparison only. NHTSA used the publicly available baseline for all analysis related to the development and evaluation of the proposed new CAFE standards.

2. How Were the Technology Inputs Developed?

As discussed above in Section II.E, for developing the technology inputs for the
MY 2012–2016 CAFE and GHG standards, the agencies primarily began with the technology inputs used in the MY 2011 CAFE final rule and in the July 2008 EPA ANPRM, and then reviewed, as requested by President Obama in his January 26 memorandum, the technology assumptions that NHTSA used in setting the MY 2011 standards
and the comments that NHTSA received in response to its May 2008 Notice of Proposed Rulemaking. In addition, the agencies supplemented their review with updated information from more current literature, new product plans and from EPA certification testing. More detail is available regarding how the agencies developed the technology inputs for this NPRM above in Section II.E, in Chapter 3 of the Draft Joint TSD,
and in Section V of NHTSA’s PRIA. a. What Technologies Does NHTSA

Consider?

Section II.E.1 above describes the fuel-saving technologies considered by the agencies that manufacturers could use to improve the fuel economy of their vehicles during MYs 2012–2016. The majority of the technologies described in this section are readily available, well known, and could be incorporated into vehicles once production decisions are made. As discussed, the technologies

considered fall into five broad categories: Engine technologies, transmission technologies, vehicle technologies, electrification/accessory technologies, and hybrid technologies.

Table IV.C.2–1 below lists all the technologies considered and provides

the abbreviations used for them in the Volpe model, 464 as well as their year of availability, which for purposes of NHTSA’s analysis means the first model year in the rulemaking period that the Volpe model is allowed to apply a technology to a manufacturer’s fleet. 465 Year of availability recognizes that technologies must achieve a level of technical viability before they can be implemented in the Volpe model, and are thus a means of constraining technology use until such time as it is considered to be technologically feasible. For a more detailed description of each technology and their costs and effectiveness, we refer the reader to Chapter 3 of the joint TSD and Section

V of NHTSA’s PRIA.



### TABLE 

### IV.C.2–1—L IST OF 

### T ECHNOLOGIES IN 

### NHTSA’ S 

### A NALYSIS

Technology Model abbreviation Year available

Low Friction Lubricants ................................................................................................ LUB .....

...................................................... 2011 Engine Friction Reduction ............................................................................................ EFR .......

.................................................... 2011 VVT—Coupled Cam Phasing (CCP) on SOHC ........................................................... CCPS ...........................

............................. 2011 Discrete Variable Valve Lift (DVVL) on SOHC ............................................................ DVVLS ...................

................................... 2011 Cylinder Deactivation on SOHC ................................................................................... DEACS ..........

............................................ 2011

464 The abbreviations are used in this section both in Docket No. NHTSA–2009–0059 and on NHTSA’s 465 A date of 2011 means the technology can be for brevity and for the reader’s reference if they Web site. applied in all model years, while a date of 2014 wish to refer to the expanded decision trees and the means the technology can only be applied in model model input and output sheets, which are available years 2014 through 2016.VerDate Nov<24>2008 23:31 Sep 25, 2009 Jkt 217001 PO 00000 Frm 00203 Fmt 4701 Sfmt 4702 E:\FR\FM\28SEP2.SGM 28SEP2mstockstill on DSKH9S0YB1PROD with PROPOSALS 

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TABLE 

### IV.C.2–1—L IST OF 

### T ECHNOLOGIES IN 

### NHTSA’ S 

### A NALYSIS

### —Continued

Technology Model abbreviation Year available

VVT—Intake Cam Phasing (ICP) ................................................................................. ICP ...............

............................................. 2011 VVT—Dual Cam Phasing (DCP) ................................................................................. DCP .................

......................................... 2011 Discrete Variable Valve Lift (DVVL) on DOHC ............................................................ DVVLD ...................

................................... 2011 Continuously Variable Valve Lift (CVVL) ..................................................................... CVVL ...............

.......................................... 2011 Cylinder Deactivation on DOHC .................................................................................. DEADD ...........

.......................................... 2011 Cylinder Deactivation on OHV ..................................................................................... DEACO .........

............................................ 2011 VVT—Coupled Cam Phasing (CCP) on OHV ............................................................. CCPO ..........................

.............................. 2011 Discrete Variable Valve Lift (DVVL) on OHV ............................................................... DVVLO .................

..................................... 2011
Conversion to DOHC with DCP ................................................................................... CDOHC ............

......................................... 2011 Stoichiometric Gasoline Direct Injection (GDI) ............................................................ SGDI .................

........................................ 2011
Combustion Restart ...................................................................................................... CBRST ..

.................................................... 2014 Turbocharging and Downsizing .................................................................................... TRBDS ..........

............................................ 2011 Exhaust Gas Recirculation (EGR) Boost ..................................................................... EGRB .................

....................................... 2013 Conversion to Diesel following CBRST ....................................................................... DSLC ................

........................................ 2011 Conversion to Diesel following TRBDS ....................................................................... DSLT ................

......................................... 2011 6-Speed Manual/Improved Internals ............................................................................ 6MAN ..............

.......................................... 2011 Improved Auto. Trans. Controls/Externals ................................................................... IATC ................

.......................................... 2011 Continuously Variable Transmission ............................................................................ CVT ..............

............................................. 2011 6/7/8-Speed Auto. Trans with Improved Internals ....................................................... NAUTO ....................

................................. 2011
Dual Clutch or Automated Manual Transmission ........................................................ DCTAM ......................

............................... 2011 Electric Power Steering ................................................................................................ EPS .....

...................................................... 2011 Improved Accessories .................................................................................................. IACC .....

.................................................... 2011 12V Micro-Hybrid .......................................................................................................... MHEV .

....................................................... 2011 Belt Integrated Starter Generator ................................................................................. BISG .........

................................................ 2011 Crank Integrated Starter Generator ............................................................................. CISG ............

............................................. 2011 Power Split Hybrid ........................................................................................................ PSHEV

...................................................... 2011 2-Mode Hybrid .............................................................................................................. 2MHE V ...................................................... 2011 Plug-in Hybrid ............................................................................................................... PH EV ........................................................ 2011 Mass Reduction 1 (1.5%) ............................................................................................. MS1 ........

................................................... 2011 Mass Reduction 2 (3.5%–8.5%) .................................................................................. MS2 ..............

............................................. 2014 Low Rolling Resistance Tires ....................................................................................... ROLL ........

................................................. 2011 Low Drag Brakes .......................................................................................................... LDB ...

........................................................ 2011 Secondary Axle Disconnect 4WD ................................................................................ SAX ...............

............................................ 2011 Aero Drag Reduction .................................................................................................... AERO ....

.................................................... 2011

For purposes of this NPRM and as discussed in greater detail in the joint TSD, NHTSA and EPA carefully reviewed the list of technologies used in the agency’s analysis for the MY 2011 final rule. Given the relatively shortamount of time, from a technologydevelopment perspective, that has elapsed since March 2009 and this NPRM, NHTSA and EPA concluded that the considerable majority of technologies were correctly defined and continued to be appropriate for use in the analysis supporting the proposed standards. However, some refinements
were made as discussed below.

Specific to its modeling, NHTSA has revised eight of the technologies used in the current analysis from those
considered in the MY 2011 final rule. Specifically, two technologies which were previously unavailable in the MY 2011 time frame are now available (in the extended MY 2012–2016 period); one technology has been combined with another; one is newly introduced; three have revised names and/or definitions; and one has been deleted entirely. These changes are discussed further below, and NHTSA seeks comment on both these changes and the validation of the unchanged technology assumptions
and estimates.

Availability: In the MY 2011 final rule, two of the engine technologies—
EGR boost and combustion restart— were unavailable because they were not considered technologically feasible until beyond that rulemaking time frame.
While both were described and discussed in the MY 2011 final rule, neither was applied in the modeling process that supported those
standards. 466 In this analysis, EGR boost becomes available in MY 2013, and combustion restart in MY 2014, so both are being applied by the Volpe model, as needed, in this analysis.

Merging of technologies: In the MY 2011 final rule, higher voltage and improved alternator (HVIA) was used to represent changes in the design of the alternator, effectively optimizing it for higher efficiency (instead of for low cost as is typically done). For purposes of

466 As an additional note, since combustion restart was unavailable in the MY 2011 time frame, the technology titled diesel following combustion restart (DSLC), which as the name indicates was only applied after combustion restart, was also unavailable. Accordingly, DSLC, which was described and discussed in the MY 2011 final rule, is now available in the current analysis. this analysis, the HVIA technology is no longer represented individually, but instead has been incorporated into a new-to-this-analysis technology called belt integrated starter generator, or BISG, as discussed next.

New technology: In the MY 2011 final rule, two levels of mild hybrid technology were defined: A 12 volt micro-hybrid (MHEV) system, which utilized a belt-driven starter generator operating at 12 volts, and the more capable integrated starter generator technology (ISG) operating at higher voltages (100 volts). ISG envisioned both belt and crank configured starter generator systems. In the current proposal, and in an effort to offer a broader spectrum of more diversified mild hybrid technologies for the modeling process to choose from, NHTSA has added the BISG technology to the electrification decision tree, and redefined the ISG technology to be a crank mounted version of ISG, accordingly renamed to CISG.

The BISG technology is a belt-coupled system like the 12-volt MHEV, but it operates at a higher voltage ( e.g., 42 volts) and thus can make use of regenerative braking, as well asVerDate Nov<24>2008 23:31 Sep 25, 2009 Jkt 217001 PO 00000 Frm 00204 Fmt 4701 Sfmt 4702 E:\FR\FM\28SEP2.SGM 28SEP2mstockstill on DSKH9S0YB1PROD with PROPOSALS 

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potentially adding some limited motive power. Since BISG is a higher voltage system, optimization of the alternator occurs as part of the BISG technology application; hence the HVIA technology is no longer needed as a separate technology. Additionally, the CISG technology is now defined as a crank mounted system that operates at higher voltages (100 volts) than BISG, yet at lower voltage than the strong hybrids (300 volts) that make greater use of regenerative braking and provide greater motive power capability. Thus, three levels of mild hybrid technology exist in the current proposal, as opposed to the
two levels offered in the MY 2011 final
rule.

Revisions and Deletions: The Mass
Reduction/Material Substitution technologies have been revised for the current proposal. In the MY 2011 final rule, the Volpe model used three levels of material substitution technologies, referred to as MS1, MS2, and MS5, which were progressively applied to vehicles with curb weights in excess of 5,000 pounds (2,268 kg) so as to reduce weight by up to a 5 percent maximum. In keeping with the agency’s desire to limit potential negative impacts to safety performance as a result of vehicle weight reduction, material substitution was not applied to vehicles with curb weights below 5,000 pounds. In contrast, in the current analysis, and in keeping with some manufacturers’ stated plans to decrease overall fleet weights regardless of subclass or curb weight, NHTSA now defines two Mass
Reduction/Material Substitution technologies as follows:

The Mass Reduction 1.5 percent (MS1) represents a 1.5 percent weight decrease through material substitution applicable to all vehicle subclasses, regardless of curb weight, that can be applied throughout the rulemaking period (and at refresh or redesign cycle times). This technology is similar to the MS1 technology used in the prior analysis in terms of the scale of the weight reduction (1 versus 1.5 percent), the methods and techniques manufacturers are anticipated to use in achieving the reductions, and when in the product cycle the model applies it (at refresh or redesign).

A second technology, Mass Reduction 3.5–8.5 percent (MS2), has also been defined. The MS2 technology is unavailable until MY 2014, and can only be applied by the Volpe model at a product redesign cycle. MS2 assumes a 3.5 to 8.5 percent weight reduction dependent on subclass (with the smaller/lighter subclasses receiving the lowest amounts of reduction, 3.5 percent, and the larger/heavier vehicles the 8.5 percent) via the types of more intrusive and complex mass reduction associated with a complete vehicle redesign. 467 MS2 is cumulative to MS1, as it is only applied after MS1, therefore the maximum weight reduction that can
occur for smaller subclass vehicles is 5 percent, while large cars, truck, and SUVs could experience 10 percent weight reductions. Restricting weight
reduction on smaller vehicle to lower limits, and vice versa for larger vehicles, is intended to mitigate or minimize the potential safety consequences from the modeled weight reductions. Postponing the availability of the technology until MY 2014 recognizes the lead time required to implement platform redesigns that would be necessary for these levels of weight reduction and
mass reduction. NHTSA seeks comment on the agency’s use of a two-step process, with the higher applications of MS in MYs 2014 and beyond, and the process of applying smaller mass reductions to lighter vehicles and higher
reductions to heavier vehicles for the purpose of maintaining safety neutrality.

The MS5 technology used in the MY
2011 final rule is deleted.

Additionally, for purposes of this NPRM, NHTSA has revised the applicability of the diesel technologies to restrict it to vehicles with engines of 6 cylinders or more. NHTSA seeks comment on its decision not to apply diesel technologies to vehicles with 4- cylinder engines. NHTSA also seeks comment on the revised costing methodology for diesel technologies.

Besides these, all other technologies considered in this analysis were also considered in the analysis for the MY 2011 final rule, and although the costs and effectiveness estimates may have been revised as discussed further below, the other technologies remain otherwise unchanged for the purposes of this analysis in terms of their definition, functionality, and configuration. Thus, with this catalog of technologies as a starting point, NHTSA could then
review and consider effectiveness and cost estimates for each technology, and, through the Volpe model analysis, how a manufacturer might feasibly apply these technologies to their MY 2012–
2016 vehicles in order to achieve compliance with the proposed
standards.

467 Examples of such weight savings associated with new platform introductions have been provided in confidential product plan information provided by some manufacturers. b. How Did NHTSA Determine the Costs
and Effectiveness of Each of These Technologies for Use in Its Modeling Analysis?

Building on NHTSA’s estimates developed for the MY 2011 CAFE final
rule and EPA’s Advanced Notice of Proposed Rulemaking, which relied on the 2008 Staff Technical Report, 468 the agencies took a fresh look at technology
cost and effectiveness values for purposes of the joint proposal under the National Program. This joint work is reflected in Chapter 3 of the Draft Joint TSD and in Section II of this preamble,
which is summarized below. For more
detailed information on the effectiveness and cost of fuel-saving technologies, please refer to Chapter 3 of the joint TSD and Section V of NHTSA’s

PRIA.

Generally speaking, while NHTSA
and EPA found that much of the cost
information used in NHTSA’s MY 2011 final rule and EPA’s 2008 staff report was consistent to a great extent, the agencies, in reconsidering information from many sources, revised several component costs of several major technologies: turbocharging/ downsizing, mild and strong hybrids, diesels, SGDI, and Valve Train Lift Technologies. These are discussed at length in the joint TSD and in NHTSA’s PRIA. Additionally, most effectiveness
estimates used in both the MY 2011 final rule and the 2008 EPA staff report
were determined to be accurate and were carried forward without significant change into this rulemaking. When
NHTSA and EPA’s estimates for effectiveness diverged slightly due to differences in how agencies apply technologies to vehicles in their respective models, we report the ranges
for the effectiveness values used in each
model. For much more information on
the costs and effectiveness of individual technologies, we refer the reader to Chapter 3 of the joint TSD and Section
V of NHTSA’s PRIA.

NHTSA notes that, in developing technology cost and effectiveness estimates, the agencies have made every effort to hold constant aspects of vehicle performance and utility typically valued by consumers, such as horsepower, carrying capacity, and towing and hauling capacity. For example, NHTSA includes in its analysis technology cost
and effectiveness estimates that are specific to performance passenger cars (i.e., sports cars), as compared to nonperformance passenger cars. When

468 EPA Staff Technical Report: Cost and Effectiveness Estimates of Technologies Used to Reduce Light-Duty Vehicle Carbon Dioxide Emissions. EPA420–R–08–008, March 2008.VerDate Nov<24>2008 23:31 Sep 25, 2009 Jkt 217001 PO 00000 Frm 00205 Fmt 4701 Sfmt 4702 E:\FR\FM\28SEP2.SGM 28SEP2mstockstill on DSKH9S0YB1PROD with PROPOSALS 

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### 49657

commenting on the agencies’ technology cost and effectiveness estimates, NHTSA urges commenters either to place any related comments within the same context, or explain any assumptions or estimates regarding
increases or decreases in vehicle performance or utility. Additionally,
NHTSA seeks comment on the extent to
which commenters believe that the agencies have been successful in holding constant these elements of vehicle performance and utility in developing the technology cost and
effectiveness estimates.

Additionally, NHTSA notes that the technology costs included in this NPRM take into account only those associated
with the initial build of the vehicle. The agencies seek comments on the additional lifetime costs, if any, associated with the implementation of advanced technologies, including warranty, maintenance and replacement costs, such as the replacement costs for low rolling resistance tires, low friction lubricants, and hybrid batteries, and
maintenance costs for diesel aftertreatment components.

While the agencies believe that the
ideal estimates for the final rule would
be based on tear down studies or BOM approach and subjected to a transparent peer-reviewed process, NHTSA and EPA are confident that the thorough review conducted, led to the best available conclusion regarding technology costs and effectiveness estimates for the current rulemaking and resulted in excellent consistency between the agencies’ respective analyses for developing the CAFE and

CO 2 standards.

NHTSA seeks comment on the

incremental cost and effectiveness estimates employed by the agency in the Volpe modeling analysis for this NPRM, examples of which are provided in table form below. These example Tables present effectiveness and cost estimates which are incremental in nature, according to the decision trees used in the Volpe modeling analysis. Thus, the

effectiveness and cost estimates are not absolute to a single baseline vehicle, but are incremental to the technology that precedes it.

**BILLING CODE 6560–50–C**VerDate Nov<24>2008 23:31 Sep 25, 2009 Jkt 217001 PO 00000 Frm 00206 Fmt 4701 Sfmt 4725 E:\FR\FM\28SEP2.SGM 28SEP2EP28SE09.029</GPH>mstockstill on DSKH9S0YB1PROD with PROPOSALS 

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**BILLING CODE 6560–50–C**

c. How Does NHTSA Use These Assumptions in Its Modeling Analysis?

NHTSA’s analysis, using the Volpe model, relies on several inputs and data files to conduct the compliance analysis, as discussed further below and
in Section V of the PRIA. For the purposes of applying technologies, the Volpe model primarily uses three data files, one that contains data on the vehicles expected to be manufactured in the model years covered by the rulemaking, one that identifies the appropriate stage within the vehicle’s life-cycle for the technology to be applied, and one that contains data/ parameters regarding the available technologies the model can apply. These inputs are discussed below.

As discussed above, the Volpe model begins with an initial state of the domestic vehicle market, which in this case is the market for passenger cars and light trucks to be sold during the period covered by the proposed standards. TheVerDate Nov<24>2008 23:31 Sep 25, 2009 Jkt 217001 PO 00000 Frm 00208 Fmt 4701 Sfmt 4702 E:\FR\FM\28SEP2.SGM 28SEP2mstockstill on DSKH9S0YB1PROD with PROPOSALS 

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vehicle market is defined on a modelby-model, engine-by-engine, and transmission-by-transmission basis,
such that each defined vehicle model refers to a separately defined engine and a separately defined transmission.

For the current proposal, which covers MYs 2012–2016, the light vehicle (passenger car and light truck) market forecast was developed jointly by NHTSA and EPA staff using MY 2008 CAFE compliance data. The MY 2008 compliance data includes about 1,100 vehicle models, about 400 specific engines, and about 200 specific transmissions, which is a somewhat
lower level of detail in the representation of the vehicle market than that used by NHTSA in recent CAFE analyses. 469 However, within the
limitations of information that can be made available to the public, it provides the foundation for a realistic analysis of manufacturer-specific costs and the analysis of attribute-based CAFE standards, and is much greater than the level of detail used by many other models and analyses relevant to light vehicle fuel economy. 470

In addition to containing data about each vehicle, engine, and transmission,
this file contains information for each technology under consideration as it pertains to the specific vehicle (whether the vehicle is equipped with it or not), the model year the vehicle is undergoing redesign, and information
about the vehicle’s subclass for purposes of technology application. In essence, the model considers whether it is appropriate to apply a technology to
a vehicle.

Is a vehicle already equipped, or can it not be equipped, with a particular technology?

The market forecast file provides NHTSA the ability to identify, on a technology by technology basis, which technologies may already be present (manufactured) on a particular vehicle, engine, or transmission, or which technologies are not applicable (due to technical considerations) to a particular vehicle, engine, or transmission. These

469 The market file for the MY 2011 final rule, which included data for MYs 2011–2015, had 5500 records, or rows, about 5 times what we are using in this analysis of the MY 2008 certification data. However, both market files had the same number of fields, or rows.

470 Because CAFE standards apply to the average performance of each manufacturer’s fleet of cars and light trucks, the impact of potential standards on individual manufacturers cannot be credibly estimated without analysis of fleets manufacturers can be expected to produce in the future. Furthermore, because required CAFE levels under an attribute-based CAFE standard depend on manufacturers’ fleet composition, the stringency of an attribute-based standard cannot be predicted without performing analysis at this level of detail. identifications are made on a model-bymodel, engine-by-engine, and transmission-by-transmission basis. For example, if the market forecast file
indicates that Manufacturer X’s Vehicle Y is manufactured with Technology Z, then for this vehicle Technology Z will be shown as used. Additionally, NHTSA has determined that some technologies are only suitable or unsuitable when certain vehicle, engine, or transmission conditions exist. For example, secondary axle disconnect is only suitable for 4WD vehicles, and cylinder deactivation is unsuitable for any engine with fewer than 6 cylinders, while CVTs can only be applied to unibody vehicles. Similarly, comments received to the 2008 NPRM indicated that cylinder deactivation could not be applied to vehicles equipped with manual transmissions, due primarily to driveability and NVH concerns. The Volpe model employs ‘‘engineering constraints’’ to address issues like these, which are a programmatic method of controlling technology application that is independent of other constraints. Thus, the market forecast file would indicate that the technology in question should not be applied to the particular vehicle/engine/transmission (i.e. , is unavailable). Since multiple vehicle models may be equipped with an engine or transmission, this may affect multiple models. In using this aspect of the market forecast file, NHTSA ensures the Volpe model only applies technologies in an appropriate manner, since before any application of a technology can occur, the model checks the market forecast to see if it is either already present or unavailable.

NHTSA seeks comment on whether this approach is reasonable and ensures that technologies are applied in an appropriate manner.

Is a vehicle being redesigned or refreshed?

Manufacturers typically plan vehicle changes to coincide with certain stages of a vehicle’s life cycle that are appropriate for the change, or in this case the technology being applied. In the automobile industry there are two
terms that describe when technology changes to vehicles occur: redesign and refresh (i.e. , freshening). Vehicle redesign usually refers to significant changes to a vehicle’s appearance, shape, dimensions, and powertrain. Redesign is traditionally associated with
the introduction of ‘‘new’’ vehicles into the market, often characterized as the ‘‘next generation’’ of a vehicle, or a new platform. Vehicle refresh usually refers to less extensive vehicle modifications, such as minor changes to a vehicle’s appearance, a moderate upgrade to a powertrain system, or small changes to the vehicle’s feature or safety equipment content. Refresh is traditionally associated with mid-cycle cosmetic changes to a vehicle, within its current generation, to make it appear ‘‘fresh.’’ Vehicle refresh generally occurs no earlier than two years after a vehicle redesign, or at least two years before a scheduled redesign. For the majority of technologies discussed today, manufacturers will only be able to apply them at a refresh or redesign, because their application would be significant enough to involve some level of engineering, testing, and calibration
work. 471

Some technologies ( e.g. , those that require significant revision) are nearly always applied only when the vehicle is expected to be redesigned, like turbocharging and engine downsizing, or conversion to diesel or hybridization. Other technologies, like cylinder deactivation, electric power steering, and aerodynamic drag reduction can be applied either when the vehicle is expected to be refreshed or when it is expected to be redesigned, while a few others, like low friction lubricants, can be applied at any time, regardless of whether a refresh or redesign event is conducted. Accordingly, the model will only apply a technology at the particular point deemed suitable. These constraints are intended to produce
results consistent with manufacturers’ technology application practices. For each technology under consideration, NHTSA stipulates whether it can be applied any time, at refresh/redesign, or only at redesign. The data forms another input to the Volpe model. NHTSA develops redesign and refresh schedules
for each of a manufacturer’s vehicles included in the analysis, essentially based on the last known redesign year for each vehicle and projected forward in a 5-year redesign and a 2–3 year refresh cycle, and this data is also stored
in the market forecast file. We note that this approach is different than NHTSA has employed previously for determining redesign and refresh schedules, where NHTSA included the redesign and refresh dates in the market forecast file as provided by manufacturers in confidential product plans. The new approach is necessary

471 For example, applying material substitution through weight reduction, or even something as simple as low rolling-resistance tires, to a vehicle will likely require some level of validation and testing to ensure that the vehicle may continue to be certified as compliant with NHTSA’s Federal Motor Vehicle Safety Standards (FMVSS). Weight reduction might affect a vehicle’s crashworthiness; low rolling-resistance tires might change vehicle’s braking characteristics or how it performs in crash
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given the nature of the new baseline which as a single year of data does not contain its own refresh and redesign cycle cues for future model years, and to ensure the complete transparency of the agency’s analysis. Vehicle redesign/ refresh assumptions are discussed in
more detail in Section V of the PRIA and in Chapter 3 of the TSD. NHTSA seeks comment on its application for this proposal of refresh and redesign
schedules to manufacturers’ vehicles counting from the last known redesign in or prior to the baseline fleet, as compared to its approach in the MY
2011 final rule.

Once the model has concluded that a technology should be applied to a vehicle, the model must evaluate which technology should be applied. This will depend on the vehicle subclass to which the vehicle is assigned; what technologies have already been applied to the vehicle (i.e. , where in the ‘‘decision tree’’ the vehicle is); when the technology is first available (i.e. , year of availability); whether the technology is still available (i.e. , ‘‘phase-in caps’’); and
the costs and effectiveness of the technologies being considered. Technology costs may be reduced, in turn, by learning effects, while technology effectiveness may be increased or reduced by synergistic effects between technologies. In the technology input file, NHTSA has developed a separate set of technology
data variables for each of the twelve
vehicle subclasses. Each set of variables is referred to as an ‘‘input sheet,’’ so for example, the subcompact input sheet holds the technology data that is appropriate for the subcompact subclass. Each input sheet contains a list of technologies available for members of the particular vehicle subclass. The following items are provided for each technology: the name of the technology, its abbreviation, the
decision tree with which it is associated, the (first) year in which it is available, the upper and lower cost and effectiveness (fuel consumption reduction) estimates, the learning type and rate, the cost basis, its applicability, and the phase-in values.

To which vehicle subclass is the vehicle assigned?

As part of its consideration of technological feasibility, the agency evaluates whether each technology could be implemented on all types and sizes of vehicles, and whether some differentiation is necessary in applying certain technologies to certain types and sizes of vehicles, and with respect to the cost incurred and fuel consumption and

CO 2 emissions reduction achieved when doing so. The 2002 NAS Report differentiated technology application using ten vehicle ‘‘classes’’ (4 cars classes and 6 truck classes), 472 but did
not determine how cost and
effectiveness values differ from class to class. NAS’s purpose in separating
vehicles into these classes was to create groups of ‘‘like’’ vehicles, i.e. , vehicles similar in size, powertrain configuration, weight, and consumer use, and for which similar technologies are applicable. NHTSA similarly differentiates vehicles by ‘‘subclass’’ for the purpose of applying technologies to vehicles and assessing their incremental costs and effectiveness. NHTSA assigns
each vehicle manufactured in the rulemaking period to one of 12 subclasses: for passenger cars, Subcompact, Subcompact Performance, Compact, Compact Performance, Midsize, Midsize Performance, Large, and Large Performance; and for light trucks, Small SUV/Pickup/Van, Midsize SUV/Pickup/Van, Large SUV/Pickup/ Van, and Minivan.

For this NPRM as for the MY 2011 final rule, NHTSA divides the vehicle
fleet into subclasses based on model inputs, and applies subclass-specific estimates, also from model inputs, of the applicability, cost, and effectiveness of each fuel-saving technology. Therefore,
the model’s estimates of the cost to improve the fuel economy of each vehicle model depend upon the
subclass to which the vehicle model is assigned.

Each vehicle’s subclass is stored in
the market forecast file. When conducting a compliance analysis, if the Volpe model seeks to apply technology to a particular vehicle, it checks the market forecast to see if the technology is available and if the refresh/redesign
criteria are met. If these conditions are satisfied, the model determines the
vehicle’s subclass from the market data file, which it then uses to reference another input called the technology input file. NHTSA reviewed its methodology for dividing vehicles into subclasses for purposes of technology application that it used in the MY 2011 final rule, and concluded that the same methodology would be appropriate for this NPRM for MYs 2012–2016, but the agency invites comments on the method of assigning vehicles to subclasses for the purposes of technology application in the CAFE model, and on the issue of technology-application subclasses generally. The subclasses and the methodology for dividing vehicles among them are discussed in more
detail in Section V of the PRIA and in Chapter 3 of the TSD.

For the reader’s reference, the subclasses and example vehicles from the market forecast file are provided in
the tables below.



### P ASSENGER 

### C AR 

### S UBCLASSES 

### E XAMPLE 

### (MY 2008) V EHICLES

Class

Subcompact .............................................................................................. Subcompact Performance ........................................................................ Compact ................................................................................................... Compact Performance ..............................................................................

Midsize ......................................................................................................

Midsize Performance ................................................................................ Large ......................................................................................................... Large Performance ................................................................................... Example vehicles

Chevy Aveo, Honda Civic

. Mazda Miata, Saturn Sky. Chevy Cobalt, Nissan Sentra and Altima. Audi S4 Quattro, Mazda RX8. Chevy Camaro (V6), Toyota Camry, Honda Accord, Hyundai Azera. Chevy Corvette, Ford Mustang (V8), Nissan G37 Coupe. Audi A8, Cadillac CTS and DTS. Bentley Arnage, Daimler CL6 00.



### L IGHT 

### T RUCK 

### S UBCLASSES 

### E XAMPLE 

### (MY 2008) V EHICLES

Class Example vehicles

Minivans .................................................................................................... Dodge Caravan, Toyota Sienna.

472 The NAS classes included subcompact cars, midsize SUVs, large SUVs, small pickups, large compact cars, midsize cars, large cars, small SUVs, pickups, and minivans.VerDate Nov<24>2008 23:31 Sep 25, 2009 Jkt 217001 PO 00000 Frm 00210 Fmt 4701 Sfmt 4702 E:\FR\FM\28SEP2.SGM 28SEP2mstockstill on DSKH9S0YB1PROD with PROPOSALS 

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L IGHT 

### T RUCK 

### S UBCLASSES 

### E XAMPLE 

### (MY 2008) V EHICLES

### —Continued

Class

Small SUV/Pickup/Van ............................................................................. Midsize SUV/Pickup/Van .......................................................................... Large SUV/Pickup/Van ............................................................................. Example vehicles

Ford Escape & Ranger, Nissan R ogue. Chevy Colorado, Jeep Wrangler 4-door, Volvo XC70, Toyota Tacoma. Chevy Silverado, Ford Econoline, Toyota Sequoia.

What technologies have already been applied to the vehicle (i.e., where in the ‘‘decision trees’’ is it)?

NHTSA’s methodology for technology application analysis developed out of the approach taken by NAS in the 2002 Report, and evaluates the application of individual technologies and their
incremental costs and effectiveness.
Incremental costs and effectiveness of individual technologies are relative to the prior technology state, which means
that it is crucial to understand what technologies are already present on a
vehicle in order to determine correct
incremental cost and effectiveness
values. The benefit of the incremental approach is transparency in accounting, insofar as when individual technologies are added incrementally to individual vehicles, it is clear and easy to
determine how costs and effectiveness adds up as technology levels increase.

To keep track of incremental costs
and effectiveness and to know which technology to apply and in which order, the Volpe model’s architecture uses a logical sequence, which NHTSA refers to as ‘‘decision trees,’’ for applying fuel economy-improving technologies to
individual vehicles. In the MY 2011 final rule, NHTSA worked with Ricardo to modify previously-employed decision
trees in order to allow for a much more accurate application of technologies to vehicles. For purposes of the NPRM, NHTSA reviewed the technology sequencing architecture and updated, as appropriate, the decision trees used in the analysis reported in the final rule for

MY 2011.

In general, and as described in great
detail in the MY 2011 final rule and in Section V of the current PRIA, each technology is assigned to one of the five following categories based on the system it affects or impacts: engine, transmission, electrification/accessory, hybrid or vehicle. Each of these categories has its own decision tree that the Volpe model uses to apply technologies sequentially during the compliance analysis. The decision trees were designed and configured to allow the Volpe model to apply technologies in a cost-effective, logical order that also considers ease of implementation. For example, software or control logic changes are implemented before replacing a component or system with a completely redesigned one, which is typically a much more expensive option. In some cases, and as appropriate, the model may combine the sequential technologies shown on a decision tree and apply them simultaneously, effectively developingdynamic technology packages on an asneeded basis. For example, if compliance demands indicate, the model may elect to apply LUB, EFR, and ICP on a dual overhead cam engine, if they are not already present, in one single step. An example simplified decision tree for engine technologies is provided below; the other simplified decision trees may be found in Chapter 3 of the joint TSD and in the PRIA. Expanded decision trees are available in
the docket for this NPRM.

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Each technology within the decision
trees has an incremental cost and an
incremental effectiveness estimate associated with it, and estimates are specific to a particular vehicle subclass ( see the tables in Section V of the PRIA). Each technology’s incremental estimate takes into account its position in the decision tree path. If a technology is located further down the decision tree,
the estimates for the costs and
effectiveness values attributed to that technology are influenced by the
incremental estimates of costs and effectiveness values for prior technology applications. In essence, this approach accounts for ‘‘in-path’’ effectiveness synergies, as well as cost effects that occur between the technologies in the same path. When comparing cost and
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rulemakings, it is important that the estimates evaluated are analyzed in the proper context, especially as concerns their likely position in the decision trees and other technologies that may be present or missing. Not all estimates available in the public domain or offered for the agencies’ consideration during the comment period can be evaluated in an ‘‘apples-to-apples’’ comparison with those used by the Volpe model, since in some cases the order of application, or included technology content, is inconsistent with
that assumed in the decision tree.

The MY 2011 final rule discussed in detail the revisions and improvements made to the Volpe model and decision trees during that rulemaking process, including the improved handling and accuracy of valve train technology application and the development and implementation of a method for accounting path-dependent correction
factors in order to ensure that technologies are evaluated within the proper context. The reader should
consult the MY 2011 final rule
documents for further information on these modeling techniques, all of which continued to be utilized in developing this proposal. 473 To the extent that the decision trees have changed for purposes of this NPRM, it was due not to revisions in the order of technology application, but rather to redefinitions of technologies or addition or subtraction of technologies. NHTSA
seeks comment on the decision trees
described here and in the PRIA.

Is the next technology available in this model year?

As discussed above, the majority of technologies considered are available on vehicles today, and thus will be available for application in the rulemaking time frame. Some technologies, however, will not become available for purposes of NHTSA’s analysis until later in the rulemaking
time frame. When the model is considering whether to add a technology to a vehicle, it checks its year of availability—if the technology is available, it may be added; if it is not available, the model will consider
whether to switch to a different decision tree to look for another technology, or will skip to the next vehicle in a manufacturer’s fleet. The year of availability for each technology is provided above in Table IV.C.2–1.

Has the technology reached the phase-in cap for this model year?

473 See, e.g., 74 FR 14238–46 (Mar. 30, 2009) for
a full discussion of the decision trees in NHTSA’s MY 2011 final rule, and Docket No. NHTSA–2009– 0062–0003.1 for an expanded decision tree used in that rulemaking. Besides the refresh/redesign cycles used in the Volpe model, which constrain the rate of technology application at the vehicle level so as to ensure a period of stability following any modeled technology applications, the other constraint on technology application employed in NHTSA’s analysis is ‘‘phase-in caps.’’ Unlike vehicle-level cycle settings, phase-in caps constrain technology application at
the vehicle manufacturer level. 474 They
are intended to reflect a manufacturer’s overall resource capacity available for implementing new technologies (such as engineering and development personnel and financial resources), thereby ensuring that resource capacity is accounted for in the modeling process. At a high level, phase-in caps and refresh/redesign cycles work in conjunction with one another to avoid the modeling process out-pacing an OEM’s limited pool of available resources during the rulemaking time frame, especially in years where many models may be scheduled for refresh or redesign. This helps to ensure technological feasibility and economic practicability in determining the stringency of the standards.

NHTSA has been developing the concept of phase-in caps over the course of the last several CAFE rulemakings, as discussed in greater detail in the MY 2011 final rule, 475 and in Section V of the PRIA and Chapter 3 of the joint TSD.The MY 2011 final rule employed nonlinear phase-in caps (that is, caps that varied from year to year) that were designed to respond to comments raising lead-time concerns in reference to the agency’s proposed MY 2011–2015 standards, but because the final rule covered only one model year, many phase-in caps for that model year were lower than had originally been proposed. NHTSA emphasized that the MY 2011 phase-in caps were based on assumptions for the full five year period of the proposal (2011–2015), and stated that it would reconsider the phase-in

474 While phase-in caps are expressed as specific percentages of a manufacturer’s fleet to which a technology may be applied in a given model year, phase-in caps cannot always be applied as precise limits, and the Volpe model in fact allows ‘‘override’’ of a cap in certain circumstances. When only a small portion of a phase-in cap limit remains, or when the cap is set to a very low value, or when a manufacturer has a very limited product line, the cap might prevent the technology from being applied at all since any application would cause the cap to be exceeded. Therefore, the Volpe model evaluates and enforces each phase-in cap constraint after it has been exceeded by the application of the technology (as opposed to evaluating it before application), which can result in the described overriding of the cap.

475 74 FR 14268–14271 (Mar. 30, 2009). settings for all years beyond 2011 in a future rulemaking analysis.

For purposes of the current proposal for MYs 2012–2016, as in the MY 2011 final rule, NHTSA combines phase-in caps for some groups of similar technologies, such as valve phasing technologies that are applicable to different forms of engine design (SOHC, DOHC, OHV), since they are very similar from an engineering and implementation standpoint. When the phase-in caps for two technologies are combined, the maximum total application of either or both to any
manufacturer’s fleet is limited to the value of the cap. 476 In contrast to the phase-in caps used in the MY 2011 final rule, NHTSA has increased the phase-in caps for most of the technologies, as
discussed below.

In developing phase-in cap values for purposes of the current proposal, NHTSA initially considered the fact that many of the technologies commonly applied by the model, those placed near the top of the decision trees, such as low friction lubes, valve phasing, electric power steering, improved automatic transmission controls, and others, have been commonly available to manufacturers for several years now. Many technologies, in fact, precede the 2002 NAS Report, which estimated that such technologies would take 4 to 8 years to penetrate the fleet. Since the current proposal would take effect in MY 2012, nearly 10 years beyond the NAS report, and extends to MY 2016,
and in the interest of harmonization with EPA’s proposal, NHTSA tentatively determined that higher phase-in caps were likely justified. Additionally, NHTSA considered the fact that manufacturers, as part of the agreements supporting the National Program, appear to be anticipating higher technology application rates than
those used in the MY 2011 final rule. This also supported higher phase-in caps for purposes of the proposal.

Thus, while phase-in caps for the MY
2011 final rule reached a maximum of 50 percent for a couple of technologies and generally fell in the range between 0 and 20 percent, phase-in caps for this NPRM for the majority of technologies are set to reach 85 or 100 percent by MY 2016, although more advanced technologies like diesels and strong hybrids reach only 15 percent by MY

2016.

Theoretically, significantly higher phase-in caps, such as those used in the current proposal as compared to those used in the MY 2011 final rule, should

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result in higher levels of technology penetration in the modeling results. Reviewing the modeling output does not, however, indicate unreasonable levels of technology penetration for the proposed standards. 477 NHTSA believes
that this is due to the interaction of the various changes in methodology for the current proposal—changes to phase-in caps are but one of a number of revisions to the Volpe model and its inputs that could potentially impact the rate at which technologies are applied in this proposal as compared to prior rulemakings. Other revisions that could impact application rates include the use of transparent CAFE certification data in
baseline fleet formulation and the use of other data for projecting it forward, 478 or the use of a multi-year planning programming technique to apply technology retroactively to earlier-MY vehicles, both of which may have a direct impact on the modeling process. Conversely the model and inputs remain unchanged in other areas that also could impact technology application, such as in the refresh/ redesign cycle settings, estimates used for the technologies, both of which remain largely unchanged from the MY 2011 final rule. These changes togethermake it difficult to predict how phasein caps should be expected to function in the new modeling process.

Thus, after reviewing the output files, NHTSA tentatively concludes that the higher phase-in caps, and the resulting technology application rates produced by the Volpe model, at both the industry and manufacturer level, are appropriate for this proposal, achieving a suitable level of stringency without requiring unrealistic or unachievable penetration rates. However, the agency will consider comments received on this approach in determining what phase-in caps to employ in the analysis for the final rule, and may change the caps in response to comments and/or further analysis. One additional question the agency has, which may be primarily academic atthis point, is what impact lower phasein caps, such as those used in earlier rulemakings, would have on compliancecosts (and whether they might counterintuitively increase costs by forcing more expensive technologies). NHTSA seeks comment on the revised phase-in caps as compared to the MY 2011 final rule, and particularly on whether,

477 The modeling output for the analysis underlying these proposed standards is available on
NHTSA’s Web site.

478 The baseline fleet sets the starting point, from a technology point of view, for where the model begins the technology application process, so changes have a direct impact on the net application of technology. combined with the refresh and redesign assumptions, they help to ensure
sufficient lead time for manufacturers to make the technology changes required by the proposed standards. Readers areinvited to review and assess the phasein caps listed and described more fully in Section V of the PRIA, along with the application and penetration rates found in the Volpe model’s output files, and after making their own assessment, provide comment and recommendations to the agency as appropriate.

Is the technology less expensive due to learning effects?

Historically, NHTSA did not explicitly account for the cost reductions a manufacturer might realize through learning achieved from experience in actually applying a technology. Since working with EPA to develop the 2008 NPRM for MYs 2011– 2015, and with Ricardo to refine the concept for the March 2009 MY 2011 final rule, NHTSA has accounted for these cost reductions through two kinds of mutually exclusive learning,
‘‘volume-based’’ and ‘‘time-based’’
which it continues to use in this proposal, as discussed below.

In the 2008 NPRM, NHTSA applied learning factors to technology costs for the first time. These learning factors were developed using the parameters of learning threshold, learning rate, and the initial cost, and were based on the ‘‘experience curve’’ concept which describes reductions in production costs as a function of accumulated production volume. The typical curve shows a relatively steep initial decline in cost which flattens out to a gentle downwardly sloping line as the volume increase to large values. In the NPRM, NHTSA applied a learning rate discount of 20 percent for each successive doubling of production volume (on a per manufacturer basis), and a learning threshold of 25,000 units was assumed (thus a technology was viewed as being fully learned out at 100,000 units). The factor was only applied to certain technologies that were considered emerging or newly implemented on the basis that significant cost improvements
would be achieved as economies of scale were realized (i.e. , the technologies were on the steep part of the curve).

In the MY 2011 final rule, NHTSA continued to use this learning factor, referring to it as volume-based learning
since the cost reductions were determined by production volume increases, and again only applied it to emerging technologies. However, and in response to comments, NHTSA revised its assumptions on learning threshold,basing them instead on an industrywide production basis, and increasing the threshold to 300,000 units annually.

Commenters to the 2008 NPRM also described another type of learning factor which NHTSA decided to adopt and implement in the MY 2011 final rule. Commenters described a relatively small negotiated cost decrease that occurred on an annual basis through contractual agreements with first tier component and systems suppliers for readily available, high volume technologies commonly in use by multiple OEMs. Based on the same experience curve principal, however at production volumes that were on the flatter part of the curve (and thus the types of volumes that represent annual industry volumes), NHTSA adopted this type learning and referred to it as time-based learning. An annual cost reduction of 3 percent in the second and each subsequent year, which was consistent
with estimates from commenters and supported by work Ricardo conducted for NHTSA, was used in the final rule.

In developing this proposal, NHTSA and EPA have reviewed both types of learning factors, and the thresholds (300,000) and reduction rates (20 percent for volume, 479 3 percent for time-based) they rely on, and as implemented in the MY 2011 final rule, and agreed that both factors continue to be accurate and appropriate; each agency has thus implemented time- and volume-based learning in their analyses. Noting that only one type of learning can be applied to any single technology, if any learning is applied at all, the agencies reviewed each to determine which learning factor was appropriate. Volume-based learning is applied to the higher complexity hybrid technologies, while no learning is applied to technologies likely to be affected by commodity costs (LUB, ROLL) or that have loosely-defined BOMs (EFR, LDB),
as was the case in the MY 2011 final rule. Chapter 3 of the joint TSD shows the specific learning factors that NHTSA has applied in this analysis for each technology, and discusses learning factors and each agencies’ use of them
further. NHTSA seeks comment on its use of learning factors, including the types, the thresholds, and the reduction rates proposed, and particularly on the revisions to the learning (time- and volume-based) logic as compared to the
MY 2011 final rule.

Is the technology more or less effective due to synergistic effects?

When two or more technologies are added to a particular vehicle model to

479 NHTSA will conduct a sensitivity analysis on the volume-based learning value of 20 percent for
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improve its fuel efficiency and reduce

CO 2 emissions, the resultant fuel consumption reduction may sometimes be higher or lower than the product of
the individual effectiveness values for
those items. 480 This may occur because one or more technologies applied to the same vehicle partially address the same source (or sources) of engine, drivetrain or vehicle losses. Alternately, this effect may be seen when one technology shifts the engine operating points, and
therefore increases or reduces the fuel consumption reduction achieved by another technology or set of technologies. The difference between the observed fuel consumption
reduction associated with a set of technologies and the product of the
individual effectiveness values in that set is referred to for purposes of this rulemaking as a ‘‘synergy.’’ Synergies may be positive (increased fuel consumption reduction compared to the product of the individual effects) or negative (decreased fuel consumption reduction). An example of a positive synergy might be a vehicle technology that reduces road loads at highway speeds ( e.g. , lower aerodynamic drag or low rolling resistance tires), that could extend the vehicle operating range over which cylinder deactivation may be employed. An example of a negative synergy might be a variable valvetrain system technology, which reduces pumping losses by altering the profile ofthe engine speed/load map, and a sixspeed automatic transmission, which shifts the engine operating points to a portion of the engine speed/load map where pumping losses are less significant. As the complexity of the technology combinations is increased, and the number of interacting technologies grows accordingly, it becomes increasingly important to account for these synergies.

NHTSA and EPA determined synergistic impacts for this rulemaking using EPA’s ‘‘lumped parameter’’ analysis tool, which EPA described at length in its March 2008 Staff Technical Report. 481 The lumped parameter tool is

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

481 EPA Staff Technical Report: Cost and Effectiveness Estimates of Technologies Used to a spreadsheet model that represents energy consumption in terms of average performance over the fuel economy test procedure, rather than explicitly analyzing specific drive cycles. The tool begins with an apportionment of fuel consumption across several loss
mechanisms and accounts for the average extent to which different technologies affect these loss mechanisms using estimates of engine,
drivetrain and vehicle characteristics that are averaged over the EPA fuel economy drive cycle. Results of this analysis were generally consistent with
those of full-scale vehicle simulation modeling performed in 2007 by Ricardo,

Inc.

For the current rulemaking, NHTSA used the lumped parameter tool as
modified in the MY 2011 CAFE final rule. NHTSA modified the lumped parameter tool from the version
described in the EPA Staff Technical Report in response to public comments received in its rulemaking. The modifications included updating the list of technologies and their associated
effectiveness values to match the updated list of technologies used in the final rule. NHTSA also expanded the list of synergy pairings based on further consideration of the technologies for which a competition for losses would be expected. These losses are described in
more detail in Section V of the PRIA.

NHTSA and EPA incorporate synergistic impacts in their analyses in slightly different manners. Because NHTSA applies technologies individually in its modeling analysis, NHTSA incorporates synergistic effects between pairings of individual technologies. The use of discrete technology pair incremental synergies is similar to that in DOE’s National Energy Modeling System (NEMS). 482 Inputs tothe Volpe model incorporate NEMSidentified pairs, as well as additional pairs from the set of technologies considered in the Volpe model.

NHTSA notes that synergies that occur within a decision tree are already
addressed within the incremental values assigned and therefore do not require a synergy pair to address. For example, all engine technologies take into account incremental synergy factors of preceding engine technologies, and all

Reduce Light-duty Vehicle Carbon Dioxide Emissions; EPA420–R–08–008, March 2008.

482 U.S. Department of Energy, Energy Information Administration, Transportation Sector Module of the National Energy Modeling System: Model Documentation 2007, May 2007, Washington, DC, DOE/EIAM070(2007), at 29–30. Available at http://tonto.eia.doe.gov/ftproot/ modeldoc/m070(2007).pdf (last accessed Jul. 6, 2009). transmission technologies take into account incremental synergy factors of preceding transmission technologies. These factors are expressed in the fuel consumption improvement factors in the input files used by the Volpe model.

For applying incremental synergy factors in separate path technologies, the Volpe model uses an input table (see the tables in Chapter 3 of the TSD and in the PRIA) which lists technology pairings and incremental synergy factors associated with those pairings, most of which are between engine technologies and transmission/electrification/hybrid technologies. When a technology is applied to a vehicle by the Volpe model, all instances of that technology in the incremental synergy table which match technologies already applied to the vehicle (either pre-existing or previously applied by the Volpe model) are summed and applied to the fuel consumption improvement factor of the technology being applied. Synergies for the strong hybrid technology fuel consumption reductions are included in the incremental value for the specific hybrid technology block since the model applies technologies in the order
of the most effectiveness for least cost and also applies all available
electrification and transmission technologies before applying strong hybrid technologies. NHTSA seeks comment on whether the synergistic effects presented are accurate, and whether there are other synergies that the agency may have overlooked.

d. Where Can Readers Find More
Detailed Information About NHTSA’s Technology Analysis?

Much more detailed information is provided in Section V of the PRIA, and
a discussion of how NHTSA and EPA jointly reviewed and updated technology assumptions for purposes of this NPRM is available in Chapter 3 of the TSD. Additionally, all of NHTSA’s model input and output files are now public and available for the reader’s
review and consideration. The technology input files can be found in the docket for this NPRM, Docket No. NHTSA–2009–0059, and on NHTSA’s Web site. And finally, because much of NHTSA’s technology analysis for purposes of this NPRM builds on the
work that was done for the MY 2011 final rule, we refer readers to that document as well for background information concerning how NHTSA’s methodology for technology application analysis has evolved over the past several rulemakings, both in response to comments and as a result of the agency’sVerDate Nov<24>2008 23:31 Sep 25, 2009 Jkt 217001 PO 00000 Frm 00215 Fmt 4701 Sfmt 4702 E:\FR\FM\28SEP2.SGM 28SEP2mstockstill on DSKH9S0YB1PROD with PROPOSALS 

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growing experience with this type of analysis. 483

3. How Did NHTSA Develop the Economic Assumption Inputs?

NHTSA’s preliminary analysis of
alternative CAFE standards for the model years covered by this proposed rulemaking relies on a range of forecast variables, economic assumptions, and parameter values. This section describes the proposed sources of these forecasts, the rationale underlying each assumption, and the agency’s preliminary choices of specific parameter values. These proposed economic values play a significant role in determining the benefits of alternative CAFE standards, as they have for the last several CAFE rulemakings. Under those alternatives
where standards would be established by reference to their costs and benefits,
these economic values also affect the
levels of the CAFE standards
themselves. Some of these variables have more important effects on the level
of CAFE standards and the benefits from requiring alternative increases in fuel economy than do others.

In reviewing these variables and the agency’s estimates of their values for purposes of this NPRM, NHTSA reconsidered previous comments it had received and reviewed newly available literature. As a consequence, the agency
elected to revise some of its economic assumptions and parameter estimates, while retaining others. Some of the most important changes, which are discussed in greater detail below, as well as in Chapter 4 of the joint TSD and in Chapter VIII of the PRIA, include significant revisions to the markup factors for technology costs; reducing
the rebound effect from 15 to 10 percent; and revising the value of reducing CO 2 emissions based on recent interagency efforts to develop estimates of this value for government-wide use. For the reader’s reference, Table IV.C.3–
1 below summarizes the values used to
calculate the economic benefits from each alternative. The agency seeks comment on the economic assumptions presented in the table and discussed
below.



### TABLE 

### IV.C.3–1—E CONOMIC 

### VALUES FOR 

### B ENEFITS 

### C OMPUTATIONS
(2007$)

Fuel Economy Rebound Effect ....................................................................................................

........................................................ ‘‘Gap’’ between test and on-road MPG ...........................................................................................

.................................................... Value of refueling time per ($ per vehicle-hour) ................................................................................................................................. Annual growth in average vehicle use ...........................................................................................

..................................................... Fuel Prices (2012–50 average, $/gallon) Retail gasoline price ..........................................................................................................

........................................................... Pre-tax gasoline price .........................................................................................................

.......................................................... Economic Benefits from Reducing Oil Imports ($/gallon) ‘‘Monopsony’’ Component ............................................................................................................................................................ Price Shock Component ............................................................................................................................................................... Military Security Component ........................................................................................................................................................ 10%

20% $ 24.64

1.1%

$3.77 $3.40

$ 0.00 $ 0.17 $ 0.00

Total Economic Costs ($/gallon) ........................................................................................................................................... Emission Damage Costs (2020, $/ton or $/metric ton) $ 0.17

Carbon monoxide ......................................................................................................................................................................... $ 0 Volatile organic compounds (VOC) .............................................................................................................................................. $ 1,283 Nitrogen oxides (NO

x)—vehicle use ............................................................................................................................................ $ 5,116 Nitrogen oxides (NO

x)—fuel production and distribution ............................................................................................................. $ 5,339 Particulate matter (PM 2.5 )—vehicle use ....................................................................................................................................... $ 238,432 Particulate matter (PM 2.5 )—fuel production and distribution ........................................................................................................ $ 292,180 Sulfur dioxide (SO 2) ..............................................................................................................................

........................................ $ 30,896 Carbon dioxide (CO 2) ..............................................................................................................................

..................................... $ 20

Annual Increase in CO 2 Damage Cost ....................................................................................................................

.................... External Costs from Additional Automobile Use ($/vehicle-mile) 3%

Congestion .....................................................................................................................

............................................................... $ 0.054

Accidents ......................................................................................................................

................................................................ $ 0.023

Noise ..........................................................................................................................

................................................................... $ 0.001

Total External Costs .............................................................................................................................................................. External Costs from Additional Light Truck Use ($/vehicle-mile) $ 0.078

Congestion .................................................................................................................................................................................... $0.048

Accidents ...................................................................................................................................................................................... $0.026

Noise ............................................................................................................................................................................................. $0.001

Total External Costs ...........................................................................................................

................................................... Discount Rate Applied to Future Benefits .......................................................................................

.................................................... $0.075

3%

a. Costs of Fuel Economy-Improving Technologies

We developed detailed estimates ofthe costs of applying fuel economyimproving technologies to vehicle models jointly with EPA for use in analyzing the impacts of alternative
standards considered in this

483 74 FR 14233–308 (Mar. 30, 2009). rulemaking. The estimates were based on those reported by the 2002 NAS Report analyzing costs for increasing fuel economy, but were modified for purposes of this analysis as a result of extensive consultations among engineers from NHTSA, EPA, and the Volpe Center. As part of this process, the agency also developed varying cost estimates for applying certain fuel economy technologies to vehicles of different sizes and body styles. We may adjust these cost estimates based on

comments received to this NPRM.

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manufacturers’ direct costs for highvolume production of vehicles with these technologies and sufficient experience with their application so that all remaining cost reductions due to ‘‘learning curve’’ effects have been fully realized. However, NHTSA recognizes

that manufacturers’ actual costs for employing these technologies include additional outlays for accompanying design or engineering changes to models that use them, development and testing of prototype versions, recalibrating engine operating parameters, and integrating the technology with other

attributes of the vehicle. Manufacturers’ indirect costs for employing these technologies also include expenses for product development and integration, modifying assembly processes and training assembly workers to install them, increased expenses for operation and maintaining assembly lines, higher initial warranty costs for new technologies, any added expenses for selling and distributing vehicles that use these technologies, and manufacturer and dealer profit. In previous CAFE rulemakings and in NHTSA’s safety rulemakings, the agency has accounted for these additional costs by using a Retail Price Equivalent (RPE) multiplier of 1.5. For purposes of this rulemaking, based on recent work by EPA, NHTSA has applied indirect cost multipliers ranging from 1.11 to 1.64 to the

estimates of vehicle manufacturers’ direct costs for producing or acquiring each technology to improve fuel

economy. 484 These multipliers vary with the complexity of each technology

and the time frame over which costs are estimated. More complex technologies are associated with higher multipliers because of the larger increases in

manufacturers’ indirect costs for developing, producing (or procuring), and deploying these more complex technologies. The appropriate multipliers decline over time for technologies of all complexity levels, since increased familiarity and experience with their application is

assumed to reduce manufacturers’ indirect costs for employing them. NHTSA seeks comment regarding the new indirect cost multiplier approach to technology costs estimates. We note additionally that this issue will be addressed in the upcoming revised NAS
report.

484 NHTSA notes that in addition to the technology cost analysis employing this ‘‘ICM’’ approach, the PRIA contains a sensitivity analysis using a technology cost multiplier of 1.5. b. Potential Opportunity Costs of Improved Fuel Economy

An important concern is whether achieving the fuel economy improvements required by alternative CAFE standards would require manufacturers to compromise the performance, carrying capacity, safety,
or comfort of their vehicle models. To the extent that it does so, the resulting
sacrifice in the value of these attributes to consumers represents an additional cost of achieving the required improvements in fuel economy. While
exact dollar values of these attributes to consumers are difficult to infer, differences in vehicle purchase prices and buyers’ choices among competing
models that feature different
combinations of these characteristics clearly demonstrate that changing vehicle attributes clearly affect the utility and economic value that vehicles provide to potential buyers. 485

NHTSA and EPA have approached this potential problem by developingcost estimates for fuel economyimproving technologies that include any additional manufacturing costs that would be necessary to maintain the originally planned levels of performance, comfort, carrying capacity, and safety of any light-duty vehicle model to which those technologies are applied. In doing so, the agencies followed the precedent established by the 2002 NAS Report, which estimated ‘‘constant performance and utility’’ costs for fuel economy technologies.
NHTSA has used these as the basis for its continuing efforts to refine the technology costs it uses to analyze manufacturer’s costs for complying with alternative passenger car and light truck
CAFE standards for MYs 2012–2016. Although the agency has revised its
estimates of manufacturers’ costs for some technologies significantly for use in this rulemaking, these revised estimates are still intended to represent
costs that would allow manufacturers to maintain the performance, carrying capacity, and utility of vehicle models while improving their fuel economy.

Although we believe that our tentativecost estimates for fuel economyimproving technologies should be generally sufficient to prevent

485 See, e.g., Kleit A.N., 1990. ‘‘The Effect of Annual Changes in Automobile Fuel Economy Standards.’’ Journal of Regulatory Economics 2: 151–172; Berry, Steven, James Levinsohn, and Ariel Pakes, 1995. ‘‘Automobile Prices in Market Equilibrium,’’ Econometrica 63(4): 841–940; McCarthy, Patrick S., 1996. ‘‘Market Price and
Income Elasticities of New Vehicle Demands.’’ Review of Economics and Statistics 78: 543–547; and Goldberg, Pinelopi K., 1998. ‘‘The Effects of the Corporate Average Fuel Efficiency Standards in the US,’’ Journal of Industrial Economics 46(1): 1–33. significant reductions in consumer welfare provided by vehicle models to which manufacturers apply those technologies, it is possible that they do not include adequate allowance for the necessary efforts by manufacturers to prevent sacrifices in these attributes on all vehicle models. If this is the case, the true economic costs of achieving higher fuel economy should include the opportunity costs to vehicle owners of any sacrifices in vehicles’ performance, carrying capacity, and utility and the agency’s estimated technology costs
would underestimate the true economic costs of improving fuel economy.

Recognizing this possibility, it may be preferable for NHTSA to estimate explicitly the changes in vehicle buyers’ welfare from the combination of higher prices for new vehicle models, increases in their fuel economy, and any accompanying changes in vehicle attributes such as performance, passenger- and cargo-carrying capacity, or other dimensions of utility. The net change in buyer’s welfare that results from the combination of these changes would provide a more accurate estimate of the true economic costs for improving fuel economy. The agency seeks comment on this or other possible ways to deal with this extremely important

issue.

c. The On-Road Fuel Economy ‘‘Gap’’

Actual fuel economy levels achieved by light-duty vehicles in on-road driving
fall somewhat short of their levels measured under the laboratory-like test conditions used by EPA to establish its published fuel economy ratings for different models. In analyzing the fuel savings from alternative CAFE standards, NHTSA has previously adjusted the actual fuel economy performance of each light truck model
downward from its rated value to reflect the expected size of this on-road fuel economy ‘‘gap.’’ On December 27, 2006, EPA adopted changes to its regulations on fuel economy labeling, which were intended to bring vehicles’ rated fueleconomy levels closer to their actual onroad fuel economy levels. 486

In its Final Rule, EPA estimated thatactual on-road fuel economy for lightduty vehicles averages 20 percent lower than published fuel economy levels. For example, if the overall EPA fuel economy rating of a light truck is 20 mpg, the on-road fuel economy actually achieved by a typical driver of that vehicle is expected to be 16 mpg (20*.80). NHTSA employed EPA’s
revised estimate of this on-road fuel economy gap in its analysis of the fuel

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savings resulting from alternative CAFE
standards evaluated in the MY 2011
final rule.

For purposes of this NPRM, NHTSA conducted additional analysis of this issue. The agency used data on the number of passenger cars and light trucks of each model year that were registered for use during calendar years 2000 through 2006, average fuel economy for passenger cars and light trucks produced during each model year, and estimates of average miles driven per year by cars and light trucks of different ages. These data were combined to develop estimates of the average fuel economy that the U.S. passenger car and light truck fleets
would have achieved from 2000 through
2006 under test conditions.

NHTSA compared these estimates to the Federal Highway Administration’s(FHWA) published values of actual onroad fuel economy for passenger cars and light trucks during each of those

years. 487 FHWA’s estimates of actual fuel economy for passenger cars averaged 22 percent lower than
NHTSA’s estimates of its fleet-wide average value under test conditions over this period, while FHWA’s estimates for light trucks averaged 17 lower than NHTSA’s estimates of average light truck fuel economy under test conditions. These results appear to confirm that the 20 percent on-road fuel economy discount or gap represents a reasonable estimate for use in evaluating the fuel savings likely to result from
alternative CAFE standards for MY
2012–2016 vehicles.

d. Fuel Prices and the Value of Saving
Fuel

Projected future fuel prices are a critical input into the preliminary economic analysis of alternative CAFE standards, because they determine the value of fuel savings both to new vehicle buyers and to society. NHTSA relied on the most recent fuel price projections from the U.S. Energy Information Administration’s (EIA) Annual Energy Outlook (AEO) for this analysis. Specifically, we used the AEO 2009 (April 2009 release) Reference Case forecasts of inflation-adjusted (constant-dollar) retail gasoline and diesel fuel prices, which represent the EIA’s most up-to-date estimate of the most likely course of future prices for petroleum products. 488

487 Federal Highway Administration, Highway Statistics, 2000 through 2006 editions, Table VM–

1; see http://www.fhwa.dot.gov/policy/ohpi/hss/ hsspubs.cfm (last accessed July 27, 2009).

488 Energy Information Administration, Annual Energy Outlook 2009, Revised Updated Reference Case (April 2009), Table 12. Available at http:// While NHTSA relied on the forecasts of fuel prices presented in AEO 2008 High Price Case in the MY 2011 final rule, we noted at the time that we were relying on that estimate primarily because volatility in the oil market appeared to have overtaken the Reference Case, and that we anticipated
that the Reference Case forecast would be significantly higher in the next AEO. In fact, EIA’s AEO 2009 Reference Case forecast projects higher retail fuel prices in most future years than those forecast in the High Price Case from AEO 2008.
NHTSA is thus confident that the AEO 2009 Reference Case is an appropriate forecast for projected future fuel prices.

Measured in constant 2007 dollars,
the Reference Case forecast of retail gasoline prices during calendar year 2020 is $3.62 per gallon, rising gradually to $3.82 by the year 2030 (these values include Federal, State and local taxes). To obtain fuel price forecasts for the years 2031 through 2050, the agency assumes that retail fuel prices will continue to increase after 2030 at the average annual rates projected for 2020–2030 in the AEO
2009 Revised Reference Case. 489 This assumption results in a projected retail price of gasoline that reaches $4.25 in 2007 dollars by the year 2050.

The value of fuel savings resulting from improved fuel economy to buyers of light-duty vehicles is determined by the retail price of fuel, which includes Federal, State, and any local taxes imposed on fuel sales. Total taxes on gasoline, including Federal, State, and local levies averaged $0.42 per gallon during 2006, while those levied on diesel averaged $0.50. Because fuel taxes represent transfers of resources from fuel buyers to government agencies, however, rather than real
resources that are consumed in the process of supplying or using fuel, their
value must be deducted from retail fuel prices to determine the value of fuel savings resulting from more stringent CAFE standards to the U.S. economy as
a whole.

NHTSA follows the assumptions used by EIA in AEO 2009 that State and local gasoline taxes will keep pace with

www.eia.doe.gov/oiaf/servicerpt/stimulus/excel/
aeostimtab

_ 12.xls (last accessed July 26, 2009). EIA’s Updated Reference Case reflects the effects of the American Reinvestment and Recovery Act of 2009, as well as the most recent revisions to the

U.S. and global economic outlook.

489 This projection uses the rate of increase in fuel prices for 2020–2030 rather than that over the complete forecast period (2009–2030) because there is extreme volatility in the forecasts for the years 2009 through approximately 2020. Using the average rate of change over the complete 2009–2030 forecast period would result in projections of declining fuel prices after 2030. inflation in nominal terms, and thus remain constant when expressed in constant 2007 dollars. In contrast, EIA
assumes that Federal gasoline taxes will remain unchanged in nominal terms, and thus decline throughout the forecast period when expressed in constant 2007 dollars. These differing assumptions about the likely future behavior of
Federal and State/local fuel taxes are
consistent with recent historical experience, which reflects the fact that
Federal as well as most State motor fuel taxes are specified on a cents-per-gallon basis, and typically require legislation to change.

The projected value of total taxes is deducted from each future year’s forecast of retail gasoline and diesel prices reported in AEO 2009 to
determine the economic value of each gallon of fuel saved during that year as a result of improved fuel economy. Subtracting fuel taxes results in a projected value for saving gasoline of $3.22 per gallon during 2020, rising to $3.45 per gallon by the year 2030.

EIA includes ‘‘High Price Case’’ and
‘‘Low Price Case’’ forecasts in each AEO, which reflect uncertainties regarding future levels of oil production
and demand. These alternative scenarios project retail gasoline prices that range from a low of $2.02 to a high of $5.04 per gallon during 2020, and from $2.04 to $5.47 per gallon during 2030. In conjunction with our assumption that fuel taxes will remain constant in real or inflation-adjusted terms over this period, these forecasts imply pre-tax values of saving fuel ranging from $1.63 to $4.65 per gallon during 2020, and from $1.67 to $5.10 per gallon in 2030. In conducting the preliminary analysis of uncertainty in
benefits and costs from alternative CAFE standards required by OMB, NHTSA evaluated the sensitivity of its
benefits estimates to these alternative forecasts of future fuel prices. The results of this sensitivity analysis can be
found in the PRIA.

e. Consumer Valuation of Fuel Economy and Payback Period

In estimating the value of fuel economy improvements that would
result from alternative CAFE standards to potential vehicle buyers, NHTSA assumes, as in the MY 2011 final rule, that buyers value the resulting fuel savings over only part of the expected lifetime of the vehicles they purchase. Specifically, we assume that buyers value fuel savings over the first five years of a new vehicle’s lifetime, and
discount the value of these future fuel savings at a 3 percent annual rate. The five-year figure representsVerDate Nov<24>2008 23:31 Sep 25, 2009 Jkt 217001 PO 00000 Frm 00218 Fmt 4701 Sfmt 4702 E:\FR\FM\28SEP2.SGM 28SEP2mstockstill on DSKH9S0YB1PROD with PROPOSALS 

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approximately the current average term
of consumer loans to finance the purchase of new vehicles. We recognize that the period over which individual buyers finance new vehicle purchases may not correspond exactly to the time horizons they apply in valuing fuel savings from higher fuel economy.

The agency deducts the discounted present value of fuel savings over the first five years of a vehicle model’s lifetime from the technology costs incurred by its manufacturer to improve that model’s fuel economy to determine the increase in its ‘‘effective price’’ to buyers. The Volpe model uses these
estimates of effective costs for increasing the fuel economy of each vehicle model to identify the order in which manufacturers would be likely to select models for the application of fuel economy-improving technologies in order to comply with stricter standards. The average value of the resulting
increase in effective cost from each manufacturer’s simulated compliance strategy is also used to estimate the impact of alternative standards on its total sales for future model years.

However, it is important to recognize that NHTSA estimates the aggregate value to the U.S. economy of fuel savings resulting from alternative
standards—or their ‘‘social’’ value—over
the entire expected lifetimes of vehicles manufactured under those standards, rather than over this shorter ‘‘payback period’’ we assume for their buyers. The procedure the agency uses for doing so is discussed in detail in the following

section.

f. Vehicle Survival and Use Assumptions

NHTSA’s first step in estimating lifetime fuel consumption by vehicles produced during a model year is to calculate the number expected to remain in service during each year following their production and sale. 490 This is calculated by multiplying the number of vehicles originally produced

490 Vehicles are defined to be of age 1 during the calendar year corresponding to the model year in which they are produced; thus for example, model year 2000 vehicles are considered to be of age 1 during calendar year 2000, age 1 during calendar year 2001, and to reach their maximum age of 26 years during calendar year 2025. NHTSA considers the maximum lifetime of vehicles to be the age after which less than 2 percent of the vehicles originally produced during a model year remain in service. Applying these conventions to vehicle registration data indicates that passenger cars have a maximum age of 26 years, while light trucks have a maximum lifetime of 36 years. See Lu, S., NHTSA, Regulatory Analysis and Evaluation Division, ‘‘Vehicle Survivability and Travel Mileage Schedules,’’ DOT HS 809 952, 8–11 (January 2006). Available at http://www-nrd.nhtsa.dot.gov/Pubs/809952.pdf (last accessed July 27, 2009). during a model year by the proportion typically expected to remain in service at their age during each later year, often
referred to as a ‘‘survival rate.’’

To estimate production volumes of passenger cars and light trucks for individual manufacturers, NHTSA
relied on a baseline market forecast constructed by EPA staff beginning with
MY 2008 CAFE certification data. After constructing a MY 2008 baseline, EPA used projected car and truck volumes for this period from Energy Information Administration’s (EIA’s) 2009 Annual Energy Outlook (AEO). 491 However, AEO projects sales only at the car and truck level, not at the manufacturer and model-specific level, which are needed
in order to estimate the effects new
standards will have on individual
manufacturers. 492 Therefore, EPA purchased data from CSM–Worldwide and used their projections of the number of vehicles of each type predicted to be sold by manufacturers in

2011–2015. 493 This provided the yearby-year percentages of cars and trucks sold by each manufacturer as well as the percentages of each vehicle segment. Although it was thus necessary to
assume the same manufacturer and segment shares in 2016 as in 2015, 2016
estimates from CSM should be available for the final rule. Using these percentages normalized to the AEO projected volumes then provided the manufacturer-specific market share and model-specific sales for model years

2011–2016.

To estimate the number of passenger cars and light trucks originally produced during model years 2012 through 2016 that will remain in use during each subsequent year the agency applied age-specific survival rates for cars and light trucks to these adjusted forecasts of passenger car and light truck sales. In 2008, NHTSA updated its previous estimates of car and light truck survival rates using the most current

491 Available at http://www.eia.doe.gov/oiaf/aeo/
index.html. NHTSA and EPA made the simplifying assumption that projected sales of cars and light trucks during each calendar year from 2012 through 2016 represented the likely production volumes for the corresponding model year. The agency did not attempt to establish the exact correspondence between projected sales during individual calendar years and production volumes for specific model

years.

492 Because AEO 2009’s ‘‘car’’ and ‘‘truck’’ classes did not reflect NHTSA’s recent reclassification (in March 2009 for enforcement beginning MY 2011) of many two wheel drive SUVs from the nonpassenger (i.e., light truck) fleet to the passenger car fleet, EPA staff made adjustments to account for such vehicles
in the baseline.

493 EPA also considered other sources of similar information, such as J.D. Powers, and concluded that CSM was better able to provide forecasts at the requisite level of detail for most of the model years
of interest. registration data for vehicles produced during recent model years, in order to ensure that they reflected recent increases in the durability and expected life spans of cars and light trucks. 494

The next step in estimating fuel use
is to calculate the total number of miles that model year 2012–2016 cars and light trucks remaining in use will be driven each year. To estimate total miles driven, the number projected to remain in use during each future year is multiplied by the average number of miles they are expected to be driven at the age they will reach in that year. The agency estimated annual usage of cars and light trucks of each age using data from the Federal Highway
Administration’s 2001 National Household Transportation Survey (NHTS). 495 Because these estimates reflect the historically low gasoline prices that prevailed at the time the 2001 NHTS was conducted, however, NHTSA adjusted them to account for the effect on vehicle use of subsequent increases in fuel prices. Details of this adjustment are provided in Chapter VIII of the PRIA and Chapter of the draft joint TSD.

Increases in average annual use of cars and light trucks have been an important source of historical growth in the total number of miles they are driven each year. To estimate future growth in their average annual use for purposes of this rulemaking, NHTSA calculated the rate of growth in the adjusted mileage schedules derived from the 2001 NHTS necessary for total car and light truck travel to increase at
the rate forecast in the AEO 2009
Reference Case. 496 This rate was
calculated to be consistent with future changes in the overall size and age distributions of the U.S. passenger car and light truck fleets that result from the agency’s forecasts of total car and light truck sales and updated survival rates. The resulting growth rate in average annual car and light truck use of approximately 1.1 percent per year was

494 Lu, S., NHTSA, Regulatory Analysis and Evaluation Division, ‘‘Vehicle Survivability and Travel Mileage Schedules,’’ DOT HS 809 952, 8–11 (January 2006). Available at http://wwwnrd.nhtsa.dot.gov/Pubs/809952.pdf (last accessed August 9, 2009). These updated survival rates suggest that the expected lifetimes of recent-model passenger cars and light trucks are 13.8 and 14.5

years.

495 For a description of the Survey, see http:// nhts.ornl.gov/quickStart.shtml (last accessed August 9, 2009).

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applied to the mileage figures derived
from the 2001 NHTS to estimate annual mileage during each year of the expected lifetimes of MY 2012–2016 cars and light trucks. 497

Finally, the agency estimated total fuel consumption by passenger cars and light trucks remaining in use each year by dividing the total number of miles surviving vehicles are driven by the fuel economy they are expected to achieve
under each alternative CAFE standard. Each model year’s total lifetime fuel consumption is the sum of fuel use by the cars or light trucks produced during that model year during each year of their life spans. In turn, the savings in a model year’s lifetime fuel use that will
result from each alternative CAFE
standard is the difference between its lifetime fuel use at the fuel economy
level it attains under the Baseline alternative, and its lifetime fuel use at the higher fuel economy level it is projected to achieve under that
alternative standard. 498

g. Accounting for the Rebound Effect of Higher Fuel Economy

The fuel economy rebound effect refers to the fraction of fuel savings expected to result from an increase in vehicle fuel economy—particularly an increase required by the adoption of higher CAFE standards—that is offset by
additional vehicle use. The increase in vehicle use occurs because higher fuel economy reduces the fuel cost of driving, typically the largest single component of the monetary cost of operating a vehicle, and vehicle owners

497 While the adjustment for future fuel prices reduces average mileage at each age from the values derived from the 2001 NHTS, the adjustment for expected future growth in average vehicle use increases it. The net effect of these two adjustments is to increase expected lifetime mileage by about 18 percent significantly for both passenger cars and about 16 percent for light trucks.

498 To illustrate these calculations, the agency’s adjustment of the AEO 2009 Revised Reference Case forecast indicates that 9.26 million passenger cars will be produced during 2012, and the agency’s updated survival rates show that 83 percent of these vehicles, or 7.64 million, are projected to remain in service during the year 2022, when they will have reached an age of 10 years. At that age, passenger achieving the fuel economy level they are projected
to achieve under the Baseline alternative are driven an average of about 800 miles, so surviving model year 2012 passenger cars will be driven a total of

82.5 billion miles (= 7.64 million surviving vehicles

× 10,800 miles per vehicle) during 2022. Summing the results of similar calculations for each year of their 26-year maximum lifetime, model year 2012 passenger cars will be driven a total of 1,395 billion
miles under the Baseline alternative. Under that alternative, they are projected to achieve a test fuel economy level of 32.4 mpg, which corresponds to actual on-road fuel economy of 25.9 mpg (= 32.4

mpg × 80 percent). Thus their lifetime fuel use under the Baseline alternative is projected to be

53.9billion gallons (= 1,395 billion miles divided by 25.9 miles per gallon). respond to this reduction in operating costs by driving slightly more. By lowering the marginal cost of vehicle use, improved fuel economy may lead to
an increase in the number of miles vehicles are driven each year and over their lifetimes. Even with their higher fuel economy, this additional driving consumes some fuel, so the rebound effect reduces the net fuel savings that
result when new CAFE standards require manufacturers to improve fuel

economy.

The magnitude of the rebound effect is an important determinant of the actual fuel savings that are likely to result from adopting stricter CAFE standards. Research on the magnitude of the rebound effect in light-duty vehicle use dates to the early 1980s, and generally concludes that a statistically significant rebound effect occurs when vehicle fuel efficiency improves. 499 The agency reviewed studies of the rebound effect it had previously relied upon, considered more recently published estimates, and developed new estimates of its magnitude for purposes of this

NPRM. 500 Recent studies provide some
evidence that the rebound effect has been declining over time, and may
decline further over the immediate
future if incomes rise faster than gasoline prices. This result appears plausible, because the responsiveness of
vehicle use to variation in fuel costs is expected to decline as they account for a smaller proportion of the total monetary cost of driving, which has been the case until very recently. At the same time, rising personal incomes would be expected to reduce the sensitivity of vehicle use to fuel costs as the time component of driving costs— which is likely to be related to income levels—accounts for a larger fraction the
total cost of automobile travel. NHTSA developed new estimates of the reboundeffect by using national data on lightduty vehicle travel over the period from 1950 through 2006 to estimate various econometric models of the relationship
between vehicle miles-traveled and factors likely to influence it, including household income, fuel prices, vehicle fuel efficiency, road supply, the number of vehicles in use, vehicle prices, and

499 Some studies estimate that the long-run rebound effect is significantly larger than the immediate response to increased fuel efficiency. Although their estimates of the adjustment period required for the rebound effect to reach its long-run magnitude vary, this long-run effect is most appropriate for evaluating the fuel savings and emissions reductions resulting from stricter standards that would apply to future model years.

500 For details of the agency’s analysis, see Chapter VIII of the PRIA and Chapter 4 of the draft joint TSD accompanying this proposed rule. other factors. 501 The results of NHTSA’s analysis are consistent with the findings from other recent research: The average long-run rebound effect ranged from 16 percent to 30 percent over the period from 1950 through 2007, while
estimates of the rebound effect in 2007 range from 8 percent to 14 percent. Projected values of the rebound effect for the period from 2010 through 2030, which the agency developed using forecasts of personal income, fuel prices, and fuel efficiency from AEO 2009’s Reference Case, range from 4 percent to 16 percent, depending on the specific model used to generate them.

In light of these results, the agency’s judgment is that the apparent decline over time in the magnitude of the rebound effect justifies using a value for future analysis that is lower than historical estimates, which average 15– 25 percent. Because the lifetimes of vehicles affected by the alternative
CAFE standards considered in this rulemaking will extend from 2012 until nearly 2050, a value that is significantly lower than historical estimates appears to be appropriate. Thus NHTSA has elected to use a 10 percent rebound effect in its analysis of fuel savings and other benefits from higher CAFE
standards for this NPRM.

NHTSA also invites comment on other alternatives for estimating the rebound effect. As one illustration, variation in the price per gallon of gasoline directly affects the per-mile cost of driving, and drivers may respond just as they would to a change in the cost of driving resulting from a change in fuel economy, by varying the number of miles they drive. Because vehicles’ fuel economy is fixed in the short run,
variation in the number of miles driven in response to changes in fuel prices will be reflected in changes in gasoline consumption. Under the assumption that drivers respond similarly to changes in the cost of driving whether they are caused by variation in fuel prices or fuel economy, the short-run price elasticity of gasoline—which measures the sensitivity of gasoline consumption to changes in its price per gallon—may provide some indication about the magnitude of the rebound
effect itself. NHTSA invites comment on
the extent to which the short-run elasticity of demand for gasoline with respect to its price can provide useful
information about the size of the rebound effect. Specifically, we seek
comment on whether it would be

501 The agency used several different model specifications and estimation procedures to control for the effect of fuel prices on fuel efficiency in
order to obtain accurate estimates of the rebound
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appropriate to use the price elasticity of demand for gasoline, or other alternative approaches, to guide the choice of a
value for the rebound effect.

Additionally, NHTSA recognizes that as the world price of oil falls in response to lower U.S. demand for oil, there is the potential for an increase in oil use and, in turn, greenhouse gas
emissions outside the U.S. This so
called international oil ‘‘take back’’
effect is difficult to estimate. Given that oil consumption patterns vary across countries, there will be different demand responses to a change in the world price of crude oil. In addition, many countries around the world subsidize their oil consumption. It is not clear how oil consumption would change due to changes in the market price of oil given the current pattern of demand and subsidies. Further, many countries, especially in the developed countries/regions (i.e., the European Union), already have or anticipate implementing policies to limit GHG emissions. Further out in the future, it is anticipated that developing countries
would take actions to reduce their GHG emissions as well. Any increases in petroleum consumption and GHG
emissions in other nations that occurs in response to a decline in world petroleum prices would be attributed to those nations, and recorded in their respective GHG emissions inventories. Thus, including the same increase in emissions as part of the impact of adopting CAFE standards in the U.S. would risk double-counting of global
emissions totals. NHTSA seeks
comment on how to estimate the
international ‘‘take back’’ effect and its impact on fuel consumption and GHG

emissions. See the Energy Security section of the TSD, 4.2.8, for more discussion of the impact of the proposed
vehicle rule on oil markets.

h. Benefits From Increased Vehicle Use

The increase in vehicle use from the rebound effect provides additional benefits to their owners, who may make more frequent trips or travel farther to
reach more desirable destinations. This additional travel provides benefits to drivers and their passengers by improving their access to social and economic opportunities away from home. As evidenced by their decisions to make more frequent or longer trips when improved fuel economy reduces their costs for driving, the benefits from
this additional travel exceed the costs drivers and passengers incur in making more frequent or longer trips.

The agency’s analysis estimates the
economic benefits from increased rebound-effect driving as the sum of fuel costs drivers incur plus the consumer surplus they receive from the additional accessibility it provides. 502 Because the increase in travel depends on the extent of improvement in fuel economy, the value of benefits it provides differs among model years and alternative
CAFE standards. Under even those alternatives that would impose the highest standards, however, the magnitude of these benefits represents a
small fraction of total benefits.

i. The Value of Increased Driving Range

Improving vehicles’ fuel economy may also increase their driving range before they require refueling. By reducing the frequency with which drivers typically refuel, and by extending the upper limit of the range they can travel before requiring refueling, improving fuel economy thus provides some additional benefits to
their owners. 503 NHTSA re-examined this issue for purposes of this rulemaking, and found no information
in comments or elsewhere that would cause the agency to revise its previous approach. Since no direct estimates of the value of extended vehicle range are available, NHTSA calculates directly the
reduction in the annual number of required refueling cycles that results from improved fuel economy, and applies DOT-recommended values of travel time savings to convert the resulting time savings to their economic
value. 504

As an illustration, a typical small light truck model has an average fuel tank size of approximately 20 gallons. Assuming that drivers typically refuel when their tanks are 55 percent full (i.e., 11 gallons in reserve), increasing this model’s actual on-road fuel economy from 24 to 25 mpg would extend its driving range from 216 miles (= 9 gallons × 24 mpg) to 225 miles (= 9 gallons × 25 mpg). Assuming that it is driven 12,000 miles/year, this reduces
the number of times it needs to be refueled each year from 55.6 (= 12,000 miles per year/216 miles per refueling) to 53.3 (= 12,000 miles per year/225

502 The consumer surplus provided by added travel is estimated as one-half of the product of the decline in fuel cost per mile and the resulting
increase in the annual number of miles driven.

503 If manufacturers respond to improved fuel economy by reducing the size of fuel tanks to maintain a constant driving range, the resulting cost savings will presumably be reflected in lower vehicle sales prices.

504 See Department of Transportation, Guidance Memorandum, ‘‘The Value of Saving Travel Time: Departmental Guidance for Conducting Economic Evaluations,’’ Apr. 9, 1997. http://ostpxweb.dot.gov/ policy/Data/VOT97guid.pdf (last accessed August 9, 2009); update available at http:// ostpxweb.dot.gov/policy/Data/VOTrevision1

_2-11- 03.pdf (last accessed August 9, 2009). miles per refueling), or by 2.3 refuelings

per year.

Weighted by the nationwide mix of urban and rural driving, personal and business travel in urban and rural areas, and average vehicle occupancy for driving trips, the DOT-recommended values of travel time per vehicle-hour is $24.64 (in 2007 dollars). 505 Assuming that locating a station and filling up requires five minutes, the annual value of time saved as a result of less frequent refueling amounts to $4.72 (calculated
as 5/60 × 2.3 × $24.64). This calculation is repeated for each future year that model year 2012–2016 cars and light
trucks would remain in service. Like fuel savings and other benefits, the
value of this benefit declines over a model year’s lifetime, because a smaller number of vehicles originally produced during that model year remain in service each year, and those remaining
in service are driven fewer miles.

NHTSA recognizes that many assumptions made in its estimate for the value of increased driving range are subject to uncertainty. Please see Chapter 4 of the TSD and Chapter 8 of
NHTSA’s PRIA for more information about the uncertainty regarding these assumptions.

j. Added Costs From Congestion,
Crashes and Noise

Increased vehicle use associated with
the rebound effect also contributes to increased traffic congestion, motor vehicle accidents, and highway noise. NHTSA relies on estimates of per-mile congestion, accident, and noise costs caused by increased use of automobiles and light trucks developed by the Federal Highway Administration to
estimate these increased costs. 506 NHTSA employed these estimates previously in its analysis accompanying the MY 2011 final rule, and continues

505 The hourly wage rate during 2008 is estimated to average $25.50 when expressed in 2007 dollars. Personal travel in urban areas (which represents 94 percent of urban travel) is valued at 50 percent of the hourly wage rate, while business travel (the remaining 6 percent of urban travel) is valued at 100 percent of the hourly wage rate. For intercity travel, personal travel (87 percent of total intercity travel) is valued at 70 percent of the wage rate, while business travel (13 percent) is valued at 100 percent of the wage rate. The resulting values of travel time are $12.67 for urban travel and $17.66 for intercity travel, and must be multiplied by vehicle occupancy (1.6) to obtain the estimated values of time per vehicle hour in urban and rural driving. Finally, about 66% of driving occurs in urban areas, while the remaining 34% takes place in rural areas, and these percentages are used to calculate a weighted average of the value of time in all driving.

506 These estimates were developed by FHWA for

use in its 1997 Federal Highway Cost Allocation Study; see http://www.fhwa.dot.gov/policy/hcas/ final/index.htm (last accessed August 9, 2009).VerDate Nov<24>2008 23:31 Sep 25, 2009 Jkt 217001 PO 00000 Frm 00221 Fmt 4701 Sfmt 4702 E:\FR\FM\28SEP2.SGM 28SEP2mstockstill on DSKH9S0YB1PROD with PROPOSALS 

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to find them appropriate for this NPRM after reviewing the procedures used by FHWA to develop them and considering
other available estimates of these values. The agency multiplies FHWA’s estimates of per-mile costs by the
annual increases in automobile and light truck use from the rebound effect to yield the estimated increases in congestion, accident, and noise externality costs during each future

year.

k. Petroleum Consumption and Import
Externalities

U.S. consumption and imports of petroleum products also impose costs on the domestic economy that are not reflected in the market price for crude petroleum, or in the prices paid by consumers of petroleum products such as gasoline. These costs include (1) higher prices for petroleum products resulting from the effect of U.S. oil import demand on the world oil price; (2)the risk of disruptions to the U.S. economy caused by sudden reductions in the supply of imported oil to the U.S.; and (3) expenses for maintaining a U.S. military presence to secure imported oil supplies from unstable regions, and for maintaining the strategic petroleum reserve (SPR) to cushion against resulting price increases. 507

Higher U.S. imports of crude oil or refined petroleum products increase the magnitude of these external economic costs, thus increasing the true economic cost of supplying transportation fuels above their market prices. Conversely, lowering U.S. imports of crude petroleum or refined fuels by reducing domestic fuel consumption can reduce these external costs, and any reduction
in their total value that results from improved fuel economy represents an economic benefit of more stringent CAFE standards, in addition to the value of saving fuel itself.

NHTSA has carefully reviewed its assumptions regarding the appropriate value of these benefits for this proposed rule. In analyzing benefits from its recent actions to increase light truck CAFE standards for model years 2005– 07 and 2008–11, NHTSA relied on a 1997 study by Oak Ridge National Laboratory (ORNL) to estimate the value

507 See, e.g., Bohi, Douglas R. and W. David Montgomery (1982). Oil Prices, Energy Security, and Import Policy, Washington, DC: Resources for the Future, Johns Hopkins University Press; Bohi,

D. R., and M. A. Toman (1993). ‘‘Energy and Security: Externalities and Policies,’’ Energy Policy 21:1093–1109; and Toman, M. A. (1993). ‘‘The Economics of Energy Security: Theory, Evidence, Policy,’’ in A. V. Kneese and J. L. Sweeney, eds. (1993). Handbook of Natural Resource and Energy Economics, Vol. III. Amsterdam: North-Holland, pp.

1167–1218. of reduced economic externalities from petroleum consumption and imports. 508 More recently, ORNL updated its
estimates of the value of these externalities, using the analytic framework developed in its original 1997 study in conjunction with recent
estimates of the variables and parameters that determine their
value. 509 The updated ORNL study was subjected to a detailed peer review by experts selected by EPA, and its estimates of the value of oil import externalities were subsequently revised
to reflect their comments and
recommendations. 510

At the request of EPA, ORNL further
revised its 2008 estimates of external costs from U.S. oil imports to reflect recent changes in the outlook for world petroleum prices and continuing changes in the structure and characteristics of global petroleum supply and demand.

These most recent revisions increase ORNL’s estimates of the ‘‘monopsony premium’’ associated with U.S. oil imports, which measures the reduced value of payments from U.S. oil purchasers to foreign oil suppliers beyond the savings from reduced purchases of petroleum itself that results when lower U.S. import demand reduces the world price of petroleum. 511 Consistency with NHTSA’s use of
estimates of the global benefits from reducing emissions of CO 2 and other greenhouse gases in this analysis, however, requires the use of a global perspective for assessing their net value. From this perspective, reducing these payments simply results in a transfer of resources from foreign oil suppliers to

U.S. purchasers (or more properly, in a savings in the value of resources previously transferred from U.S. purchasers to foreign producers), and provides no real savings in resources to the global economy. Thus NHTSA’s analysis of the benefits from adopting higher CAFE standards for MY 2012–

508 Leiby, Paul N., Donald W. Jones, T. Randall Curlee, and Russell Lee, Oil Imports: An Assessment of Benefits and Costs, ORNL–6851, Oak Ridge National Laboratory, November 1, 1997. Available at http://pzl1.ed.ornl.gov/ORNL6851.pdf (last accessed August 9, 2009).

509 Leiby, Paul N. ‘‘Estimating the Energy Security Benefits of Reduced U.S. Oil Imports,’’ Oak Ridge National Laboratory, ORNL/TM–2007/028, Revised July 23, 2007. Available at http://pzl1.ed.ornl.gov/ energysecurity.html (click on link below ‘‘Oil Imports Costs and Benefits’’) (last accessed August 9, 2009).

510 Peer Review Report Summary: Estimating the Energy Security Benefits of Reduced U.S. Oil Imports, ICF, Inc., September 2007.

511 The reduction in payments from U.S. oil purchasers to domestic petroleum producers is not included as a benefit, since it represents a transfer that occurs entirely within the U.S. economy. 2016 cars and light trucks excludes the reduced value of monopsony payments by U.S. oil consumers that might result from lower fuel consumption by these
vehicles.

The literature on the energy security for the last two decades has routinely combined the monopsony and the macroeconomic disruption components when calculating the total value of the energy security premium. However, in the context of using a global value for the Social Cost of Carbon (SCC) the question arises: How should the energy security premium be used when some benefits from the proposed rule, such as the benefits of reducing greenhouse gas emissions, are calculated at a global level? Monopsony benefits represent avoided payments by the U.S. to oil producers in foreign countries that
result from a decrease in the world oil price as the U.S. decreases its consumption of imported oil. Although there is clearly a benefit to the U.S.
when considered from the domestic perspective, the decrease in price due to
decreased demand in the U.S. alsorepresents a loss of income to oilproducing countries. Given the redistributive nature of this effect, do the negative effects on other countries ‘‘net out’’ the positive impacts to the U.S.? If this is the case, then, the monopsony portion of the energy security premium should be excluded
from the net benefits calculation for the
rule.

As the preceding discussion has indicated, the agencies omitted the reduction in monopsony payments that occurs when U.S. petroleum consumption and imports are reduced
from their estimates of economic benefits for the proposed rules. Since the reduction in monopsony payments by U.S. oil consumers is exactly offset by a decline in income to suppliers of imported oil, this omission ensures consistency of the agencies’ analysis with the inclusion of global benefits from reducing emissions of greenhouse gas emissions. The agencies seek comment on whether, from other perspectives, it would be reasonable to include both the global value of reducing GHG emissions and the reduction in monopsony payments by

U.S. consumers of petroleum products
in their estimates of total economic benefits from reducing U.S. fuel consumption.

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to the U.S. economy by $0.16 per gallon (in 2007$). The reduction in expected disruption costs represents a real savings in resources, and thus
contributes economic benefits in
addition to the savings in fuel production costs that result from increasing fuel economy. NHTSA employs this value in its evaluation of the economic benefits from adopting higher CAFE standards for MY 2012– 2016 cars and light trucks.

NHTSA’s analysis does not include savings in budgetary outlays to support

U.S. military activities among the benefits of higher fuel economy and the resulting fuel savings. 512 NHTSA’s analysis of benefits from alternative
CAFE standards for MY 2012–2016 also excludes any cost savings from maintaining a smaller SPR from its
estimates of the external benefits of reducing gasoline consumption and petroleum imports. This view concurs with that of the recent ORNL study of economic costs from U.S. oil imports, which concludes that savings in government outlays for these purposes are unlikely to result from reductions in consumption of petroleum products and oil imports on the scale of those resulting from higher CAFE standards.

Based on a detailed analysis of differences in fuel consumption, petroleum imports, and imports of refined petroleum products among the Reference Case, High Economic Growth,
and Low Economic Growth Scenarios presented in AEO 2009, NHTSA estimates that approximately 50 percent of the reduction in fuel consumption resulting from adopting higher CAFE standards is likely to be reflected in reduced U.S. imports of refined fuel, while the remaining 50 percent would be reduce domestic fuel refining. 513 Of this latter figure, 90 percent is anticipated to reduce U.S. imports of crude petroleum for use as a refinery feedstock, while the remaining 10 percent is expected to reduce U.S. domestic production of crude petroleum. 514 Thus on balance, each

512 However, the agency conducted a sensitivity analysis of the potential effect of assuming that some reduction military spending would result from fuel savings and reduced petroleum imports in order to investigate its impacts on the standards and fuel savings.

513 Differences between forecast annual U.S. imports of crude petroleum and refined products among these three scenarios range from 24–89 percent of differences in projected annual gasoline and diesel fuel consumption in the U.S. These differences average 49 percent over the forecast period spanned by AEO 2009.

514 Differences between forecast annual U.S. imports of crude petroleum among these three scenarios range from 67–97 percent of differences in total U.S. refining of crude petroleum, and 100 gallons of fuel saved as a consequence of higher CAFE standards is anticipated to reduce total U.S. imports of crude petroleum or refined fuel by 95 gallons. 515

l. Air Pollutant Emissions

i. Impacts on Criteria Air Pollutant

Emissions

Criteria air pollutants emitted by vehicles and during fuel production include carbon monoxide (CO), hydrocarbon compounds (usually referred to as ‘‘volatile organic compounds,’’ or VOC), nitrogen oxides (NO X), fine particulate matter (PM 2.5 ), and sulfur oxides (SO X). While reductions in domestic fuel refining and
distribution that result from lower fuel consumption will reduce U.S. emissions of these pollutants, additional vehicle
use associated with the rebound effect from higher fuel economy will increase
their emissions. Thus the net effect of
stricter CAFE standards on emissions of each criteria pollutant depends on the relative magnitudes of its reduced emissions in fuel refining and distribution, and increases in its
emissions from vehicle use. Because the relationship between emissions in fuel refining and vehicle use is different for each criteria pollutant, the net effect of fuel savings from the proposed
standards on total emissions of each pollutant is likely to differ. We note that any benefits in terms of criteria air pollutant reductions resulting from this
rule would not be direct benefits.

With the exception of SO 2 , NHTSA
calculated annual emissions of each criteria pollutant resulting from vehicle use by multiplying its estimates of car and light truck use during each year over their expected lifetimes by per-mile emission rates appropriate to each vehicle type, fuel, model year, and age. These emission rates were developed by

U.S. EPA using its Motor Vehicle Emission Simulator (Draft MOVES 2009). 516 Emission rates for SO 2 were calculated by NHTSA using average fuel sulfur content estimates supplied by EPA, together with the assumption that
the entire sulfur content of fuel is
emitted in the form of SO 2

. 517 Total SO 2

average 85 percent over the forecast period spanned by AEO 2009.

515 This figure is calculated as 50 gallons + 50 gallons * 90% = 50 gallons + 45 gallons = 95 gallons.

516 The MOVES model assumes that the per-mile rates at which these pollutants are emitted are determined by EPA regulations and the effectiveness of catalytic after-treatment of engine exhaust emissions, and are thus unaffected by changes in car and light truck fuel economy.

517 These are 30 and 15 parts per million (ppm, measured on a mass basis) for gasoline and diesel respectively, which produces emission rates of 0.17 emissions under each alternative CAFE standard were calculated by applying the resulting emission rates directly to estimated annual gasoline and diesel fuel use by cars and light trucks.

As with other impacts, the changes in emissions of criteria air pollutants resulting from alternative increases in
CAFE standards for MY 2012–2016 cars and light trucks were calculated from
the differences between emissions
under each alternative that would increase CAFE standards, and emissions
under the baseline alternative.

NHTSA estimated the reductions in criteria pollutant emissions from producing and distributing fuel that
would occur under alternative CAFE standards using emission rates obtained by EPA from Argonne National
Laboratories’ Greenhouse Gases and Regulated Emissions in Transportation (GREET) model. 518 The GREET model provides separate estimates of air pollutant emissions that occur in different phases of fuel production and distribution, including crude oil extraction, transportation, and storage, fuel refining, and fuel distribution and storage. 519 EPA modified the GREET model to change certain assumptions about emissions during crude petroleum extraction and transportation, as well as to update its emission rates to reflect adopted and pending EPA emission
standards. NHTSA converted these emission rates from the mass per fuel energy content basis on which GREET reports them to mass per gallon of fuel supplied using estimates of fuel energy content supplied by GREET.

The resulting emission rates were applied to the agency’s estimates of fuel consumption under each alternative CAFE standard to develop estimates of total emissions of each criteria pollutant during fuel production and distribution. The assumptions about the effects of changes in fuel consumption on domestic and imported sources of fuel supply discussed above were then employed to calculate the effects of

grams of SO 2 per gallon of gasoline and 0.10 grams per gallon of diesel.

518 Argonne National Laboratories, The Greenhouse Gas and Regulated Emissions from Transportation (GREET) Model, Version 1.8, June

2007, available at http:// www.transportation.anl.gov/modeling

_simulation/
GREET/index.html (last accessed August 9, 2009).

519 Emissions that occur during vehicle refueling at retail gasoline stations (primarily evaporative emissions of volatile organic compounds, or VOCs) are already accounted for in the ‘‘tailpipe’’ emission factors used to estimate the emissions generated by increased light truck use. GREET estimates emissions in each phase of gasoline production and distribution in mass per unit of gasoline energy content; these factors are then converted to mass per gallon of gasoline using the average energy content of gasoline.VerDate Nov<24>2008 23:31 Sep 25, 2009 Jkt 217001 PO 00000 Frm 00223 Fmt 4701 Sfmt 4702 E:\FR\FM\28SEP2.SGM 28SEP2mstockstill on DSKH9S0YB1PROD with PROPOSALS 

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reductions in fuel use from alternative CAFE standards on changes in imports of refined fuel and domestic refining. NHTSA’s analysis assumes that reductions in imports of refined fuel would reduce criteria pollutant emissions during fuel storage and distribution only. Reductions in domestic fuel refining using imported
crude oil as a feedstock are assumed to reduce emissions during fuel refining, storage, and distribution, because each
of these activities would be reduced. Reduced domestic fuel refining using domestically-produced crude oil is assumed to reduce emissions during all four phases of fuel production and
distribution. 520

Finally, NHTSA calculated the net changes in domestic emissions of each criteria pollutant by summing the increases in emissions projected to result from increased vehicle use, and the reductions anticipated to result from lower domestic fuel refining and
distribution. 521 As indicated previously, the effect of adopting higher CAFE
standards on total emissions of each criteria pollutant depends on the relative magnitudes of the resulting
reduction in emissions from fuel refining and distribution, and the
increase in emissions from additional vehicle use. Although these net changes vary significantly among individual criteria pollutants, the agency projects that on balance, adopting higher CAFE
standards would reduce emissions of all criteria air pollutants except carbon monoxide (CO).

The net changes in domestic emissions of fine particulates (PM 2.5 ) and its chemical precursors (such as

NO X , SO X , and VOCs) are converted to economic values using estimates of the reductions in health damage costs per ton of emissions of each pollutant that is avoided, which were developed and recently revised by EPA. These savings represent the estimated reductions in the value of damages to human health resulting from lower atmospheric concentrations and population exposure to air pollution that occur when emissions of each pollutant that contributes to atmospheric PM 2.5 concentrations are reduced. The value of reductions in the risk of premature death due to exposure to fine particulate

520 In effect, this assumes that the distances crude oil travels to U.S. refineries are approximately the same regardless of whether it travels from domestic oilfields or import terminals, and that the distances that gasoline travels from refineries to retail stations are approximately the same as those from import terminals to gasoline stations.

521 All emissions from increased vehicle use are assumed to occur within the U.S., since CAFE standards would apply only to vehicles produced
for sale in the U.S. pollution (PM 2.5 ) account for a majority of EPA’s estimated values of reducing criteria pollutant emissions, although the value of avoiding other health impacts is also included in these
estimates. These values do not include a number of unquantified benefits, such
as reduction in the welfare and environmental impacts of PM 2.5 pollution, or reductions in health and welfare impacts related to other criteria pollutants (ozone, NO 2 , and SO 2 ) and air
toxics. EPA estimates different PMrelated per-ton values for reducing
emissions from vehicle use than for
reductions in emissions of that occur during fuel production and
distribution. 522 NHTSA applies these separate values to its estimates of changes in emissions from vehicle use and fuel production and distribution to determine the net change in total economic damages from emissions of these pollutants.

EPA projects that the per-ton values for reducing emissions of criteria pollutants from both mobile sources (including motor vehicles) and stationary sources such as fuel refineries and storage facilities will increase over time. These projected increases reflect rising income levels, which are assumed
to increase affected individuals’ willingness to pay for reduced exposure to health threats from air pollution, as well as future population growth, which increases population exposure to future levels of air pollution.

ii. Reductions in CO 2 Emissions

Emissions of carbon dioxide and other greenhouse gases (GHGs) occur throughout the process of producing and distributing transportation fuels, as well as from fuel combustion itself. By reducing the volume of fuel consumed by passenger cars and light trucks, higher CAFE standards will reduce GHG emissions generated by fuel use, as well as throughout the fuel supply cycle. Lowering these emissions is likely to slow the projected pace and reduce the ultimate extent of future changes in the global climate, thus reducing future economic damages that changes in the global climate are expected to cause. By reducing the probability that climate changes with potentially catastrophic economic or environmental impacts will occur, lowering GHG emissions may
also result in economic benefits that exceed the resulting reduction in the expected future economic costs caused

522 These reflect differences in the typical geographic distributions of emissions of each pollutant, their contributions to ambient PM 2.5 concentrations, pollution levels (predominantly
those of PM 2.5 ), and resulting changes in population

exposure. by gradual changes in the earth’s climatic systems.

Quantifying and monetizing benefits from reducing GHG emissions is thus an important step in estimating the total economic benefits likely to result from establishing higher CAFE standards. The agency estimated emissions of CO 2 from passenger car and light truck use by multiplying the number of gallons of each type of fuel (gasoline and diesel) they are projected to consume under alternative CAFE standards by the quantity or mass of CO 2 emissions released per gallon of fuel consumed.
This calculation assumes that the entire
carbon content of each fuel is converted

to CO 2 emissions during the combustion process. Carbon dioxide emissions account for nearly 95 percent of total
GHG emissions that result from fuel combustion during vehicle use.

iii. Economic Value of Reductions in

CO 2 Emissions

NHTSA has taken the economic benefits of reducing CO 2 emission into account in this rulemaking, both in developing proposed CAFE standards and in assessing the economic benefits
of each alternative that was considered.
Since direct estimates of the economic benefits from reducing GHG emissions are generally not reported in published literature on the impacts of climate change, these benefits are typically assumed to be the ‘‘mirror image’’ of the estimated incremental costs resulting
from an increase in those emissions. That is, the benefits from reducing emissions are usually measured by the savings in estimated economic damages that an equivalent increase in emissions
would otherwise have caused.

The ‘‘social cost of carbon’’ (SCC) is intended to be a monetary measure of the incremental damage resulting from carbon dioxide (CO 2 ) emissions, including (but not limited to) net agricultural productivity loss, human health effects, property damages from sea level rise, and changes in ecosystem services. Any effort to quantify and to monetize the consequences associated with climate change will raise serious questions of science, economics, and ethics. But with full regard for the limits of both quantification and monetization, the SCC can be used to provide an
estimate of the social benefits of
reductions in GHG emissions.

For at least four reasons, any particular figure will be contestable. First, scientific and economic knowledge about the impacts of climate change continues to grow. With new
and better information about relevant questions, including the cost, burdens, and possibility of adaptation, currentVerDate Nov<24>2008 23:31 Sep 25, 2009 Jkt 217001 PO 00000 Frm 00224 Fmt 4701 Sfmt 4702 E:\FR\FM\28SEP2.SGM 28SEP2mstockstill on DSKH9S0YB1PROD with PROPOSALS 

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estimates will inevitably change over time. Second, some of the likely and potential damages from climate change—for example, the loss of endangered species—are generally not
included in current SCC estimates. These omissions may turn out to be significant; in the sense that they may
mean that the best current estimates are too low. As noted by the IPCC Fourth Assessment Report, ‘‘It is very likely that globally aggregated figures underestimate the damage costs becausethey cannot include many nonquantifiable impacts.’’ Third, it is unlikely that the damage estimates account for the directed technological change that will lead to innovations that reduce the costs of responding to climate change—for example, it is likely that scientists will develop crops that are better able to withstand high temperatures. In this respect, the current estimates may overstate the likely damages. Fourth, controversial ethical judgments, including those involving the treatment of future generations, play a role in judgments about the SCC (see in particular the discussion of the discount rate, below).

To date, SCC estimates presented in recent regulatory documents have varied within and among agencies, including DOT, DOE, and EPA. For example, a regulation proposed by DOT in 2008 assumed a value of $7 per ton

CO 2 523 (2006$) for 2011 emission reductions (with a range of $0–14 for sensitivity analysis). A regulation finalized by DOE used a range of $0–$20 (2007$). Both of these ranges were designed to reflect the value of damages to the United States resulting from carbon emissions, or the ‘‘domestic’’ SCC. In the final MY 2011 CAFE EIS,
DOT used both a domestic SCC value of $2/tCO 2 and a global SCC value of $33/tCO 2 (with sensitivity analysis at $80/tCO 2 ), increasing at 2.4 percent per year thereafter. The final MY 2011 CAFE rule also presented a range from $2 to $80/tCO 2 . EPA’s Advance Notice of Proposed Rulemaking for Greenhouse Gases discussed the benefits of reducing
GHG emissions and identified what it described as ‘‘very preliminary’’ SCC estimates ‘‘subject to revision’’ that spanned three orders of magnitude. EPA’s global mean values were $68 and $40/tCO 2 for discount rates of 2 percent

523 For the purposes of this discussion, we present all values of the SCC as the cost per ton of

CO 2 emissions. Some discussions of the SCC in the literature use an alternative presentation of a dollar per ton of Carbon. The standard adjustment factor is 3.67, which means, for example, that a SCC of $10 per ton of CO 2 would be equivalent to a cost of $36.70 for a ton of carbon emitted. and 3 percent respectively (in 2006 real dollars for 2007 emissions). 524

The current Administration has worked to develop a transparent methodology for selecting a set of

interim SCC estimates to use in regulatory analyses until a more comprehensive characterization of the distribution of SCC is developed. This discussion proposes a set of values for
the interim social cost of carbon. It should be emphasized that the analysis here is preliminary. Today’s proposed joint rulemaking presents SCC estimates
that reflect the Administration’s current understanding of the relevant literature. These interim estimates are being used for the short-term while an interagency group develops a more comprehensive
characterization of the distribution of
SCC values for future economic and regulatory analyses. The interim values
should not be viewed as a statement about the results of the longer-term process. The Administration will be evaluating and seeking comment in the preamble to today’s proposed rule on all of the scientific, economic, and ethical issues before establishing final estimates for use in future rulemakings.

The outcomes of the Administration’s process to develop interim values are judgments in favor of (a) global rather than domestic values, (b) an annual growth rate of 3%, and (c) interim global SCC estimates for 2007 (in 2006 dollars) of $55, $33, $19, $10, and $5 per ton of

CO 2 . Notably, we have centered our current attention on a SCC of $19. The proposed figures are based on the following judgments.

1. Global and domestic measures.
Because of the distinctive nature of the climate change problem, we present both a global SCC and a fraction of that value that represents impacts that may
occur within the borders of the U.S. alone, or a ‘‘domestic’’ SCC, but center our current attention on the global measure. This approach represents a departure from past practices, which relied, for the most part, on domestic measures. As a matter of law, both global and domestic values are permissible; the relevant statutory provisions are ambiguous and allow
selection of either measure. 525

524 73 FR 44416 (July 30, 2008). EPA, ‘‘Advance Notice of Proposed Rulemaking for Greenhouse Gases Under the Clean Air Act, Technical Support Document on Benefits of Reducing GHG Emissions,’’ June 2008. www.regulations.gov. Search for ID ‘‘EPA–HQ–OAR–2008–0318–0078.’’

525 It is true that Federal statutes are presumed not to have extraterritorial effect, in part to ensure that the laws of the United States respect the interests of foreign sovereigns. But use of a global measure for the SCC does not give extraterritorial
effect to Federal law and hence does not intrude on
such interests. It is true that under OMB guidance, analysis from the domestic perspective is required, while analysis from the international perspective is optional.
The domestic decisions of one nation are not typically based on a judgment
about the effects of those decisions on other nations. But the climate change problem is highly unusual in the sense that it involves (a) a global public good in which (b) the emissions of one nation may inflict significant damages on other nations and (c) the United States is actively engaged in promoting an international agreement to reduce
worldwide emissions.

In these circumstances, we believe the global measure is preferred. Use of a global measure reflects the reality of the problem and is expected to contribute to the continuing efforts of the United
States to ensure that emissions reductions occur in many nations.

Domestic SCC values are also presented. The development of a domestic SCC is greatly complicated bythe relatively few region- or countryspecific estimates of the SCC in the literature. One potential source of
estimates comes from a recent unpublished EPA modeling effort using the FUND model. The resulting estimates suggest that the ratio of domestic to global benefits varies with key parameter assumptions. With a 3 percent discount rate, for example, the

U.S. benefit is about 6 percent of the global benefit for the ‘‘central’’ (mean) FUND results, while, for the corresponding ‘‘high’’ estimates associated with a higher climate sensitivity and lower global economic growth, the U.S. benefit is less than 4 percent of the global benefit. With a 2 percent discount rate, the U.S. share is about 2–5 percent of the global estimate.

Based on this available evidence, an interim domestic SCC value equal to 6 percent of the global damages is proposed. This figure is in the middle of the range of available estimates from the literature. It is recognized that the 6 percent figure is approximate and highly speculative and alternative approaches will be explored before establishing final values for future rulemakings.

2. Filtering existing analyses. There
are numerous SCC estimates in the existing literature, and it is reasonable
to make use of those estimates in order to produce a figure for current use. Astarting point is provided by the metaanalysis in Richard Tol, 2008. 526 With

526 Richard Tol, The Social Cost of Carbon: Trends, Outliers, and Catastrophes, Economics: The Open-Access, Open-Assessment E-Journal, Vol. 2,

2008–25. http://www.economics-ejournal.org/ economics/journalarticles/2008-25 (2008).VerDate Nov<24>2008 23:31 Sep 25, 2009 Jkt 217001 PO 00000 Frm 00225 Fmt 4701 Sfmt 4702 E:\FR\FM\28SEP2.SGM 28SEP2mstockstill on DSKH9S0YB1PROD with PROPOSALS 

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that starting point, the Administration proposes to ‘‘filter’’ existing SCC estimates by using those that (1) are derived from peer-reviewed studies; (2) do not weight the monetized damages to one country more than those in other countries; (3) use a ‘‘business as usual’’ climate scenario; and (4) are based on the most recent published version of each of the three major integrated assessment models (IAMs): FUND, PAGE, and DICE.

Proposal (1) is based on the view that those studies that have been subject to peer review are more likely to be
reliable than those that have not been. Proposal (2) is based on a principle of neutrality and simplicity; it does not treat the citizens of one nation (or different citizens within the U.S.) differently on the basis of speculative or controversial considerations. Further, it is consistent with the potential compensation tests of Kaldor (1939) and Hicks (1940), which use unweighted sums of willingness to pay. Finally, this is the approach used in rulemakings across a variety of settings and consequently keeps U.S. government policy consistent across contexts.

Proposal (3) stems from the judgment that as a general rule, the proper way to assess a policy decision is by comparing the implementation of the policy against a counterfactual state where the policy is not implemented. In addition, our expectation is that most policies to be evaluated using these interim SCC estimates will constitute small enough changes to the larger economy to safely assume that the marginal benefits of emissions reductions will not change between the baseline and policy scenarios. A departure from this approach would be to consider a more dynamic setting in which other countries might implement policies to
reduce GHG emissions at an unknown
future date and the U.S. could choose to implement such a policy now or at a
future date.

Proposal (4) is based on four complementary judgments. First, the FUND, PAGE, and DICE models now stand as the most comprehensive and
reliable efforts to measure the economic damages from climate change. Second,
the latest versions of the three IAMs are likely to reflect the most recent evidence and learning, and hence they are presumed to be superior to those that preceded them. Third, any effort to choose among them, or to reject one in favor of the others, would be difficult to defend at the present time. In the
absence of a clear reason to choose among them, it is reasonable to base the SCC on all of them. Fourth, in light of
the uncertainties associated with the SCC, the additional information offered by different models is important.

3. Use a model-weighted average of
the estimates at each discount rate. At this time, a scientifically valid reason to prefer any of the three major IAMs (FUND, PAGE, and DICE) has not been identified. Accordingly, to address the concern that certain models not be given unequal weight relative to the other models, the estimates are based on an equal weighting of the means of the
estimates from each of the models. Among estimates that remain after applying the filter, we begin by taking the average of all estimates within a
model. The estimated SCC is then calculated as the average of the three model-specific averages. This approach
is used to ensure that models with a greater number of published results do not exert unequal weight on the interim

SCC estimates.

4. Apply a 3 percent annual growth
rate to the chosen SCC values. SCC is assumed to increase over time, because future emissions are expected to produce larger incremental damages as physical and economic systems become more stressed as the magnitude of climate change increases. Indeed, an implied growth rate in the SCC can be produced by most of the models that estimate economic damages caused by
increased GHG emissions in future years. But neither the rate itself nor the information necessary to derive its implied value is commonly reported. In light of the limited amount of debate thus far about the appropriate growth rate of the SCC, applying a rate of 3 percent per year seems appropriate at this stage. This value is consistent with the range recommended by IPCC (2007) and close to the latest published estimate (Hope 2008).

(1)Discount Rates

For estimation of the benefits associated with the mitigation of climate change, one of the most complex issues involves the appropriate discount rate. OMB’s current guidance offers a
detailed discussion of the relevant
issues and calls for discount rates of 3 percent and 7 percent. It also permits a sensitivity analysis with low rates (1–3 percent) for intergenerational problems: ‘‘If your rule will have important intergenerational benefits or costs you might consider a further sensitivity analysis using a lower but positive discount rate in addition to calculating net benefits using discount rates of 3 and 7 percent.’’ 527

527 See OMB Circular A–4, pp. 35–36, citing Portney and Weyant, eds. (1999), Discounting and The choice of a discount rate, especially over long periods of time, raises highly contested and exceedingly difficult questions of science, economics, philosophy, and law. See,

e.g., William Nordhaus, The Challenge of Global Warming (2008); Nicholas Stern, The Economics of Climate Change (2007); Discounting and Intergenerational Equity (Paul Portney and John Weyant eds. 1999). It is not clear that future generations would be willing to trade environmental quality for consumption at the same rate as the current generations. Under imaginableassumptions, decisions based on costbenefit analysis with high discount rates might harm future generations—at least
if investments are not made for the benefit of those generations. See Robert Lind, Analysis for Intergenerational Discounting, id. at 173, 176–177. It is also possible that the use of low discount rates for particular projects might itself harm future generations, by ensuring that resources are not used in a way that would greatly benefit them. In the context of climate change, questions of intergenerational equity are especially important.

Reasonable arguments support the use of a 3 percent discount rate. First, that rate is among the two figures suggested by OMB guidance, and hence it fits with existing national policy. Second, it is
standard to base the discount rate on the compensation that people receive for delaying consumption, and the 3 percent is close to the risk-free rate of return, proxied by the return on long term inflation-adjusted U.S. Treasury Bonds, as of this writing. Although these rates are currently closer to 2.5 percent, the use of 3 percent provides an adjustment for the liquidity premium
that is reflected in these bonds’ returns.

At the same time, others would argue that a 5 percent discount rate can be supported. The argument relies on several assumptions. First, that rate can also be justified by reference to the level of compensation for delaying consumption, because it fits with market behavior with respect to
individuals’ willingness to trade-off consumption across periods as measured by the estimated post-tax average real returns to risky private investments ( e.g., the S&P 500). In the climate setting, the 5 percent discount rate may be preferable to the riskless rate because it is based on risky investments and the return to projects to mitigate climate change is also risky. In contrast, the 3 percent riskless rate may be a more appropriate discount rate for

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projects where the return is known with a high degree of confidence ( e.g., highway guardrails). In principal, the
correct discount rate would reflect the variance in payoff from climate mitigation policy and the correlation between the payoffs of the policy and the broader economy. 528

Second, 5 percent, and not 3 percent, is roughly consistent with estimates implied by reasonable inputs to the theoretically derived Ramsey equation, which specifies the optimal time path for consumption. That equation specifies the optimal discount rate as the sum of two components. The first term (the product of the elasticity of the marginal utility of consumption and the growth rate of consumption) reflects the fact that consumption in the future is likely to be higher than consumption today, so diminishing marginal utility implies that the same monetary damage will cause a smaller reduction of utility
in the future. Standard estimates of this
term from the economics literature are in the range of 3 percent–5 percent. The second component reflects the possibility that a lower weight should be placed on utility in the future, to account for social impatience or extinction risk, which is specified by a pure rate of time preference (PRTP). A
common estimate of the PRTP is 2 percent, though some observers believe that a principle of intergenerational equity suggests that the PRTP should be
close to zero. It follows that discount rate of 5 percent is near the middle of the range of values that are able to be derived from the Ramsey equation.

It is recognized that the arguments
above—for use of market behavior and the Ramsey equation—face objections in the context of climate change, and of course there are alternative approaches. In light of climate change, it is possible that consumption in the future will not be higher than consumption today, and if so, the Ramsey equation will suggest a lower figure. However, the historical evidence is consistent with rising consumption over time.

Some critics note that using observed interest rates for inter-generational decisions imposes current preferences on future generations, which some economists say may not be appropriate. For generational equity, they argue that
the discount rate should be below
market rates to correct for market
distortions and inefficiencies in intergenerational transfers of wealth (which are presumed to compensate future generations for damage), and to treat generations equitably based on ethical principles ( see Broome 2008). 529

Additionally, some analyses attempt to deal with uncertainty with respect to interest rates over time. We explore below how this might be done. 530

(2)Proposed Interim Estimates

The application of the methodology outlined above yields interim estimates of the SCC that are reported in Table IV.C.3–2. These estimates are reported separately using 3 percent and 5 percent discount rates. The cells are empty in rows 10 and 11, because these studies did not report estimates of the SCC ata 3 percent discount rate. The modelweighted means are reported in the final or summary row; they are $33 per tCO 2 at a 3 percent discount rate and $5 per

tCO 2 with a 5 percent discount rate.



### TABLE 

### IV.C.3–2—G LOBAL 

### S OCIAL 

### C OST OF 

### C ARBON 

### (SCC) E STIMATES 

### ($/tCO 2 IN 

### 2007 (2006$)), B ASED ON 

### 3% AND 

### 5% D ISCOUNT 

### RATES 

### *

Model Study Climate scenario 3% 5%

1

2

3

4

5

6

7

8

9

10

11 FUND .................................................

FUND .................................................

FUND .................................................

FUND .................................................

FUND .................................................

FUND .................................................

FUND .................................................

FUND .................................................

PAGE .................................................

PAGE ...............................................

DICE ................................................. Anthoff et al. 2009 ...................................

Anthoff et al. 2009 ...................................

Anthoff et al. 2009 ...................................

Link and Tol 2004 ...................................

Link and Tol 2004 ...................................

Guo et al. 2006 .......................................

Guo et al. 2006 .......................................

Guo et al. 2006 .......................................

Wahba & Hope 2006 ............................... Hope 2006 ...............................................

Nordhaus 2008 ........................................ FUND default ...........................................

SRES A1b ...............................................

SRES A2 .................................................

No THC ...............

.................................... THC continues ........

................................. Constant PRTP .......................................

Gollier discount 1 ....................................

Gollier discount 2 ....................................

FUND Mean ............................................

A2-scen ..................

.................................

.................. ................................................

................... ............................................... 6

1

9

12

12

5

14

7

8.25

57

..........

.......... ¥ 1

¥ 1

¥ 1

3

2

¥ 1

0

¥ 1

0

7

7

8

528 Specifically, if the benefits of the policy are highly correlated with the returns from broader economy, then the market rate should be used to
discount the benefits. If the benefits are uncorrelated with the broader economy the long term government bond rate should be applied. Furthermore, if the benefits are negatively correlated with the broader economy a rate less than that on long term government bonds should be used (Lind, 1982 pp. 89–90).

529 See Arrow, K.J., W.R. Cline, K-G Maler, M. Munasinghe, R. Squiteri, J.E. Stiglitz, 1996. ‘‘Intertemporal equity, discounting and economic efficiency,’’ in Climate Change 1995: Economic and Social Dimensions of Climate Change, Contribution of Working Group III to the Second Assessment Report of the Intergovernmental Panel on Climate Change. See also Weitzman, M.L., 1999. In Portney,

P.R. and Weyant J.P. (eds.), Discounting and Intergenerational Equity, Resources for the Future, Washington, DC.

530 Richard Newell and William Pizer, Discounting the distant future: how much do uncertain rates increase valuations? J. Environ. Econ. Manage. 46 (2003) 52–71.

531 Most of the estimates in Table 1 rely on climate scenarios developed by the Intergovernmental Panel on Climate Change (IPCC). The IPCC published a new set of scenarios in 2000 for use in the Third Assessment Report (Special Report on Emissions Scenarios—SRES). The SRES scenarios define four narrative storylines: A1, A2, B1 and B2, describing the relationships between the forces driving greenhouse gas and aerosol emissions and their evolution during the 21st century for large world regions and globally. Each storyline represents different demographic, social, economic, technological, and environmental developments that diverge in increasingly irreversible ways. The storylines are summarized in Nakicenovic et al., 2000 ( see also http://sedac.ciesin.columbia.edu/
ddc/sres/ ). Because the B1 and B2 storylines represent policy cases rather than business-as-usual projections, estimates derived from these scenarios to be less appropriate for use in benefit-cost analysis. They are therefore excluded. 532 Guo et al. (2006) report estimates based on two Gollier discounting schemes. The Gollier discounting assumes complex specifications about individual utility functions and risk preferences. After various conditions are satisfied, declining social discount rates emerge. Gollier Discounting Scheme 1 employs a certainty-equivalent social rate of time preference (SRTP) derived by assuming the regional growth rate is equally likely to be 1% above or below the original forecast growth rate.Gollier Discounting Scheme 2 calculates a certaintyequivalent social rate of time preference (SRTP) using five possible growth rates, and applies the new SRTP instead of the original. Hope (2008) conducts Monte Carlo analysis on the PRTP component of the discount rate. The PRTP is modeled as a triangular distribution with a min value of 1%/yr, a most likely value of 2%/yr, and a max value of 3%/yr.VerDate Nov<24>2008 23:31 Sep 25, 2009 Jkt 217001 PO 00000 Frm 00227 Fmt 4701 Sfmt 4702 E:\FR\FM\28SEP2.SGM 28SEP2mstockstill on DSKH9S0YB1PROD with PROPOSALS 

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TABLE 

### IV.C.3–2—G LOBAL 

### S OCIAL 

### C OST OF 

### C ARBON 

### (SCC) E STIMATES 

### ($/tCO 2 IN 

### 2007 (2006$)), B ASED ON 

### 3% AND 

### 5% D ISCOUNT 

### RATES 

### *—Continued

Model Study Climate scenario 3% 5%

Summary ....

............................................. .................................................................. Model -weighted Mean ............................. 33 5

* The sample includes all peer reviewed, non-equity-weighted estimates included in Tol (2008), Nordhaus (2008), Hope (2008), and Anthoff et al. (2009), that are based on the most recent published version of FUND, PAGE, or DICE and use business-as-usual climate scenarios. 531 532 All values are based on the best available information from the underlying studies about the base year and year dollars, rather than the Tol (2008) assumption that all estimates included in his review are 1995 values in 1995$. All values were updated to 2007 using a 3 percent annual growth rate in the SCC, and adjusted for inflation using GDP deflator.

Analyses have been conducted at $33 and $5 as these represent the estimates associated with the 3 percent and 5 percent discount rates, respectively. 533 The 3 percent and 5 percent estimates have independent appeal, and at this time a clear preference for one over the other is not warranted. Thus, we have
also included—and centered our current attention on—the average of the
estimates associated with these discount rates, which is $19. (Based on the $19 global value, the approximate domestic fraction of these benefits would be $1.14 per ton of CO 2 assuming that domestic benefits are 6 percent of the global
benefits.

It is true that there is uncertainty about interest rates over long time horizons. Recognizing that point, Newell and Pizer (2003) have made a careful effort to adjust for that uncertainty. The Newell-Pizer approach models discount rate uncertainty as something that evolves over time. 534 This is a relatively recent contribution to the literature, and estimates based on
this method are included with the aim of soliciting comment.

There are several concerns with using this approach in this context. First, it would be a departure from current OMB guidance. Second, an approach that would average what emerges from discount rates of 3 percent and 5 percent reflects uncertainty about the discount rate, but based on a different model of uncertainty. The Newell-Pizer approach models discount rate uncertainty as something that evolves over time; in contrast, the preferred approach (outlined above) assumes that there is a single discount rate with equal probability of 3 percent and 5 percent.

Table IV.C.3–3 reports on the application of the Newell-Pizer adjustments. The precise numbers depend on the assumptions about the data generating process that governs interest rates. Columns (1a) and (1b)
assume that ‘‘random walk’’ model best describes the data and uses 3 percent and 5 percent discount rates, respectively. Columns (2a) and (2b) repeat this, except that it assumes a ‘‘mean-reverting’’ process. While the empirical evidence does not rule out a mean-reverting model, Newell and Pizer find stronger empirical support for the
random walk model.



### TABLE 

### IV.C.3–3—G LOBAL 

### S OCIAL 

### C OST OF 

### C ARBON 

### (SCC) E STIMATES 

### ($/tCO 2 IN 

### 2007 (2006$))*, U SING 

### N EWELL 

### &

P IZER 

### (2003) A DJUSTMENT FOR 

### F UTURE 

### D ISCOUNT 

### RATE 

### U NCERTAINTY

### **

Model Study Climate scenario Random-walk model Meanreverting

model

3% (1a) 5% (1b) 3% (2a) 5% (2b)

1 FUND ........................... Anthoff et al. 2009 ................................ FUND default ........................................ 10 0 7

¥ 1

2 FUND ........................... Anthoff et al. 2009 ................................ SRES A1b ............................................. 2 0 1

¥ 1

3 FUND ........................... Anthoff et al. 2009 ................................ SRES A2 ............................................... 15 0 10

¥ 1

4 FUND ........................... Link and Tol 2004 ................................. No THC .......................................

.......... 20 6 13 4

5 FUND ........................... Link and Tol 2004 ................................. THC continues ................................

...... 20 4 13 2

6 FUND ........................... Guo et al. 2006 ..................................... Constant PRTP ..................................... 9 0 6

¥ 1

7 FUND ........................... Guo et al. 2006 ..................................... Gollier discount 1 .................................. 14 0 14 0

8 FUND ........................... Guo et al. 2006 ..................................... Gollier discount 2 ..................................

FUND Mean .......................................... 7

12 ¥ 1

1 7

9 ¥ 1

0

9 PAGE ........................... Wahba & Hope 2006 ............................ A2-scen ...........................................

...... 97 13 63 8

10 PAGE ......................... Hope 2006 ............................................

........................................... .................... .......... 13

.......... 8

11 DICE .......................... Nordhaus 2008 .....................................

............................................. .................. .......... 15

.......... 9 Summary ...........................

............................................................... Model-weighted Mean ...........

............... 55 10 36 6

* The sample includes all peer reviewed, non-equity-weighted estimates included in Tol (2008), Nordhaus (2008), Hope (2008), and Anthoff et al. (2009), that are based on the most recent published version of FUND, PAGE, or DICE and use business-as-usual climate scenarios. All values are based on the best available information from the underlying studies about the base year and year dollars, rather than the Tol (2008) assumption that all estimates included in his review are 1995 values in 1995$. All values were updated to 2007 using a 3 percent annual growth rate in the SCC, and adjusted for inflation using GDP deflator. See the Notes to Table 1 for further details.

** Assumes a starting discount rate of 3 percent or 5 percent. Newell and Pizer (2003) based adjustment factors are not applied to estimates from Guo et al. (2006) that use a different approach to account for discount rate uncertainty (rows 7–8).

Note that the correction factor from Newell and Pizer is based on the DICE model. The proper adjustment may differ for other integrated assessment models that produce different time schedules of marginal damages. We would expect this difference to be minor.

533 It should be noted that reported discount rates assumptions about the individual components of represented by a probability distribution. The may not be consistently derived across models or the Ramsey equation identified earlier. Newell and Pizer, and Weitzman approaches are specific applications of models: While the discount 534 In contrast, an alternative approach based on relatively recent contributions, and we invite
rate may be identical, it may reflect different Weitzman (2001) would assume that there is a comment on the advantages and disadvantages of

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The resulting estimates of the social cost of carbon are necessarily greater. When the adjustments from the random walk model are applied, the estimates of the social cost of carbon are $10 and $55 per ton of CO 2 , with the 5 percent and 3 percent discount rates, respectively. The application of the mean-reverting adjustment yields estimates of $6 and $36. Relying on the random walk model, analyses are also conducted with the value of the SCC set at $10 and $55.

(3)Caveats

There are at least four caveats to the approach outlined above.

First, the impacts of climate change are expected to be widespread, diverse, and heterogeneous. In addition, the exact magnitude of these impacts is uncertain, because of the inherent randomness in the Earth’s atmospheric processes, the U.S. and global economies, and the behaviors of current and future populations. Current IAM do not currently individually account for and assign value to all of the important physical and other impacts of climate change that are recognized in the climate change literature. Although it is likely that our capability to quantify and monetize impacts will improve with time, it is also likely that even in future applications, there are a number of potentially significant benefits categories that will remain
unmonetized.

Second, in the opposite direction, it is unlikely that the damage estimates adequately account for the directed technological change that climate change will cause. In particular, climate change will increase the return on investment to develop technologies that allow individuals to better cope with climate change. For example, it is likely that scientists will develop crops that are better able to withstand high temperatures. In this respect, the current estimates may overstate the likely damages.

Third, there has been considerable
recent discussion of the risk of catastrophic impacts and of how best to
account for worst-case scenarios. Recent research by Weitzman (2009) specifies
some conditions under which the possibility of catastrophe would
undermine the use of IAMs and conventional cost-benefit analysis. This research requires further exploration before its generality is known and the optimal way to incorporate it into regulatory reviews is understood.

Fourth, it is also worth noting that the SCC estimates are only relevant for incremental policies relative to the projected baselines, which capture
business-as-usual scenarios. To evaluate non-marginal changes, such as might
occur if the U.S. acts in tandem with other nations, then it might be necessary to go beyond the simple expedient of using the SCC along the BAU path. In particular, it would be correct to calculate the aggregate WTP to move from the BAU scenario to the policy scenario, without imposing the restriction that the marginal benefit remains constant over this range.

All of the values derived from this process are expressed in 2006 dollars. NHTSA has adjusted them to their equivalent values in 2007 dollars for consistency with other values used in its analysis of benefits from adopting higher CAFE standards for MY 2012– 2016 passenger cars and light trucks. The resulting value upon which we have centered our analysis, which is derived from the figures reported in the tables above, is equivalent to $20 per
metric ton of CO 2 emissions avoided when expressed in 2007$, and the agency has relied on this value in its analysis. NHTSA has also analyzed the sensitivity of its benefit estimates to alternative values of $5, $10, $34, and $56 per metric ton of CO 2 emissions avoided, with all figures again in 2007$. Each of these values applies to emissions during 2007, and are assumed to grow in real terms by 3 percent annually beginning in 2007. NHTSA
seeks comments on these values and the approach used to derive them.

m. Discounting Future Benefits and

Costs

Discounting future fuel savings and
other benefits is intended to account for the reduction in their value to society when they are deferred until some future date, rather than received immediately. The discount rate expresses the percent decline in the
value of these benefits—as viewed from today’s perspective—for each year they
are deferred into the future. In evaluating the benefits from alternative
increases in CAFE standards for MY 2012–2016 passenger cars and light trucks, NHTSA has employed a discount rate of 3 percent per year. The agency has also tested the sensitivity of
these benefit and cost estimates to the use of a 7 percent discount rate. Although these are the same discount rates analyzed in the MY 2011 final rule, NHTSA has chosen to use 3 percent as the basis for the Reference Case in this proposed rule rather than the 7 percent rate it employed previously, for the reasons discussed
below.

The primary reason that NHTSA has selected 3 percent as the appropriate rate for discounting future benefits from increased CAFE standards is that most
or all of vehicle manufacturers’ costs for complying with higher CAFE standards are likely to be reflected in higher sales prices for their new vehicle models. By increasing sales prices for new cars and light trucks, CAFE regulation will thus primarily affect vehicle purchases and other private consumption decisions. Both economic theory and OMB guidance on discounting indicate that
the future benefits and costs of regulations that mainly affect private consumption should be discounted at the social rate of time preference. 535

OMB guidance also indicates that savers appear to discount future consumption at an average real (that is, adjusted to remove the effect of inflation) rate of about 3 percent when they face little risk about its likely level.
Since the real rate that savers use to discount future consumption represents
a reasonable estimate of the social rate of time preference, NHTSA has employed the 3 percent rate to discount projected future benefits and costs resulting from higher CAFE standards for MY 2012–2016 passenger cars and light trucks. 536

Because there is some uncertainty
about the extent to which vehicle
manufacturers will be able to recover their costs for complying with higher CAFE standards by increasing vehicle sales prices, however, NHTSA has also tested the sensitivity of these benefit and cost estimates to the use of a higher percent discount rate. OMB guidance indicates that the real economy-wide opportunity cost of capital is the appropriate discount rate to apply to
future benefits and costs when the primary effect of a regulation is ‘‘* * * to displace or alter the use of capital in the private sector,’’ and estimates that this rate currently averages about 7 percent. 537 Thus the agency has also tested the sensitivity of its benefit and
cost estimates for alternative MY 2012–
2016 CAFE standards to the use of a 7 percent real discount rate. NHTSA seeks
comment on whether it should evaluate CAFE standards using a discount rate of 3 percent, 7 percent, or an alternative
value.

n. Accounting for Uncertainty in
Benefits and Costs

In analyzing the uncertainty surrounding its estimates of benefits and costs from alternative CAFE standards,

535 Id.

536 Office of Management and Budget, Circular A– 4, ‘‘Regulatory Analysis,’’ September 17, 2003, 33. Available at http://www.whitehouse.gov/omb/ circulars/a004/a-4.pdf (last accessed August 9, 2009).

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NHTSA has considered alternative estimates of those assumptions and parameters likely to have the largest effect. These include the projected costs of fuel economy-improving technologies and their expected effectiveness in reducing vehicle fuel consumption, forecasts of future fuel prices, the magnitude of the rebound effect, the
reduction in external economic costs resulting from lower U.S. oil imports, the value to the U.S. economy of reducing carbon dioxide emissions, and the discount rate applied to future benefits and costs. The range for each of these variables employed in the uncertainty analysis is presented in the section of this notice discussing each
variable.

The uncertainty analysis was conducted by assuming independent normal probability distributions for each of these variables, using the low and high estimates for each variable as the values below which 5 percent and 95 percent of observed values are
believed to fall. Each trial of the uncertainty analysis employed a set of values randomly drawn from each of these probability distributions, assuming that the value of each variable is independent of the others. Benefits
and costs of each alternative standard were estimated using each combination of variables. A total of 1,000 trials were used to establish the likely probability
distributions of estimated benefits and
costs for each alternative standard.

o. Where Can Readers Find More
Information About the Economic Assumptions?

Much more detailed information is provided in Chapter VIII of the PRIA,
and a discussion of how NHTSA and EPA jointly reviewed and updated economic assumptions for purposes of this NPRM is available in Chapter 4 of the TSD. In addition, all of NHTSA’s model input and output files are now public and available for the reader’s
review and consideration. The economic input files can be found in the docket for this NPRM, NHTSA–2009– 0059, and on NHTSA’s Web site. Finally, because much of NHTSA’s economic analysis for purposes of this
NPRM builds on the work that was done for the MY 2011 final rule, we refer
readers to that document as well for background information concerning how NHTSA’s assumptions regarding economic inputs for CAFE analysis have evolved over the past several rulemakings, both in response to comments and as a result of the agency’s growing experience with this type of analysis. 538

4. How Does NHTSA Use the Assumptions in Its Modeling Analysis?

In developing today’s proposed CAFE standards, NHTSA has made significant use of results produced by the CAFE Compliance and Effects Model (commonly referred to as ‘‘the CAFE model’’ or ‘‘the Volpe model’’), which DOT’s Volpe National Transportation Systems Center developed specifically to support NHTSA’s CAFE rulemakings. The model, which has been constructed specifically for the purpose of analyzing potential CAFE standards, integrates the following core capabilities:

(1)Estimating how manufacturers could apply technologies in response to new fuel economy standards,

(2)Estimating the costs that would be incurred in applying these technologies,

(3)Estimating the physical effects resulting from the application of these technologies, such as changes in travel demand, fuel consumption, and
emissions of carbon dioxide and criteria pollutants, and

(4)Estimating the monetized societal benefits of these physical effects.

An overview of the model follows below. Separate model documentation provides a detailed explanation of the functions the model performs, the calculations it performs in doing so, and how to install the model, construct inputs to the model, and interpret the model’s outputs. Documentation of the model, along with model installation files, source code, and sample inputs are
available at NHTSA’s web site. The
model documentation is also available in the docket for today’s proposed rule, as are inputs for and outputs from analysis of today’s proposed CAFE
standards.

a. How Does the Model Operate?

As discussed above, the agency uses the Volpe model to estimate the extent to which manufacturers could attempt to comply with a given CAFE standard by adding technology to fleets that the agency anticipates they will produce in future model years. This exercise
constitutes a simulation of manufacturers’ decisions regarding compliance with CAFE standards.

This compliance simulation begins with the following inputs: (a) The
baseline market forecast discussedabove in Section IV.C.1, (b) technologyrelated estimates discussed above in Section IV.C.2, (c) economic inputs discussed above in Section IV.C.3, and (d)inputs defining the characteristics of

538 74 FR 14308–14358 (Mar. 30, 2009). potential new CAFE standards. For each manufacturer, the model applies technologies in a sequence that follows a defined engineering logic (‘‘decision
trees’’ discussed in the MY 2011 final rule and in the model documentation) and a cost-minimizing strategy in order to identify a set of technologies the manufacturer could apply in response to
new CAFE standards. The model applies technologies to each of the projected individual vehicles in a manufacturer’s fleet, until one of three things occurs:

(1)The manufacturer’s fleet achieves compliance with the applicable standard;

(2)The manufacturer ‘‘exhausts’’ 539 available technologies; or

(3) For manufacturers estimated to be willing to pay civil penalties, the manufacturer reaches the point at which doing so would be more cost-effective (from the manufacturer’s perspective) than adding further technology. 540

As discussed below, the model has also been modified in order to apply additional technology in early model years if doing so will facilitate compliance in later model years.

The model accounts explicitly for each model year, applying most technologies when vehicles are scheduled to be redesigned or freshened, and carrying forward technologies between model years. The CAFE model accounts explicitly for each model year because EPCA requires that NHTSA make a year-by-year determination of the appropriate level of

539 In a given model year, the model makes additional technologies available to each vehicle model within several constraints, including (a) whether or not the technology is applicable to the vehicle model’s technology class, (b) whether the vehicle is undergoing a redesign or freshening in the given model year, (c) whether engineering aspects of the vehicle make the technology unavailable ( e.g., secondary axle disconnect cannot be applied to two-wheel drive vehicles), and (d) whether technology application remains within ‘‘phase in caps’’ constraining the overall share of a manufacturer’s fleet to which the technology can be added in a given model year. Once enough technology is added to a given manufacturer’s fleet in a given model year that these constraints make further technology application unavailable, technologies are exhausted for that manufacturer in that model year.

540 This possibility was added to the model to account for the fact that under EPCA/EISA, manufacturers must pay fines if they do not achieve compliance with applicable CAFE standards. 49

U.S.C. 32912(b). NHTSA recognizes that some manufacturers will find it more cost-effective to pay fines than to achieve compliance, and believes that
to assume these manufacturers would exhaust available technologies before paying fines would cause unrealistically high estimates of market penetration of expensive technologies such as diesel engines and strong hybrid electric vehicles, as well as correspondingly inflated estimates of both the costs and benefits of any potential CAFE
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stringency and then set the standard at that level, while ensuring ratable increases in average fuel economy. 541

The model also calculates the costs, effects, and benefits of technologies that it estimates could be added in response to a given CAFE standard. 542 It calculates costs by applying the cost estimation techniques discussed above in Section IV.C.2, and by accounting for
the number of affected vehicles. It accounts for effects such as changes in vehicle travel, changes in fuel consumption, and changes in greenhouse gas and criteria pollutant emissions. It does so by applying the fuel consumption estimation techniques also discussed in Section IV.C.2, and the vehicle survival and mileage accumulation forecasts, the rebound effect estimate and the fuel properties
and emission factors discussed in Section IV.C.3. Considering changes in travel demand and fuel consumption,
the model estimates the monetized value of accompanying benefits to society, as discussed in Section IV.C.3.
The model calculates both the
undiscounted and discounted value of
benefits that accrue over time in the
future.

The Volpe model has other capabilities that facilitate the development of a CAFE standard. It can
be used to fit a mathematical function forming the basis for an attribute-based CAFE standard, following the steps
described below. It can also be used to evaluate many ( e.g., 200 per model year) potential levels of stringency sequentially, and identify the stringency at which specific criteria are met. For example, it can identify the stringency at which net benefits to society are maximized, the stringency at which a specified total cost is reached, or the stringency at which a given average required fuel economy level is attained. This allows the agency to compare more easily the impacts in terms of fuel

541 49 U.S.C. 32902(a) states that at least 18 months before the beginning of each model year, the Secretary of Transportation shall prescribe by regulation average fuel economy standards for automobiles manufactured by a manufacturer in that model year, and that each standard shall be the maximum feasible average fuel economy level that the Secretary decides the manufacturers can achieve in that year. NHTSA has long interpreted this statutory language to require year-by-year assessment of manufacturer capabilities. 49 U.S.C. 32902(b)(2)(C) also requires that standards increase ratably between MY 2011 and MY 2020.

542 As for all of its other rulemakings, NHTSA is required by Executive Order 12866 and DOT regulations to analyze the costs and benefits of CAFE standards. Executive Order 12866, 58 FR 51735 (Oct. 4, 1993); DOT Order 2100.5, ‘‘Regulatory Policies and Procedures,’’ 1979, available at http://regs.dot.gov/ rulemakingrequirements.htm (last accessed August 21, 2009). savings, emissions reductions, and costs and benefits of achieving different levels of stringency according to different
criteria. The model can also be used to perform uncertainty analysis (i.e., Monte Carlo simulation), in which input estimates are varied randomly according to specified probability distributions, such that the uncertainty of key measures ( e.g., fuel consumption, costs, benefits) can be evaluated.

b. Has NHTSA Considered Other
Models?

Nothing in EPCA requires NHTSA to use the Volpe model. In principle, NHTSA could perform all of these tasks through other means. For example, in developing the standards proposed today, the agency did not use the Volpe model’s curve fitting routines, because they could not be modified in time to reflect the change in the mathematical function defining the proposed CAFE standards. The Volpe model may be modified to do so for the final rule, although the agency can also continue
to fit the mathematical function outside the model. In general, though, these model capabilities have greatly increased the agency’s ability to rapidly, systematically, and reproducibly conduct key analyses relevant to the
formulation and evaluation of new
CAFE standards.

During its previous rulemaking,
which led to the final MY 2011 standards promulgated earlier this year,
NHTSA received comments from the Alliance and CARB encouraging
NHTSA to examine the usefulness of
other models. As discussed in that final rule, NHTSA, having undertaken such consideration, concluded that the Volpe
model is a sound and reliable tool for the development and evaluation of potential CAFE standards. 543

In reconsidering and reaffirming this conclusion for purposes of this NPRM, NHTSA notes that the Volpe model not only has been formally peer-reviewed and tested through three rulemakings, but also has some features especially important for the analysis of CAFE standards under EPCA/EISA. Amongthese are the ability to perform year-byyear analysis, and the ability to account for engineering differences between specific vehicle models.

EPCA requires that NHTSA set CAFE standards for each model year at the level appropriate for that year. 544 Doing so requires the ability to analyze each model year and, when developing regulations covering multiple model years, to account for the

543 74 FR 14372 (Mar. 30, 2009).

544 49 U.S.C. 32902(a). interdependency of model years in terms of the appropriate levels of stringency for each one. Also, as part of
the evaluation of the economic practicability of the standards, as required by EPCA, NHTSA has traditionally assessed the annual costs
and benefits of the standards as it is permitted to do so. The first (2002)
version of DOT’s model treated each model year separately, and did not perform this type of explicit accounting. Manufacturers took strong exception to these shortcomings. For example, GM commented in 2002 that ‘‘although the table suggests that the proposed standard for MY 2007, considered in isolation, promises benefits exceeding costs, that anomalous outcome is merely an artifact of the peculiar Volpe methodology, which treats each year independently of any other * * *.’’ In 2002, GM also criticized DOT’s analysis for, in some cases, adding a technology in MY 2006 and then replacing it with another technology in MY 2007. GM (and other manufacturers) argued that this completely failed to represent true manufacturer product-development cycles, and therefore could not be technologically feasible or economically practicable.

In response to these concerns, and related concerns expressed by other manufacturers, DOT modified the CAFE
model in order to account for dependencies between model years and to better represent manufacturers’ planning cycles, in a way that still allowed NHTSA to comply with the statutory requirement to determine the appropriate level of the standards for each model year. This was accomplished by limiting the application of many technologies to model years in which vehicle models are scheduled to be redesigned (or, for some technologies, ‘‘freshened’’), and by causing the model to ‘‘carry forward’’ applied technologies from one model year to the next.

During the recent rulemaking for MY 2011 passenger cars and light trucks,
DOT further modified the CAFE model
to account for cost reductions attributable to ‘‘learning effects’’ related to volume (i.e., economies of scale) and the passage of time (i.e., time-basedlearning), both of which evolve on yearby-year basis. These changes were implemented in response to comments by environmental groups and other
stakeholders.

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a given vehicle model. Some commenters have previously suggested that manufacturers are most likely to broadly apply generic technology ‘‘packages,’’ and the Volpe model does tend to form ‘‘packages’’ dynamically, based on vehicle characteristics, redesign schedules, and schedules for
increases in CAFE standards. For example, under the proposed CAFE standards for passenger cars, the CAFE
model estimated that manufacturers could apply turbocharged SGDI engines
mated with dual-clutch AMTs to 1.8 million passenger cars in MY 2016, about 16 percent of the MY 2016 passenger car fleet. Recent modifications to the model, discussed below, to represent multi-year planning, increase the model’s tendency to add relatively cost-effective technologies
when vehicles are estimated to be redesigned, and thereby increase the model’s tendency to form such packages.

On the other hand, some
manufacturers have indicated that especially when faced with significant progressive increases in the stringency of new CAFE standards, they are likely to also look for narrower opportunities to apply specific technologies. By progressively applying specific technologies to specific vehicle models, the CAFE model also produces such outcomes. For example, under the proposed CAFE standards for passenger cars, the CAFE model estimated that in MY 2012, some manufacturers could find it advantageous to apply SIDI to
some vehicle models without also adding turbochargers.

By following this approach of combining technologies incrementally and on a model-by-model basis, the
CAFE model is able to account for important engineering differences
between vehicle models and avoid unlikely technology combinations. For example, the model does not apply dual-clutch AMTs (or strong hybrid systems) to vehicle models with 6-speed
manual transmissions. Some vehicle buyers prefer a manual transmission; this preference cannot be assumed away. The model’s accounting for manual transmissions is also important for vehicles with larger engines: For example, cylinder deactivation cannot be applied to vehicles with manual transmissions, because there is no reliable means of predicting when the driver will change gears. By retaining cylinder deactivation as a specifictechnology rather than part of a predetermined package and by retaining
differentiation between vehicles with different transmissions, DOT’s model is able to target cylinder deactivation only to vehicle models for which it is technologically feasible.

The Volpe model also produces a single vehicle-level output file that, for each vehicle model, shows which technologies were present at the outset of modeling, which technologies were superseded by other technologies, and which technologies were ultimately present at the conclusion of modeling. For each vehicle, the same file shows resultant changes in vehicle weight, fuel economy, and cost. This provides for efficient identification, analysis, and correction of errors, a task with which the public can now assist the agency, since all inputs and outputs are public.

Such considerations, as well as those related to the efficiency with which theVolpe model is able to analyze attributebased CAFE standards and changes in vehicle classification, and to perform higher-level analysis such as stringency estimation (to meet predetermined criteria), sensitivity analysis, and uncertainty analysis, lead the agency to
conclude that the model remains the best available to the agency for the purposes of analyzing potential new
CAFE standards.

c. What Changes Has DOT Made to the
Model?

Prior to being used for analysis supporting today’s proposal, the Volpe
model was revised to make some minor improvements, and to add one significant new capability: the ability to simulate manufacturers’ ability toengage in ‘‘multi-year planning.’’ Multiyear planning refers to the fact that when redesigning or freshening vehicles, manufacturers can anticipate future fuel economy or CO 2 standards, and add technologies accounting for these standards. For example, amanufacturer might choose to overcomply in a given model year when many vehicle models are scheduled for redesign, in order to facilitate compliance in a later model year when standards will be more stringent yet few
vehicle models are scheduled for redesign. 545 Prior comments have indicated that the Volpe model, by not representing such manufacturer choices, tended to overestimate compliance costs. However, because of the technical complexity involved in representing these choices when, as in the Volpe model, each model year is accounted for separately and explicitly, the model
could not be modified to add this

545 Although a manufacturer may, in addition, generate CAFE credits in early model years for use in later model years (or, less likely, in later years for use in early years), EPCA does not allow NHTSA, when setting CAFE standards, to account
for manufacturers’ use of CAFE credits. capability prior to the statutory deadline
for the MY 2011 final standards.

The model now includes this capability, and NHTSA has applied it in analyzing the standards proposed today. Consequently, this often produces results indicating that manufacturers could over-comply in some model years (with corresponding increases in costs and benefits in those model years) and thereby ‘‘carry forward’’ technology into later model years in order to reduce compliance costs in those later model years. NHTSA believes this better represents how manufacturers would actually respond to new CAFE standards, and thereby produces more
realistic estimates of the costs and
benefits of such standards.

The Volpe model has also been modified to accommodate inputs specifying the amount of CAFE credit to be applied to each manufacturer’s fleet. Although the model is not currently capable of estimating manufacturers’ decisions regarding the generation and use of CAFE credits, and EPCA does not allow NHTSA, in setting CAFE standards, to take into account manufacturers’ potential use of credits, this additional capability in the Volpe model provides a basis for more accurately estimating costs, effects, and benefits that may actually result from
new CAFE standards. Insofar as some manufacturers actually do earn and use CAFE credits, this provides NHTSA with some ability to examine outcomes more realistically than EPCA allows for purposes of setting new CAFE
standards.

In comments on recent NHTSA rulemakings, some reviewers have suggested that the Volpe model should
be modified to estimate the extent to
which new CAFE standards would induce changes in the mix of vehicles in the new vehicle fleet. NHTSA, like EPA, agrees that a ‘‘market shift’’ model, also called a consumer vehicle choice model, could provide useful information regarding the possible effects of potential new CAFE standards. An earlier experimental version of the Volpe model included a multinomial logit model that estimated changes in sales resulting from CAFE-induced increases in new vehicle fuel economy and prices. A fuller description of this attempt can be found in Section V of the PRIA. However, NHTSA has thus far been unable to develop credible coefficients specifying such a model. In addition, as discussed in Section II.H.4,
such a model is sensitive to the coefficients used in it, and there is great variation over some key values of these coefficients in published studies. NHTSA seeks comment on ways toVerDate Nov<24>2008 23:31 Sep 25, 2009 Jkt 217001 PO 00000 Frm 00232 Fmt 4701 Sfmt 4702 E:\FR\FM\28SEP2.SGM 28SEP2mstockstill on DSKH9S0YB1PROD with PROPOSALS 

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improve on this earlier work and develop this capability effectively. If the agency is able to do so prior to conducting analysis supporting decisions regarding final CAFE standards, it will attempt to reintegrate this capability in the Volpe model and include these effects in its analysis of final standards. If not, NHTSA will continue efforts to develop and make use of this capability in future rulemakings.

d. Does the Model Set the Standards?

Although NHTSA currently uses the Volpe model as a tool to inform its consideration of potential CAFE standards, the Volpe model does not
determine the CAFE standards that NHTSA proposes or promulgates as final regulations. The results it produces are completely dependent on inputs selected by NHTSA, based on the best
available information and data available in the agency’s estimation at the time standards are set. Although the model has been programmed in previous rulemakings to estimate at what stringency net benefits are maximized,
NHTSA has not done so here and has instead used the Volpe model to estimate stringency levels that produce roughly constant rates of increase in the combined average required fuel economy. Ultimately, NHTSA’s
selection of a CAFE standard is governed and guided by the statutory requirements of EPCA, as amended by
EISA: NHTSA sets the standard at the maximum feasible average fuel economy
level that it determines is achievable during a particular model year, considering technological feasibility, economic practicability, the effect of
other standards of the Government on fuel economy, and the need of the nation to conserve energy.

NHTSA considers the results of analyses conducted by the Volpe model and analyses conducted outside of the Volpe model, including analysis of the impacts of carbon dioxide and criteria pollutant emissions, analysis of technologies that may be available in the long term and whether NHTSA could expedite their entry into the market through these standards, and analysis of the extent to which changes in vehicle prices and fuel economy might affect vehicle production and sales. Using all of this information—not solely that from the Volpe model—the agency considers the governing statutory factors, along with
environmental issues and other relevant societal issues such as safety, and promulgates the standards based on its best judgment on how to balance these
factors. This is why the agency considered eight regulatory alternatives, only one of which reflects the agency’s proposed standards, based on the agency’s determinations and assumptions. Others assess alternative standards, some of which exceed the proposed standards and/or the point at which net benefits are maximized. These comprehensive analyses, which also included scenarios with different economic input assumptions as presented in the FEIS and FRIA, are intended to inform and contribute to the agency’s consideration
of the ‘‘need of the United States to conserve energy,’’ as well as the other statutory factors. 49 U.S.C. 32902(f). Additionally, the agency’s analysis
considers the need of the nation to conserve energy by accounting for economic externalities of petroleum consumption and monetizing the
economic costs of incremental CO 2 emissions in the social cost of carbon.
NHTSA uses information from the model when considering what standards to propose and finalize, but the model
does not determine the standards.

e. How Does NHTSA Make the Model Available and Transparent?

Model documentation, which is publicly available in the rulemaking docket and on NHTSA’s web site, explains how the model is installed, how the model inputs (all of which are available to the public) 546 and outputs are structured, and how the model is used. The model can be used on any Windows-based personal computer with
Microsoft Office 2003 and the Microsoft .NET framework installed (the latter available without charge from Microsoft). The executable version of the model and the underlying source
code are also available at NHTSA’s Web site. The input files used to conduct the core analysis documented in this proposed rule are available in the public docket. With the model and these input files, anyone is capable of independently running the model to repeat, evaluate, and/or modify the agency’s analysis.

5. How Did NHTSA Develop the Shape of the Target Curves for the Proposed
Standards?

In developing the shape of the target curves for today’s proposed standards, NHTSA took a new approach, primarily in response to comments received in the MY 2011 rulemaking. NHTSA’s authority under EISA allows

546 We note, however, that files from any supplemental analysis conducted that relied in part on confidential manufacturer product plans cannot be made public, as prohibited under 49 CFR part

512. consideration of any ‘‘attribute related to fuel economy’’ and any
‘‘mathematical function.’’ While the attribute, footprint, is the same for these proposed standards as the attribute used for the MY 2011 standards, the
mathematical function is new.

Both vehicle manufacturers and public interest groups expressed concern in the MY 2011 rulemaking process that the constrained logistic function, particularly the function for the passenger car standards, was overly steep and could lead, on the one hand, to fuel economy targets that were overly stringent for small footprint vehicles, and on the other hand, to a greater incentive for manufacturers to upsize
vehicles in order to reduce theircompliance obligation (because largerfootprint vehicles have less stringent targets) in ways that could compromise energy and environmental benefits. We tentatively believe that the constrained linear function developed here significantly mitigates steepness concerns, but we seek comment on whether readers agree, and whether there are any other issues relating to the new approach that NHTSA should consider in developing the curves for
the final rule.

a. Standards Are Attribute-Based and Defined by a Mathematical Function

EPCA, as amended by EISA, expressly requires that CAFE standards for passenger cars and light trucks be based
on one or more vehicle attributes related to fuel economy, and be expressed in
the form of a mathematical function. 547 Like the MY 2011 standards, the MY 2012–2016 passenger car and light truck
standards are attribute-based and defined by a mathematical function. 548 Also like the MY 2011 standards, the
MY 2012–2016 standards are based on the footprint attribute. However, unlike the MY 2011 standards, the MY 2012– 2016 standards are defined by a
constrained linear rather than a constrained logistic function. The
reasons for these similarities and differences are explained below.

As discussed above in Section II, under attribute-based standards, the fleet-wide average fuel economy that a particular manufacturer must achieve in a given model year depends on the mix of vehicles that it produces for sale.

547 49 U.S.C. 32902(a)(3)(A).

548 As discussed in Chapter 2 of the TSD, EPA is also proposing to set attribute-based CO 2 standards that are defined by a mathematical function, given the advantages of using attribute-based standards and given the goal of coordinating and harmonizing
the CAFE and CO 2 standards as expressed by
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Until NHTSA began to set ‘‘Reformed’’ attribute-based standards for light trucks in MYs 2008–2011, and until EISA gave NHTSA authority to set attribute-based standards for passenger cars beginning in MY 2011, NHTSA set ‘‘universal’’ or ‘‘flat’’ industry-wide average CAFE
standards. Attribute-based standards are preferable to universal industry-wide average standards for several reasons. First, attribute-based standards increase fuel savings and reduce emissions when compared to an equivalent universal industry-wide standard under which each manufacturer is subject to the same numerical requirement. Absent a policy to require all full-line manufacturers to produce and sell essentially the same mix of vehicles, the stringency of the universal industry-wide standards is constrained by the capability of those full-line manufacturers whose product mix includes a relatively high proportion of larger and heavier vehicles. In effect, the standards are
based on the mix of those manufacturers. As a result, the standards are generally set below the capabilities of full-line and limited-line manufacturers that sell predominantly lighter and smaller vehicles.

Under an attribute-based system, in contrast, every manufacturer is more likely to be required to continue adding more fuel-saving technology each year because the level of the compliance obligation of each manufacturer is based on its own particular product mix. Thus, the compliance obligation of a manufacturer with a higher percentage of lighter and smaller vehicles will have a higher compliance obligation than a manufacturer with a lower percentage of such vehicles. As a result, all manufacturers must use technologies to enhance the fuel economy levels of the vehicles they sell. Therefore, fuel savings and CO 2 emissions reductionsshould be higher under an attributebased system than under a comparable industry-wide standard.

Second, attribute-based standards
minimize the incentive for manufacturers to respond to CAFE in ways harmful to safety. 549 Because each vehicle model has its own target (based on the attribute chosen), attribute-based standards provide no incentive to buildsmaller vehicles simply to meet a fleetwide average. Since smaller vehicles are subject to more stringent fuel economy targets, a manufacturer’s increasing its proportion of smaller vehicles would

549 The 2002 NAS Report described at length and quantified the potential safety problem with average fuel economy standards that specify a single numerical requirement for the entire industry. See NAS Report at 5, finding 12. simply cause its compliance obligation

to increase.

Third, attribute-based standards provide a more equitable regulatory
framework for different vehicle
manufacturers. 550 A universal industrywide average standard imposes disproportionate cost burdens and compliance difficulties on the manufacturers that need to change their product plans and no obligation on
those manufacturers that have no need to change their plans. Attribute-based standards spread the regulatory cost burden for fuel economy more broadly
across all of the vehicle manufacturers within the industry.

And fourth, attribute-based standards respect economic conditions and consumer choice, instead of having the government mandate a certain fleet mix. Manufacturers are required to invest in technologies that improve the fuel economy of their fleets, regardless ofvehicle mix. Additionally, attributebased standards help to avoid the need to conduct rulemakings to amend
standards if economic conditions change, causing a shift in the mix of vehicles demanded by the public. NHTSA conducted three rulemakings during the 1980s to amend passenger
car standards for MYs 1986–1989 in response to unexpected drops in fuel prices and resulting shifts in consumer demand that made the passenger car standard of 27.5 mpg infeasible for several years following the change in fuel prices.

As discussed above in Section II, for purposes of the CAFE standards proposed in this NPRM, NHTSA recognizes that the risk, even if small, does exist that low fuel prices in MYs 2012–2016 might lead indirectly to less than currently anticipated fuel savings and emissions reductions. Thus, we seek comment on whether backstop standards, or any other method within the agencies’ statutory authority, should and can be implemented for the import and light truck fleets in order to achieve the fuel savings that attribute-based standards might not absolutely guarantee. Commenters are encouraged, but not required, to review and respond
to NHTSA’s discussion of this issue in the MY 2011 final rule as a starting point. 551

b. What Attribute Does NHTSA Use, and Why?

Consistent with the MY 2011 CAFE standards, NHTSA is proposing to use footprint as the attribute for the MY
2012–2016 CAFE standards. There are

550 Id. at 4–5, finding 10.

551 74 FR 14409–14412 (Mar. 30, 2009). several policy reasons why NHTSA and EPA both believe that footprint is the most appropriate attribute on which to base the standards, as discussed below.

As discussed in the PRIA, in NHTSA’s judgment, from the standpoint of vehicle safety, it is important that the CAFE standards be set in a way that does not encourage manufacturers to respond by selling vehicles that are in any way less safe. While NHTSA’s
research also indicates that reductions in vehicle mass tend to compromise vehicle safety, footprint-based standards provide an incentive to use advanced lightweight materials and structures that would be discouraged by weight-based standards, because manufacturers can use them to improve a vehicle’s fuel economy without their use necessarily resulting in a change in the vehicle’s target level of fuel economy.

Further, although we recognize that weight is better correlated with fuel economy than is footprint, we continue
to believe that there is less risk of ‘‘gaming’’ (artificial manipulation of the attribute(s) to achieve a more favorable target) by increasing footprint under footprint-based standards than byincreasing vehicle mass under weightbased standards—it is relatively easy for a manufacturer to add enough weight to a vehicle to decrease its applicable fuel economy target a significant amount, as compared to increasing vehicle footprint. We also agree with concerns raised in 2008 by some commenters in the MY 2011 CAFE rulemaking that there would be greater potential for gaming under multi-attribute standards, such as standards under which targets would also depend on attributes such as weight, torque, power, towing capability, and/or off-road capability. Standards that incorporate such attributes in conjunction with footprint would not only be significantly more complex, but by providing degrees offreedom with respect to more easilyadjusted attributes, they would make it
less certain that the future fleet would actually achieve the projected average fuel economy and CO 2 reduction levels.

However, while NHTSA tentatively concludes that footprint is the most appropriate attribute upon which to base the proposed standards, recognizing strong public interest in this issue, we seek comment on whether the agency should consider setting
standards for the final rule based on
another attribute or another
combination of attributes. If commenters suggest that the agency should consider
another attribute or another combination of attributes, the agency specifically requests that the
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in the paragraphs above regarding the use of other attributes, and explain how standards should be developed using the other attribute(s) in a way that contributes more to fuel savings and

CO 2 reductions than the footprint-based

Here, TARGET is the fuel economy target (in mpg) applicable to vehicles of a given footprint (FOOTPRINT, in square feet), b and

a are the function’s lower and upper asymptotes (also in mpg), e is approximately equal to 2.718, 552 c is the footprint (in square feet) at which the inverse of the fuel economy target falls halfway between the inverses of standards, without compromising safety.

c. What Mathematical Function DidNHTSA Use for the RecentlyPromulgated MY 2011 CAFE Standards?

The MY 2011 CAFE standards are defined by a continuous, constrained



### 1 TARGET = ( FOOTPRINT

− c ) d 

### 1 ⎛ 1 1 ⎞ e

+ −

⎜ ⎟ ( FOOTPRINT

− c ) d 

### a ⎝ b a ⎠ 1 + e

the lower and upper asymptotes, and d is a parameter (in square feet) that determines how gradually the fuel economy target transitions from the upper toward the lower asymptote as the footprint increases.

After fitting this mathematical form (separately) to the passenger car and logistic function, which takes the form of an S-curve, and is defined according to the following formula:

light truck fleets and determining the stringency of the standards (i.e. , the vertical positions of the curves), NHTSA arrived at the following curves to define

the MY 2011 standards:

d. What Mathematical Function is NHTSA Proposing to Use for New CAFE Standards, and Why?

In finalizing the MY 2011 standards, NHTSA noted that the agency is not required to use a constrained logistic function and indicated that the agency may consider defining future CAFE

standards in terms of a different

mathematical function. NHTSA has done so in preparation for the proposed
CAFE standards.

In revisiting this question, NHTSA
found that the final MY 2011 CAFE standard for passenger cars, though less

552 e is the irrational number for which the slope
of the function y = number x is equal to 1 when x is equal to zero. The first 8 digits of e are 2.7182818.VerDate Nov<24>2008 23:31 Sep 25, 2009 Jkt 217001 PO 00000 Frm 00235 Fmt 4701 Sfmt 4702 E:\FR\FM\28SEP2.SGM 28SEP2EP28SE09.053</MATH>mstockstill on DSKH9S0YB1PROD with PROPOSALS 

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steep than the MY 2011 standard NHTSA proposed in 2008, continues to concentrate the sloped portion of the curve (from a compliance perspective, the area in which upsizing results in a slightly lower applicable target) within a relatively narrow footprint range (approximately 47–55 square feet). Further, most passenger car models have footprints smaller than the curve’s

51.4square foot inflection point, and many passenger car models have footprints at which the curve is relatively flat.

For both passenger cars and light trucks, a mathematical function that has some slope at most footprints where vehicles are produced is advantageous in terms of fairly balancing regulatory burdens among manufacturers, and in terms of providing a disincentive to respond to new standards by downsizing vehicles in ways that compromise vehicle safety. For example, a flat standard may be very



### TARGET = ⎞ ⎟ ⎠ 11

, a b ⎤ ⎥ ⎦

Here, TARGET is the fuel economy target (in mpg) applicable to vehicles of a given footprint (FOOTPRINT, in square feet), b and

a are the function’s lower and upper asymptotes (also in mpg), respectively, c is the slope (in gpm per square foot) of the sloped portion of the function, and d is the intercept (in gpm) of the sloped portion of the function (that is, the value the sloped portion would take if extended to a footprint of 0 square feet. The MIN and MAX functions take the minimum and maximum, respectively of the included values; for example, MIN (1,2) = 1, MAX (1,2) = 2, and

MIN[MAX (1,2),3)]=2. The following chart shows an example of a linear target function,
where a = 0.0241 gpm (41.6 mpg), b = 0.032 gpm (31.2 mpg), c = 0.000531 gpm per square foot, and d = 0.002292 gpm (436 mpg). Because the function is linear on a gpm basis, not an mpg basis, it is plotted on this basis.

e. How Did NHTSA Fit the Coefficients That Determine the Shape of the Proposed Curves?

For purposes of this NPRM, and for EPA’s use in developing new CO 2 emissions standards, the basic curve shapes were developed using methods similar to those applied by NHTSA in fitting the curves defining the MY 2011 standards. We began with the market inputs discussed above, but because the baseline fleet is technologically heterogeneous, NHTSA used the CAFE model to develop a fleet to which nearly all the technologies discussed in Section V of the PRIA and Chapter 3 of the joint difficult for a full-line manufacturer to meet, while requiring very little of a manufacturer concentrating on small vehicles, and a flat standard may provide an incentive to manufacturers to downsize certain vehicles, in order to ‘‘balance out’’ other vehicles subject to
the same standard.

As a potential alternative to the constrained logistic function, NHTSA had, in proposing MY 2011 standards, presented information regarding a
constrained linear function. As shown in the 2008 NPRM, a constrained linear function has the potential to avoid creating a localized region (in terms of vehicle footprint) over which the slope of the function is relatively steep. Although NHTSA did not receive public comments on this option, the agency
indicated that it still believed a linear function constrained by upper (on a gpm basis) and possibly lower limits
could merit reconsideration in future CAFE rulemakings.



### 1

MIN ⎡ ⎢ ⎣

MAX ⎛ ⎜ ⎝

c × FOOTPRINT + d,

TSD 553 were applied, by taking the following steps: (1) Treating all manufacturers as unwilling to pay civil penalties rather than applying technology, (2) applying any technology at any time, irrespective of scheduled vehicle redesigns or freshening, and (3) ignoring ‘‘phase-in caps’’ that constrain the overall amount of technology that can be applied by the model to a given manufacturer’s fleet. These steps helped to increase technological parity among vehicle models, thereby providing a better basis (than the baseline fleet) for estimating the statistical relationship between vehicle size and fuel economy.

More information on the process for fitting the passenger car and light truck
curves for MYs 2012–2016 is available above in Section II.C, and NHTSA refers the reader to that section and to Chapter 2 of the joint TSD. NHTSA seeks comment on this approach to fitting the
curves. We note that final decisions on this issue will play an important role in determining the form and stringency of
the final CAFE and CO 2 standards, the

553 The agencies excluded diesel engines and strong hybrid vehicle technologies from this exercise (and only this exercise) because the agencies expect that manufacturers would not need to rely heavily on these technologies in order to comply with the proposed standards. NHTSA and EPA did include diesel engines and strong hybrid vehicle technologies in all other portions of their analyses. Having re-examined a constrained linear function for purposes of the proposed standards, NHTSA tentatively concludes that for both passenger cars and light trucks, it remains meaningfully sloped over a wide footprint range, thereby providing a

well-distributed disincentive to downsize vehicles in ways that could compromise highway safety. Further,

the constrained linear function proposed today is not so steeply sloped that it would provide a strong incentive

to increase vehicle size in order to obtain a lower CAFE requirement and higher CO 2 limit, thereby compromising energy and environmental benefits. Therefore, the CAFE standards proposed today are defined by constrained linear

functions.

The constrained linear function is defined according to the following

formula:

incentives those standards will provide ( e.g. , with respect to downsizing small vehicles), and the relative compliance burden faced by each manufacturer.

D. Statutory Requirements

1. EPCA, as Amended by EISA

a. Standard Setting

NHTSA must establish separate standards for MY 2011–2020 passenger cars and light trucks, subject to two principal requirements. 554 First, the standards are subject to a minimum requirement regarding stringency: They must be set at levels high enough to ensure that the combined U.S. passenger car and light truck fleet achieves an average fuel economy level of not less than 35 mpg not later than MY 2020. 555 Second, as discussed above and at length in the March 2009 final rule establishing the MY 2011 CAFE standards, EPCA requires that the

554 EISA added the following additional requirements. Standards must be attribute-based and expressed in the form of a mathematical function. 49 U.S.C. 32902(b)(3)(A). Standards for MYs 2011–2020 must ‘‘increase ratably’’ in each model year. 49 U.S.C. 32902(b)(2)(C). NHTSA interprets this requirement, in combination with the requirement to set the standards for each model year at the level determined to be the maximum feasible level for that model year, to mean that the annual increases should not be disproportionately large or small in relation to each other.

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agency establish standards for all new passenger cars and light trucks at the maximum feasible average fuel economy level that the Secretary decides the
manufacturers can achieve in that model year. 556 The implication of this second requirement is that it calls for exceeding the minimum requirement if the agency determines that the manufacturers can achieve a higher level. When determining the level achievable by the manufacturers, EPCA requires that the agency consider the four statutory factors of technological feasibility, economic practicability, the
effect of other motor vehicle standards of the Government on fuel economy,
and the need of the United States to conserve energy. In addition, the agency has the authority to and traditionally does consider other relevant factors,
such as the effect of the CAFE standards on motor vehicle safety.

i. Statutory Factors Considered in Determining the Achievable Level of Average Fuel Economy

As none of the four factors is defined in EPCA and each remains interpreted only to a limited degree by case law,
NHTSA has considerable latitude in interpreting them. NHTSA interprets the four statutory factors as set forth below.

(1)Technological Feasibility

‘‘Technological feasibility’’ refers to whether a particular technology for improving fuel economy is available or
can become available for commercial application in the model year for which a standard is being established. Thus, the agency is not limited in determining the level of new standards to technology that is already being commercially applied at the time of the rulemaking. It can, instead, set technology-forcing standards, i.e., ones that make it necessary for manufacturers to engage in research and development in order to bring a new technology to market.

(2)Economic Practicability

‘‘Economic practicability’’ refers to
whether a standard is one ‘‘within the financial capability of the industry, but not so stringent as to’’ lead to ‘‘adverse economic consequences, such as a significant loss of jobs or the
unreasonable elimination of consumer
choice.’’ 557 In an attempt to ensure the economic practicability, the agency considers a variety of factors, including
the annual rate at which manufacturers can increase the percentage of its fleet that has a particular type of fuel saving technology, and cost to consumers.

556 49 U.S.C. 32902(a).

557 67 FR 77015, 77021 (Dec. 16, 2002). Consumer acceptability is also an element of economic practicability.

At the same time, the law does not preclude a CAFE standard that poses considerable challenges to any
individual manufacturer. The Conference Report for EPCA, as enacted in 1975, makes clear, and the case law affirms, ‘‘(A) determination of maximum feasible average fuel economy should not be keyed to the single manufacturer which might have the most difficulty achieving a given level of average fuel economy.’’ 558 Instead, the agency is compelled ‘‘to weigh the benefits to the nation of a higher fuel economy standard against the difficulties of
individual automobile manufacturers.’’
Id. The law permits CAFE standards exceeding the projected capability of any particular manufacturer as long as the standard is economically practicable for the industry as a whole. Thus, while a particular CAFE standard may pose difficulties for one manufacturer, it may also present opportunities for another. The CAFE program is not necessarily intended to maintain the competitive positioning of each particular company. Rather, it is intended to enhance fuel economy of the vehicle fleet on American roads, while protecting motor vehicle safety and being mindful of the
risk of harm to the overall United States

economy.

Thus, NHTSA believes that this term must be applied in the context of the competing concerns associated with
different levels of standards. Prior to switching to attribute-based standards in the MY 2008–2011 rulemaking, the agency sought to ensure the economy practicability of standards in part by setting them at or near the capability of the ‘‘least capable manufacturer’’ with a significant share of the market, i.e., typically the manufacturer whose vehicles are, on average, the heaviest and largest. In the first several rulemakings to establish attribute based standards, the agency applied marginal cost benefit analysis. This ensured that the agency’s application of technologies was limited to those that would pay for
themselves and thus would have significant appeal to consumers. However, the agency can and has limited its application of technologies to those technologies, with or without the use of such analysis.

(3)The Effect of Other Motor Vehicle
Standards of the Government on Fuel Economy

‘‘The effect of other motor vehicle
standards of the Government on fuel economy,’’ involves an analysis of the

558 CEI–I, 793 F.2d 1322, 1352 (D.C. Cir. 1986). effects of compliance with emission, 559 safety, noise, or damageability standards on fuel economy capability and thus on average fuel economy. In previous CAFE rulemakings, the agency has said that pursuant to this provision, it considers
the adverse effects of other motor vehicle standards on fuel economy. It said so because, from the CAFE program’s earliest years 560 until present, the effects of such compliance on fuel economy capability over the history of the CAFE program have been negative ones. In those instances in which the effects are negative, NHTSA is called upon to ‘‘mak[e] a straightforward adjustment to the fuel economy improvement projections to account for the impacts of other Federal standards, principally those in the areas of emission control, occupant safety, vehicle damageability, and vehicle noise. However, only the unavoidable consequences should be accounted for.
The automobile manufacturers must be expected to adopt those feasible methods of achieving compliance with
other Federal standards which minimize any adverse fuel economy effects of
those standards.’’ 561 For example, safety standards that have the effect of increasing vehicle weight lower vehicle fuel economy capability and thus decrease the level of average fuel economy that the agency can determine
to be feasible.

The ‘‘other motor vehicle standards’’ consideration has thus in practice
functioned in a fashion similar to the provision in EPCA, as originallyenacted, for adjusting the statutorilyspecified CAFE standards for MY 1978– 1980 passengers cars. 562 EPCA did not permit NHTSA to amend those standards based on a finding that the maximum feasible level of average fuel economy for any of those three years was greater or less than the standard specified for that year. Instead, it provided that the agency could only reduce the standards and only on one basis: if the agency found that there had
been a Federal standards fuel economy reduction, i.e. , a reduction in fuel economy due to changes in the Federal vehicle standards, e.g. , emissions and safety, relative to the year of enactment,

1975.

559 In the case of emission standards, this includes standards adopted by the Federal Government and can include standards adopted by the States as well, since in certain circumstances the Clean Air Act allows States to adopt and enforce
State standards different from the Federal ones.

560 42 FR 63184, 63188 (Dec. 15, 1977). See also 42 FR 33534, 33537 (Jun. 30, 1977).

561 42 FR 33534, 33537 (Jun. 30, 1977).

562 That provision was deleted as obsolete when
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The ‘‘other motor vehicle standards’’ provision is broader than the Federal standards fuel economy reduction provision. Although the effects analyzed
to date under the ‘‘other motor vehicle standards’’ provision have been negative, there could be circumstances in which the effects are positive. In the event that the agency encountered such circumstances, it would be required to consider those positive effects. For example, if changes in vehicle safety technology led to NHTSA’s amending a safety standard in a way that permits manufacturers to reduce the weight added in complying with that standard, that weight reduction would increase vehicle fuel economy capability and thus increase the level of average fuel economy that could be determined to be
feasible.

In the wake of Massachusetts v. EPA and of EPA’s proposed endangerment finding, granting of a waiver to
California for its motor vehicle GHG standards, and its own proposal of GHG standards, the agency is confronted with
the issue of how to treat those standards
under the ‘‘other motor vehicle standards’’ provision. To the extent the
GHG standards result in increases in fuel economy, they would do so almost exclusively as a result of inducing manufacturers to install the same types of technologies used by manufacturers in complying with the CAFE standards. The primary exception would involve increases in the efficiency of air
conditioners.

Thus, NHTSA tentatively concludes
that the effects of the EPA and California standards are neither positive nor negative because the proposed rule results in consistent standards among all components of the National Program. Comment is requested on whether and in what way the effects of the California
and EPA standards should be
considered under the ‘‘other motor vehicle standards’’ provision or other provisions of EPCA in 49 U.S.C. 32902, consistent with NHTSA’s independent obligation under EPCA/EISA to issue CAFE standards? The agency has already considered EPA’s proposal and
the harmonization benefits of the National Program in developing its own proposal.

(4)The Need of the United States To Conserve Energy

‘‘The need of the United States to conserve energy’’ means ‘‘the consumer cost, national balance of payments, environmental, and foreign policy implications of our need for large quantities of petroleum, especially imported petroleum.’’ 563 Environmental implications principally include those
associated with reductions in emissions of criteria pollutants and CO 2 . A prime example of foreign policy implications are energy independence and security

concerns.

ii. Other Factors Considered by NHTSA

The agency historically has considered the potential for adverse safety consequences in setting CAFE standards. This practice is recognized approvingly in case law. As the courts have recognized, ‘‘NHTSA has always examined the safety consequences of the
CAFE standards in its overall
consideration of relevant factors since its earliest rulemaking under the CAFE program.’’ Competitive Enterprise

Institute v. NHTSA, 901 F.2d 107, 120

n. 11 (DC Cir. 1990) (‘‘ CEI I’’) (citing 42 Fed. Reg. 33534, 33551 (June 30, 1977)). The courts have consistently upheld NHTSA’s implementation of EPCA in
this manner. See, e.g., Competitive Enterprise Institute v. NHTSA , 956 F.2d 321, 322 (D.C. Cir. 1992) (‘‘ CEI II’’) (in determining the maximum feasible fuel economy standard, ‘‘NHTSA has always taken passenger safety into account.’’) (citing CEI I, 901 F.2d at 120 n. 11); Competitive Enterprise Institute v. NHTSA , 45 F.3d 481, 482–83 (D.C. Cir. 1995) (‘‘ CEI III’’) (same); Center for Biological Diversity v. NHTSA, 538 F.3d 1172, 1203–04 (9th Cir. 2008) (upholding NHTSA’s analysis of vehicle safety issues associated with weight in connection with the MY 2008–11 light truck CAFE rule). Thus, in evaluating what levels of stringency would result in maximum feasible standards, NHTSA assesses the potential safety impacts and considers them in balancing the statutory considerations and to determine the appropriate level of the
standards.

Under the universal or ‘‘flat’’ CAFE standards that NHTSA was previously authorized to establish, the primary risk to safety came from the possibility that manufacturers would respond to higher standards by building smaller, less safe
vehicles in order to ‘‘balance out’’ the larger, safer vehicles that the public generally preferred to buy. Under the attribute-based standards being proposed today, that risk is reduced because building smaller vehicles
would tend to raise a manufacturer’s overall CAFE obligation, rather than only raising its fleet average CAFE. However, even if the manufacturers did not engage in any downsizing under attribute-based standards, there is still the possibility that manufacturers

563 42 FR 63184, 63188 (1977). would rely on downweighting to improve their fuel economy (for a given vehicle at a given footprint target) in ways that may reduce safety to a greater
or lesser extent. While NHTSA recognizes that manufacturers may nonetheless choose this option for raising their CAFE levels, in prior rulemakings we have limited the application of downweighting/material substitution in our modeling analysis to vehicles over 5,000 lbs GVWR. 564

For purposes of today’s proposed standards, however, NHTSA has revised its modeling analysis to allow some application of downweighting/material substitution for all vehicles, including those under 5,000 lbs GVWR, because
we believe that this is more consistent with how manufacturers will actually respond to the standards. However, as discussed above, NHTSA does not mandate the use of any particular technology by manufacturers in meeting
the standards. More information on the new approach to modeling manufacturer use of downweighting/
material substitution is available in Chapter 3 of the draft joint TSD and in Section V of the PRIA; and the estimated safety impacts that may be due to the proposed standards are
described below.

iii. Factors That NHTSA Is Prohibited From Considering

EPCA also provides that in determining the level at which it should set CAFE standards for a particular model year, NHTSA may not consider the ability of manufacturers to take advantage of several EPCA provisions that facilitate compliance with the CAFE standards and thereby reduce the costs of compliance. 565 As discussed further below, manufacturers can earn compliance credits by exceeding the
CAFE standards and then use those credits to achieve compliance in years in which their measured average fuel economy falls below the standards.
Manufacturers can also increase their CAFE levels through MY 2019 by producing alternative fuel vehicles. EPCA provides an incentive for producing these vehicles by specifying that their fuel economy is to be determined using a special calculation procedure that results in those vehicles being assigned a high fuel economy
level.

The effect of the prohibitions against considering these flexibilities in setting
the CAFE standards is that theflexibilities remain voluntarilyemployed measures. If the agency were

564 See 74 FR 14396–14407 (Mar. 30, 2009).

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instead to assume manufacturer use of those flexibilities in setting new standards, that assumption would result in higher standards and thus tend to require manufacturers to use those
flexibilities.

iv. Determining the Level of the Standards by Balancing the Factors

NHTSA has broad discretion in balancing the above factors in determining the appropriate levels of average fuel economy at which to set the CAFE standards for each model year. Congress ‘‘specifically delegated the process of setting * * * fuel economy
standards with broad guidelines concerning the factors that the agency
must consider.’’ 566 The breadth of those guidelines, the absence of any statutorily prescribed formula for balancing the factors, the fact that the relative weight to be given to the various factors may change from rulemaking to rulemaking as the underlying facts change, and the fact that the factors may often be conflicting with respect to whether they militate toward higher or lower standards give NHTSA discretion to decide what weight to give each of the competing policies and concerns
and then determine how to balance
them. The exercise of that discretion is subject to the necessity of ensuring that NHTSA’s balancing does not undermine the fundamental purpose of the EPCA: Energy conservation, 567 and as long as that balancing reasonably accommodates ‘‘conflicting policies that were committed to the agency’s care by
the statute.’’ 568 The balancing of the factors in any given rulemaking is highly dependent on the factual and policy context of that rulemaking. Given the changes over time in facts bearing on assessment of the various factors, such as those relating to the economic conditions, fuel prices and the state of climate change science, the agency recognizes that what was a reasonable balancing of competing statutory priorities in one rulemaking may not be a reasonable balancing of those priorities in another rulemaking. 569 Nevertheless, the agency retains
substantial discretion under EPCA to choose among reasonable alternatives.

EPCA neither requires nor precludes the use of any type of cost-benefit analysis as a tool to help inform the

566 Center for Auto Safety v. NHTSA, 793 F.2d 1322, 1341 (C.A.D.C. 1986).

567 Center for Biological Diversity v. NHTSA , 538 F.3d 1172, 1195 (9th Cir. 2008).

568 CAS, 1338 (quoting Chevron U.S.A., Inc. v. Natural Resources Defense Council, Inc. , 467 U.S. 837, 845).

569 CBD v. NHTSA , 538 F.3d 1172, 1198 (9th Cir. 2008). balancing process. While NHTSA used marginal cost-benefit analysis in the first two rulemakings to establish attribute-based CAFE standards, it was not required to do so and is not required to continue to do so. Regardless of what type of analysis is or is not used, considerations relating to costs and benefits remain an important part of CAFE standard setting.

Because the relevant considerations and factors can reasonably be balanced in a variety of ways under EPCA, and
because of uncertainties associated with the many technological and cost inputs, NHTSA considers a wide variety of alternative sets of standards, each reflecting different balancing of those policies and concerns, to aid it in discerning reasonable outcomes. Among the alternatives providing for an
increase in the standards in this rulemaking, the alternatives range in stringency from a set of standards that increase, on average, 3 percent annually to a set of standards that increase, on average, 7 percent annually.

2. Administrative Procedure Act

To be upheld under the ‘‘arbitrary and capricious’’ standard of judicial review in the APA, an agency rule must be rational, based on consideration of the relevant factors, and within the scope of the authority delegated to the agency by the statute. The agency must examine
the relevant data and articulate a satisfactory explanation for its action including a ‘‘rational connection
between the facts found and the choice
made.’’ Burlington Truck Lines, Inc. v. United States, 371 U.S. 156, 168 (1962).

Statutory interpretations included in an agency’s rule are subjected to the two-step analysis of Chevron, U.S.A.,

Inc. v. Natural Resources Defense Council, 467 U.S. 837, 104 S.Ct. 2778, 81 L.Ed.2d 694 (1984). Under step one, where a statute ‘‘has directly spoken to the precise question at issue,’’ id. at 842, 104 S.Ct. 2778, the court and the agency ‘‘must give effect to the unambiguously expressed intent of Congress,’’ id. at 843, 104 S.Ct. 2778. If the statute is silent or ambiguous regarding the specific question, the court proceeds to step two and asks ‘‘whether the agency’s answer is based on a permissible
construction of the statute.’’ Id.

If an agency’s interpretation differs from the one that it has previously adopted, the agency need not demonstrate that the prior position was wrong or even less desirable. Rather, the agency would need only to demonstrate
that its new position is consistent with the statute and supported by the record, and acknowledge that this is a departure from past positions. The Supreme Court emphasized this recently in FCC v. Fox Television, 129 S.Ct. 1800 (2009). When an agency changes course from earlier regulations, ‘‘the requirement that an agency provide reasoned explanation for its action would ordinarily demand that it display awareness that it is changing position,’’ but ‘‘need not demonstrate to
a court’s satisfaction that the reasons for the new policy are better than the reasons for the old one; it suffices that the new policy is permissible under the statute, that there are good reasons for it, and that the agency believes it to be better, which the conscious change of course adequately indicates.’’ 570

3. National Environmental Policy Act

As discussed above, EPCA requires the agency to determine what level at
which to set the CAFE standards for each model year by considering the four factors of technological feasibility, economic practicability, the effect of
other motor vehicle standards of the Government on fuel economy, and the
need of the United States to conserve energy. NEPA directs that
environmental considerations be integrated into that process. To accomplish that purpose, NEPA requires an agency to compare the potential environmental impacts of its proposed action to those of a reasonable range of
alternatives.

To explore the environmental consequences in depth, NHTSA has prepared a draft environmental impact statement. The purpose of an EIS is to ‘‘provide full and fair discussion of significant environmental impacts and [to] inform decisionmakers and the public of the reasonable alternatives
which would avoid or minimize adverse impacts or enhance the quality of the
human environment.’’ 40 CFR 1502.1.

NEPA is ‘‘a procedural statute that mandates a process rather than a particular result.’’ Stewart Park & Reserve Coal., Inc. v. Slater, 352 F.3d at

557. The agency’s overall EIS-related obligation is to ‘‘take a ‘hard look’ at the environmental consequences before taking a major action.’’ Baltimore Gas &
Elec. Co. v. Natural Res. Def. Council,
Inc., 462 U.S. 87, 97, 103 S.Ct. 2246, 76 L.Ed.2d 437 (1983). Significantly, ‘‘[i]f
the adverse environmental effects of the proposed action are adequately identified and evaluated, the agency is not constrained by NEPA from deciding that other values outweigh the
environmental costs.’’ Robertson v. Methow Valley Citizens Council, 490
U.S. 332, 350, 109 S.Ct. 1835, 104 L.Ed.2d 351 (1989).

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### 49691

The agency must identify the not command the agency to favor an passenger cars and light trucks is ‘‘environmentally preferable’’ environmentally preferable course of expressed as a mathematical function alternative, but need not adopt it. action, only that it make its decision to that defines a fuel economy target ‘‘Congress in enacting NEPA * * * did proceed with the action after taking a applicable to each vehicle model and, not require agencies to elevate hard look at environmental for each fleet, establishes a required
environmental concerns over other consequences. CAFE level determined by computing appropriate considerations.’’ Baltimore the sales-weighted harmonic average of E. What Are the Proposed CAFE Gas and Elec. Co. v. Natural Resources those targets. 571 Standards? Defense Council, Inc., 462 U.S. 87, 97 As discussed above in Section II.C, (1983). Instead, NEPA requires an NHTSA is proposing to determine fuel 1. Form of the Standards agency to develop alternatives to the economy targets using a constrained proposed action in preparing an EIS. 42 Each of the CAFE standards that linear function defined according to the

U.S.C. 4332(2)(C)(iii). The statute does NHTSA is proposing today for following formula:



### 1 TARGET =

MIN ⎡ ⎢ ⎣

MAX ⎛ ⎜ ⎝

c × FOOTPRINT + d, ⎞ ⎟ ⎠ 11

, a b ⎤ ⎥ ⎦

Here, TARGET is the fuel economy target production-weighted harmonic average The proposed standards are, therefore, (in mpg) applicable to vehicles of a given of the fuel economy targets applicable to specified by the four coefficients footprint (FOOTPRINT, in square feet), b and each vehicle model: defining fuel economy targets:

a are the function’s lower and upper

a = upper limit (mpg) asymptotes (also in mpg), respectively, c is the slope (in gpm per square foot) of the 

# ∑

= i

i 

### SALES
i 

### TARGET
i 

### SALES b = lower limit (mpg)

c = slope (gpm per square foot)

d = intercept (gpm)

The values of the coefficients are sloped portion of the function, and d is the 

### CAFE intercept (in gpm) of the sloped portion of required 

# ∑ the function (that is, the value the sloped portion would take if extended to a footprint i of 0 square feet). The MIN and MAX Here, CAFE required is the required level for different for the passenger car standards functions take the minimum and maximum, a given fleet, SALESi is the number of units and the light truck standards. respectively of the included values. of model i produced for sale in the United States, TARGET i is the fuel economy target 2. Passenger Car Standards for MYs As also discussed in Section II.C, applicable to model i (according to the 2012–2016 under the proposed standards (as under equation shown in Chapter II and based on the recently-promulgated MY 2011 For passenger cars, NHTSA is the footprint of model i), and the summations standards), the CAFE level required of in the numerator and denominator are both proposing CAFE standards defined by any given manufacturer will be performed over all models in the fleet in the following coefficients during MY determined by calculating the question. 2012–2016:



### TABLE 

### IV.E.2–1—C OEFFICIENTS 

### D EFINING 

### P ROPOSED 

### MY 2012–2016 F UEL 

### E CONOMY 

### TARGETS FOR 

### P ASSENGER 

### C ARS

Coefficient 2012 2013 2014 2015 2016

a (mpg) .......................................................................

b (mpg) .......................................................................

c (gpm/sf) ...................................................................

d (gpm) ....................................................................... 36 .23

28 .12

0 .0005308

0 .005842 37 .15

28 .67

0 .0005308

0 .005153 38 .08

29 .22

0 .0005308

0 .004498 39 .55

30 .08

0 .0005308

0 .003520 41 .38

31 .12

0 .0005308

0 .002406

These coefficients result in footprint- standard, which is specified by a a constrained linear function, is shown dependent target curves shown constrained logistic function rather than for comparison. graphically below. The MY 2011 final

571 Required CAFE levels shown here are determined until the end of each model year, when target curves, as defined by the constrained linear

estimated required levels based on NHTSA’s all of the vehicles produced by a manufacturer in function, and as embedded in the function for the current projection of manufacturers’ vehicle fleets that model year are known and their compliance sales-weighted harmonic average, are the real

in MYs 2012–2016. Actual required levels are not obligation can be determined with certainty. The ‘‘standards’’ being proposed today.VerDate Nov<24>2008 23:31 Sep 25, 2009 Jkt 217001 PO 00000 Frm 00240 Fmt 4701 Sfmt 4702 E:\FR\FM\28SEP2.SGM 28SEP2EP28SE09.033</GPH>mstockstill on DSKH9S0YB1PROD with PROPOSALS 

### 49692 Federal Register / Vol. 74, No. 186 / Monday, September 28, 2009 / Proposed Rules

As discussed, the CAFE levels sales that NHTSA has used to examine individual manufacturers during MYs required of individual manufacturers today’s proposed CAFE standards, the 2012–2016 (an updated estimate of the will depend on the mix of vehicles they agency estimates that the targets shown average required fuel economy level produce for sale in the United States. above will result in the following under the final MY 2011 standard is Based on the market forecast of future average required fuel economy levels for shown for comparison): 572



### TABLE 

### IV.E.2–2—E STIMATED 

### AVERAGE 

### F UEL 

### E CONOMY 

### R EQUIRED 

### U NDER 

### F INAL 

### MY 2011 AND 

### P ROPOSED 

### MY 2012–
2016 CAFE S TANDARDS FOR 

### P ASSENGER 

### C ARS

Manufacturer MY 2011 MY 2012 MY 2013 MY 2014 MY 2015 MY 2016

BMW

........................................................ 30.2 33.2 34.0 34.8 36.0 37.5 Chrysler

.................................................... 29.6 33.0 33.7 34.5 35.3 36.8
Daimler

..................................................... 29.4 32.6 33.1 33.8 35.0 36.4
Ford

.......................................................... 29.8 33.0 33.7 34.5 35.8 37.3
General Motors

........................................ 30.3 33.0 33.8 34.6 35.8 37.3
Honda

....................................................... 30.8 33.9 34.7 35.5 36.8 38.4 Hyundai

.................................................... 30.8 33.8 34.6 35.5 36.8 38.3
Kia

............................................................ 30.6 33.6 34.4 35.2 36.5 38.0
Mazda

...................................................... 30.7 34.1 34.8 35.7 37.0 38.6
Mitsubishi

................................................. 31.0 34.4 35.3 36.1 37.4 39.2
Nissan

...................................................... 30.7 33.5 34.2 35.0 36.2 37.8
Porsche

.................................................... 31.2 36.2 37.2 38.1 39.6 41.4
Subaru

...................................................... 31.0 34.8 35.7 36.5 37.9 39.6
Suzuki

...................................................... 31.2 35.9 36.8 37.7 39.2 41.0
Tata

.......................................................... 27.8 30.7 31.4 32.1 33.1 34.4 Toyota

...................................................... 30.8 34.1 34.9 35.7 37.0 38.6 Volkswagen

.............................................. 30.8 34.6 35.4 36.2 37.5 39.1

Average

............................................. 30.5 33.6 34.4 35.2 36.4 38.0

572 In the March 2009 final rule establishing MY under the MY 2011 passenger car standard. Based the MY 2011 final rule, NHTSA now estimates that 2011 standards for passenger cars and light trucks, on the agency’s current forecast of the MY 2011 the average required fuel economy level for NHTSA estimated that the required fuel economy passenger car market, which anticipates greater passenger cars will be 30.5 mpg in MY 2011. levels for passenger cars would average 30.2 mpg numbers of passenger cars than the forecast used inVerDate Nov<24>2008 23:31 Sep 25, 2009 Jkt 217001 PO 00000 Frm 00241 Fmt 4701 Sfmt 4700 E:\FR\FM\28SEP2.SGM 28SEP2mstockstill on DSKH9S0YB1PROD with PROPOSALS 

### Federal Register / Vol. 74, No. 186 / Monday, September 28, 2009 / Proposed Rules 

### 49693

We note that a manufacturer’s required average fuel economy level for a model year under the proposed

standards would be based on its actual production numbers in that model year. Therefore, its official required fuel economy level would not be known until the end of that model year. However, because the targets for each vehicle footprint would be established in advance of the model year, a

manufacturer should be able to estimate its required level accurately. 3. Minimum Domestic Passenger Car
Standards

EISA expressly requires each
manufacturer to meet a minimum fuel economy standard for domestically manufactured passenger cars in addition to meeting the standards set by NHTSA. According to the statute (49 U.S.C. 32902(b)(4)) the minimum standard shall be the greater of (A) 27.5 miles per gallon; or (B) 92 percent of the average fuel economy projected by the Secretary
for the combined domestic and nondomestic passenger automobile fleets
manufactured for sale in the United States by all manufacturers in the model year. The agency must publish the projected minimum standards in the Federal Register when the passenger car standards for the model year in question are promulgated.

Based on NHTSA’s current market forecast, the agency’s estimates of these minimum standards under the proposed MY 2012–2016 CAFE standards (and, for comparison, the final MY 2011 standard) are summarized below in
Table IV.E.2–1. 573 For eventual compliance calculations, the final
calculated minimum standards will be updated to reflect any changes in the average fuel economy level required
under the final standards.



### TABLE 

### IV.E.3–1—E STIMATED 

### M INIMUM 

### S TANDARD FOR 

### D OMESTICALLY 

### M ANUFACTURED 

### P ASSENGER 

### C ARS 

### U NDER 

### F INAL 

### MY 2011 AND 

### P ROPOSED 

### MY 2012–2016 CAFE S TANDARDS FOR 

### P ASSENGER 

### C ARS

2011 2012 2013 2014 2015 2016

28.0 30.9 31.6 32.4 33.5 34.9

4. Light Truck Standards

For light trucks, NHTSA is proposing CAFE standards defined by the following coefficients during MYs 2012–2016:



### TABLE 

### IV.E.4–1—C OEFFICIENTS 

### D EFINING 

### P ROPOSED 

### MY 2012–2016 F UEL 

### E CONOMY 

### TARGETS FOR 

### L IGHT 

### T RUCKS

Coefficient 2012 2013 2014 2015 2016

a (mpg) .......................................................................

b (mpg) .......................................................................

c (gpm/sf) ...................................................................

d (gpm) ....................................................................... 29 .44

22 .06

0 .0004546

0 .01533 30 .32

22 .55

0 .0004546

0 .01434 31 .30

23 .09

0 .0004546

0 .01331 32 .70

23 .84

0 .0004546

0 .01194 34 .38

24 .72

0 .0004546

0 .01045

These coefficients result in footprint- which is specified by a constrained constrained linear function, is shown dependent targets shown graphically logistic function rather than a for comparison. below. The MY 2011 final standard,

573 In the March 2009 final rule establishing MY passenger cars would be 27.8 mpg under the MY required CAFE standard will be 28.0 mpg in MY 2011 standards for passenger cars and light trucks, 2011 passenger car standard. Based on the agency’s 2011. NHTSA estimated that the minimum required current forecast of the MY 2011 passenger car CAFE standard for domestically manufactured market, NHTSA now estimates that the minimumVerDate Nov<24>2008 23:31 Sep 25, 2009 Jkt 217001 PO 00000 Frm 00242 Fmt 4701 Sfmt 4700 E:\FR\FM\28SEP2.SGM 28SEP2EP28SE09.034</GPH>mstockstill on DSKH9S0YB1PROD with PROPOSALS 

### 49694 Federal Register / Vol. 74, No. 186 / Monday, September 28, 2009 / Proposed Rules

Given these targets, the CAFE levels has used to examine today’s proposed manufacturers during MYs 2012–2016 required of individual manufacturers CAFE standards, the agency estimates (an updated estimate of the average will depend on the mix of vehicles they that the targets shown above will result required fuel economy level under the produce for sale in the United States. in the following average required fuel final MY 2011 standard is shown for Based on the market forecast NHTSA economy levels for individual comparison): 574



### TABLE 

### IV.E.4–2—E STIMATED 

### AVERAGE 

### F UEL 

### E CONOMY 

### R EQUIRED 

### U NDER 

### F INAL 

### MY 2011 AND 

### P ROPOSED 

### MY 2012–
2016 CAFE S TANDARDS FOR 

### L IGHT 

### T RUCKS

Manufacturer MY 2011 MY 2012 MY 2013 MY 2014 MY 2015 MY 2016

BMW

........................................................ 25.7 26.3 27.0 27.7 28.8 30.1 Chrysler

.................................................... 24.2 25.2 25.8 26.4 27.3 28.5
Daimler

..................................................... 24.7 25.4 26.1 26.9 27.9 29.1
Ford

.......................................................... 23.3 24.3 24.9 25.3 26.2 27.3
General Motors

........................................ 22.9 23.6 24.2 24.8 25.6 26.6
Honda

....................................................... 25.6 26.4 27.1 27.9 29.0 30.4 Hyundai

.................................................... 25.9 26.6 27.3 28.1 29.3 30.6
Kia

............................................................ 25.1 25.8 26.4 27.2 28.3 29.6
Mazda

...................................................... 26.3 27.4 28.1 28.8 29.9 31.4
Mitsubishi

................................................. 26.4 27.4 28.1 28.9 30.1 31.6
Nissan

...................................................... 24.1 25.0 25.6 26.1 27.0 28.2
Porsche

.................................................... 25.5 26.0 26.7 27.4 28.5 29.8
Subaru

...................................................... 26.5 27.5 28.3 29.2 30.4 31.8
Suzuki

...................................................... 26.3 27.2 27.9 28.7 29.9 31.3
Tata

.......................................................... 26.1 26.9 27.6 28.4 29.6 31.0 Toyota

...................................................... 25.2 25.7 26.3 27.1 28.1 29.3 Volkswagen

.............................................. 25.0 25.6 26.2 26.9 27.9 29.2

Average

............................................. 24.2 25.0 25.6 26.2 27.1 28.3

574 In the March 2009 final rule establishing MY under the MY 2011 light truck standard. Based on in MY 2011. The increase in the estimate reflects 2011 standards for passenger cars and light trucks, the agency’s current forecast of the MY 2011 light a slight decrease in the size of the average light NHTSA estimated that the required fuel economy truck market, NHTSA now estimates that the truck. levels for light trucks would average 24.1 mpg required fuel economy levels will average 24.2 mpgVerDate Nov<24>2008 23:31 Sep 25, 2009 Jkt 217001 PO 00000 Frm 00243 Fmt 4701 Sfmt 4700 E:\FR\FM\28SEP2.SGM 28SEP2mstockstill on DSKH9S0YB1PROD with PROPOSALS 

### Federal Register / Vol. 74, No. 186 / Monday, September 28, 2009 / Proposed Rules 

### 49695

As discussed above with respect to the proposed passenger cars standards, we note that a manufacturer’s required fuel economy level for a model year under the proposed standards would be based on its actual production numbers in that model year.

F. How Do the Proposed Standards Fulfill NHTSA’s Statutory Obligations?

In developing the proposed MY 2012– 16 standards, the agency developed and considered a wide variety of
alternatives. NHTSA took a new approach to defining alternatives as compared to the most recent prior CAFE rulemaking. In response to comments
received in the last round of rulemaking, in our March 2009 notice of intent to prepare an environmental impact statement, the agency selected a range of candidate stringencies that increased annually, on average, 3% to 7%. 575 That same approach has been
carried over to this NPRM and to the accompanying DEIS and PRIA. The majority of the alternatives considered in this rulemaking are defined as average percentage increases in stringency—3 percent per year, 4 percent per year, 5 percent per year, and
so on. NHTSA believes that this approach more clearly communicates the level of stringency of each
alternative and is more intuitive than
alternatives defined in terms of different cost-benefit ratios, and still allows us to identify alternatives that represent different ways to balance NHTSA’s statutory requirements under EPCA/

EISA.

In the notice of intent, we noted that
each of the listed alternatives represents, in part, a different way in which NHTSA could conceivably balance conflicting policies and

575 Notice of intent to prepare an EIS, 74 FR 14857, 14859–60, April 1, 2009. considerations in setting the standards. We were mindful that the agency would need to weigh and balance many factors, such as the technological feasibility, economic practicability, including
leadtime considerations for the introduction of technologies and impacts on the auto industry, the impacts of the standards on fuel savings
and CO 2 emissions, fuel savings by consumers; as well as other relevant factors such as safety. For example, the 7% Alternative, the most stringent alternative, weighs energy conservation and climate change considerations more heavily and technological feasibility and economic practicability less heavily. In contrast, the 3% Alternative, the least stringent alternative, places more weight on technological feasibility and economic practicability. We recognized that the ‘‘feasibility’’ of the alternatives also may reflect differences and uncertainties in the way in which key economic ( e.g., the price of fuel and the social cost of carbon) and technological inputs could be assessed and estimated
or valued.

In subsequently developing the NPRM and the associated analytical documents, the agency expanded the list of alternatives to provide a degree of analytical continuity between the old and new approach to defining alternatives in an effort help the agency and the public understand the
similarities and dissimilarities between the two approaches and to make the transition to the new approach. To that end, we included and analyzed two additional alternatives, one that sets standards at the point where net benefits are maximized, and another that sets standards at the point at which total costs are equal to total benefits. 576

576 The stringency indicated by each of these alternatives depends on the value of inputs to NHTSA’s analysis. Results presented here for these With respect to the first of those alternatives, we note that Executive
Order 12866 focuses attention on an approach that maximizes net benefits. Further, since NHTSA has thus far set
attribute-based CAFE standards at the point at which net benefits are maximized, we believed it would be
useful and informative to consider the potential impacts of that approach as compared to the new approach for MYs

2012–2016.

After working with EPA in thoroughly reviewing and in some cases reassessing
the effectiveness and costs of technologies, most of which are already being incorporated in at least some vehicles, market forecasts and economic assumptions, we used the Volpe model extensively to assess the technologies that the manufacturers could apply in order to comply with each of the alternatives. This permitted us to assess the variety, amount and cost of the technologies that could be needed to enable the manufacturers to comply
with each of the alternatives. NHTSA estimated how the application of these and other technologies could increase vehicle costs. The following five figures show industry-wide average incremental (i.e., relative to the reference fleet) per-vehicle costs, for each model year, each fleet, and the combined fleet. Estimates specific to
each manufacturer are shown in the accompanying PRIA.

two alternatives are based on NHTSA’s reference case inputs, which underlie the central analysis of the proposed standards. In the accompanying PRIA, the agency presents the results of that analysis to explore the sensitivity of results to changes in key economic inputs. Because of numerous changes in model inputs ( e.g., discount rate, rebound effect,

CO 2 value, technology cost estimates), our analysis often exhausts all available technologies before reaching the point at which total costs equal total benefits. In these cases, the stringency that exhausts all available technologies is considered.VerDate Nov<24>2008 23:31 Sep 25, 2009 Jkt 217001 PO 00000 Frm 00244 Fmt 4701 Sfmt 4725 E:\FR\FM\28SEP2.SGM 28SEP2EP28SE09.035</GPH> EP28SE09.036</GPH>mstockstill on DSKH9S0YB1PROD with PROPOSALS 

### 49696 Federal Register / Vol. 74, No. 186 / Monday, September 28, 2009 / Proposed RulesVerDate Nov<24>2008 23:31 Sep 25, 2009 Jkt 217001 PO 00000 Frm 00245 Fmt 4701 Sfmt 4725 E:\FR\FM\28SEP2.SGM 28SEP2EP28SE09.037</GPH> EP28SE09.038</GPH>mstockstill on DSKH9S0YB1PROD with PROPOSALS 

### Federal Register / Vol. 74, No. 186 / Monday, September 28, 2009 / Proposed Rules 

### 49697VerDate Nov<24>2008 23:31 Sep 25, 2009 Jkt 217001 PO 00000 Frm 00246 Fmt 4701 Sfmt 4725 E:\FR\FM\28SEP2.SGM 28SEP2EP28SE09.039</GPH> EP28SE09.040</GPH>mstockstill on DSKH9S0YB1PROD with PROPOSALS 

### 49698 Federal Register / Vol. 74, No. 186 / Monday, September 28, 2009 / Proposed Rules

Corresponding to these per-vehicle results for MY 2012–2016 for industry large U.S.-headquartered and three large cost increases, NHTSA estimated total and Chrysler, Ford, General Motors, foreign-headquartered full-line incremental outlays for additional Honda, Nissan, and Toyota. This figure manufacturers. technology in each model year. The focuses on these manufacturers as they following figure shows cumulative currently (in MY 2008) represent threeVerDate Nov<24>2008 23:31 Sep 25, 2009 Jkt 217001 PO 00000 Frm 00247 Fmt 4701 Sfmt 4700 E:\FR\FM\28SEP2.SGM 28SEP2EP28SE09.041</GPH>mstockstill on DSKH9S0YB1PROD with PROPOSALS 

### Federal Register / Vol. 74, No. 186 / Monday, September 28, 2009 / Proposed Rules 

### 49699

For each alternative, NHTSA has also each model year, including fuel savings, well as the estimated societal benefits of estimated all corresponding effects for CO 2 reductions, and other effects, as these effects.



### TABLE 

### IV.F.1—F UEL 

### S AVINGS 

### , CO 2 

### R EDUCTIONS



### , AND 

### T ECHNOLOGY 

### C OSTS FOR 

### R EGULATORY 

### A LTERNATIVES

Regulatory alternative Fuel savings (b. gal) CO 2 reductions

(mmt) Cost ($b)

3% per Year ....................................................................................................................

............. 4% per Year ....................................................................................................................

............. 5% per Year ....................................................................................................................

............. 6% per Year ....................................................................................................................

............. Maximum Net Benefit ............................................................................................................

...... 7% per Year ....................................................................................................................

............. 37

54

69

83

90

91 404

582

718

846

923

934 29

46

74

103

111

116

Total Cost = Total Benefit .................................................................................................... 9 5 977 122

The accompanying PRIA presents a economic practicability and the need of presented above to the corresponding detailed analysis of these results. the nation to conserve energy, the cumulative fuel savings. Relevant to EPCA’s requirement that following figure compares the NHTSA consider, among other factors, incremental technology outlays

The agency then assessed which alternative would represent a reasonable balancing of the statutory criteria, given the difficulties confronting the industry and the economy, and the priorities and policy goals of the President. Those priorities and goals include achieving nationally harmonized and coordinated program for regulating fuel economy
and GHG emissions.

Part of that assessment entailed an evaluation of the stringencies necessary
to achieve both Federal and State GHG emission reduction goals, especially
those of California and the States that have adopted its GHG emission
standard for motor vehicles. Given that EPCA requires attribute-based standards, NHTSA and EPA determined
the level at which an attribute-based
GHG emissions standard would need to be set to achieve the goals of California. This was done by evaluating a
nationwide CAA standard for MY 2016 that would require the levels of technology upgrade, across the country,
which California standards would require for the subset of vehicles sold in
California under the California standards for MY 2009–2016 (known as ‘‘Pavley 1’’). In essence, the stringency of the California Pavley 1 program was evaluated, but for a national standard.

For a number of reasons discussed in
section III.D, an assessment was developed of an equivalent national

new vehicle fleet-wide CO 2 performance standards for model year 2016 which

would result in the new vehicle fleet in the State of California having CO 2 performance equal to the performance from the California Pavley 1 standards. That level, 250 g/mi, is equivalent to

35.5 mpg if the GHG standard is metVerDate Nov<24>2008 23:31 Sep 25, 2009 Jkt 217001 PO 00000 Frm 00248 Fmt 4701 Sfmt 4700 E:\FR\FM\28SEP2.SGM 28SEP2mstockstill on DSKH9S0YB1PROD with PROPOSALS 

### 49700 Federal Register / Vol. 74, No. 186 / Monday, September 28, 2009 / Proposed Rules

exclusively by fuel economy improvements.

To obtain the counterpart CAFE standard, we then adjusted that level
downward to account for differences between the more prescriptive EPCA
and the more flexible CAA. These differences give EPA greater ability under the CAA to provide compliance
flexibilities that would enable manufacturers to achieve compliance with a given level of requirement under
the CAA at less cost than with the same level of requirement under EPCA. Principal among those greater
flexibilities are the credits that EPA can provide for improving the efficiency of air conditioners and reducing the leakage of refrigerants from them. The adjustments result in a figure of 34.1 mpg as the appropriate counterpart CAFE standard. This differential gives manufacturers the opportunity to reach

35.5 mpg under the CAA in ways that would significantly reduce their costs.
Were NHTSA instead to establish its standard at the same level,
manufacturers would need to make substantially greater expenditures on fuel-saving technologies to reach 35.5 mpg under EPCA.

Given the importance to this rulemaking of achieving a harmonized National Program, we created a new alternative whose annual percentage increases would achieve 34.1 mpg by
MY 2016. That alternative is one which increases on average at 4.3% annually.

This new alternative, like the seven alternative presented above, represents a unique balancing of the statutory factors
and other relevant considerations. We
have added that alternative to the table
below.

Regulatory alternative Fuel savings (b. gal) CO 2reductions (mmt) Cost ($b)

3% per Year ..... 37 404 29 4% per Year ..... 54 582 46 Regulatory alternative Fuel savings (b. gal) CO 2reductions (mmt) Cost ($b)

Proposed (4.3%

per Year) ....... 62 656 60 5% per Year ..... 69 718 74 6% per Year ..... 83 846 103
Maximum Net

Benefit ........... 90 923 111 7% per Year ..... 91 934 116

Total Cost =

Total
Benefit .... 95 977 122

As noted earlier, NHTSA has used the Volpe model to analyze each of these alternatives based on analytical inputs determined jointly with EPA. For a given regulatory alternative, the Volpe
model estimates how each manufacturer could apply technology in response to the MY 2012 standard (separately for cars and trucks), carries technologies applied in MY 2012 forward to MY 2013, and then estimates how each manufacturer could apply technology in response to the MY 2013 standard. When analyzing MY 2013, the model considers the potential to add ‘‘extra’’ technology in MY 2012 in order to carry that technology into MY 2013, thereby avoiding the use of more expensive technologies in MY 2013. The model continues in this fashion through MY 2016, and then performs calculations to estimate the costs, effects, and benefits of the applied technologies, and to estimate any civil penalties owed based on projected noncompliance. For each regulatory alternative, the model calculates incremental costs, effects, and benefits relative to the regulatory baseline (i.e., the no-action alternative),
under which the MY 2011 CAFE standards continue through MY 2016.
The model calculates results for each model year, because EPCA requires that
NHTSA set its standards for each model year at the ‘‘maximum feasible average fuel economy level that the Secretary decides the manufacturers can achieve in that model year’’ considering four statutory factors. Pursuant to EPCA’s directive notice not to consider statutory credits in establishing CAFE standards, NHTSA did not FFV credits, credits carried forward and backward, and
transferred credit. 577 578 In addition, the analysis reflects the ability of manufacturers to pay fines in lieu of compliance.

Because it entails year-by-year examination of eight regulatory alternatives for, separately, passenger cars and light trucks, NHTSA’s analysis involves a large amount of information. Detailed results of this analysis are presented separately in the PRIA accompanying today’s notice. The
remainder of this section discusses a combination of aggregated and illustrative results of this analysis.

The following figure compares average fuel economy levels required of manufacturers under the eight regulatory alternatives in MYs 2012, 2014, and 2016. Required levels for MY
2013 and MY 2015 fall between those
for MYs 2012 and 2014 and MYs 2014 and 2016, respectively. Although required levels for these interim years are not presented in the following figure to limit the complexity of the figure, they do appear in the accompanying

PRIA. 579

577 Separately, NHTSA has conducted analysis that accounts for EPCA’s provisions regarding FFVs.

578 Because NHTSA’s modeling represents every model year explicitly, accounts for estimates of when vehicle model redesigns will occur, and sets aside these compliance flexibilities, the agency’s modeling produces results that differ varyingly from EPA’s for specific manufacturers, fleets, and model years.

579 Also, the ‘‘Max NB’’ and the ‘‘TC = TB’’ alternatives depend on the inputs to the agencies’ analysis. The sensitivity analysis presented in the PRIA documents the response of these alternatives to changes in key economic inputs. For example, the combined average required fuel economy under the ‘‘Max NB’’ alternative is 36.8 mpg under the reference case economic inputs presented here, and ranges from 32.8 mpg to 37.2 mpg under the alternative economic inputs presented in the PRIA.VerDate Nov<24>2008 23:31 Sep 25, 2009 Jkt 217001 PO 00000 Frm 00249 Fmt 4701 Sfmt 4700 E:\FR\FM\28SEP2.SGM 28SEP2EP28SE09.042</GPH>mstockstill on DSKH9S0YB1PROD with PROPOSALS 

### Federal Register / Vol. 74, No. 186 / Monday, September 28, 2009 / Proposed Rules 

### 49701

As this figure illustrates, the proposed
standards involve a ‘‘faster start’’ toward increased stringency than do any of the alternatives that increase steadily (i.e., the 3%/y, 4%/y, 5%/y, 6%/y, and 7%/ y alternatives). However, by MY 2016, the stringency of the proposed standards reflects an average annual increase of 4.3%/y. The proposed standards, therefore, represent an alternative that could be referred to as ‘‘4.3% per year
with a fast start’’ or a ‘‘front-loaded 4.3% average annual increase.’’

In NHTSA’s analysis, these achieved average fuel economy levels result from the application of technology rather than changes in the mix of vehicles produced for sale in the U.S. The accompanying PRIA presents detailed estimates of additional technology penetration into the NHTSA reference fleet associated with each regulatory alternative. The following four charts illustrate the results of this analysis, considering the application of four technologies by six manufacturers and the industry as a whole. Technologies include gasoline direct injection (GDI), engine turbocharging and downsizing, diesel engines, and strong HEV systems (including CISG systems). GDI and turbocharging are among the technologies that play an important role in achieving the fuel economy improvements shown in NHTSA’s analysis, and diesels and strong HEVs represent technologies involving significant challenges for widespread use through MY 2016. These figures focus on Chrysler, Ford, General Motors, Honda, Nissan, and Toyota, as these manufacturers currently (in MY 2008) represent three large U.S.-headquartered and three large foreign-headquartered

full-line manufacturers. For each alternative, the figures show additional application of technology by MY 2016. The PRIA presents results for all model years, technologies, and manufacturers,

and NHTSA has considered these broader results when considering the eight regulatory alternatives.VerDate Nov<24>2008 23:31 Sep 25, 2009 Jkt 217001 PO 00000 Frm 00250 Fmt 4701 Sfmt 4725 E:\FR\FM\28SEP2.SGM 28SEP2EP28SE09.043</GPH> EP28SE09.044</GPH>mstockstill on DSKH9S0YB1PROD with PROPOSALS 

### 49702 Federal Register / Vol. 74, No. 186 / Monday, September 28, 2009 / Proposed Rules