1

II. Joint Technical Work Completed for This Proposal 

A. Introduction 

In this section NHTSA and EPA discuss several aspects of the joint technical analyses the 

two agencies collaborated on which are common to the development of each agency’s proposed 

standards. Specifically we discuss: the development of the baseline vehicle market forecast 

used by each agency, the development of the proposed attribute-based standard curve shapes, 

how the relative stringency between the car and truck fleet standards for this proposal was 

determined, which technologies the agencies evaluated and their costs and effectiveness, and 

which economic assumptions the agencies included in their analyses. The joint Technical 

Support Document (TSD) discusses the agencies’ joint technical work in more detail. 

 B. How did NHTSA and EPA Develop the Baseline Market Forecast? 

1. Why do the Agencies Establish a Baseline Vehicle Fleet?

In order to calculate the impacts of the EPA and NHTSA proposed regulations, it is 

necessary to estimate the composition of the future vehicle fleet absent these proposed 

regulations in order to conduct comparisons. EPA and NHTSA have developed a comparison 

fleet in two parts. The first step was to develop a baseline fleet based on model year 2008 data. 

The second step was to project that fleet into 2011-2016. This is called the reference fleet. The 

third step was to modify that 2011-2016 reference fleet such that it had sufficient technologies to 

meet the 2011 CAFE standards. This final “reference fleet” is the light duty fleet estimated to 

exist in 2012-2016 if these proposed rules are not adopted. Each agency developed a final 

reference fleet to use in its modeling. All of the agencies’ estimates of emission reductions, fuel 

economy improvements, costs, and societal impacts are developed in relation to the respective 

reference fleets. 

2. How do the Agencies Develop the Baseline Vehicle Fleet? 

EPA and NHTSA have based the projection of total car and total light truck sales on 

recent projections made by the Energy Information Administration (EIA). EIA publishes a longterm projection of national energy use annually called the Annual Energy Outlook. This 

projection utilizes a number of technical and econometric models which are designed to reflect 

both economic and regulatory conditions expected to exist in the future. In support of its 

projection of fuel use by light-duty vehicles, EIA projects sales of new cars and light trucks. 

Due to the state of flux of both energy prices and the economy, EIA published three versions of 

its 2009 Annual Energy Outlook. The Preliminary 2009 report was published early (in 

November 2008) in order to reflect the dramatic increase in fuel prices which occurred during 

2008 and which occurred after the development of the 2008 Annual Energy Outlook. The 

official 2009 report was published in March of 2009. A third 2009 report was published a month 

later which reflected the economic stimulus package passed by Congress earlier this year. We 

use the sales projections of this latest report, referred to as the updated 2009 Annual Energy 

Outlook, here. 
2

In their updated 2009 report, EIA projects that total light-duty vehicle sales will gradually 

recover from their currently depressed levels by roughly 2013. In 2016, car and light truck sales 

are projected to be 9.5 and 7.1 million units, respectively. While the total level of sales of 16.6 

million units is similar to pre-2008 levels, the fraction of car sales is higher than that existing in

the 2000-2007 timeframe. This presumably reflects the impact of higher fuel prices and that fact 

that cars tend to have higher levels of fuel economy than trucks. We note that EIA’s definition 

of cars and trucks follows that used by NHTSA prior to the MY 2011 CAFE final rule published 

earlier this year. That recent CAFE rule, which established the MY 2011 standards, reclassified a 

number of 2-wheel drive sport utility vehicles from the truck fleet to the car fleet. This has the 

impact of shifting a considerable number of previously defined trucks into the car category. 

Sales projections of cars and trucks for all future model years can be found in the draft Joint TSD 

for this proposal. 

In addition to a shift towards more car sales, sales of segments within both the car and 

truck markets have also been changing and are expected to continue to change in the future. 

Manufacturers are introducing more crossover models which offer much of the utility of SUVs 

but using more car-like designs. In order to reflect these changes in fleet makeup, EPA and 

NHTSA considered several available forecasts. After review EPA purchased and shared with 

NHTSA forecasts from two well-known industry analysts, CSM-Worldwide (CSM), and J.D. 

Powers. NHTSA and EPA decided to use the forecast from CSM, for several reasons. One, 

CSM agreed to allow us to publish the data, on which our forecast is based, in the public 

domain.1

 Two, it covered nearly all the timeframe of greatest relevance to this proposed rule 

(2012-2015 model years). Three, it provided projections of vehicle sales both by manufacturer 

and by market segment. Four, it utilized market segments similar to those used in the EPA 

emission certification program and fuel economy guide. As discussed further below, this 

allowed the CSM forecast to be combined with other data obtained by NHTSA and EPA. We 

also assumed that the breakdowns of car and truck sales by manufacturer and by market segment 

for 2016 model year and beyond were the same as CSM’s forecast for 2015 calendar year. The 

changes between company market share and industry market segments were most significant 

from 2011-2014, while for 2014-2015 the changes were relatively small. Therefore, we assumed 

2016 market share and market segments to be the same as for 2015. To the extent that the 

agencies have received CSM forecasts for 2016, we will consider using them for the final rule. 

We then projected the CSM forecasts for relative sales of cars and trucks by manufacturer 

and by market segment on to the total sales estimates of the updated 2009 Annual Energy 

Outlook. Tables II.B.1-1 and II.B.1-2 show the resulting projections for the 2016 model year 

and compare these to actual sales which occurred in 2008 model year. Both tables show sales 

using the traditional or classic definition of cars and light trucks. Determining which classic 

trucks will be defined as cars using the revised definition established by NHTSA earlier this year 

 

1

 The CSM data made public includes only the higher level volume projections by market segment and 

manufacturer. The projections by nameplate and model are strictly the agencies’ estimates based on these higher 

level CSM segment and manufacturer distribution. 
3

and included in this proposed rule requires more detailed information about each vehicle model 

which is developed next. 

Table II.B.2-1 Annual Sales of Light-Duty Vehicles by Manufacturer in 2008 and Estimated for 

2016 

 Cars Light Trucks Total 

 2008 MY 2016 MY 2008 MY 2016 MY 2008 MY 2016 MY 

BMW 291,796 380,804 61,324 134,805 353,120 515,609

Chrysler 537,808 110,438 1,119,397 133,454 1,657,205 243,891

Daimler 208,052 235,205 79,135 109,917 287,187 345,122

Ford 641,281 990,700 1,227,107 1,713,376 1,868,388 2,704,075

General 

Motors 1,370,280 1,562,791 1,749,227 1,571,037 3,119,507 3,133,827

Honda 899,498 1,429,262 612,281 812,325 1,511,779 2,241,586

Hyundai 270,293 437,329 120,734 287,694 391,027 725,024

Kia 145,863 255,954 135,589 162,515 281,452 418,469

Mazda 191,326 290,010 111,220 112,837 302,546 402,847

Mitsubishi 76,701 49,697 24,028 10,872 100,729 60,569

Porsche 18,909 37,064 18,797 17,175 37,706 54,240

Nissan 653,121 985,668 370,294 571,748 1,023,415 1,557,416

Subaru 149,370 128,885 49,211 75,841 198,581 204,726

Suzuki 68,720 69,452 45,938 34,307 114,658 103,759

Tata 9,596 41,584 55,584 47,105 65,180 88,689

Toyota 1,143,696 1,986,824 1,067,804 1,218,223 2,211,500 3,205,048

Volkswagen 290,385 476,699 26,999 99,459 317,384 576,158

Total 6,966,695 9,468,365 6,874,669 7,112,689 13,841,364 16,581,055

Table II.B.2-2 Annual Sales of Light-Duty Vehicles by Market Segment in 2008 and Estimated 

for 2016 

Cars Light Trucks 

 2008 MY 2016 MY 2008 MY 2016 MY 

Full-Size Car 730,355 466,616 Full-Size Pickup 1,195,073 1,475,881

Mid-Size Car 1,970,494 2,641,739 Mid-Size Pickup 598,197 510,580

Small/Compact 

Car 1,850,522 2,444,479 Full-Size Van 33,384 284,110

Mid-Size Van 719,529 615,349

Subcompact/Mini 

Car 599,643 1,459,138 Mid-Size MAV* 191,448 158,930

Small MAV 235,524 289,880

Luxury Car 1,057,875 1,432,162 Full-Size SUV* 530,748 90,636

Specialty Car 754,547 1,003,078 Mid-Size SUV 347,026 110,155
4

Others 3,259 21,153 Small SUV 377,262 124,397

Full-Size CUV* 406,554 319,201

Mid-Size CUV 798,335 1,306,770

Small CUV 1,441,589 1,866,580

TotalSales 6,966,695 9,468,365 6,874,669 7,152,470

* MAV – Multi-Activity Vehicle, SUV – Sport Utility Vehicle, CUV – Crossover Utility Vehicle 

The agencies recognize that CSM forecasts a very significant reduction in market share 

for Chrysler. This may be a result of the extreme uncertainty surrounding Chrysler in early 

2009. We expect to use the most recent forecasts for the final rule. The forecast from CSM used 

in this proposal is CSM’s forecast from the 2nd quarter of 2009. CSM also provided to the 

agencies an updated forecast in the 3rd quarter of 2009, which we were unable to use for this 

proposal due to time constraints. However, we have placed a copy of the 3rd Quarter CSM 

forecast in the public docket for this rulemaking, and we will consider its use, and any further 

updates from CSM or other data received during the comment period when developing the 

analysis for the final rule. 2 CSM’s forecast for Chrysler for the 3rd quarter of 2009 was 

significantly increased compared to the 2nd quarter, by nearly a factor of two increase in 

projected sales over the 2012-2015 time frame.

The forecasts obtained from CSM provided estimates of car and trucks sales by segment 

and by manufacturer, but not by manufacturer for each market segment. Therefore, we needed 

other information on which to base these more detailed market splits. For this task, we used as a 

starting point each manufacturer’s sales by market segment from model year 2008. Because of 

the larger number of segments in the truck market, we used slightly different methodologies for 

cars and trucks. 

The first step for both cars and trucks was to break down each manufacturer’s 2008 sales 

according to the market segment definitions used by CSM. For example, we found that Ford’s 

cars sales in 2008 were broken down as shown in Table II.B.2-3: 

Table II.B.2-3: Breakdown of Ford’s 2008 Car Sales 

Full-size cars 76,762 units 

Mid-size Cars 170,399 units 

Small/Compact Cars 180,249 units 

Subcompact/Mini Cars none 

Luxury cars 100,065 units 

Specialty cars 110,805 units 

 

We then adjusted each manufacturer’s sales of each of its car segments (and truck 

segments, separately) so that the manufacturer’s total sales of cars (and trucks) matched the total 

estimated for each future model year based on EIA and CSM forecasts. For example, as 

 

2

 “CSM North America Sales Forecast Comparison 2Q09 3Q09 For Docket.” 2nd and 3rd quarter forecasting results 

from CSM World Wide (Docket EPA-HQ-OAR-2009-0472). 
5

indicated in Table II.B.2-1, Ford’s total car sales in 2008 were 641,281 units, while we project 

that they will increase to 990,700 units by 2016. This represents an increase of 54.5 percent. 

Thus, we increased the 2008 sales of each Ford car segment by 54.5 percent. This produced 

estimates of future sales which matched total car and truck sales per EIA and the manufacturer 

breakdowns per CSM (and exemplified for 2016 in Table II.B.1-1). However, the sales splits by 

market segment would not necessarily match those of CSM (and exemplified for 2016 in Table 

II.B.2-2). 

In order to adjust the market segment mix for cars, we first adjusted sales of luxury, 

specialty and other cars. Since the total sales of cars for each manufacturer were already set, any 

changes in the sales of one car segment had to be compensated by the opposite change in another 

segment. For the luxury, specialty and other car segments, it is not clear how changes in sales 

would be compensated. For example, if luxury car sales decreased, would sales of full-size cars 

increase, mid-size cars, etc.? Thus, any changes in the sales of cars within these three segments 

were assumed to be compensated for by proportional changes in the sales of the other four car 

segments. For example, for 2016, the figures in Table II.B.2-2 indicate that luxury car sales in 

2016 are 1,432,162 units. Luxury car sales are 1,057,875 units in 2008. However, after 

adjusting 2008 car sales by the change in total car sales for 2016 projected by EIA and a change 

in manufacturer market share per CSM, luxury car sales increased to 1,521,892 units. Thus, 

overall for 2016, luxury car sales had to decrease by 89,730 units or 6 percent. We decreased the 

luxury car sales by each manufacturer by this percentage. The absolute decrease in luxury car 

sales was spread across sales of full-size, mid-size, compact and subcompact cars in proportion 

to each manufacturer’s sales in these segments in 2008. The same adjustment process was used 

for specialty cars and the “other cars” segment defined by CSM. 

A slightly different approach was used to adjust for changing sales of the remaining four 

car segments. Starting with full-size cars, we again determined the overall percentage change 

that needed to occur in future year full-size cars sales after 1) adjusting for total sales per EIA, 2) 

manufacturer sales mix per CSM and 3) adjustments in the luxury, specialty and other car 

segments, in order to meet the segment sales mix per CSM. Sales of each manufacturer’s large 

cars were adjusted by this percentage. However, instead of spreading this change over the 

remaining three segments, we assigned the entire change to mid-size vehicles. We did so 

because, as shown in 2008, higher fuel prices tend to cause car purchasers to purchase smaller 

vehicles. We are using AEO 2009 for this analysis, which assumes fuel prices similar in 

magnitude to actual high fuel prices seen in the summer of 2008.3

 However, if a consumer had 

previously purchased a full-size car, we thought it unlikely that they would jump all the way to a 

subcompact. It seemed more reasonable to project that they would drop one vehicle size 

category smaller. Thus, the change in each manufacturer’s sales of full-size cars was matched by 

an opposite change (in absolute units sold) in mid-size cars. 

The same process was then applied to mid-size cars, with the change in mid-size car sales 

being matched by an opposite change in compact car sales. This process was repeated one more 

 

3

 J.D.Power and Associates, Press Release, May 16, 2007. “Rising Gas Prices Begin to Sway New-Vehicle Owners 

Toward Smaller Versions of Trucks and Utility Vehicles.” 
6

time for compact car sales, with changes in sales in this segment being matched by the opposite 

change in the sales of subcompacts. The overall result was a projection of car sales for 2012-

2016 which matched the total sales projections of EIA and the manufacturer and segment splits 

of CSM. These sales splits can be found in Chapter 1 of the draft Joint Technical Support 

Document for this proposal. 

As mentioned above, a slightly different process was applied to truck sales. The reason 

for this was we could not confidently project how the change in sales from one segment 

preferentially went to or came from another particular segment. Some trend from larger vehicles 

to smaller vehicles would have been possible. However, the CSM forecasts indicated large 

changes in total sport utility vehicle, multi-activity vehicle and cross-over sales which could not 

be connected. Thus, we applied an iterative, but straightforward process for adjusting 2008 truck 

sales to match the EIA and CSM forecasts. 

The first three steps were exactly the same as for cars. We broke down each 

manufacturer’s truck sales into the truck segments as defined by CSM. We then adjusted all 

manufacturers’ truck segment sales by the same factor so that total truck sales in each model year 

matched EIA projections for truck sales by model year. We then adjusted each manufacturer’s 

truck sales by segment proportionally so that each manufacturer’s percentage of total truck sales 

matched that forecast by CSM. This again left the need to adjust truck sales by segment to 

match the CSM forecast for each model year. 

In the fourth step, we adjusted the sales of each truck segment by a common factor so that 

total sales for that segment matched the combination of the EIA and CSM forecasts. For 

example, sales of large pickups across all manufacturer’s were 1,144,166 units in 2016 after 

adjusting total sales to match EIA’s forecast and adjusting each manufacturer’s truck sales to 

match CSM’s forecast for the breakdown of sales by manufacturer. Applying CSM’s forecast of 

the large pickup segment of truck sales to EIA’s total sales forecast indicated total large pickup 

sales of 1,475,881 units. Thus, we increased each manufacturer’s sales of large pickups by 29 

percent. The same type of adjustment was applied to all the other truck segments at the same 

time. The result was a set of sales projections which matched EIA’s total truck sales projection 

and CSM’s market segment forecast. However, after this step, sales by manufacturer no longer 

met CSM’s forecast. Thus, we repeated step three and adjusted each manufacturer’s truck sales 

so that they met CSM’s forecast. The sales of each truck segment (by manufacturer) were 

adjusted by the same factor. The resulting sales projection matched EIA’s total truck sales 

projection and CSM’s manufacturer forecast, but sales by market segment no longer met CSM’s 

forecast. However, the difference between the sales projections after this fifth step was closer to 

CSM’s market segment forecast than it was after step three. In other words, the sales projection 

was converging. We repeated these adjustments, matching manufacturer sales mix in one step 

and then market segment in the next for a total of 19 times. At this point, we were able to match 

the market segment splits exactly and the manufacturer splits were within 0.1% of our goal, 

which is well within the needs of this analysis. 

The next step in developing the baseline fleet was to characterize the vehicles within each 

manufacturer-segment combination. In large part, this was based on the characterization of the 
7

specific vehicle models sold in 2008. EPA and NHTSA chose to base our estimates of detailed 

vehicle characteristics on 2008 sales for several reasons. One, these vehicle characteristics are 

not confidential and can thus be published here for careful review and comment by interested 

parties. Two, being actual sales data, this vehicle fleet represents the distribution of consumer 

demand for utility, performance, safety, etc. 

We gathered most of the information about the 2008 vehicle fleet from EPA’s emission 

certification and fuel economy database. The data obtained from this source included vehicle 

production volume, fuel economy, engine size, number of engine cylinders, transmission type, 

fuel type, etc. EPA’s certification database does not include a detailed description of the types of 

fuel economy-improving/CO2-reducing technologies considered in this proposal. Thus, we 

augmented this description with publicly available data which includes more complete 

technology descriptions from Ward’s Automotive Group.

4

 In a few instances when required 

vehicle information was not available from these two sources (such as vehicle footprint), we 

obtained this information from publicly accessible internet sites such as Motortrend.com and 

Edmunds.com.5

 

The projections of future car and truck sales described above apply to each 

manufacturer’s sales by market segment. The EPA emissions certification sales data are 

available at a much finer level of detail, essentially vehicle configuration. As mentioned above, 

we placed each vehicle in the EPA certification database into one of the CSM market segments. 

We then totaled the sales by each manufacturer for each market segment. If the combination of 

EIA and CSM forecasts indicated an increase in a given manufacturer’s sales of a particular 

market segment, then the sales of all the individual vehicle configurations were adjusted by the 

same factor. For example, if the Prius represented 30% of Toyota’s sales of compact cars in 

2008 and Toyota’s sales of compact cars in 2016 was projected to double by 2016, then the sales 

of the Prius were doubled, and the Prius sales in 2016 remained 30% of Toyota’s compact car 

sales. 

NHTSA and EPA request comment on the methodology and data sources used for 

developing the baseline vehicle fleet for this proposal and the reasonableness of the results. 

3. How is the Development of the Baseline Fleet for this Proposal 

Different from NHTSA’s Historical approach, and Why is this Approach 

Preferable?

 NHTSA has historically based its analysis of potential new CAFE standards on detailed 

product plans the agency has requested from manufacturers planning to produce light vehicles 

for sale in the United States. Although the agency has not attempted to compel manufacturers to 

submit such information, most major manufacturers and some smaller manufacturers have 

voluntarily provided it when requested. 

 

4

 Note that WardsAuto.coms Automotive is a fee-based service, but all information is public to subscribers. 

5

 Motortrend.com and Edmunds.com are free, no-fee internet sites. 
8

 As in this and other prior rulemakings, NHTSA has requested extensive and detailed 

information regarding the models that manufacturers plan to offer, as well as manufacturers’ 

estimates of the volume of each model they expect to produce for sale in the U.S. NHTSA’s 

recent requests have sought information regarding a range of engineering and planning 

characteristics for each vehicle model (e.g., fuel economy, engine, transmission, physical 

dimensions, weights and capacities, redesign schedules), each engine (e.g., fuel type, fuel 

delivery, aspiration, valvetrain configuration, valve timing, valve lift, power and torque ratings), 

and each transmission (e.g., type, number of gears, logic). 

 The information that manufacturers have provided in response to these requests has 

varied in completeness and detail. Some manufacturers have submitted nearly all of the 

information NHTSA has requested, have done so for most or all of the model years covered by 

NHTSA’s requests, and have closely followed NHTSA’s guidance regarding the structure of the 

information. Other manufacturers have submitted partial information, information for only a few 

model years, and/or information in a structure less amenable to analysis. Still other 

manufacturers have not responded to NHTSA’s requests or have responded on occasion, usually 

with partial information. 

 In recent rulemakings, NHTSA has integrated this information and estimated missing 

information based on a range of public and commercial sources (such as those used to develop 

today’s market forecast). For unresponsive manufacturers, NHTSA has estimated fleet 

composition based on the latest-available CAFE compliance data (the same data used as part of 

the foundation for today’s market forecast). NHTSA has then adjusted the size of the fleet based 

on AEO’s forecast of the light vehicle market and normalized manufacturers’ market shares 

based on the latest-available CAFE compliance data.

 Compared to this approach, the market forecast the agencies have developed for this 

analysis has both advantages and disadvantages. 

Most importantly, today’s market forecast is much more transparent. The information 

sources used to develop today’s market forecast are all either in the public domain or available 

commercially. Therefore, NHTSA and EPA are able to make public the market inputs actually 

used in the agencies’ respective modeling systems, such that any reviewer may independently 

repeat and review the agencies’ analyses. Previously, although NHTSA provided this type of 

information to manufacturers upon request (e.g., GM requested and received outputs specific to 

GM), NHTSA was otherwise unable to release market inputs and the most detailed model 

outputs (i.e., the outputs containing information regarding specific vehicle models) because 

doing so would violate requirements protecting manufacturers’ confidential business information 

from disclosure.6

 Therefore, this approach provides much greater opportunity for the public to 

review every aspect of the agencies’ analyses and comment accordingly. 

Another significant advantage of today’s market forecast is the agencies’ ability to assess 

more fully the incremental costs and benefits of the proposed standards. In the past two years, 

 

6

 See 49 CFR Part 512. 
9

NHTSA has requested and received three sets of future product plan submissions from the 

automotive companies, most recently this past spring. These submissions are intended to be the 

actual future product plans for the companies. In the most recent submission it is clear that many 

of the firms have been and are clearly planning for future CAFE standard increases for model 

years 2012 and later. The results for the product plans for many firms are a significant increase 

in their projected future application of fuel economy improvement technology. However, for the 

purposes of assessing the costs of the model year 2012-2016 standards the use of the product 

plans presents a difficulty, namely, how to assess the increased costs of the proposed future 

standards if the companies have already anticipated the future standards and the costs are 

therefore now part of the agencies’ baseline. This is a real concern with the most recent product 

plans received from the companies, and is one of the reasons the agencies have decided not to 

use the recent product plans to define the baseline market data for assessing our proposed 

standards. The approach used for this proposal does not raise this concern, as the underlying 

data comes from model year 2008 production.7

In addition, by developing a baseline fleet from common sources, the agencies have been 

able to avoid some errors—perhaps related to interpretation of requests—that have been 

observed in past responses to NHTSA’s requests. For example, while reviewing information 

submitted to support the most recent CAFE rulemaking, NHTSA staff discovered that one 

manufacturer had misinterpreted instructions regarding the specification of vehicle track width, 

leading to important errors in estimates of vehicle footprints. Although the manufacturer 

resubmitted the information with corrections, with this approach, the agencies are able to reduce 

the potential for such errors and inconsistencies by utilizing common data sources and 

procedures. 

An additional advantage of the approach used for this proposal is a consistent projection 

of the change in fuel economy and CO2 emissions across the various vehicles from the 

application of new technology. In the past, company product plans would include the application 

of new fuel economy improvement technology for a new or improved vehicle model with the 

resultant estimate from the company of the fuel economy levels for the vehicle. However, 

companies did not always provide to NHTSA the detailed analysis which showed how they 

forecasted what the fuel economy performance of the new vehicle was – that is, whether it came 

from actual test data, from vehicle simulation modeling, from best engineering judgment or some 

other methodology. Thus, it was not possible for NHTSA to review the methodology used by 

the manufacturer, nor was it possible to review what approach the different manufacturers 

utilized from a consistency perspective. With the approach used for this proposal, the baseline 

market data comes from actual vehicles which have actual fuel economy test data – so there is no 

question what is the basis for the fuel economy or CO2 performance of the baseline market data 

as it is actual measured data. 

 

7

 However, as discussed below, an alternative approach that NHTSA is exploring would be to use only 

manufacturers’ near-term product plans, e.g., from MY 2010 or MY 2011. NHTSA believes manufacturers’ nearterm plans should be less subject to this concern about missing costs and benefits already included in the baseline. 

NHTSA is also hopeful that in connection with the agencies’ rulemaking efforts, manufacturers will be willing to 

make their near-term plans available to the public.
10

Another advantage of today’s approach is that future market shares are based on a 

forecast of what will occur in the future, rather than a static value. In the past, NHTSA has 

utilized a constant market share for each model year, based on the most recent year available, for 

example from the CAFE compliance data, that is, a forecast of the 2011-2015 time frame where 

company market shares do not change. In the approach used today, we have utilized the 

forecasts from CSM of how future market shares among the companies may change over time.8

The approach the agencies have taken in developing today’s market forecast does, 

however, have some disadvantages. Most importantly, it produces a market forecast that does 

not represent some important changes likely to occur in the future. 

Some of the changes not captured by today’s approach are specific. For example, the 

agencies’ current market forecast includes some vehicles for which manufacturers have 

announced plans for elimination or drastic production cuts such as the Chevrolet Trailblazer, the 

Chrysler PT Cruiser, the Chrysler Pacifica, the Dodge Magnum, the Ford Crown Victoria, the 

Hummer H2, the Mercury Sable, the Pontiac Grand Prix, and the Pontiac G5. These vehicle 

models appear explicitly in market inputs to NHTSA’s analysis, and are among those vehicle 

models included in the aggregated vehicle types appearing in market inputs to EPA’s analysis. 

Conversely, the agencies’ market forecast does not include some forthcoming vehicle 

models, such as the Chevrolet Volt, the Chevrolet Camaro, the Ford Fiesta and several publicly 

announced electric vehicles, including the announcements from Nissan. Nor does it include 

several MY 2009 or 2010 vehicles, such as the Honda Insight, the Hyundai Genesis and the 

Toyota Venza, as our starting point for vehicle definitions was Model Year 2008. Additionally, 

the market forecast does not account for publicly announced technology introductions, such as 

Ford’s EcoBoost system, whose product plans specify which vehicles and how many are planned 

to have this technology. Were the agencies to rely on manufacturers’ product plans (that were 

submitted), the market forecast would account for not only these specific examples, but also for 

similar examples that have not yet been announced publicly. 

The agencies anticipate that including vehicles after MY 2008 would not significantly 

impact our estimates of the technology required to comply with the proposed standards. If they 

were included, these vehicles could make the standards appear to cost less relative to the 

reference case. First, the projections of sales by vehicle segment and manufacturer include these 

expected new vehicle models. Thus, to the extent that these new vehicles are expected to change 

consumer demand, they should be reflected in our reference case. While we are projecting the 

characteristics of the new vehicles with MY 2008 vehicles, the primary difference between the 

new vehicles and 2008 vehicles in the same vehicle segment is the use of additional CO2-

reducing and fuel-saving technology. Both the NHTSA and EPA models add such technology to 

facilitate compliance with the proposed standards. Thus, our future projections of the vehicle 

 

8

 We note that market share forecasts like CSM’s could, of course, be applied to any data used to create the baseline 

market forecast. If, as mentioned above, manufacturers do consent to make public MY 2010 or 2011 product plan 

data for the final rule, the agencies could consider applying market share forecast to that data as well. 
11

fleet generally shift vehicle designs towards those of these newer vehicles. The advantage of our 

approach is that it helps clarify the costs of this proposal, as the cost of all fuel economy 

improvements beyond those required by the MY 2011 CAFE standards are being assigned to the 

proposal. In some cases, the new vehicles being introduced by manufacturers are actually in 

response to their anticipation of this rulemaking. Our approach prevents some of these 

technological improvements and their associated cost from being assumed in the baseline. Thus, 

the added technology will not be considered to be free for the purposes of this rule. 

We note that, as a result of these issues, the market file may show sales volumes for 

certain vehicles during MYs 2012-2016 even though they will be discontinued before that time 

frame. Although the agencies recognize that these specific vehicles will be discontinued, we 

continue to include them in the market forecast because they are useful for representing 

successor vehicles that may appear in the rulemaking time frame to replace the discontinued 

vehicles in that market segment. 

Other market changes not captured by today’s approach are broader. For example, 

Chrysler Group LLC has announced plans to offer small- and medium-sized cars using Fiat 

powertrains. The product plan submitted by Chrysler includes vehicles that appear to reflect 

these plans. However, none of these specific vehicle models are included in the market forecast 

the agencies have developed starting with MY 2008 CAFE compliance data. The product plan 

submitted by Chrysler is also more optimistic with regard to Chrysler’s market share during MYs 

2012-2016 than the market forecast projected by CSM and used by the agencies for this 

proposal. Similarly, the agencies’ market forecast does not reflect Nissan’s plans regarding 

electric vehicles. 

 Additionally, some technical information that manufacturers have provided in product 

plans regarding specific vehicle models is, at least insofar as NHTSA and EPA have been able to 

determine, not available from public or commercial sources. While such gaps do not bear 

significantly on the agencies’ analysis, the diversity of pickup configurations necessitated 

utilizing a sales-weighted average footprint value9

 for many manufacturers’ pickups. Since our 

modeling only utilizes footprint in order to estimate each manufacturer’s CO2 or fuel economy 

standard and all the other vehicle characteristics are available for each pickup configuration, this 

approximation has no practical impact on the projected technology or cost associated with 

compliance with the various standards evaluated. The only impact which could arise would be if 

 

9

 A full-size pickup might be offered with various combinations of cab style (e.g., regular, extended, crew) and box 

length (e.g., 51⁄2’, 61⁄2’, 8’) and, therefore, multiple footprint sizes. CAFE compliance data for MY 2008 data does 

not contain footprint information, and does not contain information that can be used to reliably identify which 

pickup entries correspond to footprint values estimable from public or commercial sources. Therefore, the agencies 

have used the known production levels of average values to represent all variants of a given pickup line (e.g., all 

variants of the F-150 and the Sierra/Silverado) in order to calculate the sales-weighted average footprint value for 

each pickup family. Again, this has no impact on the results of our modeling effort, although it would require reestimation if we were to examine light truck standards of a different shape. In the extreme, one single footprint 

value could be used for every vehicle sold by a single manufacturer as long as the fuel economy standard associated 

with this footprint value represented the sales-weighted, harmonic average of the fuel economy standards associated 

with each vehicle’s footprint values.. 
12

the relative sales of the various pickup configurations changed, or if the agencies were to explore 

standards with a different shape. This would necessitate recalculating the average footprint value 

in order to maintain accuracy. 

The agencies have carefully considered these advantages and disadvantages of using a 

market forecast derived from public and commercial sources rather than from manufacturers’ 

product plans, and we believe that the advantages outweigh the disadvantages for the purpose of 

proposing standards for model years 2012-2016. NHTSA’s inability to release confidential 

market inputs and corresponding detailed outputs from the CAFE model has raised serious 

concerns among many observers regarding the transparency of NHTSA’s analysis, as well as 

related concerns that the lack of transparency might enable manufacturers to provide unrealistic 

information to try to influence NHTSA’s determination of the maximum feasible standards. 

Although NHTSA does not agree with some observers’ assertions that some manufacturers have 

deliberately provided inaccurate or otherwise misleading information, today’s market forecast is 

fully open and transparent, and is therefore not subject to such concerns. 

With respect to the disadvantages, the agencies are hopeful that manufacturers will, in the 

future, agree to make public their plans regarding model years that are very near, such as MY 

2010 or perhaps MY 2011, so that this information can be considered for purposes of the final 

rule analysis and be available for the public. In any event, because NHTSA and EPA are 

releasing market inputs used in the agencies’ respective analyses, manufacturers, suppliers, and 

other automobile industry observers and participant can submit comments on how these inputs 

should be improved, as can all other reviewers. 

4. How Does Manufacturer Product Plan Data Factor into the Baseline Used in this 

Proposal?

In the spring of 2009, many manufacturers submitted product plans in response to 

NHTSA’s request that they do so.10 NHTSA and EPA both have access to these plans, and both 

agencies have reviewed them in detail. A small amount of product plan data was used in the 

development of the baseline. The specific pieces of data are: 

• Wheelbase 

• Track Width Front 

• Track Width Rear 

• EPS (Electric Power Steering) 

• ROLL (Reduced Rolling Resistance) 

• LUB (Advance Lubrication i.e. low weight oil) 

• IACC (Improved Electrical Accessories) 

• Curb Weight 

• GVWR (Gross Vehicle Weight Rating) 

 

10 74 FR 9185 (Mar. 3, 2009) 
13

The track widths, wheelbase, curb weight, and GVWR could have been looked up on the 

internet (159 were), but were taken from the product plans when available for convenience. To 

ensure accuracy, a sample from each product plan was used as a check against the numbers 

available from Motortrend.com. These numbers will be published in the baseline file since they 

can be easily looked up on the internet. On the other hand, EPS, ROLL, LUB, and IACC are 

difficult to determine without using manufacturer’s product plans. These items will not be 

published in the baseline file, but the data has been aggregated into the EPA baseline in the 

technology effectiveness and cost effectiveness for each vehicle in a way that allows the baseline 

for the model to be published without revealing the manufacturers data. 

In addition to performing analysis using the baseline common to both agencies, NHTSA 

has conducted a separate analysis that does make use of these product plans. The analysis is 

documented in the Preliminary Regulatory Impact Analysis (PRIA) accompanying NHTSA’s 

Notice of Proposed Rulemaking. However, 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.

Considering both the publicly-available baseline used in this proposal and the product 

plans provided recently by manufacturers, however, it is possible that the latter could potentially 

be used to develop a more realistic forecast of product mix and vehicles characteristics of the 

near-future light-duty fleet. At the core of concerns about using company product plans are two 

concerns about doing so: (a) uncertainty and possible inaccuracy in manufacturers’ forecasts and 

(b) the transparency of using product plan data. With respect to the first concern, the agencies 

note that manufacturers’ near-term forecasts (i.e., for model years two or three years into the 

future) should be less uncertain and more amenable to eventual retrospective analysis (i.e., 

comparison to actual sales) than manufacturers’ longer-term forecasts (i.e., for model years more 

than five years into the future). With respect to the second concern, NHTSA has consulted with 

most manufacturers and believes that although few, if any, manufacturers would be willing to 

make public their longer-term plans, many responding manufacturers may be willing to make 

public their short-term plans. In a companion notice, NHTSA is seeking product plan 

information from manufacturers for MYs 2008 to 2020, [fill in], and the agencies will also 

continue to consult with manufacturers regarding the possibility of releasing plans for MY 2010 

and/or MY 2011 for purposes of developing and analyzing the final GHG and CAFE standards 

for MYs 2012-2016. The agencies are hopeful that manufacturers will agree to do so, and that 

NHTSA and EPA would therefore be able to use product plans in ways that might aid in 

increasing the accuracy of the baseline market forecast. 

C. Development of Attribute-Based Curve Shapes 

NHTSA and EPA are setting attribute-based CAFE and CO2 standards that are defined by 

a mathematical function for MYs 2012-2016 passenger cars and light trucks. 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 
14

mathematical function.11 The CAA has no such requirement, though in past rules, EPA has 

relied on both universal and attribute-based standards (e.g., for nonroad engines, EPA uses the 

attribute of horsepower). However, given the advantages of using attribute-based standards and 

given the goal of coordinating and harmonizing CO2 standards promulgated under the CAA and 

CAFE standards promulgated under EPCA, as expressed in the joint NOI, EPA is also proposing 

to issue standards that are attribute-based and defined by mathematical functions. 

 

Under an attribute-based standard, every vehicle model has a performance target (fuel 

economy and GHG emissions for CAFE and GHG emissions standards, respectively), the level 

of which depends on the vehicle’s attribute (for this proposal, footprint). The manufacturers’ 

fleet average performance is determined by the production-weighed12 average (for CAFE, 

harmonic average) of those targets. NHTSA and EPA are proposing CAFE and CO2 emissions 

standards defined by constrained linear functions and, equivalently, piecewise linear functions.13 

As a possible option for future rulemakings, the constrained linear form was introduced by 

NHTSA in the 2007 NPRM proposing CAFE standards for MY 2011-2015. Described 

mathematically, the proposed constrained linear function is defined according to the following 

formula:14

1

1 1

, ,

TARGET

MIN MAX c FOOTPRINT d

a b

=

      × +      

where 

TARGET = the fuel economy target (in mpg) applicable to vehicles of a given footprint 

(FOOTPRINT, in square feet), 

a = the function’s upper limit (in mpg), 

b = the function’s lower limit (in mpg), 

c = the slope (in gpm per square foot) of the sloped portion of the function, 

d = 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, and 

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. 

 Because the format is linear on a gallons-per-mile basis, not on a miles-per-gallon basis, 

it is plotted as fuel consumption below. Graphically, the constrained linear form appears as 

shown in Figure II.C.1-1. 

 

11 49 U.S.C. 32902(a)(3)(A). 

12 Production for sale in the United States. 

13 The equations are equivalent but are specified differently due to differences in the agencies’ respective models. 

14 This function is linear in fuel consumption but not in fuel economy. 
15

0.0500.0450.0400.0350.0300.0250.0200.0150.0100.0050.000

35 40 45 50 55 60 65 70

Footprint (sf)

Fuel Consumption (gpm) 1/LIMIT UPPER 1/LIMIT LOWER INTERCEPT SLOPE

Figure II.C.1-1 The Shape of the Constrained Linear Form 
16

The specific form and stringency for each fleet (passenger car and light trucks) and model 

year are defined through specific values for the four coefficients shown above. 

EPA is proposing the equivalent equation below for assigning CO2 targets to an 

individual vehicle’s footprint value. Although the general model of the equation is the same for 

each vehicle category and each year, the parameters of the equation differ for cars and trucks. 

Each parameter also changes on an annual basis, resulting in the yearly increases in stringency 

seen in the tables above. Described mathematically, EPA’s proposed piecewise linear function is 

as follows: 

Target = a, if x ≤ l 

Target = cx + d, if l < x ≤ h 

Target = b, if x > h 

 

In the constrained linear form applied by NHTSA, this equation takes the simplified form: 

Target = MIN [ MAX ( c * x + d , a) , b] 

where 

Target = the CO2 target value for a given footprint (in g/mi) 

a = the minimum target value (in g/mi CO2) 

b = the maximum target value (in g/mi CO2) 

c = the slope of the linear function (in g/mi per sq ft CO2) 

d = is the intercept or zero-offset for the line (in g/mi CO2) 

x = footprint of the vehicle model (in square feet, rounded to the nearest tenth) 

l & h are the lower and higher footprint limits or constraints or (“kinks”) or the boundary 

between the flat regions and the intermediate sloped line (in sq ft) 

 Graphically, piecewise linear form, like the constrained linear form, appears as shown in 

Figure II.C.1-2. 
17

30 35 40 45 50 55 60 65 70 Footprint (sq feet)

CO2 (gram/mile) or 

Fuel Consumption (gallons/mile)

lower limit = l Intercept = d

upper limit = h

slope = c

min = a

max = b

Figure II.C.1-2 The Shape of the Piecewise Linear Form 
18

As for the constrained linear form, the specific form and stringency for each fleet 

(passenger car and light trucks) and model year are defined through specific values for the four 

coefficients shown above. 

For purposes of this rule, NHTSA and EPA developed the basic curve shapes using 

methods similar to those applied by NHTSA in fitting the curves defining the MY 2011 

standards. The first step is defining the reference market inputs (in the form used by NHTSA’s 

CAFE model) described in Section II.B of this preamble and in Chapter 1 of the joint TSD. 

However, because the baseline fleet is technologically heterogeneous, NHTSA used the CAFE 

model to develop a fleet to which nearly all the technologies discussed in Chapter 3 of the joint 

TSD15 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 or reference fleets) for estimating the 

statistical relationship between vehicle size and fuel economy. 

In fitting the curves, NHTSA also continued to apply constraints to limit the function’s 

value for both the smallest and largest vehicles. Without a limit at the smallest footprints, the 

function—whether logistic or linear—can reach values that would be unfairly burdensome for a 

manufacturer that elects to focus on the market for small vehicles; depending on the underlying 

data, an unconstrained form could apply to the smallest vehicles targets that are simply 

unachievable. Limiting the function’s value for the smallest vehicles ensures that the function 

remains technologically achievable at small footprints, and that it does not unduly burden 

manufacturers focusing on small vehicles. On the other side of the function, without a limit at 

the largest footprints, the function may provide no floor on required fuel economy. Also, the 

safety considerations that support the provision of a disincentive for downsizing as a compliance 

strategy apply weakly—if at all—to the very largest vehicles. Limiting the function’s value for 

the largest vehicles leads to a function with an inherent absolute minimum level of performance, 

while remaining consistent with safety considerations. 

Before fitting the sloped portion of the constrained linear form, NHTSA selected 

footprints above and below which to apply constraints (i.e., minimum and maximum values) on 

the function. For passenger cars, the agency noted that several manufacturers offer small and, in 

some cases, sporty coupes below 41 square feet, examples including the BMW Z4 and Mini, 

Saturn Sky, Honda Fit and S2000, Hyundai Tiburon, Mazda MX-5 Miata, Suzuki SX4, Toyota 

Yaris, and Volkswagen New Beetle. Because such vehicles represent a small portion (less than 

10 percent) of the passenger car market, yet often have characteristics that could make it 

 

15 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. 
19

infeasible to achieve the very challenging targets that could apply in the absence of a constraint, 

NHTSA is proposing to “cut off” the linear portion of the passenger car function at 41 square 

feet. For consistency, the agency is proposing to do the same for the light truck function, 

although no light trucks are currently offered below 41 square feet. The agency further noted 

that above 56 square feet, the only passenger car model present in the MY 2008 fleet were four 

luxury vehicles with extremely low sales volumes—the Bentley Arnage and three versions of the 

Rolls Royce Phantom. NHTSA is therefore proposing to “cut off” the linear portion of the 

passenger car function at 56 square feet. Finally, the agency noted that although public 

information is limited regarding the sales volumes of the many different configurations (cab 

designs and bed sizes) of pickup trucks, most of the largest pickups (e.g., the Ford F-150, GM 

Sierra/Silverado, Nissan Titan, and Toyota Tundra) appear to fall just above 66 square feet in 

footprint. NHTSA is therefore proposing to “cut off” the linear portion of the light truck 

function at 66 square feet. 

NHTSA and EPA seek 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 CO2 standards, the incentives those standards will provide (e.g., with respect 

to downsizing small vehicles), and the relative compliance burden faced by each manufacturer. 

For purposes of the CAFE and CO2 standards proposed in this NPRM, NHTSA and EPA 

recognize that there is some possibility that low fuel prices during the years in which MY 2012-

2016 vehicles are in service might lead to less than currently anticipated fuel savings and 

emissions reductions. One way to assure that emission reductions are achieved in fact is through 

the use of explicit backstops, fleet average standards established at an absolute level. For 

purposes of the CAFE program, EISA requires a backstop for domestically-manufactured 

passenger cars—a universal minimum, non-attribute-based standard of either “27.5 mpg or 92 

percent of the average fuel economy projected by the Secretary of Transportation for the 

combined domestic and non-domestic passenger automobile fleets manufactured for sale in the 

United States by all manufacturers in the model year...,” whichever is greater.16 In the MY 2011 

final rule, the first rule setting standards since EISA added the backstop provision to EPCA, 

NHTSA considered whether the statute permitted the agency to set backstop standards for the 

other regulated fleets of imported passenger cars and light trucks. Although commenters 

expressed support both for and against a more permissive reading of EISA, NHTSA concluded 

in that rulemaking that its authority was likely limited to setting only the backstop standard that 

Congress expressly provided, i.e., the one for domestic passenger cars. A backstop, however, 

could be adopted under section 202(a) of the CAA assuming it could be justified under the 

relevant statutory criteria. EPA and NHTSA also note that the flattened portion of the car curve 

directionally addresses the issue of a backstop (i.e. a flat curve is itself a backstop). The agencies 

seek comment on whether backstop standards, or any other method within the agencies’ statutory 

authority, should and can be implemented in order to guarantee a level of CO2 emissions 

reductions and fuel savings under the attribute-based standards. 

 

16 49 U.S.C. 32902(b)(4). 
20

Having developed a set of baseline data to which to fit the mathematical fuel 

consumption function, the initial values for parameters c and d were determined for cars and 

trucks separately. c and d were initially set at the values for which the average (equivalently, 

sum) of the absolute values of the differences was minimized between the “maximum 

technology” fleet fuel consumption (within the footprints between the upper and lower limits) 

and the straight line the function defined above at the same corresponding vehicle footprints. 

That is, c and d were determined by minimizing the average absolute residual, commonly known 

as the MAD (Mean Absolute Deviation) approach, of the corresponding straight line. 

Finally, NHTSA calculated the values of the upper and lower values (a and b) based on 

the corresponding footprints discussed above (41 and 56 square feet for passenger cars, and 41 

and 66 square feet for light trucks). 

The result of this methodology is shown below in Figures II.A.2-2 and II.A.2-3 for 

passenger cars and light trucks, respectively. The fitted curves are shown with the underlying 

“maximum technology” passenger car and light truck fleets. For passenger cars, the mean 

absolute deviation of the sloped portion of the function was 14 percent. For trucks, the 

corresponding MAD was 10 percent. 
21

0.0600.0550.0500.0450.0400.0350.0300.0250.0200.0150.0100.0050.000

35 40 45 50 55 60 65 70 75 80

Footprint (sf)

Fuel Consumption (gpm)

Figure II.C.1-3 “Maximum Technology” Passenger Fleet with Fitted Constrained Linear Function 
22

0.0600.0550.0500.0450.0400.0350.0300.0250.0200.0150.0100.0050.000

35 40 45 50 55 60 65 70 75 80

Footprint (sf)

Fuel Consumption (gpm)

Figure II.C.1-4 “Maximum Technology” Light Truck with Fitted Constrained Linear Function 
23

 The agencies used these functional forms as a starting point to develop mathematical 

functions defining the actual proposed standards as discussed above. The agencies then 

transposed these functions vertically (i.e., on a gpm or CO2 basis, uniformly downward) to 

produce the relative car and light truck standards described in the next section. 

D. Relative Car-Truck Stringency 

The agencies have determined, under their respective statutory authorities, that it is 

appropriate to propose fleetwide standards with the projected levels of stringency of 34.1 mpg or 

250 g/mi (as well as the corresponding intermediate year fleetwide standards) for NHTSA and 

EPA respectively. To determine the relative stringency of passenger car and light truck 

standards, the agencies are concerned that increasing the difference between the car and truck 

standards (either by raising the car standards or lowering the truck standards) could encourage 

manufacturers to build fewer cars and more trucks, likely to the detriment of fuel economy and 

CO2 reductions. 17 In order to maintain consistent car/truck standards, the agencies applied a 

constant ratio between the estimated average required performance under the passenger car and 

light truck standards, in order to maintain a stable set of incentives regarding vehicle 

classification. 

To calculate relative car-truck stringency in this proposal, the agencies explored a 

number of possible alternatives. In the interest of harmonization, the agencies agree to use the 

Volpe model in order to estimate stringencies at which net benefits would be maximized. 

Further details of the development of this scenario approach can be found in Section IV of this 

preamble as well as in NHTSA’s PRIA and DEIS. NHTSA examined passenger car and light 

truck standards that would produce the proposed combined average fuel economy levels from 

Table I.B.2-2 above. NHTSA did so by shifting downward the curves that maximize net 

benefits, holding the relative stringency of passenger car and light truck standards constant at the 

level determined by maximizing net benefits, such that the average fuel economy required of 

passenger cars remains 34 percent higher than the average fuel economy required of light trucks. 

This methodology resulted in the average fuel economy levels for passenger cars and light trucks 

during MYs 2012-2016 as shown in Table I.D.2-1. The following chart illustrates this 

methodology of shifting the standards from the levels maximizing net benefits to the levels 

consistent with the combined fuel economy standards in this rule. 

 

17 For example, since many 2WD SUVs are classified as passenger cars, manufacturers have already warned that 

high car standards relative to truck standards could create an incentive for them to drop the 2WD version and sell 

only the 4WD version. 
24

454035302520

2011 2012 2013 2014 2015 2016

Model Year

Ave. Required FE (mpg)

Combined (National Policy)Cars (Maximizing Net Benefits)Trucks (Maximizing Net Benefits)Combined (Maximizing Net Benefits)Cars (Pending)Trucks (Pending)Combined (Pending)

Passenger CarsCombined Ave.Light Trucks

Figure II.D.1-1 Shifting the Standards from the Maximizing Net Benefit Levels to the Levels Consistent with the 

Combined Fuel Economy Standards in this Rule 
25

After this analysis was completed, EPA examined two alternative approaches to 

determine whether they would lead to significantly different outcomes. First, EPA analyzed the 

relative stringencies using a 10-year payback analysis (with the OMEGA model). This analysis 

sets the relative stringencies if increased technology cost is to be paid back out of fuel savings 

over a 10-year period (assuming a 3% discount rate). Second, EPA also conducted a technology 

maximized analysis, which sets the relative stringencies if all technologies (with the exception of 

strong hybrids and diesels) are assumed to be utilized in the fleet. (This is the same methodology 

that was used to determine the curve shape as explained in the section above and in Chapter 2 of 

the joint TSD section). Compared to NHTSA’s approach based on stringencies estimated to 

maximize net benefits, EPA staff found that these two other approaches produced very similar 

results to NHTSA’s, i.e., similar ratios of car-truck relative stringency (the ratio being within a 

range of 1.34 to 1.37 relative stringency of the car to the truck fuel economy standard). EPA 

believes that this similarity supports the proposed relative stringency of the two standards. 

The car and truck standards for EPA (Table I.D. 2-4 above) were subsequently 

determined by first converting the average required fuel economy levels to average required CO2

emission rates, and then applying the expected air conditioning credits for 2012-2016. These 

A/C credits are shown in the following table. Further details of the derivation of these factors 

can be found in Section III of this preamble or in the EPA RIA. 

Table II.D.1-1 Expected Fleet A/C Credits (in CO2 equivalent g/mi) from 2012-2016 

Average Technology 

Penetration 

Average 

credit 

for cars 

Average 

credit for 

trucks 

Average 

credit for 

combined 

fleet 

2012 25% 3.0 3.4 3.1 

2013 40% 4.8 5.4 5.0 

2014 55% 7.2 8.1 7.5 

2015 75% 9.6 10.8 10.0 

2016 85% 10.2 11.5 10.6 

The agencies seek comment on the use of this methodology for apportioning the fleet 

stringencies to relative car and truck standards for 2012-2016. 

E. Joint Vehicle Technology Assumptions

Vehicle technology assumptions, i.e., assumptions about their cost, effectiveness, and the 

rate at which they can be incorporated into new vehicles, are often very controversial as they 

have a significant impact on the levels of the standards. Agencies must, therefore, take great 

care in developing and justifying these assumptions. In developing technology inputs for MY 

2012-2016 standards, the agencies reviewed 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. This review is consistent with the request by President 

Obama in his January 26 memorandum to DOT. In addition, the agencies reviewed the 

technology input estimates identified in EPA’s July 2008 Advanced Notice of Proposed 
26

Rulemaking. The review of these documents was supplemented with updated information from 

more current literature, new product plans and from EPA certification testing. 

As a general matter, the best way to derive technology cost estimates is to conduct realworld tear down studies. These studies break down each technology into its respective 

components, evaluate the costs of each component, and build up the costs of the entire 

technology based on the contribution of each component. As such, tear down studies require a 

significant amount of time and are very costly. EPA has begun conducting tear down studies to 

assess the costs of 4-5 technologies under a contract with FEV. To date, only two technologies 

(stoichiometeric gasoline direct injection and turbo charging with engine downsizing for a 4 

cylinder engine to a 4 cylinder engine) have been evaluated. The agencies relied on the findings 

of FEV for estimating the cost of these technologies in this rulemaking—directly for the 4 

cylinder engines, and extrapolated for the 6 and 8 cylinder engines. The agencies request 

comment on the use of these estimated costs from the FEV study. For the other technologies, 

because tear down studies were not yet available, the agencies decided to pursue, to the extent 

possible, the Bill of Materials (BOM) approach as outlined in NHTSA’s MY 2011 final rule. A 

similar approach was used by EPA in the EPA 2008 Staff Technical Report. This approach was 

recommended to NHTSA by Ricardo, an international engineering consulting firm retained by 

NHTSA to aid in the analysis of public comments on its proposed standards for MYs 2011-2015 

because of its expertise in the area of fuel economy technologies. A BOM approach is one 

element of the process used in tear down studies. The difference is that under a BOM approach, 

the build up of cost estimates is conducted based on a review of cost and effectiveness estimates 

for each component from available literature, while under a tear down study, the cost estimates 

which go into the BOM come from the tear down study itself. To the extent that the agencies 

departed from the MY 2011 CAFE final rule estimates, the agencies explained the reasons and 

provided supporting analyses. As tear down studies are concluded by FEV during the 

rulemaking process, the agencies will make them available in the joint rulemaking docket of this 

rulemaking. The agencies will consider these studies and any comments received on them, as 

practicable and appropriate, as well as any other new information pertinent to the rulemaking of 

which the agencies become aware, in developing technology cost assumptions for the final rule. 

Similarly, the agencies followed a BOM approach for developing its effectiveness 

estimates, insofar as the BOM developed for the cost estimates helped to inform the appropriate 

effectiveness values derived from the literature review. The agencies supplemented the 

information with results from available simulation work and real world EPA certification testing. 

The agencies would also like to note that per the Energy Independence and Security Act 

(EISA), the National Academies of Sciences is conducting an updated study to update Chapter 3 

of the 2002 NAS Report, which outlines technology estimates. The update will take a fresh look 

at that list of technologies and their associated cost and effectiveness values. 

The report is expected to be available on September 30, 2009. As soon as the update to 

the NAS Report is received, it will be placed in the joint rulemaking docket for the public’s 

review and comment. Because this will occur during the comment period, the public is 

encouraged to check the docket regularly and provide comments on the updated NAS Report by 
27

the closing of the comment period of this notice. NHTSA and EPA will consider the updated 

NAS Report and any comments received, as practicable and appropriate, on it when considering 

revisions to the technology cost and effectiveness estimates for the final rule. Consideration of 

this report is consistent with the request by President Obama in his January 26 memorandum to 

DOT. 

 

1. What Technologies do the Agencies Consider?

The agencies considered over 35 vehicle technologies that manufacturers could use to 

improve the fuel economy and reduce CO2 emissions 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. Other technologies 

considered may not currently be in production, but are beyond the research phase and under 

development, and are expected to be in production in the next few years. These are technologies 

which can, for the most part, be applied both to cars and trucks, and which are capable of 

achieving significant improvements in fuel economy and reductions in CO2 emissions, at 

reasonable costs. The agencies did not consider technologies in the research stage because the 

leadtime available for this rule is not sufficient to move such technologies from research to 

production. 

The technologies considered in the agencies’ analysis are briefly described below. They 

fall into five broad categories: engine technologies, transmission technologies, vehicle 

technologies, electrification/accessory technologies, and hybrid technologies. For a more 

detailed description of each technology and their costs and effectiveness, we refer the reader to 

Chapter 3 of the joint TSD, Chapter III of NHTSA’s PRIA, and Chapter 1 of EPA’s DRIA. 

Technologies to reduce CO2 and HFC emissions from air conditioning systems are discussed in 

Section III of this preamble and in EPA’s DRIA. 

Types of engine technologies that improve fuel economy and reduce CO2 emissions 

include the following: 

• Low-friction lubricants – low viscosity and advanced low friction lubricants oils are 

now available with improved performance and better lubrication. If manufacturers 

choose to make use of these lubricants, they would need to make engine changes and 

possibly conduct durability testing to accommodate the low-friction lubricants. 

• Reduction of engine friction losses – can be achieved through low-tension piston 

rings, roller cam followers, improved material coatings, more optimal thermal 

management, piston surface treatments, and other improvements in the design of 

engine components and subsystems that improve engine operation. 

• Conversion to dual overhead cam with dual cam phasing – as applied to overhead 

valves designed to increase the air flow with more than two valves per cylinder and 

reduce pumping losses.
28

• Cylinder deactivation – deactivates the intake and exhaust valves and prevents fuel 

injection into some cylinders during light-load operation. The engine runs 

temporarily as though it were a smaller engine which substantially reduces pumping 

losses 

• Variable valve timing – alters the timing of the intake valve, exhaust valve, or both, 

primarily to reduce pumping losses, increase specific power, and control residual 

gases.

• Discrete variable valve lift – increases efficiency by optimizing air flow over a 

broader range of engine operation which reduces pumping losses. Accomplished by 

controlled switching between two or more cam profile lobe heights. 

• Continuous variable valve lift – is an electromechanically controlled system in which 

valve timing is changed as lift height is controlled. This yields a wide range of 

performance optimization and volumetric efficiency, including enabling the engine to 

be valve throttled. 

• Stoichiometric gasoline direct-injection technology – injects fuel at high pressure 

directly into the combustion chamber to improve cooling of the air/fuel charge within 

the cylinder, which allows for higher compression ratios and increased 

thermodynamic efficiency. 

• Combustion restart – can be used in conjunction with gasoline direct-injection 

systems to enable idle-off or start-stop functionality. Similar to other start-stop 

technologies, additional enablers, such as electric power steering, accessory drive 

components, and auxiliary oil pump, might be required. 

• Turbocharging and downsizing – increases the available airflow and specific power 

level, allowing a reduced engine size while maintaining performance. This reduces 

pumping losses at lighter loads in comparison to a larger engine.

• Exhaust-gas recirculation boost – increases the exhaust-gas recirculation used in the 

combustion process to increase thermal efficiency and reduce pumping losses. 

• Diesel engines – have several characteristics that give superior fuel efficiency, 

including reduced pumping losses due to lack of (or greatly reduced) throttling, and a 

combustion cycle that operates at a higher compression ratio, with a very lean air/fuel 

mixture, relative to an equivalent-performance gasoline engine. This technology 

requires additional enablers, such as NOx trap catalyst after-treatment or selective 

catalytic reduction NOx after-treatment. The cost and effectiveness estimates for the 

diesel engine and aftertreatment system utilized in this proposal have been revised 

from the NHTSA MY 2011 CAFE final rule, and the agencies request comment on 

these diesel cost estimates.

Types of transmission technologies considered include: 
29

• Improved automatic transmission controls – optimizes shift schedule to maximize 

fuel efficiency under wide ranging conditions, and minimizes losses associated with 

torque converter slip through lock-up or modulation. 

• Six-, seven-, and eight-speed automatic transmissions – the gear ratio spacing and 

transmission ratio are optimized for a broader range of engine operating conditions. 

• Dual clutch or automated shift manual transmissions – are similar to manual 

transmissions, but the vehicle controls shifting and launch functions. A dual-clutch 

automated shift manual transmission uses separate clutches for even-numbered and 

odd-numbered gears, so the next expected gear is pre-selected, which allows for faster 

and smoother shifting.

• Continuously variable transmission – commonly uses V-shaped pulleys connected by 

a metal belt rather than gears to provide ratios for operation. Unlike manual and 

automatic transmissions with fixed transmission ratios, continuously variable 

transmissions can provide fully variable transmission ratios with an infinite number 

of gears, enabling finer optimization of transmission torque multiplication under 

different operating conditions so that the engine can operate at higher efficiency. 

• Manual 6-speed transmission –offers an additional gear ratio, often with a higher 

overdrive gear ratio, than a 5-speed manual transmission. 

Types of vehicle technologies considered include: 

• Low-rolling-resistance tires – have characteristics that reduce frictional losses 

associated with the energy dissipated in the deformation of the tires under load, 

therefore improving fuel economy and reducing CO2 emissions. 

• Low-drag brakes – reduce the sliding friction of disc brake pads on rotors when the 

brakes are not engaged because the brake pads are pulled away from the rotors. 

• Front or secondary axle disconnect for four-wheel drive systems – provides a torque 

distribution disconnect between front and rear axles when torque is not required for 

the non-driving axle. This results in the reduction of associated parasitic energy 

losses. 

• Aerodynamic drag reduction – is achieved by changing vehicle shape or reducing 

frontal area, including skirts, air dams, underbody covers, and more aerodynamic side 

view mirrors. 

• Mass reduction and material substitution – Mass reduction encompasses a variety of 

techniques ranging from improved design and better component integration to 

application of lighter and higher-strength materials. Mass reduction is further 

compounded by reductions in engine power and ancillary systems (transmission, 
30

steering, brakes, suspension, etc.). The agencies recognize there is a range of 

diversity and complexity for mass reduction and material substitution technologies 

and there are many techniques that automotive suppliers and manufacturers are using 

to achieve the levels of this technology that the agencies have modeled in our analysis

for this proposal. The agencies seek comments on the methods, costs, and 

effectiveness estimates associated with mass reduction and material substitution 

techniques that manufacturers intend to employ for reducing fuel consumption and 

CO2 emissions during the rulemaking time frame.

Types of electrification/accessory and hybrid technologies considered include: 

• Electric power steering (EPS) – is an electrically-assisted steering system that has 

advantages over traditional hydraulic power steering because it replaces a 

continuously operated hydraulic pump, thereby reducing parasitic losses from the 

accessory drive.

• Improved accessories (IACC) – may include high efficiency alternators, electrically 

driven (i.e., on-demand) water pumps and cooling fans. This excludes other electrical 

accessories such as electric oil pumps and electrically driven air conditioner 

compressors. 

• Air Conditioner Systems – These technologies include improved hoses, connectors 

and seals for leakage control. They also include improved compressors, expansion 

valves, heat exchangers and the control of these components for the purposes of 

improving tailpipe CO2 emissions as a result of A/C use. These technologies are 

covered separately in the EPA RIA. 

• 12-volt micro-hybrid (MHEV) – also known as idle-stop or start stop and commonly 

implemented as a 12-volt belt-driven integrated starter-generator, this is the most 

basic hybrid system that facilitates idle-stop capability. Along with other enablers, 

this system replaces a common alternator with a belt-driven enhanced power starteralternator, and a revised accessory drive system.

• Higher Voltage Stop-Start/Belt Integrated Starter Generator (BISG) – provides idlestop capability and uses a high voltage battery with increased energy capacity over 

typical automotive batteries. The higher system voltage allows the use of a smaller, 

more powerful electric motor. This system replaces a standard alternator with an 

enhanced power, higher voltage, higher efficiency starter-alternator, that is belt driven 

and that can recover braking energy while the vehicle slows down (regenerative 

braking).

• Integrated Motor Assist (IMA)/Crank integrated starter generator (CISG)– provides 

idle-stop capability and uses a high voltage battery with increased energy capacity 

over typical automotive batteries. The higher system voltage allows the use of a 

smaller, more powerful electric motor and reduces the weight of the wiring harness. 

This system replaces a standard alternator with an enhanced power, higher voltage, 

Formatted: Subscript
31

higher efficiency starter-alternator that is crankshaft mounted and can recover braking 

energy while the vehicle slows down (regenerative braking).

• 2-mode hybrid (2MHEV) – is a hybrid electric drive system that uses an adaptation of 

a conventional stepped-ratio automatic transmission by replacing some of the 

transmission clutches with two electric motors that control the ratio of engine speed 

to vehicle speed, while clutches allow the motors to be bypassed. This improves both 

the transmission torque capacity for heavy-duty applications and reduces fuel 

consumption and CO2 emissions at highway speeds relative to other types of hybrid 

electric drive systems.

• Power-split hybrid (PSHEV)–a hybrid electric drive system that replaces the 

traditional transmission with a single planetary gearset and a motor/generator. This 

motor/generator uses the engine to either charge the battery or supply additional 

power to the drive motor. A second, more powerful motor/generator is permanently 

connected to the vehicle’s final drive and always turns with the wheels. The 

planetary gear splits engine power between the first motor/generator and the drive 

motor to either charge the battery or supply power to the wheels.

• Plug-in hybrid electric vehicles (PHEV) – are hybrid electric vehicles with the means 

to charge their battery packs from an outside source of electricity (usually the electric 

grid). These vehicles have larger battery packs with more energy storage and a 

greater capability to be discharged. They also use a control system that allows the 

battery pack to be substantially depleted under electric-only or blended 

mechanical/electric operation. 

• Electric vehicles (EV) – are vehicles with all-electric drive and with vehicle systems 

powered by energy-optimized batteries charged primarily from grid electricity. 

The cost estimates for the various hybrid systems have been revised from the estimates used 

in the MY 2011 CAFE final rule, in particular with respect to estimated battery costs. The 

agencies request comment on the hybrid cost estimates detailed in the draft Joint Technical 

Support Document.

2. How did the Agencies Determine the Costs and Effectiveness of Each of These 

Technologies?

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,18

the agencies took a fresh look at technology cost and effectiveness values for purposes of the 

joint proposal under the National Program. For costs, the agencies reconsidered both the direct 

or “piece” costs and indirect costs of individual components of technologies. For the direct 

 

18 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. 
32

costs, the agencies followed a bill of materials (BOM) approach employed by NHTSA in 

NHTSA’s MY 2011 final rule based on recommendation from Ricardo, Inc. EPA used a similar 

approach in the 2008 EPA Staff Technical Report. A bill of materials, in a general sense, is a list 

of components or sub-systems that make up a system—in this case, an item of fuel economyimproving technology. In order to determine what a system costs, one of the first steps is to 

determine its components and what they cost. 

NHTSA and EPA estimated these components and their costs based on a number of 

sources for cost-related information. The objective was to use those sources of information 

considered to be most credible for projecting the costs of individual vehicle technologies. For 

example, while NHTSA and Ricardo engineers had relied considerably in the MY 2011 final rule 

on the 2008 Martec Report for costing contents of some technologies, upon further joint review 

and for purposes of the MY 2012-2016 standards, the agencies decided that some of the costing 

information in that report was no longer accurate due to downward trends in commodity prices 

since the publication of that report. The agencies reviewed, then revalidated or updated cost 

estimates for individual components based on new information. Thus, while NHTSA and EPA 

found that much of the cost information used in NHTSA’s MY 2011 final rule and EPA’s staff 

report was consistent to a great extent, the agencies, in reconsidering information from many 

sources,19,20,21,22,23,24,25 revised several component costs of several major technologies: 

turbocharging with engine downsizing, mild and strong hybrids, diesels, stoichiometric gasoline 

direct injection fuel systems, and valve train lift technologies. These are discussed at length in 

the joint TSD and in NHTSA’s PRIA. 

For two technologies (stoichiometric gasoline direct injection and turbocharging with 

engine downsizing), the agencies relied, to the extent possible, on the tear down data available 

and scaling methodologies used in EPA’s ongoing study with FEV. This study consists of 

complete system tear-down to evaluate technologies down to the nuts and bolts to arrive at very 

detailed estimates of the costs associated with manufacturing them.26 The confidential 

information provided by manufacturers as part of their product plan submissions to the agencies 

 

19 National Research Council, “Effectiveness and Impact of Corporate Average Fuel Economy (CAFE) Standards,” 

National Academy Press, Washington, DC (2002) (the “2002 NAS Report”), available at 

http://www.nap.edu/openbook.php?isbn=0309076013 (last accessed August 7, 2009) 

20 Northeast States Center for a Clean Air Future (NESCCAF), “Reducing Greenhouse Gas Emissions from LightDuty Motor Vehicles,” 2004 (the “2004 NESCCAF Report”), available at 

http://www.nesccaf.org/documents/rpt040923ghglightduty.pdf (last accessed August 7, 2009) 

21 [add citation for ISOR] “Staff Report: Initial Statement of Reasons for Proposed Rulemaking, Public Hearing to 

Consider Adoption of Regulations to Control Greenhouse Gas Emissions from Motor Vehicles,” California 

Environmental Protection Agency, Air Resources Board, August 6, 2004.

22 Energy and Environmental Analysis, Inc., “Technology to Improve the Fuel Economy of Light Duty Trucks to 

2015,” 2006 (the “2006 EEA Report”), Docket EPA-HQ-OAR-2009-0472. 

23 Martec, “Variable Costs of Fuel Economy Technologies,” June 1, 2008, (the “2008 Martec Report”) available at 

Docket No. NHTSA-2008-0089-0169.1 

24 Vehicle fuel economy certification data 

25 Confidential data submitted by manufacturers in response to the March 2009 and other requests for product plans 

26 U.S. Environmental Protection Agengcy, “Draft Report – Light-Duty Technology Cost Analysis Pilot Study,” 

Contract No. EP-C-07-069, Work Assignment 1-3, September 3, 2009.Reference report/draft report of FEVwork.
33

or discussed in meetings between the agencies and the manufacturers and suppliers served 

largely as a check on publicly-available data. 

For the other technologies, considering all sources of information and using the BOM 

approach, the agencies worked together intensively during the summer of 2009 to determine 

component costs for each of the technologies and build up the costs accordingly. Where 

estimates differ between sources, we have used engineering judgment to arrive at what we 

believe to be the best cost estimate available today, and explained the basis for that exercise of 

judgment. 

Once costs were determined, they were adjusted to ensure that they were all expressed in 

2007 dollars using a ratio of GDP values for the associated calendar years,27 and indirect costs 

were accounted for using the new approach developed by EPA and explained in Chapter 3 of the 

draft joint TSD, rather than using the traditional Retail Price Equivalent (RPE) multiplier 

approach. A report explaining how EPA developed this approach can be found in the docket for 

this notice. NHTSA and EPA also reconsidered how costs should be adjusted by modifying or 

scaling content assumptions to account for differences across the range of vehicle sizes and 

functional requirements, and adjusted the associated material cost impacts to account for the 

revised content, although some of these adjustments may be different for each agency due to the 

different vehicle subclasses used in their respective models. In previous rulemakings, NHTSA 

has used the Producer Price Index (PPI) to adjust vehicle technology costs to consistent price 

levels, since the PPI measures the effects of cost changes that are specific to the vehicle 

manufacturing industry. For purposes of this NPRM, NHTSA and EPA chose to use the GDP 

deflator, which accounts for the effect of economy-wide price inflation on technology cost 

estimates, in order to express those estimates in comparable terms with forecasts of fuel prices 

and other economic values used in the analysis of costs and benefits from the proposed 

standards. Because it is specific to the automotive sector, the PPI tends to be highly volatile 

from year to year, reflecting rapidly changing balances between supply and demand for specific 

components, rather than longer-term trends in the real cost of producing a broad range of 

powertrain components. NHTSA and EPA seek comment on whether the agencies should use a 

GDP deflator or a PPI inflator for purposes of developing technology cost estimates for the final 

rule.

Regarding estimates for technology effectiveness, NHTSA and EPA also reexamined the 

estimates from NHTSA’s MY 2011 final rule and EPA’s ANPRM and 2008 Staff Technical 

Report, which were largely consistent with NHTSA’s 2008 NPRM estimates. The agencies also 

reconsidered other sources such as the 2002 NAS Report, the 2004 NESCCAF report, recent 

CAFE compliance data (by comparing similar vehicles with different technologies against each 

other in fuel economy testing, such as a Honda Civic Hybrid versus a directly comparable Honda 

Civic conventional drive), and confidential manufacturer estimates of technology effectiveness. 

NHTSA and EPA engineers reviewed effectiveness information from the multiple sources for 

each technology and ensured that such effectiveness estimates were based on technology 

 

27 NHTSA examined the use of the CPI multiplier instead of GDP for adjusting these dollar values, but found the 

difference to be exceedingly small – only $0.14 over $100. 
34

hardware consistent with the BOM components used to estimate costs. Together, they compared 

the multiple estimates and assessed their validity, taking care to ensure that common BOM 

definitions and other vehicle attributes such as performance, refinement, and drivability were 

taken into account. However, because the agencies’ respective models employ different numbers 

of vehicle subclasses and use different modeling techniques to arrive at the standards, direct 

comparison of BOMs was somewhat more complicated. To address this and to confirm that the 

outputs from the different modeling techniques produced the same result, NHTSA and EPA 

developed mapping techniques, devising technology packages and mapping them to 

corresponding incremental technology estimates. This approach helped compare the outputs 

from the incremental modeling technique to those produced by the technology packaging 

approach to ensure results that are consistent and could be translated into the respective models 

of the agencies. 

In general, 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 proposal. 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. 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 CO2 standards.

The agencies note that the effectiveness values estimated for the technologies considered 

in the modeling analyses may represent average values, and do not reflect the potentiallylimitless spectrum of possible values that could result from adding the technology to different 

vehicles. For example, while the agencies have estimated an effectiveness of 0.5 percent for low 

friction lubricants, each vehicle could have a unique effectiveness estimate depending on the 

baseline vehicle’s oil viscosity rating. Similarly, the reduction in rolling resistance (and thus the

improvement in fuel economy and the reduction in CO2 emissions) due to the application of low 

rolling resistance tires depends not only on the unique characteristics of the tires originally on the

vehicle, but on the unique characteristics of the tires being applied, characteristics which must be 

balanced between fuel efficiency, safety, and performance. Aerodynamic drag reduction is much 

the same—it can improve fuel economy and reduce CO2 emissions, but it is also highly 

dependent on vehicle-specific functional objectives. For purposes of this NPRM, NHTSA and 

EPA believe that employing average values for technology effectiveness estimates, as adjusted 

depending on vehicle subclass, is an appropriate way of recognizing the potential variation in the 

specific benefits that individual manufacturers (and individual vehicles) might obtain from 

adding a fuel-saving technology. However, the agencies seek comment on whether additional 

levels of specificity beyond that already provided would improve the analysis for the final rule, 

and if so, how those levels of specificity should be analyzed.

Chapter 3 of the draft Joint Technical Support Document contains a detailed description 

of our assessment of vehicle technology cost and effectiveness estimates. The agencies note that 
35

the technology costs included in this NPRM take into account only those associated with the 

initial build of the vehicle. The agencies seek comment on the additional lifetime costs, if any, 

associated with the implementation of advanced technologies including warranty costs, and

maintenance and replacement costs such as replacement costs for low rolling resistance tires, low 

friction lubricants, and hybrid batteries, and maintenance on diesel aftertreatment components.

F. Joint Economic Assumptions

The agencies’ preliminary analysis of alternative CAFE and GHG standards for the 

model years covered by this proposed rulemaking rely on a range of forecast information, 

economic estimates, and input parameters. This section briefly describes the agencies’ 

preliminary choices of specific parameter values. These proposed economic values play a 

significant role in determining the benefits of both CAFE and GHG standards. 

In reviewing these variables and the agency’s estimates of their values for purposes of 

this NPRM, NHTSA and EPA reconsidered previous comments that NHTSA had received and 

reviewed newly available literature. As a consequence, the agencies elected to revise some 

economic assumptions and parameter estimates, while retaining others. Some of the most 

important changes, which are discussed in greater detail in the agencies’ respective sections 

below, as well as in Chapter 4 of the joint TSD and in Chapter VIII of NHTSA’s PRIA and 

Chapter 8 of EPA’s DRIA, 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 CO2

emissions based on recent interagency efforts to develop estimates of this value for governmentwide use. The agencies seek comment on the economic assumptions described below. 

• Costs of fuel economy-improving technologies – These estimates are presented in 

summary form above and in more detail in the agencies’ respective sections of this 

preamble, in Chapter 3 of the joint TSD, and in the agencies’ respective RIAs. The 

technology cost estimates used in this analysis are intended to represent manufacturers’ 

direct costs for high-volume production of vehicles with these technologies and sufficient 

experience with their application so that all cost reductions due to “learning curve” 

effects have been fully realized. Costs are then modified by applying near-term 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, 

depending on the complexity of the technology and the time frame over which costs are 

estimated. 

• Potential opportunity costs of improved fuel economy – This estimate addresses the 

possibility that achieving the fuel economy improvements required by alternative CAFE 

or GHG standards would require manufacturers to compromise the performance, carrying 

capacity, safety, or comfort of their vehicle models. If it did so, the resulting sacrifice in 

the value of these attributes to consumers would represent an additional cost of achieving 

the required improvements, and thus of manufacturers’ compliance with stricter 

standards. Currently the agencies assume that these vehicle attributes do not change, and 

include the cost of maintaining these attributes as part of the cost estimates for 
36

technologies. However, it is possible that the technology cost estimates do not include 

adequate allowance for the necessary efforts by manufacturers to maintain vehicle 

performance, carrying capacity, and utility while improving fuel economy and reducing 

GHG emissions. While, in principle, consumer vehicle demand models can measure 

these effects, the reliability of measures from these models is not proventhese models do 

not appear to be robust across specifications, since authors derive a wide range of 

willingness-to-pay values for fuel economy from these models, and there is not clear 

guidance from the literature on whether one specification is clearly preferred over 

another. Thus, the agencies seek comment on how 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 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 NHTSA and EPA to establish compliance with the 

proposed CAFE and GHG standards The agencies use an on-road fuel economy gap for 

light-duty vehicles of 20 percent lower than published fuel economy levels. For example, 

if the measured CAFE fuel economy value 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).28 NHTSA previously used this estimate in its MY 2011 final rule, and the 

agencies confirmed it based on independent analysis for use in this NPRM 

• Fuel prices and the value of saving fuel – Projected future fuel prices are a critical input 

into the preliminary economic analysis of alternative standards, because they determine 

the value of fuel savings both to new vehicle buyers and to society. The agencies relied 

on the most recent fuel price projections from the U.S. Energy Information 

Administration’s (EIA) Annual Energy Outlook (AEO) for this analysis, 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.29

• Consumer valuation of fuel economy and payback period – In estimating the value of fuel 

economy improvements that would result from alternative CAFE and GHG standards to 

potential vehicle buyers, the agencies assume 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 that buyers discount the value of these future fuel savings using rates of 3% and 7%. 

 

28 U.S. Environmental Protection Agency, Final Technical Support Document, Fuel Economy Labeling of Motor 

Vehicle Revisions to Improve Calculation of Fuel Economy Estimates, EPA420-R-06-017, December 2006. 

29 Energy Information Administration, Annual Energy Outlook 2009, Revised Updated Reference Case (April

2009), Table 12. Available at http://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. 
37

The five-year figure represents the current average term of consumer loans to finance the 

purchase of new vehicles. 

• Vehicle sales assumptions – The first step in estimating lifetime fuel consumption by 

vehicles produced during a model year is to calculate the number that are expected to be 

produced and sold.30 The agencies relied on the AEO 2009 Reference Case for forecasts 

of total vehicle sales, while the baseline market forecast developed by the agencies (see 

Section II.B) divided total projected sales into sales of cars and light trucks. 

• Vehicle survival assumptions -- We then applied updated values of age-specific survival 

rates for cars and light trucks to these adjusted forecasts of passenger car and light truck 

sales to determine the number of these vehicles remaining in use during each year of their 

expected lifetimes. 

• Total vehicle use – We then calculated the total number of miles that cars and light trucks 

produced in each model year will be driven during each year of their lifetimes using 

estimates of annual vehicle use by age tabulated from the Federal Highway 

Administration’s 2001 National Household Transportation Survey (NHTS),31 adjusted to 

account for the effect on vehicle use of subsequent increases in fuel prices. In order to 

insure that the resulting mileage schedules imply reasonable estimates of future growth in 

total car and light truck use, we calculated the rate of growth in annual car and light truck 

mileage at each age that is necessary for total car and light truck travel to increase at the 

rates forecast in the AEO 2009 Reference Case. The growth rate in average annual car 

and light truck use produced by this calculation is approximately 1.1 percent per year.32 

This rate was 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.33

• Accounting for the rebound effect of higher fuel economy – The rebound effect refers to 

the fraction of fuel savings expected to result from an increase in vehicle fuel economy – 

 

30 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 

2 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).

31 For a description of the Survey, see http://nhts.ornl.gov/quickStart.shtml (last accessed July 27, 2009). 

32 It was not possible to estimate separate growth rates in average annual use for cars and light trucks, because of the 

significant reclassification of light truck models as passenger cars discussed previously. 

33 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 for passenger cars and about 16 

percent for light trucks. 
38

particularly an increase required by the adoption of higher CAFE and GHG 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 respond to this reduction in 

operating costs by driving slightly more. For purposes of this NPRM, the agencies have 

elected to use a 10 percent rebound effect in their analyses of fuel savings and other 

benefits from higher standards. 

• 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. The benefits from increased vehicle use include both the 

fuel expenses associated with this additional travel, and the consumer surplus it provides. 

We estimate the economic value of the consumer surplus provided by added driving 

using the conventional approximation, which is one half of the product of the decline in 

vehicle operating costs per vehicle-mile and the resulting increase in the annual number 

of miles driven. Because it depends on the extent of improvement in fuel economy, the 

value of benefits from increased vehicle use changes by model year and varies among 

alternative standards. 

• The value of increased driving range – By reducing the frequency with which drivers 

typically refuel their vehicles, and by extending the upper limit of the range they can 

travel before requiring refueling, improving fuel economy and reducing GHG emissions 

thus provides some additional benefits to their owners. No direct estimates of the value 

of extended vehicle range are readily available, so the agencies’ analysis calculates 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.34

 The agencies invite comment on the

assumptions used in this analysis. Please see the Chapter 4 of the draft Joint TSD for 

details.

•See the Chapter 4 of the draft Joint TSD for details.

• Added costs from congestion, crashes and noise – Although it provides some benefits to 

drivers, increased vehicle use associated with the rebound effect also contributes to 

increased traffic congestion, motor vehicle accidents, and highway noise. Depending on 

how the additional travel is distributed over the day and on where it takes place, 

additional vehicle use can contribute to traffic congestion and delays by increasing traffic 

volumes on facilities that are already heavily traveled during peak periods. These added 

delays impose higher costs on drivers and other vehicle occupants in the form of 

 

34 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 October 20, 2007); update available at

http://ostpxweb.dot.gov/policy/Data/VOTrevision1_2-11-03.pdf (last accessed October 20, 2007). 

Formatted: Bullets and Numbering
39

increased travel time and operating expenses, increased costs associated with traffic 

accidents, and increased traffic noise. The agencies rely on estimates of congestion, 

accident, and noise costs caused by automobiles and light trucks developed by the 

Federal Highway Administration to estimate the increased external costs caused by added 

driving due to the rebound effect.35

 

• 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. In economics literature on this subject, these costs include (1) 

higher prices for petroleum products resulting from the effect of U.S. oil import demand 

on the world oil price (“monopsony costs”); (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.36 Reducing U.S. imports of crude petroleum or refined 

fuels can reduce the magnitude of these external costs. Any reduction in their total value 

that results from lower fuel consumption and petroleum imports represents an economic 

benefit of setting more stringent standards over and above the dollar value of fuel savings 

itself. The agencies do not include a value for monopsony costs in order to be consistent 

with their use of a global value for the social cost of carbon. Based on a recently-updated 

ORNL study, we estimate that each gallon of fuel saved that results in a reduction in U.S. 

petroleum imports (either crude petroleum or refined fuel) will reduce the expected costs 

of oil supply disruptions to the U.S. economy by $0.169 (2007$). The agencies do not 

include savings in budgetary outlays to support U.S. military activities among the 

benefits of higher fuel economy and the resulting fuel savings. Each gallon of fuel saved 

as a consequence of higher standards is anticipated to reduce total U.S. imports of crude 

petroleum or refined fuel by 0.95 gallons.37

• Air pollutant emissions 

o Impacts on criteria air pollutant emissions – While reductions in domestic fuel 

refining and distribution that result from lower fuel consumption will reduce U.S. 

 

35 These estimates were developed by FHWA for use in its 1997 Federal Highway Cost Allocation Study; 

http://www.fhwa.dot.gov/policy/hcas/final/index.htm (last accessed July 29, 2009). 

36 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.

37 Each gallon of fuel saved is assumed to reduce imports of refined fuel by 0.5 gallons, and the volume of fuel 

refined domestically by 0.5 gallons. Domestic fuel refining is assumed to utilize 90% imported crude petroleum and 

10% domestically-produced crude petroleum as feedstocks. Together, these assumptions imply that each gallon of 

fuel saved will reduce imports of refined fuel and crude petroleum by 0.50 gallons + 0.50 gallons*90% = 0.50 

gallons + 0.45 gallons = 0.95 gallons. 
40

emissions of criteria pollutants, additional vehicle use associated with the rebound 

effect will increase emissions of these pollutants. Thus the net effect of stricter 

standards on emissions of each criteria pollutant depends on the relative 

magnitudes of reduced emissions from fuel refining and distribution, and 

increases in emissions resulting from added vehicle use. 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 (NOx), fine particulate matter (PM2.5), and sulfur oxides 

(SOx). It is assumed that the emission rates (per mile) stay constant for future 

year vehicles.

o EPA and NHTSA estimate the economic value of the human health benefits 

associated with reducing exposure to PM2.5 using a “benefit-per-ton” method. 

These PM2.5-related benefit-per-ton estimates provide the total monetized benefits 

to human health (the sum of reductions in premature mortality and premature 

morbidity) that result from eliminating one ton of directly emitted PM2.5, or one 

ton of a pollutant that contributes to secondarily-formed PM2.5 (such as NOx, 

SOx, and VOCs), from a specified source. Chapter 4.2.9 of the Technical 

Support Document that accompanies this proposal includes a description of these 

values. Reductions in GHG 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 

standards will thus reduce GHG emissions generated by fuel use, as well as 

throughout the fuel supply cycle. The agencies estimated the increases of GHGs 

other than CO2, including methane and nitrous oxide, from additional vehicle use 

by multiplying the increase in total miles driven by cars and light trucks of each 

model year and age by emission rates per vehicle-mile for these GHGs. These 

emission rates, which differ between cars and light trucks as well as between 

gasoline and diesel vehicles, were estimated by EPA using its recently-developed 

Motor Vehicle Emission Simulator (Draft MOVES 2009).

38 Increases in 

emissions of non-CO2 GHGs are converted to equivalent increases in CO2

emissions using estimates of the Global Warming Potential (GWP) of methane 

and nitrous oxide.

o Economic value of reductions in CO2 emissions - EPA and NHTSA assigned a 

dollar value to reductions in CO2 emissions using the marginal dollar value (i.e., 

cost) of climate-related damages resulting from carbon emissions, also referred to 

as “social cost of carbon” (SCC). The SCC is intended to measure the monetary 

 

38 The MOVES model assumes that the per-mile rates at which cars and light trucks emit these GHGs are 

determined by the efficiency of fuel combustion during engine operation and chemical reactions that occur during 

catalytic after-treatment of engine exhaust, and are thus independent of vehicles’ fuel consumption rates. Thus 

MOVES’ emission factors for these GHGs, which are expressed per mile of vehicle travel, are assumed to be 

unaffected by changes in fuel economy. 
41

value society places on impacts resulting from increased GHGs, such as property 

damage from sea level rise, forced migration due to dry land loss, and mortality 

changes associated with vector-borne diseases. Published estimates of the SCC 

vary widely as a result of uncertainties about future economic growth, climate 

sensitivity to GHG emissions, procedures used to model the economic impacts of 

climate change, and the choice of discount rates. EPA and NHTSA’s 

coordinated proposals present a set of interim SCC values reflecting a federal 

interagency group’s interpretation of the relevant climate economics literature. 

Sections III.H.6 and IV.C.3 provide more detail about SCC. 

• 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 non-climate related benefits 

of the proposed standards, the agencies have employed discount rates of both 3 percent 

and 7 percent. 

For the reader’s reference, Table II.F.1-1 below summarizes the values used to calculate 

the impacts of each proposed standard. The values presented in this table are summaries of the 

inputs used for the models; specific values used in the agencies’ respective analyses may be 

aggregated, expanded, or have other relevant adjustments. See the respective RIAs for details. 

The agencies seek comment on the economic assumptions presented in the table and discussed 

below. 

In addition, the agencies have conducted a range of sensitivities and present them in their 

respective RIAs. For example, NHTSA has conducted a sensitivity analysis on several 

assumptions including (1) forecasts of future fuel prices, (2) the discount rate applied to future 

benefits and costs, (3) the magnitude of the rebound effect, (4) the value to the U.S. economy of 

reducing carbon dioxide emissions, (5) the monopsony effect, and (6) the reduction in external 

economic costs resulting from lower U.S. oil imports. This information is provided in NHTSA’s 

PRIA. The agencies will consider additional sensitivities for the final rule as appropriate, 

including sensitivities on the markup factors applied to direct manufacturing costs to account for 

indirect costs (i.e., the Indirect Cost Markups (ICMs) which are discussed in Sections III and 

IV), and the learning curve estimates used in this analysis. 

Table II.F.1-1 Economic Values for Benefits Computations (2007$) 

Fuel Economy Rebound Effect 10%

"Gap" between test and on-road MPG 20%

Value of refueling time per ($ per vehicle-hour) $ 24.64

Annual growth in average vehicle use 1.1%

Fuel Prices (2012-50 average, $/gallon) 

Retail gasoline price $3.77

Pre-tax gasoline price $3.40

Economic Benefits from Reducing Oil Imports ($/gallon) 

"Monopsony" Component $ 0.00
42

Price Shock Component $ 0.17

Military Security Component $ 0.00

Total Economic Costs ($/gallon) $ 0.17

Emission Damage Costs (2020, $/ton or $/metric ton)

Carbon monoxide $ 0

Volatile organic compounds (VOC) $ 1,283

Nitrogen oxides (NOx) – vehicle use $ 5,116

Nitrogen oxides (NOx) – fuel production and distribution $ 5,339

Particulate matter (PM2.5) – vehicle use $ 238,432

Particulate matter (PM2.5) – fuel production and distribution $ 292,180

Sulfur dioxide (SO2) $ 30,896

Carbon dioxide (CO2) 

$ 5 

$ 10 

$ 20 

$ 34 

$ 56

Annual Increase in CO2 Damage Cost 3%

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

Congestion $ 0.054

Accidents $ 0.023

Noise $ 0.001

Total External Costs $ 0.078

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

Congestion $0.048

Accidents $0.026

Noise $0.001

Total External Costs $0.075

Discount Rates Applied to Future Benefits 3%, 7%