Final Rulemaking to Establish Light-Duty 

Vehicle Greenhouse Gas Emission 

Standards and Corporate Average Fuel 

Economy Standards 

Joint Technical Support Document 
EPA-420-R-10-901

April 2010

Assessment and Standards Division

Office of Transportation and Air Quality

U.S. Environmental Protection Agency

and

Office of International Policy, Fuel Economy, and Consumer Programs

National Highway Traffic Safety Administration

U.S. Department of Transportation

Final Rulemaking to Establish Light-Duty 

Vehicle Greenhouse Gas Emission 

Standards and Corporate Average Fuel 

Economy Standards 

Joint Technical Support Document 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

TABLE OF CONTENTS
 

Executive Summary iv
 

CHAPTER 1: The Baseline and Reference Vehicle Fleet 1-1
 

1.1	 Why do the agencies establish a baseline and reference vehicle fleet? 1-1
 

1.2	 The 2008 baseline vehicle fleet 1-2
 

1.2.1 Why did the agencies choose 2008 as the baseline model year? 1-2
 

1.3	 The MY 2011-2016 Reference Fleet 1-13
 

1.3.1 On what data is the reference vehicle fleet based?	 1-13
 

1.3.2 How do the agencies develop the reference vehicle fleet?	 1-15
 

1.3.3 How has the reference fleet changed from the NPRM to the Final Rule? 1-24
 

1.3.4 What are the sales volumes and characteristics of the reference fleet? 1-26
 

1.3.5 How is the development of the baseline and reference fleet for this final rule
 

different from NHTSA’s historical approach and why is this approach
 

preferable? 1-30
 

1.3.6 How does manufacturer product plan data factor into the baseline used in this
 

final rule? 1-31
 

CHAPTER 2: What Are the Attribute-Based Curves the Agencies Are Using, and How
 

Were They Developed? 2-1
 

2.1	 Standards are attribute-based and are defined by a mathematical function 2-1
 

2.2	 What attribute do the agencies use, and why? 2-5
 

2.3	 What mathematical function do the agencies use, and why? 2-5
 

CHAPTER 3: Technologies Considered in the Agencies’ Analysis	 3-1
 

3.1	 How do the agencies decide which technologies to include in the analysis? 3-1
 

3.1.1 Reports and papers in the literature	 3-1
 

3.1.2 Fuel economy certification data	 3-2
 

3.2	 Which technologies will be applicable during the model years to which the
 

rules apply? 3-3
 

3.3	 What technology assumptions have the agencies used for the final rule? 3-7
 

3.3.1	 How are the technologies applied in the agencies’ respective models? 3-7
 

3.3.2	 How did the agencies develop technology cost and effectiveness estimates for
 

the final rule? 3-9
 

3.4	 Specific technologies considered and estimates of costs and effectiveness 3-20
 

3.4.1 What data sources did the agencies evaluate?	 3-20
 

3.4.2 Individual technology descriptions and cost/effectiveness estimates 3-20
 

3.5	 Cost and effectiveness tables 3-83
 

3.5.1	 NHTSA cost and effectiveness tables 3-83
 

3.5.2 EPA cost and effectiveness tables 3-91
 

Appendix 3.A 3-96
 

CHAPTER 4: Economic Assumptions Used in the Agencies’ Analysis 4-1
 

4.1	 How the Agencies use the economic assumptions in their analyses 4-1
 

4.2	 What economic assumptions do the agencies use? 4-2
 

4.2.1 The on-road fuel economy “gap”	 4-2
 

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4.2.2 Fuel prices and the value of saving fuel 4-3
 

4.2.3 Vehicle survival and use assumptions 4-6
 

4.2.4 Accounting for the fuel economy rebound effect 4-16
 

4.2.5 Benefits from increased vehicle use 4-23
 

4.2.6 Added costs from increased vehicle use 4-23
 

4.2.7 Petroleum and energy security impacts 4-24
 

4.2.8 Air pollutant emissions 4-36
 

4.2.9 Reductions in emissions of greenhouse gases 4-45
 

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Executive Summary 

The Environmental Protection Agency (EPA) and the National Highway Traffic 

Safety Administration (NHTSA) are issuing a joint final rulemaking which establishes 

new standards for light-duty highway vehicles that will reduce greenhouse gas emissions 

and improve fuel economy. The joint final rulemaking is consistent with the National 

Fuel Efficiency Policy announced by President Obama on May 19, 2009, responding to 

the country’s critical need to address global climate change and to reduce oil 

consumption. EPA is finalizing greenhouse gas emissions standards under the Clean Air 

Act, and NHTSA is finalizing Corporate Average Fuel Economy standards under the 

Energy Policy and Conservation Act, as amended. These standards apply to passenger 

cars, light-duty trucks, and medium-duty passenger vehicles, covering model years 2012 

through 2016. They require these vehicles to meet an estimated combined average 

emissions level of 250 grams of CO2 per mile in MY 2016 under EPA’s GHG program, 

and 34.1 mpg in MY 2016 under NHTSA’s CAFE program and represent a harmonized 

and consistent national program (National Program). These standards are designed such 

that compliance can be achieved with a single national vehicle fleet whose emissions and 

fuel economy performance improves each year from MY2012 to 2016. This document 

describes the supporting technical analysis for areas of these jointly finalized rules which 

are consistent between the two agencies. 

NHTSA and EPA have coordinated closely to create a nationwide joint fuel 

economy and GHG program based on consistent compliance structures and technical 

assumptions. To the extent permitted under each Agency’s statutes, NHTSA and EPA 

have incorporated the same compliance flexibilities, such as averaging, banking, and 

trading of credits, and the same testing protocol for determining the agencies’ respective 

fleet-wide average standards. In addition, the agencies have worked together to create a 

common baseline fleet and to harmonize most of the costs and benefit inputs used in the 

agencies’ respective modeling processes for this joint finalized rule. 

Chapter 1 of this document provides an explanation of the agencies’ new 

methodology used to develop the baseline and reference case vehicle fleets, including the 

technology composition of these fleets, and how the agencies projected vehicle sales into 

the future. One of the fundamental features of this technical analysis is the development 

of these fleets, which are used by both agencies in their respective models. In order to 

determine technology costs associated with this joint rulemaking, it is necessary to 

consider the vehicle fleet absent a rulemaking as a “business as usual” comparison. In 

past CAFE rulemakings, NHTSA has used confidential product plans submitted by 

vehicle manufacturers to develop the reference case fleet. In responding to comments 

from these previous rulemakings that the agencies make these fleets available for public 

review, the agencies created a new methodology for creating baseline and reference fleets 

using data, the vast majority of which is publicly available. 

Chapter 2 of this document discusses how NHTSA and EPA developed the 

mathematical functions which provide the bases for manufacturers’ car and truck 

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standards. NHTSA and EPA worked together closely to develop regulatory approaches 

that are fundamentally the same, and have chosen to use an attribute-based program 

structure based on the footprint attribute, like NHTSA’s current Reformed CAFE 

program. The agencies revisited other attributes as candidates for the standard functions, 

but concluded that footprint remains the best option for balancing the numerous technical 

and social factors. However, the agencies did adjust the shape of the footprint curve, in 

contrast to the 2011 CAFE rule, the CO2 or fuel consumption curve is a piecewise linear 

or constrained linear function, rather than a constrained logistic function. In determining 

the shape of the footprint curve, the agencies considered factors such as the magnitudes 

of CO2 reduction and fuel savings, how much that shape may entice manufacturers to 

comply in a manner which circumvents the overall goals of the joint program, whether 

the standards’ stringencies are technically attainable, and the mathematical flexibilities 

inherent to such a function 

Chapter 3 contains a detailed analysis of NHTSA and EPA’s technology 

assumptions on which the finalized regulations were based. Because the majority of 

technologies that reduce GHG emissions and improve fuel economy are identical, it was 

crucial that NHTSA and EPA use common assumptions for values pertaining to 

technology availability, cost, and effectiveness. The agencies collaborated closely in 

determining which technologies would be considered in the rulemaking, how much these 

technologies would cost the manufacturers (directly) in the time frame of the rules, how 

these costs will be adjusted for learning as well as for indirect cost multipliers, and how 

effective the technologies are at accomplishing the goals of improving fuel efficiency and 

GHG emissions. 

Chapter 4 of this TSD provides a full description and analysis of the economic 

factors considered in this joint final rulemaking. EPA and NHTSA harmonized many of 

the economic and social factors, such as the discount rates, fuel prices, the magnitude of 

the rebound effect, the value of refueling time, and the social cost of importing oil and 

fuel. 

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The Baseline and Reference Vehicle Fleet
 

CHAPTER 1: The Baseline and Reference Vehicle Fleet 

The passenger cars and light trucks sold currently in the United States, and those 

which are anticipated to be sold in the MY 2012-2016 timeframe, are highly varied and 

satisfy a wide range of consumer needs. From two-seater miniature cars to 11-seater 

passenger vans to large extended cab pickup trucks, American consumers have a great 

number of vehicle options to accommodate their utility needs and preferences. Recent 

volatility in oil prices and the state of the economy have demonstrated that consumer 

demand and choice of vehicles within this wide range can be sensitive to these factors. 

Although it is impossible for anyone or any organization to precisely predict the future, a 

characterization and quantification of the future fleet are required to assess the impacts of 

rules which would affect that future fleet. In order to do this, the various leading 

publicly-available sources are examined, and a series of models are relied upon that help 

us to project the composition of a reference fleet. This chapter describes the process for 

accomplishing this. 

Most of the public comments to the NPRM supported this methodology for 

developing the inputs to the rule's analysis. Because the input sheets can be made public, 

stakeholders can verify and check EPA’s and NHTSA’s modeling, and perform their own 

analyses with these datasets. Many commenters stated that creating a transparent fleet 

from public sources was a significant improvement over previous rulemakings, although 

other commenters raised accuracy issues with regard to the continuation in the agencies’ 

analysis of MY 2008 vehicles into the future model years covered by the rulemaking. 

There were no comments on methodology, but GM did comment that they believe the 

agencies had projected more full size trucks and full size vans than they believe would be 

produced. EPA had already noticed, after the NPRM had been published, that the 

standard CSM forecast included heavy duty class 2b and class 3 vehicles. EPA requested 

that CSM make a custom forecast with these vehicles removed for the final rulemaking. 

1.1 Why do the agencies establish a baseline and reference vehicle fleet? 

In order to calculate the impacts of the EPA and NHTSA final rule, it is necessary to 

estimate the composition of the future vehicle fleet absent the final CAFE/GHG standards 

in order to conduct comparisons. EPA in consultation with NHTSA has developed a 

comparison fleet in two parts. The first step was to develop a baseline fleet based on 

model year 2008 data. EPA and NHTSA create a baseline fleet in order to track the 

volumes and types of fuel economy-improving and CO2-reducing technologies which are 

already present in today’s fleet. Creating a baseline fleet helps to keep, to some extent, 

the agencies’ models from adding technologies to vehicles that already have these 

technologies, which would result in “double counting” of technologies’ costs and 

benefits. The second step was to project the baseline fleet sales into MYs 2011-2016. 

This is called the reference fleet, and it represents the fleet that would exist in MYs 2011­

2016 absent any change from current regulations. The third step was to add technologies 

to that fleet such that each manufacturer’s average car and truck CO2 levels are in 

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compliance with their MY 2011 CAFE standards. This final “reference fleet” is the light 

duty fleet estimated to exist in MYs 2012-2016 without the final CAFE/GHG standards. 

All of the agencies’ estimates of emission reductions/fuel economy improvements, costs, 

and societal impacts are developed in relation to the respective reference fleets. The 

chapter describes the first two steps of the development of the baseline and reference 

fleets. The third step of technology addition is developed separately by each agency as 

the outputs of the OMEGA and Volpe models. The process is described in section II of 

the preamble and in each agency’s respective RIAs. 

1.2 The 2008 baseline vehicle fleet 

1.2.1 Why did the agencies choose 2008 as the baseline model year? 

The baseline vehicle fleet developed by EPA in consultation with NHTSA and is 

comprised of model year 2008 data. MY 2008 was used as the basis for the baseline 

vehicle fleet, because it is the most recent model year for which a complete set of data is 

publicly available. Vehicle manufacturers have 90 days after their last vehicle is 

produced to submit their CAFE data to EPA.1 Most manufacturers interpret this to mean 

90 days after the end of the calendar year. For example, in calendar year 2007, model 

year 2008 vehicles were tested and certified by the EPA. These MY 2008 vehicles were 

then sold in the latter part (often fall) of 2007 until the following fall of 2008. In early 

2009 (calendar year), the manufacturers then submit their total sales of MY 2008 

vehicles. After these sales figures were submitted, EPA and NHTSA combined the sales 

with the previously measured and reported fuel economies to calculate the sales-weighted 

average fleet fuel economy. Even though the fuel economies (and some other 

specifications) of the MY 2009 vehicles were known, since they were tested earlier, the 

sales were not yet known for each company exactly. Full MY 2009 sales data is not 

available until April 2010, due to the fact that manufacturers have 90 days after the end 

of the model year to submit their data.a Therefore, the agencies chose to use MY 2008 as 

the baseline since it was the latest complete transparent data set available. 

1.2.1.1 On what data is the baseline vehicle fleet based? 

As part of the CAFE program, EPA measures vehicle CO2 emissions and converts 

them to mpg and generates and maintains the federal fuel economy database. Most of the 

information about the 2008 vehicle fleet was gathered from EPA’s emission certification 

and fuel economy database, most of which is available to the public. The data obtained 

from this source included vehicle production volume, fuel economy, carbon dioxide 

emissions, fuel type, number of engine cylinders, displacement, valves per cylinder, 

engine cycle, transmission type, drive, hybrid type, and aspiration. However, EPA’s 

certification database does not include a detailed description of the types of fuel 

economy-improving/CO2-reducing technologies considered in this final rule, because this 

level of information is not necessary for emission certification or fuel economy testing. 

Thus, the agency augmented this description with publicly-available data which includes 

a § 600.512-08 Model Year Report 

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The Baseline and Reference Vehicle Fleet
 

more complete technology descriptions from Ward’s Automotive Group.2,b In a few 

instances when required vehicle information was not available from these two sources 

(such as vehicle footprint), this information was obtained from publicly-accessible 

internet sites such as Motortrend.com, Edmunds.com and other sources to a lesser extent 

(such as articles about specific vehicles revealed from internet search engine research.3,c 

The baseline vehicle fleet for the analysis in this rule is comprised of publiclyavailable data to the largest extent possible. However, a few relatively low-impact 

technologies were added based on confidential information provided from some 

manufacturers (within their product plan submissions to NHTSA and EPA). This was 

done because the data were not available from any other source. These technologies 

include low friction lubricants, electric power steering, improved accessories, and low 

rolling resistance tires. This confidential information has been excised from the baseline 

data submitted to the docket, though the summary results are still used, so that any 

specific information cannot be traced back to any specific manufacturer. This 

discrepancy between the public baseline and the one used by the agencies is relatively 

minor and results in only result small differences in the outputs of the Volpe and 

OMEGA models for certain manufacturers. 

Creating the 2008 baseline fleet Excel file was an extremely labor intensive 

process. EPA in consultation with NHTSA first considered using EPA’s CAFE 

certification data, which contains most of the required information. However, since the 

deadline for manufacturers to report this data did not allow enough time for early 

modeling review, it was necessary to start this process using an alternative data source. 

The agencies next considered using EPA’s vehicle emissions certification data, 

which contains much of the required information, however it lacked the production 

volumes that are necessary for the OMEGA and Volpe models. The data set also 

contains some vehicle models manufacturers have certified, but not produced. A second 

data source which would supply production volumes and eliminate extraneous vehicles 

was needed. Data from a paid subscription to Ward’s Automotive Group was used as the 

second source for data, which contains production volumes and vehicle specifications. 

The vehicle emissions certification dataset came in two parts, an engine file and a 

vehicle file. Since there was a common index in the two files, the engine and vehicle 

data were easily combined into one spreadsheet. The agencies had hoped to supplement 

this dataset with production volume data from Ward’s Automotive Group but the Ward’s 

data does not have production volumes for individual vehicles down to the resolution of 

the specific engine and transmission level. Although production volumes from Ward’s 

Automotive Group could not be used, the subscription did provide specific details on 

individual vehicles and engines. The Ward’s data used came in two parts (engine file and 

vehicle file), and also required mapping. In this case, mapping was more difficult since 

there was no common index between the two files. A new index was implanted in the 

engine file and a search equation in the vehicle file, which identified most of the vehicle 

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

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

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and engine combinations. Each vehicle and engine combination was reviewed and 

corrections were made manually when the search routine failed to give the correct engine 

and vehicle combination. The combined Ward’s data was then mapped to the vehicle 

emissions certification data by creating a new index in the combined Ward’s data and 

using the same process that was used to combine the Ward’s engine and vehicle files. In 

the next step, CAFE certification data had to be merged in order to fill out the needed 

production volumes. 

NHTSA and EPA reviewed the CAFE certification data for model year 2008 as it 

became available. The CAFE certification dataset could have been used with the Ward’s 

data without the vehicle emission certification dataset, but was instead appended to the 

combined Ward’s and vehicle certification dataset. The two former datasets were then 

mapped into the CAFE dataset using the same Excel mapping technique described above. 

Finally, EPA and NHTSA obtained the remaining attribute and technology data, such as 

footprint, curb weight, and others (for a complete list of data with sources see Table 1-1 

below) from other sources (such as the internet and the confidential product plan data), 

thus completing the baseline dataset. 

This was the first time a baseline fleet was created using this method. Given the 

long delay before the CAFE certification data became available, EPA explored creating 

the alternative dataset. It is possible to create the same baseline with CAFE certification 

data, the Ward’s engine data, a limited amount of product plan data, and some internet 

searches. 

Table 1-1 below shows the columns of the complete fleet file, which includes the 

2008 MY baseline data that was compiled. Each column has its name, definition 

(description) and source. The EV and Aero columns were added to the fleet file to more 

accurately describe vehicles for the final rule. The data that is marked “not available” is 

data NHTSA would normally get from product plans. As mentioned above, some of the 

desired model inputs, such as the presence of low rolling resistance tires, reduced engine 

friction, improved accessories, etc., are not available from public sources and the 

agencies had to rely on manufacturers’ confidential product plans. The Technology 

Effectiveness Basis and the Cost Effectiveness Basis values reflect the percent of a 

technology package’s effectiveness and cost present in the baseline fleet, and they are 

described in further detail in chapters 3.1 and 3.5, respectively. Those technologies that 

are not accounted for in the baseline—that is, the ones marked “not available”—run the 

risk of getting double counted by the agencies’ models, but those effects are expected to 

be small. 

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The Baseline and Reference Vehicle Fleet
 

Table 1-1 Data, Definitions, and Sources 

Data Item Definition Where The Data is From 

Manufacturer 

Common name of company that manufactured 

vehicle. May include more name plates than Cert 

Manufacturer Name. Certification data 

CERT Manufacturer Name Certification name of company that manufactured 

vehicle Certification data 

Name Plate Name of Division Certification data 

Model Name of Vehicle Certification data 

Reg Class EPA Fuel Economy Class Name Certification data 

Class 

If a car’s Footprint<43 then "SubCmpctAuto" 

If a car’s 43<=Footprint<46 then "CompactAuto" 

If a car’s 46<=Footprint<53 then "MidSizeAuto” 

If a car’s Footprint >=53 then “LargeAuto” 

If a S.U.V.’s Footprint < 43 then “SmallSuv” 

If a S.U.V.’s 43<=Footprint<46 then 

"MidSizeSuv” 

If a S.U.V’s Footprint >=46 then “LargeSuv” 

If a Truck’s Footprint < 50 then “SmallPickup” 

If a Truck’s Footprint>=50 then “LargPickup” 

If a Van’s Structure is Ladder then “CargoVan” 

If a Van’s Structure is Unibody then “Minivan” 

Derived From Certification 

data and Footprint 

CSM Class CSM Worldwides class for the vehicle. Used to 

weight vehicles based on CSM data. CSM Worldwide 

Traditional Car/Truck C= Car, T=Truck. As defined in the certification 

database. Not used in calculations. Certification data 

Total Production Volume Total number of vehicles produced for that model. Certification data 

Fuel Econ. 

(mpg) EPA Unadjusted Fuel Economy Certification data 

Curb 

Weight (lb) 

Vehicles Curb Weight 

Some from Edmonds.com or 

Motortrend.com, Others from 

product plans with a subset 

verified with Edmonds.com or 

Motortrend.com for accuracy. 

Area (sf) Average Track x Wheelbase 

Calculated from track width 

and wheel base 

Fuel Gas or Diesel Wards 

Disp 

(lit.) Engine Cylinder Displacement Size in Liters Wards/Certification data 

Effective Cyl Number of Cylinder + 2 if the engine has a turbo or 

super charger. 

Derived From Certification 

data. 

Actual Cylinders Actual Number of Engine Cylinders Certification data 

Valves Per Cylinder Number of Valves Per Actual Cylinder Certification data 

Valve Type Type of valve actuation. 

Wards (Note:Type E is from 

Cert Data) 

Engine Cycle As Defined by EPA Cert. Definition Wards 

Horsepower Max. Horsepower of the Engine Wards 

Torque Max. Torque of the Engine Wards 

Trans Type A=Auto AMT=Automated Manual M=Manual 

CVT= Continuously Variable Transmission Certification data 

Trans Type Code with number of Gears Certification data 

Num of Gears Number of Gears Certification data 

Structure Ladder or Unibody 

General Internet Searches using 

Google.com 

Drive Fwd, Rwd, Awd, 4wd Certification data 

Wheelbase 

Length of Wheelbase 

Some from Edmonds.com or 

Motortrend.com, Others from 

product plans with a subset 

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verified with Edmonds.com or 

Motortrend.com for accuracy. 

Track Width 

(front) 

Length of Track Width in inches 

Some from Edmonds.com or 

Motortrend.com, Others from 

product plans with a subset 

verified with Edmonds.com or 

Motortrend.com for accuracy. 

Track Width 

(rear) 

Length of Track Width in inches 

Some from Edmonds.com or 

Motortrend.com, Others from 

product plans with a subset 

verified with Edmonds.com or 

Motortrend.com for accuracy. 

Footprint Average Track x Wheelbase 

Calculated from track width 

and wheel base 

Curb 

Weight 

Curb Weight of the Vehicle 

Some from Edmonds.com or 

Motortrend.com, Others from 

product plans with a subset 

verified with Edmonds.com or 

Motortrend.com for accuracy. 

GVWR 

Gross Vehicle Weight Rating of the Vehicle 

Some from Edmonds.com or 

Motortrend.com, Others from 

product plans with a subset 

verified with Edmonds.com or 

Motortrend.com for accuracy. 

HYBRID Is the Vehicle a Hybrid? Certification data 

HYBRID TYPE Type of Hybrid Certification data 

Turbo Turbo Charged Engine Certification data 

Super Charged Super Charged Certification data 

Turbo and/or Super Charged Either or both Turbo charged / Super Charged Certification data 

SOHC Single Overhead Cam Engine Wards 

DOHC Dual Overhead Cam Engine Wards 

OHV Overhead Valve Engine Wards 

1 

LUB Low friction lubricants—incremental to base 

engine 

Obtained from product plans 

from manufacturers that report 

this information.2 

2 

EFR Engine friction reduction—incremental to base 

engine 

Obtained from product plans 

from manufacturers that report 

this information.2 

3 

CCPS VVT - Coupled Cam Phasing (CCP) on SOHC Wards1 

4 

DVVLS Discrete Variable Valve Lift (DVVL) on SOHC Wards1 

5 

DEACS Cylinder Deactivation on SOHC Wards1 

6 

ICP VVT - Intake Cam Phasing (ICP) Wards1 

7 

DCP VVT - Dual Cam Phasing (DCP) (independent) Wards1 

8 

DVVLD Discrete Variable Valve Lift (DVVL) on DOHC Wards1 

9 

CVVL Continuously Variable Valve Lift (CVVL) Wards1 

10 

DEACD Cylinder Deactivation on DOHC Wards1 

11 

DEACO Cylinder Deactivation on OHV Wards1 

12 

CCPO VVT - Coupled Cam Phasing (CCP) on OHV Wards1 

13 

DVVLO Discrete Variable Valve Lift (DVVL) on OHV Wards1 

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The Baseline and Reference Vehicle Fleet
 

14 

CDOHC Conversion to DOHC with DCP Not available 

15 

SGDI Stoichiometric Gasoline Direct Injection (GDI) Wards1 

16 

CBRST Combustion Restart Not available 

18 

EGRB Exhaust Gas Recirculation (EGR) Boost Not available 

19 

DSLT Conversion to Diesel (from TRBDS) Not available 

20 

DSLC Conversion to Diesel (from CBRST) Not available 

21 

6MAN 6-Speed Manual/Improved Internals Certification data 

22 

IATC Improved Auto. Trans. Controls/Externals Not available 

23 

CVT Continuously Variable Transmission Certification data 

24 

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

25 

DCTAM Dual Clutch or Automated Manual Transmission Certification data 

26 

EPS Electric Power Steering 

Obtained from product plans 

from manufacturers that report 

this information.2 

27 

IACC Improved Accessories 

Obtained from product plans 

from manufacturers that report 

this information.2 

28 

MHEV 12V Micro-Hybrid Not available 

29 

HVIA Higher Voltage/Improved Alternator Not available 

30 

BISG Belt Mounted Integrated Starter Generator Certification data 

31 

CISG Crank Mounted Integrated Starter Generator Certification data 

32 

PSHEV Power Split Hybrid Certification data 

33 

2MHEV 2-Mode Hybrid Certification data 

34 

PHEV Plug-in Hybrid Not available 

35 

MS1 Material Substitution (1%) Not available 

36 

MS2 Material Substitution (2%) Not available 

37 

MS5 Material Substitution (5%) Not available 

38 

ROLL Low Rolling Resistance Tires 

Obtained from product plans 

from manufacturers that report 

this information.2 

39 

LDB Low Drag Brakes Not available 

EV Electric Vehicle Not available 

Aero Aerodynamics Optimised Not available 

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Joint Technical Support Document
 

Import Car Cars Imported Certification data 

Index Index Used to link EPA and NHTSA baselines Created 

Volume 2011 

Projected Production Volume for 2011 

Calculated based on 2008 

volume and AEO and CSM 

adjustment factors. 

Volume 2012 

Projected Production Volume for 2012 

Calculated based on 2008 

volume and AEO and CSM 

adjustment factors. 

Volume 2013 

Projected Production Volume for 2013 

Calculated based on 2008 

volume and AEO and CSM 

adjustment factors. 

Volume 2014 

Projected Production Volume for 2014 

Calculated based on 2008 

volume and AEO and CSM 

adjustment factors. 

Volume 2015 

Projected Production Volume for 2015 

Calculated based on 2008 

volume and AEO and CSM 

adjustment factors. 

Volume 2016 

Projected Production Volume for 2016 

Calculated based on 2008 

volume and AEO and CSM 

adjustment factors. 

Vehicle Type Number 

Vehicle Type Number assigned to a vehicle based 

on its number of cylinders, valves per cylinder, and 

valve actuation technology. Mapping done by Agencies 

Footprint: PU Average 

Car Foot Print is normal. Truck footprint is the 

production weighted average for each vehicle. 

Derived from data from 

Edmunds.com or 

Motortrend.com. Production 

volumes or specific footprints 

from product plans. 

Footprint: PU Cutoff Same as above, but footprint valve for trucks 

limited to 66 before weighting. Meaning valves 

greater than 66 equal 66. 

Derived from data from 

Edmunds.com or 

Motortrend.com. Production 

volumes or specific footprints 

from product plans. 

NHTSA Defined New NHTSA 

Car/Truck 

New NHTSA Car Truck value as determined by 

NHTSA. Used in calculations. NHTSA 

CO2 

CO2 calculated from MPG. CO2 weighted 1.15 

times higher for diesel vehicles. Certification data 

Thresholded FootPrint 

Footprint valve that will be set to 41 for values less 

than 41, Will be set to 56 for car values > 56, and 

will be set to 66 for truck values >66 

Used in Summary Calculations 

Derived from data from 

Edmunds.com or 

Motortrend.com. Production 

volumes or specific footprints 

from product plans. 

TEB Tech 1 

Percentage of Tech Package 1 

Calculated using a macro in 

another spread sheet. 

Calculated based on the 

technology in the "Data" sheet. 

TEB Tech 2 

Percentage of Tech Package 2 

Calculated using a macro in 

another spread sheet. 

Calculated based on the 

technology in the "Data" sheet. 

TEB Tech 3 

Percentage of Tech Package 3 

Calculated using a macro in 

another spread sheet. 

Calculated based on the 

technology in the "Data" sheet. 

TEB Tech 4 

Percentage of Tech Package 4 

Calculated using a macro in 

another spread sheet. 

Calculated based on the 

technology in the "Data" sheet. 

TEB Tech 5 Percentage of Tech Package 5 

Calculated using a macro in 

another spread sheet. 

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The Baseline and Reference Vehicle Fleet
 

Calculated based on the 

technology in the "Data" sheet. 

TEB Tech 6 

Percentage of Tech Package 6 

Calculated using a macro in 

another spread sheet. 

Calculated based on the 

technology in the "Data" sheet. 

CEB Tech 1 

Percentage of Cost of Tech Package 1 

Calculated using a macro in 

another spread sheet. 

Calculated based on the 

technology in the "Data" sheet. 

CEB Tech 2 

Percentage of Cost of Tech Package 2 

Calculated using a macro in 

another spread sheet. 

Calculated based on the 

technology in the "Data" sheet. 

CEB Tech 3 

Percentage of Cost of Tech Package 3 

Calculated using a macro in 

another spread sheet. 

Calculated based on the 

technology in the "Data" sheet. 

CEB Tech 4 

Percentage of Cost of Tech Package 4 

Calculated using a macro in 

another spread sheet. 

Calculated based on the 

technology in the "Data" sheet. 

CEB Tech 5 

Percentage of Cost of Tech Package 5 

Calculated using a macro in 

another spread sheet. 

Calculated based on the 

technology in the "Data" sheet. 

CEB Tech 6 

Percentage of Cost of Tech Package 6 

Calculated using a macro in 

another spread sheet. 

Calculated based on the 

technology in the "Data" sheet. 

Notes: 

1. For engines not available in the WardsAuto.com engine file, an internet search was done to find this information. 

2. These data were obtained from manufacturer’s product plans. They were used to block (where possible) the model from adding 

technology that was already on a vehicle. 

3. Ward’s Automotive Group data obtained from "2008 Light Vehicle Engines." 

The sales volumes for the MY 2008 baseline fleet are included in the section 

below on reference fleet under the MY 2008 columns. Table 1-2 displays the 

technologies present in the baseline fleet. 

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Joint Technical Support Document
 

Table 1-2 2008 Technology Percentages 

Turbo

Super Charged

SOHC

DOHC

OHV

LUB

CCPS

DVVLSDEACS

ICP

DCP

DVVLD 

All Trucks 1.0% 0.2% 23.7% 47.6% 28.7% 61.4% 4.1% 5.8% 0.0% 5.3% 36.6% 1.5% 

All Cars 3.4% 0.2% 17.4% 73.3% 9.2% 48.5% 1.4% 7.4% 1.3% 3.2% 47.9% 0.2% 

BMW Trucks 0.0% 0.0% 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 94.7% 0.0% 

BMW Cars 6.3% 1.1% 13.9% 86.0% 0.0% 0.0% 11.9% 11.9% 0.0% 0.0% 85.8% 1.5% 

Chrysler Trucks 0.2% 0.0% 38.9% 4.1% 56.9% 76.9% 0.0% 0.0% 0.0% 0.0% 3.9% 0.0% 

Chrysler Cars 0.9% 0.0% 20.8% 71.5% 7.7% 28.5% 0.0% 0.0% 0.0% 0.0% 41.7% 0.0% 

Ford Trucks 0.0% 0.1% 62.9% 34.3% 2.8% 3.9% 26.5% 0.0% 0.0% 0.0% 9.3% 0.0% 

Ford Cars 3.1% 0.9% 14.2% 85.8% 0.0% 6.4% 4.0% 0.0% 0.0% 0.0% 5.2% 0.0% 

Subaru Trucks 3.2% 0.0% 70.5% 29.5% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 29.5% 27.4% 

Subaru Cars 14.5% 0.0% 69.2% 30.8% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 30.8% 0.9% 

GM Trucks 0.0% 0.0% 0.0% 30.8% 69.2% 92.7% 0.0% 0.0% 0.0% 14.7% 30.8% 0.0% 

GM Cars 2.6% 0.0% 0.0% 56.2% 43.8% 92.0% 0.0% 0.0% 0.0% 11.5% 28.8% 0.0% 

Honda Trucks 4.2% 0.0% 64.3% 35.7% 0.0% 0.0% 0.0% 64.3% 0.0% 0.0% 31.5% 4.2% 

Honda Cars 0.0% 0.0% 57.2% 42.8% 0.0% 0.0% 0.0% 57.2% 10.7% 0.0% 42.6% 0.3% 

Hyundai Trucks 0.0% 0.0% 0.0% 100.0% 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 29.6% 0.0% 

Hyundai Cars 0.0% 0.0% 0.0% 100.0% 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 32.2% 0.0% 

Tata Trucks 0.0% 20.3% 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 100.0% 0.0% 

Tata Cars 0.0% 0.0% 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 100.0% 0.0% 

Kia Trucks 0.0% 0.0% 0.0% 100.0% 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 17.2% 0.0% 

Kia Cars 0.0% 0.0% 0.0% 100.0% 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 10.2% 0.0% 

Mazda Trucks 23.5% 0.0% 0.6% 99.4% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 99.3% 0.0% 

Mazda Cars 11.4% 0.0% 0.0% 100.0% 0.0% 0.0% 0.9% 0.9% 0.9% 0.9% 99.9% 0.9% 

Daimler Trucks 15.9% 0.7% 36.2% 63.8% 0.0% 0.0% 0.0% 0.0% 0.0% 46.7% 0.0% 0.0% 

Daimler Cars 2.4% 0.3% 54.6% 45.4% 0.0% 0.0% 0.0% 0.0% 0.0% 30.3% 0.0% 0.0% 

Mitsubishi Trucks 0.0% 0.0% 100.0% 0.0% 0.0% 100.0% 37.7% 0.0% 0.0% 0.0% 0.0% 0.0% 

Mitsubishi Cars 0.0% 0.0% 100.0% 0.0% 0.0% 100.0% 42.9% 0.0% 0.0% 0.0% 0.0% 0.0% 

Nissan Trucks 0.0% 0.0% 0.0% 100.0% 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 100.0% 0.0% 

Nissan Cars 0.0% 0.0% 0.0% 100.0% 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 100.0% 0.0% 

Porsche Trucks 12.4% 0.0% 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 0.0% 100.0% 100.0% 100.0% 

Porsche Cars 17.4% 0.0% 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 0.0% 28.7% 82.6% 0.0% 

Suzuki Trucks 0.0% 0.0% 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

Suzuki Cars 0.0% 0.0% 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

Toyota Trucks 0.0% 0.0% 0.0% 100.0% 0.0% 89.1% 0.0% 0.0% 0.0% 0.0% 78.3% 0.0% 

Toyota Cars 0.0% 0.0% 0.0% 100.0% 0.0% 69.9% 0.0% 0.0% 0.0% 0.2% 99.4% 0.0% 

VW Trucks 0.6% 0.0% 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 0.0% 78.9% 99.4% 78.9% 

VW Cars 43.1% 0.0% 85.0% 15.0% 0.1% 0.0% 0.0% 0.0% 0.0% 1.9% 14.1% 0.8% 

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The Baseline and Reference Vehicle Fleet
 

Table 1-2 2008 Technology Percentages 

CVVL

DEACD

DEACO

CCPO

DVVLO

SGDI

6MAN

CVT

NAUTO

DCTAM

EPS

BISG

CISG 

All Trucks 2.8% 0.0% 10.5% 1.4% 0.0% 2.9% 1.0% 2.6% 19.9% 0.0% 0.9% 0.0% 0.0% 

All Cars 5.2% 0.0% 1.2% 5.7% 0.0% 4.7% 2.3% 11.5% 18.5% 0.8% 12.6% 0.0% 0.4% 

BMW Trucks 0.0% 0.0% 0.0% 0.0% 0.0% 6.3% 0.7% 0.0% 99.3% 0.0% 0.0% 0.0% 0.0% 

BMW Cars 0.0% 0.0% 0.0% 0.0% 0.0% 7.7% 14.6% 0.5% 83.3% 1.1% 0.0% 0.0% 0.0% 

Chrysler Trucks 0.0% 0.0% 0.0% 0.0% 0.0% 0.2% 4.3% 3.6% 21.4% 0.0% 0.0% 0.0% 0.0% 

Chrysler Cars 0.0% 0.0% 5.2% 0.0% 0.0% 0.0% 0.9% 18.5% 3.9% 0.0% 0.0% 0.0% 0.0% 

Ford Trucks 0.0% 0.0% 0.0% 0.0% 0.0% 1.0% 0.0% 1.8% 18.8% 0.0% 0.0% 0.0% 0.0% 

Ford Cars 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 1.2% 1.9% 36.8% 0.0% 0.0% 0.0% 0.0% 

Subaru Trucks 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

Subaru Cars 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 4.1% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

GM Trucks 0.0% 0.0% 39.1% 5.0% 0.0% 0.0% 0.0% 0.0% 18.6% 0.0% 0.0% 0.0% 0.0% 

GM Cars 0.0% 0.0% 4.0% 28.8% 0.0% 6.0% 1.1% 0.0% 12.3% 0.0% 0.0% 0.1% 0.0% 

Honda Trucks 31.5% 0.0% 0.0% 0.0% 0.0% 4.2% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

Honda Cars 42.6% 0.0% 0.0% 0.0% 0.0% 0.0% 3.1% 3.2% 0.0% 0.0% 0.0% 0.0% 3.2% 

Hyundai Trucks 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 25.0% 0.0% 0.0% 0.0% 0.0% 

Hyundai Cars 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.2% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

Tata Trucks 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 

Tata Cars 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 91.6% 0.0% 0.0% 0.0% 0.0% 

Kia Trucks 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

Kia Cars 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

Mazda Trucks 0.0% 0.0% 0.0% 0.0% 0.0% 23.5% 0.0% 0.1% 57.2% 0.0% 0.0% 0.0% 0.0% 

Mazda Cars 0.9% 0.9% 0.9% 0.9% 0.9% 11.4% 3.8% 0.1% 19.2% 0.0% 0.0% 0.0% 0.0% 

Daimler Trucks 0.0% 0.0% 0.0% 0.0% 0.0% 15.9% 0.0% 0.0% 99.3% 0.0% 0.0% 0.0% 0.0% 

Daimler Cars 0.0% 0.0% 0.0% 0.0% 0.0% 1.7% 0.3% 0.0% 67.4% 10.4% 0.0% 0.0% 0.0% 

Mitsubishi Trucks 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 14.4% 23.3% 0.0% 0.0% 0.0% 0.0% 

Mitsubishi Cars 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 1.4% 44.2% 7.6% 0.0% 0.0% 0.0% 0.0% 

Nissan Trucks 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 1.2% 16.5% 0.0% 0.0% 16.5% 0.0% 0.0% 

Nissan Cars 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 4.5% 69.4% 0.0% 0.0% 32.3% 0.0% 0.0% 

Porsche Trucks 0.0% 0.0% 0.0% 0.0% 0.0% 100.0% 1.4% 0.0% 98.6% 0.0% 0.0% 0.0% 0.0% 

Porsche Cars 0.0% 0.0% 0.0% 0.0% 0.0% 17.4% 51.9% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

Suzuki Trucks 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

Suzuki Cars 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

Toyota Trucks 0.0% 0.0% 0.0% 0.0% 0.0% 5.7% 1.4% 4.7% 19.2% 0.0% 0.0% 0.0% 0.0% 

Toyota Cars 0.0% 0.0% 0.0% 0.0% 0.0% 7.6% 0.1% 16.7% 22.5% 0.0% 64.0% 0.0% 0.0% 

VW Trucks 0.0% 0.0% 0.0% 0.0% 0.0% 100.0% 0.0% 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 

VW Cars 0.0% 0.0% 0.0% 0.0% 0.0% 46.0% 6.4% 5.4% 65.7% 14.3% 0.0% 0.0% 0.0% 

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Joint Technical Support Document
 

Table 1-2 2008 Technology Percentages 

PSHEV

2MHEV

PHEV

Auto 4 SpeedAuto 5 SpeedAuto 6 SpeedAuto 7 SpeedAuto 8 Speed

Manual 5 SpeedManual 6 Speed

CVT

Automated Manual

5 Speed

Automated Manual

6 Speed

Automated Manual

7 Speed 

All Trucks 0.8% 0.1% 0.0% 37.9% 37.3% 18.6% 1.4% 0.0% 1.1% 1.0% 2.6% 0.0% 0.0% 0.0% 

All Cars 2.7% 0.0% 0.0% 39.1% 22.9% 16.4% 1.7% 0.4% 4.9% 2.3% 11.5% 0.3% 0.5% 0.0% 

BMW Trucks 0.0% 0.0% 0.0% 0.0% 0.0% 99.3% 0.0% 0.0% 0.0% 0.7% 0.0% 0.0% 0.0% 0.0% 

BMW Cars 0.2% 0.0% 0.2% 0.0% 0.0% 82.6% 0.8% 0.0% 0.3% 14.6% 0.5% 0.0% 0.0% 1.1% 

Chrysler Trucks 0.0% 0.0% 0.0% 34.9% 35.5% 21.4% 0.0% 0.0% 0.3% 4.3% 3.6% 0.0% 0.0% 0.0% 

Chrysler Cars 0.0% 0.0% 0.0% 67.0% 8.2% 3.9% 0.0% 0.0% 1.4% 0.9% 18.5% 0.0% 0.0% 0.0% 

Ford Trucks 0.0% 0.0% 0.0% 55.9% 20.9% 18.8% 0.0% 0.0% 2.7% 0.0% 1.8% 0.0% 0.0% 0.0% 

Ford Cars 0.0% 0.0% 0.0% 36.3% 18.2% 36.8% 0.0% 0.0% 5.6% 1.2% 1.9% 0.0% 0.0% 0.0% 

Subaru Trucks 0.0% 0.0% 0.0% 62.1% 29.5% 0.0% 0.0% 0.0% 8.4% 0.0% 0.0% 0.0% 0.0% 0.0% 

Subaru Cars 0.0% 0.0% 0.0% 69.6% 2.4% 0.0% 0.0% 0.0% 23.9% 4.1% 0.0% 0.0% 0.0% 0.0% 

GM Trucks 0.0% 0.5% 0.0% 75.5% 5.0% 19.0% 0.0% 0.0% 0.4% 0.0% 0.0% 0.0% 0.0% 0.0% 

GM Cars 0.0% 0.1% 0.0% 77.9% 5.4% 12.3% 0.0% 0.0% 3.2% 1.1% 0.0% 0.0% 0.0% 0.0% 

Honda Trucks 0.0% 0.0% 0.0% 0.0% 99.7% 0.0% 0.0% 0.0% 0.3% 0.0% 0.0% 0.0% 0.0% 0.0% 

Honda Cars 0.0% 0.0% 0.0% 0.0% 87.3% 0.0% 0.0% 0.0% 6.4% 3.1% 3.2% 0.0% 0.0% 0.0% 

Hyundai Trucks 0.0% 0.0% 0.0% 29.8% 45.0% 25.0% 0.0% 0.0% 0.2% 0.0% 0.0% 0.0% 0.0% 0.0% 

Hyundai Cars 0.0% 0.0% 0.0% 76.4% 16.7% 0.0% 0.0% 0.0% 6.7% 0.2% 0.0% 0.0% 0.0% 0.0% 

Tata Trucks 0.0% 0.0% 0.0% 0.0% 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

Tata Cars 0.0% 0.0% 0.0% 0.0% 8.4% 91.6% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

Kia Trucks 0.0% 0.0% 0.0% 23.1% 75.7% 0.0% 0.0% 0.0% 1.2% 0.0% 0.0% 0.0% 0.0% 0.0% 

Kia Cars 0.0% 0.0% 0.0% 51.5% 41.0% 0.0% 0.0% 0.0% 7.5% 0.0% 0.0% 0.0% 0.0% 0.0% 

Mazda Trucks 0.0% 0.0% 0.0% 10.1% 29.6% 57.2% 0.0% 0.0% 3.0% 0.0% 0.1% 0.0% 0.0% 0.0% 

Mazda Cars 0.0% 0.0% 0.0% 33.3% 29.7% 19.2% 0.0% 0.0% 13.9% 3.8% 0.1% 0.0% 0.0% 0.0% 

Daimler Trucks 0.0% 0.0% 0.0% 0.0% 0.7% 0.0% 99.3% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

Daimler Cars 0.0% 0.0% 0.0% 0.0% 22.0% 0.0% 67.4% 0.0% 0.0% 0.3% 0.0% 10.4% 0.0% 0.0% 

Mitsubishi Trucks 0.0% 0.0% 0.0% 62.3% 0.0% 23.3% 0.0% 0.0% 0.0% 0.0% 14.4% 0.0% 0.0% 0.0% 

Mitsubishi Cars 0.0% 0.0% 0.0% 30.8% 4.5% 7.6% 0.0% 0.0% 11.6% 1.4% 44.2% 0.0% 0.0% 0.0% 

Nissan Trucks 0.0% 0.0% 0.0% 0.0% 81.6% 0.0% 0.0% 0.0% 0.7% 1.2% 16.5% 0.0% 0.0% 0.0% 

Nissan Cars 1.1% 0.0% 0.0% 7.0% 19.1% 0.0% 0.0% 0.0% 0.0% 4.5% 69.4% 0.0% 0.0% 0.0% 

Porsche Trucks 0.0% 0.0% 0.0% 0.0% 0.0% 98.6% 0.0% 0.0% 0.0% 1.4% 0.0% 0.0% 0.0% 0.0% 

Porsche Cars 0.0% 0.0% 0.0% 0.0% 33.7% 0.0% 0.0% 0.0% 14.4% 51.9% 0.0% 0.0% 0.0% 0.0% 

Suzuki Trucks 0.0% 0.0% 0.0% 0.0% 99.5% 0.0% 0.0% 0.0% 0.5% 0.0% 0.0% 0.0% 0.0% 0.0% 

Suzuki Cars 0.0% 0.0% 0.0% 76.2% 13.1% 0.0% 0.0% 0.0% 10.7% 0.0% 0.0% 0.0% 0.0% 0.0% 

Toyota Trucks 4.7% 0.0% 0.0% 9.0% 63.9% 19.2% 0.0% 0.0% 1.9% 1.4% 4.7% 0.0% 0.0% 0.0% 

Toyota Cars 16.9% 0.0% 0.0% 35.7% 19.5% 19.8% 0.0% 2.6% 5.5% 0.1% 16.7% 0.0% 0.0% 0.0% 

VW Trucks 0.0% 0.0% 0.0% 0.0% 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

VW Cars 0.0% 0.0% 0.0% 0.0% 0.0% 65.7% 0.0% 0.0% 8.2% 6.4% 5.4% 0.0% 14.3% 0.0% 

As noted at proposal, these technology tables indicate that some manufacturers 

are already implementing some of these technologies in their 2008 fleet. VW stands out 

as having a significant number of turbocharged direct injection engines, though it is 

1-12 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

The Baseline and Reference Vehicle Fleet
 

uncertain whether their engines are also downsized. Some of the valve and cam 

technologies are quite common today: for example, nearly half the fleet has dual cam 

phasing, while Honda and GM are have considerable levels of engines with cylinder 

deactivation (DEAC). Honda also has already implemented continuously variable valve 

lift (CVVL) on a majority of their engines. Moreover, Toyota has the highest penetration 

of hybrid technologies. Finally, regarding the technologies obtained from the product 

plans, many of the manufacturers who reported the lubricating oil information indicated 

that low friction (or low viscosity) oil will be used more frequently in the future. 

Therefore, it is likely that this technology is underreported for the fleet as a whole since it 

was assumed that the usage of low viscosity oils was zero for the companies who did not 

provide product plans. The lack of reporting of these types of technologies (if they exist 

in the fleet) will probably lead to a slightly higher cost of compliance for these 

companies. However, these technologies alone do not necessarily guarantee a better fuel 

economy compared to other vehicles in their footprint class. There are many other 

considerations at play, most notably, weight and power. 

The section below provides further detail on the conversion of the MY 2008 

baseline into the MY 2011-2016 reference fleet. It also describes more of the data 

contained in the baseline spreadsheet. 

1.3 The MY 2011-2016 Reference Fleet 

The reference fleet aims to reflect the current market conditions and expectations 

about conditions of the vehicle fleet during the model years to which the agencies’ rules 

apply. Fundamentally, constructing this fleet involved projecting the MY 2008 baseline 

fleet into the MY 2011-2016 model years. It also included the assumption that none of 

the models had changes during this period. Projecting what the fleet will look like in the 

future is a process that is inherently uncertain. NHTSA and EPA therefore relied on 

many sources of reputable information to make these projections. 

1.3.1 On what data is the reference vehicle fleet based? 

As explained at proposal, EPA and NHTSA have based the projection of total car 

and light truck sales on recent projections made by the Energy Information 

Administration (EIA). EIA publishes a mid-term projection of national energy use 

annually called the Annual Energy Outlook (AEO).4 No commenter challenged the 

appropriateness of using the AEO. At proposal, the agencies used the various AEO 2009 

reports for the sales projections used in constructing the reference fleet used as the basis 

for the proposed rule. However, EIA also recently published an early version of its 2010 

Annual Energy Outlook. This early 2010 report was published in December, in time to be 

used in this rule making. The differences in projected sales in the updated 2009 report 

and the early 2010 report are very small, so the agencies just scaled the data from the 

2009 report data to the absolute number of vehicles (cars and trucks) in the 2010 report. 

The agencies recognize that AEO 2010 Early Release does include some impacts of 

future projected increases in CAFE stringency. We have closely examined the difference 

between AEO 2009 and AEO 2010 Early Release and we believe the differences in total 

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sales and the car/truck split attributed to considerations of the standard in the final rule 

5 are small. 

Table 1-3below shows the scaling factors that were used for each model year. 

We use the sales projections from the scaled updated 2009 Annual Energy Outlook. 

Table 1-3 Annual Energy Outlook 2010 Scaling Factora 

2011 2012 2013 2014 2015 2016 

Multiplier 0.958339 1.004719 1.011593 0.997677 0.992585 0.995178 a The scaling factors were determined by dividing the absolute number of vehicles in 

AEO 2010 for each model year shown by the absolute number of vehicles in AEO 2009 

for each of those model years. 

In the AEO 2010 Early Release, EIA projects that total light-duty vehicle sales 

gradually recover from their currently depressed levels by roughly 2013. In 2016, car 

and light truck sales are projected to be 9.4 and 7.1 million units, respectively. While the 

total level of sales of 16.5 million units is similar to pre-2008 levels, the fraction of car 

sales is projected to be higher than that existing in the 2000-2007 timeframe. This 

projection reflects the impact of higher fuel prices, as well as the effects of the EISA 

(which called for a minimum fleet average of 35 mpg by 2020). 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. The MY 2011 CAFE final rule reclassified a number 

of 2-wheel drive sport utility vehicles from the truck fleet to the car fleet. EIA’s sales 

projections of cars and trucks for the 2011-2016 model years under both the new and old 

NHTSA truck definition are shown in Table 1-4 and Table 1-5 below, respectively. 

Actual vehicle sales for MY 2008 are also shown under the new NHTSA truck definition. 

Slight differences exist between the total car and truck sales projections. These 

differences are less than 0.1 percent and are due to the iterative process described further 

below to shift sales between car and truck market segments. 

Table 1-4 AEO 2010 Volumes New NHTSA Car Truck Definition After Projections 

2008 

Baseline 

Sales 

2011 

Projected 

Volume 

2012 

Projected 

Volume 

2013 

Projected 

Volume 

2014 

Projected 

Volume 

2015 

Projected 

Volume 

2016 

Projected 

Volume 

All Trucks 5,620,847 5,457,758 5,798,588 6,038,484 5,947,819 5,826,579 5,669,683 

All Cars 8,220,517 7,922,992 9,123,197 9,797,738 10,231,974 10,627,055 10,832,348 

Total 13,841,364 13,380,750 14,921,784 15,836,222 16,179,793 16,453,634 16,502,032 

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Table 1-5 AEO 2010 Volumes Old NHTSA Car Truck Definition Before Projections 

2011 

Projected 

Volume 

2012 

Projected 

Volume 

2013 

Projected 

Volume 

2014 

Projected 

Volume 

2015 

Projected 

Volume 

2016 

Projected 

Volume 

All Trucks 6,665,386 7,160,069 7,478,667 7,423,880 7,280,946 7,079,323 

All Cars 6,715,364 7,761,715 8,357,555 8,755,912 9,172,688 9,422,709 

Total 13,380,750 14,921,784 15,836,222 16,179,793 16,453,634 16,502,032 

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 continuing to introduce more crossover models which offer 

much of the utility of SUVs but use more car-like designs and unibody structures. In 

order to reflect these changes in fleet makeup, EPA and NHTSA considered several 

available forecasts. After reviewing information from a number of forecasting 

organizations, the agencies decided to purchase forecasts from two well-known industry 

analysts, CSM Worldwide (CSM) and J.D. Powers.6 NHTSA and EPA decided to use 

the forecast from CSM, for several reasons. One, CSM agreed to allow us to publish 

their high level data, on which the forecast is based, in the public domain. Two, it 

covered nearly all the timeframe of greatest relevance to this 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. EPA did need to have CSM’s 

standard forecast (that covers all vehicles below 14,000 lbs GVW) customized to cover 

only light duty vehicles and medium duty passenger vehicles. As discussed further 

below, the CSM forecast is combined with other data obtained by NHTSA and EPA. The 

breakdown of car and truck sales by manufacturer and by market segment for the 2016 

model year and beyond were assumed to be the same as CSM’s forecast for the 2015 

calendar year, because CSM forecasts for 2016 will not be available until the 1st quarter 

of 2010. 

1.3.2 How do the agencies develop the reference vehicle fleet? 

The process of producing the 2011-2016 reference fleet involved combining the 

baseline fleet with the projection data described above. This was a complex multistep 

procedure, which is described in this section. 

1.3.2.1 How was the 2008 baseline data merged with the CSM data? 

As explained at proposal, merging the 2008 baseline data with the 2011-2016 

CSM data required a thorough mapping of certification vehicles to CSM vehicles by 

individual make and model. One challenge the agencies faced when determining a 

reference case fleet was that the sales data projected by CSM had different market 

segmentation than the data contained in EPA’s internal database. In order to create a 

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common segmentation between the two databases, side-by-side comparison of the 

specific vehicle models in both datasets was performed, and an additional “CSM 

segment” modifier in the spreadsheet was created, thus mapping the two datasets. The 

reference fleet sales based on the “CSM segmentation” was then projected. 

The baseline data and reference fleet volumes are available to the public. The 

baseline Excel spreadsheet in the docket is the result of the merged files.7 It provides 

specific details on the sources and definitions for the data. The Excel file contains 

several tabs. They are: “Data”, “Data Tech Definitions”, “SUM”, “SUM Tech 

Definitions”, “Truck Vehicle Type Map”, and “Car Vehicle Type Map”. “Data” is the 

tab with the raw data. “Data Tech Definitions” is the tab where each column is defined 

and its data source named. “SUM” is the tab where the raw data is processed to be used 

in the OMEGA and Volpe models. The “SUM” tab minus columns A-F and minus the 

Generic vehicles is the input file for the models. The “Generic” manufacturer (shown in 

the “SUM” tab) is the sum of all manufacturers and is calculated as a reference, and for 

data verification purposes. It is used to validate the manufacturers’ totals. It also gives 

an overview of the fleet. Table 1-6 shows the sum of the models chosen. The number of 

models is determined by the number of unique segment and vehicle type combinations. 

These combinations of segment and vehicle type (the vehicle type number is the same as 

the technology package number) are determined by the technology packages discussed 

in.the EPA RIA (chapter 1). “SUM Tech Definitions” is the tab where the columns of 

the “SUM” tab are defined. The “Truck Vehicle Type Map” and “Car Vehicle Type 

Map” map the number of cylinder and valve actuation technology to the “tech package” 

vehicle type number. 

Table 1-6 Models from the SUM Tab Model 

Model 

Car Like LargeSuv >=V8 Vehicle Type: 13 

Car Like LargeSuv V6 Vehicle Type: 16 

Car Like LargeSuv V6 Vehicle Type: 12 

Car Like LargeSuv V6 Vehicle Type: 9 

Car Like LargeSuv I4 and I5 Vehicle Type: 7 

Car Like MidSizeSuv V6 Vehicle Type: 8 

Car Like MidSizeSuv V6 Vehicle Type: 5 

Car Like MidSizeSuv I4 Vehicle Type: 7 

Car Like SmallSuv V6 Vehicle Type: 12 

Car Like SmallSuv V6 Vehicle Type: 4 

Car Like SmallSuv I4 Vehicle Type: 3 

LargeAuto >=V8 Vehicle Type: 13 

LargeAuto >=V8 Vehicle Type: 10 

LargeAuto >=V8 Vehicle Type: 6 

LargeAuto V6 Vehicle Type: 12 

LargeAuto V6 Vehicle Type: 5 

MidSizeAuto >=V8 Vehicle Type: 13 

MidSizeAuto >=V8 Vehicle Type: 10 

MidSizeAuto >=V8 (7 or >) Vehicle Type: 6 

MidSizeAuto V6 Vehicle Type: 12 

MidSizeAuto V6 Vehicle Type: 8 

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MidSizeAuto V6 Vehicle Type: 5
 

MidSizeAuto I4 Vehicle Type: 3
 

In the combined EPA certification and CSM database, all of the 2008 vehicle 

models were assumed to continue out to 2016, though their volumes changed in 

proportion to CSM projections. Also, any new models expected to be introduced within 

the 2009-2016 timeframe are not included in the data. These volumes are reassigned to 

the existing models. All MY 2011-2016 vehicles are mapped to the existing vehicles by 

a process of mapping to manufacturer market share and overall segment distribution. The 

mappings are discussed in the next section. Further discussion of this limitation is 

discussed below in section 1.3.4. The statistics of this fleet will be presented below since 

further modifications were required to the volumes as the next section describes. 

1.3.2.2 How were the CSM forecasts normalized to the AEO forecasts? 

As also explained at proposal, the next step in the production of the reference fleet 

is one of the more complicated steps to explain. Here, the projected CSM forecasts for 

relative sales of cars and trucks by manufacturer and by market segment was normalized 

(set equal) to the total sales estimates of the preliminary 2010 Annual Energy Outlook 

(AEO). NHTSA and EPA used projected car and truck volumes for this period from 

AEO 2009. However, the AEO projects sales only at the car and truck level, not at the 

manufacturer and model-specific level, which are needed for the analysis. The CSM data 

provided year-by-year percentages of cars and trucks sold by each manufacturer as well 

as the percentages of each vehicle segment. Using these percentages normalized to the 

AEO-projected volumes then provided the manufacturer-specific market share and 

model-specific sales for model years 2011-2016 (it is worth clarifying that the agencies 

are not using the model-specific sales volumes from CSM, only the volumes by 

manufacturer and segment). This process is described in greater detail in the following 

paragraphs. 

In order to determine future production volumes, multipliers were developed by 

manufacturer and vehicle segment that could be applied to MY 2008 volumes. The 

process for developing the multipliers is complicated, but is easiest to explain as a threestep process, though the first step is combined with both the second and third step, so 

only one multiplier per manufacturer and vehicle segment is developed. 

The three steps are: 

1.	 Adjust total car and truck sales to match AEO projections. 

2.	 Adjust Car sales to match CSM market share projections for each manufacturer 

and car segment. 

3.	 Adjust Truck sales to match CSM market share projections for each manufacturer 

and truck segment. 

The first step is the adjustment of total car and truck sales in 2008 to match AEO 

projections of total car and truck sales in 2011-2016. The volumes for all of the trucks in 

2008 were added up (TruckSum2008), and so were the volumes of all the cars 

(CarSum2008). A multiplier was developed to scale the volumes in 2008 to the AEO 

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projections. The example equation below shows the general form of how to calculate a 

car or truck multiplier. The AEO projections are shown above in Table 1-4. 

Example Equation : 

TruckMultiplier(Year X) = AEOProjectionforTrucks(Year X) / TruckSum2008 

CarMultiplier(Year X) = AEOProjectionforCars(Year X) / CarSum2008 

Where: Year X is the model year of the multiplier. 

The AEO projection is different for each model year. Therefore, the multipliers 

are different for each model year. The multipliers can be applied to each 2008 vehicle as 

a first adjustment, but multipliers based solely on AEO have limited value since it can 

only give an adjustment that will give the correct total numbers of cars and trucks without 

the correct market share or vehicle mix. A correction factor based on the CSM data, 

which does contain market share and vehicle segment mix, is therefore necessary, so 

combining the AEO multiplier with CSM multipliers (one per manufacturer, segment, 

and model year) will give the best multipliers. 

There were several steps in developing an adjustment for Cars based on the CSM 

data. CSM provided data on the market share and vehicle segment distribution. The first 

step in determining the adjustment for Cars was to total the number of Cars in each 

vehicle segment by manufacturer in MY 2008. A total for all manufacturers in each 

segment was also calculated. The next step was to multiply the volume of each segment 

for each manufacturer by the CSM market share. The AEO multiplier was also applied at 

this time. This gave projected volumes with AEO total volumes and market share 

correction for Cars. This is shown in the “Adjusted for 2011 AEO and Manufacturer 

Market Share” column of Table 1-7. 

The next step is to adjust the sales volumes for CSM vehicle segment distribution. 

The process for adjusting for vehicle segment is more complicated than a simple one step 

multiplication. In order to keep manufacturers’ volumes constant and still have the 

correct vehicle segment distribution, vehicles need to move from segment to segment 

while maintaining constant manufacturers’ totals. Six rules and one assumption were 

applied to accomplish the shift. The assumption (based on the shift in vehicle sales in the 

last year) is that people are moving to smaller vehicles in the rulemaking time frame. A 

higher level (less detailed) example of this procedure is provided in the preamble section 

II. 

1.	 Vehicles from CSM’s “Luxury Car,” “Specialty Car,” and “Other Car” segments, 

if reduced will be equally distributed to the remaining four categories (“Full-Size 

Car,” “Mid-Size Car,” “Small Car,” “Mini Car”). If these sales increased, they 

were taken from the remaining four categories so that the relative sales in these 

four categories remained constant. 

2.	 Vehicles from CSM’s “Luxury Car,” “Specialty Car,” and “Other Car” segments, 

if increased will take equally from the remaining categories (“Full-Size Car,” 

“Mid-Size Car,” “Small Car,” “Mini Car”). 

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3.	 All manufacturers have the same multiplier for a given segment shift based on 

moving all vehicles in that segment to achieve the CSM distribution. Table 1-7 

shows how the 2011 vehicles moved and the multipliers that were created for 

each adjustment. This does not mean that new vehicle segments will be added 

(except for Generic Mini Car described in the next step) to manufacturers that do 

not produce them. Vehicles within each manufacturer will be shifted as close to 

the distribution as possible given the other rules. Table 1-8 has the percentages of 

Cars per CSM segment. These percentages are multiplied by the total number of 

vehicles in a given year to get the total sales in the segment. Table 1-7 shows the 

totals for 2011 in the “2011 AEO-CSM Sales Goal” column. 

4.	 When “Full-Size Car,” “Mid-Size Car,” “Small Car” are processed, if vehicles 

need to move in or out of the segment, they will move into or out of the next 

smaller segment. So, if Mid-Size Cars are being processed they can only move to 

or be taken from Small Cars. Note: In order to accomplish this, a “Generic Mini 

Car” segment was added to manufacturers who did not have a Mini (type) Car in 

production in 2008, but needed to shift down vehicles from the Small Car 

segment. 

5.	 The data must be processed in the following order: “Luxury Car,” “Specialty 

Car,” “Other Car,” “Full-Size Car,” “Mid-Size Car,” “Small Car.” The “Mini 

Car” does not need to be processed separately. By using this order, it works out 

that vehicles will always move toward the correct distribution. There are two 

exceptions, BMW and Porsche only have “Luxury Car,” “Specialty Car,” and 

“Other Car” vehicles, so their volumes were not changed or shifted since these 

rules did not apply to them. 

6.	 When an individual manufacturer multiplier is applied for a segment, the vehicles 

move to or from the appropriate segments as specified in the previous rules and as 

shown in Table 1-7. 

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Table 1-7 Example: 2011 Model Year Volume Shift* 

CSM Segment 

2008 MY 

Sales 

Adjusted for 

2011 AEO 

and 

Manufacturer 

Market Share 

Luxury, Specialty, 

Other Adjustment 

Full Size 

Adjustment 

Midsize 

Adjustment 

Small Car 

Adjustment 

2011 AEOCSM Sales 

Goal 

All Full-Size Car 730,355 501,245 520,885 411,025 411,025 411,025 411,025 

All Luxury Car 1,057,875 1,076,470 970,680 970,680 970,680 970,680 970,666 

All Mid-Size Car 1,970,494 1,946,981 2,033,087 2,142,948 1,838,095 1,838,095 1,838,095 

All Mini Car 599,643 686,738 733,339 733,339 733,339 991,309 991,349 

All Small Car 1,850,522 2,007,527 2,099,343 2,099,343 2,404,196 2,146,226 2,146,226 

All Specialty Car 754,547 783,982 637,785 637,785 637,785 637,785 637,759 

All Others 3,259 4,355 12,178 12,178 12,178 12,178 12,178 

Number Vehicles that shift and Where 

All Full-Size Car 19,640 (109,861) - -

All Luxury Car (105,790) - - -

All Mid-Size Car 86,107 109,861 (304,853) -

All Mini Car 46,601 - - 257,970 

All Small Car 91,816 - 304,853 (257,970) 

All Specialty Car (146,198) - - -

All Others 7,823 - - -

Individual Manufacturer Multiplier 

All Full-Size Car 0.789 

All Luxury Car 0.902 

All Mid-Size Car 0.858 

All Mini Car 1.352 

All Small Car 

All Specialty Car 0.814 

All Others 2.796 

*Note: The data in this table is based on the NPRM data. 

Table 1-8 Example: CSM – Percent of Cars per Segment* 

CSM Segment 2011 2012 2013 2014 2015 2016 

Compact Car 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

Full-Size Car 5.9% 4.3% 4.7% 4.9% 4.9% 4.9% 

Luxury Car 13.9% 13.7% 14.6% 15.0% 15.1% 15.1% 

Mid-Size Car 26.2% 29.0% 27.9% 27.5% 27.9% 27.9% 

Mini Car 14.1% 15.5% 15.5% 15.4% 15.4% 15.4% 

Small Car 30.6% 27.5% 26.5% 26.1% 25.8% 25.8% 

Specialty Car 9.1% 9.8% 10.7% 10.9% 10.6% 10.6% 

Others 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 

*Note: The data in this table is based on the NPRM data. 

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Mathematically, an individual manufacturer multiplier is calculated by making the 

segment the goal and dividing by the previous total for the segment (shown in Table 1-7). 

If the number is greater than 1, the vehicles are entering the segment, and if the number is 

less than 1, the vehicles are leaving the segment. So, for example, if Luxury Cars have 

an adjustment of 1.5, then for a specific manufacturer who has Luxury Cars, a multiplier 

of 1.5 is applied to its luxury car volume, and the total number of vehicles that shifted 

into the Luxury segment is subtracted from the remaining segments to maintain that 

company’s market share. On the other hand, if Large Cars have an adjustment of 0.7, 

then for a specific manufacturer who has Large Cars, a multiplier of 0.7 is applied to its 

Large Cars, and the total number of vehicles leaving that segment is transferred into that 

manufacturer’s Mid-Size Cars. 

After the vehicle volumes are shifted using the above rules, a total for each 

manufacturer and vehicle segment is maintained. The total for each manufacturer 

segment for a specific model year (e.g., 2011 General Motors Luxury Cars) divided by 

the MY 2008 total for that manufacturer segment (e.g., 2008 General Motors Luxury 

Cars) is the new multiplier used to determine the future vehicle volume for each vehicle 

model. This is done by taking the multiplier (which is for a specific manufacturer and 

segment) times the MY 2008 volume for the specific vehicle model (e.g., 2008 General 

Motors Luxury Car Cadillac CTS). This process is repeated for each model year (2011­

2016). 

The method used to adjust CSM Trucks to the AEO market share was different 

than the method used for Cars. The process for Cars is different than Trucks because it is 

not possible to predict how vehicles would shift between segments based on current 

market trends. This is because of the added utility of some trucks that makes their sales 

more insensitive to factors like fuel price. Again, CSM provided data on the market 

share and vehicle segment distribution. The process for having the fleet match CSM’s 

market share and vehicle segment distribution was iterative. 

The following totals were determined: 

•	 The total number of trucks for each manufacturer in 2008 model year. 

•	 The total number of trucks in each truck segment in 2008 model year. 

•	 The total number of truck in each segment for each manufacturer in 2008 model 

year. 

•	 The total number of trucks for each manufacturer in a specific future model year 

based on the AEO and CSM data. This is the goal for market share. 

•	 The total number of trucks in each truck segment in a specific future model year 

based on the AEO and CSM data. This is the goal for vehicle segment 

distribution. Table 1-9 has the percentages of Trucks per CSM segment 

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Table 1-9 Example: CSM – Percent of Trucks per Segment* 

CSM Segment 2011 2012 2013 2014 2015 2016 

Full-Size CUV 4.1% 3.3% 4.0% 4.4% 4.5% 4.5% 

Full-Size Pickup 20.0% 17.9% 18.1% 20.1% 20.8% 20.8% 

Full-Size SUV 3.3% 2.8% 1.6% 1.2% 1.3% 1.3% 

Full-Size Van 3.5% 3.3% 4.0% 3.9% 4.0% 4.0% 

Mid-Size CUV 14.5% 15.8% 17.5% 18.1% 18.4% 18.4% 

Mid-Size MAV 3.8% 3.3% 2.6% 2.2% 2.2% 2.2% 

Mid-Size Pickup 3.6% 5.8% 6.4% 6.4% 6.6% 6.6% 

Mid-Size SUV 3.2% 2.3% 1.8% 1.6% 1.6% 1.6% 

Mid-Size Van 11.9% 11.3% 10.7% 10.0% 8.7% 8.7% 

Small CUV 27.3% 28.4% 27.0% 26.1% 26.2% 26.2% 

Small MAV 1.9% 3.4% 4.1% 4.1% 4.1% 4.1% 

Small SUV 2.8% 2.4% 2.2% 2.0% 1.8% 1.8% 

*Note: The data in this table is based on the NPRM data. 

To start, two different types of tables were created. One table had each manufacturer 

with its total sales for 2008 (similar to Table 1-10). This table will have the goal for each 

manufacturer, and a column added for each iteration with the current total. The second 

table has a truck segment total by manufacturer. The second table starts out with a 

“Generic” manufacturer (Table 1-11) which is the table where the goal resides. Each 

manufacturer (BMW is shown in Table 1-12) is then listed below the “Generic” 

manufacturer. With each iteration, a new total is added for each segment that is 

calculated and added to the table. This is not shown in the tables below. A process of 

first adjusting the numbers in the tables to the goal for market share distribution was 

done. This was followed by adjusting to the goal for vehicle segment distribution. Each 

time an adjustment was done a new column was added. An adjustment was done by 

creating a multiplier (either segment distribution-based or manufacturer distributionbased) and applying it to each vehicle segment total in the current iteration. A 

manufacturer-based multiplier is calculated by taking the goal total for a manufacturer 

and dividing by the current total (starting with 2008 model year volumes) for a 

manufacturer. A segment distribution-based multiplier is calculated by taking the goal 

distribution volumes in the Generic manufacturer set and dividing them by the current 

volume. Table 1-10, Table 1-11, and Table 1-12 below illustrates two iterations using 

BMW as an example. 

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Table 1-10 Example:Manufacturer Truck Totals* 

2008 Model Year Sales Manufacturer Distribution 2011Volume Goal Multiplier for Iteration 1 

BMW 61,324 139948 139948/61324=2.28 

*Note: The data in this table is based on the NPRM data. 

Table 1-11 Example: Segment Specific Truck Totals for All Manufacturers* 

Manufacturer CSM Segment 

2008 Model Year 

Sales 

Segment Distribution 

2011Volume Goal Multipliers 

Generic** Full-Size Pickup 1,195,073 1,390,343 1.30 

Generic Mid-Size Pickup 598,197 251,433 0.38 

Generic Full-Size Van 33,384 244,393 8.30 

Generic Mid-Size Van 719,529 826,733 1.37 

Generic Mid-Size MAV 191,448 263,765 1.26 

Generic Small MAV 235,524 133,309 0.57 

Generic Full-Size SUV 530,748 232,375 0.46 

Generic Mid-Size SUV 347,026 221,607 0.70 

Generic Small SUV 377,262 194,496 0.75 

Generic Full-Size CUV 406,554 287,313 0.69 

Generic Mid-Size CUV 798,335 1,007,583 0.99 

Generic Small CUV 1,441,589 1,901,229 1.16 

* The data in this table is based on the NPRM data. 

** Generic means all manufacturers. 

Table 1-12 Example: Segment Specific Truck Totals for BMW* 

Manufacturer CSM Segment 

2008 Model Year 

Sales 

Iteration 1 Adjust for 

Market Share 

Iteration 2 Adjust for 

Segment Distribution 

BMW Full-Size Pickup 

BMW Mid-Size Pickup 

BMW Full-Size Van 

BMW Mid-Size Van 

BMW Mid-Size MAV 3,882 2.28*3,882=8,551 1.26*8,851=11,137 

BMW Small MAV 

BMW Full-Size SUV 

BMW Mid-Size SUV 

BMW Small SUV 

BMW Full-Size CUV 

BMW Mid-Size CUV 36,409 2.28*36,409=83,013 0.99*83,013=82,603 

BMW Small CUV 21,033 2.28*21,033=47,955 1.16*47,955=55,592 

Total BMW Vehicles 61,324 139,819 149,332 

*Note: The data in this table is based on the NPRM data. 

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Using this process, the numbers will get closer to the goal of matching CSM’s market 

share for each manufacturer and distribution for each vehicle segment after each of the 

iterations. The iterative process is carried out until the totals nearly match the goals. 

After 19 iterations, all numbers were within 0.01% of CSM’s distributions. The 

calculation iterations could have been stopped sooner, but they were continued to observe 

how the numbers would converge. 

After the market share and segment distribution were complete, the totals need to be 

used to create multipliers that could be applied to the original individual 2008 model year 

vehicle volumes (each unique manufacture models volume). The total for each 

manufacturer segment divided by the 2008 model year total for each manufacturer 

segment gives a multiplier that can be applied to each vehicle based on its manufacturer 

and segment. 

The above process is done for each model year needed (2011-2016). The multipliers 

are then applied to each vehicle in 2008 model year, which gives a volume for each 

vehicle in 2011 through 2016 model year. The “reference case” (which is the technology 

being applied to 2012-2016 fleet to meet the 2011 cafe standard) is discussed below in 

section 4.1, with the results being presented in section 4.2. 

1.3.3 How has the reference fleet changed from the NPRM to the Final Rule? 

The agencies updated the projected reference (2011-2016) fleet file for the final 

rule, though the baseline fleet (MY 2008) remains unchanged. The NPRM reference 

fleet used CSM Worldwide’s second quarter, 2009 projections and the April 2009 Annual 

Energy Outlook. The reference for the final rule uses a filtered version of CSM 

Worldwide’s fourth quarter, 2009 projections, and (as noted above) an early version of 

the 2010 Annual Energy Outlook that was released in December of 2009. 

CSM Worldwide’s standard forecast includes heavy duty class 2b and heavy duty 

class 3 vehicles. Including these extra vehicles in the second quarter forecast caused the 

baseline to have an increased number of full size trucks and full size vans. EPA did not 

discover this discrepancy between CSM’s definition of heavy truck and the one used by 

NHTSA/EPA until after the proposal had been published. For the final rule, the EPA 

requested CSM to filter their 4th quarter projections to only include light duty and 

medium duty passenger vehicles. The filtered CSM data allowed the agencies to produce 

a more accurate projection of future sales. 

Table 1-13 and Table 1-14 below show the difference in volumes per each CSM 

segment in the final rule baseline and the NPRM reference. The significant decrease in 

the number of full size vans can be attributed to the new filtered version of the CSM data. 

Full size trucks show some decrease, but did not have as obvious an impact from the data 

filtering since CSM increase their predicted volume for full size trucks. The overall 

decrease in cars and trucks for some of the year in the 2011-2016 time frame is due to the 

decreased volumes projected in the 2010 Annual Energy Outlook. 

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Table 1-13 Difference Truck Segment Volumes (Final-NPRM) 

Model 2011 2012 2013 2014 2015 2016 

Full-Size Pickup (36,639) 209,466 224,624 (74,421) (110,407) (103,495) 

Mid-Size Pickup 56,153 (91,457) (61,634) (90,397) (145,580) (180,099) 

Full-Size Van (138,346) (118,196) (214,794) (216,209) (225,771) (218,776) 

Mid-Size Van (119,480) (39,675) 136,637 159,127 224,405 219,798 

Mid-Size MAV (96911) (92,668) (68,927) (58,747) (45,077) (43,413) 

Small MAV (74545) (172,269) (231,829) (230,774) (234,930) (227,667) 

Full-Size SUV 75,909 54,140 90,095 128,390 144,629 140,861 

Mid-Size SUV 85,265 114,268 99,228 64,284 52,735 51,563 

Small SUV (42,807) (31,396) (41,079) (31,821) (16,856) (16,064) 

Full-Size CUV (48,893) 11,197 (32,095) (35,457) (62,356) (59,796) 

Mid-Size CUV 85,731 80,897 (67,541) 15,205 57,194 59,019 

Small CUV (35,350) 109,182 253,249 353,827 307,846 304,197 

All Trucks (289,913) 33,489 85,933 (16,993) (54,166) (73,873) 

Table 1-14 Difference Car Segment Volumes (Final-NPRM) 

Model 2011 2012 2013 2014 2015 2016 

Full-Size Car 245,467 219,990 117,266 60,568 52,287 54,945 

Mid-Size Car 179,734 (88,360) (109,151) (102,313) (119,548) (116,040) 

Small Car (364,311) (47,099) 91,824 39,821 26,360 33,401 

Subcompact/Mini Car 10,613 107,422 202,059 167,808 138371 146,009 

Luxury Car 1,971 98,741 32,604 55,014 78055 83,925 

Specialty Car (173,163) (176,256) (267,196) (296,546) (255347) (259,743) 

Others (12,178) (12,350) (14,739) (17,811) (20646) (21,153) 

All Cars (111,868) 102,089 52,668 (93,460) (10,0467) (78,656) 

Table 1-15below shows the differences in projected manufacturer volumes 

between the final rule projected volumes and the NPRM projected volumes. The 2010 

Annual Energy Outlook reduced the volumes for 2011, and 2014-2016. This would have 

reduced all of the manufacturer projected production volumes if we had not used the 4th 

quarter CSM projections. All manufactures that have an increase shown in those years 

are projected by CSM to have increased market share. The most notable change in 

projected production volume is from Chrysler. Chrysler was projected to have 138,602 

cars, and 109,674 trucks for 2016 in the NPRM based on using 2nd quarter CSM (and 

2009 Annual Energy Outlook). 4th quarter CSM projections for Chrysler are much more 

optimistic, and the baseline for the final rule has 399,762 for Chrysler’s cars and 462,150 

for Chrysler’s trucks in 2016. 

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Table 1-15 Difference Car and Truck Volumes by Manufacturer (Final-NPRM) 

Manufacturer 2011 2012 2013 2014 2015 2016 

Total Vehicles All Manufacturers (401,780) 135,578 138,601 (110,453) (154,633) (152,529) 

All Manufacturers Trucks (89,568) 268,005 318,204 136,923 109,279 82,731 

All Manufacturers Cars (312,212) (132,427) (179,603) (247,376) (263,912) (235,260) 

BMW Trucks 62,770 61,336 41,601 46,988 36,579 35,180 

BMW Cars (41,587) 6,160 (29,286) 17,731 39,985 42,070 

Chrysler Trucks 334,846 409,643 349,651 292,257 362,932 352,476 

Chrysler Cars 132,623 230,827 251,382 242,273 255,900 261,160 

Ford Trucks (151,056) (173,698) (167,456) (236,552) (294,109) (291,602) 

Ford Cars 114,699 119,923 61,456 121,665 39,295 47,956 

Subaru Trucks (13,064) 9,058 12,351 22,381 42,630 43,160 

Subaru Cars 16,044 34,293 39,224 49,380 74,158 75,882 

GENERAL MOTORS Trucks 33,594 301,275 215,632 (6,059) 23,993 19,092 

GENERAL MOTORS Cars 92,543 101,513 (75,325) (255,948) (302,495) (305,754) 

HONDA Trucks 14,156 (35,243) (47,772) (52,602) (130,896) (129,956) 

HONDA Cars (145,521) (248,912) (287,759) (406,166) (650,062) (662,741) 

HYUNDAI Trucks (80,886) (82,864) (34,222) (30,417) (38,372) (37,777) 

HYUNDAI Cars (188,181) (204,255) (163,401) (167,347) (79,342) (78,446) 

Tata Trucks (618) (12,521) (12,506) (13,041) (4,879) (4,879) 

Tata Cars 4,615 13,499 17,706 13,782 23,165 23,905 

Kia Trucks (9,888) 8,854 28,177 42,230 48,498 46,803 

Kia Cars 3,362 (4,913) 16,985 95,993 191,184 196,973 

Mazda Trucks (1,909) (11,147) (6,352) 861 14,853 14,160 

Mazda Cars (25,356) (30,361) 11,242 39,804 73,259 75,028 

Daimler Trucks (1,296) (1,299) (1,152) 11,265 16,512 15,740 

Daimler Cars 36,309 39,481 12,426 40,218 34,189 35,735 

Mitsubishi Trucks 36,069 38,193 34,739 43,711 49,546 48,149 

Mitsubishi Cars 36,473 45,440 43,385 31,673 29,942 29,228 

Nissan Trucks (89,599) (72,880) (87,000) (68,513) (62,754) (63,585) 

Nissan Cars (182,244) (134,666) (199,962) (219,176) (178,793) (176,968) 

PORSCHE Trucks (8,270) (7,577) (6,031) (3,346) 323 (603) 

PORSCHE Cars (5,853) 9,511 7,486 (4,446) (3,750) (3,755) 

Suzuki Trucks (18,299) (25,798) (13,193) (10,485) (6,657) (6,548) 

Suzuki Cars (41,871) (14,240) (4,439) 9,117 24,635 25,575 

TOYOTA Trucks (171,045) (132,755) (28,733) 50,277 25,938 18,761 

TOYOTA Cars (118,360) (106,708) 79,278 93,978 62,371 72,407 

VOLKSWAGEN Trucks (25,074) (4,573) 40,469 47,969 25,144 24,160 

VOLKSWAGEN Cars 92 10,979 40,000 50,093 102,448 106,486 

1.3.4 What are the sales volumes and characteristics of the reference fleet? 

Table 1-16 and Table 1-17 below contain the sales volumes and percentages that 

result from the process above for MY 2008 and 2011-2016. 

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Table 1-16 Vehicle Segment Volumes 

Reference Class 

Segmenta 

Actual and Projected Sales Volume 

2008 2011 2012 2013 2014 2015 2016 

Large Auto 557,693 479,329 438,780 422,094 438,346 461,663 474,260 

Mid-Size Auto 3,097,859 2,747,665 3,028,262 3,174,985 3,361,926 3,524,000 3,619,798 

Compact Auto 1,976,424 1,671,674 2,024,030 2,232,209 2,340,408 2,454,466 2,520,777 

Sub-Compact Auto 1,364,434 1,763,860 2,222,516 2,483,365 2,564,754 2,663,349 2,735,809 

All Cars 6,996,410 6,662,527 7,713,589 8,312,653 8,705,434 9,103,479 9,350,644 

Large Pickup 1,581,880 1,515,406 1,649,043 1,774,535 1,629,457 1,594,363 1,550,212 

Small Pickup 177,497 120,816 128,754 168,070 150,225 129,421 125,837 

Large SUV 2,783,949 2,692,979 2,808,069 2,796,947 2,904,358 2,932,045 2,851,169 

Mid-Size SUV 1,263,360 1,462,069 1,630,310 1,677,100 1,722,783 1,687,445 1,645,170 

Small SUV 285,355 113,652 111,539 97,954 90,729 80,754 78,518 

Mini Van 642,055 631,390 665,307 799,280 784,215 737,003 716,594 

Cargo Van 110,858 181,911 215,174 209,684 192,590 189,124 183,887 

All Trucks 6,844,954 6,718,223 7,208,196 7,523,569 7,474,358 7,350,155 7,151,387 a This is no longer the CSM segment, but the segment used for the subsequent analysis in this rule. 

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Table 1-17 Manufacturer Volumes 2008 Baseline Sales

2011

Projected

Volume

2012

Projected

Volume

2013

Projected

Volume

2014

Projected

Volume

2015

Projected

Volume

2016

Projected

Volume 

Total Vehicles 13,841,364 13,380,750 14,921,784 15,836,222 16,179,793 16,453,634 16,502,032 

All Trucks 6,844,954 6,718,223 7,208,196 7,523,569 7,474,358 7,350,155 7,151,387 

All Cars 6,996,410 6,662,527 7,713,589 8,312,653 8,705,434 9,103,479 9,350,644 

BMW Trucks 61,324 202,729 204,197 183,550 191,010 175,612 170,749 

BMW Cars 291,796 230,986 289,631 293,905 369,979 411,653 422,874 

Chrysler Trucks 956,792 737,504 692,115 594,092 514,802 475,312 462,150 

Chrysler Cars 700,413 326,327 409,462 426,454 411,319 392,483 399,762 

Ford Trucks 846,596 792,527 851,877 940,080 965,589 936,781 910,840 

Ford Cars 1,021,792 1,344,461 1,468,182 1,485,801 1,567,762 1,542,470 1,559,310 

Subaru Trucks 82,546 104,832 97,935 89,944 99,293 116,055 117,295 

Subaru Cars 116,035 169,688 183,486 175,170 184,521 204,746 206,903 

GM Trucks 1,512,047 1,347,521 1,510,917 1,536,070 1,336,797 1,379,813 1,341,604 

GM Cars 1,607,460 1,248,909 1,586,094 1,544,975 1,452,559 1,487,318 1,514,479 

Honda Trucks 505,140 585,334 634,705 676,729 634,606 560,745 545,217 

Honda Cars 1,006,639 850,875 906,096 1,064,848 1,087,076 912,434 930,350 

Hyundai Trucks 53,158 46,314 58,164 101,529 103,857 94,606 91,986 

Hyundai Cars 337,869 382,250 376,284 395,573 395,515 511,236 518,445 

Tata Trucks 55,584 30,726 29,647 33,749 40,294 43,703 42,493 

Tata Cars 9,596 23,187 36,377 50,527 49,316 63,751 65,489 

Kia Trucks 59,472 87,819 87,643 102,773 114,423 118,391 115,113 

Kia Cars 221,980 305,709 299,611 326,652 427,191 538,717 548,055 

Mazda Trucks 55,885 57,873 60,783 64,784 67,780 74,213 72,158 

Mazda Cars 246,661 292,592 283,128 329,911 378,291 413,328 420,516 

Daimler Trucks 79,135 96,630 108,053 114,531 136,455 129,878 126,281 

Daimler Cars 208,052 213,801 211,652 202,559 244,554 263,751 270,940 

Mitsubishi Trucks 15,371 45,337 48,290 46,179 52,835 56,896 55,320 

Mitsubishi Cars 85,358 104,252 110,284 104,555 88,150 82,310 82,688 

Nissan Trucks 305,546 331,067 405,017 391,572 406,045 391,733 380,886 

Nissan Cars 717,869 611,629 824,030 831,607 854,131 925,478 946,518 

Porsche Trucks 18,797 13,131 13,190 14,608 16,033 17,145 16,670 

Porsche Cars 18,909 25,056 41,117 43,299 34,024 32,426 33,309 

Suzuki Trucks 35,319 6,895 4,593 16,557 20,060 20,547 19,978 

Suzuki Cars 79,339 49,484 72,297 81,781 90,597 100,600 103,003 

Toyota Trucks 951,136 888,116 886,621 990,315 1,095,949 1,107,261 1,076,598 

Toyota Cars 1,260,364 1,355,639 1,591,054 1,941,480 2,079,011 2,176,644 2,226,522 

VW Trucks 26,999 83,403 104,842 141,421 151,992 127,888 124,346 

VW Cars 290,385 388,146 434,412 498,641 517,978 567,711 583,185 

Table 1-18 also shows how the change in fleet make-up may affect the footprint 

distributions over time. The resulting data indicate that footprint will not change 

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The Baseline and Reference Vehicle Fleet
 

significantly between 2008 and 2016. There will be an increase in the number of cars 

sold, which will cause the average footprints for cars and trucks combined to be slightly 

smaller (about 2%). This is the result of AEO projecting an increased number of cars, 

and CSM predicting that most of that increase will be in the subcompact segment. It is 

important to note that the AEO and CSM projections were made without accounting for 

the effects of this rulemaking, and thus are not influenced by the new regulations.d 

Table 1-18 Production Foot Print Mean 

Model Year 

Foot Print Mean 

for 

Car & Truck 

Combined 

Foot Print Mean 

for 

Car 

Foot Print Mean 

for 

Truck 

2008 49.0 45.4 54.1 

2011 48.9 45.2 54.2 

2012 48.6 45.0 54.3 

2013 48.5 44.9 54.3 

2014 48.2 44.9 54.0 

2015 48.2 44.9 54.0 

2016 48.1 44.9 54.0 

Table 1-19 and Table 1-20 below show the changes in engine cylinders over the 

model years. The current assumptions show that engines will be downsized over the 

model years to which these rules apply. The biggest projected shift occurs between MY 

2008 and 2013. This shift is a projected consequence of the expected changes in class 

and segment mix as predicted by AEO and CSM, and does not represent engine 

downsizing attributable to the rules. 

Table 1-19 Truck Percentages of 4, 6, 8 Cylinder Engines by Model Year 

Model Percent of Total Sales of 4,6,8 Cylinders Trucks 

2008 2011 2012 2013 2014 2015 2016 

All Trucks 8 Cylinders 33.7% 31.4% 30.9% 29.7% 28.3% 28.8% 28.8% 

All Trucks 6 Cylinders 55.8% 57.1% 57.1% 58.2% 59.4% 59.0% 59.0% 

All Trucks 4 Cylinders 10.5% 11.5% 12.0% 12.0% 12.4% 12.2% 12.3% 

Table 1-20 Car Percentages of 4, 6, 8 Cylinder Engines by Model Year 

Model Percent of Total Sales of 4,6,8 Cylinders Cars 

2008 2011 2012 2013 2014 2015 2016 

All Cars 8 Cylinders 5.2% 5.2% 5.0% 4.8% 4.8% 5.0% 5.1% 

All Cars 6 Cylinders 36.7% 34.5% 33.0% 31.9% 32.2% 32.3% 32.2% 

All Cars 4 Cylinders 58.1% 60.3% 62.0% 63.3% 63.0% 62.6% 62.7% 

d Refer to the memo to the docket (Docket EPA-HQ-OAR-2009-0472) for a more detailed description of 

the updated AEO2010 fleet projection. 

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1.3.5 How is the development of the baseline and reference fleet for this final 

rule different from NHTSA’s historical approach and why is this 

approach preferable? 

As described in detail at the opening of this chapter, and as explained at proposal, 

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 never compelled 

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

manufacturers have voluntarily provided it when requested. 

As in 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 this request has 

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

information NHTSA has requested, 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. 

One of the primary reasons for the request for data in 2009 was to obtain 

permission from the manufacturers to make public their product plan information for 

model years 2010 and 2011. There are a number of reasons that this could be 

advantageous in the development of a reference fleet. First some changes to the fleet 

may not be captured by this approach of solely using publicly available information. 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. 

Second, 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 

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does it include several MY 2009 and 2010 vehicles, such as the Honda Insight, the 

Hyundai Genesis and the Toyota Venza, as the starting point for vehicle definitions was 

MY 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, this market forecast would account for not only 

these specific examples, but also for similar examples that have not yet been announced 

publicly. 

Finally, some technical information manufacturers that have provided in product 

plans regarding specific vehicle models is, at least as far 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 value7 for many manufacturers’ 

pickups. Since the 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 the relative sales of the various 

pickup configurations changed, or if the agencies were to explore standards with a 

different shape or attribute. This would necessitate recalculating the average footprint 

value in order to maintain accuracy. 

In an effort to update the 2008 baseline to account for the expected changes in the 

fleet in the near-term model years 2009-2011 described above, NHTSA requested 

permission from the manufacturers to make this limited product plan information public. 

Unfortunately, virtually no manufacturers agreed to allow the use of their data after the 

2009 model year. A few manufacturers stated we could use their 2009 product plan data 

after the end of production (December 31). Unfortunately, this does not afford us 

sufficient time to do the analysis for the final rule. Also, the 2009 product plan data that 

was permitted to be used was not consistent with the data already in the baseline, and in 

some cases was determined to be inaccurate. Since the agencies were unable to obtain 

consistent updates, the baseline and reference fleets were not updated beyond 2008 model 

year for this final rule. The 2008 baseline fleet and projections were instead updated 

using the latest AEO and CSM data, as explained above. 

7 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 

MY2008 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 little impact on the 

results of the modeling effort. 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. 

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1.3.6 How does manufacturer product plan data factor into the baseline used 

in this final rule? 

In the spring and fall of 2009, many manufacturers submitted product plans in 

response to NHTSA’s recent requests that they do so. 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) 

The track widths, wheelbase, curb weight, and GVWR for vehicles 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 agencies’ 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. 

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References
 

All references can be found in the EPA DOCKET: EPA-HQ-OAR-2009-0472. 

1 CFEIS (Certification Fuel Emissions Information System) Database: CFEIS is an EPA database system.
 

2 WardsAuto.com: Used as a source for engine specifications.
 

3 Motortrend.com and Edmunds.com: Used as a source for foot print and vehicle weight data.
 

4 Energy Information Administration’s 2009 Annual Energy Outlook. 

5 The agencies have also looked at the impact of the rule in EIA’s projection, and concluded that the impact 

was small. EPA and NHTSA have evaluated the differences between the AEO 2010 (early draft) and AEO 

2009 and found little difference in the fleet projections (or fuel prices). This analysis can be found in the 

memo to the docket: (Docket EPA-HQ-OAR-2009-0472). 

6 CSM World Wide, CSM World Wide is a paid service provider; J.D. Power Automotive Forecasting, J.D. Power 

Automotive Forecasting is a paid service provider. 

7 The baseline Excel file (“2008-2016 Production Summary Data _Definitions Docket 08_27_2009”) is 

available in the docket (Docket EPA-HQ-OAR-2009-0472). 

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What are the Attribute-Based Curves the Agencies are Using, and How Were They Developed? 

CHAPTER 2: What are the Attribute-Based Curves the Agencies 

are Using, and How Were They Developed? 

2.1 Standards are attribute-based and are defined by a mathematical 

function 

As discussed in Section II.B of the final rule preamble, NHTSA and EPA are setting 

attribute-based CAFE and CO2 standards for MYs 2012-2016 passenger cars and light trucks, 

which are expressed by a mathematical function. EPCA, as amended by EISA, expressly 

requires that CAFE standards for passenger cars and light trucks be based on one or more 

vehicle attributes related to fuel economy, and be expressed in the form of a mathematical 

function.1 The CAA has no such requirement, although such an approach is permissible 

under section 202 (a) and EPA has used the attribute-based approach in issuing standards 

under analogous provisions of the CAA (e.g., standards for non-road diesel engines using 

engine size as the attribute). Given the advantages of using attribute-based standards for light 

duty vehicles, and given the goal of coordinating and harmonizing CO2 standards promulgated 

under the CAA and CAFE standards promulgated under CAA and EPCA, as expressed by 

President Obama in his announcement of the new National Program and by the agencies in 

the joint NOI and NPRM, EPA is also finalizing standards that are attribute-based and 

expressed as mathematical functions. 

Under an attribute-based standard approach, each vehicle model has a fuel economy 

(mpg) or CO2 target, the stringency of which depends on the value of the attribute for the 

vehicle model. Thus, fuel economy and CO2 targets are set for individual vehicles, and vary 

directly with the attribute, i.e., becoming more stringent as the attribute decreases and vice 

versa. For example, size-based (i.e., size-indexed) standards assign higher fuel economy 

targets (lower CO2 targets) to smaller vehicles and lower fuel economy targets (higher CO2 

targets) to larger vehicles. However, we emphasize that the targets themselves are not 

standards. The CAFE or fleet-wide average CO2 level required of a particular manufacturer is 

actually determined by calculating the production-weighted average of the applicable modelspecific fuel economy or CO2 targets. 

The form of the fuel economy or CO2 standard can have a dramatic effect on the level 

of improvement required from various types of vehicles and on the relative stringency of the 

standard for various manufacturers. These disparate impacts derive from two facts 

concerning the current automotive market: 1) consumers currently demand a wide variety of 

vehicle types and sizes, and 2) individual manufacturers focus their product offerings on 

different portions of this demand. Therefore, a comparison of the advantages and 

disadvantages of attribute-based and flat (universal) standards needs to consider both of these 

factors. 

Methods to reduce the CO2 emissions from and increase the fuel economy of 

individual vehicles can be generally grouped into four main categories: 1) reducing vehicle 

size, 2) reducing vehicle performance and/or utility, 3) increasing the efficiency of the 

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powertrain and accessories while maintaining constant performance and utility, and 4) 

reducing weight and aerodynamic drag while maintaining vehicle size and utility.a Stringent 

flat standards encourage all four of these methods. However, the level of encouragement 

differs between manufacturers. Manufacturers of generally larger vehicles face a more 

difficult task in meeting a flat standard than those of generally smaller vehicles. Thus, the 

stringency of flat standards will tend to be limited by the capabilities of the manufacturers of 

larger vehicles. Conversely, manufacturers of smaller vehicles will tend to face lower 

compliance costs and may not even need to change their vehicles at all.b Thus, the overall 

level of achieved stringency of a flat standard may be limited. A flat standard will encourage 

smaller, less powerful vehicles, as these vehicles face the least compliance costs. Sales will 

tend to be shifted to manufacturers of generally smaller vehicles as their overall compliance 

costs will be lower than those of manufacturers of average or larger vehicles. Finally, because 

smaller vehicles face lower costs than larger vehicles, consumers will be encouraged to 

purchase smaller vehicles. Or, to avoid these outcomes, the standard may not be of sufficient 

stringency to result in significant fuel economy improvements or emission reductions. 

Attribute-based fuel economy and CO2 standards are preferable to universal industrywide average standards for several reasons. First, if the shape is chosen properly, every 

manufacturer is more likely to be required to continue adding more fuel efficient technology 

each year, because the level of the compliance obligation of each manufacturer is based on its 

own particular product mix. 

Second, attribute-based standards minimize the incentive for manufacturers to respond 

to CAFE and CO2 standards in ways that may be harmful to safety.2 Because each vehicle 

model has its own target based on the attribute chosen, attribute-based standards can, 

depending on the corresponding mathematical function (i.e., the shape of the attribute based 

standards), reduce or eliminate the incentive to build smaller vehicles simply to meet a fleetwide average. Since smaller vehicles are subject to more stringent fuel economy targets, a 

manufacturer’s increasing its proportion of smaller vehicles would not necessarily ease its 

compliance obligation. For the purposes of this rule, the agencies believe that setting 

attribute-based standards with careful attention to the slope of the target curves will not 

encourage wholesale changes in fleet mix to either smaller or larger vehicles. 

Third, attribute-based standards provide a more equitable regulatory framework for 

different vehicle manufacturers.3 A flat industry-wide average standard imposes 

disproportionate compliance obligations on (full-line) manufacturers that need to significantly 

a Another method of increasing fleet fuel economy is to increase the number of high-fuel-economy vehicles and 

reduce production of low-fuel-economy vehicles, for instance, by changing their relative prices. EPA and 

NHTSA believe that existing technologies allow automakers to meet the requirements of the rules without 

changing the mix of vehicles produced (although the rules do not prohibit such changes in mix). 

b Trading of credits could encourage fuel economy improvements or CO2 emission reductions from 

manufacturers of smaller vehicles. This would probably create a shift of wealth between the two sets of 

manufacturers. However, most manufacturers have indicated that they would be unlikely to trade credits with 

their direct competitors. In addition, EPCA does not permit NHTSA, in determining maximum feasible CAFE 

standards, to consider manufacturers’ ability to earn, transfer, trade, and use CAFE credits. 

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change their product plans and little obligation on those manufacturers that have minimal 

need to change their plans because, for example, they may build smaller vehicles. Attributebased standards that have the proper shape spread the regulatory cost burden for fuel economy 

more broadly across all of the vehicle manufacturers within the industry. 

And fourth, attribute-based standards respect economic conditions and consumer 

choice. Manufacturers are required to invest in technologies that improve the fuel economy 

of their fleets, regardless of vehicle mix. Thus the consumer is likely to enjoy the same wide 

range of vehicles to choose from in 2016 as they do now. The difference will be, of course, 

that the vehicles will be more fuel efficient. 

The agencies recognize that, because manufacturers’ compliance obligations under 

attribute-based standards are based in part on the mix of vehicles that they produce, the fuel 

savings and emissions reductions produced under attribute-based standards can vary 

depending on market conditions. For example, fuel prices lower than those anticipated at the 

time of rulemaking could tend to shift consumer demand toward larger vehicles. If 

manufacturers sell a greater number of larger vehicles than the agencies anticipate, fuel 

savings and CO2 reductions would be lower than anticipated. In contrast, if fuel prices rise 

significantly, more fuel savings and CO2 reductions than anticipated should be likely. 

One potential way to mitigate the variability of results under attribute-based standards 

due to market conditions is through the use of a universal standard—that is, an average 

standard set at a (single) absolute level below which manufacturers’ fuel economy/CO2 

standards may not drop/exceed. This is often described as a “backstop standard.” For 

purposes of the CAFE program, EISA expressly requires a backstop for domesticallymanufactured 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...”4

, 

whichever is greater. Backstops are generally permissible under section 202(a) of the CAA 

(as are universal standards generally). 

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

EPA recognized that the risk, even if small, does exist that low fuel prices in MYs 2012-2016 

might lead indirectly to less than currently anticipated fuel savings and emissions reductions. 

The NPRM sought comment on whether backstop standards, or any other method within the 

agencies’ statutory authority, should and can be implemented for the import and light truck 

fleets in order to achieve the fuel savings that attribute-based standards might not absolutely 

guarantee. 

As discussed in Section II.C of the final rule preamble, the agencies have reviewed the 

comments received on this issue and have made the policy choice not to adopt backstop 

standards for MYs 2012-2016 other than those for domestic passenger cars, as required by 

EISA. EPA and NHTSA remain confident that their projections of the future fleet mix are 

reliable, and that future changes in the fleet mix of footprints and sales are not likely to lead to 

more than modest changes in projected emissions reductions or fuel savings. Both agencies 

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remain confident in these fleet projections and the resulting emissions reductions and fuel 

savings from the standards. As explained in section II.B of the preamble and chapter 1 of the 

TSD, the agencies’ projections of the future fleet are based on the best, transparent data 

obtained to date. In addition, there are only a relatively few model years at issue. Moreover, 

market trends today are consistent with the agencies’ estimates showing shifts from light 

trucks to passenger cars and increased emphasis on fuel economy. Finally, the shapes of the 

curves, including the “flattening” at the largest footprint values, tend to avoid or minimize 

regulatory incentives for a manufacturer to upsize their fleet to change their compliance 

burden. 

At the same time, adding another backstop standard would directionally impose 

increased costs for some manufacturers, with greater costs commensurate with more stringent 

backstops. This would occur unless the backstop standard was relatively weak, in which case 

it would have a minimal effect. It would be difficult to establish the level of a backstop 

standard such that costs are likely to be imposed on manufacturers only when there is a failure 

to achieve the projected reductions across the industry as a whole. An example of this could 

be when there is a significant shift to larger vehicles across the industry as a whole, or if there 

is a general market shift from cars to trucks. The problem the agencies are concerned about is 

not with respect to any single manufacturer, but rather is based on concerns over shifts across 

the fleet as a whole, as compared to shifts in one manufacturer's fleet that may be more than 

offset by shifts the other way in another manufacturer's fleet. However a backstop does not 

directly deal with fleetwide trends because it acts as a manufacturer specific standard. The 

concept of a ratchet mechanism recognizes this issue, and aims to impose the new more 

stringent standard only when the problem arises across the industry as a whole. However, this 

approach fails to provide the lead time necessary for making vehicle changes to meet a new, 

more stringent standard. 

In addition, we do not believe that the risk of vehicle upsizing or changing vehicle 

offerings to “game” the passenger car and light truck definitions is as great as commenters 

imply for the model years in question. The changes that commenters suggest manufacturers 

might make are neither so simple nor so likely to be accepted by consumers. For example, 

4WD versions of vehicles tend to be more expensive and, other things being equal, have 

inherently lower fuel economy than their 2WD equivalent models. Therefore, many 

consumers may not desire to purchase 4WD vehicles because of the cost premium; 

conversely, many manufacturers often require the 2WD option to satisfy demand for base 

vehicle models. Additionally, increasing the footprint of vehicles requires platform changes, 

which usually requires a product redesign phase (the agencies estimate that this occurs on 

average once every 5 years for a model). Alternatively, turning many 2WD SUVs into 

(2WD) light trucks would require manufacturers to squeeze a third row of seats in or 

significantly increase their GVWR, which also requires a significant change in the vehicle. 

For these reasons, and in the interest of harmonization and given that neither EISA nor 

the CAA provide specific directives on this issue plus the agencies’ belief that backstops are 

not the only mechanisms to ensure fuel savings and emissions reductions, the agencies have 

reached the conclusion not to incorporate backstops in these final rules but will consider the 

issue further if the evidence warrants. The agencies discuss this issue further in the final rule 

preamble. 

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What are the Attribute-Based Curves the Agencies are Using, and How Were They Developed? 

2.2 What attribute do the agencies use, and why? 

Consistent with the MY 2011 CAFE standards, EPA and NHTSA are using footprint 

as the attribute for the MY 2012-2016 CAFE standards and CO2 emissions standards. There 

are several policy reasons why the agencies believe that footprint is the most appropriate 

attribute on which to base the standards, as discussed below. 

It is important that the CAFE and CO2 standards be set in a way that does not 

encourage manufacturers to respond by selling vehicles that are in any way less safe. 

Although NHTSA’s research also indicates that certain types of reductions in vehicle mass 

tend to compromise vehicle safety, footprint-based standards provide an incentive to use new 

and advanced lightweight materials and structures that could be otherwise discouraged by 

weight-based standards, because manufacturers can use them to improve a vehicle’s fuel 

economy without their use necessarily resulting in a change in the vehicle’s target level of 

fuel economy or CO2 emissions and without a substantial impact on the safety (in terms of 

crashworthiness) of that vehicle. 

Further, although the agencies recognize the point raised by some commenters that 

weight is better correlated with fuel economy and CO2 emissions than is footprint, the 

agencies continue to believe that there is less risk of “gaming” (artificial manipulation of the 

attribute(s) to achieve a more favorable target) by increasing footprint under footprint-based 

standards than by increasing vehicle mass under weight-based standards—it is relatively easy 

for a manufacturer to add enough weight to a vehicle to decrease its applicable fuel economy 

target by a significant amount, as compared to increasing vehicle footprint. For a 

manufacturer to change a vehicle’s footprint, a significant redesign of the chassis and many 

components would be required. (See the related discussion of this issue above in the 

discussion of whether to adopt universal backstop standards.) In addition, despite requests 

from some manufacturers that the agencies consider multi-attribute standards, or adjust the 

target curves to “accommodate” other attributes like towing/hauling capability, the agencies 

continue to agree with concerns raised in 2008 by some commenters to NHTSA’s MY 2011 

CAFE rulemaking that there would be greater potential for gaming under multi-attribute 

standards, such as standards under which targets would also depend on attributes such as 

weight, torque, power, towing capability, and/or off-road capability. Standards that 

incorporate such attributes in conjunction with footprint would not only be significantly more 

complex, but by providing degrees of freedom with respect to more easily-adjusted attributes, 

they would make it less certain that the future fleet would actually achieve the average fuel 

economy improvement and CO2 reduction levels projected by the agencies. 

2.3 What mathematical function do the agencies use, and why? 

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

function, which takes the form of an S-curve, and is defined according to the following 

formula: 

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1 TARGET = (FOOTPRINT −c) d 1 ⎛ 1 1 ⎞ e 

+ ⎜ − ⎟ (FOOTPRINT −c) d a ⎝ b a ⎠1+ e 

Here, TARGET is the fuel economy target (in mpg) applicable to vehicles of a given 

footprint (FOOTPRINT, in square feet), b and a are the function’s lower and upper 

asymptotes (also in mpg), e is approximately equal to 2.718,c c is the footprint (in square feet) 

at which the inverse of the fuel economy target falls halfway between the inverses of the 

lower and upper asymptotes, and d is a parameter (in square feet) that determines how 

gradually the fuel economy target transitions from the upper toward the lower asymptote as 

the footprint increases. Figure 2-1 shows an example of a logistic target function, where b = 

20 mpg, a = 30 mpg, c = 40 square feet, and d = 5 square feet. 

35 

30 LIMIT UPPER 

25 

20 LIMIT LOWER 

15 

10 

5 

0 

0 10 20 30 40 50 60 70 80 90 100 

Footprint (Square Feet) 

Figure 2-1 Sample Logistic Curve 

After fitting this mathematical form (separately) to the passenger car and light truck 

fleets and determining the maximum feasible stringency of the standards (i.e., the vertical 

c e is the irrational number for which the slope of the function y = number x is equal to 1 when x is equal to zero. 

The first 8 digits of e are 2.7182818. Target (Miles per Gallon) 

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What are the Attribute-Based Curves the Agencies are Using, and How Were They Developed?
 

positions of the curves), NHTSA arrived at the curves shown in Figure 2-2 to define the MY 

2011 standards. 

20 

25 

30 

35 

40 

45 

Target (mpg) 

Passenger Cars 

Light Trucks 

35 40 45 50 55 60 65 70 

Footprint (sf) 

Figure 2-2 MY 2011 CAFE Standards for Passenger Cars and Light Trucks 

In finalizing the MY 2011 standards, NHTSA noted that the agency is not required to 

use a constrained logistic function and indicated that the agency may consider defining future 

CAFE standards in terms of a different mathematical function. NHTSA and EPA have done 

so jointly for the MYs 2012-2016 CAFE standards and CO2 emissions standards. 

In revisiting this question jointly, EPA and NHTSA found that the final MY 2011 

CAFE standard for passenger cars, though less steep than the MY 2011 standard NHTSA 

proposed in 2008, continues to concentrate the sloped portion of the curve (from a compliance 

perspective, the area in which upsizing results in a slightly lower applicable target) within a 

relatively narrow footprint range (approximately 47-55 square feet). In practical terms, this 

results in a portion of the fleet that has a large change in emissions and fuel consumption with 

just a minor change in footprint. This potentially increases the incentive for gaming by 

upsizing slightly to meet less stringent standards. Further, most passenger car models have 

footprints smaller than the curve’s 51.4 square foot inflection point, and many passenger car 

models have footprints at which the curve is relatively flat (see figure above). The resulting 

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fit to the data was forced by choice of asymptotes defining the constrained logistic function. 

The agencies discuss below the alternative function that seeks to prevent this from occurring. 

For both passenger cars and light trucks, a mathematical function that has some slope 

at most footprints where vehicles are produced is advantageous in terms of fairly balancing 

regulatory burdens among manufacturers, and in terms of providing a disincentive to respond 

to new standards by downsizing vehicles in ways that compromise vehicle safety. For 

example, a flat standard has drawbacks discussed above: it may be very difficult for a full-line 

manufacturer to meet, while requiring very little of a manufacturer concentrating on small 

vehicles, and a flat standard may provide an incentive to manufacturers to downsize certain 

vehicles, in order to “balance out” other vehicles subject to the same standard. 

As a potential alternative to the constrained logistic function, NHTSA had, in 

proposing MY 2011 standards, presented information regarding a constrained linear function 

(or piecewise linear function). As shown in the 2008 NPRM, a constrained linear function 

has the potential to avoid creating a localized region (in terms of vehicle footprint) over which 

the slope of the function is relatively steep. Although NHTSA did not receive public 

comments on this option, the agency indicated that it still believed a linear function 

constrained by upper (on a gpm basis) and possibly lower limits could merit reconsideration 

in future CAFE rulemakings. 

Having re-examined a piecewise linear function or constrained linear function for 

purposes of the proposed standards, NHTSA and EPA tentatively concluded that for both 

passenger cars and light trucks, it remained important to maintain a function with a 

meaningfully sloped section covering a wide footprint range, thereby providing a welldistributed disincentive to downsize vehicles in ways that could compromise highway safety. 

Also, the function proposed was not so steeply sloped that it would provide a strong incentive 

to increase vehicle size in order to obtain a lower CAFE requirement and higher CO2 limit, 

thereby compromising energy and environmental benefits. Therefore, the CAFE and CO2 

emissions standards proposed in the NPRM were defined by constrained linear or piecewise 

linear functions. 

The NHTSA constrained linear function was defined according to the following 

formula: 

1 TARGET = ⎡ ⎛ 1 ⎞ 1 ⎤ MIN MAX c × FOOTPRINT + d, , ⎢ ⎜ ⎟ ⎥ ⎣ ⎝ a ⎠ b ⎦

Here, TARGET is the fuel economy target (in mpg) applicable to vehicles of a given 

footprint (FOOTPRINT, in square feet), b and a are the function’s lower and upper 

asymptotes (also in mpg), respectively, c is the slope (in gpm per square foot) of the sloped 

portion of the function, and d is the intercept (in gpm) of the sloped portion of the function 

(that is, the value the sloped portion would take if extended to a footprint of 0 square feet. 

The MIN and MAX functions take the minimum and maximum, respectively of the included 

values; for example, MIN(1,2) = 1, MAX(1,2) = 2, and MIN[MAX(1,2),3)]=2. Figure 2-3 

shows an example of a linear target function, where a = 0.0241 gpm (41.6 mpg), b = 0.032 

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0.000 

0.005 

0.010 

0.015 

0.020 

0.025 

0.030 

0.035 

0.040 

0.045 

0.050 

Fuel Consumption (gpm)

1/LIMIT UPPER 

1/LIMIT LOWER 

INTERCEPT 

SLOPE 

What are the Attribute-Based Curves the Agencies are Using, and How Were They Developed? 

gpm (31.2 mpg), c = 0.000531 gpm per square foot, and d = 0.002292 gpm (436 mpg). 

Because the function is linear on a gpm basis, not an mpg basis, it is plotted on this basis. 

35 40 45 50 55 60 65 70 

Footprint (sf) 

Figure 2-3 Sample Linear Function 

The EPA piecewise linear function has the same shape as the NHTSA equation 

but has a slightly different mathematical specification. It is defined according to the 

following formula: 

Target = a, if x ≤ l
 

Target = cx + d, if l < x ≤ h
 

Target = b, if x > h
 

This equation also takes the simplified form (consistent with NHTSA’s functional 

form): 

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

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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 the boundary (“kinks”) 

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

Figure 2-4 shows the basic curve. 

CO2 (gram/mile) or

Fuel Consumption (gallons/mile) 

lower limit = l Intercept = d 

upper 

limit = h 

slope = c 

min = a 

max = b 

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

Figure 2-4 The Shape of the Piecewise Linear Attribute Based Curve 

For purposes of the NPRM, NHTSA, working with EPA, developed the basic curve 

shapes for both agencies’ respective standards, using statistical fitting methods similar to 

those applied by NHTSA in fitting the curves defining the MY 2011 standards. The first step 

began with the market inputs discussed above, but because the baseline fleet was 

technologically heterogeneous, NHTSA used the CAFE model to develop a fleet to which 

nearly all the technologies listed in Ch. 3 of the TSDd were applied, by taking the following 

d 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 

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steps: (1) applying all non-diesel and non-hybrid technologies to each vehicles according to 

their applicability, (2) treating all manufacturers as unwilling to pay civil penalties rather than 

applying technology, (3) applying any technology at any time, irrespective of scheduled 

vehicle redesigns or freshening, and (4) ignoring “phase-in caps” that constrain the overall 

amount of technology that can be applied by the model to a given manufacturer’s fleet. These 

steps helped to increase technological parity among vehicle models, thereby providing a better 

basis (than the baseline fleet) for estimating the statistical relationship between vehicle size 

and fuel economy. In other words, this process normalized the fleet such that differences in 

technology had a minimal contribution to the variation (scatter) in the data. This process also 

served to eliminate the influence of differing degrees of pre-existing fuel-saving technology at 

various footprint values. This slightly flattened the slope of the line, indicating that a 

regression without adjusting for different 2008 vehicle technologies would not only have 

overestimated the dependence of fuel economy on footprint, but would also have provided an 

inappropriate incentive to upsize vehicles. 

In fitting the curves, EPA and 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 targets to the smallest 

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

thus assures that the function remains technologically achievable and economically 

practicable for manufacturers focusing on small vehicles. Obviously, passenger 

compartments can only become so small; this means, among other things, that a vehicle’s 

frontal area—a key determinant of aerodynamic drag—can only become so small. Also, as 

vehicle size decreases, complying with federal motor vehicle safety standards can necessitate 

the addition of structural elements that add mass and thereby reduce fuel economy and 

increase CO2 emissions. 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 fuel 

economy (or maximum emissions), while remaining consistent with safety considerations. 

Before fitting the sloped portion of the constrained linear form, NHTSA and EPA 

selected footprints above and below which to apply constraints (i.e., minimum and maximum 

values) on the function. For passenger cars, the agencies 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 

order to comply with the standards. NHTSA and EPA did include diesel engines and strong hybrid vehicle 

technologies in all other portions of their analyses. 

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characteristics that could make it infeasible to achieve the very challenging targets that could 

apply in the absence of a constraint, the agencies proposed to “cut off” the linear portion of 

the passenger car function at 41 square feet. The agencies recognize that for manufacturers 

who make small vehicles in this size range, there is some incentive to downsize to make it 

easier to meet the target. However, at the same time, the agencies believe that there is a limit 

to the market for ever-smaller cars. Most consumers likely have some minimum expectation 

about interior volume, among other things. The agencies thus believe that the number of 

consumers who will want vehicles smaller than 41sq ft to be downsized even further is 

extremely small, and the incentive to downsize, if present, will be minimal. For consistency, 

the agencies proposed to “cut off” the light truck function at the same footprint, although no 

light trucks are currently offered below 41 square feet. If the agencies set the limit for small 

trucks at a level higher than that for cars, there could be an undue incentive for manufacturers 

to change their car designs to qualify as trucks between the two footprint limits. The agencies 

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. The agencies therefore proposed to “cut off” the 

linear portion of the passenger car function at 56 square feet. Finally, the agencies noted that 

although public information is limited regarding the sales volumes of the many different 

configurations (cab designs and bed sizes) of pickup trucks, the largest pickups (e.g., the Ford 

F-150, GM Sierra/Silverado, Nissan Titan, and Toyota Tundra), appear to fall above 66 

square feet in footprint. The agencies therefore proposed to “cut off” the linear portion of the 

light truck function at 66 square feet. These “cut-off” values of footprint serve as the 

constraints to the functions shown above. The lower and upper levels are determined from 

where the sloped line meets these cut-off points. 

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. Values for c and d were initially set such that 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 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. 

The curve was fit in fuel consumption (i.e., CO2) space rather than fuel economy space 

because the manufacturer targets are in terms of the harmonic average fuel economy (or 

average CO2), and so it is more important that the curve fit the fuel consumption (i.e., CO2) 

data well than that it fit the fuel economy data well. NHTSA also explained in the MY 2011 

final rule that it chose to use MAD in this step instead of minimizing the sum of the square 

errors (“least squares,” a more common approach in curve fitting) in order to lessen the 

influence of outliers. 

NHTSA and EPA currently believe that it is more appropriate to use unweighted data 

in fitting the curve rather than weighting the data by sales because of large variations in model 

sales. The agencies find that the market forecast used for analysis supporting both the NPRM 

and the final rule exhibits the two key characteristics that previously led NHTSA to use 

minimization of the unweighted Mean Absolute Deviation (MAD) rather than weighted leastsquares analysis. First, projected model-specific sales volumes in the agencies’ market 

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What are the Attribute-Based Curves the Agencies are Using, and How Were They Developed?
 

forecast cover an extremely wide range, such that, as discussed in NHTSA’s rulemaking for 

MY 2011, although unweighted regression gives low-selling vehicle models and high-selling 

vehicle models equal emphasis, sales-weighted regression would give some vehicle models 

considerably more emphasis than other vehicle models.e The agencies’ intention is to fit a 

curve that describes a technical relationship between fuel economy and footprint, given 

comparable levels of technology, and this supports weighting discrete vehicle models equally. 

On the other hand, sales weighted regression would allow the difference between other 

vehicle attributes to be reflected in the analysis, and also would reflect consumer demand. 

Second, even after NHTSA’s “maximum technology” analysis to increase 

technological parity of vehicle models before fitting curves, the agencies’ market forecast 

contains many significant outliers. As discussed in NHTSA’s rulemaking for MY 2011, 

MAD is a statistical procedure that has been demonstrated to produce more efficient 

parameter estimates than least-squares analysis in the presence of significant outliers.f In 

addition, the agencies remain concerned that the steeper curves resulting from weighted leastsquares analysis would increase the risk that energy savings and environmental benefits 

would be lower than projected, because the steeper curves would provide a greater incentive 

to increase sales of larger vehicles with lower fuel economy levels. Based on these technical 

considerations and these concerns regarding potential outcomes, the agencies have decided 

not to re-fit curves using weighted least-squares analysis, but note that they may reconsider 

using least-squares regression in future analyses. 

e For example, the agencies’ market forecast shows MY 2016 sales of 187,000 units for Toyota’s 2WD Sienna, 

and shows 27 model configurations with MY 2016 sales of fewer than 100 units. Similarly, the agencies’ market 

forecast shows MY 2016 sales of 268,000 for the Toyota Prius, and shows 29 model configurations with MY 

2016 sales of fewer than 100 units. Sales-weighted analysis would give the Toyota Sienna and Prius more than a 

thousand times the consideration of many vehicle model configurations. Sales-weighted analysis would, 

therefore, cause a large number of vehicle model configurations to be virtually ignored. See discussion in 

NHTSA’s final rule for MY 2011 passenger car and light truck CAFE standards, 74 Fed. Reg. at 14368 (Mar. 

30, 2009), and in NHTSA’s NPRM for that rulemaking, 73 Fed. Reg. at 24423-24429 (May 2, 2008). 

f Id. In the case of a dataset not drawn from a sample with a Gaussian, or normal, distribution, there is often a 

need to employ robust estimation methods rather than rely on least-squares approach to curve fitting. The leastsquares approach has as an underlying assumption that the data are drawn from a normal distribution, and hence 

fits a curve using a sum-of-squares method to minimize errors. This approach will, in a sample drawn from a 

non-normal distribution, give excessive weight to outliers by making their presence felt in proportion to the 

square of their distance from the fitted curve, and, hence, distort the resulting fit. With outliers in the sample, the 

typical solution is to use a robust method such as a minimum absolute deviation, rather than a squared term, to 

estimate the fit (see, e.g., ‘‘AI Access: Your Access to Data Modeling,’’ at 

http://www.aiaccess.net/English/Glossaries/GlosMod/e_gm_O_Pa.htm#Outlier). The effect on the estimation is 

to let the presence of each observation be felt more uniformly, resulting in a curve more representative of the 

data (see, e.g., Peter Kennedy, A Guide to Econometrics, 3rd edition, 1992, MIT Press, Cambridge, MA). 

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Finally, the agencies calculated the values of the upper and lower limits based on the 

corresponding footprint constraints 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 Figure 2-5 and Figure 2-6 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 mean absolute deviation was 10 percent. 

0.060 

0.055 

0.050 

0.045 

0.040 

Fuel Consumption (gpm) 0.035 

0.030 

0.025 

0.020 

0.015 

0.010 

0.005 

0.000
 

35 40 45 50 55 60 65 70 75 80
 

Footprint (sf) 

Figure 2-5 “Maximum Technology” Passenger Fleet with Fitted Constrained Linear Function 

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What are the Attribute-Based Curves the Agencies are Using, and How Were They Developed?
 Fuel Consumption (gpm) 

0.060 

0.055 

0.050 

0.045 

0.040 

0.035 

0.030 

0.025 

0.020 

0.015 

0.010 

0.005 

0.000 

35 40 45 50 55 60 65 70 75 80 

Footprint (sf) 

Figure 2-6 “Maximum Technology” Light Truck with Fitted Constrained Linear Function 

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

functions defining actual proposed standards. As discussed in Sections II.C of the preamble, 

the agencies transposed these functions vertically (i.e., on a gpm basis, uniformly downward) 

to produce the maximum feasible passenger car and light truck CAFE standards, and 

corresponding CO2 emissions standards. 

Having considered public comments as discussed in greater detail in the final rule 

preamble, NHTSA and EPA have re-examined the development of curves underlying the 

standards proposed in the NPRM, and are promulgating standards based on the same 

underlying curves. The agencies have made this decision considering that, while EISA 

mandates that CAFE standards be defined by a mathematical function in terms of one or more 

attributes related to fuel economy, neither EISA nor the CAA expressly require that the 

mathematical function be purely descriptive of an observed or theoretical dependence of fuel 

economy on the selected attribute or attributes. As a means by which CAFE and GHG 

standards are specified, the mathematical function plays a normative role.g Therefore, 

g That is, tending to encourages some types of responses more than others – in this case, generally preferring 

increased use of fuel-saving technologies over simple reductions in vehicle size. 

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NHTSA and EPA have concluded that the mathematical function can reasonably be selected 

based on a blend of analytical and policy considerations. 

Considering comments by UC Santa Barbara students regarding difficulties 

reproducing NHTSA’s analysis, NHTSA re-examined its analysis, and discovered some 

erroneous entries in model inputs underlying the analysis used to develop the curves proposed 

in the NPRM. These errors, which are also discussed in NHTSA’s final Regulatory Impact 

Analysis (RIA) and have since been corrected, include the following: incorrect valvetrain 

phasing and lift inputs for many BMW engines, incorrect indexing for some Daimler models, 

incorrectly enabled valvetrain technologies for rotary engines and Atkinson cycle engines, 

omitted baseline applications of cylinder deactivation in some Honda and GM engines, 

incorrect valve phasing codes for some 4-cylinder Chrysler engines, omitted baseline 

applications of advanced transmissions in some VW models, incorrectly enabled advanced 

electrification technologies for several hybrid vehicle models, and incorrect DCT 

effectiveness estimates for subcompact passenger cars. 

After correcting these errors and repeating the curve development analysis presented 

in the NPRM, NHTSA obtained curves shown below in Figures 2-7 and 2-8 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 mean absolute deviation was 10 percent. 

Figure 2-7 Revised “Maximum Technology” Passenger Fleet with Fitted Constrained Linear Function 

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What are the Attribute-Based Curves the Agencies are Using, and How Were They Developed?
 

Figure 2-8 Revised “Maximum Technology” Light Truck with Fitted Constrained Linear Function 

This refitted passenger car curve is similar to that presented in the NPRM, and the 

refitted light truck curve is nearly identical the corresponding curve in the NPRM. However, 

the slope of the passenger car curve is about 27 percent steeper (on a gpm per sf basis) than 

the curve presented in the NPRM. For passenger cars and light trucks, respectively, Figure 

2-9 and Figure 2-10 show the results of adjustment—discussed below—of the above curves to 

yield the average required fuel economy levels corresponding to the announced national 

policy. 

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20 

25 

30 

35 

40 

45 

T a 

r

g 

e t

(

m 

p 

g

) 

NPRM 

Final 

Re-Fitted 

35 40 45 50 55 60 65 70 75 80 

Footprint (sf) 

Figure 2-9 MY2016 Passenger Car Targets: NPRM, Final Rule, and if Using Re-Fitted Curve 

20 

25 

30 

35 

40 

45 

T a 

r

g 

e t

(

m 

p 

g

) 

NPRM 

Final 

Re-Fitted 

35 40 45 50 55 60 65 70 75 80 

Footprint (sf) 

Figure 2-10 MY2016 Light Truck Targets: NPRM, Final Rule, and if Using Re-Fitted Curve 

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What are the Attribute-Based Curves the Agencies are Using, and How Were They Developed?
 

As the figures show, the resultant light truck curves are visually indistinguishable from 

one another. For passenger cars, in contrast, the refitted curve would increase stringency for 

the smallest cars, decrease stringency for the largest cars, and provide a greater incentive to 

increase car size throughout the range of footprints within which NHTSA and EPA project 

most passenger car models will be sold through MY 2016. The agencies are concerned that 

these changes would make it unduly difficult for manufacturers to introduce new small 

passenger cars in the United States, and unduly risk forgoing achievable and cost-effective 

energy and environmental benefits by increasing incentives for the passenger car market to 

shift toward larger vehicles. 

Also, the agencies note that the refitted passenger car curve produces only a slightly 

closer fit to the corrected fleet than would the curve estimated in the NPRM; with respect to 

the corrected fleet (between the “cut off” footprint values, and after the “maximum 

technology” analysis discussed above), the mean absolute deviation for the refitted curve is 

13.887 percent, and that of a refitted curve held to the original slope is 13.933 percent. In 

other words, the data support the original slope very nearly as well as they support the refitted 

slope. 

Although these refitted curves reflect the agencies’ best technical judgment at this 

time regarding the use of statistical analysis for this purpose, based on NHTSA’s and EPA’s 

concerns regarding the undesirable changes in incentives that would result from a refitted 

curve for passenger cars, and given that the data support the original curves essentially as well 

as they would support refitted curves, the agencies are finalizing CAFE and GHG standards 

based on the curves presented in the NPRM. 

Finally, regarding some commenters’ inability to reproduce the agencies’ NPRM 

analysis, NHTSA believes that its correction of the errors discussed above and its release (on 

NHTSA’s web site) of the updated Volpe model and all accompanying inputs and external 

analysis files should enable outside parties to independently reproduce the agencies’ analysis. 

If outside parties continue to experience difficulty in doing so, we encourage them to contact 

NHTSA, and the agency will do its best to provide assistance. 

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References
 

1 49 U.S.C. 32902(a)(3)(A).
 

2 The 2002 NAS Report described at length and quantified the potential safety problem with average fuel
 

economy standards that specify a single numerical requirement for the entire industry. See NAS Report at 5,
 

finding 12. Docket EPA-HQ-OAR-2009-0472-0147.
 

3 Id. at 4-5, finding 10. Docket EPA-HQ-OAR-2009-0472-0147.
 

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

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Technologies Considered in the Agencies’ Analysis
 

CHAPTER 3: Technologies Considered in the Agencies’ 

Analysis 

This Chapter of the joint TSD describes the technologies NHTSA and EPA 

evaluated as potential inputs in their respective models and provides estimates of the 

technologies’ costs, effectiveness and availability. This Chapter also describes, in general 

terms, how the agencies use these inputs in their respective models. For greater detail on 

this subject, please see Section V of NHTSA’s FRIA and Chapter 4 of EPA’s RIA. 

3.1	 How do the agencies decide which technologies to include in the 

analysis? 

Technology assumptions, i.e., assumptions about their availability, 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, the agencies reviewed the technology input assumptions identified in EPA’s 

2008 Staff Technical Report,1 the comments that NHTSA received in response to its May 

2008 Notice of Proposed Rulemaking, and the comments that the agencies received in 

response to its September 2009 Notice of Proposed Rulemaking. In addition, the 

agencies supplemented their review with information from the sources described in the 

following section. 

3.1.1 Reports and papers in the literature 

NHTSA and EPA have done extensive research in identifying the most credible 

sources of information. These sources included: the 2002 NAS report on the effectiveness 

and impact of CAFE standards;2 the 2004 study done by NESCCAF;3 the California Air 

Resources Board (CARB) Initial Statement of Reasons in support of their carbon 

rulemaking;4 a 2006 study done by Energy and Environmental Analysis (EEA) for the 

Department of Energy;5 a study done by the Martec Group for the Alliance of 

Automobile Manufacturers, and an update by the Martec Group to that study;6 and 

vehicle fuel economy certification data. Both agencies also reviewed the published 

technical literature which addressed the issue of CO2 emission control and fuel economy, 

such as papers published by the Society of Automotive Engineers and the American 

Society of Mechanical Engineers, as well as papers submitted by commenters (for 

example, the Aluminum Association) to the agencies’ joint NPRM. In addition, 

confidential data submitted by vehicle manufacturers in response to NHTSA’s request for 

product plans,7 and confidential information shared by automotive industry component 

suppliers in meetings with EPA and NHTSA staff held during the second half of the 2007 

calendar year were used as a cross check of the public data mentioned above and not as a 

significant basis for this rulemaking. 

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EPA also has a contracted study ongoing with FEV that consists of complete 

system tear-downs to evaluate technologies down to the nuts and bolts to arrive at very 

detailed estimates of the costs associated with manufacturing them. For the NPRM, the 

agencies used a completed analysis to estimate costs of turbocharging with downsizing 

for I4 engines only. The NPRM stated that tear-down cost estimates from FEV for 

additional engine and transmission technologies became available shortly before the 

release of the NPRM, but not in time to be incorporated into the agencies’ cost analysis 

of the proposed standards. These preliminary results were made available for review and 

the agencies stated they would consider this information for use in the final rule analysis.8 

The NPRM also stated that a detailed report would be submitted to the docket on these 

additional technologies during the public comment period for the proposal. That deadline 

was not met but all additional technologies for which cost study tear downs have been 

completed from this study with FEV (studies have now been completed on turbocharging 

and downsizing for V6 and V8 engines, stoichiometric gasoline direct injection, 6/7/8­

speed automatic transmission and dual clutch transmission technologies) have been 

considered for the final rule and details are contained in two reports placed in the 

docket.9,10 EPA and NHTSA reviewed all of the above information in order to develop 

the best estimates of availability, cost and effectiveness of these fuel-saving/CO2­

reducing technologies. 

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

Security Act (EISA), the National Academies of Sciences has been conducting a study 

for NHTSA to update chapter 3 of their 2002 NAS Report, which presents technology 

effectiveness estimates for light-duty vehicles. The update takes a fresh look at that list 

of technologies and their associated cost and effectiveness values. The updated NAS 

report was expected to be available on September 30, 2009, but has not been completed 

and released to the public. The results from this study thus are unavailable for this 

rulemaking. The agencies look forward to considering the results from this study as part 

of the next round of rulemaking for CAFE/GHG standards. 

3.1.2 Fuel economy certification data 

Where available, data from recent model years of EPA’s Fuel Economy 

Certification Data were used for helping to determine the effectiveness of specific 

technologies. CO2 and fuel consumption reduction estimates were estimated from EPA’s 

fuel economy database on the two-cycle (FTP city & highway) fuel economy test results. 

During the standard fuel economy test cycles, direct measurements of CO2 emissions are 

made. This data, along with other measurements, are then used to calculate the estimated 

fuel economy performance in gallons of fuel consumed per mile. Vehicle certification 

data are an obviously reliable source for determination of the CO2 and fuel consumption 

reduction potential when a directly comparable vehicle was offered both with and without 

the specific CO2 and fuel consumption reducing technology, because a comparison 

between the emissions data between the two vehicles directly reflect the application of 

the technologies on the vehicle test cycles. Where possible, technology-specific 

effectiveness numbers were extracted for vehicles where only the specific technology 

would be changed from a reference vehicle, in order to eliminate any confounding of 

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Technologies Considered in the Agencies’ Analysis
 

values across several technologies. In some hybrid vehicle cases, the exact same vehicle 

may not be offered, and a similar vehicle was selected for comparison. 

3.2	 Which technologies will be applicable during the model years to 

which the rules apply? 

One of the key factors that NHTSA and EPA considered in developing the 

standards for each model year is the availability and feasibility of fuel-saving/CO2­

reducing technologies. A variety of technologies can be used to reduce fuel consumption 

and CO2 emissions, whether they be engine-related (e.g., turbocharging), transmissionrelated (e.g., six forward gears in place of four), accessory-related (e.g., electric power 

steering), or vehicle-related (e.g., low rolling resistance tires). In taking a fresh look at 

the availability and feasibility of technologies for purposes of setting the MYs 2012-2016 

standards, the agencies reviewed NHTSA’s MY 2011 CAFE final rule and EPA’s 2008 

Staff Report, as well as the public comments to the proposed rule and updated the 

assumptions based on the most current information. As a check on this new analysis, 

NHTSA and EPA also reviewed the product plans submitted by manufacturers to the 

agencies in response to NHTSA’s November 2009 request for product plans. 

Many technologies considered by the agencies, such as variable valve timing 

(VVT), low rolling resistance tires, etc., are well known and readily available during the 

rulemaking time period, and could be incorporated into vehicles once production 

decisions are made. The agencies did not consider technologies in the research stage, 

such as homogeneous charge compression ignition (HCCI), lean-burn direct injection 

technology, etc., because their effectiveness and/or cost estimates contain a high level of 

uncertainty. 

The technologies considered by the NHTSA and EPA models are briefly 

described below. They fall under the five broad categories of engine, transmission, 

vehicle, electrification/accessory, and hybrid technologies. A more detailed description 

of each technology, and the technology’s costs and effectiveness, is described in greater 

detail in section 3.5 of this TSD. 

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. 

•	 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. 

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•	 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. 

•	 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 or phase 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 cam period and phasing 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 startstop 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, than an equivalent-performance gasoline 

engine. This technology requires additional enablers, such as NOx trap 

catalyst after-treatment or selective catalytic reduction NOx after-treatment. 

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Technologies Considered in the Agencies’ Analysis
 

Types of transmission technologies considered include:
 

•	 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 to enable the engine to operate in a more 

efficient operating range over a broader range of vehicle operating conditions. 

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

transmissions, but the vehicle controls shifting and launch functions. A dualclutch automated shift manual transmission uses separate clutches for evennumbered 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 and an infinite 

number of transmission ratios that enable the engine to operate in a more 

efficient operating range over a broader range of vehicle operating conditions. 

•	 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 reducing the energy needed to move the vehicle. 

•	 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 

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integration to application of lighter and higher-strength materials. Mass 

reduction is further compounded by reductions in engine power and ancillary 

systems (transmission, steering, brakes, suspension, etc.). 

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 an 

enhanced power starter-alternator, both belt driven, and a revised accessory 

drive system. 

•	 Higher Voltage Stop-Start/Belt Integrated Starter Generator (BISG) – 

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 motor, inverter, and battery wiring harnesses. 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 motor, inverter, and battery wiring harnesses. This system 

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

efficiency starter-alternator that is crankshaft mounted and can recover 

braking energy while the vehicle slows down (regenerative braking). 

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Technologies Considered in the Agencies’ Analysis
 

•	 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. 

3.3	 What technology assumptions have the agencies used for the final 

rule? 

3.3.1 How are the technologies applied in the agencies’ respective models? 

Although both NHTSA and EPA are basing their fuel economy and emission 

modeling on the same baseline vehicle fleet and cost and effectiveness estimates for 

control technologies, differences in the Volpe and OMEGA models require that this 

common information be processed in different ways prior to their use in the respective 

models.. With respect to the vehicle fleet, the Volpe Model evaluates the addition of 

technology to individual vehicle configurations or models, while the OMEGA model 

does so for vehicle platforms broken down further by engine size. Thus, NHTSA used 

the vehicle sales estimates described above directly, while EPA combined the sales of 

certain vehicle models. The Volpe Model evaluates technologies individually. This, 

coupled with the modeling of individual vehicle models, means that only the presence or 

absence of any particular technology needs to be indicated, as described above. OMEGA 

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applies technology in combinations or packages. This, plus the grouping of individual 

vehicle models, requires that the total effectiveness of the technology already applied in 

the baseline fleet must be calculated and must be reflected as a percentage of the various 

technology packages available to be added to those vehicles. 

With respect to the cost and effectiveness of technologies, as mentioned above, 

the Volpe Model applies technologies individually. It does this following certain 

specified pathways for several categories of technologies (e.g., engine, transmission, 

accessories, etc.). The Volpe Model applies technology incrementally, so the 

effectiveness of each subsequent technology needs to be determined relative to the 

previous one. The same is true for cost. In addition, because of interaction in the 

effectiveness of certain technologies, herein referred to as the synergy/dis-synergy, any 

such interaction between the next technology on a specified pathway with those which 

have already been potentially applied in other pathways must be determined. For 

example, the incremental effectiveness of switching from a six-speed automatic 

transmission to a dual clutch transmission will depend on the level of engine technology 

already applied (e.g., intake cam phasing on a port-fuel injected engine or a down-sized, 

turbocharged, direct injection engine). 

EPA’s OMEGA model applies technologies in packages and according to a fixed 

sequence for any particular group of vehicles. This requires that the overall cost and 

effectiveness of each package be determined first, considering any and all dis-synergies 

which may exist. Then, the incremental cost and effectiveness of each subsequent 

package is determined relative to the prior one. 

Thus, while the same baseline vehicle fleet and cost and effectiveness estimates 

for technologies are being used in both the Volpe and OMEGA models, the form of the 

actual inputs to the model will appear to be different. For more information on EPA’s 

and NHTSA’s unique approaches to modeling, please refer to each agency’s respective 

final RIA. 

In order to estimate both technology costs and fuel consumption/CO2 reduction 

estimates, it is necessary for each agency to describe the baseline vehicle characteristics 

from which the estimates can be compared. This “baseline” is different from the usage in 

Chapter 1 of this joint TSD. In Chapter 1, the baseline fleet is the projected fleet in MY 

2016 before accounting for technologies needed to meet the MY 2011 CAFE standards 

and before accounting for changes in fleet composition attributable to that rule (those 

later steps accounted for independently by each agency in developing their separate 

reference fleets). In the present context, it indicates the vehicle types and technologies 

that will be used for comparison from a strict cost and effectiveness point of view. These 

baselines may be slightly different for the two agencies. For EPA, unless noted 

elsewhere, the baseline vehicle is defined as a vehicle with a port-fuel injected, naturally 

aspirated gasoline engine with two intake and two exhaust valves and fixed valve timing 

and lift. The baseline transmission is a 4-speed automatic, and the vehicle has no hybrid 

systems. For NHTSA, unless noted elsewhere, the baseline vehicle is the actual vehicle 

as it exists in the baseline fleet, because NHTSA models each unique vehicle separately. 

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For details on how the technology cost and effectiveness estimates presented below are 

used in each agency’s model, refer to each agency’s respective final RIA. 

3.3.2	 How did the agencies develop technology cost and effectiveness estimates for 

the final rule? 

3.3.2.1 Considerations that affect technology effectiveness 

As mentioned above, regarding estimates for technology effectiveness, NHTSA 

and EPA’s primary approach was to reexamine the estimates from NHTSA’s MY 2011 

CAFE final rule and EPA’s ANPRM and Staff Technical Report, which largely mirrored 

NHTSA’s NPRM estimates in the 2008 proposal to establish CAFE standards for MYs 

2011-2015. The agencies also reconsidered other sources such as the 2002 NAS Report, 

the 2004 NESCCAF report, recent CAFE compliance data (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. Using the BOM 

framework utilized in the MY 2011 CAFE final rule, NHTSA and EPA engineers 

reviewed effectiveness information from the multiple sources for each technology. 

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 

technology decision trees to arrive at the standards, direct comparison of BOMs was 

somewhat more complicated. To address this and to assure an apples-to-apple 

comparison, NHTSA and EPA developed mapping techniques, devising technology 

packages and corresponding incremental technology estimates. This approach helped 

compare incremental and packaged estimates and derive results that are consistent and 

could be translated into the respective models of the agencies. In general, most 

effectiveness estimates used in the 2008 EPA Staff Report, the MY 2011 CAFE final rule 

and the MY 2012-2016 joint NPRM were determined to be accurate and were carried 

forward without significant change into the analysis for this final rule. When NHTSA 

and EPA’s estimates for effectiveness diverged slightly due to differences in how the 

agencies apply technologies to vehicles in their respective models, the agencies report the 

ranges for the effectiveness values used in each model, as well as the reasons why the 

range is reasonable. There were only a few comments on technology effectiveness. For 

the most part, the non-confidential comments were consistent with the range of 

effectiveness reported in this rule. Where there were differences, EPA and NHTSA did 

not see sufficient evidence to warrant a change in the technology effectiveness. Further 

details can be found in EPA’s Response to Comments document (section 4.4) and 

Chapter V of NHTSA’s FRIA. 

3.3.2.1.1 Technology synergies 

The benefits of these technologies are generally but not always additive. That is, 

adding cylinder deactivation to dual cam phasing does not necessarily result in a 10 

percent improvement. This is true because some of the technologies address the same 

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shortcomings of the internal combustion engine, although in different ways. Where that 

is the case, negative (or dis-) synergies are said to exist between two (or more) 

technologies. It is also true that combining some technologies can provide more benefit 

than a simple additive effect. In those cases, positive synergies are said to exist. The 

analysis accounts for these positive and negative synergies wherever appropriate so as to 

ensure no double or insufficient counting of effects. Synergies are discussed in more 

detail in each agency’s respective final RIA. 

3.3.2.2 Technology Cost Considerations 

As a general matter, NHTSA and EPA believe the best way to derive technology 

cost estimates is to conduct real-world tear down studies. This position is supported by 

commenters and we received no comments to the contrary.a These studies are based to a 

large degree on tear downs of vehicles or vehicle systems that employ the new 

technologies, and of similar vehicles or systems without the new technologies. Analysts 

with expertise in automotive design, materials, and manufacturing then compare the tear 

down components and evaluate the differences. Using databases for materials, labor, 

manufacturing overhead, and mark-up costs, the overall costs to manufacture individual 

parts are calculated and summed into final results. However, 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 Inc., an 

independent engine and powertrain systems research, design and development company. 

To date, four technologies (downsizing and turbocharging, stoichiometric gasoline directinjection, dual clutch transmission and 6-speed automatic transmission) have been 

evaluated. The tear down study has been peer-reviewed and the report for these tear 

down studies and the peer-review report have been made public in the rulemaking 

docket.11 The agencies have considered these studies and the comments received on 

them, as practicable and appropriate, in developing technology cost assumptions for this 

final rule. 

These recently completed tear-downs include the following technologies: 

1.	 3.0L V6 port fuel-injected (PFI) downsized and turbocharged to a 2.0L I4 

gasoline direct injection (GDI) 

2.	 5.4L V8 PFI downsized and turbocharged to a 3.0L V6 twin-turbo GDI 

3.	 5-speed automatic transmission to 6-speed automatic transmission 

4.	 6-speed automatic transmission to 6-speed wet dual-clutch transmission 

A comparison between costs reported in the NPRM and the final rule can be found in 

Table 3-1 below. 

Table 3-1 A comparison of NPRM costs and the final rule for five updated technologies ($2007 in 

2012). 

Technology Incremental To NPRM Direct Final Rule Direct Change 

a See comments from ICCT (EPA-HQ-OAR-2009-0472-7156), CARB (EPA-HQ-OAR-2009-0472-7189), 

NESCAUM (EPA-HQ-OAR-2009-0472-7235) 

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Manufacturing 

Cost 

Manufacturing 

Cost 

2.0L I4 Turbo GDI 3.0L V6 MPFI $248 $152 -$96 

3.0L V6 twin-turbo GDI 5.4L V8 MPFI $1,081 $964 -$117 

5-speed auto trans 4-speed auto trans $91 $91 $0 

6-speed auto trans 4-speed auto trans $153 $101 -$52 

6-speed auto trans 5-speed auto trans $62 * $9 -$53 

6-speed dual-clutch trans (wet) 6-speed auto trans $126 -$11 -$137 

* Calculated as the difference between the 4 to 6 speed trans ($153) and the 4 to 5 speed trans ($91). 

Most notable in Table 3-1 is the change in costs for the 6-speed dual-clutch 

transmission relative to the 6-speed automatic transmission. We discuss this change in 

more detail in section 3.4.2.2.5, FEV’s tear down work found that a wet clutch DCT is 

$147 less costly than a 6 speed auto transmission (see table 3-15). This cost savings is 

not unexpected when one considers that the DCT is less complex than an auto 

transmission. However, FEV tear down cost analysis studies were conducted based on 

the assumption that the analyzed technologies and the manufacturing for those 

technologies were both fully “mature,” in that designs and manufacturing processes have 

been reasonably optimized. The studies also assumed that manufacturing facilities have 

annual production levels of 450,000 units. EPA and NHTSA recognize that in early 

implementation years, designs and manufacturing processes may not be optimized to that 

extent, and investment cost may exceed those of fully mature technologies. To account 

for higher cost in the earlier implementation years of the rulemaking period, NHTSA and 

EPA estimated MY 2012 costs as the average of the FEV tear down study cost and the 

NPRM cost for technologies for which there were completed FEV tear down studies. 

Time-based learning is used to reflect cost in later years. This approach is applied to 

downsizing and turbocharging for V6 and V8, stoichiometric gasoline direct-injection for 

V6 and V8, dual clutch transmission and 6-speed automatic transmission. In the NPRM, 

the costs for turbocharging, downsizing and SGDI for I4 engines were based on an FEV 

teardown cost study that was completed prior to release of the NPRM. For the final rule, 

these costs were carefully reviewed and updated to better account for early year 

implementation costs. We discuss all of the costs presented in Table 3-1 in greater detail 

in section 3.4.2 of this joint TSD. 

Regarding the other technologies, because tear down studies were not yet 

available, the agencies decided to continue the BOM approach as outlined in NHTSA’s 

MY 2011 final rule and EPA’s 2008 Staff 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 

similar in concept to the approach 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. To the extent 

that the agencies departed from the MY 2011 CAFE final rule and the 2008 Staff Report 

estimates, the agencies have explained the reasons and provided supporting analyses. For 

example, while NHTSA and Ricardo engineers had relied considerably in the MY 2011 

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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, revalidated or updated cost estimates for individual components based on new 

information. 

The subsequent sections describe the costs associated with the new vehicle 

technologies. The costs represent the piece costs for an individual piece of hardware or 

system, e.g., an intake cam phaser to provide variable valve timing. Costs are presented 

in terms of their hardware incremental compliance cost. This means that they include all 

potential costs associated with their application on vehicles, not just the cost of their 

physical parts. As discussed, the basis for the piece costs presented here is NHTSA’s 

MY 2011 CAFE final rule and EPA’s 2008 Staff Report.12,1 In those analyses, piece 

costs were estimated 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. As a result of almost daily 

meetings between EPA and NHTSA staff during 2009, where both piece costs and fuel 

consumption efficiencies were discussed in detail, cost estimates have been adjusted 

slightly. Where estimates differ between sources, engineering judgment was used to 

arrive at what is believed to be the best cost estimate available today, and explained the 

basis for that exercise of judgment. 

Note that EPA’s 2008 Staff Report presented costs in terms of 2006 dollars. For 

this analysis, costs are expressed in terms of 2007 dollars. Any costs presented here that 

are based on costs expressed in 2006 dollars have been adjusted to reflect 2007 dollars 

using the Gross Domestic Product price deflator (see Appendix 3.A).13,b The following 

discussion summarizes the piece cost estimates and how these data sources were used to 

arrive at the best estimate of piece costs for individual technologies, as well as their 

respective effectiveness estimates. For purposes of this joint TSD, these estimates were 

developed jointly by NHTSA and EPA. Due to differences in the modeling and 

technology application methodologies employed by the two agencies, the estimates used 

by each agency may not match exactly but will be consistent given the different modeling 

tools used. Additional details are also provided in each agency’s respective final RIAs. 

3.3.2.2.1 Direct manufacturing costs 

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, 1 the agencies took a fresh look at technology cost and effectiveness 

values for purposes of the joint final rule 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 costs, the agencies generally followed a bill 

of materials (BOM) approach as noted above. A bill of materials, in a general sense, is a 

list of components that make up a system—in this case, an item of fuel economyb The adjustment used to convert from 2006 dollars to 2007 dollars was 1.03. 

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improving 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. As discussed above, the agencies reviewed, revalidated or updated cost 

estimates for individual components based on new information. Thus although NHTSA 

and EPA found that much of the cost information used in NHTSA’s MY 2011 CAFE 

final rule and EPA’s staff report was consistent, the agencies, in reconsidering 

information from many sources (such as the new FEV cost studies), ultimately revised 

several component costs of several major technologies (turbocharging downsizing, mild 

and strong hybrids, diesels, SGDI, Valve Train Lift Technologies, DCT and 6-speed 

automatic transmissions).14 These are discussed at length below. For turbocharging 

downsizing, SGDI, DCT and 6-speed automatic transmission, the agencies relied, to the 

extent possible, on the tear down data available and scaling methodologies used in EPA’s 

ongoing study with FEV. As discussed above in detail in Section 3.3.2.2, 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.15 The confidential information provided by manufacturers under their product plan 

submissions to the agencies or discussed in meetings between the agencies and the 

manufacturers and suppliers served largely as a check on publicly-available data. 

Once costs were determined, they were adjusted to ensure that they were all 

expressed in 2007 dollars using the GDP price deflator as described in Appendix 3.A. 

Indirect costs were accounted for using a new approach developed by EPA for this 

rulemaking and explained below rather than using the Retail Price Equivalent (RPE) 

multiplier of 1.5 as was done in NHTSA’s MY 2011 final rule and EPA’s 2008 Staff 

Report. NHTSA and EPA also considered how costs should be adjusted to reflect 

manufacturer learning as discussed below. Lastly, costs were 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 these adjustments were different for each agency due to 

the different vehicle subclasses used in their respective models. 

3.3.2.2.2 Cost markups to account for indirect costs 

Indirect costs include production-related costs (research, development, and other 

engineering), business-related costs (corporate salaries, pensions, and manufacturer 

profits), and retail-sales-related costs (dealer support, marketing, and dealer profits). For 

this analysis, direct cost estimates were first developed for each technology or system at 

the auto manufacturer level, i.e., the price paid by the manufacturer to a Tier 1 

component supplier. To these costs, an indirect cost markup factor was then applied that 

varied by the best estimate of the particular technology’s complexity. This section 

describes the approach to determining the indirect cost multipliers (ICM) used in this 

analysis and the specific multipliers used for each piece of technology. 

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3.3.2.2.3 Concept behind and development of indirect cost multipliers 

If all desirable data were available, when a new technology is implemented, the 

costs of that technology would include the direct and indirect costs particular to that 

technology. For instance, some changes may involve new tooling, while others may not; 

some may affect the way the car is marketed, while others are of limited interest to 

consumers. In a world of full information, the indirect costs of a new technology would 

be calculated specifically for that technology. In practice, though, it is often difficult, if 

not impossible, to identify the indirect costs specific to a particular new technology. 

The automotive industry, EPA, and NHTSA have commonly used retail price 

equivalent (RPE) multipliers to approximate the indirect costs associated with a new 

technology. The RPE is a ratio of total revenues to direct manufacturing costs. Because, 

by definition, total revenues = direct costs + indirect costs + profit, the RPE is the factor 

that, when multiplied by direct manufacturing costs, recovers total revenue.c This 

multiplication is accurate only in the aggregate; it does not in reality apply to any specific 

technology. The RPE is a way to estimate indirect costs on the assumption that indirect 

costs are constant across all technologies and processes in a company. In the MY 2011 

CAFE final rule NHTSA utilized a 1.5 RPE multiplier. 

In fact, however, the indirect costs of new technologies vary, both with the 

complexity of the technology and with the time frame. For instance, a hybrid-electric 

engine is likely to involve greater research and development and marketing costs per 

dollar of direct costs than low-rolling-resistance tires; the research and development costs 

of any technology are likely to decrease over time. In recognition of this concern, EPA 

contracted with RTI International to provide a current estimate of the RPE multiplier and 

to examine whether the indirect costs of new technologies are likely to vary across 

technologies. The report “Automobile Industry Retail Price Equivalent and Indirect Cost 

Multipliers,” by Alex Rogozhin, Michael Gallaher, and Walter McManus,16 calculates the 

RPE multiplier as 1.46 in 2007. The report then develops indirect cost (IC) multipliers 

that vary with the complexity of technology and the time frame. While any multiplier is 

only an approximation of the true indirect costs of a new technology, the IC multipliers in 

this report move away from the assumption that the proportion of indirect costs is 

constant across all technologies and take into account some of the variation in these costs. 

The multipliers developed in this report are presented in Table 3-2. 

The indirect cost multipliers used adjustment factors, developed by a team of EPA 

engineers with expertise in the auto industry, which accounted for the differences in 

complexity of the specific technologies under study. To examine the sensitivity of the 

results to different technologies of the same complexity, and to provide more detailed 

documentation of the development of the adjustment factors, EPA convened a second 

panel,17 with NHTSA’s input, to develop adjustment factors for three different 

technologies. This latter process allowed for estimates of the variation in adjustment 

c Note that unlike the RPE, the ICM does not include profits. 

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factors, and thus in the variation of indirect cost multipliers. These results are also 

presented in Table 3-2. 

Table 3-2 Indirect Cost Multipliers 

STUDY 

TECHNOLOGY COMPLEXITY 

Short Run Long Run 

Low Medium High Low Medium High 

RTI Report 1.05 1.20 1.45 1.02 1.05 1.26 

EPA Memo: Average 1.16 1.29 1.64 1.12 1.20 1.39 

Standard Deviation 0.14 0.15 0.21 0.14 0.13 0.15 

Median 1.12 1.26 1.66 1.06 1.20 1.40 

Max 1.43 1.53 2.15 1.42 1.45 1.69 

Min 1.00 1.02 1.37 1.00 1.01 1.12 

Multipliers Used in 

this Analysis 1.11 1.25 1.45 1.64 1.07 1.13 1.26 1.39 

Table 3-2shows minor differences in the multipliers for low- and mediumcomplexity technologies (roughly 0.1), but larger differences in the high-complexity 

technologies. The EPA and NHTSA engineers who reviewed the results believed that the 

differences reflected actual differences in the technologies under study. In particular, for 

low complexity, low-rolling-resistance tires (the application in the RTI Report) would 

involve lower indirect costs than aerodynamic improvements (the application in the EPA 

memo); and, for medium complexity, dual-clutch transmissions (the application in the 

RTI Report) should have a smaller multiplier than engine downsizing done in conjunction 

with turbocharging (the application in the EPA Memo). For these two cases, EPA and 

NHTSA considered these technologies to span the range of technologies assigned to 

those classes; the costs in this study, then, use the averages of the values of the two 

reports, as shown in the last line of Table 3-2. For high complexity technologies, the 

agencies felt the technologies assigned to these categories—hybrid-electric vehicles in 

the RTI Report; plug-in hybrid electric vehicles in the EPA Memo—were sufficiently 

different that each deserved a different category. This is discussed in more detail in the 

next section which highlights the multipliers used for each specific technology. 

3.3.2.2.3.1 Application of specific indirect cost multipliers to each technology 

As noted in the previous section, and in the NPRM, a different ICM was applied 

to each technology’s direct cost to arrive at its compliance cost. These different ICMs 

were chosen based on the complexity of integrating the technology into the vehicle in the 

opinions of staff engineers at EPA and NHTSA, most of whom have several years of 

experience in the auto industry. As shown in Table 3-2, ICMs were developed via two 

separate processes: that presented in the RTI report; and that presented in the EPA 

Memo. While all of the ICMs generated via these two processes were in general 

agreement, some differences did exist. In determining how to deal with these differences, 

EPA and NHTSA agreed that, for the low and medium complexity technologies, a simple 

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average of the two values would be used. However, for the high complexity 

technologies, it was decided that two separate high-multipliers should be used. The 

lower multiplier, deemed high, would be applied to those technologies of high 

complexity but with some level of use in the marketplace today. Such technologies 

would be power-split and 2-mode hybrid electric vehicles. The higher multiplier, deemed 

high+, would be applied to those technologies of high complexity but with no, or 

essentially no, use in the current fleet. Such technologies would be plug-in hybrids and 

full electric vehicles. Table 3-3 shows the complexity level for each technology 

considered in this analysis. 

Table 3-3 Complexity Levels of Technologies 

LOW COMPLEXITY MEDIUM COMPLEXITY HIGH COMPLEXTIY HIGH+ 

COMPLEXITY 

Low friction lubes VVLT-continuous (OHV) VVLT-continuous 

(OHC) 

Plug-in hybrid 

Engine friction reduction VVLT-discrete (OHV) Camless valve actuation Full electric vehicle 

VVT-intake (OHC) GDI-lean burn Homogeneous charge 

CI 

VVT-coupled (OHC & OHV) Turbocharge with downsize Weight reduction – 30% 

VVT-dual (OHC) Conversion to diesel Integrated motor assist 

hybrid 

Cylinder deactivation (OHC & 

OHV) 

Dual clutch transmission 2-mode hybrid 

VVLT-discrete (OHC) Continually variable trans Power-split hybrid 

GDI-stoich 42 Volt conversion 

Turbocharge (with no downsize) Weight reduction – 20% 

Downsize (with no turbocharge) Integrated starter generator 

(stop-start) 

Cam configuration changes (with 

no downsize) 

Combustion restart 

Aggressive shift logic Cooled EGR/EGR boost 

Early torque converter lockup 

5-speed auto transmission 

6-speed auto transmission 

6-speed manual transmission 

Improved accessories 

Electric power steering 

Low rolling resistance tires 

Low drag brakes 

Secondary axle disconnect 

Improved aerodynamics 

Weight reduction – 3% 

Weight reduction – 5% 

Weight reduction – 10% 

A/C Leakage Reduction 

A/C Efficiency Improvement 

The estimates of vehicle compliance costs cover the years of implementation of 

the program – MYs 2012 through 2016. In EPA’s analysis, compliance costs have also 

been estimated for the years following implementation to shed light on the long term – 

2022 and later – cost impacts of the rule. The year 2022 is used by EPA because the 

short-term and long-term markup factors described above are applied in five year 

increments with the 2012 through 2016 implementation span and the 2017 through 2021 

span both representing the short-term. 

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Commenters in general agreed with this methodology for determining indirect 

cost multipliers, though there was some disagreement as to the magnitude of the 

multipliers. 

3.3.2.2.4 Cost reduction through manufacturer learning 

For some of the technologies considered in this analysis, manufacturer learning 

effects would be expected to play a role in the actual end costs. The “learning curve” or 

“experience curve” describes the reduction in unit production costs as a function of 

accumulated production volume. In theory, the cost behavior it describes applies to 

cumulative production volume measured at the level of an individual manufacturer, 

although it is often assumed—as both agencies have done in past regulatory analyses—to 

apply at the industry-wide level, particularly in industries that utilize many common 

technologies and component supply sources.18 Both agencies believe there are indeed 

many factors that cause costs to decrease over time. Research in the costs of 

manufacturing has consistently shown that, as manufacturers gain experience in 

production, they are able to apply innovations to simplify machining and assembly 

operations, use lower cost materials, and reduce the number or complexity of component 

parts. All of these factors allow manufacturers to lower the per-unit cost of production 

(i.e., the manufacturing learning curve).19 

NHTSA has a detailed description of the learning effect in the MY 2011 CAFE 

final rule, and EPA has a detailed description of the learning effect in the 2008 Staff 

Report and in several past rules.20 The description here is focused on how the learning 

effect has been used in the analyses support each agency’s rule. 

Most studies of the effect of experience or learning on production costs appear to 

assume that cost reductions begin only after some initial volume threshold has been 

reached, but not all of these studies specify this threshold volume. The rate at which 

costs decline beyond the initial threshold is usually expressed as the percent reduction in 

average unit cost that results from each successive doubling of cumulative production 

volume, sometimes referred to as the learning rate. Many estimates of experience curves 

do not specify a cumulative production volume beyond which cost reductions would no 

longer occur, instead depending on the asymptotic behavior of the effect for learning 

rates below 100 percent to establish a floor on costs. 

In past rulemaking analyses, as noted above, both agencies have used a learning 

curve factor of 20 percent for each doubling of production volume. NHTSA has used 

this approach in analyses supporting recent CAFE rules. In its analysis, EPA has 

simplified the approach by using an “every two years” based learning progression rather 

than a pure production volume progression (i.e., after two years of production it was 

assumed that production volumes would have doubled and, therefore, costs would be 

reduced by 20 percent). 

In the MY 2011 CAFE final rule, NHTSA considered not only this volume-based 

learning as described above, but also “time-based” learning. Time-based learning, 

estimated by NHTSA at three percent per year, occurs in years following the volume3-17
 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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based learning steps and represents the smaller scale learning that occurs as 

manufacturers continue to innovate. The time-based learning is, in effect, represented by 

the flattened out, asymptotic portion of the learning curve. 

For this analysis, each agency has employed both volume-based and time-based 

learning effects. In NHTSA’s analysis, volume-based learning is estimated to result in 20 

percent lower costs as production volumes of the given technology reach 300,000 units 

and again when they reach 600,000 units within the entire industry. The count of 

production units begins in the 2012 model year and/or with the first units produced, 

whichever is earlier. In EPA’s analysis, as noted above, volume-based learning is 

estimated to result in 20 percent lower costs after two full years of implementation (i.e., 

the 2014 MY costs are 20 percent lower than the 2012 and 2013 model year costs). In 

each agency’s analysis, time-based learning is estimated to result in 3 percent lower costs 

in each year following first introduction of a given technology. Once two volume-based 

learning steps have occurred, or once the five year time-based learning period is 

completed, learning is assumed to be complete, at least for purposes of the analyses done 

to support each agency’s rule. 

Both agencies considered learning impacts on most but not all of the technologies 

expected to be used because some of the expected technologies are already used rather 

widely in the industry and, presumably, learning impacts have already occurred. 

Volume-based learning was considered for only a handful of technologies that are 

considered to be new or emerging technologies such as the hybrids and electric vehicles. 

Most technologies have been considered to be more established given their current use in 

the fleet and, hence, the lower time-based learning has been applied. The learning effects 

applied to each technology are summarized in Table 3-4. 

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Table 3-4 Learning Effects Applied to Each Technology 

TECHNOLOGY LEARNING EFFECT 

Low friction lubricants None 

Engine friction reduction None 

VVT – intake cam phasing Time 

VVT – coupled cam phasing Time 

VVT – dual cam phasing Time 

Cylinder deactivation Time 

Discrete VVLT Time 

Continuous VVLT Time 

GDI – stoichiometric Time 

GDI – lean burn Time 

Turbocharge (with no downsize) Time 

Downsize (with no turbocharge) Time 

Turbocharge with downsize Time 

Cam configuration changes (with no downsize) Time 

Homogeneous charge CI Time 

Conversion to diesel Time 

Aggressive shift logic Time 

Early torque converter lockup Time 

5-speed automatic Time 

6-speed automatic Time 

6-speed DCT – dry clutch Time 

6-speed DCT – wet clutch Time 

6-speed manual Time 

CVT Time 

Stop-Start Volume 

IMA/ISA/BSG (includes engine downsize) Volume 

2-Mode hybrid electric vehicle Volume 

Power-split hybrid electric vehicle Volumed 

Plug-in hybrid electric vehicle Volume 

Full electric vehicle Volume 

Improved high efficiency alternator & electrification of accessories Time 

Upgrade to 42 volt electrical system Time 

Electric power steering (12 or 42 volt) Time 

Aero drag reduction (20% on cars, 10% on trucks) Time 

Low rolling resistance tires None 

Low drag brakes (ladder frame only) None 

Secondary axle disconnect (unibody only) None 

Front axle disconnect (ladder frame only) Time 

A/C Leakage Reduction Time 

A/C Efficiency Improvements Time 

Table 3-25 through Table 3-30 show Volpe model costs and effectiveness values 

for each particular technology described throughout this chapter. The costs shown are 

applicable for the 2012 model year. The Volpe Model handles learning effects within the 

model itself so that individual technology costs in the 2016 model year would be lower 

than those shown in the tables. 

Table 3-31 through Table 3-40 show OMEGA model costs and effectiveness 

values for each particular technology described throughout this chapter. The costs shown 

are applicable for the 2016 model year and, therefore, represent fully learned costs in the 

d Due to an error, the NPRM showed this as time-based learning. 

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context of EPA’s analysis. For technologies added in years prior to MY 2016, EPA has 

backed out the learning effects relative to the costs shown in the tables. For example, 

Table 3-33 shows the small car stop-start vehicle cost as $351 in 2016. In the 2012 

model year, this cost would be higher since the volume-based learning reflected in the 

2016 cost would not have occurred yet. Backing out two volume-based learning steps 

(i.e., dividing $351 by 80% twice) would result in a 2012 cost estimate of $548. 

The different handling of learning effects across the two analyses (that done by 

EPA and that by NHTSA) would have little impact on the overall results of the analyses. 

NHTSA handles learning effects in the CAFE model. EPA handles learning effects 

outside of the OMEGA model since OMEGA is run for only one model year - 2016. 

Because the CAFE model adds technology on a year-by-year basis, it applies volumebased learning effects once the applicable production volume thresholds have been 

exceeded. In contrast, EPA would not apply volume-based learning until year 3 

(assumed to be the point at which initial production volumes have doubled). Most 

technologies in both analyses are subject to time-based learning which is applied on an 

annual basis in the same way by both agencies (with the exception that NHTSA applies 

time-based learning in the CAFE model while EPA applies it outside the OMEGA 

model). Importantly, by the 2016 model year, both agencies will have applied two 

volume-based learning steps to the appropriate technologies (hybrid and electric vehicles 

only) and four time-based learning steps to the appropriate technologies (most other 

technologies) to arrive at the same technology costs. 

3.4 Specific	 technologies considered and estimates of costs and 

effectiveness 

It is important to note that the cost and effectiveness values presented by NHTSA 

and EPA in this Final TSD are used when creating input files for the agencies’ respective 

models, but are not literally the inputs themselves. The Docket to this final rule contains 

the specific input files EPA and NHTSA used when modeling the technology cost. 

3.4.1 What data sources did the agencies evaluate? 

Refer to section 3.1 for a discussion of the specific data sources and reference 

material used for the analysis of this rulemaking. 

3.4.2 Individual technology descriptions and cost/effectiveness estimates 

The sections that follow describe the technologies applied by both EPA and 

NHTSA. It is important to note that there are distinct differences between the agencies in 

how cost and effectiveness estimations are reported and applied. 

EPA reports the total cost and effectiveness associated with a respective 

technology. The technologies are then applied to a vehicle type as a package. To 

calculate a cost for a vehicle package, the individual technologies are summed. To 

estimate the overall effectiveness, the lumped parameter method is applied. 

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Technologies Considered in the Agencies’ Analysis
 

NHTSA applies cost and effectiveness through a series of decision trees. The 

progressive nature of this methodology results in conditions for which a portion of a 

technology has already been added. In this case, NHTSA would add an incremental 

value which is the difference between the total cost of a technology and the portion 

already existing. An example of this is cylinder deactivation. At the point in the decision 

tree that it is applied, valvetrain technologies which could enable this feature have 

already been added. NHTSA will add the delta in cost, which in this example accounts 

for the NVH attributes. 

3.4.2.1 Engine Technologies 

NHTSA and EPA have reviewed the engine technology estimates used in 

NHTSA’s MY 2011 CAFE final rule and EPA’s 2008 Staff Report and available 

comments to the NPRM. In doing so NHTSA and EPA reconsidered all available 

sources and updated the estimates as appropriate. The section below describes each of 

the engine technologies considered for this rulemaking. 

3.4.2.1.1 Low Friction Lubricants (LUB) 

One of the most basic methods of reducing fuel consumption in gasoline engines 

is the use of lower viscosity engine lubricants. More advanced multi-viscosity engine 

oils are available today with improved performance in a wider temperature band and with 

better lubricating properties. This can be accomplished by changes to the oil base stock 

(e.g., switching engine lubricants from a Group I base oils to lower-friction, lower 

viscosity Group III synthetic) and through changes to lubricant additive packages (e.g., 

friction modifiers and viscosity improvers). The use of 5W-30 motor oil is now 

widespread and auto manufacturers are introducing the use of even lower viscosity oils, 

such as 5W-20 and 0W-20, to improve cold-flow properties and reduce cold start friction. 

However, in some cases, changes to the crankshaft, rod and main bearings and changes to 

the mechanical tolerances of engine components may be required. In all cases, durability 

testing would be required to ensure that durability is not compromised. The shift to lower 

viscosity and lower friction lubricants will also improve the effectiveness of valvetrain 

technologies such as cylinder deactivation, which rely on a minimum oil temperature 

(viscosity) for operation. 

Several manufacturers have previously commented confidentially, that low 

friction lubricants could have an effectiveness value between 0 to 1 percent. For 

purposes of this final rule, NHTSA and EPA are using effectiveness estimates within this 

range. Therefore 0.5 percent was used in both models. 

The 2002 NAS study estimated the low friction lubricant RPE at $8 to $11 using a 

1.4 markup factor. The NESCCAF study showed an RPE of $5 to $15 with a 1.4 

markup. The EEA report to DOE showed manufacturer costs of $10 to $20 with no 

markup. Confidential Business Information (CBI) data estimates an average incremental 

cost of $3 for the use of low friction lubricants. EPA’s 2008 Staff Report also confirms 

this $3 cost (2006$). Both NHTSA and EPA believe that manufacturer’s estimates are 

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the most accurate, and thus continue to believe that the $3 cost estimate is appropriate 

and independent of vehicle class since the engineering work required should apply to any 

engine size. Applying an indirect cost multiplier (ICM) of 1.11, for a low complexity 

technology, results in a compliance cost of $3 per vehicle (2007$) for a MY 2012 through 

MY 2016 vehicle (learning effects are not applied to low friction lubes).e 

3.4.2.1.2 Engine Friction Reduction (EFR) 

In addition to low friction lubricants, manufacturers can also reduce friction and 

improve fuel consumption by improving the design of engine components and 

subsystems. Approximately 10 percent of the energy consumed by a vehicle is lost to 

friction, and just over half is due to frictional losses within the engine.21 Examples 

include improvements in low-tension piston rings, piston skirt design, roller cam 

followers, improved crankshaft design and bearings, material coatings, material 

substitution, more optimal thermal management, and piston and cylinder surface 

treatments. Additionally, as computer-aided modeling software continues to improve, 

more opportunities for evolutionary friction reductions may become available. 

All reciprocating and rotating components in the engine are potential candidates 

for friction reduction, and minute improvements in several components can add up to a 

measurable fuel economy improvement. The 2002 NAS, NESCCAF and EEA reports as 

well as confidential manufacturer data suggested a range of effectiveness for engine 

friction reduction to be between 1 to 3 percent. NHTSA and EPA continue to believe 

that this range is accurate. Because of the incremental nature of the CAFE model, 

NHTSA needed to continue to use the narrower range of 1-2 percent, which was also 

used in the MY 2011 CAFE final rule. 

In the MY 2011 CAFE final rule, NHTSA estimated a range from $13 to $49 

using a 1.5 RPE on a per cylinder basis, or $9 to $33 without RPE (2007$). In the 2008 

NPRM engine friction reduction was estimated to cost up to $14 without RPE on a per 

cylinder basis (2006$). EPA’s 2008 Staff Report estimated this at $11 using a 1.5 RPE 

on a per cylinder basis, or $7 without RPE (2006$). After review, NHTSA and EPA 

believe that the cost estimate is closer to the lower end of the MY 2011 CAFE final rule 

range and thus for this rulemaking is $13 per cylinder compliance cost (2007$), including 

the low complexity ICM markup value of 1.11, for a MY 2012 through MY 2016 vehicle 

(learning effects are not applied to engine friction reduction). This cost is multiplied by 

the number of engine cylinders. 

3.4.2.1.3 Variable Valve Timing (VVT) 

Variable valve timing (VVT) classifies a family of valve-train designs that alter 

the timing of the intake valve, exhaust valve, or both, primarily to reduce pumping losses, 

increase specific power, and control the level of residual gases in the cylinder. VVT 

reduces pumping losses when the engine is lightly loaded by controlling valve timing 

closer to an optimum needed to sustain horsepower and torque. VVT can also improve 

volumetric efficiency at higher engine speeds and loads. Additionally, VVT can be used 

e Note that the costs developed for low friction lubes for this analysis reflect the costs associated with any 

engine changes that would be required as well as any durability testing that may be required. 

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Technologies Considered in the Agencies’ Analysis
 

to alter (and optimize) the effective compression ratio where it is advantageous for 

certain engine operating modes (e.g., in the Atkinson Cycle). 

VVT has now become a widely adopted technology: in MY 2007, over half of all 

new cars and light trucks had engines with some method of variable valve timing.22 

Manufacturers are currently using many different types of variable valve timing, which 

have a variety of different names and methods. The three major types of VVT are listed 

below. 

Each of the three implementations of VVT uses a cam phaser to adjust the 

camshaft angular position relative to the crankshaft position, referred to as “camshaft 

phasing.” The phase adjustment results in changes to the pumping work required by the 

engine to accomplish the gas exchange process. The majority of current cam phaser 

applications use hydraulically-actuated units, powered by engine oil pressure and 

managed by a solenoid that controls the oil pressure supplied to the phaser. 

3.4.2.1.3.1 Intake Cam Phasing (ICP) 

Valvetrains with ICP, which is the simplest of the cam phasing technologies, can 

modify the timing of the inlet valves by phasing the intake camshaft while the exhaust 

valve timing remains fixed. This requires the addition of a cam phaser on each bank of 

intake valves on the engine. An in-line 4-cylinder engine has one bank of intake valves, 

while V-configured engines have two banks of intake valves. 

NHTSA’s MY 2011 CAFE final rule and EPA 2008 Staff Report estimated an 

effectiveness of 1 to 2 percent for ICP, which was supported by the NESCCAF report and 

a majority of confidential manufacturer comments. The agencies have found no 

additional sources to suggest strongly that this estimate is inaccurate, and so have 

employed it for this rulemaking. 

As for costs, NHTSA’s MY 2011 CAFE final rule estimated a $61 RPE ($41 nonRPE) cost per cam phaser, based on the 2008 Martec Report and confidential 

manufacturer data (2007$). In the 2008 staff report, EPA estimated this at $59 using a 

1.5 RPE or $40 without RPE (2006$). NHTSA and EPA believe that this estimate 

remains accurate. Using the new indirect cost multiplier of 1.11, for a low complexity 

technology, the compliance cost per cam phaser would be $45 per bank , yielding a $45 

cost for and in-line engine configurations and $90 for V-engine configurations for a MY 

2012 vehicle (2007$). Time-based learning is applied to ICP so the MY 2016 cost would 

be $40 for in in-line engine and $80 for the V-engine (2007$). 

3.4.2.1.3.2 Coupled Cam Phasing (CCPS and CCPO) 

Valvetrains with coupled (or coordinated) cam phasing can modify the timing of 

both the inlet valves and the exhaust valves an equal amount by phasing the camshaft of a 

single overhead cam (SOHC) engine or an overhead valve (OHV) engine.f For overhead 

f Although CCP appears only in the SOHC and OHV branches of the decision tree, it is noted that a single 

phaser with a secondary chain drive would allow CCP to be applied to DOHC engines. Since this would 

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Joint Technical Support Document
 

cam engines, this requires the addition of a cam phaser on each bank of the engine. Thus, 

an in-line 4-cylinder engine has one cam phaser, while SOHC V-engines have two cam 

phasers. For overhead valve (OHV) engines, which have only one camshaft to actuate 

both inlet and exhaust valves, CCP is the only VVT implementation option available and 

requires only one cam phaser.g 

Based on NHTSA’s MY 2011 CAFE final rule, previously-received confidential 

manufacturer data, and the NESCCAF report, NHTSA and EPA estimated the 

effectiveness of CCP to be between 1 to 4 percent. NHTSA and EPA reviewed this 

estimate for purposes of the final rule, and continue to find it accurate. 

The same cam phaser has been assumed for ICP and CCP applications, thus CCPs 

cost per cam phaser is identical to ICPs. This results in a cost of $45 for in-line SOHC 

and OHV engines and $90 for SOHC V-engine configurations for a MY 2012 vehicle 

(2007$). With time-based learning applied, these costs for a 2016 MY vehicle would be 

$40 and $80, respectively (2007$). 

3.4.2.1.3.3 Dual Cam Phasing (DCP) 

The most flexible VVT design is dual (independent) cam phasing, where the 

intake and exhaust valve opening and closing events are controlled independently. This 

option allows the option of controlling valve overlap, which can be used as an internal 

EGR strategy. At low engine loads, DCP creates a reduction in pumping losses, resulting 

in improved fuel consumption/reduced CO2 emissions. Increased internal EGR also 

results in lower engine-out NOX emissions. The amount by which fuel consumption is 

improved and CO2 emissions are reduced depends on the residual tolerance of the 

combustion system. Additional improvements are observed at idle, where low valve 

overlap could result in improved combustion stability, potentially reducing idle fuel 

consumption. 

NHTSA MY 2011 CAFE final rule estimated the effectiveness of DCP to be 

between 3 to 5 percent relative to a base engine or 2 to 3 relative to an engine with ICP. 

NHTSA and EPA believe that this estimate remains applicable for the final rule. 

As above, the same cam phaser has been assumed for ICP and DCP applications. 

Thus, DCP’s cost per cam phaser is identical to ICP’s. DCP requires two cam phasers 

per cylinder bank, one to control the intake valves and one to control the exhaust valves. 

This results in a cost of $90, relative to an engine without ICP, or $45 relative to an 

engine with ICP, minus $6 for the removal of the EGR valve, ultimately yielding costs of 

$84 and $39 respectively for in-line DOHC configurations (all in 2007$). For Vconfiguration engines, the cost is $180 relative to an engine without ICP, or $90 relative 

to an engine with ICP, minus $6 for the removal of the EGR valve, ultimately yielding 

potentially be adopted on a limited number of DOHC engines NHTSA did not include it in that branch of 

the decision tree. 

g It is also noted that coaxial camshaft developments would allow other VVT options to be applied to OHV 

engines. However, since they would potentially be adopted on a limited number of OHV engines NHTSA 

did not include them in the decision tree. 

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Technologies Considered in the Agencies’ Analysis
 

costs of $174 and $84, respectively (all in 2007$). These costs are appropriate for a MY 

2012 vehicle. With time-based learning applied, these costs for a 2016 MY vehicle 

would be $157 and $73, respectively (2007$). 

3.4.2.1.4 Variable Valve Lift (VVL) 

Controlling the lift of the valves provides a potential for further efficiency 

improvements. By optimizing the valve-lift profile for specific engine operating regions, 

the pumping losses can be reduced by reducing the amount of throttling required to 

produce the desired engine power output. By moving the throttling losses further 

downstream of the throttle valve, the heat transfer losses that occur from the throttling 

process are directed into the fresh charge-air mixture just prior to compression, delaying 

the onset of knock-limited combustion processes. Variable valve lift control can also be 

used to induce in-cylinder mixture motion, which improves fuel-air mixing and can result 

in improved thermodynamic efficiency. Variable valve lift control can also potentially 

reduce overall valvetrain friction. At the same time, such systems may also incur 

increased parasitic losses associated with their actuation mechanisms. A number of 

manufacturers have already implemented VVL into their fleets (Toyota, Honda, and 

BMW), but overall this technology is still available for most of the fleet. There are two 

major classifications of variable valve lift, described below: 

3.4.2.1.4.1 Discrete Variable Valve Lift (DVVLS, DVVLD, DVVLO) 

DVVL systems allow the selection between two or three discrete cam profiles by 

means of a hydraulically-actuated mechanical system. By optimizing the cam profile for 

specific engine operating regions, the pumping losses can be reduced by reducing the 

amount of throttling required to produce the desired engine power output. This increases 

the efficiency of the engine. These cam profiles consist of a low and a high-lift lobe, and 

may include an inert or blank lobe to incorporate cylinder deactivation (in the case of a 3­

step DVVL system). DVVL is normally applied together with VVT control. DVVL is 

also known as Cam Profile Switching (CPS). DVVL is a mature technology with low 

technical risk. 

NHTSA’s MY 2011 CAFE final rule, previously-received confidential 

manufacturer data, and the NESCCAF report, all estimate the effectiveness of DVVL to 

be between 1 to 4 percent above that realized by VVT systems. NHTSA and EPA 

believe this estimate continues to be applicable for the final rule. 

In the MY 2011 CAFE final rule, NHTSA estimated an RPE (1.5 markup factor) 

of $201 for an inline 4-cylinder engine, $306 for a V6 engine and $396 for a V8 engine 

or without the RPE markup $134, $204, $264, respectively (all in 2007$). In the 2008 

Staff Report, EPA estimated this at $169, $246, and $322, respectively, using a 1.5 RPE 

factor (2006$) or $113, $164 and $215, respectively without markup (all in 2006$). For 

this analysis, the agencies have estimated the compliance costs, including $25 for 

controls and using a 1.11 ICM for a low complexity technology, at $141 for an inline 4­

cylinder engine, $205 for a V6 engine and $293 for a V8 for a 2012 MY vehicle (2007$). 

With time-based learning applied, these costs would be $125, $181, and $259, 

respectively for a 2016 MY vehicle (2007$). 

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3.4.2.1.4.2 Continuously Variable Valve Lift (CVVL) 

In CVVL systems, valve lift is varied by means of a mechanical linkage, driven 

by an actuator controlled by the engine control unit. The valve opening and phasing vary 

as the lift is changed and the relation depends on the geometry of the mechanical system. 

BMW has considerable production experience with CVVL systems and has sold portinjected “Valvetronic” engines since 2001. CVVL allows the airflow into the engine to 

be regulated by means of intake valve opening reduction, which improves engine 

efficiency by reducing pumping losses from throttling the intake system further upstream 

as with a conventionally throttled engine. 

Variable valve lift gives a further reduction in pumping losses compared to that 

which can be obtained with cam phase control only, with CVVL providing greater 

effectiveness than DVVL, since it can be fully optimized for all engine speeds and loads, 

and is not limited to a two or three step compromise. There may also be a small 

reduction in valvetrain friction when operating at low valve lift, resulting in improved 

low load fuel consumption for cam phase control with variable valve lift as compared to 

cam phase control only. Most of the fuel economy effectiveness is achieved with 

variable valve lift on the intake valves only. CVVL is only applicable to double overhead 

cam (DOHC) engines. 

NHTSA’s MY 2011 CAFE final rule estimated the effectiveness for CVVL at 1.5 

to 3.5 percent over an engine with DCP, but also recognize that it could go up as high as 

5 percent above and beyond DCP to account for the implementation of more complex 

CVVL systems such as BMW’s “Valvetronic” engines. This coincides with EPA Staff 

Report estimates of the contribution of CVVL, which were based on the NESCCAF 

report, in which CVVL could improve effectiveness by 4 percent (minivans) and up to 6 

percent (large cars) over dual cam phasing. Thus, the effectiveness range for CVVL in 

this joint TSD ranges from 1.5 to 6 percent depending on the complexity level of the 

application. However, due to the complexity and cost of this technology, the GHG model 

did not consider it and the CAFE model projected no application of the less complex 

version of this technology. The most recent submission of manufacturers’ product plans 

confirmed that this technology will not be applied by most manufacturers. 

In the MY 2011 CAFE final rule, NHTSA estimated and RPE (1.5 factor) of 

continuously variable valve lift to be $306 for an inline 4-cylinder engine, $432 for a V6 

engine and $582 for a V8 engine or without RPE $204, $287, $388, respectively. In the 

2008 Staff Report, EPA estimated this at $254, $466, and $508, respectively, using a 1.5 

RPE factor (2006$) or $169, $311 and $339, respectively without markup (all in 2006$) 

For this analysis, the agencies have estimated the compliance costs, using a 1.45 ICM for 

a high complexity technology, at $277 for an inline 4-cylinder engine, $509 for a V6 

engine and $554 for a V8 engine for a MY 2012 vehicle (2007$). With time-based 

learning applied, these costs would become $245, $449, and $489, respectively for a MY 

2016 vehicle (2007$). 

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Technologies Considered in the Agencies’ Analysis
 

3.4.2.1.5 Cylinder Deactivation (DEACS, DEACD, DEACO) 

In conventional spark-ignited engines throttling the airflow controls engine torque 

output. At partial loads, efficiency can be improved by using cylinder deactivation 

instead of throttling. Cylinder deactivation (DEAC) can improve engine efficiency by 

disabling or deactivating (usually) half of the cylinders when the load is less than half of 

the engine’s total torque capability – the valves are kept closed, and no fuel is injected – 

as a result, the trapped air within the deactivated cylinders is simply compressed and 

expanded as an air spring, with reduced friction and heat losses. The active cylinders 

combust at almost double the load required if all of the cylinders were operating. 

Pumping losses are significantly reduced as long as the engine is operated in this “partcylinder” mode. 

Cylinder deactivation control strategy relies on setting maximum manifold 

absolute pressures or predicted torque within which it can deactivate the cylinders. Noise 

and vibration issues reduce the operating range to which cylinder deactivation is allowed, 

although manufacturers are exploring vehicle changes that enable increasing the amount 

of time that cylinder deactivation might be suitable. Some manufacturers may choose to 

adopt active engine mounts and/or active noise cancellations systems to address NVH 

concerns and to allow a greater operating range of activation. Manufacturers have stated 

that use of DEAC on 4 cylinder engines would cause unacceptable NVH; therefore 

cylinder deactivation has not been applied to 4-cylinder engines. 

Cylinder deactivation has seen a recent resurgence thanks to better valvetrain 

designs and engine controls. General Motors and Chrysler Group have incorporated 

cylinder deactivation across a substantial portion of their V8-powered lineups. Honda 

(Odyssey, Pilot) offers V6 models with cylinder deactivation. 

Effectiveness improvements scale roughly with engine displacement-to-vehicle 

weight ratio: the higher displacement-to-weight vehicles, operating at lower relative loads 

for normal driving, have the potential to operate in part-cylinder mode more frequently. 

NHTSA and EPA reviewed the MY 2011 CAFE final rule estimate and confirmed 

a range of 0.5 to 6 percent depending on the valvetrain configuration and other existing 

technologies. The OMEGA model, which is based on packages, applies 6 percent 

reduction in CO2 emissions in all applications. The CAFE model, due to its incremental 

nature, uses a range depending on the engine valvetrain configuration. For example, for 

DOHC engines which are already equipped with DCP and DVVLD, there is little benefit 

that can be achieved from adding cylinder deactivation since the pumping work has 

already been minimized and internal Exhaust Gas Recirculation (EGR) rates are 

maximized, so the effectiveness is only up to 0.5 percent for DEACD. For SOHC 

engines which have CCP and DVVLS applied, effectiveness ranged from 2.5 to 3 percent 

for DEACS. For OHV engines, without VVT or VVL technologies, the effectiveness for 

DEACO ranged from 3.9 to 5.5 percent. 

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NHTSA and EPA considered a range of $170 to $190 (or $28 to $190 depending 

on whether an engine already has lost motion devices, oil control valves and camshaft 

position sensors). This is a departure from NHTSA’s MY 2011 CAFE final rule, which 

used a range of $306 to $400. That range was primarily based on the 2008 Martec 

Report and applied a higher RPE value. In reviewing these assumptions, NHTSA and 

EPA amended the MY 2011 CAFE final rule estimates and adjusted the estimates to 

include the new ICM low complexity markup of 1.11. The EPA Staff Report estimated 

this ranging from $203 to $229 (RPE using a 1.5 factor) or $135 to $153 without RPE 

(2006$). NHTSA’s MYs 2011-2015 NPRM showed estimates of a $170 for a 6-cylinder 

engine and $190 for an 8-cylinder engine—when adjusted for 2007 dollars and using the 

new ICM multipliers— for engines that do not have lost motion devices. These numbers 

were within the ranges described by the 2002 NAS and NESCCAF reports. The $170 

and $190 costs are appropriate of a MY 2012 vehicle (2007$). With time-based learning 

applied, these would become $150 and $169, respectively, for a MY 2016 vehicle 

(2007$). 

If lost motion devices are on the engine, the cost of DEAC as applied to SOHC 

and DOHC engines could be as low as $28.h This $28i accounts for the potential 

additional application of active engine mounts on SOHC and DOHC engines and can 

only be applied on 50 percent of the vehicles. Further, this SOHC and DOHC engine 

estimate is relevant to the CAFE model only because the OMEGA model does not apply 

technologies in the same incremental fashion as the CAFE model. 

3.4.2.1.6	 Conversion to Double Overhead Camshaft Engine with Dual Cam Phasing 

(CDOHC) 

Double overhead camshaft engines achieve increased airflow at high engine 

speeds, improve volumetric efficiency and reductions of the valvetrain’s moving mass. 

Such engines typically develop higher power at high engine speeds. Manufacturers may 

choose to replace OHV engines with DOHC engine designs with dual cam phasing 

(DCP). EPA and NHTSA reviewed the analysis of effectiveness for CDOHC in the MY 

2011 CAFE final rule and found no information to indicate that these estimates were 

inaccurate. EPA and NHTSA estimate the effectiveness to be between 1 to 2.5 percent. 

As for costs, NHTSA’s MY 2011 CAFE final rule assumed that CDOHC would 

have an RPE of $746 ($497 non-RPE) for a V8 engine, $590 ($393 non-RPE) for a V6 

engine and $373 ($249 non-RPE) for inline 4-cylinder engine (2007$). In the 2008 Staff 

Report, EPA did not estimate a cost for this specific technology. For purposes of this 

rulemaking, NHTSA revised the costs only by identifying this technology as a low 

complexity technology and applying an indirect cost multiplier of 1.11 resulting in a 

compliance cost of $552 for V8 engine, $436 for a V6 and $276 for an inline 4-cylinder 

engine. The above costs are for a MY 2012 vehicle and with the application of timebased learning will be slightly lower for MY 2016 vehicle 

h The $28 is an adjustment from the $75 estimate used in the MY 2011 final rule to account for the new 

ICM markup factor and the fact that it could only be applied on up to 50 percent of the vehicles. 

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Technologies Considered in the Agencies’ Analysis
 

3.4.2.1.7 Stoichiometric Gasoline Direct Injection (SGDI) 

Stoichiometric gasoline direct injection (SGDI), or Spark Ignition Direct injection 

(SIDI), engines inject fuel at high pressure directly into the combustion chamber (rather 

than the intake port in port fuel injection). SGDI requires changes to the injector design, 

an additional high pressure fuel pump, new fuel rails to handle the higher fuel pressures 

and changes to the cylinder head and piston crown design. Direct injection of the fuel 

into the cylinder improves cooling of the air/fuel charge within the cylinder, which allows 

for higher compression ratios and increased thermodynamic efficiency without the onset 

of combustion knock. Recent injector design advances, improved electronic engine 

management systems and the introduction of multiple injection events per cylinder firing 

cycle promote better mixing of the air and fuel, enhance combustion rates, increase 

residual exhaust gas tolerance and improve cold start emissions. SGDI engines achieve 

higher power density and match well with other technologies, such as boosting and 

variable valvetrain designs. 

Several manufacturers have recently introduced vehicles with SGDI engines, 

including VW/Audi, BMW, Toyota (Lexus IS 350) and General Motors (Chevrolet 

Impala and Cadillac CTS 3.6L). BMW, GM, Ford and VW/Audi have announced their 

plans to increase dramatically the number of SGDI engines in their portfolios. 

NHTSA’s MY 2011 CAFE final rule estimated the effectiveness of SGDI to be 

between 2 and 3 percent. NHTSA and EPA reviewed estimates from the Alliance of 

Automobile Manufactures, which projects 3 percent gains in fuel efficiency and a 7 

percent improvement in torque. The torque increase provides the opportunity to 

downsize the engine allowing an increase in efficiency of up to a 5.8 percent. NHTSA 

and EPA also reviewed other published literature, reporting 3 percent effectiveness for 

23 24 SGDI. Another source reports a 5 percent improvement on the NEDC drive cycle.

Confidential manufacturer data reported an efficiency effectiveness range of 1 to 2 

percent. NHTSA and EPA determined that the range of 2 to 3 percent continues to be 

appropriate. Combined with other technologies (i.e., boosting, downsizing, and in some 

cases, cooled EGR), SGDI can achieve greater reductions in fuel consumption and CO2 

emissions compared to engines of similar power output. 

The NHTSA and EPA cost estimates for SGDI take into account the changes 

required to the engine hardware, engine electronic controls, ancillary and Noise Vibration 

and Harshness (NVH) mitigation systems. Through contacts with industry NVH 

suppliers, and manufacturer press releases, the agencies believe that the NVH treatments 

will be limited to the mitigation of fuel system noise, specifically from the injectors and 

the fuel lines. In the final rule, the agencies have revised the SGDI costs based on the 

FEV work that was not yet available for the NPRM. Focusing on direct manufacturing 

costs, the NPRM estimates, the FEV values, and the final values are shown in Table 3-5. 

FEV did not directly estimate the SGDI costs shown here. Instead, FEV estimated costs 

associated with downsizing and turbocharging a V8 and V6 engine to a V6 and I4 engine, 

respectively, and simultaneously converting the PFI fuel system to a SGDI fuel system. 

The agencies, working closely with FEV, then “binned” the costs into three distinct bins: 

downsize, turbocharge, and SGDI. As such, the FEV results shown in Table 3-5 cannot 

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be found in the FEV reports, but instead are detailed in a memo to the docket which also 

provides details of this binning process.25 Because the methodology used by FEV 

presumes high volume production, instead of using the FEV results directly the agencies 

have averaged those results with the NPRM results to estimate the final values (this is 

noted in section 3.3.2.2, 3.4.2.1.9, and 3.4.2.2.5). Note that the costs for the I3 and I4 

engines have changed since the NPRM but have not been averaged with the NPRM 

values. The costs changed based on a more rigorous binning process than that conducted 

for the NPRM, but were not averaged because they are based on the I4 to I4 teardown 

conducted by FEV which was, in fact, used in the NPRM. While the final value of $213 

is lower than the NPRM value of $226, the $13 difference has simply been shifted from 

SGDI to the downsizing bin. 

Table 3-5 Direct Manufacturing Costs for SGDI (2007$ in 2012) 

Technology NPRM FEV Results Final Rule 

I3 $226 $213 $213 

I4 $226 $213 $213 

V6 $293 $321 $307 

V8 $317 $386 $352 

For the final rule marked up costs, the agencies estimate SGDI costs at $236 for 

an inline 4-cylinder and $341 for V6 and $390 for V8 including the low complexity ICM 

markup value of 1.11 (2007$ in 2012). With time-based learning applied, these costs 

would be $209, $301, and $346 for a MY 2016 vehicle (2007$). As noted above, all of 

these costs differ slightly from those used in the NPRM analysis as shown in Table 3-6. 

These costs were not changed in response to public comments, but instead were changed 

due to updated information from the FEV teardown studies.26 

Table 3-6 Marked up Costs for Stoichiometric GDI (2007$ in 2012) 

Technology NPRM Final Rule 

I3 $251 $236 

I4 $251 $236 

V6 $326 $341 

V8 $353 $390 

3.4.2.1.8 Combustion Restart (CBRST) 

Combustion restart allows “start-stop” functionality of DI engines through the 

implementation of an upgraded starter with bi-directional rotation to allow precise 

crankshaft positioning prior to subsequent fuel injection and spark ignition, allowing 

engine restart. This method of implementing engine stop/start functionality allows not 

only the fuel savings from not idling the engine, but also reduces fuel consumption as the 

engine speeds up to its operational speed. A Direct Injection (DI) fuel system is required 

for implementation of this technology. 

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NHTSA reviewed the MY 2011 CAFE final rule assumptions and determined that 

due to technical risks implementation of combustion restart would likely not be feasible 

prior to MY 2014. Some of the risks are associated with unresolved issues regarding the 

impact of very high or very low ambient air temperatures on the ability to start the engine 

in the described manner. Although the starter motor can provide fail-safe starting 

capability in these temperature limited areas, strategies must be developed to manage the 

transitions. Other risks relate to production readiness. 

Additional hardware is required to implement combustion restart, beyond SGDI. 

This includes a battery sensor, incremental wiring and high current switching, an 

incremental crank position sensor, and, in the case of an automatic transmission 

applications, a transmission oil pump to allow for torque converter continuity. 

BMW has published a 3.5 percent fuel consumption effectiveness over the NEDC 

27 28 drive cycle for combustion restart, and AVL a 4.8 percent effectiveness. However, 

these reported effectiveness levels could potentially be reduced significantly on the EPA 

combined drive cycle, as combustion restart does not save fuel on the highway drive 

cycle. Therefore, NHTSA estimates the fuel consumption effectiveness for CBRST to 

range from 2 to 2.5 percent. 

Regarding the cost estimate, NHTSA determined that the estimate of $118 from 

the 2008 Martec Report cost estimates for individual pieces was the best available. The 

total RPE cost (excluding transmission pump) is $141 at high volumes, which includes 

$70 for upgrading the starter, $10 for a battery sensor and wiring, $10 for high current 

switch and $4 for crank sensor a totaling $94 (non-RPE) cost. Applying an indirect cost 

multiplier of 1.25, for a medium complexity technology, results in a compliance cost of 

$118 for a MY 2012 vehicle and will be reduced in future years with the application of 

time-based learning. 

3.4.2.1.9 Turbocharging and Downsizing (TRBDS) 

The specific power of a naturally aspirated engine is primarily limited by the rate 

at which the engine is able to draw air into the combustion chambers. Turbocharging and 

supercharging (grouped together here as boosting) are two methods to increase the intake 

manifold pressure and cylinder charge-air mass above naturally aspirated levels. 

Boosting increases the airflow into the engine, thus increasing the specific power level, 

and with it the ability to reduce engine displacement while maintaining performance. 

This effectively reduces the pumping losses at lighter loads in comparison to a larger, 

naturally aspirated engine. 

Almost every major manufacturer currently markets a vehicle with some form of 

boosting. While boosting has been a common practice for increasing performance for 

several decades, turbocharging has considerable potential to improve fuel economy and 

reduce CO2 emissions when the engine displacement is also reduced. Specific power 

levels for a boosted engine often exceed 100 hp/L, compared to average naturally 

aspirated engine power densities of roughly 70 hp/L. As a result, engines can 

conservatively be downsized roughly 30 percent to achieve similar peak output levels. In 

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the last decade, improvements to turbocharger turbine and compressor design have 

improved their reliability and performance across the entire engine operating range. New 

variable geometry turbines and ball-bearing center cartridges allow faster turbocharger 

spool-up (virtually eliminating the once-common “turbo lag”) while maintaining high 

flow rates for increased boost at high engine speeds. However, even with turbocharger 

improvements, maximum engine torque at very low engine speed conditions, for example 

launch from standstill, is increased less than at mid and high engine speed conditions. 

The potential to downsize engines may be less on vehicles with low displacement to 

vehicle mass ratios in order to provide adequate acceleration from standstill, particularly 

up grades or at high altitudes. 

Use of GDI systems with turbocharged engines and air-to-air charge air cooling 

also reduces the fuel octane requirements for knock limited combustion and allows the 

use of higher compression ratios. Ford’s “Ecoboost” downsized, turbocharged GDI 

engines introduced on MY 2010 vehicles allow the replacement of V8 engines with V6 

engines with improved in 0-60 mph acceleration and with fuel economy improvements of 

up to 12 percent.29 

NHTSA and EPA reviewed NHTSA’s MY 2011 CAFE final rule estimates that a 

turbocharged and downsized engine at equivalent performance levels would offer an 

effectiveness improvement of 2 to 5 percent over a naturally-aspirated SGDI engine of 

comparable performance. This would equate to a 12 to 14 effectiveness improvement 

over baseline fixed-valve engine, similar to the estimate for Ford’s Ecoboost. NHTSA 

and EPA kept these estimates for this final rule. 

Recently published data with advanced spray-guided injection systems and more 

aggressive engine downsizing targeted towards reduced fuel consumption and CO2 

emissions reductions indicate that the potential for reducing CO2 emissions for 

turbocharged, downsized GDI engines may be as much as 15 to 30 percent relative to 

14,15,16,17,18 port-fuel-injected engines. Confidential manufacturer data suggests an 

incremental range of fuel consumption and CO2 emission reduction of 4.8 to 7.5 percent 

for turbocharging and downsizing. Other publicly-available sources suggest a fuel 

consumption and CO2 emission reduction of 8 to 13 percent compared to currentproduction naturally-aspirated engines without friction reduction or other fuel economy 

technologies: a joint technical paper by Bosch and Ricardo suggesting fuel economy gain 

of 8 to 10 percent for downsizing from a 5.7 liter port injection V8 to a 3.6 liter V6 with 

direct injection using a wall-guided direct injection system;30 a Renault report suggesting 

a 11.9 percent NEDC fuel consumption gain for downsizing from a 1.4 liter port injection 

in-line 4-cylinder engine to a 1.0 liter in-line 4-cylinder engine, also with wall-guided 

direct injection;31 and a Robert Bosch paper suggesting a 13 percent NEDC gain for 

downsizing to a turbocharged DI engine, again with wall-guided injection.32 These 

reported fuel economy benefits show a wide range depending on the SGDI technology 

employed. Public comment was received on this issue and a summary of those 

comments with the agencies’ response can be found in section 4.4 of EPA’s Response to 

Comments document and in Chapter V of NHTSA’s FRIA. 

As noted above, the agencies relied on engine teardown analyses conducted by 

EPA, FEV and Munro to develop costs for turbocharged GDI engines.33 Teardown 

studies are one of the most effective ways to estimate technology costs. For the proposal, 

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only the 2.4L I4 DOHC to 1.6L I4 DOHC teardown study had been completed in time for 

inclusion in the NPRM, and we used that study’s results as the cost in the 2012 model 

year. For other turbo-downsize costs, the NPRM primarily used values developed for the 

2008 staff report. Since issuing the NPRM, two more teardown studies have been 

completed and those results are being used in the final analysis with some adjustment. 

The agencies have adjusted the I4 to I4 turbo-downsize costs slightly to reflect an 

j updated burden rate employed by FEV and an updated binning approach employed by 

the agencies to distinguish turbo-related costs from downsize-related costs.k NHTSA and 

EPA modified the other FEV estimated costs as well. FEV made the assumption that 

these technologies would be mature when produced in large volumes (450,000 units or 

more). The agencies believe that there is potential for near term supplier-level 

engineering, design, and testing (ED&T) costs to be in excess of those considered in the 

FEV analysis (as existing equipment and facilities must be converted to production of 

new technologies). The agencies have therefore decided to average the FEV results with 

the NPRM values. We have also used the FEV results, where possible, to estimate costs 

for turbo-downsize scenarios that were not done via actual teardown study (e.g., FEV did 

not conduct a teardown of a V8 DOHC to turbocharged V6 DOHC). We have also used 

these values to estimate costs for other camshaft configuration changes that do not 

involve engine downsizing. Table 3-7 shows the NPRM, FEV and final rule direct 

manufacturing costs used for the turbo-downsize technologies. 

Table 3-7 Direct Manufacturing Costs for Turbocharging, Downsizing, and Other Camshaft
 

Configuration Changes applicable in the 2012 Model Year (2007 dollars)
 

Technology NPRM FEV 

Result 

Final 

Rule Comment 

Turbo (I4 to I4 only) $372 $404 $404 Use FEV result directly, no averaging with NPRM cost 

Turbo (V6 to I4) $450 $404 $427 Average of NPRM & actual FEV teardown 

Turbo (V8 to V6) $675 $681 $678 Average of NPRM & actual FEV teardown 

I4 DOHC to I3 

DOHC -$101 -$193 -$147 FEV result calculated using binning of other teardown results then averaged with NPRM cost 

I4 DOHC to smaller 

I4 DOHC -$60 -$85 -$85 Use FEV result directly, no averaging with NPRM cost 

I4 SOHC to smaller 

I4 DOHC $65 -$80 -$80 FEV result calculated using binning of other teardown results 

then used directly, no averaging with NPRM cost 

I4 OHV to smaller I4 

DOHC $65 n/a $65 Not possible to calculate using binning of other teardown results; NPRM cost used as final 

V6 DOHC to I4 

DOHC -$428 -$547 -$488 Average of NPRM & actual FEV teardown 

V6 SOHC to I4 

DOHC -$68 -$382 -$225 FEV result calculated using binning of other teardown results 

then averaged with NPRM cost 

V6 OHV to I4 $270 n/a $270 Not possible to calculate using binning of other teardown 

j Burden costs include the following fixed and variable costs: rented and leased equipment; manufacturing 

equipment depreciation; plant office equipment depreciation; utilities expense; insurance (fire and general); 

municipal taxes; plant floor space (equipment and plant offices); maintenance of manufacturing equipment 

- non-labor; maintenance of manufacturing building - general, internal and external, parts, and labor; 

operating supplies; perishable and supplier-owned tooling; all other plant wages (excluding direct, indirect 

and MRO labor); returnable dunnage maintenance; and intra-company shipping costs (see EPA-HQ-OAR2009-0472-0149). 

k

This “binning” approach simply bins the turbo-downsize-GDI total cost generated by FEV into separate 

cost bins for the turbo portion, the downsize portion, and the GDI portion. 

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DOHC results; NPRM cost used as final 

V8 DOHC to V6 

DOHC -$203 -$274 -$238 FEV result calculated using binning of other teardown results then averaged with NPRM cost 

V8 SOHC 3V to V6 

DOHC $113 -$155 -$21 Average of NRPM & actual FEV teardown 

V8 SOHC 2V to V6 

DOHC $203 -$84 $60 FEV result calculated using binning of other teardown results 

then averaged with NPRM cost 

V8 OHV to V6 

DOHC $315 n/a $315 Not possible to calculate using binning of other teardown results; NPRM cost used as final 

V6 SOHC to V6 

DOHC $360 $165 $262 FEV result calculated using binning of other teardown results 

then averaged with NPRM cost 

V6 OHV to V6 

DOHC $473 n/a $473 Not possible to calculate using binning of other teardown 

results; NPRM cost used as final 

V8 SOHC 3V to V8 

DOHC $315 $119 $217 FEV result calculated using binning of other teardown results then averaged with NPRM cost 

V8 SOHC 2V to V8 

DOHC $405 $190 $298 FEV result calculated using binning of other teardown results 

then averaged with NPRM cost 

V8 OHV to V8 

DOHC $518 n/a $518 Not possible to calculated using binning of other teardown 

results; NPRM cost used as final 

For the OHV applications, the agencies maintained consistency with the EPA 

2008 Staff Report and estimated direct manufacturing costs associated with downsizing 

to be $50 per cylinder, $10 per valve, and $100 per cam shaft for the 2015 model year 

(2006$). Therefore, these costs have not changed relative to the NPRM. Table 3-8 

shows all of these turbo and downsize costs, the applicable ICMs, and the final costs for 

the 2012 MY (2007$). The agencies have applied time based learning to all of these 

costs. Estimated costs for the 2016 MY can be found in section 3.5.2 of this TSD. 

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Technologies Considered in th

Table 3-8 Direct Manufacturing and Marked up Costs for Turbocharging and Downsizing and Other Camshaft Configuratio

dollars) 

Technology Incremental to Direct Manufacturing Cost IC M

Turbo Downsize 

Turbo w/o Downsize

Downsize w/o Turbo 

Turbo (I4 to I4 only) Base engine $404 n/a 1

Turbo (V6 to I4) Base engine $427 n/a 1

Turbo (V8 to V6) Base engine $678 n/a 1

Downsize to I4 DOHC V6 DOHC n/a -$488 1

Downsize to I4 DOHC V6 SOHC n/a -$225 1

Downsize to I4 DOHC V6 OHV n/a $270 1

Downsize to I4 DOHC I4 DOHC (larger) n/a -$85 1

Downsize to I3 DOHC I4 DOHC n/a -$147 1

Downsize to V6 DOHC V8 DOHC n/a -$238 1

Downsize to V6 DOHC V8 SOHC 2V n/a $60 1

Downsize to V6 DOHC V8 SOHC 3V n/a -$21 1

Downsize to V6 DOHC V8 OHV n/a $315 1

Turbo with Downsize 

Downsize to I4 DOHC & add turbo V6 DOHC w/o turbo $427 -$488 1

Downsize to I4 DOHC & add turbo V6 SOHC w/o turbo $427 -$225 1

Downsize to I4 DOHC & add turbo V6 OHV w/o turbo $427 $270 1

Downsize to I4 DOHC & add turbo I4 DOHC (larger) w/o turbo $404 -$85 1

Downsize to I3 DOHC & add turbo I4 DOHC w/o turbo $404 -$147 1

Downsize to V6 DOHC & add twin turbo V8 DOHC w/o turbo $675 -$238 1

Downsize to V6 DOHC & add twin turbo V8 SOHC 2V w/o turbo $675 $60 1

Downsize to V6 DOHC & add twin turbo V8 SOHC 3V w/o turbo $675 -$21 1

Downsize to V6 DOHC & add twin turbo V8 OHV w/o turbo $675 $315 1

Camshaft

Changes 

Convert to V6 DOHC V6 SOHC n/a $262 1

Convert to V6 DOHC V6 OHV n/a $473 1

Convert to V8 DOHC V8 SOHC 2V n/a $298 1

Convert to V8 DOHC V8 SOHC 3V n/a $217 1

Convert to V8 DOHC V8 OHV n/a $518 1

* Note that, where downsizing results in cost savings, the compliance cost is calculated as the IC markup less 1 which is then multiplied by the absolumanufacturing cost. The absolute value of the direct manufacturing cost is then subtracted from that to arrive at the end result. For example, for the VI4 DOHC at a direct manufacturing cost of -$488, the compliance cost would be (1.11-1) x │-$488│ - │-$488│ = -$434. 

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3.4.2.1.10 Cooled Exhaust Gas Recirculation/EGR Boost (EGRB) 

Cooled exhaust gas recirculation (cooled EGR) or EGR Boost is a combustion 

concept that involves utilizing EGR as a charge dilutant for controlling combustion 

temperatures and cooling the EGR prior to its introduction to the combustion system. 

Higher exhaust gas residual levels at part load conditions reduce pumping losses for 

increased fuel economy. Cooled EGR reduces knock sensitivity which enables the use of 

more optimal spark advance or enables compression ratio to be increased for improved 

thermal efficiency, and increased fuel economy. Currently available turbo, charge air 

cooler, and EGR cooler technologies are sufficient to demonstrate the feasibility of this 

concept. 

However, this remains a technology with a number of issues that still need to be 

addressed and for which there is no production experience. EGR system fouling 

characteristics could be potentially worse than diesel EGR system fouling, due to the 

higher HC levels found in gasoline exhaust. Turbocharger compressor contamination 

may also be an issue for low pressure EGR systems. Additionally, transient controls of 

boost pressure, EGR rate, cam phasers and intake charge temperature to exploit the 

cooled EGR combustion concept will require development beyond what has already been 

accomplished by the automotive industry. These are all “implementation readiness” 

issues that must be resolved prior to putting EGR Boost into high volume production. 

NHTSA has concluded that these implementation issues could be resolved 

and this technology could be brought to production by MY 2013. Supporting this 

conclusion, MEMA has previously suggested a 5 to 7 percent effectiveness for 

cooled EGR systems, although without boosting.34 

Two public sources indicate a 10 to 20 percent fuel consumption 

effectiveness for a downsized DI engine with cooled EGR compared to a naturally 

aspirated baseline engine35 and a 4 percent fuel consumption effectiveness for 

cooled EGR compared to a conventional downsized DI turbocharged engine.36 

Based on the data from these reports, NHTSA estimates the incremental reduction 

in fuel consumption for EGR Boost to be 4 percent over a turbocharged and 

downsized DI engine. Thus, if TRBDS precedes EGRB, adding the 12 percent gain 

from TRBDS to the 4 percent gain from EGRB results in total fuel consumption 

reduction of 16 percent. This is in agreement with the range suggested in the Lotus 

report. 

Regarding costs, the addition of EGR cooler and EGR valve were estimated in 

NHTSA’s MY 2011 rule to have an incremental RPE cost impact of approximately $173 

based on confidential individual component cost data from 2008 Martec describing EGR 

cooler costs of $75, EGR valve costs of $20 and associated piping costs of $20, totaling 

$115 (non-RPE). For purposes of this rulemaking, NHTSA found no information to 

indicate that these estimates were inaccurate. To that end, NHTSA applied an indirect 

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cost multiplier of 1.25, for a medium complexity technology, resulting in a compliance 

cost of $144 for MY 2012 vehicles with time-based learning applied. 

3.4.2.1.11 Diesel Engine Technologies 

Diesel engines have several characteristics that give them superior fuel efficiency 

compared to conventional gasoline, spark-ignited engines. Pumping losses are much 

lower due to lack of (or greatly reduced) throttling. The diesel combustion cycle operates 

at a higher compression ratio, with a very lean air/fuel mixture, and turbocharged lightduty diesels typically achieve much higher torque levels at lower engine speeds than 

equivalent-displacement naturally-aspirated gasoline engines. Additionally, diesel fuel 

has a higher energy content per gallon.l However, diesel fuel also has a higher carbon to 

hydrogen ratio, which increases the amount of CO2 emitted per gallon of fuel used by 

approximately 15 percent over a gallon of gasoline. 

Diesel engines have emissions characteristics that present challenges to meeting 

federal Tier 2 NOx emissions standards. It is a significant systems-engineering challenge 

to maintain the fuel consumption advantage of the diesel engine while meeting U.S. 

emissions regulations. Fuel consumption can be negatively impacted by emissions 

reduction strategies depending on the combination of strategies employed. Emission 

compliance strategies for diesel vehicles sold in the U.S. are expected to include a 

combination of combustion improvements and aftertreatment. These emission control 

strategies are being introduced on Tier 2 light-duty diesel vehicles today 

To achieve U.S. Tier 2 emissions limits, roughly 45 to 65 percent more NOx 

reduction is required compared to the Euro VI standards. Additionally, as discussed 

below, there may be a fuel consumption penalty associated with diesel aftertreatment 

since extra fuel is needed for the aftertreatment, and this extra fuel is not used in the 

combustion process of the engine that provides torque to propel the vehicle. 

Light-duty diesel emissions control systems capable of meeting Tier 2 Bin 5 

emission standards are already in production. Several key advances in diesel technology 

have made it possible to reduce emissions coming from the engine prior to aftertreatment. 

These technologies include improved fuel systems (higher injection pressure and 

multiple-injection capability), advanced controls and sensors to optimize combustion and 

emissions performance, higher EGR levels and EGR cooling to reduce NOx, and 

advanced turbocharging systems. 

On the aftertreatment side, the traditional 3-way catalyst aftertreatment found on 

gasoline-powered vehicles is ineffective due to the lean-burn combustion of a diesel. All 

diesels will require a diesel particulate filter (DPF) or catalyzed diesel particulate filter 

(CDPF), a diesel oxidation catalyst (DOC), and a NOx reduction strategy to comply with 

Tier 2 emissions standards. The most common NOx reduction strategies include the use 

of lean NOx traps (LNT) or selective catalytic reduction (SCR), which are outlined 

below. 

l Burning one gallon of diesel fuel produces about 15 percent more carbon dioxide than gasoline due to the 

higher density and carbon to hydrogen ratio. 

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3.4.2.1.11.1 Diesel Engine with Lean NOx Trap (LNT) Catalyst After-treatment 

A lean NOx trap operates, in principle, by oxidizing NO to NO2 in the exhaust 

and storing NO2 on alkali sorbent material. When the control system determines (via 

mathematical model or a NOx sensor) that the trap is saturated with NOx, it switches the 

engine into a rich operating mode or may in some cases inject fuel directly into the 

exhaust stream to produce excess hydrocarbons that act as a reducing agent to convert the 

stored NOx to N2 and water, thereby “regenerating” the LNT and opening up more 

locations for NOx to be stored. LNTs preferentially store sulfate compounds from the 

fuel, which can reduce catalytic performance. The system must undergo periodic 

desulfurization by operating at a net-fuel-rich condition at high temperatures in order to 

retain NOx trapping efficiency. 

NHTSA and EPA have concluded that diesel engines on small vehicles would be 

LNT-based. In the proposed rule, we did not include a diesel option for small vehicles 

because it did not appear to be a cost effective solution. Based on comments received we 

are including a diesel option for small vehicles in the final rule, and we are allowing the 

respective models to choose whether to apply the technology. 

3.4.2.1.11.2 Diesel Engine with Selective Catalyst Reduction (SCR) After-treatment 

An SCR aftertreatment system uses a reductant (typically, ammonia derived from 

urea) that is injected into the exhaust stream ahead of the SCR catalyst. Ammonia 

combines with NOx in the SCR catalyst to form N2 and water. The hardware 

configuration for an SCR system is more complicated than that of an LNT, due to the 

onboard urea storage and delivery system (which requires a urea pump and injector to 

inject urea into the exhaust stream). While a rich engine-operating mode is not required 

for NOx reduction, the urea is typically injected at a rate of approximately 3 percent of 

the fuel consumed. Manufacturers designing SCR systems intend to align urea tank 

refills with standard maintenance practices such as oil changes. As is the case with LNTbased diesels, EPA and NHTSA project that SCR-based diesel engines will be available 

within the next couple of years. Mercedes-Benz recently introduced two 2009 model 

year vehicles R320 and GL320, both of which are certified to Tier 2, Bin 5 emission 

standards. Based on public announcements from several other companies, an increased 

number of product offerings from multiple companies are expected over the next few 

years. 

In order to maintain equivalent performance to comparable gasoline-engine 

vehicles, an in-line 4-cylinder diesel engine, with displacement varying around 2.0 liters 

was assumed to replace an I4 gasoline base engine for Subcompact, Compact, and 

Midsize Passenger Car, Performance Subcompact Car and Small Light Truck. An in-line 

4-cylinder diesel engine, with displacement varying around 2.8 liters was assumed to 

replace a V6 gasoline base engine for Performance Compact, Performance Midsize, 

Large Passenger Car, Minivan, and Midsize Truck for the CAFE model. A V6 diesel 

engine, with displacement varying around 4.0 liters to meet vehicle performance 

requirements, was assumed to replace a V8 gasoline base engine for Large Truck and 

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Technologies Considered in the Agencies’ Analysis
 

Performance Large Car vehicle classes for the CAFE model. It was also assumed that 

diesel engines for these classes would utilize SCR aftertreatment systems. 

Confidential manufacturer and non-confidential comment data submitted in 

response to NHTSA’s past rulemaking for diesel engines showed a fuel consumption 

reduction in the range of 16.7 to 26.7 percent or a 15 to 23 percent CO2 emission 

reduction over a baseline gasoline engine. NHTSA’s MY 2011 CAFE final rule, which 

was supported by confidential manufacturer data, estimated the fuel consumption 

reduction of a SCR-based diesel engine to be between 19 to 25 percent over a baseline 

gasoline engine. NHTSA and EPA have revisited these values and found them to be 

reasonable. EPA estimates a 15 to 26 percent CO2 reduction for light-duty diesels 

equipped with SCR. 

Diesel engines are more costly than port-injected spark-ignition gasoline engines. 

These higher costs result from: 

•	 Fuel systems (higher pressures and more responsive injectors); 

•	 Controls and sensors to optimize combustion and emissions performance; 

•	 Engine design (higher cylinder pressures require a more robust engine, but 

higher torque output means diesel engines can have reduced displacement); 

•	 Turbocharger(s); 

•	 Aftertreatment systems, which tend to be more costly for diesels; 

Due to a significant decrease in platinum group metal prices since NHTSA’s MY 

2011 CAFE final rule analysis, the agencies chose to re-analyze diesel costs. In EPA’s 

2008 Staff Report, costs were considered for two types of diesel systems: one using a 

lean-NOX trap (LNT) along with a diesel particulate filter (DPF); and one using a 

selective catalytic reduction (SCR) system along with a DPF. In that report, EPA 

estimated direct manufacturing costs to range from $1,860 for the small car (LNT plus 

DPF) to $2,710 for the large truck (SCR plus DPF). For comparison, the NESCCAF 

study showed direct manufacturing costs of $1,500 to $1,950. More recently, NHTSA’s 

MY 2011 CAFE final rule showed direct manufacturing costs of $2,670 for a 4-cylinder 

engine using a LNT plus DPF system, $3,735 for a 6-cylinder engine using a SCR plus 

DPF system, and $4,668 for an 8-cylinder engine using a SCR plus DPF system. 

NHTSA noted that estimates in the MY 2011 CAFE final rule were higher than those 

shown in this rule due largely to the spike in platinum group metal prices that had 

occurred in the months just prior to issuing the 2011 CAFE final rule. 

The following diesel engine costs were developed by first taking a look back at 

EPA’s 2008 Staff Report, which reveals a couple of factors that resulted in somewhat 

misleading costs. First, the engine costs estimated there did not take into account the 

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downsizing that would occur when moving from a gasoline engine to a diesel engine 

(provided equivalent performance was maintained). Second, the engine costs used in that 

analysis were actually stated in terms of 2002 dollars rather than 2006 dollars in which 

the report was meant to be stated. The agencies decided that an update to the enginerelated costs would provide a much better cost estimate for converting to diesel. This 

was done by starting with the source for engine costs in the 2008 staff report which was 

an October 2005 EPA Interim Report37 which, in turn, sourced estimates from a 2003 

study done by FEV for EPA contained within a 2004 EPA Interim Technical Report.38 

These direct manufacturing costs are reproduced in Table 3-9. 

Table 3-9 Diesel Engine Direct Manufacturing Source Costs, Incremental to a Baseline Gasoline
 

Engine (2002 dollars)
 

Component(s) Large SUV Midsize 

Gasoline engine (baseline) 5L V8 2.4L I4 

Diesel engine 4L V8 2.2L I4 

Add high-pressure, common rail diesel fuel injection system $980 $630 

Delete gasoline fuel injection system -$245 -$165 

Add variable geometry turbocharger $175 $126 

Delete gasoline ignition system -$120 -$75 

Delete fuel pump and other changes to fuel system -$94 -$75 

Enhance powertrain mounting system $87 $107 

Other engine changes $80 $70 

Add air intercooler, ducts, and sensor $80 $55 

Larger battery and starter, add glow plugs $72 $50 

Delete exhaust gas oxygen sensor* -$60 -$30 

Add supplemental heater $50 $15 

Modify transmission $25 $25 

Enhance sound insulation package $25 $10 

Smaller radiator -$13 -$4 

Total $1,042 $739 

Note: Table reproduced from EPA420-R-05-012, October 2005 

Building on the direct manufacturing costs shown in Table 3-9, the agencies used 

appropriate scaling to estimate the costs for replacing a baseline gasoline engine with a 

diesel engine for the following five situations: a small car converted from a 2.4L I4 

gasoline to a 2L I4 Diesel; a large car converted from a 4.5L V8 gasoline to a 3L V6 

diesel; a medium/large MPV converted from a 3.2L V6 to a 2.8L I4 diesel; a small truck 

converted from a 3.2L V6 gasoline to a 2.8L I4 diesel; and a large truck converted from a 

5.6L V8 gasoline to a 4L V6 diesel. The results for the five base gasoline to diesel 

conversions are shown in Table 3-10. Values from Table 3-9 have been updated to 2007 

dollars using the GDP price deflator factor of 1.15 (see Appendix 3.A). Since the source 

costs were developed in 2003, this analysis conservatively considers the costs shown in 

Table 3-10 as being applicable to the 2012 model year. 

Table 3-10 Diesel Engine Direct Manufacturing Scaled-Costs in 2012, Incremental to Baseline
 

Gasoline Engine (2007 dollars)
 

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Component(s) Small 

car 

Large 

car 

Med/large 

MPV 

Small 

truck 

Large 

truck 

Notes 

(see text 

below) 

Gasoline engine (baseline) 2.4L I4 4.5L 

V8 3.2L V6 3.2L V6 5.6L V8 

Diesel engine 2.0L I4 3L V6 2.8L I4 2.8L I4 4L V6 

Add high-pressure, common rail 

diesel fuel injection system $517 $1,026 $724 $724 $1,026 1 

Delete gasoline fuel injection 

system -$52 -$89 -$73 -$73 -$89 2 

Add variable geometry 

turbocharger $145 $173 $145 $145 $201 3 

Delete gasoline ignition system -$69 -$138 -$112 -$112 -$138 4 

Delete fuel pump and other 

changes to fuel system -$62 -$108 -$86 -$86 -$108 5 

Enhance powertrain mounting 

system $123 $100 $123 $123 $100 6 

Other engine changes $57 $86 $80 $80 $86 7 

Add air intercooler, ducts, and 

sensor 

$45 $78 $63 $63 $92 8 

Larger battery and starter, add 

glow plugs $57 $70 $57 $57 $70 9 

Delete exhaust gas oxygen 

sensor* $0 $0 $0 $0 $0 10 

Add supplemental heater $17 $37 $17 $17 $57 11 

Modify transmission $29 $29 $29 $29 $29 12 

Enhance sound insulation package $11 $20 $11 $11 $29 13 

Smaller radiator -$7 -$15 -$10 -$10 -$15 14 

Engine downsize credit $0 -$185 -$390 -$390 -$185 15 

Total $813 $1,085 $580 $580 $1,156 

* Note: Oxygen sensor removals are included in aftertreatment costs. 

The costs shown in Table 3-10 were scaled in the following ways: 

1.	 Large car and large truck calculated as 75% the cost of Table 3-9’s large SUV and 

25% of midsize car; medium/large MPV and small truck calculated as equal to Table 

3-9’s midsize car; small car calculated using Table 3-10’s Med/large MPV value of 

$724 and applying the ratio of diesel engine sizes (2.0/2.8). Values converted to 2007 

dollars using GDP factor of 1.15. 

2.	 The estimates generated by FEV for eliminating the gasoline fuel injection systems 

were considerably larger than EPA & NHTSA believed was appropriate. Therefore, 

for a more accurate estimate, these costs were estimated, in 2007 dollars as follows: 

large car and large truck were calculated using incremental costs of $8/injector, 

$20/fuel rail, and $5 for a pressure damper or $8x8+$20+$5=$89; medium/large 

MPV and small truck were calculated using incremental costs of $8/injector, $20/fuel 

rail, and $5 for a pressure damper or $8x6+$20+$5=$73; small car calculated using 

Table 3-10’s Med/large MPV value of -$73 and applying the ratio of diesel engine 

sizes (2.0/2.8). 

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3.	 Large car calculated as the average of Table 3-9’s large SUV and midsize car; 

medium/large MPV and small truck calculated as equal to Table 3-9’s midsize car, 

and large truck calculated as equal to Table 3-9’s large SUV; small car calculated as 

equal to medium/large MPV. Values converted to 2007 dollars using GDP factor of 

1.15. 

4.	 Medium/large MPV and small truck calculated as the average of Table 3-9’s large 

SUV and midsize car; Large car and large truck calculated as equal to Table 3-9’s 

large SUV; small car calculated as equal to half of Table 3-10’s large truck value of ­

$138. Values converted to 2007 dollars using GDP factor of 1.15. 

5.	 Medium/large MPV and small truck calculated as equal to Table 3-9’s midsize car; 

Large car and large truck calculated as equal to Table 3-9’s large SUV; small car 

calculated using Table 3-10’s Med/large MPV value of -$86 and applying the ratio of 

diesel engine sizes (2.0/2.8). Values converted to 2007 dollars using GDP factor of 

1.15. 

6.	 Medium/large MPV and small truck calculated as equal to Table 3-9’s large SUV; 

Large car and large truck calculated as equal to Table 3-9’s midsize car; small car 

calculated as equal to medium/large MPV. Values converted to 2007 dollars using 

GDP factor of 1.15. 

7.	 Medium/large MPV and small truck calculated as equal to Table 3-9’s midsize car; 

Large car and large truck calculated as the average of Table 3-9’s large SUV and 

midsize car; small car calculated using Table 3-10’s Med/large MPV value of $80 

and applying the ratio of diesel engine sizes (2.0/2.8). Values converted to 2007 

dollars using GDP factor of 1.15. 

8.	 Medium/large MPV and small truck calculated as equal to Table 3-9’s midsize car; 

Large car calculated as the average of Table 3-9’s large SUV and midsize car; Large 

truck calculated as equal to Table 3-9’s large SUV; small car calculated using Table 

3-10’s Med/large MPV value of $63 and applying the ratio of diesel engine sizes 

(2.0/2.8). Values converted to 2007 dollars using GDP factor of 1.15. 

9.	 Medium/large MPV and small truck calculated as equal to Table 3-9’s midsize car; 

Large car and large truck calculated as the average of Table 3-9’s large SUV and 

midsize car; small car calculated as equal to medium/large MPV. Values converted 

to 2007 dollars using GDP factor of 1.15. 

10. Oxygen sensor costs are included in the aftertreatment costs discussed below. 

11. Medium/large MPV and small truck calculated as equal to Table 3-9’s midsize car; 

Large car calculated as the average of Table 3-9’s large SUV and midsize car; Large 

truck calculated as equal to Table 3-9’s large SUV; small car calculated as equal to 

medium/large MPV. Values converted to 2007 dollars using GDP factor of 1.15. 

12. Values from Table 3-9 converted to 2007 dollars using GDP factor of 1.15. 

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Technologies Considered in the Agencies’ Analysis
 

13. Medium/large MPV and small truck calculated as equal to Table 3-9’s midsize car; 

Large car calculated as the average of Table 3-9’s large SUV and midsize car; Large 

truck calculated as equal to Table 3-9’s large SUV; small car calculated as equal to 

medium/large MPV. Values converted to 2007 dollars using GDP factor of 1.15. 

14. Medium/large MPV and small truck calculated as the average of Table 3-9’s large 

SUV and midsize car; Large car and large truck calculated as equal to Table 3-9’s 

large SUV; small car calculated using Table 3-10’s Med/large MPV value of -$10 

and applying the ratio of diesel engine sizes (2.0/2.8). Values converted to 2007 

dollars using GDP factor of 1.15. 

15. Based on the approach presented in the turbocharging/downsizing section (see section 

3.5.2.1.11), the savings associated with downsizing the gasoline engine were 

calculated by estimating the cost in 2007 dollars of each cylinder at $51, each valve at 

$10, and each cam at $103.m Therefore, the large car and large truck, which each lose 

two cylinders (-$102), eight valves (-$82) and no cams realize a $185 savings. The 

medium/large MPV and small truck would each lose two cylinders (-$102) and eight 

valves (-$82) and two cams (-$205) for a savings of $390. The small car downsizing 

credit was left at $0 given the small displacement change and lack of cylinder or 

valve removals. 

For the diesel aftertreatment systems, the approach taken is consistent with the 

approach taken in EPA’s 2007/2010 Highway Diesel rule and EPA’s recent locomotive 

and marine rule.39 For platinum group metal (PGM) costs, monthly average prices as of 

March 2009 as reported by Johnson-Matthey were used.40,n Those values were 

$1,085/troy ounce for platinum and $1,169/troy ounce for rhodium. Aftertreatment 

devices were sized according to the diesel engine displacement with a 1:1 ratio for both 

the SCR catalyst and the DPF, and a 0.5:1 ratio for the DOC (i.e., the DOC is half the 

displacement of the engine). The end result for aftertreatment devices, including a urea 

dosing unit, urea tank and necessary brackets and heaters, are shown in Table 3-11. Also 

shown in Table 3-11 are the savings associated with removal of the gasoline catalyst. 

Note that the gasoline catalyst was sized according to the gasoline engine that served as 

the baseline engine. 

m These are the correct costs for the 2015MY in 2007$. But, they are used here erroneously as 2012MY 

values. Technically, 3 years of time-based learning should have been backed out to get 2012MY values. 

So, the 2012MY costs are slightly underestimated by roughly $10. This was true in the proposed analysis 

and, because it has no meaningful impact on the analysis (1-2% of the estimated diesel costs), continues to 

be true in the final analysis. 

n These are the PGM prices used in the NPRM. As precious metals, PGM prices swing widely year-to-year 

and even month-to-month. Given economic conditions during the past year and the tendency of investors 

during tough economic times to invest in valuable metals such as gold and PGMs, PGM prices have 

increased considerably since the NPRM. Were the January 2010 PGM prices used, the diesel costs 

presented here would increase on the order of 10-20 percent. That would serve to make diesels less cost 

effective options for improving fuel economy or reducing GHGs. Since diesels comprise such a small 

percentage of each agency’s analysis from a technology penetration standpoint, even using the lower March 

2009 PGM prices, the choice of what PGM prices to use is of little consequence to the analytical results for 

purposes of this rulemaking. 

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Table 3-11 Diesel Aftertreatment Direct Manufacturing Costs in 2012 (2007 dollars) 

Component(s) Small car Large car Med/large 

MPV Small truck Large truck 

Gasoline engine (baseline) 2.4L I4 4.5L V8 3.2L V6 3.2L V6 5.6L V8 

Diesel engine 2.0L I4 3L V6 2.8L I4 2.8L I4 4L V6 

DOC $216 $277 $257 $257 $339 

DPF (includes a $20 pressure sensor for 

OBD & sensing) $401 $534 $503 $503 $668 

SCR system (includes a $50 NOx sensor for 

OBD & sensing) n/a $904 $904 $914 $996 

LNT System includes $50 NOx sensor for 

OBD and sensing $442 n/a n/a n/a n/a 

Removal of gasoline catalysts & sensors -$175 -$401 -$288 -$298 -$483 

Total $883 $1,314 $1,376 $1,376 $1,520 

The incremental costs to convert from a gasoline to a diesel engine—Table 3-10 

and Table 3-11 combined—are shown in Table 3-12. 

Table 3-12 Direct Manufacturing Costs to Convert from a Gasoline to Diesel System in 2012 (2007 

dollars) 

Component(s) Small car Large car Med/large 

MPV Small truck Large truck 

Gasoline engine 

(baseline) 2.4L I4 4.5L V8 3.2L V6 3.2L V6 5.6L V8 

Diesel engine 2.0L I4 3L V6 2.8L I4 2.8L I4 4L V6 

Engine-related costs $813 $1,085 $580 $580 $1,156 

Aftertreatment $883 $1,314 $1,376 $1,376 $1,520 

Total $1,697 $2,399 $1,956 $1,956 $2,676 

This analysis applies time-based learning to diesel systems and a 1.25 ICM for a 

medium complexity rating. Therefore, the MY 2012 compliance costs are as shown in 

Table 3-13. 

Table 3-13 Compliance Costs to Convert from a Gasoline to Diesel System in 2012 (2007 dollars) 

Component(s) Small car Large car Med/large 

MPV Small truck Large truck 

Gasoline engine 

(baseline) 2.4L I4 4.5L V8 3.2L V6 3.2L V6 5.6L V8 

Diesel engine 2.0L I4 3L V6 2.8L I4 2.8L I4 4L V6 

Total $2,121 $2,999 $2,445 $2,445 $3,345 

The diesel costs for the 2016 MY, which include 4 years of time-based learning, 

are shown in section 3.5.2. In the NHTSA analysis, and consistent with the above 

analysis, diesel costs for a 2.0L in-line 4-cylinder from an I4 gasoline engine were 

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Technologies Considered in the Agencies’ Analysis
 

estimated to be $2,121 in MY 2012. A 2.8L in-line 4-cylinder from a V6 gasoline engine 

were estimated to be $2,445 in MY 2012. A MY 2012 cost of $3,345 was estimated for a 

conversion to 4.0L V6 diesel from a V8 gasoline engine. These MY 2012 estimates were 

obtained using the in-line 4-cylinder 2.0L cost of $1,697, the in-line 4-cylinder 2.8L cost 

of $1956 and V6 cost of $2,676, from Table 3-12, and multiplying them by the medium 

complexity ICM markup of 1.25 and are eligible for time-based learning cost reductions. 

3.4.2.2 Transmission Technologies 

NHTSA and EPA have also reviewed the transmission technology estimates used 

in the MY 2011 CAFE final rule. In doing so, NHTSA and EPA considered or 

reconsidered all available sources and updated the estimates as appropriate. The section 

below describes each of the transmission technologies considered for this rulemaking. 

3.4.2.2.1	 Improved Automatic Transmission Control (IATC) (Aggressive Shift Logic 

and Early Torque Converter Lockup) 

Calibrating the transmission shift schedule to upshift earlier and quicker, and to 

lock-up or partially lock-up the torque converter under a broader range of operating 

conditions can reduce fuel consumption and CO2 emissions. However, this operation can 

result in a perceptible degradation in noise, vibration, and harshness (NVH). The degree 

to which NVH can be degraded before it becomes noticeable to the driver is strongly 

influenced by characteristics of the vehicle, and although it is somewhat subjective, it 

always places a limit on how much fuel consumption can be improved by transmission 

control changes. Given that the Aggressive Shift Logic and Early Torque Converter 

Lockup are best optimized simultaneously due to the fact that adding both of them 

primarily requires only minor modifications to the transmission or calibration software, 

these two technologies are combined in the modeling. 

3.4.2.2.2	 Aggressive Shift Logic 

During operation, an automatic transmission’s controller manages the operation of 

the transmission by scheduling the upshift or downshift, and locking or allowing the 

torque converter to slip based on a preprogrammed shift schedule. The shift schedule 

contains a number of lookup table functions, which define the shift points and torque 

converter lockup based on vehicle speed and throttle position, and other parameters such 

as temperature. Aggressive shift logic (ASL) can be employed in such a way as to 

maximize fuel efficiency by modifying the shift schedule to upshift earlier and inhibit 

downshifts under some conditions, which reduces engine pumping losses and engine 

friction. The application of this technology does require a manufacturer to confirm that 

drivability, durability, and NVH are not significantly degraded. 

The agencies have estimated the effectiveness of aggressive shift logic at 1 to 2 

percent, as explained below. The agencies have estimated the cost at $32 for a MY 2012 

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vehicle, regardless of size (2007$).o With time-based learning applied, this cost would be 

$28 for a MY 2016 vehicle (2007$) based on the 2002 NAS report. 

3.4.2.2.3 Early Torque Converter Lockup 

A torque converter is a fluid coupling located between the engine and 

transmission in vehicles with automatic transmissions and continuously-variable 

transmissions (CVT). This fluid coupling allows for slip so the engine can run while the 

vehicle is idling in gear (as at a stop light), provides for smoothness of the powertrain, 

and also provides for torque multiplication during acceleration, and especially launch. 

During light acceleration and cruising, the inherent slip in a torque converter causes 

increased fuel consumption, so modern automatic transmissions utilize a clutch in the 

torque converter to lock it and prevent this slippage. Fuel consumption can be further 

reduced by locking up the torque converter at lower vehicle speeds, provided there is 

sufficient power to propel the vehicle, and noise and vibration are not excessive.p If the 

torque converter cannot be fully locked up for maximum efficiency, a partial lockup 

strategy can be employed to reduce slippage. Early torque converter lockup is applicable 

to all vehicle types with automatic transmissions. Some torque converters will require 

upgraded clutch materials to withstand additional loading and the slipping conditions 

during partial lock-up. As with aggressive shift logic, confirmation of acceptable 

drivability, performance, durability and NVH characteristics is required to successfully 

implement this technology. 

Regarding the effectiveness of Improved Automatic Transmission Control, the 

MY 2011 CAFE final rule, which was supported by the 2002 NAS and NESCCAF 

reports as well as confidential manufacturer data, estimated an effectiveness 

improvement of 1 to 2 percent for aggressive shift logic and 0.5 percent for early torque 

converter lockup. These estimates are in agreement with the values stated in the 

NESCCAF report and confidential manufacturer data. For the purpose of this final rule, 

NHTSA and EPA concluded that the combined estimated effectiveness is 1.5 to 2.5 

percent reduction in CO2 emissions and fuel consumption. 

Regarding cost estimates, the agencies estimate the cost at $29, regardless of 

vehicle size and including a low complexity ICM of 1.11 for a MY 2012 vehicle (2007$). 

With time-based learning, this cost would be $25 for a MY 2016 vehicle (2007$). For 

IATC (or the combination of aggressive shift logic and early torque converter lockup), 

the cost, including the 1.11 ICM, would be $60 for a MY 2012 vehicle and $53 for a MY 

2016 vehicle after time-based learning (2007$). 

3.4.2.2.4 Automatic 6-, 7- and 8-Speed Transmissions (NAUTO) 

Manufacturers can also choose to replace 4- and 5-speed transmission with 6-, 7-, 

or 8-speed automatic transmissions. Additional ratios allow for further optimization of 

engine operation over a wider range of conditions, but this is subject to diminishing 

o This reflects a revisiting of component costs for NHTSA since the MY 2011 CAFE final rule. 

p Although only modifications to the transmission calibration software are considered as part of this 

technology, very aggressive early torque converter lock up may require an adjustment to damper stiffness 

and hysteresis inside the torque converter. 

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Technologies Considered in the Agencies’ Analysis
 

returns as the number of speeds increases. As additional planetary gear sets are added 

(which may be necessary in some cases to achieve the higher number of ratios), 

additional weight and friction are introduced. Also, the additional shifting of such a 

transmission can be perceived as bothersome to some consumers, so manufacturers need 

to develop strategies for smooth shifts. Some manufacturers are replacing 4- and 5-speed 

automatics with 6-speed automatics, and 7- and 8-speed automatics have also entered 

production, albeit in lower-volume applications in luxury and performance oriented cars. 

As discussed in the MY 2011 CAFE final rule, confidential manufacturer data 

projected that 6-speed transmissions could incrementally reduce fuel consumption by 0 to 

5 percent from a baseline 4-speed automatic transmission, while an 8-speed transmission 

could incrementally reduce fuel consumption by up to 6 percent from a baseline 4-speed 

automatic transmission. GM has publicly claimed a fuel economy improvement of up to 

4 percent for its new 6-speed automatic transmissions.41 The 2008 EPA Staff Technical 

Report found a 4.5 to 6.5 percent fuel consumption improvement for a 6-speed over a 4­

speed automatic transmission.42 Based on this information, NHTSA estimated in the MY 

2011 rule, that the conversion to a 6-,7- and 8-speed transmission (NAUTO) from a 4 or 

5-speed automatic transmission with IATC would have an incremental fuel consumption 

benefit of 1.4 percent to 3.4 percent, for all vehicle classes. From a baseline 4 or 5 speed 

transmission without IATC, the incremental fuel consumption benefit would be 

approximately 3 to 6 percent, which is consistent with the EPA Staff Report estimate. 

NHTSA and EPA reviewed these effectiveness estimates and concluded that they 

remain accurate. While the CAFE model follows the incremental approach discussed 

above, the GHG model estimates the packaged effectiveness of 4.5 to 6.5 percent. 

In the NPRM, NHTSA and EPA reviewed the cost estimates from the MY 2011 

CAFE final rule which used cost estimates from 2008 Martec report and the 2008 EPA 

Staff Report (which assumed use of a Lepelletier gear set) and concluded that some but 

not all 6-speed automatic transmissions would be equipped with Lepelletier gear set. As 

such, the estimates were revised to establish the cost for the 6 speed transmission to be 

equally divided between application using Lepelletier, and application of a standard 

planetary gear set 6-speed automatic transmission as estimated in the 2008 Martec Report 

(and the MY 2011 CAFE final rule). The 2008 Martec report estimated a cost of $323 

with RPE adjustment and $215 without RPE adjustment for converting a 4-speed to a 6­

speed transmission and a cost of $638 with RPE adjustment or $425 without RPE 

adjustment for converting a 4-speed to an 8-speed transmission. As a result, the final 

incremental cost estimate in the NPRM was $170, independent of vehicle type and size 

and including a low complexity 1.11 ICM (2007$). In the CAFE model, due to the 

structure of the vehicle classes used, an additional $102 (2007$) was included to account 

for performance vehicle classes and for medium and large trucks. This is because for 

performance classes, additional gear ratios, such as 7 and 8 speed transmissions may be 

utilized, and for medium and large trucks heavier duty transmissions are required. These 

estimates represented MY 2012 vehicle costs 

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For the final analysis, as noted earlier, the agencies have considered the teardown 

work done by FEV. In its teardown work, FEV determined the 6 speed automatic 

transmission to be $106 less costly than a 5 speed automatic transmission (direct 

manufacturing cost without markups). This is counterintuitive but can be attributed to the 

type of gear set employed in the 6 speed automatic transmission studied by FEV. That 6 

speed auto transmission was equipped with a Lepelletier-like gear set and therefore does 

not require a 1-way clutch that is essential to other designs. The cost analysis performed 

in this rule requires that the cost difference between both a 4 speed and 5 speed auto 

relative to 6 speed auto be established because there are a substantial number of both 4 

speed and 5 speed automatic transmissions used in the baseline fleet. This was done 

using the NPRM analysis for a 4 speed to 5 speed and adding the FEV results for the 

transition from a 5 speed to a 6 speed. Our analysis resulted in a cost for a 5 speed auto 

transmission relative to a 4 speed auto of $91 direct manufacturing cost (2007$ in 2012). 

As done in the NPRM, the agencies averaged the non-Lepelletier gear set cost with the 

Lepelletier-like gear set cost (FEV tear-down value of -$106). In the 2011 FRM, 

NHTSA estimated the cost of a 6 speed auto transmission, without a Lepelletier gear set, 

relative to a 4 speed auto transmission at $215 (2007$). Therefore, we have used the 

$215 value (4 speed to 6 speed) from NHTSA’s 2011 FRM and subtracted $91 from that 

(4 speed to 5 speed) to arrive at a cost of $124 as the non-Lepelletier cost for a 6 speed 

auto transmission relative to a 5 speed auto transmission. We have then averaged the 

$124 value with the FEV value of -$106 (Lepelletier-like cost) to get an end result of $9 

(2007$ in 2012). This $9 cost represents the cost for a 6 speed auto transmission relative 

to a 5 speed auto transmission in the final analysis. This $9 can then be added to the $91 

to get a cost of $101 for a 6 speed auto transmission relative to a 4 speed auto 

transmission. Table 3-14 shows the direct manufacturing costs used in the NPRM and the 

final analyses. With the low complexity markup of 1.11, the compliance cost for a 6 

speed automatic transmission relative to a 4 speed automatic transmission in 2012 is $112 

(2007$). With time based learning, the compliance costs in 2016 to move from a 4 speed 

auto to a 5 speed auto and then from a 5 speed auto to a 6 speed auto transmission would 

be $90 and $9, respectively (2007$). 

Table 3-14 Direct Manufacturing Costs for Automatic Transmissions (2007$ in 2012) 

Technology 

Non-Lepelletier 

Cost Used in 2011 

CAFE Analysis 

NPRM FEV tear down 

(Lepelletier-like) Final Rule Comments 

5s auto relative to 4s 

auto $91 n/a $91 Final rule uses NPRM 

value 

6s auto relative to 5s 

auto -$106 $9 $215-$91=$124 

[$124+(-$106)]/2=$9 

6s auto relative to 4s 

auto $215 $153 n/a $101 $91+$9=$101 (values are 

rounded) 

Notes: Blank cells represent values not considered in this analysis; n/a means that FEV did not conduct a tear down of the 

technology; refer to text for more detail on the comments. 

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Technologies Considered in the Agencies’ Analysis
 

3.4.2.2.5 Dual Clutch Transmissions / Automated Manual Transmissions (DCTAM) 

An Automated Manual Transmission (AMT) is mechanically similar to a 

conventional manual transmission, but shifting and launch functions are automatically 

controlled by the electronics. There are two basic types of AMTs, single-clutch and dualclutch (DCT). A single-clutch AMT is essentially a manual transmission with automated 

clutch and shifting. Because of shift quality issues with single-clutch designs, DCTs will 

likely be far more common in the U.S. and are the basis of the estimates that follow. A 

DCT uses separate clutches (and separate gear shafts) for the even-numbered gears and 

odd-numbered gears. In this way, the next expected gear is pre-selected, which allows 

for faster and smoother shifting. For example, if the vehicle is accelerating in third gear, 

the shaft with gears one, three and five has gear three engaged and is transmitting power. 

The shaft with gears two, four, and six is idle, but has gear four engaged. When a shift is 

required, the controller disengages the odd-gear clutch while simultaneously engaging the 

even-gear clutch, thus making a smooth shift. If, on the other hand, the driver slows 

down instead of continuing to accelerate, the transmission will have to change to second 

gear on the idling shaft to anticipate a downshift. This shift can be made quickly on the 

idling shaft since there is no torque being transferred on it. 

In addition to single-clutch and dual-clutch AMTs, there are also wet clutch and 

dry clutch designs which are used for different types of vehicle applications. Wet clutch 

AMTs offer a higher torque capacity that comes from the use of a hydraulic system that 

cools the clutches. Wet clutch systems are less efficient than the dry clutch systems due 

to the losses associated with hydraulic pumping. Additionally, wet AMTs have a higher 

cost due to the additional hydraulic hardware required. 

Overall, DCTs likely offer the greatest potential for effectiveness improvements 

among the various transmission options presented in this report because they offer the 

inherently lower losses of a manual transmission with the efficiency and shift quality 

advantages of electronic controls. The lower losses stem from the elimination of the 

conventional lock-up torque converter, and a greatly reduced need for high pressure 

hydraulic circuits to hold clutches or bands to maintain gear ratios (in automatic 

transmissions) or hold pulleys in position to maintain gear ratio (in Continuously 

Variable Transmissions). However, the lack of a torque converter will affect how the 

vehicle launches from rest, so a DCT will most likely be paired with an engine that offers 

sufficient torque at low engine speeds to allow for adequate launch performance. 

For the MY 2011 CAFE final rule, NHTSA estimated a 5.5 to 9.5 percent 

improvement in fuel consumption over a baseline 4/5-speed automatic transmission for a 

wet clutch DCT, which was assumed for all but the smallest of vehicle subclasses, 

Subcompact and Compact cars and small LT. This results in an incremental effectiveness 

estimate of 2.7 to 4.1 percent over a 6-speed automatic transmission with IATC. For 

Subcompact and Compact Cars and small LT, which were assumed to use a dry clutch 

DCT, NHTSA estimated an 8 to 13 percent fuel consumption improvement over a 

baseline 4/5-speed automatic transmission, which equates to a 5.5 to 7.5 percent 

incremental improvement over the 6-speed transmission. This is consistent with the EPA 

Staff Report, which estimated that these transmissions offer an effectiveness of 9.5 to 

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14.5 percent over a 4-speed automatic transmission. EPA and NHTSA therefore 

conclude that 8 to 13 percent effectiveness is appropriate for this rule. 

In the NPRM analysis, the agencies estimated costs for 6 speed dual-clutch 

transmissions relative to 6 speed auto transmissions. For the dry clutch DCT, the 

agencies estimated a direct manufacturing cost of $59 (2007$ in 2012). For the wet 

clutch DCT, the estimate was $126 (2007$ in 2012). For the final rule, we have 

considered the teardown work done by FEV. In that teardown work, FEV found a wet 

clutch DCT to be $147 less costly than a 6 speed auto transmission. This cost savings is 

not unexpected when one considers that the DCT is less complex than an auto 

transmission. However, such a cost savings presumes that capacity exists to produce 

dual-clutch transmissions at volumes of 450,000 units, an assumption made by FEV. 

Since such capacity may not currently exist in the US, we have not used the FEV value 

directly but have instead averaged the FEV value (-$147) with the value used in the 

NPRM analysis ($126). Therefore, in the final rule, the wet clutch DCT is estimated to 

save $11 relative to a 6 speed auto transmission (2007$ in 2012). To generate a dry 

clutch DCT cost for the final analysis, we have applied the same cost difference from the 

NPRM for wet clutch versus dry clutch. In other words, the NPRM analysis had a cost 

difference of $67 which was then subtracted from the final wet-clutch DCT cost of -$11 

to arrive at a result of -$78 for a dry-clutch DCT relative to a 6 speed auto transmission 

(2007$ in 2012). Table 3-15 shows the direct manufacturing costs used in the NPRM and 

final rule for dual clutch transmissions. Applying the medium complexity ICM of 1.25 

results in costs of -$59 for the wet-clutch DCT and -$8 for the dry clutch DCT (2007$ in 

2012), both relative to a 6 speed auto transmission. With time based learning, these costs 

become -$52 and -$7, respectively, in 2016 (2007$). 

Table 3-15 Direct Manufacturing Costs for Dual-Clutch Transmissions (2007$ in 2012) 

Technology NPRM FEV tear 

down Final Rule Comments 

6s wet DCT relative to 6s auto $126 -$147 -$11 [$126+(-$147)]/2=-$11 

6s dry DCT relative to 6s auto $59 n/a -$78 -$11-($126-$59) = -$78 

Notes: n/a means that FEV did not conduct a tear down of the technology; refer to text for more detail on 

the comments. 

3.4.2.2.6 Continuously Variable Transmission (CVT) 

A Continuously Variable Transmission (CVT) is unique in that it does not use 

gears to provide ratios for operation. Instead, the most common CVT design uses two Vshaped pulleys connected by a metal belt. Each pulley is split in half and a hydraulic 

actuator moves the pulley halves together or apart. This causes the belt to ride on either a 

larger or smaller diameter section of the pulley which changes the effective ratio of the 

input to the output shafts. Advantages of the CVT are that the engine can operate at its 

most efficient speed-load point more of the time, since there are no fixed ratios. 

However, CVTs are limited by engine power and cannot be applied to high torque 

applications. Also, CVTs often have a wider range of ratios compared to conventional 

automatic transmissions which can provide for more options in engine optimization. 

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While CVTs by definition are fully continuous, some automakers choose to emulate 

conventional stepped automatic operation because some drivers are not used to the 

sensation of the engine speed operating independently of vehicle speed. 

Considering the confidential data together with independent review, NHTSA has 

estimated the fuel consumption effectiveness for CVTs at 2.2 to 4.5 percent over a 4/5­

speed automatic transmission, which translates into a 0.7 to 2.0 incremental effectiveness 

improvement over an automatic transmission with the IATC technology. EPA and 

NHTSA reviewed these estimates and found them to be accurate. 

The agencies adjusted the original estimates used in the MY 2011 CAFE final 

rule to account for medium complexity ICM of 1.25. This resulted in estimated 

incremental compliance cost of $250 for the MY 2012 vehicles. In the Volpe model, this 

technology was only applied to vehicles manufactured with unibody construction 

methods. CVTs are an established and readily available technology, so time based 

learning is applied. 

3.4.2.2.7 6-Speed Manual Transmissions (6MAN) 

Manual transmissions are entirely dependent upon driver input to shift gears: the 

driver selects when to perform the shift and which gear to select. This is the most 

efficient transfer of energy of all transmission layouts, because it has the lowest internal 

gear losses, with a minimal hydraulic system, and the driver provides the energy to 

actuate the clutch. From a systems viewpoint, however, vehicles with manual 

transmissions have the drawback that the driver may not always select the optimum gear 

ratio for fuel economy. Nonetheless, increasing the number of available ratios in a 

manual transmission can improve fuel economy by allowing the driver to select a ratio 

that optimizes engine operation more often. Typically, this is achieved through adding 

overdrive ratios to reduce engine speed at cruising velocities (which saves fuel through 

reduced engine pumping losses) and pushing the torque required of the engine towards 

the optimum level. However, if the gear ratio steps are not properly designed, this may 

require the driver to change gears more often in city driving, resulting in customer 

dissatisfaction. Additionally, if gear ratios are selected to achieve improved launch 

performance instead of to improve fuel economy, then no fuel saving effectiveness is 

realized. 

NHTSA’s MY 2011 CAFE final rule estimated an effectiveness increase of 0.5 

percent for replacing a 5-speed manual with a 6-speed manual transmission, which was 

derived from confidential manufacturer data. NHTSA and EPA have found no evidence 

to dispute this estimate and chosen to use 0.5 percent reduction in CO2 emissions and fuel 

consumption for replacing a 5-speed manual with a 6-speed manual transmission for this 

rule. 

NHTSA updated costs to reflect the ICM low complexity markup of 1.11 which 

resulted in a incremental compliance cost of $250 as compared to $338 for MY 2012, 

with lower costs in later MYs due to the application of time-based learning factors. In 

the 2008 Staff Report, EPA estimated this RPE (1.5 RPE factor) at $107 (2006$) relative 

to a 5-speed manual transmission and for a MY 2015 vehicle. EPA updated this value to 

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2007$ and used a 1.11 ICM to get costs of $89 for a MY 2012 vehicle and $69 for a MY 

2016 vehicle, both relative to a 5-speed manual transmission (2007$). 

3.4.2.3 Hybrid and Electrification/Accessory Technologies 

A Hybrid is a vehicle that combines two or more sources of propulsion energy, 

where one uses a consumable fuel (like gasoline), and one is rechargeable (during 

operation, or by another energy source). Hybrid technology is established in the U.S. 

market and more manufacturers are adding hybrid models to their lineups. Hybrids 

reduce fuel consumption through three major mechanisms: 

•	 The internal combustion engine can be optimized (through downsizing, 

modifying the operating cycle, or other control techniques) to operate at or 

near its most efficient point more of the time. Power loss from engine 

downsizing can be mitigated by employing power assist from the secondary 

power source. 

•	 Some of the energy normally lost as heat while braking can be captured and 

stored in the energy storage system for later use. 

•	 The engine is turned off when it is not needed, such as when the vehicle is 

coasting or when stopped. 

Hybrid vehicles utilize some combination of the three above mechanisms to 

reduce fuel consumption and CO2 emissions. A fourth mechanism to reduce petroleum 

fuel consumption, available only to plug-in hybrids, is by substituting the petroleum fuel 

energy with energy from another source, such as the electric grid. The effectiveness of 

fuel consumption and CO2 reduction depends on the utilization of the above mechanisms 

and how aggressively they are pursued. One area where this variation is particularly 

prevalent is in the choice of engine size and its effect on balancing fuel economy and 

performance. Some manufacturers choose not to downsize the engine when applying 

hybrid technologies. In these cases, performance is vastly improved, while fuel 

efficiency improves significantly less than if the engine was downsized to maintain the 

same performance as the conventional version. While this approach has been used in 

cars such as the Honda Accord Hybrid (now discontinued), it is more likely to be used for 

vehicles like trucks where towing and/or hauling is an integral part of their performance 

requirements. In these cases, if the engine is downsized, the battery can be quickly 

drained during a long hill climb with a heavy load, leaving only a downsized engine to 

carry the entire load. Because towing capability is currently a heavily-marketed truck 

attribute, manufacturers are hesitant to offer a truck with downsized engine which can 

lead to a significantly diminished towing performance with a low battery, and therefore 

engines are traditionally not downsized for these vehicles. 

Although hybrid vehicles using other energy storage concepts (flywheel, 

hydraulic) have been developed, the systems currently in production in the U.S. for 

passenger cars and light trucks use battery storage and electric drive systems. Hybrid 

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electric vehicles (HEV) are part of a continuum of vehicles using systems with differing 

levels of electric drive and electric energy storage. This range of vehicles includes 

relatively basic engine start/stop systems, HEV systems with varying degrees of electric 

storage and electric drive system capability, plug-in hybrid electric vehicles (PHEV) with 

differing degrees of all electric range and battery electric vehicles (EV) that rely entirely 

on electric drive and battery electric energy storage. 

Different HEV, PHEV and EV concepts utilize these mechanisms differently, so 

they are treated separately for the purposes of this analysis. Below is a discussion of 

battery energy storage and the major hybrid concepts that were determined to be 

available in the near term. In many applications, particularly with PHEV and EV, the 

battery represents the most costly and system-limiting sub-component of the hybrid 

system. Currently, there are many battery chemistries being developed and refined for 

hybrid applications that are expected to enhance the performance of future hybrid 

vehicles. 

3.4.2.3.1 Batteries for MHEV, HEV, PHEV and EV Applications 

The design of battery secondary cells can vary considerably between MHEV, 

HEV, PHEV and EV applications. 

MHEV batteries: Due to their lower voltage (12-42 VDC) and reduced power 

and energy requirements, MHEV systems will likely continue to use lead-acid batteries. 

MHEV battery designs differ from those of current starved-electrolyte (typical 

maintenance free batteries) or flooded-electrolyte (the older style lead-acid batteries 

requiring water “top-off”) batteries used for starting, lighting and ignition (SLI) in 

automotive applications. Standard SLI batteries are primarily designed to provide highcurrent for engine start-up and then recharge immediately after startup via the vehicle’s 

charging system. Deeply discharging a standard SLI battery will greatly shorten its life. 

MHEV applications are expected to use: 

•	 Extended-cycle-life flooded (ELF) lead-acid batteries 

•	 Absorptive glass matt, valve-regulated lead-acid (AGM/VRLA) batteries – 

or – 

•	 Asymmetric lead-acid battery/capacitor hybrids (e.g., flooded 

ultrabatteries) 

MHEV systems using electrolytic double-layer capacitors are also under development. 

HEV batteries: HEV applications operate in a narrow, short-cycling, chargesustaining state of charge (SOC). Energy capacity in HEV applications is somewhat 

limited by the ability of the battery and power electronics to accept charge and by space 

and weight constraints within the vehicle design. HEV battery designs tend to be 

optimized for high power density rather than high energy density, with thinner cathode 

and anode layers and more numerous current collectors and separators (Figure 3-1). 

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EV batteries: EV batteries tend to be optimized for high energy density and are 

considerably larger than HEV batteries. PHEV battery designs are intermediate between 

power-optimized HEV and energy-optimized EV battery cell designs. 

PHEV batteries: PHEV batteries must provide both charge depleting operation 

similar to an EV and charge sustaining operation similar to an HEV. Unlike HEV 

applications, charge-sustaining operation with PHEVs occurs at a relatively low battery 

state of charge (SOC) which can pose a significant challenge with respect to attaining 

acceptable battery cycle life. In the case of the GM Volt, this limits charge depleting 

operation to a minimum SOC of approximately 30 percent.43 An alternative approach for 

PHEV applications that has potential to allow extension of charge depletion to a lower 

battery SOC is using energy-optimized lithium-ion batteries for charge depleting 

operation in combination with the use of supercapacitors for charge sustaining 

44 operation.

Figure 3-1: Schematic representation of power and energy optimized 

prismatic-layered battery cells 

Collector (-) 

Cathode (-) 

Separator 

Anode (+) 

Collector (+) 

HEV Power-optimized Battery Cell 

EV Energy-optimized Battery Cell 

Power-split hybrid vehicles from Toyota, Ford and Nissan, integrated motor assist 

hybrid vehicles from Honda and the GM 2-mode hybrid vehicles currently use nickelmetal hydride (NiMH) batteries. Lithium-ion (Li-ion) batteries offer the potential to 

approximately double both the energy and power density relative to current NiMH 

batteries, enabling much more electrical-energy-intensive automotive applications such 

as PHEVs and EVs. Li-ion batteries for high-volume automotive applications differ 

substantially from those used in consumer electronics applications with respect to 

cathode chemistry, construction and cell size. Li-ion battery designs currently under 

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Technologies Considered in the Agencies’ Analysis
 

development by CPI (LG-Chem) for the GM Volt PHEV and by AESC, GS-Yuasa and 

A123 Systems (respectively) for the upcoming Nissan, Mitsubishi and Chrysler EVs use 

large-format, layered-prismatic cells assembled into battery modules. The modules are 

then combined into battery packs. 

Cathodes for large-format, automotive Li-ion batteries are becoming increasingly 

focused on two chemistries – LiMn2O4-spinel (CPI, GS-Yuasa, AESC) and LiFePO4 

(A123 Systems). 

In addition to the purely hybrid technologies, which decrease the proportion of 

propulsion energy coming from the fuel by increasing the proportion of that energy 

coming from electricity, there are other steps that can be taken to improve the efficiency 

of auxiliary functions (e.g., power-assisted steering or air-conditioning) which also 

reduce CO2 emissions and fuel consumption. These steps, together with the hybrid 

technologies, are collectively referred to as “vehicle electrification” because they 

generally use electricity instead of engine power. In order to achieve consistency 

between the two modeling techniques, and to improve the number and range of 

technology offerings, the CAFE model was revised to include one additional mild hybrid 

technology. The high voltage or improved efficiency alternator (HVIA) technology, 

which was used in the MY 2011 CAFE final rule, is no longer represented as a separate 

technology and has instead been incorporated into this new mild hybrid technology, as 

discussed further below. 

3.4.2.3.2 Hybrid System Sizing and Cost Estimating Methodology 

NHTSA and EPA reviewed estimates of cost and effectiveness for hybrid and 

related electrical technologies and adjusted them as appropriate. Both agencies found the 

hybrid technology cost estimating methodology that Ricardo and NHTSA developed 

during the MY 2011 CAFE final rule to be reasonable and used it to estimate hybrid 

systems costs and account for variation in component sizing across both the hybrid types 

and vehicle subclasses. That method utilizes four pieces of data: (1) key component 

sizes for a midsize car by hybrid system type; (2) normalized costs for each key 

component; (3) component scaling factors that are applied to each vehicle class/subclass 

by hybrid system type; and (4) vehicle characteristics for the subclasses which are used as 

the basis for the scaling factors. During development of the methodology, NHTSA and 

Ricardo made several assumptions: 

1) Hybrid controls hardware varies with the level of functionality offered by the 

hybrid technology. Assumed hybrid controls complexity for a 12V micro 

hybrid (MHEV) and belt integrated starter generator (BISG) was 25 percent of 

a strong hybrid controls system and the complexity for a Crank Integrated 

Starter Generator (CISG) was 50 percent. These ratios were estimates based 

on the directional need for increased functionality as system complexity 

increases. 

2) Li-ion batteries for hybrid electric vehicles are currently entering production, 

including a 2010 MY Mercedes and Hyundai. One estimate from Anderman 

indicates that Li-ion market penetration will achieve 35 percent by 2015.45 

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However, as was discussed above, significant development effort is underway 

by a number of battery producers which could impact cost and overcome other 

technical concerns. Therefore it was assumed that mild hybrids (MHEV, 

BISG, CISG) and strong hybrids (PSHEV, 2MHEV, PHEV) will use either 

Li-ion or NiMH batteries, depending on cost considerations. However, plugin hybrids will use Li-ion batteries only. Battery usage is discussed further 

below. 

3) The plug-in hybrid battery pack was sized for a mid-sized car by assuming: 

the vehicle has a 20 mile all electric range and consumes an average of 300 

W-hr per mile; the battery pack can be discharged down to 30 percent depth of 

discharge;q and the capacity of a new battery pack is 20 percent greater than at 

end of life (i.e., range on a new battery pack is 24 miles). 

4) All hybrid systems included a DC/DC converter which was sized to 

accommodate vehicle electrical loads appropriate for increased vehicle 

electrification in the time frame considered. 

5) High voltage wiring scaled with hybrid vehicle functionality and could be 

represented as a fraction of strong hybrid wiring. These ratios were estimates 

based on the directional need for increased functionality as system complexity 

increases. 

6) All hybrid systems included a supplemental heater to provide vehicle heating 

when the engine is stopped; however, in this analysis, it is assumed that only 

half of the vehicles will adapt this technology, as discussed further below. 

Only the strong hybrids included electric air conditioning to enable engine 

stop/start when vehicle air conditioning was requested by the operator. 

Furthermore, NHTSA and Ricardo recognized that some strong hybrid systems 

replaced a conventional transmission with a hybrid-specific transmission, resulting in a 

cost offset (i.e., a cost credit) for the removal of a portion of the clutches and gear sets 

within the transmission. In the MY 2011 CAFE final rule, the transmission cost in Table 

3-16 below expressed hybrid transmission costs as a percentage of traditional automatic 

transmission cost, as described in the 2008 Martec Report, at $850 direct manufacturing 

costs (non-RPE/ICM). The method assumed that the mechanical aspect of a power-split 

transmission with a reduced number of gear sets and clutches resulted in a cost savings of 

50 percent ($425) over a conventional transmission with torque converter. For a 2-mode 

hybrid, the mechanical aspects of the transmission are similar in complexity to a 

conventional transmission, so no cost savings was appropriate. The plug-in hybrid 

assumed a highly simplified transmission for electric motor drive, thus 25 percent of the 

base vehicle transmission cost was applied (resulting in a $638 credit). 

The NHTSA MY 2011 CAFE final rule discusses in detail how the hybrid cost 

estimating methodology uses the information provided in the tables below to calculate 

costs for each of the strong hybrid systems used in this analysis. It also includes a stepby-step example for the midsize vehicle mild hybrid systems used in the MY 2011 CAFE 

q The GM Volt operates between 30% DOD and 85% DOD. So there is 55% useable DOD, but charge 

sustaining operation starts at 30% and cycles between 30 and 35% DOD. 

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Technologies Considered in the Agencies’ Analysis
 

final rule.46 As in that analysis, it is important to understand that the CISG technology 

replaces existing mild hybrid systems.r 

NHTSA and EPA in reviewing the above made the following revisions. First, 

NHTSA and EPA revalidated the component sizes that were estimated for a midsize car 

for each type of hybrid system as shown in Table 3-16. However, NHTSA and EPA 

added an additional component, front engine accessory drive (FEAD), because 

hybridization often involves revision to the FEAD design such that certain devices (belts, 

pulleys, idlers, etc.) as well as other engine components (alternator, A/C compressor, and 

starter) may no longer be needed and can thus be eliminated, or may be de-specified to 

lower cost alternatives. This is applicable to CISG and the strong hybrid technologies, 

and is intended to account for cost savings associated with items that changed or are no 

longer required as a result of these technology applications. 

Table 3-16. Component Sizes by Hybrid Type for a Midsize Car 

Component 

Hybrid Type 

MHEV 

BISG CISG PSHEV 2MHEV PHEV 

Primary Motor power, continuous (kW) 3 11 45 45 45 

Secondary Motor power, continuous (kW) na na 30 45 30 

Primary Inverter power, continuous (kW) 3 11 45 45 45 

Secondary Inverter power, continuous (kW) na na 30 45 30 

Controls complexity (relative to strong hybrid) 25% 50% 100% 100% 100% 

NiMH Battery Pack capacity (kW-hr)1 na 1 2 2 na 

Li-Ion Battery Pack capacity (kW-hr)1 na 1 2 2 15 

DC/DC Converter power (kW) 0.7 2 2 2 2 

High Voltage Wiring (relative to strong hybrid) na 50% 100% 100% 100% 

Supplemental heating2 50% 50% 50% 50% 50% 

Mechanical Transmission (relative to baseline vehicle) 100% 100% 50% 100% 25% 

Electric AC No No Yes Yes Yes 

Blended Brakes No Yes Yes Yes Yes 

FEAD Credit No Yes Yes Yes Yes 

Charger power, continuous (kW) na na na na 3 

1 - Assumes the use of either NiMH or Li-Ion, and not both. 

2 - Implemented through a reduction in component cost (50%) 

Second, the costs estimates of the key components were revised. The MY 2011 

CAFE final rule was developed at a time when economic conditions were significantly 

different than those that currently exist, a time when many of the commodity materials 

used in the hybrid systems were more expensive than today. These changes in economic 

conditions were one of the factors leading to some of the cost revisions EPA and NHTSA 

jointly discussed and made. Differences in estimates provided by confidential sources to 

either EPA or NHTSA also played a part in the revisions. In addition, the agencies 

applied the new ICM mark-up factors instead of the RPE that was used previously. An 

appropriate ICM factor (1.45 for most mild and strong hybrid technologies) replaces the 

r For the incremental CAFE model, before CISG is applied, the costs for MHEV and BISG are subtracted if 

they were previously applied. 

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previous RPE factor (1.5), as discussed in section 3.3.2.2.2. Specifically, the primary and 

secondary inverter cost per kilowatt were revised downward from $10 to $7, the controls 

cost was revised upward from $100 to $115, the DC/DC converter costs were revised 

from $100 to $88, the blended brake system that was revised from $400 to $310, and 

finally the fully learned, high volume production, cost per kilowatt hour (kW-hr) for 

NiMH batteries was revised from $350 to $320 to match the updated costs for Li-ion 

batteries discussed below.s 

The cost for Li-ion batteries was also revised. As previously stated, Li-ion 

batteries are being implemented in series production in model year 2010. Battery 

technology is changing rapidly in the marketplace today, as discussed above, and is 

expected to continue along this path throughout the rulemaking period. OEMs are now 

forming relationships with battery manufacturers in an effort to research and develop not 

only new and improved battery technology, but also more efficient manufacturing 

processes capable of supporting high volume production. Accordingly, as shown in 

Table 3-17, the $600 per kW-hr used in the MY 2011 CAFE final rule was revised 

downward to $320 per kW-hr. The revision downward from $600/kW-hr in the MY 

2011 CAFE final rule to $320/kW-hr in this analysis was done based a study by Deutsche 

Bank that estimated Li-ion battery costs at 300-400 €/kW-hr.47 This was converted to 

$500/kW-hr then learned twice using volume-based learning to arrive at the $320/kW-hr 

applicable in the 2015 timeframe. 

Li-ion batteries were originally restricted to plug-in hybrids only. Recent vehicle 

introductions confirm either battery technology can be used in any mild or strong HEV 

application. However, manufacturers are likely to consider cost highly in their selection 

of battery technology. If Li-ion battery prices remain high, NiMH would be the default 

battery technology for all hybrid electric vehicles. For plug-in hybrids Li-ion would 

continue to be required because plug-in hybrids demand higher energy density than 

NiMH can provide. Neither the CAFE nor OMEGA model predicts a high penetration of 

plug-in technology in achieving the standards. 

Finally, the agencies assessed the cost savings associated with the FEAD credit 

discussed above. This cost was not previously represented in the hybrid cost model. As 

shown in Table 3-17 below, a $100 credit is shown which offsets directly the costs of the 

other components specified. This is the best approximation of the value of these items, 

based on NHTSA and EPA engineering assessment. 

Estimates of each key component are shown in Table 3-17 below along with the 

sources of those estimates. The cost basis estimates assume fully learned, high-volume 

(greater than 1.2 million units per annum) production, and the costs shown are direct 

manufacturing costs that are not RPE or ICM adjusted. This table does not show a cost 

applicable to the belt integrated starter generator system (BISG) since it is a fixed cost 

that, like the automatic transmission pump cost, is not scaled by subclass as described 

later. 

s In the MY 2011 final rule NHTSA had misstated this $350 per kW-hr cost as $50 per kW-hr due to a 

typographical error. 

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Table 3-17. Component Cost Basis at High Volumes and Data Sources stated in 2015 (2007 dollars) 

COMPONENT COST BASIS DATA SOURCE 

Primary Motor ($/kW) $15 Martec 2008 Secondary Motor ($/kW) $15 

Primary Inverter ($/kW) $7 

Secondary Inverter ($/kW) $7 Confidential Business Information 

Controls $115 

NiMH Battery Pack ($/kW-hr.) $320 2011 CAFE FRM 

(with revision) 

Li-ion Battery Pack ($/kW-hr.) $320 Deutsche Bank 2008 

DC/DC Converter (Size: 2kW) $88 Confidential Business Information 

High Voltage Wiring $200 Martec 2008 Supplemental Heating $42 

Mechanical Transmission $850 Martec 2008 (to 4-spd auto) 

Electric Air Conditioning $450 

Blended Brakes $310 Confidential Business Information 

Charger $100 

Automatic Transmission Pump $75 Martec 2008 

FEAD Credit $(100) Confidential Business Information 

Third, NHTSA and EPA also revised component size/scaling assumptions for 

some vehicles (i.e., large trucks). NHTSA and EPA recognized that some manufacturers 

may choose not to use supplemental cabin heating, opting instead to continue engine 

operation in the event heat demand occurs; therefore supplemental heating is specified for 

only half of the vehicles. Table 3-17 above indicates the 50 percent application rate 

implemented in the hybrid cost estimating methodology reducing the component cost 

from $84 to $42. 

EPA and NHTSA also reviewed the choice of a 3 kW DC/DC converter as a 

component size input for a midsize vehicle, which represented a 250 amp current 

capability. In retrospect this is a high specification for a midsize vehicle, so the agencies 

revised the estimate to a 2 kW DC/DC converter, as shown in Table 3-17 above, which 

would represent a more reasonable 150 amp current capacity. 

The scaling factor used for the primary and secondary motors and invertors on the 

large truck and SUV vehicles was revised. As in the MY 2011 CAFE final rule, a linear 

extrapolation was used from the midsize vehicle and extended it out to the largest of 

vehicles, the large truck class. This resulted in projected component sizes that are larger 

than those used on a commercially realized truck in this vehicle class, the Chevrolet 

Tahoe two-mode HEV. Accordingly, the scaling factors have been revised for this class 

(and the agencies have verified scaling factors for the other classes). This more closely 

approximates the motor and inverter sizes specified in the Tahoe application. For future 

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analysis, the agencies are considering whether it may be more accurate to use one set of 

scaling for passenger cars and another different set for light trucks. 

Another revision involves the addition of a stand-alone higher voltage Start-Stop/ 

BISG mild hybrid system. NHTSA and EPA determined that by applying a cost increase 

to the MHEV technology to allow for a voltage increase (lead acid batteries) and 

efficiency improvements to the alternator, the system would then approximate the higher 

voltage Start-Stop/BISG applied by EPA. Based on confidential sources, the estimates 

provided were first converted to 2007 dollars and then reverse-learned through two 

cycles, since volume learning is applicable, to arrive at a non-RPE/ICM incremental 

compliance cost of $229. This cost is applicable to all classes that use higher voltage 

Start-Stop/BISG, and is not scaled by any vehicle attribute. 

Component scaling factors for each type of hybrid system are as shown in Table 

3-18 below. 

Table 3-18. Component Scaling Factors applied to Vehicle Class for each Hybrid System 

Component 

Hybrid Type 

MHEV CISG PSHEV 2MHEV PHEV 

Primary Motor Engine 

displacement 

Curb 

weight Curb weight1 Engine power 

Secondary Motor na na 

Engine 

displacement Curb weight2 

Primary Inverter Primary motor power 

Secondary Inverter na na Secondary motor power 

Controls Complexity 

NiMH Battery Pack na Curb weight na 

Li-Ion Battery Pack na Curb weight 

DC/DC Converter Curb weight3 

High Voltage Wiring na Vehicle footprint 

Supplemental heating Vehicle footprint 

Mechanical Transmission Same for all vehicle classes 

Electric AC na na Vehicle footprint 

Blended Brakes na Same for all vehicle classes 

Charger na na na na 

Same for all 

vehicle classes 

(1) For all vehicle classes except for performance classes which use Engine Torque 

(2) Curb weight used as surrogate for vehicle road load 

(3) Curb weight used as surrogate for vehicle electrical load 

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Regarding the market data file from the MY 2011 CAFE final rule, NHTSA and 

EPA did not make any revisions to the average vehicle characteristics for each vehicle 

subclass as shown in Table 3-19, which defines the average vehicle characteristics for 

each vehicle subclass. These characteristics were used as the basis of the scaling factors 

in the CAFE and OMEGA models. EPA used these vehicle specifications solely for the 

purpose of scaling hybrid vehicle attributes in order to remain consistent with the 

NHTSA methodology. 

Table 3-19. Key Vehicle Characteristics For Each Vehicle Subclass for CAFE Model 

Vehicle Subclass 

Curb 

Weight 

(lbs) 

Footprint 

(ft2) 

Engine 

Disp. (L) 

Engine 

Power 

(hp) 

Torque 

(ft-lb) 

Subcompact Car 2795 41 1.9 134 133 

Compact Car 3359 44 2.2 166 167 

Midsize Car 3725 47 2.9 205 206 

Large Car 4110 50 3.4 258 248 

Performance Subcompact Car 3054 40 2.7 260 260 

Performance Compact Car 3516 44 3.0 269 260 

Performance Midsize Car 3822 47 3.9 337 318 

Performance Large Car 4189 51 4.8 394 388 

Minivan 4090 50 3.3 247 242 

Small Truck 3413 45 2.6 178 185 

Medium Truck 4260 50 3.6 250 256 

Large Truck 5366 63 5.0 323 352 

3.4.2.3.3 Electrical Power Steering (EPS) 

Electric power steering (EPS) provides a potential reduction in CO2 emissions and 

fuel consumption over hydraulic power steering because of reduced overall accessory 

loads. This eliminates the parasitic losses associated with belt-driven power steering 

pumps which consistently draw load from the engine to pump hydraulic fluid through the 

steering actuation systems even when the wheels are not being turned. EPS is an enabler 

for all vehicle hybridization technologies since it provides power steering when the 

engine is off. EPS may be implemented on most vehicles with a standard 12V system. 

Some heavier vehicles may require a higher voltage system which may add cost and 

complexity. 

In the MY 2011 CAFE final rule, NHTSA estimated a 1 to 2 percent effectiveness 

based on the 2002 NAS report, a Sierra Research report, and confidential manufacturer 

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data. NHTSA and EPA reviewed these effectiveness estimates and found them to be 

accurate, thus they have been retained for this final rule. 

Regarding costs, in the MY 2011 CAFE final rule, NHTSA estimated EPS at 

$105-120 at 1.5 RPE mark-up factor. NHTSA and EPA adjusted the EPS cost for the 

current rulemaking based on a review of the specification of the system. Adjustments 

were made to include potentially higher voltage or heavier duty system operation, such as 

would be required on some hybrid trucks. Accordingly, higher costs were estimated for 

systems with higher capability. After accounting for the differences in system capability 

and applying the ICM markup of low complexity technology of 1.11, the estimated costs 

for this rulemaking are $106 for a MY 2012 vehicle (2007$). With time-based learning, 

this cost would be $94 for a MY 2016 vehicle (2007$). As EPS systems are in 

widespread usage today, time-based learning is deemed applicable. 

3.4.2.3.4 Improved Accessories (IACC) 

The accessories on an engine, including the alternator, coolant and oil pumps are 

traditionally mechanically-driven. A reduction in CO2 emissions and fuel consumption 

can be realized by driving them electrically, and only when needed (“on-demand”). 

Electric water pumps and electric fans can provide better control of engine 

cooling. For example, coolant flow from an electric water pump can be reduced and the 

radiator fan can be shut off during engine warm-up or cold ambient temperature 

conditions which will reduce warm-up time, reduce warm-up fuel enrichment, and reduce 

parasitic losses. 

Indirect benefit may be obtained by reducing the flow from the water pump 

electrically during the engine warm-up period, allowing the engine to heat more rapidly 

and thereby reducing the fuel enrichment needed during cold starting of the engine. 

Further benefit may be obtained when electrification is combined with an improved, 

higher efficiency engine alternator. Intelligent cooling can more easily be applied to 

vehicles that do not typically carry heavy payloads, so larger vehicles with towing 

capacity present a challenge, as these vehicles have high cooling fan loads.t 

The agencies considered whether to include electric oil pump technology for the 

rulemaking. Because it is necessary to operate the oil pump any time the engine is 

running, electric oil pump technology has insignificant effect on efficiency. Therefore, 

the agencies decided to not include electric oil pump technology for this rulemaking. 

NHTSA and EPA jointly reviewed the estimates of 1 to 2 percent effectiveness 

estimates used in the MY 2011 CAFE final rule and found them to be accurate for 

Improved Electrical Accessories. For its analysis, NHTSA simply adjusted the MY 2011 

CAFE final rule costs to reflect the new ICM markup of low complexity value of 1.11, 

resulting in a cost estimate for this rulemaking of $128 at MY 2012 (2007$). Improved 

t In the CAFE model, IACC refers solely to improved engine cooling. However, EPA has included a high 

efficiency alternator in this category, as well as improvements to the cooling system. 

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accessory systems are in production currently and thus time-based learning is applied. 

For its analysis, EPA considered both an accessory upgrade to a 12 Volt system and a 

combined accessory upgrade with conversion to a 42 Volt system. The accessory 

upgrade itself is estimated to cost $86 in MY 2012 and $76 in MY 2016 (with time-based 

learning; both in 2007$). The conversion to a 42 Volt electrical system is estimated to 

cost $97 for a MY 2012 vehicle and $86 for a MY 2016 vehicle (again with time-based 

learning; both in 2007$). 

3.4.2.3.5 12V Micro Hybrid (MHEV) 

12V Micro-Hybrid (MHEV) systems are the most basic of hybrid systems and 

offer only the ability to turn the engine off when the vehicle is stopped or potentially 

during deceleration (i.e., idle stop). Their low cost and adaptability to existing 

powertrains and platforms can make them attractive for some applications. The 

conventional belt-driven alternator is replaced with a belt-driven, enhanced power 

starter-alternator and a redesigned front-end accessory drive system. A conventional 

12V gear-reduction starter is retained to ensure reliable cold-weather starting. Also, 

during idle-stop, some functions such as power steering and automatic transmission 

hydraulic pressure are lost, so electric power steering and an auxiliary transmission 

pump are needed. 

Although EPA does not use the 12 V version of this stop-start technology, 

NHTSA and EPA jointly reviewed the assumptions. The effectiveness estimates for this 

technology ranged from 2.0 to 4.0 percent, depending on whether the vehicle is equipped 

with a 4-, 6- or 8-cylinder engine, with the 4-cylinder engine having the lowest range and 

the 8-cylinder having the highest. The estimates reflect the limited capability of 12 volt 

systems; sources citing higher estimates typically involve higher voltage systems that 

have increased capability. 

For this final rule, the system specifications assumed in the MY 2011 CAFE final 

rule were applied (i.e., use of a 3 kW motor and a DC/DC converter) and the hybrid 

technology cost method produced costs like those in MY 2011 final rule; however, the 

use of new ICM markups resulted in costs ranging from $288 for Subcompact subclass to 

$410 for the Large Performance subclass. This technology is not applied to the Large 

Truck and SUV subclass due to the heavy duty utility requirements of these vehicles. 

Time-based learning is considered applicable, and a schematic of the MHEV system is 

shown in Figure 3-2. 

Figure 3-2 Schematic of MHEV type System [Husted, 2003] 

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Engine 

Motor/ 

Generator 

Trans. 

Controller 

Inverter 

Final Drive 

Wheel 

+ -

Battery 

3.4.2.3.6 Higher Voltage Stop-Start/Belt Integrated Starter Generator (BISG) 

Higher Voltage Stop-Start and Belt Mounted Integrated Starter Generator 

(BISG) systems are similar to a micro-hybrid system, offering idle-stop functionality, 

except that they utilize larger electric machine and a higher capacity battery, typically 

42 volts or above, thus enabling a limited level of regenerative braking which is 

generally not applicable to 12 volt systems. The larger electric machine and battery 

also enables a limited degree of power assist, which MHEV cannot provide. However, 

because of the limited torque capacity of the belt-driven design, these systems have a 

smaller electric machine, and thus less capability than crank-integrated or stronger 

hybrid systems. These systems replace the conventional alternator with a belt-driven 

starter/alternator and may add electric power steering and an auxiliary automatic 

transmission pump. The limited electrical requirements of these systems allow the use 

of lead-acid batteries or supercapacitors for energy storage. 

The effectiveness estimates used for these technologies range from 3.0 to 7.5 

percent depending on vehicle subclass. The CAFE model, which applies this 

effectiveness incrementally to the prior 12 Volt technology, uses estimates of 4 to 6 

percent. The estimates are based on those found in the 2002 NAS and 2004 NESCCAF 

reports, and confidential manufacturer data. 

EPA estimates an incremental compliance cost range of $549 (small car) to $682 

(large truck) for a MY 2012 vehicle and including a medium complexity ICM of 1.25 

(2007$). With volume-based learning applied, these become $351 (small car) and $437 

(large truck) for a MY 2016 vehicle (2007$). The cost estimate in the CAFE model is 

incremental to the 12 Volt micro hybrid systems as noted above, and therefore is adjusted 

upwards to $286 to reflect the additional battery capacity, wiring upgrades, and a larger 

optimized electric machine only. The $286 reflects volume-based learning factors and 

the ICM medium-complexity markup of 1.25. 

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3.4.2.3.7 Integrated Motor Assist (IMA)/Crank Integrated Starter Generator (CISG) 

IMA is a system developed and marketed by Honda48 and is similar to CISG. 

They both utilize a thin axial electric motor bolted to the engine’s crankshaft and 

connected to the transmission through a torque converter or clutch. The axial motor is 

motor/generator that typically operates above 100 volts (but lower than the stronger 

hybrid systems discussed below, which typically operate at around 300 volts) and can 

provide sufficient torque for launch as well as generate sufficient current to provide 

significant levels of brake energy recovery. The motor/generator also acts as the starter 

for the engine and can replace a typical accessory-driven alternator. Current IMA/CISG 

systems typically do not launch the vehicle on electric power alone, although some 

commercially available systems can cruise on electric power and dual-clutch 

IMA/CISG systems capable of all-electric drive are under development. IMA and 

CISG could be applied to all classes of vehicles. A schematic of the Honda IMA 

system is shown in Figure 3-3. 

Figure 3-3 Schematic of Honda IMA System [Husted, 2003] 

Final Drive 

Engine Motor/ 

Generator 

Trans. 

Controller 

Inverter 

+ -

Battery 

Wheel 

NHTSA did not have an equivalent technology to CISG in the MY 2011 CAFE 

final rule (the ISG technology used in the MY 2011 final rule was envisioned to be less 

capable than the CISG technology defined here). For the CISG technology, NHTSA 

estimated a net effectiveness range of 16 to 20 percent, relative to the baseline vehicle 

and across all vehicle subclasses in this analysis. The CAFE model therefore applies an 

incremental effectiveness of approximately 8.6 to 8.9 percent relative to the BISG 

technology and dependent on vehicle subclass, except for large truck and SUV in which 

case BISG does not apply. Note that the net effectiveness assumptions used in this rule 

do not include engine downsizing, or any other effectiveness gains from engine, 

transmission, or vehicle technologies added to the vehicle by the modeling process. 

EPA relied on a combination of certification data (comparing vehicles available 

with and without a hybrid system and backing out other components where appropriate) 

and manufacturer-supplied information to determine that the effectiveness of these 

systems in terms of CO2 reduction is 30 percent for small cars, 25 percent for large cars, 

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and 20 percent for minivans and small trucks similar to the range estimated by NHTSA 

for the respective vehicle classes. The effectiveness for small cars assumes engine 

downsizing to maintain approximately equivalent performance. The large car, minivan, 

and small truck effectiveness values assume less engine downsizing in order to improve 

vehicle performance and/or maintain towing and hauling performance. 

EPA has estimated these costs, relative to a base engine/transmission, as $4,459 

(small car) to $6,924 (large truck) for a MY 2012 vehicle and including a high 

complexity ICM of 1.45 (2007$). With volume-based learning, these costs become 

$2,854 and $4,431, respectively, for a MY 2016 vehicle (2007$). NHTSA has estimated 

the MY 2012 compliance cost, relative to a BISG baseline, to range from $2,791 for 

Subcompact vehicles to $3,611 for Midsize Light Truck and Minivan vehicles. These 

include a high complexity ICM markup factor of 1.45 for this technology. As this 

technology is still in limited production use, volume-based learning is applied, resulting 

in lower costs in later years. 

3.4.2.3.8 Power Split Hybrid 

The Power Split hybrid (PSHEV) has the ability to move the vehicle on electric 

power only. It replaces the vehicle’s transmission with a single planetary gear and a 

motor/generator. A second, more powerful motor/generator is directly connected to the 

vehicle’s final drive. The planetary gear splits engine power between the first 

motor/generator and the final drive. The first motor/generator uses power from the 

engine to either charge the battery or supply power to the wheels. The speed of the first 

motor/generator determines the relative speed of the engine to the wheels. In this way, 

the planetary gear allows the engine to operate independently of vehicle speed, much like 

a CVT. The Toyota Prius and the Ford Hybrid Escape are two examples of power split 

hybrid vehicles. 

In addition to providing the functions of idle engine stop, subsequent restart and 

regenerative braking, this hybrid system allows for pure EV operation. The power split 

system provides very good fuel consumption in city driving. During highway cycles, the 

hybrid functions of regenerative braking, engine start/stop and optimal engine operation 

cannot be applied as often as in city driving, and so the effectiveness in fuel consumption 

is less. Additionally, it is less efficient at highway speeds due to the fact that the first 

motor/generator must be spinning at a relatively high speed and therefore incurs losses. 

Newer designs incorporate a gear-reduction motor to provide improved high speed 

efficiency and improved matching of motor torque to engine torque. 

The Power Split hybrid also reduces the cost of the transmission, replacing a 

conventional multi-speed unit with a single planetary gear. The electric components are 

bigger than those in mild hybrid and IMA/CISG configurations so the costs are 

correspondingly higher. 

EPA and NHTSA used a combination of manufacturer-supplied information and a 

comparison of vehicles available with and without a hybrid system from EPA’s fuel 

economy test data to determine that the effectiveness is 19 to 36 percent for the classes to 

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which it is applied. The estimate would depend on whether engine downsizing is also 

assumed. As an example, See Table 3-20 and Table 3-21, which assume some level of 

engine downsizing. In the CAFE incremental model, the range of effectiveness used was 

23 to 33 percent as engine downsizing is not assumed (and accounted for elsewhere). 

Table 3-20 Large Car Power Split Certification Data 

Tailpipe CO2 

City Hwy 55/45 comb. 

Nissan Altima 

3.5L CVT 444 306 386 

HEV 2.5L PS 317 254 286 

Net % difference -26% 

Toyota Camry 

3.0L 5-auto 404 286 355 

HEV 2.4L PS 222 234 228 

Net % difference -36% 

Lexus GS 

4.3L 6-auto 493 355 423 

HEV 3.5L PS 355 317 341 

Net % difference -19% 

Table 3-21 Small Truck Power Split Certification Data 

Tailpipe CO2 

City Hwy 55/45 comb. 

Ford Escape 4X4 

3.0L 4-auto 467 386 423 

HEV 2.3L PS 277 306 286 

Net % difference -32% 

Ford Escape 4X2 

3.0L 4-auto 444 370 404 

HEV 2.3L PS 247 286 261 

Net % difference -35% 

Toyota Highlander 4X4 

3.3L 5-auto 493 370 423 

HEV 3.3L PS 286 329 306 

Net % difference -28% 

Using the hybrid cost estimating methodology with updated component costs, 

EPA established a compliance cost range of $4,481 (small cars) to $8,145 (large trucks) 

relative to the base engine and transmission for a MY 2012 vehicle (all in 2007$). With 

time-based learning, these costs become $3,967 and $7,210, respectively, for a MY 2016 

vehicle (2007$). Using the same methodology, NHTSA established overall PSHEV 

system costs with electric power steering (EPS) and improved accessories (IACC) 

ranging from $5,509 to $11,534 for MY 2012, which leads to incremental costs in the 

CAFE model ranging from $1,600 to $6,723 depending on the subclass (all in 2007$). 

The estimates were determined with a 1.45 ICM for a high complexity technology. 

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Based on Toyota Prius and Ford Escape/Mariner market sales, PSHEV technology with 

NiMH battery is considered mature from the perspective of cost learning, and time-based 

learning is applicable. However, the Li-ion battery technology is undergoing rapid 

development and it is expected that some manufacturers may choose to use Li-ion 

batteries on PSHEV applications during this rulemaking period. Because this 

combination of technology is new, volume-based learning would be appropriate for such 

applications. For cost analysis in the NPRM and final rule, volume-based learning is 

used for PSHEV which assumes use of Li-ion batteries. This volume-based learning for 

Li-ion is supported by the 2008 Deutsche Bank study. 

3.4.2.3.9 2-Mode Hybrid 

The 2-Mode Hybrid (2MHEV) uses an adaptation of a conventional stepped-ratio 

automatic transmission which replaces some of the transmission clutches with two 

electric motor/generators, allowing the transmission to act like a CVT. The 

motor/generators control the ratio of engine speed to vehicle speed. The clutches allow 

the motors to be bypassed improving the transmission’s torque capacity and the 

efficiency for improved fuel economy at highway speeds. This type of system is used in 

the Chevrolet Tahoe Hybrid. 

In addition to providing the hybrid functions of engine stop and subsequent restart 

and regenerative braking, the 2MHEV allows for pure EV operation. The two 

motor/generators allow the engine to be run in efficient operating zones. The 2-mode 

system provides significantly reduced CO2 emissions and fuel consumption. The primary 

motor/generator is comparable in size to that in the PSHEV system, but the secondary 

motor/generator is larger. The 2-mode system cost is greater than that for the power split 

system due to the additional transmission complexity and secondary motor sizing. 

The battery pack for 2MHEV is assumed to be 300V NiMH as is used in current 

2MHEV systems today, however GM recently announced an agreement with Hitachi to 

provide Li-ion battery packs for 2012 and later 2MHEVs. 

For this rule the CAFE model considered a range of 23 to 33 percent with a 

midpoint of 28 percent, assuming no engine downsizing to preserve the utility nature of 

medium and large trucks (e.g., maintaining full towing capability even in situations with 

low battery charge) and EPA estimates CO2 emissions reduction effectiveness to be 25 

percent for large trucks (LDT3 and LDT4 categories) based on vehicle certification data. 

EPA estimates an effectiveness of 40 percent for smaller vehicles. 

EPA and NHTSA estimated MY 2012 costs using the updated component costs 

and scaling factors in the hybrid cost estimating methodology discussed above. EPA 

estimated the compliance cost to range from $5,469 for large car to $7,236 for large 

truck/SUV relative to the base engine/transmission (all in 2007$). NHTSA determined 

incremental cost estimates ranging from $3,521 to $5,779 for light-duty truck 

applications in the CAFE model. These include the 1.45 ICM markup value for high 

complexity technology. Given that the CAFE and OMEGA models classify vehicles 

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differently, these numbers varied slightly in the modeling to reflect the different 

classification methods of the OMEGA and CAFE models, as described in the individual 

NHTSA and EPA FRIAs. Volume-based learning is applicable to this technology. 

3.4.2.3.10 Plug-In Hybrid 

Plug-In Hybrid Electric Vehicles (PHEVs) are very similar to Hybrid Electric 

Vehicles, but with three significant functional differences. The first is the addition of a 

means to charge the battery pack from an outside source of electricity (e.g., the electric 

grid). Second, a PHEV would have a larger battery pack with more energy storage, and a 

greater capability to be discharged. Finally, a PHEV would have a control system that 

allows the battery pack to be significantly depleted during normal operation. 

Table 3-22 below, illustrates how PHEVs compare functionally to both hybrid 

electric vehicles (HEV) and electric vehicles (EV). These characteristics can change 

significantly within each class/subclass, so this is simply meant as an illustration of the 

general characteristics. In reality, the design options are so varied that all these vehicles 

exist on a continuum with HEVs on one end and EVs on the other. 

Table 3-22 Conventional, HEVs, PHEVs, and EVs Compared 

Increasing Electrification 

Attribute Conventional HEV PHEV EV 

Drive Power Engine Blended 

Engine/Electric 

Blended 

Engine/Electric Electric 

Engine Size Full Size Full Size or Smaller Smaller or Much 

Smaller No Engine 

Electric Range None None to Very Short Short to Medium Medium to Long 

Battery Charging None On-Board Grid/On-Board Grid Only 

Deriving some of their propulsion energy from the electric grid provides several 

advantages for PHEVs. PHEVs offer a significant opportunity to replace petroleum used 

for transportation energy with domestically-produced electricity. The reduction in 

petroleum usage does, of course, depend on the amount of electric drive the vehicle is 

capable of under its duty cycle. PHEVs also provide electric utilities the possibility to 

increase electric generation during “off-peak” periods overnight when there is excess 

generation capacity and electricity prices are lower. Utilities like to increase this “base 

load” because it increases overall system efficiency and lowers average costs. Utilities 

are also investigating the use of PHEV and EV batteries as a source of grid storage 

capacity to provide ancillary services for grid stabilization purposes. PHEVs can lower 

localized emissions of criteria pollutants and air toxics especially in urban areas by 

operating on electric power. The emissions from the power generation occur outside the 

urban area at the power generation plant which provides health benefits for residents of 

the more densely populated urban areas by moving emissions of ozone precursors out of 

the urban air shed. Unlike most other alternative fuel technologies, PHEVs can initially 

use an existing infrastructure for refueling (charging and liquid refueling) so investments 

in infrastructure may be reduced. 

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In analyzing the impacts of grid-connected vehicles like PHEVs and EVs, the 

emissions from the electrical generation can be accounted for if a full upstream and 

downstream analysis is desired. While this issue is being studied on an on-going basis, 

upstream CO2 emissions are not unique to grid-connected technologies and so are not 

included in this analysis. 

PHEVs will be considerably more costly than conventional vehicles and some 

other advanced technologies. To take advantage of their capability, consumers would 

have to be willing to charge the vehicles nightly, and would need access to electric power 

where they park their vehicles. For many urban dwellers who may park on the street, or 

in private or public lots or garages, charging may not be practical. Charging may be 

possible at an owner’s place of work, but that would increase grid loading during peak 

hours which would eliminate some of the benefits to utilities of off-peak charging versus 

on-peak. Oil savings will still be the same in this case assuming the vehicle can be 

charged fully. 

The effectiveness potential of PHEVs depends on many factors, the most 

important being the energy storage capacity designed into the battery pack. To estimate 

the fuel consumption and tailpipe CO2 reduction potential of PHEVs, EPA has developed 

an in-house vehicle energy model (PEREGRIN) to estimate the fuel consumption/CO2 

emissions reductions of PHEVs. This model is based on the PERE (Physical Emission 

Rate Estimator) physics-based model used as a fuel consumption input for EPA’s 

MOVES mobile source emissions model. 

The PHEV small car, large car, minivan and small trucks were modeled using 

parameters from a midsize car similar to today’s hybrids and scaled to each vehicle’s 

weight. The large truck PHEV was modeled separately assuming no engine downsizing. 

PHEVs can have a wide variation in the All Electric Range (AER) that they offer. Some 

PHEVs are of the “blended” type where the engine is on during most of the vehicle 

operation, but the proportion of electric energy that is used to propel the vehicle is 

significantly higher than that used in a PSHEV or 2MHEV. Each PHEV was modeled 

with enough battery capacity for a 20-mile-equivalent AER and a power requirement to 

provide similar performance to a hybrid vehicle. 20 miles was selected because it offers 

a good compromise for vehicle performance, weight, battery packaging and cost. Given 

expected near-term battery capability, a 20 mile range represents the likely capability that 

will be seen in PHEVs in the near-to-mid term. 

To calculate the total energy use of a PHEV, the PHEV can be thought of as 

operating in two distinct modes, electric (EV) mode, and hybrid (HEV) mode. At the 

tailpipe, the CO2 emissions during EV operation are zero. The EV mode fuel economy 

can then be combined with the HEV mode fuel economy using the Utility Factor 

calculation in SAE J1711 to determine a total MPG value for the vehicle. (See Table 

3-23) 

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Table 3-23 Sample Calculation of PHEV Gasoline-Equivalent CO2 Reduction 

Midsize Car Large Truck 

EV energy comb (0.55 city / 0.45 hwy) 0.252 kwh/mi 0.429 kwh/mi 

EV range (from PEREGRIN) 20 miles 20 miles 

SAE J1711 utility factor 0.30 0.30 

HEV mode comb FE (0.55 city / 0.45 hwy) 49.1 mpg 25.6 mpg 

Total UF-adjusted FE (UF*FCEV + (1-UF)*FCHEV) 70.1 mpg 36.6 mpg 

Baseline FE 29.3 mpg 19.2 mpg 

Percent FE gain 139% 90% 

Percent CO2 reduction -58% -47% 

Calculating a total fuel consumption and tailpipe CO2 reduction based on model 

outputs and the Utility Factor calculations results in a 58 percent reduction for small cars, 

large cars, minivans, and small trucks. For large trucks, the result is a 47 percent 

reduction. The lower improvement is due to less engine downsizing in the large truck 

class. The CAFE model does not apply this technology to medium and large trucks and 

SUVs. 

Using the hybrid cost estimating model and updated component costs, EPA and 

NHTSA determined MY 2012 incremental cost estimates for the CAFE model ranging 

from $11,527 for a subcompact car to $19,265 for a midsize performance car. This 

includes the 1.64 ICM markup value for very high complexity technology. In the 

OMEGA model, and relative to a IMA/ISA/BSG, these values range from $6,922 for 

small car to $12,467 for a large truck, and relative to power-split hybrid, these values 

range from $5,423 for small car to $9,643 for large truck (all values for MY 2016 in 

2007$). Volume-based learning is applied to plug-in hybrid costs since this is a newly 

emerging technology. 

3.4.2.4 Vehicle Technologies 

3.4.2.4.1 Mass Reduction 

Reducing a vehicle’s mass, or down-weighting the vehicle, decreases fuel 

consumption by reducing the energy demand needed to overcome forces resisting motion, 

and rolling resistance. Manufacturers employ a systematic approach to mass reduction, 

where the net mass reduction is the addition of a direct component or system mass 

reduction plus the additional mass reduction taken from indirect ancillary systems and 

components, as a result of full vehicle optimization, effectively compounding or 

obtaining a secondary mass reduction from a primary mass reduction. For example, use 

of a smaller, lighter engine with lower torque-output subsequently allows the use of a 

smaller, lighter-weight transmission and drive line components. Likewise, the 

compounded weight reductions of the body, engine and drivetrain reduce stresses on the 

suspension components, steering components, wheels, tires and brakes, allowing further 

reductions in the mass of these subsystems. The reductions in unsprung masses such as 

brakes, control arms, wheels and tires further reduce stresses in the suspension mounting 

points. This produces a compounding effect of mass reductions. 

Estimates of the synergistic effects of mass reduction and the compounding effect 

that occurs along with it can vary significantly from one report to another. For example, 

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in discussing its estimate, an Auto-Steel Partnership report states that “These secondary 

mass changes can be considerable—estimated at an additional 0.7 to 1.8 times the initial 

mass change.”49 This means for each one pound reduction in a primary component, up to 

1.8 pounds can be reduced from other structures in the vehicle (i.e., a 180 percent factor). 

The report also discusses that a primary variable in the realized secondary weight 

reduction is whether or not the powertrain components can be included in the mass 

reduction effort, with the lower end estimates being applicable when powertrain elements 

are unavailable for mass reduction. However, another report by the Aluminum 

Association, which primarily focuses on the use of aluminum as an alternative material 

for steel, estimated a factor of 64 percent for secondary mass reduction even though some 

powertrain elements were considered in the analysis.50 That report also notes that typical 

values for this factor vary from 50 to 100 percent. Although there is a wide variation in 

stated estimates, synergistic mass reductions do exist, and the effects result in tangible 

mass reductions. Mass reductions in a single vehicle component, for example a door side 

impact/intrusion system, may actually result in a significantly higher weight savings in 

the total vehicle, depending on how well the manufacturer integrates the modification 

into the overall vehicle design. Accordingly, care must be taken when reviewing reports 

on weight reduction methods and practices to ascertain if compounding effects have been 

considered or not. 

Mass reduction is broadly applicable across all vehicle subsystems including the 

engine, exhaust system, transmission, chassis, suspension, brakes, body, closure panels, 

glazing, seats and other interior components, engine cooling systems and HVAC systems. 

It is estimated that up to 1.25 kilograms of secondary weight savings can be achieved for 

every kilogram of weight saved on a vehicle when all subsystems are redesigned to take 

51,52 into account the initial primary weight savings.

Mass reduction can be accomplished by proven methods such as: 

• Smart Design: Computer aided engineering (CAE) tools can be used to better 

optimize load paths within structures by reducing stresses and bending moments 

applied to structures (Figure 3-4 through Figure 3-6). This allows better 

optimization of the sectional thicknesses of structural components to reduce mass 

while maintaining or improving the function of the component. Smart designs 

also integrate separate parts in a manner that reduces mass by combining 

functions or the reduced use of separate fasteners. In addition, some “body on 

frame” vehicles are redesigned with a lighter “unibody” construction. 

• Material Substitution: Substitution of lower density and/or higher strength 

materials into a design in a manner that preserves or improves the function of the 

component. This includes substitution of high-strength steels, aluminum, 

magnesium or composite materials for components currently fabricated from mild 

steel. 

• Reduced Powertrain Requirements: Reducing vehicle weight sufficiently allows 

for the use of a smaller, lighter and more efficient engine while maintaining or 

increasing performance. Approximately half of the reduction is due to these 

reduced powertrain output requirements from reduced engine power output and/or 

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displacement, changes to transmission and final drive gear ratios. The subsequent 

reduced rotating mass (e.g., transmission, driveshafts/halfshafts, wheels and tires) 

via weight and/or size reduction of components are made possible by reduced 

torque output requirements. 

Using smart design, material substitution and space efficient designs have already 

been integrated into vehicles as new platforms cycle into production although net vehicle 

weight and performance has continued to increase (Figure 3-4). 

Figure 3-4: Example of CAE optimization of the design and position of the roof rails and front seat 

cross-member for the “Future Generation Passenger Compartment.”53 The thin lines represent 

relative positions for roof bows and front cross-members considered during iterations of the CAE 

analysis of the vehicle structure. The different colors given to the structural components represent 

differences in sectional material thickness within this particular CAE modeling run. 

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Figure 3-5: Example of CAE optimization of a roof-rail design for the “Future Generation Passenger
 

Compartment” that reduces component mass by 41%.53
 

Figure 3-6: Example of CAE optimization of a door-beam design for the “Future Generation 

Passenger Compartment” that reduces component mass by 57% and reduces part count and 

assemby.53 

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Figure 3-7: Lightweight, tubular front door intrusion beams (2007 Civic) 

•	 Automotive companies have largely used weight savings in some vehicle 

subsystems to offset or mitigate weight gains in other subsystems from increased 

feature content (sound insulation, entertainment systems, improved climate 

control, panoramic roof, etc.). 

•	 Lightweight designs have also been used to improve vehicle performance 

parameters by increased acceleration performance or superior vehicle handling 

and braking. 

Many manufacturers have already announced proposed future products plans 

reducing the weight of a vehicle body through the use of high strength steel body-inwhite, composite body panels, magnesium alloy front and rear energy absorbing 

structures reducing vehicle weight sufficiently to allow a smaller, lighter and more 

efficient engine. Nissan will be reducing average vehicle curb weight by 15% by 2015.54 

Ford has identified weight reductions of 250 to 750 lbs per vehicle as part of its 

implementation of known technology within its sustainability strategy between 2011 and 

2020.55 Mazda plans to reduce vehicle weight by 220 pounds per vehicle or more as 

56,57 models are redesigned. Ducker International estimates that the average curb weight 

of light duty vehicle fleet will decrease approximately 2.8% from 2009 to 2015 and 

approximately 6.5% from 2009 to 2020 via changes in automotive materials and 

increased change-over from previously used body-on-frame automobile and light-truck 

designs to newer unibody designs.54 

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NHTSA in the CAFE model has used a tiered approach in material substitution to 

reduce mass without considering other means of vehicle mass reduction. MS1, a 1.5 

percent mass reduction, is applicable in any year at refresh or redesign. MS2, a 3.5 to 8.5 

percent mass reduction incremental to MS1, is only available in MY 2014 and beyond in 

an attempt to mimic manufacturers’ planned applications of mass reduction/material 

substitution in later years. For smaller vehicles, MS2 was limited to a 3.5 percent mass 

reduction of mass reduction/material substitution, but for larger cars and trucks up to 8.5 

percent mass reduction was allowed. 

In the CAFE model, MS2 is cumulative to MS1, as it is only applied after MS1; 

therefore the maximum weight reduction that can occur for smaller subclass vehicles is 5 

percent, while large cars, truck, and SUVs could experience up to a 10 percent weight 

reduction. Making the MS2 technology unavailable until MY 2014 is intended to 

recognize the lead time required to implement platform redesigns that would be necessary 

for these levels of weight reduction. 

NHTSA and EPA estimate that a 10 percent mass reduction with engine 

downsizing results in a 6.5 percent reduction in fuel consumption while maintaining 

equivalent vehicle performance (i.e., 0-60 mph time, towing capacity, etc.) which is 

consistent with estimates in the 2002 NAS report. For small amounts of mass reduction, 

such as the 1.5 percent used at vehicle refresh in NHTSA’s modeling, no engine 

downsizing will be used and a 10 percent mass reduction without engine downsizing will 

58,59,60 result in a 3.5 percent reduction in fuel consumption. 

NHTSA and EPA have reviewed three studies of mass reduction/material 

substitution and the associated cost. The 2002 NAS,Report estimated that vehicle weight 

could be reduced for approximately $1.50 per pound, along with 3 to 4 percent reductions 

in fuel consumption, without engine downsizing, from a 5 percent reduction in vehicle 

weight, at a cost of $210-$350. This translates into $1.50 per pound, assuming a 3,800 

pound base vehicle and using the midpoint cost. Additionally, Sierra Research estimated 

that a 10 percent reduction, with compounding, could be accomplished for a cost of $1.01 

61 62 per pound. Finally, an MIT study estimated that the weight of a vehicle could be 

reduced by 14 percent, with no compounding, for a cost of $1.36 per pound. The final 

cost estimate is $1.32 per pound and is based on the average of the three referenced 

studies. Applying an ICM factor of 1.11 for a low complexity technology results in a 

compliance cost of $1.48 per pound. For the vehicle mass reduction technologies, neither 

volume-based nor time-based cost reductions are applied, since many of the materials 

under consideration are commodity based. 

3.4.2.4.2 Low Drag Brakes (LDB) 

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 rotating disc 

either by mechanical or electric methods. While most passenger cars have already 

adopted this technology with the standardization of electronic brake control, there are 

indications that this technology is still available for body-on-frame trucks and for some 

large passenger cars. 

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NHTSA’s MY 2011 CAFE final rule estimated the effectiveness of LDB to be as 

much as 1 percent, based on confidential manufacturer data. NHTSA and EPA have 

reviewed this estimate and believe it to be applicable for this final rule. 

NHTSA and EPA have reviewed the cost estimates from the MY 2011 CAFE 

final rule and have not found any evidence to suggest that this cost is not a valid cost and 

have chosen to maintain this estimate. However, the agencies adjusted the costs to apply 

the ICM of 1.11, for a low-complexity technology. The compliance cost for LDB is 

estimated to be $63 for MY 2012 through MY 2016 vehicles as no learning effects are 

applied to this technology. 

3.4.2.4.3 Low Rolling Resistance Tires (ROLL) 

Tire rolling resistance is the frictional loss associated mainly with the energy 

dissipated in the deformation of the tires under load and thus influences fuel economy 

and CO2 emissions. Other tire design characteristics (e.g., materials, construction, and 

tread design) influence durability, traction (both wet and dry grip), vehicle handling, and 

ride comfort in addition to rolling resistance. A typical low rolling resistance tire’s 

attributes would include: increased tire inflation pressure, material changes, and tire 

construction with less hysteresis, geometry changes (e.g., reduced aspect ratios), and 

reduction in sidewall and tread deflection. These changes would generally be 

accompanied with additional changes to suspension tuning and/or suspension design. 

EPA and NHTSA estimated a 1 to 2 percent increase in effectiveness with a 10 

percent reduction in rolling resistance, which was based on the 2002 NAS report findings 

and consistent with NHTSA’s MY 2011 CAFE final rule estimate. NHTSA and EPA 

still believe that this NAS effectiveness estimate is valid for this final rule. Lower rolling 

resistance tires are widely available today. 

Based on NHTSA’ MY 2011 CAFE final rule and the 2006 NAS/NRC report, the 

agencies have estimated the cost for low rolling resistance tires to be $6 per vehicle.63 

This is based on a cost of $1 per tire as estimated by NAS/NRC 2006 report, which is $5 

per vehicle, including the spare tire. When applying the ICM low complexity markup 

factor, of 1.11, results in compliance cost of $6 per vehicle for MY 2012 through MY 

2016 vehicles (2007$).u Due to the commodity-based nature of this technology, cost 

learning is not applied. 

3.4.2.4.4 Front or Secondary Axle Disconnect for Four-Wheel Drive Systems (SAX) 

Energy is required to continually drive the front, or secondary, axle in a fourwheel drive system even when the system is not required during most operating 

conditions. This energy loss directly results in increased fuel consumption and CO2 

emissions. Many part-time four-wheel drive systems use some type of front axle 

disconnect to provide shift-on-the-fly capabilities. The front axle disconnect is normally 

part of the front differential assembly. As part of a shift-on-the-fly four-wheel drive 

u Note that the costs developed for low rolling resistance tires for this analysis do not include the increase in 

lifetime costs that would be expected at each tire replacement. Instead, the analysis includes only the 

upfront increase in costs. 

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system, the front axle disconnect serves two basic purposes. First, in two-wheel drive 

mode, it disengages the front axle from the front driveline so the front wheels do not turn 

the front driveline at road speed, saving wear and tear. Second, when shifting from twoto four-wheel drive “on the fly” (while moving), the front axle disconnect couples the 

front axle to the front differential side gear only when the transfer case’s synchronizing 

mechanism has spun the front driveshaft up to the same speed as the rear driveshaft. 

Four-wheel drive systems that have a front axle disconnect typically do not have either 

manual- or automatic-locking hubs. To isolate the front wheels from the rest of the front 

driveline, front axle disconnects use a sliding sleeve to connect or disconnect an axle 

shaft from the front differential side gear. NHTSA and EPA are not aware of any 

manufacturer offering this technology in the U.S. today on unibody frame vehicles; 

however, it is possible this technology could be introduced by manufacturers within the 

rulemaking time period. 

Based on confidential manufacturer data, NHTSA’s MY 2011 CAFE final rule 

estimated an effectiveness improvement of 1.0 to 1.5 percent for axle disconnect. 

NHTSA and EPA believe this to still be an accurate estimate for this rulemaking. 

Regarding costs, the agencies believe that the incremental compliance cost from 

NHTSA’s MY 2011 final rule is an accurate estimate. However, for this final rule, this 

estimate was revised by applying the new ICM factor markup, for a low complexity 

technology, of 1.11. Thus, the compliance cost estimate for this final rule, is $87 for a 

MY 2012 vehicle (2007$). With time-based learning this cost would be $84 for a MY 

2016 vehicle (2007$). 

3.4.2.4.5 Aerodynamic Drag Reduction (AERO) 

Many factors affect a vehicle’s aerodynamic drag and the resulting power 

required to move it through the air. While these factors change with air density and the 

square and cube of vehicle speed, respectively, the overall drag effect is determined by 

the product of its frontal area and drag coefficient. Reductions in these quantities can 

therefore reduce fuel consumption and CO2 emissions. Although frontal areas tend to be 

relatively similar within a vehicle class (mostly due to market-competitive size 

requirements), significant variations in drag coefficient can be observed. Significant 

changes to a vehicle’s aerodynamic performance may need to be implemented during a 

redesign (e.g., changes in vehicle shape). However, shorter-term aerodynamic 

reductions, with a somewhat lower effectiveness, may be achieved through the use of 

revised exterior components (typically at a model refresh in mid-cycle) and add-on 

devices that currently being applied. The latter list would include revised front and rear 

fascias, modified front air dams and rear valances, addition of rear deck lips and 

underbody panels, and lower aerodynamic drag exterior mirrors. 

The MY 2011 CAFE final rule estimated that a fleet average of 10 to 20 percent 

total aerodynamic drag reduction is attainable (with a caveat for “high-performance” 

vehicles described below) which equates to incremental reductions in fuel consumption 

and CO2 emissions of 2 to 3 percent for both cars and trucks. These numbers are 

generally supported by confidential manufacturer data and public technical literature and 

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therefore NHTSA and EPA are retaining these estimates, as confirmed by joint review, 

for the purposes of this final rule. 

In the MY 2011 CAFE final rule, NHTSA estimated a range from $60 to $116 

(1.5 RPE); $40 to $75 (without RPE). NHTSA and EPA reviewed these estimates and 

updated these estimates to reflect the new ICM markup factor of 1.11 for a low 

complexity technology. In addition, given its low complexity and the facility of its 

implementation during design and refresh cycles, NHTSA and EPA believe that a cost 

estimate at the lower end of the range is more appropriate. Thus, the cost estimate used 

in this rulemaking is $48, including the 1.11 ICM markup value, for a low complexity 

technology (2007$). This compliance cost is for a MY 2012 vehicle. With time-based 

learning, this cost would be $42 for a MY 2016 vehicle (2007$) 

3.4.2.5	 Technologies considered but not included in the final rule 

analysis 

NHTSA and EPA have identified six technologies that will not be available in the 

time frame considered under this rulemaking. These technologies while considered were 

not made available in the CAFE and OMEGA models. They are: electric vehicles, 

camless valve actuation (CVA), lean burn gasoline direct injection (LBDI), homogeneous 

charge compression ignition (HCCI), and electric assist turbocharging and full series 

hydraulic hybrids (HHV). While electric vehicles are likely become available in small 

numbers in the near future, the costs for this technology (as estimated by the agencies) 

are non-competitive with the more conventional technologies listed above. Therefore, 

the OMEGA and Volpe model analysis would preclude their selection, when estimating 

the costs of the rule. Lean Burn direct injection engines are currently available in 

Europe, however, these vehicles cannot be designed at this time to be both efficient and 

meet the NOx standards in the United States with the current sulfur levels in the fuels. 

The other technologies listed are still in the research phase of development. NHTSA and 

EPA will continue to monitor the industry and system suppliers for progress on these 

technologies, and should they become more available, consider them for use in future 

rulemaking activity. More details are described below. 

3.4.2.5.1 Electric Vehicles 

The recent intense interest in Hybrid vehicles and the development of Hybrid 

vehicle battery and motor technology has helped make Electric Vehicle technology more 

viable than it has ever been. Electric Vehicles (EVs) require much larger batteries than 

either HEVs or PHEVs, but the batteries must be of a high-energy and lower-power 

design to deliver an appropriate amount of power over the useful charge of the battery. 

These high-energy batteries are generally less expensive per kilowatt-hour than highpower batteries required for hybrids, but the size of the battery pack still incurs a 

considerable cost. 

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Electric motor and power electronics designs are very similar to HEV and PHEV 

designs, but they must be larger, more powerful, and more robust since they provide the 

only motive power for the vehicle. On the other hand, the internal combustion engine, 

fuel system, and possibly the transmission can all be removed for significant weight, 

complexity and cost savings. 

As for PHEVs, two electric vehicles were modeled, a small car and a large car, 

using the same model (PEREGRIN) and similar assumptions. EVs are only considered 

for these two classes because the larger, heavier vehicles would require too much battery 

capacity to be practical in the short-to-mid term and the agencies do not see any serious 

development activities for these vehicle types in the market. 

We chose to model the EVs with a range of 150 miles on the urban driving cycle 

because this range offers a good compromise in capability and battery cost, weight and 

size with expected technology in the near- to mid-term. Using the same methodology as 

used for PHEVs to calculate gasoline-equivalent fuel consumption, the results obtained 

are shown in Table 3-24, below. 

Table 3-24 Electric Vehicle Gasoline-Equivalent CO2 Reduction (excluding A/C) 

Small Car Large Car 

EV energy comb (0.55 city / 0.45 hwy) 0.202 kwh/mi 0.244 kwh/mi 

City cycle EV range 150 miles 150 miles 

Highway cycle EV range 166 miles 162 miles 

Baseline FE 35.5 25.3 mpg 

Tailpipe CO2 reduction 100% 100% 

3.4.2.5.2 Camless Valve Actuation 

Camless valve actuation relies on electromechanical actuators instead of 

camshafts to open and close the cylinder valves. When electromechanical actuators are 

used to replace cams and coupled with sensors and microprocessor controls, valve timing 

and lift can be optimized over all conditions. An engine valvetrain that operates 

independently of any mechanical means provides the increased flexibility for intake and 

exhaust timing and lift optimization. With it comes increased ability to vary valve 

overlap, the rapid response required to change between combustion operating modes 

(such as HCCI and GDI), intake valve throttling, cylinder deactivation, and elimination 

of the camshafts (reduced friction and rotating mass). This level of control can enable 

even further incremental reductions in fuel consumption and CO2 emissions. 

This technology has been under research for many decades and although progress 

is being made, NHTSA and EPA have not found evidence to support that the technology 

can be successfully implemented within the 2012 through 2016 timeframe of these 

regulations. Thus NHTSA and EPA have not estimated cost or effectiveness for this 

technology at this time. 

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Technologies Considered in the Agencies’ Analysis
 

3.4.2.5.3 Lean-Burn Gasoline Direct Injection Technology 

Direct injection, especially with diesel-like “spray-guided” injection systems, 

enables operation with excess air in a stratified or partially-stratified fuel-air mixture, as a 

way of reducing the amount of intake throttling. Also, with higher-pressure fuel injection 

systems, the fuel may be added late enough during the compression stroke so as to delay 

the onset of autoignition, even with higher engine compression ratios or with boosted 

intake pressure. Taken together, an optimized “lean-burn” direct injection gasoline 

engine may achieve high engine thermal efficiency which approaches that of a diesel 

engine. European gasoline direct-injection engines have implemented stratified-charge 

lean-burn GDI, although at higher NOx emissions levels than are allowed at under U.S. 

Federal Tier 2 emissions standards. Fuel system improvements, changes in combustion 

chamber design and repositioning of the injectors have allowed for better air/fuel mixing 

and combustion efficiency. There is currently a shift from wall-guided injection to spray 

guided injection, which improves injection precision and targeting towards the spark 

plug, increasing lean combustion stability. Combined with advances in NOx aftertreatment, lean-burn GDI engines may eventually be a possibility in North America. 

EPA and NHTSA’s current assessment is that the availability of ultra-low sulfur 

(ULS less than 15 ppm sulfur) gasoline is a key technical requirement for lean-burn GDI 

engines to meet EPA’s Tier 2 NOx emissions standards. Since we do not believe that 

ULS gasoline will be available during the model years applicable to these rules, the 

technology was not applied in EPA or NHTSA analyses. 

3.4.2.5.4 Homogeneous Charge Compression Ignition 

Gasoline homogeneous charge compression ignition (HCCI), also referred to as 

controlled autoignition (CAI), is an alternate engine operating mode that does not rely on 

a spark event to initiate combustion. The principles are more closely aligned with a 

diesel combustion cycle, in which the compressed charge exceeds a temperature and 

pressure necessary for spontaneous autoignition although it differs from diesel by having 

a homogenous fuel/air charge rather than being a diffusion-controlled combustion event. 

The subsequent combustion event is much shorter in duration with higher thermal 

efficiency. 

An HCCI engine has inherent advantages in its overall efficiency for two main 

reasons: 

•	 The engine is operated with a higher compression ratio, and with a shorter 

combustion duration, resulting in a higher thermodynamic efficiency, and 

•	 The engine can be operated virtually unthrottled, even at light loads, 

Combined, these effects have shown an increase in engine brake efficiency 

(typically 25 to 28 percent) to greater than 35 percent at the high end of the HCCI 

operating range.64 

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Criteria pollutant emissions are very favorable during HCCI operation. Lower 

peak in-cylinder temperatures (due to high dilution) keep engine-out NOx emissions to a 

minimum – realistically below Tier 2 levels without aftertreatment – and particulates are 

low due to the homogeneous nature of the premixed charge. 

Due to the inherent difficulty in maintaining combustion stability without 

encountering engine knock, HCCI is difficult to control, requiring feedback from incylinder pressure sensors and rapid engine control logic to optimize combustion timing, 

especially considering the transient nature of operating conditions seen in a vehicle. Due 

to the highly dilute conditions under which gasoline-HCCI combustion is stable, the 

range of engine loads achievable in a naturally-aspirated engine is somewhat limited. 

Because of this, it is likely that any commercial application would operate in a “dualmode” strategy between HCCI and spark ignition combustion modes, in which HCCI 

would be utilized for best efficiency at light engine loads and spark ignition would be 

used at higher loads and at idle. This type of dual-mode strategy has already been 

employed in diesel HCCI engines in Europe and Asia (notably the Toyota Avensis D-Cat 

and the Nissan light-duty “MK” combustion diesels). 

Until recently, gasoline-HCCI technology was considered to still be in the 

research phase. However, most manufacturers have made public statements about the 

viability of incorporating HCCI into light-duty passenger vehicles, and have significant 

vehicle demonstration programs aimed at producing a viable product within the next 5-10 

years. 

There is widespread optimism as to the CO2 reduction potential for HCCI in the 

literature. Based on confidential manufacturer information, EPA and NHTSA believe 

that a gasoline HCCI / GDI dual-mode engine might achieve 10 to 12 percent reduction 

in CO2, compared to a comparable SI engine. Despite its promise, application of HCCI 

in light duty vehicles is not yet ready for the market. It is not anticipated to be seen in 

volume for at least the next 5-10 years, which is concurrent with many manufacturers’ 

public estimates. NHTSA also noted in its MY 2011 CAFE final rule that the technology 

will not be available within the time frame considered based on a review of confidential 

product plan information. 

3.4.2.5.5 Electric Assist Turbocharging 

The Alliance commented in NHTSA’s previous rulemaking that global 

development of electric assist turbocharging has not demonstrated the fuel efficiency 

effectiveness of a 12V EAT up to 2kW power levels since the 2004 NESCCAF study, 

and stated that it saw remote probability of its application over the next decade. While 

hybrid vehicles lower the incremental hardware requirements for higher-voltage, higherpower EAT systems, NHTSA and EPA agree that significant developmental work is 

required to demonstrate effective systems and that implementation in significant volumes 

will not occur in the 2012 to 2016 time frame considered in this rulemaking. Thus, this 

technology was not included in the final rule. 

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3.4.2.5.6 Full Series Hydraulic Hybrid 

A Full Series Hydraulic Hybrid Vehicle (HHV) is somewhat similar in concept to 

a full series electric hybrid vehicle, except that the energy is stored in the form of 

compressed nitrogen gas and the power is transmitted in the form of hydraulic fluid. 

Series HHV technology currently under development by EPA is capable of a 40 percent 

decrease in tailpipe CO2 emissions in the small car, large car, minivan, and small truck 

classes. In the large truck class, a 30 percent CO2 reduction is possible. The large truck 

benefit is somewhat lower than the other classes because it is assumed that a large truck 

requires a larger engine to maintain towing and hauling performance after the energy in 

the high pressure hydraulic accumulator is exhausted. This technology is still under 

development and not yet commercialized, however there are technology demonstration 

vehicles in service with UPS in daily package delivery service. 

3.5 Cost and effectiveness tables 

The tables representing the Volpe model input files for incremental technology 

costs by vehicle subclass are presented below. The tables have been divided into 

passenger cars, performance passenger cars, and light trucks to make them easier to read. 

It should be noted that the Volpe model input files define costs for MY 2012 and learn 

down costs for subsequent years. 

3.5.1 NHTSA cost and effectiveness tables 

Table 3-25 Technology Incremental Cost Estimates for Volpe model, Passenger Cars in 2012 

VEHICLE TECHNOLOGY ICM COSTS PER VEHICLE (2007$) BY VEHICLE 

SUBCLASS - PASSENGER CARS 

Subcompact 

Car 

Compact 

Car 

Midsize 

Car 

Large 

Car 

Nominal Baseline Engine (For Cost Basis) Inline 4 Inline 4 Inline 4 V6 

Low Friction Lubricants 3 3 3 3 

Engine Friction Reduction EFR 50 50 50 75 

VVT - Coupled Cam Phasing (CCP) on SOHC CCPS 45 45 45 90 

Discrete Variable Valve Lift (DVVL) on SOHC DVVLS 142 142 142 205 

Cylinder Deactivation on SOHC DEACS n.a. n.a. n.a. 0 - 56 

VVT - Intake Cam Phasing (ICP) ICP 45 45 45 90 

VVT - Dual Cam Phasing (DCP) DCP 38 38 38 83 

Discrete Variable Valve Lift (DVVL) on DOHC DVVLD 142 142 142 205 

Continuously Variable Valve Lift (CVVL) CVVL 277 277 277 509 

Cylinder Deactivation on DOHC DEACD n.a. n.a. n.a. 0 - 56 

Cylinder Deactivation on OHV DEACO n.a. n.a. n.a. 170 

VVT - Coupled Cam Phasing (CCP) on OHV CCPO 45 45 45 45 

Discrete Variable Valve Lift (DVVL) on OHV DVVLO 142 142 142 0 - 56 

Conversion to DOHC with DCP CDOHC 276 276 276 436 

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Stoichiometric Gasoline Direct Injection (GDI) SGDI 236 236 236 341 

Combustion Restart CBRST 118 118 118 118 

Turbocharging and Downsizing TRBDS 445 445 445 325 

Exhaust Gas Recirculation (EGR) Boost EGRB 144 144 144 144 

Conversion to Diesel following CBRST DSLC 1,527. 1,527 1,527 1,556 ­

1,612 

Conversion to Diesel following TRBDS DSLT 938. 938 938 1,088­

1,143 

6-Speed Manual/Improved Internals 6MAN 250 250 250 250 

Improved Auto. Trans. Controls/Externals IATC 60 60 60 60 

Continuously Variable Transmission CVT 250 250 250 250 

6/7/8-Speed Auto. Trans with Improved 

Internals NAUTO 112 112 112 112 

Dual Clutch or Automated Manual Transmission DCTAM (59) (59) (8) (8) 

Electric Power Steering EPS 106 106 106 106 

Improved Accessories IACC 128 128 128 128 

12V Micro-Hybrid MHEV 288 311 342 367 

Belt mounted Integrated Starter Generator BISG 286 286 286 286 

Crank mounted Integrated Starter Generator CISG 2,791 3,107 3,319 3,547 

Power Split Hybrid PSHEV 1,600 2,133 2,742 3,261 

2-Mode Hybrid 2MHEV n.a. n.a. n.a. n.a. 

Plug-in Hybrid PHEV 11,520 14,135 16,215 n.a. 

Mass Reduction (1.5%) MS1 1.5 1.5 1.5 1.5 

Mass Reduction (3.5 to 8.5%) MS2 1.5 1.5 1.5 1.5 

Low Rolling Resistance Tires ROLL 6 6 6 6 

Low Drag Brakes LDB n.a. n.a. n.a. 63 

Secondary Axle Disconnect SAX 87 87 87 87 

Aero Drag Reduction AERO 48 48 48 48 

Table 3-26. Technology Incremental Cost Estimates for Volpe model, Performance Passenger Cars 

in 2012 

VEHICLE TECHNOLOGY ICM COSTS PER VEHICLE (2007$) BY VEHICLE 

SUBCLASS - PERFORMANCE PASSENGER CARS 

Perform. 

Subcomp. 

Car 

Perform. 

Compact 

Car 

Perform. 

Midsize 

Car 

Perform. 

Large 

Car 

Nominal Baseline Engine (For Cost Basis) Inline 4 V6 V6 V8 

Low Friction Lubricants LUB 3 3 3 3 

Engine Friction Reduction EFR 50 75 75 101 

VVT - Coupled Cam Phasing 

(CCP) on SOHC CCPS 45 90 90 90 

Discrete Variable Valve Lift 

(DVVL) on SOHC DVVLS 142 205 205 293 

Cylinder Deactivation on SOHC DEACS n.a. 0 - 56 0 - 56 0 – 56 

VVT - Intake Cam Phasing ICP 45 90 90 90 

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Technologies Considered in the Agencies’ Analysis
 

(ICP) 

VVT - Dual Cam Phasing 

(DCP) DCP 38 83 83 82 

Discrete Variable Valve Lift 

(DVVL) on DOHC DVVLD 142 205 205 293 

Continuously Variable Valve 

Lift (CVVL) CVVL 277 509 509 555 

Cylinder Deactivation on 

DOHC DEACD n.a. 0 - 56 0 - 56 0 – 56 

Cylinder Deactivation on OHV DEACO n.a. 170 170 190 

VVT - Coupled Cam Phasing 

(CCP) on OHV CCPO 45 45 45 45 

Discrete Variable Valve Lift 

(DVVL) on OHV DVVLO 142 0 - 56 0 - 56 0 – 56 

Conversion to DOHC with DCP CDOHC 276 436 436 552 

Stoichiometric Gasoline Direct 

Injection (GDI) SGDI 236 341 341 552 

Combustion Restart CBRST 118 118 118 118 

Turbocharging and Downsizing TRBDS 445 325 325 919 

Exhaust Gas Recirculation 

(EGR) Boost EGRB 144 144 144 144 

Conversion to Diesel following 

CBRST DSLC 1,527 1,556 – 1,612 1,556 – 1,612 2,294 – 2,349 

Conversion to Diesel following 

TRBDS DSLT 938 1,088 – 1,143 1,088 – 1,143 1,231 – 1,287 

6-Speed Manual/Improved 

Internals 6MAN 250 250 250 250 

Improved Auto. Trans. 

Controls/Externals IATC 60 60 60 60 

Continuously Variable 

Transmission CVT 250 250 250 250 

6/7/8-Speed Auto. Trans with 

Improved Internals NAUTO 112 – 214 112 – 214 112 – 214 112 - 214 

Dual Clutch or Automated 

Manual Transmission DCTAM (8) (8) (8) (8) 

Electric Power Steering EPS 106 106 106 106 

Improved Accessories IACC 128 128 128 128 

12V Micro-Hybrid MHEV 314 337 372 410 

Belt mounted Integrated Starter 

Generator BISG 286 286 286 286 

Crank mounted Integrated 

Starter Generator CISG 2,839 3,149 3,335 3,571 

Power Split Hybrid PSHEV 3,661 4,018 5,287 6,723 

2-Mode Hybrid 2MHEV n.a. n.a. n.a. n.a. 

Plug-in Hybrid PHEV 14,987 16,792 19,265 n.a. 

Mass Reduction (1.5%) MS1 1.5 1.5 1.5 1.5 

Mass Reduction (3.5 to 8.5%) MS2 1.5 1.5 1.5 1.5 

Low Rolling Resistance Tires ROLL n.a. n.a. n.a. n.a. 

Low Drag Brakes LDB n.a. n.a. n.a. 63 

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Secondary Axle Disconnect SAX 87 87 87 87 

Aero Drag Reduction AERO n.a. n.a. n.a. n.a. 

Table 3-27 . Technology Incremental Cost Estimates for Volpe model, Light Trucks in 2012 

VEHICLE TECHNOLOGY ICM COSTS PER VEHICLE (2007$) BY VEHICLE 

SUBCLASS - LIGHT TRUCKS 

Minivan 

LT 

Small 

LT 

Midsize 

LT 

Large 

LT 

Nominal Baseline Engine (For Cost Basis) V6 Inline 4 V6 V8 

Low Friction Lubricants LUB 3 3 3 3 

Engine Friction Reduction EFR 75 50 75 101 

VVT - Coupled Cam Phasing (CCP) 

on SOHC CCPS 90 45 90 90 

Discrete Variable Valve Lift (DVVL) 

on SOHC DVVLS 205 142 205 293 

Cylinder Deactivation on SOHC DEACS 0 - 56 n.a. 0 - 56 0 – 56 

VVT - Intake Cam Phasing (ICP) ICP 90 45 90 90 

VVT - Dual Cam Phasing (DCP) DCP 83 38 83 83 

Discrete Variable Valve Lift (DVVL) 

on DOHC DVVLD 205 142 205 293 

Continuously Variable Valve Lift 

(CVVL) CVVL 509 277 509 555 

Cylinder Deactivation on DOHC DEACD 0 - 56 n.a. 0 - 56 0 – 56 

Cylinder Deactivation on OHV DEACO 170 n.a. 170 190 

VVT - Coupled Cam Phasing (CCP) 

on OHV CCPO 45 45 45 45 

Discrete Variable Valve Lift (DVVL) 

on OHV DVVLO 0 - 56 142 0 - 56 0 – 56 

Conversion to DOHC with DCP CDOHC 436 276 436 552 

Stoichiometric Gasoline Direct 

Injection (GDI) SGDI 341 236 341 391 

Combustion Restart CBRST 118 118 118 118 

Turbocharging and Downsizing TRBDS 325 445 325 919 

Exhaust Gas Recirculation (EGR) 

Boost EGRB 144 144 144 144 

Conversion to Diesel following 

CBRST DSLC 1,556 ­

1,612 1,527 1,556 -1,612 2,294 – 2,349 

Conversion to Diesel following 

TRBDS DSLT 1,088-1,143 938 1,088 – 1,143 1,231 – 1,287 

6-Speed Manual/Improved Internals 6MAN 250 250 250 250 

Improved Auto. Trans. 

Controls/Externals IATC 60 60 60 60 

Continuously Variable Transmission CVT 250 250 250 n.a. 

6/7/8-Speed Auto. Trans with 

Improved Internals NAUTO 112 112 112 - 214 112 - 214 

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Technologies Considered in the Agencies’ Analysis
 

Dual Clutch or Automated Manual 

Transmission DCTAM (8) (8) (8) (8) 

Electric Power Steering EPS 106 106 106 106 

Improved Accessories IACC 128 128 128 n.a. 

12V Micro-Hybrid MHEV 367 325 376 n.a. 

Belt mounted Integrated Starter 

Generator BISG 286 286 286 n.a. 

Crank mounted Integrated Starter 

Generator CISG 3,547 3,141 3,611 5,124 

Power Split Hybrid PSHEV 3,261 2,377 3,462 n.a. 

2-Mode Hybrid 2MHEV n.a. 3,661 4,887 - 4,989 5,902 – 6,004 

Plug-in Hybrid PHEV n.a. 14,721 n.a. n.a. 

Mass Reduction (1.5%) MS1 1.5 1.5 1.5 1.5 

Mass Reduction (3.5 to 8.5%) MS2 1.5 1.5 1.5 1.5 

Low Rolling Resistance Tires ROLL 6 6 6 6 

Low Drag Brakes LDB 63 n.a. 63 63 

Secondary Axle Disconnect SAX 87 87 87 87 

Aero Drag Reduction AERO 48 48 48 48 

Table 3-28. Technology Incremental Effectiveness Estimates for Volpe model, Passenger Cars in 

2012 

VEHICLE TECHNOLOGY INCREMENTAL FUEL CONSUMPTION REDUCTION 

(-%) BY VEHICLE SUBCLASS - PASSENGER CAR 

Subcompact 

Car 

Compact 

Car 

Midsize 

Car 

Large 

Car 

Nominal Baseline Engine (For Cost Basis) Inline 4 Inline 4 Inline 4 V6 

Low Friction Lubricants LUB 0.5 0.5 0.5 0.5 

Engine Friction Reduction EFR 1.0 - 2.0 1.0 - 2.0 1.0 - 2.0 1.0 - 2.0 

VVT - Coupled Cam Phasing (CCP) 

on SOHC CCPS 1.0 - 3.0 1.0 - 3.0 1.0 - 3.0 1.0 - 3.0 

Discrete Variable Valve Lift (DVVL) 

on SOHC DVVLS 1.0 - 3.0 1.0 - 3.0 1.0 - 3.0 1.0 - 3.0 

Cylinder Deactivation on SOHC DEACS n.a. n.a. n.a. 2.5 - 3.0 

VVT - Intake Cam Phasing (ICP) ICP 1.0 - 2.0 1.0 - 2.0 1.0 - 2.0 1.0 - 2.0 

VVT - Dual Cam Phasing (DCP) DCP 2.0 - 3.0 2.0 - 3.0 2.0 - 3.0 2.0 - 3.0 

Discrete Variable Valve Lift (DVVL) 

on DOHC DVVLD 1.0 - 3.0 1.0 - 3.0 1.0 - 3.0 1.0 - 3.0 

Continuously Variable Valve Lift 

(CVVL) CVVL 1.5 - 3.5 1.5 - 3.5 1.5 - 3.5 1.5 - 3.5 

Cylinder Deactivation on DOHC DEACD n.a. n.a. n.a. 0 - 0.5 

Cylinder Deactivation on OHV DEACO n.a. n.a. n.a. 3.9 - 5.5 

VVT - Coupled Cam Phasing (CCP) 

on OHV CCPO 1.0 - 1.5 1.0 - 1.5 1.0 - 1.5 1.0 - 1.5 

Discrete Variable Valve Lift (DVVL) 

on OHV DVVLO 0.5 - 2.5 0.5 - 2.5 0.5 - 2.5 0.5 - 2.5 

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Conversion to DOHC with DCP CDOHC 1.0 - 2.5 1.0 - 2.5 1.0 - 2.5 1.0 - 2.5 

Stoichiometric Gasoline Direct 

Injection (GDI) SGDI 2.0 - 3.0 2.0 - 3.0 2.0 - 3.0 2.0 - 3.0 

Combustion Restart CBRST 2.0 - 2.5 2.0 - 2.5 2.0 - 2.5 2.0 - 2.5 

Turbocharging and Downsizing TRBDS 4.2 - 4.8 4.2 - 4.8 4.2 - 4.8 1.8 - 1.9 

Exhaust Gas Recirculation (EGR) 

Boost EGRB 3.5 - 4.0 3.5 - 4.0 3.5 - 4.0 3.5 - 4.0 

Conversion to Diesel following 

CBRST DSLC 13.5 – 13.9 13.5 – 

13.9 

13.5 – 

13.9 

10.8 ­

11.7 

Conversion to Diesel following 

TRBDS DSLT 5.3 – 6.9 5.3 – 6.9 5.3 – 6.9 5.3 - 6.9 

6-Speed Manual/Improved Internals 6MAN 0.5 0.5 0.5 0.5 

Improved Auto. Trans. 

Controls/Externals IATC 1.5 - 2.5 1.5 - 2.5 1.5 - 2.5 1.5 - 2.5 

Continuously Variable Transmission CVT 0.7 - 2.0 0.7 - 2.0 0.7 - 2.0 0.7 - 2.0 

6/7/8-Speed Auto. Trans with 

Improved Internals NAUTO 1.4 - 3.4 1.4 - 3.4 1.4 - 3.4 1.4 - 3.4 

Dual Clutch or Automated Manual 

Transmission DCTAM 5.5 – 7.5 5.5 – 7.5 2.7 - 4.1 2.7 - 4.1 

Electric Power Steering EPS 1.0 - 2.0 1.0 - 2.0 1.0 - 2.0 1.0 - 2.0 

Improved Accessories IACC 1.0 - 2.0 1.0 - 2.0 1.0 - 2.0 1.0 - 2.0 

12V Micro-Hybrid MHEV 2.0 - 3.0 2.0 - 3.0 2.0 - 3.0 2.5 - 3.5 

Belt mounted Integrated Starter 

Generator BISG 4.0 - 6.0 4.0 - 6.0 4.0 - 6.0 3.5 - 5.5 

Crank mounted Integrated Starter 

Generator CISG 8.6 - 8.9 8.6 - 8.9 8.6 - 8.9 8.7 - 8.9 

Power Split Hybrid PSHEV 6.3 - 12.4 6.3 - 12.4 6.3 - 12.4 6.3 ­

12.4 

2-Mode Hybrid 2MHEV n.a. n.a. n.a. n.a. 

Plug-in Hybrid PHEV 45.2 - 47.7 45.2 ­

47.7 

45.2 ­

47.7 n.a. 

Mass Reduction (1.5%) MS1 0.5 0.5 0.5 0.5 

Mass Reduction (3.5 to 8.5%) MS2 2.7 2.7 4.4 6.0 

Low Rolling Resistance Tires ROLL 1.0 - 2.0 1.0 - 2.0 1.0 - 2.0 1.0 - 2.0 

Low Drag Brakes LDB n.a. n.a. n.a. 0.5 - 1.0 

Secondary Axle Disconnect SAX 1.0 - 1.5 1.0 - 1.5 1.0 - 1.5 1.0 - 1.5 

Aero Drag Reduction AERO 2.0 - 3.0 2.0 - 3.0 2.0 - 3.0 2.0 - 3.0 

3-88
 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Technologies Considered in the Agencies’ Analysis
 

Table 3-29. Component Technology Incremental Effectiveness Estimates for Volpe model,
 

Performance Cars in 2012
 

VEHICLE TECHNOLOGY INCREMENTAL FUEL CONSUMPTION REDUCTION 

(-%) BY VEHICLE SUBCLASS - PERFORMANCE PASSENGER CAR 

Perform. 

Subcomp. 

Car 

Perform. 

Compact 

Car 

Perform. 

Midsize 

Car 

Perform. 

Large 

Car 

Nominal Baseline Engine (For Cost Basis) Inline 4 V6 V6 V8 

Low Friction Lubricants LUB 0.5 0.5 0.5 0.5 

Engine Friction Reduction EFR 1.0 - 2.0 1.0 - 2.0 1.0 - 2.0 1.0 - 2.0 

VVT - Coupled Cam Phasing (CCP) on 

SOHC CCPS 1.0 - 3.0 1.0 - 3.0 1.0 - 3.0 1.0 - 3.0 

Discrete Variable Valve Lift (DVVL) on 

SOHC DVVLS 1.0 - 3.0 1.0 - 3.0 1.0 - 3.0 1.0 - 3.0 

Cylinder Deactivation on SOHC DEACS n.a. 2.5 - 3.0 2.5 - 3.0 2.5 - 3.0 

VVT - Intake Cam Phasing (ICP) ICP 1.0 - 2.0 1.0 - 2.0 1.0 - 2.0 1.0 - 2.0 

VVT - Dual Cam Phasing (DCP) DCP 2.0 - 3.0 2.0 - 3.0 2.0 - 3.0 2.0 - 3.0 

Discrete Variable Valve Lift (DVVL) on 

DOHC DVVLD 1.0 - 3.0 1.0 - 3.0 1.0 - 3.0 1.0 - 3.0 

Continuously Variable Valve Lift (CVVL) CVVL 1.5 - 3.5 1.5 - 3.5 1.5 - 3.5 1.5 - 3.5 

Cylinder Deactivation on DOHC DEACD n.a. 0.0 - 0.5 0.0 - 0.5 0.0 - 0.5 

Cylinder Deactivation on OHV DEACO n.a. 3.9 - 5.5 3.9 - 5.5 3.9 - 5.5 

VVT - Coupled Cam Phasing (CCP) on OHV CCPO 1.0 - 1.5 1.0 - 1.5 1.0 - 1.5 1.0 - 1.5 

Discrete Variable Valve Lift (DVVL) on 

OHV DVVLO 0.5 - 2.5 0.5 - 2.5 0.5 - 2.5 0.5 - 2.5 

Conversion to DOHC with DCP CDOHC 1.0 - 2.5 1.0 - 2.5 1.0 - 2.5 1.0 - 2.5 

Stoichiometric Gasoline Direct Injection 

(GDI) SGDI 2.0 - 3.0 2.0 - 3.0 2.0 - 3.0 2.0 - 3.0 

Combustion Restart CBRST 2.0 - 2.5 2.0 - 2.5 2.0 - 2.5 2.0 - 2.5 

Turbocharging and Downsizing TRBDS 4.2 - 4.8 1.8 - 1.9 1.8 - 1.9 1.8 - 1.9 

Exhaust Gas Recirculation (EGR) Boost EGRB 3.5 - 4.0 3.5 - 4.0 3.5 - 4.0 3.5 - 4.0 

Conversion to Diesel following CBRST DSLC 13.5 – 

13.9 

10.8 ­

11.7 

10.8 ­

11.7 

10.8 ­

11.7 

Conversion to Diesel following TRBDS DSLT 5.3 – 6.9 5.3 - 6.9 5.3 - 6.9 5.3 - 6.9 

6-Speed Manual/Improved Internals 6MAN 0.5 0.5 0.5 0.5 

Improved Auto. Trans. Controls/Externals IATC 1.5 - 2.5 1.5 - 2.5 1.5 - 2.5 1.5 - 2.5 

Continuously Variable Transmission CVT 0.7 - 2.0 0.7 - 2.0 0.7 - 2.0 n.a. 

6/7/8-Speed Auto. Trans with Improved 

Internals NAUTO 1.4 - 3.4 1.4 - 3.4 1.4 - 3.4 1.4 - 3.4 

Dual Clutch or Automated Manual 

Transmission DCTAM 2.7 - 4.1 2.7 - 4.1 2.7 - 4.1 2.7 - 4.1 

Electric Power Steering EPS 1.0 - 2.0 1.0 - 2.0 1.0 - 2.0 1.0 - 2.0 

Improved Accessories IACC 1.0 - 2.0 1.0 - 2.0 1.0 - 2.0 1.0 - 2.0 

12V Micro-Hybrid MHEV 2.0 - 3.0 2.5 - 3.5 2.5 - 3.5 3.0 - 4.0 

Belt mounted Integrated Starter Generator BISG 4.0 - 6.0 3.5 - 5.5 3.5 - 5.5 3.0 - 5.0 

Crank mounted Integrated Starter Generator CISG 8.6 - 8.9 8.7 - 8.9 8.7 - 8.9 8.7 - 8.9 

Power Split Hybrid PSHEV 6.3 - 12.4 6.3 ­

12.4 

6.3 ­

12.4 

6.3 ­

12.4 

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2-Mode Hybrid 2MHEV n.a. n.a. n.a. n.a. 

Plug-in Hybrid PHEV 45.2 ­

47.7 

45.2 ­

47.7 

45.2 ­

47.7 n.a. 

Mass Reduction (1.5%) MS1 0.5 0.5 0.5 0.5 

Mass Reduction (3.5 to 8.5%) MS2 2.7 2.7 4.4 6.0 

Low Rolling Resistance Tires ROLL n.a. n.a. n.a. n.a. 

Low Drag Brakes LDB n.a. n.a. n.a. 

0.5 ­

1.0 

Secondary Axle Disconnect SAX 1.0 - 1.5 1.0 - 1.5 1.0 - 1.5 1.0 - 1.5 

Aero Drag Reduction AERO n.a. n.a. n.a. n.a. 

Table 3-30. Component Technology Incremental Effectiveness Estimates for Volpe model, Light
 

Trucks in 2012
 

VEHICLE TECHNOLOGY INCREMENTAL FUEL CONSUMPTION 

REDUCTION (-%) BY VEHICLE SUBCLASS - LIGHT TRUCKS 

Minivan 

LT 

Small 

LT 

Midsize 

LT 

Large 

LT 

Nominal Baseline Engine (For Cost Basis) V6 Inline 4 V6 V8 

Low Friction Lubricants LUB 0.5 0.5 0.5 0.5 

Engine Friction Reduction EFR 1.0 - 2.0 1.0 - 2.0 1.0 - 2.0 1.0 - 2.0 

VVT - Coupled Cam Phasing (CCP) on 

SOHC CCPS 1.0 - 3.0 1.0 - 3.0 1.0 - 3.0 1.0 - 3.0 

Discrete Variable Valve Lift (DVVL) on 

SOHC DVVLS 1.0 - 3.0 1.0 - 3.0 1.0 - 3.0 1.0 - 3.0 

Cylinder Deactivation on SOHC DEACS 2.5 - 3.0 n.a. 2.5 - 3.0 2.5 - 3.0 

VVT - Intake Cam Phasing (ICP) ICP 1.0 - 2.0 1.0 - 2.0 1.0 - 2.0 1.0 - 2.0 

VVT - Dual Cam Phasing (DCP) DCP 2.0 - 3.0 2.0 - 3.0 2.0 - 3.0 2.0 - 3.0 

Discrete Variable Valve Lift (DVVL) on 

DOHC DVVLD 1.0 - 3.0 1.0 - 3.0 1.0 - 3.0 1.0 - 3.0 

Continuously Variable Valve Lift (CVVL) CVVL 1.5 - 3.5 1.5 - 3.5 1.5 - 3.5 1.5 - 3.5 

Cylinder Deactivation on DOHC DEACD 0 - 0.5 n.a. 0.0 - 0.5 0.0 - 0.5 

Cylinder Deactivation on OHV DEACO 3.9 - 5.5 n.a. 3.9 - 5.5 3.9 - 5.5 

VVT - Coupled Cam Phasing (CCP) on 

OHV CCPO 1.0 - 1.5 1.0 - 1.5 1.0 - 1.5 1.0 - 1.5 

Discrete Variable Valve Lift (DVVL) on 

OHV DVVLO 0.5 - 2.5 0.5 - 2.5 0.5 - 2.5 0.5 - 2.5 

Conversion to DOHC with DCP CDOHC 1.0 - 2.5 1.0 - 2.5 1.0 - 2.5 1.0 - 2.5 

Stoichiometric Gasoline Direct Injection 

(GDI) SGDI 2.0 - 3.0 2.0 - 3.0 2.0 - 3.0 2.0 - 3.0 

Combustion Restart CBRST 2.0 - 2.5 2.0 - 2.5 2.0 - 2.5 2.0 - 2.5 

Turbocharging and Downsizing TRBDS 1.8 - 1.9 4.2 - 4.8 1.8 - 1.9 1.8 - 1.9 

Exhaust Gas Recirculation (EGR) Boost EGRB 3.5 - 4.0 3.5 - 4.0 3.5 - 4.0 3.5 - 4.0 

Conversion to Diesel following CBRST DSLC 10.8 ­

11.7 

13.5 – 

13.9. 

10.8 ­

11.7 

10.8 ­

11.7 

Conversion to Diesel following TRBDS DSLT 5.3 - 6.9 n.a. 5.3 - 6.9 5.3 - 6.9 

6-Speed Manual/Improved Internals 6MAN 0.5 0.5 0.5 0.5 

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Technologies Considered in the Agencies’ Analysis
 

Improved Auto. Trans. Controls/Externals IATC 1.5 - 2.5 1.5 - 2.5 1.5 - 2.5 1.5 - 2.5 

Continuously Variable Transmission CVT 0.7 - 2.0 0.7 - 2.0 0.7 - 2.0 n.a. 

6/7/8-Speed Auto. Trans with Improved 

Internals NAUTO 1.4 - 3.4 1.4 - 3.4 1.4 - 3.4 1.4 - 3.4 

Dual Clutch or Automated Manual 

Transmission DCTAM 2.7 - 4.1 2.7 -4.1 2.7 - 4.1 2.7 - 4.1 

Electric Power Steering EPS 1.0 - 2.0 1.0 - 2.0 1.0 - 2.0 1.0 - 2.0 

Improved Accessories IACC 1.0 - 2.0 1.0 - 2.0 1.0 - 2.0 n.a. 

12V Micro-Hybrid MHEV 2.5 - 3.5 2.0 - 3.0 2.5 - 3.5 n.a. 

Belt mounted Integrated Starter Generator BISG 3.5 - 5.5 4.0 - 6.0 3.5 - 5.5 n.a. 

Crank mounted Integrated Starter Generator CISG 8.7 - 8.9 8.6 - 8.9 8.7 - 8.9 14.1 ­

16.3 

Power Split Hybrid PSHEV 6.3 ­

12.4 

6.3 ­

12.4 

6.3 ­

12.4 n.a. 

2-Mode Hybrid 2MHEV n.a. 3.0 - 7.3 3.0 - 7.3 4.1 - 9.5 

Plug-in Hybrid PHEV n.a. 

45.2 ­

47.7 n.a. n.a. 

Mass Reduction (1.5%) MS1 0.5 0.5 0.5 0.5 

Mass Reduction (3.5 to 8.5%) MS2 6.0 4.4 4.4 6.0 

Low Rolling Resistance Tires ROLL 1.0 - 2.0 1.0 - 2.0 1.0 - 2.0 1.0 - 2.0 

Low Drag Brakes LDB 0.5 - 1.0 n.a. 0.5 - 1.0 0.5 - 1.0 

Secondary Axle Disconnect SAX 1.0 - 1.5 1.0 - 1.5 1.0 - 1.5 1.0 - 1.5 

Aero Drag Reduction AERO 2.0 - 3.0 2.0 - 3.0 2.0 - 3.0 2.0 - 3.0 

3.5.2 EPA cost and effectiveness tables 

The tables below show costs and effectiveness values for each particular 

technology described throughout this chapter (those considered by EPA in its model). 

The costs are shown as incremental to a particular baseline as noted in the tables. In 

general, the costs are meant to reflect the cost of a given technology relative to a baseline 

vehicle or engine with fixed timing 4 valves per cylinder, port fuel injection and a 4 

speed automatic transmission. Where this is not the case is made clear in the tables. The 

same is true of the effectiveness tables below. As such, the costs and effectiveness values 

shown in the tables below will differ from the values contained in the NHTSA tables due 

to the different ways that the agency’s models handles them. However, the underlying 

cost and effectiveness values used by each agency in their respective analyses are 

consistent. 

Table 3-31 EPA’s Incremental Piece Costs for Engine Technologies Marked up to include both Direct and 

Indirect Costs in 2016 (2007 Dollars per Vehicle) 

TECHNOLOGY INCREMENTAL TO VEHICLE CLASS 

Small Car Large Car Minivan Small Truck Large Truck 

Low friction lubricants Base engine $3 $3 $3 $3 $3 

Engine friction reduction Base engine $50 $75 $75 $75 $100 

OHC Engines 

VVT – intake cam 

phasing Base engine $40 $80 $80 $80 $80 

VVT – coupled cam 

phasing Base engine $40 $80 $80 $80 $80 

VVT – dual cam phasing Base engine $73 $157 $157 $157 $157 

Cylinder deactivation Base engine n/a $150 $150 $150 $169 

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Discrete VVLT Base engine $125 $181 $181 $181 $259 

Continuous VVLT Base engine $245 $449 $449 $449 $489 

OHV Engines

Cylinder deactivation Base engine n/a $150 $150 $150 $169 

VVT – coupled cam 

phasing Base engine $40 $40 $40 $40 $40 

Discrete VVLT Base engine $141 $204 $204 $204 $291 

Continuous VVLT 

(includes conversion to 

Overhead Cam) 

Base engine w/ VVTcoupled $497 $1,048 $1,048 $1,048 $1,146 

Camless valvetrain 

(electromagnetic) Base engine $501 $501 $501 $501 $501 

GDI – stoichiometric I4 Base I4 $209 209 $209 209 209 

GDI – stoichiometric V6 Base V6 n/a $301 $301 $301 $301 

GDI – stoichiometric V8 Base V8 n/a $346 $346 n/a $346 

GDI – lean burn GDI - stoich $623 $623 $623 $623 $623 

Turbo w/o downsize

Downsize w/o turbo 

Turbocharge (single) Base I4 $397 n/a $397 n/a n/a 

Turbocharge (single) Base V6 n/a $420 $420 $420 $420 

Turbocharge (twin) Base engine $666 $666 $666 $666 $666 

Downsize to I4 DOHC V6 DOHC n/a -$384 -$384 -$384 -$384 

Downsize to I4 DOHC V6 SOHC n/a -$177 -$177 -$177 -$177 

Downsize to I4 DOHC V6 OHV n/a $265 $265 $265 $265 

Downsize to I4 DOHC I4 DOHC (larger) -$67 -$67 -$67 -$67 -$67 

Downsize to I3 DOHC I4 DOHC -$116 n/a n/a n/a n/a 

Downsize to V6 DOHC V8 DOHC n/a -$188 -$188 -$188 -$188 

Downsize to V6 DOHC V8 SOHC 2V n/a $59 $59 $59 $59 

Downsize to V6 DOHC V8 SOHC 3V n/a -$17 -$17 -$17 -$17 

Downsize to V6 DOHC V8 OHV n/a $310 $310 $310 $310 

Turbo with downsize 

Downsize to I4 DOHC 

& add turbo V6 DOHC w/o turbo n/a $149 $149 $149 $149 

Downsize to I4 DOHC 

& add turbo V6 SOHC w/o turbo n/a $323 $323 $323 $323 

Downsize to I4 DOHC 

& add turbo V6 OHV w/o turbo $771 $771 $771 $771 $771 

Downsize to I4 DOHC 

& add turbo 

I4 DOHC (larger) 

w/o turbo $391 n/a $391 n/a n/a 

Downsize to I3 DOHC 

& add turbo I4 DOHC w/o turbo $349 n/a n/a n/a n/a 

Downsize to V6 DOHC 

& add twin turbo V8 DOHC w/o turbo n/a $592 $592 $592 $592 

Downsize to V6 DOHC 

& add twin turbo 

V8 SOHC 2V w/o 

turbo n/a $816 $816 $816 $816 

Downsize to V6 DOHC 

& add twin turbo 

V8 SOHC 3V w/o 

turbo n/a $736 $736 $736 $736 

Downsize to V6 DOHC 

& add twin turbo V8 OHV w/o turbo n/a $1,099 $1,099 $1,099 $1,099 

Convert to V6 DOHC V6 SOHC n/a $258 $258 $258 $258 

Convert to V6 DOHC V6 OHV n/a $464 $464 $464 $464 

Convert to V8 DOHC V8 SOHC 2V n/a $292 $292 $292 $292 

Convert to V8 DOHC V8 SOHC 3V n/a $213 $213 $213 $213 

Convert to V8 DOHC V8 OHV n/a $509 $509 $509 $509 

Gasoline HCCI dualmode GDI - stoich $253 $375 $375 $375 $659 

Diesel – Lean NOx trap Base gasoline engine $1,877 

Diesel – urea SCR Base gasoline engine $2,655 $2,164 $2,164 $2,961 

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Technologies Considered in the Agencies’ Analysis
 

Table 3-32 EPA’s Incremental Piece Costs for Transmission Technologies Marked up to include both Direct and
 

Indirect Costs in 2016 (2007 Dollars per Vehicle)
 

TECHNOLOGY INCREMENTAL TO 

VEHICLE CLASS 

Small 

Car 

Large 

Car Minivan Small 

Truck 

Large 

Truck 

Aggressive shift logic Base trans $28 $28 $28 $28 $28 

Early torque converter lockup Base trans $25 $25 $25 $25 $25 

5-speed automatic 4-speed auto trans $90 $90 $90 $90 $90 

6-speed automatic 4-speed auto trans $99 $99 $99 $99 $99 

6-speed DCT – dry clutch 6-speed auto trans -$52 -$52 -$52 -$52 -$52 

6-speed DCT – wet clutch 6-speed auto trans -$7 -$7 -$7 -$7 -$7 

6-speed manual 5-speed manual trans $79 $79 $79 $79 $79 

CVT 4-speed auto trans $192 $224 $224 n/a n/a 

Table 3-33 EPA’s Incremental Piece Costs for Hybrid Technologies Marked up to include both Direct and
 

Indirect Costs in 2016 (2007 Dollars per Vehicle)
 

TECHNOLOGY INCREMENTAL 

TO 

VEHICLE CLASS 

Small Car Large Car Minivan Small Truck Large Truck 

Stop-Start Base engine & 

trans $351 $398 $398 $398 $437 

IMA/ISA/BSG 

(includes engine 

downsize) 

Base engine & 

trans $2,854 $3,612 $3,627 $3,423 $4,431 

2-Mode hybrid 

electric vehicle 

Base engine & 

trans $4,232 $5,469 $5,451 $4,943 $7,236 

Power-split 

hybrid electric 

vehicle 

Base engine & 

trans $3,967 $5,377 $5,378 $4,856 $7,210 

Plug-in hybrid 

electric vehicle 

IMA/ISA/BSG 

hybrid $6,922 $9,519 $9,598 $9,083 $12,467 

Plug-in hybrid 

electric vehicle 

Power-split 

hybrid $5,423 $7,431 $7,351 $7,128 $9,643 

Full electric 

vehicle 

Base engine & 

trans $27,628 n/a n/a n/a n/a 

Table 3-34 EPA’s Incremental Piece Costs for Accessory Technologies Marked up to include both Direct and
 

Indirect Costs in 2016 (2007 Dollars per Vehicle)
 

TECHNOLOGY INCREMENTAL 

TO 

VEHICLE CLASS 

Small Car Large Car Minivan Small Truck Large Truck 

Improved high efficiency 

alternator & 

electrification of 

accessories 

Base accessories $76 $76 $76 $76 $76 

Upgrade to 42 volt 

electrical system 

12 volt electrical 

system $86 $86 $86 $86 $86 

Electric power steering 

(12 or 42 volt) 

Base power 

steering $94 $94 $94 $94 $94 

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Joint Technical Support Document
 

Table 3-35 EPA’s Incremental Piece Costs for Vehicle Technologies Marked up to include both Direct and
 

Indirect Costs in 2016 (2007 Dollars per Vehicle)
 

TECHNOLOGY INCREMENTAL 

TO 

VEHICLE CLASS 

Small Car Large Car Minivan Small Truck Large Truck 

Aero drag reduction (20% 

on cars, 10% on trucks) Base vehicle $42 $42 $42 $42 $42 

Low rolling resistance 

tires Base tires $6 $6 $6 $6 $6 

Low drag brakes (ladder 

frame only) Base brakes n/a n/a n/a $63 $63 

Secondary axle disconnect 

(unibody only) Base vehicle $514 $514 $514 $514 n/a 

Front axle disconnect 

(ladder frame only) Base vehicle n/a n/a n/a $84 $84 

Table 3-36 through Table 3-40 summarize the estimates for the CO2 reduction 

estimates of various technologies which can be applied to cars and light-duty trucks. 

Table 3-36 Engine Technology Effectiveness 

Technology 

Absolute CO2 Reduction (% from baseline vehicle) 

Small Car Large 

Car Minivan Small Truck 

Large 

Truck 

Low friction lubricants – incremental to base engine 0.5 0.5 0.5 0.5 0.5 

Engine friction reduction – incremental to base engine 1-3 1-3 1-3 1-3 1-3 

Overhead Cam Branch 

VVT – intake cam phasing 2 1 1 1 2 

VVT – coupled cam phasing 3 4 2 3 4 

VVT – dual cam phasing 3 4 2 2 4 

Cylinder deactivation (includes imp. oil pump, 

if applicable) n.a. 6 6 6 6 

Discrete VVLT 4 3 3 4 4 

Continuous VVLT 5 6 4 5 5 

Overhead Valve Branch 

Cylinder deactivation (includes imp. oil 

pump, if applicable) n.a. 6 6 6 6 

VVT – coupled cam phasing 3 4 2 3 4 

Discrete VVLT 4 4 3 4 4 

Continuous VVLT (includes conversion to 

Overhead Cam) 5 6 4 5 5 

Camless valvetrain (electromagnetic) 5-15 5-15 5-15 5-15 5-15 

Gasoline Direct Injection–stoichiometric (GDI-S) 1-2 1-2 1-2 1-2 1-2 

Gasoline Direct Injection–lean burn (incremental to 

GDI-S) 8-10 9-12 9-12 9-12 10-14 

Gasoline HCCI dual-mode (incremental to GDI-S) 10-12 10-12 10-12 10-12 10-12 

Turbo+downsize (incremental to GDI-S) 5-7 5-7 5-7 5-7 5-7 

Diesel – Lean NOx trap[]* 15-26 15-26 15-26 15-26 15-26 

Diesel – urea SCR []* 15-26 15-26 15-26 15-26 15-26 

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Technologies Considered in the Agencies’ Analysis
 

Table 3-37 Transmission Technology Effectiveness 

Technology 

Absolute CO2 Reduction (% from baseline vehicle) 

Small 

Car 

Large 

Car Minivan Small 

Truck 

Large 

Truck 

5-speed automatic (from 4-speed auto) 2.5 2.5 2.5 2.5 2.5 

Aggressive shift logic 1-2 1-2 1-2 1-2 1-2 

Early torque converter lockup 0.5 0.5 0.5 0.5 0.5 

6-speed automatic (from 4-speed auto) 4.5-6.5 4.5-6.5 4.5-6.5 4.5-6.5 4.5-6.5 

6-speed AMT (from 4-speed auto) 9.5-14.5 9.5-14.5 9.5-14.5 9.5-14.5 9.5-14.5 

6-speed manual (from 5-speed manual) 0.5 0.5 0.5 0.5 0.5 

CVT (from 4-speed auto) 6 6 6 n.a. n.a. 

Table 3-38 Hybrid Technology Effectiveness 

Technology 

Absolute CO2 Reduction (% from baseline vehicle) 

Small 

Car 

Large 

Car Minivan Small Truck 

Large 

Truck 

Stop-Start with 42 volt system 7.5 7.5 7.5 7.5 7.5 

IMA/ISA/BSG (includes engine downsize) 30 25 20 20 20 

2-Mode hybrid electric vehicle n.a. 40 40 40 25 

Power-split hybrid electric vehicle 35 35 35 35 n.a. 

Full-Series hydraulic hybrid 40 40 40 40 30 

Plug-in hybrid electric vehicle 58 58 58 58 47 

Full electric vehicle (EV) 100 100 n.a. n.a. n.a. 

Table 3-39 Accessory Technology Effectiveness 

Technology 

Absolute CO2 Reduction (% from baseline vehicle) 

Small 

Car 

Large 

Car Minivan Small 

Truck 

Large 

Truck 

Improved high efficiency alternator & electrification of 

accessories (12 volt) 1-2 1-2 1-2 1-2 1-2 

Electric power steering (12 or 42 volt) 1.5 1.5-2 2 2 2 

Improved high efficiency alternator & electrification of 

accessories (42 volt) 2-4 2-4 2-4 2-4 2-4 

Table 3-40 Other Vehicle Technology Effectiveness 

Technology 

Absolute CO2 Reduction (% from baseline vehicle) 

Small 

Car 

Large 

Car Minivan Small Truck 

Large 

Truck 

Aero drag reduction (20% on cars, 10% on trucks) 3 3 3 2 2 

Low rolling resistance tires (10%) 1-2 1-2 1-2 1-2 n.a. 

Low drag brakes (ladder frame only) n.a. n.a. n.a. 1 1 

Secondary axle disconnect (unibody only) 1 1 1 1 n.a. 

Front axle disconnect (ladder frame only) n.a. n.a. n.a. 1.5 1.5 

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Joint Technical Support Document
 

Appendix 3.A
 

Gross Domestic Product Adjustments Used to Adjust 2002-2006 Dollars
 

to 2007 Dollars 

Throughout this analysis, the incremental cost estimates have been presented in 

terms of 2007 dollars. However, most of the data sources relied upon as the basis for the 

estimates have presented costs in terms of 2006 dollars. To convert the 2006 dollars to 

2007 dollars, the “chain-type” Gross Domestic Product (GDP) deflator was used. Table 

3.A-1 shows the GDP deflator data used for this analysis. 

Table 3.A-1 Price Indexes for Gross Domestic Product 

LINE 2002 2003 2004 2005 2006 2007 2008 

1 Gross domestic product 104.193 106.409 109.462 113.039 116.676 119.819 122.5 

2 Personal consumption 

expenditures 103.542 105.597 108.392 111.581 114.675 117.659 121.596 

3 Durable goods 95.766 92.366 90.696 89.984 88.772 87.154 86.071 

4 Nondurable goods 102.089 104.145 107.626 111.606 114.984 118.407 124.666 

5 Services 106.018 109.379 112.929 116.7 120.752 124.712 128.752 

6 Gross private domestic 

investment 101.64 103.191 106.686 111.381 116.102 117.735 118.509 

7 Fixed investment 101.66 103.313 106.845 111.638 116.38 117.995 118.727 

8 Nonresidential 99.513 99.591 100.896 103.829 107.277 108.739 110.513 

9 Structures 110.03 113.872 120.912 135.177 151.822 157.662 163.298 

10 Equipment and software 95.956 94.912 94.6 94.534 94.594 94.87 95.485 

11 Residential 107.24 112.372 120.587 129.268 136.897 138.884 135.587 

12 Change in private inventories 

13 Net exports of goods and 

services 

14 Exports 99.273 101.429 104.997 108.814 112.618 116.586 122.788 

15 Goods 98.661 100.64 104.323 107.536 111.131 115.062 121.147 

16 Services 100.762 103.333 106.632 111.855 116.156 120.211 126.684 

17 Imports 96.341 99.685 104.526 111.154 115.932 120.168 132.698 

18 Goods 95.288 98.063 102.915 109.568 114.171 118.326 131.408 

19 Services 101.878 108.291 113.049 119.496 125.257 129.928 139.217 

20 

Government consumption 

expenditures and gross 

investment 

105.507 109.849 114.754 121.47 127.239 132.941 139.234 

21 Federal 105.631 110.094 115.322 120.834 125.806 130.076 134.289 

22 National defense 105.792 110.751 115.932 121.944 127.381 131.874 136.574 

23 Nondefense 105.345 108.898 114.218 118.744 122.803 126.636 129.869 

24 State and local 105.435 109.712 114.431 121.862 128.109 134.671 142.219 

Source: Bureau of Economic Analysis, U.S. Department of Commerce, 

http://www.bea.gov/national/nipaweb/TableView.asp?SelectedTable=4 

Using Line 1 in the table, 2006 dollars can be adjusted to 2007 dollars by 

multiplying the ratio 119.8/116.7, or 1.027, by any value expressed in 2006 dollars. For 

example, $1 in 2006 dollars would be $1 x 1.027, or $1.03 in 2007 dollars. 

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Technologies Considered in the Agencies’ Analysis
 

References
 

1 “EPA Staff Technical Report: Cost and Effectiveness Estimates of Technologies Used to Reduce Lightduty Vehicle Carbon Dioxide Emissions,” EPA420-R-08-008, March 2008, Docket EPA-HQ-OAR-2009­

0472-0132. 

2 “Effectiveness and Impact of Corporate Average Fuel Economy (CAFE) Standards,” National Research 

Council, National Academy of Sciences, 2002, Docket EPA-HQ-OAR-2009-0472-0147. 

3 “Reducing Greenhouse Gas Emissions from Light-Duty Motor Vehicles,” Northeast States Center for a 

Clean Air Future, September 2004, Docket EPA-HQ-OAR-2009-0472-0148. 

4 “Staff Report: Initial Statement of Reasons for Proposed Rulemaking,” California Environmental 

Protection Agency, Air Resources Board, Regulations to Control Greenhouse Gas Emissions from Motor 

Vehicles, August 6, 2004, Docket EPA-HQ-OAR-2009-0472-0134. 

5 “Technology to Improve the Fuel Economy of Light Duty Trucks to 2015,” Energy and Environmental 

Analysis, Inc., May 2006, Docket EPA-HQ-OAR-2009-0472-0136. 

6 “Variable Costs of Fuel Economy Technologies,” prepared for The Alliance of Automobile 

Manufacturers, June 1, 2008, Docket EPA-HQ-OAR-2009-0472-0144; and, “Variable Costs of Fuel 

Economy Technologies,” prepared for The Alliance of Automobile Manufacturers, June 1, 2008, Amended 

December 10, 2008, Docket EPA-HQ-OAR-2009-0472-0145. 

7 National Highway Traffic Safety Administration, Request for Product Plan Information, 74 FR 48192, 

September 22, 2009. 

8 Memorandum from Don Kopinski, U.S. EPA, Docket No. EPA-HQ-OAR-2009-0472-0217, dated 

September 11, 2009. 

9 U.S. Environmental Protection Agency, “Draft Report – Light-Duty Technology Cost Analysis Pilot 

Study,” Contract No. EP-C-07-069, Work Assignment 1-3, September 3, 2009, Docket EPA-HQ-OAR2009-0472-0149; EPA-420-R-09-020, EPA docket number EPA-HQ-OAR-2009-0472-11282. 

10 “Light-duty Technology Cost Analysis – Report on Additional Case Studies,” EPA-420-R-10-010. 

11 U.S. Environmental Protection Agency, “Light-Duty Technology Cost Analysis Pilot Study,” Contract 

No. EP-C-07-069, Work Assignment 1-3, December 2009, EPA-420-R-09-020, Docket EPA-HQ-OAR2009-0472-0149; peer review report dated November 6, 2009, is at EPA-HQ-OAR-2009-0472-11285; 

“Light-duty Technology Cost Analysis – Report on Additional Case Studies,” EPA-420-R-10-010.. 

12 NHTSA’s 2011 CAFE final rule at 74 FR 14196. 

13 National Income and Product Accounts Table 1.1.4: Price Indexes for Gross Domestic Product; 

downloaded on 6/25/2009 At 12:00:25 PM, Last Revised June 25, 2009; Bureau of Economic Analysis, 

U.S. Department of Commerce, www.bea.gov. 

14 The 2002 NAS Report, Docket EPA-HQ-OAR-2009-0472-0147; the 2004 study done by NESCCAF, 

Docket EPA-HQ-OAR-2009-0472-0148; the California Air Resources Board (CARB) Initial Statement of 

Reasons in support of their carbon rulemaking, Docket EPA-HQ-OAR-2009-0472-0134; a 2006 study done 

3-97
 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Joint Technical Support Document
 

by Energy and Environmental Analysis (EEA) for the Department of Energy, Docket EPA-HQ-OAR-2009­

0472-0136; a study done by Martec for the Alliance of Automobile Manufacturers, Docket EPA-HQ-OAR2009-0472-0144, and the 2008 Martec Report which updated that study, Docket EPA-HQ-OAR-2009­

0472-0145; and vehicle fuel economy certification data, and confidential data submitted by manufacturers 

in response to the March and November 2009 requests for product plans. 

15 U.S. Environmental Protection Agency, “Draft Report – Light-Duty Technology Cost Analysis Pilot 

Study,” Contract No. EP-C-07-069, Work Assignment 1-3, September 3, 2009, Docket EPA-HQ-OAR2009-0472-0149. 

16 Rogozhin, Alex, Michael Gallaher, and Walter McManus, “Automobile Industry Retail Price Equivalent 

and Indirect Cost Multipliers,” EPA 420-R-09-003, Docket EPA Docket EPA-HQ-OAR-2009-0472-0142, 

February 2009, http://epa.gov/otaq/ld-hwy/420r09003.pdf. 

17 “Memorandum: Documentation of the Development of Indirect Cost Multipliers for Three Automotive 

Technologies,” Gloria Helfand and Todd Sherwood, Office of Transportation and Air Quality, U.S. 

Environmental Protection Agency, August 2009, Docket EPA-HQ-OAR-2009-0472-0158. 

18 “Final Regulatory Analysis: Control of Emissions from Nonroad Diesel Engines,” EPA420-R-04-007, 

May 2004, Docket EPA-HQ-OAR-2009-0472-0140; “Regulatory Impact Analysis: Control of Emissions 

of Air Pollution from Locomotive Engines and Marine Compression-Ignition Engines Less than 30 Liters 

per Cylinder,” EPA420-D-08-001a, May 2008, Docket EPA-HQ-OAR-2009-0472-0139; NHTSA 2011 

CAFE final rule at 74 FR 14196. 

19 “Learning Curves in Manufacturing,” Linda Argote and Dennis Epple, Science, February 23, 1990, Vol. 

247, pp. 920-924, Docket EPA-HQ-OAR-2009-0472-0143. 

20 NHTSA 2011 CAFE final rule at 74 FR 14196; “EPA Staff Technical Report: Cost and Effectiveness 

Estimates of Technologies Used to Reduce Light-Duty Vehicle Carbon Dioxide Emissions,” EPA420-R08-008, March 2008, Docket EPA-HQ-OAR-2009-0472-0132; “Final Regulatory Analysis: Control of 

Emissions from Nonroad Diesel Engines,” EPA420-R-04-007, May 2004, Docket EPA-HQ-OAR-2009­

0472-0140; “Regulatory Impact Analysis: Control of Emissions of Air Pollution from Locomotive Engines 

and Marine Compression-Ignition Engines Less than 30 Liters per Cylinder,” EPA420-D-08-001a, May 

2008, Docket EPA-HQ-OAR-2009-0472-0139. 

21 “Impact of Friction Reduction Technologies on Fuel Economy,” Fenske, G. Presented at the March 2009 

Chicago Chapter Meeting of the ‘Society of Tribologists and Lubricated Engineers’ Meeting, March 18th, 

2009. Available at: http://www.chicagostle.org/program/2008­

2009/Impact%20of%20Friction%20Reduction%20Technologies%20on%20Fuel%20Economy%20­

%20with%20VGs%20removed.pdf (last accessed July 9, 2009). 

22 “Light-Duty Automotive Technology and Fuel Economy Trends: 1975 Through 2007”, EPA420-S-07­

001, September 2007, Docket EPA-HQ-OAR-2009-0472-0137. Available at 

http://www.epa.gov/oms/cert/mpg/fetrends/fetrends-archive.htm (last accessed July 9, 2009). 

23 Paul Whitaker, Ricardo, Inc., “Gasoline Engine Performance And Emissions – Future Technologies and 

Optimization,” ERC Symposium, Low Emission Combustion Technologies for Future IC Engines, 

Madison, WI, June 8-9, 2005, Docket EPA-HQ-OAR-2009-0472-0155. Available at 

http://www.erc.wisc.edu/symposiums/2005_Symposium/June%208%20PM/Whitaker_Ricardo.pdf (last 

accessed Nov. 9, 2008). 

24 Stefan Trampert, FEV Motorentechnik GmbH, “Engine and Transmission Development Trends - Rising 

Fuel Cost Pushes Technology,” Symposium on International Automotive Technology, Pune, India, January 

2007. 

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Technologies Considered in the Agencies’ Analysis
 

25 “Binning of FEV Costs to GDI, Turbo-charging, and Engine Downsizing,” memorandum to Docket 

EPA-HQ-OAR-2009-0472, from Michael Olechiw, U.S. EPA, dated March 25, 2010. 

26 “Binning of FEV Costs to GDI, Turbo-charging, and Engine Downsizing,” memorandum to Docket 

EPA-HQ-OAR-2009-0472, from Michael Olechiw, U.S. EPA, dated March 25, 2010. 

27 Stefan Wolff, Dirk Abendroth, Werner Weigl, Claus-Peter Linner, Rupert Neudecker, Michael 

Schneider, Wolfgang Huber, and Andreas Rau, BMW, “Introducing The Automatic Start-Stop (ASS) 

Function In Series Models,” 7th Stuttgart Automotive Vehicle and Engine Symposium, Organised by 

FKFS, Mar 2007, Vol. 1. 

28 G.K. Fraidl, P.E. Kapus, and H. Friedl, AVL List GmbH, “Future Gasoline Engine Technologies for 130 

g/Km CO2,” VKM-THD 11th Symposium on the Working Process of Combustion Engines, TU Graz, 

Sept. 2007. 

29 “Development and Optimization of the Ford 3.5L V6 EcoBoost Combustion System,” Yi,J., Wooldridge, 

S., Coulson, G., Hilditch, J. Iyer, C.O., Moilanen, P., Papaioannou, G., Reiche, D. Shelby, M., 

VanDerWege, B., Weaver, C. Xu, Z., Davis, G., Hinds, B. Schamel, A. SAE Technical Paper No. 2009-01­

1494, 2009, Docket EPA-HQ-OAR-2009-0472-2860. 

30 David Woldring and Tilo Landenfeld of Bosch, and Mark J. Christie of Ricardo, “DI Boost: Application 

of a High Performance Gasoline Direct Injection Concept,” SAE 2007-01-1410. Available at 

http://www.sae.org/technical/papers/2007-01-1410 (last accessed Nov. 9, 2008) 

31 Yves Boccadoro, Loïc Kermanac’h, Laurent Siauve, and Jean-Michel Vincent, Renault Powertrain 

Division, “The New Renault TCE 1.2L Turbocharged Gasoline Engine,” 28th Vienna Motor Symposium, 

April 2007. 

32 Tobias Heiter, Matthias Philipp, Robert Bosch, “Gasoline Direct Injection: Is There a Simplified, CostOptimal System Approach for an Attractive Future of Gasoline Engines?” AVL Engine & Environment 

Conference, September 2005. 

33 U.S. Environmental Protection Agency, “Draft Report – Light-Duty Technology Cost Analysis Pilot 

Study,” Contract No. EP-C-07-069, Work Assignment 1-3, September 3, 2009, Docket EPA-HQ-OAR2009-0472-0149. 

34 Docket No. NHTSA-2008-0089-0193.1 

35 Cairns et al., Lotus, “Low Cost Solutions for Improved Fuel Economy in Gasoline Engines,” Global 

Powertrain Congress September 27-29, 2005, vol. 33. Available at http://www.gpcicpem.org/pages/publications.html (last accessed Nov. 9, 2008). 

36 Tim Lake, John Stokes, Richard Murphy, and Richard Osborne of Ricardo and Andreas Schamel of 

Ford-Werke, “Turbocharging Concepts for Downsized DI Gasoline Engines,” VKA/ika Aachen 

Colloquium 2003. Available at http://cat.inist.fr/?aModele=afficheN&cpsidt=16973598 (last accessed 

Nov. 9, 2008). 

37 “Interim Report: New Powertrain Technologies and Their Projected Costs,” October 2005, EPA420-R05-012, Docket EPA-HQ-OAR-2009-0472-0138. 

38 “Cost and Fuel Economy Comparison of Diesel and Gasoline Powertrains in Passenger Cars and Light 

Trucks,” submitted by FEV Engine Technology, Inc., April 23, 2003, contained as Appendix I within EPA 

Interim Technical Report EPA420-R-04-002, Docket EPA-HQ-OAR-2009-0472-0141. 

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39 EPA’s 2007/2010 diesel heavy-duty highway final rule at66 FR 5002; EPA’s Locomotive and Marine 

final rule at 73 FR 37096. 

40 http://www.platinum.matthey.com/ (web page printout is contained in Docket EPA-HQ-OAR-2009-0472 

as “Johnson-Matthey PGM data for March 2009.pdf”). 

41 General Motors, news release, “From Hybrids to Six-Speeds, Direct Injection And More, GM’s 2008 

Global Powertrain Lineup Provides More Miles with Less Fuel” (released Mar. 6, 2007). Available at 

http://www.gm.com/experience/fuel_economy/news/2007/adv_engines/2008-powertrain-lineup-082707.jsp 

(last accessed Sept. 18, 2008). 

42 “EPA Staff Technical Report: Cost and Effectiveness Estimates of Technologies Used to Reduce Lightduty Vehicle Carbon Dioxide Emissions” Environmental Protection Agency, EPA420-R-08-008, March 

2008, at page 17, Docket EPA-HQ-OAR-2009-0472-0132. 

43 “Latest Chevrolet Volt Battery Pack and Generator Details and Clarifications.” Lyle Dennis interview 

of Rob Peterson (GM) regarding the all-electric drive range of the GM Volt, August 29, 2007. Accessed on 

the Internet on June 30, 2009 at: http://gm-volt.com/2007/08/29/latest-Chevrolet-volt-battery-pack-andgenerator-details-and-clarifications/ 

44 “Active Combination of Ultracapacitorsand Batteries for PHEV ESS.” Bohn, T. U.S. Department of 

Energy 2009 Vehicle Technologies Merit Review, May 20, 2009, Docket EPA-HQ-OAR-2009-0472-0163. 

45 Anderman, Advanced Automotive Battery Conference, May 2008. Proceedings available for purchase at 

http://www.advancedautobat.com/Proceedings/index.html (last accessed October 17, 2008). 

46 74 FR 14291 (Mar. 30, 2009) 

47 “Electric Cars: Plugged In, Batteries must be included,” Deutsche Bank Global Markets Research 

Company, June 9, 2008, Docket EPA-HQ-OAR-2009-0472-0154. 

48 http://automobiles.honda.com/insight-hybrid/features.aspx?Feature=ima (last accessed on March 29, 

2010; web page printout is contained in Docket EPA-HQ-OAR-2009-0472 as “Honda IMA webpage.pdf”). 

49 “Preliminary Vehicle Mass Estimation Using Empirical Subsystem Influence Coefficients,” Malen, D.E., 

Reddy, K. Auto-Steel Partnership Report, May 2007, Docket EPA-HQ-OAR-2009-0472-0169. Accessed 

on the Internet on May 30, 2009 at: http://www.a-sp.org/database/custom/Mass%20Compounding%20­

%20Final%20Report.pdf 

50 “Benefit Analysis: Use of Aluminum Structures in Conjunction with Alternative Powertrain 

Technologies in Automobiles,” Bull, M. Chavali, R., Mascarin, A., Aluminum Association Research 

Report, May 2008, Docket EPA-HQ-OAR-2009-0472-0168. Accessed on the Internet on April 30, 2009 

at: http://www.autoaluminum.org/downloads/IBIS-Powertrain-Study.pdf 

51 “Future Generation Passenger Compartment-Validation (ASP 241)” Villano, P.J., Shaw, J.R., 

Polewarczyk, J., Morgans, S., Carpenter, J.A., Yocum, A.D., in “Lightweighting Materials – FY 2008 

Progress Report,” U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Vehicle 

Technologies Program, May 2009, Docket EPA-HQ-OAR-2009-0472-0190. 

52 “Preliminary Vehicle Mass Estimation Using Empirical Subsystem Influence Coefficients,” Malen, D.E., 

Reddy, K. Auto-Steel Partnership Report, May 2007, Docket EPA-HQ-OAR-2009-0472-0169. Accessed 

on the Internet on May 30, 2009 at: http://www.a-sp.org/database/custom/Mass%20Compounding%20­

%20Final%20Report.pdf 

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Technologies Considered in the Agencies’ Analysis
 

53 “Future Generation Passenger Compartment – Final Report,” Auto-Steel Partnership Report, June 2007. 

Accessed on the Internet on May 30, 2009 at: http://www.a-sp.org/database/custom/FGPC%20Phase 

%201%20Final%20Report.pdf 

54 “Lighten Up!,” Brooke, L., Evans, H. Automive Engineering International, Vol. 117, No. 3, March 2009. 

55 “2008/9 Blueprint for Sustainability,” Ford Motor Company. Available at: http:// 

www.ford.com/go/sustainability (last accessed February 8, 2010). 

56 “Mazda to cut vehicle fuel consumption 30 percent by 2015,” Mazda press release, June 23, 2009. 

Available at: http://www.mazda.com/publicity/release/2008/200806/080623.html (last accessed February 

8, 2010). 

57 “Mazda: Don’t believe hot air being emitted by hybrid hype,” Greimel, H. Automotive News, March 30, 

2009. 

58 “Factor of Two: Halving the Fuel Consumption of New U.S. Automobiles by 2035,” Cheah, L., Evans, 

C., Bandivadekar, A., Heywood, J., Massachusetts Institute of Technology, Publication No. LFEE 2007-04 

RP, October 2007, Docket EPA-HQ-OAR-2009-0472-0174. 

59 “FY 2007 Progress Report for Lightweighting Materials”, Davis, P., Sullivan, R., Carpenter, J., U.S. 

Department of Energy Office of Vehicle Technologies, December 2008, Docket EPA-HQ-OAR-2009­

0472-0165. Accessed on the Internet on April 30, 2009 at: 

http://www1.eere.energy.gov/vehiclesandfuels/resources/vt_lm_fy07.html 

60 “Benefit Analysis: Use of Aluminum Structures in Conjunction with Alternative Powertrain 

Technologies in Automobiles,” Bull, M. Chavali, R., Mascarin, A., Aluminum Association Research 

Report, May 2008, Docket EPA-HQ-OAR-2009-0472-0168. Accessed on the Internet on April 30, 2009 

at: http://www.autoaluminum.org/downloads/IBIS-Powertrain-Study.pdf 

61 “Basic Analysis of the Cost and Long-Term Impact of the Energy Independence and Security Act Fuel 

Economy Standards” Sierra Research Report No. SR2008-04-01, Docket EPA-HQ-OAR-2009-0472-0173. 

62 “Factor of Two: Halving the Fuel Consumption of New U.S. Automobiles by 2035” L. Cheah, C. Evans, 

A. Bandivadekar and J. Heywood, Publication No. LFEE 2007-04 RP, October 2007, Docket EPA-HQOAR-2009-0472-0174. 

63 “Tires and Passenger Vehicle Fuel Economy,” Transportation Research Board Special Report 286, 

National Research Council of the National Academies, 2006, Docket EPA-HQ-OAR-2009-0472-0146. 

64 “An HCCI Engine Power Plant for a Hybrid Vehicle,” Sun, R., R. Thomas and C. Gray, Jr., SAE 

Technical Paper No. 2004-01-0933, 2004. 

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CHAPTER 4:Economic Assumptions Used in the Agencies’ Analysis 

4.1 How the agencies use the economic assumptions in their analyses 

Improving new vehicles’ fuel efficiency and reducing greenhouse gas (GHG) emissions 

provides direct benefits to their buyers and users by reducing fuel consumption and fuel costs 

throughout those vehicles’ lifetimes, stimulating increased vehicle use through the fuel economy 

rebound effect, and increasing vehicles’ driving range so that they require less frequent refueling. 

At the same time, the reduction in fuel use that results from requiring higher fuel economy and 

reducing GHGs also produces wider benefits to the U.S. economy by lowering the cost of 

economic externalities that result from U.S. petroleum consumption and imports, including 

reducing the price of petroleum, lowering the potential costs from disruption in the flow of oil 

imports, and possibly reducing federal outlays to secure imported oil supplies and cushion the 

U.S. economy against their potential interruption. Reducing fuel consumption and GHGs also 

lowers the economic costs of environmental externalities resulting from fuel production and use, 

including reducing the impacts on human health from emissions of criteria air pollutants, and 

reducing future economic damages from potential changes in the global climate caused by 

greenhouse gas emissions. 

These benefits are partly offset by the increase in fuel use that results from added vehicle 

use due to the fuel economy rebound effect, as well as by added costs from the increased 

congestion, crashes, and noise caused by increased vehicle use. They might also be offset by any 

loss in the utility that new vehicles provide to their buyers (and subsequent owners) if 

manufacturers include reductions in vehicles’ performance, carrying capacity, or comfort as part 

of their strategies to comply with higher fuel economy requirements and GHG standards. 

Nevertheless, the total economic benefits from requiring higher fuel economy and reducing 

GHGs are likely to be substantial, and the EPA and NHTSA have attempted to develop detailed 

estimates of the economic benefits from adopting more stringent standards. 

This section discusses the common economic values used by both NHTSA and EPA. 

These economic inputs incorporate a range of forecast information, economic estimates, and 

input parameters. This section describes the sources that EPA and NHTSA have relied upon for 

this information, the rationale underlying each assumption, and the agencies’ estimates of 

specific parameter values. These common economic values are then used as inputs into each 

Agencies’ respective modeling and other analyses of the economic benefits and costs of the EPA 

and NHTSA programs. While the underlying economic input values are common to both 

Agencies, it is the differences in the way each Agency assesses its program that result in 

differing benefits estimates. This issue is discussed further in Section I.C of the preamble to the 

joint rulemaking. 

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4.2 What economic assumptions do the agencies use? 

4.2.1 The on-road fuel economy “gap” 

Actual fuel economy levels achieved by vehicles in on-road driving fall significantly 

short of their levels measured under the laboratory-like test conditions used under the CAFE 

program to establish its published fuel economy ratings for different models. In analyzing the 

fuel savings from passenger car and light truck fuel efficiency and GHG standards, the agency 

adjusts the actual fuel economy performance of each passenger car and light truck model 

downward from its rated value to reflect the expected size of this on-road fuel economy “gap.” 

In December 2006, EPA adopted changes to its regulations on fuel economy labeling, which 

were intended to bring vehicles’ rated fuel economy levels closer to their actual on-road fuel 

economy levels.1 

Comparisons of on-road and CAFE fuel economy levels developed by EPA as part of its 

Final Rule implementing new fuel economy labeling requirements for new vehicles indicate that 

actual on-road fuel economy for light-duty vehicles averages 20 percent lower than published 

fuel economy levels.2 While there is great heterogeneity between individual drivers, as 

discussed in the referenced material, 20% represents an accurate average for modeling a fleet. 

For example, if the overall EPA fuel economy rating of a light truck is 20 mpg, the on-road fuel 

economy actually achieved by a typical driver of that vehicle is expected to be 16 mpg (20*.80). 

In its analysis supporting the Final Rule establishing CAFE standards for MY 2011, NHTSA 

employed EPA’s revised estimate of this on-road fuel economy gap in its analysis of the fuel 

savings resulting from alternative fuel efficiency standards. 

An analysis conducted by NHTSA confirmed that EPA’s estimate of a 20 percent gap 

between test and on-road fuel economy is well-founded. The agency used data on the number of 

passenger cars and light trucks of each model year that were in service (registered for use) during 

each calendar year from 2000 through 2006, average fuel economy for passenger cars and light 

trucks produced during each model year, and estimates of average miles driven per year by cars 

and light trucks of different ages during each calendar year over that period. These data were 

combined to develop estimates of the usage-weighted average fuel economy that the U.S. 

passenger car and light truck fleets would have achieved during each year from 2000 through 

2006 under test conditions. 

Table 4-1 compares NHTSA’s estimates of fleet-wide average fuel economy under test 

conditions for 2000 through 2006 to the Federal Highway Administration’s (FHWA) published 

estimates of actual on-road fuel economy achieved by passenger cars and light trucks during 

each of those years. As it shows, FHWA’s estimates of actual fuel economy for passenger cars 

ranged from 21 percent to 23 percent lower than NHTSA’s estimates of its fleet-wide average 

value under test conditions over this period. Similarly, FHWA’s estimates of actual fuel 

economy for light trucks ranged from 16 percent to 18 percent lower than NHTSA’s estimates of 

average light truck fuel economy under test conditions. Thus, these results appear to confirm 

that the 20 percent on-road fuel economy discount or gap represents a reasonable estimate for 

use in evaluating the fuel savings likely to result from alternative fuel efficiency standards for 

MY 2012-2016 vehicles. 

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We are aware of two potential issues involved in these estimates. One, the estimates of 

total annual car and truck VMT are developed by the states and submitted to FHWA. Each state 

uses its own definition of a car and a truck. For example, some states classify minivans as cars 

and some as trucks. Thus, there are known inconsistencies with these estimates when evaluated 

separately for cars and trucks. Also, total gasoline consumption can be reasonably estimated 

from excise tax receipts, but separate estimates for cars and trucks are not available. We are not 

aware of the precise methodology used to develop the distinct on-road fuel economy estimates 

for cars and trucks developed by FHWA. We do not believe that they are based on direct 

measurements from substantial numbers of vehicles, as no such test programs were found by 

EPA during its fuel economy labeling rule in 2006. Also, the year-to-year consistency for both 

car and truck fuel economy implies some methodology other than direct measurement. For this 

reason, NHTSA and EPA are not using distinct on-road fuel economy gaps for cars and trucks, 

but one common value of 20 percent for both vehicle classes for purposes of estimating the fuel 

savings of the standards. 

Table 4-1 Estimated Fleet-Wide Fuel Economy of Passenger Cars and Light Trucks
 

Compared to Reported Fuel Economy
 

YEAR 

PASSENGER CARS LIGHT-DUTY TRUCKS 

NHTSA 

Estimated 

Test MPG 

FHWA 

Reported 

Actual MPG 

Percent 

Difference 

NHTSA 

Estimated 

Test MPG 

FHWA 

Reported 

Actual MPG 

Percent 

Difference 

2000 28.2 21.9 -22.2% 20.8 17.4 -16.3% 

2001 28.2 22.1 -21.7% 20.8 17.6 -15.5% 

2002 28.3 22.0 -22.3% 20.9 17.5 -16.2% 

2003 28.4 22.2 -21.9% 21.0 17.2 -18.0% 

2004 28.5 22.5 -21.1% 21.0 17.2 -18.3% 

2005 28.6 22.1 -22.8% 21.1 17.7 -16.3% 

2006 28.8 22.5 -21.8% 21.2 17.8 -16.2% 

Avg., 

2000­

2006 

28.4 22.2 -22.0% 21.0 17.5 -16.7% 

4.2.2 Fuel prices and the value of saving fuel 

Projected future fuel prices are a critical input into the preliminary economic analysis of 

alternative fuel efficiency and GHG standards, because they determine the value of fuel savings 

both to new vehicle buyers and to society. As in the proposal, EPA and NHTSA relied on the 

most recent fuel price projections from the U.S. Energy Information Administration’s (EIA) 

Annual Energy Outlook (AEO) for this analysis. In the proposal, we used the AEO 2009 (April 

2009 release). For the final rulemaking presented here, we updated to the more recent AEO 

2010 Early Release.3 The Reference Case forecasts inflation-adjusted (constant-dollar) retail 

gasoline and diesel fuel prices and represents the EIA’s most up-to-date estimate of the most 

4 likely course of future prices for petroleum products.

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AEO 2010 also reflects the provisions of the Energy Independence and Security Act of 

2007 (EISA), including the requirement that the combined mpg level of U.S. cars and light 

trucks reach 35 miles per gallon by model year 2020. Because this provision would be expected 

to reduce future U.S. demand for gasoline and other fuels, the agencies conducted analysis 

whether AEO 2010 is suitable for valuing the projected reductions in fuel use. In response to this 

concern, the agencies note that EIA issued a revised version of AEO 2008 in June 2008, which 

modified its previous December 2007 Early Release of AEO 2008 to reflect the effects of the 

recently-passed EISA legislation. The fuel price forecasts reported in EIA’s Revised Release of 

AEO 2008 differed by less than one cent per gallon over the entire forecast period (2008–2030) 

from those previously issued as part of its initial release of AEO 2008. Thus, the agencies are 

reasonably confident that the fuel price forecasts presented in AEO 2010 and used to analyze the 

value of fuel savings projected to result from this rule are not unduly affected by the CAFE 

provisions of EISA, and therefore do not cause a baseline problem. For more detail, please see 

5 the docket memo.

As compared to the gasoline prices used in the proposal, AEO 2010 Early Release fuel 

prices are generally lower (in absolute 2007 dollars). A comparison table is presented below, 

Table 4-2. In broad strokes, future projections of fuel price are generally lower in AEO 2010 

than in AEO 2009. 

Table 4-2 Gasoline Prices for Selected Years in AEO 2009 and 2010 

(Presented in constant 2007$ and including all taxes) 

2015 2020 2030 

AEO 2010 $3.00 $3.27 $3.60 

AEO 2009 $3.16 $3.62 $3.82 

Federal government agencies generally use EIA’s projections in their assessments of 

future energy-related policies. The retail fuel price forecasts presented in AEO 2010 span the 

period from 2007 through 2035. Measured in constant 2007 dollars, the AEO 2010 Reference 

Case forecast of retail gasoline prices during calendar year 2020 is $3.27 per gallon, rising 

gradually to $3.83 by the year 2035 (these values include federal, state and local taxes). 

However, valuing fuel savings over the maximum lifetimes of passenger cars and light trucks 

used in this analysis requires fuel price forecasts that extend through 2050, approximately the 

last year during which a significant number of MY 2016 vehicles will remain in service.a To 

obtain fuel price forecasts for the years 2036 through 2050, the agency assumes that retail fuel 

prices will continue to increase after 2035 at the average annual rates projected for 2020-2035 in 

the AEO 2010 Reference Case.b This assumption results in a projected retail price of gasoline 

that reaches $4.49 in 2050. 

a The agency defines the maximum lifetime of vehicles as the highest age at which more than 2 percent of those 

originally produced during a model year remain in service. In the case of light-duty trucks, for example, this age has 

typically been 36 years for recent model years. 

b This projection uses the rate of increase in fuel prices for 2020-2030 rather than that over the complete forecast 

period (2009-2030) because there is extreme volatility in the forecasts for the years 2009 through approximately 

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The value of fuel savings resulting from improved fuel economy and GHG emissions to 

buyers of light-duty vehicles is determined by the retail price of fuel, which includes federal, 

state, and any local taxes imposed on fuel sales. Total taxes on gasoline, including federal, state, 

and local levies averaged $0.42 per gallon during 2008, while those levied on diesel averaged 

$0.50. Because fuel taxes represent transfers of resources from fuel buyers to government 

agencies, however, rather than real resources that are consumed in the process of supplying or 

using fuel, their value must be deducted from retail fuel prices to determine the value of fuel 

savings resulting from more stringent fuel efficiency and GHG standards to the U.S. economy as 

a whole. 

In estimating the economy-wide value of fuel savings from improved fuel efficiency 

standards and GHG emission standards, EPA and NHTSA follow the assumptions used by EIA 

in AEO 2010 that state and local gasoline taxes will keep pace with inflation in nominal terms, 

and thus remain constant when expressed in constant 2007 dollars.c In contrast, EIA assumes 

that federal gasoline taxes will remain unchanged in nominal terms, and thus decline throughout 

the forecast period when expressed in constant 2007 dollars. These differing assumptions about 

the likely future behavior of federal and state/local fuel taxes are consistent with recent historical 

experience, which reflects the fact that federal as well as most state motor fuel taxes are specified 

on a cents-per-gallon basis, and typically require legislation to change. The projected value of 

total taxes is deducted from each future year’s forecast of retail gasoline and diesel prices 

reported in AEO 2010 to determine the economic value of each gallon of fuel saved during that 

year as a result of improved fuel economy. Subtracting fuel taxes results in a projected value for 

saving gasoline of $2.89 per gallon during 2020, rising to $3.48 per gallon by the year 2035. 

EIA is widely-recognized as an impartial and authoritative source of analysis and 

forecasts of U.S. energy production, consumption, and prices. The agency has published annual 

forecasts of energy prices and consumption levels for the U.S. economy since 1982 in its AEOs. 

These forecasts have been widely relied upon by federal agencies for use in regulatory analysis 

and for other purposes. Since 1994, EIA’s annual forecasts have been based upon the agency’s 

National Energy Modeling System (NEMS), which includes detailed representation of supply 

pathways, sources of demand, and their interaction to determine prices for different forms of 

energy. 

From 1982 through 1993, EIA’s forecasts of world oil prices – the primary determinant 

of prices for gasoline, diesel, and other transportation fuels derived from petroleum – 

consistently overestimated actual prices during future years, often very significantly. Of the total 

of 119 forecasts of future world oil prices for the years 1985 through 2005 that EIA reported in 

its 1982-1993 editions of AEO, 109 overestimated the subsequent actual values for those years, 

on average exceeding their corresponding actual values by 75 percent. 

2020. Using the average rate of change over the complete 2009-2030 forecast period would result in projections of
 

declining fuel prices after 2030.
 

c To calculate economy-wide impacts, the agencies value fuel savings at pre-tax prices, since this measures the value
 

of the resources that are saved when less fuel is produced and consumed. The economy-wide value of saving fuel
 

includes this plus the reductions in economic and environmental externalities that result from consuming less fuel,
 

importing less petroleum, and so on.
 

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Since that time, however, EIA’s forecasts of future world oil prices show a more mixed 

record for accuracy. The 1994-2005 editions of AEO reported 91 separate forecasts of world oil 

prices for the years 1995-2005, of which 33 have subsequently proven too high while the 

remaining 58 have underestimated actual prices. The average absolute error (i.e., regardless of 

its direction) of these forecasts has been 21 percent, but over- and underestimates have tended to 

offset one another, so that on average EIA’s more recent forecasts have underestimated actual 

world oil prices by 7 percent. Although both its overestimates and underestimates of future 

world oil prices for recent years have often been large, the most recent editions of AEO have 

significantly underestimated petroleum prices during those years for which actual prices are now 

available. 

However, EPA and NHTSA does not regard EIA’s recent tendency to underestimate 

future prices for petroleum and refined products or the high level of current fuel prices as 

adequate justification to employ forecasts that differ from the Reference Case forecast presented 

in the AEO 2010 Reference Case. This is particularly the case because both the AEO 2009 and 

AEO 2010 Reference Cases forecast significantly higher future prices than those projected in the 

initial release of AEO 2008, which in turn represented a major upward revision from EIA’s fuel 

price forecast reported previously in AEO 2007. Comparing recent forecasts of world oil prices 

also shows that EIA’s Reference Case forecast reported in AEO 2009 is among the highest of all 

six publicly-available forecasts of world oil prices over the 2010-30 time horizon.6 Because 

world petroleum prices are the primary determinant of retail prices for refined petroleum 

products such as transportation fuels, this suggests that the Reference Case forecast of U.S. fuel 

prices reported in AEO 2010 is likely to be the highest of those projected by major forecasting 

services. 

In the estimates of costs and benefits presented in the Preamble and the agencies’ RIAs, 

the agencies have included the full fuel savings over vehicles’ expected lifetimes, discounted to 

their present values using both 3 and 7 percent discount rates. More detailed discussion of this 

approach can be found in Preamble Sections III.H.1 and IV.G.6, in Section 8.1 of EPA’s RIA, 

and in Chapter VIII of NHTSA’s RIA. 

4.2.3 Vehicle survival and use assumptions 

The agencies’ analyses of fuel savings and related benefits from adopting higher fuel 

efficiency standards and establishing GHG emission standards for MY 2012-2016 passenger cars 

and light trucks begin by estimating the resulting changes in fuel use over the entire lifetimes of 

these vehicles. The change in total fuel consumption by vehicles produced during each of these 

model years is calculated as the difference in their lifetime fuel use under the baseline 

assumption that the CAFE standard previously established for model year 2011 remains in effect, 

and under the alternative assumption that CAFE and GHG emissions standards improve for each 

model year from 2012-2016. 

The first step in estimating lifetime fuel consumption by vehicles produced during a 

model year is to calculate the number of those vehicles that is expected to remain in service 

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during each future calendar year after they are produced and sold.d This number is calculated by 

multiplying the number of vehicles originally produced during a model year by the proportion 

expected to remain in service at the age they will have reached during each subsequent calendar 

year, often referred to as a “survival rate.” 

NHTSA previously updated its estimates of car and light truck survival rates using 2008 

registration data (the most recent available) for passenger cars and light trucks produced during 

recent model years, in order to ensure that its forecasts of age-specific survival rates reflect 

recent increases in the durability and expected life spans of cars and light trucks.7 Both agencies 

used these updated survival estimates in their analyses of fuel savings and other benefits from 

adopting improved fuel efficiency and GHG standards for MY 2012-2106 cars and light trucks. 

The proportions of passenger cars and light trucks expected to remain in service at each age up to 

their maximum lifetimes (26 and 36 years, respectively) are shown in Table 4-3 and Table 4-4.e 

The next step in estimating lifetime fuel use by the cars or light trucks produced during a 

future model year is to calculate the total number of miles that they will be driven during each 

year of their expected lifetimes. To estimate total miles driven, the number of cars and light 

trucks projected to remain in use during each future calendar year calculated in the previous step 

is multiplied by the average number of miles a surviving car or light truck is expected to be 

driven at the age it will have reached in that year. 

Updated estimates of average annual miles driven by cars and light trucks at each age 

were developed by NHTSA from the Federal Highway Administration’s 2001 National 

Household Transportation Survey. These updated estimates, differ from those employed in past 

NHTSA analyses, were used previously by NHTSA to analyze the effects of the previouslyestablished CAFE standards for MY 2011 cars and light trucks.f 

Table 4-3 and Table 4-4 report NHTSA’s updated estimates of average car and light 

truck use which were used in the analyses for the MY 2011 final rule. As these tables show, the 

d Vehicles are defined to be of age 1 during the calendar year corresponding to the model year in which they are 

produced; thus for example, model year 2000 vehicles are considered to be of age 1 during calendar year 2000, age 

1 during calendar year 2001, and to reach their maximum age of 26 years during calendar year 2025. NHTSA 

considers the maximum lifetime of vehicles to be the age after which less than 2 percent of the vehicles originally 

produced during a model year remain in service. Applying these conventions to vehicle registration data indicates 

that passenger cars have a maximum age of 26 years, while light trucks have a maximum lifetime of 36 years. See 

Lu, S., NHTSA, Regulatory Analysis and Evaluation Division, “Vehicle Survivability and Travel Mileage 

Schedules,” DOT HS 809 952, 8-11 (January 2006). Available at http://www-nrd.nhtsa.dot.gov/Pubs/809952.pdf 

(last accessed Feb. 15, 2010). e The maximum age of cars and light trucks was defined as the age when the number remaining in service has 

declined to approximately two percent of those originally produced. Based on an examination of recent registration 

data for previous model years, typical maximum ages appear to be 26 years for passenger cars and 36 years for light 

trucks. 

f See also NHTSA, “Vehicle Survival and Travel Mileage Schedules,” Office of Regulatory Analysis and 

Evaluation, January 2006, pp. 15-17. The original source of information on annual use of passenger cars and light 

trucks by age used in this analysis is the 2001 National Household Travel Survey (NHTS), jointly sponsored by the 

Federal Highway Administration, Bureau of Transportation Statistics, and National Highway Traffic Safety 

Administration. 

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resulting survival-weighted mileages calculated for that rule over the 26-year maximum lifetime 

of passenger cars is 161,847 miles, while that over the 36-year maximum lifetime of light trucks 

is 190,066 miles. It should be noted, however, that survival-weighted mileage is extremely low 

(less than 1,000 miles per year) after age 20 for cars and after age 25 for light trucks, and thus 

has little impact on lifetime fuel savings or other benefits from higher fuel economy, particularly 

after discounting those benefits to their present values. 

The survival and annual mileage estimates reported in the tables reflect the convention 

that vehicles are defined to be of age 1 during the calendar year that coincides with their model 

year Thus for example, model year 2012 vehicles will be considered to be of age 1 during 

calendar year 2012. This convention is used in order to account for the fact that vehicles 

produced during a model year typical are first offered for sale in June through September of the 

preceding calendar year (for example, sales of a model year typically begin in June through 

September of the previous calendar year, depending on manufacturer). Thus virtually all of the 

vehicles produced during a model year will be in use for some or all of the calendar year 

coinciding with their model year, and they are considered to be of age 1 during that year.g 

g As an illustration, virtually the entire production of model year 2012 cars and light trucks will have been sold by 

the end of calendar year 2012, so those vehicles are defined to be of age 1 during calendar year 2012. Model year 

2012 vehicles are subsequently defined to be of age 2 during calendar year 2013, age 3 during calendar year 2014, 

and so on. One complication arises because registration data are typically collected for July 1 of each calendar year, 

so not all vehicles produced during a model year will appear in registration data until the calendar year when they 

have reached age 2 (and sometimes age 3) under this convention. 

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Economic Assumptions Used in the Agencies’ Analysis
 

Table 4-3 Survival Rates and Unadjusted Annual Miles Traveled (VMT) by Age for
 

Passenger Cars (calculated from 2001 NHTS) Used in MY 2011 Final Rule
 

Vehicle Age Estimated Survival Fraction Estimated Annual VMT Survival-Weighted Annual 

VMT 

1 0.9950 14,231 14,160 

2 0.9900 13,961 13,821 

3 0.9831 13,669 13,438 

4 0.9731 13,357 12,998 

5 0.9593 13,028 12,497 

6 0.9413 12,683 11,938 

7 0.9188 12,325 11,324 

8 0.8918 11,956 10,662 

9 0.8604 11,578 9,961 

10 0.8252 11,193 9,237 

11 0.7866 10,804 8,499 

12 0.7170 10,413 7,466 

13 0.6125 10,022 6,138 

14 0.5094 9,633 4,907 

15 0.4142 9,249 3,831 

16 0.3308 8,871 2,934 

17 0.2604 8,502 2,214 

18 0.2028 8,144 1,652 

19 0.1565 7,799 1,220 

20 0.1200 7,469 896 

21 0.0916 7,157 656 

22 0.0696 6,866 478 

23 0.0527 6,596 348 

24 0.0399 6,350 253 

25 0.0301 6,131 185 

26 0.0227 5,940 135 

Estimated Passenger Car Lifetime VMT 161,847 

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Table 4-4 Survival Rates and Unadjusted Annual Vehicle-Miles Traveled (VMT)
 

by Age for Light Trucks (calculated from 2001 NHTS) Used in MY 2011 Final Rule
 

VEHICLE AGE 

ESTIMATED 

SURVIVAL 

FRACTION 

ESTIMATED 

ANNUAL VMT 

SURVIVALWEIGHTED 

ANNUAL VMT 

1 0.9950 16,085 16,004 

2 0.9741 15,782 15,374 

3 0.9603 15,442 14,829 

4 0.9420 15,069 14,195 

5 0.9190 14,667 13,479 

6 0.8913 14,239 12,691 

7 0.8590 13,790 11,845 

8 0.8226 13,323 10,960 

9 0.7827 12,844 10,053 

10 0.7401 12,356 9,145 

11 0.6956 11,863 8,252 

12 0.6501 11,369 7,391 

13 0.6042 10,879 6,573 

14 0.5517 10,396 5,735 

15 0.5009 9,924 4,971 

16 0.4522 9,468 4,281 

17 0.4062 9,032 3,669 

18 0.3633 8,619 3,131 

19 0.3236 8,234 2,665 

20 0.2873 7,881 2,264 

21 0.2542 7,565 1,923 

22 0.2244 7,288 1,635 

23 0.1975 7,055 1,393 

24 0.1735 6,871 1,192 

25 0.1522 6,739 1,026 

26 0.1332 6,663 887 

27 0.1165 6,648 774 

28 0.1017 6,648 676 

29 0.0887 6,648 590 

30 0.0773 6,648 514 

31 0.0673 6,648 447 

32 0.0586 6,648 390 

33 0.0509 6,648 338 

34 0.0443 6,648 294 

35 0.0385 6,648 256 

36 0.0334 6,648 222 

Estimated Lifetime Light Truck VMT 190,066 

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4.2.3.1 Adjusting vehicle use for future fuel prices
 

The estimates of average annual miles driven by passenger cars and light trucks reported 

in Table 4-3 and Table 4-4 reflect the historically low gasoline prices that prevailed at the time 

the 2001 NHTS was conducted. To account for the effect on vehicle use of subsequent increases 

in fuel prices in the proposal, the estimates of annual vehicle use derived from the NHTS were 

adjusted to reflect the forecasts of future gasoline prices reported in the Energy Information 

Administration’s Annual Energy Outlook 2009 Updated Reference Case. For this final 

rulemaking, the agencies updated the analysis with the forecasts of future gasoline prices 

reported in the Energy Information Administration’s Annual Energy Outlook 2010 Early 

Release. This adjustment accounts for the difference between the average retail price per gallon 

of fuel forecast during each calendar year over the expected lifetimes of model year 2012-2016 

passenger cars and light trucks, and the average price that prevailed when the NHTS was 

conducted in 2001. The elasticity of annual vehicle use with respect to fuel cost per mile 

corresponding to the 10 percent fuel economy rebound effect used in this analysis (i.e., an 

elasticity of -0.10) was used in conjunction with the difference between each future year’s fuel 

prices and those prevailing in 2001 to adjust the estimates of vehicle use derived from the NHTS 

to reflect the effect of higher future fuel prices. This procedure was applied to the mileage 

figures reported previously in Table 4-3 and Table 4-4 to adjust annual mileage by age during 

each calendar year of the expected lifetimes of MY 2012-2016 cars and light trucks. 

4.2.3.2 Ensuring consistency with growth in total vehicle use 

The estimates of annual miles driven by passenger cars and light trucks at each age were 

also adjusted to reflect projected future growth in average vehicle use. Increases in the average 

number of miles cars and trucks are driven each year have been an important source of historical 

growth in total car and light truck use, and are expected to represent an important source of 

future growth in total light-duty vehicle travel as well. As an illustration of the importance of 

growth in average vehicle use, the total number of miles driven by passenger cars increased 35 

percent from 1985 through 2005, equivalent to a compound annual growth rate of 1.5 percent.8 

During that time, however, the total number of passenger cars registered for in the U.S. grew by 

only about 0.3 percent annually.h Thus growth in the average number of miles automobiles are 

driven each year accounted for the remaining 1.2 percent (= 1.5 percent - 0.3 percent) annual 

growth in total automobile use.i Further, the AEO 2010 Reference Case forecasts of total car and 

light truck use and of the number of cars and light trucks in use suggest that their average annual 

use will continue to increase gradually from 2010 through 2035. 

In order to develop reasonable estimates of future growth in average car and light truck 

use, the agencies calculated the rate of growth in the mileage schedules shown previously in 

Table 4-6 and Table 4-7 that would be necessary for total car and light truck travel to increase at 

h A slight increase in the fraction of new passenger cars remaining in service beyond age 10 has accounted for a 

small share of growth in the U.S. automobile fleet. The fraction of new automobiles remaining in service to various 

ages was computed from R.L. Polk vehicle registration data for 1977 through 2005 by the agency’s Center for 

Statistical Analysis. 

i See supra note k below. 

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the rate forecast in the AEO 2010 Reference Case. This calculated rate is also consistent with 

future changes in the overall size and age distributions of the U.S. passenger car and light truck 

fleets that are implied by the agency’s forecasts of total car and light truck sales and the survival 

rates reported in Table 4-3 and Table 4-4. The growth rate in average annual car and light truck 

j use produced by this calculation is approximately 1.15 percent per year. This rate was applied 

to the mileage figures reported previously in Table 4-3 and Table 4-4 to estimate annual mileage 

by age during each calendar year of the expected lifetimes of MY 2012-2016 cars and light 

k trucks.

Table 4-6 and Table 4-7 report the results of applying the adjustments for both future fuel 

prices and annual growth in car and light truck use to the mileage schedules reported previously 

in Table 4-2 and Table 4-3. Separate adjustments for projected fuel prices and growth in car and 

light truck use were made for each calendar year from 2012 through 2030. Because the effects 

of both fuel prices and cumulative growth in average vehicle use vary by year, these adjustments 

result in differing VMT schedules for each future year. The adjusted annual VMT estimates 

reported in Table 4-4 and Table 4-5 and their lifetime totals represent the averages of the 

adjusted values of annual car and light truck use by age for calendar years 2012-2030. However, 

the estimates of fuel savings and other impacts of improved fuel efficiency for individual 

calendar years over the lifetimes of model year 2012-16 cars and light trucks employ the 

adjusted values of car and light truck use by age during those specific calendar years. 

While the adjustment for future fuel prices reduces average mileage at each age from the 

values shown previously, the adjustment for expected future growth in average vehicle use 

increases it. Comparing the mileage estimates in Table 4-6 and Table 4-7 to those shown 

previously in Table 4-2 and Table 4-3 shows that the net effect of these two adjustments is to 

increase expected lifetime mileage significantly. As an illustration, expected lifetime mileage for 

passenger cars, this figure rises to 195,264 miles from the 161,847 miles reported previously in 

Table 4-3 (or by 21 percent), while expected lifetime mileage for light trucks increases from the 

190,066 miles reported previously in Table 4-4 to 225,865 miles (19 percent). As previously 

noted, the estimates of survival-weighted mileage decline to less than 1,000 miles per year after 

age 20 for cars and after age 27 for light trucks. Thus they have relatively little impact on 

j 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. 

k The agency has also identified an unintentional discrepancy between the estimates of expected lifetime VMT used 

by EPA and NHTSA in their respective analyses of proposed GHG and CAFE standards reported in the NPRM. 

EPA’s estimates of expected lifetime VMT for model year 2012-26 vehicles reflect the shared assumption that the 

average number of miles driven annually by cars and light trucks of all ages would grow at a rate of 1.15 percent per 

year beginning in 2001. Although NHTSA’s description of the procedures used to calculate its estimates of 

expected lifetime VMT for passenger cars and light trucks indicated that it had also adjusted the base-year estimates 

of vehicle use to account for annual growth (see Preliminary Regulatory Impact Assessment, Chapter VIII, p. 367), 

this adjustment was not in fact applied in NHTSA’s analysis of the effects of proposed increases in CAFE standards 

for model years 2012-16. This omission caused the estimates of expected lifetime VMT for passenger cars and light 

trucks that NHTSA actually used in its NPRM analysis of CAFE standards for model years 2012-16 to be lower than 

those used by EPA. This error has been corrected by NHTSA, and this correction is reflected in its analysis of the 

impacts of CAFE standards for model year 2012-16 passenger cars and light trucks reported in the Final Rule. This 

error did not occur in the analysis of environmental impacts of alternative increases in CAFE standards reported in 

the agency’s recent Draft Environmental Impact Statement. 

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lifetime fuel savings or other benefits from higher fuel economy, particularly after discounting 

the benefits that occur in those distant future years to their present values. 

The VMT schedules used in the proposal and in this final rulemaking are presented 

below. To summarize the earlier discussion, the slight (~2.5 percent) increase in lifetime VMT 

schedule is attributable to the lower fuel prices projected in AEO 2010 Early Release as 

compared to the updated AEO 2009. 

Table 4-5 Summary of Expected Lifetime VMT Schedules 

MY 2012-2016 MY 2012-2016 

Proposal Final Rulemaking 

Car 190,271 195,264 

Trucks 221,199 225,865 

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Table 4-6 Survival Rates and Adjusted Annual Vehicle-Miles Traveled (VMT) by Age for
 

Passenger Cars (average values for 2012-2030)
 

Vehicle Age Estimated Survival 

Fraction 

Adjusted Annual 

VMT 

Survival-Weighted 

Annual VMT 

1 0.9950 17,270 17,183 

2 0.9900 16,943 16,774 

3 0.9831 16,599 16,319 

4 0.9731 16,163 15,728 

5 0.9593 15,761 15,119 

6 0.9413 15,337 14,437 

7 0.9188 14,881 13,672 

8 0.8918 14,429 12,868 

9 0.8604 13,940 11,994 

10 0.8252 13,495 11,136 

11 0.7866 12,964 10,198 

12 0.7170 12,510 8,970 

13 0.6125 11,990 7,344 

14 0.5094 11,470 5,843 

15 0.4142 10,997 4,555 

16 0.3308 10,543 3,488 

17 0.2604 10,125 2,637 

18 0.2028 9,714 1,970 

19 0.1565 9,307 1,456 

20 0.1200 8,891 1,067 

21 0.0916 8,546 783 

22 0.0696 8,285 577 

23 0.0527 8,136 429 

24 0.0399 7,896 315 

25 0.0301 7,699 232 

26 0.0227 7,530 171 

Adjusted Lifetime Passenger Car VMT 195,264 

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Table 4-7 Survival Rates and Adjusted Annual Vehicle-Miles Traveled (VMT) by Age for
 

Light Trucks (average values for 2012-2030)
 

Vehicle Age Estimated Survival 

Fraction 

Adjusted Annual 

VMT 

Survival-Weighted 

Annual VMT 

1 0.9950 19,219 19,123 

2 0.9741 18,782 18,296 

3 0.9603 18,419 17,688 

4 0.9420 17,946 16,905 

5 0.9190 17,502 16,085 

6 0.8913 16,952 15,109 

7 0.8590 16,439 14,121 

8 0.8226 15,829 13,021 

9 0.7827 15,218 11,911 

10 0.7401 14,648 10,841 

11 0.6956 13,992 9,733 

12 0.6501 13,450 8,744 

13 0.6042 12,832 7,753 

14 0.5517 12,212 6,737 

15 0.5009 11,600 5,811 

16 0.4522 11,069 5,005 

17 0.4062 10,617 4,313 

18 0.3633 10,125 3,679 

19 0.3236 9,650 3,123 

20 0.2873 9,238 2,654 

21 0.2542 8,882 2,258 

22 0.2244 8,667 1,945 

23 0.1975 8,400 1,659 

24 0.1735 8,395 1,456 

25 0.1522 8,197 1,248 

26 0.1332 8,188 1,091 

27 0.1165 8,218 957 

28 0.1017 8,216 836 

29 0.0887 8,213 728 

30 0.0773 8,211 635 

31 0.0673 8,210 553 

32 0.0586 8,208 481 

33 0.0509 8,203 418 

34 0.0443 8,196 363 

35 0.0385 8,182 315 

36 0.0334 8,167 273 

Adjusted Lifetime Light Truck VMT 225,865 

4.2.4 Accounting for the fuel economy rebound effect 

The rebound effect refers to the fraction of fuel savings expected to result from an 

increase in vehicle fuel economy that is offset by additional vehicle use. The increase in vehicle 

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use that stems from improved fuel economy occurs because vehicle owners respond to the 

resulting reduction in vehicle fuel consumption and operating costs by driving slightly more. 

The magnitude of the rebound effect is one of the determinants of the actual fuel savings 

that are likely to result from adopting stricter fuel economy or emissions standards, and thus an 

important parameter affecting EPA’s and NHTSA’s evaluation of alternative standards for future 

model years. It can be measured directly by estimating the elasticity of vehicle use with respect 

to fuel economy itself, or indirectly by the elasticity of vehicle use with respect to fuel cost per 

mile driven.l When expressed as a positive percentage, either of these parameters gives the 

fraction of fuel savings that would otherwise result from adopting stricter standards, but is offset 

by the increase in fuel consumption that results when vehicles with increased fuel economy are 

driven more. 

The fuel economy rebound effect for light-duty vehicles has been the subject of a large 

number of studies since the early 1980s. Although they have reported a wide range of estimates 

of its exact magnitude, these studies generally conclude that a significant rebound effect occurs 

when vehicle fuel efficiency improves.m The most common approach to estimating its 

magnitude has been to analyze household survey data on vehicle use, fuel consumption, fuel 

prices (often obtained from external sources), and other determinants of household travel 

demand to isolate the response of vehicle use to higher fuel economy. Other studies have relied 

on econometric analysis of annual U.S. data on vehicle use, fuel economy, fuel prices, and other 

variables to identify the response of total or average vehicle use to changes in fleet-wide average 

fuel economy and its effect of fuel cost per mile driven. Two recent studies analyzed yearly 

variation in vehicle ownership and use, fuel prices, and fuel economy among individual states 

over an extended time period in order to measure the response of vehicle use to changing fuel 

economy. n 

This section surveys these previous studies, summarizes recent work on the rebound 

effect, and explains the basis for the 10 percent rebound effect EPA and NHTSA are using in this 

rulemaking.9 

4.2.4.1 Summary of historical literature on rebound effect 

It is important to note that a majority of the studies previously conducted on the rebound 

effect rely on data from the 1950-1990s. While these older studies provide valuable information 

on the potential magnitude of the rebound effect, studies that include more recent information 

(e.g., data within the last decade) may provide more reliable estimates of how this rule will affect 

l Fuel cost per mile is equal to the price of fuel in dollars per gallon divided by fuel economy in miles per gallon, so 

this figure declines when a vehicle’s fuel economy increases. 

m Some studies estimate that the long-run rebound effect is significantly larger than the immediate response to 

increased fuel efficiency. Although their estimates of the adjustment period required for the rebound effect to reach 

its long-run magnitude vary, this long-run effect is most appropriate for evaluating the fuel savings and emissions 

reductions resulting from stricter standards that would apply to future model years. 

n In effect, these studies treat U.S. states as a data “panel” by applying appropriate estimation procedures to data 

consisting of each year’s average values of these variables for the separate states. 

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future driving behavior. Therefore, the more recent studies have been described in more detail 

below. 

Estimates based on aggregate U.S. vehicle travel data published by the U.S. Department 

of Transportation, Federal Highway Administration, covering the period from roughly 1950 to 

1990, have found long-run rebound effects on the order of 10-30 percent. Some of these studies 

are summarized in the following table. 

Table 4-8 Estimates of the Rebound Effect Using U.S. Aggregate Time-Series Data 

on Vehicle Travel1 

AUTHOR 

(YEAR) 

SHORTRUN LONG-RUN TIME PERIOD 

Mayo & Mathis 

(1988) 

22% 26% 1958-84 

Gately (1992) 9% 9% 1966-88 

Greene (1992) Linear 5-19% 

Log-linear 

13% 

Linear 5-19% 

Log-linear 13% 

1957-89 

Jones (1992) 13% 30% 1957-89 

Schimek (1996) 5-7% 21-29% 1950-94 

1 Source: Sorrell and Dimitropolous (2007) table 4.6. 

Table 4-9 Estimates of the Rebound Effect Using U.S. State Level Data1 

AUTHOR 

(YEAR) 

SHORT-RUN LONG-RUN TIME PERIOD 

Haughton & 

Sarkar (1996) 

9-16% 22% 1973-1992 

Small and Van 

Dender (2007) 

4.5% 22% 1961-2001 

1 Source: Sorrell and Dimitropolous (2007) table 4.7. 

While national (Table 4-6) and state level (Table 4-9) data have found relatively 

consistent long-run estimates of the rebound effect, household surveys display more variability 

(Table 4-10). There are several possible explanations for this larger variability. One explanation 

is that some of these studies do not include vehicle age as an explanatory variable, thus leading 

to omitted variable bias.10 Another explanation is that it is difficult to differentiate between the 

impacts of residential density and fuel prices, since households with higher fuel prices are more 

likely to be in urban areas.11 

Table 4-10 Estimates of the Rebound Effect Using U.S. Survey Data 

AUTHOR SHORT­ LONG-RUN TIME PERIOD 

(YEAR) RUN 

Goldberg (1966) 0% CES 1984-90 

Greene (1999) 23% EIA RTECS 

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1979-1994 

Pickrell & 

Schimek (1999) 

4-34% NPTS 1995 Single year 

Puller & Greening 

(1999) 

49% CES 1980-90 

Single year, cross-sectional 

West (2004) 87% CES 1997 

Single year 

An important distinction among studies of the rebound effect is whether they assume that 

the effect is constant, or varies over time in response to the absolute levels of fuel costs, personal 

income, or household vehicle ownership. Most studies using aggregate annual data for the U.S. 

assume a constant rebound effect, although some of these studies test whether the effect can vary 

as changes in retail fuel prices or average fuel economy alter fuel cost per mile driven. Many 

studies using household survey data estimate significantly different rebound effects for 

households owning varying numbers of vehicles, although they arrive at differing conclusions 

about whether the rebound effect is larger among households that own more vehicles. Finally, 

one recent study using state-level data concludes that the rebound effect varies directly in 

response to changes in personal income and the degree of urbanization of U.S. cities, as well as 

fuel costs. 

In order to provide a more comprehensive overview of previous estimates of the rebound 

effect, EPA and NHTSA reviewed 22 studies of the rebound effect conducted from 1983 through 

2005. The agencies then performed a detailed analysis of the 66 separate estimates of the longrun rebound effect reported in these studies, which is summarized in 

Table 4-11 below.o As the table indicates, these 66 estimates of the long-run rebound 

effect range from as low as 7 percent to as high as 75 percent, with a mean value of 23 percent. 

Limiting the sample to 50 estimates reported in the 17 published studies of the rebound effect 

yields the same range, but a slightly higher mean estimate (24 percent). 

The type of data used and authors’ assumption about whether the rebound effect varies 

over time have important effects on its estimated magnitude. The 34 estimates derived from 

analysis of U.S. annual time-series data produce a mean estimate of 18 percent for the long-run 

rebound effect, while the mean of 23 estimates based on household survey data is considerably 

larger (31 percent), and the mean of 9 estimates based on state data (25 percent) is close to that 

for the entire sample. The 37 estimates assuming a constant rebound effect produce a mean of 

23 percent, identical to the mean of the 29 estimates reported in studies that allowed the rebound 

effect to vary in response to fuel prices, vehicle ownership, or household income. 

Table 4-11 Summary Statistics for Estimates of the Rebound Effect 

o In some cases, NHTSA derived estimates of the overall rebound effect from more detailed results reported in the 

studies. For example, where studies estimated different rebound effects for households owning different numbers 

of vehicles but did not report an overall value, the agency computed a weighted average of the reported values using 

the distribution of households among vehicle ownership categories. 

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Category of Estimates Number of 

Studies 

Number of 

Estimates 

Range Distribution 

Low High Median Mean Std. Dev. 

All Estimates 22 66 7% 75% 22% 23% 14% 

Published Estimates 17 50 7% 75% 22% 24% 14% 

U.S. Time-Series Data 7 34 7% 45% 14% 18% 9% 

Household Survey Data 13 23 9% 75% 31% 31% 16% 

Pooled U.S. State Data 2 9 8% 58% 22% 25% 14% 

Constant Rebound Effect (1) 15 37 7% 75% 20% 23% 16% 

Variable Rebound Effect: (1) 10 29 10% 45% 23% 23% 10% 

4.2.4.2 Summary of recent studies and analyses of the rebound effect 

More recent studies since 2007 indicate that the impacts of fuel prices and fuel economy 

have been decreasing over time as incomes increase and real gasoline prices fall. The theoretical 

argument for why the rebound effect should vary is that the responsiveness to the fuel cost of 

driving will be larger when it is a larger proportion of the total cost of driving. As incomes rise 

(or fuel prices fall), the responsiveness to the fuel cost per mile of driving will decrease if people 

view the time cost of driving – which is likely to be related to their income levels – as a larger 

component of the total cost. 

Small and Van Dender combined time series data for each of the 50 States and the 

District of Columbia to estimate the rebound effect, allowing the magnitude of the rebound to 

vary over time.12 For the time period from 1966-2001, their study found a long-run rebound 

effect of about 22 percent, which is consistent with previously published studies. But for the 

most recent five year period (1997-2001), the long-run rebound effect decreased to 11 percent. 

Furthermore, when the authors updated their estimates with data through 2004, the long-run 

rebound effect for the most recent five year period (2000-2004) dropped to 6 percent.13 

New research conducted by David Greene in 2008-2009 at EPA's request further appears 

to support the theory that the magnitude of the rebound effect is declining over time.14 Over the 

entire time period analyzed (1966-2007), Greene found that fuel prices had a statistically 

significant impact on VMT, while fuel economy did not. In spite of this result, Greene also 

tested Small and Van Dender’s formulation which allows the elasticity of fuel cost per mile to 

decrease with increasing per capita income. The results of estimation using national time series 

data confirmed the results obtained by Small and Van Dender using a time series of state level 

data. 

While Small and Van Dender did not find a statistically significant coefficient for fuel 

efficiency, they did find a statistically significant coefficient for the price of fuel. They then 

tested the hypothesis that the elasticity of vehicle travel with respect to the price of fuel was 

equal to its elasticity with respect to the rate of fuel consumption (gallons per mile), and found 

that the data could not reject that hypothesis. As a consequence, they estimated the rebound 

effect as the elasticity of vehicle travel with respect to fuel cost per mile, which constrains these 

elasticities to be equal in magnitude. Greene also attempted to re-estimate the Small and Van 

Dender results using his national data set. When using Greene’s preferred functional form, the 

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projected rebound effect is approximately 12 percent in 2007, and drops to 10 percent in 2010, 9 

percent in 2016 and 8 percent in 2030. 

4.2.4.3 NHTSA analysis of the rebound effect 

To provide additional insight into the rebound effect for the purposes of this rulemaking, 

NHTSA developed several new estimates of its magnitude. These estimates were developed by 

estimating and testing several econometric models of the relationship between vehicle milestraveled and factors that influence it, including household income, fuel prices, vehicle fuel 

efficiency, road supply, the number of vehicles in use, vehicle prices, and other factors. 

As the study by Small and Van Dender pointed out, it is important to account for the 

effect of fuel prices when attempting to estimate the rebound effect. Failing to control for 

changes in fuel prices is likely to bias estimates of the rebound effect. Therefore, changes in fuel 

prices are taken into account in Volpe’s analysis of the rebound effect. Several different 

approaches were used to estimate the fuel economy rebound effect for light duty vehicles, 

including single equation OLS regressions, multiple equation systems estimated using two- and 

three-stage least squares regression, vector autoregression, and vector error-correction models. 

With the exception of single-equation regressions estimated using OLS, all of these procedures 

attempt to account for the endogenous relationship of fuel efficiency to fuel prices. 

The results from each of these approaches are presented in Table 4-12 below. The table 

reports the value of the rebound effect calculated over the entire period from 1950 through 2006, 

as well as for the final year of that period. In addition, the table presents forecasts of the average 

rebound effect between 2010 and 2030, which utilize forecasts of personal income, fuel prices, 

and fuel efficiency from EIA’s AEO 2009 Reference Case. 

The results of NHTSA’s analysis are broadly consistent with the findings from previous 

research summarized above. The historical average long-run rebound effect is estimated to range 

from 16-30 percent, and comparing these estimates to its calculated values for 2006 (which range 

from 8-14 percent) gives some an indication that it is declining in magnitude. The forecast 

values of the rebound effect shown in the table also suggest that this decline is likely to continue 

through 2030, as they range from 4-16 percent. 

4-20
 
Economic Assumptions Used in th

Table 4-12 Summary of NHTSA Estimates of the Rebound Effect
 

Model VMT Measure Variables Included in VMT Equation Estimation Technique 

1

2

Small-Van Dender single VMT equation 

annual VMT per adult 

fuel cost per mile, per Capita income, vehicle stock, road miles per adult, fraction of population that is adult, fraction of population living in urban areas, fraction of population living in urban areas with heavy rail, dummy variables for fuel rationing, time trend 

OLS 3

Small-Van Dender three-equation system 

annual VMT per adult 

fuel cost per mile, per Capita income, vehicle stock, road miles per adult, fraction of population that is adult, fraction of population living in urban areas, fraction of population living in urban areas with heavy rail, dummy variables for fuel rationing, time trend 

3SLS 2

Single-equation VMT model 

annual VMT per adult personal income, road miles per Capita, time trend OLS 1

Single-equation VMT model 

annual VMT per vehicle 

fuel cost per mile, personal income, road miles per Capita, time trend OLS 1

Single-equation VMT model 

annual VMT per adult 

fuel cost per mile, personal income, road miles per Capita, dummy variables for fuel rationing, time trend OLS 3

Single-equation VMT model 

annual VMT per vehicle 

fuel cost per mile, personal income, vehicles per road mile, % of fleet manufactured under CAFE standards, new vehicle prices 

IV (for fuel 

cost per mile) 1

Three-equation fuel cost per mile, personal income, vehicles per capita, system for VMT, annual VMT vehicles per road mile, fraction of adult population 2SLS 2

fuel efficiency, and per vehicle licensed to drive, new vehicle prices, % of fleet 

vehicle stock manufactured under CAFE standards Three-equation fuel cost per mile, personal income, vehicles per capita, system for VMT, annual VMT vehicles per road mile, fraction of adult population 3SLS 2

fuel efficiency, and per vehicle licensed to drive, new vehicle prices, % of fleet 

vehicle stock manufactured under CAFE standards Three-equation system for VMT, 

annual VMT per vehicle 

fuel cost per mile, personal income, vehicles per capita, vehicles per road mile, fraction of adult population 

Vector autoregression 1

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4.2.4.4 Basis for rebound effect used by EPA and NHTSA in this rule 

As the preceding discussion indicates, there is a wide range of estimates for both the 

historical magnitude of the rebound effect and its projected future value, and there is some 

evidence that the magnitude of the rebound effect appears to be declining over time. 

Nevertheless, NHTSA requires a single point estimate for the rebound effect as an input to its 

analysis, although a range of estimates can be used to test the sensitivity to uncertainty about its 

exact magnitude. For the final rulemaking, the agencies chose to use 10 percent as their primary 

estimate of the rebound effect, with a range of 5-15 percent for use in sensitivity testing. 

As Tables 4-10 and 4-11 indicate, the 10 percent figure is well below those reported in 

almost all previous research, and Table 4-12 shows that it is also below most estimates of the 

historical and current magnitude of the rebound effect developed by NHTSA. However, other 

recent research – particularly that conducted by Small and Van Dender and by Greene – reports 

persuasive evidence that the magnitude of the rebound effect is likely to be declining over time, 

and the forecasts developed by NHTSA and reported in Table 4-12 also suggest that this is likely 

to be the case. As a consequence, the agencies concluded that a value below the historical 

estimates reported in Tables 4-10, 4-11, and 4-12 is likely to provide a more reliable estimate of 

its magnitude during the future period spanned by the agencies’ analyses of the impacts of this 

rule. The 10 percent estimate meets this condition, since it lies below the 15-30 percent range of 

estimates for the historical rebound effect reported in most previous research, and at the upper 

end of the 5-10 percent range of estimates for the future rebound effect reported in the recent 

studies by Small and Van Dender and by Greene. As Table 4-12 shows, it also lies within the 3­

16 percent range of forecasts of the future magnitude of the rebound effect developed by 

NHTSA in its recent research. In summary, the 10 percent value was not derived from a single 

point estimate from a particular study, but instead represents a reasonable compromise between 

the historical estimates and the projected future estimates. The Agencies will continue to review 

the estimate of the rebound effect for future rulemakings. 

In their proposed rules, the agencies sought comment on other alternatives for estimating 

the rebound effect. As one illustration, under the assumption that drivers respond similarly to 

changes in the cost of driving whether they are caused by variation in fuel prices or fuel 

economy, the short-run price elasticity of demand for gasoline – which measures the sensitivity 

of gasoline consumption to changes in its price per gallon – may provide some indication about 

the magnitude of the rebound effect itself. Thus, the agencies sought comment on whether it 

would be appropriate to use the price elasticity of demand for gasoline, or other alternative 

approaches, to guide their choice of a value for the rebound effect. 

During the public comment period, EPA and NHTSA did not receive new data or 

analysis on the level of the rebound effect, but did receive some supportive comments on the 10 

percent level. Hence, EPA and NHTSA have elected to continue to use the 10 percent estimate 

of the rebound effect in the analyses supporting this final rule. 

4.2.5 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. 

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This additional travel provides benefits to drivers and their passengers by improving their access 

to social and economic opportunities away from home. As evidenced by their decisions to make 

more frequent or longer trips when improved fuel economy reduces their costs for driving, the 

benefits from this additional travel exceed the costs drivers and passengers incur in making more 

frequent or longer trips. 

The analysis estimates the economic benefits from increased rebound-effect driving as 

the sum of fuel costs drivers incur plus the consumer surplus they receive from the additional 

accessibility it provides.p Because the increased in travel depend on the extent of improvement 

in fuel economy, the value of benefits it provides differs among model years and alternative 

CAFE standards. Under even those alternatives that would impose the highest standards, 

however, the magnitude of these benefits represents a small fraction of total benefits. 

4.2.6 Added costs from increased vehicle use 

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 increased travel 

time and operating expenses. Because drivers do not take these added costs into account in 

deciding when and where to travel, they must be accounted for separately as a cost of the added 

driving associated with the rebound effect. 

Increased vehicle use due to the rebound effect may also increase the costs associated 

with traffic accidents. Drivers may take account of the potential costs they (and their passengers) 

face from the possibility of being involved in an accident when they decide to make additional 

trips. However, they probably do not consider all of the potential costs they impose on 

occupants of other vehicles and on pedestrians when accidents occur, so any increase in these 

“external” accident costs must be considered as another cost of additional rebound-effect driving. 

Like increased delay costs, any increase in these external accident costs caused by added driving 

is likely to depend on the traffic conditions under which it takes place, since accidents are more 

frequent in heavier traffic (although their severity may be reduced by the slower speeds at which 

heavier traffic typically moves). 

Finally, added vehicle use from the rebound effect may also increase traffic noise. Noise 

generated by vehicles causes inconvenience, irritation, and potentially even discomfort to 

occupants of other vehicles, to pedestrians and other bystanders, and to residents or occupants of 

surrounding property. Because these effects are unlikely to be taken into account by the drivers 

whose vehicles contribute to traffic noise, they represent additional externalities associated with 

motor vehicle use. Although there is considerable uncertainty in measuring their value, any 

p The consumer surplus provided by added travel is estimated as one-half of the product of the decline in fuel cost 

per mile and the resulting increase in the annual number of miles driven. 

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increase in the economic costs of traffic noise resulting from added vehicle use must be included 

together with other increased external costs from the rebound effect. 

EPA and NHTSA 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.15 NHTSA employed 

these estimates previously in its analysis accompanying the MY 2011 final rule, and continues to 

find them appropriate for this NPRM after reviewing the procedures used by FHWA to develop 

them and considering other available estimates of these values. They are intended to measure the 

increases in costs from added congestion, property damages and injuries in traffic accidents, and 

noise levels caused by automobiles and light trucks that are borne by persons other than their 

drivers (or “marginal” external costs). 

Updated to 2007 dollars, FHWA’s “Middle” estimates for marginal congestion, accident, 

and noise costs caused by automobile use amount to 5.2 cents, 2.3 cents, and 0.1 cents per 

vehicle-mile (for a total of 7.6 cents per mile), while those for pickup trucks and vans are 4.7 

16, q cents, 2.5 cents, and 0.1 cents per vehicle-mile (for a total of 7.3 cents per mile). These costs 

are multiplied by the annual increases in automobile and light truck use from the rebound effect 

to yield the estimated increases in congestion, accident, and noise externality costs during each 

future year. 

4.2.7 Petroleum and energy security impacts 

4.2.7.1 Impact on U.S. petroleum imports 

In 2008, U.S. petroleum import expenditures represented 21 percent of total U.S. imports 

of all goods and services.17 In 2008, the United States imported 66 percent of the petroleum it 

consumed, and the transportation sector accounts for 70 percent of total U.S. petroleum 

consumption. This compares roughly to 37 percent of petroleum from imports and 55 percent 

consumption of petroleum in the transportation sector in 1975.18 It is clear that petroleum 

imports have a significant impact on the U.S. economy. Requiring lower-GHG vehicle 

technology and improved fuel economy in the U.S. is expected to lower U.S. petroleum imports. 

4.2.7.2 Background on U.S. energy security 

U.S. energy security is broadly defined as protecting the U.S. economy against 

circumstances that threaten significant short- and long-term increases in energy costs. Most 

discussion of U.S. energy security revolves around the topic of the economic costs of U.S. 

dependence on oil imports. The problem is that the U.S. relies on sources of imported oil from 

q The Federal Highway Administration’s estimates of these costs agree closely with some other recent estimates. 

For example, recent published research conducted by Resources for the Future (RFF) estimates marginal congestion 

and external accident costs for increased light-duty vehicle use in the U.S. to be 3.5 and 3.0 cents per vehicle-mile in 

year-2002 dollars. See Ian W.H. Parry and Kenneth A. Small, “Does Britain or the U.S. Have the Right Gasoline 

Tax?” Discussion Paper 02-12, Resources for the Future, 19 and Table 1 (March 2002). Available at 

http://www.rff.org/rff/Documents/RFF-DP-02-12.pdf (last accessed Feb. 15, 2010). 

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potentially unstable sources. In addition, oil exporters have the ability to raise the price of oil by 

exerting monopoly power through the formation of a cartel, the Organization of Petroleum 

Exporting Countries (OPEC). Finally, these factors contribute to the vulnerability of the U.S. 

economy to episodic oil supply shocks and price spikes. In 2008, U.S. imports of crude oil were 

$326 billion (in 2007$, see Figure 4-1). 

U.S. Expenditures on Crude Oil 

0 

100 

200 

300 

400 

500 

600 

700 

1970 1975 1980 1985 1990 1995 2000 2005 

Year 

(2007$ Billions) 

Domestic 

Imported 

Figure 4-1 U.S. Expenditures on Crude Oil from 1970 through 2008r 

One effect of the EPA/NHTSA joint rule is that it promotes more efficient use of 

transportation fuels in the U.S. The result is that it reduces U.S. oil imports, which reduces both 

financial and strategic risks associated with a potential disruption in supply or a spike in the cost 

of a particular energy source. This reduction in risks is a measure of improved U.S. energy 

security. For this rule, an “oil premium” approach is utilized to identify those energy-security 

related impacts which are not reflected in the market price of oil, and which are expected to 

change in response to an incremental change in the level of U.S. oil imports. 

4.2.7.2.1 Methodology Used to Estimate U.S. Energy Security Benefits 

In order to understand the energy security implications of reducing U.S. oil imports, EPA 

has worked with Oak Ridge National Laboratory (ORNL), which has developed approaches for 

r For historical data through 2006: EIA Annual Energy Review, various editions. 

For data 2006-2008: EIA Annual Energy Outlook (AEO) 2009 (Update Reference (Stimulus) Base Case). 

See file "aeostimtab_11.xls" available at http://www.eia.doe.gov/oiaf/servicerpt/stimulus/aeostim.html 

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evaluating the social costs and energy security implications of oil use. The energy security 

estimates provide below are based upon a methodology developed in a peer-reviewed study 

entitled, “The Energy Security Benefits of Reduced Oil Use, 2006-2015,” completed in March 

2008. This recent study is included as part of the docket for this rulemaking.19 This ORNL 

study is an update version of the approach to estimating the energy security benefits of U.S. oil 

import reductions developed in an ORNL 1997 Report by Leiby, Paul N., Donald W. Jones, T. 

Randall Curlee, and Russell Lee, entitled “Oil Imports: An Assessment of Benefits and Costs.” 

When conducting this recent analysis, ORNL considered the full cost of importing 

petroleum into the U.S. The full economic cost is defined to include two components in addition 

to the purchase price of petroleum itself. These are: (1) the higher costs for oil imports resulting 

from the effect of U.S. import demand on the world oil price and on OPEC market power (i.e., 

the “demand” or “monopsony” costs); and (2) the risk of reductions in U.S. economic output and 

disruption of the U.S. economy caused by sudden disruptions in the supply of imported oil to the 

U.S. (i.e., macroeconomic disruption/adjustment costs). Maintaining a U.S. military presence to 

help secure stable oil supply from potentially vulnerable regions of the world was not included in 

this analysis because its attribution to particular missions or activities is difficult (as discussed 

further below). 

Section III.H of the preamble contains a detailed discussion of how the monopsony and 

macroeconomic disruption/adjustment components were treated for this analysis. As that 

discussion indicates, the monopsony cost represents a transfer of resources from consumers of 

petroleum products to oil producers, and does not involve the consumption of real economic 

resources. Reducing the magnitude of the monopsony cost does not produce a savings in 

economic resources, although it does reduce the value of resources transferred from U.S. 

consumers of petroleum products to foreign suppliers of petroleum. In contrast, the 

macroeconomic disruption and adjustment costs that arise from U.S. petroleum imports do 

represent real economic costs, and reductions in their magnitude do represent real economic 

benefits from reducing U.S. consumption and imports of petroleum. Therefore, the remainder of 

this discussion focuses on estimating the magnitude of the macroeconomic disruption and 

adjustment costs. 

As part of the process for using the ORNL energy security estimates, EPA sponsored an 

independent-expert peer review of this ORNL study. A report compiling the peer reviewers’ 

comments is provided in the docket.20 In addition, EPA has worked with ORNL to address 

comments raised in the peer review and to develop estimates of the energy security benefits 

associated with a reduction in U.S. oil imports for this rule. In response to peer reviewer 

comments, ORNL modified its model by changing several key parameters involving OPEC 

supply behavior, the responsiveness of oil demand and supply to a change in the world oil price, 

and the responsiveness of U.S. economic output to a change in the world oil price. 

For this rule, ORNL further updated the energy security premium by incorporating the 

most recent oil price forecast and energy market trends in AEO 2009 into its model. In order for 

the energy security premium estimated to be used in EPA’s VECTOR model, ORNL developed 

energy security estimates for a number of different years; i.e., 2015, 2020, 2030, and 2040. 

NHTSA used just the 2015 estimates to estimate the energy security benefits associated with this 

rule. 

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For 2015, ORNL has estimated that the total energy security premium associated with a 

reduction of imported oil is $18.49/barrel. On a dollar per gallon basis, energy security benefits 

for 2015 are $0.44/gallon. Based upon alternative sensitivities regarding OPEC supply behavior, 

and the responsiveness of oil demand and supply to a change in the world oil price, the energy 

security premium ranges from $9.80 to $28.08/barrel. In terms of dollars per gallon, the energy 

security premium ranges from $0.23/gallon to $0.67/gallon. Please refer to Table 4-13 for 

similar information for years 2015, 2020, 2030 and 2040,s as well as a breakdown of the 

components of the energy security premium for each year. The components of the energy 

security premium and their values are discussed below. 

Table 4-13 Energy Security Premium in 2015, 2020, 2030 and 2040t 

(2007$/Barrel) 

Year Monopsony 

(Range) 

Macroeconomic 

Disruption/Adjustment Costs 

(Range) 

Total Mid-Point 

(Range) 

2015 $11.79 

($4.26 - $21.37) 

$6.70 

$3.11 – $10.67 

$18.49 

$9.80 - $28.08 

2020 $12.31 

($4.46 - $22.53) 

$7.62 

($3.77 – $12.46) 

$19.94 

($10.58 - $30.47) 

2030 $10.57 

($3.84 – 18.94) 

$8.12 

($3.90 – $13.04) 

$18.69 

($10.52 - $27.89) 

2040 $10.57 

($3.84 – $18.94) 

$8.12 

($3.90 – $13.04) 

$18.69 

($10.52 - $27.89) 

4.2.7.2.1.1 Effect of oil use on long-run oil price, U.S. import costs, and economic output 

The first component of the full economic costs of importing petroleum into the U.S. 

follows from the effect of U.S. import demand on the world oil price over the long-run. Because 

the U.S. is a sufficiently large purchaser of foreign oil supplies, its purchases can affect the world 

oil price. This monopsony power means that increases in U.S. petroleum demand can cause the 

world price of crude oil to rise, and conversely, that reduced U.S. petroleum demand can reduce 

the world price of crude oil. Thus, one benefit of decreasing U.S. oil purchases, due to the 

increased availability and use of other transportation fuels, is the potential decrease in the crude 

oil price paid for all crude oil purchased. 

The demand or monopsony effect can be readily illustrated with an example. If the U.S. 

imports 10 million barrels per day at a world oil price of $50 per barrel, its total daily bill for oil 

imports is $500 million. If a decrease in U.S. imports to 9 million barrels per day causes the 

world oil price to drop to $49 per barrel, the daily U.S. oil import bill drops to $441 million (9 

s AEO 2009 forecasts energy market trends and values only to 2030. The energy security premia post-2030 were 

assumed to be the 2030 estimate. 

t AEO 2009 world oil prices were used, as in the proposed rule. The analysis was not updated using the recentlyreleased AEO 2010 forecast of world oil prices, because EPA and NHTSA believe that the changes in the fuel prices 

would be too small to cause a change in the Energy Security Premium estimates. 

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million barrels times $49 per barrel). While the world oil price only declines $1, the resulting 

decrease in oil purchase payments of $59 million per day ($500 million minus $441 million) is 

equivalent to an incremental benefit of $59 per barrel of oil imports reduced, or $10 more than 

the newly-decreased world price of $49 per barrel. This additional $10 per barrel “import cost 

premium” represents the incremental external benefits to the U.S. for avoided import costs 

beyond the price paid oil purchases. This additional benefit arises only to the extent that 

reduction in U.S. oil imports affects the world oil price. ORNL estimates this component of the 

energy security benefit in 2015 to be $11.79/barrel, with a range of $4.26 – $21.37/barrel of 

imported oil reduced. 

4.2.7.2.1.2 Short-run disruption premium from expected costs of sudden supply disruptions 

The second component of the oil import premium, the “macroeconomic 

disruption/adjustment costs premium,” arises from the effect of oil imports on the expected cost 

of disruptions. A sudden increase in oil prices triggered by a disruption in world oil supplies has 

two main effects: (1) it increases the costs of imports in the short run, further expanding the 

transfer of U.S. wealth to foreign producers, and (2) it can lead to macroeconomic contraction, 

dislocation and Gross Domestic Product (GDP) losses. ORNL estimates the composite estimate 

of these two factors that comprise the macroeconomic disruption/adjustment costs premium to be 

$6.70/barrel in 2015, with a range of $3.11 – $10.67/barrel of imported oil reduced. 

4.2.7.2.1.2.1 Higher costs of oil imports during shocks 

During oil price shocks, the higher price of imported oil causes increased payments for 

imports from the U.S to oil exporters. This increased claim on U.S. economic output is a loss to 

the U.S. that is separate from and additional to any reduction in economic output due to the 

shock. The increased oil payments during shocks are counted as a loss to the degree that the 

expected price increase is not anticipated and internalized by oil consumers. 

4.2.7.2.1.2.2 Macroeconomic costs: potential output loss and dislocation/adjustment costs 

Macroeconomic losses during price shocks reflect both aggregate output losses and 

“allocative” losses. The former are a reduction in the level of output that the U.S. economy can 

produce fully using its available resources; and the latter stem from temporary dislocation and 

underutilization of available resources due to the shock, such as labor unemployment and idle 

plant capacity. The aggregate output effect, a reduction in “potential” economic output, will last 

so long as the price is elevated. It depends on the extent and duration of any disruption in the 

world supply of oil, since these factors determine the magnitude of the resulting increase in 

prices for petroleum products, as well as whether and how rapidly these prices return to their predisruption levels. 

In addition to the aggregate contraction, there are “allocative” or “adjustment” costs 

associated with dislocated energy markets. Because supply disruptions and resulting price 

increases occur suddenly, empirical evidence shows they also impose additional costs on 

businesses and households which must adjust their use of petroleum and other productive factors 

more rapidly than if the same price increase had occurred gradually. Dislocational effects 

include the unemployment of workers and other resources during the time needed for their 

intersectoral or interregional reallocation, and pauses in capital investment due to uncertainty. 

These adjustments temporarily reduce the level of economic output that can be achieved even 

below the “potential” output level that would ultimately be reached once the economy’s 

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adaptation to higher petroleum prices was complete. The additional costs imposed on businesses 

and households for making these adjustments reflect their limited ability to adjust prices, output 

levels, and their use of energy, labor and other inputs quickly and smoothly in response to rapid 

changes in prices for petroleum products. 

Since future disruptions in foreign oil supplies are an uncertain prospect, each of the 

disruption cost components must be weighted by the probability that the supply of petroleum to 

the U.S. will actually be disrupted. Thus, the “expected value” of these costs – the product of the 

probability that a supply disruption will occur and the sum of costs from reduced economic 

output and the economy’s abrupt adjustment to sharply higher petroleum prices – is the relevant 

measure of their magnitude. Further, when assessing the energy security value of a policy to 

reduce oil use, it is only the change in the expected costs of disruption that results from the 

policy that is relevant. The expected costs of disruption may change from lowering the normal 

(i.e., pre-disruption) level of domestic petroleum use and imports, from any induced alteration in 

the likelihood or size of disruption, or from altering the short-run flexibility (e.g., elasticity) of 

petroleum use. 

In summary, the steps needed to calculate the disruption or security premium are: 1) 

determine the likelihood of an oil supply disruption in the future; 2) assess the likely impacts of a 

potential oil supply disruption on the world oil price; 3) assess the impact of the oil price shock 

on the U.S. economy (in terms of import costs and macroeconomic losses); and 4) determine 

how these costs change with oil imports. The value of price spike costs avoided by reducing oil 

imports becomes the oil security portion of the premium. 

In the energy security literature, the macroeconomic disruption component of the energy 

security premium traditionally has included both (1) increased payments for petroleum imports 

associated with a rapid increase in world oil prices, and (2) the GDP losses and adjustment costs 

that result from projected future oil price shocks. One commenter suggested that the increased 

payments associated with rapid increases in petroleum prices (i.e., price increases in a disrupted 

market) represent transfers from U.S. oil consumers to petroleum suppliers rather than real 

economic costs, and therefore, should not be counted as a benefit. 

This approach would represent a significant departure from how the macroeconomic 

disruption costs associated with oil price shocks have been quantified in the broader energy 

security literature, and the Agencies believe it should be analyzed in more detail before being 

applied in a regulatory context. In addition, the Agencies also believe that there are compelling 

reasons to treat higher oil import costs during oil supply disruptions differently than simple 

wealth transfers that reflect the exercise of market power by petroleum sellers or consumers. 

According to the OMB definition of a transfer: “Benefit and cost estimates should reflect real 

resource use. Transfer payments are monetary payments from one group to another that do not 

affect total resources available to society...The net reduction in the total surplus (consumer plus 

producer) is a real cost to society, but the transfer from buyers to sellers resulting from a higher 

price is not a real cost since the net reduction automatically accounts for the transfer from buyers 

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to sellers.”u In other words, pure transfers do not lead to changes in the allocation or 

consumption of economic resources, whereas changes in the resource allocation or use produce 

real economic costs or benefits. 

While price increases during oil price disruptions can result in large transfers of wealth, 

they also result in a combination of real resource shortages, costly short-run shifts in energy 

supply, behavioral and demand adjustments by energy users, and other response costs. Unlike 

pure transfers, the root cause of the disruption price increase is a real resource supply reduction 

due, for example, to disaster or war. Regions where supplies are disrupted (i.e., the U.S.) suffer 

very high costs. Businesses’ and households’ emergency responses to supply disruptions and 

rapid price increases are likely to consume some real economic resources, in addition to causing 

financial losses to the U.S. economy that are matched by offsetting gains elsewhere in the global 

economy. 

While households and businesses can reduce their petroleum consumption, invest in fuel 

switching technologies, or use futures markets to insulate themselves in advance against the 

potential costs of rapid increases in oil prices, when deciding how extensively to do so, they are 

unlikely to account for the effect of their petroleum consumption on the magnitude of costs that 

supply interruptions and accompanying price shocks impose on others. As a consequence, the 

U.S. economy as a whole will not make sufficient use of these mechanisms to insulate itself from 

the real costs of rapid increases in energy prices and outlays that usually accompany oil supply 

interruptions.v Therefore, the ORNL estimate of macroeconomic disruption and adjustment 

costs that the Agencies use to value energy security benefits includes the increased oil import 

costs stemming from oil price shocks that are unanticipated and not internalized by advance 

actions of U.S. consumers of petroleum products. The Agencies believe that, as the ORNL 

analysis argues, the uninternalized oil import costs that occur during oil supply interruptions 

represents a real cost associated with U.S. petroleum consumption and imports, and that reducing 

its value by lowering domestic petroleum consumption and imports thus represents a real 

economic benefit from lower fuel consumption. 

4.2.7.2.2 Cost of existing U.S. energy security policies 

The last often-identified component of the full economic costs of U.S. oil imports is the 

costs to the U.S. taxpayers of existing U.S. energy security policies. The two primary examples 

are maintaining a military presence to help secure a stable oil supply from potentially vulnerable 

regions of the world and maintaining the Strategic Petroleum Reserve (SPR). The SPR is the 

largest stockpile of government-owned emergency crude oil in the world. Established in the 

aftermath of the 1973-74 oil embargo, the SPR provides the U.S. a response option should a 

u OMB Circular A-4, September 17, 2003. See 

http://www.whitehouse.gov/omb/assets/omb/circulars/a004/a-4.pdf 

v For a more complete discussion of the reasons why the oil import cost component of the 

macroeconomic disruption and adjustment costs includes some real costs and does not represent 

a pure transfer, see Paul N. Leiby, Estimating the Energy Security Benefits of Reduced U.S. Oil 

Imports: Final Report, ORNL-TM-2007-028, Oak Ridge National Laboratory, March 14, 2008, 

pp. 21-25. 

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Economic Assumptions Used in the Agencies’ Analysis
 

disruption in commercial oil supplies threaten the U.S. economy. It also allows the U.S. to meet 

part of its International Energy Agency obligation to maintain emergency oil stocks, and it 

provides a national defense fuel reserve. 

U.S. military costs are excluded from the analysis performed by ORNL because their 

attribution to particular missions or activities is difficult. Most military forces serve a broad 

range of security and foreign policy objectives. Attempts to attribute some share of U.S. military 

costs to oil imports are further challenged by the need to estimate how those costs might vary 

with incremental variations in U.S. oil imports. Similarly, while the costs for building and 

maintaining the SPR are more clearly related to U.S. oil use and imports, historically these costs 

have not varied in response to changes in U.S. oil import levels. Thus, while SPR is factored 

into the ORNL analysis, the cost of maintaining the SPR is excluded. 

4.2.7.2.3 Modifications to analysis based upon peer reviewer comments 

The EPA commissioned ORNL to conduct a number of sensitivity analyses to address the 

comments of the peer reviewers. Based upon the peer reviewer comments, key parameters that 

influence the “oil import” premium were assessed. Since not all the comments were in 

agreement with each other, several ranges of different parameters were developed for the 

analyses. These sensitivities used the most recent price forecasts and energy market trends 

available at the time the peer review was being conducted and completed, the AEO 2007 

Reference Case. Thus, the results presented below are suggestive of how the energy security 

premium is influenced by alternative assumptions of key parameters that influence world oil 

markets. A summary of the results of the analyses are shown in Table 4-14. 

Three key parameters were varied in order to assess their impacts on the oil import 

premium: (1) the response of OPEC supply, (2) the combined response of non-U.S., non-OPEC 

demand and supply and (3) the GDP response to a change in the world oil as a result of reduced 

U.S. oil imports. The cases used updated supply/demand elasticities for non-U.S./non-OPEC 

region after considering more recent estimates than those used in 1997 study. As a result, the 

total market responsiveness is greater than previous ORNL estimates. Only small changes to the 

world oil price are anticipated from a substantial reduction in U.S. demand, on average, about 

$0.70/barrel for every million-barrels-per-day reduction in demand. 

In the ORNL framework, OPEC-behavior is treated parametrically, with a wide range of 

possible responses represented by a range of supply elasticities. Case One in Table 4-14 below 

refers to the AEO 2007 estimates of energy market trends and uses the elasticity parameters from 

the original 1997 ORNL study. In Case Two, the OPEC supply elasticities range for 0.25 to 6 

with a mean elasticity of 1.7. Case Three alters the distribution of the OPEC supply elasticities 

so that the mean elasticity is 2.2 instead of 1.7. With the more elastic OPEC oil supply in Case 

Three, the oil premium is lower. Alternatively, a candidate rule for OPEC strategic response 

behavior, adapted from a lead article on what behavior maximizes OPEC’s long run net revenue 

in a robust way,21 would have OPEC responding to preserve its worldwide oil market share. 

This is presented as Case Seven. Application of this rule instead of the range of OPEC supply 

responses used leads to an estimate of the oil import premium that is between Case Two and 

Case Three. 

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The second key parameter that was varied based upon peer reviewer comments was nonOPEC, non-U.S. demand and supply responsiveness to a change in the U.S. oil import demand 

and, hence, the world oil price. In Case Four, the mean non-U.S./non-OPEC demand and supply 

elasticities are taken to each be 0.3 in absolute value terms. When combined together, the net 

elasticity of import demand from this region — the non-U.S./non-OPEC region is — roughly 

1.6. Case Five takes the Case Four assumptions of a more elastic OPEC supply behavior and 

combines those assumptions with the 1.6 net elasticity of import demand for the non-U.S./nonOPEC region. Case Six looks at the consequences of a yet higher net elasticity of import 

demand — 2.28 — for the non-U.S./non-OPEC region. The impact on the oil import premium is 

relatively modest. 

Cases Eight and Nine consider a reduced GDP elasticity, the parameter which 

summarizes the sensitivity of GDP to oil price shocks. Several reviewers suggested a lower 

estimate for this parameter. In response to their comments, a couple of cases were examined 

where the GDP elasticity was lowered to 0.032 in comparison to the original ORNL estimate of 

0.0495. As anticipated, this change lowered the oil import premium modestly. For example, 

compared with Case Four where OPEC supply is more elastic, lowering the GDP elasticity with 

respect to the world oil price reduced the oil import premium by roughly $0.40/barrel. This is 

because the GDP-dislocation component is only about one-quarter of the total premium, and 

there are offsetting changes in other components. The last case examined, Case Nine, looks at 

the consequences for the oil import premium with a reduced elasticity of GDP if OPEC attempts 

to maintain its share of the world oil market. 

Clearly there is an unavoidable degree of uncertainty about the magnitude of marginal 

economic costs from the U.S. importation of petroleum, and the size of the oil import premium. 

ORNL sought to reflect this with probabilistic risk analysis over key input factors, guided by the 

available literature and the best judgment of oil market experts. Cases shown in Table 4-14 

explore some reasonable variations in the ranges of input assumptions and the mean oil premium 

estimates vary in a fairly moderate range between roughly $11 and $15/barrel of imported oil. 

On balance, Case Eight suggested a reasonable and cautious assessment of the premium value to 

ORNL, and is ORNL’s recommended case. This is based on a review of important driving 

factors, the numerical evaluations and simulations over major uncertainties, and taking into 

consideration the many comments and suggestion from the reviewers, the EPA and other 

Agencies. This recommended case, and the premium range resulting from 90 percent of the 

simulated outcomes, encompasses a wide array of perspectives and potential market outcomes in 

response to a reduction of U.S. imports. 

As mentioned previously, this recommended case relied on the most recent available 

projections of the U.S. and world oil market for the next ten years based upon the AEO 2007 

Reference Case. OPEC-behavior was treated parametrically, with a wide range of possible 

responses represented by a wide range of supply elasticities, from small to quite large. This 

recommended case recognized that the OPEC response is the most uncertain single element of 

this analysis. It could vary between inelastic defense of output levels, or market share, or could 

be highly elastic in defense of price, probably at the expense of longer run cartel power and 

discounted net profits. The balance between possible elastic and inelastic OPEC response was 

essentially even over a fairly wide range of elasticities. ORNL concluded that this is the best 

way to estimate OPEC behavior until greater progress can be made in synthesizing what insights 

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Economic Assumptions Used in the Agencies’ Analysis
 

are available from the evolving strategic game-theoretic and empirical research on OPEC 

behavior, and advancing that research. An alternative would have been to use OPEC strategic 

response behavior to maximize long-run net revenue, which may well correspond to marketshare preservation behavior (e.g., Case Seven), and a somewhat higher premium value. 

Finally, ORNL’s recommended case used a GDP elasticity range, the parameter which 

summarizes the sensitivity of GDP to oil price shocks, which is reduced compared to earlier 

estimates, and compared to the full range of historically-based estimates. This helped address 

the concerns of those who either question the conclusions of past empirical estimates or expect 

that the impacts of oil shocks may well be declining. 

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Joint Technical Support Document
 

Table 4-14 Summary Results – Oil Import Premium Under Various Cases ($2007/BBL

Component Statistic 

1) AEO2007 Base Outlook, 1997 Study Elasticities 

2) AEO2007 Base Outlook, Wider Range of OPEC Supply Elasticities 

3) Case 2 with Revised Wider Range of OPEC Supply Elasticities 

4) Case 2 with Updated NonOPEC Supply/Demand Elasticities 

5) Case 4 plus Revised Wider Range of OPEC Supply Elasticities 

6) Case 4 Variant with Wider, Higher Range of NonU.S./Non-OPEC Supply/Demand Elasticities 

7) Case 6 with Applied Strategic 

OPEC Behavioral 

Rule: Maintain Market Share 

8) CReEla

Monopsony Component Mean $5.57 $10.26 $8.16 $7.77 $6.52 $6.44 $9.36 

Range ($3.60 - $8.19) ($3.10 - $21.22) ($3.12 - $19.30) ($2.94 - $13.75) ($2.90 - $13.06) ($2.69 - $11.33) ($6.69 - $12.42) ($2

Disruption Import Costs Mean $2.36 $2.33 $2.34 $1.92 $1.94 $1.93 $1.89 

Range ($0.51 - $4.66) ($0.58 - $4.57) ($0.66 - $4.61) ($0.39 - $3.75) ($0.38 - $3.84) ($0.42 - $3.70) ($0.44 - $3.66) ($

Disruption Dislocation Costs Mean $3.83 $3.70 $3.76 $3.42 $3.45 $3.47 $3.41 

Range ($1.06 - $6.69) ($1.04 - $6.57) ($1.06 - $6.74) ($0.92 - $6.23) ($0.94 - $6.10) ($0.92 - $6.13) ($0.87 - $6.26) ($

Economic Disruption/Adjustment 

Costs 

Mean $6.19 $6.03 $6.10 $5.34 $5.39 $5.40 $5.30 

Range ($2.94 - $10.01) ($2.94 - $9.75) ($2.91 - $9.85) ($2.53 - $8.69) ($2.60 - $8.62) ($2.58 - $8.92) ($2.54 - $8.74) ($

Total Mid-Point Mean $11.75 $16.29 $14.27 $13.11 $11.90 $11.86 $14.65 

Range ($8.04 - $15.96) ($8.10 - $27.42) ($7.90 - $25.49) ($7.26 - $19.59) ($6.96 - $18.62) ($6.79 - $17.23) ($11.03 - $18.61) ($7

Total Premium, in $/Gallon Mean $0.28 $0.39 $0.34 $0.31 $0.28 $0.28 $0.35 Price Reduction ($/MMBD) Mean $0.52 $1.04 $0.81 $0.75 $0.61 $0.60 $0.90 

Cases7) Applied Strategic OPEC Behavioral Rule to Case 6: Maintain Market Share (Gately 2004 paper best strategy). This rule implies that OPEC Supply elasticity matches that result non-U.S. elasticity of import demand ranges from 8) Variant on version Case 4, considered reduced GDP elasticity for future disruptions (range -0.01 to -0.054; midcase value -0.032; mean value is -0.032, reduced from meabehavior treated parametrically. 9) Revise Case 7 (which applied Strategic OPEC Behavioral Rule to Case 6: Maintain Market Share (Gately 2004 paper best strategy)) with reduced GDP elasticity for future 0.054; midcase value -0.032; mean value is -0.032, reduc 3) Revise Case 2, with OPEC behavior distributed over elasticities 0 to 6, so that 25% of response is inelastic (< 1.0), mode elasticity is 2.0 (mean elasticity is 2.2 rather than 4) Updated Case 2 supply/demand elasticities for non-OPEC region with more recent estimates. Elasticity of non-U.S. demand -0.2 to -0.4, with mean and mode -0.3, non-Umean and mode 0.3, implying (mode) net elasticity of impo 5) Revise Case 4, with OPEC behavior distributed over elasticities 0 to 6, so that 25% of response is inelastic (< 1.0), mode elasticity is 2.0 (mean elasticity is 2.2 rather than demand is -1.6 for the non-U.S./non-OPEC r 

6) Alternative to Case 4 with expanded (and higher) range of non-U.S. supply/demand elasticities. Elasticity of non-U.S. demand = -0.3 to -0.7, triangular distribution with mo

non-U.S. supply = 0.2 to 0.6, mode 0.3 and m 1) Based on AEO2007. Updated oil market outlook from AEO1994 Base Case to AEO2007 Base Case. Among other things, this means average crude price rises from $20match 1997 values. Non-U.S. elasticity of import demand = -0. 2) AEO2007 Base Outlook, with wider range of OPEC supply elasticities, 0.25 to 6.0 and a mean elasticity of 1.76. 

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4.2.7.2.4 Energy security premium values for this rule 

After the peer review process was completed, EPA worked with ORNL to further 

update the analysis for this vehicle program. Using the same methodology as the peer 

reviewed model, but updating the analysis using AEO 2009 world oil price values, ORNL 

calculated energy security premium estimates for the years 2015, 2020, and 2030. Since the 

AEO 2009 ends its forecast in the year 2030, EPA and NHTSA assumed that the post-2030 

energy security premium estimates were the same as the 2030 estimate. The results of this 

analysis are shown above in Table 4-13in Chapter 4.2.8. 

The monopsony cost represents a transfer of resources from consumers of petroleum 

products to oil producers, and does not entail the use of real economic resources. Reducing 

the magnitude of the monopsony cost does not produce a savings in economic resources, 

although it does reduce the value of resources transferred from U.S. consumers of petroleum 

products to foreign suppliers of petroleum. In contrast, the macroeconomic disruption and 

adjustment costs that arise from U.S. petroleum imports do represent real economic costs, and 

reductions in their magnitude do represent real economic benefits from reducing U.S. 

consumption and imports of petroleum. Hence the agencies’ estimates of economic benefits 

from reducing fuel consumption via CAFE and GHG emission standards include ORNL’s 

estimates of the economic value of resulting reductions in macroeconomic and disruption 

costs, but exclude reductions in the monopsony cost component of the oil import premium. 

4.2.7.2.5 The impact of fuel savings on U.S. petroleum imports 

Based on a detailed analysis of differences in fuel consumption, petroleum imports, 

and imports of refined petroleum products among the Reference Case, High Economic 

Growth, and Low Economic Growth Scenarios presented in AEO 2009, NHTSA and EPA 

estimate that approximately 50 percent of the reduction in fuel consumption resulting from 

adopting improved fuel efficiency standards and GHG standards is likely to be reflected in 

reduced U.S. imports of refined fuel, while the remaining 50 percent would be expected to be 

reflected in reduced domestic fuel refining.w Of this latter figure, 90 percent is anticipated to 

reduce U.S. imports of crude petroleum for use as a refinery feedstock, while the remaining 

10 percent is expected to reduce U.S. domestic production of crude petroleum.x Thus on 

balance, each gallon of fuel saved as a consequence of improved fuel efficiency standards and 

GHG standards is anticipated to reduce total U.S. imports of crude petroleum or refined fuel 

by 0.95 gallons.y Section 5.3 of the RIA contains a discussion regarding caveats for the fuel 

savings estimated due to implementation of this rule. 

w Differences between forecast annual U.S. imports of crude petroleum and refined products among these three 

scenarios range from 24-89 percent of differences in projected annual gasoline and diesel fuel consumption in 

the U.S. These differences average 49 percent over the forecast period spanned by AEO 2009. This analysis has 

not been updated using forecasts from AEO 2010, because to date only the Reference Case from AEO 2010 has 

been released by EIA. 

x Differences between forecast annual U.S. imports of crude petroleum among these three scenarios range from 

67-97 percent of differences in total U.S. refining of crude petroleum, and average 85 percent over the forecast 

period spanned by AEO 2009. 

y This figure is calculated as 0.50 + 0.50*0.9 = 0.50 + 0.45 = 0.95. 

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The energy security analysis conducted for this rule estimates that the world price of 

oil will fall modestly in response to lower U.S. demand for refined fuel. One potential result 

of this decline in the world price of oil would be an increase in the consumption of petroleum 

products outside the U.S., which would in turn lead to a modest increase in emissions of 

greenhouse gases, criteria air pollutants, and airborne toxics from their refining and use. 

While additional information would be needed to analyze this “leakage effect” in detail, 

NHTSA provides a sample estimate of its potential magnitude in its Final EIS.z This analysis 

indicates that the leakage effect is likely to offset only a modest fraction of the reductions in 

emissions projected to result from the rule. 

4.2.8 Air pollutant emissions 

Car and light truck use, fuel refining, and fuel distribution and retailing also generate 

emissions of certain criteria air pollutants, including carbon monoxide (CO), hydrocarbon 

compounds (usually referred to as “volatile organic compounds,” or VOC), nitrogen oxides 

(NOx), fine particulate matter (PM2.5), and sulfur dioxide (SO2). Emissions of most of these 

pollutants are associated with the number of vehicle miles driven, rather than with the 

quantity of fuel consumed. While reductions in fuel refining and distribution that result from 

lower fuel consumption will reduce emissions of criteria pollutants, additional vehicle use 

associated with the rebound effect from higher fuel economy will increase emissions of most 

of these pollutants. Sulfur dioxide is an exception, as described below. 

Thus the net effect of stricter fuel efficiency and GHG standards on total emissions of 

each criteria pollutant depends on the relative magnitudes of reduced emissions in fuel 

refining and distribution, and increases in emissions from vehicle use. Because the 

relationship between emission rates (emissions per gallon refined of fuel or mile driven) in 

fuel refining and vehicle use is different for each criteria pollutant, the net effect of fuel 

savings from increased fuel efficiency and GHG standards on total emissions of each 

pollutant is likely to differ. 

4.2.8.1 Reductions in emissions of criteria air pollutants 

For the analysis of criteria emissions over the lifetime of the model years covered by 

this rule, EPA and NHTSA estimate the increases in emissions of each criteria air pollutant 

from additional vehicle use by multiplying the increase in total miles driven by cars and light 

trucks of each model year and age by their estimated emission rates per vehicle-mile of each 

pollutant. These emission rates differ between cars and light trucks as well as between 

gasoline and diesel vehicles, and both their values for new vehicles and the rates at which they 

increase with age and accumulated mileage can vary among model years. With the exception 

of SO2, the agencies calculated the increase in emissions of these criteria pollutants from 

added car and light truck use by multiplying the estimated increases in their vehicles’ use 

z NHTSA Final Environmental Impact Statement: Corporate Average Fuel Economy Standards, Passenger Cars 

and Light Trucks, Model Years 2012-2016, February 2010, page 3-14. 

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Economic Assumptions Used in the Agencies’ Analysis
 

during each year over their expected lifetimes by per-mile emission rates appropriate to each 

vehicle type, fuel used, model year, and age as of that future year. 

These emission rates were estimated by U.S. EPA using its newly updated Motor 

Vehicle Emission Simulator (MOVES2010). For the proposal, emission rates were estimated 

using Draft MOVES2009. 

The MOVES model assumes that the per-mile rates at which these pollutants are 

emitted are determined by EPA regulations and the effectiveness of catalytic after-treatment 

of engine exhaust emissions, and are thus unaffected by changes in car and light truck fuel 

economy. As a consequence, the effects of required increases in fuel economy on emissions 

of these pollutants from car and light truck use are determined entirely by the increases in 

driving that result from the fuel economy rebound effect. As discussed in the EPA RIA, 

anticipated changes in the fuel supply composition will also affect criteria pollutant emissions. 

Emission factors in the MOVES database are expressed in the form of grams per 

vehicle-hour of operation. To convert these emission factors to grams per mile for use in EPA 

and NHTSA’s calculations, MOVES was run for the year 2050, and was programmed to 

report aggregate emissions from vehicle starting and running exhaust operation. EPA analysts 

selected the year 2050 in order to generate emission factors that were representative of 

lifetime average emission rates for vehicles meeting the agency’s Tier 2 emission standard.aa 

Separate estimates were developed for each vehicle type and model year, as well as for each 

state and month, in order to reflect the effects of regional and temporal variation in 

temperature and other relevant variables on emissions. 

The MOVES emissions estimates were then summed to the model year level and 

divided by average distance traveled in order to produce per-mile emission factors for each 

pollutant. The resulting emission rates represent average values across the nation, and 

incorporate typical temperature variations over an entire calendar year. These national 

average rates also reflect county-specific differences in fuel composition, as well as in the 

presence and type of vehicle inspection and maintenance programs.bb 

Emission rates for the criteria pollutant SO2 were calculated by EPA and NHTSA 

using average fuel sulfur content estimates supplied by EPA, together with the assumption 

that the entire sulfur content of fuel is emitted in the form of SO2. These calculations assumed 

that national average gasoline and diesel sulfur levels would remain at current levels.cc 

aa Because all light-duty emission rates in MOVES2010 are assumed to be invariant after MY 2010, a calendar-


year 2050 run produced a full set of emission rates that reflect anticipated deterioration in the effectiveness of
 

vehicles’ emission control systems with increasing age and accumulated mileage for post-MY 2010 vehicles.
 

bb The national mix of fuel types includes county-level market shares of conventional and reformulated gasoline,
 

as well as county-level variation in sulfur content, ethanol fractions, and other fuel properties.
 

Inspection/maintenance programs at the county level account for detailed program design elements such as test
 

type, inspection frequency, and program coverage by vehicle type and age.
 

cc These are 30 and 15 parts per million (ppm, measured on a mass basis) for gasoline and diesel respectively,
 

which produces emission rates of 0.17 grams of SO2 per gallon of gasoline and 0.10 grams per gallon of diesel.
 

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There, unlike many other criteria pollutants, sulfur dioxide emissions from vehicle use 

decrease proportionally to the decrease in fuel consumption. 

Emissions of criteria air pollutants also occur during each phase of fuel production and 

distribution, including crude oil extraction and transportation, fuel refining, and fuel storage 

and transportation. The reduction in emissions during each of these phases depends on the 

extent to which fuel savings result in lower imports of refined fuel, or in reduced domestic 

fuel refining. To a lesser extent, they also depend on whether reductions in domestic gasoline 

refining are reflected in reduced imports of crude oil or in reduced domestic extraction of 

petroleum. 

Both EPA’s and NHTSA’s analyses assume that reductions in imports of refined fuel 

would reduce criteria pollutant emissions during fuel storage and distribution only. 

Reductions in domestic fuel refining using imported crude oil as a feedstock are assumed to 

reduce emissions during fuel refining, storage, and distribution, because each of these 

activities would be reduced. Finally, reduced domestic fuel refining using domesticallyproduced crude oil is assumed to reduce emissions during all four phases of fuel production 

and distribution.dd 

EPA estimated the reductions in criteria pollutant emissions from producing and 

distributing fuel that would occur with alternative fuel efficiency and GHG standards using 

emission rates obtained from Argonne National Laboratories’ Greenhouse Gases and 

Regulated Emissions in Transportation (GREET) model.22 The GREET model provides 

separate estimates of air pollutant emissions that occur in four phases of fuel production and 

distribution: crude oil extraction, crude oil transportation and storage, fuel refining, and fuel 

distribution and storage.ee EPA modified the GREET model to change certain assumptions 

about emissions during crude petroleum extraction and transportation, as well as to update its 

emission rates to reflect adopted and pending EPA emission standards. EPA also 

incorporated emission factors for the air toxics estimated in this analysis: benzene, 1,3­

butadiene, acetaldehyde, acrolein, and formaldehyde. The resulting emission factors are 

shown in Table 4-15. 

dd In effect, this assumes that the distances crude oil travels to U.S. refineries are approximately the same 

regardless of whether it travels from domestic oilfields or import terminals, and that the distances that gasoline 

travels from refineries to retail stations are approximately the same as those from import terminals to gasoline 

stations. 

ee Emissions that occur during vehicle refueling at retail gasoline stations (primarily evaporative emissions of 

volatile organic compounds, or VOCs) are already accounted for in the “tailpipe” emission factors used to 

estimate the emissions generated by increased light truck use. GREET estimates emissions in each phase of 

gasoline production and distribution in mass per unit of gasoline energy content; these factors are then converted 

to mass per gallon of gasoline using the average energy content of gasoline. 

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Economic Assumptions Used in the Agencies’ Analysis
 

Table 4-15 Emissions by Stage of Fuel Production and Distribution (grams/million Btu)
 

Pollutant Fuel Type 

Petroleum 

Extraction & 

Transportation1 

Refinery 

Energy 

Use 

Upstream 

Emissions 

Petroleum 

Refining 

On-Site 

Petroleum 

Refining2 

Fuel 

Transport, 

Storage, 

Distribution3 

CO 

Conventional Gasoline 

Reformulated Gasoline 

Low Sulfur Diesel 

4.908 

4.908 

4.908 

0.928 

0.908 

0.800 

5.596 

5.662 

5.103 

6.525 

6.571 

5.903 

0.748 

0.768 

0.780 

VOC 

Conventional Gasoline 

Reformulated Gasoline 

Low Sulfur Diesel 

3.035 

3.035 

3.035 

0.602 

0.627 

0.552 

2.560 

2.584 

2.511 

3.162 

3.211 

3.063 

42.91 

42.92 

1.261 

NOx 

Conventional Gasoline 

Reformulated Gasoline 

Low Sulfur Diesel 

14.91 

14.91 

14.91 

3.328 

3.288 

2.895 

14.442 

14.575 

12.972 

17.771 

17.863 

15.866 

3.691 

3.786 

3.570 

SOx 

Conventional Gasoline 

Reformulated Gasoline 

Low Sulfur Diesel 

3.926 

3.926 

3.926 

4.398 

4.422 

3.893 

9.678 

9.922 

9.187 

14.076 

14.344 

13.080 

0.886 

0.909 

0.840 

PM2.5 

Conventional Gasoline 

Reformulated Gasoline 

Low Sulfur Diesel 

0.645 

0.645 

0.645 

1.442 

1.487 

1.309 

1.789 

1.838 

1.635 

3.231 

3.325 

2.943 

0.155 

0.159 

0.133 

Air Toxics 

1,3-Butadiene 

Conventional Gasoline 

Reformulated Gasoline 

Low Sulfur Diesel 

0.0017 

0.0017 

0.0017 

0.0003 

0.0003 

0.0003 

0.0014 

0.0014 

0.0014 

0.0017 

0.0018 

0.0017 

0.0001 

0.0001 

0.0001 

Acetaldehyde 

Conventional Gasoline 

Reformulated Gasoline 

Low Sulfur Diesel 

0.0002 

0.0002 

0.0002 

0.0000 

0.0000 

0.0000 

0.0002 

0.0002 

0.0002 

0.0002 

0.0002 

0.0002 

0.0046 

0.0047 

0.0044 

Acrolein 

Conventional Gasoline 

Reformulated Gasoline 

Low Sulfur Diesel 

0.0001 

0.0001 

0.0001 

0.0000 

0.0000 

0.0000 

0.0001 

0.0001 

0.0001 

0.0001 

0.0001 

0.0001 

0.0006 

0.0006 

0.0006 

Benzene 

Conventional Gasoline 

Reformulated Gasoline 

Low Sulfur Diesel 

0.0313 

0.0313 

0.0313 

0.0062 

0.0064 

0.0058 

0.0264 

0.0264 

0.0264 

0.0326 

0.0328 

0.0322 

0.0787 

0.0788 

0.0015 

Formaldehyde 

Conventional Gasoline 

Reformulated Gasoline 

Low Sulfur Diesel 

0.0050 

0.0050 

0.0050 

0.0010 

0.0010 

0.0009 

0.0042 

0.0042 

0.0042 

0.0052 

0.0052 

0.0051 

0.0326 

0.0335 

0.0316 1 The petroleum extraction and transport emission factors are only applied to domestic crude oil.
 2 Refinery emissions factors are applied to domestic refineries, whether refining domestic or imported crude.
 3 Fuel transport, storage, and distribution emission factors represent domestic emissions and are applied to all
 

finished fuel, whether refined domestically or internationally.
 

The agency converted these emission rates from the mass per fuel energy content basis 

on which GREET reports them to mass per gallon of fuel supplied using the estimates of fuel 

energy content reported by GREET. The resulting emission rates were applied to both EPA’s 

and NHTSA’s estimates of fuel consumption under alternative fuel efficiency standards to 

develop estimates of total emissions of each criteria pollutant during fuel production and 

distribution. The assumptions about the effects of changes in fuel consumption on domestic 

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and imported sources of fuel supply discussed above were then employed to calculate the 

effects of reductions in fuel use from alternative fuel efficiency and GHG standards on 

changes in domestic emissions of each criteria pollutant. 

Finally, EPA and NHTSA calculated the net changes in domestic emissions of each 

criteria pollutant by summing the increases in its emissions projected to result from increased 

vehicle use, and the reductions in emissions anticipated to result from lower domestic fuel 

refining and distribution.ff As indicated previously, the effect of adopting improved fuel 

efficiency and GHG standards on total emissions of each criteria pollutant depends on the 

relative magnitudes of the resulting reduction in emissions from fuel refining and distribution, 

and the increase in emissions from additional vehicle use. Although these net changes vary 

significantly among individual criteria pollutants, we project that on balance, adopting 

improved fuel efficiency and GHG standards would reduce emissions of all criteria air 

pollutants except carbon monoxide (CO). The analysis of criteria emissions during specific 

calendar years is described independently in NHTSA’s Environmental Impact Statement and 

EPA’s RIA. 

4.2.8.2 Estimated values of reducing PM related emissions in the model year analysis 

In this analysis, EPA and NHTSA estimate the economic value of the human health 

benefits associated with reducing exposure to PM2.5 using benefit-per-ton estimates for the 

analysis of PM-related benefits over the lifetime of specific model years. Due to analytical 

limitations, the estimated benefit-per-ton values do not include comparable benefits related to 

reductions in other criteria pollutants (such as ozone, NO2 or SO2) or toxic air pollutants, nor 

do they monetize all of the potential health and welfare effects associated with PM2.5 or the 

other criteria pollutants. As a result these values underestimate the benefits associated with 

reductions of these pollutants. Note that EPA conducted full-scale photochemical air quality 

modeling for the calendar year analysis. Please refer to Chapter 7.2 of the RIA for a 

description of EPA’s air quality modeling results and to Chapter 7.3 for a description of the 

quantified and monetized PM- and ozone-related health impacts of the FRM. 

The analysis of PM-related benefits over the lifetime of specific model years uses a 

“benefit-per-ton” method to estimate selected PM2.5-related health benefits. These PM2.5 ­

related benefit-per-ton estimates provide the total monetized human health benefits (the sum 

of premature mortality and premature morbidity) of reducing 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. Ideally, the human health benefits would be 

estimated based on changes in ambient PM2.5 concentrations and population exposure, as 

determined by full-scale air quality and exposure modeling. 

The dollar-per-ton estimates for reductions in PM2.5 emissions used in this analysis are 

provided in Table 4-16. 

ff All emissions from increased vehicle use are assumed to occur within the U.S., since fuel efficiency standards 

would apply only to vehicles produced for sale in the U.S. 

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Economic Assumptions Used in the Agencies’ Analysis
 

Table 4-16 Benefits-per-ton Values (2007$) Derived Using the ACS Cohort Study for
 

PM-related Premature Mortality (Pope et al., 2002)a
 

Yearc All Sourcesd Stationary (Non-EGU) 

Sources 

Mobile Sources 

SOX VOC NOX Direct PM2.5 NOX Direct PM2.5 

Estimated Using a 3 Percent Discount Rateb 

2015 $28,000 $1,200 $4,700 $220,000 $4,900 $270,000 

2020 $31,000 $1,300 $5,100 $240,000 $5,300 $290,000 

2030 $36,000 $1,500 $6,100 $280,000 $6,400 $350,000 

2040 $43,000 $1,800 $7,200 $330,000 $7,600 $420,000 

Estimated Using a 7 Percent Discount Rateb 

2015 $26,000 $1,100 $4,200 $200,000 $4,400 $240,000 

2020 $28,000 $1,200 $4,600 $220,000 $4,800 $270,000 

2030 $33,000 $1,400 $5,500 $250,000 $5,800 $320,000 

2040 $39,000 $1,600 $6,600 $300,000 $6,900 $380,000 a The benefit-per-ton estimates presented in this table are based on an estimate of premature mortality derived
 

from the ACS study (Pope et al., 2002). If the benefit-per-ton estimates were based on the Six-Cities study
 

(Laden et al., 2006), the values would be approximately 145% (nearly two-and-a-half times) larger.
 

b The benefit-per-ton estimates presented in this table assume either a 3 percent or 7 percent discount rate in the
 

valuation of premature mortality to account for a twenty-year segmented cessation lag.
 

c Benefit-per-ton values were estimated for the years 2015, 2020, and 2030. For 2040, EPA and NHTSA
 

extrapolated exponentially based on the growth between 2020 and 2030.
 

d Note that the benefit-per-ton value for SOx is based on the value for Stationary (Non-EGU) sources; no SOx
 

value was estimated for mobile sources. The benefit-per-ton value for VOCs was estimated across all sources.
 

e Non-EGU denotes stationary sources of emissions other than electric generating units.
 

As Table 4-16 indicates, EPA projects that the per-ton values for reducing emissions 

of criteria pollutants from both vehicle use and stationary sources such as fuel refineries and 

storage facilities will increase over time. These projected increases reflect rising income 

levels, which are assumed to increase affected individuals’ willingness to pay for reduced 

exposure to health threats from air pollution, as well as population growth into the future, 

expanding the population exposed to air pollution in both urban and rural areas.23 

For certain PM2.5-related pollutants (such as direct PM2.5 and NOx), EPA estimates 

different per-ton values for reducing mobile source emissions than for reductions in 

emissions of the same pollutant from stationary sources such as fuel refineries and storage 

facilities. These reflect differences in the typical geographic distributions of emissions of 

each pollutant, their contributions to ambient levels of PM2.5, and resulting changes in 

population exposure. EPA and NHTSA apply these separate values to its estimates of 

changes in emissions from vehicle use and from fuel production and distribution to determine 

the net change in total economic damages from emissions of those pollutants. 

The benefit per-ton technique has been used in previous analyses, including EPA’s 

recent Ozone National Ambient Air Quality Standards (NAAQS) RIA,24 the proposed 

Portland Cement National Emissions Standards for Hazardous Air Pollutants (NESHAP) 

25 26 RIA, and final NO2 NAAQS. Table 4-17 shows the quantified and unquantified PM2.5 ­

related co-benefits captured in these benefit-per-ton estimates. 

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Table 4-17 Human Health and Welfare Effects of PM2.5 

Pollutant / Quantified and Monetized Unquantified Effects 

Effect in Primary Estimates Changes in: 

PM2.5 Adult premature mortality Subchronic bronchitis cases 

Bronchitis: chronic and acute 

Hospital admissions: respiratory and 

Low birth weight 

Pulmonary function 

cardiovascular 

Emergency room visits for asthma 

Nonfatal heart attacks (myocardial 

Chronic respiratory diseases other than 

chronic bronchitis 

Non-asthma respiratory emergency room 

infarction) 

Lower and upper respiratory illness 

visits 

Visibility 

Minor restricted-activity days 

Work loss days 

Asthma exacerbations (asthmatic 

Household soiling 

population) 

Infant mortality 

Consistent with the NO2 NAAQS,gg the benefits estimates utilize concentrationresponse functions as reported in the epidemiology literature. Readers interested in reviewing 

the complete methodology for creating the benefit-per-ton estimates used in this analysis can 

consult the Technical Support Document (TSD)27 accompanying the recent final ozone 

NAAQS RIA. Readers can also refer to Fann et al. (2009)28 for a detailed description of the 

benefit-per-ton methodology.hh 

As described above, national per-ton estimates were developed for selected 

pollutant/source category combinations. The per-ton values calculated therefore apply only to 

tons reduced from those specific pollutant/source combinations (e.g., NO2 emitted from 

mobile sources; direct PM emitted from stationary sources). Our estimate of total PM2.5 

benefits is therefore based on the total direct PM2.5 and PM2.5–related precursor emissions 

(NOx, SOx, and VOCs) controlled from each source and multiplied by the respective per-ton 

values of reducing emissions from that source. 

The benefit-per-ton coefficients in this analysis were derived using modified versions 

of the health impact functions used in the PM NAAQS Regulatory Impact Analysis. 

Specifically, this analysis uses the benefit-per-ton estimates first applied in the Portland 

Cement NESHAP RIA, which incorporated concentration-response functions directly from 

gg Although we summarize the main issues in this chapter, we encourage interested readers to see benefits 

chapter of the NO2 NAAQS for a more detailed description of recent changes to the PM benefits presentation 

and preference for the no-threshold model. 

hh The values included in this report are different from those presented in the article cited above. Benefits 

methods change to reflect new information and evaluation of the science. Since publication of the June 2009 

article, EPA has made two significant changes to its benefits methods: (1) We no longer assume that a threshold 

exists in PM-related models of health impacts, which is consistent with the findings reported in published 

research; and (2) We have revised the Value of a Statistical Life to equal $6.3 million (year 2000$), up from an 

estimate of $5.5 million (year 2000$) used in the June 2009 report. Please refer to the following website for 

updates to the dollar-per-ton estimates: http://www.epa.gov/air/benmap/bpt.html 

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Economic Assumptions Used in the Agencies’ Analysis
 

the epidemiology studies, without any adjustment for an assumed threshold. Removing the 

threshold assumption is a key difference between the method used in this analysis to estimate 

PM co-benefits and the methods used in analyses prior to EPA’s Portland Cement NESHAP. 

As a consequence, the benefit-per-ton estimates used in this analysis include incremental 

benefits of reductions in PM2.5 concentrations down to their lowest modeled levels. 

Reductions in PM-related mortality provide the majority of the monetized value in 

each benefit-per-ton estimate. Typically, the premature mortality-related effect coefficients 

that underlie the benefits-per-ton estimates are drawn from epidemiology studies that examine 

two large population cohorts: the American Cancer Society cohort (Pope et al., 2002)29 and 

the Harvard Six Cities cohort (Laden et al., 2006).30 The concentration-response (C-R) 

function developed from the extended analysis of American Cancer Society (ACS) cohort, as 

reported in Pope et al. (2002), has previously been used by EPA to generate its primary 

benefits estimate. The extended analysis of the Harvard Six Cities cohort, as reported by 

Laden et al (2006), was published after the completion of the Staff Paper for the 2006 PM2.5 

NAAQS and has been used as an alternative estimate in the PM2.5 NAAQS RIA and PM2.5 cobenefits estimates in analyses completed since the PM2.5 NAAQS. 

These studies provide logical choices for anchor points when presenting PM-related 

benefits because, while both studies are well designed and peer-reviewed, there are strengths 

and weaknesses inherent in each. Although this argues for using both studies to generate 

benefits estimates, due to the analytical limitations associated with this analysis, EPA and 

NHTSA have chosen to use the benefit-per-ton value derived from the ACS study. The 

agencies note that benefits would be approximately 145 percent (or nearly two-and-a-half 

times) larger if the per-ton benefit values based on the Harvard Six Cities were used instead. 

As is the nature of benefits analyses, assumptions and methods evolve over time to 

reflect the most current interpretation of the scientific and economic literature. For a period 

of time (2004-2008), EPA’s Office of Air and Radiation (OAR) valued mortality risk 

reductions using a value of statistical life (VSL) estimate derived from a limited analysis of 

some of the available studies. OAR arrived at a VSL using a range of $1 million to $10 

million (2000$) consistent with two meta-analyses of the wage-risk literature. 

The $1 million value represented the lower end of the interquartile range from the 

Mrozek and Taylor (2002)31 meta-analysis of 33 studies. The $10 million value represented 

the upper end of the interquartile range from the Viscusi and Aldy (2003)32 meta-analysis of 

43 studies. The mean estimate of $5.5 million (2000$) was also consistent with the mean 

VSL of $5.4 million estimated in the Kochi et al. (2006)33 meta-analysis. However, the 

Agency neither changed its official guidance on the use of VSL in rule-makings nor subjected 

the interim estimate to a scientific peer-review process through the Science Advisory Board 

(SAB) or other peer-review group. 

Until updated guidance is available, EPA determined that a single, peer-reviewed 

estimate applied consistently best reflects the SAB-EEAC advice it has received. Therefore, 

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EPA has decided to apply the VSL that was vetted and endorsed by the SAB in the Guidelines 

for Preparing Economic Analyses (U.S. EPA, 2000)34 while they continue efforts to update 

their guidance on this issue.ii This approach calculates a mean value across VSL estimates 

derived from 26 labor market and contingent valuation studies published between 1974 and 

1991. The mean VSL across these studies is $6.3 million (2000$). The dollar-per-ton 

estimates used in this analysis are based on this revised VSL. 

The benefit-per-ton estimates are subject to a number of assumptions and 

uncertainties. 

a. They do not reflect local variability in population density, meteorology, exposure, 

baseline health incidence rates, or other local factors that might lead to an 

overestimate or underestimate of the actual benefits of controlling fine particulates in 

specific locations. EPA conducted full-scale air quality modeling for the calendar year 

analysis in an effort to capture this variability. Please refer to Chapter 7.2 of the RIA 

for a description of EPA’s air quality modeling results and to Chapter 7.3 for the 

description of the agency’s quantification and monetization of PM- and ozone-related 

health impacts for the FRM. 

b. This analysis assumes that all fine particles, regardless of their chemical composition, 

are equally potent in causing premature mortality. This is an important assumption, 

because PM2.5 produced via transported precursors emitted from stationary sources 

may differ significantly from direct PM2.5 released from engines and other industrial 

sources. At the present time, however, no clear scientific grounds exist for supporting 

differential effects estimates by particle type. 

c. This analysis assumes that the health impact function for fine particles is linear within 

the range of ambient concentrations under consideration. Thus, the estimates include 

health benefits from reducing fine particles in areas with varied initial concentrations 

of PM2.5, including both regions that are in attainment with fine particle standard and 

those that do not meet the standard, down to the lowest modeled concentrations. 

d. There are several health benefits categories that EPA and NHTSA were unable to 

quantify due to limitations associated with using benefits-per-ton estimates, several of 

which could be substantial. Because NOX and VOC emissions are also precursors to 

ozone, changes in NOX and VOC would also impact ozone formation and the health 

effects associated with ozone exposure. Benefits-per-ton estimates for ozone do not 

exist due to issues associated with the complexity of the atmospheric air chemistry and 

nonlinearities associated with ozone formation. The PM-related benefits-per-ton 

estimates also do not include any human welfare or ecological benefits. Please refer to 

Chapter 7.3 of the RIA for a description of the quantification and monetization of both 

PM- and ozone-related health impacts for the FRM and a description of the 

unquantified co-pollutant benefits associated with this rulemaking. 

ii In the (draft) update of the Economic Guidelines (U.S. EPA, 2008c), EPA retained the VSL endorsed by the 

SAB with the understanding that further updates to the mortality risk valuation guidance would be forthcoming 

in the near future. Therefore, this report does not represent final agency policy. The draft update of the 

Economic Guidelines is available on the Internet at <http://yosemite.epa.gov/ee/epa/eermfile.nsf/vwAN/EE0516-01.pdf/$File/EE-0516-01.pdf>. 

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As mentioned above, emissions changes and benefits-per-ton estimates alone are not a 

good indication of local or regional air quality and health impacts, as the localized impacts 

associated with the rulemaking may vary significantly. Additionally, the atmospheric 

chemistry related to ambient concentrations of PM2.5, ozone and air toxics is very complex. 

Full-scale photochemical modeling is therefore necessary to provide the needed spatial and 

temporal detail to more completely and accurately estimate the changes in ambient levels of 

these pollutants and their associated health and welfare impacts. For the final rule, EPA 

conducted a national-scale air quality modeling analysis in 2030 to analyze the impacts of the 

standards on PM2.5, ozone, and selected air toxics. 

4.2.9 Reductions in emissions of greenhouse gases 

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 

fuel efficiency standards will thus reduce GHG emissions generated by fuel use, as well as 

throughout the fuel supply cycle. Lowering these emissions is likely to slow the projected 

pace and reduce the ultimate extent of future changes in the global climate, thus reducing 

future economic damages that changes in the global climate are otherwise expected to cause. 

Further, by reducing the probability that climate changes with potentially catastrophic 

economic or environmental impacts will occur, lowering GHG emissions may also result in 

economic benefits that exceed the resulting reduction in the expected future economic costs 

caused by gradual changes in the earth’s climatic systems. Quantifying and monetizing 

benefits from reducing GHG emissions is thus an important step in estimating the total 

economic benefits likely to result from establishing improved fuel efficiency and GHG 

standards. 

4.2.9.1 Estimating reductions in GHG emissions 

The agencies estimated emissions of carbon dioxide (CO2) from passenger car and 

light truck use by multiplying the number of gallons of each type of fuel (gasoline and diesel) 

they are projected to consume with each alternative CAFE standard in effect by the quantity 

or mass of CO2 emissions released per gallon of fuel consumed. This calculation assumes 

that the entire carbon content of each fuel is ultimately converted to CO2 emissions during the 

combustion process. The weighted average CO2 content of gasoline is estimated to be 

approximately 8,900 grams per gallon, while that of diesel fuel is estimated to be 

approximately 10,200 grams per gallon. For details, please see EPA’s RIA and NHTSA’s 

RIA. 

Although carbon dioxide emissions account for nearly 95 percent of total GHG 

emissions that result from fuel combustion during vehicle use, emissions of other GHGs are 

potentially significant as well because of their higher “potency” as GHGs than that of CO2 

itself. EPA and NHTSA estimated the increases in emissions of methane (CH4) and nitrous 

oxide (N2O) 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 

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Joint Technical Support Document
 

Vehicle Emission Simulator (Draft MOVES 2009), as subsequently updated for use in this 

analysis. 

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. 

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. These GWPs are one way of accounting for the higher radiative forcing capacity and 

differing lifetimes of methane and nitrous oxide when they are released into the earth’s 

atmosphere, measured relative to that of CO2 itself. Because these gases differ in atmospheric 

lifetimes, their relative damages are not constant over time. Impacts other than temperature 

change also vary across gases in ways that are not captured by GWP. For instance, CO2 

emissions, unlike methane and other greenhouse gases, contribute to ocean acidification. 

Methane contributes to health and ecosystem effects arising from increases in tropospheric 

ozone, while damages from methane emissions are not offset by the positive effect of CO2 

fertilization. Noting these caveats, the CO2 equivalents of increases in emissions of these 

gases are then added to the increases in emissions of CO2 itself to summarize the effect of the 

total increase in CO2-equivalent GHG emissions from vehicle use. However, only the CO2 

emissions were monetized for purposes of valuing benefits of the rule. 

4.2.9.2 Economic benefits from reducing GHG emissions 

NHTSA and EPA have taken the economic benefits of reducing CO2 emission into 

account in this rulemaking, both in developing alternative CAFE standards and in assessing 

the economic benefits of each alternative that was considered. Specifically, 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 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 developed new SCC estimates through an interagency modeling 

exercise. The modeling exercise involved running three integrated assessment models using 

inputs agreed upon by the interagency group for climate sensitivity, socioeconomic and 

emissions trajectories, and discount rates. A more complete discussion of SCC, including the 

revised estimates and underlying methodology, can be found in the SCC TSD, Social Cost of 

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Economic Assumptions Used in the Agencies’ Analysis
 

jj Carbon for Regulatory Impact Analysis Under Executive Order 12866 (February 2010). See 

also preamble Sections II.F, III.H.6 and IV.C.3, EPA RIA 7.5, and NHTSA RIA VIII for 

discussion about the application of new SCC estimates to this final rule. The revised SCC 

estimates corresponding to assumed values of the discount rate are shown below in Table 4­

18. 

Table 4-18 Social Cost of CO2, 2010 Emissionsa (in 2007 dollars) 

Discount Rate 5% 3% 2.5% 3% 

Source 

Average 

of 

Estimates 

Average 

of 

Estimates 

Average 

of 

Estimates 

95th 

Percentile 

2010 4.7 21.4 35.1 64.9 

2015 5.7 23.8 38.4 72.8 

2020 6.8 26.3 41.7 80.7 

2025 8.2 29.6 45.9 90.4 

2030 9.7 32.8 50.0 100.0 

2035 11.2 36.0 54.2 109.7 

2040 12.7 39.2 58.4 119.3 

2045 14.2 42.1 61.7 127.8 

2050 15.7 44.9 65.0 136.2 

a Monetized GHG benefits exclude the value of reductions in non-CO2 GHG emissions (HFC, CH4 and 

N2O) expected under this final rule. Although the agencies have not monetized the benefits of reductions in 

these non-CO2 emissions, the value of these reductions should not be interpreted as zero. Rather, the reductions 

in non-CO2 GHGs will contribute to this rule’s climate benefits, as explained in Section III.F.2. The SCC TSD 

notes the difference between the social cost of non-CO2 emissions and CO2 emissions, and specifies a goal to 

develop methods to value non-CO2 emissions in future analyses. 

4.2.10 The value of increased driving range 

Improving the fuel economy of passenger cars and light-duty trucks may also increase 

their driving range before they require refueling. By reducing the frequency with which 

drivers typically refuel their vehicles and extending the upper limit of the range they can 

travel before requiring refueling, improving fuel economy thus provides some additional 

benefits to their owners. Alternatively, if manufacturers respond to improved fuel economy 

by reducing the size of fuel tanks to maintain a constant driving range, the resulting cost 

saving will presumably be reflected in lower vehicle sales prices. If manufacturers respond 

by doing so, this presumably reflects their judgment that the value to economic benefits to 

vehicle buyers from lower purchase prices exceeds that from extended refueling range. 

jj Interagency Working Group on Social Cost of Carbon, U.S. Government, with participation by Council of 

Economic Advisers, Council on Environmental Quality, Department of Agriculture, Department of Commerce, 

Department of Energy, Department of Transportation, Environmental Protection Agency, National Economic 

Council, Office of Energy and Climate Change, Office of Management and Budget, Office of Science and 

Technology Policy, and Department of Treasury, “Social Cost of Carbon for Regulatory Impact Analysis Under 

Executive Order 12866,” February 2010, available in docket EPA-HQ-OAR-2009-0472. 

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No direct estimates of the value of extended vehicle range are readily available, so the 

agencies’ analyses calculate 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.35 As a coarse 

illustration of how the value of extended refueling range is estimated, a typical small light 

truck model has an average fuel tank size of approximately 20 gallons.kk Based on a 

California Air Resources Board Study, the average fuel purchase is approximately 55% of 

tank volume.36 Therefore, increasing this model’s actual on-road fuel economy from 24 to 25 

mpg would extend its driving range from 216 miles (= 9 gallons x 24 mpg) to 225 miles (= 9 

gallons x 25 mpg). Assuming that this vehicle is driven 12,000 miles/year, this reduces the 

number of times it needs to be refueled each year from 55.5 (= 12,000 miles per year / 216 

miles per refueling) to 53.3 (= 12,000 miles per year / 225 miles per refueling), or by 2.2 

refuelings per year. 

Weighted by the nationwide mix of urban (about 2/3) and rural (about 1/3) driving and 

average vehicle occupancy for all driving trips (1.6 persons), the DOT-recommended value of 

travel time per vehicle-hour is $24.00 (in 2006 dollars).ll Assuming that locating a station and 

filling up requires five minutes, the annual value of time saved as a result of less frequent 

refueling amounts to $4.40 (calculated as 5/60 x 2.2 x $24.00). This calculation is repeated 

for each future calendar year that light trucks of each model year affected by the alternative 

fuel economy standards considered in this rule would remain in service. Like fuel savings 

and other benefits, however, the value of this benefit declines over a model year’s lifetime, 

because a smaller number of vehicles originally produced during that model year remain in 

service each year, and those remaining in service are driven fewer miles. The detailed values 

used in this calculation by each agency are available in EPA’s RIA and NHTSA’s FRIA 

respectively. 

The agencies’ estimate of benefits from less frequent refueling is subject to several 

sources of uncertainty. First, this analysis assumes that manufacturers will not adjust fuel tank 

capacities downward (from the current average of 19.3 gallons) when they improve the fuel 

economy of their vehicle models, so that the entire increase in fuel economy will be reflected 

in increased driving range. 

Should manufacturers choose to downsize fuel tanks, and all other factors have been 

estimated with no error, the current estimates of refueling benefits would be overstated. 

Should manufacturers downsize tanks to fully offset any increase in vehicle range, there 

would be no extension in driving range and no resultant benefit to consumers. However, 

kk Based on the Volpe Model Market Data file for Model Year 2011, average tank volumes for cars and trucks 

are 16.6 gallons and 23.0 gallons, respectively. This produces a production weighted average of 19.3 gallons. 

ll The hourly wage rate during 2006 is estimated to be $24.00. Personal travel (94.4 percent of urban travel) is 

valued at 50 percent of the hourly wage rate. Business travel (5.6 percent of urban travel) is valued at 100 

percent of the hourly wage rate. For intercity travel, personal travel (87 percent) is valued at 70 percent of the 

wage rate, while business travel (13 percent) is valued at 100 percent of the wage rate. The resulting values of 

travel time are $12.67 for urban travel and $17.66 for intercity travel, and must be multiplied by vehicle 

occupancy (1.6) to obtain the estimate value of time per vehicle hour. 

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should fuel tank sizes be reduced, vehicle space, utility and value would increase and vehicle 

weight and production costs would decrease, improving fuel economy and CO2 emissions. 

A larger fuel tank size requires additional manufacturing costs (if for nothing more 

than increased materials or increased molding costs to squeeze more tank volume in unusually 

shaped spaces underbody), adds weight (thus, reduces fuel economy and increases CO2 

emissions) and takes up potentially usable vehicle space. It also increases vehicle range (a 

widely advertised vehicle attribute), which reduces the frequency of vehicle refueling for at 

least some owners and (less frequently) increases peace of mind when operating a vehicle in 

areas with limited refueling options. Given this clear trade-off, manufacturers must use some 

type of optimization schema to determine the appropriate tank size for each vehicle. If 

manufacturers choose to retain the current fuel tank size, then the impact on manufacturing 

costs is obvious: zero. At least some portion of the driving public will save a considerable 

amount of time with some value. Given range is a widely publicized vehicle attribute, 

manufacturers will only reduce tank size if it lowers production costs sufficiently to overcome 

the loss in vehicle desirability. Thus, either consumers save time or production costs go 

down. Much as the agencies assume that cost increases are passed through to consumers, the 

agencies assume that cost savings from reducing fuel tank size (by reducing engineering 

complexity and materials required) would be passed through to consumers. 

Further, the agencies assume that manufacturers operate in a manner that maintains or 

increases the desirability of a vehicle. Consequently, should a manufacturer choose to reduce 

fuel tank size, this option should provide welfare gains equivalent to or greater than the loss in 

welfare from reducing driving range. In the context of the rule, the improvement in CO2 or 

fuel economy from downsizing a fuel tank will additionally decrease a manufacturers cost of 

compliance with the standards. The weight reduction estimates modeled in OMEGA and the 

CAFE Model do not include any reduction in the size and weight of fuel tanks, excluding 

potential light weight materials substitution which would not affect fuel volume, thus this 

benefit has not been counted elsewhere. 

Second, the agencies’ analysis assumes that fuel purchases average 55 percent of fuel 

tank capacity. However, as shown in the California Air Resource Board (CARB) report, 

refueling patterns vary. Moreover, the 55 percent estimate implies that drivers, on average, 

are either refueling when nearly a half tank of gas remains in their vehicles, or that they are 

habitually not filling their tanks. Since many drivers only refuel when their tanks are very 

low, and since many drivers habitually refuel, this in turn implies that many drivers in the 

CARB study are refueling when their tanks are still well above 50 percent full. While based 

on field data, this estimate may thus overestimate the impact of refueling benefits. 

For the primary analysis in both the proposal and this final rulemaking, the agencies 

assume that 100 percent of all refueling is demand-based; i.e., that every gallon of fuel which 

is saved would reduce the need to return to the refueling station. Based on anecdotal 

evidence, this value is potentially an overestimate. As an example, some people may refuel 

every Sunday morning at the same time as they buy their newspaper and chewing gum. 

Barring unusually long trips, these people would not benefit from an increased driving range. 

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In order to understand how sensitive the overall estimate of the refueling benefit is to 

this specific input, we provide the following example. If 25 percent of gallons are refueled on 

a habitual rather than demand-based schedule, and the rest of the assumptions remain 

constant, the value of increased driving range decreases by 25 percent. There would be no 

value of increased driving range derived from these gallons. Returning to the example light 

truck discussed above, which generated a value of increased driving range worth $4.40 in the 

first year, if this owner refilled 1⁄4 of his annual fuel on a habitual rather than demand based 

schedule, the value of increased driving range would decrease to $3.30. Unfortunately, the 

agencies do not have a basis for this 25 percent value, thus this example is for illustrative 

purposes only. However, as noted below, DOT is undertaking a new survey which may 

provide a data-based basis for revising our 100 percent assumption for future analysis. 

Third, the agencies’ estimate of refueling benefits assumes that refueling stops involve 

the same number of vehicle occupants as the overall average for all vehicle trips (1.6 persons). 

To the extent that drivers refuel while doing other errands or in advance of picking up 

passengers, this figure may overestimate the typical vehicle occupancy during refueling, and 

thus the total savings in refueling time. Similarly, the hourly value used to estimate the 

economic value of savings in refueling time reflects the typical mix of personal and business 

travel purposes, and drivers are likely to assign different values to their time when traveling 

for these different purposes. To the extent that drivers seek to refuel when traveling for 

purposes that typically use less valuable time, the hourly value used in the agencies’ analysis 

may overstate the benefits from saving refueling time. 

Finally, the agencies assume that both finding and using a refueling station takes, on 

average, five minutes. There are few, if any, data sources on average refueling time, and this 

estimate is subject to significant uncertainty. 

For these reasons, the agencies’ estimate of savings in refueling time is uncertain. To 

reduce the uncertainty, a new project is being planned by DOT which will include a detailed 

study of refueling events, using a random sample of refueling stations across the U.S. It is 

projected to include ~7,000 observations (time to refuel, # gallons refueled, etc.) and ~5,000 

surveys of refueling participants. The agencies anticipate that this will provide a robust data 

set on which to revise many of the key inputs to the refueling benefit calculation. Some of the 

specific data categories which will be surveyed are listed below: 

• Miles driven and time out of way to get to gas station 

• Fuel gage level before and after refueling 

• # gallons purchased 

• # people in vehicle, above and below 16 years old 

• Reasons for travel 

• Reason for stopping at gas station (e.g., fuel level too low, or other) 

While the study results are not available in time for this final rulemaking, it is 

anticipated that the data will improve future estimations of the value of increased driving 

range. 

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4.2.11 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 current perspective – for each year they are deferred into the future. 

In evaluating the benefits from alternative increases in fuel economy and GHG standards for 

MY 2012-2016 passenger cars and light trucks, EPA and NHTSA consider a discount rate of 

both 3 and 7 percent per year. 

Three percent may be the appropriate rate for discounting future benefits from 

increased fuel economy and GHG standards because most or all of vehicle manufacturers’ 

costs for complying with improved fuel economy and GHG standards are likely to be 

reflected in higher sales prices for their new vehicle models. By increasing sales prices for 

new cars and light trucks, CAFE regulation will thus primarily affect vehicle purchases and 

other private consumption decisions. 

Both economic theory and OMB guidance on discounting indicate that the future 

benefits and costs of regulations that mainly affect private consumption should be discounted 

at the consumption rate of time preference.37 OMB guidance indicates that savers appear to 

discount future consumption at an average real (that is, adjusted to remove the effect of 

inflation) rate of about 3 percent when they face little risk about its likely level, which makes 

it a reasonable estimate of the consumption rate of time preference.38 Thus EPA and NHTSA 

have employed the 3 percent rate to discount projected future benefits and costs resulting 

from improved fuel economy and GHG standards for MY 2012-2016 passenger cars and light 

trucks. 

Because there is some uncertainty about the extent to which vehicle manufacturers 

will be able to recover their costs for complying with improved fuel economy and GHG 

standards by increasing vehicle sales prices, however, the use of a higher percent discount rate 

may also be appropriate. OMB guidance indicates that the real economy-wide opportunity 

cost of capital is the appropriate discount rate to apply to future benefits and costs when the 

primary effect of a regulation is “...to displace or alter the use of capital in the private sector,” 

and estimates that this rate currently averages about 7 percent.39 Thus the agencies estimate 

net present values using both 3 and 7 percent discount rates. 

One important exception to these values are the rates used to discount benefits from 

reducing CO2 emissions from the years in which reduced emissions occur, which span the 

lifetimes of model year 2012-16 cars and light trucks, to their present values. In order to 

ensure consistency in the derivation and use of the interagency group’s estimates of the unit 

values of reducing CO2 emissions, the total benefits from reducing those emissions during 

each future year are discounted using the same rates that were used to derive the alternative 

values of reducing each ton of CO2 emissions. As described previously in Section 4.2.10.2, 

these rates are 2.5, 3.0, and 5.0 percent. 

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References
 

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6 http://www.eia.doe.gov/oiaf/aeo/forecast.html (last accessed July 26, 2009), and Table 16, 

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Schedules,” DOT HS 809 952, 8-11 (January 2006). Available at http://www-nrd.nhtsa.dot.gov/pdf/nrd30/NCSA/Rpts/2006/809952.pdf (last accessed Feb. 15, 2010). These updated survival rates suggest that the 

expected lifetimes of recent-model passenger cars and light trucks are 13.8 and 14.5 years. 

8 FHWA, Highway Statistics, Summary to 1995, Table vm201at 

http://www.fhwa.dot.gov/ohim/summary95/vm201a.xlw , and annual editions 1996-2005, Table VM-1 at 

http://www.fhwa.dot.gov/policy/ohpi/hss/hsspubs.htm (last accessed Feb. 15, 2010). 

9 Sorrell, S. and J. Dimitropoulos, 2007. “UKERC Review of Evidence for the Rebound Effect, Technical Report 

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14 Revised Report by David Greene of Oak Ridge National Laboratory to EPA,“Rebound 2007: Analysis of 

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15 FHWA. 1997 Federal Highway Cost Allocation Study; http://www.fhwa.dot.gov/policy/hcas/final/index.htm 

(last accessed July 29, 2009). 

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16 Federal Highway Administration, 1997 Federal Highway Cost Allocation Study, 

http://www.fhwa.dot.gov/policy/hcas/final/index.htm, Tables V-22, V-23, and V-24 (last accessed Feb. 15, 

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17 U.S. Bureau of Economic Analysis, U.S. International Transactions Accounts Data, as shown on June 24, 

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19 Leiby, Paul N. "Estimating the Energy Security Benefits of Reduced U.S. Oil Imports," Oak Ridge National 

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Inc., September 2007. 

21 Gately, Dermot 2004. "OPEC's Incentives for Faster Output Growth," The Energy Journal, 25(2):75-96, 

"What Oil Export Levels Should We Expect From OPEC?" The Energy Journal, 28(2):151-173, 2007 

22 Argonne National Laboratories, The Greenhouse Gas and Regulated Emissions from Transportation (GREET) 

Model, Version 1.8, June 2007, available at http://www.transportation.anl.gov/software/GREET/index.html 

(last accessed April 20, 2008). 

23 The issue is discussed in more detail in the PM NAAQS RIA from 2006. See U.S. Environmental Protection 

Agency. 2006. Final Regulatory Impact Analysis (RIA) for the Proposed National Ambient Air Quality 

Standards for Particulate Matter. Prepared by: Office of Air and Radiation. October 2006. Available at 

http://www.epa.gov/ttn/ecas/ria.html. 

24 U.S. Environmental Protection Agency (U.S. EPA). 2008. Regulatory Impact Analysis, 2008 National 

Ambient Air Quality Standards for Ground-level Ozone, Chapter 6. Office of Air Quality Planning and 

Standards, Research Triangle Park, NC. March. Available at <http://www.epa.gov/ttn/ecas/regdata/RIAs/6­

ozoneriachapter6.pdf>. EPA-HQ-OAR-2009-0472-0238 

25 U.S. Environmental Protection Agency (U.S. EPA). 2009. Regulatory Impact Analysis: National Emission 

Standards for Hazardous Air Pollutants from the Portland Cement Manufacturing Industry. Office of Air 

Quality Planning and Standards, Research Triangle Park, NC. April. Available on the Internet at 

<http://www.epa.gov/ttn/ecas/regdata/RIAs/portlandcementria_4-20-09.pdf>. EPA-HQ-OAR-2009-0472-0241 

26 U.S. Environmental Protection Agency (U.S. EPA). 2010. Final NO2 NAAQS Regulatory Impact Analysis 

(RIA). Office of Air Quality Planning and Standards, Research Triangle Park, NC. April. Available on the 

Internet at http://www.epa.gov/ttn/ecas/regdata/RIAs/FinalNO2RIAfulldocument.pdf. Accessed March 15, 

2010. EPA-HQ-OAR-2009-0472-0237 

27 U.S. Environmental Protection Agency (U.S. EPA). 2008. Technical Support Document: Calculating Benefit 

Per-Ton estimates, Ozone NAAQS Docket #EPA-HQ-OAR-2007-0225-0284. Office of Air Quality Planning 

and Standards, Research Triangle Park, NC. March. Available on the Internet at <http://www.regulations.gov>. 

EPA-HQ-OAR-2009-0472-0228 

28 Fann, N. et al. (2009). The influence of location, source, and emission type in estimates of the human health 

benefits of reducing a ton of air pollution. Air Qual Atmos Health. Published online: 09 June, 2009. EPA-HQOAR-2009-0472-0229 

29 Pope, C.A., III, R.T. Burnett, M.J. Thun, E.E. Calle, D. Krewski, K. Ito, and G.D. Thurston. 2002. “Lung 

Cancer, Cardiopulmonary Mortality, and Long-term Exposure to Fine Particulate Air Pollution.” Journal of the 

American Medical Association 287:1132-1141. EPA-HQ-OAR-2009-0472-0263 

30 Laden, F., J. Schwartz, F.E. Speizer, and D.W. Dockery. 2006. “Reduction in Fine Particulate Air Pollution 

and Mortality.” American Journal of Respiratory and Critical Care Medicine 173:667-672. Estimating the 

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Joint Technical Support Document
 

Public Health Benefits of Proposed Air Pollution Regulations. Washington, DC: The National Academies Press. 

EPA-HQ-OAR-2009-0472-1661 

31 Mrozek, J.R., and L.O. Taylor. 2002. “What Determines the Value of Life? A Meta-Analysis.” Journal of 

Policy Analysis and Management 21(2):253-270. EPA-HQ-OAR-2009-0472-1677 

32 Viscusi, V.K., and J.E. Aldy. 2003. “The Value of a Statistical Life: A Critical Review of Market Estimates 

throughout the World.” Journal of Risk and Uncertainty 27(1):5-76. EPA-HQ-OAR-2009-0472-0245 

33 Kochi, I., B. Hubbell, and R. Kramer. 2006. An Empirical Bayes Approach to Combining Estimates of the 

Value of Statistical Life for Environmental Policy Analysis. Environmental and Resource Economics. 34: 385­

406. EPA-HQ-OAR-2009-0472-0235 

34 U.S. Environmental Protection Agency (U.S. EPA). 2000. Guidelines for Preparing Economic Analyses. 

EPA 240-R-00-003. National Center for Environmental Economics, Office of Policy Economics and 

Innovation. Washington, DC. September. Available on the Internet at 

<http://yosemite.epa.gov/ee/epa/eed.nsf/webpages/Guidelines.html/$file/cover.pdf>. EPA-HQ-OAR-2009-0472­

0226 

35 See http://ostpxweb.dot.gov/policy/Data/VOT97guid.pdf and 

http://ostpxweb.dot.gov/policy/Data/VOTrevision1_2-11-03.pdf 

36 California Environmental Protection Agency, Air Resources Board. Draft Assessment of the Real-World 

Impacts of Commingling California Phase 3 Reformulated Gasoline. August 2003 

37 Id. 

38 Office of Management and Budget, Circular A-4, “Regulatory Analysis,” September 17, 2003, 33. Available 

at http://www.whitehouse.gov/omb/circulars/a004/a-4.pdf (last accessed July 24, 2009). 

39 Id. 

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