National Vehicle and Fuel Emissions Laboratory Office of Transportation and Air Quality U.S. Environmental Protection Agency Office of International Policy, Fuel Economy, and Consumer Programs National Highway Traffic Safety Administration U.S. Department of Transportation EPA-420-R-10-901



## Joint Technical Support Document:

Rulemaking to Establish Light-Duty Vehicle Greenhouse Gas Emission Standards and Corporate Average Fuel Economy Standards

April 2010ii 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 re ference vehicle fleet? 1-1
1.2 The 2008 baseline vehicle fleet 1-2 1.2.1 Why did the agencies choose 2008 as the basel ine 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 b ased? 1-13 1.3.2 How do the agencies develop the reference veh icle 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 characteristic s of the reference fleet? 1-26 1.3.5 How is the development of the baseline and re ference 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 facto r 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 b y 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? 2-6

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 th e model years to which the rules apply? 3-3 3.3 What technology assumptions have the agencies u sed for the final rule? 3-7 3.3.1 How are the technologies applied in the agen cies’ 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 Agencie s’ Analysis 4-1 4.1 How the Agencies use the economic assumptions i n their analyses 4-1 4.2 What economic assumptions do the agencies use? 4-2 4.2.1 The on-road fuel economy “gap” 4-2iii

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 effe ct 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 4-45iv



## Executive Summary

The Environmental Protection Agency (EPA) and the N ational Highway Traffic Safety Administration (NHTSA) are issuing a joint f inal rulemaking which establishes new standards for light-duty highway vehicles that will reduce greenhouse gas emissions and improve fuel economy. The joint final rulemaki ng 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 clima te change and to reduce oil consumption. EPA is finalizing greenhouse gas emis sions standards under the Clean Air Act, and NHTSA is finalizing Corporate Average Fuel Economy standards under the Energy Policy and Conservation Act, as amended. Th ese 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 a n 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 natio nal vehicle fleet whose emissions and fuel economy performance improves each year from MY 2012 to 2016. This document describes the supporting technical analysis for are as 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 complia nce structures and technical assumptions. To the extent permitted under each Ag ency’s statutes, NHTSA and EPA have incorporated the same compliance flexibilities , such as averaging, banking, and trading of credits, and the same testing protocol f or determining the agencies’ respective fleet-wide average standards. In addition, the age ncies 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 jo int finalized rule.

Chapter 1 of this document provides an explanatio n of the agencies’ new methodology used to develop the baseline and refere nce 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 thi s 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 joi nt 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 m ake 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 truckv

standards. NHTSA and EPA worked together closely t o develop regulatory approaches that are fundamentally the same, and have chosen to use an attribute-based program structure based on the footprint attribute, like NH TSA’s current Reformed CAFE program. The agencies revisited other attributes a s candidates for the standard functions, but concluded that footprint remains the best optio n for balancing the numerous technical and social factors. However, the agencies did adju st the shape of the footprint curve, in contrast to the 2011 CAFE rule, the CO2 or fuel con sumption curve is a piecewise linear or constrained linear function, rather than a const rained logistic function. In determining the shape of the footprint curve, the agencies cons idered factors such as the magnitudes of CO2 reduction and fuel savings, how much that sh ape may entice manufacturers to comply in a manner which circumvents the overall go als of the joint program, whether the standards’ stringencies are technically attaina ble, 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 f or 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 a s 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 discou nt rates, fuel prices, the magnitude of the rebound effect, the value of refueling time, an d the social cost of importing oil and
fuel.The Baseline and Reference Vehicle Fleet

1-1 

## 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 2012-201 6 timeframe, are highly varied and satisfy a wide range of consumer needs. From two-s eater 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 util ity needs and preferences. Recent volatility in oil prices and the state of the econo my have demonstrated that consumer demand and choice of vehicles within this wide rang e can be sensitive to these factors. Although it is impossible for anyone or any organiz ation to precisely predict the future, a characterization and quantification of the future f leet are required to assess the impacts of
rules which would affect that future fleet. In ord er to do this, the various leading publicly-available sources are examined, and a seri es 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 t his methodology for developing the inputs to the rule's analysis. Bec ause 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 o ver 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 c omment that they believe the agencies had projected more full size trucks and fu ll 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 refere nce vehicle fleet?

In order to calculate the impacts of the EPA and NH TSA final rule, it is necessary to estimate the composition of the future vehicle flee t absent the final CAFE/GHG standards in order to conduct comparisons. EPA in consultati on 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 basel ine fleet in order to track the volumes and types of fuel economy-improving and CO 2 -reducing technologies which are already present in today’s fleet. Creating a basel ine fleet helps to keep, to some extent, the agencies’ models from adding technologies to ve hicles that already have these technologies, which would result in “double countin g” of technologies’ costs and benefits. The second step was to project the basel ine fleet sales into MYs 2011-2016. This is called the reference fleet, and it represen ts the fleet that would exist in MYs 2011- 2016 absent any change from current regulations. T he third step was to add technologies to that fleet such that each manufacturer’s average car and truck CO 2 levels are inJoint Technical Support Document

1-2

compliance with their MY 2011 CAFE standards. This final “reference fleet” is the light duty fleet estimated to exist in MYs 2012-2016 with out the final CAFE/GHG standards. All of the agencies’ estimates of emission reductio ns/fuel economy improvements, costs, and societal impacts are developed in relation to t he respective reference fleets. The chapter describes the first two steps of the develo pment of the baseline and reference fleets. The third step of technology addition is d eveloped separately by each agency as the outputs of the OMEGA and Volpe models. The pro cess 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 mo del year?

The baseline vehicle fleet developed by EPA in cons ultation with NHTSA and is comprised of model year 2008 data. MY 2008 was use d 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 ex ample, 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 u ntil 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 econ omies to calculate the sales-weighted average fleet fuel economy. Even though the fuel e conomies (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 ma nufacturers 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 trans parent 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 C O 2 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 gather ed 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, d isplacement, 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/CO 2 -reducing technologies considered in this final rul e, because this level of information is not necessary for emission certification or fuel economy testing. Thus, the agency augmented this description with pu blicly-available data which includes

a § 600.512-08 Model Year ReportThe Baseline and Reference Vehicle Fleet

1-3 more complete technology descriptions from Ward’s A utomotive 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 o btained 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. How ever, a few relatively low-impact technologies were added based on confidential infor mation provided from some manufacturers (within their product plan submission s to NHTSA and EPA). This was done because the data were not available from any o ther source. These technologies include low friction lubricants, electric power ste ering, improved accessories, and low rolling resistance tires. This confidential inform ation has been excised from the baseline data submitted to the docket, though the summary re sults are still used, so that any specific information cannot be traced back to any s pecific 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 cons idered using EPA’s CAFE certification data, which contains most of the requ ired 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 pro cess using an alternative data source.

The agencies next considered using EPA’s vehicle em issions certification data, which contains much of the required information, ho wever it lacked the production volumes that are necessary for the OMEGA and Volpe models. The data set also
contains some vehicle models manufacturers have cer tified, but not produced. A second data source which would supply production volumes a nd 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 v olumes 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 th e two files, the engine and vehicle data were easily combined into one spreadsheet. Th e 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 individua l vehicles down to the resolution of the specific engine and transmission level. Althou gh production volumes from Ward’s Automotive Group could not be used, the subscriptio n did provide specific details on individual vehicles and engines. The Ward’s data u sed 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 fi le, which identified most of the vehicle

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

c Motortrend.com and Edmunds.com are free, no-fee in ternet sites.Joint Technical Support Document

1-4

and engine combinations. Each vehicle and engine c ombination was reviewed and corrections were made manually when the search rout ine 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 inde x 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 me rged 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 c ould have been used with the Ward’s
data without the vehicle emission certification dat aset, 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 m apping technique described above. Finally, EPA and NHTSA obtained the remaining attri bute 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 create d using this method. Given the long delay before the CAFE certification data becam e 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 p roduct plan data, and some internet
searches.

Table 1-1 below shows the columns of the complete f leet file, which includes the 2008 MY baseline data that was compiled. Each colu mn 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. T he 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 r olling resistance tires, reduced engine friction, improved accessories, etc., are not avail able from public sources and the agencies had to rely on manufacturers’ confidential product plans. The Technology
Effectiveness Basis and the Cost Effectiveness Basi s 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’ mod els, but those effects are expected to
be small.The Baseline and Reference Vehicle Fleet

1-5 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. C ertification 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/C ertification 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 dat a 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 subsetJoint Technical Support Document

1-6 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 Certif ication 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 Wards 1

4 DVVLS Discrete Variable Valve Lift (DVVL) on SOHC Wards 1

5 DEACS Cylinder Deactivation on SOHC Wards 1

6 ICP VVT - Intake Cam Phasing (ICP) Wards 1

7 DCP VVT - Dual Cam Phasing (DCP) (independent) Wards 1

8
DVVLD Discrete Variable Valve Lift (DVVL) on DOHC Wards 1

9 CVVL Continuously Variable Valve Lift (CVVL) Wards 1

10 DEACD Cylinder Deactivation on DOHC Wards 1

11 DEACO Cylinder Deactivation on OHV Wards 1

12 CCPO VVT - Coupled Cam Phasing (CCP) on OHV Wards 1

13 DVVLO Discrete Variable Valve Lift (DVVL) on OHV Wards 1The Baseline and Reference Vehicle Fleet

1-7 14 CDOHC Conversion to DOHC with DCP Not available
15 SGDI Stoichiometric Gasoline Direct Injection (GDI) Ward s 1

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 dat a 22 IATC Improved Auto. Trans. Controls/Externals Not availa ble
23 CVT Continuously Variable Transmission Certification da ta 24 NAUTO 6/7/8-Speed Auto. Trans with Improved Internals Cer tification data
25 DCTAM Dual Clutch or Automated Manual Transmission Certif ication 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 Certific ation data
31 CISG Crank Mounted Integrated Starter Generator Certifi cation 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 availableJoint Technical Support Document

1-8 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, an d

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

CO 2 CO 2 calculated from MPG. CO 2 weighted 1.15 times higher for diesel vehicles. Certification data

Thresholded FootPrint Footprint valve that will be set to 41 for values l ess 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.The Baseline and Reference Vehicle Fleet

1-9 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 th is information. 2. These data were obtained from manufacturer’s pr oduct plans. They were used to block (where possib le) 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 ar e included in the section
below on reference fleet under the MY 2008 columns. Table 1-2 displays the technologies present in the baseline fleet.Joint Technical Support Document

1-10 Table 1-2 2008 Technology PercentagesTurboSuper ChargedSOHCDOHCOHVLUBCCPSDVVLSDEACSICPDCPDVVLD

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 %The Baseline and Reference Vehicle Fleet

1-11 Table 1-2 2008 Technology PercentagesCVVLDEACDDEACOCCPODVVLOSGDI6MANCVTNAUTODCTAMEPSBISGCISG

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%Joint Technical Support Document

1-12 Table 1-2 2008 Technology PercentagesPSHEV2MHEVPHEVAuto 4 SpeedAuto 5 SpeedAuto 6 SpeedAuto 7 SpeedAuto 8 SpeedManual 5 SpeedManual 6 SpeedCVTAutomated Manual5 SpeedAutomated Manual6 SpeedAutomated Manual7 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 indic ate that some manufacturers are already implementing some of these technologies in their 2008 fleet. VW stands out as having a significant number of turbocharged dire ct injection engines, though it isThe Baseline and Reference Vehicle Fleet

1-13 uncertain whether their engines are also downsized. Some of the valve and cam technologies are quite common today: for example, n early half the fleet has dual cam phasing, while Honda and GM are have considerable l evels of engines with cylinder deactivation (DEAC). Honda also has already implem ented continuously variable valve lift (CVVL) on a majority of their engines. Moreov er, Toyota has the highest penetration of hybrid technologies. Finally, regarding the tec hnologies obtained from the product plans, many of the manufacturers who reported the l ubricating oil information indicated that low friction (or low viscosity) oil will be us ed more frequently in the future. Therefore, it is likely that this technology is und erreported for the fleet as a whole since it was assumed that the usage of low viscosity oils wa s zero for the companies who did not provide product plans. The lack of reporting of th ese types of technologies (if they exist in the fleet) will probably lead to a slightly high er cost of compliance for these companies. However, these technologies alone do no t necessarily guarantee a better fuel economy compared to other vehicles in their footpri nt class. There are many other considerations at play, most notably, weight and po wer.

The section below provides further detail on the co nversion 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 mar ket conditions and expectations about conditions of the vehicle fleet during the mo del years to which the agencies’ rules apply. Fundamentally, constructing this fleet invo lved projecting the MY 2008 baseline fleet into the MY 2011-2016 model years. It also i ncluded the assumption that none of the models had changes during this period. Project ing 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 pro jection 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 construct ing 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 p ublished in December, in time to be used in this rule making. The differences in proje cted sales in the updated 2009 report and the early 2010 report are very small, so the ag encies 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 h ave closely examined the difference between AEO 2009 and AEO 2010 Early Release and we believe the differences in totalJoint Technical Support Document

1-14

sales and the car/truck split attributed to conside rations of the standard in the final rule
are small. 5

Table 1-3below shows the scaling factors that were used for each model year. We use the sales projections from the scaled update d 2009 Annual Energy Outlook.

Table 1-3 Annual Energy Outlook 2010 Scaling Facto r a

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 th e 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 to tal light-duty vehicle sales gradually recover from their currently depressed le vels 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 simil ar 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 price s, 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 tr uck fleet to the car fleet. EIA’s sales projections of cars and trucks for the 2011-2016 mo del years under both the new and old
NHTSA truck definition are shown in Table 1-4 and T able 1-5 below, respectively.
Actual vehicle sales for MY 2008 are also shown und er 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 t o the iterative process described further
below to shift sales between car and truck market s egments.

Table 1-4 AEO 2010 Volumes New NHTSA Car Truck Defi nition 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 16,502,032The Baseline and Reference Vehicle Fleet

1-15 Table 1-5 AEO 2010 Volumes Old NHTSA Car Truck Defi nition 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, sale s of segments within both the car and truck markets have also been changing and are e xpected 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 d esigns and unibody structures. In order to reflect these changes in fleet makeup, EPA and NHTSA considered several available forecasts. After reviewing information f rom a number of forecasting organizations, the agencies decided to purchase for ecasts 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, C SM agreed to allow us to publish their high level data, on which the forecast is bas ed, in the public domain. Two, it covered nearly all the timeframe of greatest releva nce 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 gui de. EPA did need to have CSM’s standard forecast (that covers all vehicles below 1 4,000 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 an d by market segment for the 2016 model year and beyond were assumed to be the same a s CSM’s forecast for the 2015 calendar year, because CSM forecasts for 2016 will not be available until the 1 st quarter of 2010.



### 1.3.2 How do the agencies develop the reference vehicle f leet?

The process of producing the 2011-2016 reference fl eet involved combining the baseline fleet with the projection data described a bove. 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 certificati on vehicles to CSM vehicles by individual make and model. One challenge the agenc ies faced when determining a reference case fleet was that the sales data projec ted by CSM had different market segmentation than the data contained in EPA’s inter nal database. In order to create aJoint Technical Support Document

1-16

common segmentation between the two databases, side -by-side comparison of the specific vehicle models in both datasets was perfor med, and an additional “CSM segment” modifier in the spreadsheet was created, t hus mapping the two datasets. The reference fleet sales based on the “CSM segmentatio n” was then projected.

The baseline data and reference fleet volumes are a vailable to the public. The baseline Excel spreadsheet in the docket is the res ult 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 Definit ions”, “SUM”, “SUM Tech Definitions”, “Truck Vehicle Type Map”, and “Car Ve hicle 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 segmen t 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 th e 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: 8The Baseline and Reference Vehicle Fleet

1-17 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 model s expected to be introduced within the 2009-2016 timeframe are not included in the dat a. 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 a nd overall segment distribution. The mappings are discussed in the next section. Furthe r discussion of this limitation is
discussed below in section 1.3.4. The statistics o f 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 fo recasts?

As also explained at proposal, the next step in the production of the reference fleet is one of the more complicated steps to explain. H ere, the projected CSM forecasts for relative sales of cars and trucks by manufacturer a nd by market segment was normalized (set equal) to the total sales estimates of the pre liminary 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 ne eded for the analysis. The CSM data provided year-by-year percentages of cars and truck s 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 manufacture r-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 describ ed in greater detail in the following paragraphs.

In order to determine future production volumes, mu ltipliers were developed by manufacturer and vehicle segment that could be appl ied to MY 2008 volumes. The process for developing the multipliers is complicat ed, but is easiest to explain as a threestep process, though the first step is combined wit h both the second and third step, so only one multiplier per manufacturer and vehicle se gment is developed.

The three steps are:
1. Adjust total car and truck sales to match AEO proje ctions.
2. Adjust Car sales to match CSM market share projecti ons for each manufacturer and car segment.
3. Adjust Truck sales to match CSM market share projec tions for each manufacturer and truck segment.

The first step is the adjustment of total car and t ruck sales in 2008 to match AEO projections of total car and truck sales in 2011-20 16. 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 AEOJoint Technical Support Document

1-18

projections. The example equation below shows the general form of how to calculate a car or truck multiplier. The AEO projections are s hown above in Table 1-4.

Example Equation : TruckMultiplier(Year X) = AEOProjectionforTrucks(Ye ar 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 correct ion 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 adjustmen t for Cars based on the CSM data. CSM provided data on the market share and ve hicle 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 tota l 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 to tal volumes and market share correction for Cars. This is shown in the “Adjuste d for 2011 AEO and Manufacturer
Market Share” column of Table 1-7.

The next step is to adjust the sales volumes for CS M vehicle segment distribution. The process for adjusting for vehicle segment is mo re complicated than a simple one step multiplication. In order to keep manufacturers’ vo lumes 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 (b ased on the shift in vehicle sales in the last year) is that people are moving to smaller veh icles in the rulemaking time frame. A higher level (less detailed) example of this proced ure 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 remai ning 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 th at 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 c ategories (“Full-Size Car,” “Mid-Size Car,” “Small Car,” “Mini Car”).The Baseline and Reference Vehicle Fleet

1-19 3. All manufacturers have the same multiplier for a gi ven 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 multiplie rs that were created for each adjustment. This does not mean that new vehic le segments will be added (except for Generic Mini Car described in the next step) to manufacturers that do not produce them. Vehicles within each manufacture r 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 multip lied 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” co lumn.
4. When “Full-Size Car,” “Mid-Size Car,” “Small Car” a re processed, if vehicles need to move in or out of the segment, they will mo ve into or out of the next smaller segment. So, if Mid-Size Cars are being pr ocessed they can only move to
or be taken from Small Cars. Note: In order to ac complish 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 vehicl es 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 d istribution. There are two exceptions, BMW and Porsche only have “Luxury Car,” “Specialty Car,” and “Other Car” vehicles, so their volumes were not cha nged or shifted since these rules did not apply to them.
6. When an individual manufacturer multiplier is appli ed for a segment, the vehicles move to or from the appropriate segments as specifi ed in the previous rules and as
shown in Table 1-7.Joint Technical Support Document

1-20 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 4 11,025 411,025 411,025 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 2,14 2,948 1,838,095 1,838,095 1,838,095
All Mini Car 599,643 686,738 733,339 733,339 733,33 9 991,309 991,349
All Small Car 1,850,522 2,007,527 2,099,343 2,099,3 43 2,404,196 2,146,226 2,146,226 All Specialty Car 754,547 783,982 637,785 637,785 6 37,785 637,785 637,759
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 Segme nt*

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.The Baseline and Reference Vehicle Fleet

1-21 Mathematically, an individual manufacturer multipli er 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 e ntering 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 manufactu rer 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 rema ining segments to maintain that company’s market share. On the other hand, if Larg e 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 leavin g that segment is transferred into that
manufacturer’s Mid-Size Cars.

After the vehicle volumes are shifted using the abo ve rules, a total for each manufacturer and vehicle segment is maintained. Th e 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 f uture vehicle volume for each vehicle model. This is done by taking the multiplier (whic h 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 r epeated for each model year (2011- 2016).

The method used to adjust CSM Trucks to the AEO mar ket share was different than the method used for Cars. The process for Car s is different than Trucks because it is not possible to predict how vehicles would shift be tween segments based on current
market trends. This is because of the added utili ty 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 proces s for having the fleet match CSM’s market share and vehicle segment distribution was i terative.

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 fo r 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 fo r vehicle segment distribution. Table 1-9 has the percentages of Tru cks per CSM segmentJoint Technical Support Document

1-22 Table 1-9 Example: CSM – Percent of Trucks per Segm ent*

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 create d. One table had each manufacturer with its total sales for 2008 (similar to Table 1-1 0). 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. T he second table starts out with a “Generic” manufacturer (Table 1-11) which is the ta ble where the goal resides. Each manufacturer (BMW is shown in Table 1-12) is then l isted 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 sho wn in the tables below. A process of first adjusting the numbers in the tables to the go al for market share distribution was done. This was followed by adjusting to the goal f or 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 tota l in the current iteration. A manufacturer-based multiplier is calculated by taki ng the goal total for a manufacturer and dividing by the current total (starting with 20 08 model year volumes) for a manufacturer. A segment distribution-based multipl ier is calculated by taking the goal
distribution volumes in the Generic manufacturer se t and dividing them by the current volume. Table 1-10, Table 1-11, and Table 1-12 bel ow illustrates two iterations using BMW as an example.The Baseline and Reference Vehicle Fleet

1-23 Table 1-10 Example:Manufacturer Truck Totals*

2008 Model Year Sales Manufacturer Distribution 2 011Volume 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 f or 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 f or 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 61,324 139,8 19 149,332 *Note: The data in this table is based on the NPRM data.Joint Technical Support Document

1-24

Using this process, the numbers will get closer to the goal of matching CSM’s market
share for each manufacturer and distribution for ea ch vehicle segment after each of the iterations. The iterative process is carried out u ntil 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 soon er, but they were continued to observe how the numbers would converge.

After the market share and segment distribution wer e 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 vol ume). 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 e ach vehicle based on its manufacturer and segment.

The above process is done for each model year neede d (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 “refe rence case” (which is the technology being applied to 2012-2016 fleet to meet the 2011 c afe standard) is discussed below in section 4.1, with the results being presented in se ction 4.2.



### 1.3.3 How has the reference fleet changed from the NPRM t o the Final Rule?

The agencies updated the projected reference (2011- 2016) fleet file for the final rule, though the baseline fleet (MY 2008) remains u nchanged. The NPRM reference fleet used CSM Worldwide’s second quarter, 2009 pro jections and the April 2009 Annual Energy Outlook. The reference for the final rule u ses 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 du ty class 2b and heavy duty class 3 vehicles. Including these extra vehicles i n the second quarter forecast caused the
baseline to have an increased number of full size t rucks 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 publish ed. For the final rule, the EPA requested CSM to filter their 4th quarter projectio ns to only include light duty and medium duty passenger vehicles. The filtered CSM d ata 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 ref erence. The significant decrease in
the number of full size vans can be attributed to t he new filtered version of the CSM data. Full size trucks show some decrease, but did not ha ve 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 Ener gy Outlook.The Baseline and Reference Vehicle Fleet

1-25 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) (22 7,667)

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) (16,06 4)

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-NP RM)

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) (2 59,743)

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 NP RM projected volumes. The 2010 Annual Energy Outlook reduced the volumes for 2011, and 2014-2016. This would have reduced all of the manufacturer projected productio n volumes if we had not used the 4th quarter CSM projections. All manufactures that hav e 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. Chry sler 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 proje ctions 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.Joint Technical Support Document

1-26 Table 1-15 Difference Car and Truck Volumes by Man ufacturer (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 t he 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.The Baseline and Reference Vehicle Fleet

1-27 Table 1-16 Vehicle Segment Volumes

Actual and Projected Sales Volume Reference Class Segment a 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 3,174,9 85 3,361,926 3,524,000 3,619,798 Compact Auto 1,976,424 1,671,674 2,024,030 2,232,20 9 2,340,408 2,454,466 2,520,777 Sub-Compact Auto 1,364,434 1,763,860 2,222,516 2,48 3,365 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 1,774,53 5 1,629,457 1,594,363 1,550,212 Small Pickup 177,497 120,816 128,754 168,070 150,22 5 129,421 125,837 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 1,677,10 0 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 73 7,003 716,594 Cargo Van 110,858 181,911 215,174 209,684 192,590 1 89,124 183,887
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.Joint Technical Support Document

1-28

Table 1-17 Manufacturer Volumes2008BaselineSales2011ProjectedVolume2012ProjectedVolume2013ProjectedVolume2014ProjectedVolume2015ProjectedVolume2016ProjectedVolume

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 indica te that footprint will not changeThe Baseline and Reference Vehicle Fleet

1-29 significantly between 2008 and 2016. There will be an increase in the number of cars sold, which will cause the average footprints for c ars and trucks combined to be slightly smaller (about 2%). This is the result of AEO proj ecting 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 in fluenced 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 eng ines will be downsized over the model years to which these rules apply. The bigges t projected shift occurs between MY 2008 and 2013. This shift is a projected consequen ce of the expected changes in class and segment mix as predicted by AEO and CSM, and do es not represent engine downsizing attributable to the rules.

Table 1-19 Truck Percentages of 4, 6, 8 Cylinder En gines by Model Year

Percent of Total Sales of 4,6,8 Cylinders Trucks Model 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 Engi nes by Model Year

Percent of Total Sales of 4,6,8 Cylinders Cars Model 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.Joint Technical Support Document

1-30



### 1.3.5 How is the development of the baseline and referenc e 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 chap ter, and as explained at proposal, NHTSA has historically based its analysis of potent ial new CAFE standards on detailed product plans the agency has requested from manufac turers planning to produce light vehicles for sale in the United States. Although t he agency has never compelled manufacturers to submit such information, most majo r manufacturers and some smaller manufacturers have voluntarily provided it when req uested.

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 mode l they expect to produce for sale in the U.S. NHTSA’s recent requests have sought infor mation regarding a range of engineering and planning characteristics for each v ehicle 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 i n response to this request has varied in completeness and detail. Some manufactur ers 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 info rmation in a structure less amenable to analysis. Still other manufacturers have not re sponded to NHTSA’s requests or have responded on occasion, usually with partial informa tion.

One of the primary reasons for the request for data in 2009 was to obtain permission from the manufacturers to make public th eir product plan information for model years 2010 and 2011. There are a number of r easons that this could be advantageous in the development of a reference flee t. First some changes to the fleet may not be captured by this approach of solely usin g publicly available information. For example, the agencies’ current market forecast incl udes 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 mode ls appear explicitly in market inputs to NHTSA’s analysis, and are among those vehicle mo dels included in the aggregated vehicle types appearing in market inputs to EPA’s a nalysis.

Second, the agencies’ market forecast does not incl ude some forthcoming vehicle models, such as the Chevrolet Volt, the Chevrolet C amaro, the Ford Fiesta and several publicly announced electric vehicles, including the announcements from Nissan. NorThe Baseline and Reference Vehicle Fleet

1-31 does it include several MY 2009 and 2010 vehicles, such as the Honda Insight, the Hyundai Genesis and the Toyota Venza, as the starti ng point for vehicle definitions was MY 2008. Additionally, the market forecast does no t account for publicly-announced technology introductions, such as Ford’s EcoBoost s ystem, whose product plans specify which vehicles and how many are planned to have thi s technology. Were the agencies to rely on manufacturers’ product plans, this market f orecast would account for not only these specific examples, but also for similar examp les that have not yet been announced publicly.

Finally, some technical information manufacturers t hat have provided in product plans regarding specific vehicle models is, at leas t as far as NHTSA and EPA have been able to determine, not available from public or com mercial sources. While such gaps do not bear significantly on the agencies’ analysis, t he diversity of pickup configurations necessitated utilizing a sales-weighted average foo tprint value 7 for many manufacturers’ pickups. Since the modeling only utilizes footprin t in order to estimate each
manufacturer’s CO 2 or fuel economy standard and all the other vehicle characteristics are available for each pickup configuration, this appro ximation has no practical impact on the projected technology or cost associated with compli ance 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 w ere to explore standards with a different shape or attribute. This would necessita te 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 descri bed above, NHTSA requested permission from the manufacturers to make this limi ted 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 cou ld use their 2009 product plan data after the end of production (December 31). Unfortu nately, this does not afford us sufficient time to do the analysis for the final ru le. Also, the 2009 product plan data that was permitted to be used was not consistent with th e 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 flee ts were not updated beyond 2008 model year for this final rule. The 2008 baseline fleet a nd projections were instead updated using the latest AEO and CSM data, as explained abo ve.

7 A full-size pickup might be offered with various c ombinations 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 t o footprint values estimable from public or commerc ial sources. Therefore, the agencies have used the kno wn 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/Silverad o) in order to calculate the sales-weighted average footprint value for each pickup family. Again, this has little impact on t he results of the modeling effort. In the extreme, on e single footprint value could be used for every ve hicle sold by a single manufacturer as long as the fuel e conomy standard associated with this footprint valu e represented the sales-weighted, harmonic average of the fuel economy standards associated with each vehicle’s footprint values.Joint Technical Support Document

1-32



### 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 revie wed them in detail. A small amount of product plan data was used in the development of th e 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 s ample 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 diffic ult 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’ bas eline in the technology effectiveness and cost effectiveness for each vehicle in a way th at allows the baseline for the model to be published without revealing the manufacturers da ta.The Baseline and Reference Vehicle Fleet

1-33 References

All references can be found in the EPA DOCKET: EPA-HQ-OAR-2009-0472.

1 CFEIS (Certification Fuel Emissions Information Sy stem) Database: CFEIS is an EPA database system.

2 WardsAuto.com: Used as a source for engine specif ications.

3 Motortrend.com and Edmunds.com: Used as a source f or foot print and vehicle weight data.

4 Energy Information Administration’s 2009 Annual Ene rgy Outlook.

5 The agencies have also looked at the impact of the rule in EIA’s projection, and concluded that the im pact was small. EPA and NHTSA have evaluated the differ ences between the AEO 2010 (early draft) and AEO 2009 and found little difference in the fleet proje ctions (or fuel prices). This analysis can be foun d in the memo to the docket: (Docket EPA-HQ-OAR-2009-0472).

6 CSM World Wide, CSM World Wide is a paid service pr ovider; J.D. Power Automotive Forecasting, J.D. Pow er Automotive Forecasting is a paid service provider.

7 The baseline Excel file (“2008-2016 Production Sum mary Data _Definitions Docket 08_27_2009”) is available in the docket (Docket EPA-HQ-OAR-2009-047 2).What are the Attribute-Based Curves the Agencies ar e Using, and How Were They Developed?

2-1 

## 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 prea mble, NHTSA and EPA are setting
attribute-based CAFE and CO 2 standards for MYs 2012-2016 passenger cars and lig ht trucks, which are expressed by a mathematical function. EP CA, 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 attribut e-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 advantage s of using attribute-based standards for light duty vehicles, and given the goal of coordinating a nd harmonizing CO 2 standards promulgated under the CAA and CAFE standards promulgated under CAA and EPCA, as expressed by
President Obama in his announcement of the new Nati onal Program and by the agencies in the joint NOI and NPRM, EPA is also finalizing stan dards that are attribute-based and expressed as mathematical functions.

Under an attribute-based standard approach, each ve hicle model has a fuel economy (mpg) or CO 2 target, the stringency of which depends on the val ue of the attribute for the vehicle model. Thus, fuel economy and CO 2 targets are set for individual vehicles, and vary directly with the attribute, i.e. , becoming more stringent as the attribute decrease s and vice versa. For example, size-based ( i.e. , size-indexed) standards assign higher fuel econom y targets (lower CO 2 targets) to smaller vehicles and lower fuel econom y targets (higher CO 2 targets) to larger vehicles. However, we emphasize that the targets themselves are not standards. The CAFE or fleet-wide average CO 2 level required of a particular manufacturer is actually determined by calculating the production-w eighted average of the applicable modelspecific fuel economy or CO 2 targets.

The form of the fuel economy or CO 2 standard can have a dramatic effect on the level of improvement required from various types of vehic les and on the relative stringency of the standard for various manufacturers. These disparat e impacts derive from two facts concerning the current automotive market: 1) consum ers currently demand a wide variety of vehicle types and sizes, and 2) individual manufact urers focus their product offerings on different portions of this demand. Therefore, a co mparison of the advantages and disadvantages of attribute-based and flat (universa l) standards needs to consider both of these
factors.

Methods to reduce the CO 2 emissions from and increase the fuel economy of individual vehicles can be generally grouped into f our main categories: 1) reducing vehicle size, 2) reducing vehicle performance and/or utilit y, 3) increasing the efficiency of theJoint Technical Support Document

2-2

powertrain and accessories while maintaining consta nt performance and utility, and 4) reducing weight and aerodynamic drag while maintain ing vehicle size and utility. a Stringent flat standards encourage all four of these methods. However, the level of encouragement differs between manufacturers. Manufacturers of ge nerally larger vehicles face a more difficult task in meeting a flat standard than thos e of generally smaller vehicles. Thus, the stringency of flat standards will tend to be limite d by the capabilities of the manufacturers of larger vehicles. Conversely, manufacturers of smal ler vehicles will tend to face lower compliance costs and may not even need to change th eir 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 sm aller 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 vehic les, consumers will be encouraged to purchase smaller vehicles. Or, to avoid these outc omes, the standard may not be of sufficient stringency to result in significant fuel economy im provements or emission reductions.

Attribute-based fuel economy and CO 2 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 conti nue adding more fuel efficient technology each year, because the level of the compliance obli gation of each manufacturer is based on its own particular product mix.

Second, attribute-based standards minimize the ince ntive for manufacturers to respond
to CAFE and CO 2 standards in ways that may be harmful to safety. 2 Because each vehicle model has its own target based on the attribute cho sen, attribute-based standards can, depending on the corresponding mathematical functio n ( i.e. , the shape of the attribute based standards), reduce or eliminate the incentive to bu ild smaller vehicles simply to meet a fleetwide average. Since smaller vehicles are subject t o 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 ru le, 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 equ itable regulatory framework for
different vehicle manufacturers. 3 A flat industry-wide average standard imposes disproportionate compliance obligations on (full-li ne) manufacturers that need to significantly

a Another method of increasing fleet fuel economy is to increase the number of high-fuel-economy vehicl es and reduce production of low-fuel-economy vehicles, for instance, by changing their relative prices. EPA and NHTSA believe that existing technologies allow auto makers to meet the requirements of the rules withou t changing the mix of vehicles produced (although the rules do not prohibit such changes in mix).

b Trading of credits could encourage fuel economy im provements or CO 2 emission reductions from manufacturers of smaller vehicles. This would prob ably create a shift of wealth between the two sets of manufacturers. However, most manufacturers have in dicated that they would be unlikely to trade credit s with their direct competitors. In addition, EPCA does n ot permit NHTSA, in determining maximum feasible CA FE standards, to consider manufacturers’ ability to ea rn, transfer, trade, and use CAFE credits.What are the Attribute-Based Curves the Agencies ar e Using, and How Were They Developed?

2-3 change their product plans and little obligation on those manufacturers that have minimal need to change their plans because, for example, th ey may build smaller vehicles. Attributebased standards that have the proper shape spread t he regulatory cost burden for fuel economy more broadly across all of the vehicle manufacturer s within the industry.

And fourth, attribute-based standards respect econo mic conditions and consumer choice. Manufacturers are required to invest in te chnologies that improve the fuel economy of their fleets, regardless of vehicle mix. Thus t he 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 att ribute-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 dem and toward larger vehicles. If manufacturers sell a greater number of larger vehic les than the agencies anticipate, fuel savings and CO 2 reductions would be lower than anticipated. In co ntrast, if fuel prices rise significantly, more fuel savings and CO 2 reductions than anticipated should be likely.

One potential way to mitigate the variability of re sults under attribute-based standards due to market conditions is through the use of a un iversal standard—that is, an average standard set at a (single) absolute level below whi ch manufacturers’ fuel economy/CO 2 standards may not drop/exceed. This is often descr ibed as a “backstop standard.” For purposes of the CAFE program, EISA expressly requir es a backstop for domesticallymanufactured passenger cars—a universal minimum, no n-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-do mestic passenger automobile fleets manufactured for sale in the United States by all m anufacturers in the model year...” 4

, whichever is greater. Backstops are generally perm issible under section 202(a) of the CAA (as are universal standards generally).

For purposes of the CAFE and CO 2 standards proposed in the NPRM, NHTSA and EPA recognized that the risk, even if small, does e xist that low fuel prices in MYs 2012-2016 might lead indirectly to less than currently antici pated fuel savings and emissions reductions. The NPRM sought comment on whether backstop standar ds, or any other method within the agencies’ statutory authority, should and can be im plemented for the import and light truck fleets in order to achieve the fuel savings that at tribute-based standards might not absolutely guarantee.

As discussed in Section II.C of the final rule prea mble, the agencies have reviewed the comments received on this issue and have made the p olicy choice not to adopt backstop
standards for MYs 2012-2016 other than those for do mestic passenger cars, as required by EISA. EPA and NHTSA remain confident that their pr ojections 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 red uctions or fuel savings. Both agenciesJoint Technical Support Document

2-4

remain confident in these fleet projections and the resulting emissions reductions and fuel savings from the standards. As explained in sectio n 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 re latively few model years at issue. Moreover, market trends today are consistent with the agencie s’ 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 f ootprint 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 greate r costs commensurate with more stringent backstops. This would occur unless the backstop st andard was relatively weak, in which case
it would have a minimal effect. It would be diffic ult to establish the level of a backstop standard such that costs are likely to be imposed o n manufacturers only when there is a failure to achieve the projected reductions across the indu stry as a whole. An example of this could be when there is a significant shift to larger vehi cles 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 ra ther 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 manufactu rer'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 issu e, and aims to impose the new more stringent standard only when the problem arises acr oss the industry as a whole. However, this approach fails to provide the lead time necessary f or making vehicle changes to meet a new, more stringent standard.

In addition, we do not believe that the risk of veh icle upsizing or changing vehicle offerings to “game” the passenger car and light tru ck 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 b e 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 equiva lent models. Therefore, many consumers may not desire to purchase 4WD vehicles b ecause of the cost premium; conversely, many manufacturers often require the 2W D option to satisfy demand for base vehicle models. Additionally, increasing the footpr int of vehicles requires platform changes, which usually requires a product redesign phase (th e agencies estimate that this occurs on average once every 5 years for a model). Alternati vely, turning many 2WD SUVs into (2WD) light trucks would require manufacturers to s queeze a third row of seats in or significantly increase their GVWR, which also requi res a significant change in the vehicle.

For these reasons, and in the interest of harmoniza tion and given that neither EISA nor the CAA provide specific directives on this issue p lus 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 agenci es discuss this issue further in the final rule preamble.What are the Attribute-Based Curves the Agencies ar e Using, and How Were They Developed?

2-5 

### 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 standard s 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 discus sed below.

It is important that the CAFE and CO 2 standards be set in a way that does not encourage manufacturers to respond by selling vehic les that are in any way less safe. Although NHTSA’s research also indicates that certa in 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 t hat could be otherwise discouraged by weight-based standards, because manufacturers can u se 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 CO 2 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 C O 2 emissions than is footprint, the agencies continue to believe that there is less ris k 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 wei ght-based standards—it is relatively easy for a manufacturer to add enough weight to a vehicl e to decrease its applicable fuel economy target by a significant amount, as compared to increasing veh icle footprint. For a manufacturer to change a vehicle’s footprint, a sig nificant redesign of the chassis and many components would be required. (See the related dis cussion of this issue above in the discussion of whether to adopt universal backstop s tandards.) In addition, despite requests from some manufacturers that the agencies consider multi-attribute standards, or adjust the target curves to “accommodate” other attributes lik e towing/hauling capability, the agencies continue to agree with concerns raised in 2008 by s ome commenters to NHTSA’s MY 2011 CAFE rulemaking that there would be greater potenti al for gaming under multi-attribute standards, such as standards under which targets wo uld also depend on attributes such as weight, torque, power, towing capability, and/or of f-road capability. Standards that incorporate such attributes in conjunction with foo tprint would not only be significantly more complex, but by providing degrees of freedom with r espect to more easily-adjusted attributes, they would make it less certain that the future fle et would actually achieve the average fuel economy improvement and CO 2 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 continu ous, constrained logistic function, which takes the form of an S-curve, and i s defined according to the following
formula:Joint Technical Support Document

2-6 ( )

( ) 1

1 1 1

1 FOOTPRINT c d

FOOTPRINT c d TARGET

e

a b a 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 fal ls halfway between the inverses of the lower and upper asymptotes, and d is a parameter (in square feet) that determines ho w gradually the fuel economy target transitions from the upper toward the lower asymptote as the footprint increases. Figure 2-1 shows an examp le of a logistic target function, where b = 20 mpg, a = 30 mpg, c = 40 square feet, and d = 5 square feet.

0 5 10 15 20 25 30 35

0 10 20 30 40 50 60 70 80 90 100

Footprint (Square Feet)Target (Miles per Gallon) LIMIT

UPPER

LIMIT LOWER

Figure 2-1 Sample Logistic Curve

After fitting this mathematical form (separately) t o the passenger car and light truck fleets and determining the maximum feasible stringe ncy 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.What are the Attribute-Based Curves the Agencies ar e Using, and How Were They Developed?

2-7 positions of the curves), NHTSA arrived at the curv es shown in Figure 2-2 to define the MY
2011 standards.

20 25 30 35 40 45

35 40 45 50 55 60 65 70

Footprint (sf)Target (mpg) Passenger Cars

Light Trucks

Figure 2-2 MY 2011 CAFE Standards for Passenger Ca rs and Light Trucks

In finalizing the MY 2011 standards, NHTSA noted th at the agency is not required to use a constrained logistic function and indicated t hat 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 CO 2 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 slop ed 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 foo t inflection point, and many passenger car models have footprints at which the curve is relati vely flat (see figure above). The resultingJoint Technical Support Document

2-8

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 mathema tical function that has some slope at most footprints where vehicles are produced is a dvantageous in terms of fairly balancing regulatory burdens among manufacturers, and in term s of providing a disincentive to respond to new standards by downsizing vehicles in ways tha t compromise vehicle safety. For example, a flat standard has drawbacks discussed ab ove: it may be very difficult for a full-line manufacturer to meet, while requiring very little o f a manufacturer concentrating on small vehicles, and a flat standard may provide an incent ive 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 logis tic function, NHTSA had, in proposing MY 2011 standards, presented information regarding a constrained linear function (or piecewise linear function). As shown in the 20 08 NPRM, a constrained linear function has the potential to avoid creating a localized reg ion (in terms of vehicle footprint) over which the slope of the function is relatively steep. Alt hough 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 c onstrained linear function for purposes of the proposed standards, NHTSA and EPA t entatively concluded that for both passenger cars and light trucks, it remained import ant to maintain a function with a meaningfully sloped section covering a wide footpri nt range, thereby providing a welldistributed disincentive to downsize vehicles in wa ys that could compromise highway safety. Also, the function proposed was not so steeply slop ed that it would provide a strong incentive
to increase vehicle size in order to obtain a lower CAFE requirement and higher CO 2 limit, thereby compromising energy and environmental benef its. Therefore, the CAFE and CO 2 emissions standards proposed in the NPRM were defin ed by constrained linear or piecewise
linear functions.

The NHTSA constrained linear function was defined a ccording to the following
formula:

1

1 1

, , TARGET

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 slope d 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 i f extended to a footprint of 0 square feet.
The MIN and MAX

functions take the minimum and maximum, respective ly 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 = 0.032What are the Attribute-Based Curves the Agencies ar e Using, and How Were They Developed?

2-9 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.

0.000 0.005 0.010 0.015 0.020 0.025 0.030 0.035 0.040 0.045 0.050

35 40 45 50 55 60 65 70

Footprint (sf)Fuel Consumption (gpm)

1/LIMIT UPPER 1/LIMIT LOWER

INTERCEPT SLOPE

Figure 2-3 Sample Linear Function

The EPA piecewise linear function has the same shap e as the NHTSA equation but has a slightly different mathematical specifica tion. 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 (consi stent with NHTSA’s functional form):

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

2-10

Where,

Target = the CO2 target value for a given footprint (in g/mi) a = the minimum target value (in g/mi CO 2 ) b = the maximum target value (in g/mi CO 2 ) c = the slope of the linear function (in g/mi per s q ft CO 2 ) d = is the intercept or zero-offset for the line (i n g/mi CO 2 ) 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 slope d line (in sq ft)

Figure 2-4 shows the basic curve.

30 35 40 45 50 55 60 65 70 Footprint (sq feet)CO2 (gram/mile) orFuel Consumption (gallons/mile)

lower limit = l Intercept = d upper limit = h

slope = c

min = a max = b

Figure 2-4 The Shape of the Piecewise Linear Attri bute Based Curve

For purposes of the NPRM, NHTSA, working with EPA, developed the basic curve shapes for both agencies’ respective standards, usi ng statistical fitting methods similar to those applied by NHTSA in fitting the curves defini ng the MY 2011 standards. The first step began with the market inputs discussed above, but b ecause 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 TSD d were applied, by taking the following

d The agencies excluded diesel engines and strong hy brid vehicle technologies from this exercise (and o nly this exercise) because the agencies expect that manufact urers would not need to rely heavily on these techn ologies inWhat are the Attribute-Based Curves the Agencies ar e Using, and How Were They Developed?

2-11 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 mod el 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 nor malized the fleet such that differences in technology had a minimal contribution to the variat ion (scatter) in the data. This process also served to eliminate the influence of differing degr ees 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 veh icle technologies would not only have overestimated the dependence of fuel economy on foo tprint, 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 th e function’s value for the smallest vehicles thus assures that the function remains technologica lly achievable and economically practicable for manufacturers focusing on small veh icles. 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 moto r vehicle safety standards can necessitate
the addition of structural elements that add mass a nd thereby reduce fuel economy and
increase CO2 emissions. On the other side of the f unction, without a limit at the largest footprints, the function may provide no floor on re quired fuel economy. Also, the safety considerations that support the provision of a disi ncentive 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 a n inherent absolute minimum level of fuel economy (or maximum emissions), while remaining con sistent with safety considerations.

Before fitting the sloped portion of the constraine d 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 a gencies 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, H yundai 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 vehicl e technologies in all other portions of their analyse s.Joint Technical Support Document

2-12

characteristics that could make it infeasible to ac hieve 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 i s some incentive to downsize to make it easier to meet the target. However, at the same ti me, 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 age ncies 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 be tween the two footprint limits. The agencies further noted that above 56 square feet, the only p assenger 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 age ncies 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 th e sales volumes of the many different configurations (cab designs and bed sizes) of picku p trucks, the largest pickups ( e.g. , the Ford F-150, GM Sierra/Silverado, Nissan Titan, and Toyot a Tundra), appear to fall above 66 square feet in footprint. The agencies therefore p roposed 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 lowe r 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 parame ters c and d were determined for cars and trucks separately. Values for c and d were initially set such that the average (equivale ntly, sum) of the absolute values of the differences was minimized between the “maximum technology” fleet fuel consumption (within the foot prints 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. , CO 2 ) space rather than fuel economy space because the manufacturer targets are in terms of th e harmonic average fuel economy (or average CO 2 ), and so it is more important that the curve fit t he fuel consumption ( i.e. , CO 2 ) data well than that it fit the fuel economy data we ll. NHTSA also explained in the MY 2011
final rule that it chose to use MAD in this s tep 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 app ropriate 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 characteris tics that previously led NHTSA to use minimization of the unweighted Mean Absolute Deviat ion (MAD) rather than weighted leastsquares analysis. First, projected model-specific sales volumes in the agencies’ marketWhat are the Attribute-Based Curves the Agencies ar e Using, and How Were They Developed?

2-13 forecast cover an extremely wide range, such that, as discussed in NHTSA’s rulemaking for MY 2011, although unweighted regression gives low-s elling vehicle models and high-selling vehicle models equal emphasis, sales-weighted regre ssion would give some vehicle models considerably more emphasis than other vehicle model s. e The agencies’ intention is to fit a curve that describes a technical relationship betwe en 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” ana lysis to increase technological parity of vehicle models before fitti ng curves, the agencies’ market forecast contains many significant outliers. As discussed i n NHTSA’s rulemaking for MY 2011, MAD is a statistical procedure that has been demons trated 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 st eeper curves resulting from weighted leastsquares analysis would increase the risk that energ y 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 fue l economy levels. Based on these technical considerations and these concerns regarding potenti al outcomes, the agencies have decided not to re-fit curves using weighted least-squares a nalysis, 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 Sienn a, and shows 27 model configurations with MY 2016 sale s of fewer than 100 units. Similarly, the agencies ’ market forecast shows MY 2016 sales of 268,000 for the Toy ota Prius, and shows 29 model configurations with M Y 2016 sales of fewer than 100 units. Sales-weighted analysis would give the Toyota Sienna and Prius mo re than a thousand times the consideration of many vehicle mo del configurations. Sales-weighted analysis would, therefore, cause a large number of vehicle model co nfigurations to be virtually ignored. See discussion in NHTSA’s final rule for MY 2011 passenger car and li ght truck CAFE standards, 74 Fed. Reg. at 14368 (Ma r. 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 tha n rely on least-squares approach to curve fitting. The leastsquares approach has as an underlying assumption th at the data are drawn from a normal distribution, a nd hence fits a curve using a sum-of-squares method to minim ize errors. This approach will, in a sample drawn from a non-normal distribution, give excessive weight to o utliers 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 estimati on is to let the presence of each observation be felt mor e uniformly, resulting in a curve more representati ve of the data ( see , e.g. , Peter Kennedy, A Guide to Econometrics , 3 rd edition, 1992, MIT Press, Cambridge, MA).Joint Technical Support Document

2-14

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 Fi gure 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 fun ction was 14 percent. For trucks, the corresponding mean absolute deviation was 10 percen t.

0.000 0.005 0.010 0.015 0.020 0.025 0.030 0.035 0.040 0.045 0.050 0.055 0.060

35 40 45 50 55 60 65 70 75 80

Footprint (sf)Fuel Consumption (gpm)

Figure 2-5 “Maximum Technology” Passenger Fleet wi th Fitted Constrained Linear FunctionWhat are the Attribute-Based Curves the Agencies ar e Using, and How Were They Developed?

2-15 0.000 0.005 0.010 0.015 0.020 0.025 0.030 0.035 0.040 0.045 0.050 0.055 0.060

35 40 45 50 55 60 65 70 75 80

Footprint (sf)Fuel Consumption (gpm)

Figure 2-6 “Maximum Technology” Light Truck with F itted Constrained Linear Function

The agencies used these functional forms as a start ing point to develop mathematical functions defining actual proposed standards. As d iscussed 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 l ight truck CAFE standards, and corresponding CO 2 emissions standards.

Having considered public comments as discussed in g reater detail in the final rule preamble, NHTSA and EPA have re-examined the develo pment of curves underlying the standards proposed in the NPRM, and are promulgatin g standards based on the same underlying curves. The agencies have made this dec ision considering that, while EISA mandates that CAFE standards be defined by a mathem atical function in terms of one or more attributes related to fuel economy, neither EISA no r the CAA expressly require that the mathematical function be purely descriptive of an o bserved or theoretical dependence of fuel economy on the selected attribute or attributes. A s 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 respo nses more than others – in this case, generally pre ferring increased use of fuel-saving technologies over simp le reductions in vehicle size.Joint Technical Support Document

2-16

NHTSA and EPA have concluded that the mathematical function can reasonably be selected based on a blend of analytical and policy considera tions.

Considering comments by UC Santa Barbara students r egarding difficulties reproducing NHTSA’s analysis, NHTSA re-examined its analysis, and discovered some erroneous entries in model inputs underlying the an alysis used to develop the curves proposed in the NPRM. These errors, which are also discusse d in NHTSA’s final Regulatory Impact Analysis (RIA) and have since been corrected, inclu de the following: incorrect valvetrain phasing and lift inputs for many BMW engines, incor rect indexing for some Daimler models, incorrectly enabled valvetrain technologies for rot ary engines and Atkinson cycle engines, omitted baseline applications of cylinder deactivat ion in some Honda and GM engines, incorrect valve phasing codes for some 4-cylinder C hrysler engines, omitted baseline applications of advanced transmissions in some VW m odels, incorrectly enabled advanced electrification technologies for several hybrid veh icle models, and incorrect DCT effectiveness estimates for subcompact passenger ca rs.

After correcting these errors and repeating the cur ve development analysis presented in the NPRM, NHTSA obtained curves shown below in F igures 2-7 and 2-8 for passenger cars and light trucks, respectively. The fitted cu rves 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 fun ction was 14 percent. For trucks, the corresponding mean absolute deviation was 10 percen t.

Figure 2-7 Revised “Maximum Technology” Passenger Fleet with Fitted Constrained Linear FunctionWhat are the Attribute-Based Curves the Agencies ar e Using, and How Were They Developed?

2-17 Figure 2-8 Revised “Maximum Technology” Light Truc k with Fitted Constrained Linear Function

This refitted passenger car curve is similar to tha t 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 pe rcent steeper (on a gpm per sf basis) than the curve presented in the NPRM. For passenger car s 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 corr esponding to the announced national policy.Joint Technical Support Document

2-18 20 25 30 35 40 45

35 40 45 50 55 60 65 70 75 80 T a r g e t ( m p g )

Footprint (sf) NPRM

Final

Re-Fitted

Figure 2-9 MY2016 Passenger Car Targets: NPRM, Final Rule, and if Using Re-Fitted Curve

20 25 30 35 40 45

35 40 45 50 55 60 65 70 75 80 T a r g e t ( m p g )

Footprint (sf) NPRM

Final

Re-Fitted

Figure 2-10 MY2016 Light Truck Targets: NPRM, Fin al Rule, and if Using Re-Fitted CurveWhat are the Attribute-Based Curves the Agencies ar e Using, and How Were They Developed?

2-19 As the figures show, the resultant light truck curv es 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 larg est cars, and provide a greater incentive to increase car size throughout the range of footprint s within which NHTSA and EPA project most passenger car models will be sold through MY 2 016. The agencies are concerned that these changes would make it unduly difficult for ma nufacturers to introduce new small passenger cars in the United States, and unduly ris k forgoing achievable and cost-effective energy and environmental benefits by increasing inc entives 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 cu rve estimated in the NPRM; with respect to the corrected fleet (between the “cut off” footprin t values, and after the “maximum technology” analysis discussed above), the mean abs olute deviation for the refitted curve is 13.887 percent, and that of a refitted curve held t o the original slope is 13.933 percent. In other words, the data support the original slope ve ry nearly as well as they support the refitted slope.

Although these refitted curves reflect the agencies’ best te chnical 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 refi tted curve for passenger cars, and given that the data s upport 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 re produce 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 al l accompanying inputs and external analysis files should enable outside parties to ind ependently reproduce the agencies’ analysis. If outside parties continue to experience difficult y in doing so, we encourage them to contact NHTSA, and the agency will do its best to provide a ssistance.Joint Technical Support Document

2-20 References

1 49 U.S.C. 32902(a)(3)(A).

2 The 2002 NAS Report described at length and quanti fied the potential safety problem with average fuel economy standards that specify a single numerical r equirement 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-2009-0472-0 147.

4 49 U.S.C. 32902(b)(4).Technologies Considered in the Agencies’ Analysis

3-1 

## CHAPTER 3: Technologies Considered in the Agencies’

Analysis

This Chapter of the joint TSD describes the technol ogies NHTSA and EPA evaluated as potential inputs in their respective m odels 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 r espective 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 in clude in the analysis?

Technology assumptions, i.e. , assumptions about their availability, cost, effectiveness, and the rate at which they can be in corporated into new vehicles, are often very controversial as they have a significant impac t on the levels of the standards. Agencies must, therefore, take great care in develo ping and justifying these assumptions. In developing technology inputs for M Y 2012-2016 standards the agencies reviewed the technology assumptions that NHTSA used in setting the MY 2011 standards, the agencies reviewed the technology inp ut assumptions identified in EPA’s 2008 Staff Technical Report, 1 the comments that NHTSA received in response to it s May 2008 Notice of Proposed Rulemaking, and the comment s that the agencies received in response to its September 2009 Notice of Proposed R ulemaking. 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 ident ifying 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 Anal ysis (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 Mart ec Group to that study; 6 and vehicle fuel economy certification data. Both agen cies also reviewed the published
technical literature which addressed the issue of C O 2 emission control and fuel economy, such as papers published by the Society of Automoti ve 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 manufacturer s 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 pub lic data mentioned above and not as a significant basis for this rulemaking.Joint Technical Support Document

3-2 EPA also has a contracted study ongoing with FEV th at 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 man ufacturing them. For the NPRM, the agencies used a completed analysis to estimate cost s of turbocharging with downsizing for I4 engines only. The NPRM stated that tear-dow n cost estimates from FEV for additional engine and transmission technologies bec ame available shortly before the release of the NPRM, but not in time to be incorpor ated into the agencies’ cost analysis of the proposed standards. These preliminary resul ts were made available for review and the agencies stated they would consider this inform ation for use in the final rule analysis. 8 The NPRM also stated that a detailed report would b e submitted to the docket on these additional technologies during the public comment p eriod for the proposal. That deadline was not met but all additional technologies for whi ch cost study tear downs have been completed from this study with FEV (studies have no w been completed on turbocharging and downsizing for V6 and V8 engines, stoichiometri c gasoline direct injection, 6/7/8- speed automatic transmission and dual clutch transm ission technologies) have been
considered for the final rule and details are conta ined in two reports placed in the
docket. 9,10 EPA and NHTSA reviewed all of the above informati on in order to develop the best estimates of availability, cost and effect iveness of these fuel-saving/CO 2 - reducing technologies.

The agencies would also like to note that per the E nergy Independence and Security Act (EISA), the National Academies of Scie nces has been conducting a study for NHTSA to update chapter 3 of their 2002 NAS Rep ort, which presents technology effectiveness estimates for light-duty vehicles. T he update takes a fresh look at that list of technologies and their associated cost and effec tiveness 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 consideri ng the results from this study as part of the next round of rulemaking for CAFE/GHG standa rds.



### 3.1.2 Fuel economy certification data

Where available, data from recent model years of EP A’s Fuel Economy Certification Data were used for helping to determi ne the effectiveness of specific technologies. CO 2 and fuel consumption reduction estimates were esti mated from EPA’s fuel economy database on the two-cycle (FTP city & highway) fuel economy test results. During the standard fuel economy test cycles, direc t measurements of CO 2 emissions are made. This data, along with other measurements, ar e then used to calculate the estimated fuel economy performance in gallons of fuel consume d per mile. Vehicle certification data are an obviously reliable source for determina tion of the CO 2 and fuel consumption reduction potential when a directly comparable vehi cle was offered both with and without the specific CO 2 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 w here only the specific technology would be changed from a reference vehicle, in order to eliminate any confounding ofTechnologies Considered in the Agencies’ Analysis

3-3 values across several technologies. In some hybrid vehicle cases, the exact same vehicle may not be offered, and a similar vehicle was selec ted for comparison.



### 3.2 Which technologies will be applicable during the mo del years to which the rules apply?

One of the key factors that NHTSA and EPA considere d in developing the standards for each model year is the availability a nd feasibility of fuel-saving/CO 2 - reducing technologies. A variety of technologies c an be used to reduce fuel consumption
and CO 2 emissions, whether they be engine-related ( e.g. , turbocharging), transmissionrelated ( e.g. , six forward gears in place of four), accessory-re lated ( 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 fo r purposes of setting the MYs 2012-2016 standards, the agencies reviewed NHTSA’s MY 2011 CA FE 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 submi tted by manufacturers to the agencies in response to NHTSA’s November 2009 reque st 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 i nto vehicles once production decisions are made. The agencies did not consider technologies in the research stage, such as homogeneous charge compression ignition (HC CI), lean-burn direct injection technology, etc., because their effectiveness and/o r cost estimates contain a high level of uncertainty.

The technologies considered by the NHTSA and EPA mo dels are briefly described below. They fall under the five broad ca tegories of engine, transmission, vehicle, electrification/accessory, and hybrid tech nologies. 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 econ omy and reduce CO 2 emissions include the following:

• Low-friction lubricants – low viscosity and advanced low friction lubrican ts oils are now available with improved performance an d better lubrication.

• Reduction of engine friction losses – can be achieved through low-tension piston rings, roller cam followers, improved materi al coatings, more optimal thermal management, piston surface treatments, and other improvements in the design of engine components and subsystems that improve engine operation.Joint Technical Support Document

3-4 • Conversion to dual overhead cam with dual cam phasi ng – as applied to overhead valves designed to increase the air flow w ith more than two valves per cylinder and reduce pumping losses.

• Cylinder deactivation – deactivates the intake and exhaust valves and preve nts fuel injection into some cylinders during light-loa d 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 p umping 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 hei ght 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 im prove cooling of the air/fuel charge within the cylinder, which allows f or 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 functional ity. Similar to other startstop technologies, additional enablers, such as ele ctric 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 m aintaining performance. This reduces pumping losses at lighter loads in com parison to a larger engine.

• Exhaust-gas recirculation boost – increases the exhaust-gas recirculation used in the combustion process to increase thermal effic iency 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 equivale nt-performance gasoline engine. This technology requires additional enable rs, such as NO
x trap catalyst after-treatment or selective catalytic red uction NO
x after-treatment.Technologies Considered in the Agencies’ Analysis

3-5 Types of transmission technologies considered inclu de:

• Improved automatic transmission controls – optimizes shift schedule to maximize fuel efficiency under wide ranging conditi ons, and minimizes losses associated with torque converter slip through lock- up or modulation.

• Six-, seven-, and eight-speed automatic transmissio ns – 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 v ehicle operating conditions.

• Dual clutch or automated shift manual transmissions – are similar to manual transmissions, but the vehicle controls shifting an d launch functions. A dualclutch automated shift manual transmission uses sep arate clutches for evennumbered and odd-numbered gears, so the next expect ed 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 prov ide ratios for operation.
Unlike manual and automatic transmissions with fixe d transmission ratios, continuously variable transmissions can provide ful ly variable and an infinite number of transmission ratios that enable the engin e to operate in a more efficient operating range over a broader range of v ehicle 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 loss es associated with the energy dissipated in the deform ation 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 o n rotors when the brakes are not engaged because the brake p ads are pulled away from
the rotors.

• Front or secondary axle disconnect for four-wheel d rive systems – provides a torque distribution disconnect between front and re ar 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 componentJoint Technical Support Document

3-6 integration to application of lighter and higher-st rength materials. Mass reduction is further compounded by reductions in en gine power and ancillary systems (transmission, steering, brakes, suspension , etc.).

Types of electrification/accessory and hybrid techn ologies considered include:

• Electric power steering (EPS) – is an electrically-assisted steering system that has advantages over traditional hydraulic power ste ering because it replaces a continuously operated hydraulic pump, thereby reduc ing parasitic losses from the accessory drive.

• Improved accessories (IACC) – may include high efficiency alternators, electrically driven (i.e., on-demand) water pumps a nd cooling fans. This
excludes other electrical accessories such as elect ric oil pumps and electrically driven air conditioner compressors.

• Air Conditioner Systems

– These technologies include improved hoses, connectors and seals for leakage control. They als o include improved compressors, expansion valves, heat exchangers and the control of these components for the purposes of improving tailpipe C O 2 emissions as a result of A/C use. These technologies are covered separa tely 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 integ rated starter-generator, this is the most basic hybrid system that facilitat es 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 G enerator (BISG)
– provides idle-stop capability and uses a high volta ge 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 h arnesses. This system replaces a standard alternator with an enhanced pow er, higher voltage, higher efficiency starter-alternator, that is belt driven and that can recover braking energy while the vehicle slows down (regenerative b raking).

• Integrated Motor Assist (IMA)/Crank integrated star ter generator (CISG)
– provides idle-stop capability and uses a high volta ge 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 h arnesses. This system replaces a standard alternator with an enhanced pow er, higher voltage, higher efficiency starter-alternator that is crankshaft mo unted and can recover braking energy while the vehicle slows down (regene rative braking).Technologies Considered in the Agencies’ Analysis

3-7 • 2-mode hybrid (2MHEV) – is a hybrid electric drive system that uses an adaptation of a conventional stepped-ratio automati c transmission by replacing some of the transmission clutches with tw o electric motors that control the ratio of engine speed to vehicle speed, while clutches allow the motors to be bypassed. This improves both the tran smission torque capacity for heavy-duty applications and reduces fuel consum ption and CO 2 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 ge arset and a motor/generator. This motor/generator uses the engine to either char ge the battery or supply additional power to the drive motor. A second, mor e powerful motor/generator is permanently connected to the veh icle’s final drive and always turns with the wheels. The planetary gear s plits engine power between the first motor/generator and the drive motor to ei ther 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 l arger battery packs with more energy storage and a greater capability to be disch arged. They also use a control system that allows the battery pack to be s ubstantially depleted under electric-only or blended mechanical/electric operat ion.

• Electric vehicles (EV)

– are vehicles with all-electric drive and with vehic le systems powered by energy-optimized batteries charg ed 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’ r espective models?

Although both NHTSA and EPA are basing their fuel economy and emission modeling on the same baseline vehicle fleet and cos t and effectiveness estimates for control technologies, differences in the Volpe and OMEGA models require that this common information be processed in different ways p rior to their use in the respective models.. With respect to the vehicle fleet, the Vo lpe Model evaluates the addition of technology to individual vehicle configurations or models, while the OMEGA model does so for vehicle platforms broken down further b y engine size. Thus, NHTSA used
the vehicle sales estimates described above directl y, while EPA combined the sales of certain vehicle models. The Volpe Model evaluates technologies individually. This, coupled with the modeling of individual vehicle mod els, means that only the presence or absence of any particular technology needs to be in dicated, as described above. OMEGAJoint Technical Support Document

3-8

applies technology in combinations or packages. Th is, plus the grouping of individual vehicle models, requires that the total effectivene ss of the technology already applied in
the baseline fleet must be calculated and must be r eflected as a percentage of the various technology packages available to be added to those vehicles.

With respect to the cost and effectiveness of tech nologies, as mentioned above, the Volpe Model applies technologies individually. It does this following certain specified pathways for several categories of techno logies ( e.g. , engine, transmission, accessories, etc.). The Volpe Model applies techno logy incrementally, so the effectiveness of each subsequent technology needs t o be determined relative to the previous one. The same is true for cost. In addit ion, because of interaction in the effectiveness of certain technologies, herein refer red to as the synergy/dis-synergy, any such interaction between the next technology on a s pecified pathway with those which have already been potentially applied in other path ways must be determined. For example, the incremental effectiveness of switching from a six-speed automatic transmission to a dual clutch transmission will dep end 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. Thi s 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 ef fectiveness 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 a nd OMEGA models, the form of the actual inputs to the model will appear to be differ ent. For more information on EPA’s and NHTSA’s unique approaches to modeling, please r efer to each agency’s respective
final RIA.

In order to estimate both technology costs and fuel consumption/CO 2 reduction estimates, it is necessary for each agency to descr ibe the baseline vehicle characteristics from which the estimates can be compared. This “ba seline” is different from the usage in Chapter 1 of this joint TSD. In Chapter 1, the bas eline fleet is the projected fleet in MY 2016 before accounting for technologies needed to m eet the MY 2011 CAFE standards and before accounting for changes in fleet composit ion attributable to that rule (those later steps accounted for independently by each age ncy in developing their separate reference fleets). In the present context, it indi cates 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 age ncies. For EPA, unless noted elsewhere, the baseline vehicle is defined as a veh icle with a port-fuel injected, naturally aspirated gasoline engine with two intake and two e xhaust valves and fixed valve timing and lift. The baseline transmission is a 4-speed a utomatic, and the vehicle has no hybrid systems. For NHTSA, unless noted elsewhere, the ba seline vehicle is the actual vehicle as it exists in the baseline fleet, because NHTSA m odels each unique vehicle separately.Technologies Considered in the Agencies’ Analysis

3-9 For details on how the technology cost and effectiv eness 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 ef fectiveness estimates for the final rule?

3.3.2.1 Considerations that affect technology effectiveness

As mentioned above, regarding estimates for technol ogy effectiveness, NHTSA and EPA’s primary approach was to reexamine the est imates 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 esta blish CAFE standards for MYs 2011-2015. The agencies also reconsidered other so urces such as the 2002 NAS Report, the 2004 NESCCAF report, recent CAFE compliance dat a (comparing similar vehicles with different technologies against each other in f uel economy testing, such as a Honda Civic Hybrid versus a directly comparable Honda Civ ic conventional drive), and confidential manufacturer estimates of technology e ffectiveness. Using the BOM framework utilized in the MY 2011 CAFE final rule, NHTSA and EPA engineers reviewed effectiveness information from the multipl e sources for each technology. Together, they compared the multiple estimates and assessed their validity, taking care to
ensure that common BOM definitions and other vehicl e attributes such as performance, refinement, and drivability were taken into account . However, because the agencies’ respective models employ different numbers of vehic le subclasses and use different technology decision trees to arrive at the standard s, direct comparison of BOMs was somewhat more complicated. To address this and to assure an apples-to-apple comparison, NHTSA and EPA developed mapping techniq ues, devising technology packages and corresponding incremental technology e stimates. This approach helped compare incremental and packaged estimates and deri ve results that are consistent and could be translated into the respective models of t he 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 analysi s for this final rule. When NHTSA and EPA’s estimates for effectiveness diverged slig htly due to differences in how the agencies apply technologies to vehicles in their re spective models, the agencies report the ranges for the effectiveness values used in each mo del, as well as the reasons why the range is reasonable. There were only a few comment s on technology effectiveness. For the most part, the non-confidential comments were c onsistent with the range of effectiveness reported in this rule. Where there w ere 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 b ut not always additive. That is, adding cylinder deactivation to dual cam phasing do es not necessarily result in a 10 percent improvement. This is true because some of the technologies address the sameJoint Technical Support Document

3-10

shortcomings of the internal combustion engine, alt hough 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, pos itive synergies are said to exist. The analysis accounts for these positive and negative s ynergies wherever appropriate so as to ensure no double or insufficient counting of effect s. 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 bes t way to derive technology
cost estimates is to conduct real-world tear down s tudies. This position is supported by
commenters and we received no comments to the contr ary. a These studies are based to a large degree on tear downs of vehicles or vehicle s ystems that employ the new technologies, and of similar vehicles or systems wi thout the new technologies. Analysts with expertise in automotive design, materials, and manufacturing then compare the tear down components and evaluate the differences. Usin g databases for materials, labor, manufacturing overhead, and mark-up costs, the over all 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 c ostly. EPA has begun conducting tear down studies to assess the costs of 4-5 technologie s under a contract with FEV Inc., an independent engine and powertrain systems research, design and development company. To date, four technologies (downsizing and turbocha rging, stoichiometric gasoline directinjection, dual clutch transmission and 6-speed aut omatic transmission) have been evaluated. The tear down study has been peer-revie wed and the report for these tear down studies and the peer-review report have been m ade public in the rulemaking
docket. 11 The agencies have considered these studies and th e comments received on them, as practicable and appropriate, in developing technology cost assumptions for this
final rule.

These recently completed tear-downs include the fol lowing technologies:

1. 3.0L V6 port fuel-injected (PFI) downsized and turb ocharged 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 Di rect 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)Technologies Considered in the Agencies’ Analysis

3-11 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 -$1 17 5-speed auto trans 4-speed auto trans $91 $91 $0 6-speed auto trans 4-speed auto trans $153 $101 -$5 2 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 s peed trans ($153) and the 4 to 5 speed trans ($91).

Most notable in Table 3-1 is the change in costs fo r the 6-speed dual-clutch transmission relative to the 6-speed automatic tran smission. We discuss this change in more detail in section 3.4.2.2.5, FEV’s tear down w ork 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 l ess complex than an auto transmission. However, FEV tear down cost analysis studies were conducted based on the assumption that the analyzed technologies and t he manufacturing for those technologies were both fully “mature,” in that desi gns and manufacturing processes have been reasonably optimized. The studies also assume d that manufacturing facilities have annual production levels of 450,000 units. EPA and NHTSA recognize that in early implementation years, designs and manufacturing pro cesses may not be optimized to that extent, and investment cost may exceed those of ful ly 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 F EV tear down study cost and the NPRM cost for technologies for which there were com pleted FEV tear down studies. Time-based learning is used to reflect cost in late r years. This approach is applied to downsizing and turbocharging for V6 and V8, stoichi ometric gasoline direct-injection for V6 and V8, dual clutch transmission and 6-speed aut omatic transmission. In the NPRM, the costs for turbocharging, downsizing and SGDI fo r I4 engines were based on an FEV teardown cost study that was completed prior to rel ease 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. Th is approach was recommended to NHTSA by Ricardo, an international engineering cons ulting firm retained by NHTSA to aid in the analysis of public comments on its propo sed standards for MYs 2011-2015 because of its expertise in the area of fuel econom y technologies. A BOM approach is similar in concept to the approach used in tear dow n studies. The difference is that under a BOM approach, the build up of cost estimates is c onducted 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 fi nal 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 reli ed considerably in the MY 2011Joint Technical Support Document

3-12

final rule on the 2008 Martec Report for costing co ntents of some technologies, upon further joint review and for purposes of the MY 201 2-2016 standards, the agencies decided that some of the costing information in tha t report was no longer accurate due to downward trends in commodity prices since the publi cation of that report. The agencies reviewed, revalidated or updated cost estimates for individual components based on new
information.

The subsequent sections describe the costs associat ed 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 ti ming. Costs are presented in terms of their hardware incremental compliance c ost. This means that they include all potential costs associated with their application o n vehicles, not just the cost of their physical parts. As discussed, the basis for the pi ece 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 f or cost related information. The objective was to use those sources of information c onsidered to be most credible for projecting the costs of individual vehicle technolo gies. As a result of almost daily meetings between EPA and NHTSA staff during 2009, w here 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 esti mate available today, and explained the basis for that exercise of judgment.

Note that EPA’s 2008 Staff Report presented costs i n 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 b een adjusted to reflect 2007 dollars using the Gross Domestic Product price deflator (se e 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 indi vidual technologies, as well as their respective effectiveness estimates. For purposes o f this joint TSD, these estimates were developed jointly by NHTSA and EPA. Due to differe nces in the modeling and technology application methodologies employed by th e two agencies, the estimates used by each agency may not match exactly but will be co nsistent 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 M Y 2011 CAFE final rule and EPA’s Advanced Notice of Proposed Rulemaking, w hich 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 t he National Program. For costs, the agencies reconsidered both the direct or “piece” co sts 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 ca se, an item of fuel economy-

b The adjustment used to convert from 2006 dollars t o 2007 dollars was 1.03.Technologies Considered in the Agencies’ Analysis

3-13 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 objec tive was to use those sources of information considered to be most credible for proj ecting the costs of individual vehicle technologies. As discussed above, the agencies rev iewed, revalidated or updated cost estimates for individual components based on new in formation. Thus although NHTSA
and EPA found that much of the cost information use d in NHTSA’s MY 2011 CAFE final rule and EPA’s staff report was consistent, t he agencies, in reconsidering information from many sources (such as the new FEV cost studies), ultimately revised several component costs of several major technologi es (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 turboch arging downsizing, SGDI, DCT and 6-speed automatic transmi ssion, the agencies relied, to the extent possible, on the tear down data available an d scaling methodologies used in EPA’s ongoing study with FEV. As discussed above in deta il in Section 3.3.2.2, this study consists of complete system tear down to evaluate t echnologies down to the nuts and bolts to arrive at very detailed estimates of the c osts associated with manufacturing
them. 15 The confidential information provided by manufact urers under their product plan submissions to the agencies or discussed in meeting s between the agencies and the manufacturers and suppliers served largely as a che ck on publicly-available data.

Once costs were determined, they were adjusted to e nsure that they were all expressed in 2007 dollars using the GDP price defla tor as described in Appendix 3.A. Indirect costs were accounted for using a new appro ach developed by EPA for this rulemaking and explained below rather than using th e Retail Price Equivalent (RPE) multiplier of 1.5 as was done in NHTSA’s MY 2011 fi nal rule and EPA’s 2008 Staff Report. NHTSA and EPA also considered how costs sh ould be adjusted to reflect manufacturer learning as discussed below. Lastly, costs were adjusted by modifying or scaling content assumptions to account for differen ces across the range of vehicle sizes and functional requirements, and adjusted the assoc iated 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 resp ective models.

3.3.2.2.2 Cost markups to account for indirect costs Indirect costs include production-related costs (r esearch, development, and other engineering), business-related costs (corporate sal aries, pensions, and manufacturer profits), and retail-sales-related costs (dealer su pport, marketing, and dealer profits). For this analysis, direct cost estimates were first dev eloped 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 co st markup factor was then applied that varied by the best estimate of the particular techn ology’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.Joint Technical Support Document

3-14

3.3.2.2.3 Concept behind and development of indirect cost mul tipliers If all desirable data were available, when a new te chnology is implemented, the costs of that technology would include the direct a nd 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 ind irect 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 spec ific to a particular new technology.

The automotive industry, EPA, and NHTSA have common ly used retail price equivalent (RPE) multipliers to approximate the ind irect costs associated with a new technology. The RPE is a ratio of total revenues t o direct manufacturing costs. Because, by definition, total revenues = direct costs + indi rect 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; i t 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 proc esses 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 technol ogies vary, both with the complexity of the technology and with the time fram e. For instance, a hybrid-electric engine is likely to involve greater research and de velopment 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 curr ent estimate of the RPE multiplier and
to examine whether the indirect costs of new techno logies are likely to vary across technologies. The report “Automobile Industry Reta il Price Equivalent and Indirect Cost Multipliers,” by Alex Rogozhin, Michael Gallaher, a nd Walter McManus, 16 calculates the RPE multiplier as 1.46 in 2007. The report then de velops 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 acco unt some of the variation in these costs. The multipliers developed in this report are presen ted in Table 3-2.

The indirect cost multipliers used adjustment facto rs, developed by a team of EPA engineers with expertise in the auto industry, whic h 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 compl exity, 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 esti mates of the variation in adjustment

c Note that unlike the RPE, the ICM does not include profits.Technologies Considered in the Agencies’ Analysis

3-15 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

TECHNOLOGY COMPLEXITY Short Run Long Run STUDY 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 d ifferences in the high-complexity technologies. The EPA and NHTSA engineers who revi ewed the results believed that the
differences reflected actual differences in the tec hnologies under study. In particular, for low complexity, low-rolling-resistance tires (the a pplication in the RTI Report) would involve lower indirect costs than aerodynamic impro vements (the application in the EPA memo); and, for medium complexity, dual-clutch tran smissions (the application in the RTI Report) should have a smaller multiplier than e ngine 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 ran ge of technologies assigned to those classes; the costs in this study, then, use t he averages of the values of the two reports, as shown in the last line of Table 3-2. F or high complexity technologies, the agencies felt the technologies assigned to these ca tegories—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 t o 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 c ompliance 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 repo rt; and that presented in the EPA Memo. While all of the ICMs generated via these tw o processes were in general agreement, some differences did exist. In determin ing how to deal with these differences, EPA and NHTSA agreed that, for the low and medium c omplexity technologies, a simpleJoint Technical Support Document

3-16

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 market place today. Such technologies would be power-split and 2-mode hybrid electric veh icles. The higher multiplier, deemed high+, would be applied to those technologies of hi gh complexity but with no, or essentially no, use in the current fleet. Such tec hnologies would be plug-in hybrids and full electric vehicles. Table 3-3 shows the comple xity 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-conti nuous (OHC) Plug-in hybrid

Engine friction reduction VVLT-discrete (OHV) Camle ss valve actuation Full electric vehicle VVT-intake (OHC) GDI-lean burn Homogeneous charge
CI VVT-coupled (OHC & OHV) Turbocharge with downsize W eight reduction – 30% VVT-dual (OHC) Conversion to diesel Integrated moto r assist hybrid Cylinder deactivation (OHC & OHV) Dual clutch transmission 2-mode hybrid

VVLT-discrete (OHC) Continually variable trans Powe r-split hybrid
GDI-stoich 42 Volt conversion Turbocharge (with no downsize) Weight reduction – 2 0% Downsize (with no turbocharge) Integrated starter g enerator (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 anal ysis, compliance costs have also been estimated for the years following implementati on to shed light on the long term – 2022 and later – cost impacts of the rule. The yea r 2022 is used by EPA because the short-term and long-term markup factors described a bove are applied in five year increments with the 2012 through 2016 implementatio n span and the 2017 through 2021 span both representing the short-term.Technologies Considered in the Agencies’ Analysis

3-17 Commenters in general agreed with this methodology for determining indirect cost multipliers, though there was some disagreemen t 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 ana lysis, manufacturer learning effects would be expected to play a role in the act ual 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 i ndustries 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 manuf acturers gain experience in production, they are able to apply innovations to s implify machining and assembly operations, use lower cost materials, and reduce th e 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 ef fect in the MY 2011 CAFE final rule, and EPA has a detailed description of t he learning effect in the 2008 Staff Report and in several past rules. 20 The description here is focused on how the learni ng effect has been used in the analyses support each a gency’s rule.

Most studies of the effect of experience or learnin g on production costs appear to assume that cost reductions begin only after some i nitial 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 usual ly 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 beyon d which cost reductions would no longer occur, instead depending on the asymptotic b ehavior of the effect for learning rates below 100 percent to establish a floor on cos ts.
In past rulemaking analyses, as noted above, both a gencies have used a learning curve factor of 20 percent for each doubling of pro duction volume. NHTSA has used this approach in analyses supporting recent CAFE ru les. In its analysis, EPA has simplified the approach by using an “every two year s” 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 no t only this volume-based learning as described above, but also “time-based” learning. Time-based learning, estimated by NHTSA at three percent per year, occur s in years following the volume-Joint Technical Support Document

3-18

based learning steps and represents the smaller sca le learning that occurs as
manufacturers continue to innovate. The time-based learning is, in effect, represented by the flattened out, asymptotic portion of the learni ng curve.

For this analysis, each agency has employed both vo lume-based and time-based learning effects. In NHTSA’s analysis, volume-base d learning is estimated to result in 20 percent lower costs as production volumes of the gi ven 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 201 2 and 2013 model year costs). In each agency’s analysis, time-based learning is esti mated to result in 3 percent lower costs in each year following first introduction of a give n 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 l east for purposes of the analyses done to support each agency’s rule.

Both agencies considered learning impacts on most b ut not all of the technologies expected to be used because some of the expected te chnologies are already used rather widely in the industry and, presumably, learning im pacts have already occurred. Volume-based learning was considered for only a han dful 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 e stablished 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.Technologies Considered in the Agencies’ Analysis

3-19 Table 3-4 Learning Effects Applied to Each Technol ogy

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 Volume d Plug-in hybrid electric vehicle Volume
Full electric vehicle Volume Improved high efficiency alternator & electrificati on 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) Ti me 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 cost s and effectiveness values for each particular technology described throughout this chapter. The costs shown are applicable for the 2012 model year. The Volpe Mode l 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 cost s and effectiveness values for each particular technology described thr oughout this chapter. The costs shown are applicable for the 2016 model year and, therefo re, represent fully learned costs in the

d Due to an error, the NPRM showed this as time-bas ed learning.Joint Technical Support Document

3-20

context of EPA’s analysis. For technologies added in years prior to MY 2016, EPA has backed out the learning effects relative to the cos ts shown in the tables. For example, Table 3-33 shows the small car stop-start vehicle c ost as $351 in 2016. In the 2012 model year, this cost would be higher since the vol ume-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 201 2 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 o nly one model year - 2016. Because the CAFE model adds technology on a year-by -year basis, it applies volumebased learning effects once the applicable producti on 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 productio n volumes have doubled). Most technologies in both analyses are subject to time-b ased 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 app lies it outside the OMEGA model). Importantly, by the 2016 model year, both agencies will have applied two volume-based learning steps to the appropriate tech nologies (hybrid and electric vehicles only) and four time-based learning steps to the app ropriate technologies (most other technologies) to arrive at the same technology cost s.



### 3.4 Specific technologies considered and estimates of c osts and effectiveness

It is important to note that the cost and effective ness values presented by NHTSA and EPA in this Final TSD are used when creating in put 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 mo deling the technology cost.



### 3.4.1 What data sources did the agencies evaluate?

Refer to section 3.1 for a discussion of the specif ic data sources and reference material used for the analysis of this rulemaking.



### 3.4.2 Individual technology descriptions and cost/effecti veness estimates

The sections that follow describe the technologies applied by both EPA and NHTSA. It is important to note that there are dist inct differences between the agencies in how cost and effectiveness estimations are reported and applied.

EPA reports the total cost and effectiveness associ ated 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 individ ual technologies are summed. To estimate the overall effectiveness, the lumped para meter method is applied.Technologies Considered in the Agencies’ Analysis

3-21 NHTSA applies cost and effectiveness through a seri es of decision trees. The progressive nature of this methodology results in c onditions for which a portion of a technology has already been added. In this case, N HTSA would add an incremental
value which is the difference between the total cos t of a technology and the portion already existing. An example of this is cylinder d eactivation. At the point in the decision tree that it is applied, valvetrain technologies wh ich could enable this feature have already been added. NHTSA will add the delta in co st, which in this example accounts
for the NVH attributes.

3.4.2.1 Engine Technologies

NHTSA and EPA have reviewed the engine technology e stimates used in
NHTSA’s MY 2011 CAFE final rule and EPA’s 2008 Staf f Report and available comments to the NPRM. In doing so NHTSA and EPA re considered all available sources and updated the estimates as appropriate. The section below describes each of the engine technologies considered for this rulemak ing.

3.4.2.1.1 Low Friction Lubricants (LUB) One of the most basic methods of reducing fuel cons umption in gasoline engines is the use of lower viscosity engine lubricants. M ore advanced multi-viscosity engine oils are available today with improved performance in a wider temperature band and with better lubricating properties. This can be accompl ished by changes to the oil base stock ( e.g. , switching engine lubricants from a Group I base o ils to lower-friction, lower viscosity Group III synthetic) and through changes to lubricant additive packages ( e.g. , friction modifiers and viscosity improvers). The u se of 5W-30 motor oil is now widespread and auto manufacturers are introducing t he use of even lower viscosity oils, such as 5W-20 and 0W-20, to improve cold-flow prope rties 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 i mprove the effectiveness of valvetrain technologies such as cylinder deactivation, which r ely on a minimum oil temperature (viscosity) for operation.

Several manufacturers have previously commented con fidentially, that low
friction lubricants could have an effectiveness val ue between 0 to 1 percent. For purposes of this final rule, NHTSA and EPA are usin g effectiveness estimates within this range. Therefore 0.5 percent was used in both mode ls.

The 2002 NAS study estimated the low friction lubri cant 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) da ta 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 t hat manufacturer’s estimates areJoint Technical Support Document

3-22

the most accurate, and thus continue to believe tha t the $3 cost estimate is appropriate and independent of vehicle class since the engineer ing 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 t o low friction lubes). e

3.4.2.1.2 Engine Friction Reduction (EFR)
In addition to low friction lubricants, manufacture rs 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 l osses within the engine. 21 Examples include improvements in low-tension piston rings, p iston 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 modeli ng software continues to improve, more opportunities for evolutionary friction reduct ions may become available.

All reciprocating and rotating components in the en gine 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. N HTSA and EPA continue to believe that this range is accurate. Because of the increm ental 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 r ange 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 cos t up to $14 without RPE on a per cylinder basis (2006$). EPA’s 2008 Staff Report es timated 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 low er end of the MY 2011 CAFE final rule range and thus for this rulemaking is $13 per cylin der compliance cost (2007$), 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 frictio n 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 b oth, primarily to reduce pumping losses, increase specific power, and control the level of r esidual gases in the cylinder. VVT reduces pumping losses when the engine is lightly l oaded by controlling valve timing closer to an optimum needed to sustain horsepower a nd torque. VVT can also improve volumetric efficiency at higher engine speeds and l oads. Additionally, VVT can be used

e Note that the costs developed for low friction lu bes for this analysis reflect the costs associated with any engine changes that would be required as well as an y durability testing that may be required.Technologies Considered in the Agencies’ Analysis

3-23 to alter (and optimize) the effective compression r atio 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 met hod of variable valve timing. 22 Manufacturers are currently using many different ty pes 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 crankshaf t 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, powe red by engine oil pressure and managed by a solenoid that controls the oil pressur e 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 th e intake camshaft while the exhaust valve timing remains fixed. This requires the addi tion 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 es timate is inaccurate, and so have employed it for this rulemaking.

As for costs, NHTSA’s MY 2011 CAFE final rule estim ated a $61 RPE ($41 nonRPE) cost per cam phaser, based on the 2008 Martec Report and confidential manufacturer data (2007$). In the 2008 staff repor t, 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 mult iplier of 1.11, for a low complexity technology, the compliance cost per cam phaser woul d 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 appli ed to ICP so the MY 2016 cost would be $40 for in in-line engine and $80 for the V-engi ne (2007$).

3.4.2.1.3.2 Coupled Cam Phasing (CCPS and CCPO) Valvetrains with coupled (or coordinated) cam phasi ng can modify the timing of both the inlet valves and the exhaust valves an equ al amount by phasing the camshaft of a single overhead cam (SOHC) engine or an overhead va lve (OHV) engine. f For overhead

f Although CCP appears only in the SOHC and OHV bran ches of the decision tree, it is noted that a singl e phaser with a secondary chain drive would allow CCP to be applied to DOHC engines. Since this wouldJoint Technical Support Document

3-24

cam engines, this requires the addition of a cam ph aser on each bank of the engine. Thus, an in-line 4-cylinder engine has one cam phaser, wh ile SOHC V-engines have two cam phasers. For overhead valve (OHV) engines, which h ave 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, previousl y-received confidential manufacturer data, and the NESCCAF report, NHTSA an d 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 contin ue to find it accurate.

The same cam phaser has been assumed for ICP and C CP applications, thus CCPs cost per cam phaser is identical to ICPs. This res ults in a cost of $45 for in-line SOHC and OHV engines and $90 for SOHC V-engine configura tions for a MY 2012 vehicle (2007$). With time-based learning applied, these c osts 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 overl ap, which can be used as an internal EGR strategy. At low engine loads, DCP creates a r eduction in pumping losses, resulting in improved fuel consumption/reduced CO 2 emissions. Increased internal EGR also results in lower engine-out NO X emissions. The amount by which fuel consumption i s improved and CO 2 emissions are reduced depends on the residual tole rance of the combustion system. Additional improvements are obse rved at idle, where low valve overlap could result in improved combustion stabili ty, potentially reducing idle fuel consumption.

NHTSA MY 2011 CAFE final rule estimated the effecti veness 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 ap plicable 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 engin e without ICP, or $45 relative to an engine with ICP, minus $6 for the removal of the EG R valve, ultimately yielding costs of $84 and $39 respectively for in-line DOHC configura tions (all in 2007$). 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 development s would allow other VVT options to be applied to OH V engines. However, since they would potentially be a dopted on a limited number of OHV engines NHTSA
did not include them in the decision tree.Technologies Considered in the Agencies’ Analysis

3-25 costs of $174 and $84, respectively (all in 2007$). These costs are appropriate for a MY 2012 vehicle. With time-based learning applied, th ese 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 poten tial for further efficiency improvements. By optimizing the valve-lift profile for specific engine operating regions, the pumping losses can be reduced by reducing the a mount 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 mixt ure just prior to compression, delaying the onset of knock-limited combustion processes. V ariable valve lift control can also be used to induce in-cylinder mixture motion, which im proves fuel-air mixing and can result in improved thermodynamic efficiency. Variable val ve lift control can also potentially
reduce overall valvetrain friction. At the same ti me, such systems may also incur increased parasitic losses associated with their ac tuation mechanisms. A number of manufacturers have already implemented VVL into the ir fleets (Toyota, Honda, and BMW), but overall this technology is still availabl e for most of the fleet. There are two major classifications of variable valve lift, descr ibed below:

3.4.2.1.4.1 Discrete Variable Valve Lift (DVVLS, DVVLD, DVVLO) DVVL systems allow the selection between two or thr ee discrete cam profiles by means of a hydraulically-actuated mechanical system . By optimizing the cam profile for specific engine operating regions, the pumping loss es can be reduced by reducing the amount of throttling required to produce the desire d engine power output. This increases the efficiency of the engine. These cam profiles c onsist of a low and a high-lift lobe, and may include an inert or blank lobe to incorporate c ylinder 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-receive d confidential manufacturer data, and the NESCCAF report, all esti mate the effectiveness of DVVL to be between 1 to 4 percent above that realized by VV T systems. NHTSA and EPA believe this estimate continues to be applicable fo r 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, $264, respect ively (all in 2007$). 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 comp liance 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 $259, respectively for a 2016 MY vehicle (2007$).Joint Technical Support Document

3-26 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 uni t. 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 CVV L systems and has sold portinjected “Valvetronic” engines since 2001. CVVL al lows the airflow into the engine to be regulated by means of intake valve opening reduc tion, which improves engine efficiency by reducing pumping losses from throttli ng the intake system further upstream as with a conventionally throttled engine.

Variable valve lift gives a further reduction in p umping 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 opti mized for all engine speeds and loads, and is not limited to a two or three step compromis e. 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 wit h variable valve lift as compared to cam phase control only. Most of the fuel economy e ffectiveness is achieved with variable valve lift on the intake valves only. CVV L is only applicable to double overhead cam (DOHC) engines.

NHTSA’s MY 2011 CAFE final rule estimated the effec tiveness for CVVL at 1.5 to 3.5 percent over an engine with DCP, but also re cognize that it could go up as high as 5 percent above and beyond DCP to account for the i mplementation 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 b y 4 percent (minivans) and up to 6 percent (large cars) over dual cam phasing. Thus, t he effectiveness range for CVVL in this joint TSD ranges from 1.5 to 6 percent dependi ng on the complexity level of the application. However, due to the complexity and co st 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 submis sion 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 $339, respecti vely without markup (all in 2006$) 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 $554 for a V8 engine for a MY 2012 vehic le (2007$). With time-based learning applied, these costs would become $245, $4 49, and $489, respectively for a MY 2016 vehicle (2007$).Technologies Considered in the Agencies’ Analysis

3-27 3.4.2.1.5 Cylinder Deactivation (DEACS, DEACD, DEACO) In conventional spark-ignited engines throttling th e airflow controls engine torque output. At partial loads, efficiency can be improv ed by using cylinder deactivation instead of throttling. Cylinder deactivation (DEAC ) can improve engine efficiency by disabling or deactivating (usually) half of the cyl inders when the load is less than half of the engine’s total torque capability – the valves a re 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 an d heat losses. The active cylinders combust at almost double the load required if all o f 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 se tting 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 change s that enable increasing the amount of time that cylinder deactivation might be suitabl e. Some manufacturers may choose to adopt active engine mounts and/or active noise canc ellations 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-cyl inder engines.

Cylinder deactivation has seen a recent resurgence thanks to better valvetrain designs and engine controls. General Motors and Ch rysler Group have incorporated cylinder deactivation across a substantial portion of their V8-powered lineups. Honda (Odyssey, Pilot) offers V6 models with cylinder dea ctivation.

Effectiveness improvements scale roughly with engin e displacement-to-vehicle weight ratio: the higher displacement-to-weight veh icles, operating at lower relative loads for normal driving, have the potential to operate i n 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 valvet rain configuration and other existing technologies. The OMEGA model, which is based on p ackages, applies 6 percent
reduction in CO 2 emissions in all applications. The CAFE model, du e to its incremental nature, uses a range depending on the engine valvet rain configuration. For example, for DOHC engines which are already equipped with DCP an d DVVLD, there is little benefit that can be achieved from adding cylinder deactivat ion since the pumping work has already been minimized and internal Exhaust Gas Rec irculation (EGR) rates are maximized, so the effectiveness is only up to 0.5 p ercent for DEACD. For SOHC engines which have CCP and DVVLS applied, effective ness ranged from 2.5 to 3 percent for DEACS. For OHV engines, without VVT or VVL tec hnologies, the effectiveness for DEACO ranged from 3.9 to 5.5 percent.Joint Technical Support Document

3-28 NHTSA and EPA considered a range of $170 to $190 (o r $28 to $190 depending on whether an engine already has lost motion device s, 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 prim arily based on the 2008 Martec Report and applied a higher RPE value. In reviewin g these assumptions, NHTSA and
EPA amended the MY 2011 CAFE final rule estimates a nd 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 fac tor) or $135 to $153 without RPE (2006$). NHTSA’s MYs 2011-2015 NPRM showed estimat es of a $170 for a 6-cylinder engine and $190 for an 8-cylinder engine—when adjus ted for 2007 dollars and using the new ICM multipliers— for engines that do not have l ost motion devices. These numbers were within the ranges described by the 2002 NAS an d 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 $169, respecti vely, for a MY 2016 vehicle (2007$).

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 $28 i accounts for the potential additional application of active engine mounts on S OHC and DOHC engines and can only be applied on 50 percent of the vehicles. Fur ther, 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 reduction s 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 desi gns with dual cam phasing (DCP). EPA and NHTSA reviewed the analysis of effe ctiveness for CDOHC in the MY
2011 CAFE final rule and found no information to in dicate that these estimates were
inaccurate. EPA and NHTSA estimate the effectivene ss to be between 1 to 2.5 percent.

As for costs, NHTSA’s MY 2011 CAFE final rule assum ed 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 ($249 non-RPE) for inline 4-cylinde r engine (2007$). In the 2008 Staff Report, EPA did not estimate a cost for this specif ic technology. For purposes of this rulemaking, NHTSA revised the costs only by identif ying 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 V 6 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 v ehicle

h The $28 is an adjustment from the $75 estimate use d in the MY 2011 final rule to account for the new ICM markup factor and the fact that it could only b e applied on up to 50 percent of the vehicles.Technologies Considered in the Agencies’ Analysis

3-29 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 direct ly 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 rai ls to handle the higher fuel pressures and changes to the cylinder head and piston crown d esign. 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 thermod ynamic efficiency without the onset of combustion knock. Recent injector design advanc es, improved electronic engine management systems and the introduction of multiple injection events per cylinder firing cycle promote better mixing of the air and fuel, en hance combustion rates, increase residual exhaust gas tolerance and improve cold sta rt emissions. SGDI engines achieve higher power density and match well with other tech nologies, such as boosting and variable valvetrain designs.

Several manufacturers have recently introduced vehi cles with SGDI engines, including VW/Audi, BMW, Toyota (Lexus IS 350) and G eneral 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 e ngines in their portfolios.

NHTSA’s MY 2011 CAFE final rule estimated the effec tiveness of SGDI to be between 2 and 3 percent. NHTSA and EPA reviewed es timates from the Alliance of Automobile Manufactures, which projects 3 percent g ains in fuel efficiency and a 7 percent improvement in torque. The torque increase provides the opportunity to downsize the engine allowing an increase in efficie ncy of up to a 5.8 percent. NHTSA and EPA also reviewed other published literature, r eporting 3 percent effectiveness for
SGDI. 23 Another source reports a 5 percent improvement on the NEDC drive cycle. 24 Confidential manufacturer data reported an efficien cy effectiveness range of 1 to 2 percent. NHTSA and EPA determined that the range o f 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 reduct ions in fuel consumption and CO 2 emissions compared to engines of similar power outp ut.

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 co ntacts with industry NVH suppliers, and manufacturer press releases, the age ncies believe that the NVH treatments will be limited to the mitigation of fuel system no ise, specifically from the injectors and the fuel lines. In the final rule, the agencies ha ve 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 a nd 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 “binne d” the costs into three distinct bins: downsize, turbocharge, and SGDI. As such, the FEV results shown in Table 3-5 cannotJoint Technical Support Document

3-30

be found in the FEV reports, but instead are detail ed 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 t he FEV results directly the agencies have averaged those results with the NPRM results t o 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 be en 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 ar e based on the I4 to I4 teardown conducted by FEV which was, in fact, used in the NP RM. While the final value of $213 is lower than the NPRM value of $226, the $13 diffe rence 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 es timate SGDI costs at $236 for an inline 4-cylinder and $341 for V6 and $390 for V 8 including the low complexity ICM markup value of 1.11 (2007$ in 2012). With time-ba sed 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 st udies. 26

Table 3-6 Marked up Costs for Stoichiometric GDI (2 007$ 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” functionalit y of DI engines through the implementation of an upgraded starter with bi-direc tional rotation to allow precise crankshaft positioning prior to subsequent fuel inj ection and spark ignition, allowing engine restart. This method of implementing engine stop/start funct ionality allows not only the fuel savings from not idling the engine, b ut also reduces fuel consumption as the engine speeds up to its operational speed. A Direc t Injection (DI) fuel system is required for implementation of this technology.Technologies Considered in the Agencies’ Analysis

3-31 NHTSA reviewed the MY 2011 CAFE final rule assumpti ons 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 tempera tures on the ability to start the engine in the described manner. Although the starter moto r can provide fail-safe starting capability in these temperature limited areas, stra tegies must be developed to manage the transitions. Other risks relate to production read iness.

Additional hardware is required to implement combus tion 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 ef fectiveness over the NEDC drive cycle for combustion restart, 27 and AVL a 4.8 percent effectiveness. 28 However, these reported effectiveness levels could potential ly be reduced significantly on the EPA combined drive cycle, as combustion restart does no t save fuel on the highway drive cycle. Therefore, NHTSA estimates the fuel consump tion 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 individua l pieces was the best available. The total RPE cost (excluding transmission pump) is $14 1 at high volumes, which includes $70 for upgrading the starter, $10 for a battery s ensor 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 technol ogy, results in a compliance cost of $118 for a MY 2012 vehicle and will be reduced in f uture 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 co mbustion chambers. Turbocharging and supercharging (grouped together here as boosting) a re two methods to increase the intake manifold pressure and cylinder charge-air mass abov e naturally aspirated levels. Boosting increases the airflow into the engine, thu s increasing the specific power level, and with it the ability to reduce engine displaceme nt while maintaining performance. This effectively reduces the pumping losses at ligh ter 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 practic e for increasing performance for several decades, turbocharging has considerable pot ential to improve fuel economy and
reduce CO 2 emissions when the engine displacement is also red uced. 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 a chieve similar peak output levels. InJoint Technical Support Document

3-32

the last decade, improvements to turbocharger turbi ne and compressor design have improved their reliability and performance across t he entire engine operating range. New variable geometry turbines and ball-bearing center cartridges allow faster turbocharger spool-up (virtually eliminating the once-common “tu rbo lag”) while maintaining high flow rates for increased boost at high engine speed s. However, even with turbocharger improvements, maximum engine torque at very low eng ine speed conditions, for example launch from standstill, is increased less than at m id and high engine speed conditions. The potential to downsize engines may be less on ve hicles with low displacement to vehicle mass ratios in order to provide adequate ac celeration from standstill, particularly up grades or at high altitudes.
Use of GDI systems with turbocharged engines and ai r-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 re placement 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 r ule estimates that a turbocharged and downsized engine at equivalent per formance 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 es timate 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 reduc ed fuel consumption and CO 2 emissions reductions indicate that the potential fo r reducing CO 2 emissions for turbocharged, downsized GDI engines may be as much as 15 to 30 percent relative to port-fuel-injected engines. 14,15,16,17,18 Confidential manufacturer data suggests an incremental range of fuel consumption and CO 2 emission reduction of 4.8 to 7.5 percent for turbocharging and downsizing. Other publicly-a vailable sources suggest a fuel consumption and CO 2 emission reduction of 8 to 13 percent compared to currentproduction naturally-aspirated engines without fric tion 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 injecti on system; 30 a Renault report suggesting a 11.9 percent NEDC fuel consumption gain for downs izing 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 N EDC gain for downsizing to a turbocharged DI engine, again with wall-guided injection. 32 These reported fuel economy benefits show a wide range de pending on the SGDI technology employed. Public comment was received on this issu e and a summary of those comments with the agencies’ response can be found i n 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 teard own analyses conducted by EPA, FEV and Munro to develop costs for turbocharge d GDI engines. 33 Teardown studies are one of the most effective ways to estim ate technology costs. For the proposal,Technologies Considered in the Agencies’ Analysis

3-33 only the 2.4L I4 DOHC to 1.6L I4 DOHC teardown stud y had been completed in time for inclusion in the NPRM, and we used that study’s res ults as the cost in the 2012 model year. For other turbo-downsize costs, the NPRM pri marily used values developed for the 2008 staff report. Since issuing the NPRM, two mor e teardown studies have been completed and those results are being used in the f inal analysis with some adjustment. The agencies have adjusted the I4 to I4 turbo-downs ize costs slightly to reflect an updated burden rate j employed by FEV and an updated binning approach em ployed by the agencies to distinguish turbo-related costs fro m 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 potenti al 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 dec ided 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 c onfiguration changes that do not involve engine downsizing. Table 3-7 shows the NPR M, FEV and final rule direct manufacturing costs used for the turbo-downsize tec hnologies.

Table 3-7 Direct Manufacturing Costs for Turbochar ging, 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 & a ctual FEV teardown Turbo (V8 to V6) $675 $681 $678 Average of NPRM & a ctual FEV teardown
I4 DOHC to I3
DOHC -$101 -$193 -$147 FEV result calculated using binning of other teardo wn results then averaged with NPRM cost
I4 DOHC to smaller
I4 DOHC -$60 -$85 -$85 Use FEV result directly, no averagin g with NPRM cost

I4 SOHC to smaller
I4 DOHC $65 -$80 -$80 FEV result calculated using binning of other teardo wn 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 te ardown results; NPRM cost used as final
V6 DOHC to I4
DOHC -$428 -$547 -$488 Average of NPRM & actual FEV tear down

V6 SOHC to I4
DOHC -$68 -$382 -$225 FEV result calculated using binning of other teardo wn results then averaged with NPRM cost V6 OHV to I4 $270 n/a $270 Not possible to calculat e using binning of other teardown

j Burden costs include the following fixed and varia ble costs: rented and leased equipment; manufactur ing equipment depreciation; plant office equipment depr eciation; utilities expense; insurance (fire and ge neral); municipal taxes; plant floor space (equipment and p lant offices); maintenance of manufacturing equipme nt - non-labor; maintenance of manufacturing building - general, internal and external, parts, and labor; operating supplies; perishable and supplier-owned t ooling; all other plant wages (excluding direct, in direct 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-downs ize-GDI total cost generated by FEV into separate cost bins for the turbo portion, the downsize porti on, and the GDI portion.Joint Technical Support Document

3-34 DOHC results; NPRM cost used as final
V8 DOHC to V6
DOHC -$203 -$274 -$238 FEV result calculated using binning of other teardo wn results then averaged with NPRM cost
V8 SOHC 3V to V6
DOHC $113 -$155 -$21 Average of NRPM & actual FEV teardo wn

V8 SOHC 2V to V6
DOHC $203 -$84 $60 FEV result calculated using binning of other teardo wn results then averaged with NPRM cost
V8 OHV to V6
DOHC $315 n/a $315 Not possible to calculate using binning of other te ardown results; NPRM cost used as final
V6 SOHC to V6
DOHC $360 $165 $262 FEV result calculated using binning of other teardo wn results then averaged with NPRM cost
V6 OHV to V6
DOHC $473 n/a $473 Not possible to calculate using binning of other te ardown results; NPRM cost used as final
V8 SOHC 3V to V8
DOHC $315 $119 $217 FEV result calculated using binning of other teardo wn results then averaged with NPRM cost
V8 SOHC 2V to V8
DOHC $405 $190 $298 FEV result calculated using binning of other teardo wn results then averaged with NPRM cost
V8 OHV to V8
DOHC $518 n/a $518 Not possible to calculated using binning of other t eardown results; NPRM cost used as final

For the OHV applications, the agencies maintained c onsistency with the EPA 2008 Staff Report and estimated direct manufacturin g 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 r elative to the NPRM. Table 3-8 shows all of these turbo and downsize costs, the ap plicable ICMs, and the final costs for the 2012 MY (2007$). The agencies have applied tim e based learning to all of these
costs. Estimated costs for the 2016 MY can be foun d in section 3.5.2 of this TSD.Technologies Considered in the Agencies’ Analysis3-35Table 3-8 Direct Manufacturing and Marked up Costsfor Turbocharging and Downsizing and Other Camshaft Configuration Changes in 2012 (2007dollars)Direct Manufacturing CostTechnology Incremental toTurbo DownsizeIC Markup Final Cost *Turbo (I4 to I4 only) Base engine $404 n/a 1.11 $448Turbo (V6 to I4) Base engine $427 n/a 1.11 $474Turbo (V8 to V6) Base engine $678 n/a 1.11 $753Downsize to I4 DOHC V6 DOHC n/a -$488 1.11 -$434Downsize to I4 DOHC V6 SOHC n/a -$225 1.11 -$200Downsize to I4 DOHC V6 OHV n/a $270 1.11 $300Downsize to I4 DOHC I4 DOHC (larger) n/a -$85 1.11-$76Downsize to I3 DOHC I4 DOHC n/a -$147 1.11 -$131Downsize to V6 DOHC V8 DOHC n/a -$238 1.11 -$212Downsize to V6 DOHC V8 SOHC 2V n/a $60 1.11 $66Downsize to V6 DOHC V8 SOHC 3V n/a -$21 1.11 -$19

Turbo w/o Downsize Downsize w/o TurboDownsize to V6 DOHC V8 OHV n/a $315 1.11 $350Downsize to I4 DOHC & add turbo V6 DOHC w/o turbo $427 -$488 1.25 $168Downsize to I4 DOHC & add turbo V6 SOHC w/o turbo $427 -$225 1.25 $365Downsize to I4 DOHC & add turbo V6 OHV w/o turbo $427 $270 1.25 $872Downsize to I4 DOHC & add turbo I4 DOHC (larger) w/o turbo $404 -$85 1.25 $441Downsize to I3 DOHC & add turbo I4 DOHC w/o turbo $404 -$147 1.25 $395Downsize to V6 DOHC & add twin turbo V8 DOHC w/o turbo $675 -$238 1.25 $669Downsize to V6 DOHC & add twin turbo V8 SOHC 2V w/oturbo $675 $60 1.25 $923Downsize to V6 DOHC & add twin turbo V8 SOHC 3V w/oturbo $675 -$21 1.25 $832

Turbo with DownsizeDownsize to V6 DOHC & add twin turbo V8 OHV w/o turbo $675 $315 1.25 $1,242Convert to V6 DOHC V6 SOHC n/a $262 1.11 $291Convert to V6 DOHC V6 OHV n/a $473 1.11 $525Convert to V8 DOHC V8 SOHC 2V n/a $298 1.11 $330Convert to V8 DOHC V8 SOHC 3V n/a $217 1.11 $241

Camshaft ChangesConvert to V8 DOHC V8 OHV n/a $518 1.11 $575* 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 absolutevalue of the directmanufacturing cost. The absolute value of the direct manufacturing cost is then subtracted from thatto arrive at the end result. For example, for theV6 DOHC downsized to theI4 DOHC at a direct manufacturing cost of -$488, the compliance cost would be (1.11-1) x│-$488│-│-$488│= -$434.Joint Technical Support Document

3-36

3.4.2.1.10 Cooled Exhaust Gas Recirculation/EGR Boost (EGRB) Cooled exhaust gas recirculation (cooled EGR) or EG R Boost is a combustion concept that involves utilizing EGR as a charge dil utant for controlling combustion temperatures and cooling the EGR prior to its intro duction to the combustion system. Higher exhaust gas residual levels at part load con ditions reduce pumping losses for increased fuel economy. Cooled EGR reduces knock s ensitivity which enables the use of more optimal spark advance or enables compression r atio to be increased for improved thermal efficiency, and increased fuel economy. Cu rrently 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 expe rience. EGR system fouling characteristics could be potentially worse than die sel EGR system fouling, due to the higher HC levels found in gasoline exhaust. Turboc harger compressor contamination may also be an issue for low pressure EGR systems. Additionally, transient controls of boost pressure, EGR rate, cam phasers and intake ch arge temperature to exploit the cooled EGR combustion concept will require developm ent beyond what has already been accomplished by the automotive industry. These are all “implementation readiness” issues that must be resolved prior to putting EGR B oost into high volume production.

NHTSA has concluded that these implementation issue s 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 fu el consumption effectiveness for a downsized DI engine with cooled EGR compared to a naturally aspirated baseline engine 35 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 estimat es the incremental reduction in fuel consumption for EGR Boost to be 4 percent o ver 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 cos t impact of approximately $173 based on confidential individual component cost dat a from 2008 Martec describing EGR cooler costs of $75, EGR valve costs of $20 and ass ociated piping costs of $20, totaling $115 (non-RPE). For purposes of this rulemaking, N HTSA found no information to
indicate that these estimates were inaccurate. To that end, NHTSA applied an indirectTechnologies Considered in the Agencies’ Analysis

3-37 cost multiplier of 1.25, for a medium complexity te chnology, resulting in a compliance cost of $144 for MY 2012 vehicles with time-based l earning applied.

3.4.2.1.11 Diesel Engine Technologies Diesel engines have several characteristics that gi ve them superior fuel efficiency compared to conventional gasoline, spark-ignited en gines. Pumping losses are much lower due to lack of (or greatly reduced) throttlin g. 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 l evels at lower engine speeds than equivalent-displacement naturally-aspirated gasolin e 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 CO 2 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 si gnificant systems-engineering challenge to maintain the fuel consumption advantage of the d iesel engine while meeting U.S. emissions regulations. Fuel consumption can be neg atively impacted by emissions reduction strategies depending on the combination o f strategies employed. Emission compliance strategies for diesel vehicles sold in t he U.S. are expected to include a combination of combustion improvements and aftertre atment. These emission control strategies are being introduced on Tier 2 light-dut y diesel vehicles today

To achieve U.S. Tier 2 emissions limits, roughly 45 to 65 percent more NO
x reduction is required compared to the Euro VI stand ards. Additionally, as discussed below, there may be a fuel consumption penalty asso ciated 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 torq ue to propel the vehicle.

Light-duty diesel emissions control systems capable of meeting Tier 2 Bin 5 emission standards are already in production. Seve ral key advances in diesel technology have made it possible to reduce emissions coming fr om the engine prior to aftertreatment. These technologies include improved fuel systems (h igher injection pressure and multiple-injection capability), advanced controls a nd sensors to optimize combustion and emissions performance, higher EGR levels and EGR co oling to reduce NO
x , and advanced turbocharging systems.

On the aftertreatment side, the traditional 3-way c atalyst 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 (D PF) or catalyzed diesel particulate filter (CDPF), a diesel oxidation catalyst (DOC), and a NO
x reduction strategy to comply with
Tier 2 emissions standards. The most common NO
x reduction strategies include the use
of lean NO
x traps (LNT) or selective catalytic reduction (SCR) , which are outlined
below.

l Burning one gallon of diesel fuel produces about 1 5 percent more carbon dioxide than gasoline due to the higher density and carbon to hydrogen ratio.Joint Technical Support Document

3-38 3.4.2.1.11.1 Diesel Engine with Lean NOx Trap (LNT) Catalyst Aft er-treatment
A lean NO
x trap operates, in principle, by oxidizing NO to NO 2 in the exhaust and storing NO2 on alkali sorbent material. When the control system determines (via
mathematical model or a NO
x sensor) that the trap is saturated with NO
x , it switches the engine into a rich operating mode or may in some ca ses inject fuel directly into the exhaust stream to produce excess hydrocarbons that act as a reducing agent to convert the
stored NO
x to N 2 and water, thereby “regenerating” the LNT and open ing up more
locations for NO
x to be stored. LNTs preferentially store sulfate c ompounds from the fuel, which can reduce catalytic performance. The system must undergo periodic desulfurization by operating at a net-fuel-rich con dition at high temperatures in order to
retain NO
x trapping efficiency.

NHTSA and EPA have concluded that diesel engines on small vehicles would be LNT-based. In the proposed rule, we did not includ e a diesel option for small vehicles because it did not appear to be a cost effective so lution. 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 te chnology.

3.4.2.1.11.2 Diesel Engine with Selective Catalyst Reduction (SC R) After-treatment An SCR aftertreatment system uses a reductant (typi cally, ammonia derived from urea) that is injected into the exhaust stream ahea d of the SCR catalyst. Ammonia
combines with NO
x in the SCR catalyst to form N 2 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 req uires a urea pump and injector to inject urea into the exhaust stream). While a rich engine-operating mode is not required
for NO
x reduction, the urea is typically injected at a rat e of approximately 3 percent of the fuel consumed. Manufacturers designing SCR sys tems 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 rec ently introduced two 2009 model year vehicles R320 and GL320, both of which are cer tified to Tier 2, Bin 5 emission standards. Based on public announcements from seve ral 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 com parable 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 f or Subcompact, Compact, and Midsize Passenger Car, Performance Subcompact Car a nd 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 C ompact, 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 andTechnologies Considered in the Agencies’ Analysis

3-39 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 comm ent data submitted in response to NHTSA’s past rulemaking for diesel engi nes showed a fuel consumption reduction in the range of 16.7 to 26.7 percent or a 15 to 23 percent CO 2 emission reduction over a baseline gasoline engine. NHTSA’s MY 2011 CAFE final rule, which was supported by confidential manufacturer data, es timated the fuel consumption reduction of a SCR-based diesel engine to be betwee n 19 to 25 percent over a baseline gasoline engine. NHTSA and EPA have revisited thes e values and found them to be reasonable. EPA estimates a 15 to 26 percent CO 2 reduction for light-duty diesels equipped with SCR.

Diesel engines are more costly than port-injected s park-ignition gasoline engines. These higher costs result from:

• Fuel systems (higher pressures and more responsive injectors);

• Controls and sensors to optimize combustion and emi ssions 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 costl y for diesels;

Due to a significant decrease in platinum group met al prices since NHTSA’s MY 2011 CAFE final rule analysis, the agencies chose t o re-analyze diesel costs. In EPA’s 2008 Staff Report, costs were considered for two ty pes of diesel systems: one using a
lean-NO X trap (LNT) along with a diesel particulate filter (DPF); and one using a selective catalytic reduction (SCR) system along wi th 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 t o $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 usi ng a SCR plus DPF system.
NHTSA noted that estimates in the MY 2011 CAFE fina l rule were higher than those shown in this rule due largely to the spike in plat inum group metal prices that had occurred in the months just prior to issuing the 20 11 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 estimate d there did not take into account theJoint Technical Support Document

3-40

downsizing that would occur when moving from a gaso line 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 doll ars rather than 2006 dollars in which the report was meant to be stated. The agencies de cided that an update to the enginerelated costs would provide a much better cost esti mate for converting to diesel. This was done by starting with the source for engine cos ts in the 2008 staff report which was an October 2005 EPA Interim Report 37 which, in turn, sourced estimates from a 2003 study done by FEV for EPA contained within a 2004 E PA 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 injectio n 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, Octob er 2005

Building on the direct manufacturing costs shown in Table 3-9, the agencies used appropriate scaling to estimate the costs for repla cing 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 f rom 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 dies el; and a large truck converted from a 5.6L V8 gasoline to a 4L V6 diesel. The results fo r the five base gasoline to diesel
conversions are shown in Table 3-10. Values from T able 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 conserv atively considers the costs shown in Table 3-10 as being applicable to the 2012 model ye ar.

Table 3-10 Diesel Engine Direct Manufacturing Scale d-Costs in 2012, Incremental to Baseline Gasoline Engine (2007 dollars)Technologies Considered in the Agencies’ Analysis

3-41 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 $2 9 13 Smaller radiator -$7 -$15 -$10 -$10 -$15 14 Engine downsize credit $0 -$185 -$390 -$390 -$185 1 5 Total $813 $1,085 $580 $580 $1,156
* Note: Oxygen sensor removals are included in aft ertreatment costs.

The costs shown in Table 3-10 were scaled in the fo llowing ways:

1. Large car and large truck calculated as 75% the cos t of Table 3-9’s large SUV and 25% of midsize car; medium/large MPV and small truc k 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 esti mated, in 2007 dollars as follows: large car and large truck were calculated using inc remental 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 increment al 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 applyi ng the ratio of diesel engine sizes (2.0/2.8).Joint Technical Support Document

3-42

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 equa l 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 200 7 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 calc ulated 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 f actor of 1.15.

5. Medium/large MPV and small truck calculated as equa l to Table 3-9’s midsize car; Large car and large truck calculated as equal to Ta ble 3-9’s large SUV; small car calculated using Table 3-10’s Med/large MPV value o f -$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 equa l to Table 3-9’s large SUV; Large car and large truck calculated as equal to Ta ble 3-9’s midsize car; small car calculated as equal to medium/large MPV. Values co nverted to 2007 dollars using GDP factor of 1.15.

7. Medium/large MPV and small truck calculated as equa l 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 equa l 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 equa l 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 mediu m/large MPV. Values converted to 2007 dollars using GDP factor of 1.15.

10. Oxygen sensor costs are included in the aftertreatm ent costs discussed below.

11. Medium/large MPV and small truck calculated as equa l 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 usi ng GDP factor of 1.15.Technologies Considered in the Agencies’ Analysis

3-43 13. Medium/large MPV and small truck calculated as equa l 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 calc ulated 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 turbochargin g/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 o f each cylinder at $51, each valve at $10, and each cam at $103. m Therefore, the large car and large truck, which e ach lose two cylinders (-$102), eight valves (-$82) and no c ams realize a $185 savings. The medium/large MPV and small truck would each lose tw o 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 ru le and EPA’s recent locomotive
and marine rule. 39 For platinum group metal (PGM) costs, monthly ave rage 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 ounc e for rhodium. Aftertreatment devices were sized according to the diesel engine d isplacement 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 af tertreatment devices, including a urea dosing unit, urea tank and necessary brackets and h eaters, 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 learnin g 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 analys is and, because it has no meaningful impact on the ana lysis (1-2% of the estimated diesel costs), continu es to be true in the final analysis.

n These are the PGM prices used in the NPRM. As prec ious 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 m etals such as gold and PGMs, PGM prices have increased considerably since the NPRM. Were the Ja nuary 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 co st effective options for improving fuel economy or red ucing GHGs. Since diesels comprise such a small percentage of each agency’s analysis from a technol ogy penetration standpoint, even using the lower Ma rch 2009 PGM prices, the choice of what PGM prices to u se is of little consequence to the analytical resul ts for purposes of this rulemaking.Joint Technical Support Document

3-44 Table 3-11 Diesel Aftertreatment Direct Manufacturi ng 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 fr om 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 c ompliance costs are as shown in
Table 3-13.

Table 3-13 Compliance Costs to Convert from a Gasol ine 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 y ears 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-cylinde r from an I4 gasoline engine wereTechnologies Considered in the Agencies’ Analysis

3-45 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 eng ine. 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, an d 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 t echnology 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 technologi es considered for this rulemaking.

3.4.2.2.1 Improved Automatic Transmission Control (IATC) (Agg ressive Shift Logic

and Early Torque Converter Lockup) Calibrating the transmission shift schedule to upsh ift earlier and quicker, and to lock-up or partially lock-up the torque converter u nder a broader range of operating conditions can reduce fuel consumption and CO 2 emissions. However, this operation can result in a perceptible degradation in noise, vibra tion, and harshness (NVH). The degree to which NVH can be degraded before it becomes noti ceable to the driver is strongly influenced by characteristics of the vehicle, and a lthough 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 L ogic 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 contr oller manages the operation of the transmission by scheduling the upshift or downs hift, and locking or allowing the torque converter to slip based on a preprogrammed s hift 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 throttl e position, and other parameters such as temperature. Aggressive shift logic (ASL) can b e employed in such a way as to maximize fuel efficiency by modifying the shift sch edule to upshift earlier and inhibit downshifts under some conditions, which reduces eng ine pumping losses and engine friction. The application of this technology does require a manufacturer to confirm that drivability, durability, and NVH are not significan tly degraded.

The agencies have estimated the effectiveness of ag gressive shift logic at 1 to 2 percent, as explained below. The agencies have est imated the cost at $32 for a MY 2012Joint Technical Support Document

3-46

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 betw een the engine and
transmission in vehicles with automatic transmissio ns and continuously-variable transmissions (CVT). This fluid coupling allows fo r slip so the engine can run while the vehicle is idling in gear (as at a stop light), pro vides for smoothness of the powertrain, and also provides for torque multiplication during acceleration, and especially launch. During light acceleration and cruising, the inheren t slip in a torque converter causes increased fuel consumption, so modern automatic tra nsmissions utilize a clutch in the torque converter to lock it and prevent this slippa ge. 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 a nd vibration are not excessive. p If the torque converter cannot be fully locked up for maxi mum 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 l oading and the slipping conditions during partial lock-up. As with aggressive shift l ogic, confirmation of acceptable drivability, performance, durability and NVH charac teristics is required to successfully implement this technology.

Regarding the effectiveness of Improved Automatic T ransmission 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 CO 2 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 $25 fo r a MY 2016 vehicle (2007$). 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-s peed transmission with 6-, 7-, or 8-speed automatic transmissions. Additional rat ios 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 ca libration software are considered as part of this technology, very aggressive early torque converter lock up may require an adjustment to damper stiffne ss and hysteresis inside the torque converter.Technologies Considered in the Agencies’ Analysis

3-47 returns as the number of speeds increases. As addi tional planetary gear sets are added (which may be necessary in some cases to achieve th e higher number of ratios), additional weight and friction are introduced. Als o, 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 manu facturers are replacing 4- and 5-speed automatics with 6-speed automatics, and 7- and 8-sp eed 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, confid ential manufacturer data projected that 6-speed transmissions could incremen tally reduce fuel consumption by 0 to 5 percent from a baseline 4-speed automatic transmi ssion, while an 8-speed transmission could incrementally reduce fuel consumption by up t o 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 transmissio ns. 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-spe ed 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 vehi cle classes. From a baseline 4 or 5 speed transmission without IATC, the incremental fuel con sumption benefit would be approximately 3 to 6 percent, which is consistent w ith the EPA Staff Report estimate.

NHTSA and EPA reviewed these effectiveness estimate s and concluded that they
remain accurate. While the CAFE model follows the incremental approach discussed above, the GHG model estimates the packaged effecti veness of 4.5 to 6.5 percent.

In the NPRM, NHTSA and EPA reviewed the cost estima tes 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 ge ar set) and concluded that some but not all 6-speed automatic transmissions would be eq uipped with Lepelletier gear set. As such, the estimates were revised to establish the c ost for the 6 speed transmission to be equally divided between application using Lepelleti er, and application of a standard planetary gear set 6-speed automatic transmission a s 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 adju stment or $425 without RPE adjustment for converting a 4-speed to an 8-speed t ransmission. As a result, the final incremental cost estimate in the NPRM was $170, ind ependent of vehicle type and size and including a low complexity 1.11 ICM (2007$). I n the CAFE model, due to the structure of the vehicle classes used, an additiona l $102 (2007$) was included to account for performance vehicle classes and for medium and large trucks. This is because for performance classes, additional gear ratios, such a s 7 and 8 speed transmissions may be utilized, and for medium and large trucks heavier d uty transmissions are required. These estimates represented MY 2012 vehicle costsJoint Technical Support Document

3-48 For the final analysis, as noted earlier, the agenc ies have considered the teardown work done by FEV. In its teardown work, FEV determ ined the 6 speed automatic transmission to be $106 less costly than a 5 speed automatic transmission (direct manufacturing cost without markups). This is count erintuitive 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 Lepelle tier-like gear set and therefore does not require a 1-way clutch that is essential to oth er designs. The cost analysis performed in this rule requires that the cost difference betw een both a 4 speed and 5 speed auto relative to 6 speed auto be established because the re are a substantial number of both 4 speed and 5 speed automatic transmissions used in t he baseline fleet. This was done using the NPRM analysis for a 4 speed to 5 speed an d adding the FEV results for the transition from a 5 speed to a 6 speed. Our analys is resulted in a cost for a 5 speed auto transmission relative to a 4 speed auto of $91 dire ct manufacturing cost (2007$ 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 valu e of -$106). In the 2011 FRM, NHTSA estimated the cost of a 6 speed auto transmis sion, without a Lepelletier gear set, relative to a 4 speed auto transmission at $215 (20 07$). Therefore, we have used the $215 value (4 speed to 6 speed) from NHTSA’s 2011 F RM 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 transm ission. 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 analysi s. This $9 can then be added to the $91 to get a cost of $101 for a 6 speed auto transmissi on relative to a 4 speed auto
transmission. Table 3-14 shows the direct manufactu ring 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 condu ct a tear down of the technology; refer to text for more detail on the co mments.Technologies Considered in the Agencies’ Analysis

3-49 3.4.2.2.5 Dual Clutch Transmissions / Automated Manual Transm issions (DCTAM) An Automated Manual Transmission (AMT) is mechanica lly 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 issu es with single-clutch designs, DCTs will likely be far more common in the U.S. and are the b asis of the estimates that follow. A DCT uses separate clutches (and separate gear shaft s) 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 t hree engaged and is transmitting power. The shaft with gears two, four, and six is idle, bu t has gear four engaged. When a shift is required, the controller disengages the odd-gear cl utch 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 trans mission 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 transfe rred 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 typ es 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 ef ficient than the dry clutch systems due to the losses associated with hydraulic pumping. A dditionally, wet AMTs have a higher cost due to the additional hydraulic hardware requi red.

Overall, DCTs likely offer the greatest potential f or effectiveness improvements among the various transmission options presented in this report because they offer the inherently lower losses of a manual transmission wi th the efficiency and shift quality advantages of electronic controls. The lower losse s stem from the elimination of the conventional lock-up torque converter, and a greatl y reduced need for high pressure hydraulic circuits to hold clutches or bands to mai ntain gear ratios (in automatic transmissions) or hold pulleys in position to maint ain gear ratio (in Continuously Variable Transmissions). However, the lack of a to rque converter will affect how the vehicle launches from rest, so a DCT will most like ly 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 s mallest of vehicle subclasses, Subcompact and Compact cars and small LT. This res ults in an incremental effectiveness estimate of 2.7 to 4.1 percent over a 6-speed autom atic transmission with IATC. For Subcompact and Compact Cars and small LT, which wer e assumed to use a dry clutch DCT, NHTSA estimated an 8 to 13 percent fuel consum ption improvement over a baseline 4/5-speed automatic transmission, which eq uates to a 5.5 to 7.5 percent incremental improvement over the 6-speed transmissi on. This is consistent with the EPA Staff Report, which estimated that these transmissi ons offer an effectiveness of 9.5 toJoint Technical Support Document

3-50

14.5 percent over a 4-speed automatic transmission. EPA and NHTSA therefore conclude that 8 to 13 percent effectiveness is appr opriate for this rule.
In the NPRM analysis, the agencies estimated costs for 6 speed dual-clutch transmissions relative to 6 speed auto transmission s. 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 au to transmission. This cost savings is not unexpected when one considers that the DCT is l ess complex than an auto transmission. However, such a cost savings presume s that capacity exists to produce dual-clutch transmissions at volumes of 450,000 uni ts, 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 $11 relative to a 6 speed auto transmission (2 007$ in 2012). To generate a dry clutch DCT cost for the final analysis, we have app lied the same cost difference from the NPRM for wet clutch versus dry clutch. In other wo rds, the NPRM analysis had a cost difference of $67 which was then subtracted from th e 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 manuf acturing 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-Clut ch Transmissions (2007$ in 2012)

Technology NPRM FEV tear down Final Rule Comments

6s wet DCT relative to 6s auto $126 -$147 -$11 [$12 6+(-$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 d own of the technology; refer to text for more detai l on
the comments.

3.4.2.2.6 Continuously Variable Transmission (CVT) A Continuously Variable Transmission (CVT) is uniqu e in that it does not use gears to provide ratios for operation. Instead, th e most common CVT design uses two Vshaped pulleys connected by a metal belt. Each pul ley 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 wh ich 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, s ince there are no fixed ratios. However, CVTs are limited by engine power and canno t 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.Technologies Considered in the Agencies’ Analysis

3-51 While CVTs by definition are fully continuous, some automakers choose to emulate conventional stepped automatic operation because so me drivers are not used to the sensation of the engine speed operating independent ly of vehicle speed.

Considering the confidential data together with ind ependent review, NHTSA has estimated the fuel consumption effectiveness for CV Ts 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 i n 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 manufacture d with unibody construction methods. CVTs are an established and readily avail able technology, so time based learning is applied.

3.4.2.2.7 6-Speed Manual Transmissions (6MAN) Manual transmissions are entirely dependent upon dr iver 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 la youts, because it has the lowest internal gear losses, with a minimal hydraulic system, and t he driver provides the energy to actuate the clutch. From a systems viewpoint, howe ver, vehicles with manual transmissions have the drawback that the driver may not always select the optimum gear ratio for fuel economy. Nonetheless, increasing th e number of available ratios in a manual transmission can improve fuel economy by all owing the driver to select a ratio that optimizes engine operation more often. Typica lly, 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 torq ue required of the engine towards the optimum level. However, if the gear ratio step s are not properly designed, this may require the driver to change gears more often in ci ty driving, resulting in customer dissatisfaction. Additionally, if gear ratios are selected to achieve improved launch performance instead of to improve fuel economy, the n no fuel saving effectiveness is
realized.

NHTSA’s MY 2011 CAFE final rule estimated an effect iveness increase of 0.5 percent for replacing a 5-speed manual with a 6-spe ed 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 perc ent reduction in CO 2 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 complexi ty markup of 1.11 which resulted in a incremental compliance cost of $250 a s compared to $338 for MY 2012, with lower costs in later MYs due to the applicatio n 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 toJoint Technical Support Document

3-52

2007$ and used a 1.11 ICM to get costs of $89 for a MY 2012 vehicle and $69 for a MY 2016 vehicle, both relative to a 5-speed manual tra nsmission (2007$).

3.4.2.3 Hybrid and Electrification/Accessory Technologies

A Hybrid is a vehicle that combines two or more sou rces of propulsion energy, where one uses a consumable fuel (like gasoline), a nd one is rechargeable (during operation, or by another energy source). Hybrid te chnology is established in the U.S. market and more manufacturers are adding hybrid mod els to their lineups. Hybrids reduce fuel consumption through three major mechani sms:

• The internal combustion engine can be optimized (th rough downsizing, modifying the operating cycle, or other control tec hniques) to operate at or near its most efficient point more of the time. Po wer loss from engine downsizing can be mitigated by employing power assi st from the secondary

power source.

• Some of the energy normally lost as heat while brak ing can be captured and stored in the energy storage system for later use.

• The engine is turned off when it is not needed, suc h as when the vehicle is coasting or when stopped.

Hybrid vehicles utilize some combination of the thr ee above mechanisms to reduce fuel consumption and CO 2 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 CO 2 reduction depends on the utilization of the above mechanisms and how aggressively they are pursued. One area wh ere this variation is particularly prevalent is in the choice of engine size and its e ffect on balancing fuel economy and performance. Some manufacturers choose not to down size the engine when applying hybrid technologies. In these cases, performance i s vastly improved, while fuel efficiency improves significantly less than if the engine was downsized to maintain the same performance as the conventional version. Whil e this approach has been used in cars such as the Honda Accord Hybrid (now discontin ued), 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 dow nsized, 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 i s currently a heavily-marketed truck attribute, manufacturers are hesitant to offer a tr uck with downsized engine which can lead to a significantly diminished towing performan ce with a low battery, and therefore engines are traditionally not downsized for these v ehicles.

Although hybrid vehicles using other energy storage concepts (flywheel, hydraulic) have been developed, the systems current ly in production in the U.S. for passenger cars and light trucks use battery storage and electric drive systems. HybridTechnologies Considered in the Agencies’ Analysis

3-53 electric vehicles (HEV) are part of a continuum of vehicles using systems with differing levels of electric drive and electric energy storag e. This range of vehicles includes relatively basic engine start/stop systems, HEV sys tems 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 stora ge.

Different HEV, PHEV and EV concepts utilize these m echanisms differently, so they are treated separately for the purposes of thi s analysis. Below is a discussion of battery energy storage and the major hybrid concept s 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-limit ing sub-component of the hybrid system. Currently, there are many battery chemistr ies being developed and refined for hybrid applications that are expected to enhance th e 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 cons iderably between MHEV, HEV, PHEV and EV applications.

MHEV batteries : Due to their lower voltage (12-42 VDC) and reduc ed power and energy requirements, MHEV systems will likely c ontinue to use lead-acid batteries. MHEV battery designs differ from those of current s tarved-electrolyte (typical maintenance free batteries) or flooded-electrolyte (the older style lead-acid batteries requiring water “top-off”) batteries used for start ing, lighting and ignition (SLI) in automotive applications. Standard SLI batteries ar e primarily designed to provide highcurrent for engine start-up and then recharge immed iately 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 batteri es

• Absorptive glass matt, valve-regulated lead-acid (A GM/VRLA) batteries –

or –

• Asymmetric lead-acid battery/capacitor hybrids ( e.g. , flooded ultrabatteries)

MHEV systems using electrolytic double-layer capaci tors are also under development.

HEV batteries : HEV applications operate in a narrow, short-cycl ing, chargesustaining state of charge (SOC). Energy capacity in HEV applications is somewhat limited by the ability of the battery and power ele ctronics 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 e nergy density, with thinner cathode and anode layers and more numerous current collecto rs and separators (Figure 3-1).Joint Technical Support Document

3-54 EV batteries : EV batteries tend to be optimized for high energ y density and are considerably larger than HEV batteries. PHEV batte ry designs are intermediate between power-optimized HEV and energy-optimized EV battery cell designs.

PHEV batteries : PHEV batteries must provide both charge depletin g operation similar to an EV and charge sustaining operation si milar to an HEV. Unlike HEV applications, charge-sustaining operation with PHEV s 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 perc ent. 43 An alternative approach for PHEV applications that has potential to allow exten sion of charge depletion to a lower battery SOC is using energy-optimized lithium-ion b atteries for charge depleting operation in combination with the use of supercapac itors for charge sustaining operation. 44

Figure 3-1 : Schematic representation of power and energy opti mized

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 N issan, integrated motor assist hybrid vehicles from Honda and the GM 2-mode hybrid vehicles currently use nickelmetal hydride (NiMH) batteries. Lithium-ion (Li-io n) batteries offer the potential to approximately double both the energy and power dens ity relative to current NiMH batteries, enabling much more electrical-energy-int ensive automotive applications such as PHEVs and EVs. Li-ion batteries for high-volume automotive applications differ substantially from those used in consumer electroni cs applications with respect to cathode chemistry, construction and cell size. Li -ion battery designs currently underTechnologies Considered in the Agencies’ Analysis

3-55 development by CPI (LG-Chem) for the GM Volt PHEV a nd by AESC, GS-Yuasa and A123 Systems (respectively) for the upcoming Nissan , Mitsubishi and Chrysler EVs use large-format, layered-prismatic cells assembled int o battery modules. The modules are then combined into battery packs.

Cathodes for large-format, automotive Li-ion batter ies are becoming increasingly focused on two chemistries – LiMn2O4-spinel (CPI, G S-Yuasa, AESC) and LiFePO4 (A123 Systems).

In addition to the purely hybrid technologies, whic h decrease the proportion of propulsion energy coming from the fuel by increasin g 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) whic h also
reduce CO 2 emissions and fuel consumption. These steps, toge ther with the hybrid technologies, are collectively referred to as “vehi cle 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 efficienc y alternator (HVIA) technology, which was used in the MY 2011 CAFE final rule, is n o longer represented as a separate technology and has instead been incorporated into t his new mild hybrid technology, as
discussed further below.

3.4.2.3.2 Hybrid System Sizing and Cost Estimating Methodolog y
NHTSA and EPA reviewed estimates of cost and effect iveness for hybrid and related electrical technologies and adjusted them a s 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 componen t 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 a pplied to each vehicle class/subclass by hybrid system type; and (4) vehicle characterist ics for the subclasses which are used as the basis for the scaling factors. During developm ent of the methodology, NHTSA and Ricardo made several assumptions:

1) Hybrid controls hardware varies with the level of f unctionality offered by the hybrid technology. Assumed hybrid controls complexi ty 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 rat ios were estimates based on the directional need for increased functionality as system complexity
increases. 2) Li-ion batteries for hybrid electric vehicles are c urrently entering production, including a 2010 MY Mercedes and Hyundai. One esti mate from Anderman indicates that Li-ion market penetration will achie ve 35 percent by 2015. 45Joint Technical Support Document

3-56 However, as was discussed above, significant develo pment 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 conside rations. 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 co nsumes an average of 300 W-hr per mile; the battery pack can be discharged d own to 30 percent depth of discharge; q and the capacity of a new battery pack is 20 perce nt 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 fo r increased vehicle
electrification in the time frame considered. 5) High voltage wiring scaled with hybrid vehicle func tionality and could be represented as a fraction of strong hybrid wiring. These ratios were estimates
based on the directional need for increased functio nality as system complexity
increases. 6) All hybrid systems included a supplemental heater t o provide vehicle heating when the engine is stopped; however, in this analys is, it is assumed that only half of the vehicles will adapt this technology, as discussed further below. Only the strong hybrids included electric air condi tioning to enable engine stop/start when vehicle air conditioning was reques ted 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 th e 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 m echanical 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 a ppropriate. The plug-in hybrid assumed a highly simplified transmission for electr ic motor drive, thus 25 percent of the base vehicle transmission cost was applied (resulti ng in a $638 credit).

The NHTSA MY 2011 CAFE final rule discusses in deta il how the hybrid cost estimating methodology uses the information provide d 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 betwe en 30 and 35% DOD.Technologies Considered in the Agencies’ Analysis

3-57 final rule. 46 As in that analysis, it is important to understan d that the CISG technology replaces existing mild hybrid systems. r

NHTSA and EPA in reviewing the above made the follo wing 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 accesso ry drive (FEAD), because hybridization often involves revision to the FEAD d esign such that certain devices (belts, pulleys, idlers, etc.) as well as other engine comp onents (alternator, A/C compressor, and starter) may no longer be needed and can thus be el iminated, 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 associa ted with items that changed or are no longer required as a result of these technology app lications.

Table 3-16

. Component Sizes by Hybrid Type for a Midsize Car

Component 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% 5 0% 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 heating 2 50% 50% 50% 50% 50% Mechanical Transmission (relative to baseline vehic le) 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

2 - Implemented through a reduction in compo nent cost (50%) 1 - Assumes the use of either NiMH or Li-Ion , and not both. Hybrid Type

Second, the costs estimates of the key components w ere revised. The MY 2011 CAFE final rule was developed at a time when econom ic conditions were significantly different than those that currently exist, a time w hen 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 estimat es provided by confidential sources to either EPA or NHTSA also played a part in the revis ions. 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 stro ng hybrid technologies) replaces the

r For the incremental CAFE model, before CISG is app lied, the costs for MHEV and BISG are subtracted if they were previously applied.Joint Technical Support Document

3-58

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 d ownward from $10 to $7, the controls cost was revised upward from $100 to $115, the DC/D C 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 mat ch 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 productio n in model year 2010. Battery technology is changing rapidly in the marketplace t oday, 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 product ion. 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 le arning 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 re main 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 dem and 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 ass ociated with the FEAD credit discussed above. This cost was not previously repr esented in the hybrid cost model. As shown in Table 3-17 below, a $100 credit is shown w hich offsets directly the costs of the other components specified. This is the best approx imation 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 estimat es assume fully learned, high-volume (greater than 1.2 million units per annum) producti on, and the costs shown are direct manufacturing costs that are not RPE or ICM adjuste d. 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.Technologies Considered in the Agencies’ Analysis

3-59 Table 3-17. Component Cost Basis at High Volumes a nd Data Sources stated in 2015 (2007 dollars)

COMPONENT COST BASIS DATA SOURCE Primary Motor ($/kW) $15 Secondary Motor ($/kW) $15 Martec 2008

Primary Inverter ($/kW) $7 Secondary Inverter ($/kW) $7 Controls $115 Confidential Business Information

NiMH Battery Pack ($/kW-hr.) $320 2011 CAFE FRM (with revision) Li-ion Battery Pack ($/kW-hr.) $320 Deutsche Bank 2 008 DC/DC Converter (Size: 2kW) $88 Confidential Busine ss Information High Voltage Wiring $200 Supplemental Heating $42 Martec 2008

Mechanical Transmission $850 Martec 2008 (to 4-spd auto) Electric Air Conditioning $450 Blended Brakes $310 Charger $100 Confidential Business Information

Automatic Transmission Pump $75 Martec 2008 FEAD Credit $(100) Confidential Business Informatio n

Third, NHTSA and EPA also revised component size/sc aling assumptions for some vehicles ( i.e. , large trucks). NHTSA and EPA recognized that som e manufacturers may choose not to use supplemental cabin heating, o pting instead to continue engine operation in the event heat demand occurs; therefor e supplemental heating is specified for only half of the vehicles. Table 3-17 above indica tes the 50 percent application rate implemented in the hybrid cost estimating methodolo gy 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 r epresented a 250 amp current capability. In retrospect this is a high specifica tion 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 c apacity.

The scaling factor used for the primary and seconda ry motors and invertors on the large truck and SUV vehicles was revised. As in th e 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 facto rs 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 futureJoint Technical Support Document

3-60

analysis, the agencies are considering whether it m ay be more accurate to use one set of scaling for passenger cars and another different se t for light trucks.

Another revision involves the addition of a stand-a lone 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 incre ase (lead acid batteries) and efficiency improvements to the alternator, the syst em would then approximate the higher voltage Start-Stop/BISG applied by EPA. Based on c onfidential sources, the estimates provided were first converted to 2007 dollars and t hen reverse-learned through two cycles, since volume learning is applicable, to arr ive at a non-RPE/ICM incremental compliance cost of $229. This cost is applicable t o all classes that use higher voltage Start-Stop/BISG, and is not scaled by any vehicle a ttribute.

Component scaling factors for each type of hybrid s ystem are as shown in Table
3-18 below.

Table 3-18. Component Scaling Factors applied to V ehicle Class for each Hybrid System

Component MHEV CISG PSHEV 2MHEV PHEV

Primary Motor Engine displacement Curb

weight Engine power

Secondary Motor na na Curb weight 2

Primary Inverter Secondary Inverter na na
Controls NiMH Battery Pack na na Li-Ion Battery Pack na Curb weight

DC/DC Converter High Voltage Wiring na Supplemental heating

Mechanical Transmission
Electric AC na na

Blended Brakes na

Charger na na na na Same for all
vehicle classes

(1) For all vehicle classes except for performance cla sses which use Engine Torque

(2) Curb weight used as surrogate for vehicle road loa d

(3) Curb weight used as surrogate for vehicle electric al load Same for all vehicle classes Hybrid Type

Engine displacement Curb weight 1

Curb weight 3 Vehicle footprint

Same for all vehicle classes Vehicle footprint

Vehicle footprint Primary motor power Secondary motor power Complexity

Curb weightTechnologies Considered in the Agencies’ Analysis

3-61 Regarding the market data file from the MY 2011 CAF E final rule, NHTSA and EPA did not make any revisions to the average vehic le 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 vehic le specifications solely for the purpose of scaling hybrid vehicle attributes in ord er to remain consistent with the NHTSA methodology.

Table 3-19

. Key Vehicle Characteristics For Each Vehicle Subcla ss 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 CO 2 emissions and fuel consumption over hydraulic power steering beca use of reduced overall accessory loads. This eliminates the parasitic losses associ ated 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 vehi cles 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 rep ort, and confidential manufacturerJoint Technical Support Document

3-62

data. NHTSA and EPA reviewed these effectiveness e stimates and found them to be accurate, thus they have been retained for this fin al rule.

Regarding costs, in the MY 2011 CAFE final rule, NH TSA 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 specifi cation 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. According ly, higher costs were estimated for systems with higher capability. After accounting f or the differences in system capability and applying the ICM markup of low complexity techn ology 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 deem ed applicable.

3.4.2.3.4 Improved Accessories (IACC) The accessories on an engine, including the alterna tor, coolant and oil pumps are traditionally mechanically-driven. A reduction in CO 2 emissions and fuel consumption can be realized by driving them electrically, and o nly when needed (“on-demand”).

Electric water pumps and electric fans can provide better control of engine cooling. For example, coolant flow from an electri c 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 w arm-up fuel enrichment, and reduce parasitic losses.

Indirect benefit may be obtained by reducing the fl ow from the water pump electrically during the engine warm-up period, allo wing the engine to heat more rapidly and thereby reducing the fuel enrichment needed dur ing cold starting of the engine. Further benefit may be obtained when electrificatio n is combined with an improved, higher efficiency engine alternator. Intelligent c ooling 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 hav e 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 insignifi cant effect on efficiency. Therefore, the agencies decided to not include electric oil pu mp technology for this rulemaking.

NHTSA and EPA jointly reviewed the estimates of 1 t o 2 percent effectiveness
estimates used in the MY 2011 CAFE final rule and f ound 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.Technologies Considered in the Agencies’ Analysis

3-63 accessory systems are in production currently and t hus 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 V olt electrical system is estimated to cost $97 for a MY 2012 vehicle and $86 for a MY 201 6 vehicle (again with time-based learning; both in 2007$).

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 e xisting powertrains and platforms can make them attractive for some applications. The conventional belt-driven alternator is replaced wit h a belt-driven, enhanced power starter-alternator and a redesigned front-end acces sory drive system. A conventional 12V gear-reduction starter is retained to ensure re liable cold-weather starting. Also, during idle-stop, some functions such as power stee ring and automatic transmission hydraulic pressure are lost, so electric power stee ring 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. Th e effectiveness estimates for this technology ranged from 2.0 to 4.0 percent, dependin g on whether the vehicle is equipped with a 4-, 6- or 8-cylinder engine, with the 4-cyli nder engine having the lowest range and the 8-cylinder having the highest. The estimates r eflect 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 assu med in the MY 2011 CAFE final rule were applied ( i.e. , use of a 3 kW motor and a DC/DC converter) and th e hybrid technology cost method produced costs like those in MY 2011 final rule; however, the use of new ICM markups resulted in costs ranging fr om $288 for Subcompact subclass to $410 for the Large Performance subclass. This tech nology is not applied to the Large Truck and SUV subclass due to the heavy duty utilit y 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, 2 003]Joint Technical Support Document

3-64

3.4.2.3.6 Higher Voltage Stop-Start/Belt Integrated Starter G enerator (BISG) Higher Voltage Stop-Start and Belt Mounted Integrat ed Starter Generator (BISG) systems are similar to a micro-hybrid system , offering idle-stop functionality, except that they utilize larger electric machine an d 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 l arger electric machine and battery also enables a limited degree of power assist, whic h 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 conventi onal alternator with a belt-driven starter/alternator and may add electric power steer ing and an auxiliary automatic transmission pump. The limited electrical requirem ents of these systems allow the use of lead-acid batteries or supercapacitors for energ y storage.

The effectiveness estimates used for these technolo gies range from 3.0 to 7.5 percent depending on vehicle subclass. The CAFE mo del, which applies this effectiveness incrementally to the prior 12 Volt te chnology, 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 c ost 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 c apacity, 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.

Engine Motor/ Generator

Trans.

Controller Inverter

Final Drive Wheel

+

- BatteryTechnologies Considered in the Agencies’ Analysis

3-65 3.4.2.3.7 Integrated Motor Assist (IMA)/Crank Integrated Star ter Generator (CISG) IMA is a system developed and marketed by Honda 48 and is similar to CISG. They both utilize a thin axial electric motor bolte d to the engine’s crankshaft and connected to the transmission through a torque conv erter or clutch. The axial motor is motor/generator that typically operates above 100 v olts (but lower than the stronger hybrid systems discussed below, which typically ope rate at around 300 volts) and can provide sufficient torque for launch as well as gen erate sufficient current to provide significant levels of brake energy recovery. The m otor/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 elec tric power alone, although some commercially available systems can cruise on electr ic 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, 2 003]

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). Fo r the CISG technology, NHTSA estimated a net effectiveness range of 16 to 20 per cent, 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, excep t for large truck and SUV in which case BISG does not apply. Note that the net effect iveness assumptions used in this rule do not include engine downsizing, or any other effe ctiveness 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 ot her components where appropriate) and manufacturer-supplied information to determine that the effectiveness of these systems in terms of CO 2 reduction is 30 percent for small cars, 25 percent for large cars, Final Drive Engine Motor/

Generator Trans.

+ -
Battery Controller
Inverter WheelJoint Technical Support Document

3-66

and 20 percent for minivans and small trucks simila r to the range estimated by NHTSA for the respective vehicle classes. The effectiven ess for small cars assumes engine downsizing to maintain approximately equivalent per formance. The large car, minivan,
and small truck effectiveness values assume less en gine downsizing in order to improve vehicle performance and/or maintain towing and haul ing performance.

EPA has estimated these costs, relative to a base e ngine/transmission, as $4,459 (small car) to $6,924 (large truck) for a MY 2012 v ehicle and including a high complexity ICM of 1.45 (2007$). With volume-based learning, these costs become $2,854 and $4,431, respectively, for a MY 2016 vehi cle (2007$). NHTSA has estimated the MY 2012 compliance cost, relative to a BISG bas eline, to range from $2,791 for Subcompact vehicles to $3,611 for Midsize Light Tru ck 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, volu me-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 m ove 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/gen erator is directly connected to the vehicle’s final drive. The planetary gear splits e ngine power between the first motor/generator and the final drive. The first mot or/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 th e engine to the wheels. In this way, the planetary gear allows the engine to operate ind ependently 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 engi ne 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 st art/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 hig hway speeds due to the fact that the first motor/generator must be spinning at a relatively hi gh 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 planeta ry gear. The electric components are bigger than those in mild hybrid and IMA/CISG confi gurations so the costs are correspondingly higher.

EPA and NHTSA used a combination of manufacturer-su pplied information and a comparison of vehicles available with and without a hybrid system from EPA’s fuel economy test data to determine that the effectivene ss is 19 to 36 percent for the classes toTechnologies Considered in the Agencies’ Analysis

3-67 which it is applied. The estimate would depend on w hether 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 assume d (and accounted for elsewhere).

Table 3-20 Large Car Power Split Certification Dat a

Tailpipe CO 2 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 Da ta

Tailpipe CO 2 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 u pdated 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 l eads 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.Joint Technical Support Document

3-68

Based on Toyota Prius and Ford Escape/Mariner marke t sales, PSHEV technology with NiMH battery is considered mature from the perspect ive of cost learning, and time-based learning is applicable. However, the Li-ion batter y technology is undergoing rapid development and it is expected that some manufactur ers may choose to use Li-ion batteries on PSHEV applications during this rulemak ing period. Because this combination of technology is new, volume-based lear ning would be appropriate for such applications. For cost analysis in the NPRM and fi nal rule, volume-based learning is
used for PSHEV which assumes use of Li-ion batterie s. 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 c onventional stepped-ratio automatic transmission which replaces some of the t ransmission clutches with two electric motor/generators, allowing the transmissio n 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 transmissio n’s torque capacity and the efficiency for improved fuel economy at highway spe eds. This type of system is used in the Chevrolet Tahoe Hybrid.

In addition to providing the hybrid functions of en gine 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 effi cient operating zones. The 2-mode system provides significantly reduced CO 2 emissions and fuel consumption. The primary motor/generator is comparable in size to that in th e 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 complexit y and secondary motor sizing.

The battery pack for 2MHEV is assumed to be 300V Ni MH 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 2MH EVs.

For this rule the CAFE model considered a range of 23 to 33 percent with a midpoint of 28 percent, assuming no engine downsizi ng to preserve the utility nature of medium and large trucks ( e.g. , maintaining full towing capability even in situat ions with low battery charge) and EPA estimates CO 2 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 sm aller vehicles.

EPA and NHTSA estimated MY 2012 costs using the upd ated component costs and scaling factors in the hybrid cost estimating m ethodology 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 OME GA models classify vehiclesTechnologies Considered in the Agencies’ Analysis

3-69 differently, these numbers varied slightly in the m odeling 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 appl icable to this technology.

3.4.2.3.10 Plug-In Hybrid Plug-In Hybrid Electric Vehicles (PHEVs) are very s imilar to Hybrid Electric Vehicles, but with three significant functional dif ferences. The first is the addition of a means to charge the battery pack from an outside so urce 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 PH EV would have a control system that allows the battery pack to be significantly deplete d during normal operation.

Table 3-22 below, illustrates how PHEVs compare fun ctionally to both hybrid electric vehicles (HEV) and electric vehicles (EV). These characteristics can change significantly within each class/subclass, so this i s simply meant as an illustration of the general characteristics. In reality, the design op tions are so varied that all these vehicles
exist on a continuum with HEVs on one end and EVs o n the other.

Table 3-22 Conventional, HEVs, PHEVs, and EVs Compa red

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 Med ium Medium to Long Battery Charging None On-Board Grid/On-Board Grid O nly

Deriving some of their propulsion energy from the e lectric grid provides several advantages for PHEVs. PHEVs offer a significant op portunity to replace petroleum used for transportation energy with domestically-produce d electricity. The reduction in petroleum usage does, of course, depend on the amou nt of electric drive the vehicle is capable of under its duty cycle. PHEVs also provid e electric utilities the possibility to increase electric generation during “off-peak” peri ods overnight when there is excess generation capacity and electricity prices are lowe r. Utilities like to increase this “base load” because it increases overall system efficienc y and lowers average costs. Utilities are also investigating the use of PHEV and EV batte ries as a source of grid storage capacity to provide ancillary services for grid sta bilization purposes. PHEVs can lower localized emissions of criteria pollutants and air toxics especially in urban areas by operating on electric power. The emissions from th e power generation occur outside the urban area at the power generation plant which prov ides health benefits for residents of the more densely populated urban areas by moving em issions of ozone precursors out of
the urban air shed. Unlike most other alternative fuel technologies, PHEVs can initially use an existing infrastructure for refueling (charg ing and liquid refueling) so investments in infrastructure may be reduced.Joint Technical Support Document

3-70 In analyzing the impacts of grid-connected vehicles like PHEVs and EVs, the emissions from the electrical generation can be acc ounted for if a full upstream and downstream analysis is desired. While this issue i s being studied on an on-going basis, upstream CO 2 emissions are not unique to grid-connected technol ogies and so are not included in this analysis.

PHEVs will be considerably more costly than convent ional 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 dwe llers 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 woul d 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 thi s case assuming the vehicle can be charged fully.
The effectiveness potential of PHEVs depends on man y factors, the most important being the energy storage capacity designe d into the battery pack. To estimate the fuel consumption and tailpipe CO 2 reduction potential of PHEVs, EPA has developed an in-house vehicle energy model (PEREGRIN) to esti mate the fuel consumption/CO 2 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 tr ucks were modeled using parameters from a midsize car similar to today’s hy brids and scaled to each vehicle’s weight. The large truck PHEV was modeled separatel y 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 th at is used to propel the vehicle is significantly higher than that used in a PSHEV or 2 MHEV. Each PHEV was modeled with enough battery capacity for a 20-mile-equivale nt AER and a power requirement to provide similar performance to a hybrid vehicle. 2 0 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 ra nge 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 PH EV can be thought of as operating in two distinct modes, electric (EV) mode , and hybrid (HEV) mode. At the tailpipe, the CO 2 emissions during EV operation are zero. The EV mo de 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 v alue for the vehicle. (See Table 3-23)Technologies Considered in the Agencies’ Analysis

3-71 Table 3-23 Sample Calculation of PHEV Gasoline-Equi valent CO 2 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 CO 2 reduction -58% -47%

Calculating a total fuel consumption and tailpipe C O 2 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 en gine downsizing in the large truck class. The CAFE model does not apply this technolo gy 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 co mplexity technology. In the OMEGA model, and relative to a IMA/ISA/BSG, these v alues range from $6,922 for small car to $12,467 for a large truck, and relativ e 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-i n 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 ve hicle, decreases fuel consumption by reducing the energy demand needed to overcome forces resisting motion, and rolling resistance. Manufacturers employ a sys tematic approach to mass reduction,
where the net mass reduction is the addition of a d irect component or system mass reduction plus the additional mass reduction taken from indirect ancillary systems and components, as a result of full vehicle optimizatio n, effectively compounding or obtaining a secondary mass reduction from a primary mass reduction. For example, use of a smaller, lighter engine with lower torque-outp ut subsequently allows the use of a smaller, lighter-weight transmission and drive line components. Likewise, the compounded weight reductions of the body, engine an d drivetrain reduce stresses on the suspension components, steering components, wheels, tires and brakes, allowing further reductions in the mass of these subsystems. The re ductions in unsprung masses such as brakes, control arms, wheels and tires further redu ce stresses in the suspension mounting points. This produces a compounding effect of mass reductions.
Estimates of the synergistic effects of mass reduct ion and the compounding effect that occurs along with it can vary significantly fr om one report to another. For example,Joint Technical Support Document

3-72

in discussing its estimate, an Auto-Steel Partnersh ip report states that “These secondary mass changes can be considerable—estimated at an ad ditional 0.7 to 1.8 times the initial mass change.” 49 This means for each one pound reduction in a prima ry 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 i n the realized secondary weight reduction is whether or not the powertrain componen ts can be included in the mass reduction effort, with the lower end estimates bein g applicable when powertrain elements are unavailable for mass reduction. However, anoth er 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 sec ondary 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 ex ist, and the effects result in tangible mass reductions. Mass reductions in a single vehic le component, for example a door side impact/intrusion system, may actually result in a s ignificantly higher weight savings in the total vehicle, depending on how well the manufa cturer integrates the modification into the overall vehicle design. Accordingly, care must be taken when reviewing reports on weight reduction methods and practices to ascert ain if compounding effects have been
considered or not.
Mass reduction is broadly applicable across all veh icle subsystems including the engine, exhaust system, transmission, chassis, susp ension, brakes, body, closure panels, glazing, seats and other interior components, engin e cooling systems and HVAC systems. It is estimated that up to 1.25 kilograms of second ary weight savings can be achieved for every kilogram of weight saved on a vehicle when al l subsystems are redesigned to take into account the initial primary weight savings. 51,52

Mass reduction can be accomplished by proven method s such as:

• Smart Design: Computer aided engineering (CAE) too ls can be used to better optimize load paths within structures by reducing s tresses and bending moments applied to structures (Figure 3-4 through Figure 3- 6). This allows better optimization of the sectional thicknesses of struct ural components to reduce mass while maintaining or improving the function of the component. Smart designs also integrate separate parts in a manner that redu ces mass by combining functions or the reduced use of separate fasteners. In addition, some “body on frame” vehicles are redesigned with a lighter “unib ody” construction.

• Material Substitution: Substitution of lower densi ty 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-stre ngth steels, aluminum, magnesium or composite materials for components cur rently fabricated from mild
steel.

• Reduced Powertrain Requirements: Reducing vehicle w eight sufficiently allows for the use of a smaller, lighter and more efficien t engine while maintaining or increasing performance. Approximately half of the r eduction is due to these reduced powertrain output requirements from reduced engine power output and/orTechnologies Considered in the Agencies’ Analysis

3-73 displacement, changes to transmission and final dri ve 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 cycl e into production although net vehicle weight and performance has continued to increase (F igure 3-4).

Figure 3-4: Example of CAE optimization of the des ign and position of the roof rails and front seat cross-member for the “Future Generation Passenger C ompartment.” 53 The thin lines represent relative positions for roof bows and front cross-me mbers considered during iterations of the CAE analysis of the vehicle structure. The different c olors given to the structural components represent differences in sectional material thickness within this particular CAE modeling run.Joint Technical Support Document

3-74

Figure 3-5: Example of CAE optimization of a roof-r ail 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. 53Technologies Considered in the Agencies’ Analysis

3-75 Figure 3-7: Lightweight, tubular front door intrusi on beams (2007 Civic)

• Automotive companies have largely used weight savin gs in some vehicle subsystems to offset or mitigate weight gains in ot her subsystems from increased feature content (sound insulation, entertainment sy stems, 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 u se 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 susta inability strategy between 2011 and
2020. 55 Mazda plans to reduce vehicle weight by 220 pounds per vehicle or more as models are redesigned. 56,57 Ducker International estimates that the average c urb weight of light duty vehicle fleet will decrease approxima tely 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. 54Joint Technical Support Document

3-76 NHTSA in the CAFE model has used a tiered approach in material substitution to reduce mass without considering other means of vehi cle mass reduction. MS1, a 1.5 percent mass reduction, is applicable in any year a t 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 applicat ions 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 i t is only applied after MS1; therefore the maximum weight reduction that can occ ur for smaller subclass vehicles is 5 percent, while large cars, truck, and SUVs could ex perience up to a 10 percent weight reduction. Making the MS2 technology unavailable u ntil MY 2014 is intended to recognize the lead time required to implement platf orm redesigns that would be necessary for these levels of weight reduction.
NHTSA and EPA estimate that a 10 percent mass reduc tion with engine downsizing results in a 6.5 percent reduction in fu el 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 reduc tion without engine downsizing will result in a 3.5 percent reduction in fuel consumpti on. 58,59,60

NHTSA and EPA have reviewed three studies of mass r eduction/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, fro m a 5 percent reduction in vehicle weight, at a cost of $210-$350. This translates in to $1.50 per pound, assuming a 3,800 pound base vehicle and using the midpoint cost. Ad ditionally, Sierra Research estimated that a 10 percent reduction, with compounding, coul d be accomplished for a cost of $1.01 per pound. 61 Finally, an MIT study 62 estimated that the weight of a vehicle could be reduced by 14 percent, with no compounding, for a c ost of $1.36 per pound. The final cost estimate is $1.32 per pound and is based on th e 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 vehicl e mass reduction technologies, neither volume-based nor time-based cost reductions are app lied, 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 p ulled away from the rotating disc either by mechanical or electric methods. While mo st 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.Technologies Considered in the Agencies’ Analysis

3-77 NHTSA’s MY 2011 CAFE final rule estimated the effec tiveness of LDB to be as much as 1 percent, based on confidential manufactur er data. NHTSA and EPA have reviewed this estimate and believe it to be applica ble 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 sugge st that this cost is not a valid cost and have chosen to maintain this estimate. However, th e 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 veh icles 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 asso ciated mainly with the energy dissipated in the deformation of the tires under lo ad and thus influences fuel economy
and CO 2 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 t uning and/or suspension design.

EPA and NHTSA estimated a 1 to 2 percent increase i n 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 200 6 NAS/NRC report, the agencies have estimated the cost for low rolling re sistance 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 applyi ng the ICM low complexity markup factor, of 1.11, results in compliance cost of $6 p er vehicle for MY 2012 through MY 2016 vehicles (2007$). u Due to the commodity-based nature of this technol ogy, cost learning is not applied.

3.4.2.4.4 Front or Secondary Axle Disconnect for Four-Wheel D rive Systems (SAX) Energy is required to continually drive the front, or secondary, axle in a fourwheel drive system even when the system is not requ ired during most operating conditions. This energy loss directly results in i ncreased fuel consumption and CO 2 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 resi stance tires for this analysis do not include the i ncrease in lifetime costs that would be expected at each tire replacement. Instead, the analysis includes only t he upfront increase in costs.Joint Technical Support Document

3-78

system, the front axle disconnect serves two basic purposes. First, in two-wheel drive mode, it disengages the front axle from the front d riveline 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), th e 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 s ame speed as the rear driveshaft. Four-wheel drive systems that have a front axle dis connect 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 sle eve 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. t oday on unibody frame vehicles; however, it is possible this technology could be in troduced 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 incr emental 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 esti mate 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 an d the resulting power required to move it through the air. While these f actors 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 coefficien t. Reductions in these quantities can therefore reduce fuel consumption and CO 2 emissions. Although frontal areas tend to be relatively similar within a vehicle class (mostly d ue to market-competitive size requirements), significant variations in drag coeff icient 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, ma y be achieved through the use of revised exterior components (typically at a model r efresh in mid-cycle) and add-on devices that currently being applied. The latter l ist 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 exteri or mirrors.

The MY 2011 CAFE final rule estimated that a fleet average of 10 to 20 percent total aerodynamic drag reduction is attainable (wit h a caveat for “high-performance” vehicles described below) which equates to incremen tal reductions in fuel consumption
and CO 2 emissions of 2 to 3 percent for both cars and truc ks. These numbers are generally supported by confidential manufacturer da ta and public technical literature andTechnologies Considered in the Agencies’ Analysis

3-79 therefore NHTSA and EPA are retaining these estimat es, as confirmed by joint review, for the purposes of this final rule.

In the MY 2011 CAFE final rule, NHTSA estimated a r ange 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 mark up 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, NH TSA and EPA believe that a cost estimate at the lower end of the range is more appr opriate. Thus, the cost estimate used in this rulemaking is $48, including the 1.11 ICM m arkup value, for a low complexity technology (2007$). This compliance cost is for a MY 2012 vehicle. With time-based learning, this cost would be $42 for a MY 2016 vehi cle (2007$)

3.4.2.5 

### Technologies considered but not included in the fin al 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. T hey are: electric vehicles, camless valve actuation (CVA), lean burn gasoline d irect injection (LBDI), homogeneous charge compression ignition (HCCI), and electric as sist turbocharging and full series hydraulic hybrids (HHV). While electric vehicles a re likely become available in small numbers in the near future, the costs for this tech nology (as estimated by the agencies) are non-competitive with the more conventional tech nologies listed above. Therefore, the OMEGA and Volpe model analysis would preclude t heir 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 th e current sulfur levels in the fuels. The other technologies listed are still in the rese arch phase of development. NHTSA and EPA will continue to monitor the industry and syste m suppliers for progress on these technologies, and should they become more available , consider them for use in future rulemaking activity. More details are described be low.

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 mak e Electric Vehicle technology more viable than it has ever been. Electric Vehicles (E Vs) 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 ov er the useful charge of the battery. These high-energy batteries are generally less expe nsive per kilowatt-hour than highpower batteries required for hybrids, but the size of the battery pack still incurs a
considerable cost.Joint Technical Support Document

3-80 Electric motor and power electronics designs are ve ry similar to HEV and PHEV designs, but they must be larger, more powerful, an d more robust since they provide the only motive power for the vehicle. On the other ha nd, 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 assumpt ions. EVs are only considered for these two classes because the larger, heavier v ehicles would require too much battery capacity to be practical in the short-to-mid term a nd the agencies do not see any serious development activities for these vehicle types in t he 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 capa bility and battery cost, weight and size with expected technology in the near- to mid-t erm. Using the same methodology as used for PHEVs to calculate gasoline-equivalent fue l consumption, the results obtained are shown in Table 3-24, below.

Table 3-24 Electric Vehicle Gasoline-Equivalent CO 2 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 CO 2 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. W hen electromechanical actuators are used to replace cams and coupled with sensors and m icroprocessor 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 come s increased ability to vary valve overlap, the rapid response required to change betw een combustion operating modes (such as HCCI and GDI), intake valve throttling, cy linder deactivation, and elimination of the camshafts (reduced friction and rotating mas s). This level of control can enable even further incremental reductions in fuel consump tion and CO 2 emissions.

This technology has been under research for many de cades and although progress is being made, NHTSA and EPA have not found evidenc e to support that the technology can be successfully implemented within the 2012 thr ough 2016 timeframe of these regulations. Thus NHTSA and EPA have not estimated cost or effectiveness for this technology at this time.Technologies Considered in the Agencies’ Analysis

3-81 3.4.2.5.3 Lean-Burn Gasoline Direct Injection Technology Direct injection, especially with diesel-like “spra y-guided” injection systems, enables operation with excess air in a stratified o r partially-stratified fuel-air mixture, as a way of reducing the amount of intake throttling. A lso, with higher-pressure fuel injection systems, the fuel may be added late enough during t he 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 “lea n-burn” direct injection gasoline engine may achieve high engine thermal efficiency w hich approaches that of a diesel engine. European gasoline direct-injection engines have implemented stratified-charge lean-burn GDI, although at higher NOx emissions lev els than are allowed at under U.S. Federal Tier 2 emissions standards. Fuel system im provements, changes in combustion chamber design and repositioning of the injectors h ave allowed for better air/fuel mixing and combustion efficiency. There is currently a sh ift from wall-guided injection to spray guided injection, which improves injection precisio n and targeting towards the spark plug, increasing lean combustion stability. Combin ed with advances in NO
x aftertreatment, lean-burn GDI engines may eventually be a possibility in North America.

EPA and NHTSA’s current assessment is that the avai lability of ultra-low sulfur (ULS less than 15 ppm sulfur) gasoline is a key tec hnical requirement for lean-burn GDI engines to meet EPA’s Tier 2 NO
x emissions standards. Since we do not believe that ULS gasoline will be available during the model yea rs 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 (H CCI), also referred to as controlled autoignition (CAI), is an alternate engi ne operating mode that does not rely on a spark event to initiate combustion. The principl es are more closely aligned with a diesel combustion cycle, in which the compressed ch arge exceeds a temperature and pressure necessary for spontaneous autoignition alt hough it differs from diesel by having a homogenous fuel/air charge rather than being a di ffusion-controlled combustion event. The subsequent combustion event is much shorter in duration with higher thermal efficiency.

An HCCI engine has inherent advantages in its overa ll efficiency for two main

reasons:

• The engine is operated with a higher compression ra tio, and with a shorter combustion duration, resulting in a higher thermody namic efficiency, and

• The engine can be operated virtually unthrottled, e ven at light loads,

Combined, these effects have shown an increase in e ngine brake efficiency (typically 25 to 28 percent) to greater than 35 per cent at the high end of the HCCI operating range. 64Joint Technical Support Document

3-82 Criteria pollutant emissions are very favorable dur ing 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 c harge.

Due to the inherent difficulty in maintaining combu stion stability without encountering engine knock, HCCI is difficult to con trol, requiring feedback from incylinder pressure sensors and rapid engine control logic to optimize combustion timing, especially considering the transient nature of oper ating conditions seen in a vehicle. Due to the highly dilute conditions under which gasolin e-HCCI combustion is stable, the range of engine loads achievable in a naturally-asp irated engine is somewhat limited. Because of this, it is likely that any commercial a pplication would operate in a “dualmode” strategy between HCCI and spark ignition comb ustion modes, in which HCCI would be utilized for best efficiency at light engi ne loads and spark ignition would be used at higher loads and at idle. This type of dua l-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 consid ered to still be in the research phase. However, most manufacturers have m ade public statements about the viability of incorporating HCCI into light-duty pas senger 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 CO 2 reduction potential for HCCI in the
literature. Based on confidential manufacturer inf ormation, EPA and NHTSA believe that a gasoline HCCI / GDI dual-mode engine might a chieve 10 to 12 percent reduction
in CO 2 , compared to a comparable SI engine. Despite its promise, application of HCCI in light duty vehicles is not yet ready for the mar ket. It is not anticipated to be seen in volume for at least the next 5-10 years, which is c oncurrent 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 conside red based on a review of confidential product plan information.

3.4.2.5.5 Electric Assist Turbocharging The Alliance commented in NHTSA’s previous rulemak ing that global development of electric assist turbocharging has no t demonstrated the fuel efficiency effectiveness of a 12V EAT up to 2kW power levels s ince the 2004 NESCCAF study, and stated that it saw remote probability of its ap plication over the next decade. While hybrid vehicles lower the incremental hardware requ irements for higher-voltage, higherpower EAT systems, NHTSA and EPA agree that signifi cant developmental work is required to demonstrate effective systems and that implementation in significant volumes
will not occur in the 2012 to 2016 time frame consi dered in this rulemaking. Thus, this technology was not included in the final rule.Technologies Considered in the Agencies’ Analysis

3-83 3.4.2.5.6 Full Series Hydraulic Hybrid A Full Series Hydraulic Hybrid Vehicle (HHV) is som ewhat 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 transmitte d in the form of hydraulic fluid. Series HHV technology currently under development b y EPA is capable of a 40 percent decrease in tailpipe CO 2 emissions in the small car, large car, minivan, and small truck classes. In the large truck class, a 30 percent CO 2 reduction is possible. The large truck
benefit is somewhat lower than the other classes be cause it is assumed that a large truck requires a larger engine to maintain towing and hau ling performance after the energy in the high pressure hydraulic accumulator is exhauste d. This technology is still under development and not yet commercialized, however the re are technology demonstration vehicles in service with UPS in daily package deliv ery 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 lig ht 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 fo r Volpe model, Passenger Cars in 2012

VEHICLE TECHNOLOGY ICM COSTS PER VEHICLE (2007$) BY VEHICLE

SUBCLASS - PASSENGER CARS

Subcompact Compact Midsize Large

Car Car Car Car
Nominal Baseline Engine (For Cost Basis) Inline 4 I nline 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 436Joint Technical Support Document

3-84

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 mod el, Performance Passenger Cars
in 2012

VEHICLE TECHNOLOGY ICM COSTS PER VEHICLE (2007$) BY VEHICLE

SUBCLASS - PERFORMANCE PASSENGER CARS

Perform. Perform. Perform. Perform. Subcomp. Compact Midsize Large

Car Car Car 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 90Technologies Considered in the Agencies’ Analysis

3-85 (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 – 2,34 9

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. 63Joint Technical Support Document

3-86

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 Small Midsize Large

LT LT LT 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 - 214Technologies Considered in the Agencies’ Analysis

3-87 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 E stimates for Volpe model, Passenger Cars in
2012

VEHICLE TECHN OLOGY INCREMENTAL FUEL CONSUMPTION REDUCTION

(-%) BY VEHICLE SUBCLASS - PASSENGER CAR

Subcompact Compact Midsize Large

Car Car Car 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 - 2.5Joint Technical Support Document

3-88

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 - 3.0Technologies Considered in the Agencies’ Analysis

3-89 Table 3-29. Component Technology Incremental Effec tiveness Estimates for Volpe model, Performance Cars in 2012

VEHICLE TECHNOLOGY INCREMENTAL FUEL CONSUMPTION RED UCTION (-%) BY VEHICLE SUBCLASS - PERFORMANCE PASSENGER CAR

Perform. Perform. Perform. Perform. Subcomp. Compact Midsize Large

Car Car Car Car Nominal Baseline Engine (For Cost Basis) Inline 4 V 6 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 -

12.4Joint Technical Support Document

3-90

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 Effect iveness Estimates for Volpe model, Light Trucks in 2012

VEHICLE TECHNOLOGY INCREMENTAL FUEL CONSUMPTION REDUCTION (-%) BY VEHICLE SUBCLASS - LIGHT TRUCKS

Minivan Small Midsize Large

LT LT LT 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 0.5Technologies Considered in the Agencies’ Analysis

3-91 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 valu es 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 cy linder, port fuel injection and a 4 speed automatic transmission. Where this is not th e 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 valu es contained in the NHTSA tables due to the different ways that the agency’s models hand les them. However, the underlying cost and effectiveness values used by each agency i n 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)

VEHICLE CLASS TECHNOLOGY INCREMENTAL TO 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
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 $1 57 $157OHC Engines

Cylinder deactivation Base engine n/a $150 $150 $15 0 $169Joint Technical Support Document

3-92 Discrete VVLT Base engine $125 $181 $181 $181 $259 Continuous VVLT Base engine $245 $449 $449 $449 $48 9 Cylinder deactivation Base engine n/a $150 $150 $15 0 $169 VVT – coupled cam phasing Base engine $40 $40 $40 $40 $40

Discrete VVLT Base engine $141 $204 $204 $204 $291OHV Engines

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 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 - $17Turbo w/o downsizeDownsize w/o turbo

Downsize to V6 DOHC V8 OHV n/a $310 $310 $310 $310
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 $736Turbo with downsize

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 $2,961Technologies Considered in the Agencies’ Analysis

3-93 Table 3-32 EPA’s Incremental Piece Costs for Transm ission Technologies Marked up to include both Direc t and Indirect Costs in 2016 (2007 Dollars per Vehicle)

VEHICLE CLASS TECHNOLOGY INCREMENTAL TO 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 $2 5 $25 $25 5-speed automatic 4-speed auto trans $90 $90 $90 $9 0 $90 6-speed automatic 4-speed auto trans $99 $99 $99 $9 9 $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)

VEHICLE CLASS TECHNOLOGY INCREMENTAL
TO 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 Access ory Technologies Marked up to include both Direct a nd Indirect Costs in 2016 (2007 Dollars per Vehicle)

VEHICLE CLASS TECHNOLOGY INCREMENTAL
TO 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 $94Joint Technical Support Document

3-94

Table 3-35 EPA’s Incremental Piece Costs for Vehicl e Technologies Marked up to include both Direct and Indirect Costs in 2016 (2007 Dollars per Vehicle)

VEHICLE CLASS TECHNOLOGY INCREMENTAL
TO 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 estimat es for the CO 2 reduction estimates of various technologies which can be appl ied to cars and light-duty trucks.

Table 3-36 Engine Technology Effectiveness

Absolute CO 2 Reduction (% from baseline vehicle) Technology Small Car Large
Car Minivan Small

Truck Large

Truck Low friction lubricants – incremental to base engin e 0.5 0.5 0.5 0.5 0.5 Engine friction reduction – incremental to base eng ine 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) 10-1 2 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 1 5-26 Diesel – urea SCR []* 15-26 15-26 15-26 15-26 15-26Technologies Considered in the Agencies’ Analysis

3-95 Table 3-37 Transmission Technology Effectiveness

Absolute CO 2 Reduction (% from baseline vehicle) Technology 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

Absolute CO 2 Reduction (% from baseline vehicle) Technology 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

Absolute CO 2 Reduction (% from baseline vehicle) Technology Small
Car Large

Car Minivan Small

Truck Large

Truck Improved high efficiency alternator & electrificati on 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 & electrificati on of accessories (42 volt) 2-4 2-4 2-4 2-4 2-4

Table 3-40 Other Vehicle Technology Effectiveness

Absolute CO 2 Reduction (% from baseline vehicle) Technology 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 1.5Joint Technical Support Document

3-96 

### Appendix 3.A

Gross Domestic Product Adjustments Used to Adjust 2 002-2006 Dollars to 2007 Dollars

Throughout this analysis, the incremental cost esti mates have been presented in terms of 2007 dollars. However, most of the data s ources relied upon as the basis for the estimates have presented costs in terms of 2006 dol lars. To convert the 2006 dollars to 2007 dollars, the “chain-type” Gross Domestic Produ ct (GDP) deflator was used. Table
3.A-1 shows the GDP deflator data used for this ana lysis.

Table 3.A-1 Price Indexes for Gross Domestic Produ ct

LINE 2002 2003 2004 2005 2006 2007 2008 1 Gross domestic product 104.193 106.409 109.462 11 3.039 116.676 119.819 122.5

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 12 4.712 128.752

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 1 16.38 117.995 118.727 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 107.24 112.372 120.587 129.268 136.8 97 138.884 135.587 12 Change in private inventories

13 Net exports of goods and

services 14 Exports 99.273 101.429 104.997 108.814 112.618 1 16.586 122.788 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 12 0.168 132.698 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 105.345 108.898 114.218 118.744 122.8 03 126.636 129.869 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. Departmen t of Commerce, http://www.bea.gov/national/nipaweb/TableView.asp?S electedTable=4

Using Line 1 in the table, 2006 dollars can be adju sted 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.Technologies Considered in the Agencies’ Analysis

3-97 References

1 “EPA Staff Technical Report: Cost and Effectivene ss 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 Fue l 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 Pr oposed Rulemaking,” California Environmental Protection Agency, Air Resources Board, Regulations to Control Greenhouse Gas Emissions from Motor Vehicles, August 6, 2004, Docket EPA-HQ-OAR-2009-04 72-0134.

5 “Technology to Improve the Fuel Economy of Light D uty Trucks to 2015,” Energy and Environmental Analysis, Inc., May 2006, Docket EPA-HQ-OAR-2009-04 72-0136.

6 “Variable Costs of Fuel Economy Technologies,” pre pared 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, Re quest 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 Repor t – 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 A dditional 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 Ad ditional 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 Revise d June 25, 2009; Bureau of Economic Analysis, U.S. Department of Commerce, www.bea.gov.

14 The 2002 NAS Report, Docket EPA-HQ-OAR-2009-0472-0 147; the 2004 study done by NESCCAF, Docket EPA-HQ-OAR-2009-0472-0148; the California Ai r Resources Board (CARB) Initial Statement of Reasons in support of their carbon rulemaking, Dock et EPA-HQ-OAR-2009-0472-0134; a 2006 study doneJoint Technical Support Document

3-98

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 up dated that study, Docket EPA-HQ-OAR-2009- 0472-0145; and vehicle fuel economy certification d ata, and confidential data submitted by manufacture rs in response to the March and November 2009 requests for product plans.

15 U.S. Environmental Protection Agency, “Draft Repor t – 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 McMan us, “Automobile Industry Retail Price Equivalent and Indirect Cost Multipliers,” EPA 420-R-09-003, D ocket 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, Of fice of Transportation and Air Quality, U.S. Environmental Protection Agency, August 2009, Docke t 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; “Regula tory 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 E PA-HQ-OAR-2009-0472-0139; NHTSA 2011 CAFE final rule at 74 FR 14196.

19 “Learning Curves in Manufacturing,” Linda Argote a nd 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 St aff 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-2009-0472-013 2; “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 3 0 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 Tribolog ists and Lubricated Engineers’ Meeting, March 18th, 2009. Available at: http://www.chicagostle.org/pr ogram/2008- 2009/Impact%20of%20Friction%20Reduction%20Technolog ies%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-2009-0472-01 37. Available at http://www.epa.gov/oms/cert/mpg/fetrends/fetrends-a rchive.htm (last accessed July 9, 2009).

23 Paul Whitaker, Ricardo, Inc., “Gasoline Engine Per formance And Emissions – Future Technologies and Optimization,” ERC Symposium, Low Emission Combusti on 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/J une%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 Internat ional Automotive Technology, Pune, India, January
2007.Technologies Considered in the Agencies’ Analysis

3-99 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. EP A, 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. EP A, dated March 25, 2010.

27 Stefan Wolff, Dirk Abendroth, Werner Weigl, Claus- Peter Linner, Rupert Neudecker, Michael Schneider, Wolfgang Huber, and Andreas Rau, BMW, “I ntroducing The Automatic Start-Stop (ASS) Function In Series Models,” 7th Stuttgart Automotiv e Vehicle and Engine Symposium, Organised by FKFS, Mar 2007, Vol. 1.

28 G.K. Fraidl, P.E. Kapus, and H. Friedl, AVL List G mbH, “Future Gasoline Engine Technologies for 130 g/Km CO2,” VKM-THD 11th Symposium on the Working Pr ocess 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 M ark J. Christie of Ricardo, “DI Boost: Application of a High Performance Gasoline Direct Injection Con cept,” SAE 2007-01-1410. Available at http://www.sae.org/technical/papers/2007-01-1410 (l ast accessed Nov. 9, 2008)

31 Yves Boccadoro, Loïc Kermanac’h, Laurent Siauve, a nd Jean-Michel Vincent, Renault Powertrain Division, “The New Renault TCE 1.2L Turbocharged Ga soline Engine,” 28th Vienna Motor Symposium, April 2007.

32 Tobias Heiter, Matthias Philipp, Robert Bosch, “Ga soline Direct Injection: Is There a Simplified, Co stOptimal System Approach for an Attractive Future of Gasoline Engines?” AVL Engine & Environment Conference, September 2005.

33 U.S. Environmental Protection Agency, “Draft Repor t – 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 Econ omy 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 D I Gasoline Engines,” VKA/ika Aachen Colloquium 2003. Available at http://cat.inist.fr/?aModele=afficheN&cpsidt=169735 98 (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 G asoline 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 EP A-HQ-OAR-2009-0472-0141.Joint Technical Support Document

3-100 39 EPA’s 2007/2010 diesel heavy-duty highway final ru le 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-O AR-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 L ess 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 Effectivenes s Estimates of Technologies Used to Reduce Lightduty Vehicle Carbon Dioxide Emissions” Environmenta l 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 intervie w of Rob Peterson (GM) regarding the all-electric dri ve range of the GM Volt, August 29, 2007. Accessed on the Internet on June 30, 2009 at: http://gm-volt.c om/2007/08/29/latest-Chevrolet-volt-battery-pack-an dgenerator-details-and-clarifications/

44 “Active Combination of Ultracapacitorsand Batteri es 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.ht ml (last accessed October 17, 2008).

46 74 FR 14291 (Mar. 30, 2009)

47 “Electric Cars: Plugged In, Batteries must be inc luded,” Deutsche Bank Global Markets Research Company, June 9, 2008, Docket EPA-HQ-OAR-2009-0472- 0154.

48 http://automobiles.honda.com/insight-hybrid/feature s.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 Empiric al 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-s p.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-Pow ertrain-Study.pdf

51 “Future Generation Passenger Compartment-Validatio n (ASP 241)” Villano, P.J., Shaw, J.R., Polewarczyk, J., Morgans, S., Carpenter, J.A., Yocu m, 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-2 009-0472-0190.

52 “Preliminary Vehicle Mass Estimation Using Empiric al 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-s p.org/database/custom/Mass%20Compounding%20- %20Final%20Report.pdfTechnologies Considered in the Agencies’ Analysis

3-101 53 “Future Generation Passenger Compartment – Final R eport,” 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 Engi neering 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 hyb rid hype,” Greimel, H. Automotive News, March 30,
2009.

58 “Factor of Two: Halving the Fuel Consumption of N ew U.S. Automobiles by 2035,” Cheah, L., Evans, C., Bandivadekar, A., Heywood, J., Massachusetts In stitute of Technology, Publication No. LFEE 2007-04 RP, October 2007, Docket EPA-HQ-OAR-2009-0472-0174.

59 “FY 2007 Progress Report for Lightweighting Materi als”, 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, 2 009 at: http://www1.eere.energy.gov/vehiclesandfuels/resour ces/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-Powertra in-Study.pdf

61 “Basic Analysis of the Cost and Long-Term Impact o f the Energy Independence and Security Act Fuel Economy Standards” Sierra Research Report No. SR20 08-04-01, Docket EPA-HQ-OAR-2009-0472-0173.

62 “Factor of Two: Halving the Fuel Consumption of Ne w U.S. Automobiles by 2035” L. Cheah, C. Evans, A. Bandivadekar and J. Heywood, Publication No. LFE E 2007-04 RP, October 2007, Docket EPA-HQOAR-2009-0472-0174.

63 “Tires and Passenger Vehicle Fuel Economy,” Transp ortation 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.Economic Assumptions Used in the Agencies’ Analysis

4-1 

## CHAPTER 4: Economic Assumptions Used in the Agencies’ Analysis

4.1 How the agencies use the economic assumptions in th eir analyses

Improving new vehicles’ fuel efficiency and reducin g greenhouse gas (GHG) emissions provides direct benefits to their buyers and users by reducing fuel consumption and fuel costs throughout those vehicles’ lifetimes, stimulating i ncreased vehicle use through the fuel economy rebound effect, and increasing vehicles’ driving ra nge so that they require less frequent refueling. At the same time, the reduction in fuel use that re sults 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. petrol eum consumption and imports, including reducing the price of petroleum, lowering the poten tial costs from disruption in the flow of oil imports, and possibly reducing federal outlays to s ecure 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 external ities 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 cha nges 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 th eir buyers (and subsequent owners) if manufacturers include reductions in vehicles’ perfo rmance, carrying capacity, or comfort as part of their strategies to comply with higher fuel econ omy requirements and GHG standards. Nevertheless, the total economic benefits from requ iring 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 mo re stringent standards.

This section discusses the common economic values u sed by both NHTSA and EPA. These economic inputs incorporate a range of foreca st information, economic estimates, and input parameters. This section describes the sourc es that EPA and NHTSA have relied upon for this information, the rationale underlying each ass umption, and the agencies’ estimates of specific parameter values. These common economic v alues 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 Age ncy assesses its program that result in differing benefits estimates. This issue is discus sed further in Section I.C of the preamble to the joint rulemaking.Joint Technical Support Document

4-2



### 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 rat ings for different models. In analyzing the fuel savings from passenger car and light truck fue l 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 expect ed size of this on-road fuel economy “gap.” In December 2006, EPA adopted changes to its regula tions 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 r equirements 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 indivi dual drivers, as discussed in the referenced material, 20% represent s 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 th at vehicle is expected to be 16 mpg (20*.80). In its analysis supporting the Final Rule establish ing CAFE standards for MY 2011, NHTSA employed EPA’s revised estimate of this on-road fue l 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-found ed. 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 estimat es of average miles driven per year by cars and light trucks of different ages during each cale ndar 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 ach ieved 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 Hig hway Administration’s (FHWA) published estimates of actual on-road fuel economy achieved b y 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 NHT SA’s estimates of its fleet-wide average value under test conditions over this period. Simi larly, 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 conditi ons. Thus, these results appear to confirm that the 20 percent on-road fuel economy discount o r 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.Economic Assumptions Used in the Agencies’ Analysis

4-3 We are aware of two potential issues involved in th ese 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 inconsis tencies with these estimates when evaluated separately for cars and trucks. Also, total gasoli ne consumption can be reasonably estimated from excise tax receipts, but separate estimates fo r cars and trucks are not available. We are not aware of the precise methodology used to develop th e distinct on-road fuel economy estimates for cars and trucks developed by FHWA. We do not b elieve 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-roa d 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

PASSENGER CARS LIGHT-DUTY TRUCKS

YEAR 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 i nto the preliminary economic analysis of alternative fuel efficiency and GHG standards, beca use they determine the value of fuel savings both to new vehicle buyers and to society. As in t he proposal, EPA and NHTSA relied on the most recent fuel price projections from the U.S. En ergy 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 likely course of future prices for petroleum produc ts. 4Joint Technical Support Document

4-4

AEO 2010 also reflects the provisions of the Energy Independence and Security Act of 2007 (EISA), including the requirement that the com bined 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 projec ted reductions in fuel use. In response to this concern, the agencies note that EIA issued a revise d version of AEO 2008 in June 2008, which modified its previous December 2007 Early Release o f AEO 2008 to reflect the effects of the recently-passed EISA legislation. The fuel price fo recasts 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 ba seline problem. For more detail, please see
the docket memo. 5

As compared to the gasoline prices used in the prop osal, AEO 2010 Early Release fuel prices are generally lower (in absolute 2007 dollar s). 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 taxe s)

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 pro jections in their assessments of future energy-related policies. The retail fuel pr ice forecasts presented in AEO 2010 span the period from 2007 through 2035. Measured in constan t 2007 dollars, the AEO 2010 Reference Case forecast of retail gasoline prices during cale ndar year 2020 is $3.27 per gallon, rising gradually to $3.83 by the year 2035 (these values i nclude federal, state and local taxes). However, valuing fuel savings over the maximum life times 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 2 016 vehicles will remain in service. a To obtain fuel price forecasts for the years 2036 thro ugh 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 pr ice of gasoline that reaches $4.49 in 2050.

a The agency defines the maximum lifetime of vehicle s as the highest age at which more than 2 percent o f those originally produced during a model year remain in s ervice. In the case of light-duty trucks, for exam ple, 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 comp lete forecast period (2009-2030) because there is extreme volatil ity in the forecasts for the years 2009 through app roximatelyEconomic Assumptions Used in the Agencies’ Analysis

4-5 The value of fuel savings resulting from improved f uel 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 2 008, while those levied on diesel averaged $0.50. Because fuel taxes represent transfers of r esources 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 retai l fuel prices to determine the value of fuel savings resulting from more stringent fuel efficien cy and GHG standards to the U.S. economy as
a whole.

In estimating the economy-wide value of fuel saving s 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 wil l 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 i n 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/loc al 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 for ecast of retail gasoline and diesel prices reported in AEO 2010 to determine the economic valu e of each gallon of fuel saved during that year as a result of improved fuel economy. Subtrac ting fuel taxes results in a projected value for saving gasoline of $2.89 per gallon during 2020, ri sing to $3.48 per gallon by the year 2035.

EIA is widely-recognized as an impartial and author itative source of analysis and forecasts of U.S. energy production, consumption, a nd prices. The agency has published annual forecasts of energy prices and consumption levels f or the U.S. economy since 1982 in its AEOs. These forecasts have been widely relied upon by fed eral agencies for use in regulatory analysis and for other purposes. Since 1994, EIA’s annual f orecasts have been based upon the agency’s National Energy Modeling System (NEMS), which inclu des 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 oi l prices – the primary determinant of prices for gasoline, diesel, and other transport ation fuels derived from petroleum – consistently overestimated actual prices during fut ure 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 th e subsequent actual values for those years, on average exceeding their corresponding actual val ues by 75 percent.

2020. Using the average rate of change over the co mplete 2009-2030 forecast period would result in pr ojections of declining fuel prices after 2030.

c To calculate economy-wide impacts, the agencies va lue fuel savings at pre-tax prices, since this meas ures the value of the resources that are saved when less fuel is p roduced and consumed. The economy-wide value of sa ving fuel includes this plus the reductions in economic and e nvironmental externalities that result from consumi ng less fuel, importing less petroleum, and so on.Joint Technical Support Document

4-6

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 su bsequently proven too high while the remaining 58 have underestimated actual prices. Th e average absolute error ( i.e. , regardless of its direction) of these forecasts has been 21 perce nt, but over- and underestimates have tended to offset one another, so that on average EIA’s more r ecent forecasts have underestimated actual world oil prices by 7 percent. Although both its o verestimates and underestimates of future world oil prices for recent years have often been l arge, the most recent editions of AEO have significantly underestimated petroleum prices durin g 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 dif fer from the Reference Case forecast presented in the AEO 2010 Reference Case. This is particular ly the case because both the AEO 2009 and AEO 2010 Reference Cases forecast significantly hig her future prices than those projected in the initial release of AEO 2008, which in turn represen ted a major upward revision from EIA’s fuel price forecast reported previously in AEO 2007. Co mparing recent forecasts of world oil prices also shows that EIA’s Reference Case forecast repor ted in AEO 2009 is among the highest of all six publicly-available forecasts of world oil price s 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 suggest s that the Reference Case forecast of U.S. fuel prices reported in AEO 2010 is likely to be the hig hest 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 ov er vehicles’ expected lifetimes, discounted to their present values using both 3 and 7 percent dis count 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 consumpti on by vehicles produced during each of these model years is calculated as the difference in thei r lifetime fuel use under the baseline assumption that the CAFE standard previously establ ished 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 consumpt ion by vehicles produced during a model year is to calculate the number of those vehi cles that is expected to remain in serviceEconomic Assumptions Used in the Agencies’ Analysis

4-7 during each future calendar year after they are pro duced and sold. d This number is calculated by multiplying the number of vehicles originally produ ced 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 l ight truck survival rates using 2008 registration data (the most recent available) for p assenger cars and light trucks produced during recent model years, in order to ensure that its for ecasts of age-specific survival rates reflect recent increases in the durability and expected lif e spans of cars and light trucks. 7 Both agencies used these updated survival estimates in their anal yses 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, respectiv ely) are shown in Table 4-3 and Table 4-4. e

The next step in estimating lifetime fuel use by th e 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 tota l miles driven, the number of cars and light trucks projected to remain in use during each futur e calendar year calculated in the previous step is multiplied by the average number of miles a surv iving 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 Ad ministration’s 2001 National Household Transportation Survey. These updated est imates, differ from those employed in past NHTSA analyses, were used previously by NHTSA to an alyze the effects of the previouslyestablished CAFE standards for MY 2011 cars and lig ht trucks. f

Table 4-3 and Table 4-4 report NHTSA’s updated est imates of average car and light truck use which were used in the analyses for the M Y 2011 final rule. As these tables show, the

d Vehicles are defined to be of age 1 during the cal endar year corresponding to the model year in which they are produced; thus for example, model year 2000 vehicle s are considered to be of age 1 during calendar yea r 2000, age 1 during calendar year 2001, and to reach their max imum age of 26 years during calendar year 2025. NHTSA considers the maximum lifetime of vehicles to be th e age after which less than 2 percent of the vehicl es originally produced during a model year remain in service. Ap plying these conventions to vehicle registration da ta indicates that passenger cars have a maximum age of 26 years, while light trucks have a maximum lifetime of 36 y ears. See Lu, S., NHTSA, Regulatory Analysis and Evaluation D ivision, “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 defin ed as the age when the number remaining in service has declined to approximately two percent of those orig inally 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 ye ars for light
trucks.
f See also NHTSA, “Vehicle Survival and Travel Mileag e Schedules,” Office of Regulatory Analysis and Evaluation, January 2006, pp. 15-17. The original source of information on annual use of passenger ca rs and light trucks by age used in this analysis is the 2001 Nat ional Household Travel Survey (NHTS), jointly spons ored by the Federal Highway Administration, Bureau of Transport ation Statistics, and National Highway Traffic Safe ty Administration.Joint Technical Support Document

4-8

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, tha t survival-weighted mileage is extremely low (less than 1,000 miles per year) after age 20 for c ars 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 v alues.

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 w ill be considered to be of age 1 during calendar year 2012. This convention is used in ord er to account for the fact that vehicles produced during a model year typical are first offe red for sale in June through September of the preceding calendar year (for example, sales of a mo del 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 us e for some or all of the calendar year coinciding with their model year, and they are cons idered to be of age 1 during that year. g

g As an illustration, virtually the entire productio n of model year 2012 cars and light trucks will hav e been sold by the end of calendar year 2012, so those vehicles ar e 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 registr ation data are typically collected for July 1 of ea ch calendar year, so not all vehicles produced during a model year wi ll appear in registration data until the calendar y ear when they have reached age 2 (and sometimes age 3) under this convention.Economic Assumptions Used in the Agencies’ Analysis

4-9 Table 4-3 Survival Rates and Unadjusted Annual Mile s 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 161,847Joint Technical Support Document

4-10

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 190,066Economic Assumptions Used in the Agencies’ Analysis

4-11 4.2.3.1 Adjusting vehicle use for future fuel prices

The estimates of average annual miles driven by pas senger 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 ef fect on vehicle use of subsequent increases in fuel prices in the proposal, the estimates of an nual vehicle use derived from the NHTS were adjusted to reflect the forecasts of future gasolin e 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 differenc e 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 pr ice that prevailed when the NHTS was conducted in 2001. The elasticity of annual vehicl e use with respect to fuel cost per mile corresponding to the 10 percent fuel economy reboun d effect used in this analysis ( i.e. , an elasticity of -0.10) was used in conjunction with t he difference between each future year’s fuel prices and those prevailing in 2001 to adjust the e stimates 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 u se

The estimates of annual miles driven by passenger c ars 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 yea r have been an important source of historical growth in total car and light truck use, and are expected to repres ent 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 com pound annual growth rate of 1.5 percent. 8 During that time, however, the total number of pass enger cars registered for in the U.S. grew by only about 0.3 percent annually. h Thus growth in the average number of miles automo biles are driven each year accounted for the remaining 1.2 pe rcent (= 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 t hrough 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 accoun ted for a small share of growth in the U.S. automobile fleet. The fraction of new automobiles remaining in serv ice to various ages was computed from R.L. Polk vehicle registrati on data for 1977 through 2005 by the agency’s Cente r for Statistical Analysis.

i See supra note k below.Joint Technical Support Document

4-12

the rate forecast in the AEO 2010 Reference Case. This calculated rate is also consistent with future changes in the overall size and age distribu tions of the U.S. passenger car and light truck fleets that are implied by the agency’s forecasts o f total car and light truck sales and the survival rates reported in Table 4-3 and Table 4-4. The gro wth rate in average annual car and light truck use produced by this calculation is approximately 1 .15 percent per year. j 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 li fetimes of MY 2012-2016 cars and light
trucks. k

Table 4-6 and Table 4-7 report the results of apply ing 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 f or projected fuel prices and growth in car and light truck use were made for each calendar year fr om 2012 through 2030. Because the effects of both fuel prices and cumulative growth in averag e vehicle use vary by year, these adjustments result in differing VMT schedules for each future y ear. The adjusted annual VMT estimates reported in Table 4-4 and Table 4-5 and their lifet ime totals represent the averages of the adjusted values of annual car and light truck use b y 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 201 2-16 cars and light trucks employ the adjusted values of car and light truck use by age d uring 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 expecte d future growth in average vehicle use increases it. Comparing the mileage estimates in T able 4-6 and Table 4-7 to those shown previously in Table 4-2 and Table 4-3 shows that th e 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 lifeti me mileage for light trucks increases from the 190,066 miles reported previously in Table 4-4 to 2 25,865 miles (19 percent). As previously noted, the estimates of survival-weighted mileage d ecline 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 ra tes 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 di screpancy between the estimates of expected lifetim e VMT used by EPA and NHTSA in their respective analyses of pr oposed 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 descripti on of the procedures used to calculate its estimate s 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 Pr eliminary Regulatory Impact Assessment, Chapter VII I, p. 367), this adjustment was not in fact applied in NHTSA’s analysis of the effects of proposed increases in CA FE standards for model years 2012-16. This omission caused the e stimates of expected lifetime VMT for passenger car s and light trucks that NHTSA actually used in its NPRM analysi s of CAFE standards for model years 2012-16 to be l ower than those used by EPA. This error has been corrected b y NHTSA, and this correction is reflected in its an alysis of the impacts of CAFE standards for model year 2012-16 pa ssenger cars and light trucks reported in the Final Rule. This error did not occur in the analysis of environmenta l impacts of alternative increases in CAFE standard s reported in the agency’s recent Draft Environmental Impact Stat ement.Economic Assumptions Used in the Agencies’ Analysis

4-13 lifetime fuel savings or other benefits from higher fuel economy, particularly after discounting the benefits that occur in those distant future yea rs 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 s light (~2.5 percent) increase in lifetime VMT schedule is attributable to the lower fuel prices p rojected in AEO 2010 Early Release as compared to the updated AEO 2009.

Table 4-5 Summary of Expected Lifetime VMT Schedules

MY 2012-2016 Proposal MY 2012-2016

Final Rulemaking
Car 190,271 195,264
Trucks 221,199 225,865Joint Technical Support Document

4-14

Table 4-6 Survival Rates and Adjusted Annual Vehicle-Miles Tr aveled (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 195,264Economic Assumptions Used in the Agencies’ Analysis

4-15 Table 4-7 Survival Rates and Adjusted Annual Vehicl e-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 vehicleJoint Technical Support Document

4-16

use that stems from improved fuel economy occurs be cause 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 d eterminants of the actual fuel savings that are likely to result from adopting stricter fu el economy or emissions standards, and thus an important parameter affecting EPA’s and NHTSA’s eva luation of alternative standards for future model years. It can be measured directly by estima ting the elasticity of vehicle use with respect to fuel economy itself, or indirectly by the elasti city of vehicle use with respect to fuel cost per
mile driven. l When expressed as a positive percentage, either o f these parameters gives the fraction of fuel savings that would otherwise resul t from adopting stricter standards, but is offset by the increase in fuel consumption that results wh en vehicles with increased fuel economy are
driven more.

The fuel economy rebound effect for light-duty vehi cles 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 con clude 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 hi gher fuel economy. Other studies have relied on econometric analysis of annual U.S. data on vehi cle use, fuel economy, fuel prices, and other variables to identify the response of total or aver age vehicle use to changes in fleet-wide average fuel economy and its effect of fuel cost per mile d riven. 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 th e response of vehicle use to changing fuel

economy. n

This section surveys these previous studies, summar izes recent work on the rebound effect, and explains the basis for the 10 percent r ebound 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 stud ies previously conducted on the rebound effect rely on data from the 1950-1990s. While the se older studies provide valuable information on the potential magnitude of the rebound effect, s tudies that include more recent information (e.g., data within the last decade) may provide mor e reliable estimates of how this rule will affect

l Fuel cost per mile is equal to the price of fuel i n dollars per gallon divided by fuel economy in mil es per gallon, so this figure declines when a vehicle’s fuel economy increases.

m Some studies estimate that the long-run rebound ef fect is significantly larger than the immediate res ponse to increased fuel efficiency. Although their estimate s of the adjustment period required for the rebound effect to reach its long-run magnitude vary, this long-run effect i s most appropriate for evaluating the fuel savings and emissions reductions resulting from stricter standards that w ould apply to future model years.

n In effect, these studies treat U.S. states as a da ta “panel” by applying appropriate estimation proce dures to data consisting of each year’s average values of these v ariables for the separate states.Economic Assumptions Used in the Agencies’ Analysis

4-17 future driving behavior. Therefore, the more recent studies have been described in more detail
below.

Estimates based on aggregate U.S. vehicle travel da ta 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 or der 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 Travel 1

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 Data 1

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 explanati ons for this larger variability. One explanation
is that some of these studies do not include vehicl e age as an explanatory variable, thus leading
to omitted variable bias. 10 Another explanation is that it is difficult to di fferentiate between the impacts of residential density and fuel prices, sin ce 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 D ata

AUTHOR (YEAR) SHORTRUN LONG-RUN TIME PERIOD

Goldberg (1966) 0% CES 1984-90 Greene (1999) 23% EIA RTECSJoint Technical Support Document

4-18 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 rebou nd effect is whether they assume that the effect is constant, or varies over time in resp onse to the absolute levels of fuel costs, personal income, or household vehicle ownership. Most studi es 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 ec onomy alter fuel cost per mile driven. Many studies using household survey data estimate signif icantly different rebound effects for households owning varying numbers of vehicles, alth ough they arrive at differing conclusions about whether the rebound effect is larger among ho useholds that own more vehicles. Finally, one recent study using state-level data concludes t hat the rebound effect varies directly in response to changes in personal income and the degr ee of urbanization of U.S. cities, as well as
fuel costs.

In order to provide a more comprehensive overview o f previous estimates of the rebound effect, EPA and NHTSA reviewed 22 studies of the re bound effect conducted from 1983 through 2005. The agencies then performed a detailed analy sis 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 e stimate (24 percent).

The type of data used and authors’ assumption about whether the rebound effect varies over time have important effects on its estimated m agnitude. 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 base d on household survey data is considerably larger (31 percent), and the mean of 9 estimates ba sed 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 estimat es 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 overa ll rebound effect from more detailed results report ed in the studies. For example, where studies estimated dif ferent rebound effects for households owning differ ent numbers of vehicles but did not report an overall value, th e agency computed a weighted average of the reporte d values using the distribution of households among vehicle owners hip categories.Economic Assumptions Used in the Agencies’ Analysis

4-19 Range Distribution Category of Estimates Number of Studies Number of

Estimates 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 rebou nd effect

More recent studies since 2007 indicate that the im pacts 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 proporti on of the total cost of driving. As incomes rise (or fuel prices fall), the responsiveness to the fu el 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 f ound a long-run rebound effect of about 22 percent, which is consistent wit h 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 estimat es 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 reb ound effect is declining over time. 14 Over the entire time period analyzed (1966-2007), Greene fou nd that fuel prices had a statistically significant impact on VMT, while fuel economy did n ot. In spite of this result, Greene also
tested Small and Van Dender’s formulation which all ows the elasticity of fuel cost per mile to decrease with increasing per capita income. The re sults of estimation using national time series data confirmed the results obtained by Small and Va n Dender using a time series of state level
data.

While Small and Van Dender did not find a statistic ally significant coefficient for fuel efficiency, they did find a statistically significa nt coefficient for the price of fuel. They then tested the hypothesis that the elasticity of vehicl e 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 res pect 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 u sing Greene’s preferred functional form, theJoint Technical Support Document

4-20

projected rebound effect is approximately 12 percen t 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 effe ct for the purposes of this rulemaking, NHTSA developed several new estimates of its magnit ude. These estimates were developed by estimating and testing several econometric models o f the relationship between vehicle milestraveled and factors that influence it, including h ousehold 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, i t is important to account for the effect of fuel prices when attempting to estimate t he 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 o f the rebound effect. Several different approaches were used to estimate the fuel economy r ebound effect for light duty vehicles, including single equation OLS regressions, multiple equation systems estimated using two- and three-stage least squares regression, vector autore gression, and vector error-correction models. With the exception of single-equation regressions e stimated 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 prese nted 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 a ddition, the table presents forecasts of the averag e rebound effect between 2010 and 2030, which utilize forecasts of personal income, fuel prices, and fuel efficiency from EIA’s AEO 2009 Reference C ase.

The results of NHTSA’s analysis are broadly consist ent 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 t o 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 als o suggest that this decline is likely to continue through 2030, as they range from 4-16 percent.Economic Assumptions Used in the Agencies’ Analysis4-21Table 4-12 Summary of NHTSA Estimates of the Rebound EffectRebound Effects:ModelVMTMeasureVariables Included in VMT EquationEstimationTechnique1950-200620062010-2030*Small-Van Dendersingle VMTequationannual VMTper adultfuel cost per mile, per Capita income, vehicle stock, roadmiles per adult, fraction of population that is adult,fraction of population living in urban areas, fraction ofpopulation living in urban areas with heavy rail, dummyvariables for fuel rationing, time trendOLS 33.0% 15.8% 8.0%Small-Van Denderthree-equationsystemannual VMTper adultfuel cost per mile, per Capita income, vehicle stock, roadmiles per adult, fraction of population that is adult,fraction of population living in urban areas, fraction ofpopulation living in urban areas with heavy rail, dummyvariables for fuel rationing, time trend3SLS 21.6% 5.8% 3.4%Single-equationVMT modelannual VMTper adultpersonal income, road miles per Capita, time trendOLS 18.4% 11.7% 9.2%Single-equationVMT modelannual VMTper vehiclefuel cost per mile, personal income, road miles perCapita, time trendOLS 17.6% 15.2% 15.7%Single-equationVMT modelannual VMTper adultfuel cost per mile, personal income, road miles perCapita, dummy variables for fuel rationing, time trendOLS 34.0% 20.8% 13.6%Single-equationVMT modelannual VMTper vehiclefuel cost per mile, personal income, vehicles per roadmile, % of fleet manufactured under CAFE standards,new vehicle pricesIV (for fuelcost per mile)16.3% 9.2% 7.0%Three-equationsystem for VMT,fuel efficiency, andvehicle stockannual VMTper vehiclefuel cost per mile, personal income, vehicles per capita,vehicles per road mile, fraction of adult populationlicensed to drive, new vehicle prices, % of fleetmanufactured under CAFE standards2SLS 29.5% 13.4% 15.9%Three-equationsystem for VMT,fuel efficiency, andvehicle stockannual VMTper vehiclefuel cost per mile, personal income, vehicles per capita,vehicles per road mile, fraction of adult populationlicensed to drive, new vehicle prices, % of fleetmanufactured under CAFE standards3SLS 29.8% 13.7% 16.2%Three-equationsystem for VMT,annual VMTper vehiclefuel cost per mile, personal income, vehicles per capita,vehicles per road mile, fraction of adult populationVector autoregression19.9% 10.8% --Joint Technical Support Document

4-22

4.2.4.4 Basis for rebound effect used by EPA and NHTSA in t his rule

As the preceding discussion indicates, there is a w ide 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 a ppears to be declining over time. Nevertheless, NHTSA requires a single point estimat e 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 age ncies chose to use 10 percent as their primary estimate of the rebound effect, with a range of 5-1 5 percent for use in sensitivity testing.

As Tables 4-10 and 4-11 indicate, the 10 percent fi gure 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 eff ect developed by NHTSA. However, other recent research – particularly that conducted by Sm all and Van Dender and by Greene – reports persuasive evidence that the magnitude of the rebou nd effect is likely to be declining over time, and the forecasts developed by NHTSA and reported i n Table 4-12 also suggest that this is likely to be the case. As a consequence, the agencies con cluded that a value below the historical estimates reported in Tables 4-10, 4-11, and 4-12 i s likely to provide a more reliable estimate of its magnitude during the future period spanned by t he 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 reporte d 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 magnitu de of the rebound effect developed by NHTSA in its recent research. In summary, the 10 p ercent 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 e stimates. The Agencies will continue to review
the estimate of the rebound effect for future rulem akings.

In their proposed rules, the agencies sought commen t 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 cau sed by variation in fuel prices or fuel economy, the short-run price elasticity of demand f or 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 th e 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 di d receive some supportive comments on the 10 percent level. Hence, EPA and NHTSA have elected t o continue to use the 10 percent estimate of the rebound effect in the analyses supporting th is 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.Economic Assumptions Used in the Agencies’ Analysis

4-23 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 ec onomy reduces their costs for driving, the
benefits from this additional travel exceed the cos ts drivers and passengers incur in making more frequent or longer trips.

The analysis estimates the economic benefits from i ncreased rebound-effect driving as the sum of fuel costs drivers incur plus the consum er surplus they receive from the additional accessibility it provides. p Because the increased in travel depend on the ext ent 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, incr eased vehicle use associated with the
rebound effect also contributes to increased traffi c congestion, motor vehicle accidents, and highway noise. Depending on how the additional tra vel is distributed over the day and on where it takes place, additional vehicle use can contribu te to traffic congestion and delays by increasing traffic volumes on facilities that are already heav ily traveled during peak periods. These added delays impose higher costs on drivers and other veh icle occupants in the form of increased travel time and operating expenses. Because drivers do no t take these added costs into account in deciding when and where to travel, they must be acc ounted 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 o f the potential costs they (and their passengers) face from the possibility of being involved in an a ccident 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 ano ther cost of additional rebound-effect driving. Like increased delay costs, any increase in these e xternal 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 severit y 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, irritat ion, and potentially even discomfort to occupants of other vehicles, to pedestrians and oth er bystanders, and to residents or occupants of surrounding property. Because these effects are un likely to be taken into account by the drivers whose vehicles contribute to traffic noise, they re present 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 e stimated as one-half of the product of the decline in fuel cost per mile and the resulting increase in the annual n umber of miles driven.Joint Technical Support Document

4-24

increase in the economic costs of traffic noise res ulting from added vehicle use must be included together with other increased external costs from t he rebound effect.

EPA and NHTSA rely on estimates of congestion, acci dent, and noise costs caused by automobiles and light trucks developed by the Feder al Highway Administration to estimate the increased external costs caused by added driving du e to the rebound effect. 15 NHTSA employed these estimates previously in its analysis accompan ying 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 t hese 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), w hile those for pickup trucks and vans are 4.7 cents, 2.5 cents, and 0.1 cents per vehicle-mile (f or a total of 7.3 cents per mile). 16, q These costs are multiplied by the annual increases in automobil e and light truck use from the rebound effect to yield the estimated increases in congestion, acc ident, 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 represe nted 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 fo r 70 percent of total U.S. petroleum consumption. This compares roughly to 37 percent o f petroleum from imports and 55 percent consumption of petroleum in the transportation sect or in 1975. 18 It is clear that petroleum imports have a significant impact on the U.S. econo my. 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 protect ing 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 es timates. For example, recent published research conducted by Resources for the Future (RFF) estimates marginal congestion and external accident costs for increased light-dut y vehicle use in the U.S. to be 3.5 and 3.0 cents p er 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 Fut ure, 19 and Table 1 (March 2002). Available at http://www.rff.org/rff/Documents/RFF-DP-02-12.pdf (last accessed Feb. 15, 2010).Economic Assumptions Used in the Agencies’ Analysis

4-25 potentially unstable sources. In addition, oil exp orters have the ability to raise the price of oil b y 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 spi kes. 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 2008 r

One effect of the EPA/NHTSA joint rule is that it p romotes more efficient use of transportation fuels in the U.S. The result is tha t it reduces U.S. oil imports, which reduces both financial and strategic risks associated with a pot ential disruption in supply or a spike in the cost of a particular energy source. This reduction in r isks 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 mark et 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 B enefits

In order to understand the energy security implicat ions 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 Ener gy 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://ww w.eia.doe.gov/oiaf/servicerpt/stimulus/aeostim.htmlJoint Technical Support Document

4-26

evaluating the social costs and energy security imp lications of oil use. The energy security estimates provide below are based upon a methodolog y developed in a peer-reviewed study entitled, “ The Energy Security Benefits of Reduced Oil Use, 20 06-2015 ,” 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 estim ating 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 consider ed 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 a re: (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 ris k of reductions in U.S. economic output and disruption of the U.S. economy caused by sudden dis ruptions in the supply of imported oil to the U.S. ( i.e. , macroeconomic disruption/adjustment costs). Main taining a U.S. military presence to help secure stable oil supply from potentially vuln erable 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 d iscussion 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 i nvolve 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 val ue of resources transferred from U.S. consumers of petroleum products to foreign supplier s 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 th eir 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 se curity 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 e stimates 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 sever al key parameters involving OPEC supply behavior, the responsiveness of oil demand a nd 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 secu rity premium by incorporating the most recent oil price forecast and energy market tr ends 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.Economic Assumptions Used in the Agencies’ Analysis

4-27 For 2015, ORNL has estimated that the total energy security premium associated with a reduction of imported oil is $18.49/barrel. On a d ollar 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 bel ow.

Table 4-13 Energy Security Premium in 2015, 2020, 2030 and 204 0 t

(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. impor t costs, and economic output The first component of the full economic costs of i mporting petroleum into the U.S. follows from the effect of U.S. import demand on th e world oil price over the long-run. Because the U.S. is a sufficiently large purchaser of forei gn oil supplies, its purchases can affect the world oil price. This monopsony power means that increas es in U.S. petroleum demand can cause the world price of crude oil to rise, and conversely, t hat 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 transportat ion fuels, is the potential decrease in the crude oil price paid for all crude oil purchased.

The demand or monopsony effect can be readily illus trated with an example. If the U.S. imports 10 million barrels per day at a world oil p rice of $50 per barrel, its total daily bill for oi l imports is $500 million. If a decrease in U.S. imp orts to 9 million barrels per day causes the world oil price to drop to $49 per barrel, the dail y 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-203 0 were
assumed to be the 2030 estimate.

t AEO 2009 world oil prices were used, as in the pro posed rule. The analysis was not updated using the recentlyreleased AEO 2010 forecast of world oil prices, bec ause 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.Joint Technical Support Document

4-28

million barrels times $49 per barrel). While the w orld oil price only declines $1, the resulting decrease in oil purchase payments of $59 million pe r day ($500 million minus $441 million) is equivalent to an incremental benefit of $59 per bar rel 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 benefi ts to the U.S. for avoided import costs beyond the price paid oil purchases. This addition al 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 t he effect of oil imports on the expected cost of disruptions. A sudden increase in oil prices tr iggered by a disruption in world oil supplies has two main effects: (1) it increases the costs of imp orts 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 macroeconomi c disruption/adjustment costs premium to be $6.70/barrel in 2015, with a range of $3.11 – $10.6 7/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 import ed oil causes increased payments for imports from the U.S to oil exporters. This increa sed claim on U.S. economic output is a loss to the U.S. that is separate from and additional to an y reduction in economic output due to the shock. The increased oil payments during shocks ar e counted as a loss to the degree that the expected price increase is not anticipated and inte rnalized by oil consumers.

4.2.7.2.1.2.2 Macroeconomic costs: potential output loss and disl ocation/adjustment costs Macroeconomic losses during price shocks reflect bo th 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 th e 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 red uction in “potential” economic output, will last so long as the price is elevated. It depends on th e 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 a nd 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 u se of petroleum and other productive factors more rapidly than if the same price increase had oc curred gradually. Dislocational effects include the unemployment of workers and other resou rces during the time needed for their intersectoral or interregional reallocation, and pa uses in capital investment due to uncertainty. These adjustments temporarily reduce the level of e conomic output that can be achieved even below the “potential” output level that would ultim ately be reached once the economy’sEconomic Assumptions Used in the Agencies’ Analysis

4-29 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 in puts quickly and smoothly in response to rapid changes in prices for petroleum products.

Since future disruptions in foreign oil supplies ar e 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 “ex pected 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 sharp ly higher petroleum prices – is the relevant measure of their magnitude. Further, when assessin g the energy security value of a policy to reduce oil use, it is only the change in the expect ed costs of disruption that results from the policy that is relevant. The expected costs of dis ruption may change from lowering the normal ( i.e. , pre-disruption) level of domestic petroleum use a nd imports, from any induced alteration in the likelihood or size of disruption, or from alter ing the short-run flexibility (e.g., elasticity) of petroleum use.

In summary, the steps needed to calculate the disru ption or security premium are: 1) determine the likelihood of an oil supply disruptio n in the future; 2) assess the likely impacts of a potential oil supply disruption on the world oil pr ice; 3) assess the impact of the oil price shock on the U.S. economy (in terms of import costs and m acroeconomic 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 pre mium.

In the energy security literature, the macroeconomi c 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 price s, 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 petrole um 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 counte d as a benefit.

This approach would represent a significant departu re from how the macroeconomic disruption costs associated with oil price shocks h ave been quantified in the broader energy security literature, and the Agencies believe it sh ould 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 marke t power by petroleum sellers or consumers. According to the OMB definition of a transfer: “Be nefit and cost estimates should reflect real resource use. Transfer payments are monetary paymen ts 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 transf er from buyers to sellers resulting from a higher price is not a real cost since the net reduction au tomatically accounts for the transfer from buyersJoint Technical Support Document

4-30

to sellers.” u In other words, pure transfers do not lead to cha nges 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 pr ice increase is a real resource supply reduction due, for example, to disaster or war. Regions wher e supplies are disrupted (i.e., the U.S.) suffer very high costs. Businesses’ and households’ emerg ency responses to supply disruptions and rapid price increases are likely to consume some re al economic resources, in addition to causing financial losses to the U.S. economy that are match ed by offsetting gains elsewhere in the global

economy.

While households and businesses can reduce their pe troleum consumption, invest in fuel switching technologies, or use futures markets to i nsulate themselves in advance against the potential costs of rapid increases in oil prices, w hen deciding how extensively to do so, they are unlikely to account for the effect of their petrole um 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 us e 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 dis ruption and adjustment costs that the Agencies use to value energy securit y benefits includes the increased oil import costs stemming from oil price shocks that are unant icipated and not internalized by advance actions of U.S. consumers of petroleum products. Th e Agencies believe that, as the ORNL analysis argues, the uninternalized oil import cost s that occur during oil supply interruptions represents a real cost associated with U.S. petrole um consumption and imports, and that reducing its value by lowering domestic petroleum consumptio n 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 eco nomic 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 cru de oil in the world. Established in the aftermath of the 1973-74 oil embargo, the SPR provi des 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 inclu des 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.Economic Assumptions Used in the Agencies’ Analysis

4-31 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. A ttempts 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. S imilarly, 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 se nsitivity analyses to address the comments of the peer reviewers. Based upon the pee r 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 differ ent 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 con ducted 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 c ombined response of non-U.S., non-OPEC demand and supply and (3) the GDP response to a cha nge in the world oil as a result of reduced U.S. oil imports. The cases used updated supply/de mand 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 previou s 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 redu ction in demand.

In the ORNL framework, OPEC-behavior is treated par ametrically, 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 t rends and uses the elasticity parameters from the original 1997 ORNL study. In Case Two, the OPE C supply elasticities range for 0.25 to 6 with a mean elasticity of 1.7. Case Three alters t he 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 behav ior maximizes OPEC’s long run net revenue in a robust way, 21 would have OPEC responding to preserve its worldwi de oil market share. This is presented as Case Seven. Application of th is rule instead of the range of OPEC supply responses used leads to an estimate of the oil impo rt premium that is between Case Two and
Case Three.Joint Technical Support Document

4-32

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 v alue 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 elastic ity 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 elastic ity, the parameter which summarizes the sensitivity of GDP to oil price shoc ks. 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 co mparison to the original ORNL estimate of 0.0495. As anticipated, this change lowered the oi l import premium modestly. For example, compared with Case Four where OPEC supply is more e lastic, lowering the GDP elasticity with respect to the world oil price reduced the oil impo rt 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 uncertain ty about the magnitude of marginal economic costs from the U.S. importation of petrole um, 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 m arket 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 r oughly $11 and $15/barrel of imported oil. On balance, Case Eight suggested a reasonable and c autious assessment of the premium value to ORNL, and is ORNL’s recommended case. This is base d 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 r ange resulting from 90 percent of the simulated outcomes, encompasses a wide array of per spectives and potential market outcomes in response to a reduction of U.S. imports.

As mentioned previously, this recommended case reli ed on the most recent available projections of the U.S. and world oil market for th e next ten years based upon the AEO 2007 Reference Case. OPEC-behavior was treated parametr ically, with a wide range of possible responses represented by a wide range of supply ela sticities, 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 def ense 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 possib le elastic and inelastic OPEC response was essentially even over a fairly wide range of elasti cities. ORNL concluded that this is the best way to estimate OPEC behavior until greater progres s can be made in synthesizing what insightsEconomic Assumptions Used in the Agencies’ Analysis

4-33 are available from the evolving strategic game-theo retic and empirical research on OPEC behavior, and advancing that research. An alternat ive 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 elastic ity range, the parameter which summarizes the sensitivity of GDP to oil price shoc ks, which is reduced compared to earlier estimates, and compared to the full range of histor ically-based estimates. This helped address the concerns of those who either question the concl usions of past empirical estimates or expect that the impacts of oil shocks may well be declinin g.Joint Technical Support Document4-34Table 4-14 Summary Results – Oil Import Premium Under Various Cases ($2007/BBL)Component Statistic1) AEO2007Base Outlook,1997 StudyElasticities2) AEO2007Base Outlook,Wider Range ofOPEC SupplyElasticities3) Case 2 withRevised WiderRange of OPECSupplyElasticities4) Case 2 withUpdated NonOPECSupply/DemandElasticities5) Case 4 plusRevised WiderRange of OPECSupplyElasticities6) Case 4Variant withWider, HigherRange of NonU.S./Non-OPECSupply/DemandElasticities7) Case 6 withApplied StrategicOPEC BehavioralRule: MaintainMarket Share8) Case 4 withReduced GDPElasticity9) Case 7 withReduced GDPElasticityMean $5.57 $10.26 $8.16 $7.77 $6.52 $6.44 $9.36 $7.86 $9.42Range ($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) ($2.94 - $13.89) ($6.72 - $12.54)Mean $2.36 $2.33 $2.34 $1.92 $1.94 $1.93 $1.89 $2.20 $2.16Range ($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) ($0.63 - $4.07) ($0.62 - $3.96)Mean $3.83 $3.70 $3.76 $3.42 $3.45 $3.47 $3.41 $2.67 $2.64Range ($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) ($0.90 - $4.84) ($0.87 - $4.87)Mean $6.19 $6.03 $6.10 $5.34 $5.39 $5.40 $5.30 $4.87 $4.81Range ($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) ($2.23 - $7.85) ($2.25 - $7.77)Mean $11.75 $16.29 $14.27 $13.11 $11.90 $11.86 $14.65 $12.71 $14.23Range ($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) ($7.07 - $19.02) ($10.76 - $18.25)Total Premium, in $/Gallon Mean $0.28 $0.39 $0.34 $0.31 $0.28 $0.28 $0.35 $0.30 $0.34Price Reduction ($/MMBD) Mean $0.52 $1.04 $0.81 $0.75 $0.61 $0.60 $0.90 $0.75 $0.90Cases7) 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 of all non-OPEC supply. As aresult non-U.S. elasticity of import demand rangesfrom8) 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 mean value of -0.0495). OPECbehavior 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 disruptions (range -0.01 to -0.054; midcase value -0.032; mean value is -0.032,reduc3) Revise Case 2, with OPEC behavior distributed over elasticities 0 to 6, so that 25% of response isinelastic (< 1.0), mode elasticity is 2.0 (mean elasticity is 2.2 rather than 1.76)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-U.S. Supply = 0.2 to 0.4, withmean and mode 0.3, implying (mode) net elasticity of impo5) Revise Case 4, with OPEC behavior distributed over elasticities 0 to 6, so that 25% of response isinelastic (< 1.0), mode elasticity is 2.0 (mean elasticity is 2.2 rather than 1.76). Net elasticity of importdemand is -1.6 for the non-U.S./non-OPEC r6) 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 mode -0.4 mean -0.467, elasticity ofnon-U.S. supply = 0.2 to 0.6, mode 0.3 and mEconomic Disruption/AdjustmentCostsTotal Mid-Point1) 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 $20.33 to $48.34. All elasticitiesmatch 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.Monopsony ComponentDisruption Import CostsDisruption Dislocation CostsEconomic Assumptions Used in the Agencies’ Analysis

4-35 4.2.7.2.4 Energy security premium values for this rule After the peer review process was completed, EPA wo rked with ORNL to further update the analysis for this vehicle program. Usin g 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 th e years 2015, 2020, and 2030. Since the AEO 2009 ends its forecast in the year 2030, EPA an d 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 resourc es 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 produc e a savings in economic resources, although it does reduce the value of resources tran sferred from U.S. consumers of petroleum products to foreign suppliers of petroleum. In con trast, the macroeconomic disruption and adjustment costs that arise from U.S. petroleum imp orts do represent real economic costs, and reductions in their magnitude do represent real eco nomic benefits from reducing U.S. consumption and imports of petroleum. Hence the ag encies’ estimates of economic benefits from reducing fuel consumption via CAFE and GHG emi ssion standards include ORNL’s estimates of the economic value of resulting reduct ions 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 impor ts 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 reduc tion 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 rem aining 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 prod uction 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. i mports of crude petroleum or refined fuel by 0.95 gallons. y Section 5.3 of the RIA contains a discussion rega rding caveats for the fuel savings estimated due to implementation of this rul e.

w Differences between forecast annual U.S. imports o f crude petroleum and refined products among these three scenarios range from 24-89 percent of differences i n projected annual gasoline and diesel fuel consump tion in the U.S. These differences average 49 percent over the forecast period spanned by AEO 2009. This ana lysis has not been updated using forecasts from AEO 2010, bec ause to date only the Reference Case from AEO 2010 has been released by EIA.

x Differences between forecast annual U.S. imports o f 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 th e forecast period spanned by AEO 2009.

y This figure is calculated as 0.50 + 0.50*0.9 = 0.5 0 + 0.45 = 0.95.Joint Technical Support Document

4-36

The energy security analysis conducted for this rul e estimates that the world price of oil will fall modestly in response to lower U.S. de mand 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 airb orne toxics from their refining and use.
While additional information would be needed to ana lyze this “leakage effect” in detail, NHTSA provides a sample estimate of its potential m agnitude in its Final EIS. z This analysis indicates that the leakage effect is likely to offs et 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 di stribution and retailing also generate emissions of certain criteria air pollutants, inclu ding carbon monoxide (CO), hydrocarbon compounds (usually referred to as “volatile organic compounds,” or VOC), nitrogen oxides (NO
x ), fine particulate matter (PM 2.5 ), and sulfur dioxide (SO 2 ). Emissions of most of these pollutants are associated with the number of vehicl e 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 cri teria 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 exceptio n, 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 mag nitudes of reduced emissions in fuel refining and distribution, and increases in emissio ns 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 stan dards 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 lif etime 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 incr ease in total miles driven by cars and light trucks of each model year and age by their estimate d emission rates per vehicle-mile of each pollutant. These emission rates differ between car s 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 SO 2 , the agencies calculated the increase in emissions of these criteria pollutants from added car and light truck use by multiplying the es timated increases in their vehicles’ use

z NHTSA Final Environmental Impact Statement: Corpor ate Average Fuel Economy Standards, Passenger Cars and Light Trucks, Model Years 2012-2016, February 2 010, page 3-14.Economic Assumptions Used in the Agencies’ Analysis

4-37 during each year over their expected lifetimes by p er-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 usi ng its newly updated Motor Vehicle Emission Simulator (MOVES2010). For the pr oposal, emission rates were estimated using Draft MOVES2009.

The MOVES model assumes that the per-mile rates a t which these pollutants are emitted are determined by EPA regulations and the e ffectiveness of catalytic after-treatment of engine exhaust emissions, and are thus unaffecte d 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 ar e determined entirely by the increases in driving that result from the fuel economy rebound e ffect. 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 expresse d in the form of grams per vehicle-hour of operation. To convert these emissi on factors to grams per mile for use in EPA and NHTSA’s calculations, MOVES was run for the yea r 2050, and was programmed to report aggregate emissions from vehicle starting an d running exhaust operation. EPA analysts selected the year 2050 in order to generate emissio n factors that were representative of lifetime average emission rates for vehicles meetin g 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 emissio ns.

The MOVES emissions estimates were then summed to t he model year level and divided by average distance traveled in order to pr oduce 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 differen ces in fuel composition, as well as in the presence and type of vehicle inspection and mainten ance programs. bb

Emission rates for the criteria pollutant SO 2 were calculated by EPA and NHTSA using average fuel sulfur content estimates supplie d by EPA, together with the assumption
that the entire sulfur content of fuel is emitted i n the form of SO 2 . These calculations assumed that national average gasoline and diesel sulfur le vels would remain at current levels. cc

aa Because all light-duty emission rates in MOVES2010 are assumed to be invariant after MY 2010, a calen daryear 2050 run produced a full set of emission rates that reflect anticipated deterioration in the effe ctiveness of vehicles’ emission control systems with increasing age and accumulated mileage for post-MY 2010 vehicl es. bb The national mix of fuel types includes county-lev el market shares of conventional and reformulated g asoline, 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, measur ed on a mass basis) for gasoline and diesel respect ively, which produces emission rates of 0.17 grams of SO 2 per gallon of gasoline and 0.10 grams per gallon o f diesel.Joint Technical Support Document

4-38

There, unlike many other criteria pollutants, sulfu r dioxide emissions from vehicle use decrease proportionally to the decrease in fuel con sumption.

Emissions of criteria air pollutants also occur dur ing each phase of fuel production and distribution, including crude oil extraction and tr ansportation, fuel refining, and fuel storage and transportation. The reduction in emissions dur ing each of these phases depends on the extent to which fuel savings result in lower import s of refined fuel, or in reduced domestic fuel refining. To a lesser extent, they also depen d 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 reducti ons in imports of refined fuel would reduce criteria pollutant emissions during fu el 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 dome stic fuel refining using domesticallyproduced crude oil is assumed to reduce emissions d uring 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 Labor atories’ 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 trans portation and storage, fuel refining, and fuel distribution and storage. ee EPA modified the GREET model to change certain as sumptions about emissions during crude petroleum extraction a nd transportation, as well as to update its emission rates to reflect adopted and pending EPA e mission standards. EPA also incorporated emission factors for the air toxics es timated 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 o il travels to U.S. refineries are approximately the same regardless of whether it travels from domestic oilf ields or import terminals, and that the distances t hat gasoline travels from refineries to retail stations are appr oximately the same as those from import terminals t o gasoline
stations.

ee Emissions that occur during vehicle refueling at r etail gasoline stations (primarily evaporative emis sions of volatile organic compounds, or VOCs) are already ac counted for in the “tailpipe” emission factors used to estimate the emissions generated by increased light truck use. GREET estimates emissions in each phas e of gasoline production and distribution in mass per un it of gasoline energy content; these factors are th en converted to mass per gallon of gasoline using the average en ergy content of gasoline.Economic Assumptions Used in the Agencies’ Analysis

4-39 Table 4-15 Emissions by Stage of Fuel Production an d Distribution (grams/million Btu)

Pollutant Fuel Type Petroleum Extraction & Transportation 1 Refinery

Energy Use Upstream Emissions Petroleum

Refining On-Site Petroleum

Refining 2 Fuel

Transport, Storage, Distribution 3

Conventional Gasoline 4.908 0.928 5.596 6.525 0.748
Reformulated Gasoline 4.908 0.908 5.662 6.571 0.768 CO
Low Sulfur Diesel 4.908 0.800 5.103 5.903 0.780

Conventional Gasoline 3.035 0.602 2.560 3.162 42.91
Reformulated Gasoline 3.035 0.627 2.584 3.211 42.92 VOC

Low Sulfur Diesel 3.035 0.552 2.511 3.063 1.261

Conventional Gasoline 14.91 3.328 14.442 17.771 3.6 91
Reformulated Gasoline 14.91 3.288 14.575 17.863 3.7 86 NOx

Low Sulfur Diesel 14.91 2.895 12.972 15.866 3.570

Conventional Gasoline 3.926 4.398 9.678 14.076 0.88 6
Reformulated Gasoline 3.926 4.422 9.922 14.344 0.90 9 SOx

Low Sulfur Diesel 3.926 3.893 9.187 13.080 0.840

Conventional Gasoline 0.645 1.442 1.789 3.231 0.155
Reformulated Gasoline 0.645 1.487 1.838 3.325 0.159 PM2.5

Low Sulfur Diesel 0.645 1.309 1.635 2.943 0.133
Air Toxics
Conventional Gasoline 0.0017 0.0003 0.0014 0.0017 0 .0001

Reformulated Gasoline 0.0017 0.0003 0.0014 0.0018 0 .0001 1,3-Butadiene

Low Sulfur Diesel 0.0017 0.0003 0.0014 0.0017 0.000 1

Conventional Gasoline 0.0002 0.0000 0.0002 0.0002 0 .0046
Reformulated Gasoline 0.0002 0.0000 0.0002 0.0002 0 .0047 Acetaldehyde

Low Sulfur Diesel 0.0002 0.0000 0.0002 0.0002 0.004 4

Conventional Gasoline 0.0001 0.0000 0.0001 0.0001 0 .0006
Reformulated Gasoline 0.0001 0.0000 0.0001 0.0001 0 .0006 Acrolein

Low Sulfur Diesel 0.0001 0.0000 0.0001 0.0001 0.000 6

Conventional Gasoline 0.0313 0.0062 0.0264 0.0326 0 .0787
Reformulated Gasoline 0.0313 0.0064 0.0264 0.0328 0 .0788 Benzene

Low Sulfur Diesel 0.0313 0.0058 0.0264 0.0322 0.001 5

Conventional Gasoline 0.0050 0.0010 0.0042 0.0052 0 .0326
Reformulated Gasoline 0.0050 0.0010 0.0042 0.0052 0 .0335 Formaldehyde

Low Sulfur Diesel 0.0050 0.0009 0.0042 0.0051 0.031 6

1 The petroleum extraction and transport emission f actors are only applied to domestic crude oil.

2 Refinery emissions factors are applied to domesti c refineries, whether refining domestic or imported crude.

3 Fuel transport, storage, and distribution emissio n factors represent domestic emissions and are appl ied to all finished fuel, whether refined domestically or inte rnationally.

The agency converted these emission rates from the mass per fuel energy content basis on which GREET reports them to mass per gallon of f uel supplied using the estimates of fuel energy content reported by GREET. The resulting em ission rates were applied to both EPA’s and NHTSA’s estimates of fuel consumption under alt ernative fuel efficiency standards to develop estimates of total emissions of each criter ia pollutant during fuel production and distribution. The assumptions about the effects of changes in fuel consumption on domesticJoint Technical Support Document

4-40

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 poll utant.

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 antici pated to result from lower domestic fuel refining and distribution. ff As indicated previously, the effect of adopting i mproved fuel efficiency and GHG standards on total emissions of each criteria pollutant depends on the relative magnitudes of the resulting reduction in e missions from fuel refining and distribution,
and the increase in emissions from additional vehic le use. Although these net changes vary significantly among individual criteria pollutants, we project that on balance, adopting improved fuel efficiency and GHG standards would re duce emissions of all criteria air pollutants except carbon monoxide (CO). The analys is 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 i n the model year analysis

In this analysis, EPA and NHTSA estimate the econom ic value of the human health benefits associated with reducing exposure to PM 2.5 using benefit-per-ton estimates for the analysis of PM-related benefits over the lifetime o f specific model years. Due to analytical limitations, the estimated benefit-per-ton values d o not include comparable benefits related to reductions in other criteria pollutants (such as oz one, NO 2 or SO 2 ) or toxic air pollutants, nor do they monetize all of the potential health and we lfare effects associated with PM 2.5 or the other criteria pollutants. As a result these value s underestimate the benefits associated with reductions of these pollutants. Note that EPA cond ucted full-scale photochemical air quality modeling for the calendar year analysis. Please re fer to Chapter 7.2 of the RIA for a description of EPA’s air quality modeling results a nd to Chapter 7.3 for a description of the quantified and monetized PM- and ozone-related heal th impacts of the FRM.

The analysis of PM-related benefits over the lifeti me of specific model years uses a “benefit-per-ton” method to estimate selected PM 2.5 -related health benefits. These PM 2.5 - 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
PM 2.5 , or one ton of a pollutant that contributes to sec ondarily-formed PM 2.5 (such as NOx, SOx, and VOCs) from a specified source. Ideally, t he human health benefits would be estimated based on changes in ambient PM 2.5 concentrations and population exposure, as determined by full-scale air quality and exposure m odeling.

The dollar-per-ton estimates for reductions in PM 2.5 emissions used in this analysis are provided in Table 4-16.

ff All emissions from increased vehicle use are assum ed to occur within the U.S., since fuel efficiency standards would apply only to vehicles produced for sale in t he U.S.Economic Assumptions Used in the Agencies’ Analysis

4-41 Table 4-16 Benefits-per-ton Values (2007$) Derived Using the ACS Cohort Study for

PM-related Premature Mortality (Pope et al., 2002) a

All Sources d Stationary (Non-EGU)
Sources Mobile Sources Year c

SO X VOC NO X Direct PM2.5 NO X Direct PM2.5 Estimated Using a 3 Percent Discount Rate b 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 Rate b 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 tab le are based on an estimate of premature mortality derived from the ACS study (Pope et al., 2002). If the ben efit-per-ton estimates were based on the Six-Cities study (Laden et al., 2006), the values would be approxima tely 145% (nearly two-and-a-half times) larger.

b The benefit-per-ton estimates presented in this ta ble assume either a 3 percent or 7 percent discount rate in the valuation of premature mortality to account for a t wenty-year segmented cessation lag.

c Benefit-per-ton values were estimated for the year s 2015, 2020, and 2030. For 2040, EPA and NHTSA extrapolated exponentially based on the growth betw een 2020 and 2030.

d Note that the benefit-per-ton value for SOx is bas ed on the value for Stationary (Non-EGU) sources; n o SOx value was estimated for mobile sources. The benefi t-per-ton value for VOCs was estimated across all s ources.

e Non-EGU denotes stationary sources of emissions ot her 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 st ationary 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 indi viduals’ willingness to pay for reduced exposure to health threats from air pollution, as w ell as population growth into the future, expanding the population exposed to air pollution i n both urban and rural areas. 23

For certain PM 2.5 -related pollutants (such as direct PM 2.5 and NOx), EPA estimates different per-ton values for reducing mobile sourc e emissions than for reductions in emissions of the same pollutant from stationary sou rces such as fuel refineries and storage facilities. These reflect differences in the typic al geographic distributions of emissions of each pollutant, their contributions to ambient leve ls of PM 2.5 , and resulting changes in population exposure. EPA and NHTSA apply these sep arate 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 emiss ions of those pollutants.

The benefit per-ton technique has been used in prev ious analyses, including EPA’s recent Ozone National Ambient Air Quality Standards (NAAQS) RIA, 24 the proposed
Portland Cement National Emissions Standards for Ha zardous Air Pollutants (NESHAP) RIA, 25 and final NO 2 NAAQS. 26 Table 4-17 shows the quantified and unquantified PM 2.5 - related co-benefits captured in these benefit-per-t on estimates.Joint Technical Support Document

4-42 Table 4-17 Human Health and Welfare Effects of PM 2.5

Pollutant / Effect Quantified and Monetized

in Primary Estimates Unquantified Effects Changes in:
PM 2.5 Adult premature mortality

Bronchitis: chronic and acute Hospital admissions: respiratory and
cardiovascular Emergency room visits for asthma Nonfatal heart attacks (myocardial infarction) Lower and upper respiratory illness Minor restricted-activity days Work loss days Asthma exacerbations (asthmatic population) Infant mortality Subchronic bronchitis cases Low birth weight Pulmonary function Chronic respiratory diseases other than
chronic bronchitis Non-asthma respiratory emergency room
visits Visibility Household soiling

Consistent with the NO 2 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-p er-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-to n values calculated therefore apply only to tons reduced from those specific pollutant/source c ombinations ( e.g. , NO 2 emitted from mobile sources; direct PM emitted from stationary s ources). Our estimate of total PM 2.5 benefits is therefore based on the total direct PM 2.5 and PM 2.5 –related precursor emissions (NOx, SOx, and VOCs) controlled from each source an d multiplied by the respective per-ton values of reducing emissions from that source.

The benefit-per-ton coefficients in this analysis w ere derived using modified versions of the health impact functions used in the PM NAAQS Regulatory Impact Analysis. Specifically, this analysis uses the benefit-per-to n 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 chap ter, we encourage interested readers to see benefit s chapter of the NO2 NAAQS for a more detailed descri ption of recent changes to the PM benefits presenta tion and preference for the no-threshold model.

hh The values included in this report are different f rom those presented in the article cited above. Be nefits methods change to reflect new information and evalu ation of the science. Since publication of the Jun e 2009 article, EPA has made two significant changes to it s benefits methods: (1) We no longer assume that a threshold exists in PM-related models of health impacts, whic h is consistent with the findings reported in publi shed research; and (2) We have revised the Value of a St atistical Life to equal $6.3 million (year 2000$), up from an estimate of $5.5 million (year 2000$) used in the J une 2009 report. Please refer to the following web site for updates to the dollar-per-ton estimates: http://www .epa.gov/air/benmap/bpt.htmlEconomic Assumptions Used in the Agencies’ Analysis

4-43 the epidemiology studies, without any adjustment fo r an assumed threshold. Removing the threshold assumption is a key difference between th e method used in this analysis to estimate PM co-benefits and the methods used in analyses pri or to EPA’s Portland Cement NESHAP. As a consequence, the benefit-per-ton estimates use d in this analysis include incremental
benefits of reductions in PM 2.5 concentrations down to their lowest modeled levels.

Reductions in PM-related mortality provide the majo rity of the monetized value in each benefit-per-ton estimate. Typically, the prem ature mortality-related effect coefficients that underlie the benefits-per-ton estimates are dr awn from epidemiology studies that examine two large population cohorts: the American Cancer S ociety 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 Am erican 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 Ha rvard Six Cities cohort, as reported by Laden et al (2006), was published after the complet ion of the Staff Paper for the 2006 PM 2.5 NAAQS and has been used as an alternative estimate in the PM 2.5 NAAQS RIA and PM 2.5 cobenefits estimates in analyses completed since the PM 2.5 NAAQS.

These studies provide logical choices for anchor po ints when presenting PM-related benefits because, while both studies are well desig ned and peer-reviewed, there are strengths and weaknesses inherent in each. Although this arg ues for using both studies to generate benefits estimates, due to the analytical limitatio ns 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 o n 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 scie ntific and economic literature. For a period of time (2004-2008), EPA’s Office of Air and Radiat ion (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 VS L using a range of $1 million to $10 million (2000$) consistent with two meta-analyses o f the wage-risk literature.

The $1 million value represented the lower end of t he interquartile range from the Mrozek and Taylor (2002) 31 meta-analysis of 33 studies. The $10 million valu e represented the upper end of the interquartile range from the V iscusi and Aldy (2003) 32 meta-analysis of 43 studies. The mean estimate of $5.5 million (200 0$) 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 pr ocess 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,Joint Technical Support Document

4-44

EPA has decided to apply the VSL that was vetted an d 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 valuati on studies published between 1974 and 1991. The mean VSL across these studies is $6.3 mi llion (2000$). The dollar-per-ton estimates used in this analysis are based on this r evised VSL.

The benefit-per-ton estimates are subject to a numb er of assumptions and
uncertainties.

a. They do not reflect local variability in population density, meteorology, exposure, baseline health incidence rates, or other local fac tors that might lead to an
overestimate or underestimate of the actual benefit s of controlling fine particulates in specific locations. EPA conducted full-scale air q uality 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 res ults and to Chapter 7.3 for the description of the agency’s quantification and mone tization of PM- and ozone-related health impacts for the FRM.
b. This analysis assumes that all fine particles, rega rdless of their chemical composition, are equally potent in causing premature mortality. This is an important assumption,
because PM 2.5 produced via transported precursors emitted from st ationary sources may differ significantly from direct PM 2.5 released from engines and other industrial sources. At the present time, however, no clear sc ientific grounds exist for supporting differential effects estimates by particle type.

c. This analysis assumes that the health impact functi on for fine particles is linear within the range of ambient concentrations under considera tion. Thus, the estimates include health benefits from reducing fine particles in are as with varied initial concentrations
of PM 2.5, including both regions that are in attainment with fine particle standard and those that do not meet the standard, down to the lo west modeled concentrations.
d. There are several health benefits categories that E PA and NHTSA were unable to quantify due to limitations associated with using b enefits-per-ton estimates, several of
which could be substantial. Because NO X and VOC emissions are also precursors to ozone, changes in NO X and VOC would also impact ozone formation and the h ealth effects associated with ozone exposure. Benefits-p er-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. Th e 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 qua ntification and monetization of both PM- and ozone-related health impacts for the FRM an d 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 fort hcoming in the near future. Therefore, this report does no t 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>.Economic Assumptions Used in the Agencies’ Analysis

4-45 As mentioned above, emissions changes and benefits- per-ton estimates alone are not a good indication of local or regional air quality an d health impacts, as the localized impacts associated with the rulemaking may vary significant ly. Additionally, the atmospheric chemistry related to ambient concentrations of PM 2.5 , ozone and air toxics is very complex. Full-scale photochemical modeling is therefore nece ssary to provide the needed spatial and temporal detail to more completely and accurately e stimate the changes in ambient levels of these pollutants and their associated health and we lfare impacts. For the final rule, EPA conducted a national-scale air quality modeling ana lysis in 2030 to analyze the impacts of the
standards on PM 2.5 , ozone, and selected air toxics.



### 4.2.9 Reductions in emissions of greenhouse gases

Emissions of carbon dioxide and other greenhouse ga ses (GHGs) occur throughout the process of producing and distributing transportatio n 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 emis sions generated by fuel use, as well as throughout the fuel supply cycle. Lowering these e missions is likely to slow the projected pace and reduce the ultimate extent of future chang es 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 c hanges with potentially catastrophic economic or environmental impacts will occur, lower ing GHG emissions may also result in economic benefits that exceed the resulting reducti on in the expected future economic costs caused by gradual changes in the earth’s climatic s ystems. Quantifying and monetizing benefits from reducing GHG emissions is thus an imp ortant step in estimating the total economic benefits likely to result from establishin g improved fuel efficiency and GHG
standards.

4.2.9.1 Estimating reductions in GHG emissions

The agencies estimated emissions of carbon dioxide (CO 2 ) from passenger car and light truck use by multiplying the number of gallon s 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 CO 2 emissions released per gallon of fuel consumed. T his calculation assumes
that the entire carbon content of each fuel is ulti mately converted to CO 2 emissions during the combustion process. The weighted average CO 2 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 nearl y 95 percent of total GHG emissions that result from fuel combustion during v ehicle use, emissions of other GHGs are potentially significant as well because of their hi gher “potency” as GHGs than that of CO 2 itself. EPA and NHTSA estimated the increases in e missions of methane (CH 4 ) and nitrous oxide (N 2 O) from additional vehicle use by multiplying the i ncrease 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 c ars and light trucks as well as between gasoline and diesel vehicles, were estimated by EPA using its recently-developed MotorJoint Technical Support Document

4-46

Vehicle Emission Simulator (Draft MOVES 2009), as s ubsequently 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 afte r-treatment of engine exhaust, and are thus independent of vehicles’ fuel consumption rates. T hus MOVES emission factors for these GHGs, which are expressed per mile of vehicle trave l, are assumed to be unaffected by changes in fuel economy.

Increases in emissions of non-CO 2 GHGs are converted to equivalent increases in CO 2 emissions using estimates of the Global Warming Pot ential (GWP) of methane and nitrous oxide. These GWPs are one way of accounting for th e higher radiative forcing capacity and differing lifetimes of methane and nitrous oxide wh en they are released into the earth’s atmosphere, measured relative to that of CO 2 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, CO 2 emissions, unlike methane and other greenhouse gase s, 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 CO 2 fertilization. Noting these caveats, the CO 2 equivalents of increases in emissions of these gases are then added to the increases in emissions of CO 2 itself to summarize the effect of the
total increase in CO 2 -equivalent GHG emissions from vehicle use. Howeve r, only the CO2 emissions were monetized for purposes of valuing be nefits of the rule.

4.2.9.2 Economic benefits from reducing GHG emissions

NHTSA and EPA have taken the economic benefits of reducing CO 2 emission into account in this rulemaking, both in developing alte rnative 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 CO 2 emissions using the marginal dollar value (i.e., cost) of climate-related damages resulting from car bon emissions, also referred to as “social cost of carbon” (SCC). The SCC is intended to meas ure the monetary value society places on impacts resulting from increased GHGs, such as prop erty damage from sea level rise, forced migration due to dry land loss, and mortality chang es associated with vector-borne diseases. Published estimates of the SCC vary widely as a res ult of uncertainties about future economic growth, climate sensitivity to GHG emissions, proce dures used to model the economic impacts of climate change, and the choice of discou nt rates.

EPA and NHTSA developed new SCC estimates through a n interagency modeling exercise. The modeling exercise involved running t hree integrated assessment models using inputs agreed upon by the interagency group for cli mate sensitivity, socioeconomic and emissions trajectories, and discount rates. A more complete discussion of SCC, including the revised estimates and underlying methodology, can b e found in the SCC TSD, Social Cost ofEconomic Assumptions Used in the Agencies’ Analysis

4-47 Carbon for Regulatory Impact Analysis Under Executi ve Order 12866 (February 2010). jj See also preamble Sections II.F, III.H.6 and IV.C.3, EP A RIA 7.5, and NHTSA RIA VIII for discussion about the application of new SCC estimat es to this final rule. The revised SCC estimates corresponding to assumed values of the di scount rate are shown below in Table 4-
18.

Table 4-18 Social Cost of CO 2 , 2010 Emissions a (in 2007 dollars)

Discount Rate 5% 3% 2.5% 3%

Source Average

of Estimates Average

of Estimates Average

of Estimates 95 th

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 reducti ons in non-CO 2 GHG emissions (HFC, CH 4 and
N 2 O) expected under this final rule. Although the ag encies have not monetized the benefits of reduction s in
these non-CO 2 emissions, the value of these reductions should not be interpreted as zero. Rather, the reductions in non-CO 2 GHGs will contribute to this rule’s climate benefit s, as explained in Section III.F.2. The SCC TSD
notes the difference between the social cost of non -CO 2 emissions and CO 2 emissions, and specifies a goal to develop methods to value non-CO 2 emissions in future analyses.



### 4.2.10 The value of increased driving range

Improving the fuel economy of passenger cars and li ght-duty trucks may also increase their driving range before they require refueling. By reducing the frequency with which drivers typically refuel their vehicles and extendi ng the upper limit of the range they can travel before requiring refueling, improving fuel e conomy thus provides some additional benefits to their owners. Alternatively, if manufa cturers respond to improved fuel economy by reducing the size of fuel tanks to maintain a co nstant driving range, the resulting cost saving will presumably be reflected in lower vehicl e sales prices. If manufacturers respond by doing so, this presumably reflects their judgmen t that the value to economic benefits to vehicle buyers from lower purchase prices exceeds t hat 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, Offic e of Management and Budget, Office of Science and Technology Policy, and Department of Treasury, “Soc ial Cost of Carbon for Regulatory Impact Analysis U nder Executive Order 12866,” February 2010, available in docket EPA-HQ-OAR-2009-0472.Joint Technical Support Document

4-48

No direct estimates of the value of extended vehicl e range are readily available, so the agencies’ analyses calculate the reduction in the a nnual number of required refueling cycles that results from improved fuel economy, and applie s 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 approx imately 20 gallons. kk Based on a California Air Resources Board Study, the average f uel 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 d riven 12,000 miles/year, this reduces the number of times it needs to be refueled each year f rom 55.5 (= 12,000 miles per year / 216 miles per refueling) to 53.3 (= 12,000 miles per ye ar / 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 dol lars). 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 woul d remain in service. Like fuel savings and other benefits, however, the value of this bene fit declines over a model year’s lifetime, because a smaller number of vehicles originally pro duced during that model year remain in service each year, and those remaining in service a re driven fewer miles. The detailed values used in this calculation by each agency are availab le in EPA’s RIA and NHTSA’s FRIA respectively.

The agencies’ estimate of benefits from less freque nt refueling is subject to several sources of uncertainty. First, this analysis assume s 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 r efueling 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 resul tant benefit to consumers. However,

kk Based on the Volpe Model Market Data file for Mode l Year 2011, average tank volumes for cars and truc ks are 16.6 gallons and 23.0 gallons, respectively. T his 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. Busin ess travel (5.6 percent of urban travel) is valued at 100 percent of the hourly wage rate. For intercity tra vel, personal travel (87 percent) is valued at 70 p ercent of the wage rate, while business travel (13 percent) is va lued at 100 percent of the wage rate. The resultin g values of travel time are $12.67 for urban travel and $17.66 for intercity travel, and must be multiplied by veh icle occupancy (1.6) to obtain the estimate value of tim e per vehicle hour.Economic Assumptions Used in the Agencies’ Analysis

4-49 should fuel tank sizes be reduced, vehicle space, u tility and value would increase and vehicle weight and production costs would decrease, improvi ng fuel economy and CO 2 emissions.

A larger fuel tank size requires additional manufac turing 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, reduce s fuel economy and increases CO 2 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 p eace of mind when operating a vehicle in areas with limited refueling options. Given this c lear trade-off, manufacturers must use some type of optimization schema to determine the approp riate tank size for each vehicle. If
manufacturers choose to retain the current fuel tan k size, then the impact on manufacturing costs is obvious: zero. At least some portion of t he driving public will save a considerable amount of time with some value. Given range is a w idely publicized vehicle attribute, manufacturers will only reduce tank size if it lowe rs production costs sufficiently to overcome the loss in vehicle desirability. Thus, either con sumers save time or production costs go down. Much as the agencies assume that cost increa ses are passed through to consumers, the agencies assume that cost savings from reducing fue l tank size (by reducing engineering complexity and materials required) would be passed through to consumers.

Further, the agencies assume that manufacturers ope rate in a manner that maintains or increases the desirability of a vehicle. Consequen tly, 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 contex t of the rule, the improvement in CO 2 or fuel economy from downsizing a fuel tank will addit ionally 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 pu rchases 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 dri vers 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 ref ueling 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 t heir newspaper and chewing gum. Barring unusually long trips, these people would no t benefit from an increased driving range.Joint Technical Support Document

4-50

In order to understand how sensitive the overall es timate of the refueling benefit is to this specific input, we provide the following examp le. If 25 percent of gallons are refueled on a habitual rather than demand-based schedule, and t he rest of the assumptions remain constant, the value of increased driving range decr eases 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 i ncreased 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 woul d decrease to $3.30. Unfortunately, the agencies do not have a basis for this 25 percent va lue, thus this example is for illustrative purposes only. However, as noted below, DOT is und ertaking a new survey which may provide a data-based basis for revising our 100 per cent 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 typica l vehicle occupancy during refueling, and thus the total savings in refueling time. Similarl y, the hourly value used to estimate the economic value of savings in refueling time reflect s the typical mix of personal and business travel purposes, and drivers are likely to assign d ifferent values to their time when traveling for these different purposes. To the extent that d rivers 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 ti me.

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 saving s in refueling time is uncertain. To reduce the uncertainty, a new project is being plan ned 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 re fuel, # gallons refueled, etc.) and ~5,000 surveys of refueling participants. The agencies an ticipate that this will provide a robust data set on which to revise many of the key inputs to th e 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 stat ion

• 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 f or this final rulemaking, it is anticipated that the data will improve future estim ations of the value of increased driving

range.Economic Assumptions Used in the Agencies’ Analysis

4-51 

### 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 d eferred 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 increas es in fuel economy and GHG standards for MY 2012-2016 passenger cars and light trucks, EPA a nd NHTSA consider a discount rate of both 3 and 7 percent per year.

Three percent may be the appropriate rate for disco unting future benefits from increased fuel economy and GHG standards because mo st 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 vehi cle models. By increasing sales prices for new cars and light trucks, CAFE regulation will thu s primarily affect vehicle purchases and other private consumption decisions.

Both economic theory and OMB guidance on discountin g indicate that the future benefits and costs of regulations that mainly affec t 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 (tha t is, adjusted to remove the effect of inflation) rate of about 3 percent when they face l ittle 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 projec ted 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 w ith improved fuel economy and GHG standards by increasing vehicle sales prices, howev er, the use of a higher percent discount rate may also be appropriate. OMB guidance indicates th at 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 abo ut 7 percent. 39 Thus the agencies estimate net present values using both 3 and 7 percent disco unt rates.

One important exception to these values are the rat es used to discount benefits from reducing CO 2 emissions from the years in which reduced emission s occur, which span the lifetimes of model year 2012-16 cars and light truc ks, 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 CO 2 emissions, the total benefits from reducing those emissions during each future year are discounted using the same rate s that were used to derive the alternative values of reducing each ton of CO 2 emissions. As described previously in Section 4.2 .10.2, these rates are 2.5, 3.0, and 5.0 percent.Joint Technical Support Document

4-52 References

1 EPA, Fuel Economy Labeling of Motor Vehicles: Revi sions To Improve Calculation of Fuel Economy Estimates; Final Rule, 40 CFR Parts 86 and 600, Fed eral Register, December 27, 2006, pp. 77872-77969, http://www.epa.gov/fedrgstr/EPA-AIR/2006/December/D ay-27/a9749.pdf

.

2 EPA, Final Technical Support Document: Fuel Economy Labe ling of Motor Vehicle Revisions to Improve Calculation of Fuel Economy Estimates , Office of Transportation and Air Quality EPA420-R -06-017 December 2006, Chapter II, http://www.epa.gov/fueleconomy/420r06017.pdf

.

3 Energy Information Administration, Annual Energy O utlook 2010, Early Release Reference Case (Decembe r 2009), Table 12. Available at http://www.eia.doe.gov/oiaf/servicerpt/stimulus/exc el/aeostimtab_12.xls

4 Energy Information Administration, Annual Energy O utlook 2009, Revised Updated Reference Case (April 2009), Table 12. Available at http://www.eia.doe.gov/oiaf/servicerpt/stimulus/exc el/aeostimtab_12.xls (last accessed July 26, 2009). EIA’s Updated Reference Case reflects the effects of the American Reinvestm ent and Recovery Act of 2009, as well as the most recent re visions to the U.S. and global economic outlook.

5 Kahan, A. and Pickrell D. “Energy Information Adm inistration’s Annual Energy Outlook 2009 and 2010” Memo to Docket EPA-HQ-OAR-2009-0472 and NHTSA–2009– 0059.

6 http://www.eia.doe.gov/oiaf/aeo/forecast.html (las t accessed July 26, 2009), and Table 16, http://www.eia.doe.gov/oiaf/aeo/pdf/table16.pdf (la st accessed July 26, 2009).

7 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/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 a nd light trucks are 13.8 and 14.5 years.

8 FHWA, Highway Statistics, Summary to 1995, Table v m201at 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 Rev iew of Evidence for the Rebound Effect, Technical R eport 2: Econometric Studies”, UKERC/WP/TPA/2007/010, UK Energy Research Centre, London, October and Greening, L.A., D.L. Greene and C. Difiglio, 2000. “Energy Efficiency and Consumption – The Rebound Ef fect – A Survey”, Energy Policy, vol. 28, pp. 389-401.

10 Greening et al. 2000.

11 Pickrell, D. and P. Schimek, 1999. “Growth in Moto r Vehicle Ownership and Use: Evidence from the Nationwide Personal Transportation Survey”, Journal of Transportation and Statistics, vol. 2, no. 1, p p. 1-17.

12 Small, K. and K. Van Dender, 2007a. “Fuel Efficien cy and Motor Vehicle Travel: The Declining Rebound Effect”, The Energy Journal, vol. 28, no. 1, pp. 25 -51.

13 Small, K. and K. Van Dender, 2007b. "Long Run Tren ds in Transport Demand, Fuel Price Elasticities and Implications of the Oil Outlook for Transport Polic y," OECD/ITF Joint Transport Research Centre Discus sion Papers 2007/16, OECD, International Transport Forum

.

14 Revised Report by David Greene of Oak Ridge Nation al Laboratory to EPA,“Rebound 2007: Analysis of National Light-Duty Vehicle Travel Statistics,” Feb ruary 9, 2010 (Docket EPA-HQ-OAR-2009-0472-0220). This paper has been accepted for an upcoming specia l issue of Energy Policy, although the publication date has not yet been determined.

15 FHWA. 1997 Federal Highway Cost Allocation Study ; http://www.fhwa.dot.gov/policy/hcas/final/index.h tm (last accessed July 29, 2009).Economic Assumptions Used in the Agencies’ Analysis

4-53 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. 1 5, 2010).

17 U.S. Bureau of Economic Analysis, U.S. Internation al Transactions Accounts Data, as shown on June 24,
2009.

18 U.S. Department of Energy, Annual Energy Review 20 08, Report No. DOE/EIA-0384(2008), Tables 5.1 and 5.13c, June 26, 2009.

19 Leiby, Paul N. "Estimating the Energy Security Ben efits of Reduced U.S. Oil Imports," Oak Ridge Natio nal Laboratory, ORNL/TM-2007/028, Final Report, 2008.

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

21 Gately, Dermot 2004. "OPEC's Incentives for Faste r 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 Tra nsportation (GREET) Model , Version 1.8, June 2007, available at http://www.transportation.anl.gov/software/GREET/in dex.html (last accessed April 20, 2008).

23 The issue is discussed in more detail in the PM NA AQS RIA from 2006. See U.S. Environmental Protecti on Agency. 2006. Final Regulatory Impact Analysis (R IA) for the Proposed National Ambient Air Quality Standards for Particulate Matter. Prepared by: Off ice 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 Ozon e, Chapter 6. Office of Air Quality Planning and Standards, Research Triangle Park, NC. March. Ava ilable 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 Emissio n Standards for Hazardous Air Pollutants from the Por tland Cement Manufacturing Industry. Office of Air Quality Planning and Standards, Research Triangle P ark, NC. April. Available on the Internet at <http://www.epa.gov/ttn/ecas/regdata/RIAs/portlandc ementria_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 Standard s, Research Triangle Park, NC. April. Available o n the
Internet at http://www.epa.gov/ttn/ecas/regdata/RIAs/FinalNO2RI Afulldocument.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 Benef it Per-Ton estimates, Ozone NAAQS Docket #EPA-HQ-OAR-2 007-0225-0284. Office of Air Quality Planning and Standards, Research Triangle Park, NC. March. Available on the Internet at <http://www.regulatio ns.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 hum an health benefits of reducing a ton of air pollution. Air Q ual 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. Cal le, D. Krewski, K. Ito, and G.D. Thurston. 2002. “ Lung Cancer, Cardiopulmonary Mortality, and Long-term Ex posure 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. Doc kery. 2006. “Reduction in Fine Particulate Air Pol lution and Mortality.” American Journal of Respiratory an d Critical Care Medicine 173:667-672. Estimating t heJoint Technical Support Document

4-54

Public Health Benefits of Proposed Air Pollution Re gulations. Washington, DC: The National Academies Press. EPA-HQ-OAR-2009-0472-1661

31 Mrozek, J.R., and L.O. Taylor. 2002. “What Deter mines the Value of Life? A Meta-Analysis.” Journa l of Policy Analysis and Management 21(2):253-270. EPA-H Q-OAR-2009-0472-1677

32 Viscusi, V.K., and J.E. Aldy. 2003. “The Value of a Statistical Life: A Critical Review of Market Es timates throughout the World.” Journal of Risk and Uncerta inty 27(1):5-76. EPA-HQ-OAR-2009-0472-0245

33 Kochi, I., B. Hubbell, and R. Kramer. 2006. An E mpirical Bayes Approach to Combining Estimates of t he Value of Statistical Life for Environmental Policy Analysis. Environmental and Resource Economics. 3 4: 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 Environmenta l Economics, Office of Policy Economics and Innovation. Washington, DC. September. Available on the Internet at <http://yosemite.epa.gov/ee/epa/eed.nsf/webpages/Gu idelines.html/$file/cover.pdf>. EPA-HQ-OAR-2009-047 2-
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 Re sources Board. Draft Assessment of the Real-World Impacts of Commingling California Phase 3 Reformula ted Gasoline. August 2003

37 Id.

38 Office of Management and Budget, Circular A-4, “Re gulatory Analysis,” September 17, 2003, 33. Available

at http://www.whitehouse.gov/omb/circulars/a004/a-4.p df (last accessed July 24, 2009).

39 Id.