April 6, 2009 

Impacts of the California 

Greenhouse Gas Emission 

Standards on Motor Vehicle 

Sales 

4/6/09
Apr 6, 09 

Project Team 

The NERA/Sierra study was directed by David Harrison, Jr., Ph.D., at NERA 

Economic Consulting (“NERA”) and by James Lyons at Sierra Research, Inc. 

(“Sierra”). Other NERA participants (in alphabetical order) are James Johndrow, 

David Nagler, and Bernard Reddy, Ph.D. The other Sierra participant is Tom Carlson. 
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NERA Economic Consulting i 

Contents 

List of Figures............................................................................................................................iii 

List of Tables.............................................................................................................................iv 

Executive Summary................................................................................................................ E-1 

A. Background................................................................................................................. E-1 

B. Study Objectives ......................................................................................................... E-1 

C. Study Results .............................................................................................................. E-2 

I. Introduction.....................................................................................................................1 

A. Background.....................................................................................................................1 

B. Objectives of this Report .................................................................................................1 

C. Outline of the Report.......................................................................................................1 

II. Methodology and Data ....................................................................................................2 

A. Overview of the California Motor Vehicle Greenhouse Gas Emission Standards.............2 

1. California Standards..................................................................................................2 

2. States Adopting the California Standards...................................................................2 

B. Compliance Plans............................................................................................................4 

C. New Vehicle Market Model ............................................................................................5 

1. Nested Logit Model Formulation...............................................................................5 

2. Use of the New Vehicle Market Model......................................................................7 

III. Study Results ..................................................................................................................8 

A. U.S. New Vehicle Sales...................................................................................................8 

B. Other Impacts..................................................................................................................8 

APPENDIX A. Analysis of Manufacturer Compliance ......................................................A-1 

A. Background.................................................................................................................A-1 

B. Determination of Federal CAFE Compliance ..............................................................A-3 

C. Analysis of Dropped Sales Needed for GHG Compliance..........................................A-20 

D. Summary of Results ..................................................................................................A-24 

APPENDIX B. New Vehicle Market Model......................................................................B-1 

A. Conceptual Approach: Nested Logit Model.................................................................B-1 

1. Basic Framework...................................................................................................B-1 

2. Nesting Assumptions.............................................................................................B-2 

B. Simulation of Aggregate Market Shares ......................................................................B-4 

C. Market Simulation.......................................................................................................B-5 

D. Solving for Parameters................................................................................................B-7 

E. Specific Implementation Parameters and Data .............................................................B-7 

1. Vehicle Sales.........................................................................................................B-7 

2. Vehicle Prices........................................................................................................B-7 

3. Categorization of Vehicle Models into Nests.........................................................B-8 
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4. Price Elasticity.......................................................................................................B-8 

ATTACHMENT A. Analysis of Employment Impacts.........................................................A-1 
List of Figures 

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List of Figures 

Figure ES-1. Change in Sales in Relevant States due to Adoption of the California Standards E-2 

Figure 1. Hierarchy of NVMM nesting structure.........................................................................6 

Figure B-1. Nesting structure for NVMM ...............................................................................B-3 
List of Tables 

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NERA Economic Consulting iv 

List of Tables 

Table 1. California GHG Emissions Standards............................................................................2 

Table 2. States that Have Adopted the California Standards........................................................3 

Table 3. Model Year 2006 Vehicle Sales in States that Have Adopted the California Standards 

and National Vehicle Sales (thousands)...........................................................................4 

Table 4. Change in Sales in Relevant States due to the California Standards ...............................8 

Table A-1 Characteristics of the MY2006 U.S. Light-Dutya Vehicle Fleet ............................A-5 

Table A-2 DDR Constrained Logistic Fuel Economy vs. Footprint Attribute Model 

Coefficients.................................................................................................................A-6 

Table A-3 Sierra and DDR Fleet-Based Fuel Economy Targets for Passenger Cars...............A-8 

Table A-4 Sierra and DDR Fleet-Based Fuel Economy Targets for Light-Duty Trucks.........A-8 

Table A-5 EISA Alternative Fuel (E85) Credits by Model Year............................................A-9 

Table A-6 Technology Combinations Modeled by Vehicle Type for Compliance with NHTSA 

NPRM CAFE Standards............................................................................................A-10 

Table A-7 Base Technology Costs (Based on Midsize Sedan/Wagon with 3,226 lb Reference 

Weight).....................................................................................................................A-12 

Table A-8. Results Sheet Layout for GM Fleet Compilation..................................................A-14 

Table A-9 Technology Phase-In Categories ........................................................................A-15 

Table A-10 Additional Technology Phase-In Schedule for MY2011-2015 CAFE Standards 

(Percent of Production Changed by Model Year).......................................................A-15 

Table A-11. Model-Year-Specific CAFE Compliance Analysis for GM................................A-18 

Table A-12 CARB GHG Standards and Equivalent Fuel Economy Model Years 2011-2016..A21 

Table A-13 A/C Credit and Exhaust Gas Levels Assumed in CO2-Equivalent to Fuel Economy 

Conversions ..............................................................................................................A-22 
Executive Summary 

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Executive Summary 

This study evaluates the effects of the California motor vehicle greenhouse gas emission 

standards (“California Standards”) on sales of new motor vehicles.

1

 The study includes the 

effects on motor vehicle sales of the California Standards, assuming that they are implemented in 

the 13 states that have adopted California’s standards. 

A. Background 

California AB 1493, passed by the legislature and signed by Governor Davis in 2002, called for 

the California Air Resources Board (“CARB”) to develop regulations for greenhouse gas 

emissions from motor vehicles. On September 24, 2004, CARB adopted greenhouse gas 

(“GHG”) emission regulations for passenger cars, light-duty trucks (“LDT”) and medium-duty 

passenger vehicles (“MDPV”) beginning with the 2009 model year. Light duty trucks are broken 

down into two categories: LDT1 and LDT2. LDT1s are all light trucks below the test weight cutoff of 3,750 pounds. LDT2s are all light trucks between the test weight cut-off but below 8,500 

pounds Gross Vehicle Weight (“GVW”). Medium-duty passenger vehicles are vehicles between 

8,500 and 10,000 pounds GVW (generally large sports utility vehicles and pickups). There is one 

standard for passenger cars and LDT1s (PC/LDT1) and another standard for LDT2s and MDPVs 

(LDT2+). 

Twelve states other than California have already adopted the California Standards. Our analyses 

are based upon these 13 states, which represent about 34 percent of overall U.S. motor vehicle 

sales. 

In a letter dated December 21, 2005, CARB submitted a request that the U.S. Environmental 

Protection Agency (“EPA”) grant a waiver allowing California to promulgate these regulations. 

EPA denied this request on March 6, 2008. On February 12, 2009, EPA published a notice in the 

Federal Register announcing that it was reconsidering its previous denial of the waiver. 

B. Study Objectives 

The objective of this report is to evaluate the effects of the California Standards on new motor 

vehicle sales in the 13 states that have adopted the California Standards. Our estimates are based 

upon assessments of the likely changes in vehicle mix that various new vehicle manufacturers 

would make to meet the California Standards in the relevant states. These changes are relative to 

a baseline that assumes compliance with the proposed federal corporate average fuel economy 

(“CAFE”) standards. 

 

1 This report is being submitted to EPA on behalf of some but not all members of the Alliance of Automobile 

Manufacturers. 
Executive Summary 

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NERA Economic Consulting E-2 

C. Study Results 

Figure ES-1 summarizes the results of our analysis of the effects of the California Standards on 

motor vehicle sales in the 13 states over the period from 2012 to 2016. The results are provided 

as a percentage of baseline sales of motor vehicles in each year. (These results are based upon a 

case in which manufacturers generate partial credits related to air conditioning GHG emissions; 

the report provides results for another case in which the manufacturers generate full credits.) 

These results indicate that the California Standards will result in a substantial reduction in new 

motor vehicle sales in these 13 states. In 2012, the California Standards would result in a 8.5 

percent reduction in motor vehicle sales. In 2016, the reduction in sales would be about 8.0 

percent. 

The California Standards will have impacts beyond reductions in new motor vehicle sales in the 

states that adopt the California Standards. These other impacts, which have not been quantified, 

include the following: 

§ Motor vehicle fleet effects. Reductions in new vehicle sales will lead to changes in the motor 

vehicle fleet over time. In particular, the California Standards have the effect of changing the 

age distribution of the vehicle population. In future years (e.g., 2020), the numbers of motor 

vehicles of older vintages will increase and the numbers of motor vehicles of newer vintages 

will decrease as a result of the California Standards. 

-5.4%

-8.0%

-6.1%

-5.7%

-8.5%

-10%

-5%

0%

2012 2013 2014 2015 2016

Model Year

P

erc

e

nt C

h

a

n

g

e i

n

S

ales 

R

elativ

e t

o

B

aseline

Note: For partial air conditioning credit scenario. 

Source: NERA-Sierra calculations as described in text 

Figure ES-1. Change in Sales in Relevant States due to Adoption of the California Standards 

 
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NERA Economic Consulting E-3 

§ Emissions effects. The shift toward an older vehicle fleet will lead in the near term to 

increases in emissions of conventional pollutants because emissions tend to increase as 

vehicles age. Moreover, improvements in fuel economy will lead to reductions in the per 

mile cost of travel, which will tend to lead to increases in vehicle miles of travel and thus to 

additional increases in emissions of conventional pollutants. 

§ Consumer welfare effects. Changes in the new vehicle market due to the California Standards 

lead to reductions in consumer welfare due to increases in the prices of certain new vehicle 

models and reductions in vehicle choice. 

§ Employment effects. Reductions in the sales of new vehicles will lead to reductions in overall 

employment in the motor vehicle sector. These effects include the direct effects of reductions 

in motor vehicle manufacturing employment as well as the indirect effects of changes in 

motor vehicle demand on demand for related goods and services. 
Introduction 

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NERA Economic Consulting 1 

I. Introduction 

This report examines the effects of the California motor vehicle greenhouse gas emission 

standards (“California Standards”) on sales of new motor vehicles. The study includes the effects 

on motor vehicle sales of the California Standards, assuming that they are implemented in the 13 

states that have adopted California’s standards. 

A. Background 

California AB 1493, passed by the legislature and signed by Governor Davis in 2002, called for 

the California Air Resources Board (“CARB”) to develop regulations for greenhouse gas 

emissions from motor vehicles. On September 24, 2004, CARB adopted greenhouse gas 

emission regulations for passenger cars, light-duty trucks and medium-duty passenger vehicles 

beginning with the 2009 model year

1

. Thereafter, 12 additional states adopted the California 

Standards. 

In a letter dated December 21, 2005, CARB submitted a request that the U.S. Environmental 

Protection Agency (“EPA”) grant a waiver allowing California to enact these regulations. EPA 

denied this request on March 6, 2008. On February 12, 2009, EPA published a notice in the 

Federal Register announcing that it was reconsidering its previous denial of the waiver

2

. 

B. Objectives of this Report 

The objective of this report is to evaluate the effects of the California Standards on sales of new 

motor vehicles in the 13 states that have adopted the standards (“adopting states”). Our estimates 

are based upon assessments of the likely compliance options that various new vehicle 

manufacturers would pursue to meet the California Standards in each of the relevant states. 

These changes are relative to a baseline that includes compliance with the proposed federal 

corporate average fuel economy (“CAFE”) standards. 

C. Outline of the Report 

The remainder of the report is organized as follows. Chapter II provides an overview of the 

methodologies and data that are used in the study. Chapter III presents the results of the study. 

Two appendices provide technical details of the analysis. 

 

1

http://www.arb.ca.gov/newsrel/nr092404.htm

2

http://www.epa.gov/fedrgstr/EPA-AIR/2009/February/Day-12/a2913.htm
Methodology and Data 

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NERA Economic Consulting 2 

II. Methodology and Data 

This chapter provides information on the methodologies and data used to estimate the effects of 

the California Standards on new motor vehicle sales in the adopting states. As noted, appendices 

to this report provide details on data and methodologies. 

A. Overview of the California Motor Vehicle Greenhouse Gas Emission 

Standards 

This section summarizes the California Standards and provides information on the states that 

have adopted the standards or expressed an interest in adopting the standards. 

1. California Standards 

The California Standards establish a set of greenhouse gas emission rate standards for large 

volume manufacturers that apply to 2009 and subsequent model years. Table 1 summarizes the 

CO2-equivalent standards for the two categories of vehicles to which they apply: passenger cars 

(“PCs”) and class 1 light-duty trucks (“LDT1s”); and class 2 light-duty trucks (“LDT2s”) and 

certain medium duty vehicles (“MDVs”). (We refer to the second category as “LDT2+” to reflect 

the addition of these other vehicles.) The standards are slated to start in model year 2009 and 

become increasingly stringent until model year 2016. The model year 2009 to 2012 standards are 

designated the “near-term” standards, and those for the 2013 to 2016 model years are the “midterm” standards. In a letter dated January 21, 2009, CARB contends that all manufacturers will 

be in compliance for model year 2009 and, as a result, CARB should be allowed to implement 

the standards beginning in model year 2009. 

2. States Adopting the California Standards 

Table 2 lists the adopting states. Twelve states (in addition to California) have either passed 

legislation to adopt the California standard or taken executive action to do so. While 

Pennsylvania is often included as an adopting state, its regulation requires only that vehicles be 

certified to California standards. Therefore, fleet GHG emission levels in Pennsylvania may be 

Table 1. California GHG Emissions Standards 

Model Year PC/LDT1 LDT2+

2009 323 439

2010 301 420

2011 267 390

2012 233 361

2013 227 355

2014 222 350

2015 213 341

2016 205 332

Note: All entries in grams of CO2-equivalent per mile 

Source: Table II-1 of California Air Resources Board, Final Statement of Reasons 
Methodology and Data 

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NERA Economic Consulting 3 

higher or lower than the California standards depending on the manufacturer’s sales mix in 

Pennsylvania. Florida also is excluded from the list because, although a regulation has been 

developed, it has not been adopted by the state legislature. 

In model year 2006, new motor vehicle sales in the thirteen adopting states accounted for over 

34 percent of new vehicles sold in the United States. National sales and sales in each of the 

adopting states for model year 2006 are shown in Table 3. 

Table 2. States that Have Adopted the California Standards 

State

Arizona

California

Connecticut

Massachusetts

Maryland

Maine

New Jersey

New Mexico

New York

Oregon

Rhode Island

Vermont

Washington

 

Source: Pew Climate (http://www.pewclimate.org/sites/default/modules/usmap/pdf.php?file=5905) as modified as 

explained in text. 
Methodology and Data 

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NERA Economic Consulting 4 

Table 3. Model Year 2006 Vehicle Sales in States that Have Adopted the California Standards and National 

Vehicle Sales (thousands) 

B. Compliance Plans 

Vehicle manufacturers would be required simultaneously to meet both the CAFE standards and 

the California Standards. A given vehicle manufacturer would have several different options for 

complying with the California Standards in addition to the CAFE requirements. These options 

include: 

§ Incorporation of additional fuel economy improvement technology into vehicles intended for 

sale in states that have adopted the California Standards (e.g., a two-car strategy); 

§ Incorporation of additional fuel economy improvement technology into the entire product 

line so that compliance is assured in all states that have adopted the California Standards, a 

strategy that would likely result in over compliance with the CAFE standards; and 

§ Restricting the sales of certain models in states that have adopted the California Standards in 

order to achieve the California Standards. 

At this time, at least three manufacturers have indicated that they will be forced to follow the 

model restriction or “mix-shifting” strategy (option 3) because of the form of the California 

Standards, the characteristics of the models they produce, the lack of lead time and engineering 

resources, and the lack of financial resources to develop the necessary additional technology 

within this time frame. 

State Vehicle Sales Percent of National Sales

Arizona 369 2.5%

California 1,895 12.8%

Connecticut 177 1.2%

Massachusetts 314 2.1%

Maryland 348 2.3%

Maine 49 0.3%

New Jersey 560 3.8%

New Mexico 89 0.6%

New York 780 5.3%

Oregon 146 1.0%

Rhode Island 52 0.4%

Vermont 34 0.2%

Washington 258 1.7%

Sum of Adopting States 5,071 34.3%

National 14,792

Source: New Vehicle Registration data from R.L. Polk for model year 2006 
Methodology and Data 

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NERA Economic Consulting 5 

In order to estimate the models that would be dropped by those three manufacturers to comply 

using a mix shifting strategy in each of the states that have adopted the California Standards, 

Sierra Research performed a two step analysis. 

The first step involved an assessment of the likely strategy to comply with the CAFE 

requirements. In particular, we developed assessments of the technologies that all manufacturers 

would be expected to deploy during the 2011 to 2016 model-years in order to comply with 

federal CAFE standards as well as the impacts on vehicle fuel economy and the associated costs 

of the technologies. Because CAFE standards for all of these model years had not been adopted 

at the time this analysis was initiated, Sierra assumed that the 2011 to 2015 standards in the 

NHTSA December Draft Rule (“DDR”) would apply and then estimated a 2016 standard based 

on the minimum 35 mile per gallon requirement for the 2020 model-year fleet. Given the limited 

duration of the EPA comment period for reexamination of the California waiver request, Sierra 

used vehicle fleet datasets that were assembled and vehicle simulation modeling runs that were 

filed under Docket No. NHTSA-2008-0089.3In addition, Sierra assumed that vehicle 

manufacturers would make full use of the CAFE credits available for E-85 FFVs during the 2011 

to 2016 model-years. 

The second step in the analysis was to assess strategies to comply with the California Standards, 

given compliance with CAFE requirements. In particular, we assessed which models the three 

relevant manufacturers would restrict or eliminate during model-years 2011 through 2016 in 

order to comply with the California Standards in the states where they have been adopted. Sierra 

developed two cases depending upon manufacturer choices with respect to obtaining available 

air conditioning credits; one case assumes that manufacturers would generate limited (partial) air 

conditioning credits and the other case assumes that manufacturers would generate maximum 

(full) air conditioning credits. In order to identify the models to be eliminated an algorithm was 

developed to determine the means by which a manufacturer could comply with the California 

Standards in a given state in a given model-year while minimizing the loss in sales volume 

associated with model restriction or elimination. 

C. New Vehicle Market Model 

This section summarizes the major data and methodologies used to develop the New Vehicle 

Market Model (“NVMM”) and to apply it to the case of the California Standards. A detailed 

description of the NVMM is provided in Appendix B. 

1. Nested Logit Model Formulation 

The NVMM is based upon a “nested logit” formulation. (Appendix B provides details of the 

“nested logit” framework and the data used to develop the model.) Economists and other analysts 

have used nested logit models to evaluate factors affecting the demand for motor vehicles and 

 

3 T. C. Austin, et al., “Attachment to Comments Regarding the NHTSA Proposal for Average Fuel Economy 

Standards Passenger Cars and Light Trucks Model Years 2011-2015,” Docket No. NHTSA-2008-0089, Report 

No. SR2008-06-01, June 27, 2008. 
Methodology and Data 

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NERA Economic Consulting 6 

other goods.

4 Nested logit models have also been used in court proceedings to evaluate the 

effects of mergers and other changes in market conditions5and in various settings to evaluate the 

potential market demand for new products and services.6

The NVMM allows consumers to choose between individual vehicle models (e.g., Chevrolet 

Malibu, Toyota Corolla). The model uses data on transaction prices (as distinct from 

manufacturers’ suggested retail prices) and sales volumes in the base year for the analysis (model 

year 2006). 

The “nests” in the nested logit model refer to the structure assumed for consumer choices in the 

new vehicle market. Our model assumes that consumers make hierarchical decisions regarding 

the purchase of new motor vehicles, as shown in Figure 1 (described in more detail in Appendix 

B). A consumer chooses whether to purchase a new vehicle or not. Conditional on the choice to 

purchase a new vehicle, a consumer selects the type of vehicle from among three major vehicle 

types—cars, SUVs/minivans, and trucks. Conditional on the choice of a vehicle type, a consumer 

selects a specific vehicle category. Our model includes a total of 14 vehicle categories, including 

five passenger car categories, six SUVs/minivan categories, and three truck/van categories (e.g., 

pick-ups and full vans). Finally, conditional on the choice of a vehicle class, a consumer chooses 

from the vehicle models that are available in that class. 

Buy/Don’t Buy

Vehicle Type

Vehicle Class

Vehicle Model

Figure 1. Hierarchy of NVMM nesting structure 

This structure provides for a rich pattern of own- and cross-price elasticities for different vehicle 

models. The empirical estimates provide information on approximately 200 separate vehicle 

models. The aggregate new vehicle price elasticity is assumed to be –1.0, a value consistent with 

the empirical literature.7 New vehicle manufacturers are assumed to be profit-maximizing firms. 

 

4 Dr. Daniel McFadden was awarded the 2000 Nobel Prize in Economics largely for his development of the logit 

model. 

5

 See Werden, Gregory J, Luke M. Froeb, and Timothy J. Tardiff, “The Use of Logit Model in Applied Industrial 

Organization,” International Journal of the Economics of Business, Vol. 3, No. 1. 

6

. See Timothy Tardiff, “Pricing and New Product Options with Telecommunications Competition,” Proceedings of 

the Thirty-First Hawaii International Conference on System Sciences, Vol. V, Dolk, Daniel R., ed. Computer 

Society: Los Alamitos, California, pp. 416-425. 

7

c.f. Gruenspecht (2000) in Appendix B. 
Methodology and Data 

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NERA Economic Consulting 7 

2. Use of the New Vehicle Market Model 

The NVMM allows us to estimate the effects of the California Standards on new vehicle sales 

and prices. The baseline for estimating the impacts of the California Standards is compliance 

with the proposed federal CAFE standards. 

As summarized above, compliance with the California Standards for each of the relevant 

manufacturers is based upon reductions in sales of various models in the 13 states that have 

adopted the standards. We develop average results for these 13 states, a method that implicitly 

assumes that manufacturers covered by the regulation would be able to average compliance 

across the various states.8(The reductions in sales likely would be greater if manufacturers had 

to comply on a state-by-state basis.) The model allows for consumer substitution among vehicles 

that are affected differentially by the regulations. As noted, vehicles produced by manufacturers 

with lower sales volumes are subject to less stringent emissions standards under the California 

Standards in the early years of the regulation and thus have lower compliance requirements 

during those years. The model allows for these differences to be reflected in changes in vehicle 

sales by model and manufacturer. The sales impacts thus account for the shift in sales from 

models produced by manufacturers that comply by dropping and/or reducing the sales of models 

toward models produced by manufacturers that do not withdraw models. The model also 

accounts for consumer decisions not to purchase new vehicles in light of the restricted range of 

choices. 

 

8 Results were not developed for model year 2011 because of the small mix changes due to the California Standards 

for that model year. 
Study Results 

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NERA Economic Consulting 8 

III. Study Results 

This chapter summarizes the results of our analyses of the effects of the California Standards on 

the U.S. new vehicle market.9

A. U.S. New Vehicle Sales 

Table 4 provides a summary of the results of our analysis of the California Standards. The results 

are shown for the two cases, one based upon partial air conditioning credit and the other based 

upon full air conditioning credit (as explained above). The figure shows the percentage reduction 

in sales by model year in the 13 states covered by the California Standards under these two cases. 

These results indicate that the California Standards would result in a substantial reduction in new 

motor vehicle sales in the adopting states, particularly in the case with partial air conditioning 

credit. In the partial A/C scenario, the California Standards would result in a 8.5 percent 

reduction in motor vehicle sales in 2012 and a 8.0 percent reduction in 2016. With the full air 

conditioning credit, the California Standards would result in a 5.1 percent reduction in motor 

vehicle sales in 2012 and a 3.2 percent reduction in 2016. 

B. Other Impacts 

The California Standards will have impacts beyond reductions in new motor vehicle sales in the 

states that adopt the California Standards. These other impacts, which have not been quantified, 

include the following: 

§ Motor vehicle fleet effects. Reductions in new vehicle sales will lead to changes in the motor 

vehicle fleet over time. In particular, the California Standards have the effect of changing the 

age distribution of the vehicle population. In future years (e.g., 2020), the numbers of motor 

vehicles in older vintages will increase and the numbers of motor vehicles in newer vintages 

will decrease as a result of the California Standards. 

 

9 Backup materials for the Sierra and NERA analyses are provided in accompanying materials. 

Table 4. Change in Sales in Relevant States due to the California Standards 

Full A/C Credit Partial A/C Credit

2012 -5.1% -8.5%

2013 -1.8% -5.7%

2014 -1.7% -6.1%

2015 -0.6% -5.4%

2016 -3.2% -8.0%

Note: Percent change from baseline sales 

Source: NERA-Sierra calculations as described in text. 
Study Results 

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NERA Economic Consulting 9 

§ Emissions effects. The shift toward an older vehicle fleet will lead in the near term to 

increases in emissions of conventional pollutants because emissions tend to increase as 

vehicles age. Moreover, improvements in fuel economy will lead to reductions in the per 

mile cost of travel, which will tend to lead to increases in vehicle miles of travel and thus to 

additional increases in emissions of conventional pollutants. 

§ Consumer welfare effects. Changes in the new vehicle market due to the California Standards 

lead to reductions in consumer welfare due to increases in the prices of certain new vehicle 

models and reductions in vehicle choice. 

§ Employment effects. Reductions in the sales of new vehicles will lead to reductions in overall 

employment in the motor vehicle sector. These effects include the direct effects of reductions 

in motor vehicle manufacturing employment as well as the indirect effects of changes in 

motor vehicle demand on demand for related goods and services. 
Analysis of Manufacturer Compliance 

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APPENDIX A. Analysis of Manufacturer Compliance 

This appendix describes the analysis performed by Sierra Research (Sierra) to assess the 

technologies that manufacturers would have to deploy in order to comply with the 2011 to 2015 

standards in the NHTSA December Draft Rule (DDR), as well as the associated costs. The 

analysis also involved determining, after manufacturers complied with the CAFE standards in 

the DDR, the number and types of vehicle models that manufacturers would have to restrict or 

no longer sell in states that have adopted the California Greenhouse Gas (GHG) standards in 

order to comply with those standards. The “dropped sales” analysis was then performed 

individually for these manufacturers’ vehicle fleets in each of 13 GHG-adopting states. 

Following a brief discussion of the background issues, the analysis methodologies and results are 

presented. 

A. Background 

Federal Regulations – The federal Energy Independence and Security Act of 2007 (EISA), 

signed by the President on December 19, 2007, specifies a national corporate average fuel 

economy (CAFE) standard of at least 35 miles per gallon by 2020. The 2007 model year CAFE 

standards were 22.2 mpg for light-duty trucks and 27.5 mpg for passenger cars. Since there was 

approximately a 50/50 sales mix of cars and light trucks, the average fuel economy of the 2007 

models was approximately 25 mpg. Thus, the EISA standard represents a minimum 40% 

improvement in fuel economy in 2020 from current levels. 

A significant change in the CAFE standards that must be adopted under EISA is that the 

Secretary of Transportation is directed to set standards for both cars and trucks “based on one or 

more vehicle attributes related to fuel economy and express each standard in the form of a 

mathematical function.” In other words, manufacturers are no longer going to be subject to the 

same “miles per gallon” standard. In its April 2008 Notice of Proposed Rulemaking (NPRM), 

the National Highway Traffic Safety Administration (NHTSA) selected vehicle “footprint”1as 

the attribute upon which vehicle fuel economy targets would be based. The DDR recently 

developed by NHTSA also uses footprint as the attribute upon which fuel economy targets are 

calculated for each vehicle. 

As in the NPRM, the DDR sets separate CAFE fuel economy standards for passenger cars and 

light-duty trucks2for each manufacturer using a mathematical equation that is based on the 

distribution of vehicle footprint in each manufacturer’s fleet. Standards are set individually for 

model years 2011 through 2015, the maximum number of model years for which NHTSA is 

permitted under EISA to establish standards in a single rulemaking. The model year standards in 

the DDR also reflect different stringency between cars and trucks compared to the NPRM. In 

2015, the DDR standards are roughly 4% higher for passenger cars and 5% lower for light trucks 

 

1

Footprint is defined as the area determined by the wheelbase multiplied by the average of a vehicle’s front and rear 

track width. Track width is the distance along each axle from the tire centerlines. 

2 Under the new federal CAFE statutes, sport utility vehicles (SUVs) up to 10,000 pounds gross vehicle weight 

(GVW) are now included with light-duty trucks (all vans, pickups and SUVs less than 8,500 pounds GVW). 
Analysis of Manufacturer Compliance 

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NERA Economic Consulting A-2 

than those in the NPRM. The DDR standards in the earlier model years (2011-2012) also reflect 

a slower rate of fuel economy improvement (i.e., a “softer” start) than contained in the NPRM. 

State Regulations – GHG emission regulations for cars and light trucks were adopted by the 

California Air Resources Board (CARB) in 2004. These regulations set standards for vehicle 

carbon dioxide (CO2) equivalent emissions in grams per mile for model years 2009 through 

2016. Because of the direct relationship between fuel economy and CO2 emissions (exhaust CO2

represents over 99.9% of a typical vehicle’s emissions on a mass basis and nearly 97% on the 

basis of global warming potential), the GHG regulations are, in practice, fuel economy standards. 

Based on assumed levels of other carbon-containing compounds emitted by motor vehicles (HC 

and CO) and other GHG gases (N2O, CH4, and refrigerants used in air conditioning systems), the 

CO2 standards (in grams per mile) can be easily translated into fuel economy equivalent mile per 

gallon values. 

In addition to California, 12 other states3have also adopted or are planning to adopt the CARB 

GHG standards: 

§ Arizona; 

§ Connecticut; 

§ Maine; 

§ Maryland; 

§ Massachusetts; 

§ New Jersey; 

§ New Mexico; 

§ New York; 

§ Oregon; 

§ Rhode Island; 

§ Vermont; and 

§ Washington. 

Under the CARB regulations, vehicles are grouped differently for comparison to the standards 

than under federal CAFE. Under the GHG regulations, passenger cars (PCs) and light-duty 

 

3 Although Pennsylvania is adopting California’s program, it is not requiring fleet average compliance with the 

GHG standards, just the sale of California certified vehicles. Thus, it was not included in the analysis. 
Analysis of Manufacturer Compliance 

4/6/09 

NERA Economic Consulting A-3 

trucks (LDT1s) under 3,750 pounds test weight must meet the same standard. A separate 

standard exists for light-duty trucks at and above 3,750 pounds (LDT2). Under federal CAFE, 

there is no distinction between LDT1s and LDT2s. 

Unlike the new “attribute-based” federal CAFE standards, which are dependent on the 

distribution of the vehicle footprint in each manufacturer’s fleet, the GHG standards are single

standards applied across all manufacturers subject to the regulations. In addition, although the 

CARB regulations set separate standards for both PCs+LDT1s and LDT2s, they allow 

compliance to be met using a sales-weighted composite of these standards. On the other hand, 

the federal CAFE standards must be met separately for cars and light-duty trucks (although the 

NHTSA rulemaking contains provisions that allow limited “transfer” of CAFE credits between a 

manufacturer’s car and truck fleets). 

Compliance by Dropping Sales - Given the complexities of simultaneous compliance with CAFE 

and GHG standards and the fact that GHG regulations allow compliance on the basis of salesweighted average LDV CO2 emissions, one option available to manufacturers is to restrict the 

sales of certain models in states that have adopted the GHG standards. 

As noted earlier, the analysis was performed in a staged manner. First, using a baseline model 

year 2006 vehicle fleet, vehicle simulation modeling was performed to determine the level and 

costs of technology improvements that would be required to meet the footprint-based federal 

CAFE standards reflected in NHTSA’s DDR for each manufacturer’s U.S. vehicle fleet. Once 

the costs and technology requirements for federal CAFE compliance were determined, the next 

step consisted of evaluating which vehicles could stop being sold in each adopting state in order 

to further meet the sales-weighted LDV composite GHG standards reflected by each state’s 

individual fleet mix. As explained in detail later, this dropped sales analysis was performed in a 

manner that attempted to minimize the number of vehicles that would have to be dropped in each 

state. 

To be conservative our analysis of dropped sales was restricted to those three Large Volume 

Manufacturers (as defined under the GHG regulations) that have publicly indicated4the need to 

engage in mix-shifting in order to comply with the GHG standards. 

The next two sub-sections describe the details of this staged analysis. 

B. Determination of Federal CAFE Compliance 

Given the time constraints governing this effort, Sierra utilized vehicle fleet datasets that were 

assembled and vehicle simulation modeling runs that were performed in support of previous 

work5for the Alliance on the April 2008 CAFE NPRM and filed under Docket No. NHTSA-

 

4 ZANGARA DODGE, INC., et al., v. RON CURRY, in his official capacity as Secretary of the New Mexico 

Department of Environment, et al., U.S District Court for the District of New Mexico, Case No. CV 07-1305 

MCA/LFG. 

5 T. C. Austin, et al., “Attachment to Comments Regarding the NHTSA Proposal for Average Fuel Economy 

Standards Passenger Cars and Light Trucks Model Years 2011-2015,” Docket No. NHTSA-2008-0089, Report 

No. SR2008-06-01, June 27, 2008. 
Analysis of Manufacturer Compliance 

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NERA Economic Consulting A-4 

2008-0089. The methodologies employed under that work that were re-used under this effort are 

summarized below. Differences between the earlier work and the work performed here are also 

discussed (e.g., use of a different set of fuel economy targets contained in the DDR vs. those in 

the NPRM). For further details on these methods and datasets, the reader is referred to the report 

cited above that was filed under the NHTSA docket. 

Baseline Fleet Development – The first step in the analysis consisted of the development of a 

detailed fleet database that characterized the baseline specifications, technology implementation, 

and fuel economy performance of light-duty vehicles and medium-duty passenger vehicles 

subject to federal CAFE standards. Data for the model year (MY) 2006 U.S. fleet were compiled 

from several sources, including the following: 

§ AutoSite Pro – Subscription service database of detailed vehicle parameters and 

specifications down to the individual vehicle model, engine size, transmission type, and (in 

some cases) trim level; 

§ NHTSA CAFE Sales – The MY2006 “CAFE Sales” database exchanged between NHSTA 

and manufacturers to track existing CAFE compliance containing CAFE fuel economy 

values and U.S. fleet sales by vehicle model and configuration; and 

§ Polk Registrations – Detailed new vehicle registrations for MY2006 purchased from R.L. 

Polk by state for each of the 50 states broken down by vehicle model, number of cylinders, 

body style, and several other parameters. 

These datasets were merged to produce a detailed, highly disaggregated representation of the 

baseline MY2006 light-duty fleet with sales/registration at both the entire U.S. level and by 

individual state. During the baseline fleet assembly, vehicles were classified into a set of 25 

distinct vehicle categories (13 for cars and 12 for light trucks) that were developed earlier by 

Sierra. The vehicle simulation model used to determine compliance technology requirements 

and costs was performed by manufacturer and vehicle category since it was impractical to 

perform the modeling at the individual model level. (The vehicle classification scheme used by 

NHTSA and EPA to track CAFE is based primarily on seating and interior volume and as a 

result can group vehicles with dramatically different fuel economy together. Sierra’s 

classification system uses different set of criteria to categorize vehicles into groups that better 

account for fuel economy and performance differences.) 

Table A-1 summarizes key characteristics of the MY2006 baseline fleet broken down by these 

25 vehicle categories. To perform the compliance modeling, detailed tabulations by individual 

manufacturer were also prepared and are contained in an Excel spreadsheet called 

Baseline_Stats.xls in sheets named BStats, CStats, etc., where the first letter represents an 

individual manufacturer (B=BMW, C=Chrysler, etc.). 

Calculation of Fuel Economy Targets – NHTSA has developed its footprint-based reformed 

CAFE standards for model years 2011 through 2015 using a constrained logistic function defined 

as follows: 
Analysis of Manufacturer Compliance 

4/6/09 

NERA Economic Consulting A-5 

Table A-1 

Characteristics of the MY2006 U.S. Light-Dutya Vehicle Fleet

Passenger Cars 

Unadj. Fuel Econ. (mpg)

No. Name 

Test

Vehs.

%Car

Sales 

%LDV

Sales City Hwy Wtd 

Curb

Wt (lb)

Power

(hp) 

Disp

(cid) %2V 

%Auto

Trans 

1 MINI 6 0.53% 0.26% 28.45 43.21 33.62 2,612 142.6 96.8 0.00% 21.10%

2 SCSW 38 11.46% 5.66% 29.54 44.05 34.68 2,809 138.8 120.0 0.70% 75.53%

3 CSW 137 25.11% 12.41% 29.36 42.88 34.21 2,901 144.4 126.7 12.14% 67.75%

4 MSW 76 25.80% 12.75% 24.71 39.17 29.63 3,226 177.9 164.4 20.81% 95.80%

5 LSW 21 11.35% 5.61% 22.54 37.56 27.48 3,615 221.5 214.4 50.44% 94.90%

6 SCC 34 0.91% 0.45% 22.75 36.35 27.35 3,363 194.6 158.5 0.00% 43.66%

7 MCC 33 2.30% 1.13% 22.53 36.21 27.15 3,374 204.8 189.1 21.10% 87.51%

8 LCC 3 0.51% 0.25% 22.54 37.69 27.52 3,370 240.6 245.3 100.00% 100.00%

9 SSL 72 5.36% 2.65% 21.70 34.75 26.11 3,494 253.0 196.7 7.37% 83.25%

10 MSL 70 7.81% 3.86% 21.30 35.12 25.88 3,632 254.7 202.1 3.88% 95.93%

11 LSL 36 5.88% 2.90% 19.47 32.05 23.64 4,019 285.2 262.1 23.43% 99.32%

12 SC 22 0.96% 0.47% 22.16 35.31 26.62 2,991 214.6 172.7 0.00% 45.29%

13 HPSC 69 2.03% 1.00% 19.55 31.91 23.68 3,331 331.3 268.4 20.84% 55.90%

All Cars 617 100% 49.43% 24.77 38.89 29.61 3,236 190.7 168.7 17.72% 83.54%

Light Trucks

Unadj. Fuel Econ. (mpg)

No. Name 

Test

Vehs.

%Truck

Sales 

%LDV

Sales City Hwy Wtd 

Curb

Wt (lb)

Power

(hp) 

Disp

(cid) %2V 

%Auto

Trans 

1 SVAN 37 14.43% 7.30% 20.39 32.79 24.57 4,216 210.6 213.1 54.79% 100.00%

2 LVAN 23 1.22% 0.62% 16.52 24.08 19.24 5,156 260.4 311.5 98.30% 100.00%

3 S2P 37 2.56% 1.30% 21.29 31.95 25.05 3,434 174.5 177.2 16.98% 76.46%

4 S4P 10 0.04% 0.02% 18.83 27.14 21.84 3,568 177.2 190.2 51.06% 78.02%

5 L2P 85 12.55% 6.35% 17.18 25.53 20.14 4,680 259.2 286.6 57.81% 88.55%

6 L4P 84 12.93% 6.54% 16.58 24.38 19.37 4,901 270.9 293.6 52.86% 96.05%

7 S2SU 8 3.24% 1.64% 24.71 36.64 28.95 3,163 155.9 144.2 0.00% 94.71%

8 S4SU 7 1.56% 0.79% 19.36 27.96 22.47 3,427 184.5 196.1 48.93% 68.17%

9 M2SU 75 15.06% 7.61% 21.03 31.51 24.73 3,829 211.8 198.8 19.28% 87.89%

10 M4SU 112 24.18% 12.23% 19.39 28.86 22.75 4,188 230.5 213.1 22.47% 91.12%

11 L2SU 30 4.22% 2.14% 16.53 25.63 19.68 4,863 267.9 282.4 45.96% 100.00%

12 L4SU 42 8.00% 4.04% 15.99 23.96 18.83 5,137 272.1 285.3 46.04% 99.52%

All Trucks 550 100% 50.57% 18.71 28.14 22.04 4,343 234.3 237.0 38.38% 92.76%

All Light-Duty Vehicles 

Test Unadj. Fuel Econ. (mpg)

Vehs. City Hwy Wtd 

Curb

Wt (lb)

Power

(hp) 

Disp

(cid) %2V 

%Auto

Trans 

All LDVs 1,267 21.28 32.60 25.22 3,796 212.7 203.3 28.17% 88.20%

a

Also includes medium-duty passenger vehicles.

]

1

)

1 1

(

1

[

(( / )

(( / )

FP c d

FP c d

i

e

e

a b a

FE

i

−

−

+

= + − ×

where FEi is the target fuel economy (in mpg); e is the exponential function; FPi is the footprint 

for vehicle I; and a, b, c, and d are the fitted coefficients. 
Analysis of Manufacturer Compliance 

4/6/09 

NERA Economic Consulting A-6 

This constrained version of the logistic function flattens out at both ends of the range of vehicle 

footprint (coefficients a and b establish the portion of the footprint spectrum where the curve 

flattens) and was specifically chosen by NHTSA to provide a disincentive to designing vehicles 

with very small or large footprints. Coefficients c and d define the midpoint between the flat 

portions and the rate at which the curve transitions between the flat portions, respectively. 

Table A-2 lists the coefficients used by NHTSA in the DDR to determine the fuel economy 

targets by model year for each manufacturer’s passenger car and light truck fleets. 

Table A-2 

DDR Constrained Logistic Fuel Economy vs. Footprint 

Attribute Model Coefficients

Mode Passenger Car Footprint Model Coefficients l 

Year a b c d 

2011 31.20 24.00 51.90 1.90 

2012 33.30 25.20 51.40 1.90 

2013 35.80 26.60 51.40 1.90 

2014 36.80 27.20 51.40 1.90 

2015 38.70 28.20 51.40 1.90 

M Light-Duty Truck Footprint Model Coefficients odel 

Year a b c d 

2011 27.10 21.10 56.40 4.30 

2012 28.10 21.70 56.40 4.30 

2013 29.50 22.60 56.40 4.30 

2014 30.30 23.00 56.40 4.30 

2015 30.50 23.20 56.40 4.30 

In its CAFE rulemaking, NHTSA utilized confidential product plan data obtained from 

manufacturers, and it calculated compliance requirements and costs incremental to these product 

plan-based projections. Since Sierra does not have access to these projections, our baseline fleet 

reflects different distributions of vehicle footprint than represented in the fleet projections used 

by NHTSA. As explained in our earlier report, we cannot simply use the CAFE targets by model 

year and manufacturer published by NHTSA in the DDR since those targets reflect different 

vehicle footprint distributions based on the product plan projections. 

Instead, we applied the attribute coefficients in Table A-2 to the MY2006 baseline fleet to 

determine CAFE fuel economy targets by model year and manufacturer that are consistent with 

the use of our baseline fleet. These exact attribute equations for each model year (2011-2015) 

were applied separately to Sierra’s baseline passenger car and light truck fleet databases on an 

un-weighted individual vehicle basis, consistent with NHTSA’s practice in the DDR. Thus, a 

fuel economy target was computed for each car and truck using each of the five model-yearspecific equations. The resulting individual vehicle targets by fleet (car and light truck) and 

model year (2011-2015) were then harmonically averaged on a sales-weighted basis using the 

sales distributions in Sierra’s baseline fleets for each manufacturer. 
Analysis of Manufacturer Compliance 

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NERA Economic Consulting A-7 

Tables 1-3 and 1-4 present these calculated targets by model year and manufacturer for 

passenger cars and light trucks, respectively, and compare them to those targets published in the 

DDR using NHTSA’s product-plan-based footprint distributions. Sierra’s baseline fleet salesweighted average footprints calculated for each manufacturer are shown near the left side of the 

tables. (As noted, we did not compute average footprint for every manufacturer listed. These 

averages were not needed for every manufacturer for the analysis, but are shown for most 

manufacturers for illustrative purposes.) 

To the right of the footprint column in each table, the targets are shown by model year for 2011-

2016, first for the Sierra fleet, then for the DDR fleet. The 2016 targets for both fleets were 

simply calculated by applying NHTSA’s assumed 1.9% annualized increase between 2015 and 

2020 to reflect ultimate 2020 EISA compliance to the 2015 targets listed in the tables. Targets 

were calculated for 2016 to be consistent with the final model year for which GHG standards 

apply. (The columns under the “DDR Fleet CAFE Targets” heading are the actual required 

CAFE fuel economy levels by manufacturer and model year published in DDR. The order of 

manufacturers listed in the DDR tables differs slightly from those shown here due to 

manufacturer naming.) 

Note the difference in the corresponding values for each fleet. For example, in Table A-3 the 

NHTSA passenger attribute model for MY2015 produces a target fuel economy for GM of 37.4 

mpg using Sierra’s fleet and 36.5 mpg using the DDR fleet. This 1.1 mpg difference can be 

attributed only to differences in individual vehicle footprints and their distributions between each 

fleet (i.e., Sierra vs. NHTSA). A lower target in the NHTSA fleet reflects a trend toward higher 

average vehicle footprint in the product-plan-based projections. Conversely, a higher NHTSA 

target (compared to the Sierra fleet) indicates a shift toward a lower future average footprint. 

Several individual manufacturers have confirmed to Sierra that these footprint trends are in fact 

based on the product plan data submitted to NHTSA. 

Finally, the CAFE compliance analysis assumed that manufacturer would use the full FFV (e.g., 

E85) fuel economy credits allowed under the EISA. These credits, which diminish by model 

year, are listed below in Table A-5. 

Consistent with the EISA provisions, these credits were applied as simple decrements to the 

Sierra targets for passenger cars and light trucks shown in Tables 1-3 and 1-4. (The same credit 

level applies to both cars and trucks.) For example, the fuel economy target for Chrysler 

passenger cars in 2015 after applying the full E85 credit for that model year was calculated as 

34.6 mpg – 1.0 mpg = 33.6 mpg target (with E85 credit).
Analysis of Manufacturer Compliance 

4/6/09 

NERA Economic Consulting A-8 

Table A-3 

Sierra and DDR Fleet-Based Fuel Economy Targets for Passenger Cars

Sierra Fleet-Based CAFE Targets 

(mpg) 

DDR Fleet-Based CAFE Targets 

Parent (mpg) 

Manufacturer

Baseline 

Footprint 

(ft2) 2011 2012 2013 2014 2015 2016 2011 2012 2013 2014 2015 2016

BMW 45.02 30.3 32.1 34.4 35.4 37.1 37.9 30.2 32.2 34.5 35.5 37.2 37.9

Chrysler 47.78 28.8 30.2 32.3 33.1 34.6 35.3 28.6 29.9 32.6 33.6 35.2 35.9

Daimler 46.08 30.2 32.0 34.3 35.2 36.9 37.7 28.9 30.7 32.8 33.7 35.3 36.0

Ford 46.01 30.2 32.0 34.2 35.2 36.9 37.6 30.1 32.3 34.2 35.3 37.0 37.7

GM 45.55 30.5 32.3 34.7 35.6 37.4 38.1 30.0 31.8 33.9 34.8 36.5 37.2

Honda 44.75 30.9 32.9 35.4 36.3 38.2 38.9 30.6 32.6 35.0 35.9 37.8 38.5

Hyundai 43.61 31.0 33.0 35.4 36.4 38.3 39.0 30.3 32.3 34.6 35.5 37.2 37.9

Mitsubishi n/a 30.7 32.6 35.0 36.0 37.8 38.5 30.9 33.0 35.5 36.4 38.3 39.0

Nissan 45.29 31.2 33.3 35.8 36.8 38.7 39.4 30.5 32.5 34.9 35.9 37.7 38.4

Porsche 38.63 31.2 33.2 35.7 36.7 38.6 39.4 31.2 33.3 35.8 36.8 38.7 39.5

Subaru n/a 31.0 33.0 35.4 36.4 38.2 39.0 30.9 33.0 35.4 36.4 38.3 39.0

Suzuki n/a 30.8 32.8 35.2 36.2 38.0 38.8 31.0 33.0 35.5 36.5 38.3 39.0

Toyota 43.20 30.5 32.4 34.7 35.7 37.5 38.2 30.6 32.6 35.0 36.0 37.8 38.5

VW 43.57 30.3 32.1 34.4 35.4 37.1 37.9 30.9 32.9 35.4 36.3 38.2 38.9

All Mfrs 44.92 28.8 30.2 32.3 33.1 34.6 35.3 30.2 32.1 34.4 35.4 37.1 37.8

n/a – Baseline average footprints were not computed for these manufacturers. 

Table A-4 

Sierra and DDR Fleet-Based Fuel Economy Targets for Light-Duty Trucks

Sierra Fleet-Based CAFE Targets 

(mpg) 

DDR Fleet-Based CAFE Targets 

Parent (mpg) 

Manufacturer

Baseline 

Footprint 

(ft2) 2011 2012 2013 2014 2015 2016 2011 2012 2013 2014 2015 2016

BMW 46.98 26.3 27.3 28.6 29.4 29.6 30.1 25.7 26.6 27.8 28.5 28.7 25.7

Chrysler 50.13 25.3 26.1 27.4 28.0 28.2 28.8 24.2 24.9 26.0 26.8 27.2 24.2

Daimler 52.82 24.9 25.7 26.9 27.5 27.7 28.3 24.5 25.3 26.5 27.1 27.3 24.5

Ford 54.25 24.2 25.0 26.1 26.7 26.9 27.4 23.6 24.8 25.5 26.0 26.6 23.6

GM 54.18 24.3 25.1 26.3 26.9 27.1 27.6 23.3 24.4 25.6 26.1 26.3 23.3

Honda 49.97 25.4 26.3 27.5 28.2 28.4 28.9 25.4 26.3 27.5 28.2 28.4 25.4

Hyundai 46.76 26.1 27.0 28.3 29.0 29.2 29.8 25.3 25.9 27.0 27.3 27.8 25.3

Mitsubishi n/a 24.6 25.4 26.5 27.2 27.4 27.9 26.7 27.5 28.9 29.6 29.8 26.7

Nissan 53.42 25.5 26.4 27.6 28.3 28.5 29.1 24.0 24.9 26.1 26.6 26.8 24.0

Porsche 50.94 26.8 27.7 29.1 29.9 30.1 30.7 25.5 26.4 27.6 28.3 28.5 25.5

Subaru n/a 25.1 25.9 27.2 27.8 28.0 28.6 26.6 27.6 28.9 29.7 29.9 26.6

Suzuki n/a 25.5 26.3 27.6 28.3 28.5 29.0 26.4 27.3 28.6 29.4 29.6 26.4

Toyota 51.05 24.8 25.7 26.9 27.5 27.7 28.2 24.8 25.5 26.6 27.4 27.6 24.8

VW 51.09 26.3 27.3 28.6 29.4 29.6 30.1 24.9 25.8 27.0 27.6 27.8 24.9

All Mfrs 51.90 25.3 26.1 27.4 28.0 28.2 28.8 24.1 25.1 26.1 26.7 27.1 24.1

n/a – Baseline average footprints were not computed for these manufacturers. 
Analysis of Manufacturer Compliance 

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NERA Economic Consulting A-9 

Table A-5 

EISA Alternative Fuel (E85) Credits by Model Year

Model 

Year 

Alt Fuel 

Credit (mpg) 

2011 1.2 

2012 1.2 

2013 1.2 

2014 1.2 

2015 1.0 

2016 0.8 

2017 0.6 

2018 0.4 

2019 0.2 

2020 0.0 

The fuel economy targets by model year, manufacturer, and vehicle type were computed in a 

similar manner and used as the basis for determining compliance with the federal CAFE 

standards reflected in the DDR. 

Vehicle Simulation Modeling – Starting with the baseline fleet characteristics for model year 

2006, vehicle simulation modeling was performed to evaluate the improvement in fuel economy 

associated with various combinations of design changes applied to 25 different vehicle categories 

for 11 different manufacturers. 

Table A-6 lists the technology combinations that were modeled based on those deemed to be 

cost-effective for CAFE compliance. As shown in the table, the deployment sequence was 

slightly different for pickup trucks and SUVs than for other vehicles because 

2-valve/OHVengines are retained when the vehicle category has a relatively high towing 

capacity. The order in which the technologies were applied is consistent with the order presented 

in Table A-6 (i.e., top to bottom = first to last). After each technology, the notation in italicized 

parentheses indicates whether the fuel economy benefits of the technology were modeled using 

Sierra’s VEHSIM model (V) or by a simple off-model multiplicative adjustment (O). 

As indicated in Table A-6, most of the technologies were modeled using VEHSIM. Use of the 

model ensured that interaction between technologies would be properly accounted for and the 

benefits of reduced throttling losses would not be double-counted. (Throttling losses are the 

work done to draw air into the engine across the throttle. Because many of the technologies 

considered reduce throttling losses, the benefits of an individual technology are reduced when it 

is applied in combination with other technologies that also reduce throttling loss.) In the case of 

parasitic loss reductions, where throttling losses were not significantly affected and where the 

impact was small, fuel economy benefits could be correctly accounted for with an off-model 

adjustment. 
Analysis of Manufacturer Compliance 

4/6/09 

NERA Economic Consulting A-10 

Table A-6 

Technology Combinations Modeled by Vehicle Type for 

Compliance with NHTSA NPRM CAFE Standards 

Passenger Cars / Vans Pickup Trucks / SUVs 

Major Weight Reduction with Ultralight Steel (V) Major Weight Reduction with Ultralight Steel (V)

Lower Friction/Parasitics (O) Lower Friction/Parasitics (O)

Electric Power Steering (V) 5-Speed Automatic Replaces 4-Speed (V)

5-Speed Automatic Replaces 4-Speed (V) Cyl. Deactivation on 2V engines with >4 cyl. (V)

Lower Friction/Parasitics-2 (O) Lower Friction/Parasitics-2 (O)

2V engines replaced with 4V engines (V) Electro-Hydraulic Power Steering (V)

All engines get CVVLT (V) 6-Speed AMT Replaces 5-Speed Automatic (V)

Improved Alternator/Charging (V) 4V engines get CVVLT (V)

6-Speed AMT Replaces 5-Speed Automatic (V) Improved Alternator/Charging (V)

Gasoline Direct Injection on CVVLT engines (V) Gasoline Direct Injection on CVVLT engines (V)

Strong Hybrid (V) Strong Hybrid (V)

Additional Weight Reduction using Aluminum (V) Additional Weight Reduction using Aluminum (V)

Notes: (V) = applied using the VEHSIM vehicle simulation model 

(O) = applied using an off-model multiplicative adjustment 

A basic description of the VEHSIM model and its operation can be found in the earlier report 

filed under the NHTSA docket. As explained there, specific vehicle, engine, and drivetrain 

characteristics are input to the model along with a second-by-second driving cycle over which 

vehicle operation is simulated and fuel economy/consumption is calculated. 

VEHSIM runs were set up and executed for each of 11 manufacturers and for each of the 25 

vehicle categories used to classify the vehicle fleet. A total of 139 sets of manufacturer/vehicle 

category runs were conducted. (Not all 25 categories are represented in an individual 

manufacturer’s fleet.) 

The VEHSIM modeling was conducted in a manner that preserved the acceleration performance 

of the baseline fleet (represented using simulated 0-30 mph and 0-60 mph times). Benchmark 

performance times were established by manufacturer, vehicle category, engine type (OHV vs. 

OHC), and transmission from the baseline runs and are internally stored within VEHSIM during 

a future technology build-up simulation. 

Only under certain conditions was performance not maintained at the individual transmission 

level. For example, if a manufacturer/vehicle category included only 5-speed manuals and 5-

speed automatics in the baseline, in the future technology step for adding 6-speed automatics the 

resized engine for the manual was assumed to be that of the automatic. (This logic was intended 

to represent instances where manuals and automatics were likely to exist on the same engine and 

when a resize would be done to maintain equal performance with a new automatic. In that case, 

the resized engine would be used with the manual also.) 

To maintain towing performance, acceleration performance for pickup trucks and SUVs was 

maintained with added towing weight of 7,900 lbs and 5,500 lbs, respectively. This was 

accomplished in the VEHSIM modeling by executing “parallel” model runs for these vehicle 
Analysis of Manufacturer Compliance 

4/6/09 

NERA Economic Consulting A-11 

categories that included the added towing loads and maintaining acceleration performance (by 

engine resizing) using these added load runs. 

In modeling advanced hybrid technology, further downward resizing of the internal combustion 

engine to maintain acceleration performance was applied for passenger cars and minivans to 

account for the increase in acceleration provided by the electric motor. For pickups, no resizing 

was performed because of the need to maintain towing capacity (towing depletes the battery and 

makes it impossible to rely on the electric motor). For SUVs, resizing was simulated for half of 

the fleet to reflect the mixed towing use of this category of vehicles. 

Calculation of CAFE Compliance Technologies and Costs – The final element of the analysis of 

the federal CAFE standards in the DDR consisted of combining the future technology simulation 

results with retail price-equivalent technology costs and calculated fuel economy targets by 

manufacturer to determine the amount of technology required and the costs to comply with those 

standards. This CAFE compliance analysis was performed within a series of spreadsheets for 

each manufacturer that contained the technology deployment or “supply curve” calculations for 

each vehicle category in that manufacturer’s fleet. These supply curves calculate the fuel 

economy and incremental cost-effectiveness (in dollars per percent change in fuel consumption) 

at each step in the build-up of modeled technologies. The technologies were generally6

ordered 

by increasing incremental cost-effectiveness. 

These cost-effectiveness (CE) spreadsheets were named using the following convention: 

CE_AdjNPRM_M_FFV.xls 

Where: M is the manufacturer code (B=BMW, C=Chrysler, etc.). 

The cost-effectiveness spreadsheets were loaded with the vehicle simulation modeling results 

described earlier. Within the CE spreadsheets, several other sets of data were loaded into a sheet 

called “LOOKUPS” and were used in calculating incremental cost-effectiveness. These data 

included the following: 

§ Sales Fractions, Weights, and Other Specifications – Baseline sales mixes, curb weights, 2V 

and 4V engine fractions, transmission type fractions, fleet fractions of vehicles with more 

than 4 cylinders, and advanced technology penetration fractions by manufacturer for each 

vehicle category; 

§ Adjusted NPRM-Based CAFE Targets – Calculated MY2011-2015 and 2020 fuel economy 

targets by manufacturer and sub-fleet (cars and trucks) based on the NPRM attribute curves 

aligned with Sierra’s underlying fleet database; and

 

6

It quickly became apparent during the analysis that most, if not all, manufacturers would need to get near the end 

of the technology supply curve to comply with the CAFE targets. Since the cost-effectiveness for several 

technologies was bunched within a fairly narrow range within the supply sequence, we were less concerned with 

perfectly ordering each supply curve sequence by manufacturer and vehicle category (representing well over 100 

unique sequences). The sequences were checked to ensure they followed increasing incremental costeffectiveness near the points on the curves where compliance would be met. 
Analysis of Manufacturer Compliance 

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NERA Economic Consulting A-12 

§ Base Technology Costs – Individual technology costs based on a “reference” mid-size sedan 

and factors for each technology that indicate how strongly their costs are estimated to vary by 

weight. 

The sales and fleet fractions were used to calculate the fraction of a modeled vehicle category for 

a given manufacturer that is affected by each technology. For example, Ford mid-size 

sedans/wagons (MSWs) are 39.5% 2V engines and 65.2% engines with over 4 cylinders. Since 

cylinder deactivation is applied only on 2V engines with over 4 cylinders, the Ford MSW fleet 

fraction “eligible” for cylinder deactivation is 25.7% (0.395 × 0.652). 

The advanced technology penetration fractions were used to account for the amounts of 

advanced technology already in the baseline. For example, assume the fuel economy benefit of a 

particular technology is 10%, but the baseline fleet (for a given manufacturer and vehicle 

category) already contained 20% of that technology on a sales basis. This 20% technology 

penetration fraction is used within the CE spreadsheet to adjust the simulation modeling outputs 

(which reflect the full 10% fuel economy improvement) to a discounted value of 8% 

improvement [10% × (1-20%)]. 

The DDR-based CAFE targets are used later to determine the point on the technology supply 

curves where compliance with the new standards would occur. Finally, the base technology 

costs and scaling factors are used to calculate individual technology costs for each manufacturer 

and vehicle category based on the relative difference between the reference vehicle weight 

(3,226 lb) and that of each individual manufacturer and vehicle category. 

These base costs and weight scaling factors are shown in Table A-7 and are consistent with those 

files under the NHTSA docket. As shown, all technologies except PARA2 are assumed to vary 

with weight. (Note that the advanced hybrid cost is in dollars per 1,000 lb curb weight.) 

As described later, the costs shown in Table A-7 were applied in conjunction with phase-in 

schedules specific to each type of technology that represented the maximum rate at which the 

technology could be phased in without incurring additional stranded capital costs. 

Table A-7 

Base Technology Costs 

(Based on Midsize Sedan/Wagon with 3,226 lb Reference Weight) 
Analysis of Manufacturer Compliance 

4/6/09 

NERA Economic Consulting A-13 

Tech Code Tech Description 

Base 

Cost 

Wt Scale 

Factor 

ASLA5 A5 w/Aggressive Shift Logic over A4 w/o ASL $238 50% 

PARA Lower Eng. Fric./Parasitics $14 50% 

PARA2 Lower Eng. Fric./Parasitics-2 $122 0% 

CD2V(Deac) Cylinder Deactivation on 2V Engines $451 50% 

CVVLT CVVL&T on 4V After 2V to 4V $899 50% 

CVVLT4 CVVL&T on 4V $899 50% 

EPS Electric Power Steering $82 50% 

EHPS Electro-Hydraulic Power Steering $123 50% 

ULS UL Steel vs. closures only, 50% Penetration $169 100% 

DOHC DOHC V6 vs OHV V6 $593 50% 

IMPALT Imp. Alternator/Charging $73 50% 

AMT6 6-Speed AMT vs. 5-Speed Automatic $326 50% 

DVGDI Gasoline Direct Injection on 4V DVVLT $553 50% 

CVGDI Gasoline Direct Injection on 4V CVVLT $553 50% 

ALM Aluminum Incremental to ULSteel $1,596 100% 

AHYB Advanced Hybrid w/Regen Braking $1,850 a 100% 

a

Advanced hybrid cost shown is in dollars per 1,000 lb curb weight 

The weight scaling factor represents the amount of variation in cost for a given change in weight. 

For example, assume a technology is applied to a 4,000 lb vehicle with a base cost of $100 and a 

50% weight scale factor. The weight-scaled technology cost is then calculated as follows: 

Scaled Cost = Base Cost × [1 + ((TechWt/RefWt) – 1) × ScaleFactor] 

= $100 × [1 + ((4000/3200) – 1)×50%] 

= $112.50 

The data tabulation in the LOOKUPS sheet was then used in conjunction with the vehicle 

simulation modeling data to generate technology deployment sequences for each vehicle 

category for a given manufacturer. (There are 25 individual deployment sheets within each 

spreadsheet file, one for each vehicle category.) 

Within these deployment sheets, fuel economy, cumulative cost, and incremental cost 

effectiveness is calculated for each “step” or additional technology deployed. (Sierra’s earlier 

report provides details of these deployment sheet calculations.) The results from these sheets are 

then used elsewhere within the CE workbook files to calculate compliance costs and technology 

requirements by model year, as described below. 

These compliance calculations were performed in sheets labeled “ResultsUS” and 

“MYCAFEComp” and are described separately below. 

Fleet Compilations (ResultsUS) – Table A-8 provides an example of the ResultsUS sheet for 

GM. Calculations in this sheet are focused on compiling sales-weighted composite fuel 

economies for the passenger car and light truck sub-fleets from their individual vehicle 

categories (13 for cars and 12 for trucks) at each technology step. 
Analysis of Manufacturer Compliance 

4/6/09 

NERA Economic Consulting A-14 

The ResultsUS sheet also includes logic that can be invoked to simulate the effect of different 

sales distributions that result in cases where a manufacturer would stop selling specific vehicle 

models in order to achieve compliance instead of adding further technology. These “mix-shift” 

adjustments are contained in Column G and applied in Columns H and I, but were not used under 

the CAFE compliance stage of the analysis. As explained later in Section 1.3, this logic is used 

to optimize dropped sales for state-specific GHG compliance. (In Table A-8, the mix shift 

adjustments are set to unity to reflect no mix shifting at this point in the analysis.) 

Sales-weighted composite fuel economy and incremental cost calculations are performed in 

Columns K through W for the baseline and each successive technology step. The sales fractions 

contained in Column H are combined with the fuel economy values for each vehicle category to 

compute harmonically averaged passenger car and light-duty truck sub-fleet composite fuel 

economy by technology step in Rows 23, 33, 43, and 55.7 Note that the light-truck compilations 

are performed at the individual sub-fleet level in this sheet (i.e., separately for vans, pickups, and 

SUVs). 

Sales-weighted composite technology costs for each sub-fleet are shown in Rows 24, 34, 44, and 

56, just below the composite fuel economy values. 

 

7 These calculations are not shown in Table A-8, but are performed in columns to the right of those shown. 

Table A-8. Results Sheet Layout for GM Fleet Compilation. 

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A B C D E F G H I J K L M N O P Q R S T U V W

Hypothetical

Model Shift 2006 Modeled Proj Base ULS PARA EPS ASLA5 PARA2 DOHC CVVLT IMPALT AMT6 CVGDI AHYB ALM

# Category Abbrev Auto LDV Factor Auto LDV Actual Baseline 0 1 2 3 4 5 6 7 8 9 10 11 12

1 Minicars MINI 0.00% 0.00% 1.00 0.00% 0.00% - - - - - - - - - - - - - - -

2 Subcomp Sedans/Wgns SCSW 17.76% 8.24% 1.00 17.76% 8.24% 32.595 35.849 35.849 36.916 37.102 37.927 39.549 40.356 40.366 44.854 45.379 46.706 48.930 61.645 64.082

3 Compact Sedans/Wgns CSW 19.63% 9.10% 1.00 19.63% 9.10% 31.165 30.235 30.235 31.301 31.458 32.037 33.662 34.349 36.296 40.313 40.730 42.124 44.150 55.584 57.919

4 Mid-Size Sedans/Wgns MSW 20.01% 9.28% 1.00 20.01% 9.28% 28.607 26.829 26.829 27.782 27.922 28.341 30.464 31.086 33.645 37.697 38.056 39.580 41.654 53.922 56.335

5 Large Sedans/Wgns LSW 23.67% 10.98% 1.00 23.67% 10.98% 27.476 24.998 24.998 25.950 26.080 26.439 28.576 29.159 32.186 36.310 36.626 37.968 40.018 53.023 55.338

6 Small Coupes/Conv SCC 0.00% 0.00% 1.00 0.00% 0.00% - - - - - - - - - - - - - - -

7 Mid Coupes/Conv MCC 0.82% 0.38% 1.00 0.82% 0.38% 25.577 27.947 27.947 29.225 29.372 29.889 30.508 31.130 32.870 36.608 36.958 38.325 40.168 51.688 54.001

8 Large Coupes/Conv LCC 2.37% 1.10% 1.00 2.37% 1.10% 27.517 24.474 24.474 25.363 25.490 25.890 27.979 28.550 31.833 36.226 36.564 37.703 39.793 51.855 54.266

9 Sm. Sports/Lux Sedans SSL 1.82% 0.84% 1.00 1.82% 0.84% 23.270 20.653 20.653 22.571 22.684 22.971 24.310 24.806 28.111 32.395 32.658 33.480 35.400 45.943 48.131

10 Mid Sports/Lux Sedans MSL 6.68% 3.10% 1.00 6.68% 3.10% 24.513 25.461 25.461 26.439 26.572 26.947 27.367 27.925 28.614 32.788 33.082 34.229 36.196 47.250 49.549

11 Lg. Sports/Lux Sedans LSL 4.05% 1.88% 1.00 4.05% 1.88% 23.179 22.123 22.123 23.010 23.126 23.395 25.640 26.163 26.159 30.275 30.495 31.558 33.486 44.566 46.803

12 Sports Cars SC 1.23% 0.57% 1.00 1.23% 0.57% 25.571 28.876 28.876 23.355 23.472 23.779 30.759 31.387 31.385 35.487 35.806 29.696 31.728 41.023 43.020

13 Hi-Perf Sports Cars HPSC 1.95% 0.90% 1.00 1.95% 0.90% 23.974 19.185 19.185 20.270 20.372 20.612 20.608 21.029 24.909 29.814 30.040 30.498 32.612 44.230 46.218

All Cars 100.00% 46.37% 100.00% 46.37% 28.497 27.460 27.460 28.404 28.547 29.001 30.857 31.486 33.544 37.772 38.133 39.325 41.406 53.578 55.930

PC Baseline Price Increase: $0 $0 $87 $101 $185 $392 $516 $854 $1,477 $1,552 $1,848 $2,413 $8,640 $10,282

All PCs 46.37% 46.37% 28.497 27.460 27.460 28.547 29.001 30.857 31.486 33.544 37.772 38.133 39.325 41.406 53.578 55.930 #DIV/0!

Hypothetical

Model Shift 2006 Modeled Proj Base ULS PARA EPS ASLA5 PARA2 DOHC CVVLT IMPALT AMT6 CVGDI AHYB ALM

# Category Abbrev LT LDV Factor LT LDV Actual Baseline 0 1 2 3 4 5 6 7 8 9 10 11 12

14 Small Vans SVAN 8.11% 4.35% 1.00 8.11% 4.35% 24.172 24.105 24.105 24.863 24.988 25.425 26.778 27.324 29.984 33.123 33.419 34.909 36.594 47.935 49.908

15 Large Vans LVAN 2.71% 1.45% 1.00 2.71% 1.45% 19.534 15.429 15.429 15.795 15.874 16.018 17.555 17.913 19.809 22.138 22.264 23.213 24.400 30.224 30.844

10.82% 5.80% 10.82% 5.80% 22.815 21.128 21.128 21.736 21.845 22.163 23.662 24.145 26.564 29.460 29.691 30.995 32.521 41.796 43.214

SVAN Baseline Price Increase: $0 $0 $100 $116 $213 $428 $572 $1,272 $1,722 $1,808 $2,175 $2,827 $10,945 $12,806

Hypothetical

Model Shift 2006 Modeled Proj Base ULS PARA ASLA5 CD2V PARA2 EHPS AMT6 CVVLT4 IMPALT CVGDI AHYB ALM

# Category Abbrev LT LDV Factor LT LDV Actual Baseline 0 1 2 3 4 5 6 7 8 9 10 11 12

16 Small 2WD Pickups S2P 4.84% 2.59% 1.00 4.84% 2.59% 24.305 24.430 24.430 24.559 24.682 26.521 26.521 27.062 27.453 28.324 31.385 31.651 33.161 39.757 40.347

17 Small 4WD Pickups S4P 0.09% 0.05% 1.00 0.09% 0.05% 22.780 23.015 23.015 23.167 23.283 24.933 24.929 25.437 25.804 26.642 29.480 29.722 31.147 37.361 37.900

18 Large 2WD Pickups L2P 14.98% 8.03% 1.00 14.98% 8.03% 20.959 18.566 18.566 18.749 18.843 20.345 22.509 22.968 23.277 24.103 24.103 24.255 24.255 31.388 31.702

19 Large 4WD Pickups L4P 16.93% 9.08% 1.00 16.93% 9.08% 19.767 17.196 17.196 17.398 17.486 18.945 20.712 21.134 21.379 22.096 22.340 22.477 22.596 28.994 28.852

36.83% 19.75% 36.83% 19.75% 20.763 18.480 18.480 18.671 18.765 20.285 22.072 22.522 22.807 23.586 23.974 24.175 24.349 31.139 31.236

PU Baseline Price Increase: $0 $0 $69 $87 $366 $813 $963 $1,113 $1,479 $2,061 $2,151 $2,275 $10,889 $12,134

Hypothetical

Model Shift 2006 Modeled Proj Base ULS PARA ASLA5 CD2V PARA2 EHPS AMT6 CVVLT4 IMPALT CVGDI AHYB ALM

# Category Abbrev LT LDV Factor LT LDV Actual Baseline 0 1 2 3 4 5 6 7 8 9 10 11 12

20 Small 2WD SUVs S2SU 6.13% 3.29% 1.00 6.13% 3.29% 30.701 31.570 31.570 32.599 32.763 33.709 33.709 34.396 35.076 37.247 41.019 41.493 43.426 53.873 55.931

21 Small 4WD SUVs S4SU 0.00% 0.00% 1.00 0.00% 0.00% - - - - - - - - - - - - - - -

22 Midsize 2WD SUVs M2SU 14.90% 7.99% 1.00 14.90% 7.99% 24.158 24.184 24.184 24.620 24.743 25.842 26.697 27.242 27.668 28.487 30.558 30.848 31.751 40.856 41.963

23 Midsize 4WD SUVs M4SU 15.40% 8.26% 1.00 15.40% 8.26% 21.517 21.079 21.079 21.560 21.668 22.810 23.353 23.829 24.145 24.960 27.258 27.472 28.466 36.615 37.699

24 Large 2WD SUVs L2SU 3.74% 2.01% 1.00 3.74% 2.01% 19.499 17.630 17.630 17.904 17.994 19.455 21.064 21.494 21.775 22.579 22.975 23.125 23.288 30.397 31.041

25 Large 4WD SUVs L4SU 12.19% 6.54% 1.00 12.19% 6.54% 18.992 17.068 17.068 17.332 17.419 18.653 19.900 20.306 20.553 21.218 21.936 22.072 22.423 28.686 29.267

52.35% 28.08% 52.35% 28.08% 22.132 21.223 21.223 21.639 21.748 22.972 23.914 24.402 24.745 25.607 27.264 27.481 28.217 36.184 37.131

SUV Baseline Price Increase: $0 $0 $92 $109 $327 $528 $671 $815 $1,174 $1,976 $2,062 $2,442 $10,485 $12,198

All LDTs 100.00% 53.63% 53.63% 21.676 20.114 20.114 20.554 21.821 23.175 23.648 24.168 25.169 26.173 26.472 27.022 34.621 35.219 #DIV/0!

All LDVs 100.00% 100.00% 24.382 22.962 22.962 23.621 24.651 26.199 26.734 27.767 29.775 30.627 31.201 32.211 41.416 42.520 #DIV/0!

Pickups

SUVs

"Combined" Fuel Economy for Technology Combinations (mpg)

"Combined" Fuel Economy for Technology Combinations (mpg)

Shift-Adjusted

Shift-Adjusted

U.S. Sales Fractions

CAFE Fuel Econ (mpg)

CAFE Fuel Econ (mpg)

CAFE Fuel Econ (mpg)

"Combined" Fuel Economy for Technology Combinations (mpg)

GM Fuel Economy and Cost Forecasts

Shift-Adjusted

U.S. Sales Fractions

Sales Fractions

Actual U.S. CAFE Fuel Econ (mpg)

Actual U.S.

Sales Fractions

Actual U.S.

U.S. Fleet

Cars

Sales Fractions

U.S. Sales Fractions

Shift-Adjusted

U.S. Sales Fractions

Vans

Sales Fractions

Actual U.S.

"Combined" Fuel Economy for Technology Combinations (mpg)
Analysis of Manufacturer Compliance 

4/6/09 

NERA Economic Consulting A-15 

Model-Year-Specific Compliance Technology and Costs (MYCAFEComp) – Calculation of 

model-year-specific technology requirements and per-vehicle retail price equivalent (RPE) costs 

was performed by individual model year from 2011-2016 in the MYCAFEComp sheet within 

each manufacturer-specific workbook. These model year-specific calculations were performed 

utilizing phase-in schedules developed by Sierra for each type of technology that represented the 

maximum rate at which the technology could be deployed into the fleet without incurring 

additional stranded capital costs. 

Table A-9 lists the technology groups or categories assigned to each technology for which phasein schedules were developed. Table A-10 shows the specific production phase-in schedules 

assumed for each technology category. Note that some schedules differ between passenger cars 

and light trucks. 

Table A-9 

Technology Phase-In Categories 

Technology Category 

Ultralight Steel Body and Suspension (ULS) All New Bodies 

Aluminum Intensive Vehicle (over UL Steel) (ALM) All New Bodies 

5-Speed Automatic vs. 4-Speed Automatic (ASLA5) 5AT/6AT 

Reduced Parasitics-I (PARA) Minor Engine Changes 

Reduced Parasitics-II (PARA2) Minor Engine Changes 

Replace OHV Engine with OHC Engine (DOHC) All New Engines 

Continuously Variable Valve Lift & Timing (CVVLT) Major Engine Changes 

Cylinder Deactivation (DEAC) Major Engine Changes 

6-Speed Automated Manual vs. 4-Speed Auto (AMT6) AMT 

Electric / Electro-Hydraulic Power Steering (EPS) Misc. Minor Changes 

Higher Efficiency Alternator (IMPALT) Misc. Minor Changes 

Gasoline Direct Injection-Stoichiometric (GDI) Major Engine Changes 

Strong Hybrid (7kW/1000 lbs battery power) (AHYB) Hybrids 

Table A-10 

Additional Technology Phase-In Schedule for MY2011-2015 CAFE Standards 

(Percent of Production Changed by Model Year) 

Technology 2008 2009 2010 2011 2012 2013 2014 2015 

All New Bodies 0% 0% 0% 0% 12.5% 25.0% 37.5% 50.0% 

Major Body 

Changes 

0% 0% 25.0% 50.0% 75.0% 100% 100% 100% 

5AT/6AT 33.4% 41.7% 50.0% 58.3% 66.7% 75.0% 83.3% 91.7% 

AMT 0% 0% 8.3% 16.7% 25.0% 33.3% 41.7% 50.0% 

All New 

Engines, Cars 

0% 0% 0% 0% 8.3% 16.7% 25.0% 33.3% 

All New 

Engines, LDTs 

8.3% 16.7% 25.0% 33.3% 41.7% 50.0% 58.3% 66.7% 

Major Engine 

Changes, Cars 

0% 0% 16.7% 33.3% 50.0% 66.7% 83.3% 100% 

Major Engine 16.7% 33.3% 50.0% 66.7% 83.3% 91.7% 100% 100% 
Analysis of Manufacturer Compliance 

4/6/09 

NERA Economic Consulting A-16 

Changes, LDTs 

Minor Engine 

Changes 

25.0% 50.0% 75.0% 100% 100% 100% 100% 100% 

Hybrids 0% 0% 16.7% 33.3% 50.0% 66.7% 83.3% 100% 

Miscellaneous 

Minor Changes 

0% 0% 25.0% 50.0% 75.0% 100% 100% 100% 

The phase-in schedules for technology changes were developed based on industry-average 

implementation of new designs. It assumes that engines and transmissions have a 12-year life 

cycle and bodies have an 8-year life cycle. As a result, on an industry-wide basis, 8.33% of 

engines and transmissions are replaced with all new products each year and 12.5% of bodies are 

replaced with all new bodies each year. “Major” redesigns not involving all new designs can be 

phased in at approximately twice the rate of all new designs. In the case of transmissions, it is 

assumed that a transition to 6-speed transmissions was underway before the adoption of the 

regulation and there is essentially a “head start” in converting to widespread use of 5- and 6-

speed automatic transmissions. 

Except in cases where a phase-in was already in process, approximately 3.5 years lead time is 

assumed for all new bodies, engines, or transmissions. Major changes are assumed to have 2.5 

years lead time. Phase-ins were assumed to be in process for new 5- and 6-speed automatic 

transmissions, minor engine changes, major engine design changes for light-duty trucks, and all 

new engines for light-duty trucks. When applied in the analysis by individual manufacturer, they 

were implemented incrementally over and above the level of technology already contained in the 

MY2006 baseline. For example, if a manufacturer already had 10% AMT use in the baseline, 

additional AMT penetration was assumed at 8.3% per year. 

Using these phase-in schedules, technology requirements and compliance costs were calculated 

in the MYCAFEComp sheet by individual model year from 2011-2016 incremental to the 

existing MY2006 baseline fleet. 

Table A-11 shows the layout of each of these phased-in compliance worksheets, again using 

GM’s fleet. (Although the compliance analysis was performed for model years 2011–2016, a 

2020 column is also shown in the sheet and was used to ensure that technology phase-ins to meet 

the standards from 2011 through 2016 did not need to be phased in any earlier or fastest in order 

to ultimately meet the 2020 standard inferred from the EISA. Given the way NHTSA reshaped 

the model year standards in the DDR, we found no instances where compliance from 2011-2016 

would not require further or fastest technology phase-in to ultimately meet the 2020 standards.) 

As shown in Table A-11, the composite calculations in this sheet were performed for four 

subfleet groupings: (1) Cars, (2) Vans, (3) Pickups, and (4) SUVs. Phased-in compliance by 

model year is then analyzed only for the sales-weighted composite passenger car and light-duty 

truck standards. 

The basic logic to calculate CAFE compliance consists of applying the individual technologies in 

the build-up sequence using their model-year-specific phase-in schedules shown earlier in Table 

A-10. The phase-in schedules are applied at the rates shown in Table A-10 until they are not 

needed at those rates. This process is done in a manner in which the less expensive technologies 
Analysis of Manufacturer Compliance 

4/6/09 

NERA Economic Consulting A-17 

(topmost in the sequence) are applied at the “standard” rate reflected in Table A-10 until the last 

(most expensive) technology (or technologies) can be applied at a slower rate. Application of the 

standard or modified rates is indicated with either an “S” or “M”, respectively, in the rightmost 

column in Table A-11. 

Next, the modeling-based fuel economy values in the Results sheet were translated into the form 

of relative fuel economy efficiencies. The efficiency of each technology is relative to the 

preceding technology in the deployment sequence. This was done so they could be easily 

combined with the model-year-specific technology phase-in rates and used to recalculate yearby-year fuel economy levels. 

For each subfleet, technologies are listed in Column B of Table A-11. Column C shows the 

incremental cost of each technology step. Column D gives the fraction of each technology 

already in the MY2006 baseline fleet. Technology-specific phase-in rates are loaded from the 

data presented in Table A-11 into Columns F through O for model years 2011-2016 and 2020, 

respectively. As indicated earlier, standard rates are used as a starting point and replaced 

(beginning with the least-expensive technology) with modified, less aggressive rates on a trialand-error basis to optimize phased-in compliance in a least-cost manner. At the bottom of 

columns F and O, phased-in costs by model year are calculated by combining the amount of 

applied technologies in each year. This is performed for each of the four subfleets (Cars, Vans, 

Pickups, and SUVs). 
Analysis of Manufacturer Compliance 

4/6/09 

NERA Economic Consulting A-18 

Table A-11. Model-Year-Specific CAFE Compliance Analysis for GM 

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119

A B C D E F G H I J K O P Q R S T U V W X AB AC

Std Modified

Baseline PhaseIn VEHSIME Actual Phase-In

Combo # Tech. Name Incr. Cost Fraction Row 2011 2012 2013 2014 2015 2016 2020 Efficiency Efficiency Baseline 2011 2012 2013 2014 2015 2016 2020 Schedule

1 ULS $87 0.0% 15 0.0% 12.5% 25.0% 37.5% 50.0% 62.5% 100.0% 1.033 1.033 1.000 1.004 1.008 1.012 1.016 1.021 1.033 S

2 PARA $14 0.0% 7 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 1.005 1.005 1.005 1.005 1.005 1.005 1.005 1.005 1.005 S

3 EPS $84 0.0% 13 50.0% 75.0% 100.0% 100.0% 100.0% 100.0% 100.0% 1.016 1.016 1.008 1.012 1.016 1.016 1.016 1.016 1.016 S

4 ASLA5 $206 6.6% 6 65.0% 73.3% 81.6% 90.0% 98.3% 100.0% 100.0% 1.060 1.060 1.037 1.042 1.048 1.053 1.059 1.060 1.060 S

5 PARA2 $125 0.0% 8 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 1.020 1.020 1.020 1.020 1.020 1.020 1.020 1.020 1.020 S

6 DOHC $337 45.0% 17 45.0% 53.4% 61.7% 70.0% 78.4% 86.7% 100.0% 1.061 1.061 1.000 1.009 1.018 1.027 1.036 1.046 1.061 S

7 CVVLT $624 0.0% 12 14.4% 31.1% 47.8% 64.4% 81.1% 97.8% 100.0% 1.112 1.112 1.015 1.032 1.051 1.069 1.089 1.109 1.112 M

8 IMPALT $75 0.0% 18 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 100.0% 1.009 1.009 1.000 1.000 1.000 1.000 1.000 1.000 1.009 M

9 AMT6 $296 0.0% 20 16.7% 25.0% 33.3% 41.7% 50.0% 58.3% 91.7% 1.030 1.030 1.005 1.007 1.010 1.012 1.015 1.017 1.028 S

10 CVGDI $565 0.0% 25 0.0% 0.0% 0.0% 0.0% 11.4% 28.0% 94.7% 1.050 1.050 1.000 1.000 1.000 1.000 1.005 1.014 1.047 M

- - - - - - - - - - - - - 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

- - - - - - - - - - - - - 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

- - - - - - - - - - - - - 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

Total Cost: $2,413 $449 $679 $909 $1,118 $1,392 $1,680 $2,358 1.508 1.094 1.143 1.195 1.246 1.309 1.374 1.499

Car Fraction: 46.37% Achieved CAFE (mpg): 27.46 30.05 31.38 32.81 34.21 35.93 37.72 41.17

Required CAFE (mpg): 29.31 31.13 33.49 34.43 36.41 37.33 41.17

Difference (Ach'd - Req'd): +0.74 +0.24 -0.68 -0.22 -0.47 +0.39 +0.00

Running Credit Balance (mpg): +0.74 +0.98 +0.30 +0.08 -0.39 +0.00 +0.00

Baseline PhaseIn VEHSIME Actual Projected Phase-In

Combo # Tech. Name Incr. Cost Fraction Row 2011 2012 2013 2014 2015 2016 2020 Efficiency Efficiency Baseline 2011 2012 2013 2014 2015 2016 2020 Schedule

1 ULS $100 0.0% 15 0.0% 12.5% 25.0% 37.5% 50.0% 62.5% 100.0% 1.028 1.028 1.000 1.003 1.007 1.010 1.014 1.017 1.028 S

2 PARA $17 0.0% 7 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 1.005 1.005 1.005 1.005 1.005 1.005 1.005 1.005 1.005 S

3 EPS $97 0.0% 13 50.0% 75.0% 100.0% 100.0% 100.0% 100.0% 100.0% 1.014 1.014 1.007 1.011 1.014 1.014 1.014 1.014 1.014 S

4 ASLA5 $216 24.2% 6 82.5% 90.9% 99.2% 100.0% 100.0% 100.0% 100.0% 1.063 1.063 1.048 1.055 1.063 1.063 1.063 1.063 1.063 S

5 PARA2 $144 0.0% 8 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 1.020 1.020 1.020 1.020 1.020 1.020 1.020 1.020 1.020 S

6 DOHC $700 0.0% 17 33.3% 41.7% 50.0% 58.3% 66.7% 75.0% 100.0% 1.091 1.091 1.029 1.036 1.044 1.051 1.059 1.067 1.091 S

7 CVVLT $450 0.0% 12 66.7% 83.3% 100.0% 100.0% 100.0% 100.0% 100.0% 1.098 1.098 1.063 1.081 1.098 1.098 1.098 1.098 1.098 S

8 IMPALT $86 0.0% 18 50.0% 75.0% 100.0% 100.0% 100.0% 100.0% 100.0% 1.008 1.008 1.004 1.006 1.008 1.008 1.008 1.008 1.008 S

9 AMT6 $367 0.0% 20 16.7% 25.0% 33.3% 41.7% 50.0% 58.3% 91.7% 1.042 1.042 1.007 1.010 1.014 1.017 1.021 1.024 1.038 S

10 CVGDI $653 0.0% 25 0.0% 2.6% 19.3% 36.0% 52.6% 69.3% 100.0% 1.047 1.047 1.000 1.001 1.009 1.016 1.024 1.032 1.047 M

11 AHYB $8,118 0.0% 27 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 48.8% 1.222 1.222 1.000 1.000 1.000 1.000 1.000 1.000 1.097 M

- - - - - - - - - - - - - 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

- - - - - - - - - - - - - 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

Total Cost: $10,945 $1,012 $1,275 $1,629 $1,842 $2,052 $2,262 $6,755 1.978 1.205 1.264 1.336 1.368 1.400 1.433 1.698

Van Fraction: 5.80%

Achieved CAFE (mpg): 21.13 25.47 26.71 28.22 28.89 29.57 30.28 35.88

Baseline PhaseIn VEHSIME Actual Projected Phase-In

Combo # Tech. Name Incr. Cost Fraction Row 2011 2012 2013 2014 2015 2016 2020 Efficiency Efficiency Baseline 2011 2012 2013 2014 2015 2016 2020 Schedule

1 ULS $69 0.0% 15 0.0% 12.5% 25.0% 37.5% 50.0% 62.5% 100.0% 1.010 1.010 1.000 1.001 1.003 1.004 1.005 1.006 1.010 S

2 PARA $17 0.0% 7 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 1.005 1.005 1.005 1.005 1.005 1.005 1.005 1.005 1.005 S

3 ASLA5 $280 0.0% 6 58.3% 66.7% 75.0% 83.3% 91.7% 100.0% 100.0% 1.075 1.075 1.042 1.049 1.055 1.062 1.068 1.075 1.075 S

4 CD2V $447 0.0% 10 66.7% 83.3% 100.0% 100.0% 100.0% 100.0% 100.0% 1.081 1.081 1.053 1.067 1.081 1.081 1.081 1.081 1.081 S

5 PARA2 $149 0.0% 8 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 1.020 1.020 1.020 1.020 1.020 1.020 1.020 1.020 1.020 S

6 EHPS $151 0.0% 14 50.0% 75.0% 100.0% 100.0% 100.0% 100.0% 100.0% 1.012 1.012 1.006 1.009 1.012 1.012 1.012 1.012 1.012 S

7 AMT6 $365 0.0% 20 16.7% 25.0% 33.3% 41.7% 50.0% 58.3% 91.7% 1.033 1.033 1.005 1.008 1.011 1.013 1.016 1.019 1.030 S

8 CVVLT4 $583 0.0% 11 66.7% 83.3% 100.0% 100.0% 100.0% 100.0% 100.0% 1.016 1.016 1.011 1.013 1.016 1.016 1.016 1.016 1.016 S

9 IMPALT $89 0.0% 18 50.0% 75.0% 100.0% 100.0% 100.0% 100.0% 100.0% 1.008 1.008 1.004 1.006 1.008 1.008 1.008 1.008 1.008 S

10 CVGDI $124 0.0% 25 0.0% 2.6% 19.3% 36.0% 52.6% 69.3% 100.0% 1.007 1.007 1.000 1.000 1.001 1.003 1.004 1.005 1.007 M

11 AHYB $8,614 0.4% 27 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 48.8% 1.218 1.218 0.999 0.999 0.999 0.999 0.999 0.999 1.095 M

- - - - - - - - - - - - - 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

- - - - - - - - - - - - - 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

Total Cost: $10,889 $1,163 $1,460 $1,775 $1,858 $1,941 $2,025 $6,427 1.685 1.160 1.200 1.243 1.258 1.274 1.290 1.452

Pickup Fraction: 19.75%

Achieved CAFE (mpg): 18.48 21.44 22.17 22.97 23.25 23.54 23.84 26.83

Baseline PhaseIn VEHSIME Actual Projected Phase-In

Combo # Tech. Name Incr. Cost Fraction Row 2011 2012 2013 2014 2015 2016 2020 Efficiency Efficiency Baseline 2011 2012 2013 2014 2015 2016 2020 Schedule

1 ULS $92 0.0% 15 0.0% 12.5% 25.0% 37.5% 50.0% 62.5% 100.0% 1.019 1.019 1.000 1.002 1.005 1.007 1.010 1.012 1.019 S

2 PARA $16 0.0% 7 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 1.005 1.005 1.005 1.005 1.005 1.005 1.005 1.005 1.005 S

3 ASLA5 $218 21.0% 6 79.4% 87.7% 96.0% 100.0% 100.0% 100.0% 100.0% 1.053 1.053 1.039 1.045 1.050 1.053 1.053 1.053 1.053 S

4 CD2V $201 4.0% 10 70.7% 87.4% 100.0% 100.0% 100.0% 100.0% 100.0% 1.039 1.039 1.027 1.034 1.039 1.039 1.039 1.039 1.039 S

5 PARA2 $143 0.0% 8 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 1.020 1.020 1.020 1.020 1.020 1.020 1.020 1.020 1.020 S

6 EHPS $144 0.0% 14 50.0% 75.0% 100.0% 100.0% 100.0% 100.0% 100.0% 1.014 1.014 1.007 1.010 1.014 1.014 1.014 1.014 1.014 S

7 AMT6 $359 0.0% 20 16.7% 25.0% 33.3% 41.7% 50.0% 58.3% 91.7% 1.034 1.034 1.005 1.008 1.011 1.014 1.017 1.019 1.031 S

8 CVVLT4 $802 0.0% 11 66.7% 83.3% 100.0% 100.0% 100.0% 100.0% 100.0% 1.061 1.061 1.040 1.050 1.061 1.061 1.061 1.061 1.061 S

9 IMPALT $86 0.0% 18 50.0% 75.0% 100.0% 100.0% 100.0% 100.0% 100.0% 1.008 1.008 1.004 1.006 1.008 1.008 1.008 1.008 1.008 S

10 CVGDI $380 0.0% 25 0.0% 2.6% 19.3% 36.0% 52.6% 69.3% 100.0% 1.026 1.026 1.000 1.001 1.005 1.009 1.014 1.018 1.026 M

11 AHYB $8,043 0.0% 27 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 48.8% 1.220 1.220 1.000 1.000 1.000 1.000 1.000 1.000 1.096 M

- - - - - - - - - - - - - 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

- - - - - - - - - - - - - 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

Total Cost: $10,485 $1,170 $1,470 $1,816 $1,931 $2,036 $2,141 $6,333 1.705 1.161 1.203 1.249 1.265 1.277 1.290 1.467

SUV Fraction: 28.08%

Achieved CAFE (mpg): 21.22 24.63 25.52 26.51 26.85 27.11 27.37 31.14

2011 2012 2013 2014 2015 2016 2020 2011 2012 2013 2014 2015 2016 2020

LDT Fraction: 53.63% Total Cost: $1,150 $1,446 $1,781 $1,895 $2,003 $2,111 $6,414 Achieved CAFE (mpg): 23.43 24.29 25.25 25.59 25.90 26.21 29.80

Required CAFE (mpg): 23.12 23.92 25.07 25.67 26.07 26.80 29.80

Difference (Ach'd - Req'd): +0.31 +0.37 +0.17 -0.08 -0.18 -0.59 +0.00

Running Credit Balance (mpg): +0.31 +0.68 +0.85 +0.77 +0.59 +0.00 +0.00

2011 2012 2013 2014 2015 2016 2020 2011 2012 2013 2014 2015 2016 2020

Total Cost: $825 $1,090 $1,377 $1,535 $1,719 $1,911 $4,533 Achieved CAFE (mpg): 26.10 27.13 28.27 28.97 29.75 30.53 34.18

Light-Duty Vehicle (and MDPV) Sales-Weighted Composite

Pickups

Vans

Sport Utility Vehicles

Fuel Economy (mpg) by Model Year

Phase-In Schedule by Model Year

Phase-In Schedule by Model Year

Fuel Economy (mpg) by Model Year

Fuel Economy (mpg) by Model Year

All Light-Duty Trucks

Phase-In Schedule by Model Year Relative Fuel Efficiency Factors by Model Year

Phase-In Schedule by Model Year

TECHNOLOGY FRACTIONS, COSTS AND FUEL ECONOMY FOR ADJUSTED DRM CAFE COMPLIANCE BY MODEL YEAR -- GM

Passenger Cars
Analysis of Manufacturer Compliance 

4/6/09 

NERA Economic Consulting A-19 

Columns P and Q contain efficiency factors (at full phase-in) for each technology calculated 

from fuel economy data in the Results sheet. (Column Q is a hold-over from an earlier version 

of this spreadsheet model that was used to adjust the efficiency factors in certain isolated cases 

and was not used in this analysis.) 

Columns S through AB are used to scale the “full” efficiency for each technology based on the 

corresponding phase-in rate in Columns F through O. At the bottom of these tables, the 

combined CAFE fuel economy level achieved by applying each technology with the assumed 

phase-in for each model year is shown in Rows 25, 52, 78, and 104, respectively. 

For passenger cars, the achieved fuel economy in Row 25 is compared to the required CAFE 

levels shown by model year in the row below (Row 26). (The difference between the two levels, 

in mpg, is shown in Row 27.) For light-trucks, the achieved van, pickup, and SUV fuel economy 

levels and costs in Rows 52, 78, and 104 are sales-weighted into light-duty truck composite 

levels by model year in Row 111 and similarly compared to required CAFE levels listed in Row 

112. (The difference between achieved and required fuel economy by model year for light-duty 

trucks is calculated in Row 113.) 

Rows 28 and 114 show the “running” fuel economy balances from model year to model year for 

passenger cars and light trucks, respectively. 

Finally, light-duty fleet (combined passenger car and light truck) composite costs and achieved 

fuel economy levels are shown at the bottom of Table A-11 in Row 119. 

As indicated earlier, the phase-in schedules were applied or modified in a least-cost manner by 

trial and error. As indicated by the “Difference” values shown in Rows 27 and 113 of Table A11 and the running balances in the rows beneath them, this trial-and-error process was done in a 

manner that focused only on ensuring compliance with a zero running balance by 2016. This 

was done because provisions in the DDR allow manufacturers’ CAFE balances to be carried 

forward up to five model years. As was the case for GM as shown in Table A-11 (and other 

manufacturers as well), over-compliance occurs in earlier model years (because of the “soft 

start” nature of the standards in the DDR) when technology is applied at rates needed to ensure 

zero balances by 2016. Thus over-compliance credits in early model years (2011-2013 for GM 

as shown in Table A-11) were used to offset under-compliance in later years, with a zero balance 

being demonstrated by 2016. 

Model-year-specific CAFE compliance technology levels and costs from 2011-2016 were 

calculated in this manner for each of the 11 manufacturers analyzed under this effort. 

Summaries of the CAFE compliance costs by manufacturer and model year are presented later in 

Section 1.4. 

These manufacturer-specific CAFE compliance analyses are contained in the 

CE_AdjNPRM_*_FFV.xls Excel workbooks. 
Analysis of Manufacturer Compliance 

4/6/09 

NERA Economic Consulting A-20 

C. Analysis of Dropped Sales Needed for GHG Compliance 

The second and final stage of the analysis consisted of first translating the technology phase-ins 

needed for federal CAFE compliance for a U.S. fleet mix to state-specific vehicle fleets for each 

of the 13 GHG-adopting states as the “starting point” for the dropped sales analysis and for the 

relevant manufacturers. Then a series of calculations were programmed and executed to 

determine the minimum number of unit sales that would need to be dropped (i.e., no longer sold) 

to meet the sales-weighted light-duty vehicle GHG compliance target for each state. This 

analysis was performed by individual model year from 2011 through 2016. These steps are 

explained below. 

State-Specific Baseline Fleet Mix Development – As explained earlier in Section 1.2, U.S. sales 

were compiled for the model year 2006 baseline fleet using the “CAFE Sales” database 

exchanged between NHTSA and the manufacturer to track federal CAFE compliance. Statespecific new vehicle registrations for model year 2006 that had been purchased from R.L. Polk 

under a previous study were used to develop baseline fleet mixes for each of the 13 GHGadopting states considered in this analysis. 

For each individual state, the Polk registrations were merged with the baseline fleet database by 

vehicle make, model, body style, GVWR class, number of cylinders, fuel type (gas, Diesel, FFV 

or hybrid) and method of aspiration (naturally aspirated or turbocharged). Beyond this level of 

disaggregation, the state sales were apportioned down to the engine size, transmission, and trim 

level in the baseline database using splits at the level from the entire U.S. fleet. 

These state-specific fleet data were then grouped into the same 25 vehicle category classification 

scheme used for the U.S. baseline fleet. In addition, light-duty trucks were also classified into 

the LDT1 and LDT2 categories used in the GHG standards. (PCs and LDT1s must meet a 

combined CO2 standard under the GHG regulations; a separate standard applies to LDT2s.) 

Complete tabulations of the 2006 baseline vehicle sales and fuel economy levels for the state 

fleets in each of the GHG-adopting states by manufacturer are contained in the 

Baseline_Stats.xls spreadsheet file cited earlier. These tabulations are stored in sheets named 

BLTs, CLTs, DLTs, FLTs, etc., where the first letter of each sheet represents the manufacturer 

code (e.g., B=BMW, C=Chrysler, etc.). 

Large-Volume Manufacturer Identification and Grouping – The three manufacturers whom we 

believe at a minimum will have to mix-shift are large-volume manufacturers (LVMs). The 

dropped sales analysis was restricted to those three manufacturers. An LVM is essentially a 

manufacturer that sells more than 60,000 units in California using three-year running sales 

averages. In addition, the GHG regulations also use different rules than the federal CAFE 

program based on equity ownership that require manufacturers to be grouped together in 

determining whether they fall under the LVM criteria. (In short, if one manufacturer owns 10% 

or more of another manufacturer, those sales volumes must be added together for purposes of the 

CARB GHG volume requirements.) 
Analysis of Manufacturer Compliance 

4/6/09 

NERA Economic Consulting A-21 

State-Specific GHG Compliance Target Calculations – The state-by-state model year 2006 sales 

data and fuel economy levels for each LVM were loaded into a series of Excel workbooks named 

as follows: 

GHG_StateSales_M_FFV_VAC.xls 

where M is the manufacturer code and V is the version of alternative A/C system credits 

employed (either Full or Partial as described later). 

Several worksheets in these workbooks named “Sales,” “FE,” “FESales1,” “FESales2,” and 

“FESales” were used to load the state-by-state 2006 fleet sales and fuel economy data from the 

baseline fleet database and, for manufacturer groups, to properly sales-weight the individual 

manufacturer fleet statistics together. 

A sheet named “Lookups” in these workbooks was used to calculate and hold GHG compliance 

targets by model year and manufacturer for each state. The CARB regulations specify separate 

CO2-equivalent standards in grams per mile (g/mi) by model year for PCs+LDT1s and LDT2s. 

As noted earlier, manufacturer can comply with these standards separately, or based on a 

combined sales-weighted average of the PC+LDT1 and LDT2 standards using state-specific 

sales. 

Table A-12 list the CARB GHG standards by model year from 2011 through 2016. It also shows 

the equivalent fuel economy values (in mpg) corresponding to the CO2-equivalent standards. As 

noted earlier and shown in Table A-12, the dropped sales analysis was performed for two 

separate sets of assumed A/C credit levels (Full and Partial). Under the CARB GHG regulations, 

manufacturers receive credits toward the CO2 standard for using an alternative refrigerant HFC152a that has lower global warming potential than that typically used in today’s air conditioning 

systems, HFC-134a. 

Table A-12 

CARB GHG Standards and Equivalent Fuel Economy 

Model Years 2011-2016 

CO2 Equiv Std (g/mi) 

Equivalent FE (mpg) 

Full A/C Credit 

Equivalent FE (mpg) 

Model Partial A/C Credit 

Year PC+LDT1 LDT2 PC+LDT1 LDT2 PC+LDT1 LDT2 

2011 267 390 31.412 21.778 32.734 22.450 

2012 233 361 35.713 23.447 37.432 24.229

2013 227 355 36.598 23.825 38.405 24.633 

2014 222 350 37.369 24.150 39.255 24.980 

2015 213 341 38.842 24.757 40.883 25.629 

2016 205 332 40.252 25.395 42.449 26.314

Table A-13 lists Full and Partial A/C credit levels for PCs, LDT1s, and LDT2s. It also shows the 

levels of exhaust HC and CO that were used to convert CO2-equivalent standards to equivalent 

MPG values. The A/C credit and trace gas (CH4 and N2O) levels are consistent with CARB 
Analysis of Manufacturer Compliance 

4/6/09 

NERA Economic Consulting A-22 

assumptions.8 The assumed CO and HC emission levels were based on in-use emission test data 

collected in New York City for 2008 model year light-duty vehicles. 

Table A-13 

A/C Credit and Exhaust Gas Levels Assumed in 

CO2-Equivalent to Fuel Economy Conversions 

Parameter PC+LDT1 LDT2 

Full HFC-152a Refrigerant Credit (g/mi) 16.6 18.5 

Partial HFC-152a Refrigerant Credit (g/mi) 5.2 6.3 

Methane (CH4) + Nitrous Oxide (N2O) (g/mi): 1.9 1.9 

Exhaust CO (g/mi) 0.35 0.35 

Exhaust HC (g/mi) 0.02 0.02 

The following equation was used to compute equivalent fuel economy (FE) values in mpg for the 

CARB GHG standards as a function of the CO2 standard (in g/mi) and the exhaust gas and A/C 

credit assumptions listed in Table A-13: 

[0.273 ( ) 0.429 0.866 ]

2421 ( )

CO2 TG AC CO HC

FE mpg

× − + + +

=

where CO2 is the GHG standard (for either PC+LDT1 or LDT2, in g/mi), TG is the combined 

trace gas (CH4+N2O) emission rate (in CO2-equivalent g/mi), AC is the alternative A/C credit (in 

CO2-equivalent g/mi), and CO and HC are the assumed exhaust emission rates for those gases (in 

g/mi). 

Once the GHG standards by model year were converted to the equivalent fuel economy levels 

contained in Table A-12 using the assumptions in Table A-13 and the above equation, the next 

step consisted of computing the state- and manufacturer-specific sales-weighted LDV composite 

GHG targets (expressed in equivalent mpg) using the sales data for each manufacturer by state. 

These results (calculated by model year) were computed and stored in the Lookups sheet in these 

Excel workbooks. 

The final piece of information loaded into the Lookups sheet consisted of the model year by 

model year federal CAFE compliance efficiency factors explained earlier in Section 1.2. These 

efficiency factors represent the relative improvement (over baseline 2006 levels) in fuel economy 

by model year for each manufacturer that produces compliance with the federal CAFE standards 

in the DDR. 

Dropped Sales Compliance Calculations – The final step in the analysis consisted of loading the 

sales and fuel economy data, GHG compliance targets (in equivalent MPG) and CAFE 

compliance factors into separate worksheets for each state for the relevant manufacturers. These 

 

8

“Comparison of Greenhouse Gas Reductions for the United States and Canada Under U.S. CAFE Standards and 

California Air Resources Board Greenhouse Gas Regulations, California Air Resources Board, February 25, 

2008. 
Analysis of Manufacturer Compliance 

4/6/09 

NERA Economic Consulting A-23 

sheets were named “ResultsXX” where XX is a two character state abbreviation (e.g., ResultsAZ 

for Arizona, ResultsCA for California, etc.) 

These sheets were where the dropped sales calculations were performed and are laid out 

similarly to the ResultsUS sheets in the federal CAFE compliance spreadsheets. The data are 

stratified by the 25 categories used to represent the vehicle fleet. In these sheets, however, the 

light truck categories are further stratified into the LDT1 and LDT2 groups needed for the GHG 

compliance calculations. Columns D and E show the baseline 2006 sales data for the given 

manufacturer and state fleet (GM and California in this case). 

Columns F through K in these sheets titled “Hypothetical Model Shift Factors,” for model years 

2011 through 2016 are relative factors (between 0 and 1) calculated within the spreadsheet that 

are used to represent dropped sales factors that result in GHG compliance by model year for a 

manufacturer in a given state. Columns L through Q represent the calculated shift adjusted sales 

(in absolute sales units) by model year after dropping specific models that produce GHG 

compliance. (Similarly, Columns R through Q show these dropped sales on a percentage basis 

compared to baseline 2006 levels.) 

Columns X and Y in these sheets contain actual and simulation-modeling-based 2006 baseline 

fuel economy, respectively, loaded from other data in the spreadsheet. Columns AA through AF 

contain the fuel economy levels by model year, which were calculated by applying the federal 

CAFE compliance factors loaded in the Lookups sheet to the modeled baseline 2006 fuel 

economy. These values thus represent CAFE compliance fuel economy levels by vehicle 

category and model year. 

To minimize the effort required to properly calculate the dropped sales factors, an Excel macro 

named LeastDroppedSales was programmed into each workbook. This macro utilized Excel’s 

built-in Solver utility to optimally determine the shift factors for each vehicle category and 

model year that minimized the number of dropped sales in each model year needed for GHG 

compliance. In other words, it was assumed that all vehicle models were treated equally in 

determining how each manufacturer might drop specific models to meet state GHG compliance 

targets. 

A series of interrelated formulas at the bottom of the worksheet in Rows 96 through 100 were 

used by the LeastDroppedSales macro to run these optimizations. In cells AA96 through AF96, 

LDV compliance fuel economy is dynamically calculated as a function of the dropped salesadjusted fleet mix in each model year. Below these cells in Row 99, the required GHG fuel 

economy-equivalent targets are dynamically calculated (and updated as the fleet mix changes 

due to dropped sales in each model year). The net difference (actual vs. target) is calculated in 

Row 100. 

The LeastDroppedSales macro operates to minimize the values in Cells L100 through Q100, 

total dropped sales by model year. By running this macro for similar sheets representing each of 

the GHG-adopting states, it was possible to optimally determine (treating each model equally) 
Analysis of Manufacturer Compliance 

4/6/09 

NERA Economic Consulting A-24 

the number and fractions of dropped sales required for GHG compliance by state and model year 

for each manufacturer. 

D. Summary of Results 

Tabular summaries of the federal CAFE compliance and state-specific GHG dropped sales 

compliance results are presented and discussed separately in this sub-section. 

Federal CAFE Compliance Results – The sheet “TotalFFV” in the attached spreadsheet named 

CAFE_Compliance_AdjDRM_Fnl.xls shows CAFE fuel economy levels, hybrid technology 

percentages, and retail price equivalent (RPE) per vehicle compliance costs to meet federal 

CAFE by model year for each manufacturer. 

The results in the TotalFFV sheet are shown separately for each manufacturer’s passenger car 

and light-duty truck fleets. At the bottom of each subfleet, the row labeled “U.S. Fleet” shows 

the harmonically weighted average fuel economy across all manufacturers. The row below 

marked “U.S. Fleet Stds” represented the actual compliance targets in NHTSA’s DDR, adjusted 

for consistency with Sierra’s baseline fleet footprint distributions. The difference between these 

values reflects over-compliance or under-compliance margins in specific model years. As 

explained earlier in Section 1.2, the “soft start” nature of the DDR CAFE standards leads to overcompliance in early model years (2011-2013) for which a manufacturer can carry forward credit 

for up to five years. Thus, under-compliance is reflected in the later model years. Within the 

analysis spreadsheets, these balances were tracked by model year to ensure no net deficit 

occurred in the running credit balance for each manufacturer. 

U.S. fleet sales weighted averages are listed at the bottom of the worksheet. As shown, average 

CAFE compliance costs across all manufacturer range from $546 in model year 2011 up to 

$1,481 in model year 2016, although substantial variations exist across the individual 

manufacturers. Daimler and Porsche are estimated to have the highest compliance costs—

$4,877 and $5,040, respectively, in 2016. As seen from the middle columns in the worksheet, 

the large cost differences are directly related to the amount of advanced hybrid technology 

needed for compliance. 

Again, these costs and technology requirements assume manufacturers will fully use the 

alternative fuel credits specified in the EISA. 

GHG Dropped Sales Compliance Results – The attached spreadsheet named 

DroppedSales_Summary_Fnl.xls summarizes the amounts of dropped sales needed by 

manufacturer averaged across all 13 GHG-adopting states considered in this analysis. Separate 

sheets labeled “Partial” and “Full” in this spreadsheet summarize dropped sales based on partial 

and full A/C credits, respectively. The dropped sales are shown by manufacturer and model year 

and are expressed as a percentage of baseline 2006 sales. 

As shown in in the Partual sheet, the dropped sales percentages range up to 37.9% of a 

manufacturer’s state-averaged fleet. The sales-weighted averages across the manufacturers at the 

bottom of the worksheet indicate that roughly one-quarter to one-third of the sales will need to be 
Analysis of Manufacturer Compliance 

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NERA Economic Consulting A-25 

dropped for each model year from 2012 through 2016. The steep increase in dropped sales 

between 2011 and 2012 reflects the rate of increase in the stringency of the GHG standards 

between those years. This is in direct contrast with the federal CAFE standards, which increase 

more mildly over the same period. 

The sheet named Full presents similar dropped sales results by model year and manufacturer 

assuming full A/C credits. As shown, the dropped sales percentages are less than half of those 

computed assuming partial A/C credits but still average about 10% or higher across 

manufacturers for four of the six model years. 

Detailed tabulations of the dropped sales percentages by individual state are contained in the 

GHG_StateSales_*.xls workbooks in sheets named Summary. 
New Vehicle Market Model 

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NERA Economic Consulting B-1 

APPENDIX B. New Vehicle Market Model 

This appendix provides information regarding the NERA model of new vehicle markets, which 

we refer to as the New Vehicle Market Model (“NVMM”). The NVMM is used in this study to 

develop estimates of the impact of the California Standards on new vehicle prices and sales. 

A. Conceptual Approach: Nested Logit Model 

Logit discrete choice analysis provides a method for predicting consumer choices, and therefore 

demand, based on previously observed consumer behavior and other assumptions about demand 

(see, e.g., Ben-Akiva and Lerman 1985). The most basic logit model, also referred to as the 

simple logit, groups all product alternatives together and therefore allows only limited variation 

in own-price and cross-price elasticity between different alternatives. This limitation is often 

referred to as the “Independence of Irrelevant Alternatives” (“IIA”) problem. The nested logit 

model builds on this simple framework while allowing for a much richer pattern of crosssubstitution between different alternatives through the nesting structure. 

1. Basic Framework 

In the NVMM, consumers choose among a set of vehicle models, and may also choose not to 

purchase a vehicle at all. Our model uses the following utility specification. For alternative 

(vehicle model) j, the utility that a given consumer i obtains from choosing that alternative can 

be written as a function of an alternative-specific parameter and the ratio of that alternative’s 

price to the consumer’s income: 

ij

i

j

ij j

y

p

u = a − b + e (1) 

Alternative “0” is defined as the no-purchase alternative, and the remaining alternatives represent 

decisions to purchase individual vehicle models. The parameter aj measures the attractiveness of 

good j to consumers and is assumed not to differ across consumers. We assume that the price for 

the outside good is zero. pj is the price of alternative j, yi is income for consumer i, and b is a 

positive coefficient. The random error terms eij are assumed to be distributed as a multivariate 

generalization of the standard extreme value distribution. It is this assumption about the 

distribution of the eij that gives rise to the logit model. 

The potential purchaser is assumed to choose the alternative that yields the highest utility, taking 

into account both the deterministic and random components of utility. Given the nested logit 

demand assumptions, we calculate the probability for that each consumer will purchase each 

vehicle. Conditional upon the consumer’s decision to purchase a vehicle model within a vehicle 

group (or “nest”) A (as described below), the probability that consumer i purchases vehicle 

model j can be written as: 
New Vehicle Market Model 

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NERA Economic Consulting B-2 

∑∈













−













−

=

k A

A

i

k

k

A

i

j

j

i

y

p

y

p

j A y

a b l

a b l

exp

exp

Pr( | , ) (2) 

where lA is the “nesting parameter” for the appropriate vehicle group, or “nest” (as described 

below).9 We write Pr(j | A,yi) to emphasize that this probability is conditional on choosing a 

vehicle in nest A and conditional on the income of consumer i. 

2. Nesting Assumptions 

Our nested logit model uses the nesting structure shown in 

Figure B-1. We divide the choice problem first into the decision of whether to buy a new vehicle. 

Conditional upon the choice to purchase a new vehicle, consumers choose the vehicle type—in 

this case, passenger cars, pickup trucks and full-size vans, or SUVs and minivans. Conditional on 

the choice of vehicle type, consumers choose the vehicle class—for example, small cars or 

sporty cars (among others) in the passenger-car group. Conditional on the vehicle class (e.g., 

sporty car, small SUV, etc.), consumers choose one of the individual vehicle models available. 

The bottom level of the nesting structure includes over 200 vehicle models from which 

consumers may choose. 

The market model allows the utility that consumers derive from the purchase of different models 

to depend on the vehicle category and class via the nesting parameters (lA), which take values 

between zero and one.10 One nesting parameter applies to the purchase decision (buy or do not 

buy a vehicle); another nesting parameter applies to the choice of vehicle type; and a third 

applies to the choice of vehicle classes. The nesting parameter for the purchase decision must be 

at least as large as the nesting parameter for vehicle type. For each vehicle-type nest, the nesting 

parameter must be at least as large as the nesting parameter for all vehicle nests contained within 

the “parent” nest. The nesting structure implies that vehicles within one group are closer 

substitutes for each other than they are for vehicles in different groups. The cross-price 

elasticities between vehicles within the same group are therefore higher than the cross-price 

elasticities for vehicles in different groups. 

As noted above, one advantage of the nested logit model over the simple logit model is that it 

provides for a richer pattern of own- and cross-price elasticities. In the nested logit model, the 

IIA property need not hold across groups. That is, the ratio of the share for a particular car model 

in one bottom-level nest to the share of a vehicle in a different bottom-level nest, for example, 

depends not only on the characteristics of those two vehicle models but also on the substitution 

patterns implied by the nesting structure and nesting parameters. The nesting parameters 

 

9 Only differences in utility affect the predicted market shares in the nested logit model (see e.g. Train 2003). 

Thus, identification requires that one of the alternative-specific terms be normalized (fixed at a specific value). 

As a normalization rule, we set αj equal to zero for one vehicle model. Adding or subtracting a common amount 

to all the αjterms leaves the choice of alternatives unchanged, so any other normalization rule would yield 

identical results. 

10 The nesting parameters are sometimes called “inclusive value coefficients.” 
New Vehicle Market Model 

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NERA Economic Consulting B-3 

therefore enrich the simple logit model. If all nesting parameters equal one, then the nested logit 

model becomes a simple logit model. 

We next define some terms that we will use in describing the purchase probabilities in the model. 

Consumer i’s inclusive value term ( ) A iI y for bottom-level group A (the vehicle class) is defined 

as: 









































= ∑ −

j∈A

A

i

j

A i j y

P

I y l

b

( ) ln exp a (3) 

Where the index j runs across vehicles in bottom-level nest A, and i indexes consumers. We 

write ( ) A iI y to emphasize that the inclusive-value term is a function of consumer i’s income, 

and thus the values of IA will vary across consumers. Consumer i’s inclusive value term ( ) X iI y

for the top-level group X (the vehicle type) is defined as: 













= ∑

A∈X

X i A i A X

I ( y ) ln exp(I ( y )l / l ) (4) 

Consumer Decisons

Sports Wagon SUV

Buy Don’t Buy

Car

Truck/Van

SUV/Minivan

Premium SUV

Minivan

Large SUV

Mid-Range SUV

Small SUV

Small Pickup

Full Pickup

Full Van

Small Car

Mid-Range Car

Upscale Car

Sporty Car

Traditional Car

Consumer Decisons

Sports Wagon SUV

Buy Don’t Buy

Car

Truck/Van

SUV/Minivan

Premium SUV

Minivan

Large SUV

Mid-Range SUV

Small SUV

Small Pickup

Full Pickup

Full Van

Small Car

Mid-Range Car

Upscale Car

Sporty Car

Traditional Car

Figure B-1. Nesting structure for NVMM 
New Vehicle Market Model 

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NERA Economic Consulting B-4 

Where the sum is taken over all bottom-level nests A in the top-level nest X. Finally, the 

inclusive value term ( ) Buy iI y for the purchase alternative is defined as: 













= ∑

X

Buy i X i X Buy I ( y ) ln exp(I ( y )l / l ) (5) 

where the sum is taken across all top-level nests X. The probability that consumer i chooses a 

vehicle in the bottom-level group A within the top-level group X to which A belongs can be 

written as: 

∑∈

=

B X

B i A X

A i A X

i

I y

I y

A X y

exp( ( ) )

exp( ( ) )

Pr( | , )

l l

l l

. (6) 

The probability that consumer i chooses a vehicle in a given top-level group X conditional on 

consumer i purchasing a vehicle is: 

∑

=

Y

Y i X Buy

X i X Buy

i

I y

I y

X Buy y

exp( ( ) )

exp( ( ) )

Pr( | , )

l l

l l

. (7) 

Finally, the probability that consumer i chooses to purchase a vehicle at all is given by: 

exp( ( ) ) exp( )

exp( ( ) )

Pr( | )

l a0

l

+

=

Buy i Buy

Buy i Buy

i

I y

I y

Buy y , (8) 

where a0 is the value derived by the consumer from a no-purchase decision. 

The probability that consumer i chooses good j can be written as the product of the purchase 

probability and the conditional probabilities: 

Pr( | ) Pr( | ) Pr( | , ) Pr( | , )Pr( | , ) i i j i j j i j ij y = Buy y X Buy y A X y j A y , (9) 

where Aj is the bottom-level group to which j belongs and Xj is the top-level group to which Aj

belongs. 

B. Simulation of Aggregate Market Shares 

The probability expressions described above are for individual consumers. That is, the nested 

logit model gives expressions for the probability that consumer i chooses good j. In order to 

relate this demand system to observed data (e.g., market shares), it is necessary to somehow 

aggregate across consumers i. The expression for the unconditional market share of product j is: 
New Vehicle Market Model 

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NERA Economic Consulting B-5 

∫

s = Pr( j | y ) f ( y) j i

That is, the aggregate market share is calculated by integrating the consumer-level probabilities 

conditional on consumer characteristics (in our model this is the income of consumer i, or yi) 

over a density of income f(y). To create a probability density function for income, we use data 

from the Current Population Survey to estimate the parameters of a lognormal income 

distribution corresponding to U.S. household income in the base year. Because the market shares 

based on the lognormal cannot be integrated analytically, we develop a simulation estimator for 

the aggregate market shares. In brief, we create pseudorandom draws of a population of N

consumers from the lognormal income distribution for the base year. We then calculate the 

purchase probabilities for each consumer. A simple empirical approximation of the unconditional 

market shares is the following: 

∑=

=

N

i

j i

j y

N

s

1

Pr( | )

1

ˆ

i.e., the unconditional market shares can be approximated as the average of the unconditional 

choice probabilities over the N consumers in the population. It can be shown that j jsˆ → s almost 

surely as N → ∞ , that is, the simulated market shares approach the actual market shares as the 

number of simulation draws grows large. 

We must also account for the fact that we do not observe sj directly, but rather sj|Buy, that is, the 

market share of each vehicle model conditional on the buy decision. Thus, in order to calibrate 

the model to our aggregate price and quantity data, we must have an estimate of sj|Buy rather than 

sj. This is accomplished by noting that the buy-share, sBuy, is: 

∫

s = Pr(Buy | y ) f ( y) Buy i

and forming a similar simulation approximation to the buy-share. We can then calculate the 

model shares conditional on Buy as: 

Buy

j

j Buy

s

s

s | =

C. Market Simulation 

To simulate market substitution patterns, we must also have a means of calculating market-level 

substitution parameters conditional on the parameters of the model and the distribution of 

income. In other words, we must calculate a matrix of own- and cross-price elasticities, where 

the elasticity of good j with respect to a change in the price of good k is defined as: 

j

k

k

j

jk

s

p

p

s

∂

∂

e =
New Vehicle Market Model 

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NERA Economic Consulting B-6 

where pk is the price of good k and sj is the market share of good j. In order to calculate the 

market-level elasticities, first note that the market-level partial derivatives 

k

j

p

s

∂

∂

are found as 

follows: 

∫

∂

∂

=

∂

∂

( )

Pr( | )

f y

p

j y

p

s

k

i

k

j

As with the aggregate market share expressions, this integral does not have a closed form 

solution, and thus we approximate the integral as the mean across consumers of 

k

i

p

j y

∂

∂ Pr( | )

. We 

then calculate the market level elasticities (εjk) as above. 

An analytic expression exists for the aggregate price elasticity of demand (that is, the elasticity of 

demand for vehicles with respect to the price of vehicles): 

Buy

Buy

s

p

p

s

∂

∂

where p is the weighted-average price. However, in principle it is easier to approximate the 

aggregate price elasticity using finite-differencing. 

We assume that the motor vehicle market is characterized by Bertrand competition where each 

manufacturer sets prices to maximize its overall profits, taking into account the fact that it is a 

multi-product firm. In a Nash equilibrium for this Bertrand competition, the profit-maximizing 

price for a single-product firm is characterized by the Lerner condition (where mcj is the 

marginal cost for alternative j, assumed to be constant):11

j j

j j

p

p mc

e

1

= −

−

(13) 

If one observes pj, then knowing either the marginal cost or the elasticity provides enough 

information to calculate the other. 

For a multi-product firm (as is the case for all major auto firms in the United States), the pricing 

equations include additional terms that reflect the unit profits on other products made by the firm 

and the cross-elasticities of demand between good j and these other products. The basic logic is 

that as the price of good j increases, some of the lost sales of that good will be replaced by 

increased sales of other goods sold by the same firm. Our model takes these effects into account. 

Specifically, the profit-maximization condition implies that manufacturers calculate the markup 

for each product (that is, the value of p-mc) according to the following matrix equation: 

 

11

 See, e.g., Carlton and Perloff (1999) for a discussion of the Bertrand-Nash assumptions.
New Vehicle Market Model 

4/6/09 

NERA Economic Consulting B-7 

T

= Δ ⋅s

−1

b

Where b is a vector of markups for each model, s is a vector of shares for each model, and Δ is 

p-by-p matrix (where p is the number of products) whose entries are defined by: 















∂

∂

−

Δ =

0 if j and k are made by different firms

 if j and k are made by the same firm

k

j

jk

p

s

D. Solving for Parameters 

As described above, the nested logit choice framework provides a method to estimate consumer 

demand for differentiated products. Using the nested logit framework, we solve simultaneously 

for the beta parameter (the coefficient on price over income) and “alternative-specific” 

parameters that are consistent with the observed market shares and prices. 

We use the nested logit framework to solve for the alternative-specific parameters for each 

vehicle model. We make assumptions concerning the nesting parameters, the aggregate price 

elasticity of demand, and the price elasticity of demand for one specific alternative. Given the 

structure of our nested logit model, these assumptions, the observed prices, and the observed 

market shares are sufficient to derive estimates of the alternative-specific parameters (including 

that for the outside good). 

E. Specific Implementation Parameters and Data 

1. Vehicle Sales 

We use quarterly vehicle sales data from J.D. Power and Associates to determine the market 

share for each vehicle model, aggregated across trim levels, for the base year.

12 If a vehicle 

model sold less than 5,000 units in a given year, we eliminated it from the dataset for that year. 

We also eliminated observations where models sold substantially more or fewer units than the 

following or previous model year (in general due to a partial production year related to model 

introduction or discontinuation) to avoid biasing the substitution parameters in the model. 

2. Vehicle Prices 

We use data on transaction prices for each model from J.D. Power and Associates for the United 

States. The transaction prices are sales weighted and reflect the different prices charged for 

 

12 JD Power requests the following disclaimer regarding use of their data: "The information supplied by Power 

Information Network, LLC (“PIN”) is based on data believed to be reliable but is neither all inclusive nor 

guaranteed by PIN. Without limiting the generality of the foregoing, specific data points may vary considerably 

from other information sources. Any opinions expressed herein reflect the judgment of NERA at this date and 

are subject to change." 
New Vehicle Market Model 

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NERA Economic Consulting B-8 

different trim levels. The prices are actual transaction prices, not MSRPs, and are net of any cash 

rebates. 

3. Categorization of Vehicle Models into Nests 

We use vehicle categorizations from the 2007 Automotive News Market Data Book to define the 

vehicle nesting structure depicted in Figure B-1. Where the appropriate category for a particular 

model could not be determined based on the 2007 Automotive News Market Data Book, we used 

older Automotive News Market Data Books. 

4. Price Elasticity 

Consistent with various literature sources, we assume an aggregate elasticity for the new vehicle 

market of –1.0.13 We set the own-price elasticity of the “normalized” vehicle model (whose 

alternative-specific parameter is normalized to zero) to be –4.0, which is consistent with various 

other literature estimates of individual model own-price elasticities.

14

The nesting parameters for nested logit models represent the similarity between choices for 

vehicles falling within the same “nest.” The nesting parameters influence the relative 

substitutability within each nest, and also between different nests. Nesting parameters may take 

any value between zero and one, with lower values indicating greater similarity between the 

alternatives within the respective nest. For the “Buy” nest we use a nesting parameter equal to 

0.9, for vehicle types we use a nesting parameter equal to 0.6 and for vehicle classes we use a 

nesting parameter equal to 0.3. 

References 

Automotive News. 2001. 2001 Market Data Book. Crain Communications, Inc. May. 

Automotive News. 2002. 2002 Market Data Book. Crain Communications, Inc. May. 

Automotive News. 2003. 2003 Market Data Book. Crain Communications, Inc. May. 

Automotive News. 2005. 2005 Market Data Book. Crain Communications, Inc. May. 

Automotive News. 2006. 2006 Market Data Book. Crain Communications, Inc. May. 

Automotive News. 2007. 2007 Market Data Book. Crain Communications, Inc. May. 

Ben-Akiva, Moshe and Steven R. Lerman. 1985. Discrete Choice Analysis. MIT Press, 

Cambridge. 

 

13 See, for example, Gruenspecht (2000). 

14 See, for example, Berry, Levinsohn, and Pakes (1995). 
New Vehicle Market Model 

4/6/09 

NERA Economic Consulting B-9 

Berry, Steven, James Levinsohn, and Ariel Pakes. 1995. “Automobile Prices in Market 

Equilibrium,” Econometrica, Vol. 63, No. 4, July 1995, pp. 841-890. 

Carlton, Dennis W. and Jeffrey M. Perloff. 1999. Modern Industrial Organization. Third 

Edition. Addison-Wesley: New York. 

Goldberg, Pinelopi K. 1995. “Product Differentiation and Oligopoly in International Markets: 

The Case of the U.S. Automobile Industry,” Econometrica, pp. 891-951. 

Gruenspecht, Howard K. 2000. Comments on 2000 Biennial Review of Zero Emission Vehicle 

Program before the California Air Resources Board. September 5. 

J.D. Power and Associates, Power Information Network. PIN Markets: National. Purchase of 

proprietary data, December 2007. 

United States Department of Labor, Bureau of Labor Statistics (BLS). Consumer Price Index—

All Urban Consumers, U.S. All Items, 1966-2007. http://www.bls.gov/cpi/home.htm. 
Analysis of Employment Impacts 

NERA Economic Consulting A-1 

ATTACHMENT A. Analysis of Employment Impacts 

To: NERA Economic Consulting and Sierra Research, Inc. 

From: Michael J. Whinihan, Ph.D. 

Date: April 6, 2009 

Subject: Employment Losses Predicted from Lost Vehicle Sales Caused by the 

California Motor Vehicle Greenhouse Gas Emissions Regulations. 

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

I have been asked by the Alliance of Automobile Manufacturers to provide an estimate of the 

impacts of the California motor vehicle greenhouse gas standards on employment in the 

automobile industry in the United States, based on the vehicle sales losses that have been 

predicted in your joint research.1

Summary 

My results are based on the attached spreadsheet. As indicated in the spreadsheet, the California 

GHG standards under the assumptions used in your work and applying the methodology 

described here will result in significant employment losses in the national automobile industry. 

The estimated job losses for the vehicle manufacturing industry, its suppliers, and distributors are 

87,000 jobs in 2012 and 89,000 jobs in 2016. 

Methodology 

My analysis begins with the Department of Labor’s Bureau of Labor Statistics employment 

requirement matrix for the United States economy, which is based on the Bureau of Economic 

Analysis input/output table for the United States. (Chain-weighted 2000 dollars) Real Domestic 

Employment Requirements Table for 1998-2006 http://www.bls.gov/emp/empind4.htm). Use of 

input/output tables is a generally accepted technique for estimations of this nature in the field of 

economics.

2

 The table from BLS that I relied on lists the estimated number of jobs necessary to 

produce $1 million dollars of goods or services for any one of 201 industry sectors. In addition 

to providing the total jobs for a given sector’s output, the table lists the jobs needed in each of the 

201 sectors that produce that total. For example, column 88, Motor Vehicle Manufacturing, 

computes that 6.35 jobs are needed to produce $1 million of output, but only 0.88 of those jobs 

are in Motor Vehicle Manufacturing itself. An additional 1.02 jobs are required in sector 90, 

Motor Vehicle Parts Manufacturing, and so on, throughout all represented sectors. 

My initial job computation runs “from the mine mouth to the factory gate”, and I will term those 

jobs the “wholesale jobs.” Total employment effects for reduced motor vehicle sales will also 

include employment in new car dealerships and in the transport industry that moves the vehicles 

 

1

I am a practicing economist. I hold a Ph.D. in Business Administration from the University of Chicago, and a B.A. 

from Harvard University. I retired from General Motors Corporation in 2008, where I was a Senior Economist 

and spent much of my career analyzing the motor vehicle industry. 

2 Wassily Leontief was awarded the 1973 Nobel Prize in economics for pioneering the concept of input-output 

analysis.
Analysis of Employment Impacts 

NERA Economic Consulting A-2 

from the factory gate to the dealerships. Hence, to differentiate those from “wholesale jobs,” I 

will term this latter figure “distribution jobs.” 

The calculation derived from the input/output table must be adjusted for inflation. The 

input/output table I used gave figures in 2000 dollars. I adjusted the employment numbers in the 

input/output table from 2000 dollars to 2008 dollars by dividing the 6.35 wholesale jobs figure 

by the ratio of the 2008 CPI to the 2000 CPI. That ratio is 1.2503, and so the resulting wholesale 

jobs per million now reduces to 5.08. 

The Energy Information Administration’s Annual Energy Outlook 

(http://www.eia.doe.gov/oiaf/aeo/aeoref_tab.html, Year by Year Reference Case Tables, Table 

20) forecasts vehicle sales 17.02 million in 2016 and 16.47 million in 2012. Those figures are 

used on the attached spreadsheet. 

The current States that have chosen to adopt California greenhouse gas standards had a vehicle 

sales market share of 34.3% in 2006, the last non-recessionary year for which Sierra and NERA 

had complete data. (NERA also calibrated its model with data from 2006.) It is beyond the 

scope of this analysis to speculate whether that figure might change based on new States signing 

on to the California program, States withdrawing from the program, or changes in the population 

and demographics of various States. Hence, the 34.3% figure is used in the attached spreadsheet. 

Based on this market share, the volume of California vehicles is estimated to be 5.65 million in 

2012 and 5.84 million in 2016. 

The NERA model estimates that volume will decrease 8.0% and prices will decrease 3.1% in 

2016, and volume will decrease 8.5% and prices decrease 2.6% in 2012. Due to the current 

recession, car prices are much lower than normal. Once the recession is over, normal pricing is 

expected to return. Estimates of transaction prices are purchased from vehicle data vendors and 

then must be entered into a weighted average, and the results are quite imprecise. Based on 

numbers purchased by GM and adjusted for inflation to 2008 dollars from the last nonrecessionary year, 2006, I estimate average transaction costs to consumers in 2016 at about 

$25,630. I led a project several years ago that was assessing optimum dealer inventory levels. 

One of the results of that project were that although dealer gross margins vary widely, the 

average was about 5% above dealer invoice, and 8% after allowing for dealer “holdback.” This 

estimate is consistent with websites such as Car Buying Tips 

(http://www.carbuyingtips.com/car4.htm), which suggests offering a dealer 5% above dealer 

invoice, and Edmunds (http://www.edmunds.com/advice/incentives/holdback/index.html) which 

documents that most vehicle manufacturers use a 3% “hold back.” Dealer gross margin then 

averages about $2,050 and reducing average transaction price by 8% estimates average dealer 

average cost at $23,580. 

Another web site lists dealer destination charges for different models 

(http://www.destinationcharge.com/). Using that site’s data to compute an unweighted average 

of destination charges for all listed models of the three largest volume vehicle manufacturers 

results in an estimated average destination charge of $775. Subtracting dealer destination 

charges from dealer cost gives an estimated average factory gate price of $22,805. 
Analysis of Employment Impacts 

NERA Economic Consulting A-3 

Since volume and price change with a waiver, we need to compute baseline revenue as baseline 

price multiplied by baseline volume, and then multiply baseline revenue by the jobs figure to 

estimate baseline employment. I then compute waiver revenue as waiver price multiplied by 

waiver volume, and then multiply waiver revenue by the jobs figure to estimate waiver 

employment. Then waiver job losses are computed by subtracting waiver employment from 

baseline employment. These calculations estimate wholesale job losses in to be: 71,153 jobs in 

2012 and 73,347 in 2016. 

In addition, implementation of the greenhouse gas standards would cause a loss in distribution 

jobs proportional to volume losses. Based on the I/O table for column 101, Retail Trade (value 

of jobs per million dollars of 15.94) and based on an average dealer gross margin of $2,050 (as 

noted three paragraphs above), about 3.23 retail jobs are needed to sell 100 vehicles. Based on 

column 105, Truck Delivery (value of jobs per million dollars of 12.08), based on and an average 

freight charge of about $775, about 0.94 jobs are needed to deliver 100 vehicles. Adjusted for 

inflation, these numbers are 2.61 and 0.75, giving a total of 3.36 distribution jobs per 100 

vehicles sold. As a result, distribution job losses for 2012 and 2016 are 16,143 and 15,701. 

Overall, then, if EPA grants the waiver California is requesting on reconsideration, I estimate 

total job loss in the motor vehicle industry, its suppliers, and distributors at 87,296 in 2012 and 

89,048 in 2016. Details of job losses if it is assumed that the industry receives full A/C credit are 

shown in the Appendix tab in the attached spreadsheet (submitted as electronic backup material). 

In that case job losses are estimated to be 52,352 in 2012 and 36,414 in 2016.