Peer Review of the Optimization Model 

for Reducing Emissions of Greenhouse 

Gases from Automobiles (OMEGA) 

and EPA’s Response to Comments
EPA-420-R-09-016

September 2009

Assessment and Standards Division

Office of Transportation and Air Quality

U.S. Environmental Protection Agency

Prepared for EPA by

Southwest Research Institute

EPA Contract No. EP-C-05-018

Work Assignment No. 4-01

Peer Review of the Optimization Model 

for Reducing Emissions of Greenhouse 

Gases from Automobiles (OMEGA) and 

EPA’s Response to Comments

NOTICE

This technical report does not necessarily represent final EPA decisions or 

positions. It is intended to present technical analysis of issues using data 

that are currently available. The purpose in the release of such reports is to 

facilitate the exchange of technical information and to inform the public of 

technical developments.
In May, 2009, EPA contracted with the Southwest Research Institute (SwRI) to conduct a 

peer review of a new model which evaluates the technology and cost associated with reducing 

greenhouse gas (GHG) emissions from light-duty motor vehicles. This model is currently 

referred to as the Optimization Model for reducing Emissions of Greenhouse gases from 

Automobiles (OMEGA). When peer review was initiated, the model was referred to as the 

Vehicle Greenhouse Gas Emissions Cost and Compliance Model (VECTOR). 

 The three peer reviewers selected by SwRI were John German, Dr. Paul Leiby and Dr. 

Rubin. EPA would like to extend its appreciation to all three reviewers for their efforts in 

evaluating this model. The three reviewers brought useful and distinctive views to all aspects of 

the model’s use. This included setting up the input files appropriately, running the model 

efficiently, and understanding the model’s outputs. There are two major sections to this report. 

The first section contains the final SwRI report summarizing the peer review of OMEGA, 

including the detailed comments of each peer reviewer and an overview of the most significant 

comments compiled by SwRI. The SwRI report also contains the peer review charge letter. The 

second major section contains our responses to the peer reviewers’ comments. In this section, 

we repeat the detailed comments from each commenter and, after each section of comments, 

provide our response. We have retained the organization reflected in each reviewer’s comments 

to aid the reader in moving from the SwRI report to our responses. 

Table of Contents 

1. Peer Review of the EPA Optimization Model for reducing Emissions of Greenhouse 

gases from Automobiles (OMEGA) conducted by Southwest Research Institute 

I. Introduction 

II. Technical Discussion 

III. Summary 

IV. Appendicies 

A. Resumes of Peer Review Panel Members 

B. Charge Letter to Reviewers 

Attachment 1 – EPA Vehicle GHG Emission Cost and Compliance 

Model Description 

Attachment 2 – Appendicies to GHG Model Description 

Attachment 3 – Model Reference Guide 

Attachment 4 – Benefits Calculations Instructions 

C. John German’s Review Document 

D. Paul Leiby’s Review Document 

E. Jonathan Rubin’s Review Document 

2. EPA’s Response to Peer Review Comments 

A. Comments by John German 

B. Comments by Paul Leiby 

C. Comments by Jonathan Rubin 
SOUTHWEST RESEARCH INSTITUTE ®

6220 CULEBRA ROAD  POST OFFICE DRAWER 28510  SAN ANTONIO, TEXAS, USA 78228-0510  (210) 684-5111  www.swri.org 

ENGINE, EMISSIONS, AND VEHICLE RESEARCH DIVISION HTTP://ENGINEANDVEHICLE .SWRI.ORG

FAX: (210) 522-3950 ISO 9001 Certified 

ISO 14001 Certified 

SAN ANTONIO, TEXAS 

HOUSTON, TEXAS  WASHINGTON, DC  ANN ARBOR, MI 

Peer Review of the EPA Optimization Model for 

reducing Emissions of Greenhouse gases from Automobiles 

(OMEGA) 

Office of Transportation Air Quality 

U.S. Environmental Protection Agency 

September, 2009 
SOUTHWEST RESEARCH INSTITUTE ®

6220 CULEBRA ROAD  POST OFFICE DRAWER 28510  SAN ANTONIO, TEXAS, USA 78228-0510  (210) 684-5111  www.swri.org 

ENGINE, EMISSIONS, AND VEHICLE RESEARCH DIVISION HTTP://ENGINEANDVEHICLE .SWRI.ORG

FAX: (210) 522-3950 ISO 9001 Certified 

ISO 14001 Certified 

SAN ANTONIO, TEXAS 

HOUSTON, TEXAS  WASHINGTON, DC  ANN ARBOR, MI 

 In May, 2009, EPA contracted with the Southwest Research Institute (SwRI) to conduct a 

peer review of a new model which evaluates the technology and cost associated with reducing 

greenhouse gas (GHG) emissions from light-duty motor vehicles. This model is currently 

referred to as the Optimization Model for reducing Emissions of Greenhouse gases from 

Automobiles (OMEGA). When peer review was initiated, the model was referred to as the 

Vehicle Greenhouse Gas Emissions Cost and Compliance Model (VECTOR). 

 The three peer reviewers selected by SwRI were John German, Dr. Paul Leiby and Dr. 

Rubin. EPA would like to extend its appreciation to all three reviewers for their efforts in 

evaluating this model. The three reviewers brought useful and distinctive views to all aspects of 

the model’s use. This included setting up the input files appropriately, running the model 

efficiently, and understanding the model’s outputs. There are two major sections to this report. 

The first section contains the final SwRI report summarizing the peer review of OMEGA, 

including the detailed comments of each peer reviewer and an overview of the most significant 

comments compiled by SwRI. The SwRI report also contains the peer review charge letter. The 

second major section contains our responses to the peer reviewers’ comments. In this section, 

we repeat the detailed comments from each commenter and, after each section of comments, 

provide our response. We have retained the organization reflected in each reviewer’s comments 

to aid the reader in moving from the SwRI report to our responses. 
SOUTHWEST RESEARCH INSTITUTE ®

6220 CULEBRA ROAD  POST OFFICE DRAWER 28510  SAN ANTONIO, TEXAS, USA 78228-0510  (210) 684-5111  www.swri.org 

ENGINE, EMISSIONS, AND VEHICLE RESEARCH DIVISION HTTP://ENGINEANDVEHICLE .SWRI.ORG

FAX: (210) 522-3950 ISO 9001 Certified 

ISO 14001 Certified 

SAN ANTONIO, TEXAS 

HOUSTON, TEXAS  WASHINGTON, DC  ANN ARBOR, MI 

June 9, 2009 

To: Environmental Protection Agency 

 Contracts Management Division 

 26 West Martin Luther King Drive 

 Cincinnati, OH 45268 

Attention: Ms. Almethyist A. Chambers 

 Contracting Officer 

From: Patrick M. Merritt 

 Senior Research Scientist 

 Emissions Research and Development Department 

 Southwest Research Institute 

 P.O. Drawer 28510 

 San Antonio, Texas 78228-0510 

Subject: Final Report for Work Assignment 4-1, “Facilitation of an Independent 

Peer Review Process for EPA's VGHG Model” 

Contract No. EP-C-05-018, under SwRI Project 03.14658.01 

Contract Title: “Testing and Analytical Support for Regulation of Motor Vehicles, 

Engines, Fuels, and Fuel Additives” 

I. INTRODUCTION

 On-road vehicles are the predom inant source of greenhouse gas (GHG) em issions in the 

transportation sector (principally, CO2 and hydrocarbon emissions from vehicle air conditioners). 

Of all on-road vehicles, light- duty passenger cars and trucks pr oduce the majority of these GHG 

emissions. As EPA's Office of Transportation and Air Quality explores the regulation of CO2 and 

other GHG emission control measures in on-road a nd non-road vehicles and equipment, there is 

a need to evaluate the costs and benefits of any such regulations. As such, EPA has developed its 

Vehicle Greenhouse Gas E missions Cost and Co mpliance Model, or VGHG model, to facilitate 

its analysis of the costs and benefits of th e control of GHG e missions fro m cars and trucks. 

Broadly speaking, the primary cost of GHG emission control is the cost of adding technology to 

the vehicles, while the p rimary benefit is the value of reduced fuel cons umption in those sam e 

vehicles. 
Ms. Almethyist A. Chambers 

Environmental Protection Agency 

June 9, 2009 

Page 4 of 8 

The EPA VGHG model is used to apply various technologies to a defined set of vehicles 

in order to meet a specified GHG e mission target, and to then calculate the costs and benefits of 

doing so. The GHG target can be a flat standard app licable to all vehicles within a vehicle class 

(e.g., cars, trucks or both cars and trucks) or the 'target' can be in the for m of a c urve which 

varies the target as a function of a defined vehicle ' fleet.' GHG emission targets are specified in 

terms of CO2-equivalent emissions. They can simply be CO 2 emissions from the tailpipe or can 

be a combination of tailpipe CO 2 and refrigerant e missions. Fleet wide average GHG e missions 

can also be used to estim ate a wide array of societal cos ts and benefits associated with the 

reduction of GHG emissions. 

To assure the highest quality science in its p redictive as sessments, EPA has engaged 

SwRI to facilitate an independent peer review of its model for determining the costs and benefits 

of changes to vehicle technology for the reduc tion of GHG em issions from passenger cars and 

trucks. EPA needs assurance that the proposed structure (and development process) of its VGHG 

model will r esult in a model tha t is viable, a ccurate, and we ll-suited for the dive rsity of uses to 

which it may be applied. This report documents the process followed towards that end. 

II. TECHNICAL DISCUSSION

EPA's peer review gu idelines specify that all h ighly significant scientific and technical 

work products undergo independent peer review per specific agency protocols. The first task was 

to select panel members (three) who were qualified, interested, and had time available to devote 

to an in-depth review. 

Selection of candidates was made from a list of persons familiar with the issues involved 

in GHG emissions from ecological, socio-economic, regulatory, and manufacturing perspectives. 

The panel m embers who were selected h ave impressive standings in their respective fields and 

comprise a balanced an d diverse point of view. The peer review panel consists of the following 

individuals: 

 John German, The International Council on Clean Transportation 

 Paul Leiby, Oakridge National Laboratory

 Jonathan Rubin, University of Maine, School of Economics

Their résum és are presented in Appendix A. Fo llowing selection and determ ination of their 

availability, consultancy agreements had to be put in place with each individual. 

SwRI Project 03.14658.01 FR 
Ms. Almethyist A. Chambers 

Environmental Protection Agency 

June 9, 2009 

Page 5 of 8 

 As soon as it was practical, the docum ents and software were distributed to the review 

panel members. The distribution was accomplished by preparing a charge letter d escribing what 

was requested in detail, and attaching the supporting documents. Please see Appendix B. 

SwRI then arranged a teleconference between the peer reviewers and EPA technical staff 

as a kick-off m eeting. In that c onference, held on May 1, 2009, th e charge to the reviewers was 

discussed, and the presentation of the supporting docum ents was reviewed. As there were seven 

attachments, it was i mportant to have assura nce that everyone understood how the docum ents, 

appendices, and attachments are related. 

During the kick-off m eeting, an infor mal question dialog allowe d pa nel me mbers t o 

interact with EPA' s work assignm ent m anager (WAM) and the technical staff who are m ost 

familiar with the GHG model. It was agreed that any further questions would be subm itted in 

writing to S wRI, and the question and EPA’s r esponse would be distributed to all reviewers. 

Only one s uch questio n was sub mitted. Fina lly, it was agreed tha t each would attem pt to 

complete their work prior to the next teleconference, which was scheduled for May 28, 2009. 

 The review documents of two of the three reviewers had been received and forwarded to 

EPA before the teleconference held on May 28 th. The third followed shortly thereafter. The May 

28th teleconference was nonetheless productive, w ith discussion am ong EPA technical staff and 

the panel members. Those documents are presented in Appendices C, D, and E. 

 Because the third docu ment had not been re ceived before the teleco nference, it was 

determined that another teleconf erence would be held. The idea wa s that all parties could have 

additional time to read the comm ents of each of the p anel members and determine if there are 

other topics or issues that need clarification or discussion. That discussion was also productive, 

with discussion of whether one equation had an error or if it simply needed more explanation, for 

example. In addition, investm ent costs of technology with regards to tooling and plant 

conversions, capital budgets, and lead time were also discussed. 

III. SUMMARY 

A very brief summary of each of the three reviewers’ comments is presented below. 

SwRI Project 03.14658.01 FR 
Ms. Almethyist A. Chambers 

Environmental Protection Agency 

June 9, 2009 

Page 6 of 8 

Paul Leiby:

 Stay focused first on clearly and rigorously modeling the fuel-economy technology choice 

and cost-effectiveness considerations, for various GHG emission levels. 

 It is essential to be very explicit about whose behavior and objectives are being modeled. 

 Some confusing term s are includ ed in the TA RF, m ost notably the non-standard way in 

which VMT is discounted for the purposes of this TARF (See equation top of page 11, 

line 1). 

 The inclusion of “IR” (“the annu al increase in the value of CO2”) in the discount f actor is 

done without explanation or ju stification. (Is it meant to be the growth rate in GHG 

damages, abatement cost, or a CO2 tax?). 

 CostEff TARF would not seem to be a cons ideration f or vehicle manufacturers whose 

objective is to produce a new-car fleet m eeting consum er needs and a GHG em ission 

standard at least cost. What objective was intended with this hybrid aspect of the TARF? 

 Model Documentation: 

 Restructure the presentation, perhaps follo wing the pattern of a journal article 

(e.g., begin with stated purpose and b ackground. Place this m odel in the 

constellation of related models and indica te what is different and why. Describe 

approach, data sources, sample results.) 

 Bringing description of the “Core P rogram” and what the model does toward the 

front. 

 Clarify and condense the model description 

 State model objective (typically stating wh at is m aximized, minimized, or what 

final solution condition is sought) 

 State model constraints 

 State and discrim inate between principl e decision variables, exogenous inputs, 

parameters, and internally calculated results 

 State the solution algorithm and termination condition 

 Be rigorous in use of notation. 

 Use consistent variable names 

 Clarify subscripts and carefully apply them 

 Carefully state units. 

 Appropriateness and completeness of the contents of the sample input files: 

 In all data input files, specify units 

 The “Data Validation” capability and error report is a very useful feature 

 Fuels input file, Appendix 4 

 This list does not yet reflect biofuels or renewable fuels 

 Some provision m ay be needed for th e variable energy and GHG content of 

gasoline 

 Provision may also be needed for E 85, and the uncertain fraction of E85 used by 

FFVs. 

SwRI Project 03.14658.01 FR 
Ms. Almethyist A. Chambers 

Environmental Protection Agency 

June 9, 2009 

Page 7 of 8 

 The net fuel economy and emissions by PHEVs 

 Calculation of compliance to attribute-based standards: 

 On page 7, equation for the logistic-based footprint, the re a ppears to b e a s ign 

error in the denom inator (should be 1+exp((x -C)/D) not 1-exp((x-C)/D)). This is 

likely a typo in the documentation alone. 

 No discussion or provision for m arket-based (perm it trading) standards is yet 

made. This should at least be acknowledged. 

 One strategy for doing m ore flexible st andards would be to sim ply m erge the 

datasets and technology-sequence stage for all manufacturers and vehicle types in 

a trading group. However, this would not provide information about potential 

permit prices and burdens across manufacturers. 

 Clarity, co mpleteness, and accuracy of the model's visualization output, in which the 

technology application is displayed: 

 It would be very helpf ul to have som e graphical summ aries of the input and 

output results. 

 All output files should embed clear documentation on the inputs used. 

 The .log file does list names of the 4 input files, which is essential. 

 The “Visualization Output” file does not (yet) report the input files (but the 

information could be retrieve from the XML file). 

John German:

 “Accounting m odel” has advantage of si mplicity, avoids “overm odeling.” However, i t 

“requires a great deal more sophistication and work by anyone using the model to prepare the 

inputs properly.” 

 Modeling by redesign cycles (rather than annually) is a good idea. 

 Leadtime issues modeled far too simplistically 

 One of the most important issues in standard setting 

 Inappropriate to treat all m anufacturers the sam e, regards potential penetration 

rates and costs for adding technologies (given differing experience) 

 No such thing as a hard cap on technology penetration rates 

 Recommends handling leadtim e c onstraints and tech penetrati on with an assessm ent of 

capital costs by each manufacturer, with a capital budget each design cycle 

 [Q: How would that budget be set?] 

 Short-term: use manufacturer-specific caps on max penetration rate per year

 Use m ax pe netration caps (total) to ref lect m arket re strictions (dem and lim its) r ather than 

leadtime constraints 

 Re: rank-ordering technologies for each vehicle type: “Requiring the user to input technology 

in rank order of cost-effectiveness” has some challenges. 

 Requires a great d eal of analysis to cr eate m odel inpu ts: “real analyses and 

modeling are in these input files” 

SwRI Project 03.14658.01 FR 
Ms. Almethyist A. Chambers 

Environmental Protection Agency 

June 9, 2009 

Page 8 of 8 

SwRI Project 03.14658.01 FR 

 “It only works if the lear ning rate is the same for all technologies and if no 

technology changes effectiveness over time.” 

 “model must be able to handle multiple pathways” of technical progression 

 Synergies depend on order of introduction (?) and must be assessed according to 

different pathways 

 Very valuab le, perhap s ultim ately n ecessary for regulatory evaluation, to allow model to 

“maximize net social value” 

Jonathan Rubin:

 Be clear and explicit about “accounting stance” 

 “costs to whom?” 

 Describe and report costs and benefits to as many as three groups (consumers, manufacturers, 

society) 

 Account for subsidies and taxes 

 Show component costs 

 Allow distinct discount rates and treatment of risk for the three groups 

 Improve notation, consistent use of subscripts 

 Concerns about certain formulations in model equations 

 1/i in FS equation 

 Discounting factors used 

 Avoid discounting physical quan tities, and mixing physical phenomena with economic costs 

and benefits 

 Need to account for environm ental and fuel econo my implications of al ternative fuels m ore 

carefully 

 Renewable and biofuels 

 Electricity 

 Future work: 

 Significant enhancement: make probabilistic, reflecting uncertainty 

 Account for hedonic (vehicle attribute) implications of large changes in GHG 

emissions 

 Consider implications for gas excise tax revenue 

 User manual, describing impact and power of key assumptions 

 Output in SI units 
Ms. Almethyist A. Chambers 

Environmental Protection Agency 

June 9, 2009 

Page 9 of 8 

IV. CLOSING

 Southwest Research In stitute has p repared this final report to W ork Assignment 4-1 to 

describe the process followed in the peer re view of the EPA’s Vehicle Greenhouse Gas Model. 

Please contact Patrick Merr itt at 210-522-5422 (e-m ail pmerritt@swri.org) with any questions. 

Thank you for this opportunity to be of service. 

Prepared by: Reviewed by: 

 

Patrick M. Merritt E. Robert Fanick 

Senior Research Scientist Manager 

Emissions Chemistry Emissions Chemistry 

Emissions Research and Development Emissions Research and Development 

Approved: 

Jeff J. White, Director 

Emissions Research and Development 

Engine, Emissions and Vehicle 

 Research Division 

/tyd U:\Davison\wp\PROJECTS\14658 EPA FR WA 4-1 VGHG Model Peer Review PMM.doc

c: Christine Brunner, EPA NVFEL 

 Kent Helmer, EPA NVFEL 

 Richard Rykowski, EPA NVFEL 

 Sherry Twilligear, SwRI Contracts 

This report shall not be reproduced, except in full, without the written approval of Southwest Research 

Institute. Results and discussion given in this report relate only to the test items described in this report. 

SwRI Project 03.14658.01 FR 
Ms. Almethyist A. Chambers 

Environmental Protection Agency 

June 9, 2009 

Page 10 of 8 

ATTACHMENT

Appendices Number of pages 

A RÉSUMÉS OF PEER REVIEW PANEL MEMBERS ..........................................17 

B CHARGE LETTER TO REVIEWERS ....................................................................2 

 ATTACHMENT 1 - EPA VEHICLE GHG EMISSION COST AND 

COMPLIANCE MODEL DESCRIPTION.......................................................45 

 ATTACHMENT 2 - APPENDICES TO GHG MODEL DESCRIPTION 

 ATTACHMENT 3 - MODEL REFERENCE GUIDE 

 ATTACHMENT 4 - BENEFITS CALCULATIONS INSTRUCTIONS 

C JOHN GERMAN’S REVIEW DOCUMENT .........................................................11 

D PAUL LEIBY’S REVIEW DOCUMENT ...............................................................13 

E JONATHAN RUBIN’S REVIEW DOCUMENT .....................................................8 
 APPENDIX A 

 RÉSUMÉS OF PEER REVIEW PANEL MEMBERS 

 John German, The International Council on Clean Transportation 

 Paul Leiby, Oakridge National Laboratory

 Jonathan Rubin, University of Maine, School of Economics
John M. German

730 Brooks St., Ann Arbor, MI 48103 

(734) 213-0537 (H) (734) 222-5962 (W) 

PROFESSIONAL EMPLOYMENT 

January 

2009 to 

present 

SENIOR FELLOW, INTERNATIONAL COUNCIL ON CLEAN TRANSPORTATION

• Primary responsibility for technology innovation and U.S. policy development. 

• Managing project to track technology costs and benefits worldwide.

February 

1998 to 

January 

2009 

MANAGER, ENVIRONMENTAL AND ENERGY ANALYSIS, PRODUCT REGULATORY 

OFFICE, AMERICAN HONDA MOTOR CORPORATION

• Provide policy and technical analyses on vehicle-related emissions and energy issues. 

• Liaison between Honda R&D, both in the U.S. and Japan, and external organizations, 

including government agencies, environmental groups, other manufacturers, 

academia, and state representatives. 

• Primary Honda representative on fuel economy and global warming issues, including 

testifying before Congress, writing testimony, writing responses to CAFE 

rulemaking, and making presentations.

October, 

1986 to 

January, 

1998 

SENIOR TECHNICAL ADVISOR, U.S. EPA OFFICE OF MOBILE SOURCES. Supervised up to 8 

employees, managed development of regulations and guidance, and served as 

technical consultant on a wide variety of issues. 

• Technical manager for study on Tier II emission standards for cars and light trucks. 

• Designed and managed extensive research project evaluating in-use driving behavior 

and its impact on emissions in support of revisions to the Federal Test Procedure. 

Created and managed extensive usage of teams across organizational boundaries. 

• Managed the development of a nonroad emission inventory and the issuance of the 

Nonroad Engine and Vehicle Emission Study. 

• Managed rulemaking for Cold Temperature Carbon Monoxide Standards. 

• Worked with transporatation planners to help create and develop a computer 

simulation model for vehicle emissions. 

• EPA senior technical advisor on greenhouse gas and fuel economy issues, including 

CAFE alternatives, in-use fuel economy factors, and advanced technology. Active 

member of EPA global warming team and an inter-agency modeling team. 

• Developed initial concepts for On-Board Diagnostics. 

• Created and managed rulemaking assessing LDT CAFE test procedure adjustments. 

• Developed policy guidance for 48" roll electric dynamometer, driver-selectable 

devices, mileage accumulation fuel requirements, coastdown procedures and 

dynamometer power absorption settings, and model year definition and duration. 

May, 1985 

to Sept., 

1986 

TEAM LEADER, U.S. EPA OFFICE OF MOBILE SOURCES. Supervised 3 employees and 

managed manufacturer motor vehicle emissions compliance program. 

• Wrote guidance on numerous certification procedure issues. 
December, 

1981 

to May, 

1985 

ENGINEERING SUPERVISOR, CHRYSLER POWERTRAIN. Supervised 6 engineers, 

supported product planning, and developed strategies to optimize vehicle fuel economy 

and to ensure compliance with all fuel economy requirements. 

• Planned and coordinated activities of staff. 

• Chrysler's principal technical advisor on fuel economy and methods to improve CAFE 

• Provided technical analyses and written responses to proposed regulations. 

• Represented Chrysler on fuel economy matters with EPA and NHTSA. 

• Provided CAFE projections and analyzed impacts of future product changes on 

CAFE. 

• Team leader on a project with all areas of engineering to implement Shift Indicator 

Lights. Independently developed computer algorithms to eliminate cost of a sensor. 

November, 

1976 to 

December, 

1981 

ENGINEER, CHRYSLER POWERTRAIN. Designed and implemented, from scratch, 

Chrysler's system to comply with extensive EPA fuel economy regulations issued in 

1975. Also the corporate expert on fuel economy regulations, coordinated fuel 

economy testing, served as liaison with EPA, helped write responses to proposed 

regulations, and worked on special projects. 

AWARDS and ADVISORY COMMITTEES

2008 National Research Council – COMMITTEE FOR A STUDY OF POTENTIAL ENERGY 

SAVINGS AND GREENHOUSE GASS REDUCTIONS FROM TRANSPORTATION

2006 SAE Engineering Meetings Outstanding Oral Presentation Award, FOR “IT’S A HIGHMPG VEHICLE ISSUE, NOT A HYBRID ISSUE” AT SAE GOVERNMENT/INDUSTRY MTG.

2004 Barry McNutt Award for Excellence in Automotive Policy Analysis

1ST RECEIPIENT OF ANNUAL AWARD FROM THE SAE

2002-2003 advisory board, ADVANCED POWER TECHNOLOGY ALLIANCE, CENTER FOR 

AUTOMOTIVE RESEARCH, ANN ARBOR, MI

2002-2003 

2001-2002 

sae industrial lectureship program, TO PROMOTE INTERACTION BETWEEN PRACTICING 

ENGINEERS AND FACULTY AND STUDENTS VIA CAMPUS VISITS

1995 SILVER MEDAL, U.S. EPA for strategies to reduce air pollution from nonroad engines

1994 EPA SCIENCE ACHIEVEMENT AWARD in Air Quality. Only person in EPA’s Office of 

Mobile Sources ever to receive this award.

1993 OUTSTANDING TECHNICAL COMMUNICATION in the 1992-93 Society for Technical 

Communication of Southeastern Michigan Technical Publications Competition, for 

"Nonroad Engine and Vehicle Emission Study" 

1992 BRONZE MEDAL, U.S. EPA for the "Nonroad Engine and Vehicle Emission Study" 

1991 BRONZE MEDAL, U.S. EPA for the Cold Temperature Carbon Monoxide Rulemaking 
LEADERSHIP TRAINING 

2000 

1997 

1996-7 

1995 

1993 

1992 

1991 

1990 

1989 

1988 

1987 

1985 

1984 

1982 

1982 

1981 

Honda Leader’s Program – Center for Creative Leadership 

Modeling and Computer Simulation of Internal Combustion Engine--U. of Mich. course 

Excellence in Government Fellows Program--Council for Excellence in Government 

Diversity Workshops - University of Michigan 

Total Quality Management 

Looking Glass Workshop: Leadership in Multilevel Organizations – Creative Leadership 

Use of Consultative Methods - EPA Institute 

Work Group Leadership - Conservation Foundation 

Regulation Development in EPA - EPA 

Planning Effective Meetings - EPA 

Zenger-Miller Supervision program on Behavior Modeling - EPA 

Personnel Management for Managers and Supervisors - OPM 

Interaction Management - Chrysler Institute 

Organizational Leadership and Productivity - Mansare Corp. 

Leadership Effectiveness Training - Chrysler Institute 

Supervisory Skills Training - Chrysler Institute 

PUBLICATIONS

John German, " Leadtime, Customers, and Technology: Technology Opportunities and Limits on the 

Rate of Deployment". Reducing Climate Impacts in the Transportation Sector. D. Sperling and J. 

Cannon, Springer Press, 2008. 

D. Greene, J. German, and M. Delucchi, " Fuel Economy: The Case for Market Failure ". Reducing 

Climate Impacts in the Transportation Sector. D. Sperling and J. Cannon, Springer Press, 2008. 

J. German, "Reducing Vehicle Emissions Through Cap and Trade Schemes". Driving Climate Change: 

Cutting Carbon from Transportation. D. Sperling and J. Cannon, Elseview & Academic Press, 2006. 

Hybrid Gaseoline-Electric Vehicle Development, edited by John German, SAE PT-117, 2005. 

John German, “Hybrid Electric Vehicles”, Encyclopedia of Energy, Elsevier & Academic Press, 2004 

John German, Hybrid Powered Vehicles, SAE Technology Profile T-119, book published by Society 

of Automotive Engineers, Warrendate, Pa., 2003. 

John German, “Hybrid Vehicles Go to Market”, TR News #213, March-April 2001. 

K. Aoki, K. Nakano, J. German, S. Kajiwara, H. Sato, and Y. Yamamoto, “An Integrated Motor 

Assist Hybrid System – Development of the Insight, a Personal Hybrid Coupe”, SAE 2000-01-2216, 

2000. 

John German, "VMT and Emission Implications of Growth in Light Truck Sales", Air and Waste 

Management Association Emission Inventory Conference proceedings, Oct. 1997. 

J. Alson, J. German, K. Gold, R. Larson, and M. Wolcott, "Transportation Energy Demand Models: 

Why They Underestimate Greenhouse Gas Emissions", Climate Change Analysis Workshop 

Proceedings, June 6-7, 1996. 

John German, "Off-Cycle Emission and Fuel Efficiency Considerations", Asilomar conference on 
Transportation and Energy, 1995. 

John German, "Observations Concerning Current Motor Vehicle Emissions", SAE 950812, Feb. 1995. 

J. Koupal and J. German, "Real-Time Simulation of Vehicle Emissions Using VEMISS", CRC OnRoad Vehicle Emissions Workshop, April 1995. 

S. Sheppard, J. Fieber, J. Cohen, and J. German, "Cold Start Motor Vehicle Emissions Model", Air 

and Waste Management Association, Cincinnati, 1994. 

P. Enns, J. German, and J. Markey, "EPA's Survey of In-Use Driving Patterns: Implications for 

Mobile Source Emission Inventories", AWMA/CARB Specialty Conference on Emission Inventory, 

Pasadena, CA, October, 1993.

EDUCATION

1980-1984 University of Michigan. Completed 34 hours towards M.B.A. GPA: 7.9 (A=8.0) 

1970-1975 University of Michigan, B.S., Physics (minor in Math). 

Honors: National Merit Finalist, Honors Program, Dean's List 

Activities: U. of Michigan Marching Band and Concert Band 
Insert file 

Leiby Paul N. resume_2.0page_2008May02.pdf 
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Rubin%20CV.pdf 
APPENDIX B 

CHARGE LETTER TO REVIEWERS 
 SOUTHWEST RESEARCH INSTITUTE ®

6220 CULEBRA ROAD  POST OFFICE DRAWER 28510  SAN ANTONIO, TEXAS, USA 78228-0510  (210) 684-5111  www.swri.org 

ENGINE, EMISSIONS, AND VEHICLE RESEARCH DIVISION HTTP://ENGINEANDVEHICLE .SWRI.ORG

FAX: (210) 522-3950 ISO 9001 Certified 

ISO 14001 Certified 

SAN ANTONIO, TEXAS 

HOUSTON, TEXAS  WASHINGTON, DC  ANN ARBOR, MI 

Gentlemen: 

Thank you for agreeing to review EPA' s proposed vehicle emission effects model which 

estimates the technology necessary for vehicle manufacturers to meet a specified greenhouse gas 

(GHG) standard. The m odel is contained in the enclosed com puter program and the 

documentation, Description and Methodologies of the EPA Vehicle Greenhouse Gas Emissions 

Cost and Compliance Model and its appen dices. This report illu strates the concepts an d 

methodologies behind EPA' s proposed GHG model. No independen t data analy sis will be 

required for this review. Specifically, EPA staff are seeking your expert opinion on the concepts 

and methodologies upon which the model relies and whether or not the model will execute these 

algorithms correctly. Toward this end, we as k that you please review and comm ent on the 

following items: 

1) The overall approach to the specifi ed m odeling purpose and the particular 

methodologies chosen to achieve that purpose; 

2) The appropriateness and completeness of the contents of the sam ple input f iles. 

EPA staff are not seekin g comment on the particular values of the contents of the 

input files, which are samples only. Thes e input files are included as Appendices 

to the model description: 

a) The elem ents of the Market input f ile, as shown in Appendix 1 of the m odel 

description, which characterize the vehicle fleet; 

b) The elem ents of the Technology input file, in Appendix 2, that constrain the 

application of technology; 

c) The definition of the standard and econom ic conditions in the Scenario input file, 

as shown in Appendix 3; 

d) The elements of the Fuels input file, as shown in Appendix 4, which characterize 

the fuel types, properties, and prices; and 

e) The reference data contained in Appe ndix 5 which are currently hard-coded into 

the model but, in the very near future, w ill be contained in a user contro lled input 

file. 

NOTE: The types of infor mation which can be input to the m odel point to both the 

flexibilities and constraints of the model. 
3) The accuracy and app ropriateness of the m odel's conceptual algorithm s and 

equations for technology application and calculation of compliance; 

4) The congruence between the con ceptual m ethodologies and the program 

execution; 

NOTE: Thi s can be verified by com paring spreadsheet calculation s to the outputs 

provided by EPA or by changing the input va lues and exam ining the results with good 

engineering judgment. 

5) Clarity, co mpleteness and accu racy of th e calcu lations in the Benefits 

Calculations output file, in which costs and benefits are calculated; 

6) Clarity, completeness, and accuracy of the model's visualization output, in which 

the technology application is displayed; and 

7) Recommendations for any functionali ties beyond what we have described as 

"future work." 

 In making your comments, you should disti nguish between recommendations for clearly 

defined improvements that can be readily m ade based on data or literature reasonably available 

to EPA and im provements that are more explor atory or dependent on in formation not readily 

available to EPA. Comm ents should be suffici ently clear and detailed to allow a thorough 

understanding by EPA or other parties familiar with the model. EPA requests that you not release 

the peer review m aterials or you r comments until the Agency makes its model and supporting 

documentation public. EPA will notify the reviewers when this occurs. 

 If you have questions about what is required in order to com plete this review or need 

additional background m aterial, please contact Patric k Merritt at 210-522-5422 or 

pmerritt@swri.org. If you have any questions about the EPA peer review process itself, please 

contact Ms. Ruth Sche nk in EPA's Quality Off ice, National Vehic le and Fuel Em issions 

Laboratory by phone (734-214-4017) or through e-mail (schenk.ruth@epa.gov). 

With best regards, 

 Patrick M. Merritt 

Senior Research Scientist 

Emissions Research and Development Dept. 

Attachments (4) 
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ATTACHMENT 1 

EPA Vehicle GHG Emission Cost and Compliance Model Description 

Background and Overview 

 On-road vehicles are the predominant source of GHG emissions from the 

transportation sector. Of all on-road vehicles, light-duty vehicles and light-duty trucks 

(hereafter referred to as cars and trucks) produce the majority of the GHG emissions. 

There are many methods for reducing GHG emissions from cars and trucks due to the 

myriad of technology options available to improve the efficiency of vehicles. A detailed 

analysis of the costs and benefits of various GHG emissions reduction requires a 

specialized application that optimizes and accounts for all the promising technologies, 

going beyond what can be accomplished with simple spreadsheet tools . Therefore, 

EPA’s Office of Transportation and Air Quality (OTAQ) has developed the Vehicle 

Greenhouse Gas Emissions Cost and Compliance Model (hereafter referred to as the 

“EPA model”) to help facilitate the analysis of the costs and benefits of reducing GHG 

emissions from cars and trucks.. 

 Broadly speaking, the EPA model applies technologies with varying degrees of 

cost and effectiveness to a defined vehicle fleet in order to meet a specified GHG 

emission target and calculates the costs and benefits of doing so. The technologies are 

combined into a series of vehicle “packages” over a series of model years, which defines 

the fleet input file. 

The vehicle fleet can be characterized very simplistically (one vehicle) or more 

precisely (over a thousand vehicle models). Vehicle sales can vary over time in the 

model. The vehicle description includes the baseline level of GHG emissions along with 

any other attribute used in setting the target GHG emission level, such as footprint, which 

is discussed further below. 

 GHG control technology packages can be applied “one at a time” or in groups or 

bundles. The costs and effectiveness of these technologies are assumed to be the same 

for all vehicle models falling within a vehicle type category, such as midsize cars with V6 

engines or minivans. The model considers whether a specific vehicle model already has 

a specific technology package or whether a technology can or cannot be applied to it. 

The volume of a specific vehicle model’s sales which can receive a technology package 

can be limited by indicating a fraction of its baseline that already contains some 

effectiveness and cost of each specific technology package. The volume of a given 

vehicle type’s sales which can receive a specific technology package can also be limited 

with a market penetration “cap”, if desired. The effectiveness and application limits of 

each technology package can vary over time, if desired. 
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 Technology is applied to individual vehicles using a ranking process. Within a 

vehicle type, the order of technology packages is set by the user. Across vehicles, 

technology is applied to that vehicle with the lowest Technology Application Ranking 

Factor (hereafter referred to as the TARF). The TARF considers the cost of the 

technology, the value of any reduced fuel consumption considered by the vehicle 

purchaser, and the mass of GHG emissions reduced over the life of the vehicle. Fuel 

costs by calendar year and annual vehicles travelled per vehicle are provided by the user. 

 Technology is applied to vehicles until all the technologies have reached their 

caps or until the sales-weighted GHG emission average of a given manufacturer’s 

vehicles complies with the specified GHG emission target or stringency. The GHG target 

can be a flat standard applicable to all vehicles within a vehicle class (e.g., cars, trucks or 

both cars and trucks). Or, the GHG target can be in the form of a linear or logistic 

function, which varies the target as a function of vehicle footprint (vehicle track width 

times wheelbase). 

 The GHG emission target can vary over time, but not on a model or calendar year 

basis. One of the fundamental features of the EPA model is that, over a specified vehicle 

redesign cycle, a manufacturer has the capability to redesign any or all of its vehicles. 

The EPA model does not attempt to determine exactly which vehicle will be redesigned 

by each manufacturer in any given model year. Instead, it focuses on the GHG emission 

goal several model years in the future, reflecting the capability of longer term planning 

on the part of auto manufacturers. Any need to further restrict the application of 

technology can be affected through the caps on the application of technology to each 

vehicle type mentioned above. Approximate costs and benefits of complying with 

gradually decreasing GHG emission targets within the endpoints of a redesign cycle are 

produced via linear interpolation, despite that in reality these functions may resemble step 

functions more closely for any given vehicle. 

 GHG emission targets are specified in terms of CO2 equivalent emissions. They 

can simply be CO2 emissions from the tailpipe or a combination of tailpipe CO2 

emissions and air conditioner refrigerant emissions. In the case of the latter, the 

descriptions of vehicles and technologies must include baseline refrigerant emissions and 

the effectiveness of each technology in reducing these emissions. 

 Once technology has been added so that every manufacturer meets the specified 

targets (or exhausts all of the available technologies), average costs per vehicle by 

manufacturer and industry fleet are determined. Fleet-wide average GHG emissions are 

also approximated for each calendar year and are used to estimate a wide array of societal 

costs and benefits associated with the GHG emission control. 

 The model outputs the costs and the benefits of the control scenario. The primary 

cost of GHG emission control is the cost of the added technology as compared to the 

baseline. The primary benefit is the value of reduced fuel consumption and can be 

sensitive to the user assumed price of fuel. However, the value of a number of other 

costs and benefits are also evaluated, as listed below: 
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2 1) The reduction or co-benefits in VOC, CO, NOx, sulfur dioxide (SOx), and PM 

emissions associated with reduced fuel production are estimated, as well as their 

value to society, 

5 2) Societal benefits associated with reduced crude oil use which are not reflected in 

the price of crude oil, 

7 3) The value of reduced time necessary to refuel vehicles, and 

8 4) The value of GHG emission reductions. 

 GHG emission control tends to encourage technologies that improve vehicle fuel 

efficiency and reduces the cost of driving, which in turn can result in more driving. This 

feedback effect is commonly referred to as the “rebound effect”, the extent of which can 

be specified by the user. Estimates are made of a number of potential costs and benefits 

associated with driving, as follows: 

1) The increase in vehicular VOC, CO, NOx, SOx, and PM emissions, 

2) The increased vehicular noise, congestion, and accidents, 

3) The value of the increased driving, and 

4) The cost of fuel required by the increased driving. 

 Where the EPA model differs from other similar models is that it adds technology 

to vehicles on a redesign cycle basis. Some models try to predict the model year when 

each vehicle model (of hundreds) will be redesigned and then adds technologies to each 

vehicle on its redesign year. In reality, the timing of vehicle redesign is difficult to 

predict, because it is constantly adjusting to changes in the market, corporate needs, or 

government regulations, which these other models are not able to capture. In addition, 

since there are hundreds of specific vehicle models being sold in any given model year, 

the technology application process becomes computationally intensive. This 

methodology creates a situation where the model attempts to achieve a level of precision 

greater than its accuracy. 

 The EPA model avoids these two issues by taking a mid to long term approach to 

vehicle redesign, assuming a 5+ year planning horizon.. In this methodology, the user 

designates a redesign cycle length (currently this is hard-coded as 5 years, but in the 

future this will be a user input), and the model redesigns the entire fleet in the final model 

year of the redesign cycle. This philosophy is based on the assumption that vehicles 

undergo redesign at a rate consistent throughout the fleet. In addition, the EPA model 

looks much further into the future than other models, with the capability of adding 

technology to vehicles over four redesign cycles. Because it is next to impossible to 

determine each vehicle model’s subconfiguration (there are over 1000) 20+ years in the 

future, the methodology of adding technologies incrementally to each vehicle model by 

model year does not add value to the model results. Moreover, the EPA model avoids 

“overmodeling” by allowing for the simplification of the fleet by dozens (or fewer) 

representative vehicles, rather than the hundreds that are currently forecasted by 

manufacturers. 
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Input Files 

 

 The EPA model is designed to be flexible in almost every respect. Very little data 

is hard-coded in the model; since the model relies heavily on its input files, the user can 

alter them at will to create new vehicle models, types, and technologies, or to change the 

model’s operating parameters for sensitivity analysis or “what-if” scenarios. For example 

the following can all be modified by the user: vehicle descriptions, the technologies 

which are available to be applied to each vehicle, as well as their costs and effectiveness. 

11 Vehicle Fleet Characterization

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 The sample input file, “Market-1”, contains a list of the vehicle models that 

describe the vehicle fleet and five different categories of data that describe them: 1) 

Vehicle efficiency data, such as baseline fuel economy and GHG emissions (columns N 

and O); 2) Vehicle attribute data that the model uses in its technology ranking and 

compliance calculations, such as footprint, weight, and refrigerant type (columns P, Q, 

and AB respectively); 3) Vehicle attribute data that the model will eventually use when 

calculating output statistics, such as engine displacement, horsepower, and drive type, 

although output statistics are not calculated in the current version (columns R, S, and W); 

4) Vehicle sales data, which is presented in the form of annual sales in the baseline and in 

the final “redesign” year of each redesign cycle (columns E through M); 5) An indication 

of whether a technology package exists in the reference case, and if so, to what degree the 

technology package effectivenesses and costs are reflected. An example of the Market 

input file is in Appendix 1. 

 Reference case technology is tracked to avoid double counting technology costs 

and GHG improvement. Columns AD through AW represent the fraction of the 

technology package effectiveness for the different vehicles that is present in the reference 

case; for example, a value of 35% for technology package 1 means that 35% of the 

effectiveness of technology package 1 on that vehicle type is already present in the 

reference case. Columns AY through BQ represent what fraction of the technology 

packages’ cost is reflected in the reference case. Likewise, a value of 75% in any of 

these columns means that 75% of the technology package’s cost on the particular vehicle 

has been included in the reference case. This is to prevent double counting of 

technologies that are already in the baselines. 

38 Technology Characterization

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 EPA designed the model to allow the user to add GHG reducing technologies one 

at a time or in packages or bundles that would reasonably and likely be added by 

manufacturers within a redesign cycle. In addition, the user can combine similar vehicle 

models into “vehicle type” groups which are likely to receive the same list of technology 

packages. For each vehicle type, the user must rank the technology packages in order of 

how the EPA model should add them to that specific vehicle type. This approach puts 

some onus on the user to develop a reasonable sequence of technologies. However, the 
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model also produces information which helps the user determine when a particular 

technology or bundle of technologies might be “out of order”. The approach also 

simplifies the model’s calculations and enables synergistic effects among technology 

packages to be included to the fullest degree possible. 

 The sample input file entitled “Technology-1” defines the technology packages 

that are available for the model to add to the fleet. The data in this file can be categorized 

in three ways: 1) Parameters the model uses to calculate CO2 improvement, such as 

effectiveness and market penetration cap (the latter being the cap of sales for each vehicle 

model of a vehicle type that can receive a technology package) (columns D through T); 

2) Data the model uses to calculate technology costs, such as the package cost and cost 

learning coefficients (columns X through AC); 3) Properties of the technology package, 

such as refrigerant type and fuel type (columns U through W). 

 Each worksheet in the file contains a user-defined list of technology packages for 

a different vehicle type. In order to avoid the complexity of synergistic effects, the user 

must record this list within each worksheet from top to bottom in the order of how the 

model should add them to the particular vehicle. Since the user defines the technology 

packages contained in the input file, technologies are dynamic (user defined) and not 

“hard-coded” within the model. Values in columns L through S represent the Average 

Incremental Effectiveness (AIE) of the technology package over the previous package (or 

over the baseline in the case of technology package 1) which the user can adjust on a 

redesign cycle basis. For example, a value of 7.0% in the AIE column would denote a 

7.0% tailpipe CO2 improvement beyond any CO2 improvements already realized. 

(“Tailpipe CO2” refers to the CO2 emitted over a test cycle - that relates it to vehicle 

efficiency.) This value of 7% would include any synergistic effects that components of 

the technology package may have with technologies that have already been added to the 

vehicle type. Currently, the refrigerant effectiveness noted in column T is based on the 

fraction reduction in direct refrigerant leakage emissions and is separate from tailpipe 

CO2. An example of the Technology input file is in Appendix 2. 

 When technology is sufficiently new, or the leadtime available prior to the end of 

the redesign cycle is such that it is not reasonable to project that it could be applied to all 

vehicle models that are of the same specific vehicle type, for example, all minivans, the 

user can limit its application to minivans through the use of a market cap of less than 

100% in columns D through K. This cap can vary by redesign cycle. When a technology 

package is applied to fewer than 100% of the sales of a vehicle model due to the market 

cap, the effectiveness of the technology group is simply reduced proportionately to reflect 

the total net effectiveness of applying that technology package to that vehicle’s sales. 

The EPA model does not create a new vehicle with the technology package and retain the 

previous vehicle which did not receive the technology package, splitting sales between 

the old and new vehicles. If subsequent technology packages can be applied to the 

vehicle, the user should consider whether in reality the new technology would likely be 

applied to those vehicles which received the previous technology or those which did not, 

or a combination of the two. The effectiveness of adding the subsequent technology may 

depend on which vehicles are receiving it. 
VGHG Model Documents Page 6 of 43 

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Fuel and Energy Prices

The Fuels-1 input file contains data relevant to fuel and electricity, including energy, 

mass, and carbon density (columns B through D in the Fuel tab), and annual price 

forecasts for up to 20 years (columns E through X). There is a small subset of fuel 

information not included in this file that has been hard-coded into the model, which 

reflects the societal cost of importing fuel, which is discussed briefly below and in the 

section on benefits calculations. An example of the Fuels-1 file is in Appendix 4. 

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Regulatory Scenarios

 The Scenario input file contains all data specifying the number and types of 

model runs. The Scenarios tab acts as a directory for different model runs, where the user 

can create an entry for any number of runs that the model can perform in succession. At 

present, the model is only capable of performing one scenario at a time, but in the future, 

it will be capable of batch processing. In the Scenarios tab, the user must specify the 

base year, type of compliance target (CO2 or MPG), type of compliance function 

(universal = 1, linear attribute = 2, or logistic attribute = 3), the number of redesign 

cycles, and the names of the other input files that describe the vehicle fleet, technology 

packages, and fuel properties. At present, the model is limited to a CO2 standard, but our 

future plans include the capability to analyze an MPG standard as well. These elements 

are entered in the Scenario input file in columns C through K, and the user can create a 

name for the run in column B. 

 As stated above, there are three options for compliance targets. The universal 

target option is simply a numerical designation which the manufacturers’ average fleet 

CO2 cannot exceed. In contrast, the attribute-based linear target function is described by 

up to four coefficients and has the following piecewise linear mathematical form: 

 

Where A: CO2 minimum 

B: CO2 ceiling 

xmin: Intersection of lower asymptote with slope 

xmax: Intersection of upper asymptote with 

slope 

 

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x: Vehicle footprint 
VGHG Model Documents Page 7 of 43 

Example of Footprint-Based CO2 Piecewise Linear Target Function

Footprint (ft2)

CO2 (g/mi)

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The footprint-based logistic curve (shown below) is described by four coefficients and 

has the mathematical form described below. 

  

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Where A: CO2 minimum 

B: CO2 maximum 

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D: A sort of “inverse slope” 

 x: Vehicle footprint 
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Example of Footprint-Based CO2 Logistic Target Function

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 The Scenario input file also contains the economic parameters that the model uses 

to calculate the Technology Application Ranking Factors (TARFs), which are the 

optimization equations that determine the order of technology application (See the Model 

Logic section below). Such economic data includes the discount rate, vehicle payback 

period, cafe fine cost, the “gap” between on-road CO2 and test cycle CO2, threshold 

technology cost (the cost at which manufacturers add technology to only enough vehicles 

to meet the standard as opposed to adding technology to all of a model line), and the 

increase in price of CO2 over time. The user enters these economic values into columns 

B through G in the Economics tab of the Scenario file. An example of the Scenario input 

file is in Appendix 3. 

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Input data “hard-coded” into the model

 There is a set of data that has been hard-coded into the model in the development 

phase, which will be moved to a new “References” input file in the very near future. This 

data is illustrated in Appendix 5 and is comprised of vehicle age data, scrappage rate, 

vehicle miles travelled, upstream criteria pollutant emissions from fuel/energy 

production, storage, and distribution, regression coefficients for downstream criteria 
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pollutant emissions, emissions damage cost (cost per ton), economic cost of importing 

fuel, external costs of driving, and global warming potentials for various GHGs. 

Model Operation 

 Because the model depends so heavily on the input files, the current graphic user 

interface (GUI) is simple to operate. After installing the program and double clicking on 

the VGHG icon, click on the file menu and choose “Open,” and a window will appear 

with a set of input files. Click on the sample “Scenario” and it loads the data. The 

Scenario file indicates which of the other input files are to be used in the run, and a 

portion of the scenario description is shown in the top box of the GUI. The Market and 

Technology input files identified in the Scenario file are shown in the two boxes below. 

When everything is loaded, the car icon turns green. Mouse click on the car button to run 

the program. In less than a minute, a text file showing the sequence of technology 

addition by manufacturer and by redesign cycle appears. This file is automatically named 

Results-“date-time”. This file can be saved by the user with a more descriptive name, if 

desired, by simply using the standard Windows “File”, “Save As” actions. There is an 

additional saved output file entitled “Tarf = “date-time”, which includes the results of the 

TARF calculations for each vehicle type-technology package combination for each 

redesign cycle. Go back to the file menu in the GUI, and click on “Save,” which is 

necessary to enable the “visualization” function. In the file menu, now click on 

“visualization” to examine the results of the technology application process for each 

vehicle model in the market data file. In the near future, the information in the 

visualization will be generated directly in an output spreadsheet, but at present it is only 

available in the visualization, and if desired, the user can copy and paste the results 

manually. Economic costs and benefits can be loaded in a spreadsheet file entitled 

“Benefits Calculations,” located in the “Output” folder. Open the “Benefits 

Calculations” excel file, and in the “Load” tab, press the “Load” button to get the 

economic impact results. 

 

Model Logic 

 The model’s programming is organized into three main sections. At first, a preprocessing section reads in the input files and performs intermediate calculations to get 

the input data into the desired form for calculations in the second section, the “core” 

calculations. The core model backs out the existing technology from the baseline, and 

calculates the Technology Application Ranking Factor (the TARF) for each vehicle typetechnology package combination and determines the order to which technology packages 

should be added to vehicles. The core model then adds the effectivenesses and the costs 

of the technology addition until each manufacturer has met the standard or until all 

technology packages have been exhausted. Finally, a post-processing section computes 

the societal costs and benefits of the GHG emissions reduction scenario, including the 

tons of CO2 reduced and the gallons of fuel saved and outputs these results in a 

Microsoft© Excel© file. 
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 Before the model runs the optimization algorithms, a the user can validate the data 

in the input files, and the model then reads in the data. Each redesign cycle “starts from 

scratch” in that every vehicle will include only those technologies present in the base 

year. 

 The model then executes three time loops in the following order: 1) A calendar 

year loop that interpolates the annual sales in between redesign cycles; 2) A redesign 

cycle loop adds technology packages to vehicles until the fleet is in compliance (the 

“core” programming); 3) A final loop calculates the costs and benefits of the CO2 or 

MPG program and exports the economic impact results to a Microsoft Excel file (a post 

processing step). 

 EPA provided the contractor with the equations and conceptual methodologies for 

the model with a series of spreadsheets, and the contractor developed the program. These 

equations and methodologies are outlined after the variable and parameter definitions, 

below. 

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Pre-processing:

22 1) The user can validate the data in the spreadsheets, if desired. 

23 2) Read in the input files 

24 3) Perform calculations to convert input data into useful form for later calculations: 

A) First, the model calculates the annual per-vehicle VMT from the survival 

fraction and annual miles driven for cars and trucks provided in the Reference 

file. 

VMT  SurvivalFraction * AnnualMilesDriven

B) The model then calculates the discounted annual VMT and discounted 

refrigerant leakage (RCO2) for year 1 through the vehicle’s useful life 

according to the following equations. The subscript, i, represents the specific 

year of calculation. DR is the discount rate and IR is the annual increase in 

value of CO2. 

i. Discounted VMT for fuel savings calculations 

  iFSD i i DR

DR

VMT VMT







1

2

1

40 ,,

41 

42 

43 

ii. Discounted VMT for GHG calculations 
VGHG Model Documents Page 11 of 43 

  iCOD i i IRDR

IRDR

VMT VMT



 



1

2

1

1 ,2,

2 

3 

4 

iii. Discounted refrigerant leakage 

 i i i

IRDR

IRDR

LeakRate



 

 

1

2

1

5 RefLeakage

6 

7 

8 

9 

10 

11 

C) Finally, the model calculates the approximate per-mile refrigerant leakage 

emissions in CO2 equivalents from the discounted leakage rate (calculated 

above), the Global Warming Potential (GWP) of the refrigerant provided in 

the Reference file, and the discounted VMT (calculated above). 

 

 

 

LifetimeVMT

LifetimeLeakage GWP

IRDR

IRDR

VMT

GWP

IRDR

IRDR

LeakRate

VMT

GWP

RCO

years

Lifetime

i

i i

years

Lifetime

i

i i

years

Lifetime

i

iCOD

years

Lifetime

i

i

mig

 



























 































 

























)(

1

)(

1

)

1

,2,

)(

1

1

2

1

1

2

1

RefLeakage

/ )(2 12 

13 

14 

15 

16 

17 

18 

19 

20 

21 

22 

23 

D) At this point, the model will linearly interpolate the sales in between redesign 

cycles in order to estimate intermediate annual sales. The final version will 

incorporate cost learning based on sales volume. Since the current version of 

the model does not account for cost learning, this calculation is currently 

performed in the start of the “post processing” section of code, which 

calculates the social costs and benefits of the modeled regulation, rather than 

in the core code. Therefore, it will be discussed in the section below on post 

processing. 

24 Core Program:

25 

26 

27 

28 

29 

30 

31 

32 

33 

34 

 The core program is divided into two main parts: The first part determines the 

order in which the model must add technology packages to vehicle types, and the second 

part is the technology application until all manufacturers have met the standard provided 

in the Scenarios input file (or the penetration caps have been met). The core code 

contains the baseline technology accounting, the technology application ranking 

optimization, the technology application and cost calculations, and whether the 

manufacturers have met the standard. Subscripts (t-1) and (t) indicate vehicle conditions 

before and after technology package “t” addition, respectively.
VGHG Model Documents Page 12 of 43 

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1 1) Determine the order of technology application 

3 A) First, the model “backs out” any advanced technology that might have been 

present in the baseline. This step ensures that costs and benefits aren’t double 

counted when the model is optimizing the order of technology package 

application. This is done for both Tailpipe and Refrigerant CO2. The subscript, i, 

refers to the current technology package and the subscript, n is the final 

technology package for the vehicle type. 

   

  i i i

n

i

REF i i i

i TEBCapAIE

CO CapAIE TEB

BackedoutCO



 



   

1

12

2

1 1 1

11 

12 

13 

14 

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19 

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   

  i i i

n

i

REF i i i

i TEBCapRIE

CO CapRIE TEB

BackedoutRCO



 



   

1

12

2

1 1 1

whereas AIE is the average incremental effectiveness of the technology 

package, RIE is the refrigerant incremental effectiveness of the technology 

package, CAP is the market cap, and TEB is the percent of the technology 

package’s benefit that has been reflected in the baseline. The RIE reflects 

either a change in refrigerant or a reduction in refrigerant leaks, as opposed to 

an improvement in A/C efficiency resulting in a fuel efficiency gain (from a 

smaller compressor, for example) are reflected in the AIE. The backed-out 

CO2 is calculated for each technology package on each vehicle. 

B) Intermediate calculations for each vehicle type-technology package combination: 

i. Calculate the new tailpipe and refrigerant CO2 after technology is 

added to the baseline fleet (any advanced technology in the baseline 

has been removed via the “backout” calculations) and thus CO2t-1 and 

RCO2t-1 are equivalent to the BackedoutCO2i and BackedoutRCO2i

from above. 

COCO  AIE 36 t  t1  122 

 

1

1 122



  

t

t

t t GWP

GWP 37 RCO RCO RIE

38 

39 

40 

41 

The model uses this “backed out” CO2 in the equations above because it 

helps compare the effectiveness of the various vehicle-technology 

package combinations on a level playing field. If the model did not back 
VGHG Model Documents Page 13 of 43 

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out the effectiveness of the technology in the baseline, the model would 

not be able to appropriately compare the effectiveness of the vehicletechnology package combinations, and the technology application order 

would not be as robust. 

ii. Calculate the fuel consumption (FC) before and after technology 

addition: 





 















gC

gCO CD

CO FC

t

t

t

12

244

2

1

1 9 1





 









gC

gCO CD

CO FC

t

t

t

12

244

2 10 

11 

12 

13 

14 

15 

16 

17 

18 

19 

20 

21 

22 

Whereas CDt-1 and CDt represent the carbon density of the liquid fuel. 

The model does not use the equation, since it does not account for a 

change in fuel type. The electric consumption of the vehicle before 

and after technology addition is a direct input from the Market and 

Technology file, respectively. 

iii. Calculate the fuel savings (FS). Vehicles can have up to two separate 

energy sources, which are distinguished by subscripts 1 and 2. 

Currently, the model recognizes the subscript “1” as the liquid fuel and 

“2” as electricity, for plug-in hybrid vehicles. 

t

PP

i

PP

i t

PP

i

PP

i i

FP FC

i

FP FC

i

FP FC

i

FP FCFS 



 



  



 



          1

2

2

1

1

1

1 1

2

2

1

1

1 23 

24 

25 

26 

27 

28 

29 

30 

31 

32 

33 

34 

35 

36 

37 

38 

39 

Since the fuel price (FP) changes from year to year, the fuel savings 

will be calculated based on the average fuel price from the year of 

manufacturer to a “Payback Period” (PP), specified by the user. 

C) The model then calculates the Technology Application Ranking Factors (TARFs), 

which are used to compare the effectiveness technology packages on the different 

vehicle types, and optimize the order in which the model should add technology 

packages to the fleet. At this point, the model calculates one TARF for every 

technology package-vehicle type combination. 

Currently, there are two TARF equations from which the user can choose: 

“Effective Cost” and “Cost Effectiveness – Manufacturer.” 

i. The “Effective Cost” TARF is defined as the cost of the technology 

(variable plus amortized fixed) minus the discounted fuel savings over 
VGHG Model Documents Page 14 of 43 

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3 

a specified payback period of vehicle use minus the implicit reduction 

in CAFE non-compliance fee. Quantitatively, it is defined as follows: 

  















      



1 1

,,

11

)1(

1

t t

PP

i

iFSD FCFC

FEE

GAP

4 EffCost TechCost FS VMT

5 

6 

7 

8 

9 

10 

11 

12 

13 

14 

15 

16 

17 

18 

Where the GAP is the difference between real-world and test cycle 

CO2 and the FEE is a fee for non-compliance, both of which are inputs 

to the model. Appendix 7 contains the full equation form (without 

intermediate steps). 

ii. The Cost Effectiveness-Manufacturer incorporates the effective cost 

(illustrated above) but also accounts for the GHG benefit. It is defined 

as the cost of the technology minus the discounted fuel savings over a 

specified payback period minus the implicit reduction in CAFE noncompliance fee (which is the “effective cost” definition), all divided by 

the amount of lifetime GHG emission reduction in kg of CO2 

equivalent. It is represented quantitatively as the following: 

 

          





 

 

  

















   

 35

1 1 1 ,2,

1 1

,,

1

1 2222

11

)1(

1

i

i

t t t iCOD

tt

PP

i

i iFSD

MFR

Gap

RCO RCO CO CO VMT

FCFC

FEE

GAP

TechCost VMTFS

19 CostEff

20 

21 

22 

This equation can be rewritten as: 

          



    

 35

1 1 1 ,2, 1

1 2222

i

i

t t t iCOD

MFR

Gap

RCO RCO CO CO VMT

EffCost 23 CostEff

24 

25 

26 

27 

28 

29 

30 

31 

32 

33 

Appendix 7 contains the full equation form (without intermediate 

steps). 

Finally, the model determines the order in which technology packages are 

added to vehicles. The model first compares the TARFs corresponding to 

technology package 1 on all of the different vehicle types in the fleet and 

chooses the combination with the lowest TARF. (The lowest effective 

cost/most cost effective combination is represented by the smallest TARF, 

whereas a negative TARF indicates a negative cost.) 
VGHG Model Documents Page 15 of 43 

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2 D) Application of technology packages and compliance calculations 

 

 At this point, the model has determined the optimal order of technology 

application and must apply the technologies’ effectivenesses and costs until each 

manufacturer has achieved compliance or exhausted all technology package options. 

A) Starting with the vehicle type-technology package combination having the 

lowest TARF, the model adds the technology package and calculates the new 

vehicle CO2 emissions according to the following equations: 

i. First, the model calculates the tailpipe CO2 (TCO2); i.e., this is the 

CO2 the vehicle would emit on a test cycle. 

 

TEBAIE

TCO AIECAP TCO t

t 

    

1

12 2 1 15 

16 

17 

18 

19 

20 

21 

22 

23 

Where TEB is the “Technology Effectiveness Basis,” which is the 

percent of the technology package’s effectiveness that is reflected 

in the baseline for the vehicle to which it is being applied. 

ii. The model also calculates the refrigerant CO2 (RCO2), which is 

based on improvements in leakage or a change in refrigerant 

 

1

1

1

12 2



 



   

t

t t

t GWP

GWP

TEBRIE

RCO RIECAP 24 RCO

25 

26 

27 

28 

29 

30 

31 

32 

33 

34 

35 

iii. The model must then calculate the cost this technology addition. It 

calculates average incremental cost, the average per-vehicle cost 

(averaged over a manufacturer’s fleet), and the cumulative cost up 

to this point for all manufacturers. 

 The average incremental cost is performed for each technology 

addition (but is not reported). It is the cost of the technology added 

up to its market cap minus the cost of the technology present in the 

baseline. 

36 IncrementalCost  TechCost (* CEBCAP )

37 

38 

39 

40 

41 

42 

43 

Where CEB is the “Cost Effectiveness Basis,” which is the amount 

of the technology package’s cost that is in the baseline for the 

particular vehicle. 

Next, the average vehicle cost for each manufacturer is calculated 

and reported. 
VGHG Model Documents Page 16 of 43 

1 

MFR

MFR TotalFleetSales

TechCost ModelSales AvgVehicleCost 



 



  *

2 

3 

4 

5 

6 

7 

8 

9 

10 

11 

12 

13 

14 

The model then updates the cumulative costs of the program. In 

future versions of the model this can also be expressed in terms of 

cost per redesign cycle. 

MFR

Compliance

T

MFR MFR CumulativeCost AvgVehicleCost TotalFleetSales 













  1

whereas T is the number of the technology package. 

iv. The next step is to add the tailpipe and refrigerant CO2 equivalent. 

The result is the vehicle’s total CO2 and is used in the calculation 

for compliance. 

15 CO t  TCO t  RCO222 t

16 

17 

18 

19 

B) The model must then recalculate the fleet CO2 for each manufacturer that 

produces the vehicle type that received the technology package. 

 



 



MFRModels

Models

t

Sales

COSales

FleetCO

2

20 2 

21 

22 

23 

24 

25 

26 

27 

28 

29 

30 

31 

32 

33 

34 

35 

The model must calculate this value for each manufacturer and compare the 

result with the standard that the user has indicated for that manufacturer in the 

Scenario file input. If the manufacturer has not met the standard, the model 

iterates back and compares the remaining TARFS for technology package 1 

along with the TARF for technology 2 on the same vehicle type to which the 

previous technology package was just added. The model then repeats the 

process of choosing the next lowest TARF out of the new group, applying the 

technology package effectiveness and cost values, calculating the new total 

CO2, and recalculating the new fleet averages. The model performs this loop 

until each manufacturer has met the standard or until the technology packages 

have been exhausted. 

Appendix 6 contains a compilation of variable definitions used in this section. 

36 

37 

38 

39 

40 

Post-Processing - Calculation of Costs and Benefits

 The model estimates discounted costs and benefits on both calendar year and 

model year bases. We describe these calculations on a calendar year basis first, followed 

by that for an entire model year’s vehicle sales. In both cases, the discount rate is 
VGHG Model Documents Page 17 of 43 

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specified by the user, as described above. Except for technology costs, all costs and 

benefits are estimated on a fleetwide basis. Industry fleetwide technology costs are 

estimated, but they are also estimated on a disaggregated basis by manufacturer. 

Costs and Benefits by Calendar Year 

 A list of the types of costs and benefits evaluated by the EPA Model was provided 

in the Background and Overview section above. The first step in estimating these costs 

and benefits is to develop estimates of vehicle costs, GHG emissions and fuel 

consumption by model year. This is done by linearly interpolating vehicle sales, the cost 

of GHG emission control per vehicle, GHG emissions per mile, and fuel consumption per 

mile between the baseline case and the first redesign cycle and subsequently between 

redesign cycles. For example, if the baseline year is 2010, redesign year 1 is 2015 and 

redesign year 2 is 2020, we linearly interpolate between the 2010 and 2015 sales to 

estimate annual sales for years 2011-2016. Likewise, we linearly interpolate between the 

2015 and 2020 sales to estimate annual sales for years 2016-2019. The same is done for 

the other factors. The following equation depicts the arithmetic involved. 

 

2 1

2 1

1 redesignyear redesignyear

Sales Sales

AnnualSales Sales redesignyear redesignyear

yearX yearX 

 19   

20 

21 

22 

23 

24 

25 

26 

27 

28 

29 

30 

31 

32 

33 

34 

35 

36 

37 

38 

39 

40 

41 

 With an estimated cost per vehicle and vehicle sales by model year, the cost of 

added technology is simply the product of these two values. For simplicity, we assume 

that all the sales of a given model year’s vehicles occurs in the calendar year of the same 

value. 

 Most of the other costs and benefits depend on an estimation of the amount of 

VMT occurring in each calendar year by vehicles of a certain model year vintage. As 

reduced fuel consumption per mile can lead to increased driving, the amount of VMT in 

the baseline and control cases can differ and increases the complexity of the calculations. 

Thus, the next step, conceptually, is to estimate the percentage increase in VMT that 

might result from reduced driving costs. 

The rebound effect is defined as the ratio of the percentage change in VMT to the 

percentage change in incremental driving cost, which is typically assumed to simply the 

incremental cost of fuel consumed per mile. As mentioned above, the economic concept 

is that as driving becomes cheaper, people tend to drive more. Since VMT increases with 

a reduction in fuel consumption, the sign of the rebound effect is negative. The rebound 

effect (REB) is an input on the “Economics” worksheet of the “Scenario.xls” file. The 

percentage increase in VMT for a given change in fuel consumption per mile is 

calculated as follows: 

 

old

old new

reb FleetFC

FleetFC FleetFC VMT REB  42  *

43 
VGHG Model Documents Page 18 of 43 

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Since fuel consumption changes by model year, each model year’s vehicles will reflect a 

different change in VMT. This change in VMT is assumed to continue throughout the 

life of the vehicle, since fuel economy is assumed to be constant throughout vehicle life. 

 Only new vehicles are affected by the additional technology. Thus, in any given 

calendar year, some of the VMT will be by vehicles whose GHG emissions and fuel 

consumption has changed and the remainder will be by vehicles which were unaffected 

by the addition of technology. The model estimates this split by predicting each model 

year’s vehicles contribution to total VMT in each calendar year. 

 The model currently contains estimates of annual VMT per vehicle by vehicle 

age, as well as the fractions of new vehicles still on the road as a function of age. These 

estimates are taken from EPA’s MOBILE6 model and are contained in cells M1:Q39 of 

the Shared Inputs worksheet of the Benefits Calculation spreadsheet. The values for 

these two parameters are currently hard-coded in the model. However, they will soon be 

made a part of a References spreadsheet and modifiable by the user. For the purposes of 

benefit calculation, the user can already change these values in the Benefits Calculation 

spreadsheet and the changes will flow through to the calculation of fuel consumption and 

VMT-related costs and benefits. 

 The total VMT by a specific model year’s vehicles in specific calendar year is 

determined by multiplying 1) new vehicle sales for that model year, 2) the fraction of 

new vehicles remaining on the road according to the age of those vehicles in that calendar 

year and 3) the annual VMT for that vehicle class at that age. Historic vehicle sales are 

currently hard-coded in the model, like annual VMT and survival fractions. However, 

they will soon be made a part of a References spreadsheet and modifiable by the user. 

For the purposes of benefit calculation, the user can already change these values in the 

Benefits Calculation spreadsheet and the changes will flow through to the calculation of 

fuel consumption and VMT-related costs and benefits. Vehicle sales starting with the 

baseline year are input to the model through the Market spreadsheet, as described above. 

Historic sales are shown in cells V2:W39 of the Shared Inputs worksheet of the Benefits 

Calculation spreadsheet. Vehicle sales starting in the baseline year are shown on the 

Benefits2 (Sales) worksheet of the Benefits Calculation spreadsheet. 

These VMT values for each combination of model year and calendar year are then 

multiplied by fuel consumption per mile and emission factors and summed by calendar 

year to determine total fuel consumption and emission levels by calendar year both 

before and after GHG emission control. Fuel consumption per mile and CO2-equivalent 

emissions before and after control are direct outputs of the compliance model. Precontrol levels are specified in the Market spreadsheet, while post-control values are a 

direct function of the GHG standard specified. The resultant fuel consumption and 

emission levels are presented on the Emissions_Fuel_Consv worksheet of the Benefits 

Calculation spreadsheet. Intermediate calculations are performed on the VMT_Lookup 

and VMT_Rebound_Effect worksheets of the Benefits Calculation spreadsheet. 
VGHG Model Documents Page 19 of 43 

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 The model also estimates the additional emissions of CO, VOC, NOx, PM and 

SOx which result from the rebound effect. The emission factors for these pollutants are 

currently hard-coded in the model, like annual VMT and survival fractions. However, 

they will soon be made a part of a References spreadsheet and modifiable by the user. 

For the purposes of benefit calculation, the user can already change these values in the 

Benefits Calculation spreadsheet and the changes will flow through to the calculation of 

social costs and benefits. These emissions are currently specified as a function of age 

using either linear or quadratic equations. The coefficients for these emission factors are 

taken from EPA’s MOBILE6 emission model and are shown in cells H1:R11 of the 

Exclusive Inputs worksheet of the Benefits Calculation spreadsheet. The resultant 

emission levels are presented on the Emissions_Fuel_Consv worksheet of the Benefits 

Calculation spreadsheet. 

 The value of the change in fuel consumption is determined by simply multiplying 

the change in fuel consumption by calendar year by the price of fuel less taxes. Fuel 

prices are input to the model via the Fuels spreadsheet, as described above. They are 

shown in cells D1:E44 of the Shared Inputs worksheet of the Benefits Calculation 

spreadsheet. Fuel taxes are not currently input to the model, but are simply part of the 

Benefits Calculation spreadsheet. We plan to add these taxes by fuel type to the Fuels 

spreadsheet. Fuel taxes are assumed to be constant over time and are shown in cells 

AE2:AF2 of the VMTAdjCosts worksheet of the Benefits Calculation spreadsheet. The 

value of fuel savings are shown on column E of the Externaties worksheet of the Benefits 

Calculation spreadsheet, and also carried forward to the Non-Tech Costs and All Costs 

worksheets. 

 The model also estimates the value of externalities related to crude oil use. These 

externalities include, for example, monopsony effects within the crude oil market, the 

economic impact of periodic price shocks, and military costs related to protecting oil 

production and supply overseas. These values are all in terms of $ per gallon. They are 

currently hard-coded in the model, like annual VMT and survival fractions. However, 

they will soon be made a part of a References spreadsheet and modifiable by the user. 

For the purposes of benefit calculation, the user can already change these values in the 

Benefits Calculation spreadsheet and the changes will flow through to the calculation of 

costs and benefits. The value of crude oil related externalities are shown on column F of 

the Externaties worksheet of the Benefits Calculation spreadsheet, and also carried 

forward to the Non-Tech Costs and All Costs worksheets. 

 The value of the changes in CO2 and other pollutant emissions are based on 

estimates of the value of these pollutants per ton of emission. The value of CO2 emission 

reductions is input to the model through the Scenario spreadsheet. This value in real 

terms can vary over time (e.g., increase at 2.4% per year in real dollars). The values for 

the other pollutants are currently hard-coded in the model, like annual VMT and survival 

fractions. However, they will soon be made a part of a References spreadsheet and 

modifiable by the user. They are shown in cells A15:B23 of the Shared Inputs worksheet 

of the Benefits Calculation spreadsheet. For the purposes of benefit calculation, the user 

can already change these values in the Benefits Calculation spreadsheet and the changes 
VGHG Model Documents Page 20 of 43 

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will flow through to the calculation of costs and benefits. The value of changes in 

vehicle emissions are shown on the DownstreamCosts ($) worksheet of the Benefits 

Calculation spreadsheet, and also carried forward to the Non-Tech Costs and All Costs 

worksheets. 

 The model also estimates the amount of emission reduction related to reduced fuel 

production and distribution. The estimates of the emissions of each pollutant associated 

with the production and distribution of each fuel are currently hard-coded in the model, 

like annual VMT and survival fractions. However, they will soon be made a part of a 

References spreadsheet and modifiable by the user. They are shown in Columns E and F 

of the Shared Inputs worksheet of the Benefits Calculation spreadsheet. For the purposes 

of benefit calculation, the user can already change these values in the Benefits 

Calculation spreadsheet and the changes will flow through to the calculation of costs and 

benefits. The value of changes in upstream emissions are shown on the UpstreamCosts 

($) worksheet of the Benefits Calculation spreadsheet, and also carried forward to the 

Non-Tech Costs and All Costs worksheets. 

 The model estimates five additional vehicle related costs and benefits. Three are 

related to the additional VMT resulting from reduced fuel consumption: noise, congestion 

and accidents. The values for these factors are expressed in terms of $ per mile. Thus, 

their total values are simply the product of these per mile values and the additional VMT 

resulting from the reduced fuel consumption. The values of these three vehicle related 

impacts are currently hard-coded in the model, like annual VMT and survival fractions. 

However, they will soon be made a part of a References spreadsheet and modifiable by 

the user. They are shown in cells A11:B14 of the Exclusive Inputs worksheet of the 

Benefits Calculation spreadsheet. For the purposes of benefit calculation, the user can 

already change these values in the Benefits Calculation spreadsheet and the changes will 

flow through to the calculation of costs and benefits. The value of changes in these 

vehicle impacts on society are shown on the ExternalVMTCosts ($) worksheet of the 

Benefits Calculation spreadsheet, and also carried forward to the Non-Tech Costs and All 

Costs worksheets. 

 The fourth vehicle related impact is related to the time required to refuel vehicles. 

As fuel consumption per mile decreases, if fuel tank size doesn’t decrease, or at least 

does not decrease proportional to fuel consumption, the number of vehicle refuelings will 

decrease. The parameters involved in estimating the reduction in refuelings are not 

currently input to the model, but are simply part of the Benefits Calculation spreadsheet. 

We plan to add these parameters to the References spreadsheet. The value of the 

reduction in the number of refueling is estimated as the product of: 

1) The reduction in fleetwide fuel consumption for a specific calendar year, 

2) Average fuel tank size, 

3) Average refueling volume, as a percentage of fuel tank capacity, 

4) Ratio of the change in fuel tank size to the change in fuel consumtion, 

5) Average time required to refuel a vehicle, 

6) Value of time to the driver and other occupants, 
VGHG Model Documents Page 21 of 43 

1 

2 

3 

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17 

18 

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24 

25 

26 

27 

28 

29 

30 

31 

32 

33 

34 

7) The number of total occupants in the vehicle, including the driver. 

The input values and the annual benefits are in columns H and I of the 

ExternalVMTCosts ($) worksheet of the Benefits Calculation spreadsheet, and also 

carried forward to the Non-Tech Costs and All Costs worksheets. 

 

 The final vehicle related benefit category is the value of the increased driving 

associated with the rebound effect. People decide to drive more because the value of 

travelling somewhere exceeds the cost. This value consists of two components. The first 

is the sum of the direct costs of the additional driving. This is assumed to be cost of fuel 

consumed plus that of the additional congestion caused. The additional noise and 

accidents are not considered to be direct costs. Noise is primarily experienced by nondrivers and therefore not considered by drivers in their decision to take an additional trip. 

While accidents tend to be proportional to mileage, especially for a given driver, we 

assume that most of the cost of accidents is borne by insurance, where there is only a 

weak association with mileage driven. Congestion, on the other hand, is totally 

experienced by drivers and experienced fully each trip. 

 The second component of the value of the additional driving is the change in the 

consumer surplus of the demand for VMT versus the cost of driving. We estimate this 

change in surplus as one half the change in VMT times the change in the cost of driving, 

which here is the reduction in fuel cost per mile. All of these terms have already been 

estimated for other purposes within the Benefits Calculation spreadsheet. The value of 

the additional driving is calculated and shown in column I of the ExternalVMTCosts ($) 

worksheet of the Benefits Calculation spreadsheet, and also carried forward to the NonTech Costs and All Costs worksheets. 

 The first step in loading the Benefits Calculation model is to “save” the results of 

a model run (click on “File”, then “Save” in the menu bar of the model window). This 

saves the model results in an “.html” file, whose name is the current date/time stamp. 

The next step is to open the Benefits Calculation spreadsheet (if it is not already opened), 

go to the Load worksheet, and click on the Load button. From the list of files available to 

load, choose the last one on the list. This loads the latest model results into the 

spreadsheet and updates all the cost and benefit estimates. 
VGHG Model Documents Page 22 of 43 

1 

2 

3 

ATTACHMENT 2 

Appendices to GHG Model Description 
1 Appendix 1: Examples of Market input file (Vehicle Fleet Chara

2 

3 
1 
1 Appendix 2: Technology input file (technology package characterization) 

2 

3 4 5 6 

Appendix 3: Scenario input file 
1 
1 
1 Appendix 4: Fuels input file 

2 

3 
1 Appendix 5: Tables of hard-coded input data, which will soon be included in an ed

2 

Downstream Criteria Pollutan

Regression Coeffi

LDV LD

a 4.5689 b 

c 

b*[x] + 

CO 

1.5073 1

y = b*[x] + a

y = c*[x2] + 3 

4 6 

5 

Upstream Emissions from Fuel/Energy Production, Storage, and Distribution Pollutant Fuel Type Total Upstream Emissions (grams/mmBtu) 

Conventional Gasoline 14.45 

CO Low Sulfur Diesel 12.67 

Electricity Generation 58.55 Conventional Gasoline 27.42 

VOC Low Sulfur Diesel 7.78 

Electricity Generation 19.73 Conventional Gasoline 48.11 

NOx Low Sulfur Diesel 42.92 

Electricity Generation 239.85 Conventional Gasoline 4.30 

PM2.5 Low Sulfur Diesel 3.48 

Electricity Generation 76.31 Conventional Gasoline 24.13 

SOx Low Sulfur Diesel 20.94 

Electricity Generation 527.33 Conventional Gasoline 17067 

CO2 Low Sulfur Diesel 15560 

Electricity Generation 219933 
1 

2 4 35 6 7 8 9 

10 11 12 13 14 15 

Average CO2

emissions 

(gpm) New Vehicle Sales 

Model Year Cars Trucks Cars Trucks 

2010 364 514 8100000 63000002009 365 514 8000000 67000002008 365 517 7919000 70200002007 365 518 7885000 72900002006 366 519 8130000 69320002005 365 519 7964000 78860002004 365 523 7538000 81730002003 366 524 7951000 78240002002 366 519 8304000 78150002001 366 520 8408000 72020002000 360 494 9128000 74470001999 360 495 8379000 68390001998 357 495 7972000 64850001997 356 496 8335000 61240001996 362 458 7890000 52540001995 358 465 9396000 57490001994 362 456 8415000 57100001993 358 457 8456000 47540001992 364 453 8108000 40640001991 355 447 8524000 40490001990 375 483 8810000 38050001989 375 497 10018000 44350001988 372 510 10736000 45590001987 375 538 10731000 41340001986 381 463 11015000 43500001985 436 513 10791000 36690001984 443 497 10675000 33450001983 456 799 8002000 23000001982 446 956 7819000 19140001981 455 593 8733000 18210001980 538 1088 9443000 18630001979 616 1197 10794000 30880001978 646 1245 11175000 32730001977 661 1027 11300000 28230001976 691 1124 9722000 26120001975 698 1124 8237000 1987000
Vehicle Age Data 

Proportion of Original Sales Surviving to 

Age: Average Annual Miles Driven Vehicle 

Age 

Car Truck Car Truck 

1 0.9950 0.9950 13,389 15,133 2 0.9900 0.9741 13,135 14,849 3 0.9831 0.9603 12,860 14,529 4 0.9731 0.9420 12,567 14,178 5 0.9593 0.9190 12,257 13,799 6 0.9413 0.8590 11,933 13,396 7 0.9188 0.8226 11,596 12,974 8 0.8918 0.7827 11,248 12,535 9 0.8604 0.7401 10,893 12,084 

10 0.8252 0.6956 10,531 11,625 11 0.7866 0.6956 10,165 11,161 12 0.7170 0.6501 9,797 10,697 13 0.6125 0.6042 9,429 10,235 14 0.5094 0.5517 9,063 9,781 15 0.4142 0.5009 8,702 9,337 16 0.3308 0.4522 8,346 8,908 17 0.2604 0.4062 7,999 8,498 18 0.2028 0.3633 7,662 8,109 19 0.1565 0.3236 7,337 7,747 20 0.1200 0.2873 7,028 7,415 21 0.0916 0.2542 6,734 7,117 22 0.0696 0.2244 6,459 6,857 23 0.0527 0.1975 6,206 6,638 24 0.0399 0.1735 5,974 6,464 25 0.0301 0.1522 5,768 6,340 26 0.0227 0.1332 5,589 6,269 27 0.0000 0.1165 5,438 6,254 28 0.0000 0.1017 5,319 6,254 29 0.0000 0.0887 5,233 6,254 30 0.0000 0.0773 5,182 6,254 31 0.0000 0.0673 5,182 6,254 32 0.0000 0.0586 5,182 6,254 33 0.0000 0.0509 5,182 6,254 34 0.0000 0.0443 5,182 6,254 35 0.0000 0.0385 5,182 6,254 36 0.0000 0.0334 5,182 6,254 

1 2 3 4 5 6 7 

8 GWPt-1 = global warming potential of the refrigerant before te

9 GWP t = global warming potential of the refrigerant after technology 

10 CDt-1 = carbon density of fuel before technology addition 

11 CDt = carbon density of fuel after technology addition 

12 FCt-1 = fuel applicable to prior technology 

13 FCt = fuel applicable to new technology 

Appendix 6: Definitions of variables in Model Equations 

RIE = refrigerant incremental effectiveness of the technology packCO2t-1 = tailpipe CO2 emissions before technology addition 

CO2t = tailpipe CO2 emissions after technology addition 
1 DR = discount rate 

2 3 4 5 6 7 8 9 

10 11 12 13 14 15 16 17 18 

IR = annual increase in value of CO2 PP = payback period FEE = fine for non-compliance (CAFE) GAP = difference between test-cycle fuel economy and real-world fuVMTi = annual miles traveled in year i 

VMTD,FS,i = annual miles traveled in year i, discounted 

VMTD,CO2,i= annual miles traveled in year i, discounted 

RCO2i = refrigerant leakage rate in year i (g/mi) 

RCO2t-1= refrigerant leakage before technology addition 

RCO2t = refrigerant leakage after technology addition 

TCO2 = tailpipe CO2 (e.g., the test cycle CO2 emissions) REB = rebound coefficient (% change in VMT for every 1% change TEB = technology effectiveness basis, which is the effectiveness of treflected in the baseline CEB = cost effectiveness basis, which is the cost of the technology pbaseline 
1 Appendix 7: TARF Equations (full) 

2 3 4 

  

         

   

2 1 1 1 1 1 2 2 1 1 1 

PPi t PPi PPi 

FC i FP FC i FP FC i FP 5 EffCost TechCost FC

6 7 8 

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

35

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RCO RCO CO CO VMT

EffCost CostEff

11 12 

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i

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i

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VGHG Model Documents Page 34 of 43 

ATTACHMENT 3 

MODEL REFERENCE GUIDE 
VGHG Model Documents Page 35 of 43 

Vehicle Greenhouse Gas (VGHG) Application 

Quick Reference Guide 

Model Version: .9 

Introduction: 

This document is to serve as a quick reference guide for using the Vehicle Greenhouse Gas 

application and the associated input spreadsheet files. 

Input Spreadsheets: 

There are four spreadsheets, in the current version of the application, that are required for a given 

scenario. Each spreadsheet consists of multiple worksheets as follows: 

1. Scenario Spreadsheet (Scenario.xls) 

a. Scenarios – contains scenario run parameters and references to associated input 

files, suffixed by the scenario id number, e.g. Market-1.xls 

b. Economics– provides economic parameters such as discount rate and payback 

period 

c. Targets – contains cycle-specific, user-entered target values for cars and trucks 

2. Market Spreadsheet (Market-1.xls) 

a. Market Data – provides sales and engineering information for each vehicle for each 

given scenario run 

b. MFR Sales – provides inputs corresponding to sales percentages by manufacturer 

used to generate generic model records 

c. Vehicle Type – provides lookup information associated to inputs and linkage 

between the market file spreadsheets for each vehicle type 

3. Technology Spreadsheet (Technology-1.xls) 

a. Vehicle Type (1...X) Worksheet – contains technology cost, efficiency, and market 

cap assumptions and other related information specific to a vehicle type 

4. Fuels Spreadsheet (Fuels-1.xls) 

a. Fuel – contains the forecasted fuel prices by year as well as fuel’s chemical 

properties 

Each spreadsheet also contains an Error Worksheet that provides the 

Validate Data button. If errors exist throughout the separate worksheets, 

the error messages will be presented after the data validation. Note 

that skipping the data validation can result in unexpected behavior in 

the application.

All spreadsheet column headers include special color coding to 

indicate if and how the associated column values are used. 

Green background indicates, columns that contain lookup values, 

e.g. Vehicle Type column in the Market spreadsheet. 

Yellow background indicates values that are auto-generated by the 

spreadsheet and/or read-only, e.g. ID column in the Scenario 
VGHG Model Documents Page 36 of 43 

spreadsheet. 

Gray background indicates columns with values that are read in but 

not currently used, e.g. Horsepower in the Market spreadsheet. 

Turquoise background indicates calculated values, e.g. Combine FE in 

the Market spreadsheet. 

Instructions: 

Updating the Spreadsheets: 

1. Navigate to the folder containing the spreadsheets. (C:\Program Files\VGHG\Input) 

2. Open input spreadsheet(s), starting with the Scenario spreadsheet and modify input 

parameters as needed. (Note not to add any data rows in the Scenario.xls) 

3. Click on the Error tab. 

4. To verify the accuracy of the new data, click on the Validate Data button. 

a. If no errors exist, the Error Worksheet will remain blank. 

b. If errors exist, the Error Worksheet will have a row populated per error. The column 

headers convey the following information: 

i. Sheet – worksheet the error will be found 

ii. Row – row number on the proceeding worksheet where error will be located 

iii. Col – column where error will be located 

iv. Error – explains why the data isn’t correct 

5. If no errors exist, save the spreadsheet. 

6. If errors exist, the individual cells will be highlighted in red. Make the appropriate changes 

and then save the spreadsheet. 

7. Follow similar process with the other 3 related spreadsheets until all run data has been 

entered and validated. 

Common Error Examples 
VGHG Model Documents Page 37 of 43 

Issue Fix 

Blank Field – Value () is not a 

number. 

Enter a number into the field 

String Field – Value (1) is not a 

string. 

Enter an alphabetic string into 

the cell 

Number Field – Value (A) is not a 

number. 

Enter a numeric value into the 

cell 

Percentage Field – Value (1200) 

must be equal to or less than 1. 

Enter a numeric value between 

0 and 1 into the cell 

Running the application: 

1. On the desktop, click on the VGHG icon to open the VGHG application. 

 You will be presented with the VGHG Model user interface. 

2. Click on the File drop-down and select the ‘Open’ option. You will be presented with the 

Open Scenario File pop-up box. 
VGHG Model Documents Page 38 of 43 

3. Select the Scenario.xls file and Click Open. The tables will be populated with data from 

the four spreadsheets. 

4. Verify that the correct data has been populated into the VGHG Model. 
VGHG Model Documents Page 39 of 43 

5. Click on the green car button. 

6. The system will run the model and present the step wise results for each cycle and 

manufacturer in a text file at the end of the execution. 
VGHG Model Documents Page 40 of 43 

At the end of the run you have the option to save the results as an XML file to load into the 

Benefits workbook or use it for other types of post processing analysis. To save the result data to 

an XML file, select File|Save from the menu. 

You may also run this XML file thru a built in transformation package to “Visualize” the results. 

Below is a partial display of the html page produced using the “Visualize” option:
VGHG Model Documents Page 41 of 43 
VGHG Model Documents Page 42 of 43 
VGHG Model Documents Page 43 of 43 

ATTACHMENT 4 

BENEFITS CALCULATIONS INSTRUCTIONS 
VGHG Model Documents Page 44 of 43 

Benefit Calculation Spreadsheet 

Vehicle Green House Gas Model 

The Benefit Calculation spreadsheet is an analysis tool for determining the total cost savings 

from CO2 reductions forecast by the Vehicle GHG model. The January 23, 2009 version of the 

spreadsheet includes a number of modifications to facilitate both expandability and error 

checking 

The original spreadsheet has been reorganized to facilitate a smooth connection to the Vehicle 

GHG core model. Major changes include: 

 The original input worksheet has been divided into four separate sheets: 

o Load, which serves as the point of entry for output from the model. The name 

of the most recent loaded XML file is displayed on this page as well as some 

additional information. 

o Model Results, which contains the manufacturer/model/redesign results from 

the Vehicle GHG model. This sheet also includes a number of aggregations to 

the Car/Truck level by redesign cycle. 

o Exclusive Inputs, which include inputs required only by the Benefits 

Calculation spreadsheet. These are primarily physical constants and emissions 

damage costs. 

o Shared Inputs, which are required by both the Benefits Calculation spreadsheet 

and the Vehicle GHG model (e.g. the discount rate, payback period and fuel 

price forecast.) These values are also read from the Vehicle GHG output to 

insure that the Benefits Calculations use the same values used by the model. 

 VBA code has been added to handle data import from the model. 

 The Benefits2 (Sales) tab has been reoriented so that the sales forecast goes down 

rather than across. This is for easier viewing of the data. 

 The VMT_Lookup sheet includes a table of VMT by vehicle age with separate columns 

that reflect different first year starting points – these are calculated from the rebound 

effect. 

 The VMT_Rebound_Effect table includes all of the Model Year / Calendar year data 

that was originally included in the triangular tables. 

 The AnnualTotals table summarizes information from the VMT_Rebound_Effect sheet 

into Calendar year totals. 

 The remaining pages in the Benefit Calculation spreadsheet have been updated so that 

all references are to the new pages. 

 Note that the cell background color scheme (see the legend on the Load sheet), is used 

on all pages. 

 Several pages were no longer required as their functionality has been moved to the new 

sheets. These have been removed from the spreadsheet to eliminate confusion (and to 

greatly reduce the size of the spreadsheet) 
VGHG Model Documents Page 45 of 43 

Loading Model Results:

The system transfers data from the Vehicle GHG model to the Benefits Calculation spreadsheet 

via an XML file that is exported by the model after all redesign cycles are complete. The XML 

file contains and organizes all of the spreadsheet input data and the model output data. This 

approach of using a separate XML output file allows for flexible integration into other future 

post-processing spreadsheets since all of the important data is organized into a single file. 

Results from a Vehicle GHG scenario run may be loaded automatically into the appropriate cells 

in the Benefit Calculation spreadsheet via the Load button that appears on the “Load” tab. Data 

read from the Vehicle GHG scenario includes: 

 CO2 emission and cost forecasts for each make and model in each redesign cycle. 

 Vehicle sales data for each make and model in each redesign cycle. 

 Shared Inputs 

The spreadsheet contains the following sheets: 

Sheet Name Purpose Comments 

Load 

Specify scenario and read in the XML 

outputs from Vehicle GHG. 

Loads data for up to four manufacturers 

and a maximum of four redesign cycles. 

Note that the XML file is usually located 

in the .\output subdirectory of the VGHG 

installation directory. 

MODEL RESULTS 

Summarizes model outputs in a flat table. 

Also includes Redesign cycle totals and 

averages for Cars and Trucks 

If data is read in for less than eight 

cycles, values from the last cycle read in 

will be copied forward to fill in a full 

eight cycles. 

EXCLUSIVE INPUTS 

Data values required by the Benefits 

Calculation spreadsheet, but NOT by the 

core Vehicle GHG mode 

SHARED INPUTS 

Data values require by both the Vehicle 

GHG model AND the Benefits Calculation 

spreadsheet. 

BENEFITS2 (SALES) 

Interpolates annual Car and Truck sales 

based on the input data for sales in the 

redesign cycles. 

Sales forecast now extends through all 

eight redesign cycles (2050) 

VMT_Lookup 

Tables of VMT by vehicle age with 

increasing values calculated from the 

rebound effect. 

VMT_Rebound_Effect 

All Model Year / Calendar year calculations 

for impacts due to the rebound effect. This 

includes both costs and benefits. 

AnnualTotals 

Calendar year totals for the model year / 

calendar year data on the 

VMT_Rebound_Effect pag. 

Note that the Baseline and earlier CO2 

values for Cars and Trucks are included 

as the last two columns on this tab. 

EXTERNALVMTCOSTS ($) Calculate negative impact of rebound VMT 

due to Congestion, Accidents and Noise 

DOWNSTREAMCOSTS ($) 

Summarizes downstream costs calculated on 

the above six worksheets. Applies a 

discount rate to determine the net present 

value of the future cost stream. 
VGHG Model Documents Page 46 of 43 

Sheet Name Purpose Comments 

FUELCOSTS ($) Calculates cost savings due to reduced fuel 

consumption. 

EXTERNALCRUDECOSTS ($) Calculates cost savings due to reduced need 

for crude oil. 

UPSTREAMCOSTS ($) 

Calculates the upstream savings due to 

reduced emissions of CO2, CO, VOX, NOX, 

PM and SO2 due to reduced need for 

gasoline production from crude oil. 

ALL NON-TECH COSTS Summary of all Non-Technology costs and 

benefits for a given Vehicle GHG scenario. 

TECH COSTS Summary of Technology costs from the 

model. 

ALL COSTS Summary of Technology and NonTechnology costs. 
APPENDIX C 

JOHN GERMAN’S REVIEW DOCUMENT 
Appendix C-1 John German Review 

The charge to reviewers was, “EPA staff are seeking your expert opinion on the concepts and 

methodologies upon which the model relies and whether or not the model will execute these 

algorithms correctly.” The emphasis of my review is on the inputs to the model and the model 

concepts and methodologies. I did not devote much attention to the outputs and whether or not 

the model executes the algorithms correctly, as the model will certainly evolve and improve over 

time. Thus, assessing outputs and proper execution of algorithms will be a living, constantly 

changing challenge, as the model itself changes. My time and expertise is better spent focusing 

on inputs and model structure. (Not to mention that I just ran out of time.) 

 

Concepts and Methodologies Upon Which the Model Relies:

(A) Model structure 

The model is an accounting model. This is neither good nor bad. The advantage is that it avoids 

overmodeling and embedding errors in the model itself. The disadvantage is that the factors 

affecting the results are all inputs to the model. This requires a great deal more sophistication 

and work by anyone using the model to prepare the inputs properly. It will also make it more 

difficult for anyone outside EPA to use the model, unless EPA is willing to provide the detailed 

inputs to other users. 

With this type of model, it is essential that EPA release the data in the Technology and 

Economics input files and discuss them in the Notice of Proposed Rulemaking, as the real 

analyses and modeling are in these input files. But as long as this is done, the overall model 

construction is fine. 

(B) Redesign cycles 

I completely agree with EPA’s logic in creating a model based upon vehicle redesign cycles. As 

EPA states, adding technologies incrementally to each vehicle model by model year does not add 

value to the model results. Using redesign cycles also allows for simplification of the fleet. It is 

impossible to predict the direction of vehicle redesigns for each manufacturer. It is just as 

accurate to assume, for example, that future mid-size cars from each manufacturer will be 

identical; as it is to assume that current differences in mid-size cars from one manufacturer to the 

next will be continued into the future. As a recent example, Honda left their compact crossover, 

the CR-V, virtually unchanged in size during the latest redesign. However, Toyota chose to 

lengthen their compact crossover, the RAV4, by 14” during its latest redesign. It is pointless to 

try to predict differences in vehicles from different manufacturers in the future and it is pointless 

to try to predict the exact year when redesigns will occur. This is a welcome simplification. 

Another advantage of using redesign cycles is that GHG standards for interim model years can 

only be set, reasonably, as a straight line (or a constant % decrease) between the baseline year 

and the end of the redesign cycle. This is appropriate. Constant yearly % reductions provide a 

consistent signal to manufacturers for investment decisions. 

However, there is one potential problem with using redesign cycles. It masks the investment 
Appendix C-2 John German Review 

needed to bring new technology to the market. The auto industry is extremely capital intensive. 

Initial investment in a new technology is expensive, both for tooling and the resources necessary 

to assess (and fix) system-level effects and effects on reliability, durability, safety, and 

manufacturing. Redesign cycles tend to assess only the costs for high-volume production and 

skip over the high initial costs. Care must be taken to properly assess costs in the inputs. 

(C) Leadtime 

The model handles leadtime issues far too simplistically. This was also a problem with the 

Volpe model. Leadtime is one of the most important issues in setting standards and one of the 

most difficult issues to assess properly. Thus, it is disappointing to see both NHTSA and EPA 

provide so little attention to the issue. 

The only leadtime constraints in the draft model are industry-wide caps on the maximum 

technology penetration by redesign cycle and vehicle type. There are several problems with this 

approach: 

 The largest problem is that it is inappropriate to treat all manufacturers the same. A 

manufacturer that has already invested in a particular technology in the baseline year will be 

capable of higher penetration rates than a manufacturer that has never used the technology 

before – and also of producing the technology at lower cost. An obvious example is hybrid 

vehicles. Over 10% of Toyota’s vehicles already have hybrid systems on them. After 

introduction of the CR-Z next year, Honda should also have more than 10% hybrids. Due to 

their experience and head start with hybrids, both manufacturers will be capable of much 

higher penetration rates than most other manufacturers. They are also further along the 

learning curve, so their costs will be lower. Similar situations exist with most technologies. 

 Another problem is that costs will vary from manufacturer to manufacturer. As noted in my 

comments on redesign cycles, above, there are large upfront costs when a manufacturer 

introduces a new technology. For example, Toyota has already amortized large R&D and 

system-level costs for hybrid vehicles. They will be able to produce hybrids cheaper than 

manufacturers that are just starting to offer hybrids. The point is that the “Initial Incremental 

Cost” in the Technology Input File should not be applied to all manufacturers at the same 

time, but rather to each manufacturer at the time they first introduce a new technology. 

 The third problem is that there is no such thing as a hard cap on technology penetration rates. 

There is a tradeoff that exists between cost and leadtime. Technology introduction can be 

accelerated by increasing investment – and cost and risk. 

Long-Term Recommendation – The best way to handle leadtime constraints and technology 

penetration is to assess capital investments by manufacturer. This would require adding a new 

section on capital expenditures. In addition to assessing the cost of each technology, the capital 

expenditure would also be assessed. Ideally, there would be two components to the capital 

expenditure assessment for each technology, one for R&D expenditures for the first 

implementation of the technology and one for the capital investment needed to add the 

technology to additional models. However, the second is more important. Each manufacturer 

would be assigned a total capital expenditure budget for the redesign cycle and technologies 

could only be added up to the point where the sum of the technology capital expenditures did not 

exceed the manufacturer cap. Alternatively, some increase in technology penetration over the 
Appendix C-3 John German Review 

cap could be allowed, but only if coupled with increasing technology costs. This would 

appropriately handle leadtime constraints and technology penetration rates. 

Short-Term Recommendation – The long-term recommendation would require a lot of new work 

and is clearly not feasible in the timeframe needed for EPA’s rulemaking. As a short-term fix, 

instead of using industry-wide caps on maximum penetration for each technology, EPA should: 

(a) Set caps on the maximum increase permitted per year. This would be applied to each 

manufacturers’ individual technology penetration; and 

(b) Establish the model year for initial introduction. For technology that has not been 

introduced to the market yet, this year could be the same for all manufacturers. For a 

technology that is already being used by a manufacturer, the baseline year would be used 

for that manufacturer. However, if a manufacturer were not using a technology yet, even 

if another manufacturer is using it, a year of introduction would need to be set for that 

manufacturer. 

(c) Some technologies would still need caps on maximum penetration. However, this should 

reflect market restrictions, not leadtime constraints. This would incorporate consumer 

values for particular technologies that go beyond just efficiency and performance. For 

example, even though manual transmissions are more efficient than automatics, most 

consumers will not give up the convenience of an automatic. PHEVs do not have much 

benefit for people driving a lot of highway miles each day. Diesels are desired for trailer 

towing and have advantages on highway fuel economy, while hybrids have advantages in 

stop-and-go driving. These types of market considerations can be handled by 

establishing maximum penetration caps, but they should be handled separately from how 

leadtime is handled by manufacturer. 

Note that the yearly cap and introduction date violates the design cycle principal, but it is 

important to create the proper cap for each manufacturer and technology combination. Instead of 

using a model year for (b), above, the user could specify how many years into the design cycle a 

technology could be introduced. 

(D) Technology Assessment 

Requiring the user to input technology in rank order of cost-effectiveness is an interesting 

attempt to handle the synergy issue. Unfortunately, it fails to work in other ways: 

 It only works if the learning rate is the same for all technologies and if no technology 

changes effectiveness over time. If one technology has a steeper learning curve than another, 

or if a technology increases benefits in the future, then the cost-effective order will change 

over time. For example, high-tech diesels are a relatively mature technology, as over 5 

million per year have been sold in Europe for several years. Their future cost reduction 

potential is much less than that of hybrid vehicles, whose sales are at least an order of 

magnitude lower and which are still at early stages of development. Also, the high power Liion batteries just starting to penetrate the market will allow much smaller battery packs for 

conventional hybrids, with large cost reductions. In addition, analyses by MIT (2007) 

suggest that hybrid benefits will increase in the future as manufacturers figure out how to use 

the hybrid system to minimize operation at less efficient engine speed/load points. 

 The synergies will differ depending on the specific technologies into which an individual 
Appendix C-4 John German Review 

manufacturer has already invested. For example, consider one manufacturer that has 

invested in MPI turbos and a second that has invested in DI naturally aspirated engines. If 

both manufacturers move to DI turbo engines, the first manufacturer will gain the benefits of 

DI adjusted for the DI/turbo synergies, while the 2nd manufacturer will gain the benefits of 

turbocharging adjusted for the same DI/turbo synergies. Thus, the synergy impact of 

DI/turbo must be assessed independently of each technology. Even if the model ignores the 

leadtime constraints imposed by baseline technology investment and assumes every 

manufacturer will adopt the exact same technology packages for a given vehicle type (not a 

good idea, as discussed, above), a problem still exists in backing out “any advanced 

technology that might have been present in the baseline” (page 12, line 3-4). In order to back 

out the baseline technology for different vehicles and manufacturers, the technology input 

file must contain independent assessments of MPI turbo, DI naturally aspirated, and DI turbo. 

The DI turbo line includes the synergies, but the other two lines do not. How does the model 

add them back in? If the turbo lines and DI lines occur before the DI turbo line, then the 

technologies will be added together first without consideration of the synergy effect. 

 It does not allow for different markets for different technologies. For example, diesel 

engines have additional value for (a) customers who tow and (b) customers in rural areas. 

Towing is valued only by a small part of the market, but it is an important feature for that 

market. Customers in rural areas do a lot of highway driving and value the high efficiency of 

the diesel on the highway, while hybrids excel in urban areas. Thus, the markets for diesels 

and hybrids will be self-selected to some extent by their relative city and highway mpg, not 

the combined mpg used to select all technology. 

In order to work properly, the model must be able to handle multiple pathways. For example, the 

model cannot allow turbo and DI benefits to be added sequentially, but must force each to go to a 

DI turbo input. A similar situation exists with the various variable valve timing systems and 

VCM. All offer primarily pumping loss reductions and all options must be present in the input 

file in order to back out technologies in the baseline. All these options cannot be added back by 

the model one after the other – the model must also be able to handle these multiple pathways. 

Another example is transmissions, where the input file must list 5-, 6-, 7-, and 8-speed 

automatics, as well as DCTs and CVTs (even ignoring manual transmissions). I could go on. 

The point is that I do not see how the model can avoid handling multiple technology pathways 

and depend only on the input order to handle synergies. 

The model must also be able to handle technologies with different rates of change in benefits and 

costs in the future. This also requires that the model process the lines independently and not rely 

on the input order. 

The market considerations could perhaps be handled with maximum penetration caps. For 

example, it could be considered that diesel engines will not compete well with hybrids in urban 

areas, so that the maximum penetration of diesels would be equal to their sale in rural areas plus 

trucks designed to tow, with the reverse true for hybrids. Of course, this will differ by 

manufacturer, which is a problem if universal caps, instead of manufacturer-specific caps, are 

maintained. 
Appendix C-5 John German Review 

(E) Maximizing Net Social Value 

The model only outputs total costs and benefits. It presents these with great amounts of detailed 

information. But it is impossible to tell if the scenario has maximized net social value. 

To put it another way, the model is only capable of counting up the benefits and costs of 

complying with pre-determined GHG standards. It is not able to do the reverse, which is to input 

the desired benefit and have the model determine the resulting GHG standard. 

This is not a trivial issue. The 2007 EISA specifically mandates “maximum feasible” CAFÉ 

standards after 2020. NHTSA has long interpreted existing statutory authority to also require 

maximum feasible standards and established long ago that “maximum feasible” is determined by 

the point at which the costs of adding the next technology exceed the benefits. Even without a 

mandate, any credible analysis must be able to compare the costs and benefits of the chosen 

GHG standard to the maximum net social value. 

Given the existing complexity of the model, it is not unreasonable for the model to also 

determine the GHG standard that maximizes net social value. The Volpe model calculates this 

point even with a much more complex model. EPA’s model will lose considerable credibility if 

it is not capable of calculating the maximum net social value point. 

Appropriateness and Completeness of the Contents of the Sample Input Files:

(F) Market Input File 

The market input file appears to be appropriate and complete – perhaps too complete in one way. 

The file contains separate inputs for reference case technology benefits and costs. The 

percentages in these columns should simply reflect the existing market penetration of each 

technology package. They should be identical for both costs and benefits. Is there a reason why 

these would be different? If so, the Model Description should explain this. If not, the duplicate 

columns can be removed. 

Minor Suggestions: 

 If the model wants to “back out” existing technologies, you will need a lot more than 20 

columns to do this. You’ll need 10 columns just to handle transmissions and another 10 just 

to handle different valve timing systems. Not to mention differing levels of high strength 

steel and aluminum use. 

 The Model Description should state that vehicle types are a user input defined in the 

“Vehicle Type” tab of the Market Input File (I looked around for a while before I found this.) 

 If you maintain separate columns for reference case technology costs and benefits, it would 

help the user to add a row above the existing descriptions and define columns AD-AW as 

“reference case benefits” and columns AX-BQ as “reference case costs”. 
Appendix C-6 John German Review 

(G) Technology Input File 

As discussed above, the technology input files need to be substantially modified in conjunction 

with changing the model to handle multiple technology paths. 

In addition, also as discussed above, the “Cap Cycle” numbers need to be replaced with generic 

caps on the maximum increase permitted per year and manufacturer-specific model years for 

initial introduction. The annual technology penetration increase cap would be applied to each 

manufacturers’ individual baseline technology penetration, from the Market Input File, or 

starting with the manufacturer-specific initial model year for technology packages that have not 

been used yet by individual manufacturers. 

The Average Incremental Effectiveness fields are fine, although, as noted above, if these change 

for future redesign cycles, the cost-effective order of the technology packages can also change. 

I could not find any explanation of how the Initial Incremental Cost, , Decay, seedV, kD, and 

Cycle Learning Available fields are used in the model. Even the detailed algorithms on pages 9-

16 of the Model Description contain no reference to how technology costs are adjusted for the 

TARF calculations. Thus, I was not able to assess the appropriateness of these fields. However, 

in general, the cost reduction curve is not likely to be the same for all technologies. Some 

flexibility may be needed here. 

The Technology Input File does not address weight impacts associated with different 

technologies. For example, both diesel engines and hybrids add considerable weight to the 

vehicle, which negatively impacts both performance and efficiency. It is possible to handle this 

off-board in the efficiency benefit estimation. However, if so the Model Description should 

explicitly state that weight impacts are expected to be assessed by the user and included in the 

technology inputs. 

(H) Scenario Input File 

The compliance options – universal standard, linear attribute, or logistic attribute – are fine. 

However, there are columns in the Scenario input file that are not described in the Model 

Description on page 6: 

 TARF Option (column E) – Is this the “two TARF equations from which the user can 

choose”, described on page 13? If so, should state this on page 6. 

o Why is the “Effective Cost” TARF equation limited to fuel savings over the payback 

period? Why aren’t the discounted lifetime fuel savings considered? Is this done to 

try to mimic what technologies will be most acceptable to the customer? If so, this 

should be explained in the Model Description. I’m also not sure this is appropriate. 

Most technologies will be invisible to the customer. In addition, the primary point of 

CAFÉ and GHG standards is to fill in the gap between the consumers’ value of fuel 

savings and the value to society. So, the standards should be targeted towards 

society’s values, not the customers. 

o The equation for “Cost Effectiveness – Manufacturer” equation does not make sense. 
Appendix C-7 John German Review 

Unless a technology includes a fuel change, this equation will produce virtually 

identical results for all technologies. The CO2 summed in the denominator is directly 

proportional to fuel consumed summed in the numerator. The ratio should be 

virtually the same for all technologies, unless there is a fuel change. What is this 

equation trying to do? 

o Why is the fuel savings only summed over the payback period, while the CO2 

savings are summed over the useful life? Why are they not the same? 

 Target Function Type (column F) – I could not find a description of this field anywhere in 

the Model Description. 

 Fleet type (column G) – The description in Rykowski’s email response to Rubin should be 

added to the Model Description. 

 Trading limit (column I) – The description in Rykowski’s email response to Rubin should be 

added to the Model Description. 

Economic parameters – The “CAFÉ fine” and “CO2 value increase rate” are fine. However, the 

other parameters may need modification: 

 Discount rate – There is some thought that the CO2 discount rate should be different from the 

economic discount rate. I am not sure I agree with these arguments, but you may want to 

include flexibility to have a different discount rate for CO2 in the model. 

 Payback period – As discussed, above, I am not sure this is needed. Any use of payback 

period should be explained and justified in the Model Description. 

 CO2 fine – While the CAFE fine is used appropriately in the model, there is no consideration 

of a manufacturer paying CO2 fines instead of complying with CO2 standards. Of course, 

this is dependent on the compliance strategy adopted by EPA for its CO2 standards. But the 

model should have the flexibility to model CO2 fines; similar to how it handles CAFÉ fines. 

 Gap – It is appropriate to adjust the test values for differences in real-world fuel 

consumption. However, the gap is not linear. As EPA demonstrated in their fuel economy 

label rulemaking, the gap increases as fuel consumption decreases. While the fuel economy 

label adjustments overstate the actual gap, the curves for city and highway fuel economy 

labels from the generic equations are illustrative. The model should add the ability for the 

user to input a nonlinear gap function. 

 I do not understand the value of “threshold cost” or how it is used. Lines 8-10 of page 8 

state, “threshold technology cost (the cost at which manufacturers add technology to only 

enough vehicles to meet the standard as opposed to adding technology to all of a model 

line)”. The detailed calculations later in the Model Description do not discuss how this is 

done. From a practical point of view, how does the model know whether or not the 

technology is needed to meet the standard when the technologies are feed into the model one 

at a time? More importantly, manufacturers have limited resources and the standards will 

drive technology development well beyond what a manufacturer would have done without 

them. Thus, why would a manufacturer add any technology to more vehicles than are 

required to meet the standard? Unless these concerns can be addressed in the Model 

Description, the “threshold cost” should be eliminated. 

 Rebound effect – Line 38 on page 17 states that the rebound effect is an input in the 

“Economics” worksheet. However, it is not listed in the worksheet. In any case, the rebound 

effect is not handled appropriately in the model. The rebound effect is a sensitivity factor. 
Appendix C-8 John German Review 

But it is determined from a regression. Which means that the change in VMT is NOT a 

linear function of the change in fleet fuel consumption. Thus, the equation on lines 41-43 of 

page 17 is wrong. The actual relationship is logarithmic or exponential or something like 

that (I don’t remember exactly what). The correct equation should be built into the model. 

o The rebound effect is also impacted by the price of fuel and household income. This 

should be added to the model (see medium- to long-term recommendations, below). 

Minor suggestions: 

 It appears that the “Cars A”, “Cars B”, “Cars C”, and “Cars D” columns in the Target tab are 

intended to describe the footprint-based logistic curve. Does this mean that “Cars C” and 

Cars D” are also the Xmax and Xmin under the linear attribute option? If so, both 

descriptions should be in the column headings. Also, while the Model Description (page 6-7) 

includes a good explanation of the how the linear target and logistic curve work, it should 

also specifically state where the A, B, C, D, and X coefficients can be found in the 

spreadsheet. 

 The economic parameters are discussed as part of the Scenario input file on page 8. Lines 

12-13 also state that an example of the Scenario input file is in Appendix 3. However, 

Appendix 3 only includes the “Scenarios” tab and the “Target” tab. The “Economics” tab 

should also be added to Appendix 3. 

(I) Fuels Input File 

The fuels file works fine for conventional gasoline and diesel. The Model Description does not 

address biofuels, but if needed the Fuel Input and the Upstream Emissions worksheets should be 

able to handle them. 

Electricity is a special problem. A minor issue is that the Energy Density (column B), Mass 

Density (column C), and Carbon density (column D) are different than for liquid fuels. Liquid 

fuels are generally expressed in units per gallon. This doesn’t work for electricity. The units for 

electricity in the Fuels Input sheet need to be defined. Also, I’m not sure what Mass Density 

would be for electricity – kg/kWh? And isn’t carbon density meaningless, as the carbon is all 

upstream? 

More importantly, the energy density and mass density for electricity are not fixed, but are 

dependent on battery construction. High-power Li-ion batteries for conventional hybrids may 

only have about 15 Wh/kg energy density, while high-energy batteries for PHEVs and EVs may 

have over 100 Wh/kg. In addition, start/stop systems and belt-alternator/starter systems may use 

lead-acid batteries and some conventional hybrids may continue to use NiMH batteries through 

the 2013-2015 timeframe. All will have different energy densities. 

Minor suggestions: 

 The Model Description, line 6 page 6, says, “There is a small subset of fuel information not 

included in this file”. This is not accurate. Appendix 5 contains upstream emissions, which 

is an extremely important factor for fuels. This connection should be discussed in the Model 

Description. 

 The appendices should be ordered to match the order they are discussed in the Model 
Appendix C-9 John German Review 

Description (i.e. the fuels Appendix should be before the Scenario appendix). 

(J) Reference Data in Appendix 5 

Downstream Criteria Pollutant Emissions: 

The fields and the regressions as a function of age are appropriate. However, there is not enough 

flexibility to handle differences in fuel, future emission standards, and future fuel sulfur control: 

 The model should be able to handle future reductions in emission control standards. This 

means that the model should allow the user to specify effective years for future emission 

standards and enter new regression coefficients. 

 SO2 emissions are almost entirely a function of the sulfur level in the fuel. Thus, the model 

should also handle changes in fuel sulfur level. The model should allow the user to specify 

effective years for future sulfur reduction and the fuel sulfur level for both current and future 

fuels. If desired, the user would not have to enter regression coefficients for SO2, as there is 

a fixed relationship between fuel sulfur, fuel consumption, and SO2 emissions (much like 

CO2 to fuel consumption) that could be hard-coded in the model if the user specifies fuel 

sulfur levels. 

 The regression coefficients will be different for gasoline, diesel, and electric vehicles. 

Average coefficients can be used for the current fleet, but these will not be appropriate if 

there is a substantial change in the future mix of diesels, PHEVs, or EVs. The model needs 

to allow input of different coefficients for diesel and gasoline – and possibly biofuels. 

Downstream emissions of electric operation should be zero and do not have to be input. 

 It appears that the model does NOT calculate downstream pollutant emissions as part of the 

normal model accounting, only the additional emissions caused by the VMT rebound effect. 

This is not appropriate. If there is a switch to diesels or EVs, the downstream pollutant 

impact needs to be assessed by the model. 

Upstream Emissions: 

 The upstream emission inputs are fine for gasoline and diesel, although addition rows will 

likely be needed to handle biofuels and unconventional oils. 

 It is not clear if the efficiency of battery recharging is included in electricity upstream 

emissions. The model likely calculates only the mmBtu actually used by PHEVs and EVs 

during use. However, the mmBtu draw from the utility will be larger due to losses in the 

battery charger and in the battery chemical process. To ensure that the user handles this 

properly, it would be best to add an input somewhere for charging efficiency. Otherwise, the 

Model Description should explicitly state that the upstream grams/mmBtu for electricity 

must be incremented to include the losses in the charger and battery. 

 Upstream emissions, both carbon and pollutant, for electricity will vary by region. While it 

is the responsibility of the user to input proper factors, there is a potential issue with 

stratification of PHEV and EV sales across the nation. Customers in urban areas are most 

likely to buy PHEVs and EVs will likely be limited primarily to a few, dense urban cores. It 

might be useful to have the Model Description briefly discuss the need for the user to input 

upstream values for electricity that are consistent with utility emissions in the urban areas 

most likely to purchase PHEVs and EVs. 
Appendix C-10 John German Review 

Vehicle Age Data and historical data on average CO2 emissions and new vehicle sales: 

These fields and inputs are fine. 

(K) Other Reference Data 

Externalities related to crude oil use: 

The externalities in the Externalities worksheet of the Benefits Calculation are only listed for 

imported oil. This is appropriate for military costs for protecting oil supplies, but it is not for the 

economic impact of periodic price shocks (and possibly for monopsony effects as well). Oil is a 

global commodity. Any reduction in oil use, either domestic or imported, will help reduce the 

economic impact of periodic price shocks. 

Rebound effects: 

The discussion of the rebound effects on lines 10-19 of page 3 and on pages 20-21 both imply 

that rebound effects are NOT considered in assessing the societal benefits from reduced crude oil 

use and GHG emission reductions. However, I would assume that these benefits are based upon 

total fuel consumption, which includes the additional VMT from the rebound effect. If my 

assumption is not accurate, then the social benefits associated with reduced crude oil use and the 

value of GHG emission reductions must be revised to include the rebound effect. If the benefits 

do include the additional VMT from the rebound effect, this should be clarified in the discussion 

on both page 3 and page 20. 

Recommendations for Improved Model Functionality – beyond “future work”:

(L) Recommendations for Short-Term Functionality

The functionality of the model is good. My only recommendations are those already described 

above, for improved handling of leadtime (section C), ability to handle multi-path technology 

inputs, (section D), and ability to calculate “maximum net social benefits” (section E). 

(M) Important Medium-Term and Long-Term Recommendations 

1) By far the most important improvement is to use budgets for capital expenditures to assess 

leadtime. The need for this and suggestions on how to implement it were discussed in section 

(C), above. 

2) The rebound effect is impacted by both the price of fuel and household income. These 

should be added to the model. The work has already been done by Small and vanDender. Their 

equations should be added to the model, along with the necessary user input fields for future 

household income. An option to skip the fuel and income effects can be maintained, but it is 

important that the model be capable of properly calculating rebound effects. 

 The time value of congestion and vehicle refueling are also related to household 

income. While this is of lesser importance than the rebound effect, it should be 

relatively easy to add household income effects to the value of congestion and vehicle 

refueling in conjunction with adding household income to the VMT rebound effect. 
Appendix C-11 John German Review 

(N) Less Important Long-Term Suggestions 

3) Inclusion of the city and highway fuel economy/CO2 values may help with assessing 

market penetration caps, although this can be done externally. Also, separate city and highway 

values could help calculate an appropriate in-use fuel economy/CO2 “gap” for different 

technologies with different city/highway fuel economy ratios. Separate city and highway 

numbers might also be useful for other purposes. EPA should consider adding these to the 

model. 

4) Value of time required to refuel vehicles: 

The model handles this appropriately for liquid-fuel vehicles. However, PHEVs and EVs will 

add refueling time, both because of the need to plug in and, in the case of EVs, the shorter range. 

This should be added to the model. Ideally, it should also be added to the TARF assessment. 
APPENDIX D 

PAUL LEIBY’S REVIEW DOCUMENT 
Appendix D-1 Paul Leiby Review 

May 29, 2009 

Paul N. Leiby 

pleiby@gmail.com 

Review Comments on 

EPA’s GHG Model and the documentation, “Description and Methodologies of the EPA 

Vehicle Greenhouse Gas Emission Cost and Compliance Model” 

(Based on Drafts received May 1, 2009) 

Thank you for the opportunity to review this model and its documentation. This is an important 

project, and the EPA team has made great progress in developing a coherent, informative, and 

very usable system. I understand that this is a work in progress and, regrettably, many comments 

can only refer to its current (May 1, 2009) state. Also most of the comments are in the form of 

what might be changed or improved, with the hope that these might be most useful. I would like 

to say at the outset that everything achieved so far is well worthwhile, and some features are 

quite marvelous. Please also interpret statements below of the form “the model does/does not” 

as meaning “as far as I could discern so far, it seems like the model does/does not.” Statements 

like “the model/documentation should” really mean “Perhaps it would be helpful if the 

model/documentation were adjusted to....” In sum, this work is to be applauded and I look 

forward to its next iteration. Comments are offered in order of the questions posed, and in 

structured bullet form. 

Questions to address: 

1) Comments on: The overall approach to the specified modeling purpose and the 

particular methodologies chosen to achieve that purpose; 

 This model fills an important need for an independent capability to assess how 

manufacturers might respond to GHG emission regulations on light-duty vehicles. 

 There is much to recommend this model, which grapples with some key challenges of 

assessing how progress toward tighter fuel use or GHG emissions standards can be 

achieved through incremental vehicle technological change, and at what cost. 

 The essential approach of this model is consistent with others in a similar vein, with the 

most notable predecessor being the NHTSA “Volpe Model.” It describes the set of 

technological possibilities for improving vehicle fuel economy, or reducing GHG 

emissions, characterizing for each technology the cost and incremental change in 

emissions and fuel use. It determines a sequence of introduction for fuel-economy (or 

fuel switching) technologies necessary to meet a fleet-average CO2 emission constraint 

for each manufacturer. However it differs from some other approaches in significant 

ways: 

o 1. The sequence of discrete technologies that can be used for any single “Vehicle 

Type” is exogenously specified by the user. Those fixed technology successions t, 

t+1 ... for each vehicle type v ,essentially define a vehicle-type-specific supply 

(marginal cost) curve for emissions reduction. The model determines the 

sequence in vehicle types each separately progress in an orderly fashion down 

their emissions reduction technology curve. 
Appendix D-2 Paul Leiby Review 

o 2. The model makes vehicle technology redesign decisions not annually, but for 

each vehicle “design cycle,” which is typically specified as a fixed number of 

years. 

o 3. The algorithm does not do a simultaneous choice of the set of technologies that 

minimize vehicle net costs such that the GHG emission standard is met. Rather it 

iteratively “dispatches” discrete new technologies by choosing which vehicle is to 

progress next by one more step through its sequence of technologies. It repeats 

this dispatching over vehicle types until the fleet average GHG emission standard 

is finally met. The choice of which vehicle type is to receive more advanced 

technology is based on one of two figures of merit, called “TARFs.” 

 It is wisely stated that effective model design hinges on a careful definition of its purpose 

or purposes, and an acknowledgement of its bounds and limitations. The documentation 

could be much strengthened in this regard. Here is my impression of its suitability: 

o This model is currently most suited to estimating the incremental net 

technological cost of any single manufacturer achieving various GHG emission 

levels, specified as an average for that manufacturer’s new-car fleet. It accounts 

for technology costs and lifetime fuel cost savings in its dispatching of 

technologies for each manufacturer’s fleet. Other attributes and societal impacts 

may be monitored ex post (e.g. the extensive and somewhat disparate list on the 

top half of p. 3, including criteria pollutant emissions, noise, congestion, refueling 

time, etc.) but these are not considerations in the model’s solution, i.e. in the core 

algorithm that sequences the application of vehicle technologies. 

o A compact way to describe the models approach is that, like the Volpe Model, its 

solution has two phases: “manufacturer compliance simulation” (with cost-based 

technology choice) and “effects estimation” (based on a diverse set of ex post

calculations). 

o The model does not project vehicle sales, or sales mix, or aspects of vehicle 

design and vehicle appeal to consumers, apart from altered lifetime vehicle capital 

and fuel use costs. This is not mentioned as a flaw, but as an important design 

choice that should be stated. Large changes in fuel economy and GHG emissions 

could have important indirect impacts on the design and appeal of the vehicle, 

particularly if tradeoffs are made in the areas of vehicle size, weight, 

performance, range, and, for alternative fuels, fuel availability and convenience. 

o The model treats each manufacturer’s regulatory attainment problem 

independently, and is not currently designed to model “flexible” emission 

standards that allow permit trading among manufacturers, permit banking or 

borrowing, or economy-wide GHG trading systems. 

 Suitability of method 

o To some extent the discussion of the manifold ancillary benefits and costs can be 

a distraction, since a coherent and complete framework for their endogenous 

analysis is currently outside the scope of this model. I suggest that the model 

developers may wish to stay focused first on clearly and rigorously modeling the 

fuel-economy technology choice and cost-effectiveness considerations, for 

various GHG emission levels. Where possible, one reasonable design approach 
Appendix D-3 Paul Leiby Review 

might be to assume that other vehicle attributes are essentially held relatively 

constant, for each vehicle size and type. 

o Overall, the model documentation suggests that model developers may be hopeful 

of doing too much soon, with many (over 10) stated intentions for future 

extensions. Better and sounder results may follow from strategically limiting the 

model scope, carefully testing the model (in full, with real datasets), and then 

selectively adding features over time. 

o One feature of this model approach is its comparative analytical simplicity but 

heavy reliance on specialized data inputs (discussed further in Item 2 below). 

This should be viewed as a model strength: its contribution need not rely on 

analytical sophistication, but also on the coherent application of good quality, 

widely reviewed data. 

 Two major methodological points: 

o In any model, particularly any model of markets with social externalities and 

government intervention, it is essential to be very explicit about whose behavior 

and objectives are being modeled. Otherwise there is danger that nobody is really 

being described, or that we might impute particular knowledge and incentives to 

market actors who actually have neither. Naturally a model can be both 

normative, saying what should be done optimally, or descriptive, saying what we 

think will be done by some actors in certain circumstances even if it is not clearly 

optimal. And it can apply to what would or should best be done for different 

agents: vehicle consumers, manufacturers, or the government/society as a whole. 

I am a little unclear about whose behavior is being modeled in the succession of 

technology decisions made. It appears the intent is to model market behavior of 

competitive vehicle manufacturers facing cost-minimizing consumers and a firmwide emission constraint. But the objective of such a firm is not explicitly stated, 

and the solution rules are not clearly mapped to that objective. 

 In this matter it seems that the Volpe Model has set a good example by 

succinctly and specifically stating up-front whose behavior is being 

modeled: “The system first estimates how manufacturers might respond to 

a given CAFE scenario, and from that the system estimates what impact 

that response will have on fuel consumption, emissions, and economic 

externalities.” [P. 1, 

http://www.nhtsa.gov/staticfiles/DOT/NHTSA/Traffic%20Injury%20Cont

rol/Articles/Associated%20Files/811112.pdf] 

 Would a similar description not also apply to the EPA GHG model? 

o Given this idea of modeling the behavior of particular actors, e.g. manufacturers, 

in mind, the objectives of the actors should be reflected in the solution method or 

optimization condition. Bearing this in mind, there are some concerns with each 

of the two TARFs proposed as technology-dispatching figures of merit. 

 The “EffectiveCost” TARF is essentially the cost of each technology net 

of its discounted lifetime fuel savings (omitting the problematic “FEE” 

component, which seems mis-specified). Arguably, minimizing this 

would be a correct objective of new-vehicle consumers who discount fuel 
Appendix D-4 Paul Leiby Review 

savings in the same way and given no change in non-cost vehicle 

attributes. This could also be the objective of competitive firms acting on 

behalf of prospective consumers. In a mixed integer program these costs 

would be minimized subject to meeting the emission standard, and the 

algorithm would choose the least cost combination of technologies. The 

possible problem is that the EPA GHG Model algorithm sequentially 

dispatches new technologies in order of EffectiveCost, but without regard 

to their effectiveness in reducing GHGs. Some technologies with low netcost could do little for GHG reduction. In the limit a low EffectiveCost 

technology, say using a high-GHG alternative fuel could even increase 

GHGs (FFVs with coal-fired corn-ethanol?). Regardless, there is no 

assurance that the suite of technologies finally assembled to reach the 

GHG standard in this way would be the low-cost suite. The authors may 

wish to consider when they recommend that the first, EffectiveCost 

TARF, is appropriate. 

 The “CostEff” TARF on the other hand leads to an algorithm sensitive to 

both cost and cost-effectiveness for GHG reductions. Such a cost-benefit 

ratio can lead to optimal selection rules for packing (knapsack or budget) 

problems. But some confusing terms are included in the TARF, most 

notably the non-standard way in which VMT is discounted for the 

purposes of this TARF (See equation top of page 11, line 1). The 

inclusion of “IR” (“the annual increase in the value of CO2”) in the 

discount factor is done without explanation or justification. While the 

term IR is never really defined (is it meant to be the growth rate in GHG 

damages, abatement cost, or a CO2 tax?). It inclusion seems to conflate 

considerations of social benefit (value of GHG avoidance over time with 

cost (of technologies). The vehicle manufacturer’s cost of GHG 

avoidance is already embodied in the TARF numerator. The denominator 

should perhaps only reflect the quantity of GHGs avoided. As currently 

written, this CostEff TARF would not seem to be a consideration for 

vehicle manufacturers whose objective is to produce a new-car fleet 

meeting consumer needs and a GHG emission standard at least cost. What 

objective was intended with this hybrid aspect of the TARF? 

o There are other important methodological points to raise, that are discussed below 

in Section 3 on conceptual algorithms. 

o At this point, please allow an extended comment on the model documentation. 

Clearly it is in draft form only, and there would be much benefit from improving 

and clarifying it. This is not simply a matter of fastidiousness, but is an essential 

aspect of making the intellectual case for this model. As it stands, understanding 

the model was much more work than need be. Some specific suggestions are: 

 Restructure the presentation, perhaps following the pattern of a journal 

article. (E.g., begin with stated purpose and background. Place this 

model in the constellation of related models and indicate what is different 

and why. Describe approach, data sources. Sample results.) 
Appendix D-5 Paul Leiby Review 

 Bringing description of the “Core Program” and what the model does 

toward the front. 

 Clarify and condense the model description. Classically, this would 

involve: 

 State model objective (typically stating what is maximized, 

minimized, or what final solution condition is sought) 

 State model constraints 

 State and discriminate between principle decision variables, 

exogenous inputs, parameters, and internally calculated results. 

(This is not done in the variable list of Appendix 6, which also is 

incomplete. It omits AIE, PF, CAP, TCO2, IncrementalCost, 

TechCost, TARF, VMT, SurvivalFraction, AnnualMilesDriven, 

Leakrate, RefLeakage). 

 State the solution algorithm and termination condition 

 Rigorous use of notation. Currently, for example, the subscript i usually 

refers to “year” (eqns on page 10 and 11) but sometimes indexes 

technology (eqns at line 10 on p.12). 

 Use consistent variable names. For example, on pp. 16 and 17, it appears 

that the same variable is called “ModelSales”, “Sales,”, and “Annual 

Sales.” 

 Clarify subscripts and carefully apply them. The principle subscripts that 

seem to apply are: 

 t: technology number in sequence for each vehicle type 

 i: actually vehicle age, which is to be distinguished from year 

 y: year (which indexes, eg. fuel prices) 

 v: vehicle type 

 m: ma nufacturer 

 For example, equation at bottom of p. 12 is missing subscripts on 

AIE and RIE (presumably t), while GWP in that equation is 

indexed by technology t yet elsewhere (e.g. middle of page 11) it is 

not. 

 Carefully state units. Physical equations cannot be fully understood 

without a statement of the dimensions. For example, the equation in the 

middle of page 11 can be more readily understood if “Leakrate” is known 

to be in [g-GHG/yr], not [g-GHG/mi]. 

o Overall, the authors might wish to look at the documentation of the NHTSA 

Volpe model as a helpful template. 

 That documentation is actually reasonably compact (35 pp plus an 

extended guide to operation). 
Appendix D-6 Paul Leiby Review 

 It gives an excellent, succinct prose summary of what the model does in 

the first 3 pages (1-3), and much of the wording might be applicable to the 

EPA model. 

 It clearly states what is being modeled: 

 There is a flow chart and a technology sequencing flow chart 

 Equations are then presented in orderly manner with consistent notation 

and subscripting. 

2) Comments on: The appropriateness and completeness of the contents of the sample 

input files. (EPA staff are not seeking comment on the particular values of the contents 

of the input files, which are samples only.) 

 First, an overall point on data. While the instructions urge reviewers to not consider the 

particular values of sample data, it must be born in mind that models are essentially 

datasets, the equations which link the data, and the algorithms for achieving the solution 

of those equations. In this case the model equations (in the documentation) are 

reasonably straightforward, although the algorithm for their solution is somewhat opaque 

(not explicitly stated and embedded within a compiled module). Assuming a reliable 

solution algorithm (something hard to test in this review and with limited data), model 

quality will then depend strongly on the quality of model data. This is particularly worth 

mentioning because many of the data needed for this model are not readily available from 

established sources. The model calls for detailed, specialized, knowledge about vehicle 

technologies, their costs, incremental contributions and interactions, their availability 

over time and across vehicle types, and the data-providers must determine the sequence 

of technology application within each vehicle type. Ultimately, this dataset is likely to be 

the most valuable and significant component of this model. Particularly if it becomes 

publicly available, and serves as a standard. Thus the data issues should not be 

minimized. 

 In all data input files, it would help minimize errors if units were specified. Kilograms or 

grams, etc. The “Fuel” datasheet does not indicate the unit for price ($/gge, in nominal 

$?. What are the units for electricity?) 

 The “Data Validation” capability and error report is a very useful feature. Ultimately the 

modelers may wish to error check almost all inputs for acceptable range, if that is not 

already done. 

2a) The elements of the Market input file, Appendix 1, which characterize the vehicle 

fleet; 

 This file describes vehicle sales by manufacturer and vehicle type, and provides the 

attributes of those vehicle types. 

 No specific comments at this time. 

2b) The elements of the Technology input file, in Appendix 2, that constrain the 

application of technology; 
Appendix D-7 Paul Leiby Review 

 As discussed above, this could be said to be the heart of the model. It requires both 

detailed technological knowledge and considerable judgment about the sequence, timing 

and impact of each technology. 

o It may be worth a special task just considering what range of technology attributes 

can reasonably be specified, even by a technology or industry expert. 

o The possible strong-sensitivity to data specification may also call for formal 

method of risk or sensitivity analysis, given limits on the ability to refine the data. 

 How are technology interdependencies across vehicle types represented? Given 

outsourcing and the cost reductions from component sharing, would the application of a 

technology for one vehicle type make it more likely to be applied to another vehicle type? 

I could not discern how such considerations are represented in the data, and reflected in 

the solution algorithm, if they are. 

 The data challenge is even greater if the stated goal of representing technological learning 

is pursued. While ultimately technological progress (through autonomous gains from 

R&D, scale economies and learning-by-doing) should probably be acknowledged in a 

later model version, benchmarking that progress is never easy. Moreover, technological 

learning and progress will be a function not of choices for each Vehicle Type (as the 

spreadsheet organizations suggests), but of industry-wide developments across vehicle 

types and manufacturers. 

o In our models on new vehicle technology introduction, we have found it useful to 

distinguish between 3 types of technological progress: autonomous progress over 

time due to R&D; progress or cost reduction due to production scale (units 

produced per plant); and progress from Learning By Doing (LBD). All three of 

these play a role, but the proper benchmarking of each is quite challenging. I 

agree learning should be approached, but cautiously because its specification and 

parameterization can have such a pronounced effect on model results. 

 Spot-checking these entries, I did not see any items associated with changing vehicle size 

and weight. This may be a design choice rather than happenstance for the sample data: 

technologies that substantially change the vehicle design and hedonic attributes for the 

consumer would call for a more rigorous assessment of net-value to the consumer, and a 

potential re-statement of objective (TARF sequencing rule). 

2c) Scenario input file, definition of the standard and economic conditions (Appendix 

3) 

2d) The elements of the Fuels input file, Appendix 4 

 This list does not yet reflect biofuels or renewable fuels, which are a growing 

consideration, in no small part due to recent law and EPA RFSs. 

 Some provision may be needed for the variable energy and GHG content of gasoline, 

as the ethanol content varies over time. 

 Provision may also be needed for E85, and the uncertain fraction of E85 use by FFVs. 

 The net fuel economy and emissions by PHEVs remains an area of continued study. 

EPA is well aware that fuel use by fuel type and resulting emissions depend on PHEV 

design (AER), consumer use patterns, time of recharging, and the fuel used for 

regional grid generation. Nonetheless, some simplified representation of the 
Appendix D-8 Paul Leiby Review 

alternative PHEV designs will be needed soon. I was unable to ascertain what 

progress EPA has made in this area. 

2e) The reference data contained in Appendix 5. (Implied flexibilities and constraints 

of the model) 

 No specific comments 

3) The accuracy and appropriateness of the model's conceptual algorithms and equations 

for technology application and calculation of compliance; 

 Equations for technology application: 

o The sequence of technology application, and timing and extent of application, for 

each vehicle type, is exogenous. 

o Modelers acknowledge that “This approach puts some onus on the user to develop 

a reasonable sequence of technologies.” As noted, the onus may in fact be quite 

substantial. Therefore, it is helpful that the model “produces information which 

helps the user determine when a particular technology or bundle of technologies 

might be ‘out of order.’” [p. 7] Any such capability to assist the user with stage-1 

exogenous technology sequencing for individual vehicle types is worthy of further 

development and greater prominence in the documentation and model. 

o The Volpe model seems to currently offer more facility for specifying the 

structured sequences introduction of technologies or groups of technologies. The 

EPA GHG Modelers may also wish to develop some tools that make it easier for 

users to group and sequence technologies, perhaps even with logical diagrams that 

map to or from the Technology.xls dataset. This would help experts represent 

their best judgement about technologies can or would be applied. 

o While this model allows for substantial technological detail, there will always 

arise further, potentially important, complexities. In this review I could not 

determined the degree to which the model can account for cross-vehicle-type, or 

cross-manufacturer, interactions in the selection and sequencing of technologies. 

For example, various forms of hybridization are mentioned as technology options. 

We already see that one manufacturer, Toyota, develops a hybridization 

technology for one vehicle it quickly spread to other vehicles from that 

manufacturer, and that same technology is also sourced to other manufacturers 

(Nissan). Can this be represented in some way? 

o P. 17 says: “Finally, the model determines the order in which technology 

packages are added to vehicles. The model first compares the TARFs 

corresponding to technology package 1 on all of the different vehicle types in the 

fleet and chooses the combination with the lowest TARF.” 

 What does “combination” mean here? I understand it to mean the model 

chooses a combination (pair) of particular vehicle v and technology step t

(advancing from t-1 to t). 

o Technical points on the TARF-based rules for technology application (Equations 

p. 14): 

 As mentioned, net cost (“EffCost”) alone would not seem to be adequate 

for sequencing GHG-reduction technologies 
Appendix D-9 Paul Leiby Review 

 The inclusion of a FEE for non-compliance has some issues (admittedly, 

the Volpe Model does something like this as well, but the justification is 

not compelling): 

 It embeds the cost of non-compliance in an algorithm that ends 

only with compliance. Hence the fee should ultimately be zero. Is 

the intent here to employ some sort of penalty-function based 

algorithm for constrained optimization? 

 “Non-compliance” is a manufacturer-wide condition, and cannot 

be associated with a specific individual vehicle or technology 

(Note: I believe the TARF measures should be subscripted with 

m,v, and t, to highlight that they are specific at that level). 

 As written, the FEE is applied to the change in fuel economy 

(mi/gge, MPG) for that particular technology step. This is not a 

measure of non-compliance, and its essential effect is to exaggerate 

the relative importance of fuel savings. Note that the fuel-savings 

term is proportional to (FCt-1 - FC t) while the Fee term is 

proportional to (1/FCt – 1/FC t-1), essentially a monotonic nonlinear transformation of fuel-savings. So even though there will be 

compliance an no fee, the effect will be to boost the weighting of 

fuel savings in a non-linear way. 

 A maintained assumption is that fuel economy technology will not alter 

sales volume or share. But does or could vehicle sales volume influence 

the choice of technology introduction? I only noted “Sales” being 

referenced in the post-processing calculations, and it is used in the tests for 

compliance. But sales is not a consideration in the TARF for a vehicletechnology pair, nor in the terms leading up to it, so the technology 

sequencing is based entirely on per-vehicle cost analysis. This approach is 

taken in other models and is not unreasonable. But if technology learning 

or scale economies matter, for example, the choice of which vehicle to 

apply the next technology to could be related to the sales volume of 

particular vehicle-types. 

 As mentioned, the non-standard adjustment of VMT discounting in the 

denominator of the CostEff TARF should either be eliminated or more 

explicitly and rigorously motivated. As it stands it seems to either mix 

social benefits of GHG reduction with the manufacturer’s objective of 

meeting the emission standard. 

o On p. 13, the equation for Fuel Savings (FS) seems to be in error. Fuel price (FP) 

is divided by i, which denotes the age of the vehicle (year after its production). Is 

this simply a typographical error and a discount factor was intended (e.g. 

(1+DR)i

?) 

 In all cases where the lifetime value of fuel savings in considered, the 

challenge is to be clear about whose valuation of fuel savings is being 

calculated. It is widely observed that consumers, when making new 

vehicle purchase, may “undervalue” fuel savings either with a higher 

discount rate or a short planning period than actual vehicle operating life. 

I understand that these issues are probably behind the formulation used 
Appendix D-10 Paul Leiby Review 

here, but it would help to be more explicit. If manufacturer decisions are 

being modeled, the relevant question seems to be “How many years of 

discounted fuel savings would the manufacturer assume it will be able to 

recover from the consumer through the vehicle sale price?” 

 Calculation of compliance to Attribute-based standards: 

o An overarching feature of the methodology is that progress in reducing 

GHGs/fuel-use occurs by advancing drivetrain technology and other attributes 

largely transparent to the consumer. Technologies are sequenced based pervehicle figures of merit, assuming no impact on vehicle designs (apart from fuel 

use technology) and constant vehicle sales shares. One issue to consider is 

whether these assumptions of unchanged vehicle and unchanged sales mix 

become less defensible for attribute standards like the footprint standard. 

o On page 7, equation for the logistic-based footprint, there appears to be a sign 

error in the denominator (should be 1+exp((x-C)/D) not 1-exp((x-C)/D)). This is 

likely a typo in the documentation alone. 

 Calculation of compliance to possible market-based standards 

o No discussion or provision for market-based (permit trading) standards is yet 

made. This should at least be acknowledged. 

o One strategy for doing more flexible standards would be to simply merge the 

datasets and technology-sequence stage for all manufacturers and vehicle types in 

a trading group. However, this would not provide information about potential 

permit prices and burdens across manufacturers. 

4) The congruence between the conceptual methodologies and the program execution 

(examining the results with good engineering judgment) 

 This is difficult to assess and a careful validation of this model’s execution would require 

further examination. The results appear generally reasonable, but that is a weak test. 

 I was only able to experiment with cases for one design cycle. The longer-term cases 

involving multiple design cycles are more challenging. It has been noted the model 

solves for design cycles independently of one another. So it would be worthwhile to test 

what this implies for the sequence of technologies used from one cycle to the next. 

 One observation is that the inclusion of the non-compliance FEE does affect the model 

solution and choice of technologies. As mentioned above, the theoretical justification for 

this is not well formed, given that all manufacturers are typically assumed to end in 

compliance. However, I did not that the impact of including the FEE is modest, only 

changing per-vehicle costs by a few dollars. However, for at least one manufacturer (#9) 

the cost and technology sequence changes significantly. I am not sure this is a desirable 

outcome. 

 Also, simple tests with the sample dataset show a relative insensitivity to the choice of 

TARF. This was surprising, and needs more investigation. 

5) Clarity, completeness and accuracy of the calculations in the Benefits Calculations output 

file, in which costs and benefits are calculated; 

 This system produces a large number of useful side calculations. 

 Again, further investigation is necessary to investigate their accuracy. 
Appendix D-11 Paul Leiby Review 

 Overall, a careful independent validation of the two phases of this model’s execution 

(manufacturer compliance simulation and effects calculation) would be well worthwhile. 

The code for compliance simulation is compiled and not visible. Working through the 

logic in the post-processing calculations of the BenefitsCalculation spreadsheet would 

take a bit of time. But it would be worthwhile. Overall a useful validation effort could 

probably be complete in about a week of focused attention. 

6) Clarity, completeness, and accuracy of the model's visualization output, in which the 

technology application is displayed; and 

 The XML format for data transfer and display is a very good design choice, allowing 

flexibility, modern data-exchange capability, ready output to internet, and easy extension 

of the report. 

 This display in the visualization output is useful overall, but it seems more oriented 

toward “expert users” who are willing to wade through details to find understanding and 

the information they need. 

o TechPack are reference by number only, but perhaps could easily be labeled with 

the full name or 4-character abbreviation, or cross-reference by hyperlink to a 

description of the technology. 

o Additionally, hyperlinks could be added that would allow the user to easily jump 

to the table for a particular manufacturer or vehicle type. 

 It would be very helpful to have some graphical summaries of the input and output 

results. 

 All output files should embed clear documentation on the inputs used. E.g. 

o The .log file does list names of the 4 input files, which is essential. 

o The “Visualization Output” file does not (yet) report the input files (but the 

information could be retrieve from the XML file). 

7) Recommendations for any functionalities beyond what we have described as "future 

work." 

 Clearly defined improvements that can be readily made based on data or literature reasonably 

available to EPA 

o First I note that there were multiple references to “future work.” It may be helpful for 

EPA to construct a list of these prospective improvements, and establish priorities and 

a staged, progressive approach for revision. Specific releases of the model with 

carefully specified functionality will allow prospective users at EPA and elsewhere be 

clear about what the model is and can do at any point in time. 

o While the model has a number of valuable aids to execution and reporting (input 

validation, automated generation of run logs, XML data, and “Visualization” tables 

for web/browser display), more could be done here to improve usability and provide 

greater insight about each case run. Comparatively simple revisions and extensions 

to the operational procedures and output could be well worthwhile. 

 Provision for side-by-side case comparisons, reporting or graphing difference. 

 Case management and logging facilities. 
Appendix D-12 Paul Leiby Review 

 Currently the system labels every file with generic name concatenated 

to a time-date stamp. Very quickly a directory can be cluttered with 

cryptically named log, xml, htm files. 

 A case archiving facility, that compresses all input and output files to 

document the case, might be useful 

 The ability to specify a CaseName in the Scenario file, that then 

becomes part of each output file, would also be helpful. 

 When the VGHG.exe file reads a scenario file, it does not record, or at 

least display, the name of the file read. It is easy to forget which case 

was read if you step away, or are doing many cases. 

 Relatedly, the purpose of the VGHG.exe’s separate menu options is 

not yet clear to me. 

o It seems that once a scenario and the associated datafiles are 

read, execution would be the logical next step. The scrollable 

tables from data input are really too constrained a view to 

allow useful review or verification of the data. 

o Once the case is run, it seems “Save” to XML might be 

automatic, otherwise one is limited to the text-based log files, 

that omit summary information. “Saving” seems needed for 

Visualization and Benefits Calculation in the spreadsheet. 

o So perhaps VGHG.exe might load-run-save in one step, 

although I may be missing something important. 

 Graphical capabilities [more thought required here about exactly what graphs 

would be most useful. But there are many data in the tables, and they are not 

simple to process mentally.] 

 Improvements that are more exploratory. 

o Extension to accommodate flexible/market-based emission or fuel-economy 

regulations. 

 Permit trading extensions, constructed by pooling selected vehicle 

types/classes, and/or manufacturers, during the compliance phase of the 

analysis. 

 Ex post calculation of implied permit prices based on marginal costs of 

compliance (measured by the cost/GHG reduction of the final technology 

pack applied). 

 Ex post calculation of economic implications for individual manufacturers, by 

comparing results with and without trading/pooling, and accounting for the 

implied costs and revenues from permit exchanges between manufacturers. 

o Extensions to consider endogenous (standards-induced) changes in vehicle attributes. 

These are a higher challenge, but would be very valuable for an improved 

understanding of the market responses to regulations. 

 Endogenous changes in sales volume/mix 

 Endogenous changes in vehicle size/footprint 
Appendix D-13 Paul Leiby Review 
APPENDIX E 

JONATHAN RUBIN’S REVIEW DOCUMENT 
Review: 

Vehicle Greenhouse Gas Emission (VGHG) Emissions Cost and Compliance Model 

Jonathan Rubin 

23 September 2009 

Dear Sir/Madam: 

I would like to congratulate the EPA for undertaking to build this tool which will be very useful 

for possible regulatory compliance and anticipated and unanticipated policy analyses. The 

construction of such a tool requires extensive expertise, professional judgment, necessary 

compromises and assumptions. The validity of the output will of course depend on these factors 

as well as the data available to populate the model. 

My comments are based on my review of the materials provided to me by Southwest Research 

Institute: the EPA vehicle GHG Emission Cost and Compliance Model Description and 

associated attachments and appendices and the VGHG model and the associated spreadsheets. 

These comments reflect my understanding of EPA’s possible use for this model for regulatory 

compliance as well as use by external researchers and policy analysts who may use the model for 

analyses of state and regional policies. 

My comments below respond to the particular questions posed in the transmittal letter from 

Southwest Research Institute. 

Overall Approach to the specified modeling purpose and the particular methodologies 

chosen to achieve that purpose 

The authors have clearly put in a great deal of work on this challenging project and should be 

commended for an excellent start. That said, more effort and thought needs to go into what I call 

the accounting stance. On page 2, line 42-43 (p. 2, l. 42-3) the documentation states that “The 

primary cost of the GHG emission control is the cost of the added technology compared to the 

baseline.” My question is: “cost to whom?” Costs to consumers will differ from costs to society 

or costs to manufacturers. At times, the documentation reads as though these are costs to 

manufacturers – since CAFE fines are considered; other times the costs seem to be towards 

consumers or society. These accounting stances will differ for several reasons: 1) private and 

social discount rates differ, 2) social and private risk differs (on average technology performs as 

well as expected, but not for each vehicle), 3) subsidies to purchase plug-in vehicles or other 

advanced technology vehicles drive a wedge between private and social costs, 4) subsidies to 

biofuels and electricity at the state level (exemption for some or all road-use tax) mean that 

consumer costs are not equal to full resource costs. Clarifying the accounting stance is a high 

priority, because many further calculations rely on its clear definition. 

Since the potentially regulated agents are vehicle manufacturers, my recommendation is to 

define costs as the costs to manufacturers of incremental technology and vehicle re-design costs. 

The net costs to manufacturers are equivalent to the incremental costs of fuel economy 
technology less any increase in retail prices that manufacturers can charge for more fuel efficient 

vehicles. This should be equal to some portion of the expected fuel savings plus any changes in 

the hedonic value of vehicles due to changes in vehicle performance, noise, size, and refueling 

time (more on this later). By separating out manufacturing costs more clearly from consumer 

valuation of vehicles, the presentation will be more transparent. This also will make clearer the 

distinctions between consumers’ rates of discount from manufacturers’ costs of capital from 

society’s rate of time preference. 

Additionally, I recommend that the net costs clearly incorporate and identify all subsidies (for 

electric or plug-in hybrid vehicles and alternative fuels) but display costs and benefits separately 

to private agents (manufacturers, consumers) and society. These will generally not be the same. 

For example, the benefit calculation spreadsheet “Externalities” adds together consumer money 

saved on fuel with savings from lower oil imports. I would be very surprised to learn that the 

assumptions of the discount rate or risk premium or both in the calculation of benefits of reduced 

crude oil imports are the same as consumers’ discount rates for expected future gasoline savings. 

2) The appropriateness and completeness of the contents of the sample input files. 

a) The elements of the Market input file, as shown in Appendix 1 of the model description, 

which characterize the vehicle fleet 

If the data are available, it would be useful to have the cross-price elasticities for makes and 

models or model segments such that mix-shift impacts could be taken into account as vehicle 

prices rise in response to additional technology packages. 

Some of the market data are interesting, but do not seem necessary. For example, what is the use 

of knowing a vehicle’s structure (e.g., unibody) or the maximum seating capacity? 

Does the market spreadsheet contain data for mid-size trucks, gross vehicle weight 8,500 -

10,000? If not, I would think it should, given that they are now covered under the revised light 

truck CAFE rules. 

b) The elements of the Technology input file, in Appendix 2, that constrain the application 

of technology 

Are the incremental costs shown in column X retail or wholesale? What do they assume about 

the volume of production? If I read the file correctly the incremental price for plug-in hybrid 

technology often has a low first cycle cap of 5%. Is the incremental cost of this technology 

consistent with its use on 5% of a market segment of a given manufacturer? It is important to 

clearly define the relationship between scale of use and incremental technology cost. The 

columns “a”, “Decay”, “seedV”, “kD”, and “cycle learning available” need further clarification. 

P. 2, l. 14 notes that the GHG target can be set as a function of vehicle footprint. The technology 

input file does not show an indication of how down-weighting and changes in footprints may be 
used to meet a set of given standards. This may not be able to be accomplished immediately 

given available data, but it should be considered as more experience with the footprint standards 

is gained from CAFE compliance. 

c) The definition of the standard and economic conditions in the Scenario input file, as 

shown in Appendix 3 

As per my earlier comments, I think there ought to be a place for 3 different discount rates: 

consumers, manufacturers and society. Similarly, their ought to be a places for payback periods 

for consumers and society. 

d) The elements of the Fuels input file, as shown in Appendix 4, which characterize the fuel 

types, properties, and prices 

It would be useful to reference the data sources for many/most of the data items. For example, 

energy density – please see EIA report XYZ. The value shown for gasoline, for example, at 

115,000 is different than that published by the USDOE, Transportation Energy Data Book v 27 

(Davis, Diegel, Boundy, 2008, Table B4), which shows a (lower heating) value of 115,400 

Btu/gallon. 

The units should also be displayed for all inputs. Again, using the gasoline example, being 

familiar with the data, it is clear that the unit of analysis is Btu/gallon (lower heating value). For 

other data, the units are less obvious. For electricity, the input file or the documentation, or both, 

should give the assumed conversions from kilowatts to energy density or motive energy such that 

users can adjust for different end-use efficiencies. Also for electricity, the assumed grid mix 

should be given with conversion rates such that users can make appropriate adjustments for 

different policy analyses. 

I do not see a statement indicating whether the fuel price data is in nominal or real dollars. 

I do not see a row for ethanol giving its energy density, mass, and density. I am assuming that 

fuel type “EL” is electricity. Also, should you not have at least two types of ethanol – corn and 

cellulosic – with different price paths? 

As I indicated in my earlier comments, I think it is important to explicitly note the role of 

subsidies when determining costs. Given this assertion, the fuels data file ought to explicitly note 

federal and state average subsidies (i.e., the federal blender’s tax credit and foregone state excise 

taxes) for ethanol and other alternative fuels. As I note below in 7) Extended Functionality, 

accounting for foregone taxes is a logical addition to the model, especially when considering 

plug-in electric hybrid vehicles. 

e) The reference data contained in Appendix 5 which are currently hard-coded into the 

model but, in the very near future, will be contained in a user controlled input file. 

The Exclusive Inputs spreadsheet anticipates E10 and E85. It would seem fairly straightforward 

to allow for other blends such as E15. The proportion of the ethanol that comes from cellulosic 
sources in each year should be accounted for such that upstream CO2 emissions can be properly 

credited, similarly for petrodiesel and biodiesel. 

3) The accuracy and appropriateness of the model's conceptual algorithms and equations 

for technology application and calculation of compliance; 

On p. 9, l. 40, the documentation states: “The core model then adds the effectivenesses and the 

costs of the technology addition until each manufacturer has met the standard or until all 

technology packages have been exhausted.” Given that existing law allows credit averaging 

across all vehicles sold by a manufacturer, this requires that compliance would be checked 

through an iterative routine. Please describe this routine including mechanisms to prevent 

cycling so that convergence is assured. 

p. 10. VMT is given by: . I believe this is this 

done by vehicle class (from the data file). The documentation should index the function with 

separate subscripts. 

VMT  SurvivalFraction * AnnualMilesDriven

p. 10. Discounted VMT. I have two issues with this calculation. The first is mechanical. Why 

  iFSD i i DR

DR

VMT VMT







1

2

1

,,

 does the numerator have the term 1+DR/2? Is the discount rate not 

understood to be the simple annual rate? (Also what do the indices D and FS represent?). 

Conceptually, however, I do not think this VMT should be discounted. Costs and benefits are 

appropriately discounted, but I think it is a mistake to discount a physical calculation. It blurs the 

distinction between consumer and society valuation of VMT and can lead to misleading outputs. 

This point is further emphasized by calculation of VMT for GHG calculations (p.11) 

  iCOD i i IRDR

IRDR

VMT VMT



 



1

2

1

,2,

, where VMT is enhanced by the rate in change in the value of 

CO2, IR. I strongly suggest that this equation be re-done to separate out measurement of 

physical units (VMT) from cost and value calculations. 

p. 11. RCO2 

 

 

 

LifetimeVMT

LifetimeLeakage GWP

IRDR

IRDR

VMT

GWP

IRDR

IRDR

LeakRate

VMT

GWP

RCO

years

Lifetime

i

i i

years

Lifetime

i

i i

years

Lifetime

i

iCOD

years

Lifetime

i

i

mig

 



























 































 

























)(

1

)(

1

)

1

,2,

)(

1

1

2

1

1

2

1

RefLeakage

/ )(2

I have two comments. First, it seems to me that, as with VMT, the numerator ought to be 

multiplied by the survival function. Second, as with VMT, the leakage rate ought not to be 
adjusted by DR and IR. Also, again, I do not understand the form of the adjustment – why 

multiply the numerator by 1+ (DR-IR)/2? Should not the GWP be indexed by i? 

p. 12. Determine the order of Technology Application. On the previous page the subscript i

represented “year” here it represents technology package. The use of subscripts should be unique 

throughout the documents. 

P. 12. Intermediate calculations for each vehicle type. It appears that the subscripts have changed 

again. CO2 is indexed by t and AIE, RIE are missing subscripts altogether. 

p. 13. Calculate the fuel consumption before and after technology additions. 





 















gC

gCO CD

CO FC

t

t

t

12

244

2

1

1

1

. Given that CD is in units of carbon, this equation looks unit-less 

(CO2/CO2). Where do gallons per mile units come in? 

P. 13, l. 18. In step iii, calculating fuel savings we see the following equation. 

t

PP

i

PP

i t

PP

i

PP

i i

FP FC

i

FP FC

i

FP FC

i

FP FCFS 



 



  



 



          1

2

2

1

1

1

1 1

2

2

1

1

1

First, why is FP divided by i? Second, where is the adjustment for vehicle age? How does this 

equation account for consumers’ choosing to drive more miles using one fuel v. another? 

(Consumer’s may want to maximize the time they spend in electric power mode.) Even if the 

data do not exist to parameterize the model yet, I suggest that the functionality be built in to 

allow for consumers’ choosing to use one fuel type or another. 

P. 20, l. 38-46. In calculating the impact of the reduced time required to refuel vehicles, I do not 

see a mention of the estimated driving that will occur using electricity in PHEVs. 

4) The congruence between the conceptual methodologies and the program execution; 

As suggested, I made changes to input values in the spreadsheets and re-ran the model. The 

changes as displayed in the benefits calculation spreadsheet were what I had qualitatively 

expected. 

5) Clarity, completeness and accuracy of the calculations in the Benefits Calculations 

output file, in which costs and benefits are calculated; 

Please see my comments in the beginning of the document. I believe that the benefits 

calculations should more clearly reflect benefits and costs to three different agents: 

manufacturers, consumers and the nation. 

Recognizing that the benefits data (Benefits Calculation workbook) is subject to change, it would 

be really useful to list the data sources for all inputs. For example, if the VMT data is coming 
from MOBILE6, the VMT_Lookup spreadsheet should clearly state MOBILE6 as its source and 

similarly for the other inputs and spreadsheets. 

Similar to the formula used to discount VMT, the spreadsheet “ExternalVMTCosts($)” discounts 

externalities using the formula:  

i DR

DR





1

2

1

. My question is why? Most commonly used discount 

factors are simple  

i 1 DR

1 annual rates. In some senses it does not really matter because the 

user can set the discount rate, but by using a non-standard discount rate this is likely to lead to 

unnecessary confusion. 

In the “Benefits Calculation” workbook, the worksheet, “Emissions_Fuel Conservation” shows 

upstream savings from NOx, VOC, CO, PM, and SOx. These emissions savings are all 

calculated based on upstream conventional gasoline emission savings. I would think that either: 

1) these should be based on a weighted average of gasoline, diesel, ethanol, and electricity 

upstream emissions, or 2) the gallons saved should have been weighted gallons. I cannot readily 

determine if the saved gasoline gallons are weighed by the proportion of gasoline, electricity, 

ethanol and diesel (and the weights would be emission-gallon weights.) This needs to be clarified 

or corrected. 

In the “Benefits Calculation” workbook, the worksheet, “ExternalVMTcosts($)” displays the 

discount factor applied to future costs as the common discount factor used throughout the model. 

As I earlier suggest, society’s rate of discount for accidents costs (human life) are not likely to be 

the same as consumers’ rate of discounting future gasoline savings. These should be separate 

inputs. 

In the “Benefits Calculation” workbook, the worksheet, “DownstreamCosts($)”, the units on 

CO2 are shown as “$/ton”. I believe that the label is missing the modifier, “metric”. 

In the “Benefits Calculation” workbook, the worksheet, “UpstreamCosts($)” shows benefits 

determined for CO, VOC, NOx, SO2, PM2.5 all based on emission factors for conventional 

gasoline. As per my earlier comment, I think these ought to use separate emission factors for 

each fuel. 

In the “Benefits Calculation” workbook, the worksheet, “All Costs” shows costs in aggregate for 

the nation. It would be useful to also display the average, per vehicle costs. 

6) Clarity, completeness, and accuracy of the model's visualization output, in which the 

technology application is displayed; and 

In displaying the results Average Incremental Costs, please round to the nearest dollar; showing 

two digits to the right of the decimal point gives a false sense of precision and makes the output 

harder to read. 
7) Recommendations for any functionalities beyond what we have described as "future 

work." 

The model (VGHG) window box should be made larger – perhaps fill the screen. It is really too 

small to perform step 4 in running the model (i.e., Verify that the correct data has been populated 

into the VGHG model). There is also no side-to-side scroll to see the whole data field. 

Given the renewable and advanced biofuel requirement in the Energy Independence and Security 

Act of 2007, it would seem that the model ought to have data input fields to allow users to 

specify the quantities (or proportions of total fuel) of ethanol and biodiesel used in each year. 

Moreover, the proportion of biofuels which come from cellulosic sources should also be able to 

be specified. Accordingly, the GHG emission accounting framework will need to capture that 

proportion of the reductions due to changes in vehicles and that proportion due to changes in 

fuels. In anticipation of future developments in the biofuels market, it may be worthwhile to 

build in placeholder functionality to account for domestic versus imported biofuels or biofuel 

feedstocks. 

The model would be significantly enhanced if it were made probabilistic. Given that input data 

contains underlying uncertainty (What is the actual cost of a given technology? What will be the 

price of gasoline in 5 years?), the model should be made to run hundreds or thousands of times 

using Monte Carlo analysis on some of the key input data to generate a distribution of outcomes. 

Even if this is not done in the near term, having the output columns show results for “high and 

low” cost/interest rate scenarios would be convenient. It would save having to run the model 

multiple times and pulling the results in to some other summary worksheet. 

The documentation notes (p. 2) that the primary cost of the GHG emission control is the cost of 

the added technology as compared to the baseline. I do not think this is a valid presumption for 

large changes in GHG emission control. The NRC’s study on CAFE assumed that vehicles were 

hedonically equivalent. Given the likely wide-spread adoption of diesel technology and, quite 

possibly, plug-in hybrid vehicles (PHEVs), vehicle driving experiences are not likely to be the 

same. Quite possibly, PHEVs will provide a superior level of driving satisfaction. If vehicle 

manufacturers downsize or reduce performance (acceleration) to meet compliance, vehicle 

satisfaction could diminish. I do not have a good suggestion on how to adjust for these possible 

hedonic costs or benefits. Perhaps the model could incorporate placeholder equations that would 

allow users to specify hedonic gains and losses. Nonetheless, the model documentation should be 

forthright in acknowledging this limitation. 

The model should provide for an estimate of the likely gasoline excise tax implications for 

different levels of GHG emission reduction. Particularly useful would be to present this 

information in the context of different compliance strategies. For example, with tax credits for 

PHEVs, and no change in federal gasoline excise tax policy, the revenue losses could be 

significant. This functionality could be very useful for policymakers. 

As described in the documentation, the model development foresees an increased ability for 

users to change input assumptions. Changes to these assumptions may have significant impacts 

on costs and GHG emission reductions. It would be useful for the Model Reference Guide 
accompanying this model to describe in qualitative terms the impact of or assumptions behind 

choosing to adjust certain parameters. For example, the user manual could indicate that lowering 

the years of payback for technology would be consistent with a view that consumers only value 

the first years of fuel economy gains or place little or no value on GHG emission reduction that 

occur near the end of a vehicle’s lifetime. If practicable, it would also be useful to point out 

inconsistent choices. 

It would be very useful to have the model output be available in units that are used 

internationally – grams CO2 /kilometer or grams CO2 equivalent/KM. 

Clearly falling into the work for the future, would be to have a time profile of upstream CO2

emissions for conventional gasoline and diesel reflecting regional or national low carbon fuel 

standards. 
EPA Responses to Peer Review Comments

A. Comments by John German 

Concepts and Methodologies Upon Which the Model Relies:

(A) Model structure 

The model is an accounting model. This is neither good nor bad. The advantage is that it avoids 

overmodeling and embedding errors in the model itself. The disadvantage is that the factors 

affecting the results are all inputs to the model. This requires a great deal more sophistication 

and work by anyone using the model to prepare the inputs properly. It will also make it more 

difficult for anyone outside EPA to use the model, unless EPA is willing to provide the detailed 

inputs to other users. 

With this type of model, it is essential that EPA release the data in the Technology and 

Economics input files and discuss them in the Notice of Proposed Rulemaking, as the real 

analyses and modeling are in these input files. But as long as this is done, the overall model 

construction is fine. 

Response: EPA will publish all the input files in their entirety as part of its proposed GHG 

emission rule for model year 2012-2016 cars and light trucks (hereafter referred to as the “EPA 

vehicle GHG proposal” or “proposed vehicle GHG standards”). 

(C) Redesign cycles 

I completely agree with EPA’s logic in creating a model based upon vehicle redesign cycles. As 

EPA states, adding technologies incrementally to each vehicle model by model year does not add 

value to the model results. Using redesign cycles also allows for simplification of the fleet. It is 

impossible to predict the direction of vehicle redesigns for each manufacturer. It is just as 

accurate to assume, for example, that future mid-size cars from each manufacturer will be 

identical; as it is to assume that current differences in mid-size cars from one manufacturer to the 

next will be continued into the future. As a recent example, Honda left their compact crossover, 

the CR-V, virtually unchanged in size during the latest redesign. However, Toyota chose to 

lengthen their compact crossover, the RAV4, by 14” during its latest redesign. It is pointless to 

try to predict differences in vehicles from different manufacturers in the future and it is pointless 

to try to predict the exact year when redesigns will occur. This is a welcome simplification. 

Another advantage of using redesign cycles is that GHG standards for interim model years can 

only be set, reasonably, as a straight line (or a constant % decrease) between the baseline year 

and the end of the redesign cycle. This is appropriate. Constant yearly % reductions provide a 

consistent signal to manufacturers for investment decisions. 

However, there is one potential problem with using redesign cycles. It masks the investment 

needed to bring new technology to the market. The auto industry is extremely capitol intensive. 

Initial investment in a new technology is expensive, both for tooling and the resources necessary 
to assess (and fix) system-level effects and effects on reliability, durability, safety, and 

manufacturing. Redesign cycles tend to assess only the costs for high-volume production and 

skip over the high initial costs. Care must be taken to properly assess costs in the inputs. 

Response: EPA agrees that capital investment is an important consideration when assessing the 

feasibility of GHG standards. EPA intended to include explicit accounting for and limitations in 

capital investment when developing OMEGA. However, this proved to be a difficult task and 

we decided to leave this until later versions of the model. The user can track capital investment 

outside of the model based on the types and levels of technology used. The user can also adjust 

costs upward during the interim years of a redesign cycle to represent the higher costs which are 

typical during technology introduction. EPA did this as part of its cost analysis for its recently 

proposed rule for the control of GHG emissions from cars and light trucks. 

(C) Leadtime 

The model handles leadtime issues far too simplistically. This was also a problem with the 

Volpe model. Leadtime is one of the most important issues in setting standards and one of the 

most difficult issues to assess properly. Thus, it is disappointing to see both NHTSA and EPA 

provide so little attention to the issue. 

The only leadtime constraints in the draft model are industry-wide caps on the maximum 

technology penetration by redesign cycle and vehicle type. There are several problems with this 

approach: 

 The largest problem is that it is inappropriate to treat all manufacturers the same. A 

manufacturer that has already invested in a particular technology in the baseline year will be 

capable of higher penetration rates than a manufacture that has never used the technology 

before – and also of producing the technology at lower cost. An obvious example is hybrid 

vehicles. Over 10% of Toyota’s vehicles already have hybrid systems on them. After 

introduction of the CR-Z next year, Honda should also have more than 10% hybrids. Due to 

their experience and head start with hybrids, both manufacturers will be capable of much 

higher penetration rates than most other manufacturers. They are also further along the 

learning curve, so their costs will be lower. Similar situations exist with most technologies. 

 Another problem is that costs will vary from manufacturer to manufacturer. As noted in my 

comments on redesign cycles, above, there are large upfront costs when a manufacturer 

introduces a new technology. For example, Toyota has already amortized large R&D and 

system-level costs for hybrid vehicles. They will be able to produce hybrids cheaper than 

manufacturers that are just starting to offer hybrids. The point is that the “Initial Incremental 

Cost” in the Technology Input File should not be applied to all manufacturers at the same 

time, but rather to each manufacturer at the time they first introduce a new technology. 

 The third problem is that there is no such thing as a hard cap on technology penetration rates. 

There is a tradeoff that exists between cost and leadtime. Technology introduction can be 

accelerated by increasing investment – and cost and risk. 

Long-Term Recommendation – The best way to handle leadtime constraints and technology 

penetration is to assess capitol investments by manufacturer. This would require adding a new 

section on capitol expenditures. In addition to assessing the cost of each technology, the capitol 
expenditure would also be assessed. Ideally, there would be two components to the capitol 

expenditure assessment for each technology, one for R&D expenditures for the first 

implementation of the technology and one for the capitol investment needed to add the 

technology to additional models. However, the second is more important. Each manufacturer 

would be assigned a total capitol expenditure budget for the redesign cycle and technologies 

could only be added up to the point where the sum of the technology capitol expenditures did not 

exceed the manufacturer cap. Alternatively, some increase in technology penetration over the 

cap could be allowed, but only if coupled with increasing technology costs. This would 

appropriately handle leadtime constraints and technology penetration rates. 

Short-Term Recommendation – The long-term recommendation would require a lot of new work 

and is clearly not feasible in the timeframe needed for EPA’s rulemaking. As a short-term fix, 

instead of using industry-wide caps on maximum penetration for each technology, EPA should: 

(d) Set caps on the maximum increase permitted per year. This would be applied to each 

manufacturers’ individual technology penetration; and 

(e) Establish the model year for initial introduction. For technology that has not been 

introduced to the market yet, this year could be the same for all manufacturers. For a 

technology that is already being used by a manufacturer, the baseline year would be used 

for that manufacturer. However, if a manufacturer were not using a technology yet, even 

if another manufacturer is using it, a year of introduction would need to be set for that 

manufacturer. 

(f) Some technologies would still need caps on maximum penetration. However, this should 

reflect market restrictions, not leadtime constraints. This would incorporate consumer 

values for particular technologies that go beyond just efficiency and performance. For 

example, even though manual transmissions are more efficient than automatics, most 

consumers will not give up the convenience of an automatic. PHEVs do not have much 

benefit for people driving a lot of highway miles each day. Diesels are desired for trailer 

towing and have advantages on highway fuel economy, while hybrids have advantages in 

stop-and-go driving. These types of market considerations can be handled by 

establishing maximum penetration caps, but they should be handled separately from how 

leadtime is handled by manufacturer. 

Note that the yearly cap and introduction date violates the design cycle principal, but it is 

important to create the proper cap for each manufacturer and technology combination. Instead of 

using a model year for (b), above, the user could specify how many years into the design cycle a 

technology could be introduced. 

Response: EPA agrees that the consideration of leadtime constraint is important. The current 

model was designed with the implicit assumption that the first year of the first redesign cycle 

being modeled was sufficiently in the future so that a manufacturer could completely alter the 

design of vehicles being redesigned in that year. For example, in EPA’s vehicle GHG proposal, 

the first year of the redesign cycle was the 2012 model year. The start of this model year is 

approximately two years from the publication of the proposal and the final rule is not expected to 

be promulgated until sometime in 2010. Therefore, leadtime for the 2012 model year is quite 

short. Therefore, EPA adjusted the technologies caps for all technologies which might be 

restricted to years when a vehicle was being refreshed or redesigned to 85% or less, rather than 
the more typical 100%. This figure is based on an estimate of the percentage of vehicles which 

can be equipped with these technology packages from 2012-2016, though not in a linear fashion. 

Due to leadtime constraints, a lower percentage of vehicles was projected to be convertible in the 

early years than the later years of the program (i.e., redesign cycle). This is indicative of how the 

model inputs can be set in order to approximate leadtime constraints. 

We also agree with Mr. German that leadtime is not a hard and fast concept and can be a 

function of cost (i.e., a manufacturer can shorten the required leadtime involved in making a 

technological change if it is willing to increase costs, though in the very near term there are realworld lead time constraints such as the time needed for construction of new manufacturering 

facilities or capital tooling upgrades). At this same time, coupling cost and technology 

penetration would be challenging to simulate in a model such as OMEGA in a way which 

addresses all the possible factors involved. Mr. German does not point out studies which 

estimate the degree to which costs might increase in return for shortening leadtime. However, if 

such relationships can be found, it may be possible to include such flexibility in the model when 

EPA adds the effect of learning into the cost estimations. At the present time, the model can be 

run with a series of scenarios, each of which contains varying levels of technology penetration 

and varying costs. The user can evaluate the results of these runs and determine which level of 

cost and leadtime is most appropriate. 

We agree with Mr. German that manufacturers which have already implemented technologies 

such as hybridization have an advantage over those which haven’t. It is probable that such 

manufacturers could hybridize a greater percentage of their fleet than other manufacturers. 

However, on a practical level, this advantage may not be that important to include in OMEGA at 

this time. Manufacturers which have already implemented technologies, especially major ones 

like hybridization, are generally in a better position to meet GHG standards than those which 

have not implemented such technologies. Thus, there would not be any practical change in the 

model’s results if we allowed Toyota and Honda to have a greater hybrid penetration than 

applicable to other manufacturers, since these manufacturers do not require a greater hybrid 

penetration in order to meet the GHG standard. While true for hybrids, this relationship may not 

always hold true. We will consider moving the technology caps to the level of the manufacturer, 

or even the individual vehicle as we continue to develop OMEGA in the future. 

If the user believed this factor was important and should be reflected in the model results, the 

user could simply group manufacturers by their estimated technology caps and perform one 

model run for each set of technology caps and then combine the results. To use Mr. German’s 

example, vehicles produced by Toyota and Honda could be modeled in one run with high hybrid 

penetration caps and those of other manufacturers modeled separately with a lower technology 

cap. 

This issue applies primarily to technologies which require sophisticating application at the 

vehicle or manufacturer level. Hybridization is probably the best example of this due to the 

complex integration of electric motor, battery and engine operation. However, there are many 

other technologies which may actually be purchased pre-assembled from a supplier, such as 

dual-clutch transmissions. Certainly having some experience with such technologies could 

increase the speed at which a manufacturer might be able to convert most or all of its vehicles to 
the technology. However, much of the experience is also being gained by the supplier and 

available to all manufacturers. The cost paid by each manufacturer may still be a function of 

sales volume, but this can be reflecting through learning factors when appropriate. Thus, we 

believe that this issue does not apply to most of the technologies which, for example, EPA 

included in its modeling runs in support of the EPA vehicle GHG proposal. 

Regarding the variation of costs across manufacturers, this again will be addressed in large part 

when we incorporate learning into the cost estimation processes of the model. Currently, this 

can and has been done outside of the model. 

 

EPA initially intended to incorporate capital costs into the core model of technology application 

as it began development of OMEGA. However, this proved difficult for several reasons. One, 

the number of units over which the capital investment should be amortized is not easy to 

determine. The model currently applies technology to either individual vehicles which could 

represent anywhere from individual vehicle models to vehicle platforms or all of a 

manufacturer’s cars, for instance. Should the OMEGA model assume that only the sales of the 

vehicle being evaluated bear the burden of the investment or all the manufacturer’s sales? 

Should sales over one or more than one redesign cycle be considered? Some technologies, as 

mentioned above, will be manufactured by suppliers. In this case, the capital cost will be borne 

by more than one vehicle manufacturer and so should be amortized over the sales of more than 

one vehicle manufacturer. 

Our initial plan to incorporate capital cost and learning was to base technology ranking (and thus 

technology application) on the assumption that all the sales in a particular redesign cycle 

received the technology. Then, once the run was completed, the model would recalculate costs 

based on the actual application of the technology. This approach recognizes that no technology 

would be introduced if it was only going to be applied to a single vehicle. Costs for new 

technologies are always high early on, but manufacturers often do not fully recover their costs 

until the technology spreads to more vehicles. We will consider this approach, as well as others 

as we continue to develop OMEGA in the future. At the present time, the required capital cost 

associated with technology application can be assessed outside of the model. Should the results 

indicate that the required capital investment is inappropriate in some way, the inputs to the model 

can be modified to eliminate the issue. 

(D) Technology Assessment 

Requiring the user to input technology in rank order of cost-effectiveness is an interesting 

attempt to handle the synergy issue. Unfortunately, it fails to work in other ways: 

 It only works if the learning rate is the same for all technologies and if no technology 

changes effectiveness over time. If one technology has a steeper learning curve than another, 

or if a technology increases benefits in the future, then the cost-effective order will change 

over time. For example, high-tech diesels are a relatively mature technology, as over 5 

million per year have been sold in Europe for several years. Their future cost reduction 

potential is much less than that of hybrid vehicles, whose sales are at least an order of 

magnitude lower and which are still at early stages of development. Also, the high power Liion batteries just starting to penetrate the market will allow much smaller battery packs for 
conventional hybrids, with large cost reductions. In addition, analyses by MIT (2007) 

suggest that hybrid benefits will increase in the future as manufacturers figure out how to use 

the hybrid system to minimize operation at less efficient engine speed/load points. 

 The synergies will differ depending on the specific technologies into which an individual 

manufacturer has already invested. For example, consider one manufacturer that has 

invested in MPI turbos and a second that has invested in DI naturally aspirated engines. If 

both manufacturers move to DI turbo engines, the first manufacturer will gain the benefits of 

DI adjusted for the DI/turbo synergies, while the 2nd manufacturer will gain the benefits of 

turbocharging adjusted for the same DI/turbo synergies. Thus, the synergy impact of 

DI/turbo must be assessed independently of each technology. Even if the model ignores the 

leadtime constraints imposed by baseline technology investment and assumes every 

manufacturer will adopt the exact same technology packages for a given vehicle type (not a 

good idea, as discussed, above), a problem still exists in backing out “any advanced 

technology that might have been present in the baseline” (page 12, line 3-4). In order to back 

out the baseline technology for different vehicles and manufacturers, the technology input 

file must contain independent assessments of MPI turbo, DI naturally aspirated, and DI turbo. 

The DI turbo line includes the synergies, but the other two lines do not. How does the model 

add them back in? If the turbo lines and DI lines occur before the DI turbo line, then the 

technologies will be added together first without consideration of the synergy effect. 

 It does not allow for different markets for different technologies. For example, diesel 

engines have additional value for (a) customers who tow and (b) customers in rural areas. 

Towing is valued only by a small part of the market, but it is an important feature for that 

market. Customers in rural areas do a lot of highway driving and value the high efficiency of 

the diesel on the highway, while hybrids excel in urban areas. Thus, the markets for diesels 

and hybrids will be self-selected to some extent by their relative city and highway mpg, not 

the combined mpg used to select all technology. 

In order to work properly, the model must be able to handle multiple pathways. For example, the 

model cannot allow turbo and DI benefits to be added sequentially, but must force each to go to a 

DI turbo input. A similar situation exists with the various variable valve timing systems and 

VCM. All offer primarily pumping loss reductions and all options must be present in the input 

file in order to back out technologies in the baseline. All these options cannot be added back by 

the model one after the other – the model must also be able to handle these multiple pathways. 

Another example is transmissions, where the input file must list 5-, 6-, 7-, and 8-speed 

automatics, as well as DCTs and CVTs (even ignoring manual transmissions). I could go on. 

The point is that I do not see how the model can avoid handling multiple technology pathways 

and depend only on the input order to handle synergies. 

The model must also be able to handle technologies with different rates of change in benefits and 

costs in the future. This also requires that the model process the lines independently and not rely 

on the input order. 

The market considerations could perhaps be handled with maximum penetration caps. For 

example, it could be considered that diesel engines will not compete well with hybrids in urban 

areas, so that the maximum penetration of diesels would be equal to their sale in rural areas plus 

trucks designed to tow, with the reverse true for hybrids. Of course, this will differ by 
manufacturer, which is a problem if universal caps, instead of manufacturer-specific caps, are 

maintained. 

Response: Regarding Mr. German’s concern that the technology ranking will change over time 

due to differing learning rates and changing effectiveness over time, it is not clear whether his 

concern applies to changes within a redesign cycle or across numerous redesign cycles. We do 

not believe that this issue exists within a redesign cycle. Certainly, technologies can differ in 

their learning rates and strictly speaking, this means that their costs change each year and this 

could affect the order of technologies. However, manufacturers focus on the mid to long term 

when redesigning their vehicles. Focusing on costs at the end of the redesign cycle is consistent 

with this. Technology rankings are more likely to change across redesign cycles. This can be 

accomplished in the current model by listing a specific technology twice, once in the correct 

position for one redesign cycle and second, in the correct position for the second or later redesign 

cycle. The technology cap for the first listing would be zero in the second or later cycle. The 

technology cap for the second listing would be zero in the first redesign cycle. Of course, this 

can only be done for a few technologies before the user runs into the limit on the number of 

technologies which can be handled in the model. If the desired order of technologies cannot be 

accommodated in this way, the model could simply be run with two separate scenarios, one for 

each redesign cycle with its own technology file. The results could then be combined in the 

same benefits calculation worksheet if calendar year impacts were desired. Since the core model 

of technology application starts over for each redesign cycle, the results of the scenarios 

evaluating the second or later redesign cycles would be exactly the same as a single, multiple 

redesign cycle run with that technology file. In future versions of the OMEGA model, it may be 

possible to provide separate effectiveness estimates for each redesign cycle, as well as separate 

technology order for each redesign cycle. 

We believe that Mr. German’s second comment above about dis-synergies is incorrect. In fact, 

the set order of technology application is what allows OMEGA to accurately estimate dissynergies. This estimation is not in the effectiveness estimate included in the Technology file, 

but in the Technology Effectiveness Basis (TEB) for the DI Turbo technology which is input for 

each vehicle. To use Mr. German’s example, let us assume that the effectiveness of 

turbocharging alone is 5% and that of direct injection 7%. However, combining the two only 

reduces CO2 emissions by 10%. The effectiveness for DI Turbo technology in the Technology 

file will be the full 10%. Vehicles which already are turbocharged will have a TEB for the DI 

Turbo technology of 50% (5%/10%). Vehicles which already equipped with direct injection will 

have a TEB for the DI Turbo technology of 70% (7%/10%). The result is that the incremental 

benefit of moving the first vehicle to DI Turbo technology is 5%, while that for the second 

vehicle is 3%. 

The advantage of the approach taken in the OMEGA model is that the technology path for each 

vehicle is fully known. The user can use any level of vehicle simulation modeling or vehicle 

testing to assess the overall effectiveness of the technology already on a vehicle and that which 

would exist after the application of each technology made available to it. At each point, the dissynergies can be fully assessed because the full regimen of technologies on the vehicle is 

completely known. The TEB values in the Market File contain exactly the type of information 

which Mr. German says must be included. The only difference is that this information is in the 
Market file and not the Technology file, to which Mr. German alludes. It is likely that this 

confusion arose due to a lack of clarity in our description of the critical role played by the TEB 

values in the draft model documentation provided to the peer reviewers. 

Mr. German is correct in that OMEGA does not contain representations of distinct segments of 

the vehicle market (e.g., towing, rural drivers, etc.). However, OMEGA can still be designed to 

reflect such market segments if distinctions in the preferences or needs of these segments can be 

related to the acceptability of various technologies. For example, the need to tow can affect the 

acceptability of turbocharged downsized engines. The user can place vehicles which are used to 

tow trailers or haul heavy loads in different vehicle types from those vehicles which are not used 

in these ways. The technologies made available to vehicles with towing or hauling requirements 

then differ to the appropriate degree. A review of the lists of technologies made available to 

different vehicle types in the modeling which it performed in the EPA vehicle GHG proposal 

reflect such differences. 

Another approach would be to limit the application of certain technologies to less than 100% of 

sales. For example, the user may believe that all electric vehicles would be acceptable to only 

50% of the users of subcompact cars. Range limitations could severely limit their desirability to 

the remaining 50%. The user can simply set the technology penetration cap to 50% for the 

electrification technology for the vehicle type applicable to subcompact cars. The same can be 

done for factors which would affect the applicability or desirability of technology associated 

with rural driving, etc. Such limits would apply to all vehicles within a given technology type 

and thus, in general, to all manufacturers. If the user desires to limit the application of 

technology at the vehicle level, this can be approximated by setting the TEB and CEB values for 

that vehicle above the level actually present, so that the model will apply the technology to less 

than 100% of the sales of that vehicle. 

(E) Maximizing Net Social Value 

The model only outputs total costs and benefits. It presents these with great amounts of detailed 

information. But it is impossible to tell if the scenario has maximized net social value. 

To put it another way, the model is only capable of counting up the benefits and costs of 

complying with pre-determined GHG standards. It is not able to do the reverse, which is to input 

the desired benefit and have the model determine the resulting GHG standard. 

This is not a trivial issue. The 2007 EISA specifically mandates “maximum feasible” CAFÉ 

standards after 2020. NHTSA has long interpreted existing statutory authority to also require 

maximum feasible standards and established long ago that “maximum feasible” is determined by 

the point at which the costs of adding the next technology exceed the benefits. Even without a 

mandate, any credible analysis must be able to compare the costs and benefits of the chosen 

GHG standard to the maximum net social value. 

Given the existing complexity of the model, it is not unreasonable for the model to also 

determine the GHG standard that maximizes net social value. The Volpe model calculates this 

point even with a much more complex model. EPA’s model will lose considerable credibility if 
it is not capable of calculating the maximum net social value point. 

Response: We do not address Mr. German’s comments about the need for either EPA or 

NHTSA to set GHG or fuel economy standards using a model which automatically identifies a 

standard which maximizes the difference between societal benefits and costs which can be 

estimated and monetized. These issues are beyond the scope of this peer review. 

EPA agrees with Mr. German that it would be useful for OMEGA to be able to perform such a 

task. We have developed a spreadsheet which combines two of OMEGA’s output files and 

identifies the level of GHG control which maximizes net societal benefits. The two files are: 1) 

the results file in text format which shows each manufacturer’s emissions and cost after each step 

of technology application and 2) an abbreviated version of the benefit calculation file. The 

OMEGA model only needs to be run once with a GHG standard which is sufficiently stringent to 

require the addition of all available technologies to all vehicles. The spreadsheet adjusts each 

manufacturer’s standard in a consistent manner off a predetermined universal or footprint-based 

standard until net benefits reach their maximum. EPA will consider publishing this spreadsheet 

once a set of instructions for its set up and use are drafted. 

EPA will also add the automatic capability to determine the standard at which societal benefits 

are maximized to OMEGA at some time in the future. While such an approach can provide 

useful insight during rulemaking development., as Mr. German points out elsewhere, there are 

many factors, such as feasibility and leadtime, which are difficult to quantify and other factors 

which differ across manufacturers which are difficult to simulate in a model. One practical issue 

with model runs which maximize benefits is that they usually show that the standard is infeasible 

for a number of manufacturers with the technology that is projected to be available. Thus, EPA 

did not rely on the principle of maximizing net societal benefit in setting the standards contained 

in the EPA vehicle GHG proposal. 

Appropriateness and Completeness of the Contents of the Sample Input Files:

(F) Market Input File 

The market input file appears to be appropriate and complete – perhaps too complete in one way. 

The file contains separate inputs for reference case technology benefits and costs. The 

percentages in these columns should simply reflect the existing market penetration of each 

technology package. They should be identical for both costs and benefits. Is there a reason why 

these would be different? If so, the Model Description should explain this. If not, the duplicate 

columns can be removed. 

Minor Suggestions: 

 If the model wants to “back out” existing technologies, you will need a lot more than 20 

columns to do this. You’ll need 10 columns just to handle transmissions and another 10 just 

to handle different valve timing systems. Not to mention differing levels of high strength 

steel and aluminum use. 

 The Model Description should state that vehicle types are a user input defined in the 

“Vehicle Type” tab of the Market Input File (I looked around for a while before I found this.) 
 If you maintain separate columns for reference case technology costs and benefits, it would 

help the user to add a row above the existing descriptions and define columns AD-AW as 

“reference case benefits” and columns AX-BQ as “reference case costs”. 

Response: Mr. German’s comments in this section (and our response) intersect with his 

comments in Section D. above concerning the ability of OMEGA to recognize and represent 

potential dis-synergies between technologies. First, the TEBs and CEBs in the Market file will 

usually differ. One reason for this occurs with technology packages which include several 

distinct technologies. Using Mr. German’s example from Section D. a technology package 

might include both converting an engine to direct injection and adding turbocharging. If a 

baseline vehicle has a direct injection engine, but is not turbocharged, then the TEB would be the 

emission effect of only converting the engine to direct injection compared to adding both 

technologies. The CEB would be the cost of only converting the engine to direct injection 

compared to adding both technologies. Due to dis-synergies, as discussed in Section D. above, 

the emission effect of direct injection might be 60% of the total benefit of both technologies, 

while the cost might only be 50% of the total. In general, no two technologies will have exactly 

the same ratio of incremental cost and incremental effectiveness, which would be necessary for 

the TEB and CEB values for vehicles to be the same. Adding dis-synergies can markedly affect 

effectiveness, but generally has minor effect on cost. Thus, with dis-synergies, the chances of 

two technologies will have exactly the same ratio of incremental cost and incremental 

effectiveness in terms of the percentage of package cost and effectiveness is very small. 

When Mr. German refers to the need for more than 20 columns in order to back out technologies, 

he again misunderstands the nature of the TEB and CEB values. (Again, this is likely due to a 

lack of clarity in the draft model documentation on this subject which EPA provided to the peer 

reviewers.) The units of the TEB and CEB values are the percentage of technology package 

effectiveness and cost which are already present on the vehicle. These percentages are best 

determined using a vehicle efficiency simulation model which can estimate fuel consumption 

over the certification driving cycles for various combinations of technologies. EPA’s lumped 

parameter model is one example of this type of model, as is the Ricardo Easy5 full vehicle 

simulation model. The presence of individual technologies is not an input to the OMEGA 

model. As this is different from NHTSA’s Volpe Model, this may be one of the causes of 

confusion. 

We agree that the headings of the inputs files could be made more clear and descriptive. 

However, we have found that adding header lines to the input file has been more complicated 

than anticipated. Thus, we are taking the approach of adding more detailed descriptions of each 

column of each input file to the model documentation and directing the user to review these 

descriptions in order to obtain a fuller understanding of the nature of each of the inputs to the 

model. 

(G) Technology Input File 

As discussed above, the technology input files need to be substantially modified in conjunction 

with changing the model to handle multiple technology paths. 
In addition, also as discussed above, the “Cap Cycle” numbers need to be replaced with generic 

caps on the maximum increase permitted per year and manufacturer-specific model years for 

initial introduction. The annual technology penetration increase cap would be applied to each 

manufacturers’ individual baseline technology penetration, from the Market Input File, or 

starting with the manufacturer-specific initial model year for technology packages that have not 

been used yet by individual manufacturers. 

The Average Incremental Effectiveness fields are fine, although, as noted above, if these change 

for future redesign cycles, the cost-effective order of the technology packages can also change. 

I could not find any explanation of how the Initial Incremental Cost, , Decay, seedV, kD, and 

Cycle Learning Available fields are used in the model. Even the detailed algorithms on pages 9-

16 of the Model Description contain no reference to how technology costs are adjusted for the 

TARF calculations. Thus, I was not able to assess the appropriateness of these fields. However, 

in general, the cost reduction curve is not likely to be the same for all technologies. Some 

flexibility may be needed here. 

The Technology Input File does not address weight impacts associated with different 

technologies. For example, both diesel engines and hybrids add considerable weight to the 

vehicle, which negatively impacts both performance and efficiency. It is possible to handle this 

off-board in the efficiency benefit estimation. However, if so the Model Description should 

explicitly state that weight impacts are expected to be assessed by the user and included in the 

technology inputs. 

Response: As Mr. German alludes, several of the comments in this section have already been 

mentioned in earlier sections and are addressed there. Several of Mr. German’s other 

suggestions would involve the model applying technology on an annual basis. OMEGA is 

explicitly designed to apply technology over an entire vehicle redesign cycle. This has received 

favorable comment from all three peer reviewers, including Mr. German. As discussed in 

Section C above, limits on the annual rate of technology application which the user believes 

apply can be input to the current model by simply summing up these limits over the redesign 

cycle. 

Mr. German is correct that the draft model documentation provided to the peer reviewers did not 

describe how the Initial Incremental Cost, , Decay, seedV, kD, and Cycle Learning Available 

fields are used in the model. These inputs are related to the prediction of cost reductions due to 

learning, which has not yet been implemented in the OMEGA model. These columns appear in 

the Technology file as place holders for future version of the model. The same is true for several 

vehicle parameters, such as weight, seating capacity, etc., which are included in the Market file. 

We will consider Mr. German’s comment that the cost reduction curve is not likely to be the 

same for all technologies when we add learning to the model. 

We agree with Mr. German that the impact of each technology on vehicle weight and 

performance should be included in estimating the effectiveness and cost of each technology. 

This is the approach followed in the EPA vehicle GHG proposal. We have modified the model 

documentation to clarify this. 
(H) Scenario Input File 

The compliance options – universal standard, linear attribute, or logistic attribute – are fine. 

However, there are columns in the Scenario input file that are not described in the Model 

Description on page 6: 

 TARF Option (column E) – Is this the “two TARF equations from which the user can 

choose”, described on page 13? If so, should state this on page 6. 

o Why is the “Effective Cost” TARF equation limited to fuel savings over the payback 

period? Why aren’t the discounted lifetime fuel savings considered? Is this done to 

try to mimic what technologies will be most acceptable to the customer? If so, this 

should be explained in the Model Description. I’m also not sure this is appropriate. 

Most technologies will be invisible to the customer. In addition, the primary point of 

CAFÉ and GHG standards is to fill in the gap between the consumers’ value of fuel 

savings and the value to society. So, the standards should be targeted towards 

society’s values, not the customers. 

o The equation for “Cost Effectiveness – Manufacturer” equation does not make sense. 

Unless a technology includes a fuel change, this equation will produce virtually 

identical results for all technologies. The CO2 summed in the denominator is directly 

proportional to fuel consumed summed in the numerator. The ratio should be 

virtually the same for all technologies, unless there is a fuel change. What is this 

equation trying to do? 

o Why is the fuel savings only summed over the payback period, while the CO2 

savings are summed over the useful life? Why are they not the same? 

 Target Function Type (column F) – I could not find a description of this field anywhere in 

the Model Description. 

 Fleet type (column G) – The description in Rykowski’s email response to Rubin should be 

added to the Model Description. 

 Trading limit (column I) – The description in Rykowski’s email response to Rubin should be 

added to the Model Description. 

Economic parameters – The “CAFÉ fine” and “CO2 value increase rate” are fine. However, the 

other parameters may need modification: 

 Discount rate – There is some thought that the CO2 discount rate should be different from the 

economic discount rate. I am not sure I agree with these arguments, but you may want to 

include flexibility to have a different discount rate for CO2 in the model. 

 Payback period – As discussed, above, I am not sure this is needed. Any use of payback 

period should be explained and justified in the Model Description. 

 CO2 fine – While the CAFE fine is used appropriately in the model, there is no consideration 

of a manufacturer paying CO2 fines instead of complying with CO2 standards. Of course, 

this is dependent on the compliance strategy adopted by EPA for its CO2 standards. But the 

model should have the flexibility to model CO2 fines; similar to how it handles CAFÉ fines. 

 Gap – It is appropriate to adjust the test values for differences in real-world fuel 

consumption. However, the gap is not linear. As EPA demonstrated in their fuel economy 

label rulemaking, the gap increases as fuel consumption decreases. While the fuel economy 
 I do not understand the value of “threshold cost” or how it is used. Lines 8-10 of page 8 

state, “threshold technology cost (the cost at which manufacturers add technology to only 

enough vehicles to meet the standard as opposed to adding technology to all of a model 

line)”. The detailed calculations later in the Model Description do not discuss how this is 

done. From a practical point of view, how does the model know whether or not the 

technology is needed to meet the standard when the technologies are feed into the model one 

at a time? More importantly, manufacturers have limited resources and the standards will 

drive technology development well beyond what a manufacturer would have done without 

them. Thus, why would a manufacturer add any technology to more vehicles than are 

required to meet the standard? Unless these concerns can be addressed in the Model 

Description, the “threshold cost” should be eliminated. 

 Rebound effect – Line 38 on page 17 states that the rebound effect is an input in the 

“Economics” worksheet. However, it is not listed in the worksheet. In any case, the rebound 

effect is not handled appropriately in the model. The rebound effect is a sensitivity factor. 

But it is determined from a regression. Which means that the change in VMT is NOT a 

linear function of the change in fleet fuel consumption. Thus, the equation on lines 41-43 of 

page 17 is wrong. The actual relationship is logarithmic or exponential or something like 

that (I don’t remember exactly what). The correct equation should be built into the model. 

o The rebound effect is also impacted by the price of fuel and household income. This 

should be added to the model (see medium- to long-term recommendations, below). 

Minor suggestions: 

 It appears that the “Cars A”, “Cars B”, “Cars C”, and “Cars D” columns in the Target tab are 

intended to describe the footprint-based logistic curve. Does this mean that “Cars C” and 

Cars D” are also the Xmax and Xmin under the linear attribute option? If so, both 

descriptions should be in the column headings. Also, while the Model Description (page 6-7) 

includes a good explanation of the how the linear target and logistic curve work, it should 

also specifically state where the A, B, C, D, and X coefficients can be found in the 

spreadsheet. 

 The economic parameters are discussed as part of the Scenario input file on page 8. Lines 

12-13 also state that an example of the Scenario input file is in Appendix 3. However, 

Appendix 3 only includes the “Scenarios” tab and the “Target” tab. The “Economics” tab 

should also be added to Appendix 3. 

Response: Mr. German is correct in that the TARF column in the Scenario file refers to the 

choice of one of the two available TARF equations. The model documentation has been clarified 

in this regard. The payback period is the period of time over which manufacturers believe that 

vehicle purchasers value fuel saving when purchasing a vehicle. If the user prefers to use 

lifetime fuel savings in the TARF calculation, the user can specify a payback period sufficiently 

long to cover the life of the vehicle 

The point of including the fuel savings over a period of time in the TARF is to recognize that 

there is some increase in vehicle fuel economy which would neutralize the consumer’s negative 
perception of an increase in vehicle price, thus nullifying any negative sales impact. This level 

of fuel economy increase is often estimated to be the fuel savings accruing over a specified 

number of years of vehicle operation which is usually less than the life of the vehicle. Thus, 

when either TARF is negative (i.e., the fuel savings exceed the cost of technology), this implies 

that the manufacturer could add the technology at its full cost and potentially increase vehicle 

sales (all other factors being held constant). Similarly, when either TARF is positive, this 

implies that if the manufacturer added the technology at its full cost, sales would decrease. We 

have modified the model documentation to better explain the rationale behind the TARF 

equations. 

The fuel savings are typically summed only over a portion of the vehicle life because the 

timeframe considered by the vehicle purchaser is typically less than the life of the vehicle. (We 

have not included an estimate of the residual value of the added technology at the end of this 

time period, but will consider adding this in future model versions.) The lifetime CO2 emission 

reduction are included in the denominator since that represents the form of the GHG standard, 

particularly when car and truck trading is considered. For a single vehicle class, there is no need 

to include lifetime GHG reductions; the reduction in terms of g/mi would be sufficient. 

However, the lifetime GHG emission reduction provides the same ranking as the reduction in 

g/mi and also applies when car-truck trading is allowed. So including lifetime CO2 emissions in 

both cases allowed the same equation to be used in all cases. 

Mr. German suggests that the point of GHG standards is to fill in the gap between the 

consumers’ value of fuel savings and the value to society. That can be true, but this is 

accomplished primarily through the level of the GHG standard. The current TARFs are focused 

on the order in which manufacturers are likely to add technology to meet the standard. The 

TARF does not set the level of the standard. Manufacturers’ primary goal is to maximize profits. 

OMEGA does not address all of the numerous factors which affect profit maximization. For a 

specified level of sales across a fixed model mix, profits are maximized by maximizing the profit 

per vehicle, or the difference between cost and price. The numerator of both TARFs attempt to 

represent this difference. Thus, the more negative the TARF, the greater the potential profit per 

vehicle and a manufacturer’s desirability to add the technology. 

The level of a GHG standard can be based on many factors, societal benefits being one of them. 

The benefit calculation worksheet is designed to facilitate the calculation of total societal costs 

and benefits and to assist in this evaluation. 

The Cost Effectiveness – Manufacturer TARF is not constant for every technology. The Cost 

Effectiveness – Manufacturer TARF (ignoring the CAFE fee) is basically : 

[ Technology Cost less Fuel Savings over Payback Period ] / Lifetime GHG Emission Reduction 

 Or 

 Technology Cost less Fuel Savings over Payback Period

 Lifetime GHG Emission Reduction Lifetime GHG Emission Reduction 

Mr. German is correct that the ratio of fuel savings to GHG emission reduction will tend to be 
constant across technologies (at least those not aimed at reducing refrigerant leakage). However, 

the ratio of technology cost to lifetime GHG emission reduction will not be constant across 

technologies. The Cost Effectiveness – Manufacturer TARF can be presented as the difference 

between these two ratios, so it will differ markedly between technologies. Except for the 

inclusion of discounting in the calculation of the GHG emission reduction, Paul Lieby’s 

comment in section xx of his comments presents an excellent description of the rationale behind 

the Cost Effectiveness – Manufacturer TARF. 

We have clarified the description of all the model inputs in the model documentation. 

We have also added a separate discount rate for the valuation of CO2 emissions. 

As discussed above, including the payback period as an input allows the user to evaluate fuel 

savings over less than the life of the vehicle or over the entire life of the vehicle. 

EPA has not typically allowed the payment of a fee in lieu of non-compliance for car and light 

truck emission standards. The typical fine for non-compliance is far in excess of the cost of 

technology and is retroactive in that it applies to past sales of vehicles which were found to 

violate the applicable emission standards. There is no provision for actively producing vehicles 

which do not meet applicable emission standards. Thus, we do not plan to add a separate CO2 

fine to the Scenario file. The inclusion of the CAFÉ fee in the TARFs is to allow the user to use 

the OMEGA model under conditions which are similar to those possible with the Volpe Model. 

In model runs evaluating GHG emission standards, EPA would set this fee to zero. 

Mr. German is correct that EPA’s current MPG-based formulae for fuel economy labeling imply 

that the “gap” increases as fuel economy increases. The model currently assumes a constant gap 

with changing fuel economy. EPA will consider incorporating a more flexible definition of the 

gap into future versions of the model. 

We have clarified the role of the threshold value in the model documentation. Basically, if the 

per vehicle cost of the last technology added by the model in order to enable compliance exceeds 

the threshold value, the model reduces the percentage of vehicle sales receiving that technology 

to just the degree needed to enable compliance. If modified the per vehicle cost of the last 

technology added by the model in order to enable compliance is below the threshold value, the 

model leaves the percentage of vehicle sales receiving that technology at the technology 

penetration cap for that technology. This flexibility was included in the model to reflect the 

different ways in which manufacturers apply various technologies. For example, when adding 

basic engine technology such as variable valve timing, the manufacturer would generally convert 

the entire production volume of a specific engine to this technology. Two different engines, one 

with the technology and one without, would not be maintained. However, with more extreme 

technologies, such as dieselization or hybridization, the manufacturer often maintains two 

versions, one with and one without these technologies. By setting the threshold in between the 

costs of these two examples, the model will reflect these two approaches to technology 

application on the part of a manufacturer. 

If the threshold is set to zero, the model simply backs off from any predicted over-compliance. 
The higher the threshold cost is set, the greater the degree of over-compliance which is accepted 

in a model run. Since the value of the threshold cost is set by the user, its inclusion only 

provides more flexibility. 

The rebound effect has been evaluated in the literature in a number of different ways from a 

variety of datasets. It is typically defined as the percentage change in per vehicle VMT divided 

by the percentage change in the cost of driving one mile. Thus, it is a function of both fuel 

economy and fuel price. As the base cost per mile of driving in the various studies varies, there 

is some ambiguity in defining the rebound effect in this manner. Still, this is the norm used in 

the literature and we apply it accordingly. The benefit calculation worksheet determines the 

percentage in VMT per vehicle by multiplying the percentage change in fuel consumption per 

mile by the rebound effect. 

The rebound effect is included in the benefits calculation file, in cell B3 on the Exclusive Inputs 

tab. It currently only applies to changes in fuel economy. However, future versions of the 

benefits calculation file will apply it to changes in fuel price, as well. As Mr. German notes, 

VMT per vehicle has also been observed to be increasing over time due to other factors, income 

probably one of them. An input for a secular increase in VMT per vehicle will also be included. 

We have modified out descriptions of the values which are represented on the Target tab of the 

Scenario file to better describe their role in both the constrained logistic and segmented linear 

standard curves. We also have added a description of the values to be entered on the Economics 

tab of this file. 

(I) Fuels Input File 

The fuels file works fine for conventional gasoline and diesel. The Model Description does not 

address biofuels, but if needed the Fuel Input and the Upstream Emissions worksheets should be 

able to handle them. 

Electricity is a special problem. A minor issue is that the Energy Density (column B), Mass 

Density (column C), and Carbon density (column D) are different than for liquid fuels. Liquid 

fuels are generally expressed in units per gallon. This doesn’t work for electricity. The units for 

electricity in the Fuels Input sheet need to be defined. Also, I’m not sure what Mass Density 

would be for electricity – kg/kWh? And isn’t carbon density meaningless, as the carbon is all 

upstream? 

More importantly, the energy density and mass density for electricity are not fixed, but are 

dependent on battery construction. High-power Li-ion batteries for conventional hybrids may 

only have about 15 Wh/kg energy density, while high-energy batteries for PHEVs and EVs may 

have over 100 Wh/kg. In addition, start/stop systems and belt-alternator/starter systems may use 

lead-acid batteries and some conventional hybrids may continue to use NiMH batteries through 

the 2013-2015 timeframe. All will have different energy densities. 

Minor suggestions: 

 The Model Description, line 6 page 6, says, “There is a small subset of fuel information not 
included in this file”. This is not accurate. Appendix 5 contains upstream emissions, which 

is an extremely important factor for fuels. This connection should be discussed in the Model 

Description. 

 The appendices should be ordered to match the order they are discussed in the Model 

Description (i.e. the fuels Appendix should be before the Scenario appendix). 

Response: Please see our response to xx comments on the inclusion of renewable fuels in the 

model. Mr. German is correct that the benefits calculation spreadsheet could be modified by the 

user to accommodate any different costs or emissions from the use of other fuels in vehicles 

which are certified on gasoline or diesel fuel. 

The inputs for electricity in the Fuels file have been clarified in the model documentation. The 

energy density value for electricity does not apply to that of the battery type being used on the 

vehicle, so the variability in the latter value is not an issue. We have also modified our 

description of fuel-related inputs, incorporating Mr. German’s comments, as well as other 

changes. 

(J) Reference Data in Appendix 5 

Downstream Criteria Pollutant Emissions: 

The fields and the regressions as a function of age are appropriate. However, there is not enough 

flexibility to handle differences in fuel, future emission standards, and future fuel sulfur control: 

 The model should be able to handle future reductions in emission control standards. This 

means that the model should allow the user to specify effective years for future emission 

standards and enter new regression coefficients. 

 SO2 emissions are almost entirely a function of the sulfur level in the fuel. Thus, the model 

should also handle changes in fuel sulfur level. The model should allow the user to specify 

effective years for future sulfur reduction and the fuel sulfur level for both current and future 

fuels. If desired, the user would not have to enter regression coefficients for SO2, as there is 

a fixed relationship between fuel sulfur, fuel consumption, and SO2 emissions (much like 

CO2 to fuel consumption) that could be hard-coded in the model if the user specifies fuel 

sulfur levels. 

 The regression coefficients will be different for gasoline, diesel, and electric vehicles. 

Average coefficients can be used for the current fleet, but these will not be appropriate if 

there is a substantial change in the future mix of diesels, PHEVs, or EVs. The model needs 

to allow input of different coefficients for diesel and gasoline – and possibly biofuels. 

Downstream emissions of electric operation should be zero and do not have to be input. 

 It appears that the model does NOT calculate downstream pollutant emissions as part of the 

normal model accounting, only the additional emissions caused by the VMT rebound effect. 

This is not appropriate. If there is a switch to diesels or EVs, the downstream pollutant 

impact needs to be assessed by the model. 

Upstream Emissions: 

 The upstream emission inputs are fine for gasoline and diesel, although addition rows will 

likely be needed to handle biofuels and unconventional oils. 

 It is not clear if the efficiency of battery recharging is included in electricity upstream 
emissions. The model likely calculates only the mmBtu actually used by PHEVs and EVs 

during use. However, the mmBtu draw from the utility will be larger due to losses in the 

battery charger and in the battery chemical process. To ensure that the user handles this 

properly, it would be best to add an input somewhere for charging efficiency. Otherwise, the 

Model Description should explicitly state that the upstream grams/mmBtu for electricity 

must be incremented to include the losses in the charger and battery. 

 Upstream emissions, both carbon and pollutant, for electricity will vary by region. While it 

is the responsibility of the user to input proper factors, there is a potential issue with 

stratification of PHEV and EV sales across the nation. Customers in urban areas are most 

likely to buy PHEVs and EVs will likely be limited primarily to a few, dense urban cores. It 

might be useful to have the Model Description briefly discuss the need for the user to input 

upstream values for electricity that are consistent with utility emissions in the urban areas 

most likely to purchase PHEVs and EVs. 

Vehicle Age Data and historical data on average CO2 emissions and new vehicle sales: 

These fields and inputs are fine. 

Response: We agree with Mr. German’s comments about the inability to reflect step changes in 

the downstream emission equations. Future versions of the benefits calculation file will specify 

downstream emissions by model year and age. Future versions will also include distinct 

emission estimates for vehicles operating on different fuels, such as gasoline, diesel fuel and 

electric vehicles. These estimates will apply to both base levels of VMT and rebound-related 

VMT. 

Regarding upstream emissions, we agree with Mr. German that the inclusion of an efficiency 

value for battery changing (and electricity distribution) should be included so that the user can 

input upstream emission estimates based on kw-hr of power generation at the power plant. 

Changing the upstream emission calculations to reflect regional differences would involve 

substantial changes throughout the benefit calculation worksheet, as regional vehicle sales, VMT 

per vehicle, etc. would likely also differ. We believe that such regionalization of the model 

should be performed by those knowledgeable of the particular region of interest. However, we 

agree with Mr. German that the emissions input to the spreadsheet should reflect the emissions 

from the incremental increase or decrease in the production of that fuel and not the average 

emissions over the entire production of that fuel. We will modify the model documentation to 

reflect this point. 

(K) Other Reference Data 

Externalities related to crude oil use: 

The externalities in the Externalities worksheet of the Benefits Calculation are only listed for 

imported oil. This is appropriate for military costs for protecting oil supplies, but it is not for the 

economic impact of periodic price shocks (and possibly for monopsony effects as well). Oil is a 

global commodity. Any reduction in oil use, either domestic or imported, will help reduce the 

economic impact of periodic price shocks. 

Rebound effects: 
The discussion of the rebound effects on lines 10-19 of page 3 and on pages 20-21 both imply 

that rebound effects are NOT considered in assessing the societal benefits from reduced crude oil 

use and GHG emission reductions. However, I would assume that these benefits are based upon 

total fuel consumption, which includes the additional VMT from the rebound effect. If my 

assumption is not accurate, then the social benefits associated with reduced crude oil use and the 

value of GHG emission reductions must be revised to include the rebound effect. If the benefits 

do include the additional VMT from the rebound effect, this should be clarified in the discussion 

on both page 3 and page 20. 

Response: We have removed the word Imports from the title of the Oil Externality Section of 

the benefits calculation file. These externalities were applied to all reductions in crude oil use, 

not just to reduced imports. To the degree that an externality only applies to imported oil, the 

user should decrease the value of the externality by the ratio of the expected reduction in 

imported oil to the expected reduction in total oil use. 

We have corrected the discussion of rebound effect in the model documentation. The reductions 

in crude oil use and GHG emissions always included the rebound effect. 

Recommendations for Improved Model Functionality – beyond “future work”:

(L) Recommendations for Short-Term Functionality

The functionality of the model is good. My only recommendations are those already described 

above, for improved handling of leadtime (section C), ability to handle multi-path technology 

inputs, (section D), and ability to calculate “maximum net social benefits” (section E). 

Response: None required. 

(L) Important Medium-Term and Long-Term Recommendations 

1) By far the most important improvement is to use budgets for capitol expenditures to assess 

leadtime. The need for this and suggestions on how to implement it were discussed in section 

(C), above. 

2) The rebound effect is impacted by both the price of fuel and household income. These 

should be added to the model. The work has already been done by Small and vanDender. Their 

equations should be added to the model, along with the necessary user input fields for future 

household income. An option to skip the fuel and income effects can be maintained, but it is 

important that the model be capable of properly calculating rebound effects. 

 The time value of congestion and vehicle refueling are also related to household 

income. While this is of lesser importance than the rebound effect, it should be 

relatively easy to add household income effects to the value of congestion and vehicle 

refueling in conjunction with adding household income to the VMT rebound effect. 

Response: We have added a secular growth rate to the calculation of VMT per vehicle to 

represent the impact of real income and other factors which have been increasing total VMT over 
time beyond growth in the vehicle pool. At the present time, the rebound is still assumed to 

constant over time. We are aware of the Small and vanDender study which has found that the 

rebound effect appears to be decreasing over time. Fortunately, this is not a factor for analyses 

which just evaluate one or two redesign cycles. Longer term analyses face even greater 

uncertainties with VMT per vehicle and other factors. Still, we will consider modifying the 

benefits calculation file to accommodate a changing rebound rate with time. 

(M) Less Important Long-Term Suggestions 

3) Inclusion of the city and highway fuel economy/CO2 values may help with assessing 

market penetration caps, although this can be done externally. Also, separate city and highway 

values could help calculate an appropriate in-use fuel economy/CO2 “gap” for different 

technologies with different city/highway fuel economy ratios. Separate city and highway 

numbers might also be useful for other purposes. EPA should consider adding these to the 

model. 

Response: It is not clear how tracking or regulating city and highway CO2 emissions separately 

would address issues which Mr. German has raised related to the technology penetration caps. 

Regarding the gap between onroad and certification CO2 emissions or fuel economy, this gap 

can theoretically vary between city and highway driving. However, as EPA described in its 

supporting analysis to its 5-cycle fuel economy labeling rule, data on onroad fuel economy 

during city and highway driving is very scarce. Thus, assessing the distinct impact of technology 

on certification and onroad CO2 emissions during city and highway driving would have to be 

based on vehicle simulation modeling. Such models are commonly used to simulate vehicle 

operation over the EPA city and highway certification cycles at the test temperature of 75 F. 

However, few vehicles, and even fewer control technologies have been modeled over other 

driving cycles, such as the US06 high speed, aggressive driving test, the SC03 air conditioning 

test and the standard test cycles at low ambient temperatures. Thus, at the present time, there are 

insufficient data available to determine how various technologies would affect the “gap” over 

city and highway driving. Until such information becomes available, the value of expanding the 

model to include separate city and highway estimates of the gap would be of limited use. If such 

information becomes available, it would be a simple task to add such capability to the benefits 

calculation worksheet. Incorporating this into the core model would be a more significant task. 

The primary requirements would be to input onroad city and highway gaps for each technology. 

It would probably also require the use of separate effectiveness estimates for city and highway 

emissions for each technology. Compliance would still be determined based on combined 

city/highway emissions. 

4) Value of time required to refuel vehicles: 

The model handles this appropriately for liquid-fuel vehicles. However, PHEVs and EVs will 

add refueling time, both because of the need to plug in and, in the case of EVs, the shorter range. 

This should be added to the model. Ideally, it should also be added to the TARF assessment. 

Response: Mr. German raises important points about PHEVs and EVs which need to be factored 

into the consideration of their expanded use in the future. At present, the only consumer benefit 
which OMEGA includes in either of the TARFs is the value of fuel savings. Other effects, such 

as a change in refueling time, are included in the calculation of societal benefits, but not the 

TARF. There are other attributes of PHEV and EV use which will also affect their value to the 

consumer. For example, the reduced range of EVs relative to conventional vehicles is a serious 

limitation for some consumers, but not for others. Some PHEVs are designed to run most 

efficiently on battery power and only resort to liquid fuel use when the battery has run out of 

useful energy. Other PHEVs operate best on a mix of electricity and liquid fuel. However, even 

these PHEVs eventually run out of stored battery power and convert to operation solely on liquid 

fuel. Their operational cost varies depending on daily driving distance, as well as climate. 

Unfortunately, there are significant uncertainties surrounding the details of how people drive and 

how they would drive if they owned a PHEV or EV. Thus, limitations exist today regarding both 

the appropriate inputs and modeling capability before these issues can be fully represented in an 

automatic fashion in a model run. In the near term, users modeling GHG standards which 

require or reflect significant levels of PHEV and EV penetration should take care to limit their 

penetration to portions of the driving public whose driving patterns are compatible with the range 

of these vehicles. Or, if the penetration of PHEVs is such that they would be driven significant 

distances on all liquid fuel power, that their CO2 efficiencies reflect such use. 

B. Comments by. Paul Leiby 

Thank you for the opportunity to review this model and its documentation. This is an important 

project, and the EPA team has made great progress in developing a coherent, informative, and 

very usable system. I understand that this is a work in progress and, regrettably, many comments 

can only refer to its current (May 1, 2009) state. Also most of the comments are in the form of 

what might be changed or improved, with the hope that these might be most useful. I would like 

to say at the outset that everything achieved so far is well worthwhile, and some features are 

quite marvelous. Please also interpret statements below of the form “the model does/does not” 

as meaning “as far as I could discern so far, it seems like the model does/does not.” Statements 

like “the model/documentation should” really mean “Perhaps it would be helpful if the 

model/documentation were adjusted to....” In sum, this work is to be applauded and I look 

forward to its next iteration. Comments are offered in order of the questions posed, and in 

structured bullet form. 

Questions to address: 

1) Comments on: The overall approach to the specified modeling purpose and the 

particular methodologies chosen to achieve that purpose; 

 This model fills an important need for an independent capability to assess how 

manufacturers might respond to GHG emission regulations on light-duty vehicles. 

 There is much to recommend this model, which grapples with some key challenges of 

assessing how progress toward tighter fuel use or GHG emissions standards can be 

achieved through incremental vehicle technological change, and at what cost. 

 The essential approach of this model is consistent with others in a similar vein, with the 

most notable predecessor being the NHTSA “Volpe Model.” It describes the set of 

technological possibilities for improving vehicle fuel economy, or reducing GHG 

emissions, characterizing for each technology the cost and incremental change in 
emissions and fuel use. It determines a sequence of introduction for fuel-economy (or 

fuel switching) technologies necessary to meet a fleet-average CO2 emission constraint 

for each manufacturer. However it differs from some other approaches in significant 

ways: 

o 1. The sequence of discrete technologies that can be used for any single “Vehicle 

Type” is exogenously specified by the user. Those fixed technology successions t, 

t+1 ... for each vehicle type v ,essentially define a vehicle-type-specific supply 

(marginal cost) curve for emissions reduction. The model determines the 

sequence in vehicle types each separately progress in an orderly fashion down 

their emissions reduction technology curve. 

o 2. The model makes vehicle technology redesign decisions not annually, but for 

each vehicle “design cycle,” which is typically specified as a fixed number of 

years. 

o 3. The algorithm does not do a simultaneous choice of the set of technologies that 

minimize vehicle net costs such that the GHG emission standard is met. Rather it 

iteratively “dispatches” discrete new technologies by choosing which vehicle is to 

progress next by one more step through its sequence of technologies. It repeats 

this dispatching over vehicle types until the fleet average GHG emission standard 

is finally met. The choice of which vehicle type is to receive more advanced 

technology is based on one of two figures of merit, called “TARFs.” 

 It is wisely stated that effective model design hinges on a careful definition of its purpose 

or purposes, and an acknowledgement of its bounds and limitations. The documentation 

could be much strengthened in this regard. Here is my impression of its suitability: 

o This model is currently most suited to estimating the incremental net 

technological cost of any single manufacturer achieving various GHG emission 

levels, specified as an average for that manufacturer’s new-car fleet. It accounts 

for technology costs and lifetime fuel cost savings in its dispatching of 

technologies for each manufacturer’s fleet. Other attributes and societal impacts 

may be monitored ex post (e.g. the extensive and somewhat disparate list on the 

top half of p. 3, including criteria pollutant emissions, noise, congestion, refueling 

time, etc.) but these are not considerations in the model’s solution, i.e. in the core 

algorithm that sequences the application of vehicle technologies. 

o A compact way to describe the models approach is that, like the Volpe Model, its 

solution has two phases: “manufacturer compliance simulation” (with cost-based 

technology choice) and “effects estimation” (based on a diverse set of ex post

calculations). 

o The model does not project vehicle sales, or sales mix, or aspects of vehicle 

design and vehicle appeal to consumers, apart from altered lifetime vehicle capital 

and fuel use costs. This is not mentioned as a flaw, but as an important design 

choice that should be stated. Large changes in fuel economy and GHG emissions 

could have important indirect impacts on the design and appeal of the vehicle, 

particularly if tradeoffs are made in the areas of vehicle size, weight, 

performance, range, and, for alternative fuels, fuel availability and convenience. 

o The model treats each manufacturer’s regulatory attainment problem 

independently, and is not currently designed to model “flexible” emission 
standards that allow permit trading among manufacturers, permit banking or 

borrowing, or economy-wide GHG trading systems. 

Response: Dr. Leiby states that the current model is not able to reflect credit trading between 

manufacturers when determining compliance, or banking of credits. We agree that the model 

does not allow these types of credit programs to be modeled explicitly. However, completely 

flexible credit trading between manufacturers can be simulated by labeling all vehicles as being 

produced by a single manufacturer. The model then estimates the costs and benefits of bringing 

the entire industry’s new vehicle sales into compliance. Also, the flexibility to bank and borrow 

credits within a redesign cycle is implicitly assumed by the model. OMEGA assumes that a 

manufacturer’s entire fleet of vehicles can be redesigned within one redesign cycle. (Actually, 

less than 100% of vehicle sales can be assumed to be redesigned through the technology 

penetration caps included in the Technology file.) However, rarely will a manufacturer redesign 

exactly 20% of its vehicle sales in each of five straight model years. The base emissions and 

emission reductions of the vehicles being redesigned will vary. Thus, the banking and borrowing 

of credits will be needed to enable compliance with standards in the intermediate years of a 

redesign cycle using the technology projected for the final year of the cycle, assuming that the 

intermediate standards require gradual improvement each year. 

 Suitability of method 

o To some extent the discussion of the manifold ancillary benefits and costs can be 

a distraction, since a coherent and complete framework for their endogenous 

analysis is currently outside the scope of this model. I suggest that the model 

developers may wish to stay focused first on clearly and rigorously modeling the 

fuel-economy technology choice and cost-effectiveness considerations, for 

various GHG emission levels. Where possible, one reasonable design approach 

might be to assume that other vehicle attributes are essentially held relatively 

constant, for each vehicle size and type. 

o Overall, the model documentation suggests that model developers may be hopeful 

of doing too much soon, with many (over 10) stated intentions for future 

extensions. Better and sounder results may follow from strategically limiting the 

model scope, carefully testing the model (in full, with real datasets), and then 

selectively adding features over time. 

o One feature of this model approach is its comparative analytical simplicity but 

heavy reliance on specialized data inputs (discussed further in Item 2 below). 

This should be viewed as a model strength: its contribution need not rely on 

analytical sophistication, but also on the coherent application of good quality, 

widely reviewed data. 

Response: The current model does not allow the vehicle sales mix to change as a function of 

technology. When applying the model itself, EPA has developed effectiveness and cost 

estimates for the various technologies which hold vehicle attributes such as size and performance 

constant. Vehicle weight may change, as for example with dieselization or hybridization. 

However, in these cases, the effectiveness of the technology should reflect the change in weight. 
 Two major methodological points: 

o In any model, particularly any model of markets with social externalities and 

government intervention, it is essential to be very explicit about whose behavior 

and objectives are being modeled. Otherwise there is danger that nobody is really 

being described, or that we might impute particular knowledge and incentives to 

market actors who actually have neither. Naturally a model can be both 

normative, saying what should be done optimally, or descriptive, saying what we 

think will be done by some actors in certain circumstances even if it is not clearly 

optimal. And it can apply to what would or should best be done for different 

agents: vehicle consumers, manufacturers, or the government/society as a whole. 

I am a little unclear about whose behavior is being modeled in the succession of 

technology decisions made. It appears the intent is to model market behavior of 

competitive vehicle manufacturers facing cost-minimizing consumers and a firmwide emission constraint. But the objective of such a firm is not explicitly stated, 

and the solution rules are not clearly mapped to that objective. 

 In this matter it seems that the Volpe Model has set a good example by 

succinctly and specifically stating up-front whose behavior is being 

modeled: “The system first estimates how manufacturers might respond to 

a given CAFE scenario, and from that the system estimates what impact 

that response will have on fuel consumption, emissions, and economic 

externalities.” [P. 1, 

http://www.nhtsa.gov/staticfiles/DOT/NHTSA/Traffic%20Injury%20Cont

rol/Articles/Associated%20Files/811112.pdf] 

 Would a similar description not also apply to the EPA GHG model? 

o Given this idea of modeling the behavior of particular actors, e.g. manufacturers, 

in mind, the objectives of the actors should be reflected in the solution method or 

optimization condition. Bearing this in mind, there are some concerns with each 

of the two TARFs proposed as technology-dispatching figures of merit. 

 The “EffectiveCost” TARF is essentially the cost of each technology net 

of its discounted lifetime fuel savings (omitting the problematic “FEE” 

component, which seems mis-specified). Arguably, minimizing this 

would be a correct objective of new-vehicle consumers who discount fuel 

savings in the same way and given no change in non-cost vehicle 

attributes. This could also be the objective of competitive firms acting on 

behalf of prospective consumers. In a mixed integer program these costs 

would be minimized subject to meeting the emission standard, and the 

algorithm would choose the least cost combination of technologies. The 

possible problem is that the EPA GHG Model algorithm sequentially 

dispatches new technologies in order of EffectiveCost, but without regard 

to their effectiveness in reducing GHGs. Some technologies with low netcost could do little for GHG reduction. In the limit a low EffectiveCost 

technology, say using a high-GHG alternative fuel could even increase 

GHGs (FFVs with coal-fired corn-ethanol?). Regardless, there is no 

assurance that the suite of technologies finally assembled to reach the 
GHG standard in this way would be the low-cost suite. The authors may 

wish to consider when they recommend that the first, EffectiveCost 

TARF, is appropriate. 

 The “CostEff” TARF on the other hand leads to an algorithm sensitive to 

both cost and cost-effectiveness for GHG reductions. Such a cost-benefit 

ratio can lead to optimal selection rules for packing (knapsack or budget) 

problems. But some confusing terms are included in the TARF, most 

notably the non-standard way in which VMT is discounted for the 

purposes of this TARF (See equation top of page 11, line 1). The 

inclusion of “IR” (“the annual increase in the value of CO2”) in the 

discount factor is done without explanation or justification. While the 

term IR is never really defined (is it meant to be the growth rate in GHG 

damages, abatement cost, or a CO2 tax?). It inclusion seems to conflate 

considerations of social benefit (value of GHG avoidance over time with 

cost (of technologies). The vehicle manufacturer’s cost of GHG 

avoidance is already embodied in the TARF numerator. The denominator 

should perhaps only reflect the quantity of GHGs avoided. As currently 

written, this CostEff TARF would not seem to be a consideration for 

vehicle manufacturers whose objective is to produce a new-car fleet 

meeting consumer needs and a GHG emission standard at least cost. What 

objective was intended with this hybrid aspect of the TARF? 

o There are other important methodological points to raise, that are discussed below 

in Section 3 on conceptual algorithms. 

o At this point, please allow an extended comment on the model documentation. 

Clearly it is in draft form only, and there would be much benefit from improving 

and clarifying it. This is not simply a matter of fastidiousness, but is an essential 

aspect of making the intellectual case for this model. As it stands, understanding 

the model was much more work than need be. Some specific suggestions are: 

 Restructure the presentation, perhaps following the pattern of a journal 

article. (E.g., begin with stated purpose and background. Place this 

model in the constellation of related models and indicate what is different 

and why. Describe approach, data sources. Sample results.) 

 Bringing description of the “Core Program” and what the model does 

toward the front. 

 Clarify and condense the model description. Classically, this would 

involve: 

 State model objective (typically stating what is maximized, 

minimized, or what final solution condition is sought) 

 State model constraints 

 State and discriminate between principle decision variables, 

exogenous inputs, parameters, and internally calculated results. 

(This is not done in the variable list of Appendix 6, which also is 

incomplete. It omits AIE, PF, CAP, TCO2, IncrementalCost, 
TechCost, TARF, VMT, SurvivalFraction, AnnualMilesDriven, 

Leakrate, RefLeakage). 

 State the solution algorithm and termination condition 

 Rigorous use of notation. Currently, for example, the subscript i usually 

refers to “year” (eqns on page 10 and 11) but sometimes indexes 

technology (eqns at line 10 on p.12). 

 Use consistent variable names. For example, on pp. 16 and 17, it appears 

that the same variable is called “ModelSales”, “Sales,”, and “Annual 

Sales.” 

 Clarify subscripts and carefully apply them. The principle subscripts that 

seem to apply are: 

 t: technology number in sequence for each vehicle type 

 i: actually vehicle age, which is to be distinguished from year 

 y: year (which indexes, eg. fuel prices) 

 v: vehicle type 

 m: ma nufacturer 

 For example, equation at bottom of p. 12 is missing subscripts on 

AIE and RIE (presumably t), while GWP in that equation is 

indexed by technology t yet elsewhere (e.g. middle of page 11) it is 

not. 

 Carefully state units. Physical equations cannot be fully understood 

without a statement of the dimensions. For example, the equation in the 

middle of page 11 can be more readily understood if “Leakrate” is known 

to be in [g-GHG/yr], not [g-GHG/mi]. 

o Overall, the authors might wish to look at the documentation of the NHTSA 

Volpe model as a helpful template. 

 That documentation is actually reasonably compact (35 pp plus an 

extended guide to operation). 

 It gives an excellent, succinct prose summary of what the model does in 

the first 3 pages (1-3), and much of the wording might be applicable to the 

EPA model. 

 It clearly states what is being modeled: 

 There is a flow chart and a technology sequencing flow chart 

 Equations are then presented in orderly manner with consistent notation 

and subscripting. 

Response: The TARFs are intended to reflect the decision making of a manufacturer. Since the 

manufacturer must satisfy its customers and regulatory mandates, a manufacturer’s decision 

making processes will reflect these needs, as well. More explicitly, the technology cost is the 

full cost of that technology at the consumer level, including research and development costs, 

amortization of capital investment, etc. This cost is generally the same cost as EPA estimates in 

its regulatory support analyses when estimating the cost of new standards. This cost is not 
necessarily the increment in price that the manufacturer would charge for that technology, since 

price is a function of many factors which can change fairly quickly depending on market 

conditions. The fuel savings are those assumed to be valued by the customer, so they are based 

on fuel prices including taxes and reflect the timeframe which a customer might consider when 

purchasing a vehicle. The residual value of the added technology is not currently reflected in 

either TARF, but could be added in the future. The rationale behind the TARFs will be clarified 

in the model documentation to reflect these points. 

The Effective Cost TARF was included in the OMEGA model since it is the equivalent of the 

technology ranking process used in NHTSA’s Volpe Model. It allows a user to match this aspect 

of the Volpe Model when modeling equivalent standards using both models, if this is desired. 

We agree with Dr. Leiby that this TARF does not factor in the degree to which adding a 

technology will move a manufacturer’s fleet toward the regulatory target. The CostEff TARF 

was designed to incorporate this factor. 

We agree with Dr. Leiby that the inclusion of discounted GHG emission reductions in the 

denominator of the CostEff TARF is not consistent with the manufacturer focus of the numerator 

of this TARF. Future versions of the model will remove the discounting. The use of lifetime 

emission reduction in the denominator of this TARF will then be consistent with the standards 

contained in the EPA vehicle GHG proposal, where car-truck trading is based on lifetime 

emissions of each type of vehicle. 

The discounting of CO2 emissions is more appropriate for a TARF whose focus is societal 

effectiveness. Thus, we plan to add a third TARF which is similar to the CostEff TARF and 

which retains the discounting of GHG emission reductions in the denominator. Newer versions 

of the model allow for an increase in the real value of CO2 emissions per annum. Thus, we 

believe that the discount rate used in this new TARF should reflect the difference between the 

broad economic discount rate specified and the rate of increase in the value of CO2 emissions. 

When this TARF is used, it will be most appropriate to value fuel savings over the life of the 

vehicle and this will be suggested in the model documentation. 

We have significantly revised the model documentation, including the consideration of all of Dr. 

Leiby’s comments above. 

3) Comments on: The appropriateness and completeness of the contents of the sample 

input files. (EPA staff are not seeking comment on the particular values of the contents 

of the input files, which are samples only.) 

 First, an overall point on data. While the instructions urge reviewers to not consider the 

particular values of sample data, it must be born in mind that models are essentially 

datasets, the equations which link the data, and the algorithms for achieving the solution 

of those equations. In this case the model equations (in the documentation) are 

reasonably straightforward, although the algorithm for their solution is somewhat opaque 

(not explicitly stated and embedded within a compiled module). Assuming a reliable 

solution algorithm (something hard to test in this review and with limited data), model 
quality will then depend strongly on the quality of model data. This is particularly worth 

mentioning because many of the data needed for this model are not readily available from 

established sources. The model calls for detailed, specialized, knowledge about vehicle 

technologies, their costs, incremental contributions and interactions, their availability 

over time and across vehicle types, and the data-providers must determine the sequence 

of technology application within each vehicle type. Ultimately, this dataset is likely to be 

the most valuable and significant component of this model. Particularly if it becomes 

publicly available, and serves as a standard. Thus the data issues should not be 

minimized. 

 In all data input files, it would help minimize errors if units were specified. Kilograms or 

grams, etc. The “Fuel” datasheet does not indicate the unit for price ($/gge, in nominal 

$?. What are the units for electricity?) 

 The “Data Validation” capability and error report is a very useful feature. Ultimately the 

modelers may wish to error check almost all inputs for acceptable range, if that is not 

already done. 

Response: EPA will publish a complete set of input files which it used in its OMEGA model 

runs in support of its recent proposal to regulate GHG emissions from cars and light trucks. 

These input files were developed from publically available data explicitly to allow their full and 

complete release to the public for review, comment and use. 

We agree that better descriptions of the input data are needed. Incorporating these into the input 

file headings themselves involves changes to the core model. In the near term, we have included 

detailed descriptions of each type of input value in the model documentation for easy reference 

by the user. 

The validation criteria included in each of the model’s input files generally prevent the inclusion 

of clearly inappropriate values (e.g., negative values where only positive values make sense). 

The current criteria apply such restrictions to nearly all the input fields other than labels. In 

addition, the criteria can be modified by the user to incorporate additional or more restrictive 

criteria which are deemed helpful. This flexibility will be described in more detail in the model 

documentation. 

2a) The elements of the Market input file, Appendix 1, which characterize the vehicle 

fleet; 

 This file describes vehicle sales by manufacturer and vehicle type, and provides the 

attributes of those vehicle types. 

 No specific comments at this time. 

2b) The elements of the Technology input file, in Appendix 2, that constrain the 

application of technology; 
 As discussed above, this could be said to be the heart of the model. It requires both 

detailed technological knowledge and considerable judgment about the sequence, timing 

and impact of each technology. 

o It may be worth a special task just considering what range of technology attributes 

can reasonably be specified, even by a technology or industry expert. 

o The possible strong-sensitivity to data specification may also call for formal 

method of risk or sensitivity analysis, given limits on the ability to refine the data. 

 How are technology interdependencies across vehicle types represented? Given 

outsourcing and the cost reductions from component sharing, would the application of a 

technology for one vehicle type make it more likely to be applied to another vehicle type? 

I could not discern how such considerations are represented in the data, and reflected in 

the solution algorithm, if they are. 

 The data challenge is even greater if the stated goal of representing technological learning 

is pursued. While ultimately technological progress (through autonomous gains from 

R&D, scale economies and learning-by-doing) should probably be acknowledged in a 

later model version, benchmarking that progress is never easy. Moreover, technological 

learning and progress will be a function not of choices for each Vehicle Type (as the 

spreadsheet organizations suggests), but of industry-wide developments across vehicle 

types and manufacturers. 

o In our models on new vehicle technology introduction, we have found it useful to 

distinguish between 3 types of technological progress: autonomous progress over 

time due to R&D; progress or cost reduction due to production scale (units 

produced per plant); and progress from Learning By Doing (LBD). All three of 

these play a role, but the proper benchmarking of each is quite challenging. I 

agree learning should be approached, but cautiously because its specification and 

parameterization can have such a pronounced effect on model results. 

 Spot-checking these entries, I did not see any items associated with changing vehicle size 

and weight. This may be a design choice rather than happenstance for the sample data: 

technologies that substantially change the vehicle design and hedonic attributes for the 

consumer would call for a more rigorous assessment of net-value to the consumer, and a 

potential re-statement of objective (TARF sequencing rule). 

Response: Cost reductions due to learning are not yet incorporated into the model. Thus, there 

are currently no connections between the costs of technologies applied to different vehicle types. 

When learning is added to the model, the user will likely be able to specify whether this learning 

is based on the number of vehicles which receive this technology by manufacturer or industrywide. The latter approach will provide a connection between technology costs across vehicle 

types. We will consider the suggestions provided by Dr. Leiby as we develop the learning 

related algorithms for future versions of the model. Prior to the inclusion of learning, the user 

can input technology costs which reflect the anticipated use of a technology across vehicle types 

and manufacturers. These projections can be compared to the results of model runs and adjusted 

accordingly. 
As mentioned above, the current model does not allow the vehicle sales mix to change as a 

function of technology. Holding vehicle attributes such as size and performance constant when 

applying technologies simplifies the treatment of hedonics. A user could include a technology 

which included a change in vehicle size or other attribute. In this case, the user should adjust the 

cost of the technology to reflect the anticipated change in the vehicle’s value from a consumer 

perspective. However, the limitation of this approach is that it would not adjust the applicable 

footprint-based GHG standard if the reduction in vehicle size would actually change the 

vehicle’s footprint. There is currently no mechanism included in the OMEGA model for 

changing a vehicle’s footprint from its base value. The model would also not reflect any change 

in sales which might accompany such a change in vehicle size or other attribute. The user could 

project a change in vehicle size in future redesign cycles and estimate the technology and cost 

necessary to bring this adjusted fleet into compliance. The cost of the change in vehicle size 

could then be added outside of the model. EPA does not have any plans in the near term to 

incorporate a change in vehicle size and resultant changes in consumer choice into OMEGA in 

the near future. One researcher, David Greene, recently concluded that, given time for vehicle 

redesign, on the order of 95% of the fuel economy improvement induced by feebates is likely to 

be achieved through the application of improved technology rather than a shift in vehicle sales 

patterns.1

 Thus, ignoring changes in the fleet mix may not be a substantial limitation.

2c) Scenario input file, definition of the standard and economic conditions (Appendix 

3) 

2d) The elements of the Fuels input file, Appendix 4 

 This list does not yet reflect biofuels or renewable fuels, which are a growing 

consideration, in no small part due to recent law and EPA RFSs. 

 Some provision may be needed for the variable energy and GHG content of gasoline, 

as the ethanol content varies over time. 

 Provision may also be needed for E85, and the uncertain fraction of E85 use by FFVs. 

 The net fuel economy and emissions by PHEVs remains an area of continued study. 

EPA is well aware that fuel use by fuel type and resulting emissions depend on PHEV 

design (AER), consumer use patterns, time of recharging, and the fuel used for 

regional grid generation. Nonetheless, some simplified representation of the 

alternative PHEV designs will be needed soon. I was unable to ascertain what 

progress EPA has made in this area. 

2e) The reference data contained in Appendix 5. (Implied flexibilities and constraints 

of the model) 

 No specific comments 

Response: The current version of OMEGA focuses on gasoline, diesel fuel and electricity 

because the vast majority of current vehicle sales are certified on these fuels. Very few 

dedicated alternative fueled vehicles are sold and flex fuel vehicles are certified on either 

 

1

 “Feebates, footprints and highway safety,” Transportation Research Part D 14 (2009): pp. 375-

384.
gasoline or diesel fuel and numerical adjustments made to their fuel economy or emissions to 

reflect incentivizing regulatory credits. Future versions of the model will allow the user to 

include an anticipated level of FFV credits by manufacturer and by redesign cycle which will 

effectively adjust the required level of fuel economy or GHG emission control. 

Current legislation and enabling EPA regulations encourage the use of renewable fuels. 

However, to date, these requirements are not integrated with the regulations governing vehicle 

fuel economy, nor the standards contained in the EPA vehicle GHG proposal. Thus, the primary 

place which they intersect with the OMEGA model is in the calculation of benefits. As this is 

done in a spreadsheet, the user could easily modify the calculations to reflect an anticipated use 

of renewable fuels over time. EPA may develop a standard version of the benefits calculation 

spreadsheet in the future which facilitates this use. However, as suggested by Dr. Leiby above, 

this is not the first priority at this time. 

We agree that gasoline quality changes over time, but these changes are relatively small. We 

will consider including a varying quality for gasoline and diesel fuel over time in the benefits 

calculation spreadsheet as improvements are made to it. 

At present, the model assumes that PHEVs will be driven like any other vehicle. Given the 

difference in the economics of their use when driving over short and long distances, it is possible 

that PHEVs will be driven differently than other vehicles. Unless this is reflected in GHG 

regulations, however, the core model should treat PHEVs like any other vehicle. They could be 

treated differently in the benefits calculation spreadsheet. This difference could be reflected by 

the user using information already included in the spreadsheet (i.e., emission and sales per 

vehicle after the application of technology). EPA will consider incorporating the potential for 

such a difference once better estimates of how the operation of PHEVs might differ from 

conventional vehicles becomes available.

3) The accuracy and appropriateness of the model's conceptual algorithms and equations 

for technology application and calculation of compliance; 

 Equations for technology application: 

o The sequence of technology application, and timing and extent of application, for 

each vehicle type, is exogenous. 

o Modelers acknowledge that “This approach puts some onus on the user to develop 

a reasonable sequence of technologies.” As noted, the onus may in fact be quite 

substantial. Therefore, it is helpful that the model “produces information which 

helps the user determine when a particular technology or bundle of technologies 

might be ‘out of order.’” [p. 7] Any such capability to assist the user with stage-1 

exogenous technology sequencing for individual vehicle types is worthy of further 

development and greater prominence in the documentation and model. 

o The Volpe model seems to currently offer more facility for specifying the 

structured sequences introduction of technologies or groups of technologies. The 

EPA GHG Modelers may also wish to develop some tools that make it easier for 

users to group and sequence technologies, perhaps even with logical diagrams that 
map to or from the Technology.xls dataset. This would help experts represent 

their best judgement about technologies can or would be applied. 

o While this model allows for substantial technological detail, there will always 

arise further, potentially important, complexities. In this review I could not 

determined the degree to which the model can account for cross-vehicle-type, or 

cross-manufacturer, interactions in the selection and sequencing of technologies. 

For example, various forms of hybridization are mentioned as technology options. 

We already see that one manufacturer, Toyota, develops a hybridization 

technology for one vehicle it quickly spread to other vehicles from that 

manufacturer, and that same technology is also sourced to other manufacturers 

(Nissan). Can this be represented in some way? 

o P. 17 says: “Finally, the model determines the order in which technology 

packages are added to vehicles. The model first compares the TARFs 

corresponding to technology package 1 on all of the different vehicle types in the 

fleet and chooses the combination with the lowest TARF.” 

 What does “combination” mean here? I understand it to mean the model 

chooses a combination (pair) of particular vehicle v and technology step t

(advancing from t-1 to t). 

o Technical points on the TARF-based rules for technology application (Equations 

p. 14): 

 As mentioned, net cost (“EffCost”) alone would not seem to be adequate 

for sequencing GHG-reduction technologies 

 The inclusion of a FEE for non-compliance has some issues (admittedly, 

the Volpe Model does something like this as well, but the justification is 

not compelling): 

 It embeds the cost of non-compliance in an algorithm that ends 

only with compliance. Hence the fee should ultimately be zero. Is 

the intent here to employ some sort of penalty-function based 

algorithm for constrained optimization? 

 “Non-compliance” is a manufacturer-wide condition, and cannot 

be associated with a specific individual vehicle or technology 

(Note: I believe the TARF measures should be subscripted with 

m,v, and t, to highlight that they are specific at that level). 

 As written, the FEE is applied to the change in fuel economy 

(mi/gge, MPG) for that particular technology step. This is not a 

measure of non-compliance, and its essential effect is to exaggerate 

the relative importance of fuel savings. Note that the fuel-savings 

term is proportional to (FCt-1 - FC t) while the Fee term is 

proportional to (1/FCt – 1/FC t-1), essentially a monotonic nonlinear transformation of fuel-savings. So even though there will be 

compliance an no fee, the effect will be to boost the weighting of 

fuel savings in a non-linear way. 

 A maintained assumption is that fuel economy technology will not alter 

sales volume or share. But does or could vehicle sales volume influence 

the choice of technology introduction? I only noted “Sales” being 

referenced in the post-processing calculations, and it is used in the tests for 
compliance. But sales is not a consideration in the TARF for a vehicletechnology pair, nor in the terms leading up to it, so the technology 

sequencing is based entirely on per-vehicle cost analysis. This approach is 

taken in other models and is not unreasonable. But if technology learning 

or scale economies matter, for example, the choice of which vehicle to 

apply the next technology to could be related to the sales volume of 

particular vehicle-types. 

 As mentioned, the non-standard adjustment of VMT discounting in the 

denominator of the CostEff TARF should either be eliminated or more 

explicitly and rigorously motivated. As it stands it seems to either mix 

social benefits of GHG reduction with the manufacturer’s objective of 

meeting the emission standard. 

o On p. 13, the equation for Fuel Savings (FS) seems to be in error. Fuel price (FP) 

is divided by i, which denotes the age of the vehicle (year after its production). Is 

this simply a typographical error and a discount factor was intended (e.g. 

(1+DR)i

?) 

 In all cases where the lifetime value of fuel savings in considered, the 

challenge is to be clear about whose valuation of fuel savings is being 

calculated. It is widely observed that consumers, when making new 

vehicle purchase, may “undervalue” fuel savings either with a higher 

discount rate or a short planning period than actual vehicle operating life. 

I understand that these issues are probably behind the formulation used 

here, but it would help to be more explicit. If manufacturer decisions are 

being modeled, the relevant question seems to be “How many years of 

discounted fuel savings would the manufacturer assume it will be able to 

recover from the consumer through the vehicle sale price?” 

With respect to the flexibility afforded by the Volpe Model, the Volpe Model separates 

technologies by the aspect of the vehicle being modified (e.g., engine, transmission, accessories, 

vehicle (aerodynamic drag), etc.). A path is specified for the application of technology within 

each group. These paths are embedded in the model code and cannot be modified by the user. 

In contrast, with OMEGA, the user can modify the order in which technology is applied. 

We agree with Dr. Leiby that the development of the technology steps is both integral to the 

model’s operation and a challenging task. EPA will consider developing spreadsheet tools and 

procedures which will assist a user in developing such inputs. However, since modifying a 

vehicle is a complex engineering task, developing model inputs which reflect such changes will 

never be simple. EPA will publish its Technology input file which was used in its OMEGA 

modeling to support its recent proposal of GHG standards. The regulatory support documents to 

this proposal also describe how EPA developed these inputs. In general, the cost of the 

flexibility afforded by the approach taken in this area is greater responsibility with regard to the 

technological inputs to the model. 

The OMEGA model applies technology to one vehicle at a time, but does so by evaluating the 

costs and benefits of technology applicable to a manufacturer’s entire vehicle line. This is 

possible, since essentially every vehicle model is redesigned once during every redesign cycle. 
This causes OMEGA to apply more consistent levels of technology to all of a manufacturer’s 

vehicles. Thus, OMEGA would generally not predict the application of hybrid technology to one 

vehicle, while applying little or no more conventional technology to another vehicle. The 

exception would be if the TARF for the hybrid technology was less than that for the conventional 

technology, meaning that the former was generally more cost effective than the latter. Models 

evaluating compliance annually can sometimes apply very disparate levels of technology from 

one year to the next based on the number of vehicles which can receive major technological 

change in each year. Also, in our analyses in support of the EPA vehicle GHG proposal, EPA 

grouped vehicles by platform and engine size. This avoids applying one level of technology to 

the sedan configuration and another to the coupe configuration of a vehicle built on the same 

platform. 

At the same time, OMEGA would predict that Toyota, to use Dr. Leiby’s example, might 

hybridize the sales of the Camry up to the cap set for hybridization of this vehicle type and none 

of the Corolla sales. In reality, Toyota might choose to hybridize a portion of both vehicles. 

EPA does not believe that any model can predict the precise use of technology on every vehicle 

for a given fuel economy or GHG standard. Models such as OMEGA produce a reasonable 

estimate of the total application of various technologies and their overall cost. The user must 

interpret the results at this level and avoid putting too much confidence in the model’s 

predictions for any specific vehicle. 

Manufacturers can also introduce technologies for various reasons. Some technologies, such as 

the early hybrid models, were introduced for marketing purposes and to develop experience. 

Some technologies were developed for overseas markets and are sold in small numbers in the 

U.S. A model which uses economic efficiency as its primary tool for applying technology will 

not be able to capture these vagaries in technological application except by including them in the 

baseline fleet (i.e., as being outside of the impact of the GHG controls being evaluated). 

Incorporating manufacturer-based learning into the cost estimation will help somewhat, as this 

will lower the cost of technology for those companies which have already applied certain 

technologies in the past. However, again using Dr. Leiby’s example, no regulatory model would 

predict that Toyota would introduce hybrids over a number of their vehicle lines, as the use of 

this technology was not driven by regulation. 

On page 17 (of the model documentation), “combination” referred to a combination of vehicle 

and the next technology available to that vehicle. This has been clarified. 

The CAFE compliance fee is included so that the user can match this aspect of the DOT Volpe 

Model if desired. As discussed in Section H of John German’s comments, such a fee or fine is 

not applicable to an EPA GHG standard and would normally be set to zero by the user. We 

agree that the calculation of the impact of the CAFE fee was performed incorrectly in the version 

of the model which was reviewed. This has been corrected. 

Regarding the potential impact of sales volume on the TARF, this will need to be considered 

when EPA incorporates learning into the model. Some projection of the sales volume over 

which a technology might be applied will likely have to be made when calculating the TARFs. 

Then, at the end of the model run, the technology cost can be adjusted to reflect the actual use of 
each technology. If the TARF is based on the level of technology use up to that point in the 

model run, the cost of the same technology could decrease (for TARF calculation purposes) 

during a run when in fact it is the same. Manufacturers can generally be assumed to be forward 

looking when deciding to apply technology, considering the sales volume which will receive the 

technology over at least a full redesign cycle of all of their vehicles. Of course, if the learning is 

occurring at the supplier level, costs will decrease based on total industry sales, which will not 

bear any semblance to the level of application occurring to the first manufacturer’s vehicles. In 

this case, the model output could even be dependent on the order in which the model evaluated 

the various manufacturers, which is not desirable. Thus, it will likely be best to predict the 

market share of various technologies, learn costs accordingly, calculate TARFs, apply the 

technology and adjust costs as necessarily to reflect lesser or greater application of each 

technology. 

The model documentation on page 13 has been corrected. 

The fuel savings are those believed to be valued by the consumer when purchasing a new 

vehicle. The user sets these savings primarily through the payback period. The model then 

discounts the savings using the standard economic discount rate used elsewhere in the model. If 

the user believes that a consumer discounts fuel savings at a greater or lesser rate, she or he can 

adjust the estimated payback period to reflect the fact that the model uses a different discount 

rate in this calculation. 

 Calculation of compliance to Attribute-based standards: 

o An overarching feature of the methodology is that progress in reducing 

GHGs/fuel-use occurs by advancing drivetrain technology and other attributes 

largely transparent to the consumer. Technologies are sequenced based pervehicle figures of merit, assuming no impact on vehicle designs (apart from fuel 

use technology) and constant vehicle sales shares. One issue to consider is 

whether these assumptions of unchanged vehicle and unchanged sales mix 

become less defensible for attribute standards like the footprint standard. 

o On page 7, equation for the logistic-based footprint, there appears to be a sign 

error in the denominator (should be 1+exp((x-C)/D) not 1-exp((x-C)/D)). This is 

likely a typo in the documentation alone. 

 Calculation of compliance to possible market-based standards 

o No discussion or provision for market-based (permit trading) standards is yet 

made. This should at least be acknowledged. 

o One strategy for doing more flexible standards would be to simply merge the 

datasets and technology-sequence stage for all manufacturers and vehicle types in 

a trading group. However, this would not provide information about potential 

permit prices and burdens across manufacturers. 

Response: EPA believes that the vehicle sales mix will actually be less affected by attributebased standards compared to universal or flat standards. A universal standard encourages 

smaller vehicles. An attribute-based standard applies a more stringent standard to smaller 

vehicles, negating some or all of the natural reduction in GHG emissions which comes with 

reducing vehicle size and weight. In either case, the relationship between consumer purchase 
preferences, vehicle cost and fuel economy is very complex and not well assessed. A number of 

models have been developed to simulate these relationships, but they appear to differ 

substantially, especially regarding consumers’ valuation of fuel economy. EPA may incorporate 

such effects into future versions of OMEGA. However, a first step in this direction would be to 

couple the two types of models and run them iteratively and see if they converge. 

The documentation of the constrained logistic curve formula has been corrected. 

The inclusion of permit-based trading beyond the light-duty vehicle market is currently beyond 

the scope of the model. We agree with Dr. Leiby that the user could simulate the net impact of 

flexible credit trading across manufacturers by labeling all vehicles with the same manufacturer 

name. In addition, examination and analysis of the compliance cost per vehicle should provide 

sufficient information to estimate the permit prices implied. However, the user would have to 

develop these algorithms. 

4) The congruence between the conceptual methodologies and the program execution 

(examining the results with good engineering judgment) 

 This is difficult to assess and a careful validation of this model’s execution would require 

further examination. The results appear generally reasonable, but that is a weak test. 

 I was only able to experiment with cases for one design cycle. The longer-term cases 

involving multiple design cycles are more challenging. It has been noted the model 

solves for design cycles independently of one another. So it would be worthwhile to test 

what this implies for the sequence of technologies used from one cycle to the next. 

 One observation is that the inclusion of the non-compliance FEE does affect the model 

solution and choice of technologies. As mentioned above, the theoretical justification for 

this is not well formed, given that all manufacturers are typically assumed to end in 

compliance. However, I did not that the impact of including the FEE is modest, only 

changing per-vehicle costs by a few dollars. However, for at least one manufacturer (#9) 

the cost and technology sequence changes significantly. I am not sure this is a desirable 

outcome. 

 Also, simple tests with the sample dataset show a relative insensitivity to the choice of 

TARF. This was surprising, and needs more investigation. 

Response: The limited role of the FEE was discussed earlier. We assume that Dr. Leiby is 

referring to an insensitivity of the TARF to a change in the value of the FEE. This is not 

surprising, since the CAFE fine of $55 per mpg is much smaller than the fuel savings associated 

with a 1 mpg change in fuel economy. The level of the FEE has little effect on the order of 

technology application since it tends to reduce the value of the TARF for all technologies 

roughly proportionately to the fuel savings already included in the TARF calculation. 

5) Clarity, completeness and accuracy of the calculations in the Benefits Calculations output 

file, in which costs and benefits are calculated; 

 This system produces a large number of useful side calculations. 

 Again, further investigation is necessary to investigate their accuracy. 

 Overall, a careful independent validation of the two phases of this model’s execution 

(manufacturer compliance simulation and effects calculation) would be well worthwhile. 
The code for compliance simulation is compiled and not visible. Working through the 

logic in the post-processing calculations of the BenefitsCalculation spreadsheet would 

take a bit of time. But it would be worthwhile. Overall a useful validation effort could 

probably be complete in about a week of focused attention. 

Response: The inclusion of a value in the benefits calculation is not meant to automatically 

convey accuracy. This will be clarified in the model documentation. The user is ultimately 

responsible for all input values used in the modeling. Of course, if the user uses an input file 

published by EPA in some context other than simply providing an example, then the EPA 

analysis referencing that model run will support the choice of values used. As Dr. Leiby noted 

above, review of the inputs to the model or benefits calculation spreadsheet was not part of the 

peer review charge. 

EPA’s publishing of the results of its OMEGA modeling and estimated benefits of the proposed 

vehicle GHG standards should accomplish much of the task referred to in Dr. Leiby’s last 

comment, as such inputs and outputs will be subject to a full review by the public during the 

comment period for that proposal. 

6) Clarity, completeness, and accuracy of the model's visualization output, in which the 

technology application is displayed; and 

 The XML format for data transfer and display is a very good design choice, allowing 

flexibility, modern data-exchange capability, ready output to internet, and easy extension 

of the report. 

 This display in the visualization output is useful overall, but it seems more oriented 

toward “expert users” who are willing to wade through details to find understanding and 

the information they need. 

o TechPack are reference by number only, but perhaps could easily be labeled with 

the full name or 4-character abbreviation, or cross-reference by hyperlink to a 

description of the technology. 

o Additionally, hyperlinks could be added that would allow the user to easily jump 

to the table for a particular manufacturer or vehicle type. 

 It would be very helpful to have some graphical summaries of the input and output 

results. 

 All output files should embed clear documentation on the inputs used. E.g. 

o The .log file does list names of the 4 input files, which is essential. 

o The “Visualization Output” file does not (yet) report the input files (but the 

information could be retrieve from the XML file). 

EPA has improved the formatting of the output files, including better labeling of technology 

packages which have been applied. We will consider the use of hyperlinks and graphical outputs 

in the future. Output files now include date and time stamps plus the names of the input files 

used. 

7) Recommendations for any functionalities beyond what we have described as "future 

work." 
 Clearly defined improvements that can be readily made based on data or literature reasonably 

available to EPA 

o First I note that there were multiple references to “future work.” It may be helpful for 

EPA to construct a list of these prospective improvements, and establish priorities and 

a staged, progressive approach for revision. Specific releases of the model with 

carefully specified functionality will allow prospective users at EPA and elsewhere be 

clear about what the model is and can do at any point in time. 

o While the model has a number of valuable aids to execution and reporting (input 

validation, automated generation of run logs, XML data, and “Visualization” tables 

for web/browser display), more could be done here to improve usability and provide 

greater insight about each case run. Comparatively simple revisions and extensions 

to the operational procedures and output could be well worthwhile. 

 Provision for side-by-side case comparisons, reporting or graphing difference. 

 Case management and logging facilities. 

 Currently the system labels every file with generic name concatenated 

to a time-date stamp. Very quickly a directory can be cluttered with 

cryptically named log, xml, htm files. 

 A case archiving facility, that compresses all input and output files to 

document the case, might be useful 

 The ability to specify a CaseName in the Scenario file, that then 

becomes part of each output file, would also be helpful. 

 When the VGHG.exe file reads a scenario file, it does not record, or at 

least display, the name of the file read. It is easy to forget which case 

was read if you step away, or are doing many cases. 

 Relatedly, the purpose of the VGHG.exe’s separate menu options is 

not yet clear to me. 

o It seems that once a scenario and the associated datafiles are 

read, execution would be the logical next step. The scrollable 

tables from data input are really too constrained a view to 

allow useful review or verification of the data. 

o Once the case is run, it seems “Save” to XML might be 

automatic, otherwise one is limited to the text-based log files, 

that omit summary information. “Saving” seems needed for 

Visualization and Benefits Calculation in the spreadsheet. 

o So perhaps VGHG.exe might load-run-save in one step, 

although I may be missing something important. 

 Graphical capabilities [more thought required here about exactly what graphs 

would be most useful. But there are many data in the tables, and they are not 

simple to process mentally.] 

 Improvements that are more exploratory. 

o Extension to accommodate flexible/market-based emission or fuel-economy 

regulations. 
 Permit trading extensions, constructed by pooling selected vehicle 

types/classes, and/or manufacturers, during the compliance phase of the 

analysis. 

 Ex post calculation of implied permit prices based on marginal costs of 

compliance (measured by the cost/GHG reduction of the final technology 

pack applied). 

 Ex post calculation of economic implications for individual manufacturers, by 

comparing results with and without trading/pooling, and accounting for the 

implied costs and revenues from permit exchanges between manufacturers.

o Extensions to consider endogenous (standards-induced) changes in vehicle attributes. 

These are a higher challenge, but would be very valuable for an improved 

understanding of the market responses to regulations. 

 Endogenous changes in sales volume/mix 

 Endogenous changes in vehicle size/footprint 

Response: EPA appreciates these suggestions and will consider them for future model 

development activities 

C. Comments by Dr. Jonathan Rubin 

I would like to congratulate the EPA for undertaking to build this tool which will be very useful 

for possible regulatory compliance and anticipated and unanticipated policy analyses. The 

construction of such a tool requires extensive expertise, professional judgment, necessary 

compromises and assumptions. The validity of the output will of course depend on these factors 

as well as the data available to populate the model. 

My comments are based on my review of the materials provided to me by Southwest Research 

Institute: the EPA vehicle GHG Emission Cost and Compliance Model Description and 

associated attachments and appendices and the VGHG model and the associated spreadsheets. 

These comments reflect my understanding of EPA’s possible use for this model for regulatory 

compliance as well as use by external researchers and policy analysts who may use the model for 

analyses of state and regional policies. 

My comments below respond to the particular questions posed in the transmittal letter from 

Southwest Research Institute. 

Overall Approach to the specified modeling purpose and the particular methodologies 

chosen to achieve that purpose 

The authors have clearly put in a great deal of work on this challenging project and should be 

commended for an excellent start. That said, more effort and thought needs to go into what I call 

the accounting stance. On page 2, line 42-43 (p. 2, l. 42-3) the documentation states that “The 

primary cost of the GHG emission control is the cost of the added technology compared to the 

baseline.” My question is: “cost to whom?” Costs to consumers will differ from costs to society 

or costs to manufacturers. At times, the documentation reads as though these are costs to 
manufacturers – since CAFE fines are considered; other times the costs seem to be towards 

consumers or society. These accounting stances will differ for several reasons: 1) private and 

social discount rates differ, 2) social and private risk differs (on average technology performs as 

well as expected, but not for each vehicle), 3) subsidies to purchase plug-in vehicles or other 

advanced technology vehicles drive a wedge between private and social costs, 4) subsidies to 

biofuels and electricity at the state level (exemption for some or all road-use tax) mean that 

consumer costs are not equal to full resource costs. Clarifying the accounting stance is a high 

priority, because many further calculations rely on its clear definition. 

Since the potentially regulated agents are vehicle manufacturers, my recommendation is to 

define costs as the costs to manufacturers of incremental technology and vehicle re-design costs. 

The net costs to manufacturers are equivalent to the incremental costs of fuel economy 

technology less any increase in retail prices that manufacturers can charge for more fuel efficient 

vehicles. This should be equal to some portion of the expected fuel savings plus any changes in 

the hedonic value of vehicles due to changes in vehicle performance, noise, size, and refueling 

time (more on this later). By separating out manufacturing costs more clearly from consumer 

valuation of vehicles, the presentation will be more transparent. This also will make clearer the 

distinctions between consumers’ rates of discount from manufacturers’ costs of capital from 

society’s rate of time preference. 

Additionally, I recommend that the net costs clearly incorporate and identify all subsidies (for 

electric or plug-in hybrid vehicles and alternative fuels) but display costs and benefits separately 

to private agents (manufacturers, consumers) and society. These will generally not be the same. 

For example, the benefit calculation spreadsheet “Externalities” adds together consumer money 

saved on fuel with savings from lower oil imports. I would be very surprised to learn that the 

assumptions of the discount rate or risk premium or both in the calculation of benefits of reduced 

crude oil imports are the same as consumers’ discount rates for expected future gasoline savings. 

Response: Broadly speaking, the model is designed to project the application of technology 

which is controlled by the manufacturer of the vehicle, but which is also influenced by consumer 

preferences and governmental requirements. Then, once this technology has been selected, the 

model sums up the costs and benefits associated with the application and use of this technology 

from the view of society in the benefits calculation spreadsheet. This is consistent with EPA’s 

approach to the estimation of costs and benefits in its mobile source rulemaking analyses, 

including the recently proposed vehicle GHG standards. EPA often evaluates costs and benefits 

using two or more discount rates, reflecting the time value of money from different perspectives 

(e.g., private and public). The user of the OMEGA model can perform this task by modifying 

the discount rate in the benefits calculation worksheet after the results for any particular 

OMEGA model run have been loaded. In the analyses supporting the proposed vehicle GHG 

rule, EPA developed technology costs which are based on piece costs, the cost of assembly plus 

an intermediate markup factor which accounts for indirect corporate level costs and a reasonable 

level of profit. These costs were used in the OMEGA model to estimate the average cost of 

added technology per vehicle for the rule. Thus, they were used to represent the cost per vehicle 

from both manufacturers’ and society’s perspective. As EPA develops the OMEGA model 

further, particularly if the explicit treatment of capital investment requirements is incorporated in 

to the technology application process, it may be more important to explicitly treat technology 
costs differently depending on entity experiencing the cost (e.g., manufacturer, consumer, 

society). 

A special case where such separate treatment of costs could be very important is the availability 

of subsidies of the purchase of vehicles equipped with certain technologies (e.g., plug in hybrids, 

electric vehicles, etc.). As discussed further below, if sizeable subsidies apply to vehicles 

equipped with technologies which are being added by the model, these should be reflected in the 

manufacturer’s choice of technology. Currently, the model does not facilitate the availability of 

purchase subsidies. Their existence must be addressed by using different costs per vehicle when 

technology is being selected and when societal costs are being determined. 

In addition to the use of these costs when summing up the cost of technology at the vehicle, 

manufacturer and industry levels, the model also uses the same technology costs to calculate the 

TARF, which is in turn used to decide which technologies get applied to specific vehicles. The 

TARF does not necessarily reflect the perception of costs by society. The two TARFs currently 

included in the model are intended to reflect the decision making of a manufacturer and thus, 

reflect costs from the point of view of the manufacturer. Since the manufacturer must satisfy its 

customers and regulatory mandates, a manufacturer’s decision making processes will reflect 

these needs, as well. More explicitly, the technology cost is the full cost of that technology at the 

consumer level, including research and development costs, amortization of capital investment, 

etc. This cost is generally the same cost as EPA estimates in its regulatory support analyses 

when estimating the cost of new standards. This cost is not necessarily the increment in price 

that the manufacturer would charge for that technology, since price is a function of many factors 

which can change fairly quickly depending on market conditions. The fuel savings are those 

valued by the customer, so they are based on fuel prices including taxes and reflect the 

timeframe which a customer might consider when purchasing a vehicle. The residual value of 

the added technology is not currently reflected in either TARF, but could be added in the future. 

The rationale behind the TARFs will be clarified in the model documentation to reflect these 

points. 

The same technology costs are used in summing up the cost of all the technology which is 

applied to vehicles in benefits calculation worksheet. This is consistent with the treatment of 

technology costs in regulatory analyses supporting recent EPA rulemakings, including the 

recently proposed vehicle GHG standards. These analyses often develop the consumer level 

costs from material costs, labor, capital investment and profit at the supplier and manufacturer 

level. 

The current OMEGA model does not account for the availability of subsidies toward the 

purchase of certain types of vehicles, such as PHEVs or EVs. Such subsidies clearly affect the 

consumer’s valuation of these vehicles and the likelihood that manufacturers would implement 

these technologies. In terms of the model’s proceses, these subsidies change the cost of 

technology as perceived by the consumer as reflected in the TARF. A user could reflect this by 

including the subsidy in the cost of these technologies in the Technology file. The OMEGA 

model would then apply the technology considering the subsidized price. The user would then 

have to add the value of the subsidy to the costs as estimated in the benefits calculation 
spreadsheet (and other output formats) in order to fully estimate societal costs. This limitation 

does not affect EPA’s use of the OMEGA model in support of its proposed vehicle GHG 

standards, as none of the technologies projected to be required currently receive subsidies. 

However, this issue could be important for analyses evaluating vehicle GHG standards further 

out into the future. EPA will consider ways to incorporate such subsidies into future versions of 

OMEGA. 

2) The appropriateness and completeness of the contents of the sample input files. 

d) The elements of the Market input file, as shown in Appendix 1 of the model description, 

which characterize the vehicle fleet 

If the data are available, it would be useful to have the cross-price elasticities for makes and 

models or model segments such that mix-shift impacts could be taken into account as vehicle 

prices rise in response to additional technology packages. 

Some of the market data are interesting, but do not seem necessary. For example, what is the use 

of knowing a vehicle’s structure (e.g., unibody) or the maximum seating capacity? 

Does the market spreadsheet contain data for mid-size trucks, gross vehicle weight 8,500 -

10,000? If not, I would think it should, given that they are now covered under the revised light 

truck CAFE rules. 

Response: EPA agrees that it would be desirable at some point to incorporate the impact of 

increased vehicle cost, improved fuel economy and other factors on vehicle sales. However, this 

is beyond the scope of the OMEGA model at this point. The relationship between consumer 

purchase preferences and vehicle cost and fuel economy is very complex and not well assessed. 

A number of models have been developed to simulate these relationships, but they appear to 

differ substantially, especially regarding consumers’ valuation of fuel economy. EPA may 

incorporate such effects into future versions of OMEGA. However, a first step in this direction 

would be to couple the two types of models and run them iteratively and see if they converge. 

As mentioned in Section G of Mr. German’s comments, the current market file format includes 

several vehicle parameters, such as weight, seating capacity, etc., which are not currently used by 

the model. These aspects of the vehicle were included in the Market file as place holders for 

potential attribute based standards which could be based on these factors. We will modify the 

model documentation to clarify that these data fields are not used by the model. 

The example market file provided the peer reviewers does not necessarily include all vehicle 

classes potentially addressed by future GHG regulations. The OMEGA market file which will 

be published as part of EPA’s proposed vehicle GHG rule will include medium-duty passenger 

vehicles which are above 8500 pounds GVWR. 

e) The elements of the Technology input file, in Appendix 2, that constrain the application 

of technology 
Are the incremental costs shown in column X retail or wholesale? What do they assume about 

the volume of production? If I read the file correctly the incremental price for plug-in hybrid 

technology often has a low first cycle cap of 5%. Is the incremental cost of this technology 

consistent with its use on 5% of a market segment of a given manufacturer? It is important to 

clearly define the relationship between scale of use and incremental technology cost. The 

columns “a”, “Decay”, “seedV”, “kD”, and “cycle learning available” need further clarification. 

P. 2, l. 14 notes that the GHG target can be set as a function of vehicle footprint. The technology 

input file does not show an indication of how down-weighting and changes in footprints may be 

used to meet a set of given standards. This may not be able to be accomplished immediately 

given available data, but it should be considered as more experience with the footprint standards 

is gained from CAFE compliance. 

Response: Please see the response under Section #1 above for a discussion of how technology 

costs are treated in the model. As described in the peer review charge, the specific inputs 

provided to the reviewers were for example purposes only. Therefore, they do not represent any 

particular sales volume. EPA has published the Technology file which it used in its OMEGA 

modeling in support of its proposed vehicle GHG standards. This file contains official EPA cost 

estimates and the Draft Joint Technical Support Document for the proposed rule describes how 

they were developed. 

We agree with Dr. Rubin that the model documentation did not describe how the Initial 

Incremental Cost, , Decay, seedV, kD, and Cycle Learning Available fields are used in the 

model. These inputs are related to the prediction of cost reductions due to learning, which has 

not yet been implemented in the model. These columns appear in the Technology file as place 

holders for future version of the model. 

Weight reduction can be a technology which is input to the model or part of a broader 

technology package input to the model. The effectiveness and cost of this weight reduction is 

estimated in the same manner as any other technology. EPA included weight reduction in the 

technology packages which it evaluated in support of its recent proposed vehicle GHG standards. 

In doing so, EPA held vehicle size, footprint, utility and performance constant. 

It is currently not possible to include a technology in the model which changes a vehicle’s 

footprint. The TARF for such a technology would be quite complex, since both the 

manufacturer’s corporate-wide emission standard and the vehicle’s emissions would change 

simultaneously. It is possible that the technology could move the manufacturer further from 

compliance. Such a change would also likely change a vehicle’s utility and its perceived value. 

Thus, this would be a step towards projecting a change in sales mix as a function of technology 
cost, which is currently beyond the scope of the model. It is possible that such capability could 

be added in the future.

f) The definition of the standard and economic conditions in the Scenario input file, as 

shown in Appendix 3 

As per my earlier comments, I think there ought to be a place for 3 different discount rates: 

consumers, manufacturers and society. Similarly, their ought to be a places for payback periods 

for consumers and society. 

Response: As mentioned above, the TARF calculation focuses on the point of view of the 

manufacturer. As discussed in Section 1 above, a manufacturer may view technology costs 

differently than society. This difference can be reflected in the development of the per vehicle 

technology cost (i.e., the amortization of any capital equipment or other investment required to 

implement the technology). In the TARF calculation, the technology cost occurs at the time of 

vehicle purchase, so it is not affected by the discount rate assumed. The treatment of the 

increased cost of vehicles across model years occurs in the benefits calculation spreadsheet. The 

costs and benefits addressed there are intended to reflect those of society. Thus, use of societal 

discount rate is appropriate at that point. 

The primary place where a consumer discount rate comes into play is in the value of the fuel 

savings in the TARF calculation. It is likely that the typical new vehicle purchaser discounts fuel 

expenditures differently than society. However, the user also has the flexibility to set the 

payback period over which these fuel savings are determined. To the degree that the consumer 

discount rate differs from the societal discount rate, the user can adjust the otherwise appropriate 

payback period to compensate. 

d) The elements of the Fuels input file, as shown in Appendix 4, which characterize the fuel 

types, properties, and prices 

It would be useful to reference the data sources for many/most of the data items. For example, 

energy density – please see EIA report XYZ. The value shown for gasoline, for example, at 

115,000 is different than that published by the USDOE, Transportation Energy Data Book v 27 

(Davis, Diegel, Boundy, 2008, Table B4), which shows a (lower heating) value of 115,400 

Btu/gallon. 

The units should also be displayed for all inputs. Again, using the gasoline example, being 

familiar with the data, it is clear that the unit of analysis is Btu/gallon (lower heating value). For 

other data, the units are less obvious. For electricity, the input file or the documentation, or both, 

should give the assumed conversions from kilowatts to energy density or motive energy such that 

users can adjust for different end-use efficiencies. Also for electricity, the assumed grid mix 

should be given with conversion rates such that users can make appropriate adjustments for 

different policy analyses. 

I do not see a statement indicating whether the fuel price data is in nominal or real dollars. 
I do not see a row for ethanol giving its energy density, mass, and density. I am assuming that 

fuel type “EL” is electricity. Also, should you not have at least two types of ethanol – corn and 

cellulosic – with different price paths? 

As I indicated in my earlier comments, I think it is important to explicitly note the role of 

subsidies when determining costs. Given this assertion, the fuels data file ought to explicitly note 

federal and state average subsidies (i.e., the federal blender’s tax credit and foregone state excise 

taxes) for ethanol and other alternative fuels. As I note below in 7) Extended Functionality, 

accounting for foregone taxes is a logical addition to the model, especially when considering 

plug-in electric hybrid vehicles. 

Response: We will attempt to document the values contained in the input files distributed with 

the model. This has been done for the input files published with the proposed GHG vehicle 

standards. However, some of the inputs are for example only. This will be indicated in the 

model documentation. 

Incorporating the units of the various input fields into the input file headings themselves involves 

changes to the core model. In the near term, we have included detailed descriptions of each type 

of input value in the model documentation for easy reference by the user. 

Fuel prices are intended to be in terms of real dollars. This will be clarified in the model 

documentation. 

The current version of OMEGA focuses on gasoline, diesel fuel and electricity because the vast 

majority of current vehicle sales are certified on these fuels. Very few dedicated alternative 

fueled vehicles are sold and flex fuel vehicles are certified on either gasoline or diesel fuel and 

numerical adjustments made to their fuel economy or emissions to reflect incentivizing 

regulatory credits. 

Current legislation and enabling EPA regulations encourage the use of renewable fuels. 

However, to date, these requirements are not integrated with the regulations governing vehicle 

fuel economy, nor the recently proposed vehicle GHG standards. Thus, the primary place which 

they intersect with the OMEGA model is in the calculation of benefits. As this is done in a 

spreadsheet, the user could easily modify the calculations to reflect an anticipated use of 

renewable fuels over time. EPA may develop a standard version of the benefits calculation 

spreadsheet in the future which facilitates this use. However, as suggested by Dr. Leiby, this is 

not a first order priority at this time. It is not clear, however, that two types of ethanol would be 

needed. The price of ethanol in each calendar year would simply have to reflect the price 

expected given the two sources of ethanol. The upstream emissions would also reflect the mix of 

the two production paths. 

The model currently does not convert electrical energy into liquid fuel energy or vice versa. The 

two types of energy are tracked separately. The benefits calculation spreadsheet currently only 

tracks gasoline use. The capability to track diesel fuel and electricity use will be added soon. 
We plan to reflect fuel excise taxes in the benefits calculation in the near future. Changes in 

these taxes could then be tracked separately from changes in fuel costs from a societal 

perspective. 

e) The reference data contained in Appendix 5 which are currently hard-coded into the 

model but, in the very near future, will be contained in a user controlled input file. 

The Exclusive Inputs spreadsheet anticipates E10 and E85. It would seem fairly straightforward 

to allow for other blends such as E15. The proportion of the ethanol that comes from cellulosic 

sources in each year should be accounted for such that upstream CO2 emissions can be properly 

credited, similarly for petrodiesel and biodiesel. 

Response: EPA agrees that when ethanol blends and other renewable fuels are added to the 

benefits calculation spreadsheet, it would be reasonable to include the annual split of ethanol 

from corn and cellulosic feedstocks. 

3) The accuracy and appropriateness of the model's conceptual algorithms and equations 

for technology application and calculation of compliance; 

On p. 9, l. 40, the documentation states: “The core model then adds the effectivenesses and the 

costs of the technology addition until each manufacturer has met the standard or until all 

technology packages have been exhausted.” Given that existing law allows credit averaging 

across all vehicles sold by a manufacturer, this requires that compliance would be checked 

through an iterative routine. Please describe this routine including mechanisms to prevent 

cycling so that convergence is assured. 

p. 10. VMT is given by: . I believe this is this 

done by vehicle class (from the data file). The documentation should index the function with 

separate subscripts. 

VMT  SurvivalFraction * AnnualMilesDriven

p. 10. Discounted VMT. I have two issues with this calculation. The first is mechanical. Why 

  iFSD i i DR

DR

VMT VMT







1

2

1

,,

 does the numerator have the term 1+DR/2? Is the discount rate not 

understood to be the simple annual rate? (Also what do the indices D and FS represent?). 

Conceptually, however, I do not think this VMT should be discounted. Costs and benefits are 

appropriately discounted, but I think it is a mistake to discount a physical calculation. It blurs the 

distinction between consumer and society valuation of VMT and can lead to misleading outputs. 

This point is further emphasized by calculation of VMT for GHG calculations (p.11) 

  iCOD i i IRDR

IRDR

VMT VMT



 



1

2

1

,2,

, where VMT is enhanced by the rate in change in the value of 
CO2, IR. I strongly suggest that this equation be re-done to separate out measurement of 

physical units (VMT) from cost and value calculations. 

p. 11. RCO2 

 

 

 

LifetimeVMT

LifetimeLeakage GWP

IRDR

IRDR

VMT

GWP

IRDR

IRDR

LeakRate

VMT

GWP

RCO

years

Lifetime

i

i i

years

Lifetime

i

i i

years

Lifetime

i

iCOD

years

Lifetime

i

i

mig

 



























 































 

























)(

1

)(

1

)

1

,2,

)(

1

1

2

1

1

2

1

RefLeakage

/ )(2

I have two comments. First, it seems to me that, as with VMT, the numerator ought to be 

multiplied by the survival function. Second, as with VMT, the leakage rate ought not to be 

adjusted by DR and IR. Also, again, I do not understand the form of the adjustment – why 

multiply the numerator by 1+ (DR-IR)/2? Should not the GWP be indexed by i? 

p. 12. Determine the order of Technology Application. On the previous page the subscript i

represented “year” here it represents technology package. The use of subscripts should be unique 

throughout the documents. 

P. 12. Intermediate calculations for each vehicle type. It appears that the subscripts have changed 

again. CO2 is indexed by t and AIE, RIE are missing subscripts altogether. 

p. 13. Calculate the fuel consumption before and after technology additions. 





 















gC

gCO CD

CO FC

t

t

t

12

244

2

1

1

1

. Given that CD is in units of carbon, this equation looks unit-less 

(CO2/CO2). Where do gallons per mile units come in? 

P. 13, l. 18. In step iii, calculating fuel savings we see the following equation. 

t

PP

i

PP

i t

PP

i

PP

i i

FP FC

i

FP FC

i

FP FC

i

FP FCFS 



 



  



 



          1

2

2

1

1

1

1 1

2

2

1

1

1

First, why is FP divided by i? Second, where is the adjustment for vehicle age? How does this 

equation account for consumers’ choosing to drive more miles using one fuel v. another? 

(Consumer’s may want to maximize the time they spend in electric power mode.) Even if the 

data do not exist to parameterize the model yet, I suggest that the functionality be built in to 

allow for consumers’ choosing to use one fuel type or another. 

P. 20, l. 38-46. In calculating the impact of the reduced time required to refuel vehicles, I do not 

see a mention of the estimated driving that will occur using electricity in PHEVs. 
Response: The model does not currently require any iteration to determine compliance after 

each step of technology addition. Prior to technology addition, the model determines the 

corporate average standard for the manufacturer’s fleet of vehicles. The model then checks to 

see if the manufacture complies with its baseline vehicles coupled with vehicle sales in that 

redesign cycle. If so, the model does not add any technology. If not, the model begins to add 

technology to individual vehicles using the TARF to make it decisions. After each step of 

technology addition, the model recalculates the manufacturer’s corporate average emission level 

to determine if compliance has been achieved. This continues until compliance is achieved or 

there is no more technology to apply. 

The issue of discounting the CO2 emission reduction is discussed in Section 1 of Dr. Leiby’s 

comments. Discounting has been removed from the CostEff TARF which takes the point of 

view of the manufacturer. However, we are considering leaving the discounting in for a third 

TARF, which would take the viewpoint of society. In theory, it is the value of CO2 emissions 

which is being discounted. However, since the base value of CO2 emissions would be the same 

for each TARF, its inclusion in the formula has no effect on the relative TARF ranking. Thus, 

we will continue to simply discount emissions. This will be explained in the model 

documentation. 

The inclusion of the factor of one plus one half of the discount rate is to discount to the middle of 

the year, to recognize that emissions occur throughout the year and not at the end of the year. 

CO2 emissions from the tailpipe occur in proportion to VMT. Thus, the measurement or 

calculation of CO2 emissions per mile is straightforward. Refrigerant emissions do not occur in 

proportion to VMT. These emissions can be placed on a per mile basis, but only by measuring 

or calculating refrigerant emissions over a period of time and dividing by the typical amount of 

driving occurring over that period of time. Also, due to gradual vehicle scrappage and a gradual 

reduction in VMT per year as vehicles age, CO2 emissions are somewhat front-loaded towards 

the beginning of a vehicle’s life. In contrast, refrigerant leakage is near zero when the vehicle is 

new and increases as the system ages and begins to leak. Therefore, when putting lifetime 

refrigerant emissions on a per mile basis, it is important not to simply divide by the vehicle’s 

lifetime miles, but to also consider the timing of these miles through discounting. This places a 

unit g/mi reduction in both tailpipe CO2 and refrigerant emissions (in terms of their CO2 

equivalent) on a comparable basis. The suggested changes to model documentation have been 

made. 

The equation for fuel consumption (FC) is correct. CO2 represents CO2 emissions per mile and 

CD represents grams of carbon per gallon of fuel. Thus, the units of FC are gallons of fuel per 

mile. 

The equation for fuel savings in the model documentation is incorrect. (The equation in the 

model itself is correct.) The fuel price should not be divided by i. Instead, it should be divided 

by the payback period. Also, the fuel price should be a function of calendar year (i.e., be 

subscripted with “i”. This will be corrected in the model documentation. 
The reduction in refueling time does not yet consider the impact of recharging PHEV batteries. 

This will be noted in the model documentation. Future versions of the model will reflect an 

estimate of the time that it takes to connect and disconnect the vehicle to an outlet, probably each 

action performed once per day. 

4) The congruence between the conceptual methodologies and the program execution; 

As suggested, I made changes to input values in the spreadsheets and re-ran the model. The 

changes as displayed in the benefits calculation spreadsheet were what I had qualitatively 

expected. 

5) Clarity, completeness and accuracy of the calculations in the Benefits Calculations 

output file, in which costs and benefits are calculated; 

Please see my comments in the beginning of the document. I believe that the benefits 

calculations should more clearly reflect benefits and costs to three different agents: 

manufacturers, consumers and the nation. 

Recognizing that the benefits data (Benefits Calculation workbook) is subject to change, it would 

be really useful to list the data sources for all inputs. For example, if the VMT data is coming 

from MOBILE6, the VMT_Lookup spreadsheet should clearly state MOBILE6 as its source and 

similarly for the other inputs and spreadsheets. 

Similar to the formula used to discount VMT, the spreadsheet “ExternalVMTCosts($)” discounts 

externalities using the formula:  

i DR

DR





1

2

1

. My question is why? Most commonly used discount 

factors are simple  

i 1 DR

1 annual rates. In some senses it does not really matter because the 

user can set the discount rate, but by using a non-standard discount rate this is likely to lead to 

unnecessary confusion. 

In the “Benefits Calculation” workbook, the worksheet, “Emissions_Fuel Conservation” shows 

upstream savings from NOx, VOC, CO, PM, and SOx. These emissions savings are all 

calculated based on upstream conventional gasoline emission savings. I would think that either: 

1) these should be based on a weighted average of gasoline, diesel, ethanol, and electricity 

upstream emissions, or 2) the gallons saved should have been weighted gallons. I cannot readily 

determine if the saved gasoline gallons are weighed by the proportion of gasoline, electricity, 

ethanol and diesel (and the weights would be emission-gallon weights.) This needs to be clarified 

or corrected. 

In the “Benefits Calculation” workbook, the worksheet, “ExternalVMTcosts($)” displays the 

discount factor applied to future costs as the common discount factor used throughout the model. 

As I earlier suggest, society’s rate of discount for accidents costs (human life) are not likely to be 

the same as consumers’ rate of discounting future gasoline savings. These should be separate 

inputs. 
In the “Benefits Calculation” workbook, the worksheet, “DownstreamCosts($)”, the units on 

CO2 are shown as “$/ton”. I believe that the label is missing the modifier, “metric”. 

In the “Benefits Calculation” workbook, the worksheet, “UpstreamCosts($)” shows benefits 

determined for CO, VOC, NOx, SO2, PM2.5 all based on emission factors for conventional 

gasoline. As per my earlier comment, I think these ought to use separate emission factors for 

each fuel. 

In the “Benefits Calculation” workbook, the worksheet, “All Costs” shows costs in aggregate for 

the nation. It would be useful to also display the average, per vehicle costs. 

Response: Dr. Rubin’s comments referring to the calculation of costs and benefits to 

manufacturers, consumers and society, referencing input values, and discounting procedures are 

addressed in previous sections. As mentioned above, the primary focus of the benefits 

calculation spreadsheet is the estimation of societal costs and benefits. 

The benefits calculation spreadsheet currently assumes that all changes in fuel consumption are 

in terms of gallons of gasoline. The properties of and emissions from the production and use of 

this fuel can and should consider that “gasoline” in the U.S. includes a substantial volume of 

ethanol. This is clearly an approximation, but a reasonably good one for the light-duty motor 

vehicle fleet in the U.S. The explicit consideration of the cost and emission impacts related to 

other fuels will be added to a future version of the benefits calculation spreadsheet. 

Labeling of units in the benefits calculation spreadsheet has been made more specific. “Metric” 

has been added, where appropriate. We agree that displaying the average cost per vehicle would 

be useful. Other model output files show this figure, but the benefits calculation spreadsheet 

should, as well. 

6) Clarity, completeness, and accuracy of the model's visualization output, in which the 

technology application is displayed; and 

In displaying the results Average Incremental Costs, please round to the nearest dollar; showing 

two digits to the right of the decimal point gives a false sense of precision and makes the output 

harder to read. 

Response: We agree that showing costs in terms of dollars and cents is overly precise. This will 

be revised. 

7) Recommendations for any functionalities beyond what we have described as "future 

work." 

The model (VGHG) window box should be made larger – perhaps fill the screen. It is really too 

small to perform step 4 in running the model (i.e., Verify that the correct data has been populated 

into the VGHG model). There is also no side-to-side scroll to see the whole data field. 
Given the renewable and advanced biofuel requirement in the Energy Independence and Security 

Act of 2007, it would seem that the model ought to have data input fields to allow users to 

specify the quantities (or proportions of total fuel) of ethanol and biodiesel used in each year. 

Moreover, the proportion of biofuels which come from cellulosic sources should also be able to 

be specified. Accordingly, the GHG emission accounting framework will need to capture that 

proportion of the reductions due to changes in vehicles and that proportion due to changes in 

fuels. In anticipation of future developments in the biofuels market, it may be worthwhile to 

build in placeholder functionality to account for domestic versus imported biofuels or biofuel 

feedstocks. 

The model would be significantly enhanced if it were made probabilistic. Given that input data 

contains underlying uncertainty (What is the actual cost of a given technology? What will be the 

price of gasoline in 5 years?), the model should be made to run hundreds or thousands of times 

using Monte Carlo analysis on some of the key input data to generate a distribution of outcomes. 

Even if this is not done in the near term, having the output columns show results for “high and 

low” cost/interest rate scenarios would be convenient. It would save having to run the model 

multiple times and pulling the results in to some other summary worksheet. 

The documentation notes (p. 2) that the primary cost of the GHG emission control is the cost of 

the added technology as compared to the baseline. I do not think this is a valid presumption for 

large changes in GHG emission control. The NRC’s study on CAFE assumed that vehicles were 

hedonically equivalent. Given the likely wide-spread adoption of diesel technology and, quite 

possibly, plug-in hybrid vehicles (PHEVs), vehicle driving experiences are not likely to be the 

same. Quite possibly, PHEVs will provide a superior level of driving satisfaction. If vehicle 

manufacturers downsize or reduce performance (acceleration) to meet compliance, vehicle 

satisfaction could diminish. I do not have a good suggestion on how to adjust for these possible 

hedonic costs or benefits. Perhaps the model could incorporate placeholder equations that would 

allow users to specify hedonic gains and losses. Nonetheless, the model documentation should be 

forthright in acknowledging this limitation. 

The model should provide for an estimate of the likely gasoline excise tax implications for 

different levels of GHG emission reduction. Particularly useful would be to present this 

information in the context of different compliance strategies. For example, with tax credits for 

PHEVs, and no change in federal gasoline excise tax policy, the revenue losses could be 

significant. This functionality could be very useful for policymakers. 

As described in the documentation, the model development foresees an increased ability for 

users to change input assumptions. Changes to these assumptions may have significant impacts 

on costs and GHG emission reductions. It would be useful for the Model Reference Guide 

accompanying this model to describe in qualitative terms the impact of or assumptions behind 

choosing to adjust certain parameters. For example, the user manual could indicate that lowering 

the years of payback for technology would be consistent with a view that consumers only value 

the first years of fuel economy gains or place little or no value on GHG emission reduction that 

occur near the end of a vehicle’s lifetime. If practicable, it would also be useful to point out 

inconsistent choices. 
It would be very useful to have the model output be available in units that are used 

internationally – grams CO2 /kilometer or grams CO2 equivalent/KM. 

Clearly falling into the work for the future, would be to have a time profile of upstream CO2

emissions for conventional gasoline and diesel reflecting regional or national low carbon fuel 

standards. 

Response: EPA agrees that the dialog box would be more useful if it were larger and included 

the ability to scroll through the entirety of each input file. 

The capability to perform probabilistic modeling runs is planned for the future. Of course, 

accurately reflecting the uncertainties involved in the cost and effectiveness of future 

technologies is a significant challenge aside from enabling the model to reflect such 

uncertainties. The modeling of discrete options, like several discount rates has already been 

made easier. The latest model version includes the ability to run multiple scenarios with one 

model run. Creating several Scenario files with differing emission standards, discount rates, 

payback periods, etc. is fairly simple. Comparing the results from these multiple cases still 

requires opening a separate output file from each run. EPA is considering an output file which 

would compare the output from several cases automatically. However, given the common 

format of the output, a user may also be able to develop a single spreadsheet which refers to the 

relevant cells of several output files and provides a quick comparison of the output of interest for 

several cases automatically. 

The difficulty in simply and accurately reflecting changes in vehicle desirability and utility has 

already been discussed above. We will note this limitation in the model documentation when we 

describe the fact that the model holds the mix of vehicles constant during any particular model 

run. 

The treatment of excise taxes was already discussed above under Section 2.d. 

We appreciate Dr. Rubin’s desire to have the model documentation aid the user in making good 

choices regarding input values. We will consider adding suggestions at various parts of the 

model documentation. This is certainly needed for the development of the values of TEB and 

CEB in the market file and the ordering of technology in the Technology file. However, 

OMEGA is not a model which is designed to be used by someone not experienced in the area of 

motor vehicle fuel economy and emissions and environmental and economic analysis. It will not 

be possible to provide a complete tutorial on all these topics in one model’s documentation. If a 

user decides to modify a value from that which was published and supported by EPA, the user 

will have to support the appropriateness of that modification. 

We agree that there would be some value to the presentation of emissions in international units. 

However, given the complexity of the benefits calculation spreadsheet using just one set of units, 

it would seem most appropriate to create a separate file which used a different set of units 

throughout.