Economic Assumptions Used in the Agencies’ Analyses

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Table of Contents 

CHAPTER 4: ECONOMIC ASSUMPTIONS USED IN THE AGENCIES’ ANALYSES

24-2

4.1 How the Agencies Use the Economic Assumptions in their Analyses ....................... 24-2

4.2 What Economic Assumptions Do the Agencies Use? .................................................. 34-3

4.2.1 Potential Opportunity Costs of Improved Fuel Economy and Reduced GHG 

Emissions .............................................................................................................................. 34-3

4.2.2 The On-Road Fuel Economy “Gap” ............................................................. 44-34-4

4.2.3 Fuel Prices and the Value of Saving Fuel ..................................................... 64-34-6

4.2.4 Vehicle Survival and Use Assumptions ......................................................... 84-34-8

4.2.5 Accounting for the Fuel Economy Rebound Effect ................................. 264-34-17

4.2.6 Benefits from Increased Vehicle Use ......................................................... 334-34-22

4.2.7 Added Costs from Increased Vehicle Use ................................................. 334-34-23

4.2.8 Petroleum and Energy Security Impacts .................................................. 344-34-24

4.2.9 Air Pollutant Emissions ..............................................................................444-34-33

4.2.10 Reductions in Emissions of Greenhouse Gases ........................................ 544-34-43

4.2.11 The Value of Increased Driving Range ..................................................... 554-34-44

4.2.12 Discounting Future Benefits and Costs ..................................................... 584-34-45

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CHAPTER 4:Economic Assumptions Used in the Agencies’ Analyses 

4.1 How the Agencies Use the Economic Assumptions in their Analyses 

Improving new vehicles’ fuel efficiency and reducing greenhouse gas (GHG) emissions 

provides direct benefits to their buyers and users by reducing fuel consumption and fuel costs 

throughout those vehicles’ lifetimes, stimulating increased vehicle use through the fuel economy 

rebound effect, and increasing vehicles’ driving range so that they require less frequent refueling. 

At the same time, the reduction in fuel use that results from requiring higher fuel economy and 

reducing GHGs also produces wider benefits to the U.S. economy by lowering the cost of 

economic externalities that result from U.S. petroleum consumption and imports, including 

reducing the price of petroleum, lowering the potential costs from disruption in the flow of oil 

imports, and possibly reducing federal outlays to secure imported oil supplies and cushion the 

U.S. economy against their potential interruption. Reducing fuel consumption and GHGs also 

lowers the economic costs of environmental externalities resulting from fuel production and use, 

including reducing the impacts on human health impacts from emissions of criteria air pollutants, 

and reducing future economic damages from potential changes in the global climate caused by 

greenhouse gas emissions. 

These benefits are partly offset by the increase in fuel use that results from added vehicle 

use due to the fuel economy rebound effect, as well as by added costs from the increased 

congestion, crashes, and noise caused by increased vehicle use. They might also be offset by any 

loss in the utility that new vehicles provide to their buyers (and subsequent owners) as a 

consequence of reductions in their performance, carrying capacity, or comfort that manufacturers 

may implement as part of their strategies to comply with higher fuel economy requirements and 

GHG standards. Nevertheless, the total economic benefits from requiring higher fuel economy 

and reducing GHGs are likely to be substantial, and the EPA and NHTSA have attempted to 

develop detailed estimates of the economic benefits from adopting more stringent standards. 

This section discusses the common economic values used by both NHTSA and EPA. 

These economic inputs incorporate a range of forecast information, economic estimates, and 

input parameters. This section describes the sources that EPA and NHTSA have relied upon for 

this information, the rationale underlying each assumption, and the agencies’ proposed estimates 

of specific parameter values. These common economic values are then used as inputs into each 

Agencies’ respective modeling and other analyses of the economic benefits and costs of the EPA 

and NHTSA programs. While the underlying economic input values are common to both 

Agencies, it is the differences in the way each Agency assesses its program, that result in 

differing proposed benefits estimates. This issue is discussed further in Section 1.C of the 

preamble to the joint rulemaking. 
Economic Assumptions Used in the Agencies’ Analyses

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4.2 What Economic Assumptions Do the Agencies Use? 

4.2.1 Potential Opportunity Costs of Improved Fuel Economy and Reduced GHG 

Emissions 

An important concern is whether achieving the fuel economy improvements required by 

alternative fuel economy or CO2 emissions standards would require manufacturers to

compromise the performance, carrying capacity, safety, or comfort of their vehicle models. If it 

did so, the resulting sacrifice in the value of these attributes to consumers would represent an 

additional cost of achieving the required improvements in fuel economy, and thus of 

manufacturers’ compliance with stricter standards. Although the exact values that vehicle buyers 

attach to vehicle attributes such as fuel economy, performance, passenger- and cargo-carrying 

capacity, and other dimensions of vehicle utility are difficult to infer from their purchasing 

decisions and vehicle prices, changing vehicle attributes can significantly affect the overall 

utility that vehicles provide to their owners, and thus their value to potential buyers. 

EPA and NHTSA have approached this potential problem by developing proposed cost 

estimates for fuel economy-improving technologies that include any additional manufacturing 

costs that would be necessary to maintain the reference fleet levels of performance, comfort, 

capacity, or safety of any light-duty vehicle model to which those technologies are applied. In 

doing so, the agencies primarily followed the precedent established by the 2002 NAS Report, 

although EPA and NHTSA have extensively updated their assumptions as necessary for the 

purposes of the current rulemaking. The NAS study estimated “constant performance and 

utility” costs for fuel economy technologies, and EPA and NHTSA have used these as the basis 

for their continuing efforts to refine the technology costs they employ in analyzing 

manufacturer’s costs complying with alternative passenger car and light truck fuel efficiency and 

GHG standards for MYs 2012-2016. Although the agencies have revised its estimates of 

manufacturers’ costs for some technologies significantly for use in this rulemaking, these revised 

estimates are still intended to represent costs that would allow manufacturers to maintain the 

performance, carrying capacity, and utility of vehicle models while improving their fuel 

economy. 

EPA and NHTSA acknowledge the difficulty of estimating technology costs that include 

costs for the accompanying changes in vehicle design that are necessary to maintain 

performance, capacity, and utility. We believe that our proposed cost estimates for fuel 

economy-improving technologies should be generally sufficient to prevent significant reductions 

in consumer welfare provided by vehicle models to which manufacturers apply those 

technologies. However, it is possible that the agencies’ technology cost estimates do not include 

adequate allowance for the necessary efforts by manufacturers to maintain vehicle performance, 

carrying capacity, and utility while improving fuel economy. If this is the case, the true 

economic costs of achieving higher fuel economy would include the opportunity costs to vehicle 

owners of any sacrifices in vehicles’ performance, carrying capacity, and utility that 

accompanied increases in their fuel economy. In that event, the agencies’ estimated technology 

costs would underestimate the true economic costs of achieving it. 

Recognizing this possibility, EPA and NHTSA may consider in the future whether there 

are credible methods available to estimate explicitly the changes in vehicle buyers’ welfare from 
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the combination of higher prices for new vehicle models, increases in their fuel economy, and 

any accompanying changes in vehicle attributes such as performance, passenger- and cargocarrying capacity, or other dimensions of utility. The net change in buyer’s welfare that results 

from the combination of these changes would provide an estimate of the true economic costs for 

improving fuel economy, which would fall on vehicle purchasers and subsequent owners rather 

than on manufacturers. As EPA discusses in its DRIA in Section 8.1.2, though, EPA believes 

that the state of the art for developing these estimates may not be sufficient to provide credible 

estimates as indicated by wide variation in modeling methods, few assessments of the accuracy 

or consistency of results, and inconsistent results when comparisons have been made. NHTSA, 

in its PRIA in Section XX, also discusses its difficulties with implementing vehicle choice 

models that, among other features, could estimate changes in consumer welfare. Although EPA 

and NHTSA have continued to rely on estimates of what it believes are “constant performance 

and utility” technology costs for this proposed analysis of the costs of complying with 

alternative fuel efficiency standards for MY 20120-2016 cars and light trucks, the agencies seek 

comment on alternative ways to deal with this important issue. 

4.2.2 The On-Road Fuel Economy “Gap” 

Actual fuel economy levels achieved by vehicles in on-road driving fall significantly 

short of their levels measured under the laboratory-like test conditions used under the CAFE 

program to establish its published fuel economy ratings for different models. In analyzing the 

fuel savings from passenger car and light truck fuel efficiency and GHG standards, the agency 

adjusts the actual fuel economy performance of each passenger car and light truck model 

downward from its rated value to reflect the expected size of this on-road fuel economy “gap.” 

In December 2006, EPA adopted changes to its regulations on fuel economy labeling, which 

were intended to bring vehicles’ rated fuel economy levels closer to their actual on-road fuel 

economy levels.1

 

Comparisons of on-road and CAFE fuel economy levels developed by EPA as part of its 

Final Rule implementing new fuel economy labeling requirements for new vehicles indicate that 

actual on-road fuel economy for light-duty vehicles averages 20 percent lower than published 

fuel economy levels.2

 For example, if the overall EPA fuel economy rating of a light truck is 20 

mpg, the on-road fuel economy actually achieved by a typical driver of that vehicle is expected 

to be 16 mpg (20*.80). In its analysis supporting the Final Rule establishing CAFE standards for 

MY 2011, NHTSA employed EPA’s revised estimate of this on-road fuel economy gap in its 

analysis of the fuel savings resulting from alternative fuel efficiency standards. 

An analysis conducted by NHTSA confirmed that EPA’s estimate of a 20 percent gap 

between test and on-road fuel economy is well-founded. The agency used data on the number of 

passenger cars and light trucks of each model year that were in service (registered for use) during 

each calendar year from 2000 through 2006, average fuel economy for passenger cars and light 

trucks produced during each model year, and estimates of average miles driven per year by cars 

and light trucks of different ages during each calendar year over that period. These data were 

combined to develop estimates of the usage-weighted average fuel economy that the U.S. 

passenger car and light truck fleets would have achieved during each year from 2000 through 

2006 under test conditions. 
Economic Assumptions Used in the Agencies’ Analyses

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Table 4-1Table 4-1Table 4-1 compares NHTSA’s estimates of fleet-wide average fuel 

economy under test conditions for 2000 through 2006 to the Federal Highway Administration’s 

(FHWA) published estimates of actual on-road fuel economy achieved by passenger cars and 

light trucks during each of those years. As it shows, FHWA’s estimates of actual fuel economy 

for passenger cars ranged from 21 percent to 23 percent lower than NHTSA’s estimates of its 

fleet-wide average value under test conditions over this period. Similarly, FHWA’s estimates of 

actual fuel economy for light trucks ranged from 16 percent to 18 percent lower than NHTSA’s 

estimates of average light truck fuel economy under test conditions. Thus, these results appear to 

confirm that the 20 percent on-road fuel economy discount or gap represents a reasonable 

estimate for use in evaluating the fuel savings likely to result from alternative fuel efficiency 

standards for MY 2012-2016 vehicles. 

We are aware of two potential issues involved in these estimates. One, the estimates of 

total annual car and truck VMT are developed by the states and submitted to FHWA. Each state 

uses its own definition of a car and a truck. For example, some states classify minivans as cars 

and some as trucks. Thus, there are known inconsistencies with these estimates when evaluated 

separately for cars and trucks. Also, total gasoline consumption can be reasonably estimated 

from excise tax receipts, but separate estimates for cars and trucks are not available. We are not 

aware of the precise methodology used to develop the distinct on-road fuel economy estimates 

for cars and trucks developed by FHWA. We do not believe that they are based on direct 

measurements from substantial numbers of vehicles, as no such test programs were found by 

EPA during its fuel economy labeling rule in 2006. Also, the year-to-year consistency for both 

car and truck fuel economy implies some methodology other than direct measurement. For this 

reason, NHTSA and EPA are not proposing to use distinct on-road fuel economy gaps for cars 

and trucks, but one common value of 20% for both vehicle classes for purposes of estimating the 

fuel savings of the proposed standards. 

Table 4-1 Estimated Fleet-Wide Fuel Economy of Passenger Cars and Light Trucks 

Compared to Reported Fuel Economy

YEAR 

PASSENGER CARS LIGHT-DUTY TRUCKS 

NHTSA 

Estimated 

Test MPG 

FHWA 

Reported 

Actual MPG

Percent 

Difference 

NHTSA 

Estimated 

Test MPG 

FHWA 

Reported 

Actual MPG

Percent 

Difference 

2000 28.2 21.9 -22.2% 20.8 17.4 -16.3% 

2001 28.2 22.1 -21.7% 20.8 17.6 -15.5% 

2002 28.3 22.0 -22.3% 20.9 17.5 -16.2% 

2003 28.4 22.2 -21.9% 21.0 17.2 -18.0% 

2004 28.5 22.5 -21.1% 21.0 17.2 -18.3% 

2005 28.6 22.1 -22.8% 21.1 17.7 -16.3% 

2006 28.8 22.5 -21.8% 21.2 17.8 -16.2% 

Avg., 

2000-

2006 

28.4 22.2 -22.0% 21.0 17.5 -16.7% 

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4.2.3 Fuel Prices and the Value of Saving Fuel 

Projected future fuel prices are a critical input into the preliminary economic analysis of 

alternative fuel efficiency and GHG standards, because they determine the value of fuel savings 

both to new vehicle buyers and to society. EPA and NHTSA relied on the most recent fuel price 

projections from the U.S. Energy Information Administration’s (EIA) Annual Energy Outlook

(AEO) for this analysis. Specifically, we used the AEO 2009 (April 2009 release) Reference 

Case forecasts of inflation-adjusted (constant-dollar) retail gasoline and diesel fuel prices, which 

represent the EIA’s most up-to-date estimate of the most likely course of future prices for 

petroleum products.3 While NHTSA relied on the forecasts of fuel prices presented in AEO 

2008 High Price Case in the MY 2011 CAFE final rule, we noted at the time that we were 

relying on that estimate primarily because volatility in the oil market appeared to have overtaken 

the Reference Case, and that we anticipated that the Reference Case forecast would be 

significantly higher in the next AEO. In fact, EIA’s AEO 2009 Reference Case forecast projects 

higher retail fuel prices in most future years than those forecast in the High Price Case from 

AEO 2008. EPA and NHTSA are thus confident that the AEO 2009 Reference Case is an 

appropriate forecast for projected future fuel prices. 

Federal government agencies generally use EIA’s projections in their assessments of 

future energy-related policies. The retail fuel price forecasts presented in AEO 2009 span the 

period from 2009 through 2030. Measured in constant 2007 dollars, the Reference Case forecast 

of retail gasoline prices during calendar year 2020 is $3.62 per gallon, rising gradually to $3.82 

by the year 2030 (these values include federal, state and local taxes). However, valuing fuel 

savings over the maximum lifetimes of passenger cars and light trucks used in this analysis 

requires fuel price forecasts that extend through 2050, approximately the last year during which a 

significant number of MY 2016 vehicles will remain in service.a

 To obtain fuel price forecasts 

for the years 2031 through 2050, the agency assumes that retail fuel prices will continue to 

increase after 2030 at the average annual rates projected for 2020-2030 in the AEO 2009 Revised 

Reference Case.b This assumption results in a projected retail price of gasoline that reaches 

$4.25 in 2050. 

The value of fuel savings resulting from improved fuel economy and GHG emissions to 

buyers of light-duty vehicles is determined by the retail price of fuel, which includes federal, 

state, and any local taxes imposed on fuel sales. Total taxes on gasoline, including federal, state, 

and local levies averaged $0.42 per gallon during 2006, while those levied on diesel averaged 

$0.50. Because fuel taxes represent transfers of resources from fuel buyers to government 

agencies, however, rather than real resources that are consumed in the process of supplying or 

 

a

 The agency defines the maximum lifetime of vehicles as the highest age at which more than 2 percent of those 

originally produced during a model year remain in service. In the case of light-duty trucks, for example, this age has 

typically been 36 years for recent model years. 

b

 This projection uses the rate of increase in fuel prices for 2020-2030 rather than that over the complete forecast 

period (2009-2030) because there is extreme volatility in the forecasts for the years 2009 through approximately 

2020. Using the average rate of change over the complete 2009-2030 forecast period would result in projections of 

declining fuel prices after 2030. 
Economic Assumptions Used in the Agencies’ Analyses

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using fuel, their value must be deducted from retail fuel prices to determine the value of fuel 

savings resulting from more stringent fuel efficiency and GHG standards to the U.S. economy as 

a whole. 

In estimating the economy-wide or “social” value of fuel savings from improved fuel 

efficiency standards and GHG emission standards, EPA and NHTSA follow the assumptions 

used by EIA in AEO 2009 that state and local gasoline taxes will keep pace with inflation in 

nominal terms, and thus remain constant when expressed in constant 2007 dollars.c

 In contrast, 

EIA assumes that federal gasoline taxes will remain unchanged in nominal terms, and thus 

decline throughout the forecast period when expressed in constant 2007 dollars. These differing 

assumptions about the likely future behavior of federal and state/local fuel taxes are consistent 

with recent historical experience, which reflects the fact that federal as well as most state motor 

fuel taxes are specified on a cents-per-gallon basis, and typically require legislation to change. 

The projected value of total taxes is deducted from each future year’s forecast of retail gasoline 

and diesel prices reported in AEO 2009 to determine the economic value of each gallon of fuel 

saved during that year as a result of improved fuel economy. Subtracting fuel taxes results in a 

projected value for saving gasoline of $3.22 per gallon during 2020, rising to $3.45 per gallon by 

the year 2030. 

EIA is widely-recognized as an impartial and authoritative source of analysis and 

forecasts of U.S. energy production, consumption, and prices. The agency has published annual 

forecasts of energy prices and consumption levels for the U.S. economy since 1982 in its AEOs. 

These forecasts have been widely relied upon by federal agencies for use in regulatory analysis 

and for other purposes. Since 1994, EIA’s annual forecasts have been based upon the agency’s 

National Energy Modeling System (NEMS), which includes detailed representation of supply 

pathways, sources of demand, and their interaction to determine prices for different forms of 

energy. 

From 1982 through 1993, EIA’s forecasts of world oil prices – the primary determinant 

of prices for gasoline, diesel, and other transportation fuels derived from petroleum – 

consistently overestimated actual prices during future years, often very significantly. Of the total 

of 119 forecasts of future world oil prices for the years 1985 through 2005 that EIA reported in 

its 1982-1993 editions of AEO, 109 overestimated the subsequent actual values for those years, 

on average exceeding their corresponding actual values by 75 percent. 

Since that time, however, EIA’s forecasts of future world oil prices show a more mixed 

record for accuracy. The 1994-2005 editions of AEO reported 91 separate forecasts of world oil 

prices for the years 1995-2005, of which 33 have subsequently proven too high while the 

remaining 58 have underestimated actual prices. The average absolute error (i.e., regardless of 

 

c

 By “social” value of fuel savings, the agencies mean that we value fuel savings at pre-tax prices, since this 

measures the value of the resources that are saved when less fuel is produced and consumed. The social value of 

saving fuel includes this plus the reductions in economic and environmental externalities that result from consuming 

less fuel, importing less petroleum, and so on.
Draft Joint Technical Support Document 

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its direction) of these forecasts has been 21 percent, but over- and underestimates have tended to 

offset one another, so that on average EIA’s more recent forecasts have underestimated actual 

world oil prices by 7 percent. Although both its overestimates and underestimates of future 

world oil prices for recent years have often been large, the most recent editions of AEO have 

significantly underestimated petroleum prices during those years for which actual prices are now 

available. 

However, EPA and NHTSA does not regard EIA’s recent tendency to underestimate 

future prices for petroleum and refined products or the high level of current fuel prices as 

adequate justification to employ forecasts that differ from the Reference Case forecast presented 

in the AEO 2009 Reference Case. This is particularly the case because this forecast has been 

revised upward significantly since the initial release of AEO 2008, which in turn represented a 

major upward revision from EIA’s fuel price forecast reported previously in AEO 2007. 

Comparing different forecasts of world oil prices also shows that EIA’s Reference Case forecast 

reported in AEO 2009 is among the highest of all six publicly-available forecasts of world oil 

prices over the 2010-30 time horizon.4 Because world petroleum prices are the primary 

determinant of retail prices for refined petroleum products such as transportation fuels, this 

suggests that the Reference Case forecast of U.S. fuel prices reported in AEO 2009 is likely to be 

the highest of those projected by major forecasting services. 

4.2.4 Vehicle Survival and Use Assumptions 

The agencies’ analyses of fuel savings and related benefits from adopting higher fuel 

efficiency standards and establishing GHG emission standards for MY 2012-2016 passenger cars 

and light trucks is based on estimates of the resulting changes in fuel use over their entire 

lifetimes in the U.S. vehicle fleet. The first step in estimating lifetime fuel consumption by 

vehicles produced during a model year is to calculate the number of those vehicles that is 

expected to remain in service during each future year after they are produced and sold.d

 This 

number is calculated by multiplying the number of vehicles originally produced during a model 

year by the proportion expected to remain in service at the age they will have reached during 

each subsequent year, often referred to as a “survival rate.” 

In 2008, NHTSA updated its previous estimates of car and light truck survival rates using 

the most current registration data for vehicles produced during recent model years, in order to 

ensure that its forecasts of the number of these vehicles remaining in use during future years 

reflect recent increases in the durability and expected life spans of cars and light trucks.5 Both 

 

d

 Vehicles are defined to be of age 1 during the calendar year corresponding to the model year in which they are 

produced; thus for example, model year 2000 vehicles are considered to be of age 1 during calendar year 2000, age 

1 during calendar year 2001, and to reach their maximum age of 26 years during calendar year 2025. NHTSA 

considers the maximum lifetime of vehicles to be the age after which less than 2 percent of the vehicles originally 

produced during a model year remain in service. Applying these conventions to vehicle registration data indicates 

that passenger cars have a maximum age of 26 years, while light trucks have a maximum lifetime of 36 years. See 

Lu, S., NHTSA, Regulatory Analysis and Evaluation Division, “Vehicle Survivability and Travel Mileage 

Schedules,” DOT HS 809 952, 8-11 (January 2006). Available at http://www-nrd.nhtsa.dot.gov/Pubs/809952.pdf

(last accessed July 27, 2009).
Economic Assumptions Used in the Agencies’ Analyses

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agencies used these updated survival estimates in their analyses of fuel savings and other benefits 

from adopting improved fuel efficiency and GHG standards for MY 2012-2106 cars and light 

trucks. The proportions of passenger cars and light trucks expected to remain in service at each 

age up to their maximum lifetimes (26 and 36 years, respectively) are shown in Field Code Changed
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Table 4-2
Economic Assumptions Used in the Agencies’ Analyses

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Table 4-2Table 4-2 and Table 4-3.e 

The next step in estimating fuel use is to calculate the total number of miles that cars and 

light trucks produced in each model year affected by the proposed fuel efficiency and GHG 

standards will be driven during each year of their lifetimes. To estimate total miles driven, the 

number of cars and light trucks projected to remain in use during each future year is multiplied 

by the average number of miles they are expected to be driven at the age they will have reached 

in that year. 

Updated estimates of average annual miles driven by age were developed by NHTSA 

from the Federal Highway Administration’s 2001 National Household Transportation Survey, 

and these also differ from the estimates of annual mileage employed in past NHTSA analyses.f 

 

e

 The maximum age of cars and light trucks was defined as the age when the number remaining in service has 

declined to approximately two percent of those originally produced. Based on an examination of recent registration 

data for previous model years, typical maximum ages appear to be 26 years for passenger cars and 36 years for light 

trucks. 

f

See also NHTSA, “Vehicle Survival and Travel Mileage Schedules,” Office of Regulatory Analysis and 

Evaluation, January 2006, pp. 15-17. The original source of information on annual use of passenger cars and light 

trucks by age used in this analysis is the 2001 National Household Travel Survey (NHTS), jointly sponsored by the 

Federal Highway Administration, Bureau of Transportation Statistics, and National Highway Traffic Safety 

Administration. 

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Table 4-2
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Table 4-2Table 4-2 and Table 4-3 also report NHTSA’s updated estimates of average car and 

light truck use. The total number of miles driven by passenger cars or light trucks produced 

during a model year, during each year of its lifetime, is estimated by multiplying these agespecific estimates of average car and light truck use by the number of vehicles projected to 

remain in service during that year. 

As 

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Table 4-2
Economic Assumptions Used in the Agencies’ Analyses

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Table 4-2Table 4-2 and Table 4-3 also show, the resulting survival-weighted mileage over the 

26-year maximum lifetime of passenger cars is 161,847 miles, while that over the 36-year 

maximum lifetime of light trucks is 190,066 miles. Fuel savings and other benefits resulting 

from improved fuel efficiency and GHG standards for passenger cars and light trucks are 

calculated over their respective lifetimes and total expected mileage. It should be noted, 

however, that survival-weighted mileage is extremely low (less than 1,000 miles per year) after 

age 20 for cars and after age 25 for light trucks, and thus has little impact on lifetime fuel savings

or other benefits from higher fuel economy, particularly after discounting those benefits to their 

present values. 

The survival and annual mileage estimates reported in the tables reflect the convention 

that vehicles are defined to be of age 1 during the calendar year that coincides with their model 

year Thus for example, model year 2012 vehicles will be considered to be of age 1 during 

calendar year 2012. This convention is used in order to account for the fact that vehicles 

produced during a model year typical are first offered for sale in June through September of the 

preceding calendar year (for example, sales of a model year typically begin in June through 

September of the previous calendar year, depending on manufacturer). Thus virtually all of the 

vehicles produced during a model year will be in use for some or all of the calendar year 

coinciding with their model year, and they are considered to be of age 1 during that year.g

 

 

g

 As an illustration, virtually the entire production of model year 2012 cars and light trucks will have been sold by 

the end of calendar year 2012, so those vehicles are defined to be of age 1 during calendar year 2012. Model year 

2012 vehicles are subsequently defined to be of age 2 during calendar year 2013, age 3 during calendar year 2014, 

and so on. One complication arises because registration data are typically collected for July 1 of each calendar year, 

so not all vehicles produced during a model year will appear in registration data until the calendar year when they 

have reached age 2 (and sometimes age 3) under this convention. 

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Table 4-2 Survival Rates and Unadjusted Annual Miles Traveled (VMT) 

by Age for Passenger Cars

Vehicle 

Age 

Estimated Survival 

Fraction

Estimated Annual 

VMT 

Survival-Weighted 

Annual VMT 

1 0.9950 14,231 14,160 

2 0.9900 13,961 13,821 

3 0.9831 13,669 13,438 

4 0.9731 13,357 12,998 

5 0.9593 13,028 12,497 

6 0.9413 12,683 11,938 

7 0.9188 12,325 11,324 

8 0.8918 11,956 10,662 

9 0.8604 11,578 9,961 

10 0.8252 11,193 9,237 

11 0.7866 10,804 8,499 

12 0.7170 10,413 7,466 

13 0.6125 10,022 6,138 

14 0.5094 9,633 4,907 

15 0.4142 9,249 3,831 

16 0.3308 8,871 2,934 

17 0.2604 8,502 2,214 

18 0.2028 8,144 1,652 

19 0.1565 7,799 1,220 

20 0.1200 7,469 896 

21 0.0916 7,157 656 

22 0.0696 6,866 478 

23 0.0527 6,596 348 

24 0.0399 6,350 253 

25 0.0301 6,131 185 

26 0.0227 5,940 135 

Estimated Passenger Car Lifetime VMT 161,847 
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Table 4-3 Survival Rates and Unadjusted Annual Vehicle-Miles Traveled (VMT) 

by Age for Light Trucks 

VEHICLE AGE 

ESTIMATED 

SURVIVAL 

FRACTION

ESTIMATED 

ANNUAL VMT 

SURVIVALWEIGHTED 

ANNUAL VMT 

1 0.9950 16,085 16,004 

2 0.9741 15,782 15,374 

3 0.9603 15,442 14,829 

4 0.9420 15,069 14,195 

5 0.9190 14,667 13,479 

6 0.8913 14,239 12,691 

7 0.8590 13,790 11,845 

8 0.8226 13,323 10,960 

9 0.7827 12,844 10,053 

10 0.7401 12,356 9,145 

11 0.6956 11,863 8,252 

12 0.6501 11,369 7,391 

13 0.6042 10,879 6,573 

14 0.5517 10,396 5,735 

15 0.5009 9,924 4,971 

16 0.4522 9,468 4,281 

17 0.4062 9,032 3,669 

18 0.3633 8,619 3,131 

19 0.3236 8,234 2,665 

20 0.2873 7,881 2,264 

21 0.2542 7,565 1,923 

22 0.2244 7,288 1,635 

23 0.1975 7,055 1,393 

24 0.1735 6,871 1,192 

25 0.1522 6,739 1,026 

26 0.1332 6,663 887 

27 0.1165 6,648 774 

28 0.1017 6,648 676 

29 0.0887 6,648 590 

30 0.0773 6,648 514 

31 0.0673 6,648 447 

32 0.0586 6,648 390 

33 0.0509 6,648 338 

34 0.0443 6,648 294 

35 0.0385 6,648 256 

36 0.0334 6,648 222 

Estimated Lifetime Light Truck VMT 190,066 
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4.2.4.1 Adjusting Vehicle Use for Future Fuel Prices 

The estimates of average annual miles driven by passenger cars and light trucks reported in Field Code Changed
Economic Assumptions Used in the Agencies’ Analyses

19 

Table 4-2
Draft Joint Technical Support Document 

20 

Table 4-2Table 4-2 and Table 4-3 reflect the historically low gasoline prices that prevailed at the time 

the 2001 NHTS was conducted. To account for the effect on vehicle use of subsequent increases in 

fuel prices, the estimates of annual vehicle use derived from the NHTS were adjusted to reflect the 

forecasts of future gasoline prices reported in the AEO 2009 Reference Case. This adjustment 

accounts for the difference between the average price per gallon of fuel forecast for each year over the 

expected lifetimes of model year 2012-2016 passenger cars and light trucks, and the average price that

prevailed when the NHTS was conducted in 2001. The elasticity of annual vehicle use with respect to 

fuel cost per mile corresponding to the 10 percent fuel economy rebound effect used in this analysis 

(i.e., an elasticity of -0.10) was applied to the percent difference between each future year’s fuel prices 

and those prevailing in 2001 to adjust the estimates of vehicle use derived from the NHTS to reflect 

the effect of higher future fuel prices. 

4.2.4.2 Ensuring Consistency with Growth in Total Vehicle Use 

The estimates of annual miles driven by passenger cars and light trucks at each age were 

also adjusted to reflect projected future growth in average vehicle use. Increases in the average 

number of miles cars and trucks are driven each year have been an important source of historical 

growth in total car and light truck use, and are expected to represent an important source future 

growth in total light-duty vehicle travel as well. As an illustration of the importance of growth in 

average vehicle use, the total number of miles driven by passenger cars increased 35 percent 

from 1985 through 2005, equivalent to a compound annual growth rate of 1.5 percent.6

 During 

that time, however, the total number of passenger cars registered for in the U.S. grew by only 

about 0.3 percent annually.h Thus growth in the average number of miles automobiles are driven 

each year accounted for the remaining 1.2 percent (= 1.5 percent - 0.3 percent) annual growth in 

total automobile use.i

 Further, the AEO 2009 Reference Case forecasts of total car and light 

truck use and of the number of cars and light trucks in use suggest that their average annual use 

will continue to increase gradually from 2010 through 2030. 

In order to develop reasonable estimates of future growth in average car and light truck 

use, NHTSA calculated the rate of growth in the mileage schedules shown in 

 

h

 A slight increase in the fraction of new passenger cars remaining in service beyond age 10 has accounted for a 

small share of growth in the U.S. automobile fleet. The fraction of new automobiles remaining in service to various 

ages was computed from R.L. Polk vehicle registration data for 1977 through 2005 by the agency’s Center for 

Statistical Analysis. 

i

See supra note [2 above here] 

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Economic Assumptions Used in the Agencies’ Analyses

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Table 4-4
Draft Joint Technical Support Document 

22 

Table 4-4Table 4-4 and Field Code Changed
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Table 4-5
Draft Joint Technical Support Document 

24 

Table 4-5Table 4-5 that would be necessary for total car and light truck travel to increase at the 

rate forecast in the AEO 2009 Reference Case. This rate was calculated in a manner that is also 

consistent with future changes in the overall size and age distributions of the U.S. passenger car 

and light truck fleets that are implied by the agency’s adjusted forecasts of total car and light 

truck sales, together with the survival rates reported in Table 4-2Table 4-2Table 4-2 and Table 

4-3. The growth rate in average annual car and light truck use produced by this calculation is 

approximately 1.1 percent per year.j

 This rate was applied to the mileage figures reported in 

Table 4-2Table 4-2Table 4-2 and Table 4-3 to estimate annual mileage by age during each 

calendar year of the expected lifetimes of MY 2012-2016 cars and light trucks. 

 

j

 It was not possible to estimate separate growth rates in average annual use for cars and light trucks, because of the 

significant reclassification of light truck models as passenger cars discussed previously. 

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Economic Assumptions Used in the Agencies’ Analyses

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Table 4-4
Draft Joint Technical Support Document 

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Table 4-4Table 4-4 and Field Code Changed
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Table 4-5
Draft Joint Technical Support Document 

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Table 4-5Table 4-5 report the results of applying the adjustments for both future fuel prices and 

annual growth in car and light truck use to the figures reported previously. While the adjustment 

for future fuel prices reduces average mileage at each age from the values shown previously, the 

adjustment for expected future growth in average vehicle use increases it. As Field Code Changed
Economic Assumptions Used in the Agencies’ Analyses

29 

Table 4-4
Draft Joint Technical Support Document 

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Table 4-4Table 4-4 and Field Code Changed
Economic Assumptions Used in the Agencies’ Analyses

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Table 4-5
Draft Joint Technical Support Document 

32 

Table 4-5Table 4-5 show, the net effect of these two adjustments is to increase expected lifetime 

mileage significantly; for passenger cars, this figure rises to 190,971 miles from the 161,847 

miles reported previously in Field Code Changed
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Table 4-2
Draft Joint Technical Support Document 

34 

Table 4-2Table 4-2 (or by 18 percent), while expected lifetime mileage for light trucks increases 

from the 190,066 miles reported previously in Table 4-3 to 221,199 miles (16 percent). 

Separate adjustments for projected fuel prices and growth in car and light truck use were 

made for each calendar year from 2012 through 2030. The values reported in the tables below 

are the averages of the adjusted values of annual car and light truck use by age for calendar years 

2012-2030. These averages were used to analyze the impacts of improved fuel efficiency over 

the lifetimes of MY 2012-2016 cars and light trucks. In contrast, the estimated fuel savings and 

other impacts of improved fuel efficiency reported for individual calendar years used the 

adjusted values of car and light truck use by age during those specific calendar years. 

As previously stated, the estimates of survival-weighted mileage decline to less than 

1,000 miles per year after age 20 for cars and after age 27 for light trucks. Thus they have 

relatively little impact on lifetime fuel savings or other benefits from higher fuel economy, 

particularly after discounting the benefits that occur in those distant future years to their present 

values. 

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Table 4-4 Survival Rates and Adjusted Annual Vehicle-Miles Traveled (VMT) 

by Age for Passenger Cars 

Vehicle Age Estimated Survival 

Fraction

Estimated Annual 

VMT 

Survival-Weighted 

Annual VMT 

1 0.9950 16,932 16,847 

2 0.9900 16,603 16,437 

3 0.9831 16,257 15,983 

4 0.9731 15,814 15,389 

5 0.9593 15,414 14,787 

6 0.9413 14,993 14,113 

7 0.9188 14,545 13,364 

8 0.8918 14,105 12,578 

9 0.8604 13,624 11,722 

10 0.8252 13,192 10,886 

11 0.7866 12,668 9,964 

12 0.7170 12,222 8,763 

13 0.6125 11,705 7,170 

14 0.5094 11,191 5,700 

15 0.4142 10,727 4,443 

16 0.3308 10,283 3,402 

17 0.2604 9,878 2,572 

18 0.2028 9,482 1,923 

19 0.1565 9,090 1,423 

20 0.1200 8,691 1,043 

21 0.0916 8,366 766 

22 0.0696 8,126 566 

23 0.0527 8,003 422 

24 0.0399 7,774 310 

25 0.0301 7,587 228 

26 0.0227 7,424 169 

Adjusted Lifetime Passenger Car VMT 190,971 
Draft Joint Technical Support Document 

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Table 4-5 Survival Rates and Adjusted Annual Vehicle-Miles Traveled (VMT) 

by Age for Light Trucks

Vehicle Age Estimated Survival 

Fraction

Estimated Annual 

VMT 

Survival-Weighted 

Annual VMT 

1 0.9950 18,847 18,752 

2 0.9741 18,408 17,931 

3 0.9603 18,050 17,333 

4 0.9420 17,575 16,556 

5 0.9190 17,142 15,753 

6 0.8913 16,593 14,790 

7 0.8590 16,095 13,826 

8 0.8226 15,493 12,745 

9 0.7827 14,891 11,655 

10 0.7401 14,336 10,610 

11 0.6956 13,689 9,522 

12 0.6501 13,160 8,555 

13 0.6042 12,554 7,585 

14 0.5517 11,945 6,590 

15 0.5009 11,342 5,681 

16 0.4522 10,822 4,894 

17 0.4062 10,383 4,218 

18 0.3633 9,900 3,597 

19 0.3236 9,433 3,053 

20 0.2873 9,033 2,595 

21 0.2542 8,692 2,210 

22 0.2244 8,499 1,907 

23 0.1975 8,246 1,629 

24 0.1735 8,261 1,433 

25 0.1522 8,066 1,228 

26 0.1332 8,066 1,074 

27 0.1165 8,101 944 

28 0.1017 8,098 824 

29 0.0887 8,096 718 

30 0.0773 8,095 626 

31 0.0673 8,093 545 

32 0.0586 8,092 474 

33 0.0509 8,086 412 

34 0.0443 8,080 358 

35 0.0385 8,064 310 

36 0.0334 8,050 269 

Adjusted Lifetime Light Truck VMT 221,199 
Economic Assumptions Used in the Agencies’ Analyses

37 

4.2.5 Accounting for the Fuel Economy Rebound Effect 

 The rebound effect refers to the fraction of fuel savings expected to result from an 

increase in vehicle fuel economy that is offset by additional vehicle use. The increase in vehicle 

use that stems from improved fuel economy occurs because vehicle owners respond to the 

resulting reduction in vehicle fuel consumption and operating costs by driving slightly more. 

The magnitude of the rebound effect is one of the determinants of the actual fuel savings 

that are likely to result from adopting stricter fuel economy or emissions standards, and thus an 

important parameter affecting EPA’s and NHTSA’s evaluation of alternative standards for future 

model years. It can be measured directly by estimating the elasticity of vehicle use with respect 

to fuel economy itself, or indirectly by the elasticity of vehicle use with respect to fuel cost per 

mile driven.k

 When expressed as a positive percentage, either of these parameters gives the 

fraction of fuel savings that would otherwise result from adopting stricter standards, but is offset 

by the increase in fuel consumption that results when vehicles with increased fuel economy are 

driven more. 

The fuel economy rebound effect for light-duty vehicles has been the subject of a large 

number of studies since the early 1980s. Although they have reported a wide range of estimates 

of its exact magnitude, these studies generally conclude that a significant rebound effect occurs 

when vehicle fuel efficiency improves.l The most common approach to estimating its magnitude 

has been to analyze household survey data on vehicle use, fuel consumption, fuel prices (often 

obtained from external sources), and other determinants of household travel demand to isolate 

the response of vehicle use to higher fuel economy. Other studies have relied on econometric 

analysis of annual U.S. data on vehicle use, fuel economy, fuel prices, and other variables to 

identify the response of total or average vehicle use to changes in fleet-wide average fuel 

economy and its effect of fuel cost per mile driven. Two recent studies analyzed yearly variation 

in vehicle ownership and use, fuel prices, and fuel economy among individual states over an 

extended time period in order to measure the response of vehicle use to changing fuel economy.m

 

This section surveys these previous studies, summarizes recent work on the rebound 

effect, and explains the basis for the 10 percent rebound effect EPA and NHTSA are using in this 

proposed rulemaking.7

 

k

 Fuel cost per mile is equal to the price of fuel in dollars per gallon divided by fuel economy in miles per gallon, so 

this figure declines when a vehicle’s fuel economy increases. 

l

 Some studies estimate that the long-run rebound effect is significantly larger than the immediate response to 

increased fuel efficiency. Although their estimates of the adjustment period required for the rebound effect to reach 

its long-run magnitude vary, this long-run effect is most appropriate for evaluating the fuel savings and emissions 

reductions resulting from stricter standards that would apply to future model years. 

m

 In effect, these studies treat U.S. states as a data “panel” by applying appropriate estimation procedures to data 

consisting of each year’s average values of these variables for the separate states. 
Draft Joint Technical Support Document 

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4.2.5.1 Summary of Historical Literature on Rebound Effect 

It is important to note that a majority of the studies previously conducted on the rebound 

effect rely on data from the 1950-1990s. While these older studies provide valuable information 

on the potential magnitude of the rebound effect, studies that include more recent information 

(e.g., data within the last decade) may provide more reliable estimates of how this proposal will 

affect future driving behavior. Therefore, the more recent studies have been described in more 

detail below. 

Estimates based on aggregate U.S. vehicle travel data published by the U.S. Department 

of Transportation, Federal Highway Administration, covering the period from roughly 1950 to 

1990, have found long-run rebound effects on the order of 10-30 percent. Some of these studies 

are summarized in the following table. 

Table 4-6 Estimates of the Rebound Effect 

Using U.S. Aggregate Time-Series Data on Vehicle Travel1

AUTHOR 

(YEAR) 

SHORTRUN LONG-RUN TIME PERIOD 

Mayo & Mathis 

(1988) 

22% 26% 1958-84 

Gately (1992) 9% 9% 1966-88 

Greene (1992) Linear 5-

19% 

Log-linear 

13% 

Linear 5-19% 

Log-linear 

13% 

1957-89 

Jones (1992) 13% 30% 1957-89 

Schimek (1996) 5-7% 21-29% 1950-94 

1 Source: Sorrell and Dimitropolous (2007) table 4.6.

Table 4-7 Estimates of the Rebound Effect Using U.S. State Level Data1

AUTHOR 

(YEAR) 

SHORT-RUN LONG-RUN TIME PERIOD 

Haughton & 

Sarkar (1996) 

9-16% 22% 1973-1992 

Small and Van 

Dender (2007) 

4.5% 22% 1961-2001 

1 Source: Sorrell and Dimitropolous (2007) table 4.7.

While national ( Field Code Changed
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39 

Table 4-6

Table 4-6Table 4-6) and state level (Table 4-7Table 4-7Table 4-7) data have found 

relatively consistent long-run estimates of the rebound effect, household surveys display more 

variability (Table 4-8Table 4-8

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40 

Table 4-8). There are several possible explanations for this larger variability. One explanation is 

that some of these studies do not include vehicle age as an explanatory variable, thus leading to 

omitted variable bias.8 Another explanation is that it is difficult to differentiate between the 

impacts of residential density and fuel prices, since households with higher fuel prices are more 

likely to be in urban areas.9 

Table 4-8 Estimates of the Rebound Effect Using U.S. Survey Data 

AUTHOR 

(YEAR) 

SHORTRUN LONG-RUN TIME PERIOD 

Goldberg (1966) 0% CES 1984-90 

Greene (1999) 23% EIA RTECS 

1979-1994 

Pickrell & 

Schimek (1999) 

 4-34% NPTS 1995 Single year 

Puller & 

Greening (1999) 

49% CES 1980-90 

Single year, cross-sectional 

West (2004) 87% CES 1997 

Single year 

An important distinction among studies of the rebound effect is whether they assume that 

the effect is constant, or varies over time in response to the absolute levels of fuel costs, personal

income, or household vehicle ownership. Most studies using aggregate annual data for the U.S. 

assume a constant rebound effect, although some of these studies test whether the effect can vary 

as changes in retail fuel prices or average fuel economy alter fuel cost per mile driven. Many 

studies using household survey data estimate significantly different rebound effects for 

households owning varying numbers of vehicles, although they arrive at differing conclusions 

about whether the rebound effect is larger among households that own more vehicles. Finally, 

one recent study using state-level data concludes that the rebound effect varies directly in 

response to changes in personal income and the degree of urbanization of U.S. cities, as well as 

fuel costs. 

In order to provide a more comprehensive overview of previous estimates of the rebound 

effect, EPA and NHTSA reviewed 22 studies of the rebound effect conducted from 1983 through 

2005. The agencies then performed a detailed analysis of the 66 separate estimates of the longrun rebound effect reported in these studies, which is summarized in Table 4-9Table 4-9Table 

4-9 below.n As the table indicates, these 66 estimates of the long-run rebound effect range from 

as low as 7 percent to as high as 75 percent, with a mean value of 23 percent. Limiting the 

sample to 50 estimates reported in the 17 published studies of the rebound effect yields the same 

range, but a slightly higher mean estimate (24 percent). 

 

n In some cases, NHTSA derived estimates of the overall rebound effect from more detailed results reported in the 

studies. For example, where studies estimated different rebound effects for households owning different numbers 

of vehicles but did not report an overall value, we computed a weighted average of the reported values using the 

distribution of households among vehicle ownership categories. 

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41 

The type of data used and authors’ assumption about whether the rebound effect varies 

over time have important effects on its estimated magnitude. The 34 estimates derived from 

analysis of U.S. annual time-series data produce a mean estimate of 18 percent for the long-run 

rebound effect, while the mean of 23 estimates based on household survey data is considerably 

larger (31 percent), and the mean of 9 estimates based on state data (25 percent) is close to that 

for the entire sample. The 37 estimates assuming a constant rebound effect produce a mean of 

23 percent, identical to the mean of the 29 estimates reported in studies that allowed the rebound 

effect to vary in response to fuel prices, vehicle ownership, or household income. 

Table 4-9 Summary Statistics for Estimates of the Rebound Effect 

Range Distribution 

Category of Estimates Number of

Studies 

Number of

Estimates Low High Median Mean Std. Dev. 

All Estimates 22 66 7% 75% 22% 23% 14% 

Published Estimates 17 50 7% 75% 22% 24% 14% 

U.S. Time-Series Data 7 34 7% 45% 14% 18% 9% 

Household Survey Data 13 23 9% 75% 31% 31% 16% 

Pooled U.S. State Data 2 9 8% 58% 22% 25% 14% 

Constant Rebound Effect (1) 15 37 7% 75% 20% 23% 16% 

Variable Rebound Effect: (1) 10 29 10% 45% 23% 23% 10% 

4.2.5.2 Summary of Recent Studies and Analyses of the Rebound Effect 

More recent studies since 2007 indicate that the impacts of fuel prices and fuel economy 

have been decreasing over time as incomes increase and real gasoline prices fall. The theoretical 

argument for why the rebound effect should vary is that the responsiveness to the fuel cost of 

driving will be larger when it is a larger proportion of the total cost of driving. As incomes rise 

(or fuel prices fall), the responsiveness to the fuel cost per mile of driving will decrease if people

view the time cost of driving – which is likely to be related to their income levels – as a larger 

component of the total cost. 

Small and Van Dender combined time series data for each of the 50 States and the 

District of Columbia to estimate the rebound effect, allowing the magnitude of the rebound to 

vary over time.10 For the time period from 1966-2001, their study found a long-run rebound 

effect of about 22 percent, which is consistent with previously published studies. But for the 

most recent five year period (1997-2001), the long-run rebound effect decreased to 11 percent. 

Furthermore, when the authors updated their estimates with data through 2004, the long-run 

rebound effect for the most recent five year period (2000-2004) dropped to 6 percent.11

 

New research conducted by David Greene in 2008-2009 at EPA's request further supports 

the theory that the magnitude of the rebound effect is declining over time.12 Over the entire time 

period analyzed (1966-2007), Greene found that fuel economy had no statistically significant 

impact on VMT. One potential explanation for Greene’s conclusion is that there is limited 

variation in fleet-wide fuel economy in the later time period (1982-2007), so that most of the 

variation in VMT over that period is attributed to factors other than fuel economy. While Small 
Draft Joint Technical Support Document 

42 

and Van Dender did not find a statistically significant coefficient for fuel efficiency, they did 

find a statistically significant coefficient for the price of fuel. They then tested the hypothesis 

that the elasticity of vehicle travel with respect to the price of fuel was equal to its elasticity with 

respect to the rate of fuel consumption (gallons per mile), and found that the data could not reject 

that hypothesis. As a consequence, they estimated the rebound effect as the elasticity of vehicle 

travel with respect to fuel cost per mile, which constrains these elasticities to be equal in 

magnitude. Greene also attempted to re-estimate the Small and Van Dender results using his 

national data set. When using Greene’s preferred functional form, the projected rebound effect is 

approximately 12 percent in 2007, and drops to 10 percent in 2010, 9 percent in 2016 and 8 

percent in 2030. 

4.2.5.3 NHTSA analysis of the rebound effect 

To provide additional insight into the rebound effect for the purposes of this rulemaking, 

NHTSA developed several new estimates of its magnitude. These estimates were developed by 

estimating and testing several econometric models of the relationship between vehicle milestraveled and factors that influence it, including household income, fuel prices, vehicle fuel 

efficiency, road supply, the number of vehicles in use, vehicle prices, and other factors. 

As the study by Small and Van Dender pointed out, it is important to account for the 

effect of fuel prices when attempting to estimate the rebound effect. Failing to control for 

changes in fuel prices is likely to bias estimates of the rebound effect. Therefore, changes in fuel 

prices are taken into account in Volpe’s analysis of the rebound effect. Several different 

approaches were used to estimate the fuel economy rebound effect for light duty vehicles, 

including single equation OLS regressions, multiple equation systems estimated using two- and 

three-stage least squares regression, vector autoregression, and vector error-correction models. 

With the except of single-equation regressions estimated using OLS, all of these procedures 

attempt to account for the endogenous relationship of fuel efficiency to fuel prices. 

The results from each of these approaches are presented in Table 4-10Table 4-10Table 

4-10 below. The table reports the value of the rebound effect calculated over the entire period 

from 1950 through 2006, as well as for the final year of that period. In addition, the table 

presents forecasts of the average rebound effect between 2010 and 2030, which utilize forecasts 

of personal income, fuel prices, and fuel efficiency from EIA’s AEO 2009 Reference Case. 

The results of NHTSA’s analysis are broadly consistent with the findings from previous 

research summarized above. The historical average long-run rebound effect is estimated to range 

from 16-30 percent, and comparing these estimates to its calculated values for 2006 (which range 

from 8-14 percent) gives some an indication that it is declining in magnitude. The forecast 

values of the rebound effect shown in the table also suggest that this decline is likely to continue 

through 2030, as they range from 4-16 percent. 

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Table 4-10 Summary of NHTSA Estimates of the Rebound Effect

Rebound Effects: 

Model VMT 

Measure Variables Included in VMT Equation Estimation Technique 1950-

2006 2006 2010-2030* 

Small-Van Dender 

single VMT 

equation 

annual VMT 

per adult 

fuel cost per mile, per Capita income, vehicle stock, road 

miles per adult, fraction of population that is adult, 

fraction of population living in urban areas, fraction of 

population living in urban areas with heavy rail, dummy 

variables for fuel rationing, time trend 

OLS 33.0% 15.8% 8.0% 

Small-Van Dender 

three-equation 

system 

annual VMT 

per adult 

fuel cost per mile, per Capita income, vehicle stock, road 

miles per adult, fraction of population that is adult, 

fraction of population living in urban areas, fraction of 

population living in urban areas with heavy rail, dummy 

variables for fuel rationing, time trend 

3SLS 21.6% 5.8% 3.4% 

Single-equation 

VMT model 

annual VMT 

per adult personal income, road miles per Capita, time trend OLS 18.4% 11.7% 9.2% 

Single-equation 

VMT model 

annual VMT 

per vehicle 

fuel cost per mile, personal income, road miles per

Capita, time trend OLS 17.6% 15.2% 15.7% 

Single-equation 

VMT model 

annual VMT 

per adult 

fuel cost per mile, personal income, road miles per

Capita, dummy variables for fuel rationing, time trend OLS 34.0% 20.8% 13.6% 

Single-equation 

VMT model 

annual VMT 

per vehicle 

fuel cost per mile, personal income, vehicles per road 

mile, % of fleet manufactured under CAFE standards,

new vehicle prices 

IV (for fuel 

cost per mile) 16.3% 9.2% 7.0% 

Three-equation 

system for VMT, 

fuel efficiency, and 

vehicle stock 

annual VMT 

per vehicle 

fuel cost per mile, personal income, vehicles per capita, 

vehicles per road mile, fraction of adult population 

licensed to drive, new vehicle prices, % of fleet 

manufactured under CAFE standards 

2SLS 29.5% 13.4% 15.9% 

Three-equation 

system for VMT, 

fuel efficiency, and 

vehicle stock 

annual VMT 

per vehicle 

fuel cost per mile, personal income, vehicles per capita, 

vehicles per road mile, fraction of adult population 

licensed to drive, new vehicle prices, % of fleet 

manufactured under CAFE standards 

3SLS 29.8% 13.7% 16.2% 

Three-equation 

system for VMT, 

annual VMT 

per vehicle 

fuel cost per mile, personal income, vehicles per capita, 

vehicles per road mile, fraction of adult population 

Vector autoregression 19.9% 10.8% 

4.2.5.4 Basis for Rebound Effect Used by EPA and NHTSA in This Proposed Rule 

Based on a combination of historical estimates of the rebound effect and more recent 

analyses conducted by EPA and NHTSA, an estimate of 10 percent for the rebound effect was 

used for this rulemaking. Although this value represents the lower bound of the historical range 

of values for the rebound effect, more recent analysis strongly suggests that the rebound effect is 

declining over time. As part of this proposal, we are also providing information on options for 

projecting the rebound effect in the future. This projection approach, as described in the Small 

Report,13 takes into account changes in income and gasoline prices when estimating the 

magnitude of the rebound effect in the future. We will continue to evaluate this methodology 

between the proposed and final rules, and we will take into account public comments when 

determining whether it is more appropriate to use this projection approach for predicting the 

rebound effect. 

EPA and NHTSA also seeks comment on other alternatives for estimating the rebound 

effect. As one illustration, variation in the price per gallon of gasoline directly affects the permile cost of driving, and drivers may respond just as they would to a change in the cost of 

driving resulting from a change in fuel economy, by varying the number of miles they drive. 

Because vehicles’ fuel economy is fixed in the short run, variation in the number of miles driven 

in response to changes in fuel prices will be reflected in changes in gasoline consumption. 

Under the assumption that drivers respond similarly to changes in the cost of driving whether 

they are caused by variation in fuel prices or fuel economy, the short-run price elasticity of 

demand for gasoline – which measures the sensitivity of gasoline consumption to changes in its 

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44 

price per gallon – may provide some indication about the magnitude of the rebound effect itself. 

NHTSAThe agencies also invites comment on the extent to which the short run elasticity of 

demand for gasoline with respect to its price can provide useful information about the size of the 

rebound effect. 

Specifically, the agenciesy seeks comment on whether it would be appropriate to use the 

price elasticity of demand for gasoline, or other alternative approaches, to guide itstheir choice of 

a value for the rebound effect.

4.2.6 Benefits from Increased Vehicle Use 

The increase in vehicle use from the rebound effect provides additional benefits to their 

owners, who may make more frequent trips or travel farther to reach more desirable destinations. 

This additional travel provides benefits to drivers and their passengers by improving their access 

to social and economic opportunities away from home. As evidenced by their decisions to make 

more frequent or longer trips when improved fuel economy reduces their costs for driving, the 

benefits from this additional travel exceed the costs drivers and passengers incur in making more 

frequent or longer trips. 

The analysis estimates the economic benefits from increased rebound-effect driving as 

the sum of fuel costs drivers incur plus the consumer surplus they receive from the additional 

accessibility it provides.o

 Because the increased in travel depend on the extent of improvement 

in fuel economy, the value of benefits it provides differs among model years and alternative 

CAFE standards. Under even those alternatives that would impose the highest standards, 

however, the magnitude of these benefits represents a small fraction of total benefits. 

4.2.7 Added Costs from Increased Vehicle Use 

Although it provides some benefits to drivers, increased vehicle use associated with the 

rebound effect also contributes to increased traffic congestion, motor vehicle accidents, and 

highway noise. Depending on how the additional travel is distributed over the day and on where 

it takes place, additional vehicle use can contribute to traffic congestion and delays by increasing 

traffic volumes on facilities that are already heavily traveled during peak periods. These added 

delays impose higher costs on drivers and other vehicle occupants in the form of increased travel 

time and operating expenses. Because drivers do not take these added costs into account in 

deciding when and where to travel, they must be accounted for separately as a cost of the added 

driving associated with the rebound effect. 

Increased vehicle use due to the rebound effect may also increase the costs associated 

with traffic accidents. Drivers may take account of the potential costs they (and their passengers) 

face from the possibility of being involved in an accident when they decide to make additional 

trips. However, they probably do not consider all of the potential costs they impose on 

occupants of other vehicles and on pedestrians when accidents occur, so any increase in these 

 

o

 The consumer surplus provided by added travel is estimated as one-half of the product of the decline in fuel cost 

per mile and the resulting increase in the annual number of miles driven. 

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45 

“external” accident costs must be considered as another cost of additional rebound-effect driving. 

Like increased delay costs, any increase in these external accident costs caused by added driving 

is likely to depend on the traffic conditions under which it takes place, since accidents are more 

frequent in heavier traffic (although their severity may be reduced by the slower speeds at which 

heavier traffic typically moves). 

Finally, added vehicle use from the rebound effect may also increase traffic noise. Noise 

generated by vehicles causes inconvenience, irritation, and potentially even discomfort to 

occupants of other vehicles, to pedestrians and other bystanders, and to residents or occupants of 

surrounding property. Because these effects are unlikely to be taken into account by the drivers 

whose vehicles contribute to traffic noise, they represent additional externalities associated with 

motor vehicle use. Although there is considerable uncertainty in measuring their value, any 

increase in the economic costs of traffic noise resulting from added vehicle use must be included 

together with other increased external costs from the rebound effect. 

EPA and NHTSA rely on estimates of congestion, accident, and noise costs caused by 

automobiles and light trucks developed by the Federal Highway Administration to estimate the 

increased external costs caused by added driving due to the rebound effect.14 NHTSA employed 

these estimates previously in its analysis accompanying the MY 2011 final rule, and continues to 

find them appropriate for this NPRM after reviewing the procedures used by FHWA to develop 

them and considering other available estimates of these values. They are intended to measure the 

increases in costs from added congestion, property damages and injuries in traffic accidents, and 

noise levels caused by automobiles and light trucks that are borne by persons other than their 

drivers (or “marginal” external costs). 

Updated to 2007 dollars, FHWA’s “Middle” estimates for marginal congestion, accident, 

and noise costs caused by automobile use amount to 5.2 cents, 2.3 cents, and 0.1 cents per 

vehicle-mile (for a total of 7.6 cents per mile), while those for pickup trucks and vans are 4.7 

cents, 2.5 cents, and 0.1 cents per vehicle-mile (for a total of 7.3 cents per mile).15, p These costs 

are multiplied by the annual increases in automobile and light truck use from the rebound effect 

to yield the estimated increases in congestion, accident, and noise externality costs during each 

future year. 

4.2.8 Petroleum and Energy Security Impacts 

4.2.8.1 Impact on U.S. Petroleum Imports 

 In 2008, U.S. petroleum import expenditures represented 21 percent of total U.S. imports 

of all goods and services.16 In 2008, the United States imported 66 percent of the petroleum it 

 

p

 The Federal Highway Administration’s estimates of these costs agree closely with some other recent estimates. 

For example, recent published research conducted by Resources for the Future (RFF) estimates marginal congestion 

and external accident costs for increased light-duty vehicle use in the U.S. to be 3.5 and 3.0 cents per vehicle-mile in 

year-2002 dollars. See Ian W.H. Parry and Kenneth A. Small, “Does Britain or the U.S. Have the Right Gasoline 

Tax?” Discussion Paper 02-12, Resources for the Future, 19 and Table 1 (March 2002). Available at

http://www.rff.org/rff/Documents/RFF-DP-02-12.pdf (last accessed July 27, 2009).
Draft Joint Technical Support Document 

46 

consumed, and the transportation sector accounts for 70 percent of total U.S. petroleum 

consumption. This compares roughly to 37 percent of petroleum from imports and 55 percent 

consumption of petroleum in the transportation sector in 1975.17 It is clear that petroleum 

imports have a significant impact on the U.S. economy. Requiring lower-GHG vehicle 

technology and improved fuel economy in the U.S. is expected to lower U.S. petroleum imports. 

4.2.8.2 Background on U.S. Energy Security 

 U.S. energy security is broadly defined as protecting the U.S. economy against 

circumstances that threaten significant short- and long-term increases in energy costs. Most 

discussion of U.S. energy security revolves around the topic of the economic costs of U.S. 

dependence on oil imports. The problem is that the U.S. relies on sources of imported oil from 

potentially unstable sources. In addition, oil exporters have the ability to raise the price of oil by 

exerting monopoly power through the formation of a cartel, the Organization of Petroleum 

Exporting Countries (OPEC). Finally, these factors contribute to the vulnerability of the U.S. 

economy to episodic oil supply shocks and price spikes. In 2008, U.S. imports of crude oil were 

$326 billion (in 2007$, see Figure 4-1Figure 4-1Figure 4-1). Field Code Changed
Economic Assumptions Used in the Agencies’ Analyses

47 

U.S. Expenditures on Crude Oil

0

100

200

300

400

500

600

700

1970 1975 1980 1985 1990 1995 2000 2005

Year

(2007$ Billions)

Domestic

Imported

Figure 4-1 U.S. Expenditures on Crude Oil from 1970 through 2008q

One effect of the EPA/NHTSA joint proposal is that it promotes more efficient use of 

transportation fuels in the U.S. The result is that it reduces U.S. oil imports, which reduces both 

financial and strategic risks associated with a potential disruption in supply or a spike in the cost 

of a particular energy source. This reduction in risks is a measure of improved U.S. energy 

security. For this proposal, an “oil premium” approach is utilized to identify those energysecurity related impacts which are not reflected in the market price of oil, and which are 

expected to change in response to an incremental change in the level of U.S. oil imports. 

4.2.8.2.1 Methodology Used to Estimate U.S. Energy Security Benefits

In order to understand the energy security implications of reducing U.S. oil imports, EPA 

has worked with Oak Ridge National Laboratory (ORNL), which has developed approaches for 

 

q

 For historical data through 2006: EIA Annual Energy Review, various editions. 

For data 2006-2008: EIA Annual Energy Outlook (AEO) 2009 (Update Reference (Stimulus) Base Case). 

See file "aeostimtab_11.xls" available at http://www.eia.doe.gov/oiaf/servicerpt/stimulus/aeostim.html
Draft Joint Technical Support Document 

48 

evaluating the social costs and energy security implications of oil use. The energy security 

estimates provide below are based upon a methodology developed in a peer-reviewed study 

entitled, “The Energy Security Benefits of Reduced Oil Use, 2006-2015,” completed in March 

2008. This recent study is included as part of the docket for this rulemaking.18 This ORNL 

study is an update version of the approach to estimating the energy security benefits of U.S. oil 

import reductions developed in an ORNL 1997 Report by Leiby, Paul N., Donald W. Jones, T. 

Randall Curlee, and Russell Lee, entitled “Oil Imports: An Assessment of Benefits and Costs.” 

When conducting this recent analysis, ORNL considered the full cost of importing 

petroleum into the U.S. The full economic cost is defined to include two components in addition 

to the purchase price of petroleum itself. These are: (1) the higher costs for oil imports resulting 

from the effect of U.S. import demand on the world oil price and on OPEC market power (i.e., 

the “demand” or “monopsony” costs); and (2) the risk of reductions in U.S. economic output and 

disruption of the U.S. economy caused by sudden disruptions in the supply of imported oil to the 

U.S. (i.e., macroeconomic disruption/adjustment costs). Maintaining a U.S. military presence to 

help secure stable oil supply from potentially vulnerable regions of the world was not included in 

this analysis because its attribution to particular missions or activities is difficult (as discussed 

further below). Section III.H.8.b of the preamble contains a detailed discussion of how the 

monopsony and macroeconomic disruption/adjustment components were treated for this 

analysis. 

As part of the process for using the ORNL energy security estimates, EPA sponsored an 

independent-expert peer review of this ORNL study. A report compiling the peer reviewers’ 

comments is provided in the docket.19 In addition, EPA has worked with ORNL to address 

comments raised in the peer review and to develop estimates of the energy security benefits 

associated with a reduction in U.S. oil imports for this proposal. In response to peer reviewer 

comments, ORNL modified its model by changing several key parameters involving OPEC 

supply behavior, the responsiveness of oil demand and supply to a change in the world oil price, 

and the responsiveness of U.S. economic output to a change in the world oil price. 

For this proposal, ORNL further updated the energy security premium by incorporating 

the most recent oil price forecast and energy market trends in AEO 2009 into its model. In order 

for the energy security premium estimated to be used in EPA’s VECTOR model, ORNL 

developed energy security estimates for a number of different years; e.g., 2015, 2020, 2030, and 

2040. NHTSA used just the 2015 estimates to estimate the energy security benefits associated 

with this rule. 

For 2015, ORNL has estimated that the total energy security premium associated with a 

reduction of imported oil is $18.49/barrel. On a dollar per gallon basis, energy security benefits 

for 2015 are $0.44/gallon. Based upon alternative sensitivities regarding OPEC supply behavior, 

and the responsiveness of oil demand and supply to a change in the world oil price, the energy 

security premium ranges from $9.80 to $28.08/barrel. In terms of dollars per gallon, the energy 

security premium ranges from $0.23/gallon to $0.67/gallon. Please refer to Table 4-11Table 

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49 

4-11Table 4-11 for similar information for years 2015, 2020, 2030 and 2040,r as well as a 

breakdown of the components of the energy security premium for each year. The components of 

the energy security premium and their values are discussed below. 

Table 4-11 Energy Security Premium in 2015, 2020, 2030 and 2040 

 (2007$/Barrel) 

Year Monopsony 

(Range) 

Macroeconomic 

Disruption/Adjustment Costs 

(Range) 

Total Mid-Point 

(Range) 

2015 $11.79 

($4.26 - $21.37) 

$6.70 

$3.11 – $10.67 

$18.49 

$9.80 - $28.08 

2020 $12.31 

($4.46 - $22.53) 

$7.62 

($3.77 – $12.46) 

$19.94 

($10.58 - $30.47) 

2030 $10.57 

($3.84 – 18.94) 

$8.12 

($3.90 – $13.04) 

$18.69 

($10.52 - $27.89) 

2040 $10.57 

($3.84 – $18.94) 

$8.12 

($3.90 – $13.04) 

$18.69 

($10.52 - $27.89) 

4.2.8.2.1.1 Effect of Oil Use on Long-Run Oil Price, U.S. Import Costs, and 

Economic Output 

The first component of the full economic costs of importing petroleum into the U.S. 

follows from the effect of U.S. import demand on the world oil price over the long-run. Because 

the U.S. is a sufficiently large purchaser of foreign oil supplies, its purchases can affect the world

oil price. This monopsony power means that increases in U.S. petroleum demand can cause the 

world price of crude oil to rise, and conversely, that reduced U.S. petroleum demand can reduce 

the world price of crude oil. Thus, one benefit of decreasing U.S. oil purchases, due to the 

increased availability and use of other transportation fuels, is the potential decrease in the crude 

oil price paid for all crude oil purchased. 

The demand or monopsony effect can be readily illustrated with an example. If the U.S. 

imports 10 million barrels per day at a world oil price of $50 per barrel, its total daily bill for oil 

imports is $500 million. If a decrease in U.S. imports to 9 million barrels per day causes the 

world oil price to drop to $49 per barrel, the daily U.S. oil import bill drops to $441 million (9 

million barrels times $49 per barrel). While the world oil price only declines $1, the resulting 

decrease in oil purchase payments of $59 million per day ($500 million minus $441 million) is 

equivalent to an incremental benefit of $59 per barrel of oil imports reduced, or $10 more than 

the newly-decreased world price of $49 per barrel. This additional $10 per barrel “import cost 

premium” represents the incremental external benefits to the U.S. for avoided import costs 

beyond the price paid oil purchases. This additional benefit arises only to the extent that 

reduction in U.S. oil imports affects the world oil price. ORNL estimates this component of the 

 

r

 AEO 2009 forecasts energy market trends and values only to 2030. The energy security premia post-2030 were 

assumed to be the 2030 estimate. 

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50 

energy security benefit in 2015 to be $11.79/barrel, with a range of $4.26 – $21.37/barrel of 

imported oil reduced. 

4.2.8.2.1.2 Short-Run Disruption Premium from Expected Costs of Sudden Supply 

Disruptions 

The second component of the oil import premium, the “macroeconomic 

disruption/adjustment costs premium,” arises from the effect of oil imports on the expected cost 

of disruptions. A sudden increase in oil prices triggered by a disruption in world oil supplies has 

two main effects: (1) it increases the costs of imports in the short run, further expanding the 

transfer of U.S. wealth to foreign producers, and (2) it can lead to macroeconomic contraction, 

dislocation and Gross Domestic Product (GDP) losses. ORNL estimates the composite estimate 

of these two factors that comprise the macroeconomic disruption/adjustment costs premium to be 

$6.70/barrel in 2015, with a range of $3.11 – $10.67/barrel of imported oil reduced.

4.2.8.2.1.2.1 Higher Costs of Oil Imports and Wealth Transfer during Shocks 

During oil price shocks, the higher price of imported oil causes increased payments for 

imports and a transfer of wealth from the U.S to oil exporters. This increased claim on U.S. 

economic output is a loss to the U.S. that is separate from and additional to any reduction in 

economic output due to the shock. The increased wealth transfer during shocks is counted as a 

loss to the degree that the expected price increase is not anticipated and internalized by oil 

consumers. 

4.2.8.2.1.2.2 Macroeconomic Costs: Potential Output Loss and 

Dislocation/Adjustment Costs 

Macroeconomic losses during price shocks reflect both aggregate output losses and 

“allocative” losses. The former are a reduction in the level of output that the U.S. economy can 

produce fully using its available resources; and the latter stem from temporary dislocation and 

underutilization of available resources due to the shock, such as labor unemployment and idle 

plant capacity. The aggregate output effect, a reduction in “potential” economic output, will last 

so long as the price is elevated. It depends on the extent and duration of any disruption in the 

world supply of oil, since these factors determine the magnitude of the resulting increase in 

prices for petroleum products, as well as whether and how rapidly these prices return to their predisruption levels. 

In addition to the aggregate contraction, there are “allocative” or “adjustment” costs 

associated with dislocated energy markets. Because supply disruptions and resulting price 

increases occur suddenly, empirical evidence shows they also impose additional costs on 

businesses and households which must adjust their use of petroleum and other productive factors 

more rapidly than if the same price increase had occurred gradually. Dislocational effects 

include the unemployment of workers and other resources during the time needed for their 

intersectoral or interregional reallocation, and pauses in capital investment due to uncertainty. 

These adjustments temporarily reduce the level of economic output that can be achieved even 

below the “potential” output level that would ultimately be reached once the economy’s 

adaptation to higher petroleum prices was complete. The additional costs imposed on businesses 
Economic Assumptions Used in the Agencies’ Analyses

51 

and households for making these adjustments reflect their limited ability to adjust prices, output 

levels, and their use of energy, labor and other inputs quickly and smoothly in response to rapid 

changes in prices for petroleum products. 

Since future disruptions in foreign oil supplies are an uncertain prospect, each of the 

disruption cost components must be weighted by the probability that the supply of petroleum to 

the U.S. will actually be disrupted. Thus, the “expected value” of these costs – the product of the 

probability that a supply disruption will occur and the sum of costs from reduced economic 

output and the economy’s abrupt adjustment to sharply higher petroleum prices – is the relevant 

measure of their magnitude. Further, when assessing the energy security value of a policy to 

reduce oil use, it is only the change in the expected costs of disruption that results from the 

policy that is relevant. The expected costs of disruption may change from lowering the normal 

(i.e., pre-disruption) level of domestic petroleum use and imports, from any induced alteration in 

the likelihood or size of disruption, or from altering the short-run flexibility (e.g., elasticity) of

petroleum use. 

In summary, the steps needed to calculate the disruption or security premium are: 1) 

determine the likelihood of an oil supply disruption in the future; 2) assess the likely impacts of a 

potential oil supply disruption on the world oil price; 3) assess the impact of the oil price shock 

on the U.S. economy (in terms of import costs and macroeconomic losses); and 4) determine 

how these costs change with oil imports. The value of price spike costs avoided by reducing oil 

imports becomes the oil security portion of the premium. 

4.2.8.2.2 Costs of Existing U.S. Energy Security Policies 

The last often-identified component of the full economic costs of U.S. oil imports is the 

costs to the U.S. taxpayers of existing U.S. energy security policies. The two primary examples 

are maintaining a military presence to help secure a stable oil supply from potentially vulnerable 

regions of the world and maintaining the Strategic Petroleum Reserve (SPR). The SPR is the 

largest stockpile of government-owned emergency crude oil in the world. Established in the 

aftermath of the 1973-74 oil embargo, the SPR provides the U.S. a response option should a 

disruption in commercial oil supplies threaten the U.S. economy. It also allows the U.S. to meet 

part of its International Energy Agency obligation to maintain emergency oil stocks, and it 

provides a national defense fuel reserve. 

U.S. military costs are excluded from the analysis performed by ORNL because their 

attribution to particular missions or activities is difficult. Most military forces serve a broad 

range of security and foreign policy objectives. Attempts to attribute some share of U.S. military 

costs to oil imports are further challenged by the need to estimate how those costs might vary 

with incremental variations in U.S. oil imports. Similarly, while the costs for building and 

maintaining the SPR are more clearly related to U.S. oil use and imports, historically these costs 

have not varied in response to changes in U.S. oil import levels. Thus, while SPR is factored 

into the ORNL analysis, the cost of maintaining the SPR is excluded. 
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52 

4.2.8.2.3 Modifications to Analysis Based Upon Peer Reviewer Comments 

The EPA commissioned ORNL to conduct a number of sensitivity analyses to address the 

comments of the peer reviewers. Based upon the peer reviewer comments, key parameters that 

influence the “oil import” premium were assessed. Since not all the comments were in 

agreement with each other, several ranges of different parameters were developed for the 

analyses. These sensitivities used the most recent price forecasts and energy market trends 

available at the time the peer review was being conducted and completed, the AEO 2007 

Reference Case. Thus, the results presented below are suggestive of how the energy security 

premium is influenced by alternative assumptions of key parameters that influence world oil 

markets. A summary of the results of the analyses are shown in Table 4-12Table 4-12Table 

4-12. 

Three key parameters were varied in order to assess their impacts on the oil import 

premium: (1) the response of OPEC supply, (2) the combined response of non-U.S., non-OPEC 

demand and supply and (3) the GDP response to a change in the world oil as a result of reduced 

U.S. oil imports. The cases used updated supply/demand elasticities for non-U.S./non-OPEC 

region after considering more recent estimates than those used in 1997 study. As a result, the 

total market responsiveness is greater than previous ORNL estimates. Only small changes to the 

world oil price are anticipated from a substantial reduction in U.S. demand, on average, about 

$0.70/barrel for every million-barrels-per-day reduction in demand. 

In the ORNL framework, OPEC-behavior is treated parametrically, with a wide range of 

possible responses represented by a range of supply elasticities. Case One in Table 4-12Table 

4-12Table 4-12 below refers to the AEO 2007 estimates of energy market trends and uses the 

elasticity parameters from the original 1997 ORNL study. In Case Two, the OPEC supply 

elasticities range for 0.25 to 6 with a mean elasticity of 1.7. Case Three alters the distribution of

the OPEC supply elasticities so that the mean elasticity is 2.2 instead of 1.7. With the more 

elastic OPEC oil supply in Case Three, the oil premium is lower. Alternatively, a candidate rule 

for OPEC strategic response behavior, adapted from a lead article on what behavior maximizes 

OPEC’s long run net revenue in a robust way,20 would have OPEC responding to preserve its 

worldwide oil market share. This is presented as Case Seven. Application of this rule instead of 

the range of OPEC supply responses used leads to an estimate of the oil import premium that is 

between Case Two and Case Three. 

The second key parameter that was varied based upon peer reviewer comments was nonOPEC, non-U.S. demand and supply responsiveness to a change in the U.S. oil import demand 

and, hence, the world oil price. In Case Four, the mean non-U.S./non-OPEC demand and supply 

elasticities are taken to each be 0.3 in absolute value terms. When combined together, the net 

elasticity of import demand from this region — the non-U.S./non-OPEC region is — roughly 

1.6. Case Five takes the Case Four assumptions of a more elastic OPEC supply behavior and 

combines those assumptions with the 1.6 net elasticity of import demand for the non-U.S./nonOPEC region. Case Six looks at the consequences of a yet higher net elasticity of import 

demand — 2.28 — for the non-U.S./non-OPEC region. The impact on the oil import premium is 

relatively modest. 

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53 

Cases Eight and Nine consider a reduced GDP elasticity, the parameter which 

summarizes the sensitivity of GDP to oil price shocks. Several reviewers suggested a lower 

estimate for this parameter. In response to their comments, a couple of cases were examined 

where the GDP elasticity was lowered to 0.032 in comparison to the original ORNL estimate of 

0.0495. As anticipated, this change lowered the oil import premium modestly. For example, 

compared with Case Four where OPEC supply is more elastic, lowering the GDP elasticity with 

respect to the world oil price reduced the oil import premium by roughly $0.40/barrel. This is 

because the GDP-dislocation component is only about one-quarter of the total premium, and 

there are offsetting changes in other components. The last case examined, Case Nine, looks at 

the consequences for the oil import premium with a reduced elasticity of GDP if OPEC attempts 

to maintain its share of the world oil market. 

Clearly there is an unavoidable degree of uncertainty about the magnitude of marginal 

economic costs from the U.S. importation of petroleum, and the size of the oil import premium. 

ORNL sought to reflect this with probabilistic risk analysis over key input factors, guided by the 

available literature and the best judgment of oil market experts. Cases shown in Table 4-12Table 

4-12Table 4-12 explore some reasonable variations in the ranges of input assumptions and the 

mean oil premium estimates vary in a fairly moderate range between roughly $11 and $15/barrel 

of imported oil. On balance, Case Eight suggested a reasonable and cautious assessment of the 

premium value to ORNL, and is ORNL’s recommended case. This is based on a review of 

important driving factors, the numerical evaluations and simulations over major uncertainties, 

and taking into consideration the many comments and suggestion from the reviewers, the EPA 

and other Agencies. This recommended case, and the premium range resulting from 90% of the 

simulated outcomes, encompasses a wide array of perspectives and potential market outcomes in 

response to a reduction of U.S. imports. 

As mentioned previously, this recommended case relied on the most recent available 

projections of the U.S. and world oil market for the next ten years based upon the AEO 2007 

Reference Case. OPEC-behavior was treated parametrically, with a wide range of possible 

responses represented by a wide range of supply elasticities, from small to quite large. This 

recommended case recognized that the OPEC response is the most uncertain single element of 

this analysis. It could vary between inelastic defense of output levels, or market share, or could 

be highly elastic in defense of price, probably at the expense of longer run cartel power and 

discounted net profits. The balance between possible elastic and inelastic OPEC response was 

essentially even over a fairly wide range of elasticities. ORNL concluded that this is the best 

way to estimate OPEC behavior until greater progress can be made in synthesizing what insights 

are available from the evolving strategic game-theoretic and empirical research on OPEC 

behavior, and advancing that research. An alternative would have been to use OPEC strategic 

response behavior to maximize long-run net revenue, which may well correspond to marketshare preservation behavior (e.g., Case Seven), and a somewhat higher premium value.

Finally, ORNL’s recommended case used a GDP elasticity range, the parameter which 

summarizes the sensitivity of GDP to oil price shocks, which is reduced compared to earlier 

estimates, and compared to the full range of historically-based estimates. This helped address 

the concerns of those who either question the conclusions of past empirical estimates or expect 

that the impacts of oil shocks may well be declining. 

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54 

Table 4-12 Summary Results – Oil Import Premium Under Various Cases ($2007/BBL) 

Component Statistic

1) AEO2007 Base Outlook, 1997 Study Elasticities

2) AEO2007 Base Outlook, Wider Range of OPEC Supply Elasticities

3) Case 2 with Revised Wider Range of OPEC Supply Elasticities

4) Case 2 with Updated NonOPEC Supply/Demand Elasticities

5) Case 4 plus Revised Wider Range of OPEC Supply Elasticities

6) Case 4 Variant with Wider, Higher Range of NonU.S./Non-OPEC Supply/Demand Elasticities

7) Case 6 with Applied Strategic 

OPEC Behavioral Rule: Maintain Market Share

8) Case 4 with Reduced GDP Elasticity

9) Case 7 with Reduced GDP Elasticity

Mean $5.57 $10.26 $8.16 $7.77 $6.52 $6.44 $9.36 $7.86 $9.42Range ($3.60 - $8.19) ($3.10 - $21.22) ($3.12 - $19.30) ($2.94 - $13.75) ($2.90 - $13.06) ($2.69 - $11.33) ($6.69 - $12.42) ($2.94 - $13.89) ($6.72 - $12.54)

Mean $2.36 $2.33 $2.34 $1.92 $1.94 $1.93 $1.89 $2.20 $2.16Range ($0.51 - $4.66) ($0.58 - $4.57) ($0.66 - $4.61) ($0.39 - $3.75) ($0.38 - $3.84) ($0.42 - $3.70) ($0.44 - $3.66) ($0.63 - $4.07) ($0.62 - $3.96)

Mean $3.83 $3.70 $3.76 $3.42 $3.45 $3.47 $3.41 $2.67 $2.64Range ($1.06 - $6.69) ($1.04 - $6.57) ($1.06 - $6.74) ($0.92 - $6.23) ($0.94 - $6.10) ($0.92 - $6.13) ($0.87 - $6.26) ($0.90 - $4.84) ($0.87 - $4.87)

Mean $6.19 $6.03 $6.10 $5.34 $5.39 $5.40 $5.30 $4.87 $4.81Range ($2.94 - $10.01) ($2.94 - $9.75) ($2.91 - $9.85) ($2.53 - $8.69) ($2.60 - $8.62) ($2.58 - $8.92) ($2.54 - $8.74) ($2.23 - $7.85) ($2.25 - $7.77)Mean $11.75 $16.29 $14.27 $13.11 $11.90 $11.86 $14.65 $12.71 $14.23Range ($8.04 - $15.96) ($8.10 - $27.42) ($7.90 - $25.49) ($7.26 - $19.59) ($6.96 - $18.62) ($6.79 - $17.23) ($11.03 - $18.61) ($7.07 - $19.02) ($10.76 - $18.25)

Total Premium, in $/Gallon Mean $0.28 $0.39 $0.34 $0.31 $0.28 $0.28 $0.35 $0.30 $0.34Price Reduction ($/MMBD) Mean $0.52 $1.04 $0.81 $0.75 $0.61 $0.60 $0.90 $0.75 $0.90Cases7) Applied Strategic OPEC Behavioral Rule to Case 6: Maintain Market Share (Gately 2004 paper best strategy). This rule implies that OPEC Supply elasticity matches that of all non-OPEC supply. As a result non-U.S. elasticity of import demand ranges from8) Variant on version Case 4, considered reduced GDP elasticity for future disruptions (range -0.01 to -0.054; midcase value -0.032; mean value is -0.032, reduced from mean value of -0.0495). OPECbehavior treated parametrically.

9) Revise Case 7 (which applied Strategic OPEC Behavioral Rule to Case 6: Maintain Market Share (Gately 2004 paper best strategy)) with reduced GDP elasticity for future disruptions (range -0.01 to -

0.054; midcase value -0.032; mean value is -0.032, reduc3) Revise Case 2, with OPEC behavior distributed over elasticities 0 to 6, so that 25% of response is inelastic (< 1.0), mode elasticity is 2.0 (mean elasticity is 2.2 rather than 1.76)4) Updated Case 2 supply/demand elasticities for non-OPEC region with more recent estimates. Elasticity of non-U.S. demand -0.2 to -0.4, with mean and mode -0.3, non-U.S. Supply = 0.2 to 0.4, with 

mean and mode 0.3, implying (mode) net elasticity of impo5) Revise Case 4, with OPEC behavior distributed over elasticities 0 to 6, so that 25% of response is inelastic (< 1.0), mode elasticity is 2.0 (mean elasticity is 2.2 rather than 1.76). Net elasticity of import demand is -1.6 for the non-U.S./non-OPEC r

6) Alternative to Case 4 with expanded (and higher) range of non-U.S. supply/demand elasticities. Elasticity of non-U.S. demand = -0.3 to -0.7, triangular distribution with mode -0.4 mean -0.467, elasticity of non-U.S. supply = 0.2 to 0.6, mode 0.3 and mEconomic Disruption/Adjustment CostsTotal Mid-Point1) Based on AEO2007. Updated oil market outlook from AEO1994 Base Case to AEO2007 Base Case. Among other things, this means average crude price rises from $20.33 to $48.34. All elasticities 

match 1997 values. Non-U.S. elasticity of import demand = -0.2) AEO2007 Base Outlook, with wider range of OPEC supply elasticities, 0.25 to 6.0 and a mean elasticity of 1.76.Monopsony ComponentDisruption Import CostsDisruption Dislocation Costs
Economic Assumptions Used in the Agencies’ Analyses

55 

4.2.8.2.4 Energy Security Premium Values for This Proposal 

After the peer review process was completed, EPA worked with ORNL to further 

update the analysis for this proposed vehicle program. Using the same methodology as the 

peer reviewed model, but updating the analysis using AEO 2009 world oil price values, 

ORNL calculated energy security premium estimates for the years 2015, 2020, and 2030. 

Since the AEO 2009 ends its forecast in the year 2030, EPA assumed that the post-2030 

energy security premium estimates were the same as the 2030 estimate. The results of this 

analysis are shown above in Table 4-11Table 4-11Table 4-11in Chapter 4.2.8. 

4.2.8.3 The Impact of Fuel Savings on U.S. Petroleum Imports 

Based on a detailed analysis of differences in fuel consumption, petroleum imports, 

and imports of refined petroleum products among the Reference Case, High Economic 

Growth, and Low Economic Growth Scenarios presented in AEO 2009, NHTSA and EPA 

estimate that approximately 50 percent of the reduction in fuel consumption resulting from 

adopting improved fuel efficiency standards and GHG standards is likely to be reflected in 

reduced U.S. imports of refined fuel, while the remaining 50 percent would be expected to be 

reflected in reduced domestic fuel refining.s

 Of this latter figure, 90 percent is anticipated to 

reduce U.S. imports of crude petroleum for use as a refinery feedstock, while the remaining 

10 percent is expected to reduce U.S. domestic production of crude petroleum.t Thus on 

balance, each gallon of fuel saved as a consequence of improved fuel efficiency standards and 

GHG standards is anticipated to reduce total U.S. imports of crude petroleum or refined fuel 

by 0.95 gallons.u

 Section 5.3 of the RIA contains a discussion regarding caveats for the fuel 

savings estimated due to implementation of this rule. 

4.2.9 Air Pollutant Emissions 

Car and light truck use, fuel refining, and fuel distribution and retailing also generate 

emissions of certain criteria air pollutants, including carbon monoxide (CO), hydrocarbon 

compounds (usually referred to as “volatile organic compounds,” or VOC), nitrogen oxides 

(NOx), fine particulate matter (PM2.5), and sulfur dioxide (SO2). Emissions of most of these 

pollutants are associated with the number of vehicle miles driven, rather than with fuel 

consumption. While reductions in fuel refining and distribution that result from lower fuel 

consumption will reduce emissions of criteria pollutants, additional vehicle use associated 

with the rebound effect from higher fuel economy will increase emissions of most of these 

pollutants. Sulfur dioxide is an exception, as described below. 

 

s

 Differences between forecast annual U.S. imports of crude petroleum and refined products among these three 

scenarios range from 24-89% of differences in projected annual gasoline and diesel fuel consumption in the U.S. 

These differences average 49% over the forecast period spanned by AEO 2009. 

t

 Differences between forecast annual U.S. imports of crude petroleum among these three scenarios range from 

67-97% of differences in total U.S. refining of crude petroleum, and average 85% over the forecast period 

spanned by AEO 2009. 

u

 This figure is calculated as 0.50 + 0.50*0.9 = 0.50 + 0.45 = 0.95. 

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Draft Joint Technical Support Document 

56 

Thus the net effect of stricter fuel efficiency and GHG standards on total emissions of 

each criteria pollutant depends on the relative magnitudes of reduced emissions in fuel 

refining and distribution, and increases in emissions from vehicle use. Because the 

relationship between emission rates (emissions per gallon refined of fuel or mile driven) in 

fuel refining and vehicle use is different for each criteria pollutant, the net effect of fuel 

savings from increased fuel efficiency and GHG standards on total emissions of each 

pollutant is likely to differ. 

4.2.9.1 Reductions in Emissions of Criteria Air Pollutants 

EPA and NHTSA estimate the increases in emissions of each criteria air pollutant 

from additional vehicle use by multiplying the increase in total miles driven by cars and light 

trucks of each model year and age by their estimated emission rates per vehicle-mile of each 

pollutant. These emission rates differ between cars and light trucks as well as between 

gasoline and diesel vehicles, and both their values for new vehicles and the rates at which they 

increase with age and accumulated mileage can vary among model years. With the exception 

of SO2, the agencies calculated the increase in emissions of these criteria pollutants from 

added car and light truck use by multiplying the estimated increases in their vehicles’ use 

during each year over their expected lifetimes by per-mile emission rates appropriate to each 

vehicle type, fuel used, model year, and age as of that future year. 

These emission rates were estimated by U.S. EPA using its recently-developed Motor 

Vehicle Emission Simulator (Draft MOVES 2009). The MOVES model assumes that the permile rates at which these pollutants are emitted are determined by EPA regulations and the 

effectiveness of catalytic after-treatment of engine exhaust emissions, and are thus unaffected 

by changes in car and light truck fuel economy. As a consequence, the effects of required 

increases in fuel economy on emissions of these pollutants from car and light truck use are 

determined entirely by the increases in driving that result from the fuel economy rebound 

effect. 

Emission factors in the MOVES database are expressed in the form of grams per 

vehicle-hour of operation. To convert these emission factors to grams per mile for use in EPA 

and NHTSA’s calculations, MOVES was run for the year 2050, and was programmed to 

report aggregate emissions from vehicle starting, operation, storage, and refueling. EPA 

analysts selected the year 2050 in order to generate emission factors that were representative 

of lifetime average emission rates for vehicles meeting the agency’s Tier 2 emission 

standard.v

 Separate estimates were developed for each vehicle type and model year, as well as 

for each state and month, in order to reflect the effects of regional and temporal variation in 

temperature and other relevant variables on emissions. 

 

v

 Because all light-duty emission rates in Draft MOVES 2009 are assumed to be invariant after MY 

2010, a calendar-year 2050 run produced a full set of emission rates that reflect anticipated deterioration 

in the effectiveness of vehicles’ emission control systems with increasing age and accumulated mileage

for post-MY 2010 vehicles. 
Economic Assumptions Used in the Agencies’ Analyses

57 

The MOVES emissions estimates were then summed to the model year level and 

divided by average distance traveled in order to produce per-mile emission factors for each 

pollutant. The resulting emission rates represent average values across the nation, and 

incorporate typical temperature variations over an entire calendar year. These national 

average rates also reflect county-specific differences in fuel composition, as well as in the 

presence and type of vehicle inspection and maintenance programs.w 

Emission rates for the criteria pollutant SO2 were calculated by EPA and NHTSA 

using average fuel sulfur content estimates supplied by EPA, together with the assumption 

that the entire sulfur content of fuel is emitted in the form of SO2. These calculations assumed 

that national average gasoline and diesel sulfur levels would remain at current levels.x There, 

unlike many other criteria pollutants, sulfur dioxide emissions from vehicle use decrease 

proportionally to the decrease in fuel consumption.

Emissions of criteria air pollutants also occur during each phase of fuel production and 

distribution, including crude oil extraction and transportation, fuel refining, and fuel storage 

and transportation. The reduction in emissions during each of these phases depends on the 

extent to which fuel savings result in lower imports of refined fuel, or in reduced domestic 

fuel refining. To a lesser extent, they also depend on whether reductions in domestic gasoline 

refining are reflected in reduced imports of crude oil or in reduced domestic extraction of 

petroleum. 

Both EPA’s and NHTSA’s analyses assume that reductions in imports of refined fuel 

would reduce criteria pollutant emissions during fuel storage and distribution only. 

Reductions in domestic fuel refining using imported crude oil as a feedstock are assumed to 

reduce emissions during fuel refining, storage, and distribution, because each of these 

activities would be reduced. Finally, reduced domestic fuel refining using domesticallyproduced crude oil is assumed to reduce emissions during all four phases of fuel production 

and distribution.y

EPA estimated the reductions in criteria pollutant emissions from producing and 

distributing fuel that would occur with alternative fuel efficiency and GHG standards using 

emission rates obtained from Argonne National Laboratories’ Greenhouse Gases and 

Regulated Emissions in Transportation (GREET) model.

21 The GREET model provides 

 

w

 The national mix of fuel types includes county-level market shares of conventional and reformulated gasoline, 

as well as county-level variation in sulfur content, ethanol fractions, and other fuel properties. 

Inspection/maintenance programs at the county level account for detailed program design elements such as test 

type, inspection frequency, and program coverage by vehicle type and age. 

x

 These are 30 and 15 parts per million (ppm, measured on a mass basis) for gasoline and diesel respectively, 

which produces emission rates of 0.17 grams of SO2 per gallon of gasoline and 0.10 grams per gallon of diesel. 

y

 In effect, this assumes that the distances crude oil travels to U.S. refineries are approximately the same 

regardless of whether it travels from domestic oilfields or import terminals, and that the distances that gasoline 

travels from refineries to retail stations are approximately the same as those from import terminals to gasoline 

stations. 
Draft Joint Technical Support Document 

58 

separate estimates of air pollutant emissions that occur in four phases of fuel production and 

distribution: crude oil extraction, crude oil transportation and storage, fuel refining, and fuel 

distribution and storage.z EPA modified the GREET model to change certain assumptions 

about emissions during crude petroleum extraction and transportation, as well as to update its 

emission rates to reflect adopted and pending EPA emission standards. EPA also 

incorporated emission factors for the air toxics estimated in this analysis: benzene, 1,3-

butadiene, acetaldehyde, acrolein, and formaldehyde. The resulting emission factors are 

shown in Table 4-13Table 4-13Table 4-13. 

 

z

 Emissions that occur during vehicle refueling at retail gasoline stations (primarily evaporative emissions of 

volatile organic compounds, or VOCs) are already accounted for in the “tailpipe” emission factors used to 

estimate the emissions generated by increased light truck use. GREET estimates emissions in each phase of 

gasoline production and distribution in mass per unit of gasoline energy content; these factors are then converted 

to mass per gallon of gasoline using the average energy content of gasoline. 

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Economic Assumptions Used in the Agencies’ Analyses

59 

Table 4-13 Emissions by Stage of Fuel Production and Distribution (grams/million Btu) 

Pollutant Fuel Type Petroleum 

Extraction & 

Transportation1

Refinery 

Energy 

Use 

Upstream 

Emissions

Petroleum

Refining 

On-Site 

Petroleum 

Refining2

Fuel 

Transport, 

Storage, 

Distribution3

CO Conventional Gasoline 4.908 0.928 5.596 6.525 0.748 

Reformulated Gasoline 4.908 0.908 5.662 6.571 0.768 

Low Sulfur Diesel 4.908 0.800 5.103 5.903 0.780 

VOC Conventional Gasoline 3.035 0.602 2.560 3.162 42.91 

Reformulated Gasoline 3.035 0.627 2.584 3.211 42.92 

Low Sulfur Diesel 3.035 0.552 2.511 3.063 1.261 

NOx Conventional Gasoline 14.91 3.328 14.442 17.771 3.691 

Reformulated Gasoline 14.91 3.288 14.575 17.863 3.786 

Low Sulfur Diesel 14.91 2.895 12.972 15.866 3.570 

SOx Conventional Gasoline 3.926 4.398 9.678 14.076 0.886 

Reformulated Gasoline 3.926 4.422 9.922 14.344 0.909 

Low Sulfur Diesel 3.926 3.893 9.187 13.080 0.840 

PM2.5 Conventional Gasoline 0.645 1.442 1.789 3.231 0.155 

Reformulated Gasoline 0.645 1.487 1.838 3.325 0.159 

Low Sulfur Diesel 0.645 1.309 1.635 2.943 0.133 

Air Toxics 

1,3-Butadiene 

Conventional Gasoline 0.0017 0.0003 0.0014 0.0017 0.0001 

Reformulated Gasoline 0.0017 0.0003 0.0014 0.0018 0.0001 

Low Sulfur Diesel 0.0017 0.0003 0.0014 0.0017 0.0001 

Acetaldehyde Conventional Gasoline 0.0002 0.0000 0.0002 0.0002 0.0046 

Reformulated Gasoline 0.0002 0.0000 0.0002 0.0002 0.0047 

Low Sulfur Diesel 0.0002 0.0000 0.0002 0.0002 0.0044 

Acrolein Conventional Gasoline 0.0001 0.0000 0.0001 0.0001 0.0006 

Reformulated Gasoline 0.0001 0.0000 0.0001 0.0001 0.0006 

Low Sulfur Diesel 0.0001 0.0000 0.0001 0.0001 0.0006 

Benzene Conventional Gasoline 0.0313 0.0062 0.0264 0.0326 0.0787 

Reformulated Gasoline 0.0313 0.0064 0.0264 0.0328 0.0788 

Low Sulfur Diesel 0.0313 0.0058 0.0264 0.0322 0.0015 

Formaldehyde Conventional Gasoline 0.0050 0.0010 0.0042 0.0052 0.0326 

Reformulated Gasoline 0.0050 0.0010 0.0042 0.0052 0.0335 

Low Sulfur Diesel 0.0050 0.0009 0.0042 0.0051 0.0316 

1

 The petroleum extraction and transport emission factors are only applied to domestic crude oil. 

2

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

3

 Fuel transport, storage, and distribution emission factors represent domestic emissions and are applied to all 

finished fuel, whether refined domestically or internationally. 

The agency converted these emission rates from the mass per fuel energy content basis 

on which GREET reports them to mass per gallon of fuel supplied using the estimates of fuel 

energy content reported by GREET. The resulting emission rates were applied to both EPA’s 

and NHTSA’s estimates of fuel consumption under alternative fuel efficiency standards to 

develop estimates of total emissions of each criteria pollutant during fuel production and 

distribution. The assumptions about the effects of changes in fuel consumption on domestic 

and imported sources of fuel supply discussed above were then employed to calculate the 

Formatted Table
Draft Joint Technical Support Document 

60 

effects of reductions in fuel use from alternative fuel efficiency and GHG standards on 

changes in domestic emissions of each criteria pollutant. 

Finally, EPA and NHTSA calculated the net changes in domestic emissions of each 

criteria pollutant by summing the increases in its emissions projected to result from increased 

vehicle use, and the reductions in emissions anticipated to result from lower domestic fuel 

refining and distribution.aa As indicated previously, the effect of adopting improved fuel 

efficiency and GHG standards on total emissions of each criteria pollutant depends on the 

relative magnitudes of the resulting reduction in emissions from fuel refining and distribution, 

and the increase in emissions from additional vehicle use. Although these net changes vary 

significantly among individual criteria pollutants, we project that on balance, adopting 

improved fuel efficiency and GHG standards would reduce emissions of all criteria air 

pollutants except carbon monoxide (CO). 

4.2.9.2 Estimated Values of Reducing Criteria Pollutant Emissions 

In this analysis, EPA and NHTSA estimate the economic value of the human health 

benefits associated with reducing exposure to PM2.5. Due to analytical limitations, the 

estimated values do not include comparable benefits related to reductions in other criteria 

pollutants (such as ozone, NO2 or SO2) or toxic air pollutants, nor do they monetize all of the 

potential health and welfare effects associated with PM2.5 or the other criteria pollutants. As a 

result these values underestimate the benefits associated with reductions of these pollutants. 

This analysis uses a “benefit-per-ton” method to estimate selected PM2.5-related health 

benefits. These PM2.5-related benefit-per-ton estimates provide the total monetized human 

health benefits (the sum of premature mortality and premature morbidity) of reducing one ton 

of directly emitted PM2.5, or one ton of a pollutant that contributes to secondarily-formed 

PM2.5 (such as NOx, SOx, and VOCs) from a specified source. Ideally, the human health 

benefits would be estimated based on changes in ambient PM2.5 concentrations and population 

exposure, as determined by full-scale air quality and exposure modeling. However, 

conducting such detailed modeling was not possible within the timeframe for this proposal. 

The dollar-per-ton estimates for reductions in PM2.5 emissions used in this analysis are 

provided in Table 4-14Table 4-14Table 4-14. 

 

aa All emissions from increased vehicle use are assumed to occur within the U.S., since Fuel efficiency standards 

would apply only to vehicles produced for sale in the U.S. 

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Economic Assumptions Used in the Agencies’ Analyses

61 

Table 4-14 Benefits-per-ton Values (2007$) Derived Using the ACS Cohort Study for 

PM-related Premature Mortality (Pope et al., 2002)a and a 3% Discount Rateb

All SourcesdStationary (Non-EGU) 

Sourcese 

Year Mobile Sources c

SOx VOC NOx Direct PM2.5 NOx Direct PM2.5 

2015 $28,000 $1,200 $4,700 $220,000 $4,900 $270,000

2020 $31,000 $1,300 $5,100 $240,000 $5,300 $290,000

2030 $36,000 $1,500 $6,100 $280,000 $6,400 $350,000

2040 $43,000 $1,800 $7,200 $330,000 $7,600 $420,000

a The benefit-per-ton estimates presented in this table are based on an estimate of premature mortality derived 

from the ACS study (Pope et al., 2002). If the benefit-per-ton estimates were based on the Six Cities study 

(Laden et al., 2006), the values would be approximately 145% (nearly two-and-a-half times) larger. 

b

 The benefit-per-ton estimates presented in this table assume a 3% discount rate in the valuation of premature 

mortality to account for a twenty-year segmented cessation lag. If a 7% discount rate had been used, the values 

would be approximately 9% lower. 

c

 Benefit-per-ton values were estimated for the years 2015, 2020, and 2030. For 2040, EPA and NHTSA 

extrapolated exponentially based on the growth between 2020 and 2030. 

d

 Note that the benefit-per-ton value for SOx is based on the value for Stationary (Non-EGU) sources; no SOx 

value was estimated for mobile sources. The benefit-per-ton value for VOCs was estimated across all sources. 

e

 Non-EGU denotes stationary sources of emissions other than electric generating units. 

As Table 4-14Table 4-14Table 4-14 indicates, EPA projects that the per-ton values for 

reducing emissions of criteria pollutants from both vehicle use and stationary sources such as 

fuel refineries and storage facilities will increase over time. These projected increases reflect 

rising income levels, which are assumed to increase affected individuals’ willingness to pay 

for reduced exposure to health threats from air pollution, as well as population growth into the 

future, expanding the population exposed to air pollution in both urban and rural areas. 

For certain PM2.5-related pollutants (such as direct PM2.5 and NOx), EPA estimates 

different per-ton values for reducing mobile source emissions than for reductions in 

emissions of the same pollutant from stationary sources such as fuel refineries and storage 

facilities. These reflect differences in the typical geographic distributions of emissions of 

each pollutant, their contributions to ambient levels of PM2.5, and resulting changes in 

population exposure. EPA and NHTSA apply these separate values to its estimates of 

changes in emissions from vehicle use and from fuel production and distribution to determine 

the net change in total economic damages from emissions of those pollutants. 

The benefit per-ton technique has been used in previous analyses, including EPA’s 

recent Ozone National Ambient Air Quality Standards (NAAQS) RIA (U.S. EPA, 2008a),22

Portland Cement National Emissions Standards for Hazardous Air Pollutants (NESHAP) RIA 

(U.S. EPA, 2009a),23 and NO2 NAAQS (U.S. EPA, 2009b).24

 Table 4-15Table 4-15

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Draft Joint Technical Support Document 

62 

Table 4-15 shows the quantified and unquantified PM2.5-related co-benefits captured in these 

benefit-per-ton estimates. 

Table 4-15 Human Health and Welfare Effects of PM2.5

Pollutant / 

Effect 

Quantified and Monetized 

in Primary Estimates 

Unquantified Effects 

Changes in: 

PM2.5 Adult premature mortality 

Bronchitis: chronic and acute 

Hospital admissions: respiratory and 

cardiovascular 

Emergency room visits for asthma 

Nonfatal heart attacks (myocardial 

infarction) 

Lower and upper respiratory illness 

Minor restricted-activity days 

Work loss days 

Asthma exacerbations (asthmatic 

population) 

Infant mortality 

Subchronic bronchitis cases 

Low birth weight 

Pulmonary function 

Chronic respiratory diseases other 

than chronic bronchitis 

Non-asthma respiratory emergency 

room visits 

Visibility 

Household soiling 

Consistent with the NO2 NAAQS,bb the benefits estimates utilize concentrationresponse functions as reported in the epidemiology literature. Readers interested in reviewing 

the complete methodology for creating the benefit-per-ton estimates used in this analysis can 

consult the Technical Support Document (TSD)25 accompanying the recent final ozone 

NAAQS RIA (U.S. EPA, 2008a). Readers can also refer to Fann et al. (2009)26 for a detailed 

description of the benefit-per-ton methodology.cc

As described above, national per-ton estimates were developed for selected 

pollutant/source category combinations. The per-ton values calculated therefore apply only to 

tons reduced from those specific pollutant/source combinations (e.g., NO2 emitted from 

mobile sources; direct PM emitted from stationary sources). Our estimate of total PM2.5 

benefits is therefore based on the total direct PM2.5 and PM2.5–related precursor emissions 

(NOx, SOx, and VOCs) controlled from each source and multiplied by the respective per-ton 

values of reducing emissions from that source. 

 

bb Although we summarize the main issues in this chapter, we encourage interested readers to see 

benefits chapter of the NO2 NAAQS for a more detailed description of recent changes to the PM benefits

presentation and preference for the no-threshold model. 

cc The values included in this report are different from those presented in the article cited above. Benefits 

methods change to reflect new information and evaluation of the science. Since publication of the June 2009 

article, EPA has made two significant changes to its benefits methods: (1) We no longer assume that a threshold 

exists in PM-related models of health impacts; and (2) We have revised the Value of a Statistical Life to equal 

$6.3 million (year 2000$), up from an estimate of $5.5 million (year 2000$) used in the June 2009 report. Please 

refer to the following website for updates to the dollar-per-ton estimates: 

http://www.epa.gov/air/benmap/bpt.html 
Economic Assumptions Used in the Agencies’ Analyses

63 

The benefit-per-ton coefficients in this analysis were derived using modified versions 

of the health impact functions used in the PM NAAQS Regulatory Impact Analysis. 

Specifically, this analysis uses the benefit-per-ton estimates first applied in the Portland 

Cement NESHAP RIA (U.S. EPA, 2009a), which incorporated concentration-response 

functions directly from the epidemiology studies, without any adjustment for an assumed 

threshold. Removing the threshold assumption is a key difference between the method used 

in this analysis to estimate PM co-benefits and the methods used in analyses prior to EPA’s 

Portland Cement NESHAP. As a consequence, the benefit-per-ton estimates used in this 

analysis include incremental benefits of reductions in PM2.5 concentrations down to their 

lowest modeled levels. 

Reductions in PM-related mortality provide the majority of the monetized value in 

each benefit-per-ton estimate. Typically, the premature mortality-related effect coefficients 

that underlie the benefits-per-ton estimates are drawn from epidemiology studies that examine 

two large population cohorts: the American Cancer Society cohort (Pope et al., 2002)27 and 

the Harvard Six Cities cohort (Laden et al., 2006).28 The concentration-response (C-R) 

function developed from the extended analysis of American Cancer Society (ACS) cohort, as 

reported in Pope et al. (2002), has previously been used by EPA to generate its primary 

benefits estimate. The extended analysis of the Harvard Six Cities cohort, as reported by 

Laden et al (2006), was published after the completion of the Staff Paper for the 2006 PM2.5 

NAAQS and has been used as an alternative estimate in the PM2.5 NAAQS RIA and PM2.5 cobenefits estimates in analyses completed since the PM2.5 NAAQS. 

These studies provide logical choices for anchor points when presenting PM-related 

benefits because, while both studies are well designed and peer-reviewed, there are strengths 

and weaknesses inherent in each. Although this argues for using both studies to generate 

benefits estimates, due to the analytical limitations associated with this analysis,dd EPA and 

NHTSA have chosen to use the benefit-per-ton value derived from the ACS study. The 

agencies note that benefits would be approximately 145 percent (or nearly two-and-a-half 

times) larger if the per-ton benefit values based on the Harvard Six Cities were used instead. 

As is the nature of benefits analyses, assumptions and methods evolve over time to 

reflect the most current interpretation of the scientific and economic literature. For a period 

of time (2004-2008), EPA’s Office of Air and Radiation (OAR) valued mortality risk 

reductions using a value of statistical life (VSL) estimate derived from a limited analysis of 

some of the available studies. OAR arrived at a VSL using a range of $1 million to $10 

million (2000$) consistent with two meta-analyses of the wage-risk literature. 

The $1 million value represented the lower end of the interquartile range from the 

Mrozek and Taylor (2002)29 meta-analysis of 33 studies. The $10 million value represented 

the upper end of the interquartile range from the Viscusi and Aldy (2003)30 meta-analysis of 

43 studies. The mean estimate of $5.5 million (2000$) was also consistent with the mean 

VSL of $5.4 million estimated in the Kochi et al. (2006)31 meta-analysis. However, the 

 

dd Add note about VECTOR/VOLPE input limitations
Draft Joint Technical Support Document 

64 

Agency neither changed its official guidance on the use of VSL in rule-makings nor subjected 

the interim estimate to a scientific peer-review process through the Science Advisory Board 

(SAB) or other peer-review group. 

Until updated guidance is available, EPA determined that a single, peer-reviewed 

estimate applied consistently best reflects the SAB-EEAC advice it has received. Therefore, 

EPA has decided to apply the VSL that was vetted and endorsed by the SAB in the Guidelines 

for Preparing Economic Analyses (U.S. EPA, 2000)32 while they continue efforts to update 

their guidance on this issue.ee This approach calculates a mean value across VSL estimates 

derived from 26 labor market and contingent valuation studies published between 1974 and 

1991. The mean VSL across these studies is $6.3 million (2000$). The dollar-per-ton 

estimates used in this analysis are based on this revised VSL. 

The benefit-per-ton estimates are subject to a number of assumptions and 

uncertainties. 

a. They do not reflect local variability in population density, meteorology, exposure, 

baseline health incidence rates, or other local factors that might lead to an 

overestimate or underestimate of the actual benefits of controlling fine particulates in 

specific locations. EPA will conduct full-scale air quality modeling for the final 

rulemaking in an effort to capture this variability. Please refer to Chapter 7.2 of the 

DRIA for a description of EPA’s modeling plans and to Chapter 7.3 for the description 

of the agency’s plans for quantification and monetization of health impacts for the 

FRM. 

a.b. This analysis assumes that all fine particles, regardless of their chemical composition, 

are equally potent in causing premature mortality. This is an important assumption, 

because PM2.5 produced via transported precursors emitted from stationary sources 

may differ significantly from direct PM2.5 released from diesel engines and other 

industrial sources. At the present time, however, no clear scientific grounds exist for 

supporting differential effects estimates by particle type. 

a.c. This analysis assumes that the health impact function for fine particles is linear within 

the range of ambient concentrations under consideration. Thus, the estimates include 

health benefits from reducing fine particles in areas with varied initial concentrations 

of PM2.5, including both regions that are in attainment with fine particle standard and 

those that do not meet the standard, down to the lowest modeled concentrations. 

a.d. There are several health benefits categories that EPA and NHTSA were unable to 

quantify due to limitations associated with using benefits-per-ton estimates, several of 

which could be substantial. Because the NOX and VOC emission reductions 

associated with this proposal are also precursors to ozone, reductions in NOX and VOC 

 

ee In the (draft) update of the Economic Guidelines (U.S. EPA, 2008c), EPA retained the VSL endorsed by the 

SAB with the understanding that further updates to the mortality risk valuation guidance would be forthcoming 

in the near future. Therefore, this report does not represent final agency policy. The draft update of the 

Economic Guidelines is available on the Internet at <http://yosemite.epa.gov/ee/epa/eermfile.nsf/vwAN/EE0516-01.pdf/$File/EE-0516-01.pdf>. 

Formatted: Bullets and Numbering
Economic Assumptions Used in the Agencies’ Analyses

65 

would also reduce ozone formation and the health effects associated with ozone 

exposure. Unfortunately, benefits-per-ton estimates for ozone do not exist due to 

issues associated with the complexity of the atmospheric air chemistry and 

nonlinearities associated with ozone formation. The PM-related benefits-per-ton 

estimates also do not include any human welfare or ecological benefits. Please refer to 

Chapter 7.3 of the DRIA for a description of the quantification and monetization of 

health impact for the FRM and a description of the unquantified co-pollutant benefits 

associated with this rulemaking. 

As mentioned above, emissions changes and benefits-per-ton estimates alone are not a 

good indication of local or regional air quality and health impacts, as the localized impacts 

associated with the proposed rulemaking may vary significantly. Additionally, the 

atmospheric chemistry related to ambient concentrations of PM2.5, ozone and air toxics is very 

complex. Full-scale photochemical modeling is therefore necessary to provide the needed 

spatial and temporal detail to more completely and accurately estimate the changes in ambient 

levels of these pollutants and their associated health and welfare impacts. As discussed 

above, timing and resource constraints precluded EPA and NHTSA from conducting a fullscale photochemical air quality modeling analysis in time to include in the NPRM. For the 

final rule, however, a national-scale air quality modeling analysis will be performed to 

analyze the impacts of the standards on PM2.5, ozone, and selected air toxics. 

4.2.10 Reductions in Emissions of Greenhouse Gases

Emissions of carbon dioxide and other greenhouse gases (GHGs) occur throughout the 

process of producing and distributing transportation fuels, as well as from fuel combustion 

itself. By reducing the volume of fuel consumed by passenger cars and light trucks, higher 

fuel efficiency standards will thus reduce GHG emissions generated by fuel use, as well as 

throughout the fuel supply cycle. Lowering these emissions is likely to slow the projected 

pace and reduce the ultimate extent of future changes in the global climate, thus reducing 

future economic damages that changes in the global climate are otherwise expected to cause. 

Further, by reducing the probability that climate changes with potentially catastrophic 

economic or environmental impacts will occur, lowering GHG emissions may also result in 

economic benefits that exceed the resulting reduction in the expected future economic costs 

caused by gradual changes in the earth’s climatic systems. Quantifying and monetizing 

benefits from reducing GHG emissions is thus an important step in estimating the total 

economic benefits likely to result from establishing improved fuel efficiency and GHG 

standards. 

4.2.10.1 Estimating Reductions in GHG Emissions 

The agencies estimated emissions of carbon dioxide (CO2) from passenger car and 

light truck use by multiplying the number of gallons of each type of fuel (gasoline and diesel) 

they are projected to consume with each alternative CAFE standard in effect by the quantity 

or mass of CO2 emissions released per gallon of fuel consumed. This calculation assumes 

that the entire carbon content of each fuel is ultimately converted to CO2 emissions during the 

combustion process. The weighted average CO2 content of gasoline is estimated to be 

approximately 8,900 grams per gallon, while that of diesel fuel is estimated to be 
Draft Joint Technical Support Document 

66 

approximately 10,200 grams per gallon. For details, please see EPA’s DRIA and NHTSA’s 

PRIA. 

Although carbon dioxide emissions account for nearly 95 percent of total GHG 

emissions that result from fuel combustion during vehicle use, emissions of other GHGs are 

potentially significant as well because of their higher “potency” as GHGs than that of CO2

itself. EPA and NHTSA estimated the increases in emissions of methane (CH4) and nitrous 

oxide (N2O) from additional vehicle use by multiplying the increase in total miles driven by 

cars and light trucks of each model year and age by emission rates per vehicle-mile for these 

GHGs. These emission rates, which differ between cars and light trucks as well as between 

gasoline and diesel vehicles, were estimated by EPA using its recently-developed Motor 

Vehicle Emission Simulator (Draft MOVES 2009), as subsequently updated for use in this 

analysis. 

The MOVES model assumes that the per-mile rates at which cars and light trucks emit 

these GHGs are determined by the efficiency of fuel combustion during engine operation and 

chemical reactions that occur during catalytic after-treatment of engine exhaust, and are thus 

independent of vehicles’ fuel consumption rates. Thus MOVES emission factors for these 

GHGs, which are expressed per mile of vehicle travel, are assumed to be unaffected by 

changes in fuel economy. 

Increases in emissions of non-CO2 GHGs are converted to equivalent increases in CO2

emissions susing estimates of the Global Warming Potential (GWP) of methane and nitrous 

oxide. These GWPs account for the higher radiative forcing capacity of methane and nitrous 

oxide when they are released into the earth’s atmosphere, measured relative to that of CO2

itself. The CO2 equivalents of increases in emissions of these gases are then added to the 

increases in emissions of CO2 itself to determine the effect of the total increase in CO2-

equivalent GHG emissions from vehicle use. 

4.2.10.2 Economic Benefits from Reducing GHG Emissions 

EPA and NHTSA assigned a dollar value to reductions in CO2 emissions using the 

marginal dollar value (i.e., cost) of climate-related damages resulting from carbon emissions, 

also referred to as “social cost of carbon” (SCC). The SCC is intended to measure the 

monetary value society places on impacts resulting from increased GHGs, such as property 

damage from sea level rise, forced migration due to dry land loss, and mortality changes 

associated with vector-borne diseases. Published estimates of the SCC vary widely as a result 

of uncertainties about future economic growth, climate sensitivity to GHG emissions, 

procedures used to model the economic impacts of climate change, and the choice of 

discount rates. EPA and NHTSA’s coordinated proposals present a set of interim SCC values 

reflecting a federal interagency group’s interpretation of the relevant climate economics 

literature. Sections III.H.6 and IV.C.3.l of the preamble provide more detail about SCC. 

4.2.11 The Value of Increased Driving Range 

Improving the fuel economy of passenger cars and light-duty trucks may also increase 

their driving range before they require refueling. By reducing the frequency with which 
Economic Assumptions Used in the Agencies’ Analyses

67 

drivers typically refuel their vehicles and extending the upper limit of the range they can 

travel before requiring refueling, improving fuel economy thus provides some additional 

benefits to their owners. Alternatively, if manufacturers respond to improved fuel economy 

by reducing the size of fuel tanks to maintain a constant driving range, the resulting cost 

saving will presumably be reflected in lower vehicle sales prices. If manufacturers respond 

by doing so, this presumably reflects their judgment that the value to economic benefits to 

vehicle buyers from lower purchase prices exceeds that from extended refueling range. 

No direct estimates of the value of extended vehicle range are readily available, so the 

agencies’ analyses calculate the reduction in the annual number of required refueling cycles 

that results from improved fuel economy, and applies DOT-recommended values of travel 

time savings to convert the resulting time savings to their economic value.33 As an coarse 

illustration of how the value of extended refueling range is estimated, a typical small light 

truck model has an average fuel tank size of approximately 20 20 gallons.ff Based on a 

California Air Resources Board Study, the average fill fuel purchase is approximately 55% of 

tank volume.34 Assuming that drivers typically refuel when their tanks are 20 percent 

fullTherefore, (i.e., 4 gallons in reserve), increasing this model’s actual on-road fuel economy

from 24 to 25 mpg would extend its driving range from 384 216 miles (= 916 gallons x 24 

mpg) to 400 225 miles (= 16 9 gallons x 25 mpg). Assuming that this vehicle is driven 

12,000 miles/year, this reduces the number of times it needs to be refueled each year from 

31.355.5 (= 12,000 miles per year / 384 216 miles per refueling) to 30.053.3 (= 12,000 miles 

per year / 400 225 miles per refueling), or by 1.32.2 refuelings per year. 

Weighted by the nationwide mix of urban (about 2/3) and rural (about 1/3) driving and 

average vehicle occupancy for all driving trips (1.6 persons), the DOT-recommended value of 

travel time per vehicle-hour is $24.00 (in 2006 dollars).gg Assuming that locating a station and 

filling up requires ten five minutes, the annual value of time saved as a result of less frequent 

refueling amounts to $2.604.00 (calculated as 5/60 x 21.32 x $24.00). This calculation is 

repeated for each future calendar year that light trucks of each model year affected by the 

alternative fuel economy standards considered in this rule would remain in service. Like fuel 

savings and other benefits, however, the value of this benefit declines over a model year’s 

lifetime, because a smaller number of vehicles originally produced during that model year 

remain in service each year, and those remaining in service are driven fewer miles. The 

detailed values used in this calculation by each agency are available in EPA’s DRIA and 

NHTSA’s PRIA respectively. 

 

ff Based on the Volpe Model Market Data file for Model Year 2011, average tank volumes for cars and trucks are 

16.6 gallons and 23.0 gallons, respectively. This produces a production weighted average of 19.3 gallons. 

gg The hourly wage rate during 2006 is estimated to be $24.00. Personal travel (94.4% of urban travel) is valued 

at 50 percent of the hourly wage rate. Business travel (5.6% or urban travel) is valued at 100 percent of the 

hourly wage rate. For intercity travel, personal travel (87%) is valued at 70 percent of the wage rate, while 

business travel (13%) is valued at 100 percent of the wage rate. The resulting values of travel time are $12.67 

for urban travel and $17.66 for intercity travel, and must be multiplied by vehicle occupancy (1.6) to obtain the 

estimate value of time per vehicle hour. 

Formatted: Indent: First line: 0"
Draft Joint Technical Support Document 

68 

The agencies’ estimate of benefits from less frequent refueling is subject to several 

sources of uncertainty.

 First, this analysis assumes that manufacturers will not adjust fuel tank capacities 

downward (from the current average of 19.3 gallons) when they improve the fuel economy of 

their vehicle models, so that the entire increase in fuel economy will be reflected in increased 

driving range. Should manufacturers choose to downsize fuel tanks, vehicle space, utility and 

value will increase and vehicle weight will decrease, improving fuel economy and CO2

emissions (all other things being equal). In the context of the rule, this will decrease the cost 

of compliance with the proposed standards. 

Second, the agencies’ analysis assumes that fuel purchases on average, a refill is 55% 

of fuel tank capacity. However, as shown in the California Air Resource Board (CARB) 

report, refueling patterns vary. Moreover, the 55% estimate implies that drivers, on average, 

are either refueling with when nearly a half tank of gas remains in their vehicles, or that they 

are habitually not filling their tanks. Since many drivers only refuel when their tanks are very 

low, and since many drivers habitually fill up their tanks, this in turn implies that many 

drivers in the CARB study are refueling with when their tanks are still well above 50% full. 

Certainly instances of this type of behavior occur, but the CARB study implies they are that it 

is the norm. Behavior that maximizes the number of fill-ups implies a very low value of 

time, but it is also possible that the results of the CARB study are not representative of typical 

behavior across the country. While based on field data, this estimate may thus overestimate 

the impact of refueling benefits.

Third, the agencies’ estimate of refueling benefits assumes that refueling stops involve 

the same number of vehicle occupants as the overall average for all vehicle trips (1.6 persons). 

To the extent that drivers refuel while doing other errands or in advance of picking up 

passengers, this figure may overestimate the typical vehicle occupancy during refueling, and 

thus the total savings in refueling time. Similarly, the hourly value used to estimate the 

economic value of savings in refueling time reflects the typical mix of personal and business 

travel purposes, and drivers are likely to assign different values to their time when traveling 

for these different purposes. To the extent that drivers seek to refuel when traveling for 

purposes that typically use less valuable time, the hourly value used in the agencies’ analysis 

may overstate the benefits from saving refueling time.

Finally, the agencies assume that both finding and using a refueling station takes, on 

average, five minutes. There are few, if any, data sources on average refueling time, and this 

estimate is subject to significant uncertainty.

For these reasons, the agencies’ estimate of savings in refueling time is uncertain. As 

noted in section II of the preamble, the agencies seek comment or data on of each of the 

assumptions they use to estimate this benefit, including alternative empirical estimates of the 

parameters used in its calculations.
Economic Assumptions Used in the Agencies’ Analyses

69 

4.2.12 Discounting Future Benefits and Costs 

Discounting future fuel savings and other benefits is intended to account for the 

reduction in their value to society when they are deferred until some future date, rather than 

received immediately. The discount rate expresses the percent decline in the value of these 

benefits – as viewed from current perspective – for each year they are deferred into the future. 

In evaluating the benefits from alternative increases in fuel economy and GHG standards for 

MY 2012-2016 passenger car sand light trucks, EPA and NHTSA have employed a discount 

rate of 3 percent per year. 

The primary reason that the agencies have selected 3 percent as the appropriate rate 

for discounting future benefits from increased fuel economy and GHG standards is that most 

or all of vehicle manufacturers’ costs for complying with improved fuel economy and GHG 

standards are likely to be reflected in higher sales prices for their new vehicle models. By 

increasing sales prices for new cars and light trucks, CAFE regulation will thus primarily 

affect vehicle purchases and other private consumption decisions. 

Both economic theory and OMB guidance on discounting indicate that the future 

benefits and costs of regulations that mainly affect private consumption should be discounted 

at the consumption rate of time preference.35 OMB guidance indicates that savers appear to 

discount future consumption at an average real (that is, adjusted to remove the effect of 

inflation) rate of about 3 percent when they face little risk about its likely level, which makes 

it a reasonable estimate of the consumption rate of time preference.36 Thus EPA and NHTSA 

have employed the 3 percent rate to discount projected future benefits and costs resulting 

from improved fuel economy and GHG standards for MY 2012-2016 passenger cars and light 

trucks. 

Because there is some uncertainty about the extent to which vehicle manufacturers 

will be able to recover their costs for complying with improved fuel economy and GHG 

standards by increasing vehicle sales prices, however, the agencies have also tested the 

sensitivity of these benefit and cost estimates to the use of a higher percent discount rate. 

OMB guidance indicates that the real economy-wide opportunity cost of capital is the 

appropriate discount rate to apply to future benefits and costs when the primary effect of a 

regulation is “...to displace or alter the use of capital in the private sector,” and estimates that 

this rate currently averages about 7 percent.37 Thus the agencies have also tested the 

sensitivity of its benefit and cost estimates for alternative MY 2012-2016 fuel economy and 

GHG standards to the use of a 7 percent real discount rate. 
Draft Joint Technical Support Document 

70 

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