JOINT TRANSPORT RESEARCH CENTRE

Discussion Paper No. 2007-16
December 2007

Long Run Trends in Transport
Demand, Fuel Price Elasticities
and Implications of the Oil
Outlook for Transport Policy
Kenneth A. SMALL
University of California
Irvine, California, USA
Kurt VAN DENDER
Joint Transport Research Centre
Paris, France

ABSTRACTThis paper discusses the role of transportation in policies to address energy security andclimate change. It focuses on three elements: the impact of energy prices on transportdemand, the potential contributions of the transport sector to energy policies, and theinteraction between energy and other policy concerns in transport. Transport is relatively
unresponsive to broad-based price signals, in particular to changes in prices of fuels, butthere nevertheless is considerable scope to improve the fuel efficiency of vehicle fleets.
As a result, we should not expect energy policies to trigger dramatic changes in thenature of transport systems. Furthermore, this unresponsiveness suggests that it isrelatively costly to reduce energy use in transport, and thus that efficient policies willprobably not extract as much energy savings (in percentage terms) from transport asfrom other sectors. Reducing energy use in transport can be done with price incentives
or with regulatory measures. But if reducing climate change is a primary goal, measuresthat mandate conservation need to be accompanied by others that make fossil fuels
economically unattractive – for example broad-based carbon taxes. Otherwise, fossil-fuelreserves will remain economically usable and therefore will constitute a future source ofcarbon dioxide emissions. We argue that other transport problems, notably congestion,
local air pollution, and accidents, are associated with considerably higher marginalexternal costs than are climate change and energy security. It follows that policies to deal
directly with these other problems deserve high priority, regardless of energy policies.

K. Small, K. Van Dender21. INTRODUCTION

Recent years have seen the re-emergence of public debates on the desirability ofmanaging energy consumption and on the effectiveness of various ways of doing so, intransport and other sectors of the economy. The impetus for increased interest in energy
issues is twofold. First, oil is a prime source of energy, so that high and volatile oil prices
and increased dependence on oil imports have strengthened concerns regarding energy
security. Long term projections for oil prices are on the rise. For example, in 2000 theInternational Energy Agency used a price of $33/barrel for its baseline projection for2030 (prices of 2005); in 2004 the figure was $40, and in 2005 it had risen to $55,
reflecting a concern that higher oil prices are not a transitory phenomenon.1 The second
impetus is a growing consensus that the expected costs of climate change warrant
measures to reduce greenhouse gas emissions, although just how quickly remains
controversial (e.g. Arrow 2007, Schelling 2007).
The goal of this paper is to assess three factors: the impact of expected higher energy
prices on transport demand, the potential contributions of the transport sector to energy
policies, and the interaction between energy and other policy concerns in transport. Our
focus is mostly on road passenger transport because it is a particularly large energy
consumer and is often targeted for energy policies.
Section 2 starts with a review of past trends and projections for the future. Nearly all
sources suggest continued strong growth in transport demand everywhere, especially indeveloping countries like China. This demand growth is part of the reason why oil prices
have increased; but higher prices so far have not tempered growth much because theireffects have been swamped by those of population and income.
Section 2.4 provides a more analytical perspective to determine the main factors driving
energy demand in road passenger transport. We review recent econometric evidence,
especially for the US, paying particular attention to studies that distinguish explicitly thecomponents of fuel use (vehicle stock, average vehicle mileage, and average fueleconomy). This review confirms that income is a key driver of transport demand; fuelprices matter as well, but less so. In addition, the impact of the fuel cost of driving, bothon the demand for driving and on the demand for fuel, appears to decline as incomes1

Figures taken from CEC (2007, p. 26) and converted to 2005 prices using the US Consumer Price Index(CPI).
3

K. Small, K. Van Denderrise. A consequence is that the impact of fuel prices on fuel demand works increasingly
through fuel economy improvements rather than through reductions in the amount for
driving.
A broad conclusion from section 2 is that the elasticity of demand for oil will very likely
decline. A consequence is that fuel taxes would need to increase more strongly in order
to curb fuel consumption by a given amount. This does not affect the economic case forfuel taxes over alternative policies, but it does affect their political feasibility. When highfuel taxes are not politically feasible, then regulations of fuel economy become more
attractive. In this sense, lower elasticities are “good news”, as the increased driving
resulting from the lower fuel cost of driving caused by better fuel economy – the so-called“rebound effect” – is limited; this of course enhances the effectiveness of fuel-efficiency
regulations in achieving their objective of reducing fuel consumption. So, from theperspective of energy security, policy responses that mandate improved fuel economy
seem to make sense. But if the goal also is to reduce greenhouse gas emissions, such
regulations probably should be complemented by fuel or carbon taxes, because better
fuel economy in itself may primarily alter the time pattern of oil usage rather than its
cumulative total.
In section 3 we discuss the interaction between energy and other policy concerns in thetransport sector. Energy policies affect transport problems, like local air pollution andcongestion, that are more closely linked with the amount of driving than with the amountof fuel consumed. One implication is that even small increases in the amount of driving,
resulting from fuel economy regulations that reduce fuel consumption, may have costly
side-effects. Policy measures to control these side-effects are warranted, and some arein place: for example, local air pollution is controlled through per-mile emission limits. But
apart from a few well-known cordon pricing schemes (Singapore, London, Stockholm)and some value pricing experiments (Southern California, Texas, Minnesota), congestionis largely uncontrolled.
In thinking about transport policy more broadly, one needs a yardstick by which tocompare the importance of the various goals being addressed. We focus on one suchyardstick: the marginal external costs of motor vehicle use from various sources. Using
this, we find that more prosaic problems such as congestion, air pollution, and motor
vehicle accidents demand a higher priority than energy problems as we search for ways
to improve transport. We also suggest that transport is probably a sector of the economy
that should contribute less than proportionally to reductions in energy use, as othersectors offer cheaper opportunities for fuel switching and conservation. These findings
do not negate the significance of energy policy in transport, but they do offer a cautionthat energy must not become the only, or even the primary, consideration.

K. Small, K. Van Dender4

2. THE DETERMINANTS OF ROAD PASSENGER TRANSPORT DEMAND AND
DERIVED ENERGY DEMAND IN THE LONG RUN

In this section, we review the main determinants of demand for passenger transport
using motor vehicles, with a focus on energy consumption. We pay special attention to
the US (section 2.1), which is the world’s largest consumer of energy for transport
purposes and for which abundant data are available. In section 2.2, we briefly consider
trends in other countries, and in section 2.3 we look at projections and policies for the
next few decades. In section 2.4, we consider evidence on the price sensitivity oftransport demand for energy, including its size, manner of variation, and thedecomposition of this price response into changes in the amount and the energy intensityof travel. Lastly, section 2.5 summarizes the insights obtained.

2.1

Trends in the US, 1970-2005

We first discuss trends in energy use and then look at its components (vehicle stock, fuelintensity, and mileage). The Transportation Energy Data Book (Davis and Diegel 2007)
conveniently collects the relevant data.
Transport relies on petroleum, and usage keeps increasing
Petroleum consumption by all sectors in the US increased from 17.3 million barrels perday (mb/day) in 1973 to 20.8 mb/day in 2005.2 That growth was not uninterrupted:
consumption hit a low of 15.2 mb/day in 1983. Petroleum consumption from transport,
however, grew steadily, from 9.05 mb/day in 1973 to 13.9 mb/day in 2005; consumption
fell in all other sectors (residential, commercial, and electricity) except the industrial
sector, where it grew slightly. As a consequence, the share of transport in total petroleum
consumption grew strongly, from 52% in 1975 to 67% in 2005. The share of transport in
total US energy consumption also grew, from 24.6% in 1973 to 28.2% in 2005. Transportrelies almost exclusively on petroleum for its energy, with a 96% share in 1973 and in
2005. The petroleum shares are much lower, and declining, in other sectors.3

2

Crude oil accounted for 90.6% of all petroleum used in 2005 (TEDB, Tables 1.2 and 1.3).

3

See Davis and Diegel (2007), Tables 1.13 and 2.1 for the figures quoted in this paragraph.
5K. Small, K. Van Dender

These numbers illustrate that transport has not substituted out of petroleum, in contrast
to other sectors. Combined with the fact that petroleum is relatively highly taxed intransport, this suggests that technological substitution to other sources of energy isparticularly difficult in transport. An alternative response to high oil prices is to improvefuel economy. Such responses have taken place, in part because for some transportmodes, the market mechanism has been complemented by regulatory interventions,most notably the corporate average fuel economy (CAFE) regulations on new passenger
vehicles. But these responses have not been sufficient to offset the strong growth intransport activity.
Light-duty vehicle energy use dominates transport energy use
Next, we consider the breakdown of transport energy use by mode, expressed in trillionsof British Thermal Units (btu); see Table 1. While total energy use in transport increased
by 78% between 1970 and 2005, that increase was much more pronounced for highway
modes (88%) than elsewhere (45%). The greatest growth was for light trucks, partly
induced by the CAFE regulations;4 but growth was substantial in the overall light vehicle
fleet as well. Energy use by heavy trucks and aviation grew rapidly as well. Cars andlight trucks together accounted for just under two-thirds of all transport energy use, in1970 as well as in 2005. The shares accounted for by heavy trucks and aviation rose,
while those of most other modes declined.
Energy intensity has declined strongly for most modes
Table 2 shows how energy intensity has evolved in transport, leading to several
observations. First, all modes except water-borne freight are considerably more fuelefficient in 2005 than in 1970. Second, the strongest reduction in energy intensity
occurred for commercial air transport, probably because of increasing aircraft size,
higher occupancy rates, and improved technology. (General aviation did not share in thisreduction.) On a per passenger-mile basis, commercial air travel is now as efficient as
driving a car, but of course air travel tends to be over longer distances. Third, light trucks
consume 1.4 times as much energy per passenger-mile as cars, even under the
optimistic assumption that occupancy rates for trucks are as high as those for cars.
Fourth, fuel efficiency improvements are no stronger for regulated light-duty vehicles
than for unregulated modes. This suggests that, if there is a basis for targeting light-duty
vehicles for fuel-economy regulation, it is that households make “worse” decisions on

4

CAFE regulations were much stricter for cars, which probably induced manufacturers to produce lighttrucks as substitutes. We suspect furthermore that the prevalence of light trucks in the vehicle fleet
became self-reinforcing by inducing changes in consumer preferences, due to a fad effect and/or an “armsrace” as each driver seeks to avoid colliding with vehicles much larger than his or her own.
K. Small, K. Van Dender6fuel economy than do commercial transport operators like trucking firms or air carriers.5
Finally, buses are now less efficient per passenger-mile than cars, because of declining
occupancy rates.
The number of light-duty vehicles and their usage increased on a per capita basis
In 1970 there were 0.48 vehicles per capita, against 0.80 in 2005; for the same years,
average annual per capita vehicle use increased from 5,440 to 10,087 miles. Businessfleet vehicles are used even more intensively, at approximately 25,000 miles per year.
The net impact is that total highway travel in the US grew at a 3.2% annual rate for
1970–1995, and a somewhat lower 2.1% rate for 1995–2005.6 However, recent traffic
data suggest that extended high gasoline prices can eventually interrupt such trends:
travel on all roads and streets apparently declined by 0.4% between April 2006 and April
2007.7 Thus high fuel prices can sometimes reduce travel by enough to outweigh thepositive (and possibly declining) effect of rising income.
The average trip length in the US has crept upward from 8.7 miles in 1983 to 10.0 milesin 2001 (Pisarski 2006, Fig. 3-7). The average length of a work trip has risen
considerably faster, from 8.5 miles to 12.1 miles during the same time period. Much of
the growth in commuting trip length is driven by a dramatic rise in suburb-to-suburb
commuting trips and a less dramatic but still important rise in commuting trips fromsuburbs to central cities (Pisarski 2006, Fig. 3-9). It is noteworthy that only 22% of all
trips are commuting trips (NHTS 2001). Trips for shopping and for personal business
now represent 46% of all trips; however, some of this travel is probably as hard to avoid
as commuting travel and harder to shift to other modes.
Summary
Improvements in energy efficiency are not strong enough to compensate for the rise inenergy demand caused by increased travel, so that transport energy use continues toincrease. There are, however, recent indications that sufficiently high energy prices canslow the growth in travel sufficiently to curtail energy use.

5

Various explanations of why households under-invest in fuel economy have been put forward, forexample “consumer myopia” (possibly because private discount rates exceed social ones) and loss
aversion (consumers undervalue future fuel savings because they are unsure about them and risk averse).
See Greene and German (2007). There also is evidence that consumers do not accurately calculate
benefits from fuel economy (e.g. Turrentine and Kurani, 2007), but this does not imply systematic errors in
the direction of underinvestment.
6

The source for these figures is Davis and Diegel (2007), Tables 8.2, 7.3, and 3.4.

7

US FHWA (2007). These preliminary data are extrapolated from a fairly small number of traffic counting
locations, and are less reliable than the final estimates (derived from the Highway Performance Monitoring
System) published for earlier periods.
7

K. Small, K. Van DenderTable 1. Transport energy use (measured in btu): modal composition and change, USA, 1970 and 2005
Car

Light
Truck

HighwayMotorBus
cycle

1970
1990
2005

55.06
40.22
33.38

9.99
20.61
29.61

0.05
0.11
0.10

1990
2005

102.5
107.8

289.2
526.8

342.9
385.7

Heavy
All Hwy
Air
Water
Truck
% overall energy use
0.84
10.09
76.02
8.49
5.45
0.77
15.44
77.15
9.62
6.69
0.70
16.71
80.49
9.05
4.99
Change in energy use by mode, 1970 = 100
129.5
214.7
142.3
158.9
172.0
148.1
294.7
188.3
189.5
162.6

Non Hwy
Pipe-lineRail

All Non
Hwy

All

6.43
4.27
3.07

3.60
2.27
2.40

23.98
22.85
19.51

100.00100.00100.00

93.2
85.1

88.5
118.4

133.7
144.7

140.27177.84

Source: derived from Davis and Diegel, 2007, Tables 2.7 and 2.8
Table 2 . Energy intensity by mode (btu per "output mile"), USA, 1970 - 2005
Passenger modescarLT
btu/pm ind. btu/pm
1970
4868
100
6709
1975
4733
97
6252
1980
4279
88
5527
1985
4110
84
5008
1990
3856
79
4842
1995
3689
76
4505
2000
3611
74
4545
2005
3445
71
4874

Freight
Transit bus
Cert. air carrier
Rail transitHT
ind. btu/pm
ind.
btu/pm ind. btu/pm
ind. btu/vm
100
2472
100
10282 100
2157
100
24960
93
2814
114
7826
76
2625
122
24631
82
2813
114
5561
54
2312
107
24757
75
3423
138
5053
49
2809
130
23343
72
3794
153
4875
47
3024
140
22795
67
4310
174
4349
42
3340
155
22096
68
4515
183
3952
38
2729
127
23448
73
na
na
3264
32
2784
129
20539

Rail
ind. btu/tm
100
691
99
687
99
597
94
497
91
420
89
372
94
352
82
337

Water
ind. btu/tm
100
545
99.4
549
86.4
358
71.9
446
60.8
387
53.8
374
50.9
473
48.8
514

Legend: pm is passenger-mile, vm is vehicle-mile, tm is ton-mile
Source: Davis and Diegel, 2007, Tables 2.13, 2.14, 2.16
Note: Davis and Diegel, 2007, do not provide btu/pm for light trucks - we converted btu/vm using car occupancy rates(which are likely higher)
K. Small, K. Van Dender

8

ind.
100
101
66
82
71
69
87
94

2.2

Trends in other IEA countries

Oil consumption for transport in all countries belonging to the International Energy
Agency (IEA) grew from roughly 600 million tons of oil equivalent (Mtoe) to 1,000
Mtoe between 1970 and 2000. Growth in transport was steady, despite a drop in
overall oil consumption in the late 1970s and early 1980s (IEA 2001). Transport relies
nearly entirely on oil as its source of energy (approximately 97% in OECD countries:
IEA 2002).
Within surface transport, the pattern of energy consumption by mode tends to lie
between two extremes: US and Japan. The US has the highest share accounted forby cars and light trucks (66% in 1995), whereas Japan has the lowest (52%). The
situation is reversed for truck freight (29% share in the US, 38% in Japan) and forpassenger transport by bus and rail (just 1% in the US, 7% in Japan). Thus while the
US stands out in its dominance of light-duty passenger vehicles, such vehicles
account for the majority of energy consumption for land transport in all IEA countries.
According to IEA (2001), new car fuel economy in the US was roughly on the samelevel as in Japan and Australia over the period 1980–2000. (It was considerably
higher in Europe.) However, the fuel economy of light trucks, used mostly forpassenger transport and especially prevalent in the US, is much lower than that ofcars. Overall fuel efficiency of passenger-vehicle fleets rose modestly from 1980 to2000 in most IEA nations; but in the US such improvements came to a complete
standstill in the late 1990’s, mainly because of the explosion of light truck use.
Another way to look at this is that fuel use per unit weight has declined strongly
everywhere, but vehicle weight has increased, especially in the US.
Regarding travel demand, IEA (2002) shows that in several European countries(France, UK, the Netherlands), vehicle-km per capita increased from roughly 3,000 to
6,000 between 1970 and 1997. This growth rate is greater than that in the US;
however, since the level in these European countries is still less than half that in theUS, it does not seem likely that Europe will converge anytime soon to US levels of
per capita driving.
To summarize, patterns and trends in the US and other IEA countries are notfundamentally different. However, the levels are quite different: the US exhibits
persistently higher rates of vehicle ownership and usage and lower fuel efficiency, thelatter due in part to the higher prevalence of light trucks in US passenger fleets.

9

K. Small, K. Van Dender2.3

Future developments

OECD countries now represent 70% of worldwide transport energy use; but
according to IEA projections this will decline to 55% in 2030 and to 45% in 2050. The
growing share of transport in oil demand is not limited to IEA or OECD Countries, and
it is likely to continue. Transport in 1997 already represented 54% of oil consumption
in the OECD and 33% in the rest of the world; these shares are projected to increase
to 62% and 42%, respectively, by 2030 (IEA, 2006). Fulton and Eads (2004) remarkthat the growing share of transport in total oil demand leads to a decline in the overall
price sensitivity of oil demand, because transport demand is less price-elastic thandemand for other energy services. This decline may lead to larger price volatility inresponse to supply shocks, which in turn is one of the factors driving the recent surgeof popular interest in energy policy.
The growth in energy use can be decomposed into changes in transport demand andin the energy intensity of vehicles in use. Eads (2006) projects that globally, light-duty
vehicle usage will grow by 1.9% per year for 2000–2050, while energy intensity
declines by 0.4% per year; the net result is that total energy demand for passenger
transport will grow by 1.5% per year. For air transport, energy use grows by 2.6% per
year, deriving from annual demand growth of 3.3% and fuel economy improvementsof 0.7%. For trucks, energy use grows by 1.9% per year, with demand increasing at a
rate of 2.6% and efficiency improving by 0.7% per year. Projected growth is faster indeveloping countries, but vehicle ownership there is not expected to reach US levels
by 2050, nor will per-capita transport demand reach OECD levels. The main driver fortransport demand growth is per capita income. (The IEA assumptions about income
growth imply roughly that by 2050, the former Soviet Union, Eastern Europe, andChina will reach OECD income levels of 2000, whereas India will by 2050 reach thesame level attained by China in 2025.)
Energy consumption in transport is a policy concern in many nations with large
markets for motor vehicles. The US, Europe, and Japan use various combinations offuel taxes, vehicle taxes, and energy efficiency regulation of new cars, but theemphasis varies. Fuel economy is regulated in many parts of the world, but it was
introduced in the EU and in Japan only recently, and there is no regulation in India
and Mexico, where transport demand is likely to grow strongly in the near future(Plotkin 2004). In addition, there are large differences in the stringency of fueleconomy regulation, differences that are likely to continue (An et al., 2007). The EU
and Japan have the most ambitious targets. The US has laxer standards than mostother countries, but the current policy impetus seems to be towards stricter
regulation. China plans to converge to the strictest standards, but will need time to do

K. Small, K. Van Dender

10

so. Regulations are in place in Brazil and South-Korea, but they are not very
ambitious.
An et al. (2007), reviewing policy developments, suggest that there is increasingly
widespread reliance on fuel efficiency regulations. A main reason is that further
increases in fuel prices are politically difficult – in rich countries because they aremostly high already, and in poor countries because of concerns about equity (and,we would add, because of political stability). Thus, it is reasonable to expect further
regulation-induced declines in the fuel intensity of motor vehicles in many parts of theworld.
While increased fuel-efficiency regulations may generate benefits, their value in
mitigating climate change is reduced by the fact that they affect the timing, rather
than the cumulative amount, of emissions from fossil fuels. The advantages fromlowering greenhouse-gas emissions today will to some extent be at risk in the futureas the unused stock of fossil fuels remains attractive economically in the absence ofmeasures to raise its price. Strong investments in the efficiency of conventional,
carbon-intensive transport technology will if anything tend to reduce the price of fossil
fuels – indeed, when the discussion turns to energy security that is one of its explicit
purposes. Many analysts seem not to have recognized that in this sense, the goals of
less climate change and less petroleum dependence are at odds with each other.
Thus if fuel-efficiency regulations are used in lieu of price increases, it is important toalso use complementary policies to prevent prices from falling and/or to promotetechnological innovation in alternative energy sources.
Of course, the effectiveness of such complementary policies is subject to limitations.
One is that that before investors will embark on major projects to find alternative
ways of producing energy, they need assurance that the motivating policies willremain in place for a long time. Another is that high fuel prices for consumers create
incentives for manufacturers only to the extent that consumers respond to the fuelcosts of travel. This latter concern is the subject of the next subsection, where recent
research is shown to suggest that this responsiveness is small and likely declining.
2.4

The price elasticity of fuel demandThis section discusses our own recent work on estimations of the price elasticity ofdemand for gasoline. There is already a lot of research on this elasticity, but we thinkours is particularly informative for the topic at hand, for several reasons. First, wemeasure not only the price elasticity but two distinct responses that underlie it:changes in amount of driving and changes in fuel intensity. Second, the first of these
distinct responses also tells us the magnitude of the “rebound effect,” which is one
potentially important by-product of fuel efficiency regulations. Third, we investigate
11

K. Small, K. Van Denderhow these underlying responses to fuel prices depend on factors like income, thedegree of urbanization, and fuel costs. We find that the responsiveness of driving tofuel costs declines with income and urbanization, and increases with the initial levelof fuel costs. Considering the magnitudes of these dependencies, by far the most
likely outcome for the future is that this responsiveness will decline further, aprediction that substantially affects how transport policies should be designed.
Our work uses a 39-year cross-sectional time series of US states (plus District ofColumbia) covering years 1966-2004. This is a three-year extension of the data set
used in Small and Van Dender (2007a); otherwise, the methodology reported here isvery similar to that paper.8
We decompose changes in fuel consumption into three parts: changes in travel per
adult (M) for a given vehicle stock, changes in vehicle stock per adult (V), andchanges in the average fuel intensity of vehicles (Fint). These changes are specified
as three simultaneous equations, estimated simultaneously in logarithms.9 In this way
we overcome one of the confusing aspects of the past literature, which only rarely
has accounted for the fact that fuel efficiency (the inverse of Fint) is chosen jointlywith travel and vehicle stock. (We envision although do not formally model a decision
process in which consumers and motor-vehicle manufacturers interact in new-vehicle
markets while responding to constraints or incentives set by regulation.) We can thenmeasure the “structural elasticity” of travel with respect to fuel cost per mile, ε Mˆ , PM ,accounting for responses both through fleet expansion and through utilization of agiven size fleet.10 Empirically, we find that ignoring the simultaneous determination of
travel and fuel intensity would seriously overestimate the magnitude of that elasticity.

8

See Small and Van Dender (2007a,b) for further methodological details.

9

The equations are estimated using three-stage least squares. In order to account for fixed factors
affecting a given state, we use a “fixed effects” specification which estimates a separate constant termin each equation for each state.
10This structural elasticity comes from the first two of our equations, explaining travel and vehicle fleet
size. Small and Van Dender (2007a) show that it can be written in terms of the elasticities measured
within these two equations:

ε Mˆ ,PM =ε M ,PM + ε M ,V εV ,PM
1 − ε M ,V ε V ,M

where εM,PM denotes the elasticity of travel with respect to fuel cost per mile in the travel equation; εM,Vdenotes the elasticity of travel with respect to vehicle fleet in that same equation; and where εV,M andεV,PM denote the elasticities of vehicle fleet size with respect to amount of travel and to cost per mile,both within the second equation. Cost per mile of travel, PM, is defined as the price of fuel times fuelintensity. Thus ε Mˆ , PM depicts the result of a hypothetical exogenous change in PM (or of an
endogenous change in PM caused by an exogenous change in regulations affecting fuel intensity); yet
the actual estimation of these component elasticities accounts for the simultaneous determination of
K. Small, K. Van Dender12

Travel (M) is generally assumed by us and others to respond to fuel cost per mile PMrather than responding separately to its separate components (fuel price and fuelintensity). For this reason, the elasticity ε Mˆ , PM provides information about two
different policies. It is part of the price elasticity of gasoline (since gasoline price is
one component of PM); at the same time it measures the responsiveness of travel tochanges in fuel efficiency (the other component of PM). In the latter context, theresponsiveness is often called the “rebound effect,” so called because it offsets a
portion of the fuel savings that would result from an increase in fuel efficiency in theabsence of any behavioural response.11 Following convention, we define the positive
quantity b ≡ −ε Mˆ , PM as the rebound effect, and express it as a percentage. For
example, if ε Mˆ ,PM =-0.20, the rebound effect is said to be 20%.
Our empirical system also accounts for the slowness with which changes can occur,e.g. because changes in the vehicle fleet require purchases and retirements ofvehicles. In this way we distinguish between short and long run responses.
Technically, this is achieved by including lagged values of the dependent variables. 12
In the travel equation, this is equivalent to assuming that there is a desired level oftravel and that any difference between this desired level and the level attained in theprevious year is diminished in one year by a fraction (1-αm), where αm is thecoefficient of the lagged value of the variable. Thus the short-run response (thatoccurring in the same year) is smaller than the long-run response. The long-run
rebound effect is approximately: 13

vehicle stock, vehicle usage, and fuel intensity within the estimation sample. Empirically, we find that
εM,V is very small, so that ε Mˆ , PM differs little from ε M ,PM .
11

A distinction can be made between direct and economy-wide rebound effects. Our estimate
concerns the direct effect, as it is limited to the effect of increased fuel economy on travel while holding
constant all other factors except vehicle stock. But wider economic effects clearly exist that affectenergy consumption: for example, consumers will spend some of their saved energy expenditures onother goods that also consume energy. Empirical evidence on such indirect effects is relatively scarce.
According to one review, computable general equilibrium models generate values of the economywide rebound effect of around 50%, whereas a UK macro-econometric model generates a value of 7%(Sorrell 2007, p.58).

12

Another dynamic effect we account for is autocorrelation among the error terms in each equation;
however, our specification is comprehensive enough that autocorrelation, which is indicative ofimportant omitted variables, is quite small. In previous research, using shorter time periods, it has
been difficult to distinguish between autocorrelation and lagged dependent variables, which isimportant to do in order to distinguish short- and long-run responses.

13

A more precise relationship accounts for the fact that in the three-equation system, the lagged
values in more than one equation can affect the long-run response; specifically:bL =− ε M ,PM ⋅ (1 − α v ) − α mv β 2v
(1 − α m )(1 − α v ) − α mvα vm

13

K. Small, K. Van DenderbL ≅− ε Mˆ ,PM
bS
=
.
m
1− α
1− α mAs indicated, our main innovation over previous studies is to specify the equation
determining vehicle travel so that the “rebound effect” is not a constant, but rather
varies with income, fuel price, and urbanization. This is accomplished by specifying
the equation for vma (the logarithm of vehicle-miles travelled per adult) so that thelogarithm of fuel cost per mile, pm, appears not only as a single variable (withcoefficient βpm), but also interacted with other variables including itself. We define
three such variables: pm*inc, pm*pm≡pm2, and pm*Urban; we call their coefficients
β1, β2, and β3. Then the structural elasticity in this equation, which is approximately
the negative of the rebound effect, consists of four terms:14

ε M ,PM =∂ (vma )= β pm + β 1 ⋅ inc + 2β 2 ⋅ pm + β 3 ⋅ Urban .
∂ ( pm)(1)

The results of estimating the model are quite similar to what we found for the slightly
shorter time period used in Small and Van Dender (2007a). The most important
coefficients are summarized in Table 3.

where αv is the coefficient of the lagged dependent variable in the equation explaining vehicle stock,
αmv is the coefficient of vehicle stock in the equation explaining travel, αvm is the coefficient of travel in
explaining vehicle stock, and β 2v is the coefficient of pm (the logarithm of cost per mile) in the equation
explaining vehicle stock. All the dependent variables are expressed in logarithms. See Small and Van
Dender (2007a), equation (7).
14

The factor 2 in this equation is a consequence of properties of the derivative of the quadratic
function (pm)2.

K. Small, K. Van Dender14

Table 3. Selected estimation results for the three-equation model, 1966-2004
Equation and variable
Equation for vma:pm
pm*incpm*pm
pm*Urbaninc
Lagged vma
Equation for fint:
pf+vmacafe
Lagged fint

Coefficientsymbol
pm
1
2
3
m

f

Coefficient estimate Standard error-0.04070.0696-0.0169
0.02550.1044
0.79800.00420.01320.00640.01000.01340.0120-0.0297-0.0882
0.84500.00640.01100.0127

Notes to Table 3:vma = logarithm of vehicle-miles traveled per adultpm = logarithm of fuel cost per mile (normalized)
inc = logarithm of income per capita
Urban = fraction of population living in urban areas
fint = logarithm of fuel intensity, i.e. log (1/E) where E = fuel efficiency
pf = logarithm of fuel pricecafe = variable reflecting how far the CAFE standard is above the desired fuel efficiency
based on other variables (Small and Van Dender 2007a, section 3.3.3)
pf+vma is log (price of fuel * vehicle-miles traveled), representing the logarithm of theincremental annual fuel cost of a unit change in fuel intensity; thus it may be interpreted
as the logarithm of the “price” to the user, in terms of extra annual operating cost, ofvehicle features that cause higher fuel intensity.

For measuring the “rebound effect,” our primary interest is in the first four coefficientsshown in Table 3. The short-run rebound effect for average conditions in this sample
is approximately -βpm=0.0407, i.e. 4.07%, while the long-run rebound is 4.95 times
this value, or 20.1%. The coefficients for the three interacted variables involving pm(i.e., β1, β2, and β3) show that the magnitude of the rebound effect declines withincreasing income and urbanization, and increases with increasing fuel cost ofdriving. The net result is that the rebound effect declined substantially over time –which we confirmed by estimating the equation (without the three interaction terms)
separately for time periods 1966-1989 and 1990-2004, with the result that therebound effect fell by half from the earlier time period to the later one.15
The coefficient of inc confirms the conventional expectation that vehicle-miles
travelled rises with rising income: the income-elasticity is approximately 0.1 in the
15

More precisely, the short-run rebound fell from 4.8% to 2.9%, while the long run rebound fell from
21.1% to 7.7%. These declines are not in the same proportion because the estimated coefficient of thelagged dependent variable also changed between time periods.
15

K. Small, K. Van Dendershort run and 0.5 in the long run. The coefficient αm of lagged vma shows that thelong-run effect of any variable on vehicle miles travelled (VMT) is about
1/(1-αm)=4.95 times larger than the corresponding short-run effect. This may seem
surprising given our finding that changes in the size of the fleet play only a small role.
However, changes in travel can occur either quickly, for example through carpoolingor trip chaining, or over a longer period, for example through changes in home andworkplace locations or even in land-use patterns.
Our equation system also measures the extent to which the fleet-average fuelefficiency is adjusted in response to fuel prices. The short-run elasticity isapproximately the coefficient of pf+vma in Table 3, or -0.03, implying a long-run
elasticity of -0.15.16
We show in Table 4 various implied elasticities, computed at two different sets ofvalues for the explanatory variables inc, pm, and Urban. One set is the averagevalues over our sample and the other is the average values over the last five years of
the sample.17

16

The precise equations for the short-run and long-run elasticities of fuel efficiency with respect to fuelprice, again accounting for feedbacks across all three equations, are shown as equation (9) in Smalland Van Dender (2007a).

17

The third elasticity shown is the price elasticity of gasoline consumption, calculated as follows:

ε F ,PF = ε Mˆ ,PM ⋅ (1 + ε I~ ,PF ) + ε I~ ,PF , where ε Mˆ ,PM and ε I~ ,PF are the elasticities reported in the previous
two rows of the table. This equation is derived by USDOE (1996, p. 5-11) and Small and Van Dender(2005, eqn. 6); the term (1 + ε I~ ,PF ) was inadvertently omitted in Small and Van Dender (2007a) whencalculating elasticities of gasoline consumption, causing those elasticities to be slightly overstated –see Small and Van Dender (2007b) for the correct values for the shorter (1966-2001) data set used inthose papers.
K. Small, K. Van Dender16

Table 4. Estimated Elasticitites
1966-2004

2000-2004

26,506
1.91

33,669
1.69

Average values (real 2006 $):Household income ($/year)
Fuel price ($/gal)
Calculated elasticities:
Vehicle-miles traveled
Fuel intensity
Fuel consumption

Short run-0.041-0.035-0.074

Long run-0.210-0.193-0.363

Short run-0.011-0.031-0.041Long run-0.057-0.191-0.237

Rebound effect (%)

4.1%

21.0%

1.1%

5.7%

Source: Small and Van Dender (2007c), estimating using full data set.
Elasticities are with respect to fuel cost per mile for Vehicle-miles traveled, and fuel pricefor other quantities.
Columns labeled "1966-2004" use average income and fuel cost for the entire sample
period.
Columns labeled "2000-2004" use average income and fuel cost for the last five years of
the sample period.
Standard errors are approximately as follows:(a) 1966-2004, short run: 0.004 for "Vehicle-miles traveled", 0.020 for "Fuel efficiency"and "Fuel consumption".
(b) 2000-2004, short run: 0.007 for "Vehicle-miles traveled", 0.020 for "Fuel efficiency"and "Fuel consumption".
(c) Long run: 5-6 times as large as for short run.

Under average conditions over our entire sample period, the measured reboundeffect is 4.1% short run and 21.0% long run. However, these values are found to fall
dramatically when we consider conditions that prevailed in 2000-2004: over those
years the rebound effect on average is just 1.1% short run and 5.7% long run.
Why should rising income diminish the rebound effect? Our model provides no direct
answer, but there are some plausible explanations. First, higher incomes cause theshare of fuel expenditures in total expenditures to decline, which may lead to lower
elasticities. Second, higher incomes lead to higher values of time, so that time costsof travel become relatively more important than fuel costs. Higher fuel costs thentranslate into proportionally smaller increases in the generalized price of travel (whichis the sum of time and money costs), and assuming that drivers respond mainly tothis generalized price, this reduces the elasticity with respect to the money costs.
However, there are reasons why higher incomes could lead instead to larger
elasticities: the share of discretionary driving is likely higher for higher income
households, and it is easier to cut back on such driving than on “mandatory” travel.
Hughes et al. (2006) find larger price elasticities of gasoline demand for higher

17

K. Small, K. Van Denderincomes than for lower income households, while at the same time finding that thiselasticity declines over time.18
The elasticity of fuel intensity, unlike that of travel, is found to be almost constant,even though we tried specifications that would allow it to vary. This elasticity issomewhat under-researched, with results varying widely depending largely on type ofdata set. Our results for its absolute magnitude, namely short-run and long-run
values of 0.031 and 0.191 over the recent period, are quite similar to the estimates of
0.017 and 0.150 obtained by Li, Timmins and von Haefen (2006), who more directly
measure the responses of consumers in the form of model-specific decisions aboutscrappage and new-vehicle purchases.
The long-run price elasticity of gasoline in our estimates is -0.363 over the entire
sample, declining modestly in magnitude to -0.237 over the last five years. With thetravel component declining sharply and the fuel-intensity component approximately
constant, travel is becoming a notably smaller component of the response to fuelprices. The finding that responses to fuel prices take place through changes in fueleconomy more than through changes in the amount of driving is confirmed by a study
for twelve OECD countries by Johansson and Schipper (1997), and more recently ina meta-analysis (an econometric analysis of earlier estimates of the fuel priceelasticity of the demand for fuel) by Brons et al. (2007).19
What about the future? In a nutshell, our results suggest that fuel consumption bypassenger vehicles has become more price-inelastic over time, and that it isincreasingly dominated by changes in fuel efficiency rather than in amount of driving.
Furthermore, our results identify two main reasons for this: rising incomes and falling
real fuel prices. One of these – rising incomes – can be presumed to characterize thefuture as well, whereas the other – falling real fuel prices – probably cannot. So weneed to consider the relative magnitudes of these two factors.

18

Yet another factor is that richer households own more vehicles, allowing them to respond to fuelprice increases by using the more fuel-efficient vehicles more intensively. This seems mainly a shortrun reaction, and would tend to make them respond less through changes in travel but more throughchanges in average realized fuel efficiency. Basso and Oum (2007) discuss conflicting evidence fromcross-sectional studies on the relationship between income and the price elasticity. Most recently,
Wadud et al. (2007a,b) find, as we do, that the price elasticity is smaller at higher incomes.

19

The meta-analysis does not investigate whether the price-elasticity of fuel consumption depends on
income. However it does test whether it depends on a pure time trend, after all other measured
determinants are controlled for, finding no evidence of such dependence. This result is consistent with
our model, which also finds no significant time trend in the rebound effect, even when the threeinteraction terms in the estimating equation are removed. We interpret this to indicate that a simple
time trend is inadequate to capture the effects of the complex changes in income, urbanization, andfuel cost over the time periods covered by the various studies.

K. Small, K. Van Dender18

Real income in the US grew at 1.4% per year over the period 1984–2004 (US Bureauof Labor Statistics 2007). As for gasoline prices, the US Energy Information Agency(EIA) projects in its “reference case” that they will be roughly constant in real terms
after declining slightly from a spike in 2005-2006.20 The EIA also considers low- andhigh-price cases; in the latter, real prices rise on average by 1.4% per year. In thehigh-price scenario, then, rising incomes are causing the rebound effect to diminish
by 0.097 percentage points per year, while rising fuel prices are causing it to rise by
0.047 percentage points per year.21 Thus even in a scenario projecting high growth offuel prices, the influence of income growth dominates; it would do so even more if weused the 2.3%/year income growth projections from US EIA (2007) over the period
2005-2030. Thus we should expect the rebound effect and the price-elasticity of fuelconsumption both to continue to become smaller.22
2.5

Summary

The demand for oil as a source of energy is likely to grow along with income,
especially outside the OECD. With an upward sloping supply curve, this results inhigher prices. In addition, the share of the transport sector in total oil demand will
likely increase: this reduces the overall elasticity of oil demand, given that transport
has very limited access to alternative technologies in the short term. Moreover, theelasticity of fuel demand in transport declines as incomes increase, a pattern which
we identified for the US and which we expect applies elsewhere also. This further
reduces the price elasticity of oil demand. The declining elasticity implies that shortrun supply shocks have bigger price effects and that long-run demand will not be
curbed strongly as prices rise.
A common policy response to (real or perceived) excessive costs of reliance on oil is
to mandate fuel-economy improvements in the transport sector. Such policies may
well be justified, especially when households are thought to under-invest in fueleconomy, and when higher fuel taxes are difficult to implement. It is notstraightforward, however, that regulation of fuel economy in itself contributes to themitigation of climate change. The reason is that better fuel economy alters the rate ofemissions, but not necessarily their time-aggregated total. This suggests that, if

20

See US EIA (2007), “Year-by-Year Reference Case Tables (2004-2030),” Table 3, for average priceof motor gasoline. Prices are projected to decline slightly in real (inflation-adjusted) terms, by anaverage of 0.2% per year, over the period 2005-2030.

21

These numbers are calculated from the figures in Table 3, using equation (1), as 0.0696*0.014*100
and 2*0.0169*0.014*100, respectively.

22

At some point our equations predict that the rebound effect would become zero and then negative;
obviously this is contrary to theory and must be regarded as a limitation of extrapolating our equations
beyond the primary range of the data set on which they are estimated.
19

K. Small, K. Van Denderstrong reductions in CO2 emissions are desired, fuel economy regulation needs to becomplemented by other policies such as a carbon tax.23
We have in this section focussed on the connection between energy and transport. In
the next section, we put the discussion in the broader framework of transport policy.

3. ENERGY POLICY IN TRANSPORT

We now turn to factors that shape the relative advantages of various transport
policies toward energy and other goals. We attempt to create a uniform framework byconsidering the marginal external costs of fuel-related and other transport
externalities.
Long before energy issues rose to their present degree of prominence, transport wasan important and often problematic sector in the economies of nations and cities. The
many problems identified with the transport sector include large and irreversible
investments, financial mechanisms, subsidies, implications for regional economic
growth and inter-regional integration, congestion, safety, and negative spillovers to
non-users through air pollution, noise, aesthetics, wildlife disruption, water quality,
availability of open space, and other mechanisms. These problems have elicited
numerous policy responses, some of which increase and some of which lessen theamount of transport undertaken. Furthermore, these policies are often thought tohave far-reaching implications for local, regional, and national economies, and theycertainly involve strong impacts on government budgets.
One must consider, then, the interaction of energy objectives with the objectives ofthese other policies. Will attention to energy make other goals easier or harder toachieve? Do these other goals alter in significant ways the optimal response toenergy problems? Even aside from other goals, how much of a role should transport
policy play in achieving energy objectives? And just how big is energy when viewed
as a part of the overall policy environment for the transport sector?
One way to tackle these issues is by asking what responses would markets bringabout in an ideal world where prices could be brought into perfect alignment with
marginal social costs – i.e., with the extra costs incurred by all members of society,23

If economy-wide rebound effects are large, which is uncertain but possible (cf. footnote 8), this is anadditional reason for combining fuel economy regulations with fuel or carbon taxes.

K. Small, K. Van Dender20

including the decision maker, due to particular economic decisions.24 This involves
looking at each as a market failure, and asking what would happen if that failure
could be eliminated within the market system. For example, if we knew the costs
global climate change, of macroeconomic disruptions due to reliance on unstable ormonopolistic energy suppliers, and of consumers’ myopia or lack of information
enabling them to optimally trade off energy efficiency against purchase price, and if
we could trace these costs to specific economic decisions, then we would know how
much prices would have to change in order to confront each decision-maker with themarginal social costs of those consumption decisions. We could then ask how
decision-makers would react to such changes in price signals. How much would they
curtail transport energy use, and through what mechanisms? Such an analysis
provides a guide as to what changes would be the most efficient ones to target, using
public policy, if in fact it is not feasible to bring about the theoretically desirable pricesignals.
The same type of analysis can be done for other transport problems, and has in fact
been done in considerable detail for two of the most important – congestion and air
pollution – and in a more sketchy fashion for others including noise and safety. Onceagain, this produces a set of hypothetical responses that thereby become appropriate
candidate targets for public policy.
By comparing the resulting behavioural responses across the problems targeted, we
obtain answers to the questions asked above. In some cases, behavioural responses
to remedy one problem would exacerbate another; in other cases, the responses
may work together, “killing two birds with one stone”. Furthermore, by considering therelative magnitudes of the price signals involved, one can quantify the judgment
involved in the last question posed: how big are these problems relative to eachother?
A comprehensive analysis of this type would consume a work the size of anencyclopaedia. We provide here a first cut, by considering two questions. First, what
are the relative sizes of the marginal external costs of various transport problems,
when averaged over a large class of users? (Marginal external cost means that partof the marginal social cost not incurred by the decision maker.) Second, how24This is a simplification, as the presence of market failures and policy concerns outside the transport
sector implies that optimal transport prices likely deviate from marginal social costs; in the jargon, we
are in “second-best”. The exact nature of such deviations is difficult to determine, but recent researchsuggests that the deviations may be smaller in the transport sector than in other sectors, because
price changes in transport do not strongly exacerbate other inefficiencies in the economy, notably
those related to labor taxes (West and Williams, 2007). At any rate, it is very likely that second-best
transport pricing would align charges more closely with external costs than is the case for the current
price structure, so that the comparison discussed in the text is a useful one.
21

K. Small, K. Van Denderdramatically do those costs vary across user groups or local situations? And if they
do vary, do the responses that would be undertaken in response to internalizing
those costs also vary? If so, then there is a strong case for looking at closely targeted
policies that can bring about such diverse responses; if not, then a blunt instrumentthat changes average behaviour may be adequate. Local air pollution, and to astronger extent congestion, are examples of externalities where blunt approaches are
not usually considered to be effective because what is needed is a set of changes invery specific situations like driving in big cities during peak periods, or driving avehicle whose emission control mechanisms are not working.
Table 5 collects estimates of marginal external cost due to several types of transport
problems. They are classified according to whether they vary mainly in proportion tofuel consumption, which is the case for climate change and oil dependency, or inproportion to vehicle-miles travelled. For comparison, the former are converted to amarginal cost per vehicle-mile, using the fleet average fuel efficiency for passenger
vehicles (e.g. 22.9 mi/gal for the US in 2005). Note however that in terms of thethought experiment described above, the best policy responses to fuel-related andmileage-related externalities are quite different. Raising the price of fuel induces not
only a mileage reduction but a substantial increase in fuel efficiency, the latter
increasingly dominating as described in the previous section. As emphasized by Ian
Parry and Small (2005), this difference dramatically affects the (second-best) optimaluse of a fuel tax to address mileage-related externalities: using their numbers, the tax
rate would be set at only roughly 40 percent of the value that would be calculated bymultiplying the cost/mile figures by fuel efficiency. Conversely, using a distancerelated tax (sometimes called a VMT tax) to address a fuel-related externality such as
global warming would fail to elicit one of the most important responses needed, which
is an increase in fuel efficiency of vehicles.

K. Small, K. Van Dender22

Table 5. Marginal external costs from automobiles, US cents/mile, 2005 prices
HarringtonMcConnell(US &Europe)Low
High

Sansom et al.
(UK)

Parry etal.
(US)High Fuelrelateda
(US)

Low
High
Fuel-related: aClimate change
0.3
1.2
0.5
2.0
0.3
3.7
Oil dependency
1.6
2.7
n.a.
n.a.
0.6
2.4
Driving-related:
Congestion
4.2
15.8
31.0
35.7 5.0
5.0
Air pollution
1.1
14.8
1.1
5.4
2.0
2.0
Noise, Water
0.2
9.5
0.1
2.5
n.a.
n.a.
Accidents
1.1
10.5
2.6
4.5
3.0
3.0
8.5
54.5
35.3
50.1 10.9
16.1
Total
22
7
1
4
8
38
Percent fuelrelated
Sources: Harrington and McConnell (2003), Table 3; Sansom et al. (2001); Ian
Parry, Walls and Harrington (2007), Table 2. “High Fuel-related”.: same as
Parry et al. except for climate change ($0.76/gal, from Stern 2005) and oil
dependency ($0.55/gal, from the high end of range in Lieby (2007), Table 1.
Notes: All numbers converted to 2005 US price levels. n.a. means not
estimated, in some cases due to an explicit argument that the quantity issmall. Fuel-related costs are converted from per gallon to per mile using
prevailing average fuel efficiency.
The fuel-related costs portrayed in Table 5 are potentially very large in aggregate.
Taking the “high” values of the last column, and multiplying by just the 2.99 trillion
vehicle-miles travelled in the US in 2005, they come to $111 billion for global
warming and $72 billion for oil dependency annually.
Yet it appears that other, more prosaic, transport problems are even larger. The threestudies listed in Table 5 (excluding the last column) are unanimous in finding thatcongestion involves larger external costs than fuel-related externalities, and except
for the “low” Harrington-McConnell values, the same is true of air pollution andaccidents. In nearly all cases, congestion alone is found to outweigh the fuel-related
externalities by a large margin. These findings may seem surprising until one realizes
that congestion and air pollution have tangible and serious effects on most urban
residents on a daily basis. Congestion consumes huge amounts of time, and air
pollution produces demonstrable mortality. Climate change and oil dependency, bycontrast, have effects that, as best as can be determined from the admittedly
imperfect modelling available, are in the distant future, capable of substantial

23

K. Small, K. Van Denderamelioration by other means, and/or simply not very large when spread over theenormous number of vehicle-miles producing the estimated aggregate impacts.
What about variation? The figures in Table 5 are national averages, but some ofthese costs vary strongly over time and place. For example, a recent French study,
discussed in Grange (2007), finds that the marginal external congestion costs ofdriving in urban traffic are about ten times as high as those of driving in interurban
traffic. This conclusion is corroborated by other studies, which in addition point out
that the congestion costs depend strongly on time of day (e.g. Proost et al. 2002).
This is a second reason why fuel taxes are not well suited to deal with congestion.
There is strong evidence that the response to imposing targeted congestion charges(i.e., ones that vary by time and place) would involve a lot of shifting of trips acrosstime periods, modes, and routes, and much less overall reduction of trips; thus themost efficient policies would aim at shifting trips in this manner rather than simply
reducing all trips.
Similarly, pollution costs from motor vehicle-use vary widely depending on location,
fuel type, age of vehicle, and vehicle maintenance practices. for example, pollution
costs are higher for diesel than gasoline cars, because of the high health costs
associated with emissions of small particulates. This casts some doubt on whether
the European “dieselization” strategy to increase fuel economy is opportune, as itincreases emissions of particulates unless particulate filters become universal. The
US may embark on a “hybridization” strategy, which avoids the particulates issue but
which also involves expensive technology. More generally, given the high costs offurther improvements of emission abatement technology, one may question thedesirability of this policy approach, as policies to reduce emissions from small
numbers of gross polluters become more attractive (Small, 1997).
If we use the higher fuel-related figures in the last column of the table, the picturechanges somewhat – although even then fuel-related externalities do not dominate
other externalities. However, we think these higher figures are not well supported byexisting evidence. In order to support this claim, we now consider more carefully thesources of the estimates shown for fuel-related externalities, first for climate change(section 3.1) and then for oil dependency (section 3.2).

K. Small, K. Van Dender

24

3.1

Marginal external costs of motor-fuel consumption due to climatechange

The climate-change cost calculated by Parry et al. (2007), shown in the next to lastcolumn of the table, is based on a damage estimate of US$25 per tonne carbon, i.e.
$25/tC, at 2005 prices.25 This figure is consistent with results from a number ofreviews including Cline (1990), Nordhaus (1994), ECMT (1998, p. 70), and Tol et al.
(2000).26 More recent reviews include Tol (2005), who reaffirms the validity ofrelatively low costs,27 and Stern (2006, pp. 287-288), who argues for much higher
costs as discussed below.
Quantifying such costs is of course highly speculative, due especially to three
features of climate change. The first is the highly uncertain effect of emissions on
specific climate outcomes; it is usually handled by acknowledging the uncertainty andstating results in terms of a specific assumed climate outcome, most often based onreports of the Intergovernmental Panel on Climate Change (IPCC).
25

One tC means one metric ton or tonne (1000 kg) of carbon. Given that carbon comprises a fraction
12/44=0.27 of the weight of a carbon dioxide molecule, $1 per tC is equivalent to $3.67 per metric tonof CO2. According to National Research Council (2002, p. 85), one tC is the carbon content of 413
gallons of gasoline.

26

See, for example, the discussions in Small and Verhoef (2007, ch. 3) and Parry and Small (2005).
The damage estimate of $25/tC is also consistent with the “shadow price” of carbon coming out of
optimization models of economically efficient paths toward greenhouse gas control, the most wellknown being a series of models developed over many years by William Nordhaus, of which a recent
version is the regional integrated model of climate and the economy, or RICE model (Nordhaus 1994).
Nordhaus (2007b) describes a recent calculation using this model as leading to a carbon price of$17/tC in year 2010 (stated at year 2005 prices) rising to $70/tC in 2050. The “optimal” calibration
reported in Nordhaus (2007a) produces a price of $35/tC in 2015, $85/tC in 2050, and $206/tC in
2100. These numbers encompass all the estimates underlying Table 5 except for Stern’s. The damage
estimate can also be compared to the actual trading prices of carbon permits in the EU’s Emissions
Trading Scheme. If the market is working smoothly (a highly debated proposition in this case), theseprices should reflect the marginal costs to industry of enacting controls to meet the mandates of the
EU under the Kyoto Protocol. In actual experience this market has fluctuated substantially, with prices
between €6 and €30 per tonne of carbon dioxide during a period covering most of 2004-06 (Convery
and Redmond 2007, Fig. 2), which equate to $27–$138/tC (given that the carbon atom constitutes
12/44 of the atomic weights in a molecule of CO2, and using the average 2004-06 exchange rate of1€=$1.25). These figures suggest that in Europe, at least, the marginal control cost has been pushed
to well above the lower estimates of marginal damage cost in Table 5.

27

Tol (2005) reviews 103 estimates of marginal CO2 damage costs, taken from 28 separate studies
done by 18 distinct research groups. The median estimate is $14/tC, but the estimates are highly
skewed to the right, with unweighted mean $93/tC and standard deviation $203/tC. The mean
estimate declines substantially, to $43/tC, when only peer-reviewed studies are included; so does thestandard deviation, to $83/tC. There is a clear effect of methodology – especially using a very low
interest rate for discounting and using “equity weights” to aggregate across countries – in accounting
for most of the higher estimates. Tol concludes: “Using standard assumptions about discounting andaggregation, the marginal damage costs … are unlikely to exceed $50/tC, and [are] probably muchsmaller” (p. 2064).

25

K. Small, K. Van Dender

The other two features, however, give rise to two sources of major differences among
analysts. One is the unknown form of human adaptation to problems building up overdecades and centuries. The other is differences of opinion about the appropriate
analytical procedure for aggregating effects occurring over long time intervals. We
consider each of these sources of controversy in turn.
Human adaptation to climate changes may occur in many ways, including changes incrops (e.g. Mendelsohn et al., 1994), public health measures, new water storage
facilities, coastline protection measures, and human migrations. Tol (2005) and aworking group of the IPCC (Martin Parry et al. 2007) provide thorough discussions.
Such adaptive measures are expected to greatly reduce the damage that would
otherwise occur. To take one example, the European Commission (2007, p. 10)
estimates that European damages from a 56 cm rise in sea level in the 2080s would
be approximately €18 billion per year without adaptation but €3 billion with adaptive
measures. Similarly, the relevant IPCC working group notes that “adaptation costs forvulnerable coasts are much less than the costs of inaction” (Parry et al. 2007, p. 40).
Some adaptive measures will be extremely costly, but these costs will be spread overmany decades. Some, like migrations, may turn out to exact a terrible human toll, justas do natural catastrophes and various failures of governmental policies today. And
adaptive measures cannot mitigate all damage: species extinctions, flooding,damage to deteriorating aquatic environments, fresh water shortages, and manyother adverse effects are very likely to occur despite adaptation. Of course, suchadverse events are already occurring today, primarily for other reasons; so therelevant questions become quantitative ones of how much and at what cost.
Measurement of the ultimate costs is full of hazards, but real progress has been
made, especially with respect to converting damages to monetary costs. The
evidence so far does not indicate that such costs dominate the more prosaic costs ofcongestion and air pollution that we have become accustomed to in the field oftransport. An analogy with a different transport problem may be useful. The collapseof a well-used bridge in Minneapolis in August 2007 elicited expressions of great
urgency for dealing with the problem of US infrastructure deterioration. Yet theresulting 13 deaths are far less than just one day’s average fatalities from US motorvehicle accidents (116 in 2004). So which is the larger national problem:
infrastructure deterioration or routine safety? It is this kind of question that is implicitly
addressed by cost figures like those in Table 5. To the extent they are valid, theappropriate conclusion is not to ignore the problems with smaller costs; it is rather tomaintain perspective relative to other problems, even prosaic ones, when setting
priorities.

K. Small, K. Van Dender

26

The second major source of differences among analysts is the matter of “discounting”
future costs in order to aggregate them into a number applicable to an emission
produced at a specified time (e.g. today). This is a technical debate, largely over the
ethical meaning and economic interpretation of parameters that characterize modern
models of economic growth. In what Weitzman (2007) calls the “majority view” ofmost economists, distant economic consequences should be discounted at interest
rates on the order of 4%–6%. This view relies in large part on the fact that observedsavings behaviour appears to be roughly consistent with a long-term growth model inwhich people discount their own or their descendants’ future utility at very modest
interest rates (the so-called “pure rate of time preference”), and simultaneously seek
to smooth their consumption in a world where long-term growth is making themricher. The consumption smoothing part of this justification can be stated equivalently
as an ethical position against income inequality across generations. In thisinterpretation, since future generations are likely to be richer than us, we would
discount the advantage to them resulting from any sacrifice by us. Yet anotherjustification is that the world economy is capable of generating returns on
investments of at least 4%–6%, and these returns may be used to mitigate orcompensate for the adverse future consequences of climate change.
There is actually little disagreement among economic analysts about the principles
just stated. The disagreement comes in the form of numerical parameters. The“majority view” infers from savings behaviour that people apply a pure rate of time
preference of 1–3% per year, both for themselves and for descendants to whom they
bequeath wealth. Others, however, argue that for purposes of policy any suchpreferences must be overridden by an ethical principle that future generations are
just as important as current ones. Most prominently, the Stern Review issued by the
UK Treasury (Stern 2006) argues that the only legitimate basis for a pure rate of time
preference is uncertainty over whether those future generations will actually be alive,
resulting in use of a pure rate of time preference of just 0.1%. As for the aversion to
income inequality, Stern uses a parameter that implies indifference between a given
percentage loss in world output at any point in time; whereas the “majority view” is fora greater aversion to income inequality so that one would not accept a 1% cut inliving standards today in order to achieve a 1% increase at some time when people
are ten times richer. The implications of Stern’s assumptions is an actual discountrate of only 1.4% per year (Weitzman 2007).
Nordhaus (2007) argues that these two parameter assumptions used by Stern, taken
together, are inconsistent with people’s observed behaviour, in particular implying
they would choose to save much more than they do. (See however the rebuttal to thistype of argument in Stern, 2006, pp. 47-48.) More transparently, Nordhaus provides
some numerical examples of the implications of using Stern’s 1.4% social discount
27

K. Small, K. Van Denderrate. Suppose a “wrinkle in the climatic system” threatens to reduce worldconsumption by 0.1% forever, starting in year 2200. It could be averted by sacrificing
56% of one year’s world consumption today. Stern’s methodology produces the resultthat we should undertake that expense; the low interest rate for discounting turns theclimate wrinkle, which might never even be noticed, into a catastrophe in present
value terms.
It is worth noting that the Stern Review itself, despite its language of catastrophe,
does not project world per capita consumption to decline in real terms, even withuncontrolled climate change. Rather, in the worst of all the cases calculated, it isprojected to grow to 8.6 times today’s level by year 2200, instead of to 13.2 times as
it would in the absence of climate change.28 Yet because this reduced income
continues forever, and is discounted at only 1.4% per year, it has a present value
equivalent to reducing per capita consumption by 14.4% every year from now to
forever (Stern 2006, Table 6.1). Thus Stern would evidently recommend that we cut
world consumption if necessary by 14%, starting today and lasting forever, in order toprevent our descendants from having to live with a lower income growth than they
otherwise would enjoy. Would we really accept such a bargain? These examples
illustrate the hazards to common sense that accompany arguments about long time
periods with very low discount rates.
Weitzman (2007) provides an insightful discussion of a possible alternative rationale
for the parameter values used by Stern. In Weitzman’s view, the most important
issue is uncertainty about the prospects and consequences of unlikely but extremely
damaging results of climate change – events such as collapse of a continental ice
sheet or reversal of a major ocean current. Neither Stern nor his critics have a way tomodel this type of uncertainty rigorously. Weitzman posits that because of this, Stern
may have “tweaked” his parameter values intuitively to reflect it. The trouble is, such
uncertainty takes us into possibilities that we know little about and cannot model well.
Weitzman’s own conclusion is that the “majority view” provides a good starting point
for immediate policy, but that the uncertainty justifies a crash program of researchand policy debate aimed at learning about the potential adaptations to and ultimate
consequences of small-probability catastrophes. Weitzman also shows, using several
examples, that the response called for may well be closer to that coming out of
Stern’s model than that from the majority view.

28

These numbers are calculated from Stern’s assumed 1.3 percent per year growth rate of per capita
output in the absence of human-induced climate change, applied for a period of 200 years, anddiminished by 35.2 percent according to the 95th-percentile loss in the worst scenario shown, thatlabelled “High Climate, market impacts + risk of catastrophe + non-market impacts” (described on p.
156 and in Figure 6.5c). Nordhaus (2006, p. 18) makes a somewhat similar calculation in a workingpaper version of his 2007 critique.

K. Small, K. Van Dender28

This technical discussion may seem to disconnect from the main thrust of Stern’s andmany other people’s analyses of climate change. These writings are filled withdescriptions not of happy people enjoying living standards ten times greater than
today’s, but rather of terrible disruptions to their well-being. Yet the technical analysis
just described is the one that underlies Stern’s damage figure of US$96 per tonne
CO2 (at 2005 prices), equivalent to $352/tC or $0.85/gal for gasoline.29 Part of
Weitzman’s critique is that Stern may have adapted the parameters of a highly
technical and, perhaps, ultimately unsatisfactory analysis using conventional growththeory in order to capture the possibilities, even if remote, that the world will turn out
much worse than the scenarios being modelled. Unfortunately, there does not atpresent seem to be an adequate basis for analysis of such contingencies within adecision-theoretic framework.

3.2

Marginal external costs of motor-fuel consumption due to oil
dependency

Some sophisticated analysis has gone into measuring a marginal social cost for fuelconsumption due to oil dependency. One of the most thorough and recent is Leiby(2007). Ian Parry and Darmstadter (2003) provide a useful review, citing studies
producing estimates of from zero to US$0.33/gal; their own preference is $0.125/gal.
(See also Davis and Diegel 2007, table 1.8.) Leiby (2007, Table 1) obtains a range
by considering likely parameter values within a single model: when divided by 42
gal/bbl, his range is $0.16–$0.55 per gallon, with a preferred value of $0.32. We
include the value $0.55/gal in our “High fuel-related” column of Table 5.
However, we have severe reservations about accepting these numbers as indicatorsof the marginal value of reducing oil imports. The costs of oil dependency are
essentially the total cost to a national or regional economy (specifically that of theUS) of various features of the world oil market that cause problems to a nation relying
heavily on oil imports. Specifically, the features considered by these authors are a“monopsony premium” and the costs of macroeconomic disruptions. The first is
rightly described Leiby as a foregone opportunity: because the US is a large part of
the world oil market (on the buying side), it could, by exerting coordinated national
policy, reduce our import demand as seen by the Organization of the Petroleum
Exporting Countries (OPEC) and thereby reduce OPEC’s monopoly power. But thelion’s share of the monopsony premium consists of curtailing the transfer of wealthabroad to OPEC nations, not a saving of world resources. Indeed, the analysis takes
29

We have restated Stern’s figure of $85 (Stern 2006, p. 287) from year 2000 to year 2005 prices,using the 13.4% growth of US Consumer Price Index over that time.
29

K. Small, K. Van Denderas given that the inefficiency of OPEC’s monopoly power is by reducing worldconsumption below efficient levels; so it is unclear that further reducing worldconsumption would create worldwide benefits. Rather, it is mainly an attempt toreduce a transfer occurring through the workings of world trade. It seems to us
inconsistent to use a worldwide perspective in valuing climate-change costs while
adopting a parochial perspective in valuing oil dependency costs.
This leaves costs of macroeconomic disruption. There is evidence that normal pricefluctuations in world oil markets are magnified by the distortion of monopoly power,
and that resulting fluctuations in oil prices tend to cause macroeconomic instability, inparticular recessions following oil-price increases. These recessions carry an
economic cost that can be regarded as an external cost to the consumption decisionsof individual economic decision-makers. We do not disagree with this analysis,
although it must be qualified by recognizing that both of these pathways are subject
to institutional factors which may change – in particular, national banks are becoming
more savvy about counteracting oil-price shocks. But as with OPEC monopoly power,
the obvious implication is that market prices are too high, not too low. Thus
macroeconomic disruption is not an argument for raising the price facing decisionmakers, in the usual manner of an unpriced external cost. Rather, as acknowledged
by Leiby, it is an argument for other policies that reduce the extent of pricefluctuations or their adverse impacts on macroeconomic performance.
Thus both components of the oil-dependency costs, as measured by current studies,
may be seen as indicators of the potential value to the economy of a large nation or
region of reducing the proportion of its supply consisting of imports from monopolized
and/or unstable sources. It is less clear how exactly reducing transport use of
conventional oil fuels brings about this desired result. Curtailing demand, for example
by fuel-efficiency standards or incentives to reduce motor-vehicle travel, would comepartly from domestic sources (which are currently producing some oil at very highmarginal cost due to high world prices). Thus such reductions cannot be taken one
for one as reductions in imports, and in fact it’s unclear whether they would evenchange the fraction of US consumption represented by imports. Thus while oil
dependency may well be a problem that warrants action, the relevant factors are
more country-specific and the relevant policies more specific to trade andmacroeconomic conditions than the other problems discussed here. Furthermore,
simply raising the price is not obviously a solution, since many of the drawbacks of oil
dependency result from the price being artificially high.
To summarize, oil dependency is an argument for interventions to reduce the market
power of oil producers by promoting conservation or substitutes for oil. It has
significance for transport policy, but it does not provide an argument for fuel taxes or
K. Small, K. Van Dender30

for other interventions that would raise the domestic price. On the contrary, oil
dependency and climate change have offsetting effects on world oil consumption, thefirst reducing it and the second increasing it relative to a social optimum. To put it
differently, if one takes climate change as the truly overriding policy problem, thenone must welcome the possibility that world oil markets are organized in such a way
as to keep current oil consumption artificially low.

3.3

Implications of analysis of marginal external costs

We believe that damage estimates of the orders of magnitude shown in Table 5,
excluding the right-most column, are the best guides to transport policy within thelimitations of quantifiable uncertainty. Several unpriced external costs of motor
vehicle travel appear to have larger measurable external costs – when traced
specifically to motor vehicle use – than those of climate change and oil dependency.
This finding does not imply that control measures are unwarranted. On the contrary,when totalled over the trillions of vehicle-miles currently being driven throughout theworld, these costs are large and warrant significant policy interventions. It is lessclear that they are amenable to amelioration through transport policy. Furthermore,even from a broader policy perspective, there are tradeoffs. Reducing greenhousegases and energy insecurity are important and valuable activities, but so are otheruses of our resources. For example, IPPC (2007) notes that the ability of poorer
nations to cope with the climate change that is already certain to occur is greatly
affected by their development path; so one must weigh greenhouse gas control
against development needs in circumstances where they compete for funds orattention.
More specifically for transport, we reach two conclusions. First, one must pay
attention to the side effects of control measures on such prosaic but real costs as air
pollution, traffic accidents, and above all congestion. The idea that climate change isso overwhelmingly catastrophic that it trumps all other environmental or transport
policies – an idea expressed or implied by some recent writings – is wrong and quite
dangerous.
Second, an ideal approach to controlling energy use is not likely to reduce motor
vehicle travel very much. We know from our study of demand elasticities that users
would curtail travel only slightly if faced with its fuel-related external costs.
Furthermore, it seems likely that abatement costs are higher in transport than insome other sectors (e.g. Knockaert and Proost, 2005), which suggests that it is more
effective to focus abatement efforts elsewhere. An ideal approach will accomplish
31

K. Small, K. Van Dendermost of its results through technological changes specifically targeted to energy
savings, mostly through the use of more fuel-efficient vehicles and perhaps also
through alternative fuels. By choosing technological solutions when permitted,
consumers will avoid more thoroughgoing behavioural changes such as changes intravel mode, trip patterns, and home and work location, which evidently are morecostly for them.
Combining the marginal-cost analysis with our review of fuel-consumption elasticities,
it appears that transport is not the ideal sector to target for solutions to energy
problems. It surely can and should play a role, but not the dominant one that someassume. Where, then, might we find a better avenue for energy policy? Many
analysts have identified electricity production from fossil fuels as a promising one
because it entails more economical opportunities for fuel switching or conservation.
To review the electricity sector would take us outside our scope, but we can cite one
statistic that helps make the point. Ian Parry (2005) discusses the implications of the“majority view” of the external cost of carbon emissions, taking it to be $30/tC.
Applying an externality tax of this magnitude would raise the price of gasoline by$0.07 per gallon, not enough to have much effect on motor-vehicle fuel consumption.
But applying such a carbon charge to coal would more than double coal prices!30
This would have significant effects on producer, and maybe even consumer,
decisions about electricity production and use. It would of course also affect otherindustrial uses of coal, which are increasing at a frightening rate in fast-growing
economies like that of China.

4. CONCLUSION

Our analysis suggests that transport is relatively unresponsive to broad-based pricesignals, in particular to changes in prices of fuels. The main exception to this is that
there is considerable scope to improve the fuel efficiency of vehicle fleets, mainly
through technological changes but also to some extent through consumer choices
among vehicle sizes and types. As a result, we should not expect to see dramaticchanges in modal shares or in the nature of transport systems. Furthermore, thisunresponsiveness suggests that it is costly to reduce energy use in transport, relative
to other economic activities, and thus that efficient policies will probably not extract asmuch energy savings (in percentage terms) from transport as from other sectors.
30

The US price of coal in 2003 was $19.68 per metric ton (US EIA 2006, Table 7.8, converted tometric tons) with carbon content 0.75 (O’Hara 1990, Table 6), implying a price of $26/tC.

K. Small, K. Van Dender32

A perennial policy issue is whether to address problems with price incentives (in thiscase, higher prices) or with regulatory measures. Our review suggests that either
approach can work. Using prices has the disadvantage that quite large priceincreases are needed to obtain much response, and this may be beyond the political
capacity of most countries. Fuel efficiency regulations are a relatively quick way to
reduce oil imports where energy security is a concern, and the danger of inducing
more travel as a side effect is probably minor. But if reducing climate change is a
primary goal, we think it is important to supplement any regulations with either
technology policies or some price-oriented policies because otherwise the stock offossil fuels remains available and attractive for future use, making it that much harder
to move toward a global path of lower carbon-dioxide emissions.
Broad-based carbon taxes remain an excellent tool for climate control. Their impacts
on transport would be modest, mainly in the form of promoting technologicalimprovements and vehicle-mix shifts that increase fuel efficiency. This is at it shouldbe, because it reflects relatively high costs of reducing oil use in transport. There areother sectors, especially those that burn coal, that make better targets for energy
policy.
Our review of marginal external costs suggests that energy policy could be the “tailwagging the dog” in transport. Other transport problems, notably congestion, local air
pollution, and accidents, are associated with considerably higher marginal externalcosts than are climate change and energy security. It follows that policies to deal
directly with these other problems deserve high priority, regardless of energy policies.

33

K. Small, K. Van Dender

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