Survay of Transport Fuel Demand Elasticities ISBN 91-620-5586-0 · 2012-11-29 · Survey of...

Survey of Transport Fuel Demand Elasticities

REPORT 5586 • SEPTEMBER 2006


Author Professor Thomas Sterner University of Gothenburg

SWEDISH ENVIRONMENTAL PROTECTION AGENCY

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ISBN 91-620-5586-0.pdf ISSN 0282-7298

© Naturvårdsverket 2006

Elektronisk publikation

Cover photo: Digital Vision

SWE DI SH EN VI R O NM ENTA L PR OTE CT I ON AG ENCY


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Preface One of the tasks of the Swedish National Environmental Protection Agency is to develop and propose methods of control in the environmental field. Carbon dioxide emissions from transport is one area where development is heading in the wrong direction. An issue that is often debated is the extent to which in-creased taxes on fuel really lead to lower usage and thereby to a reduction in the impact traffic has on climate. What is the price elasticity of petrol in the short and the long term? Thomas Sterner, Professor of Economics at Göteborg University, is internationally one of the most knowledgeable within this field. Mats Björsell at The Swedish National Environmental Protection Agency has asked him to write this report, which represents a synthesis of the collected international research in the area. The report has a summary in Swedish.

The author have the sole responsibility for the content of the report and as such it can not be taken as the view of the Swedish Environmental Agency.

Swedish EPA Stockholm in September 2006



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Contents PREFACE 3

SUMMARY 7

SAMMANFATTNING 9 Priselasticitet för fordonsbränslen 9

1. MOTIVATION 11

2. MODEL TYPES 12

3 TIME SERIES, CROSS SECTIONS AND POOLED ESTIMATORS 14

4 COMPARISON OF MODELS AND ESTIMATION TECHNIQUES 15

5. SURVEYING STUDIES ON THE DEMAND FOR TRANSPORT FUEL 18

6. FACTORS THAT DECIDE FUEL DEMAND ELASTICITIES 21

7. SUMMARY VIEW ON ELASTICITIES 23

8. POLICY IMPLICATIONS OF FUEL DEMAND ELASTICITIES 24

9. POLITICAL ECONOMY OBSTACLES TO ACTION 29

10. SUGGESTIONS FOR FURTHER STUDY 30

REFERENCES 31



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Summary This paper summarises the extensive research on the price (and income) sensitivity of fuel demand. It therefore casts light on the conditions under which a fuel tax can operate as an instrument of environmental policy. It may be right to say that ‘‘it won’t make much difference because people still have to take their cars to work or our lifestyle will not be changed’’ but only in the short run. The hundreds of stud-ies are surprisingly consistent over a wide range countries, time periods and meth-ods that there is a significant response to increased prices, albeit a less than propor-tionate one. Price elasticities are in the range -0.65 to – 0.8 or maybe even higher in the really long run. The main component of this is probably vehicle fuel efficiency but the miles driven and the number of vehicles per capita are also important adap-tation mechanisms. In the true long run, there are also further changes in popula-tion density, urban architecture, lifestyles and other factors. The countries that have higher fuel taxes such as most of Europe and Japan have reduced fuel demand so much that this factor significantly affects global carbon emissions – in fact by more than the Kyoto phase 1 agreements. It is therefore cru-cial to better understand the political economy of fuel taxation in order to use this instrument more effectively in the future. The present report is divided into a number of sections

1. Motivation 2. Model Types 3. Time Series, Cross sections and pooled estimators 4. Comparison of models and estimation techniques 5. Surveying studies on the demand for transport fuel 6. Factors that decide fuel demand elasticities 7. Summary view on elasticity values 8. Policy Implications of Fuel Demand Elasticities 9. Political economy obstacles to action 10. Suggestions for further study

Section 1 provides a motivation, sections 2-3 are somewhat technical and may be skipped without (too much) loss of context by the non-technical readers. Section 4-5 survey a large number of studies that actually have estimated demand elasticities. It turns out that income elasticities are, in the long run around (maybe somewhat above) 1 and price elasticities in the long run are around -0.8. These numbers are not physical constants given by nature – they are a summary of past behaviour in response to various technical, economic and social parameters. Naturally they may change – both over time, with the size and pace of change contemplated and - not least – depending on the estimation model used. They may even be amenable to change as a result of policy measures. On the other hand, the order of magnitude is more or less confirmed from hundreds of studies covering quite a variety of coun-tries and time periods.



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Section 6 looks into the factors that decide fuel demand elasticities and section 7 summarises these numerical results. Section 8 looks at some policy conclusions emerging from this, the most prominent of which is that fuel taxes are indeed a powerful economic instrument for environmental policy making. Section 9 ad-dresses some of the political economy obstacles to a more aggressive use of fuel taxes and it this context it is distinctly a disadvantage that the shortrun elasticity is so much lower than the long-run value. This implies that the politicians taking the decisions will take the full heat of the resistance to this policy while the environ-mental benefits occur so many years later that the same politicians are unlikely to reap the benefits. Section 10 contains a few suggestions for further study.



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Sammanfattning Priselasticitet för fordonsbränslen Denna studie sammanfattar den mycket omfattande forskning som finns kring ben-sinefterfrågans pris- (och inkomst-) känslighet. Den kan därmed upplysa oss om de villkor som gäller för bränsleskatter som styrmedel på miljöområdet. Många brukar påstå att ”En bensinskatt inte har någon större effekt eftersom folk ändå måste ta bilen till jobbet och i övrigt kommer de knappast att ändra sin livsstil bara för en bensinskatt”. Det kan ligga en del i detta – men det är i så fall en sanning som en-dast gäller på kort sikt. På lång sikt är priskänsligheten betydligt större. Vi ger en översikt av detta forskningsfält där hundratals studier redan estimerat priskänslig-heten för olika tidsperioder, länder och modeller. De långsiktiga priselasticiteterna ligger i allmänhet i intervallet -0.65 – 0.8 eller till och med ännu högre för riktigt lång sikt. Detta betyder att om priset stiger med 10 % så minskar förbrukningen med 6 – 8 %. Det bör dock observeras att detta betyder 6-8% lägre än vad det an-nars skulle varit. Under tiden kan ju inkomsten stiga vilket motverkar minskningen och kan till och med vända den i en ökning – men i så fall är det hur som helst en mindre ökning än man annars skulle sett! Den viktigaste komponenten på riktigt kort sikt är ändringar i kör- och resbeteende, på medellång sikt har vi ändringar i fordonsstocken (såväl antal bilar som val av bilar med lägre bränsleförbrukning) och på längre sikt får vi ändringar i utbudet av teknik (utvecklingen av mer eller mindre bränsleeffektiva fordon) och förändringar i samhällsstruktur (folk bosätter sig närmare eller längre från sina arbeten), hela städers infrastruktur påverkas inklusive tillgång på kollektivtrafik. Den viktigaste komponenten är troligtvis just bränsleeffektiviteten men även antalet personkilo-meter per person förändras en hel del på lång sikt. På kort sikt är alltså föränd-ringsmöjligheterna mindre och priselasticiteten mycket lägre – kanske omkring -0,2 på ett års sikt. Detta är en förklaring till varför det är politiskt svårt att höja bensin-skatter: Det politiska motståndet och olägenheterna kommer på kort sikt medan de miljömässiga fördelarna och även den politiska acceptansen kommer först på lång sikt. De länder som har högre bränsleskatter – såsom de flesta i Europa och Japan, har redan reducerat sin bränsleförbrukning så pass mycket att detta har en högst påtag-lig – men underskattad effekt på de fossila kolutsläppen till atmosfären. Effekten är till och med i klass med eller större än minskningarna som avtalas inom Kyoto-avtalets första fas. Det är därför viktigt att förstå hur drivmedelsbeskattningen fun-gerar så att detta instrument kan utnyttjas och utvecklas effektivt i framtiden.



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Rapporten är indelad i 10 stycken

11. Syfte 12. Modelltyper 13. Tidserier, tvärsnitts- och panelestimat 14. Jämförelse av modeller och estimeringstekniker 15. Översikt av studier 16. Påverkande faktorer 17. Sammanfattning angående elasticiteter 18. Policy implikationer 19. Politisk/ekonomiska hinder 20. Förslag till vidare studier

Avsnitt 1 ger syfte, avsnitt 2-3 innehåller en översikt över de modeller som an-vänds för att statistiskt skatta elasticiteter och de är något tekniska. Texten har dock skrivits så att dessa avsnitt kan läsas kursivt utan att läsaren förlorar sammanhang-et. Avsnitt 4-5 innehåller en översikt av det stora antal studier som genomförts för att skatta efterfråge-elasticiteterna. Det visar sig att den långsiktiga elasticiteten är kring 1 för inkomst och -0.8 för pris. Dessa siffror får inte misstolkas som fysiska konstanter av samma typ som en metallfjäders elasticitet: de tillhandahåller helt enkelt ett bekvämt sätt att sammanfatta tidigare observerat beteende som ju i sin tur är reaktion på förändringar i bland annat inkomst och priser – men också socio-tekniska och kulturella förändringar. Avsnitt 6 beskriver de faktorer som formar elasticiteterna och avsnitt 7 sammanfat-tar. I avsnitt 8 försöker jag dra några policyslutsatser. Den viktigaste av dessa är att bränsleskatter faktiskt är mycket potenta styrmedel. I avsnitt 9 diskuteras en del politiska hinder att använda dem mer aktivt: det visar sig ju att ganska starka lob-bykrafter mobiliseras för att stoppa bensinskatter i många länder. I detta samman-hang är det ett stort problem att priselasticiteten är så mycket lägre på kort än på lång sikt. Det betyder att dagens politiker som fattar beslut om högre skatter får ta alla negativa konsekvenser i form av politiskt motstånd medan de miljömässiga fördelarna med styrmedlet uppenbarar sig först på så lång sikt att de beslutande politikerna riskerar att vara borta från scenen. Avsnitt 10 ger några förslag på vidare studier.



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1. Motivation The carbon content of the atmosphere has oscillated between 200 and 280 ppm for at least the latest half million years. It is well known that these variations are im-portant for the environment and climate and for instance this variation is strongly corelated to differences in temperature. The ice ages coincide with carbon contents of around 200 ppm and the interglacials with higher carbon content in the atmos-phere of around 280 ppm1 . We have now a carbon content in the atmosphere of 380 ppm and human society is at present adding around 7 Gtons of carbon per year and the “business as usual” could give another doubling within a century but the IPCC and various governments want to restrain emissions in order not to let the atmospheric carbon content get too high. The IPCC for instance recommends car-bon abatement of 50-80 %. Since transport is a very major contributor to carbon emissions worldwide and a very fast growing contributor, it is natural to seek means to reduce carbon emissions from transport. Fuel taxes, carbon emission permits and other policies are possible instruments but in order to design as well as possible these instruments in different contexts, we need to understand how the economy responds to the various factors that determine the transport demand for fuel. The purpose of this paper is to provide a brief overview of the factors that deter-mine the fuel demand from the transport sector.

1 Note that as a non-climatologist, I restrict myself to saying that the carbon content is important and correlated with climate and temperature. The issue of causation which may go both ways and of feed-backs is complex and I refrain from commenting on it.



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2. Model Types Since earlier reviews have shown that the elasticity estimates depend quite cru-cially on the type of model used, we start by a brief introduction to the most com-mon types of model used. For a more detailed discussion of modeling the reader is referred to Bohi(1981) or Sterner and Dahl(1991). The purpose of this section is briefly to present the functional forms and models chosen concentrating primarily on the issue of lag structure. Let us start with the simple static model (M0) in which G is fuel, Y income and P price2. Git = c + αPit + ßYit + μit (0) i=1...N countries; t=1...T years Static models can not in general (at least not with time-series data) be relied on to capture the complex process of adaptation to changes in prices and income. The way we model dynamic adaptation depends on the lag structure assumed. More complex demand models will include lagged variables, One particular form of lag structure is the geometrically declining lag. By using the Koyck transformation geometric lags on the exogenous variables can be more effectively modeled through the inclusion of a lagged endogenous variable as in (1). Git = c + αPit + ßYit + λGi,t-1 + μit (1) Although the geometrically decreasing lags in the lagged endogenous model M1, may be a fairly strong restriction3 , its ease of estimation and the difficulty of ob-taining sufficiently long series of data for the estimation of other more complicated lag structures have given the lagged endogenous model considerable popularity. A slightly less restrictive lag structure can be obtained through the further inclusion of lagged exogenous variables as in (2). Model M2 is often referred to as an in-verted-v lag4 because it allows for an adjustment that is first low but increasing (because of a "perception lag") and then geometrically decreasing. Git = c + αPit + ßYit + λG i,t-1 + γP i,t-1 + δY i,t-1 + μit (2) (3) shows another form of inverted-v lag that we will refer to as model M3 which can be derived by assuming the lags follow a Pascal distribution. 2 Typically we use fuel consumption per capita, Y is deflated per capita income and P is deflated fuel price. Frequently the variables are in logarithms which allows the parameters to be interpreted directly as elasticities. 3 This model can be derived by assuming that desired consumption is G*t = c + αPt + ßYt + εt1 but that adaptation is partial: Gt Gt 1 = s(G*t Gt 1) + εt2. Note that this implies that μt = sεt1 + εt2. 4 The static model and the models M1 and M2 are the same as in chapter 2 which does however not use the models M3 and M4 defined here.



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Git = c + αPit + ßYit + λG i,t-1 - ½λ2G i,t-2 + μit (3) The final form of dynamic model used here is the polynomial distributed lag model M4 in which only lagged observations of the exogenous variables are included. Usually the αi and ßi are constrained by some form of polynomial. Git = c + ΣατPi,t-τ + ΣßτYi,t-τ + μit (4) In this model the short run elasticities are αt and ßt while the long run elasticities are given by the sums of all the αs and ßs. In the lagged endogenous model the short-run is given by α and ß while the long-run is given by α and ß divided by (1- λ). In addition to this there are a number of models that include other variables such as the number of vehicles (and their properties), variables such as population density and the price/availability of alternatives to driving (public transport). This intro-duces a whole series of new complications since these variables in turn may be influenced by the fuel price and income that determine fuel demand directly. Ide-ally one might therefore want a system of equations. Naturally there are many ways such a system of equations can be built and there are relatively few such studies and they cannot easily be compared since the very interpretation of the elasticities is model specific.



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3 Time Series, Cross sections and pooled estimators Another crucial issue is the type of data used. Some studies have data for one coun-try for a number of years (time series TS) while other studies may have a cross-setion (CS) for a given year comprising a number of countries. Others again may have a panel of data with both geographical and temporal variation (CSTS). The data may have different frequency; monthly quarterly or more frequently annual. Finally some studies are based on individual rather than aggregate data- When a full panel of data is available then we may estimate either TS, CS or CSTS. The time series approach in which each country data set is analyzed separately highlights the individual country specifics but does not fully use the information implied by the comparison between countries. The pure cross section gives us separate elasticities ßkt for each year on a cross section of countries. Baltagi and Griffin (1983) show that the most efficient way to use both the variation along time and between countries simultaneously in a data set such as ours must be to base estimations on combined cross section time series. This can be done by assuming there is one unique elasticity for each explanatory variable estimated for the whole data set. There are however many ways of doing this (for instance with or without common intercepts) and the choice of model must take into account the character of data used. The use of dynamic models such as M1 and M2 with pooled estima-tors may result in large biases.



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4 Comparison of models and estimation techniques Before we survey the literature, it is useful to illustrate the importance of the choice of model by showing different estimators for one particular set of data. This is done in an externsive study by Franzén and Sterner (1995) for a large OECD dataset. Table 1 contains a number of estimates. These include the ordinary pooled OLS estimator with common intercepts, the fixed effects, so-called "within" estimator5 that allows for individual country intercepts and the generalized least square esti-mator, (GLS). For the latter we report the results of several models. For the sake of comparison we also include the aggregate cross section or so called "between" estimator (a CS regression on average data) together with both the aggregate time series (all countries added) and the mean group estimates (which is the average of all the individual country timeseries estimates.

TABLE 1 A summary of long run elasticities for the OECD using cross-section dataa)

a) OECD except Luxembourg, Iceland and New Zealand, 1963-1985. PPP used for conversion. b) Even for the Static model, OLS produces high price elasticities: -0.98 and 1.26. c) For the Static model, 'Within' estimates do however give a typical "intermediate" value of -0.4 for price. d) PDL of 2nd degree, with 8 price and 4 income lags for 1970-85. e) 'Between' is a cross section for the Static model on average data.

5 "Within" is basically an estimator designed to pick up elasticities due to variation within countries. It is estimated as an OLS with country dummies on the intercept (for the lagged endogenous model specifi-cation).

Estimation- technique

Model Price elast. Short run

Price elast. Long run

Income elast. Short run

Income elast. Long Run

Pooled OLSb) M1 -0.12 (-9.3)

-1.39 0.05 (3.8)

0.58

Pooled (fixed effects) "Within"c)

M1 -0.22 (-10.4)

-1.27 0.13 (4.2)

0.75

Pooled GLS (Fuller and Battese method)

M1 M2 M3

-0.18 (-10.2) -0.22 (-8.5) -0.15 (-11.4)

-1.35 -1.34 -1.16

0.10 (4.1) 0.25 (2.4) 0.14 (11.0)

0.73 0.69 0.63

Pooled GLS (Yule-Walker)

M4d) -0.38

(-7.5) -1.05 1.13

(27.0) 1.63

Betweene) cross sec-tion

Static M0

-1.19 (-5.4)

1.09 (6.08)

Mean group estimates M1 -0.76 0.79 Aggregate time series M1 -0.31 -1.28 0.29 1.19



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Table 1 shows that the choice of model has some fairly sizeable effects giving aggregate elasticity estimates ranging from -0.76 to -1.4 for price and 0.58 to 1.1 for long run income elasticities. The static model M0 gives high elasticities that can be interpreted as true long-run only with cross-section data. If used with time series data it gives more intermediate values. Naturally it is not only the model that decides the elasticity values. This is shown by the fairly large variation found with different models for various countries, see table 2.

TABLE 2 Comparison of long run elasticities with different models on timeseries data

PRICE ELASTICITY INCOME ELASTICITY MODEL M4 M1 M2 M4 M1 M2 Canada -2.0 -1.1 -0.9 0.6 0.5 0.6

USA -1.2 -1.0 -0.6 1.2 1.0 0.8

Austria -1.2 -0.6 -0.7 0.9 1.2 1.2

Belgium -1.5 -0.7 -0.6 1.0 1.3 1.3

Denmark -0.8 -0.6 -0.6 0.3 0.6 0.6

Finland -1.2 -1.0 -0.9 1.2 1.4 1.4

France -0.4 -0.7 -0.6 0.9 1.2 1.1

Germany +0.1 -0.6 +0.3 1.6 0.5 1.9

Greece +0.2 -1.1 -0.6 1.8 2.0 1.9

Ireland -1.0 -1.7 -0.8 1.3 0.9 1.3

Italy -0.7 -1.2 -0.9 0.9 1.3 1.2

Netherlands -3.2 -2.3 -1.5 0.4 0.6 0.8

Norway -2.5 -0.9 -0.8 1.3 1.3 1.3

Portugal -0.7 -0.3 -0.1 1.1 2.1 2.2

Spain -1.2 -0.3 -0.1 1.4 2.1 2.4

Sweden -0.1 -0.5 -0.4 1.2 1.2 1.3

Switzerland +0.2 +0.1 +0.1 1.8 1.5 1.6

Great Britain -1.4 -0.5 -0.4 1.6 1.5 1.6

Australia -0.2 -0.2 -0.1 1.2 0.7 0.8

Japan -0.3 -0.8 -0.8 0.8 0.8 0.8

Turkey -1.1 -0.6 -0.5 1.1 1.3 1.2

MEAN -1.0 -0.8 -0.6 1.1 1.2 1.3

As shown in table 2 some countries have very low (or even positive) estimates. Sometimes this applies to several models – as is the case of Sweden and Switzer-land. In the latter case it is conceivable that transit traffic is creating a bias in data. In the case of Sweden it is possible that elasticities were low and not very signifi-cant because of the combination of very high marginal taxes and generous rules for making gasoline and other driving expenses tax exempt. During the 1970s and 1980s Sweden combined a uniquely high rate of income taxation with a number of



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policies apparently destined to support the auto industry. Sweden had a record (for such a very a very small country) of two powerful domestic car producers and many policies were designed to support these producers. The combination of re-cord marginal taxes and generous rules for deducting the expenses needed to earn your income – including costs of driving to your job – actually implied that the costs of motoring were “socialized” – that is born not by the individual motorist but by society. Some groups of employees who could not raise their standard of living by longer hours or negotiating higher salaries (due to egalitarian policies and high taxes) could actually make significant amounts of money by claiming generous mileage allowances! These are largely conditions of the past but they still affect estimations which build on many years of data. Today, it is natural to think that the elasticities might be more normal in Sweden. Just as time-series estimates have the advantage of letting us study individual coun-tries elasticities, cross-section studies can allow us to study the evolution (if any) of elasticites over time. Table 3 illustrates the evolution of OECD-wide elasticities from 1970 to 1985.

Table 3 Pure Cross section estimates for all OECD countries (Model M0)

Year Price

Elast. Income Elast.

1970 -1.27 (-4.92)

1.15 (6.16)

1975 -0.87 (-3.69)

1.20 (5.62)

1980 -1.25 (-6.24)

0.88 (4.83)

1985 -0.98 (-3.92)

1.02 (4.60)

Mean 70-85a) -1.09 1.08

These results were obtained using the simple static model. T-values in brackets. a) This is the weighted mean (using the inverse of standard deviations as weights). When

using a static vehicle model, G = f(Y,P,V) where V is the vehicle stock, the mean price elas-ticity was -1.18 but the mean income elasticity was practically zero, this effect being picked up by the Vehicle variable.



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5. Surveying studies on the demand for transport fuel This is a well studied field with very many studies inspired by the oil price hikes and discussion of OPEC market power of the 1970s . Already around 1990 there were several extensive surveys of the literature (for example, Drollas, 1984; Oum, 1989; Dahl and Sterner, 1991a, 1991b; Goodwin, 1992). These surveys already then covered over a hundred different studies, many of which had several estimates each (for different countries, time periods or methodologies). Since then there have been some more studies the most interesting of which have focused either on ex-plaining the differences between earlier estimates or on breaking down the mecha-nisms behind these estimates (number of vehicles, miles driven, efficiency etc). Much of this later literature has been surveyed recently in two parallel surveys undertaken within a study commissioned by the UK Department of Transport which resulted in a number of publications (Goodwin et al 2004, Hanly et al 2002, Graham and Gleister 2002, 2004). The total number of individual studies is now several hundred and this survey will synthesize some of the main lessons of this body of work building on the surveys mentioned and some of the more notable individual studies therein. Graham and Gleister 2002 start their survey by summarising some of the earlier surveys to give a picture of the general results as concern aggregate elasticity val-ues. One of the first is Drollas (1984) which provides an early comprehensive re-view of fuel demand characteristics. He surveys both academic and non-academic studies that focus on gasoline demand elasticities. There are few studies on diesel demand – maybe because it is too close to fuel oil so that demand statistics are contaminated by the use of fuel oil (which is often untaxed or taxed at a very low rate). The author cites price and income elasticities from previous studies predominantly estimated for the US. His survey includes various modelling techniques including static cross-sectional specifications and time-series and pooled cross-section time-series models with a variety of lagged structures. While a range of estimates is found, the consen-sus is that the long-run price elasticity of demand is around -0:80, while the long-run income elasticity is slightly below unity. Typically the short-run price elastic-ities are about one-third the magnitude of the long run, and short-run income elas-ticities between a quarter and a half of the long-run value. The author does not find big differences between the US and other countries. Dahl and Sterner, 1991a, 1991b find long-run price elasticities falling in the interval -0.6 to -1 and for income between 0.6 and 1.4 depending very much on methodology. They stratify earlier studies by the type of model and data used, and calculated averages for each category. The short-run price elasticity of gasoline demand varies between -0.10 to -0.25 depending on the model estimated.



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The short-run income elasticity is between 0.2 and 0.4 see table 4. Models with annual data appear to capture long run elasticities better than monthly or quarterly data which may be less reliable. Models which include vehicle numbers and char-acteristics give intermediate values and are often too diverse to compare (many of these values are not included in the summary table below).

Table 4 Summary of average elasticities in Dahl and Sterner

Model type Price Elasticities Income Elasticities Short or Long-run (or intermediate) SR IR LR SR IR LR Static M0; TS data -0.53 1.16 Static M0 and vehicle models; CS data -1.01 0.76a Lag endog. M1 with yearly TS data -0.26 -0.86 0.43 1.38 Lag endog. M1 with yearly CSTS data -0.17 -0.95 0.40 1.21 Lag endog. M1 with quarterly TS data -0.14 -0.59 0.20 0.75 Lag endog. M1 with monthly TS data -0.19 -0.88 0.23 0.64 Inverted V-lag M2-3 yearly TS data -0.22 -0.94 0.39 1.09

Based on more extensive summary tables in Dahl and Sterner 1991a&b. a) some estimates also had a “vehicle elasticity” that picks up part of the income effect.

Goodwin (1992) also explores the issues, updating previous surveys of gasoline price elasticities in his review of academic and non-academic studies undertaken in the 1980s and 1990s. His paper shows that more recent work has generally revised the magnitude of elasticity estimates upwards. The unweighted mean value of 120 elasticities of gasoline consumption with respect to fuel prices considered in the review is -0:48, compared with similar values from previous reviews of -0:1 to -0:4. Table 5 summarises his averages by type (excluding intermediate values)

Table 5 Summary of Price Elasticities of Gasoline Consumption

Short Run Long run

Time series data -0.27 (0.18)

-0.71 (0.41)

Cross section data -0.28 (0.13)

-0.84 (0.18)

Adapted from Graham and Gleister (2002) (standard deviations in brackets).

The ‘‘short-term’’ period generally refers to less than one year. Long-term elastic-ities tend to be between two and three times higher than the short term. Having reviewed a wide range of studies, Goodwin shows that the time-series and cross-section Methods broadly concur in giving long run elasticities of around -0.8. Dahl (1995) reviews a number of previous gasoline demand surveys conducted since 1977 and updates this work with evidence from the most recent US studies. Table 6 summarises the results reviewed by Dahl.



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Table 6 Demand Elasticity Estimates Reported by Dahl (1995)

Price Elast Income Elast SR LR SR LR Taylor (1977) -0.1 to -0.5 -0.25 to -1.0 - - Bohi (1981) -0.2 -0.7 - ~1 Bohi & Zimmerman(1984) 0 to -0.8 0 to -1.6 -0.2 to 1.2 0.3 to 1.4 Dahl (1986) -0.3 -1.0 0.5 1.4 Dahl & Sterner (1991a,1991b) -0.3 -0.9 0.5 1.2 Goodwin (1992) -0.3 -0.7 to -0.8 0.5 1.2

Graham and Gleister conclude their survey of surveys and studies which build on many hundreds of studies, by saying that despite some variation in individual val-ues, elasticities of fuel demand generally fall within a fairly narrow range. Short-term price elasticities tend to be between -0.2 and -03, while long-run values go from -0.6 to -0.8. For income, the long-run elasticity is often slightly higher than unity (1.1 to 1.3) while the short-run elasticity is from 0.35 to 0.55.



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6. Factors that decide fuel demand elasticities While the overwhelming evidence points towards values within these ranges earlier review articles do not generally account for the variation in the estimates that ex-ists. Graham and Gleister (2002 and 2004) attempt to fill in some of the blanks by drawing upon recent studies. Eltony (1993) uses household data to study the behavioural foundations that give rise to price elasticities of demand for fuel. He uses a panel of Canadian household data 1969-88 over gasoline demand and distinguishes between three different re-sponses to price changes: mileage per car, number of cars and vehicle efficiency. buy more efficient vehicles. The short-run price elasticity of gasoline is estimated at -0.31. Almost 75 per cent of this is due to driving fewer miles. A further 10 per cent results from an alteration in the composition of the fleet to be more fuel-efficient and the remaining 15 per cent are due to changes in the number of vehi-cles. The long-term elasticities are around -1. Rouwendal (1996) uses Dutch data and finds a vehicle stock efficiency elasticity with respect to fuel price of -0,15, somewhat higher than Eltony. Hensher et al (1990) find similar short run responses and also estimate vehicle use elasticities (w r t fuel price) of between -0.26 and -0.39 for families with differing numbers of cars. Using aggregate data, Epsey (1996) finds a fuel price elasticity of fuel econ-omy of -0.2 and a corresponding income elasticity that was not significantly differ-ent from 0. Epsey also includes an “autonomous” rate of technical progress in fuel efficiency found to be 2.8 %/year and an elasticity of fuel economy with respect to vehicle taxation of 0.1. One of the clearest analyses is provided by Johansson and Schipper (1997) who examine the three most important determinants of fuel demand, vehicle stock, mean fuel intensity, and average annual driving distance for 12 OECD countries 1973 to 1992. Using a variety of estimation techniques and models, the authors obtain estimates for long-run car fuel and travel demand. The results confirm the aggregate results found above with a long-run fuel price elasticity of approximately -0.7- The authors find that the largest portion of this , just under 60 %, is due to changes in fuel efficiency. The gasoline demand figure is more than double the estimated price elasticity of travel demand. The long-run income elasticity of fuel demand is approximately 1.2, almost all due to the number of cars, and is of identi-cal magnitude with respect to travel demand. The fuel efficiency effect is found to arise from both increased technical efficiency and the imposition of environmental standards. Johansson and Schipper also consider the effects of different taxation measures on fuel and travel demand. They find a fuel tax increase will reduce



22

overall long-run fuel consumption much more than an increase in the other car related taxes, for example, taxes on car ownership. The finding that fuel efficiency is such an important factor in explaining gasoline demand elasticities implies that in the long run, fuel price changes will have a greater impact on fuel demand and emissions than on vehicle use (miles driven) and congestion. Graham and Gleister (2004) focuses even more on traffic demand elasticities and show for instance that the long-run elasticity (w r t fuel price) of kilometers driven is larger (-0.26) than the corresponding elasticity for the number of car trips (-0.19) since the average trip length is one of the many parameters that the consumer may adjust. They also look at the elasticities for car ownership where aside from in-come, the costs of car ownership are naturally a central factor. However the defini-tion of that variable turns out to be so complicated as to make comparisons be-tween studies virtually impossible. They also attempt to review elasticities for freight which, as they rightly point out is often assumed to be less elastic. They report that many studies do in fact find significant (negative) elasticities with re-spect to fuel and other costs but again the definitions of cost are many and varying making it hard to draw definite numerical conclusions from the material: elastic-ities w r t freight rate are often quite large (around -1) but few studies appear ex-plicitly to calculate the fuel price elasticities – which must be considerably lower since the fuel cost is only a small fraction of freight rates.



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7. Summary view on elasticities There are two recent and comprehensive reviews in this area. Graham and Gleister (2002) draws three central conclusions from its survey of the literature which pretty much concur with earlier findings:

21. Short-term elasticities are around -0.3 and long-term values from -0.6 to -0.8.

22. The effects of traffic levels are smaller than the effects on fuel consump-tion and this applies both long- and short-term. Traffic elasticities are about -0.15 in the short term and about -0.30 in the long-term.

23. The demand for car ownership depends mainly on income. The long-run income elasticity of fuel demand is typically 1.1 to 1.3. Short-run in-come elasticities are in the range 0.35 to 0.55. The implication is that fuel prices must rise faster than the rate of income growth, even to stabi-lise consumption at existing levels.

The other study (Hanly et al 2002), summarised in Goodwin et al 2004, gives a similar picture albeit with elasticity values that are slightly lower: Their fuel elas-ticities are -0.25 and -0.64 in the short/long run while their traffic volume elastic-ities are -0.1 and -0.3 respectively. Vehicle efficiency elasticities are -0.15 and -0.4 respectively while the elasticities w r t the number of vehicles are -0.1 and -0.25 respectively. The corresponding income elasticities are 0.4/1.0 for number of vehi-cles but only 0.2/0.5 for the volume of traffic however as the authors point out these figures are drawn from studies some of which are partial in nature and thus the effects may not add up. An interesting conclusion of this study is that price elasticities do not appear to be decreasing over time or with increasing fuel prices. It should be pointed out that in the true long run, there are also changes in popula-tion density, urban architecture, availability of public transport, lifestyles and other factors that are not readily amenable to measurement.



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8. Policy Implications of Fuel Demand Elasticities In table 7, I have chosen to show the average rate of taxation on gasoline. This indicator is a weighted average reflecting the varying composition of fuels with different octane (premium and regular) in the proportions actually used in each country. It is expressed in international cents converted by purchasing power par-ity. This is a useful way to provide an indicator of the actual burden the tax places on the representative motorist. This makes it attractive as an indicator of the inten-sity or strength of a policy instrument. The reader should be aware, however, that the comparisons would be different if we had used market exchange rates. The difference for most high-income countries is small but for some low-income coun-tries it is more substantial. In our sample of countries, the US would have had a somewhat higher tax and the Eastern European countries considerably lower val-ues.

Table 7 Gasoline taxes in cents/liter in selected countries, 2005

Western Europe Gas Tax Eastern Europe Gas tax Italy 90 Hungary 125 UK 97 Czech Re 117 Netherlands 100 Poland 118 France 89 Average 120 Belgium 94 Germany 90 Non European Finland 85 Japan 46 Norway 74 Australia 35 Portugal 103 NewZealand 42 Sweden 80 Canada 26 Denmark 70 Mexico 21 Spain 72 USA 10 Austria 68 Average 30 Ireland 62 Luxembourg 60 Switzerland 50 Average 80

Sources --IEA 2006; Leaded/unleaded and premium/regular weighted by shares. All figures in purchasing power parity constant dollar cents.

As we can see in table 7 the average for Western Europe is 80 cents per liter, which is clearly high compared to all the non-European countries and most notably with respect to the USA, countries such as Japan and Australia being intermediate. Within Western Europe variation is still quite significant considering that many are actually neighboring countries. Looking at gasoline prices and taxes in the 1970s



25

and 80s there was a wide divergence within Europe and several important countries such as Germany and the UK had low taxes and prices, (Angelier and Sterner, 1990). Looking at the figures today, all the major EU economies, Germany, France, Italy, UK, Belgium, Holland now have high taxes and are fairly well har-monized at quite a high level (89-100 cents/l). This reflects a fairly long and con-scious effort in countries such as the UK where the “fuel tax escalator” has implied a pre-announced, long-run program of fuel tax increases. Some of the smaller and more peripheral economies now have somewhat lower values. This actually in-cludes some of the countries that earlier were renowned for having high taxes such as Denmark. When comparing these policies one should however keep in mind that Denmark has a set of very draconian vehicle taxes so that motoring as a whole is very heavily taxed in that country. The two countries with the lowest taxes are Luxemburg and Switzerland. Luxem-burg is a very special case where at least earlier they actually appeared to be con-sciously profiteering on the tax difference and attracting motorists from surround-ing countries to fuel their cars thereby giving Luxemburg high revenues through a low tax rate. This cannot be interpreted as environmental policy; it appears to be a case of very simple local tax maximization without regard for any other principles. The case of Switzerland is quite distinct and they are currently using regulations and advanced road pricing to deter the transit traffic that is a considerable local environmental problem as well as causing considerable costs to this alp country where road maintenance is problematic. In other countries such as Austria and Ireland with low gasoline tax rates, it seems these countries have sizeable road or vehicle taxes. In countries such as Italy and the UK with high gasoline taxes these other road and vehicle taxes appear to be relatively lower. The most eye-catching and perhaps politically interesting comparison is that be-tween the US and Europe. Fuel taxes are very small in the US compared to the European average – and even to the lowest of tax rates in Europe. This is clearly correlated to higher fuel use. In the US annual gas consumption per capita is at 1300 liters. The only European country with such levels is Luxembourg while most countries are less than a third (Germany 360, France 240, UK 360, Italy 300); see also figures 1 and 2. Similar differences appear to apply to a number of other forms of fossil energy since the aggregate emissions of carbon dioxide per capita are considerably higher in the US, 5.5 tons, compared to 2.5 tons in Europe. It seems clear that if the EU had followed a similar tax policy to that in the US, then aggre-gate carbon emissions would have been substantially higher.



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Figure 1-2. Development of Price and Demand for gasoline in selected countries

Gasoline prices are in US$/Litre (converted by purchasing power index). Gasoline consumption is

in litres per capita. Source IEA 2006. In order to calculate the effect of fuel taxation we can use the elasticities discussed above to calculate what would be the equilibrium gasoline consumption for each country if it had had lower or higher prices. To do this we assume that a given country i today has consumption Qit as a response to income and prices Yit and Pit. Assume now the country i had instead had different taxes and thus prices ρit – not only today but sufficiently long for the demanded quantity to be in equilibrium, then that country’s hypothetical demand Qh would be given by (5)6 : Qh = Qit * (ρit/ Pit)ε (5)

We see in (5) that the crucial parameter is the price elasticity ε for which we use a value of -0.8 in accordance with the surveys discussed.

6 This follows from the definition of elasticity or from the models of demand presented earlier. We chose the highest current gasoline price in Europe which for 2003 was in the Netherlands.

0

0,2

0,4

0,6

0,8

1

1,2

1,4

1,6

0

200

400

600

800

1000

1200

1400

1600

US

Canada

Australia

UK

Italy



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Table 8 Effect of higher fuel price in the OECD

COUNTRY Fuel Use Price Hypo fuel use % change

AUSTRALI 13900 0,68 8315 -40,2 CANADA 29568 0,63 16717 -43,5 FRANCE 12116 1,16 11193 -7,6 GERMANY 25850 1,16 23815 -7,9 ITALY 15829 1,26 15562 -1,7 JAPAN 44566 0,77 29499 -33,8 MEXICO 25122 0,89 18716 -25,5 NETHLAND 4185 1,28 4185 0,0 SPAIN 8040 1,14 7299 -9,2 SWEDEN 4105 1,03 3449 -16,0 UK 19918 1,26 19657 -1,3 USA 384175 0,45 164678 -57,1 OECD (24)7 612487 342447 -44,1

Table 8 shows the considerable environmental importance that European environ-mental policy has had. It shows the hypothetical effect on the OECD if all coun-tries in the OECD had applied (for several decades) the price (tax) policy pursued by the European countries with the highest tax level (such as notably Italy, UK and the Netherlands). Not all individual countries are shown but the calculation has been done individually for each of them, assuming a price elasticity of -0.8 and the results then summed to the OECD level. It shows that the whole OECD emissions of carbon from transport would have been 44% lower. Had we used a price elastic-ity of -0,7 we would instead had found differences of 40% (and 36% with a long run elasticity of only -0.6). In a similar manner, another calculation shows that if all the OECD countries had, instead, pursued a tax policy such as that of the OECD country with the lowest taxes (the US), then total OECD fuel use would have been 30% higher. Sometimes we do not fully appreciate just how much gasoline tax policies have achieved: It may be true that their origin and intent were not always necessarily environmental but the fact remains that their effect on global carbon emissions is numerically quite sizeable. The hypothetical OECD total with Dutch prices is almost 60% lo-wer than the corresponding hypothetical OECD total with US prices. Thus Europe-an (and Japanese) gasoline taxes have already led to a 25% reduction compared to what they would have been – if the US and other countries also had followed the same policy the reduction would have been almost 60%8. This means that the 7 Some newer OECD countries (Hungary, Czeck republic, Poland, Korea, Slovakia and Turkey are not included. These countries have very high gasoline prices using PPP exchange rates. Thus their hypo-thetical gas consumption appears higher when using Dutch prices but this seems somewhat misleading. Including these countries changes the overall reduction from 44% to 42% so it is not vital for the conclu-sions. 8 It should be emphasized once more that our results are hypothetical rather than forecasts. If the whole World followed alternative tax policies there would of course be secondary effects on the actions of oil producers and other agents. The markets for energy resources are complicated: they exhibit both market power and political aspects. With falling/increasing demand there would be changes in these markets that are not included here.



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gasoline tax policies should be seen, alongside the Kyoto agreement, as a policy of considerable importance even for overall carbon emissions. However we believe the policy maker needs to be very careful to follow up on this lesson and not to loose its implications. Currently there are discussions of including the transport sector into the European Trading Scheme. Naturally such a scheme could in the long run have a number of potential benefits such as cost savings through equaliza-tion of marginal abatement costs. However there is also some considerable risks –particularly if the design is badly done- that the transport demand for permits dri-ves up the price of permits causing industries to question the system. There is also the opposite risk that the transport sector manages to argue (maybe illogically) that carbon and gas taxes should be phased out in return for their joining voluntarily into the ETS. This could easily have the perverse effect that the effective price of gasoline goes down instead of up- thus causing transport fuel demand to explode. The reaction shown in figures 1-2 is quite extreme. Prices vary by a factor 1:3 while consumption per capita varies by almost a factor of 5. Part of this is ex-plained by an increased use of diesel9 in Europe as is illustrated in table 9, in which we have simply added litres of diesel and gasoline (without taking any differences in energy content or transformation efficiency into account).

Table 9 Share of Diesel in the sum of total fuels (diesel + gasoline)

Australia Canada Italy UK US 960 35 50 52 43 35 1980 36 46 66 47 32 2003 45 45 64 55 32

An increased share of diesel is in itself an important mechanism of adaptation to higher fossil fuel prices since the efficiency of diesel vehicles is typically signifi-cantly higher. Disregarding the rather complex issues of differences in health ef-fects of local environmental pollution (which hinges very much on the emissions of ultrafine particles and their toxicity), it is clearly an environmental improvement to use diesel fuels. Still a high (and increasing) level of diesel use also means that the comparison made for total fuel consumption per capita between the US and Europe is less dramatic than it seems in figure. The difference is still there however. If the US uses 3,9 times as much gasoline per capita as the UK but the diesel share is lower then (as in table 9) then total fuel (diesel + gasoline) will still be 2,6 times higher in the US than in the UK. This shows that part of the adaptation to higher fuel prices is in fuel choice while part is in total fuel efficiency and in miles driven. We refrain from analyzing diesel elasticities or total fuel elasticities for the reasons mentioned above (the data may be confounded with use of industrial fuel oil and heating oil which are essentially the same but untaxed)10.

9 Possibly other fuels such as CNG, alcohol etc also play a role but as far as we have been able to ascertain, this is a very minor role. 10 For the same reasons we are cannot be absolutely certain of the accuracy of figures in table 9 but we still include this for the sake of being complete and since this may indeed be one important mechanism.



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9. Political economy obstacles to action If gasoline taxes are such a good policy the natural question to ask is why they are not used even more prominently – or even why they are not discussed more. This is a large area which cannot be fully covered here but let us mention a couple of fac-tors.

1. Gasoline taxes are historically not intended as environmental policy in-

struments and it appears that many people still do not fully understand that they actually are very potent environmental policy instruments. Their ef-fect is not decided by the intentions or psychology of the decision makers. Even if, for instance, the decision makers believed that demand was inelas-tic and that they simply were raising revenue, this would still not be true – not in the long run at least.

2. It appears that there strong lobbies acting in this area. This issue is studied by Hammar, Löfgren and Sterner (2004). They show that not only do high fuel taxes lead to lower consumption but that there is, additionally. a re-verse causation at play: Low consumption levels makes it politically easier to further raise taxes whereas high fuel consumption levels makes it politi-cally harder to further raise taxes. Thus it seems that a country may embark on one of two diametrically opposite – and self-reinforcing paths: With a low price comes high gasoline dependency and further political resistance to taxes while a policy with higher taxes weakens the auto-lobby and makes it easier to further raise taxes.

3. Many arguments are used to attack the policy of higher gas taxes. Many of these are presumably incorrect but that does not necessarily mean that they lack political effect!

4. One of these “arguments” is that a fuel tax is inflationary. Naturally it does imply that one price goes up and hence has some logical appeal. If we speak of a tax reform with a balanced budget – for instance raising a gas tax but lowering other taxes so that the net effect on the budget is neutral then it is most likely to be incorrect.

5. Similarly it is often argued that a gas tax will have regressive distributional effects. This is of course an empirical issue and it may indeed be the case in some countries such as the US where practically everyone has a car and there is little public transport. The argument is however frequently used also in low-income countries where it is bound to be incorrect since the very poor clearly use no autofuel.

6. The political economy is also heavily affected by the time perspective con-cerned. Since short run elasticities are so much smaller than the long run elasticities, the political cost (in terms of popularity) are immediately size-able while the environmental benefits accrue only in the long run – and even then they are masked by other effects so that you need an economet-rician to show them.



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10. Suggestions for further study As should be clear from this review, the area of fuel demand elasticities is already well covered and hardly a subject that needs more routine research. There is still room for innovative approaches on some topics and it would be undoubtedly be of interest to update earlier studies with new data and consider for instance the effect of new trends in the auto and fuel industries (such as the trends to use of more biofuels, diesel and gas). A number of important consequences or mechanisms would also be well worth more study. One of these concerns a better understand-ing of the political economy of policy making in this area. The most important issue is the development of a simultaneous estimation model that re-estimates de-mand elasticities taking into account, the political economy effect of consumption on tax levels and thus prices. We now know that there is a line of causality from high consumption levels to low taxes (via the impopularity of higher taxes in economies that are heavily dependent on gasoline). The question to analyse is if the estimated demand elasticities would be different when we take this political econ-omy connection on the supply side into account. Other important areas that may need work concern the income distribution effects of fuel taxes and the conse-quences of integrating policy instruments in this area with local congestion policies and global carbon policies.



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References Angelier J P and T Sterner, 1990, "Tax harmonization for petroleum products in the EC", Energy Policy July 1990.

Baltagi, B. and J. Griffin (1983): ‘‘Gasoline demand in the OECD: an application of pooling and testing procedures’’, European Economic Review, 22, 117-37.

Dahl, C. (1986): ‘‘Gasoline demand surveys’’, The Energy Journal, 7, 67-82.

Dahl, C. (1995): ‘‘Demand for transportation fuels: a survey of demand elasticities and their components’’, The Journal of Energy Literature, 1, 3-27.

Dahl, C. and T. Sterner (1991a): ‘‘Analysing gasoline demand elasticities: a sur-vey’’, Energy Economics, 13, 203-210.

Dahl, C. and T. Sterner (1991b): ‘‘A survey of econometric gasoline demand elas-ticities’’, International Journal of Energy Systems, 11, 53-76.

Drollas,L. (1984): ‘‘The demand for gasoline: further evidence’’, Energy Econom-ics, 6, Pp 71-82.

Eltony, M. (1993): ‘‘Transport gasoline demand in Canada’’ Journal of Transport Economics and Policy, 27, 193-208.

Espey, M. (1996b): ‘‘Watching the fuel gauge: an international model of automo-bile fuel economy’’, Energy Economics, 18, 93-106.

De Jong, G. and Gunn, H. (2001) Recent evidence on car cost and time elasticities of travel demand in Europe, Journal of Transport Economics and Policy, 35, pp. 137–160.

Goodwin, P. (1992) A review of new demand elasticities with special reference to short and long run effects of price changes, Journal of Transport Economics and Policy, 26, pp. 155–163.

Graham, D. and Glaister, S. (2002) The demand for automobile fuel: a survey of elasticities, Journal of Transport Economics and Policy, 36, pp. 1–26.

Graham, D. and Glaister, S. (2004) Road traffic demand: a review Transport Re-view, 24, pp. 261–274.

Hanly, M., Dargay, J. and Goodwin, P. (2002) Review of Income and Price Elas-ticities in the Demand for Road Traffic (London: Department for Transport).

Hammar, H., Å. Löfgrem and T. Sterner, (2004), ‘Political Economy Obstacles to Fuel Taxation’, Energy Journal, ISSN0195-6574, Vol. 25(3), pp 1-17.

Johansson, O. and L. Schipper (1997): ‘‘Measuring the long run fuel demand of cars: separate estimations of vehicle stock, mean fuel intensity, and mean annual driving distance’’, Journal of Transport Economics and Policy, 31, 277-292.



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Oum, T. (1989): ‘‘Alternative demand models and their elasticity estimates’’, Journal of Transport Economics and Policy, 23, 163-87.

Rouwendal, J. (1996): ‘‘An economic analysis of fuel use per kilometre by private cars’’, Journal of Transport Economics and Policy, 30, 3-14.

Sterner, T. (1990): The Pricing of and Demand for Gasoline, Swedish Transport Research Board: Stockholm.

Sterner, T. (2002), Policy Instruments for Environmental and Natural Resource Management, RFF press Washington DC. ISBN 1-891853-13-9 & ISBN 1-891853-12-0.

Sterner, T. and C. Dahl (1992): ‘‘Modelling transport fuel demand’’, in T Sterner (ed) International Energy Economics, Chapman and Hall, London, 65-79.

Sterner, T., C. Dahl and M. Franzén (1992): ‘‘Gasoline tax policy: carbon emis-sions and the global environment’’, Journal of Transport Economics and Policy, 26, 109-19.

Survey of Transport Fuel Demand Elasticites

There is a fairly widespread perception that the price

of petrol is not particularly significant for the amount

of petrol that is used – many people seem to act on the

basis that ”I need my car and I’ll pay whatever it costs

to drive it”.

Over the years a large number of empirical studies

into the price elasticity of petrol have been carried out

throughout the world. This report contains a synthesis

of the results of these studies. The results show quite

clearly that in the longer term, e.g. 10 years, the price

of petrol is of great significance for demand. A couple of

the long-term mechanisms that lie behind this are that

car owners buy more fuel-efficient cars if fuel is expensi-

ve and that people change their transport requirements.

It is also of great significance that high fuel prices give

car manufacturers a considerable incentive to develop

more energy efficient engines.

One conclusion is that tax on fuel is an effective me-

ans of long-term control to reduce climatic and other en-

vironmental impacts deriving from the transport sector.

The Swedish Environmental Protection Agency SE-106 48 Stockholm, Sweden. Visitor address: Blekholmsterrassen 36. Tel: +46 8-698 10 00, Fax: +46 8-20 29 25, e-mail: [email protected] Internet: www.naturvardsverket.se Orders Tel: +46 8-505 933 40, Fax: +46 8-505 933 99, e-mail: [email protected] Address: CM-Gruppen, Box 110 93, SE-161 11 Bromma. Internet: www.naturvardsverket.se/bokhandeln

REPORT 5586

THE SWEDISH

ENVIRONMENTAL

PROTECTION AGENCY

ISBN 91-620-5586-0

ISSN 0282-7298

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