Gasoline and diesel demand in the EU: Implications for the 2030 emission goal

Renewable and Sustainable Energy Reviews 118 (2020) 109530

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Gasoline and diesel demand in the EU: Implications for the 2030 emission goal Abenezer Zeleke Aklilu Department of Economics, Swedish University of Agricultural Sciences (SLU), Box 7013, 756 51, Uppsala, Sweden

A R T I C L E I N F O

A B S T R A C T

Keywords: Gasoline demand Diesel demand Price elasticity Income elasticity ARDL bounds The EU-28 2030 emissions policy Fuel tax

Methodologically consistent demand estimates are necessary to analyze and forecast the effect of a common fuel policy across the EU-28. This study estimates short-run and long-run price and income elasticities for gasoline and diesel demands using the ARDL Bounds approach that also tests the existence of a long-run relationship using data from 1978 to 2013. The results show that elasticity estimates between the EU-28 countries vary and the estimated long-run elasticities are higher than their short-run counterparts, which is in line with expectations based on the existing literature. The short-run and long-run income elasticities of gasoline and diesel demand are found to be more elastic than their price equivalents implying that if a charge on fuel is designed to decrease emissions by increasing the price, the charge needs to rise at a higher rate than income. An analysis of the EU’s 2030 emission and fuel consumption reduction targets using the estimated long-run elasticities shows that, with the current tax scheme, it cannot be guaranteed that emission targets will be achieved and thus a more stringent fuel tax policy is essential.

1. Introduction With the aim of controlling emissions, many countries have been implementing policies such as energy and carbon taxation, and subsidies for renewable energy. One of the most widely used policies is a fuel tax [1–3]. The effectiveness and welfare impact of a fuel tax depend on how the fuel-consuming sectors of an economy react and adjust to the mea­ sure. This responsiveness is measured by the elasticity of fuel demand. Elasticities can signal important information about the development of fuel consumption as income and prices change [4]. There is a relatively large body of literature on gasoline and diesel demand elasticity estimates, and several reviews have been carried out [5–9]. Most studies estimate gasoline or diesel demand elasticities for an individual country. Only a few studies, such as Baltagi and Griffin [10] and Sterner et al. [11], use the same methodology for a group of countries, and they cover at most 20 Organization for Economic Coop­ eration and Development (OECD) countries. Other studies, such as Brons et al. [1] and Espey [7], estimate aggregate demand elasticities for re­ gions covering several countries, and in some cases the world. The applicability of such an elasticity to a specific country is limited because it ignores differences between countries, such as habit formation, pro­ ductivity, social structure and environmental awareness [4,12,13]. Dahl [6] has reviewed the results of gasoline and diesel demand

studies and developed elasticities for more than 124 countries, including the EU-28 countries. To the best of the author’s knowledge, this has been the only study that provides gasoline and diesel demand elasticities covering the EU-28 countries. However, Dahl [6] does not estimate these elasticities, but rather compiles them from several fuel demand studies. For countries not covered by gasoline and diesel demand studies, Dahl [6] identifies systematic patterns between elasticities and other factors in the existing studies and then guesstimates. However, as Ajanovic et al. [5] and Graham and Glaister [8] show, elasticity esti­ mates for individual countries from different studies are not comparable because the estimated elasticities vary depending on the estimation methods, underlying theoretical models and data. Thus, existing elas­ ticity estimates do not allow for a consistent comparison of fuel policy effectiveness and welfare impact analysis across several countries such as the EU-28. The aim of this study is twofold. First, it provides a consistent esti­ mate of short-run and long-run price and income elasticities of gasoline and diesel demand for the EU-28 countries. The estimates are consistent in that the same econometric approach, type of data, types of variables and units of measurement are used to estimate each country’s elasticity and thus there are no methodological differences between the estimated elasticities of each of the EU-28 countries. The elimination of method­ ological differences means that differences between elasticities come

E-mail address: [email protected]. https://doi.org/10.1016/j.rser.2019.109530 Received 9 October 2018; Received in revised form 8 October 2019; Accepted 25 October 2019 1364-0321/© 2019 Elsevier Ltd. All rights reserved.

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Table 1 Brief summary of the main literature review studies. Study

Number of studies reviewed

Period

Method

Fuel type

Espey [7]

101

1929 to 1993

Meta-analysis

Gasoline

Graham & Glaister [8] Goodwin et al. [4] Graham & Glaister [15] Basso & Oum [12] Brons et al. [1]

50

1950 to 2000

Review

Gasoline

69

1929 to 1991

Review

113

1966 to 2000

Review

Gasoline/ Diesel Gasoline

100s

1980s–2000s

Critical assessment

43

Meta-analysis

Dahl [6]

300

1970s to 2000 1929 to 2006

60 13

1977 to 2010

Survey

428

1990 to 2016

Meta-analysis

Ajanovic et al. [5] Labandeira et al. [9]

Review and systematic deduction

Gasoline Diesel Gasoline Gasoline Diesel Gasoline Diesel Gasoline Diesel

Price elasticity

Income elasticity

Short-run

Long-run

Short-run

Long-run

0 to 1.36 (avg. 0.26) 0.2 to 0.3

0 to 2.72 (avg. 0.58) 0.6 to 0.8

0 to 2.92 (avg. 0.47) 0.35 to 0.55

0.05 to 2.73 (avg. 0.88) 1.1 to 1.3

0.01 to 0.57 (avg. 0.25) 2.13 to 0.59 (avg. 0.25) 0.2 to 0.3 0.13 1.36 to 0.37 (avg. 0.34) 1.65 to 0.63 (avg. 0.18) Avg. 0.16 0.20 to 0.30 0.10 0.293 0.153

0 to 1.81 (avg. 0.64) 22.00 to 0.85 (avg. 0.77) 0.6 to 0.8 0.67 2.04 to 0.12 (avg. 0.84) 61.11 to 5.89 (avg. 1.61)

0 to 0.89 (avg. 0.39) 0.0 to 1.71 (avg. 0.47) 0.3 to 0.5 0.57 to 2.14 –

0.27 to 1.71 (avg. 1.08) 0.0 to 2.68 (avg. 0.93) 0.9 to 1.3 2.16 –

2.63 to 3.00 (avg. 0.28) Avg. 1.23 0.30 to 0.50 0.39 – –

40.00 to 38.89 (avg. 1.57)

0.60 to 0.31 0.773 0.443

0.85

0.90 to 1.40 1.36 – –

Note: avg. is average.

from gasoline and diesel demand characteristics of each country but not from differences from estimation strategies and estimated elasticities are comparable across the countries. The elasticities are estimated using the Autoregressive Distributed Lag (ARDL) Bounds testing approach of Pesaran and Shin [14], which enables the existence of a long-run coin­ tegration relationship in gasoline and diesel demands to be tested while estimating short and long-run elasticities. The second aim of the paper is to examine whether the extant EU fuel tax policy is sufficient to achieve the 2030 transport emissions target using the estimated elasticities. The paper is organized as follows. Section 2 provides insights from the literature on variations in the gasoline and diesel demand elastici­ ties. The dataset used for estimation is then discussed in Section 3. Section 4 presents the econometric approach. Gasoline and diesel de­ mand elasticities are estimated, and the results are discussed in Section 5. In Section 6, the estimation results are applied to examine the fuel charges required to achieve the EU’s 2030 transport emissions goal. The final section provides concluding remarks.

two broad categories: static and dynamic models. Brons et al. [1], Espey [7] and Labandeira et al. [9] show that the choice of a static or dynamic model is a significant determinant of variations in elasticity estimates. A large number of studies base their analysis on static models, and assume that the observed demand is in a long-run equilibrium. These static models overlook the fact that impacts of price and income shocks linger for more than one period and there are adjustment lags in de­ mands. Basso and Oum [12] and Dahl [6] show that static models capture the price elasticity of an intermediate run, which falls some­ where between short run and long run unless a long-run cointegration relationship exists. When there is a long-run equilibrium relationship, the estimates from static models are close to the long-run estimates from dynamic models (Dahl, 2012) [6]. Static models have been shown to deliver the same income elasticity as long-run income elasticity from more sophisticated models. Studies that use dynamic models report separate results of short-run and long-run elasticities accounting for adjustment lags. A common approach that accounts for adjustment lags in dynamic models is to include a lagged dependent variable in the estimated model, and the first lag is most common. Basso and Oum [12] and Espey [7] suggest that this is too restrictive because it assumes a constant geometric adjustment process over time. Distributed lag and inverted-v lag models can be more flexible approaches for capturing adjustment lags. However, Basso and Oum [12] show that these models do not perform any better, they require many parameters to be estimated and lead to a multicollinearity problem. Thus, using the first lag of a dependent variable to capture the dynamic property of demand has remained a dominant practice. Basso and Oum [12] show that dynamic models of cointegration and error correction models, which take into consideration the possible non-stationarity of time series data used in fuel demand studies, provide robust estimates of short-run and long-run elasticities. Studies that use these models report relatively inelastic long-run gasoline and diesel demand elasticities compared to studies that use other dynamic models [4,7,12]. Studies that use cointegration techniques argue that this is because of a correct handling of time series data. Goodwin et al. [4] compare error correction models, partial adjustment models and inverted-v lag models and show that model choice has a significant ef­ fect on the difference in estimated elasticities. In addition, the choice of estimation methods, such as ordinary least squares and instrumental variables, is found to be a significant determinant of elasticity variations between studies by Goodwin et al. [4]. The results of Labandeira et al. [9] indicate that instrumental variable estimation generates slightly higher long-term price elasticities in absolute value.

2. Variations in gasoline and diesel demand elasticities: insights from the literature Independent studies of gasoline and diesel demand elasticities report varying results depending on the methodologies employed. This section provides an insight into the variations based on studies that have sur­ veyed the literature. Surveys of the literature are found in Basso and Oum [12], Brons et al. [1], Dahl [6], Espey [7], Goodwin et al. [4], Graham and Glaister [8,15] and Labandeira et al. [9]. In total, these eight studies review and analyze more than 1000 studies that include over 3000 estimated gasoline and diesel demand-related elasticities covering a wide geographical area for the years 1929–2016. Most of the reviews examine gasoline. For instance, among the studies reviewed by Dahl [6], 240 are gasoline-demand studies for 70 countries, 60 are diesel-demand studies for 55 countries, and 23 consider other fuel types such as natural gas and biofuels. In Goodwin et al. [4], among the sur­ veyed 69 studies, 43 consider both gasoline and diesel demand. The five of the remaining six literature review studies focus mainly on gasoline. A brief summary of the literature surveys is provided in Table 1. The survey of the studies summarized in Table 1 shows that there is a high variation between estimated elasticities. The highest variation is reported in Dahl [6] for the long-run price elasticity of gasoline ranging from 61.11 to 5.89 and long-run income elasticity of gasoline from 40.00 to 38.89. One of the main determinants of this variation is the models employed by the studies [12]. The models can be classified into 2

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Results of gasoline and diesel demand estimates also vary depending on the choice of functional forms [8,15]. The log-linear functional form has been popular in the literature. Basso and Oum [12] suggest that more flexible functional forms, such as trans-log and non-parametric approaches, could provide a better estimation when household data are used. Goodwin et al. [4] compare different functional forms (linear, log-linear, semilog, Box-Cox, and other non-linear) and show that the functional form has no significant effect on the elasticity estimate. In contrast, Espey [7] finds that functional forms are important and that the log-linear form is the most appropriate for gasoline demand estimation. Data type (time series, cross-sectional or panel data) and the interval of data (monthly, quarterly or annual) introduces a significant variation between estimated elasticities [4,9]. A comparison of elasticities esti­ mated from time series data and cross-sectional data by Basso and Oum [12] and Brons et al. [1] highlights mixed findings regarding the magnitude of the differences. However, cross-sectional data usually result in higher short-run and long-run price elasticities in absolute value and lower short-run income elasticity than estimates from time series data, while no significant difference is found for long-run income elasticity. Dahl [6] and Labandeira et al. [9] also find higher price elasticities in absolute value from cross-sectional data than from time series and panel data. Espey [7] finds a higher short-run price elasticity from cross-sectional data and a lower short-run price elasticity from panel data compared to time series data. Long-run estimates from cross-sectional, panel and time series data are not found to be signifi­ cantly different from one another by Espey [7]. Furthermore, it is not clear whether to classify the elasticities estimated from cross-sectional data as short run, long run or intermediate run. As cross-sectional data are a one-time observation across several entities, they do not have the time components of dynamic models. In addition, cross-sectional data do not take into account the time lag in consumer response to a change in price and income, and can therefore lead to specification bias. It is shown that unbiased estimation of elasticities requires dynamic models that include the time component of consumer behavior [12]. Another common practice in the literature is to use panel data for pooled estimation that assumes common elasticities across countries [12]. Goodwin et al. [4] show that pooled estimation tends to result in a lower elasticity estimate in absolute value. However, Espey [7] shows that panel data estimation results in more elastic short-run price elas­ ticity and less elastic long-run price elasticity, while no significant dif­ ference is observed between panel data and time series for income elasticity. Pooled panel data estimation, just like cross-sectional data, does not enable a country or region-specific estimation of elasticities. It has been shown by Basso and Oum [12] that the assumption of common elasticities of fuel demand across countries is implausible. The problems with pooled panel estimation can be mitigated using dummy variables and allowing the intercept in the estimation to vary across countries or regions (fixed effect estimation). However, the reviewed studies have shown that this does not lead to fully capturing regional or country-specific characteristics. The review by Basso and Oum [12] of findings of dynamic and cointegration estimation results shows that individual time series estimation is preferred and more likely to capture country-specific characteristics. Periodicity of the data is another source of variation in diesel and gasoline demand elasticity estimates but the results on its effect are mixed. Dahl [6] shows that elasticities estimated on monthly and quarterly data are significantly different from estimates on annual data, but their mean values are not significantly different from one another. The comparison of elasticities by Basso and Oum [12] estimated on yearly and seasonal data shows that there is no clear pattern in the reviewed studies. However, Goodwin et al. [4] show that lower price elasticities and higher income elasticities are reported from annual data. Espey [7] argues that data periodicity does not have an impact on long-run elasticities, but short-run elasticities from monthly data are higher because fuel demand responses occur within a month. This is

better captured by seasonal data, leading to higher estimates in absolute value. Espey [7] argues that as long as due caution is exercised, seasonal data are just as appropriate for estimating long-run adjustments as annual data. The level of data aggregation, whether household, regional or country, can have significant effects on the estimated elasticities [15]. Basso and Oum [12] show that elasticities are usually estimated from aggregate country-level data, mainly because of the relative availability of data and ease in interpreting results. However, it is argued that gas­ oline demand should be analyzed using disaggregated household data because a large share of gasoline consumption decisions are made at the household level [12]. Studies that use household data report the same price elasticities, but lower income elasticities than studies that use aggregate data. Goodwin et al. [4] compare results from aggregate, per capita and per household data and show that per capita data gives lower price elasticities and higher income elasticities than the others. In contrast, Espey [7] shows that the estimation of elasticities based on aggregate, per household, per capita or per vehicle bases does not pro­ duce a significant difference. Basso and Oum [12] show that the use of disaggregated household data highlights important determinants that would otherwise not be detected, such as household income level, location, demographic char­ acteristics such as the age of the household head, gender, race, education and the number of licensed drivers in the household. Graham and Glaister [8] report that the use of disaggregated household data can provide insights into the time dependency of consumers’ fuel demand response. In addition, disaggregated data allow for more flexibility of exogenous variables included in the estimated models than aggregate data. However it has been shown that it is difficult to determine the income effect from disaggregated data as the coefficient on income is found to be insignificant in the studies reviewed by Graham and Glaister [8]. Basso and Oum [12] point out that among the studies surveyed by Espey [7], only 5% use disaggregated household data. Thus, aggregated data have remained the main input to elasticity estimation. Despite data time length, cross-sectional width and methodological differences, there is a consensus among the studies that long-run elas­ ticities are higher than short-run elasticities for both price and income by a factor of 2–3 on average. In absolute value, income elasticity is greater than price elasticity implying that price needs to rise more quickly than income for fuel consumption to remain the same. In the short run, households react to price change by adjusting vehicle utili­ zation and in the long run by adjusting vehicle stock. The main de­ terminants of gasoline and diesel demand are the respective prices, income, number of vehicles and population. However, several other factors are shown to affect gasoline and diesel demand, such as vehicle efficiency, urbanization, female labor force participation, industrial production, seasons, weather, family demographics, price of transit, price volatility, price or availability of public transit, and speed limits. It is also shown that, generally, elasticities vary considerably depending on the underlying model, estimation technique, data type, study area and the number of exogenous factors considered in the study. 3. Data The quantity of gasoline and diesel consumed is measured by total final consumption in the transport sector from the IEA [16] for OECD countries and from the IEA [17] for non-OECD countries. The per capita consumption is calculated using data on population from the United Nations [18]. The transport sector consumes the largest percentage of the two fuels in each of the 28 EU countries. From 1960 to 2015, the average annual consumption by the transport sector in the EU was about 98% of gasoline and 51% of gasoil/diesel. Gasoline is mainly consumed by passenger vehicles and diesel is consumed by passenger vehicles and heavy vehicles such as buses, lorries and tractors. Based on the behav­ ioral differences of consumers of the two fuels, the regression results of gasoline and diesel demand may behave differently [9]. 3

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Data on the prices of gasoline and diesel are obtained from the IEA [19] for OECD countries and the IEA [20] for non-OECD countries. For Bulgaria, Estonia, Croatia, Latvia, Lithuania, Malta, Romania and Slovenia, additional data from the World Bank world development in­ dicators are used. The prices are tax-inclusive end-user prices in local currencies per liter. Real prices are calculated using CPI from the World Bank world development indicators, except for the United Kingdom for which the data are from its national statistics website listed on the United Nations Information on National Statistical Systems. Per capita income used in the estimation is real GDP per capita in local currencies obtained from the World Bank world development in­ dicators. Data on the number of vehicles per capita are collected from EUROSTAT and the individual countries’ national statistics websites listed on the United Nations Information on National Statistical Systems. It was impossible to obtain sufficient data on the number of vehicles disaggregated into diesel and gasoline-powered vehicles across the 28 EU countries. Therefore, only the total number of vehicles is used in the estimation. The total number of vehicles is sufficient for a consistent estimation, but it is not possible to infer substitution between gasoline and diesel engine-powered vehicles and utilization behaviors from the estimated coefficients [21]. Data are collected for each country from 1978 to 2013, but the sample period varies between countries. Summary statistics showing data availability, maximum, minimum, averages and standard de­ viations of the variables per country is provided in the appendix Table A1. Number of vehicles is not included in the gasoline demand estimation for Malta and the diesel demand estimation for Latvia because the available data are not sufficient for optimal choice of lag length in the estimated models.

countries, GDPe is GDP per capita, Ve is the total number of vehicles per capita and ε is the error term. Δ is difference operator and n*e;j are the

optimal lag lengths. The Bounds test tests H0 : γe;j;0 ¼ 0; α1;e;j ¼ α2;e;j ¼ α3;e;j ¼ 0; 8 e; j, no long-run relationship against H1 : γ e;j;0 6¼ 0; α1;e;j 6¼ 0; α2;e;j 6¼ 0; α3;e;j 6¼ 0. The computed F-statistic has a non-standard distribution for which Pesaran et al. [22] provide lower and higher bound critical values for the case when all regressors are I(0) and I(1), respectively, and Narayan [25] provides the small sample equivalent of the critical values. Equation (1) is estimated with or without dummies, and then the F-statistic is compared to the critical values. If the test statistic is above the critical value then it shows the existence of cointegration and a long-run equilibrium relationship. If the test statistic falls within the bounds of the critical values, the test is inconclusive. If the test statistic falls below the critical values, then the test shows that there is no long-run relationship. The variables are estimated in log form and the estimated β1;e;j;0 gives the short-run price elasticity and β2;e;j;0 gives the short-run income elasticities (see Pesaran and Shin [14]). The long-run price and income elasticities, respectively, are:

1

þ α3;e;j Ve;t

X 1

þ α1;e;j Pe;j;t

1

i¼0

þ α2;e;j GDPe;t

β2;e;j;i ΔGDPe;t i þ i¼0

β3;e;j;i ΔVe;t

i

i¼0

*

ne;j X

þ i¼1

i¼0 γ e;j;i

i¼0 β2;e;j;i

; β2;e;j;i ¼ 1

Pn*e;j

i¼0 γ e;j;i

(2)

selected using Akaike information criterion (AIC) and Schwarz criterion (SC), while controlling for serial correlation with the inclusion of dummies. The estimated models stability is checked using the cumula­ tive sum of recursive residuals (CUSUM) and CUSUM of squares (CUSUMSQ) plots. In addition, serial correlation in the models is checked using the Breusch-Godfrey Lagrange multiplier test (LM) [27]. Details of the estimation results showing the selected model, cointe­ grating coefficient, Bounds test, LM test, and stability tests are provided in the appendix. In an attempt to estimate similar demand models for all the EU-28 countries, the specification in Eq. (1) does not include diesel price in the gasoline demand estimation and gasoline price in the diesel demand estimation, and the total number of vehicles is not disaggregated into

1

*

ne;j X

X β1;e;j;i ΔPe;j;t i þ

þ

1

n*e;j

Pn*e;j

Pn*e;j

*

The standard errors for the long-run elasticities can be calculated from the standard errors of the original regression using the delta method [27]. Before proceeding to the estimation of an ARDL model and Bounds testing, the variables are tested for unit roots in order to ensure that none of the variables are I(2). Usually economic variables are susceptible to external shocks that occur outside the economic framework, such as natural disasters and unforeseen political measures. These kinds of exogenous shocks induce structural breaks in the flow of the observed data of variables. Thus, the unit root tests are conducted using Augmented Dickey-Fuller and Phillips-Perron unit root tests that do not consider structural breaks and the Zivot-Andrews unit root test, which allows for an endogenous structural break in the test. The test results are shown in Tables B1, B2 and B.3 in the appendix. The test results of the variables considered in the estimation of individual countries show that they are a mixture of I(0) and I(1), but none of them are I(2). The actual time of the structural breaks in the data set is determined using the Bai-Perron test [28]. The Bai-Perron test provides a flexible approach to determining multiple structural breaks at unknown dates. Once the break points are determined, it is accounted for in the model using impulse dummies that are one at the break year and zero else­ where. The variant types of the Bai-Perron tests sometimes select several different break points. In this case, impulse dummies are included in the initial estimation for all the break points, both individually and in a group, and their significance and their effect on the models properties are compared. Only those that are considered important are included in the final estimation. The appropriate lag lengths of n*e;j in the estimation of Eq. (1) are then

Gasoline and diesel demands are estimated using the ARDL Bounds testing approach, which was first proposed by Pesaran and Shin [14] and later extended by Pesaran et al. [22]. The ARDL Bounds testing approach fits the purposes of this study well because it captures the dynamic properties of fuel demand and tests the existence of a long-run rela­ tionship while estimating long-run and short-run elasticities. The method has several advantages. Generally, it provides unbiased long-run estimates and valid t-statistics in the presence of endogenous regressors, and it performs well in small samples [23–26]. It enables the existence of a long run-relationship among the dependent and independent variables to be tested, even if they are not integrated of the same order [22]. Other cointegration tests, such as the Engle-Granger and Johansen tests, require regressors to be integrated of the same order, specifically order one, and do not perform well when the variables are not integrated of the same order. In the ARDL Bounds setup, the variables can be I(0) and/or I(1), however the method is not applicable if any of the variables are I(2) [23], [25]. The ARDL Bounds test of long-run cointegration based on the esti­ mation of the unrestricted error correction model (ECM) is given as (see Pesaran et al. [22]): n*e;j

i¼0 β1;e;j;i

β1;e;j;i ¼

4. Econometric approach

ΔDe;j;t ¼ c0;e;j þ c1;e;j de;j þ c2;e;j te þ γe;j;0 De;j;t

Pn*e;j

*

γ e;j;i ΔDe;t i þ εe;j;t (1)

where De;j;t is fuel demand at time t with j ¼ 1,2 (1 for gasoline and 2 for diesel) and e ¼ 1; 2; …; 28 represents the 28 EU countries. d represents impulse dummies of structural breaks, t is trend variable, Pe;j , j ¼ 1, 2, P1 is the price of gasoline and P2 is the price of diesel for the 28 EU 4

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Fig. 1. Gasoline demand elasticities (in absolute value). Vertical lines indicate confidence interval.

gasoline and diesel. The reason is that because of the data demanding setup of the ARDL Bounds method, the number of observations for some countries such as Estonia, Latvia and Lithuania was not sufficient despite the considerable effort put to obtain data. Further, gasoline and diesel prices follow one another closely making disentangling their effects on each other unattainable in this study’s setting. The discussion in the next section will be based on the specification in Eq. (1) which is the most common in the literature and it provides sufficiently unbiased estimates [6].

countries where at least either the short run or the long run elasticity is significant. The countries are sorted by the magnitudes of the long-run elasticities. The full estimation results are provided in Appendix C, Table C.1 and Table C.2. The estimation results of both gasoline and diesel demand have the expected negative sign for the price and positive sign for the income long-run and short-run elasticities of all countries. The estimated shortrun price elasticity of gasoline demand is significant for 18 countries and the long-run price elasticity of gasoline demand is significant for 15 countries. The short-run income elasticity of gasoline demand is signif­ icant for 19 countries and the long-run income elasticity of gasoline demand is also significant for 16 countries. The estimated short-run price elasticity of diesel demand is significant for 17 countries and the long-run price elasticity of diesel demand is significant for 13 countries.

5. Results and discussion Figs. 1 and 2 present the price and income elasticities from the estimation results of gasoline and diesel demand for each of the EU-28 5

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Fig. 2. Diesel demand elasticities (in absolute values). Vertical lines indicate confidence interval.

The short-run income elasticity of diesel demand is significant for 20 countries and the long-run income elasticity of diesel demand is signif­ icant for 19 countries. The adjustment factors of both gasoline and diesel demands captured by the cointegrating coefficient from the estimations are between 0 and -1, except in the gasoline demand estimation of Slovakia. The adjust­ ment factors are significant in 23 countries estimations, for both fuels. In absolute value, the average adjustment factor of gasoline demand is 0.36, with the smallest being 0.08 in France and the largest 0.91 in Malta. For the diesel demand, the average adjustment factor is 0.44; the smallest adjustment factor is 0.05 for Czech Republic and the largest is 0.81 for Poland in absolute value. This means that whenever there is a shock, gasoline demand adjusts on average by 36% per period and diesel

demand adjusts by 44% per period towards a long-run equilibrium. The adjustments are in opposite directions to the shock, i.e. negative if the shock is positive and positive if the shock is negative. The adjustment factor estimates assert the theoretical stance that fuel demand gradually adjusts towards the long-run equilibrium. In the estimated demands, the existence of a long-run relationship is tested using the Bounds test. As shown in Appendix D, the Bounds test of long-run cointegration of gasoline demand shows that there is a long-run equilibrium relationship between gasoline consumption, gasoline price, income and number of vehicles in 21 of the countries. The same test for diesel demand shows that there is a long-run equilibrium relationship between diesel consumption, diesel price, income and number of vehi­ cles for 18 of the EU-28 countries. The selected models for each country 6

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for both fuel types pass the LM test of serial correlation. In addition, stability tests using CUSUM and CUSUMSQ presented in Appendix E show that all the estimated models are stable. Thus, the long-run elas­ ticities of countries where the Bounds test shows a long-run relationship can be used for policy analysis and forecasting. The reviews of Basso and Oum [12], Brons et al. [1] and Dahl [6] show the average price elasticities of gasoline demand to be close to 0.20 in the short run and 1 in the long run. In this study the estimated average gasoline price elasticity among the EU-28 countries is 0.24 in the short run and 0.82 in the long run. In line with the consensus in the existing literature, the long-run price elasticities of gasoline demand are found to be higher than the short-run price elasticities for all countries in absolute value, except in Malta and Slovakia. As the long-run elasticities depend on the adjustment factor, they are found to be higher than the corresponding short-run elasticities because the estimated adjustment factor is between 0 and -1 as expected. The short-run price elasticity varies from 0.007 in Cyprus to 0.76 in Slovakia. The smallest long-run price elasticity of gasoline demand in absolute value is found to be for Malta at 0.09 and the largest is for Spain at 2.81. The long-run price elasticity of gasoline demand shows a greater variation between countries than the short-run price elasticities. The short-run income elasticity of gasoline demand varies from the smallest estimate of 0.03 in Cyprus to the largest estimate of 1.21 in Slovakia. The long-run income elasticity of gasoline demand ranges from the least estimated value of 0.1 in Cyprus to the highest value of 5.03 in Spain, with a higher variation than their short-run counterparts. The average estimated income elasticity of gasoline demand in this study is 0.41 in the short run and 1.46 in the long run, which is not considerably different from the literature reviewed by Ajanovic et al. [5]. The long-run income elasticities are higher than the short-run elas­ ticities because of the adjustment coefficient. In addition, the long-run income elasticity estimates of gasoline demand have higher variation between countries than the short-run estimates. The elasticity variation between the countries shows that using the same elasticity across several countries for policy analysis is likely to result in biased outcomes. The bias is likely to become higher in the long run because of the higher variations in the long-run estimates. Moreover, in absolute value, both average short-run and long-run income elasticities of gasoline demand are higher than the respective price elasticities. This implies that on average gasoline demand is more income sensitive than price. In the estimated short-run price elasticity of diesel demand, in ab­ solute value, Finland has the lowest at 0.011 and the highest estimate is 0.385 for Malta. Overall, the short-run price elasticity of diesel demand varies less than the short-run price elasticity of gasoline demand. As a result of the estimated adjustment factors being within the expected range, the entire long-run price elasticity estimates of diesel demand are higher in absolute values than their short-run counterparts except for Denmark. The long-run price elasticity of diesel demand has a larger variation than the gasoline counterparts. The lowest estimated long-run price elasticity of diesel demand, in absolute values, is for Denmark at 0.02 and the highest is for the Czech Republic at 2.727. The average estimated short-run and long-run price elasticities of diesel demand ( 0.15 and 0.69 respectively) are lower than the average of the studies reviewed by Brons et al. [1]. The likely reason for the lower estimates is the consideration of the number of vehicles, demographic effects and the model specification [12,21]. The short-run income elasticity of diesel demand varies slightly more than the short-run income elasticity of gasoline demand. The long-run income elasticity of gasoline demand has a larger variation than the long-run income elasticity of diesel. The lowest short-run income elas­ ticity of diesel demand estimated is for Estonia at 0.055 and the highest is for Malta at 1.55. The average estimated income elasticities of diesel demand are 0.39 in the short run and 1.19 in the long run. The average short-run elasticity is the same as the average of the studies reviewed by Ajanovic et al. [5] but the long-run estimates are lower. In addition, both

the average short-run and long-run income elasticities of diesel demand are higher than the average short-run and long-run price elasticities in absolute value. Thus, diesel demand is more income elastic than price elastic. Moreover, both average long-run income and price elasticities of diesel demand are less elastic than their gasoline counterparts, indi­ cating that a policy affecting both gasoline and diesel would have a higher impact on average on gasoline than on diesel in the long run. In contrast to the results of Dahl [6], a simple line plot of elasticities against average real per capita incomes during the sample period and simple correlation coefficients that are shown in Appendix F do not indicate any strong systematic relationship. However, while all of the estimated elasticities appear to vary unsystematically between coun­ tries, the fitted linear trend line has a small but negative slope. The correlation coefficients are small in magnitude as well but negative, suggesting that there is a weak relationship between the estimated elasticities and average real GDP per capita. The relationship is that both short run and long run price and income elasticities of gasoline and diesel demand decrease as income increases. The trend variable included in all estimations is significant and negative for 16 countries in the gasoline demand estimations and it is significant for 6 countries and negative for 3 countries in the diesel demand estimations in the long run. In the case of gasoline, it appears that the trend variable is capturing either vehicle fuel efficiency improvement through the years or the effects of environmental policies. In the diesel case, the trend variable could be capturing fuel efficiency improvement, the effect of diesel favoring policies, the effects of envi­ ronmental policies or a combination of the three. This is further inves­ tigated by running a simple regression of gasoline and diesel consumption per vehicle on trend and country fixed effects. The result presented in Appendix G shows that a one period change is associated with an average of 3% decrease in gasoline consumption per vehicle and a 1.6% decrease in diesel consumption per vehicle across the EU-28 indicating that the trend variable is capturing fuel efficiency improve­ ments, among other things. 6. Implications for the EU 2030 emission goal The European Commission [29] reports that between 1990 and 2007, greenhouse gas emissions have increased by 33% in the transport sector and has since started to fall due to high oil prices, increasing passenger car fuel efficiency and decreasing growth of mobility. In an effort to cut pollution, the EU has set a goal to reduce greenhouse gas emissions from fuel consumption in the transport sector by about 20% by 2030 compared to emissions in 2008. In order to achieve this goal, a minimum fuel tax has been set. The European Commission [29] analysis shows that EU emission targets lead to a significant reduction in air pollution. The economic valuation of different scenarios by the report estimates that reduced mortality and better health from lower pollution rates has an estimated value of € 2.9 to € 35.5 billion. The 2003 EU Energy Tax Directive put forward a minimum tax level on gasoline and diesel for road transport in each EU member country, which has applied since 2013. However, the directive bans tax on in­ ternational aviation and shipping. The minimum tax rate imposed is € 0.359/liter on gasoline and € 0.330/liter on diesel [30]. It corresponds to 48% of the average EU member countries’ tax on diesel and 43% of the average EU member countries’ tax on gasoline in 2013. There is an ongoing discussion and proposal to raise the minimum tax rate and introduce a tax in the excluded sectors. The implication of a minimum tax rate on total fuel consumption and emissions is determined by, among other things, how consumers in each country respond. An examination follows of whether the current tax scheme is sufficient to reach the EU’s 2030 goals using the elasticity estimates in Section 5. For the countries where the Bounds test did not confirm the existence of a long-run relationship, the average EU longrun elasticity is used to project their fuel demand response to varying tax levels. 7

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Renewable and Sustainable Energy Reviews 118 (2020) 109530

Table 2 Projected emissions, targets, and required tax increment to achieve the EU’s 2030 emission goals. Country

Austria Belgium Bulgaria Croatia Cyprus Czech Republic Denmark Estonia Finland France Germany Greece Hungary Ireland Italy Latvia Lithuania Luxembourg Malta Netherlands Poland Portugal Romania Slovakia Slovenia Spain Sweden United Kingdom EU-28

Projected annual percentage change

2030 targets

2030 gasoline emission million tons CO2e

2030 diesel emission million tons CO2e

Required gasoline price change in the form of tax increase

Required diesel price change in the form of tax increase

2010 to 2020

2020 to 2030

Gasoline emission million tons CO2e

Diesel emission million tons CO2e

0.52 1.17 0.09 0.80 1.13 0.35

0.55 0.37 0.27 0.05 0.33 0.15

4.37 3.50 1.49 1.69 0.93 5.03

13.17 18.36 3.89 3.02 0.84 9.35

4.82 3.38 1.88 1.83 1.12 5.41

14.90 21.19 4.71 3.40 0.97 10.60

8% – 37% 12% 18% 7%

13% 66% 22% 78% 40% 4%

1.33 0.62 0.74 0.08 0.40 1.38 0.95 0.11 0.86 0.64 0.24 0.21 0.32 0.77 1.07 1.06 0.41 0.06 0.03 1.01 1.56 0.60

0.27 0.32 0.92 0.36 1.02 0.82 0.47 0.60 0.55 0.15 0.45 0.72 0.15 0.75 0.34 0.13 0.99 0.59 0.62 0.14 0.81 0.75

4.25 0.80 3.94 20.61 49.80 10.07 3.73 4.26 27.20 0.92 1.02 1.02 0.18 10.37 9.97 3.72 3.62 1.62 1.61 15.34 8.40 41.30

6.82 1.11 5.87 77.35 66.89 6.36 6.66 6.53 61.23 1.90 2.55 4.41 0.26 17.26 21.26 11.24 8.14 3.28 3.255 67.09 8.70 55.85

4.00 0.78 3.94 21.92 49.07 9.20 3.82 4.70 26.48 0.83 0.85 1.20 0.24 10.60 14.18 3.92 4.88 1.91 1.68 15.19 7.61 39.78

7.04 1.29 6.41 94.38 76.63 6.84 7.30 7.44 61.63 2.17 2.88 6.06 0.34 18.16 34.49 12.44 11.34 4.81 3.256 68.79 8.94 61.48

– – – 6% – – 3% 18% – – – 10% 1219% 11% 69% 4% 28% 24% 11% – – –

156% 17% 73% 31% 16% 3% 11% 37% 1% 16% 26% 22% 46% 6% 129% 14% 35% 40% 0.1% 1% 3% 21%

0.52

0.38

240.75

492.62

247.97

556.20

3%

14%

For the purpose of simplicity, it is assumed that the EU’s goal to reduce greenhouse gas emissions from the transport sector translates to reducing emissions from gasoline and diesel by 20% each in 2030 compared to emissions in 2008. Emissions from gasoline and diesel are calculated from their respective consumption using the UK Government emission conversion factors for greenhouse gas company reporting. In order to examine whether the EU’s 2030 goal could be reached with the current trends, projected emissions in 2030 were calculated. The projected emission level from gasoline and diesel consumption in 2030 in the transport sector, shown in the first two columns of Table 2, is calculated using projected annual percentage change of emissions of energy consumption in the transport sector from Capros et al. [31]. The basic assumption behind the projections of Capros et al. [31] is that the EU and member states would implement agreed policies by December 2014 and that the 2020 targets of greenhouse gas emission and renew­ able energy sources would be achieved. The projection takes into ac­ count, among other things, population growth, GDP growth, technology development, vehicle market dynamics and recent developments in fuel prices. When it is assumed that each country is expected to achieve the 2030 target individually then, as shown in Table 2, the projected 2030 emissions level from gasoline consumption in 16 countries and all of the EU-28 countries’ projected emissions from diesel consumption are higher than the target. When it is assumed that the EU-28 countries are expected to achieve the 2030 goal collectively, then the last row of Table 2 shows that the projected 2030 emission from gasoline and diesel consumption would be higher than the targets. Consequently, the required percentage change in price in the form of a tax increment in order to reach the 2030 emission goal is calculated from: � De;j;2030 * 1 þ Taxe;j * εLP;e;j ¼ 80%De;j;2008 (3)

Taxe;j ¼

80%De;j;2008 De;j;2030 εLP;e;j *De;j;2030

(4)

where εLP;e;j is long-run price elasticity of j ¼ 1 (gasoline), 2 (diesel) for the 28 EU countries e ¼ 1; 2; …; 28, Taxe;j is the required percentage change in price in the form of a tax increment. De;j;2008 and De;j;2030 are fuel demands in 2008 and 2030 respectively. When it is assumed that the EU-28 countries are expected to achieve the target independently, the required tax increment varies highly be­ tween countries for both fuel types. To keep the emissions close to the target level, the gasoline tax needs to increase by at least 3% in Hungary or by a maximum of 1219% in Malta, and the range of diesel tax increase is from 0.1% in Slovenia to 156% in Denmark. When it is assumed that the EU-28 countries are expected to achieve the goal collectively, the required tax increment is 3% on gasoline and 14% on diesel in all of the EU-28 countries. This corresponds to a rise in the minimum tax rate by € 0.0108/liter on gasoline and € 0.0462/liter on diesel. The required tax increments are calculated using long-run price elasticities, therefore the tax change needs to be implemented well ahead of the target so that the full long-run effect materializes by 2030. As shown by the wide variation in the required tax increase between countries, if the EU were able to increase the minimum tax rate for in­ dividual countries separately, it would have highly varying effects on each country in terms of tax revenue and change in consumer surplus. However, the EU can practically control only the common minimum tax rate. Countries that require a high tax increase would be able to avoid a loss in consumer surplus while the EU collectively achieves its 2030 goal.

8

A.Z. Aklilu

Renewable and Sustainable Energy Reviews 118 (2020) 109530

The estimation results have provided methodologically similar elasticities. Both short-run and long-run income elasticities of gasoline and diesel demand are found to be more elastic than their price equiv­ alents. This implies that if a policy intends to decrease emissions by increasing price, then price needs to rise faster than income, which is in line with the findings of Graham and Glaister [8]. The variation in elasticities between countries did not show any strong systematic rela­ tionship with income. This is in contrast to the argument of Dahl [6], but in line with the findings of Goodwin et al. [4], who were also not able to formulate a systematic pattern to explain the variations. Using the estimated elasticities, the final section evaluated the ach­ ievability of the EU’s 2030 environmental goal of emission reduction from the transport sector. Each country’s demand characteristics are reflected in the estimated elasticity and different tax levels for each country would be required to achieve the environmental goals. How­ ever, this approach is not practical on an EU level and the goal can be achieved by raising the common minimum tax rate on gasoline and diesel. Differences in the countries’ demand characteristics imply that imposing a common tax benefits some countries more than others. The analysis of the EU’s long-term transport emission reduction targets using the estimated long-run elasticities shows that there is no guarantee that the goal will be achieved with the current tax level, and thus a more stringent fuel tax policy is essential.

7. Conclusions The review in this study showed that gasoline and diesel demand studies cover multiple theoretical and empirical frameworks. However, a majority of the studies have been based on the premise that fuel de­ mand follows a partial adjustment structure, where it is assumed that demand converges to a long-run equilibrium. In this framework, whenever a shock happens, demand is assumed to adjust slowly over time towards the long-run equilibrium. Several studies within this framework have used time series data for a single country or panel data for multiple countries that are usually part of an economic alliance such as the OECD. Most studies focus on gasoline rather than diesel. The general consensus among fuel demand studies is that both long-run price elasticities and long-run income elasticities of gasoline and diesel de­ mand are more elastic than their short-run counterparts. Literature review studies have shown that elasticity estimates across gasoline and diesel demand studies vary based on the underlying model, estimation technique, data type, study area and number of exogenous factors considered in the study [12]. When these varying elasticities are used for analyses, they will give results that differ because of the dis­ similarities between the elasticities. Gasoline and diesel demand elas­ ticities that are consistent in terms of estimation methodology are needed to analyze and forecast the effects of EU-level fuel policy across each member state. However, no study has estimated such elasticities. The present study sets out to address this literature gap and provide comparable gasoline and diesel demand elasticities across the EU-28 that will eliminate the dissimilarities. To this end, short-run and long-run price and income elasticities of gasoline and diesel demand are estimated using the ARDL Bounds testing approach of Pesaran et al. [22]. For the estimation of each country’s elasticities, a time series data on gasoline and diesel consumption and price, number of vehicles and population is used. There are caveats in relation to the differences in data availability between the countries. The time length of the data for some countries is longer than others. Due to a lack of sufficient data, the number of vehicles in the estimation is not disaggregated into diesel and gasoline-powered vehicles. Therefore, it is not possible to observe fuel-switching behavior from the estimation.

Funding This study was supported by the funding from Responses of European Forests and Society to Invasive Pathogens (RESIPATH) (Grant No. 20357000) and the BaltCoast EU/BONUS project (Grant No. 21397000) funded jointly from the European Union’s Seventh Program for research, technological development and demonstration, and from the Swedish Environmental Protection Agency. Declarations of competing interest None.

9

10

0.49 0.43 0.01 0.24 0.01 0.28 0.51 0.45 0.54 0.44 0.53 0.50 0.23 0.59 0.44 0.01 0.01 1.83 0.01 0.38 0.19 0.28 0.00 0.19 0.63 0.32 0.70 0.57

0.28 0.15 0.001 0.14 0.00 0.13 0.33 0.20 0.38 0.16 0.30 0.19 0.16 0.32 0.18 0.001 0.00 0.83 0.001 0.31 0.11 0.10 0.00 0.10 0.30 0.13 0.37 0.28

max min

0.39 0.31 0.001 0.20 0.01 0.20 0.44 0.29 0.46 0.33 0.44 0.35 0.19 0.42 0.33 0.01 0.00 1.36 0.01 0.34 0.14 0.19 0.00 0.14 0.46 0.24 0.60 0.46

0.06 0.09 0.001 0.03 0.00 0.05 0.05 0.05 0.05 0.10 0.07 0.10 0.02 0.08 0.07 0.001 0.00 0.33 0.001 0.02 0.02 0.06 0.00 0.03 0.09 0.05 0.07 0.08

mean sd

Gasoline consumption per capita in kiloliters

35 35 35 24 35 36 35 24 36 36 36 36 36 36 36 24 24 36 36 36 35 35 35 36 24 36 36 36

N 1.18 1.35 0.01 0.50 0.01 0.48 1.54 0.61 1.20 0.92 1.03 0.79 0.46 1.06 0.65 0.01 0.01 5.41 0.01 0.60 0.41 0.63 0.01 0.35 1.14 0.92 1.33 0.53

0.37 0.82 0.001 0.23 0.00 0.29 0.75 0.27 0.88 0.69 0.66 0.30 0.19 0.40 0.49 0.001 0.00 1.58 0.001 0.37 0.14 0.17 0.00 0.18 0.40 0.31 0.57 0.34

max min 0.74 1.14 0.01 0.38 0.01 0.40 0.96 0.42 0.95 0.82 0.83 0.55 0.32 0.71 0.55 0.01 0.01 3.17 0.01 0.49 0.22 0.40 0.00 0.26 0.78 0.57 0.76 0.43

0.29 0.15 0.001 0.10 0.00 0.06 0.20 0.11 0.07 0.08 0.08 0.14 0.08 0.22 0.05 0.001 0.00 1.29 0.001 0.06 0.08 0.17 0.00 0.05 0.20 0.21 0.19 0.06

mean sd

Diesel consumption per capita in kiloliters 35 35 35 24 35 36 35 24 36 36 36 36 36 36 36 24 24 36 36 36 35 35 35 36 24 36 36 36

N 2.74 3.21 978994 194.03 3.66 0.11 0.41 13.94 3.32 3.21 3.09 159 0.36 3.65 8.36 45.26 65.17 2.61 1.75 3.47 362653 38.81 86333 2.73 7.08 8.22 0.50 4.74

max 0.65 0.65 0.51 0.12 0.70 0.02 0.10 0.04 1.02 0.74 0.24 0.83 0.00 0.87 1.04 1.01 0.53 0.53 0.54 0.61 0.22 0.91 0.36 0.85 0.86 0.85 0.10 1.52

min 1.52 1.74 188159 11.94 1.59 0.05 0.25 2.04 1.93 1.79 1.38 18.60 0.09 1.89 2.87 4.08 4.16 1.39 1.32 1.78 34639 5.97 4016 1.65 2.07 2.42 0.23 2.60

mean 0.56 0.69 369353 41.73 0.66 0.02 0.09 2.82 0.64 0.69 0.90 40.99 0.12 0.62 1.85 9.31 13.64 0.56 0.31 0.74 90691 9.78 17980 0.48 1.32 1.77 0.09 0.82

sd

Real price of diesel in local currencies per liter

Note: max is maximum, min is minimum, sd is standard deviation and N is the number of observations.

Austria Belgium Bulgaria Croatia Cyprus Czech Republic Denmark Estonia Finland France Germany Greece Hungary Ireland Italy Latvia Lithuania Luxembourg Malta Netherlands Poland Portugal Romania Slovakia Slovenia Spain Sweden United Kingdom

Country

Table A.1Data summary statistics.

A. Summary statistics

Appendix

37 36 28 25 35 36 36 22 36 36 37 36 36 36 36 23 22 36 24 36 36 36 23 36 22 36 36 36

N 2.71 2.74 553345 165.57 1.68 0.11 0.38 13.94 2.74 2.76 2.89 52.55 0.20 2.88 3.68 37.53 55.86 2.44 1.72 2.82 241769 13.11 71098 2.93 6.00 4.07 0.33 4.78

max 0.59 0.39 0.44 0.10 0.21 0.01 0.05 0.04 0.74 0.57 0.22 0.68 0.00 0.80 0.84 0.74 0.40 0.39 0.45 0.40 0.17 0.65 0.28 0.39 0.81 0.71 0.08 1.48

min 1.34 1.28 106351 10.20 0.83 0.04 0.16 1.92 1.49 1.39 1.16 7.75 0.05 1.67 1.84 3.46 3.61 1.08 1.04 1.27 23104 2.86 3293 1.34 1.88 1.71 0.17 2.56

mean 0.54 0.66 208764 35.60 0.44 0.03 0.10 2.83 0.56 0.64 0.79 14.10 0.06 0.53 0.71 7.71 11.68 0.54 0.39 0.65 60456 3.18 14806 0.62 1.09 0.80 0.07 0.89

sd

Real price of gasoline in local currencies per liter 37 36 28 25 35 36 36 22 36 36 37 36 36 36 36 23 22 36 24 36 36 36 23 36 22 36 36 36

N 60.12 56.48 1802 2.33 35.61 0.89 9.81 24.83 62.13 52.65 55.17 143.56 0.23 73.98 47.46 17.77 38.86 124.53 23.70 66.13 1714 44.62 3028 21.56 32.22 40.32 7.04 83.17

max 16.89 16.83 1.31 1.00 12.16 0.26 3.21 0.34 23.73 17.95 8.50 17.30 0.03 15.57 23.07 4.21 3.43 25.13 6.88 18.00 1.57 12.67 1.15 6.64 0.00 15.21 3.42 31.45

min 36.79 36.12 439.98 1.52 21.80 0.42 6.38 11.05 38.79 35.42 31.45 41.05 0.10 40.27 36.64 8.34 9.83 69.99 14.13 39.93 217.61 22.93 816.53 13.19 21.74 28.20 5.15 53.19

mean

12.60 10.68 633.35 0.39 6.36 0.20 1.77 6.11 10.43 9.00 15.61 33.09 0.07 17.96 6.48 3.71 7.54 28.79 5.11 13.41 449 7.58 1180 5.67 6.78 6.41 1.06 12.48

sd

Real GDP per capita in 1000 local currencies 36 36 34 25 36 36 36 22 36 36 36 36 37 36 36 23 22 36 36 36 37 36 34 23 23 36 36 36

N

0.54 0.50 0.38 0.35 0.42 0.46 0.40 0.48 0.57 0.51 0.57 0.50 0.32 0.43 0.63 0.43 0.60 0.74 0.60 0.50 0.49 0.56 0.22 0.48 0.52 0.48 0.47 0.47

0.29 0.31 0.09 0.14 0.13 0.17 0.27 0.09 0.34 0.32 0.28 0.09 0.11 0.11 0.29 0.10 0.06 0.32 0.31 0.31 0.07 0.13 0.03 0.27 0.22 0.18 0.35 0.26

max min

0.43 0.41 0.23 0.25 0.27 0.30 0.33 0.32 0.42 0.43 0.45 0.25 0.22 0.29 0.50 0.25 0.33 0.57 0.49 0.39 0.22 0.36 0.11 0.35 0.37 0.36 0.42 0.38

0.08 0.06 0.08 0.08 0.09 0.09 0.04 0.10 0.07 0.05 0.10 0.13 0.07 0.11 0.11 0.10 0.17 0.14 0.08 0.06 0.13 0.16 0.06 0.07 0.11 0.10 0.04 0.07

mean sd

35 34 29 23 35 36 34 25 36 36 36 35 36 36 36 24 26 36 24 36 35 34 33 22 34 36 36 36

N

Number of vehicles per capita

A.Z. Aklilu

Renewable and Sustainable Energy Reviews 118 (2020) 109530

Renewable and Sustainable Energy Reviews 118 (2020) 109530

A.Z. Aklilu

B. Unit root tests Table B.1Results of Augmented Dickey-Fuller unit root test. Country

lg Level

Austria Belgium Bulgaria Croatia Cyprus Czech Republic Denmark Estonia Finland France Germany Greece Hungary Ireland Italy Latvia Lithuania Luxembourg Malta Netherlands Poland Portugal Romania Slovakia Slovenia Spain Sweden United Kingdom

0.692 1.662 1.618 1.179 0.377 1.188 0.711 3.781* 0.82 3.840 2.147 2.357 1.789 0.658 0.460 0.429 1.423 0.375 3.074** 2.244 1.175 1.480 3.388** 2.166 0.057 1.531 3.984 3.262

ld 1st D. 5.088* 4.134* 6.349* 3.319** 2.208** 8.221* 1.984** 4.448* 3.511* 2.577*** 3.772* 3.431*** 3.89* 2.428** 2.328** 2.622*** 3.645* 3.309** 8.655* 4.883* 5.451* 1.392* 6.660*** 6.305* 2.611*** 3.294** 3.771** 2.675***

Dec. Level I(1) 0.531 I(1) 1.293 I(1) 1.96 I(1) 0.537 I(1) 2.648*** I(1) 0.831 I(1) 2.393 I(0) 0.94 I(1) 3.754* I(1) 1.317 I(1) 3.124** I(1) 1.424 I(1) 1.422 I(1) 0.988 I(1) 1.532 I(1) 0.807 I(1) 1.137 I(1) 0.592 I(0) 3.028** I(1) 2.26 I(1) 0.79 I(1) 2.379** I(0) 1.746 I(1) 0.954 I(1) 1.666 I(1) 1.45 I(1) 2.923** I(1) 0.404

lpg 1st D. 5.363* 5.078* 6.532* 4.89* 5.471* 4.337* 5.038* 4.338* 4.544* 3.876* 6.656* 4.270*** 4.413* 3.539* 4.113* 2.983** 2.739*** 3.179** 9.112* 6.027* 3.406** 2.141 6.178* 5.587* 6.026* 3.333** 5.069* 4.47*

Dec. Level I(1) I(1) I(1) I(1) I(0) I(1) I(1) I(1) I(0) I(1) I(0) I(1) I(1) I(1) I(1) I(1) I(1) I(1) I(0) I(1) I(1) I(0) I(1) I(1) I(1) I(1) I(0) I(1)

lpd 1st D.

1.358 1.137 1.55 5.589* 1.056 0.799 1.504 2.303 1.659 1.555 1.102 4.076* 1.141 1.688 2.549 7.797* 9.441* 1.144 2.207 1.175 2.297 3.5* 11.12* 1.23 3.572* 2.282 1.763 1.771

Dec. Level

4.888* 3.96* 3.772* 1.842 3.775* 5.096* 4.084* 3.761* 4.093* 3.962* 4.849* 3.11** 4.263* 4.364* 4.101* 3.088** 13.255* 4.105* 6.541* 4.534* 3.268** 3.213** 2.952** 4.945* 3.291** 3.711* 4.261* 4.361*

I(1) I(1) I(1) I(0) I(1) I(1) I(1) I(1) I(1) I(1) I(1) I(0) I(1) I(1) I(1) I(0) I(0) I(1) I(1) I(1) I(1) I(0) I(0) I(1) I(0) I(1) I(1) I(1)

lgdp 1st D.

1.365 0.903 1.566 5.461* 0.973 1.203 0.407 2.332 1.459 1.243 1.015 2.85*** 1.334 1.516 1.674 6.802* 7.597* 0.925 0.964 1.057 2.311 2.423 11.983* 1.883 3.42** 1.715 1.295 1.546

4.924* 4.475* 3.907* 1.845 3.753* 4.904* 4.385* 3.8* 4.299* 4.044* 5.378* 3.6* 5.083* 4.788* 4.43* 3.039** 10.623* 4.717* 4.96* 4.871* 3.162** 3.623* 3.113** 4.885* 3.739* 3.952* 5.15* 4.401*

Dec. Level

lvt 1st D.

I(1) 0.955 I(1) 0.818 I(1) 0.904 I(0) 0.739 I(1) 1.166 I(1) 0.001 I(1) 0.863 I(1) 1.876 I(1) 1.222 I(1) 0.884 I(1) 1.067 I(0) 3.69* I(1) 1.114 I(1) 0.383 I(1) 1.998 I(1) 1.78 I(1) 2.949** I(1) 0.65 I(1) 1.008 I(1) 0.788 I(1) 2.419 I(1) 2.611*** I(0) 0.921 I(1) 0.691 I(0) 1.816 I(1) 1.419 I(1) 1.417 I(1) 1.194

4.627* 3.943* 4.073* 3.035** 4.512* 4.051* 4.175* 4.113* 4.072* 4.008* 5.475* 2.771*** 3.348** 4.131* 4.151* 2.201 8.72* 3.704* 4.446* 4.348* 3.236** 3.214** 2.201 4.199* 3.684* 3.624* 4.468* 3.991*

Dec. Level I(1) I(1) I(1) I(1) I(1) I(1) I(1) I(1) I(1) I(1) I(1) I(0) I(1) I(1) I(1) I(1) I(0) I(1) I(1) I(1) I(1) I(0) I(1) I(1) I(1) I(1) I(1) I(1)

3.408** 3.456* 0.903 0.584 1.363 0.971 0.475 2.631*** 2.673 3.296** 2.055 1.22 3.43* 7.661* 6.386* 1.271 2.549 6.155* 3.101** 0.136 0.356 3.477* 1.229 0.334 1.574 6.362* 1.948 3.909*

1st D. 3.786* 2.253 6.19* 2.803*** 6.348* 6.925* 2.629*** 3.119** 3.212** 2.685*** 4.127* 5.28* 3.493* 2.118 2.609*** 4.554* 2.721*** 2.841*** 4.883* 5.764* 8.008* 0.813 3.38** 4.983* 3.81* 2.411 2.054 2.832***

Dec. I(0) I(0) I(1) I(1) I(1) I(1) I(1) I(0) I(1) I(0) I(1) I(1) I(0) I(0) I(0) I(1) I(1) I(0) I(0) I(1) I(1) I(0) I(1) I(1) I(1) I(0) I(1) I(0)

Note: lg is log of gasoline consumption per capita, ld is log of diesel consumption per capita, lpg is log of real price of gasoline, lpd is log of real price of diesel, lgdp is log of real GDP per capita, lvt is log of number of vehicles per capita, 1st D. is first difference of the variables and Dec. is decision of the level of integration. *, ** and *** denote the significance of the test statistic at 1%, 5% and 10% respectively. Table B.2Results of Phillips–Perron unit root test. Country

lg Level

Austria Belgium Bulgaria Croatia Cyprus Czech Republic Denmark Estonia Finland France Germany Greece Hungary Ireland Italy Latvia Lithuania Luxembourg Malta Netherlands Poland Portugal Romania Slovakia Slovenia Spain Sweden United Kingdom

ld 1st D.

Dec. Level

0.552 5.083* I(1) 0.599 1.471 4.139* I(1) 1.46 1.296 –7.115* I(1) 1.941 1.661 3.315** I(1) 0.694 0.548 2.182** I(1) 2.634*** 1.109 7.889* I(1) 1.217 0.615 2.122** I(1) 2.401 3.785* 4.477* I(0) 1.074 1.321 3.703* I(1) 3.709* 2.935 3.565 ** I(1) 1.649 1.228 3.9* I(1) 3.099** 1.883 3.443*** I(1) 1.467 2.172 3.807* I(1) 1.592 1.190 2.447** I(1) 1.05 0.495 2.252** I(1) 1.842 0.906 2.696*** I(1) 1.167 1.531 3.754* I(1) 1.467 0.996 3.329** I(1) 0.722 3** 9.855* I(0) 3.119** 2.349 5.018* I(1) 2.406 1.389 5.58* I(1) 0.354 1.427 3.782** I(1) 1.845 3.338** 6.834* I(0) 1.589 2.178 6.37* I(1) 1.113 0.614 2.692*** I(1) 1.69 0.325 3.389** I(1) 1.302 1.553 2.851* I(1) 3.048** 1.681 2.573*** I(1) 0.611

lpg 1st D. 5.484* 5.123* 6.499* 4.925* 5.586* 4.5* 5.147* 4.326* 4.688* 3.841* 6.674* 4.921*** 4.644* 3.676* 4.295* 3.142** 2.643*** 3.127** 9.736* 6.024* 3.337** 2.091 6.277* 5.6* 5.926* 3.473* 5.196* 4.584*

Dec. Level I(1) I(1) I(1) I(1) I(0) I(1) I(1) I(1) I(0) I(1) I(0) I(1) I(1) I(1) I(1) I(1) I(1) I(1) I(0) I(1) I(1) I(1) I(1) I(1) I(1) I(1) I(0) I(1)

1.57 1.56 1.484 5.215* 1.444 1.093 1.835 2.442 1.929 1.928 1.182 3.451* 1.092 1.959 2.53 6.133* 6.547* 1.548 2.077 1.38 1.931 3.125** 10.85* 1.428 3.131** 2.292 2.007 2.073

lpd 1st D. 4.848* 3.952* 3.751* 1.868 3.743* 5.142* 4.031* 3.765* 3.96* 3.972* 4.827* 3.185** 4.356* 4.326* 4.048* 3.593* 18.992* 4.1* 6.845* 4.477* 3.277** 3.163** 3.562* 4.902* 3.309** 3.75* 4.197* 4.307*

Dec. Level I(1) I(1) I(1) I(0) I(1) I(1) I(1) I(1) I(1) I(1) I(1) I(0) I(1) I(1) I(1) I(0) I(0) I(1) I(1) I(1) I(1) I(0) I(0) I(1) I(0) I(1) I(1) I(1)

1.565 1.223 1.51 5.018* 1.401 1.303 0.643 2.464 1.759 1.607 1.052 2.60*** 1.298 1.806 1.953 5.235* 5.545* 1.208 0.946 1.264 1.94 2.32 10.673* 1.881 3.102** 1.962 1.512 1.803

lgdp 1st D. 4.875* 4.472* 3.893* 1.873 3.762* 4.986* 4.379* 3.806* 4.189* 4.049* 5.363* 3.637* 5.174* 4.756* 4.388* 3.699* 14.542* 4.714* 4.967* 4.864* 3.162** 3.584* 3.801* 4.876* 3.78* 3.938* 5.121* 4.352*

Dec. Level I(1) I(1) I(1) I(0) I(1) I(1) I(1) I(1) I(1) I(1) I(1) I(0) I(1) I(1) I(1) I(0) I(0) I(1) I(1) I(1) I(1) I(1) I(0) I(1) I(0) I(1) I(1) I(1)

1.023 1.113 0.983 1.006 1.292 0.344 1.107 2.059 1.446 1.239 1.092 3.088** 1.171 0.598 2.33 0.769 2.984** 0.89 1.052 0.946 2.044 2.631*** 0.434 0.68 1.983 1.91 1.705 1.439

lvt 1st D. 4.579* 3.917* 4.1* 3.027** 4.527* 4.094* 4.147* 4.119* 3.979* 4.006* 5.478* 2.84*** 3.345** 4.141* 4.136* 3.262** 11.05* 3.691* 4.391* 4.31* 3.258** 3.161** 2.809* 4.182* 3.684* 3.708* 4.401* 3.986*

Dec. Level I(1) I(1) I(1) I(1) I(1) I(1) I(1) I(1) I(1) I(1) I(1) I(0) I(1) I(1) I(1) I(1) I(0) I(1) I(1) I(1) I(1) I(0) I(1) I(1) I(1) I(1) I(1) I(1)

2.88** 6.580*** 0.793 0.677 1.488 1.201 0.056 2.389 1.771 2.497 1.898 1.267 3.003** 5.924* 5.476*** 1.27 2.242 4.797* 3.367** 0.236 0.338 3.562*** 1.085 0.507 1.406 4.629* 2.953*** 3.02**

1st D. 3.84* 2.343 6.502* 2.767*** 6.537* 8.054* 2.736*** 3.131** 3.23** 2.729*** 4.11* 5.264* 3.412** 1.834 3.264** 4.556* 2.698*** 2.761*** 4.871* 5.799* 8.494* 0.175 3.304** 4.97* 3.789* 2.326 2.330 2.788***

Dec. I(0) I(0) I(1) I(1) I(1) I(1) I(1) I(1) I(1) I(1) I(1) I(1) I(0) I(0) I(0) I(1) I(1) I(0) I(0) I(1) I(1) I(0) I(1) I(1) I(1) I(0) I(0) I(0)

Note: lg is log of gasoline consumption per capita, ld is log of diesel consumption per capita, lpg is log of real price of gasoline, lpd is log of real price of diesel, lgdp is log of real GDP per capita, lvt is log of number of vehicles per capita, 1st D. is first difference of the variables and Dec. is decision of the level of integration. *, ** and *** denote the significance of the test statistic at 1%, 5% and 10% respectively.

11

12

United Kingdom

Sweden

Spain

Slovenia

Slovakia

Romania

Portugal

Poland

Netherlands

Malta

Luxembourg

Lithuania

Latvia

Italy

Ireland

Hungary

Greece

Germany

France

Finland

Estonia

Czech Republic Denmark

Cyprus

Croatia

Bulgaria

Belgium

Austria

Country

1st D.

3.084(0) 6.094*(0) 1986 1992 2.851(0) 5.182**(0) 1986 1984 4.953**(0) 6.765*(1) 1991 1991 3.524(2) 5.189**(0) 2008 1994 3.755(1) 5.71***(0) 1995 2003 3.103(1) 5.374**(2) 1992 1997 1.126(1) 4.646**(0) 1990 1984 4.162(0) 9.884*(1) 1997 1996 2.464(1) 4.867**(0) 1985 1984 3.047(0) 6.188*(0) 2003 1997 2.68(0) 1986 6.072*(0) 2000 0.787(2) 5.772*(0) 1992 1990 3.571(1) 5.169**(1) 1986 1990 3.58(2) 2008 5.176**(0) 2007 1.835(2) 5.649*(0) 1989 1993 2.192(1) 5.681*(0) 2003 2008 5.07**(1) 5.406*(2) 2006 2004 2.611(1) 5.291**(0) 1989 1989 5.794*(0) 9.79*(0) 1986 1986 4.101(0) 6.816*(0) 1993 1989 3.174(2) 4.863**(2) 1991 2000 0.877(1) 5.499*(0) 1987 1985 4.297(0) 7.049*(0) 2001 1987 4.818**(0) 6.669*(0) 1983 1992 2.022(0) 6.022*(0) 1994 1997 2.57(2) 1986 4.848**(1) 1986 0.324(0) 2007 4.911**(0) 1984 1.82(0) 1986 5.806*(0) 1984

Level

lg 3.482(2) 2007

1st D.

7.934*(0) 1985 I(1) 4.113(2) 2006 7.752*(0) 1984 I(0) 4.698***(0) 7.828*(0) 1991 1991 I(1) 1.595(2) 2009 5.593*(2) 2008 I(1) 2.956(0) 2001 7.187*(0) 2007 I(1) 3.979(2) 1990 6.642*(0) 1998 I(1) 3.617(0) 1988 6.254*(0) 1984 I(1) 4.537(0) 2001 6.36*(0) 1994 I(1) 5.885*(2) 4.179(1) 1991 1985 I(1) 4.807**(1) 3.193(2) 1991 1989 I(1) 4.747***(2) 7.587*(0) 1991 1984 I(1) 0.474(1) 2007 6.557*(0) 2007 I(1) 3.368(2) 1991 6.145*(0) 1997 I(1) 3.914(2) 2008 5.416*(0) 1985 I(1) 2.197(2) 2001 5.478*(0) 1995 I(1) 4.98**(2) 3.668(2) 2000 1996 I(0) 3.346(0) 2004 5.332**(0) 1995 I(1) 5.010***(1) 4.197(0) 2003 2006 I(0) 6.807*(1) 5.574*(2) 1986 1986 I(1) 3.882(0) 1999 7.343*(0) 1984 I(1) 4.907**(1) 4.054(0) 1989 1995 I(1) 0.546(1) 2006 4.510**(0) 1987 I(1) 3.056(0) 2006 6.237*(2) 2003 I(0) 3.474(0) 1991 6.364*(0) 1991 I(1) 3.733(2) 2009 6.602*(0) 1998 I(1) 4.874**(1) 3.829(2) 2006 2006 I(1) 4.749***(2) 4.307(2) 1987 2000 I(1) 9.143*(1) 6.781*(0) 2008 1984

I(1)

Dec. Level

ld 3.691(1) 1997

1st D.

Dec. Level

lpd 1st D.

Dec. Level

lgdp 1st D.

Dec. Level

lvt 1st D.

Dec.

5.351*(0) I(1) 3.538(1) 1996 5.241**(0) I(1) 3.985(1) 1986 5.46*(0) 1986 I(1) 5.962*(0) 6.483*(0) I(0) 1986 1986 2002 2002 I(1) 3.888(1) 2003 4.861**(0) I(1) 3.485(1) 1997 5.187**(0) I(1) 4.255(1) 1990 5.101**(0) I(1) 2.514(1) 1998 4.697**(0) I(1) 1985 1986 1985 1986 I(0) 2.387(0) 1991 5.283**(0) I(1) 2.502(0) 1991 5.43*(0) 1991 I(1) 3.178(1) 1991 5.396*(0) I(1) 7.778*(0) 6.519*(0) I(0) 1991 1991 2006 2008 I(1) 23.552*(2) 8.956*(2) I(0) 19.151*(2) 8.427*(2) I(0) 3.658(1) 2003 4.723***(0) I(1) 2.548(2) 2009 4.874**(0) I(1) 2003 2001 2003 2001 2002 1994 I(1) 2.725(0) 2004 4.450(0) 2002 I(1) 4.593***(1) 5.52*(0) 2001 I(0) 3.374(1) 1988 5.236**(0) I(1) 3.393(0) 1986 6.974*(0) I(1) 2001 1986 1988 I(1) 3.172(0) 2003 6.656*(0) I(1) 2.817(0) 1996 4.58***(2) I(1) 3.729(1) 2003 4.873**(0) I(1) 4.695***(0) 6.644*(2) I(0) 2002 2002 2001 1991 1986 I(1) 4.337(1) 2003 6.233*(1) I(1) 3.718(1) 2003 5.441*(0) I(1) 4.571(1) 1990 5.408*(0) I(1) 5.835*(2) 5.009**(2) I(0) 1986 1986 1985 1993 1995 I(1) 3.696(0) 1996 5.978*(0) I(1) 3.776(0) 1996 5.987*(0) I(1) 4.369(2) 2002 8.064*(2) I(1) 4.549(2) 2001 5.060**(0) I(1) 1998 1998 1999 2003 I(0) 4.063(1) 2006 5.968*(1) I(1) 3.628(1) 2006 5.598*(1) I(1) 2.778(1) 1998 4.849**(0) I(1) 3.44(2) 1991 5.151**(0) I(1) 1986 1991 1994 1998 I(0) 4.104(1) 2003 5.3**(0) 1985 I(1) 4.194(1) 2003 5.197**(0) I(1) 4.125(1) 1990 5.43*(0) 1985 I(1) 4.075(1) 1996 4.884*(0) I(1) 1985 2002 I(0) 4.438(1) 1990 5.474*(0) I(1) 3.257(0) 1990 5.913*(0) I(1) 5.118**(0) 6.386*(0) I(0) 3.27(0) 2007 4.808**(0) I(1) 1986 1986 1990 1986 1995 I(1) 2.481(0) 2003 5.885*(0) I(1) 2.666(0) 2003 5.087**(0) I(1) 4.921**(1) 4.663***(0) I(0) 2.274(0) 2000 6.158*(0) I(1) 1986 1986 2002 2008 1997 I(1) 2.519(2) 1991 6.701*(0) I(1) 2.536(0) 2003 7.126*(0) I(1) 2.989(1) 2002 5.234**(0) I(1) 3.559(1) 2008 4.884**(0) I(1) 2002 2002 2001 2000 I(1) 3.247(1) 2006 4.937**(0) I(1) 2.85(0) 2003 5.325**(0) I(1) 5.108**(2) 5.798*(0) I(0) 2.875(0) 2008 5.546*(0) I(1) 1985 2003 1990 1985 1994 I(1) 3.511(1) 2003 5.153**(0) I(1) 3.283(1) 2003 5.06**(0) I(1) 4.058(1) 1993 5.14**(0) I(1) 3.205(0) 1985 6.113*(0) I(1) 1986 1986 1986 1993 I(0) 8.687*(0) 3.84(0) 2006 I(0) 8.176*(0) 3.846(0) 2006 I(0) 5.919*(1) 3.613(0) 2008 I(0) 2.851(0) 2009 6.051*(0) I(1) 2005 2002 2003 1995 I(1) 4.641***(1) 12.119*(0) I(0) 4.992**(1) 5.069**(2) I(0) 4.547(1) 2009 7.516*(0) I(1) 1.665(0) 1996 6.670*(0) I(1) 1999 1996 2009 1999 2008 2001 I(0) 3.951(1) 2003 4.907**(0) I(1) 3.577(1) 2003 5.4*(0) 1986 I(1) 4.508(1) 1990 5.045**(0) I(1) 1.262(0) 1987 7.154*(0) I(1) 1986 1985 1987 I(0) 3.412(0) 2004 8.615*(1) I(1) 5.097**(0) 6.792*(0) I(0) 4.009(1) 1999 5.051**(0) I(1) 7.077*(2) 8.892*(0) I(0) 2004 2004 2004 1986 1997 1996 I(1) 3.903(1) 1997 5.169**(0) I(1) 3.704(1) 1997 5.357*(0) I(1) 3.954(1) 1990 5.443*(0) I(1) 5.168**(0) 6.2*(0) 1994 I(0) 1986 1986 1986 1991 I(0) 6.208(2) 2000 5.524*(0) I(1) 4.997**(1) 4.21(0) 1987 I(0) 5.433*(1) 4.252(0) 1994 I(0) 6.132*(0) 8.844*(0) I(0) 1991 1989 1989 1991 1986 I(1) 3.433(1) 2003 4.74***(0) I(1) 3.171(0) 2003 4.848**(0) I(1) 3.667(1) 2003 5.131**(0) I(1) 0.742(2) 2007 4.473**(0) I(1) 1986 2003 1986 1999 I(1) 7.563*(0) 3.69(0) 1995 I(0) 7.633*(0) 3.674(0) 1995 I(0) 5.164**(1) 3.997(0) 1990 I(0) 3.496(1) 2002 4.834**(0) I(1) 1997 1997 1991 2007 I(1) 3.258(0) 2003 6.268*(0) I(1) 2.694(0) 1994 7.003*(0) I(1) 3.398(0) 2003 5.696*(0) I(1) 2.903(0) 2008 6.153*(0) I(1) 2003 2003 2009 2006 I(1) 4.125(0) 1999 5.013**(0) I(1) 4.446(0) 1999 5.155**(0) I(1) 3.672(0) 2003 5.194**(0) I(1) 1.213(0) 1993 5.002**(0) I(1) 2001 2002 2002 1989 I(0) 3.714(1) 2003 4.94**(0) I(1) 3.539(1) 2003 4.712***(0) I(1) 4.585***(1) 5.032**(0) I(0) 1.245(0) 1987 4.959**(0) I(1) 1986 1986 1996 1985 1987 I(0) 3.45(1) 2003 4.893**(0) I(1) 2.835(0) 2004 5.383*(0) I(1) 4.135(1) 1993 5.062**(0) I(1) 5.303**(1) 4.433(2) 1990 I(0) 1985 2002 1985 1986 I(0) 4.32(1) 2002 4.735***(0) I(1) 3.927(1) 2002 4.905**(0) I(1) 4.145(1) 2002 4.799***(0) I(1) 1.676(1) 2006 4.642***(0) I(1) 1986 2008 1985 2006

I(1)

Dec. Level

lpg

Table B.3Results of Zivot-Andrews endogenous structural break unit root test.

A.Z. Aklilu

Renewable and Sustainable Energy Reviews 118 (2020) 109530

A.Z. Aklilu

Renewable and Sustainable Energy Reviews 118 (2020) 109530

Note: lg is log of gasoline consumption per capita, ld is log of diesel consumption per capita, lpg is log of real price of gasoline, lpd is log of real price of diesel, lgdp is log of real GDP per capita, lvt is log of number of vehicles per capita, 1st D. is first difference of the variables and Dec. is decision of the level of integration. *, ** and *** denote the significance of the test statistic at 1%, 5% and 10% respectively. Lag order is given in parentheses and the break year is given below each test statistic. C. Estimation results

Table C.1Gasoline demand estimation results. Country

Price Short run

Austria

Long run

Income

Short run Long run Short run

0.277*** 1.099*** (0.057) (0.388) 0.375*** 0.704** (0.127) (0.332)

0.444*** (0.097) 0.862*** (0.208)

Bulgaria

0.131* (0.064)

0.484*** 1.116** (0.112) (0.391)

Croatia

0.289*** 0.619 (0.091) (0.417)

0.342** (0.145)

0.731 (0.614)

Cyprus

0.007 (0.018)

0.026 (0.046)

0.095 (0.182)

Czech Republic Denmark

0.12 0.425 (0.133) (0.402) 0.223*** 0.923*** (0.03) (0.044)

0.374 (0.248) 0.351*** (0.057)

0.233 (0.745) 1.552*** (0.097)

Estonia

0.201 1.649 (0.148) (3.353) 0.298*** 1.394 (0.041) (0.974) 0.285** 0.784 (0.101) (0.532)

0.194 (0.157) 0.564*** (0.075) 0.485** (0.171)

2.03 (4.086) 2.323* (1.349) 1.156 (1.12)

Germany

0.204*** 0.778** (0.057) (0.319)

0.319*** 1.216** (0.086) (0.46)

Greece

0.168*** 0.946*** (0.02) (0.219) 0.075 0.212 (0.05) (0.218)

0.294*** (0.039) 0.154* (0.086)

1.658*** (0.406) 0.435 (0.396)

0.196*** (0.054) 0.163 (0.145) 0.067 (0.127) 0.501*** (0.096)

0.407*** (0.095) 0.363 (0.25) 0.207 (0.241) 0.79*** (0.168)

1.238*** (0.099) 2.538** (0.991) 0.426 (0.321) 2.196*** (0.693)

Belgium

Finland France

Hungary Ireland Italy Latvia Lithuania Luxembourg

0.553** (0.213)

0.025 (0.074)

0.51*** (0.083) 0.76* (0.427) 0.137 (0.185) 1.393*** (0.43)

0.432*** 1.477** (0.087) (0.606)

Malta

0.514*** 2.73** (0.126) (1.109)

0.021 (0.152) 0.201 (0.136)

0.188 (0.403) 0.182** (0.071)

0.911 (0.563) 0.746** (0.354)

Poland

0.431*** (0.098) 1.259*** (0.278) 0.155 (0.241) 0.621*** (0.047) 0.366 (0.358) 2.808** (1.202)

0.356*** (0.118) 0.653*** (0.103) 0.079 (0.241) 1.21*** (0.267) 0.178 (0.153) 0.849*** (0.25)

0.764*** (0.179) 2.192*** (0.477) 0.128 (0.4) 0.979*** (0.117) 0.459 (0.458) 5.035** (2.087)

Portugal Romania Slovakia Slovenia Spain

Trend

Long run Short run

1.759*** 0.642*** 2.542* (0.605) (0.161) (1.251) 1.945** 0.52 (0.773) (0.888)

0.089 (0.156) Netherlands 0.06 (0.039) 0.169** (0.063) 0.333*** (0.056) 0.118 (0.177) 0.767*** (0.139) 0.142 (0.119) 0.52*** (0.168)

Vehicle

0.001 (0.003) 1.174 (1.497)

Long run

Constant

Cointegrating coefficient

Dummies

0.003 (0.014) 0.037** (0.014)

3.584*** 0.252*** [2003] 0.013 (1.074) (0.078) (0.029) 0.083*** 5.228 (3.057) 0.443*** [2002] [2007] (0.009) (0.153) 0.006 0.048 (0.038) (0.039) 0.257 0.394 (0.4) 0.037*** 0.086** 2.202** 0.434*** [1998] [2002] (0.162) (0.012) (0.033) (0.753) (0.131) 0.348*** 0.133* (0.113) (0.064) 0.169 0.361 0.012* 0.026*** 0.089 (2.337) 0.467** [2000] 0.021 [2008] (0.225) (0.358) (0.007) (0.006) (0.176) (0.024) 0.094** (0.035) 0.196** 0.723*** 0.0004 0.001 0.535 0.271** [1983] 0.018 [1993] [1998] (0.084) (0.244) (0.002) (0.008) (0.483) (0.113) (0.03) 0.031 0.031 (0.029) (0.028) 0.832** 2.948 0.018 0.065 2.069 0.282 (0.173) [1992] 0.18* (0.39) (2.013) (0.013) (0.061) (1.324) (0.098) 0.256 0.484 0.008*** 0.021** 3.564*** 0.397*** [1989] [1996] [2007] (0.182) (0.465) (0.002) (0.007) (0.652) (0.065) 0.029** 0.011 0.014* (0.009) (0.012) (0.006) 0.067 5.638 0.034** 0.282 0.112 0.122 (0.278) (0.361) (12.314) (0.015) (0.596) (1.376) 0.208 0.868 0.006*** 0.057 1.561** 0.099 (0.106) (0.344) (2.001) (0.001) (0.056) (0.578) 1.223** 4.448 0.001 0.006 0.855 0.084 (0.17) [1991] [2009] 0.001 (0.528) (7.219) (0.009) (0.091) (1.973) 0.011 (0.019) (0.019) 0.054 0.207 0.006 0.024*** 1.593** 0.263* (0.13) [1989] 0.025 [2007] (0.109) (0.569) (0.004) (0.006) (0.636) (0.026) 0.028 (0.023) 0.03 (0.064) 0.167 0.007* 0.037 1.609*** 0.177*** (0.44) (0.003) (0.029) (0.346) (0.053) 0.328** 0.927** 0.011** 0.032* 1.628 0.354*** [2008] (0.152) (0.338) (0.004) (0.017) (1.143) (0.124) 0.008 (0.046) 0.235 0.06 0.015*** 0.04*** 2.304*** 0.384*** (0.386) (0.406) (0.005) (0.014) (0.824) (0.071) 0.502* 3.508 0.016*** 0.112* 2.27 0.143* [2009] 0.001 (0.246) (2.609) (0.003) (0.056) (2.377) (0.077) (0.037) 0.003 0.371 0.028** 0.059** 0.079 0.485* (0.11) (0.279) (0.013) (0.02) (1.61) (0.241) 0.205 0.569 0.035** 0.099** 5.866*** 0.36*** [1995] [2001] [2004] (0.211) (0.599) (0.015) (0.045) (1.563) (0.093) 0.308*** 0.134* 0.134* (0.073) (0.069) (0.069) 0.429 1.369 0.017 0.06*** 6.501*** 0.292*** [2003] 0.07* [2009] (0.695) (2.106) (0.011) (0.018) (2.282) (0.092) (0.037) 0.104** (0.038) – – 0.037* 0.041** 5.496 0.914*** (0.02) (0.016) (5.79) (0.201) 0.189 0.634 0.002 0.006 0.724 0.298*** [1991] (0.166) (0.59) (0.003) (0.012) (0.811) (0.102) 0.048* (0.024) 0.374* 0.803* 0.037*** 0.079*** 0.761 0.466*** (0.216) (0.46) (0.012) (0.026) (0.903) (0.094) 0.124 0.47 0.011*** 0.042*** 3.985*** 0.264*** (0.089) (0.369) (0.003) (0.006) (0.852) (0.095) 0.347 0.565 0.01 (0.023) 0.017 (0.03) 1.039 0.614** (0.458) (0.654) (2.193) (0.25) 0.184 0.149 0.042** 0.034*** 3.594** 1.236*** (0.288) (0.152) (0.017) (0.01) (1.494) (0.199) 0.092 0.237 0.016 0.041 0.994 0.388** (0.536) (1.635) (0.013) (0.033) (2.024) (0.162) 0.851 4.451 0.004 0.011 17.688** 0.364 (0.208) [1999] 0.026 (0.631) (3.218) (0.019) (0.038) (6.532) (0.028) (continued on next page)

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(continued ) Country

Price Short run

Sweden

Long run

0.379*** 2.291* (0.042) (1.103)

United 0.064* Kingdom (0.036)

0.465* (0.247)

Income

Vehicle

Trend

Constant

Cointegrating coefficient

Dummies

2.17** (0.867)

0.092** (0.041)

[1993] 0.09*** (0.016)

Short run Long run Short run

Long run Short run

Long run

0.546*** 3.702* (0.077) (1.861)

0.211 (0.383)

3.577* (2.028)

0.01*** (0.002)

0.105** (0.049)

0.087 (0.065)

0.754*** (0.259)

1.303 (0.965)

0.007* (0.004)

0.054*** 0.245 (0.012) (0.902)

0.63 (0.456)

0.138* (0.071)

[1999] 0.017 (0.011)

Note: ***, **, * indicate significance at 1%, 5% and 10%, respectively. White – heteroskedastic robust standard errors are in parentheses. Dummy years are in square brackets. Table C.2 Diesel demand estimation results. Country

Price

Income

Vehicle

Trend

Constant

Cointegrating Dummies coefficient

2.541 (2.169) 1.987 (1.583) 1.52 (1.372)

0.259*** (0.083) 0.65*** (0.132) 0.344** (0.134) 0.477** (0.217) 0.492*** (0.13)

Short run

Long run

Short run Long run Short run

Long run

Short run

Long run

0.224** (0.1) 0.131** (0.06) 0.017 (0.108)

0.865 (0.537) 0.201** (0.081) 0.381 (0.389)

0.416** (0.173) 0.335** (0.146) 0.258 (0.191)

1.607* (0.829) 0.516*** (0.178) 0.749 (0.686)

0.279 (2.053) 1.252** (0.557) 0.402 (0.477)

0.001 (0.006) 0.009* (0.005) 0.004 (0.018)

0.003 (0.029) 0.022 (0.012) 0.016 (0.064)

0.356*** (0.106) 0.269*** (0.082)

0.373** 0.089 (0.13) (0.192) 0.547*** 0.292 (0.147) (0.176)

1.179*** (0.196) 0.136 (0.194)

0.021*** 0.066** (0.006) (0.024) 0.006 0.019 (0.006) (0.015)

2.773* (1.29) 1.584* (0.777)

Czech 0.149*** 2.876 Republic (0.033) (4.74) Denmark 0.3* 0.02 (0.128) (0.251)

0.331*** (0.087) 0.802* (0.385)

2.038 (2.791) 0.919* (0.389)

0.273 (0.216) 2.306 (1.189)

16.286 (23.319) 2.62 (3.076)

0.03* (0.015) 0.047 (0.043)

0.613 (0.89) 0.105 (0.086)

2.481*** 0.052 (0.715) (0.095) 2.3 (6.172) 0.804* (0.32)

Estonia

Austria Belgium Bulgaria Croatia Cyprus

0.027 0.143* (0.023) (0.068) 0.161*** 0.328*** (0.047) (0.044)

0.072 (0.401) 1.557 (1.558) 0.138 (0.275)

0.092 (0.107) 0.011 (0.03) 0.1*** (0.034)

0.271 (0.329) 0.117*** (0.032) 0.586 (0.42)

0.055 (0.114) 0.068 (0.045) 0.205*** (0.052)

0.208 (0.317) 0.109* (0.058) 1.199* (0.681)

0.751** (0.302) 0.287*** (0.069) 0.216 (0.367)

2.205 (1.48) 0.458*** (0.098) 1.266 (2.569)

0.042*** (0.013) 0.006*** (0.001) 0.002 (0.004)

0.164* (0.074) 0.015*** (0.003) 0.014 (0.031)

0.418 (1.683) 3.945*** (0.772) 1.195 (1.106)

0.627*** (0.102) 0.171** (0.072)

Germany

0.122 (0.072)

0.26 (0.171)

0.176* (0.095)

0.375 (0.237)

0.772** (0.307)

0.228 (0.451)

0.001 (0.004)

0.001 (0.012)

1.235 (1.515)

0.469*** (0.129)

Greece

0.255 (0.151)

2.371 (2.394)

0.422 (0.267)

4.351 (4.465)

0.036 (0.367)

0.127 (1.319)

0.012 (0.019)

0.054 (0.126)

10.054*** 0.283 (3.257) (0.315)

Hungary

0.169** (0.073)

0.758 (0.667)

0.317** (0.114)

1.421* (0.827)

0.207 (0.206)

0.93 (0.862)

0.001 (0.009)

0.006 (0.044)

1.11 (1.023)

Ireland

0.247*** 0.329*** (0.057) (0.048) 0.109* 1.233** (0.06) (0.442)

0.579*** (0.123) 0.195 (0.121)

0.771*** 0.32*** (0.075) (0.11) 2.044** 0.072 (0.861) (0.332)

0.425** (0.206) 2.117** (0.755)

0.014*** 0.034*** 0.395 (0.004) (0.01) (0.63) 0.008*** 0.044*** 3.772** (0.002) (0.013) (1.562)

0.751*** (0.138) 0.227** (0.078)

0.053 (0.122)

0.583* (0.274)

0.622** (0.254)

–

0.01 (0.007)

0.027 (0.016)

2.169 (1.729)

0.546** (0.245)

0.339*** 0.442*** (0.097) (0.138) Luxembourg 0.29*** 1.214* (0.085) (0.678)

0.761*** (0.196) 0.385*** (0.129)

0.994*** 0.723** (0.224) (0.257) 1.613* 2.542** (0.817) (1.007)

0.944*** 0.046** (0.141) (0.016) 0.207 0.001 (1.339) (0.008)

0.106** (0.027) 0.005 (0.035)

7.335*** (1.915) 2.253 (1.865)

0.766*** (0.143) 0.239* (0.12)

Malta

0.385** (0.133)

0.483** (0.185)

1.552*** 1.946** (0.497) (0.8)

0.145 (0.389)

0.182 (0.624)

0.028* (0.016)

0.063 (0.043)

12.575** (4.792)

0.798*** (0.257)

Netherlands

0.161** (0.073)

0.445 (0.272)

0.315** (0.148)

0.873* (0.499)

0.392 (0.506)

1.086 (1.105)

0.005 (0.008)

0.019 (0.029)

0.611 (1.668)

0.361** (0.14)

Poland

0.093 0.298*** (0.063) (0.043) 0.197*** 0.678** (0.064) (0.307)

0.245* (0.119) 0.353** (0.129)

0.678*** 0.258 (0.082) (0.186) 1.217** 0.249 (0.581) (0.222)

0.32** (0.136) 0.523* (0.257)

0.015 (0.01) 0.008 (0.005)

0.034 (0.019) 0.036 (0.023)

0.079 (0.923) 1.267 (1.443)

0.807*** (0.145) 0.29*** (0.095)

Finland France

Italy Latvia

0.097 (0.188)

Lithuania

Portugal

–

0.34 (0.202)

0.223 (0.179)

[2000] 0.123 (0.12) [1986] [2008] 0.079 (0.06) 0.053 (0.049) [1985] 0.105* (0.048)

[1990] [1995] [2005] 0.124** 0.198** 0.02 (0.04) (0.057) (0.033)

[1994] 0.047* (0.023) [1989] 0.137*** (0.049) [1991] 0.077 (0.083) [1990] 0.114* (0.066)

[2001] 0.05* (0.025) [2008] 0.107* (0.055) [1996] 0.106 (0.084) [2005] 0.143** (0.063)

[1984] 0.088*** (0.026) [1994] 0.281** (0.104)

[1998] 0.137 (0.083)

[2005] 0.051 (0.054) [1993] 0.121 (0.143) [2009] 0.075 (0.071)

[1998] 0.078 (0.148)

[2007] 0.017 (0.024)

[2004] 0.152* (0.083)

[2009] 0.02 (0.096)

[2001] 0.231 (0.149)

[2009] 0.002 (0.129)

(continued on next page)

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Table C.2 (continued ) Country

Price

Income

Vehicle

Trend

Constant

Cointegrating Dummies coefficient

0.013 (0.021)

0.473 (2.098)

0.773*** (0.251)

0.018 (0.022)

0.139 (0.394)

3.125 (3.506)

0.151 (0.449)

1.309 (0.782) 0.766 (1.132) 2.999*** (0.777)

0.004 (0.017) 0.001 (0.004) 0.005* (0.003)

0.009 (0.034) 0.001 (0.033) 0.019 (0.01)

3.35 (3.484) 2.807** (1.113) 1.13 (1.351)

0.802*** (0.265) 0.132** (0.061) 0.374*** (0.108)

0.117 (1.347)

0.001 (0.002)

0.009 (0.014)

0.287 (1.204)

0.197* (0.11)

Short run

Long run

Short run Long run Short run

Long run

Short run

Long run

Romania

0.079 (0.089)

0.102 (0.134)

0.237 (0.171)

0.307 (0.258)

0.308 (0.464)

0.399 (0.427)

0.01 (0.018)

Slovakia

0.03 (0.162)

2.142 (7.891)

0.503* (0.261)

3.328 1.174** (10.614) (0.47)

1.069 (2.479)

Slovenia

0.124 0.155 (0.154) (0.212) 0.229*** 1.729** (0.059) (0.748) 0.158* 0.421** (0.077) (0.199)

0.304 (0.214) 0.357*** (0.096) 0.396*** (0.122)

0.379 (0.278) 2.697** (1.11) 0.697* (0.409)

1.05 (0.955) 0.101 (0.175) 1.123*** (0.371)

0.139 (0.083)

0.707 (0.81)

0.023 (0.184)

Spain Sweden

United 0.086** Kingdom (0.041)

0.437 (0.481)

[2007] 0.036 (0.113) [2003] 0.267** (0.088)

[2006] 0.067 (0.07)

[2002] 0.008 (0.044)

[2009] 0 (0.049)

Note: ***, **, * indicate significance at 1%, 5% and 10%, respectively. White – heteroskedastic robust standard errors are in parentheses. Dummy years are in square brackets.

D. Estimated model summary, cointegration test and serial correlation test

Table D.1Summary of gasoline demand estimation. Country

Estimated Model (lg lpg lgdp lvt)

Bounds test Test statistic value

Critical value bounds 10%

Austria Belgium Bulgaria Croatia Cyprus Czech Republic Denmark Estonia Finland France Germany Greece Hungary Ireland Italy Latvia Lithuania Luxembourg Malta Netherlands Poland Portugal Romania Slovakia Slovenia Spain Sweden United Kingdom

ARDL(1, 0, ARDL(1, 4, ARDL(2, 2, ARDL(1, 0, ARDL(2, 0, ARDL(2, 0, ARDL(3, 4, ARDL(3, 0, ARDL(1, 2, ARDL(2, 2, ARDL(1, 0, ARDL(1, 0, ARDL(2, 0, ARDL(1, 0, ARDL(1, 4, ARDL(2, 0, ARDL(2, 0, ARDL(1, 0, ARDL(1, 1, ARDL(2, 0, ARDL(1, 1, ARDL(1, 0, ARDL(1, 1, ARDL(1, 0, ARDL(1, 0, ARDL(3, 4, ARDL(1, 2, ARDL(1, 0,

0, 0) 0, 0) 0, 1) 0, 0) 0, 0) 1, 0) 4, 4) 2, 1) 2, 1) 3, 1) 0, 0) 0, 0) 0, 0) 2, 2) 0, 0) 0, 1) 0, 0) 1, 2) 3) 3, 0) 0, 0) 1, 0) 0, 0) 0, 0) 0, 0) 4, 4) 2, 2) 0, 1)

5%

1%

F-statistic

I(0)

I(1)

I(0)

I(1)

I(0)

I(1)

LM(1)

8.729*** 9.085*** 6.101** 19.469*** 2.701 1.939 16.259*** 2.37 7.739*** 1.633 7.934*** 70.118*** 3.868 11.989*** 18.786*** 2.271 17.802*** 9.557*** 5.824** 5.708** 7.678*** 34.166*** 3.469 9.584*** 6.634** 4.296* 49.04*** 5.027**

3.29 3.378 3.378 3.378 3.29 3.29 3.378 3.378 3.29 3.29 3.29 3.29 3.29 3.29 3.29 3.378 3.378 3.29 3.77 3.29 3.29 3.29 3.378 3.378 3.378 3.29 3.29 3.29

4.176 4.274 4.274 4.274 4.176 4.176 4.274 4.274 4.176 4.176 4.176 4.176 4.176 4.176 4.176 4.274 4.274 4.176 4.535 4.176 4.176 4.176 4.274 4.274 4.274 4.176 4.176 4.176

3.936 4.048 4.048 4.048 3.936 3.936 4.048 4.048 3.936 3.936 3.936 3.936 3.936 3.936 3.936 4.048 4.048 3.936 4.535 3.936 3.936 3.936 4.048 4.048 4.048 3.936 3.936 3.936

4.918 5.09 5.09 5.09 4.918 4.918 5.09 5.09 4.918 4.918 4.918 4.918 4.918 4.918 4.918 5.09 5.09 4.918 5.415 4.918 4.918 4.918 5.09 5.09 5.09 4.918 4.918 4.918

5.654 5.666 5.666 5.666 5.654 5.654 5.666 5.666 5.654 5.654 5.654 5.654 5.654 5.654 5.654 5.666 5.666 5.654 6.428 5.654 5.654 5.654 5.666 5.666 5.666 5.654 5.654 5.654

6.926 6.988 6.988 6.988 6.926 6.926 6.988 6.988 6.926 6.926 6.926 6.926 6.926 6.926 6.926 6.988 6.988 6.926 7.505 6.926 6.926 6.926 6.988 6.988 6.988 6.926 6.926 6.926

0.483 1.176 0.27 0.523 1.905 1.098 0.047 0.935 0.02 0.068 0.104 0.21 0.602 0.033 0.061 0.822 0.678 2.569 0.01 0.02 1.032 2.177 2.551 2.636 0.156 2.58 2.338 0.037

Note: lg is log of gasoline consumption per capita, lpg is log of real price of gasoline, lgdp is log of real GDP per capita, lvt is log of number of vehicles per capita. (lg lpg lgdp lvt) indicates the order of the variables’ lag in the estimated model. ***, ** and * denote significance at 1%, 5% and 10%, respectively. LM(1) is Breusch-Godfrey Lagrange multiplier test of up to 1 lag. Critical values are generated by Eviews 10.

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Table D.2Summary of diesel demand estimation. Country

Estimated Model (lg lpg lgdp lvt)

Bounds test Test statistic value

Critical value bounds 10%

Austria Belgium Bulgaria Croatia Cyprus Czech Republic Denmark Estonia Finland France Germany Greece Hungary Ireland Italy Latvia Lithuania Luxembourg Malta Netherlands Poland Portugal Romania Slovakia Slovenia Spain Sweden United Kingdom

ARDL(2, 0, ARDL(1, 0, ARDL(1, 1, ARDL(2, 1, ARDL(1, 0, ARDL(1, 0, ARDL(1, 5, ARDL(2, 0, ARDL(4, 1, ARDL(1, 0, ARDL(1, 0, ARDL(1, 2, ARDL(3, 0, ARDL(1, 0, ARDL(1, 4, ARDL(3, 0, ARDL(1, 0, ARDL(1, 0, ARDL(1, 0, ARDL(1, 0, ARDL(2, 1, ARDL(1, 0, ARDL(1, 0, ARDL(2, 1, ARDL(1, 0, ARDL(1, 0, ARDL(1, 0, ARDL(1, 0,

0, 0) 0, 1) 0, 0) 1, 1) 0, 1) 1, 4) 4, 5) 1, 0) 0, 0) 0, 0) 0, 1) 3, 0) 0, 0) 0, 0) 3, 2) 2) 0, 0) 0, 4) 0, 0) 0, 0) 1, 0) 0, 4) 0, 0) 0, 1) 0, 0) 0, 0) 1, 0) 0, 0)

5%

1%

F-statistic

I(0)

I(1)

I(0)

I(1)

I(0)

I(1)

LM(1)

5.798** 6.282** 3.081 7.01*** 5.729** 6.276** 5.638** 2.575 11.796*** 9.249*** 3.939 8.293*** 3.286 12.172*** 11.541*** 2.625 18.475*** 4.497* 6.019** 3.76 6.729** 5.806** 2.425 3.607 2.444 11.805*** 3.946 4.797*

3.29 3.29 3.378 3.378 3.29 3.29 3.378 3.378 3.29 3.29 3.29 3.29 3.29 3.29 3.29 3.77 3.378 3.29 3.378 3.29 3.29 3.378 3.378 3.378 3.378 3.29 3.29 3.29

4.176 4.176 4.274 4.274 4.176 4.176 4.274 4.274 4.176 4.176 4.176 4.176 4.176 4.176 4.176 4.535 4.274 4.176 4.274 4.176 4.176 4.274 4.274 4.274 4.274 4.176 4.176 4.176

3.936 3.936 4.048 4.048 3.936 3.936 4.048 4.048 3.936 3.936 3.936 3.936 3.936 3.936 3.936 4.535 4.048 3.936 4.048 3.936 3.936 4.048 4.048 4.048 4.048 3.936 3.936 3.936

4.918 4.918 5.09 5.09 4.918 4.918 5.09 5.09 4.918 4.918 4.918 4.918 4.918 4.918 4.918 5.415 5.09 4.918 5.09 4.918 4.918 5.09 5.09 5.09 5.09 4.918 4.918 4.918

5.654 5.654 5.666 5.666 5.654 5.654 5.666 5.666 5.654 5.654 5.654 5.654 5.654 5.654 5.654 6.428 5.666 5.654 5.666 5.654 5.654 5.666 5.666 5.666 5.666 5.654 5.654 5.654

6.926 6.926 6.988 6.988 6.926 6.926 6.988 6.988 6.926 6.926 6.926 6.926 6.926 6.926 6.926 7.505 6.988 6.926 6.988 6.926 6.926 6.988 6.988 6.988 6.988 6.926 6.926 6.926

1.531 0.155 0.056 0.024 2.32 1.676 1.102 2.232 0.866 0.009 0.509 0.135 1.592 0.097 0.177 1.195 0.013 1.879 2.565 1.262 0.218 0.56 0.078 0.02 0.342 2.162 0.002 0.645

Note: lg is log of gasoline consumption per capita, lpd is log of real price of diesel, lgdp is log of real GDP per capita, lvt is log of number of vehicles per capita. (lg lpd lgdp lvt) indicates the order of the variables’ lag in the estimated model. ***, ** and * denote significance at 1%, 5% and 10%, respectively. LM(1) is Breusch-Godfrey Lagrange multiplier test of up to 1 lag. Critical values are generated by Eviews 10.

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E. Stability tests of estimated models E.1. Gasoline demand CUSUM and CUSUMSQ plots, respectively

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. (continued).

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E.2. Diesel demand CUSUM and CUSUMSQ plots, respectively

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. (continued).

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F. Relationship between average income and elasticities

Figure F.1Plot of the relationship between gasoline demand elasticities and average real GDP per capita during the sample period. Countries are ordered in ascending order of real GDP per capita.

Figure F.2Plot of the relationship between diesel demand elasticities and average real GDP per capita during the sample period. Countries are ordered in ascending order of real GDP per capita. Table F.1Correlation between estimated elasticities and average real GDP per capita during the sample period. Price

Gasoline Diesel

Income

Short run

Long run

0.193 0.277

0.225 0.194

Short run

Long run 0.193 0.182

0.237 0.206

G. Trend and fuel efficiency

Table G.1Estimation results of gasoline and diesel consumption per vehicle on trend and country fixed effects Variables

Dependent variables Log of gasoline consumption per vehicle

Trend Country fixed effects Belgium Bulgaria Croatia Cyprus Czech Republic Denmark Estonia Finland France Germany Greece Hungary Ireland Italy

Log of diesel consumption per vehicle

0.031*** (0.001)

0.016*** (0.001)

0.200*** (0.033) 4.076*** (0.074) 0.099*** (0.020) 3.504*** (0.047) 0.270*** (0.047) 0.394*** (0.044) 0.217*** (0.053) 0.221*** (0.044) 0.177*** (0.031) 0.121*** (0.017) 0.563*** (0.026) 0.017 (0.018) 0.567*** (0.030) 0.317*** (0.034)

0.541*** (0.070) 4.234*** (0.103) 0.016 (0.067) 3.723*** (0.077) 0.157** (0.076) 0.590*** (0.069) 0.098 (0.088) 0.368*** (0.066) 0.189*** (0.069) 0.171** (0.068) 0.429*** (0.080) 0.051 (0.089) 0.459*** (0.068) 0.355*** (0.067) (continued on next page)

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(continued ) Variables

Dependent variables Log of gasoline consumption per vehicle

Log of diesel consumption per vehicle

Latvia Lithuania Luxembourg Malta Netherlands Poland Portugal Romania Slovakia Slovenia Spain Sweden United Kingdom Constant

3.534*** (0.072) 4.056*** (0.126) 0.993*** (0.036) 4.096*** (0.026) 0.005 (0.034) 0.209*** (0.040) 0.427*** (0.022) 3.553*** (0.081) 0.523*** (0.041) 0.375*** (0.033) 0.268*** (0.030) 0.465*** (0.033) 0.305*** (0.022) 7.356*** (0.021)

3.850*** (0.098) 4.329*** (0.109) 1.197*** (0.086) 4.336*** (0.071) 0.237*** (0.069) 0.354*** (0.078) 0.304*** (0.071) 3.548*** (0.121) 0.737*** (0.076) 0.206*** (0.077) 0.024 (0.076) 0.097 (0.071) 0.349*** (0.069) 7.682*** (0.067)

Observations R-squared

900 0.980

900 0.977

Notes: ***, ** and * denote significance at 1%, 5% and 10%, respectively. Robust standard errors in parentheses. Austria is benchmark.

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