Investigating the causal relationship between transport infrastructure, transport energy consumption and economic growth in Tunisia

Renewable and Sustainable Energy Reviews 56 (2016) 988–998

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Renewable and Sustainable Energy Reviews journal homepage: www.elsevier.com/locate/rser

Investigating the causal relationship between transport infrastructure, transport energy consumption and economic growth in Tunisia Houda Achour a, Mounir Belloumi b,n a b

Higher Institute of Transport and Logistics of Sousse, LAMIDED, University of Sousse, Tunisia College of Administrative Sciences, Najran University, Saudi Arabia and LAMIDED, University of Sousse, Tunisia

art ic l e i nf o

a b s t r a c t

Article history: Received 12 May 2015 Received in revised form 18 July 2015 Accepted 11 December 2015

This study investigates the causal relationships between transportation infrastructure (rail and road), the transport value added, gross capital formation, transportation energy consumption and transport CO2 emissions in Tunisia over the period of 1971–2012. We use the Johansen multivariate cointegration approach, generalized impulse response functions and variance decomposition technique to examine the effect of transportation infrastructure on economic growth and the environment. These findings show the existence of unidirectional long run causality running from transport value added, road transport related energy consumption, transport CO2 emissions and gross capital formation to road infrastructure. It also finds a unidirectional long run causality running from railway infrastructure, the transport value added, gross capital formation and transport CO2 emissions to rail transport related energy consumption. Besides, we find a unidirectional short run causality running from the road infrastructure to transport value added and a unidirectional short run causality running from road transport related energy consumption to transport CO2 emissions. Furthermore, there are unidirectional short run causality running from railway infrastructure to rail transport related energy consumption and a unidirectional short run causality running from transport CO2 emissions to rail transport related energy consumption. These results are very important in terms of the choice of government policy decisions. Our results cast a new dimension to the importance of investing in infrastructure as a promising device to generate higher economic growth. & 2015 Elsevier Ltd. All rights reserved.

Keywords: Transportation infrastructure The transport value added Transportation energy consumption Transport CO2 emissions Johansen's cointegration approach Tunisia

Contents 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. A brief literature review . . . . . . . . . . . . . . . . . . . . . . . . . 3. An overview of transportation infrastructure in Tunisia 4. The Data and econometric modeling . . . . . . . . . . . . . . . 5. Results and discussion. . . . . . . . . . . . . . . . . . . . . . . . . . . 6. Conclusions and policy implications. . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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988 989 990 991 992 995 997

Abbreviations: ktoe, Kg tone oil equivalent; km, Kilometer; CO2PC, Per capita transport CO2 emissions; GCFPC, Per capita gross capital formation; ROADECPC, Per capita road transport related energy consumption; RAILECPC, Per capita rail transport related energy consumption; TVAPC, Per capita transport value added; ROADPC, Per capita road infrastructure; RAILPC, Per capita rail infrastructure n Corresponding author. Tel.: þ 966 530948710; fax: þ966 175428887. E-mail addresses: [email protected] (H. Achour), [email protected], [email protected] (M. Belloumi). http://dx.doi.org/10.1016/j.rser.2015.12.023 1364-0321/& 2015 Elsevier Ltd. All rights reserved.

H. Achour, M. Belloumi / Renewable and Sustainable Energy Reviews 56 (2016) 988–998

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1. Introduction

2. A brief literature review

Transportation infrastructure represents a key facilitator of economic growth and welfare. More substantively, it is perceived, more as a factor of improvement of the product and the investment in the private sector [1,2]. The adequacy of this crucial infrastructure is an important wheel of the success of a country's effort to increase its productivity growth [3]. It reduces travel time and passenger and freight transport gain from cost time and saving [4], attracts foreign direct investment [5], expanding trade and linking together resources and markets in an integrated economy [6,7]. However, there are major challenges related to the environmental impact of transport infrastructure. The transportation infrastructure such as railways, expressways, and highways are a significant consumer of fossil fuels and growing contributor to CO2 emissions, which are gradually increasing in all the regions of the world. In fact, transportation infrastructure motives people to install in the periphery, and thereby, increase the rate of urbanization and spatial distribution of households and activities. With considering this potential consequence of this new development, communities end up with urban sprawl [8–11]. Low-density metropolitan areas exhibit an almost total predominance of automobile uses, so more traffic congestion, more energy consumption and more carbon emission [8, 12–18]. Thus, there is a strong relationship between transportation infrastructure, economic growth in transport sector energy consumption and CO2 emissions. In order to make transport sector more sustainable, recent studies have investigated the long-run relationship between carbon emissions, economic growth and energy consumption. However, none of the previous studies explored the effect of transportation infrastructure on economic growth and environment. This motivated the research to explore the nexus among road and rail infrastructure, transport value added, road and rail related energy consumption, gross capital formation and transport CO2 emissions. The contribution of this study is twofold: firstly, there is little literature, which concentrates on the relationship between economic growth, CO2 emissions and energy consumption in the transportation sector. In fact, the study of energy consumption of the transport sector, especially for each mode, has received little attention in the existing literature. Then, this study would explore whether adding transport infrastructure provokes economic growth and environment degradation or economic growth and environment degradation act as a stimulus for any consequent growth in transport infrastructure. The existence of a positive or negative relationship between variables may make policy implications easier for policy makers, environmentalists and economists to boost transportation infrastructure and hence sustainable development in Tunisia. Second, this study investigates the generalized impulse response to trace the effect of a shock on current and future values of endogenous variables, and variance decomposition technique to compare the influencing magnitude between various variables. The remaining part of this paper is organized as follows. In Section 2, we provide an overview of the related literature followed by a brief description of the Tunisian transportation infrastructure in Section 3. The data and methodology, including unit root and Granger causality between economic growth and economic infrastructure investment, capital formation, transportation energy consumption and CO2 emissions are presented in Section 4. Section 5 shows empirical results and their analysis. The final section concludes the paper.

In this section, we are interested in reviewing briefly the literature investigating the causal relationships between transportation infrastructure, economic growth and energy consumption using the multivariate cointegration techniques and Granger causality tests. Yet, the several studies are as well as controversial in terms of time periods, the differences in country specific analysis and the causal sense; the causal relationships may be represented in three possible ways: unidirectional, bidirectional or absent. There are four research groups in the literature related to economic growth, transport sector, energy consumption, and carbon emissions. The first group focuses mainly on the relationship between economic growth and energy consumption. A positive nexus among energy consumption and economic growth was found since the original study of Kraft and Kraft [19–26]. The second group investigates the relationship between economic growth and CO2 emissions. This highlights the link among environmental impacts and economic growth, which has been investigated in [27–30]. The third group concentrates principally on the relationship between transportation infrastructure and economic growth. This relationship has been explored by numerous studies such and proved by Rudra and Tapan [31]. They studied the causal relationship between transportation infrastructure (road, rail and both), economic growth and gross capital formation in India over the period 1970–2010 using the vector error correction model (VECM). The authors have found bidirectional causality between road transportation and economic growth. They also found bidirectional causality between gross capital formation and economic growth, unidirectional causality running from rail transportation to gross capital formation and economic growth and unidirectional causality running from rail transportation to gross capital formation. Canning and Bennathan [32] showed that the length of paved roads is highly correlated with capital for a panel of forty one countries. Fedderke and Bogeti [33] investigated the direct effect of infrastructure investment on labor productivity and the indirect impact of infrastructure on total factor productivity using the panel data analysis. The fourth strand emerged in modern literature, which combines the earlier three groups to check the dynamic relationship between environmental degradation, transport sector, energy consumption and economic growth. In the international literature, there exist numerous studies. Indeed, the most important studies that considered energy consumption in the transportation sector are in [34–36]. In fact, Ramanathan [37] examined the nexus between variable representing transport performances (passenger-kilometers and tone-kilometers) and some other macroeconomic variables in India using cointegration analysis. In addition, Samimi [38] identified cointegrating relationship between road transport energy, road demand and other macro-economic variables in Australia. Liddle [39] examined whether a systemic, mutually causal cointegrated relationship exists among mobility demand, income, gasoline price and ownership in the United States during the period 1946–2006. The results showed that the mobility demand has a long run systemic causal relationship with the rest of time series. Equally, Liddle [40] has investigated the long run causal relationship between transport energy consumption and Gross Domestic Product (GDP) using the panel method. Results show the existence of unidirectional causality running from transport energy consumption to GDP. In the case of China, Yaobin [41] found that urbanization causes energy consumption in both long and short runs over the period of 1978–2008. In this sense, Usama et al. [42] examined the relationship between urbanization, energy consumption, and CO2 emissions, in Middle

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H. Achour, M. Belloumi / Renewable and Sustainable Energy Reviews 56 (2016) 988–998

East and North African (MENA) countries using a panel data model over the period of 1980–2009.Their findings showed that there was a long run bidirectional relationship between urbanization, energy consumption and CO2 emissions. This finding is in line with that of Ang [43] examined the long-run relationship between GDP, CO2 and energy consumption in Malaysia. The results indicate that CO2 emissions, energy consumption and GDP are cointegrated in the long term. The results of the Granger causality show that there is evidence of unidirectional causality running from GDP to energy consumption in the long term. Evenly, Results show a weak causality running from CO2 emissions to economic growth in the long-run. Similarly, Hossain [44] explored the relationship between CO2 emissions, energy consumption, economic growth, trade openness and urbanization for a panel of nine newly-industrialized countries that included Malaysia, the Philippines and Thailand. The results showed that income and energy consumption have a long-run significant impact on CO2 emissions in the Thailand and Philippines but not for Malaysia. The panel Granger causality test indicates that there is no long-run causality between income, energy consumption and CO2 emissions. Nevertheless, in the short run, the causality runs from income to CO2 emissions. In the case of the organization for Economic Cooperation in Development countries (OECD), Saboori et al. [45] explores the bi-directional long-run relationship between energy consumption in the road transport sector with CO2 emissions and economic growth over the period 1960–2008 using the Fully Modified Ordinary Least Squares cointegration approach. Their findings showed that there was a bidirectional relationship between CO2 emissions and economic growth, road sector energy consumption and economic growth and CO2 emissions and road sector energy consumption in all the OECD countries. In the same context, Rudra [46] explored the nexus between transportation infrastructure, energy consumption and economic growth in India over the period 1970–2007. Results show a unidirectional causality from transport infrastructure to economic growth, a unidirectional causality from economic growth to energy consumption and a unidirectional causality from transport infrastructure to energy consumption. However, in the national literature, only one study put the accent on the causal relationship between transport sector, income, energy consumption and CO2 in the case of Tunisia. Indeed, Ben Abdallah et al. [47] examined the nexus between value added of transport, road transport energy consumption, road infrastructure, fuel price and CO2 emissions using the Johansen's cointegration technique over the period of 1980–2010. They found a unidirectional causality from road pricing to energy consumption, bidirectional causality between road transport infrastructure and road energy consumption.

3. An overview of transportation infrastructure in Tunisia It is well known that the competitiveness of a country or a region depends in particular on the quality of the public infrastructures. Since its independence in 1956, Tunisia has taken important works of modernization and renovation of its road infrastructures. According to report of African Development Bank [48], the Tunisian road network accounts for virtually all movement of persons and over 80% of goods transport and contributes to the exchanges inter-regions on all the territories. Over the 1997–2006 period, traffic recorded an average annual growth rate of 6.1%, with light vehicles (LV) and heavy duty vehicles (HDV) taking up 86.5% and 13.5%, respectively. The HDV traffic increasingly involves articulating units (tractors, articulated trucks and semi-trailers). It has also evolved more rapidly than the overall average traffic. This

has resulted in a reduction in the road capacity, increased degradation of carriageway and increased risks of accidents. Moreover, during the last decades, we note that road networks have been threatened because of the increase in HDV traffic and the aging of the pavement structure (Table 1). The Tunisian railway network runs from north to south with a backbone linking Tunis to the major industrial areas of the Center and Southeast and an international line connected to the Algerian network and lines providing phosphate transport in the south. According to report of African Development Bank [50], 64% (1190 km) of the total line in operation is for mixed passenger and goods traffic and 36% (670 km) for goods traffic. Phosphates traffic traditionally accounts for nearly 70% of the goods traffic. Railway transport handles annually the movement of 35 million passengers on the main lines and 13 million tons of goods, including 8 million tons of phosphate, representing 4.4% and 14.1% respectively of the market share of passenger and goods transport. The global annual traffic has remained almost static in the last two decades, resulting in the loss from the railway of a significant share of the transport market. Because of these decreases, the Tunisian rail lines have also known a full decrease (Table 2). According to the Tunisian ministry of transport, the statistical data show that the public investment represents on average 2.5% of the GDP and approximately 12% of the total investment over the period 1997–2010. Consequently, the evolution of transportation infrastructure and the urbanization phenomenon are strongly linked. Indeed, the evolution in the annual average rate growth of 1.2% of urban density allows automatically the increase of ownership cars. The rate of motorization has known an important growth during the last decades. In fact, it passes from about 32 cars per 1000 people in 1990 to about 75 cars per 1000 people in 2010 [51]. Hence, the increase of individual mobility demand results in an important consumption of energy, especially the fossil and petroleum products. Conventional fuels stemming from some oil assure at present the immense majority of needs in energy for the mobility of the people and the goods. In brief, the sector of transport occupies a dominating place in the consumption of oil that is 33% of the Tunisian national consumption [52]. The energy consumed by the ground sector of transport increases more and more and reaches 78% of the total of the energy consumption following the evolution Table1 Evolution of road infrastructure in Tunisia. Source: [49].

Road network (Km)

1971

1980

1985

1995

2005

2010

17999

18010

23127

21900

19114

19379

Table 2 Evolution of rail lines in Tunisia. Source: [49]. Years

1971

1980

1985

1995

2005

2010

Rail lines (Km)

1863

2047

2189

1860

1909

1119

Table 3 Distribution of greenhouse gas emissions by modal transport in Tunisia. Source: author's calculation based on [54,52]. Modal transport

Road

Railway

Maritime

Airway

Pipelines

MtCO2e Percent

46.40 82

1.17 2

41 0.7

11.5 0.3

8.39 15

H. Achour, M. Belloumi / Renewable and Sustainable Energy Reviews 56 (2016) 988–998

of the motorization of mass and the emergence of the notion of just in time when the flow of the tense goods. The energy consumption of the sector of transport in Tunisia generates some negative external effects such as CO2 emissions. Transport is the second sector in charge of greenhouse gas emissions by 5648.5 million tons CO2 equivalent (MtCO2e) after the industrial sector (Table 3). In order to reduce energy consumption, the Tunisian government intervened by setting up several actions such as implementation of logistic platform, station diagnostics of vehicle motors, energy audit and contract programs in the transport sector [53].

4. The Data and econometric modeling In this analysis, our data cover the period 1971–2012. They are obtained from the indicators of the World Bank, National Agency of Energy Conservation, UN energy statistics and National Institute of Statistics (NIS). They embrace the per capita transport value added (TVAPC) expressed in constant 2005 prices, the per capita gross capital Formation (GCFPC) expressed in constant 2005 prices and used as proxy for infrastructure investment, per capita road infrastructure (Roadpc) expressed in kilometers (km), per capita rail infrastructure (Railpc) expressed in kilometers, per capita road transport related energy consumption (Roadecpc) and per capita rail transport related energy consumption (Railecpc) expressed in kg tone oil equivalent (ktoe) and per capita transport CO2 emissions (CO2pc) expressed in metric tons. We use the road transport infrastructure and rail transport infrastructure separately and collectively [55,31]. In this study, all the variables are transformed to their natural logarithms before the analysis. Table 4 provides the summary statistics for the various variables used. In this analysis, the Johansen [56] multivariate cointegration approach is employed to investigate the relationship between the transport infrastructure, economic growth and energy consumption. However, this approach is conditional on the stationary of the time series involved. According to Engle and Granger [57], if the underlying time series are non-stationary at level, cointegration and vector error correction model (VECM) are used to investigate the nexus among such variables. That is why the important and first step is to test for the existence of unit roots in each series. In order to verify whether this preliminary condition is fulfilled, we use the augmented Dickey–Fuller [58] and Phillips–Perron tests [59]. Before proceeding with the cointegration analysis, it is necessary to determine the optimal lag length using Schwartz criteria (SC) and Akaike information criteria (AIC) [60]. It may so happen that a particular of two or more non stationary series (with the same order of integration) may be stationary. In such case, the non-stationary variables are said to be cointegrated. Dramatically cointegration relationship implies the existence of long run equilibrium relationships among the variables. Indeed, Granger [61]

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argued that a test for cointegration can thus be thought of as a pretest to avoid “spurious regression” situations and to know the common trend of the variables [62]. The Johansen [63] test for multivariate cointegration is used to identify the number of cointegrating vectors of equations. That test produces two likelihood statistics: the trace statistics and the maximum Eigenvalue statistics [64]. If two or more variables are cointegrated, then the relationships among the variables can be modeled using a VECM which can be employed to reveal the direction of Granger causality among pairs of variables. The VECM is employed to detect the long and short run relationships among the variables and can identify sources of causation. It is presented by Eqs. (1)–(5). In each equation, the endogenous variable is explained by itself, the others explanatory variables and the error correction term. Xp Xp Xp α ΔX t  l þ l ¼ 1 β12l ΔY t  l þ l ¼ 1 β13l ΔZ t  l ΔX t ¼ φ1 þ l ¼ 1 11l þ

Xp

l¼1

ΔY t ¼ φ2 þ þ

þ

Xp

Xp

l¼1

l¼1

ΔZ t ¼ φ3 þ

β14l ΔW t  l þ

Xp

l¼1

l¼1

l¼1

β21l ΔY t  l þ

β24l ΔW t  l þ

Xp

Xp

β34l ΔW t  l þ

Xp

Xp

l¼1

l¼1

β31l ΔZ t  l þ

β15l ΔV t  l þ δ1 ECT t  1 þ εt

l¼1

l¼1

Xp l¼1

β23l ΔZ t  l

β25l ΔV t  l þ δ2 ECT t  1 þ εt

Xp

Xp

β22l ΔX t  l þ

β32l ΔX t  l þ

Xp l¼1

ð1Þ

ð2Þ

β33l ΔY t  l

β35l ΔV t  l þ δ3 ECT t  1 þ εt

ð3Þ

Xp Xp ΔW t ¼ φ4 þ β ΔW t  l þ l ¼ 1 β42l ΔX t  l l ¼ 1 41l Xp Xp Xp þ β ΔY t  l þ l ¼ 1 β44l ΔZ t  l þ l ¼ 1 β45l ΔV t  l l ¼ 1 43l þ δ4 ECT t  1 þ εt

ð4Þ

Xp Xp ΔV t ¼ φ5 þ β ΔV t  l þ l ¼ 1 β52l ΔX t  l l ¼ 1 51l Xp Xp Xp þ β ΔY t  l þ l ¼ 1 β54l ΔZ t  l þ l ¼ 1 β55l ΔW t  l l ¼ 1 53l þ δ5 ECT t  1 þ εt

ð5Þ

Where X t ,Y t ,Z t ,W t , and V t denote respectively Roadpc (in model 1) or Railpc (in model 2), TVAPC, GCFPC, roadecpc (in model 1) or railecpc (in model 2), CO2pc; Δ is the difference operator; ECT refers to the error correction terms derived from the long run cointegrating relationships; εt are the error terms. The error correction terms allow for an additional channel for Granger causality to emerge. The short run dynamics are captured through the   coefficients ðβ i Þ of the explanatory variables. The coefficients δ of the ECTs detect the deviation of the dependent variables from the long run equilibrium. To check causality among variables, we use the standard Granger test: Firstly, the long run causality is detected using a t-test for the significance of speed adjustment in ECT terms. Secondly, the short–run causality is detected using a standard X 2 Wald Statistic.

Table 4 Summary of descriptive statistics. Variables

Variables description

Mean

Maximum

Minimum

Standard deviation

CO2PC GCFPC ROADECPC RAILECPC TVAPC ROADPC RAILPC Observations

Per Per Per Per Per Per Per 42

0.344796 644.2833 0.000110 1.45E  05 1043.554 0.002424 0.000254 42

0.587335 897.4594 0.000188 2.15E  05 2478.049 0.003390 0.000351 42

0.166216 381.7633 5.16E  05 9.91E  06 281.3116 0.001797 0.000105 42

0.109173 130.1887 3.87E  05 2.65E  06 658.4607 0.000459 6.85E  05 42

capita capita capita capita capita capita capita

transport CO2 emissions (metric tons) gross capital Formation (constant 2005 prices) road transport related energy consumption (ktoe) rail transport related energy consumption (ktoe) transport value added (constant 2005 prices) road infrastructure (km) rail infrastructure (km)

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5. Results and discussion The results of the Augmented Dickey–Fuller [58] ADF and Phillips–Perron [59] PP tests are presented in Table 5. They indicate that not all the variables are stationary in levels, but stationary in first differences. Hence, the differences become stationary and consequently the related variables are specified as individually integrated of order one (I (1)) and they can be cointegrated. In this case, we continue to check whether the series indicate the existence of cointegrating relationships among the variables. Table 5 Results of unit root tests. Variables

ADF test statistic Level

TVAPC 3.237 GCFPC 0.909 RAILECPC  0.491745 ROADECPC 3.639676 1% critical value  2.622585 5% critical value  1.949097 10% critical value  1.611824 RAILPC  2.164184 ROADPC  2.345761 CO2PC  1.190844 1% critical value  4.198503 5% critical value  3.523623 10% critical value  3.192902

PP test statistic

First difference Level

First difference

 4.538  7.323435  7.748226  5.025692  2.624057  1.949319  1.611711  5.595830  5.723687  7.457628  4.205004  3.526609  3.194611

 4.512  7.251  7.705833  5.075418  2.624057  1.949319  1.611711  5.805916  5.704038  7.491292  4.205004  3.526609  3.194611

3.175 0.909  0.479438 10.60484  2.622585  1.949097  1.611824  2.164184  2.43802  1.190844  4.198503  3.523623  3.192902

Table 6 Selection of lag length. Models

Lag

LogL

FPE

AIC

SC

Model 1

0 1 2 3 4 5

253.4769 416.5314 433.3829 455.2076 482.3980 550.6176

1.01E  12 5.89E  16 9.89E  16 1.45E  15 2.04E  15 5.03E  16n

 13.43118  20.89359  20.45313  20.28149  20.39989  22.73609n

 13.21349  19.58744n  18.05852  16.79842  15.82837  17.07611

Model 2

0 1 2 3 4 5

349.2977 507.0845 533.4386 548.1941 577.4446 617.8787

5.69E  15 4.41E  18n 4.43E  18 9.52E  18 1.20E  17 1.33E 17

 18.61069  25.78835  25.86155  25.30779  25.53754  26.37182

 18.39299  24.48220n  23.46694  21.82472  20.96602  20.71184

n Indicates lag order selected by the criterion; LR: sequential modified LR test statistic (each test at 5% level); FPE: Final prediction error; AIC: Akaike information criterion; SC: Schwarz information criterion.

Before we proceed to the next step, we determine the optimum lag length based on the minimum of AIC and SC criteria (Table 6). Table 6 shows that we choose the optimal lag length of p* ¼1. Following that the five series are integrated and have the same order, the cointegration relationships among them are detected by employing Johansen's cointegration test. The results of the Johansen cointegration test are given in Table 7. They indicate that the variables are cointegrated and there is only one long run relationship. In model 1, the max-eigenvalue test statistics determine one cointegrating equation at 10% significance level, but in model 2, the trace test statistics indicate likewise the existence of one cointegrating equation at 10% significance level. Hence, in Tunisia, a long run equilibrium relationship among road and rail transportation infrastructure, gross capital formation, transport value added, road and rail transport related energy consumption and CO2 emissions may exist between them. Based on cointegration test results, the VECM presented in Eqs. (1)–(5) is employed to determine the direction of causality using Granger causality tests. The results of Granger causality tests are shown in Table 8. In model 1, only the coefficient of the error correction term in equation (1), where the dependent variable is per capita road infrastructure, is statistically significant at a 5% level. Thus the results show the existence of unidirectional long run causality running from transport value added, road transport related energy consumption, transport CO2 emissions and gross capital formation to road infrastructure with no feedback. The results of Wald statistics show that in the short-run, there is a unidirectional causality running from the road infrastructure to transport value added and a unidirectional causality running from road energy consumption to CO2 emissions. These results imply that investments and economic growth in the transport sector determines highway transport infrastructure in the long term and vice versa in the short run. Our results suggest that economic growth in transport sector can play a significant role in the creation of highway transportation infrastructure. It can be consistent with the findings of [1–7, 31–33]. Therefore, the standard Granger test would have concluded that there is a unidirectional long run nexus running from road transport related energy consumption and CO2 emissions to road transportation infrastructure. This implies that energy consumption and CO2 emissions can be seen as major results of urbanization and urban sprawl. Indeed, in order to curb excess of urban sprawl, reduce congestion and facilitate mobility, policymakers adopted the strategy based on expansion of road infrastructure. However, this strategy presents negatives effects on the environment. These findings are consistent with those of [12–18]. When considering model 2, the coefficient of the error correction term is statistically significant at the 5% level in Eq. (4). Hence, results show a unidirectional long run causality running

Table 7 Results of Johansen cointegration tests. Models

Rank test (Trace) Number of cointegration

Model 1

Model 2

n

None At most At most At most Nonen At most At most At most

1 2 3 1 2 3

Rank test (Maximum eigenvalue) Eigenvalue

Trace statistic

5% Critical value

Prob.

Max-eigen statistic

5% Critical value

Prob.

0.544935 0.285797 0.194858 0.153505 0.533401 0.332950 0.232029 0.157486

60.30800 28.81540 15.35185 6.682384 68.88267 38.39127 22.19563 11.63552

69.81889 47.85613 29.79707 15.49471 69.81889 47.85613 29.79707 15.49471

0.2260 0.7770 0.7568 0.6148 0.0592 0.2852 0.2878 0.1753

31.49260 13.46354 8.669470 6.666031 30.49140 16.19564 10.56011 6.854608

33.876 27.584 21.131 14.264 33.87687 27.58434 21.13162 14.26460

0.0938 0.8578 0.8581 0.5294 0.1203 0.6491 0.6909 0.5065

n Denotes a rejection of the null hypothesis at the 10% level. Note: Model 1: Cointegration between road transportation infrastructure and the rest of variables. Model 2: Cointegration between rail transportation infrastructure and the rest of variables.

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Table 8 Results of causality tests. Models

Dependent variable

Source of causation (short-run)

Source of causation (long-run) ECT

ΔX t

ΔY t

ΔZ t

ΔW t

ΔV t

Model 1 ΔX t ΔY t ΔZ t ΔW t ΔV t

– 421283.4** (0.05) 157559.3 (0.18) 0.004 (0.71)  3.889 (0.89)

1.53E07 (0.24) –  0.03 (0.74) 1.41E09 (0.87) 1.55E06 (0.94)

6.90E  08 (0.77) 0.0406 (0.9) – 2.49E  08 (0.13) 1.6E  05 (0.72)

 5.013 (0.33)  6962834 (0.32)  1004701 (0.79) – 1325.478 (0.10)

0.002 (0.16) 2797.8 (0.26) 225.650 (0.86)  0.0001 (0.18) –

Model 2 ΔX t

–

 2.31E  08 (0.75)

 2.76E  08 (0.53) 1.73 (0.35)

0.0286 [0.44] (0.65)

1065877 (0.44) 952946 (0.19)  0.032* (0.03)  194.2 (0.32)

– 0.002 (0.98) 1.6E  09 (0.42)  2.97E 07 (0.99)

0.1137 (0.73) – 5E  09 (0.17) 3.34E–05 (0.48)

 446176.1 [0.912] (0.36)  147525 [  0.57] (0.56)  0.013* [  2.29] (0.02) 145.26 [2.086] (0.03)

ΔY t ΔZ t ΔW t ΔV t

 0.0006*** (0.0005)  20420546 (0.14) 1867.6 (0.2)  3278798 (0.65) 400.14 (0.6) –  1.45E  05 (0.37)  78.3 (0.96) –

 0.124* [  2.24] (0.03) -190.25 [  0.002] (0.99)  6339.022 [  0.16] (0.87) 0.003 [0.88] (0.38) 27.18 [  2.759] (0.01)

Note: numbers in parentheses are p-value, while those in square brackets are t-statistics. * **

Represent 5% level of significance. Represent 10% level of significance. Represent 1% level of significance.

***

from railway infrastructure, the transport value added, gross capital formation and transport CO2 emissions to rail transport related energy consumption. In the short term, there is unidirectional causality running from transport CO2 emissions to railway infrastructure, also, the results show a unidirectional causality running from railway infrastructure to rail transport related energy consumption. These results imply that economic growth in the transport sector determines rail transport related energy consumption in the long term and not vice versa in the short run. In addition, this analysis seems to be consistent with the neoclassical theory, which assumes that energy is neutral to economic growth in the long-run [26]. It can be seen that there is in the long and short run bidirectional causality among railway transport infrastructure and transport related energy consumption. It implies likewise that transportation energy consumption is viewed as a cause of urbanization structure and urban sprawl. Indeed, the rise of infrastructure can cause consequently the rise of the rate of traffic, spatial distribution of activities and households. Therefore, the results imply that the increases of transport CO2 emissions are linked to energy consumption and the rate or quality of railway infrastructure too. The results are further checked with generalized impulse response functions. These functions trace the impact of each variable in this study to one shocks or innovations on current and future values; furthermore, these functions identify the responsiveness of the dependent variable (endogenous variable) in the VECM due to a shock on the error term. The results of these response functions are reported for transportation infrastructure, the transport value added, gross capital formation, road and rail transport related energy consumption and transport CO2 emissions are represented respectively in Figs. 1 and 2. The analysis of generalized impulse response functions provides that all the variables are transitory if our time series found their long run equilibrium at maximum 10 years [64]. Indeed, if a positive shock of one Standard Deviation is given to the residual of road infrastructure, we note that all the rest of variables reacted to this innovation. In Fig. 1, the generalized impulse response function displays the reaction in one variable due to shocks stemming in other variables. The response in transport value added and CO2 emissions first increases, then stagnates due to shocks stemming in road infrastructure. Whereas, the responses of gross capital formation and road transport related energy consumption to road infrastructure decrease and become

negative over the horizon. According to these results, innovations in road infrastructure initially have a significantly positive impact on the transport value added and CO2 emissions. These results are very responsive to the result of VECM. These results imply that a higher disposable income may be operated for expanding demand for better road infrastructure. Because of urbanization and urban sprawl, the rise of CO2 emissions causes indirectly the expansion of road infrastructure. However, the response in the gross capital formation due shocks stemming in road infrastructure is negative in most cases. This result is contradictory and not consistent with our findings, but it can be attributed to the problem of industrial centralization under Tunisia which means that there are special zones to create investments, and most of them are devoid of transportation infrastructure. Indeed, since the uprising of 2011, several Tunisian regions request more development which underlies more public investment and more projects that are public. Furthermore, the response of road related energy consumption to road infrastructure is negative. This result is not responsive, but it can be explained by the degradation and aging of Tunisian road infrastructure which cause consequently more energy consumption. Concerning model 2, the results of impulse response function are shown in Fig. 2. The response in CO2 emissions first decrease, goes up and then stagnates due to shocks stemming in rail transport related energy consumption. This result is not surprising for Tunisia, where the overuse of fossil energy may lead to greater CO2 emissions. What is surprising is that the responses in all the variables go down and become negative due to shocks stemming in railway infrastructure. These results reflect Tunisia reality and can be attributed to the lack of investments and the bad quality of railway infrastructure. Whereas the response in railway infrastructure to shocks stemming in transport value adding is increasing and positive in time. In order to compare the contribution extents of various time series, the variance decomposition approach is used over the sample period. First, in the model one, we apply the variance decomposition approach based on the vector error correction model (VECM), to compare the influence magnitude among variables. The results are shown in Table 9. It is shown that a 64.571% of road infrastructure is explained by its own innovative shocks whereas the contributions of gross capital formation, road transport related energy consumption, transport CO2 emissions and transport value added to road infrastructure are equal to 22.87%, 5.099%, 3.89% and 3.56%, respectively. This result indicates that the share of transport value added and CO2 emissions explaining road

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Fig. 1. Results of impulse response functions (model 1).

infrastructure are minimum comparatively for those of gross capital formation and road transport related energy consumption. The results also show that a 1.18% of transport CO2 emissions are explained by its own innovative shocks. The contribution share of road transport related energy consumption to CO2 emissions is significantly important. It represents 63.18%. This result justifies the unidirectional causality running from road transport related energy consumption to carbon dioxide emissions, which is expected as the overuse of fossil energy may lead to greater CO2 emissions. Results prove also that a 21% of road infrastructure is explained by one standard deviation shock in transport value added. This implies that road infrastructure represents the driver of economic growth in the transport sector. Thus, the results of variance decomposition seem to be conforming to those given by Granger causality. Considering model 2, results of the variance decomposition analysis based on VECM are reported in Table 10. It is shown that a

37.81% of rail infrastructure is explained by its own innovative shocks whereas the contributions of rail transport related energy consumption (2.96%), gross capital formation (5.18%) and transport value added (6.5%) are low comparing to those of transport CO2 (11.53%) emissions to rail infrastructure. The results of variance decomposition for CO2 emissions have conformed those given by the Granger causality test where transport CO2 emissions cause CO2 rail infrastructure in the short run. The results also show that a 37.52% of rail transport related energy consumption are explained by its own innovative shocks. The contributions of rail infrastructure, gross capital formation, transport value added and transport CO2 emissions to rail transport related energy consumption are equal to 54.13%, 3.84%, 3.26% and 1.23%, respectively. These results are conform to those given by Granger causality; and show that railway infrastructure seems to be the most important factor explaining innovation to rail transport related energy consumption. We believe that these results are expected given that

H. Achour, M. Belloumi / Renewable and Sustainable Energy Reviews 56 (2016) 988–998

995

Fig. 2. Results of impulse response functions (model 2).

the establishing of railways infrastructure motivates people to sprawl, so more travels and more energy consumption [18]. The transport value added and the gross capital formation seems to be sizable than CO2 emissions in explaining the variation in rail transport related energy consumption for the 10-year horizon. The contribution of CO2 emissions in explaining energy consumption is minimal whereas CO2 emissions are the only variable that causes energy consumption in the long term. This result seems to be surprising and not correctly. To complement this study, we check for the problems of autocorrelation and heteroskedasticity of errors in VECM. The results of Breusch–Godfrey Serial Correlation LM test are shown in Table 11. They indicate the absence of autocorrelation of the error terms in the two models. The results of Breusch–Pagan–Godfrey heteroskedasticity test are shown in Table 12. They indicate that the error terms are homoskedastistic in the two models.

6. Conclusions and policy implications The present study tries to provide the causal relationships between road and rail transportation infrastructure, the transport value added, gross capital formation, energy related transport consumption and transport CO2 emissions in Tunisia over the period of 1971–2012. Using the Johansen multivariate cointegration technique, this study concluded the following:

 Unidirectional positive relationship running from transport



value added to road infrastructure in the long run and vice versa in the short run. Thus, road infrastructure can boost economic growth. A higher disposable income may be operated for expanding road infrastructure. Unidirectional causality running from gross capital formation to road infrastructure. Whereas, the results of the impulse response functions provide evidence that an unanticipated

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Table 9 Variance decomposition analysis of model 1. Period Variance 1 5 10 Variance 1 5 10 Variance 1 5 10 Variance 1 5 10 Variance 1 5 10

S.E.

ROADPC

Decomposition of ROADPC 8.22E  05 0.000191 0.000263 Decomposition of ROADECPC 5.53E  06 1.29E  05 1.81E  05 Decomposition of CO2PC 0.015687 0.038581 0.063391 Decomposition of GCFPC 59.79213 114.4423 150.2317 Decomposition of TVAPC 110.2759 317.1188 470.4826

100.0000 83.18121 64.57021

ROADECPC

0.000000 1.773250 5.099725

CO2PC

GCFPC

TVAPC

0.000000 2.621512 3.898071

0.000000 8.921032 22.87450

0.000000 3.502999 3.557491

0.020941 2.145499 2.405945

99.97906 87.77044 87.65336

0.000000 2.207124 2.076672

0.000000 7.720119 7.632203

0.000000 0.156815 0.231816

1.689085 10.25684 16.75766

84.38583 73.66303 63.18257

13.92509 2.852709 1.184713

0.000000 13.14167 18.62980

0.000000 0.085759 0.245252

97.62325 96.36938 97.36583

0.000000 0.108715 0.137573

0.000508 2.514673 1.821698

0.949102 0.387736 0.235318

1.427143 0.619498 0.439585

2.519462 18.79505 21.00867

2.087956 1.684485 1.571689

6.531253 1.690828 1.238133

RAILPC

RAILECPC

0.087754 0.050267 0.036274

88.77357 77.77937 76.14523

Table 10 Variance decomposition analysis of model 2. Period Variance 1 5 10 Variance 1 5 10 Variance 1 5 10 Variance 1 5 10 Variance 1 5 10

S.E. decomposition of RAILPC 1.51E  05 3.70E 05 4.91E  05 decomposition of RAILECPC 1.26E  06 2.60E  06 4.16E  06 decomposition of CO2PC 0.016246 0.032557 0.048565 decomposition of GCFPC 59.97066 116.2444 155.2395 decomposition of TVAPC 114.0689 310.1144 447.1879

100.0000 82.52039 73.81542

0.000000 1.785079 2.961995

0.423220 34.74542 54.13744

99.57678 61.33113 37.52387

34.36034 13.56808 6.897110

23.14503 33.44947 35.42619

Model 2

F-statistic Obs*R-squared F-statistic Obs*R-squared

1.004394 14.58606 1.656439 1.968644

0.000000 3.045896 6.502713

0.000000 1.697336 1.230934

0.000000 1.423779 3.844117

0.000000 0.802332 3.263647

0.000000 9.450800 15.58065

0.000000 4.334734 9.244481

88.20693 82.60614 83.42354

0.000000 0.586739 1.366217

2.599473 1.228882 0.899443

3.416159 3.468968 3.404843

5.826309 5.280717 5.570521

1.993034 6.538764 7.082123

0.025112 1.400511 1.326482

0.4779 0.2649 0.2073 0.1606

Table 12 Results of Breusch–Pagan–Godfrey Heteroskedasticity test. Model 1

F-statistic Obs*R-squared

0.547102 6.348540

Prob. F (10,29) Prob. Chi (10)

0.8420 0.7852

Model 2

F-statistic Obs*R-squared Scaled explained SS

0.711446 7.879901 9.184330

Prob. F (10,29) Prob. Chi-Square (10) Prob. Chi-Square (10)

0.7067 0.6406 0.5147

innovation in road infrastructure has a significantly negative impact on the gross capital formation. This implies that in Tunisia, the level of road infrastructure is not fairly distributed in all the territory. In order to raise the economic prosperity,



TVAPC

0.000000 2.365889 5.188870

5.777441 12.10927 10.90596

Prob. F (12,21) Prob. Chi-Square (12) Prob. F (1,32) Prob. Chi-Square (1)

GCFPC

0.000000 10.28275 11.53100

42.49464 39.19691 32.85157

Table 11 Results of Breusch–Godfrey Serial Correlation LM test. Model 1

CO2PC

0.436612 0.295385 0.237768

91.71893 86.48462 85.78311

Tunisian government must realize the justice in the development of transportation infrastructure between regions, in all the territory. Unidirectional causality running from road transport related energy consumption and transport CO2 emissions to road infrastructure in the long term. In addition, unidirectional causality running from railway infrastructure to rail transport related energy consumption in the long and short run. Also, transport CO2 emissions causes rail infrastructure in the short run. The results imply that the road and rail transport related energy consumption present at the same time the cause and results of urban sprawl. Indeed, the extension of transportation infrastructure motivates people to sprawl and so more use of private cars, long travels, more congestion and so more energy consumption and hence more greenhouses emissions. Consequently, in order to curb the overuse of energy, Tunisian policymakers adopted the strategy based on extension of infrastructure. But, this strategy is not suitable. Measures undertaken should pay considerable attention to the adverse effects of transportation infrastructure on the environment. The urban planners and policymakers in Tunisia should slow the rapid

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increase in urbanization and urban sprawl. In fact, they should increase energy efficiency, increase density and create compact city, strength the role of local authorities and efficient management which are examples that should be used as appropriate policies for the stated problem of urban sprawl. Moreover, rewriting the spatial distribution of households and activities in order to mitigate mobility can be deteriorating the urban density, the urbanized kilometers number and so transport-related energy consumption in urban areas. New equity in term of spatial distribution of activities between all cities could reduce the concentration of populations and economic activities in megacities and thus urbanized kilometers. Unidirectional long run causality running from gross capital formation and transport value added to rail transport related energy consumption these results seem to be consistent with the neoclassical theory which assumes that energy is neutral to economic growth in the long-run [26]. Unidirectional causality running from CO2 emissions to rail transport related energy consumption in the long term and the increase relationship running from road transport related energy consumption to CO2 emissions. This implies that most of CO2 emissions come from energy consumption. Thus, the Tunisian policy makers should sensitize the motorists to environmental problem and substitute clean energy resources for fossil fuels. This necessitates the implementation of long term energy and climate policies such as building a green transport sector free of coal, oil and natural gas and the promotion of smart grids to reach the environmental sustainability.

This study can be vital in the effective implementation of transport policies to boost economic growth. These findings indicate that economic growth in transport sector can play an important role in the creation of transportation infrastructure. However, in Tunisia, the level of transport infrastructure is not so nice, in both quantity and quality, in contrast to developed countries. The result would be much better, if there is sufficient transport infrastructure in the economy. Government authorities must integrate environmental dimension in their strategy. In fact, they should think to the absolute or relative decoupling phenomenon between mobility demand and economic development [65]. Such policies are reduction of the number of travels, spatial organization of the activities, the incorporation of the transport policy in the questions of town and country planning, piggyback, improvement of the technology of engines and existing fuels, biofuels, battery-driven or hybrid vehicles. Shifting over public transport is one of the important solutions of urban transport planning.

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