Investigating the causal relationship between transport infrastructure, transport energy consumption and economic growth in Tunisia Houda Achoura, Mounir Belloumib* a Higher Institute of Transport and Logistics of Sousse, LAMIDED, University of Sousse, Tunisia Email:
[email protected] b College of Administrative Sciences, Najran University, Saudi Arabia and LAMIDED, University of Sousse, Tunisia. Phone: 00966 530948710 Fax : 00966 175428887 Email :
[email protected] /
[email protected] * Corresponding author
Abstract 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. Keywords: Transportation infrastructure, the transport value added, transportation energy consumption, transport CO2 emissions, Johansen's cointegration approach, Tunisia. 1
1. Introduction 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 (Banister and Berechman 2001, Eisner, 1991). The adequacy of this crucial infrastructure is an important wheel of the success of a countryβs effort to increase its productivity growth (Esfahani and Ramirez, 2003). It reduces travel time and passenger and freight transport gain from cost time and saving (Satya, 2003), attracts foreign direct investment (Esther et al, 2004), expanding trade and linking together resources and markets in an integrated economy (Aschauer, 1993, Yoshino and Nakahigashi, 2000). 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 is 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 (Ewing, 1997; Peiser, 1989; Burchell et al., 1998; Razin and Rosentraub, 2000). Low-density metropolitan areas exhibit an almost total predominance of automobile uses, so more traffic congestion and more carbon emission (Bhatta, 2010; Ewing 1997; Wasserman, 2000; Bruekner, 2000; Pedersen et al., 1999; Stoel 1999; Stone, 2008). 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, the 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 2
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 πΆπ2 emissions are presented in section 4. Section 5 shows empirical results and their analysis. The final section concludes the paper.
2. A brief literature review 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 (1978), Akara and Long (1980); Wolde-Rufael, (2009); Narayan and Smyth, (2008); Oh and Lee, (2004); Cheng, (1999); Glasure and Lee, (1997) and Belloumi (2009). The second group investigates the relationship between economic growth and πΆπ2 emissions. This highlights the link among environmental impacts and economic growth, which has been investigated by Grossman and Krueger (1991), Fodha and Zaghdoud (2010), Andreoni and Galmarini (2012) and Abid (2014). 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 (2013). They studied the causal relationship between 3
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 (2000) showed that the length of paved roads is highly correlated with capital for a panel of forty one countries. Fedderke and Bogeti (2006) 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 of Bentzen, (1994); Eltony et al., (1995) and Ramanathan, (1999). In fact, Ramanathan (2001) 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 (1995) identified cointegrating relationship between road transport energy, road demand and other macroeconomic variables in Australia. Liddle (2009) 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 (2013) 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 (2009) found that urbanization causes energy consumption in both long and short runs over the period of 1978-2008. In this sense, Usama et al. (2013) examined the relationship between urbanization, energy consumption, and CO2 emissions, in Middle 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 (2008) examined the long-run relationship between GDP, πΆπ2 And energy consumption in Malaysia. The results indicate that πΆπ2 emissions, energy consumption and 4
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 πΆπ2 emissions to economic growth in the long-run. Similarly, Hossain (2011) explored the relationship betweenπΆπ2 emissions, energy consumption, economic growth, trade openness and urbanization for a panel of nine newly-industrialised countries that included Malaysia, the Philippines and Thailand. The results showed that income and energy consumption have a long-run significant impact on πΆπ2 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 πΆπ2 emissions. Nevertheless, in the short run, the causality runs from income to πΆπ2 emissions. In the case of the organization for Economic Cooperation in Development countries (OECD), Saboori et al (2014) explores the bi-directional long-run relationship between energy consumption in the road transport sector with πΆπ2 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 πΆπ2 emissions and economic growth, road sector energy consumption and economic growth and πΆπ2 emissions and road sector energy consumption in all the OECD countries. In the same context, Rudra (2010) 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 πΆπ2 in the case of Tunisia. Indeed, Ben Abdallah et al. (2013) examined the nexus between value added of transport, road transport energy consumption, road infrastructure, fuel price and πΆπ2 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. 5
According to report of African Development Bank (2010), 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 (Table1).
Table1. Evolution of road infrastructure in Tunisia
Road network (Km)
1971
1980
1985
1995
2005
2010
17999
18010
23127
21900
19114
19379
Source: National Institute of statistics (2012).
The Tunisian railway network runs from north to south with a backbone linking Tunis to the major industrial areas of the Centre 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 (2012), 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). Table 2. Evolution of rail lines in Tunisia Years
1971
1980
1985
1995
2005
2010
Rail lines (Km)
1863
2047
2189
1860
1909
1119
Source: National Institute of statistics
(2012).
6
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 (Mraihi et al., 2013). 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 (NAEC, 2009). 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 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 πΆπ2 emissions. Transport is the second sector in charge of greenhouse gas emissions by 5648.5million tons πΆπ2equivalent (MtπΆπ2e) 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 (NAEC, 2002). Table 3. Distribution of greenhouse gas emissions by modal transport in Tunisia Modal transport
Road
Railway
Maritime
Airway
Pipelines
MtπΆπ2 e
46.40
1.17
41
11.5
8.39
Percent
82
2
0.7
0.3
15
Source: authorβs calculation based on World Development Indicators and National Agency of Energy Conservation
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 7
(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, 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 kiloton oil equivalent and per capita transport πΆπ2 emissions (πΆπ2pc) expressed in metric tons. We use the road transport infrastructure and rail transport infrastructure separately and collectively (Pradhan, 2007, Pradhan and Baghchi, 2013). 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. Table 4. Summary of descriptive statistics Statistics
Mean Median Maximum Minimum Std. Dev. Skewness Kurtosis Jarque-Bera Probability Observations
CO2PC 0.344796 0.305788 0.587335 0.166216 0.109173 0.518436 2.532311 2.264214 0.322353
GCFPC 644.2833 641.8115 897.4594 381.7633 130.1887 0.247533 2.584390 0.731189 0.693784
ROADECPC 0.000110 0.000102 0.000188 5.16E-05 3.87E-05 0.383346 2.109118 2.417601 0.298555
RAILECPC 1.45E-05 1.38E-05 2.15E-05 9.91E-06 2.65E-06 1.020470 3.418528 7.596047 0.022415
TVAPC 1043.554 763.7934 2478.049 281.3116 658.4607 0.954399 2.646038 6.595400 0.036968
ROADPC 0.002424 0.002436 0.003390 0.001797 0.000459 0.371988 2.206066 2.071707 0.354923
RAILPC 0.000254 0.000268 0.000351 0.000105 6.85E-05 -0.427454 2.374049 1.964692 0.374432
42
42
42
42
42
42
42
In this analysis, the Johansen (1990) multivariate cointegration approach Granger 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 (1987), 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 (Dickey and Fuller, 1979) and Phillips-Perron tests (Phillips and Perron, 1988). Before proceeding with the cointegration analysis, it is necessary to determine the optimal lag length using Schwartz criteria (SC) and Akaike information criteria (AIC) (Akaike, 1994). 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 8
cointegrated. Dramatically cointegration relationship implies the existence of long run equilibrium relationships among the variables. Indeed, Granger (1986) argued that a test for cointegration can thus be thought of as a pre-test to avoid βspurious regressionβ situations and to know the common trend of the variables (Engle and Yoo, 1987). The Johansen (1988) 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. 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 the following equations: βππ‘ = π1 + βππ=1 πΌ11π βππ‘βπ + βππ=1 π½12π βππ‘βπ + βππ=1 π½13π βππ‘βπ + βππ=1 π½14π βππ‘βπ + βππ=1 π½15π βππ‘βπ + πΏ1 πΈπΆππ‘β1 + ππ‘
(1)
βππ‘ = π2 + βππ=1 π½21π βππ‘βπ + βππ=1 π½22π βππ‘βπ + βππ=1 π½23π βππ‘βπ + βππ=1 π½24π βππ‘βπ + βππ=1 π½25π βππ‘βπ + πΏ2 πΈπΆππ‘β1 + ππ‘
(2)
βππ‘ = π3 + βππ=1 π½31π βππ‘βπ + βππ=1 π½32π βππ‘βπ + βππ=1 π½33π βππ‘βπ + βππ=1 π½34π βππ‘βπ + βππ=1 π½35π βππ‘βπ + πΏ3 πΈπΆππ‘β1 + ππ‘
(3)
βππ‘ = π4 + βππ=1 π½41π βππ‘βπ + βππ=1 π½42π βππ‘βπ + βππ=1 π½43π βππ‘βπ + βππ=1 π½44π βππ‘βπ + βππ=1 π½45π βππ‘βπ + πΏ4 πΈπΆππ‘β1 + ππ‘
(4)
βππ‘ = π5 + βππ=1 π½51π βππ‘βπ + βππ=1 π½52π βππ‘βπ + βππ=1 π½53π βππ‘βπ + βππ=1 π½54π βππ‘βπ + βππ=1 π½55π βππ‘βπ + πΏ5 πΈπΆππ‘β1 + ππ‘
(5)
Where ππ‘ ,ππ‘ ,ππ‘ ,ππ‘ , πππ ππ‘ denote respectively Roadpc (in model 1) or Railpc (in model 2), TVAPC, GCFPC, roadecpc (in model 1) or railecpc (in model2), πΆπ2pc; β is the difference operator; ECT refers to the error correction terms derived from the long run cointegrating relationships; ππ‘ 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 (π½π ) of the explanatory variables. The coefficients (Ξ΄) 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 shortβrun causality is detected using a standard X 2 Wald Statistic. 9
5. Results and discussion The results of the Augmented Dickey Fuller (1979) ADF and Phillips Perron (1988) 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 First difference Level 3.237 -4.538
TVAPC GCFPC RAILECPC ROADECPC 1%critical value 5% critical value 10% critical value RAILPC ROADPC πΆπ2 PC 1%critical value 5% critical value 10% critical value
PP test statistic Level 3.175
First difference
-4.512
0.909 -0.491745 3.639676
-7.323435 -7.748226 -5.025692
0.909 -0.479438 10.60484
-7.251 -7.705833 -5.075418
-2.622585 -1.949097 -1.611824 -2.164184 -2.345761 -1.190844
-2.624057 -1.949319 -1.611711 -5.595830 -5.723687
-2.622585 -1.949097 -1.611824 -2.164184
-2.43802
-2.624057 -1.949319 -1.611711 -5.805916 -5.704038
-7.457628 -4.205004 -3.526609 -3.194611
-1.190844 -4.198503 -3.523623 -3.192902
-7.491292 -4.205004 -3.526609 -3.194611
-4.198503 -3.523623 -3.192902
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*=1. Table 6. Selection of lag length Models Model 1
Model 2
Lag
LogL
FPE
AIC
SC
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-16*
-13.43118 -20.89359 -20.45313 -20.28149 -20.39989 -22.73609*
-13.21349 -19.58744* -18.05852 -16.79842 -15.82837 -17.07611
0 1 2 3 4 5
349.2977 507.0845 533.4386 548.1941 577.4446 617.8787
5.69e-15 4.41e-18* 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 -24.48220* -23.46694 -21.82472 -20.96602 -20.71184
* 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.
10
Following that the five series are integrated and have the same order, the cointegration relationships among them are detected by employing Johansenβs 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 πΆπ2 emissions may exist between them. Table 7. Results of Johansen cointegration tests Models
Rank test (Trace) Number of cointegrati on
Eigenvalue
None * At most 1 At most 2 At most 3 None * At most 1 At most 2 At most 3
Trace Statistic
5% Critical Value
Prob.**
MaxEigen Statistic
0.544935 0.285797 0.194858 0.153505
60.30800 28.81540 15.35185 6.682384
69.81889 47.85613 29.79707 15.49471
0.2260 0.7770 0.7568 0.6148
31.49260 13.46354 8.669470 6.666031
33.876 27.584 21.131 14.264
0.0938 0.8578 0.8581 0.5294
0.533401 0.332950 0.232029 0.157486
68.88267 38.39127 22.19563 11.63552
69.81889 47.85613 29.79707 15.49471
0.0592 0.2852 0.2878 0.1753
30.49140 16.19564 10.56011 6.854608
33.87687 27.58434 21.13162 14.26460
0.1203 0.6491 0.6909 0.5065
Model 1
Model 2
Rank test (Maximum Eigenvalue)
5% Critical Value
Prob.**
* 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.
Based on cointegration test results, the VECM presented in equations (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 11
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 Rudra (2013), Satya (2003), Banister and Barchemanan (2003). Therefore, the standard Granger test would have concluded that there is a unidirectional long run nexus running from road transport related energy consumption and πΆπ2 emissions to road transportation infrastructure. This implies that energy consumption and πΆπ2 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 Newman and Kenworthy (1988), Stoel (1999) and Bruekner (2000). When considering model 2, the coefficient of the error correction term is statistically significant at the 5% level in equation (4). Hence, results show 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. 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 (Belloumi, 2009). 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 πΆπ2 emissions are linked to energy consumption and the rate or quality of railway infrastructure too.
12
Table 8. Results of causality tests Models
Depend ent variable
Source of causation (short-run) βπ π
βππ
βππ
βWt
βππ
βXt
-
1.53E07 (0.24)
6.90E-08 (0.77)
-5.013 (0.33)
0.002 (0.16)
βYt
421283.4** (0.05)
-
0.0406 (0.9)
-6962834 (0.32)
2797.8 (0.26)
βZt
157559.3 (0.18)
-0.03 (0.74)
-
-1004701 (0.79)
225.650 (0.86)
βWt
0.004 (0.71)
1.41E09 (0.87)
2.49E-08 (0.13)
-
-0.0001 (0.18)
βVt
-3.889 (0.89)
1.55E06 (0.94)
1.6E-05 (0.72)
1325.478 (0.10)
-
βXt
-
-2.31E-08 (0.75)
-2.76E-08 (0.53)
1.73 (0.35)
-0.0006*** (0.0005)
βYt
1065877 (0.44)
-
0.1137 (0.73)
-20420546 (0.14)
1867.6 (0.2)
βZt
952946 (0.19)
0.002 (0.98)
-
-3278798 (0.65)
400.14 (0.6)
βWt
-0.032* (0.03)
1.6E-09 (0.42)
5E-09 (0.17)
-
-1.45E-05 (0.37)
βVt
-194.2 (0.32)
-2.97 E-07 (0.99)
3.34E-05 (0.48)
-78.3 (0.96)
-
Model 1
Model 2
Source of causation (long-run) ECT
-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) 0.0286 [0.44] (0.65) -446176.1 [0.912] (0.36) -147525 [-0.57] (0.56) -0.013* [-2.29] (0.02) 145.26 [2.086] (0.03)
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.
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 πΆπ2 emissions are represented respectively in Figures 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 ten years (Enders, 2004). 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 figure 1, the generalized impulse response function displays the reaction in one variable due to shocks 13
stemming in other variables. It shows whether the effect of the innovation remains or dies out quickly. According to these results, innovations in road infrastructure initially have a significantly positive impact on the transport value added and πΆπ2 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 πΆπ2 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 isnβt 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 figure 2. The response in πΆπ2 emissions is increasing 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 significantly positive in time.
14
Res pons e of ROADPC to Choles ky One S.D. Innovations
Res pons e of ROADECPC to Choles ky One S.D. Innovations
.00012
.000006
.00008
.000004
.00004 .000002 .00000 .000000
-.00004
-.00008
-.000002 1
2
3
4
5
ROADPC GCFPC
6
7
8
ROADECPC T VAPC
9
1
10
2
CO2PC
3
4
5
ROADPC GCFPC
Res pons e of CO2PC to Choles ky One S.D. Innovations
6
7
8
ROADECPC T VAPC
9
10
CO2PC
Res pons e of GCFPC to Choles ky One S.D. Innovations
.020
60 50
.016 40 .012
30
.008
20 10
.004 0 .000
-10 1
2
3
4
5
ROADPC GCFPC
6
7
8
ROADECPC T VAPC
9
1
10
CO2PC
160 120 80
40 0
-40 2
3 ROADPC GCFPC
4
5
6
7
ROADECPC T VAPC
8
9
3 ROADPC GCFPC
Res pons e of TVAPC to Choles ky One S.D. Innovations
1
2
10
CO2PC
Figure 1. Results of impulse response functions (model 1)
15
4
5
6
7
ROADECPC T VAPC
8
9 CO2PC
10
Response of RAILPC to Cholesky One S.D. Innovations
Response of RAILECPC to Cholesky One S.D. Innovations
.000020
.0000015
.000016
.0000010
.000012
.0000005
.000008 .0000000 .000004 -.0000005
.000000
-.0000010
-.000004 -.000008
-.0000015 1
2
3
4
5
RAILPC GCFPC
6
7
8
RAILECPC T VAPC
9
1
10
2
CO2PC
3
4
5
RAILPC GCFPC
Response of CO2PC to Cholesky One S.D. Innovations
6
7
8
RAILECPC T VAPC
9
10
CO2PC
Response of GCFPC to Cholesky One S.D. Innovations
.015
60 50
.010 40 .005
30 20
.000
10 -.005 0 -.010
-10 1
2
3
4
5
RAILPC GCFPC
6
7
8
RAILECPC T VAPC
9
1
10
CO2PC
150
100
50
0
-50 2
3 RAILPC GCFPC
4
5
6
7
RAILECPC T VAPC
8
9
3 RAILPC GCFPC
Response of TVAPC to Cholesky One S.D. Innovations
1
2
10
CO2PC
Figure 2. Results of impulse response functions (model 2)
16
4
5
6
7
RAILECPC T VAPC
8
9 CO2PC
10
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 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. Table 9. Variance decomposition analysis of model 1 Period S.E. ROADPC Variance Decomposition of ROADPC
ROADECPC
CO2PC
GCFPC
TVAPC
1 8.22E-05 100.0000 5 0.000191 83.18121 10 0.000263 64.57021 Variance Decomposition of ROADECPC 1 5.53E-06 0.020941 5 1.29E-05 2.145499 10 1.81E-05 2.405945 Variance Decomposition of CO2PC 1 0.015687 1.689085 5 0.038581 10.25684 10 0.063391 16.75766 Variance Decomposition of GCFPC 1 59.79213 0.000508 5 114.4423 2.514673 10 150.2317 1.821698 Variance Decomposition of TVAPC 1 110.2759 2.519462 5 317.1188 18.79505 10 470.4826 21.00867
0.000000 1.773250 5.099725
0.000000 2.621512 3.898071
0.000000 8.921032 22.87450
0.000000 3.502999 3.557491
99.97906 87.77044 87.65336
0.000000 2.207124 2.076672
0.000000 7.720119 7.632203
0.000000 0.156815 0.231816
84.38583 73.66303 63.18257
13.92509 2.852709 1.184713
0.000000 13.14167 18.62980
0.000000 0.085759 0.245252
0.949102 0.387736 0.235318
1.427143 0.619498 0.439585
97.62325 96.36938 97.36583
0.000000 0.108715 0.137573
2.087956 1.684485 1.571689
6.531253 1.690828 1.238133
0.087754 0.050267 0.036274
88.77357 77.77937 76.14523
17
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 the establishing of railways infrastructure motivates people to sprawl, so more travels and more energy consumption (Newomann and Kenworthy, 1988). 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. Table10. Variance decomposition analysis of model 2 Period S.E. ROADPC Variance Decomposition of Railpc
ROADECPC
CO2PC
GCFPC
TVAPC
1 1.51E-05 100.0000 5 3.70E-05 82.52039 10 4.91E-05 73.81542 Variance Decomposition of RAILECPC 1 1.26E-06 0.423220 5 2.60E-06 34.74542 10 4.16E-06 54.13744 Variance Decomposition of CO2PC 1 0.016246 34.36034 5 0.032557 13.56808 10 0.048565 6.897110 Variance Decomposition of GCFPC 1 59.97066 5.777441 5 116.2444 12.10927 10 155.2395 10.90596 Variance Decomposition of TVAPC 1 114.0689 5.826309 5 310.1144 5.280717 10 447.1879 5.570521
0.000000 1.785079 2.961995
0.000000 10.28275 11.53100
0.000000 2.365889 5.188870
0.000000 3.045896 6.502713
99.57678 61.33113 37.52387
0.000000 1.697336 1.230934
0.000000 1.423779 3.844117
0.000000 0.802332 3.263647
23.14503 33.44947 35.42619
42.49464 39.19691 32.85157
0.000000 9.450800 15.58065
0.000000 4.334734 9.244481
2.599473 1.228882 0.899443
3.416159 3.468968 3.404843
88.20693 82.60614 83.42354
0.000000 0.586739 1.366217
1.993034 6.538764 7.082123
0.025112 1.400511 1.326482
0.436612 0.295385 0.237768
91.71893 86.48462 85.78311
18
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. Table 11. Results of Breusch-Godfrey Serial Correlation LM test Model 1
F-statistic
Prob. F (12,21) 1.004394
0.4779 Prob. Chi-Square (12)
Model 2
Obs*R-squared F-statistic Obs*R-squared
14.58606 1.656439 1.968644
0.2649 Prob. F (1,32) Prob. Chi-Square (1)
0.2073 0.1606
Table 12. Results of Breusch-Pagan-Godfrey Heteroskedasticity test Model 1
Model 2
F-statistic
Prob. F (10,29)
Obs*R-squared
0.547102 6.348540
F-statistic Obs*R-squared Scaled explained SS
0.711446 7.879901 9.184330
Prob. Chi (10) Prob. F (10,29) Prob. Chi-Square (10) Prob. Chi-Square (10)
0.8420 0.7852 0.7067 0.6406 0.5147
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 πΆπ2 emissions in Tunisia over the period of 1971-2012. Using the Johansen multivariate cointegration technique, this study concluded the following: ο·
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.
ο·
Unidirectional causality running from gross capital formation to road infrastructure. Whereas, the results of the impulse response functions provide evidence that an unanticipated innovation in road infrastructure has a significantly negative impact on the gross capital formation. This implies that in Tunisia, the level of road 19
infrastructure is not fairly distributed in all the territory. In order to raise the economic prosperity, 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 πΆπ2 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 πΆπ2 emissions causesβ 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 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 transportrelated 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 (Belloumi, 2009). Unidirectional causality running from πΆπ2 emissions to rail transport related energy consumption in the long term and the increase relationship running from road transport related energy consumption to πΆπ2 emissions. This implies that most of πΆπ2emissions come from energy consumption. Thus, the Tunisian policy makers should sensitize the motorists to environmental problem and substitute clean energy 20
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 (Stead, 2001). 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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Suggested Reviewers: 1- Abdullah Alshehri Department of Electrical Engineering, Faculty of Engineering, King Abdulaziz University, P. O. Box 13134, Jeddah 21493, Kingdom of Saudi Arabia
[email protected] 2- Εukasz Lach AGH University of Science and Technology
[email protected] 3- Chor Foon Tang Department of Economics, Faculty of Economics and Administration, University of Malaya, 50603 Kuala Lumpur, Malaysia
[email protected]
25
