Abstract—The relationship between economic growth and development of freight transport demand generates generally an increase in energy consumption. Measures are implemented to improve the energy efficiency of freight transport. The purpose of this paper is to evaluate the effectiveness of Tunisian policies proposed through the decomposition technique of the intensity of road freight transport.
Keywords-transport intensity ; energy effeciency ; coupling ; decomposion method
I. INTRODUCTION Generally, economic growth is a key generator for
transport. In this case, the relationship is characterized by the coupling phenomenon that appears when a strong correlation between economic growth accompanied by the application of carriage. The coupling is "the relationship of induction of transport demand from economic growth."[1]. Economic growth is synonymous with an increase in domestic production that involves a substantial exchange of goods and thus an increase in transport demand. In other words, GDP growth will be accompanied by a change in the intensity of transport 1 . The need to shift to consumer markets, raw materials and works hands increases.
However, demand for transport is not controlled sources of negative externalities that especially the increased consumption of nonrenewable energy. The limitation of oil reserves and the preservation of the environment require exploring all the leeway to reduce energy consumption and its impacts on the environment without affecting the pace of economic growth. Thus, the issue of energy efficiency is a goal sought by energy and transport policies of the country. The energy efficiency of different transport modes is represented by the amount of traffic carried on average load (ton-km for freight and passenger-kilometer for people) per unit of energy consumed. This is an indicator used in evaluating the efficiency of transport modes.
Efficiency in land transport is defined as the annual consumption of gasoline or diesel fuel for heavy trucks or commercial vehicles by average annual number of miles traveled by heavy trucks or commercial vehicles and diesel fuel multiplied by the average tonnage carried for each of
1 The transport intensity measures the transport demand needed to create
a unit of GDP.
these vehicles [2]. Improving the energy efficiency of transportation is used as a means to achieve a decoupling between economic growth and transport demand. The need to respond to increased demand for mobility is emphasized [3]. Some authors define the decoupling. It is to grow economically without using the same extent resources and pollute the environment [4]. A study on the phenomenon of coupling to Austria made aimed to develop indicators to be introduced into European policies decoupling in the transport sector [5]. Many practices are suggested for decoupling based on the modal and the absolute focus on reorganization and restructuring of logistics chains and production units [1]. We must act in advance to cope with negative externalities from transport [6]. Appropriate policies for the establishment of a transportation system more energy efficient in several areas of transportation (technological, technical, legislative, regulatory and monetary) instruments such as the restructuring logistics and relocation of production units [1], tariffs and regulations [7], modal shift [8] and multimodality and intermodality [9]. The effectiveness of these measures and instruments depends on the process of evaluating the energy efficiency in the transport sector. The evaluation process is generally performed, using models of transport intensity and the method of decomposition.
The objective of this work is to analyze the relationship between the intensity of freight transport and energy intensity consumed by the transportation of goods in Tunisia. Moreover, its problems are to get into how some policies are more effective to ensure the decoupling between the two intensities? To do so, we have used the decomposition of the intensity of road freight factor coupling and decoupling to see the interactions between them and their impact on the intensity of freight transport.
This paper is organized as follows: Section 2 is devoted to a review of the literature on the problems of coupling, decoupling and energy efficiency. Section 3 is reserved for the presentation of both models needed to assess the coupling that the decomposition of the intensity of freight transports. Section 4 presents the estimation results and their interpretation. Section 5 concludes.
II. LITTERATURE REVIEW In order to examine the linkage between economic growth
and transport demand, there are two approaches in the literature. The first is an estimate of an aggregate indicator of coupling. This indicator is the intensity of freight transport that is the number of freight units needed to produce one unit
of economic output. The evolution of the intensity of freight transportation allows distinguishing a situation of coupling a decoupling situation. For the case of freight transport, this indicator is the ratio between the number of transport units observed in a country (ton-kilometers for freight transport) and the value of its GDP. The transport intensity of country is, denoted by ITI, and written as:
ITR = TKi / GDP
TKi is the number of ton-kilometers transported in country i.
A decrease in the intensity of freight transport is marked in Germany, but, instead, increased the intensity of road transport and industrial production [10]. Some papers compared changes in transport intensity in the U.S. versus the European Union (EU) and they explained its reduction in the EU is made to action to realize the decoupling [4]. Another study underlined the relationship between transport intensity and efficiency of transport in Europe for the transport of goods [11]. They use different indicators in this respect as the tonnage per kilometer. However, some criticism about the model of transport intensity exists [12]. The variable ton-km does not provide a quantitative measure of demand for freight transport. The model does not distinguish situations of coupling or decoupling. Even if the variables are operational, this approach is descriptive. It offers no explanation for the relationship between transport demand and economic activity.
The second approach is based on the idea of decomposing the transport intensity factor of coupling or decoupling. This idea comes mainly from the energy economy. An extensive literature review exists. At this level, we distinguish two approaches. The first corresponds to models that incorporate CO2 emissions or the amount of energy consumed. This approach involves the direct application of decomposition models of energy intensity in transport sector. In this context, different works to decompose at about this subject are made [14].The main factors frequently used in this work correspond to the transport activity (transport volume produced), the structure of activity (mode share) and energy intensity (energy used per unit of transport). This work shows the limits of a strategy of decoupling on only concerned with technological progress.
The second approach introduces or energy consumption or GHG emissions as It focuses on the correlation between demand for freight transport and GDP and is mainly European and extends the research program REDEFINE [15]. Studies on the relationship between economic growth and demand for transportation purposes, primarily on the phenomenon of coupling between the two, have used the time series econometrics. Others estimated the elasticity of travel demand caused by changes in GDP [3].
Many papers used time series methods to forecast travel demand due to economic growth [16]. In this context, several works have addressed the relationship between CO2 emissions and changes in travel demand caused in turn by economic growth. So, the energy efficiency on the road transport sector in Germany is examined and its relation to CO2 emissions [17]. Another study emphasized the correlation between
economic growth and road transport in Austria and a relationship between transport, CO2 emissions and global warming [18].
III. MODELING AND ESTIMATION
A. Construction of variales and models The study of interactions between transport intensity and
the energy and the impact of coupling and decoupling of energy use for transport of goods assume the existence of causal relationships between several variables. This work from a combination of variables belonging to different spheres (transport, economic growth and energy). Indeed, variables may combine factors revolving around freight. They can help develop the transport sector, to participate in economic growth expressed by GDP, and at the same time influence its energy consumption. The data are not readily available in their usual forms and statistics used in this study is the result of a personal reconstruction from various data available from different sources (Appendix 1). In fact, the variables are expressed as ratios (indicators) such that the intensity of freight transport (IT), the intensity of energy consumed by the transportation of freight (IE), the intensity of road freight transport (ITR), the total energy consumed by road transport (ET), the share of energy needed to transport one ton of freight by road (TR), the share of freight transport sector in the economy (TP), the modal share of road freight transport (TT) and the distance traveled by a ton of freight (TKR). To analyze the relationship between transport demand and intensity of energy and determine the status of coupling the decoupling between the evolution of energy consumption and the demand for freight transport, we adopted the model following:
IE = a IT + b
Where a and b are the unknown coefficients of the model.
In addition, to examine factors that promote improved energy efficiency of freight transport sector, we adopt the method of decomposition of the intensity of road freight transport in factors coupling and decoupling. Subsequently, an application of the techniques of time series has taken place, to show the influence of each factor on the intensity of road transport and energy efficiency. The decomposition of intensity cargo is modeled as follows:
ITR = ER / TKR × TR / RE × TKT / GDP × TKR / TKR × TKT / TR = TKR / GDP with:
ET = ER / TKR: Energy consumed by road transport (the energy efficiency of road transport).
TR = TR / ER = Share of energy required for each ton transported by road.
TP = TKT / GDP = Share of transportation (freight) in the economy.
TT = TKR / TKT = Modal share of road transport.
TKR TKR = / TR = Distance traveled by a ton of freight (road).
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TKR = ITR / GDP = Intensity of road freight transport
We see, through this identity, five ratios that represent coupling factors and other factors decoupling. In reality, there is a real link between these indicators. Indeed, in parallel with economic growth, the share of road freight transport in the economy has greatly increased in recent years. It has generated an increase in distance traveled and energy consumption per ton carried. Therefore, the energy intensity of road freight transport has experienced an important development. Among the factors cited, it is important to identify factors that degrade and those that improve energy efficiency of the system of road freight transport in Tunisia.
B. Emprical estimation In the first part, our goal is to study the relationship
between the intensity of freight transport and energy intensity consumed by freight transport and the correlation between them. The variables are subjected to econometric tests such as the stationary, cointegration and Granger causality. The equation for the normalized relationship is then written as follows:
D (Log (IE)) = 8.340956 + 0.717821 * D (Log (IT)) (0.12535) (0.32092)
The results concerning the coefficients of the equation of the normalized relationship demonstrate the existence of a long-term relationship between the intensity of the energy and transport. The increase in freight traffic induces an increase of 71% of energy consumption (Annex 2). In the sense of Granger causality, the null hypothesis of Granger causality test is verified. In fact, F-statistic is less than 5% in either direction of causality (transport intensity because energy intensity and vice versa), so the test is substantially zero, we accept the hypothesis H0 and void. No causality has occurred between the intensity and the energy transport. An alternative hypothesis is interpreted from the probability (p-value) if p-value> 5%, therefore, we accept H0 and in this case there is no causality. If pv <5%, therefore, H0 is rejected and there is a causality in one direction or another. In this case, pv <5%, then H0 is rejected, H1 is accepted. There is thus a causal relationship between transport intensity and the energy. The Granger causality test shows the existence of a causal relationship, but instead, it does not determine the direction of causality.
In a second part, our goal is to decompose the transport intensity estimate the impacts of factors. To do this, we also apply a series of test ratios such that the stationary tests, cointegration and causality (Appendix 3). Of course, the raw series were taken in logarithmic form to avoid fluctuations (Appendix 3). According to the ADF test, and from the results generated, the values associated with variables are greater than the critical values of MacKinnon degrees of significance of 1%, 5% and 10%. The hypothesis of the existence of unit root for selected ratios is rejected. The series is not stationary. Integration of first order I (1) took place. Examining values of statistical evidence shows that there is a cointegration between the intensity of road transport (ITR) and ratios chosen. In fact, the assumption of the absence of coevolution between transport intensity and ratios chosen is rejected, whereas the
hypothesis of the existence of a relationship is more accepted. So it follows from Johansen cointegration test that there is a phenomenon of cointegration between these variables. There is therefore a cointegrating relationship between the intensity of road freight transport in Tunisia and the selected ratios representing the coupling factors, and factors of decoupling. The equation for the normalized relationship is written as follows: D (Log (ITR)) = 5.944327 - 0.686203 D (Log (ET)) +
(-1.1335) (-0.2138)
0.347195 D (Log (TR)) + 0.583911 D (Log (TP)) + (0.2871) (-1.3559) 0.656342 D (Log (TT)) - 0.420246 D (Log (TKR)) (0.1443) (2.6733)
Values in parentheses are standard errors (Appendix 4).
IV. INTERPRETATION
A. Interpretation of the equation: intensity of freight transport-energy intensity The analysis of the relationship between the intensity of
freight transport and energy intensity is aimed at understanding the causes of this relationship and create a favorable framework for the establishment of a policy of transport as objective to develop the transport system and make it more efficient and sustainable. It is clearly therefore, to evaluate its efficiency. Returning to the equation cointegration, the estimated value of the constant is significantly positive. This means that the hypothesis of co-evolution can be accepted. On the other hand, the transport intensity because energy intensity. With a correlation coefficient of 78%, we can conclude by emphasizing the close relationship between the increase in demand for freight transport and energy consumption and greater interdependence among them. This relationship between transport intensity and energy intensity has been reproduced and has intensified in recent years due to several factors and characteristics of the freight sector in Tunisia, as well as increased demand and requirements of the Tunisian economy. The supply of freight was a significantly improved especially in recent years under policies reflecting investment in road infrastructure. The rolling stock on its part has seen remarkable growth over the same period. The number of trucks and heavy vehicles has evolved continuously from 1980 until 2008 with a trend rate of close to 6%. The improvement and growth of transport flows of goods were performed in the framework of the objectives of development plans to ensure the fluidity of the national economy and meet the needs economic actors and production units.
B. Interpretation of results from decompostion The analysis of the relationship between transport intensity
and ratios deals with the impact thereof on the intensity of transport. The aim of this decomposition is to see the effect of coupling factors and decoupling the transport intensity and its evolution. In fact, it is to evaluate the effectiveness of energy policy adopted in the sector of road haulage. The relationship between transport intensity and the first coupling factor, the share of freight transport, reflects the relationship between it and GDP (58%). The increase in the intensity of road transport
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increases the coupling phenomenon and reinforces the link between economic growth and development of transport demand. However, road transport is the second largest consumer of energy in Tunisia. Therefore, this coupling relationship is unfavorable for energy efficiency in this sector. Given the scarcity of oil and rising prices of energy exports due to global economic and political circumstances, the relationship between economic growth and that of road transport and above will be more favorable. It would then question whether the gains in productivity or not replace the energy consumption costs of road transport. In addition, it is convenient to check whether these costs will be lower or higher than the costs of implementing energy efficiency policies. The second report is the relationship between the intensity of freight transport by road and the modal share of road transport. The link between these two ratios is evident (66%). The coupling between the GDP of transport is strongly supported by increasing the modal share of road transport. The use of road transport for freight transport within the country of Tunisia is more advantageous compared to rail through the extensive road network compared to the rail network; it is more accessible and flexible. Transits with countries neighborhood, road transport is more advantageous compared to other modes in terms of cost. Increasing the modal share of road transport increased its share of energy consumption. The third report concerns the relationship between the intensity of road transport from every ton transported by road to energy. This report is closely linked to the previous report on the modal share of road transport. Sure, when the modal share of road increases its share in energy increases. Subsequently, the volume of energy needed to transport one ton of freight on the road increases. This ratio contributes to the energy inefficiency of road transport. More energy consumption of a tone of goods transported by road, the higher energy mode is ineffective, the higher the transportation system is far from being sustainable. This report represents the linkage between economic growth and energy consumption for road transport of goods (58%). It measures the impact of economic growth and increasing the intensity of road transport energy consumption for this mode. Surely, the rise of energy consumption for this mode requires work on mastering the energy consumption for road transport.
Regarding decoupling factors, represent the axes on which transport policies and actions taken by politicians and decision makers must take action to improve energy efficiency and develop a transport system that meets durability requirements. Indeed, the first factor looks to the distance traveled by a ton of goods on the road. The distance is an important axis on which transport policies are. In fact, to decouple the link between GDP and transport demand, it is not easy to reduce the quantities transported. These goods represent the needs of markets to sustain economic growth. It is easier to act on the distance that reduces the quantities transported for a given economic situation. The idea to separate the link between economic growth and the transport is to reduce the share of road freight over other alternative and greener energy requirements. Reducing the modal share of road transport in favor of alternative energy is the most effective solution. The modal shift to rail transport and increase its share requires the improvement of supply conditions for this mode (accessibility,
capacity, network quality). Thus, implementation of logistics platforms and equipment of the terminal equipment required for the execution of an efficient multimodal service is becoming increasingly necessary for that rail becomes more attractive and advantageous compared to heavyweight. User awareness of the advantages of rail transport is also necessary for successful policy of modal shift. Modal shift is a kind of relative decoupling. The latest report is on energy efficiency for road transport. Indeed, when the intensity of road transport increases, the efficiency decreases. Of course, when the share of road transport, the largest energy user, increased its share of consumption increases thereafter, reducing its efficiency. Dissociation of the relationship between energy consumption and demand for road transport on the one hand, and the relationship between the consumption for this mode and economic growth is a solution. Moreover, corrective actions must contribute to the mastery and the economy in the consumption of energy. Indeed, pricing instruments are aimed to reduce environmental pollution through the internalization of external costs. Implementations in urban areas, environmental fiscal measures and / or road tolls are now required to balance costs and achieve a sustainable transport system. However, the toll contributes to a more massive carry freight on the rail, yet desirable. Mobility can also be limited by restricting access to certain roads or removing them altogether which reduces mobility by temporarily reducing the total capacity of the road network and increases the cost of driving and thus may have thereon an effect moderator. In general, the access limitations or deletions of roads force drivers to take alternative routes, to focus on other modes of transport to choose other destinations, or at least move. On the other hand, it is also possible to act on other tax benefits related to the use and maintenance of vehicles. The truck is specified by a tax on traffic in central city whose goal of limiting the movement and its impacts in the urban centers. On the other hand, taxes on fuels are the main source of tax revenue in the transport sector as in consumption taxes. Higher taxes on gasoline is a relatively straightforward user charge because it is more or less linked to mileage, it acts directly on consumption and hence emissions of carbon dioxide (CO2), which offers the prospect of influencing directly and more immediately.
V. CONCLUSION The objective of our work is to seek the causalities among
the variables used in both models. The estimate of the first model showed that changes in the intensity of freight transport in Tunisia, followed by co-evolution of energy intensity. Econometric tests show the existence of a causal relationship between these two variables. This interdependence can be explained by the greater reliance on road transport and the inadequacy and lack of equipment and logistics facilities to help promote multimodality. Consequently, this leads to increased intensive energy sector for transportation of freight.
The application of the decomposition method in the second model and estimated coefficients associated with each ratio show a correlation between economic growths, increased modal share of road transport fuel consumption. This close relationship negatively influences the efficiency of road transport. In addition, estimates show a co-evolution of ratios
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and between them and the intensity of road freight transport. Actions should be on reducing the distance traveled by road and energy consumption in this mode. This decomposition aims to highlight the role of modal measures, taxation and tariff reduction in energy intensity. Certainly, the integration of these measures in sustainable transport policies can improve the energy efficiency of this sector. The analysis of the causes of the increase in transport intensity in Tunisia is meaningless without an insight into the logistics and location of production units in the area. In fact, several remarks made there under. The logistics and location of production units depend on the characteristics of the Tunisian economy, but in terms of strategies and policies for several sectors (industry, employment, trade, energy, transport, planning).
Indeed, the production units are highly localized in Tunis (especially the area of Rades) and on the coastal zone. Production units are generally capable food and textiles. They are strongly linked to agriculture sector (10% of the economy). The distribution of these units between these areas is justified by research to be located at ports (sea and air) to facilitate the exchange with the outside. However, with logistics platforms that the Tunisian government is planning to install the short term it would be possible to reduce distances and improve the energy efficiency of road freight transport.
REFERENCES [1] G.Joignaux, J.Verny (2004): "The decoupling of freight transport and
growth: productive organizations, locations and transport demand" Journal of Urban and Regional Economics No.5.
[2] ADEME (2003): "Indicators of Energy and Environmental Efficiency Transport ", the technical specification 3 of ADEME.
[3] H.Meersmann, E.Van de Voorde (1997): "The growth of freight transport is it preventable? ", in ECMT, Which Changes for Transport in the next century?, 14th International Symposium on the Theory and Practice in Transport Economics, Paris: OECD Publications, pp, 23-51, France.
[4] R.Gilbert, K.Nadeau. (2001): "Decoupling Economic growth and transport demand: a requirement " for sustainability ", Paper Prepared for Transportation and Economic Development, 2001,Portland, Oregon, 5-7
[5] A.Niederl, K.Steininger, M. Herry, N.Sedlacek,V.Gaube, H.Schandl (2003): "Decoupling Economic Growth and Transport Demand : Case Study Austria", OECD Project, 2003.
[6] H.D.Waisman (2006): "Environmental impacts of freight mobility: Master of upstream traffic flows through a reorganization of logistics systems", Paris X-Nanterre University, Graduate School of Economics and Mathematics from Paris-Ouest, France
[7] A.Kossak (2001): "Tolls for trucks - the correct time? "International; The transport sector, 2001
[8] S.Bologna (2002): "Market players rail against open" ECMT, 125th Report, Panel on Transport Economics, European integration of rail freight, pp 29-60, OECD, Paris, 2002.
[9] M.H.Massot (2002), "Intermodal and multimodal in the field of urban transport ; MASTER SIT Meeting, 2002.
[10] H.Baum, J.Kurte (2000): "Transport and Economic Development, Report of Round Table 108 on the economies of transport, ECMT, pp 5-47, Paris, France, 2001.
[11] D.Stead (2001): "Transport intensity in Europe: Indicators and trends", Bartlett School of Planning, University College London, Transport Policy 8 (2001).29-46; UK.
[13] B.W. Ang ; F. Q. Zhang. (2000): "A Survey of index decomposition analysis and environmental studies. » Energy, 25, 1149-1176.
[14] J.Brunel (2005): "Freight transport and economic growth, " ASRDLF. Cities and territories face challenges of globalization - XLI ASRDLF symposium, 5-7. 2005, Dijon, France.
[15] REDEFINE (1999): "Relationship Between Demand for Freight-transport and Industrial Effects. " Summary Report, NIS
[16] Lenormand (2002): "Weather in the co-integrated models with rupture: application to demand land transport freight and passengers. " Thesis for PhD-Economics, University of Paris 1 Panthéon-Sorbonne,France.
[17] J.Leonardi; M.Baumgartner (2004): "CO2 Efficiency in road freight transportation: Status quo, measurements and potential". Transportation Research Part D 9 (2004) 451-464, Hamburg,
[18] Tanczos.K, Torok.A (2007): "The Linkages Between Climate Change and Energy Consumption of Hungary in the road transportation sector ". Transportation - 2007, Vol XXII, No. 2, 134-138,2007
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Appendix1: Statistical Sources of Variables
Appendix 2: Trends in transport intensity and energy intensity during
the period (1990-2008)
Appendix 3: Changes in series over time
-10.2
-10.1
-10.0
-9.9
-9.8
-9.7
-9.6
-9.5
-9.4
-9.3
90 92 94 96 98 00 02 04 06 08
LOG(ITR)
-8.65
-8.60
-8.55
-8.50
-8.45
-8.40
-8.35
-8.30
90 92 94 96 98 00 02 04 06 08
LOG(ET)
-2.0
-1.8
-1.6
-1.4
-1.2
-1.0
-0.8
90 92 94 96 98 00 02 04 06 08
LOG(TR)
-8.8
-8.4
-8.0
-7.6
-7.2
-6.8
90 92 94 96 98 00 02 04 06 08
LOG(TP)
-2.2
-2.1
-2.0
-1.9
-1.8
-1.7
-1.6
-1.5
-1.4
90 92 94 96 98 00 02 04 06 08
LOG(TT)
6.8
6.9
7.0
7.1
7.2
7.3
7.4
7.5
7.6
7.7
90 92 94 96 98 00 02 04 06 08
LOG(TKR)
Appendix 4. Average, Standard deviation, Variance
ER TKR TKT
Average 0,50573684 15038,4211 73177,7368Standard deviation
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