**1. Introduction**

Economic growth based on use of non renewable energy constitutes a serious problem because of, especially, negative environmental externalities. Growth of energy consumption and gas emissions are the principal negative impacts of these modes of development. However, the sustainable development requires modes of development which demand few of energy and produce few of pollutant gas. Literature has interested of this problematic in an aggregate or disaggregate contexts. The first is concerning the relationship between economic growth, domestic energy consumption and gas emissions. The second is corresponding to the same relationship but per economic sector. Industry and transport sectors are more studied because of their important link with economic and environmental spheres. They have an important contribution in economic growth but they are responsible of several environmental externalities.

The transport is one of the major activities which consume more energy and produce gas pollutants. Majority of freight and passengers is transported by road mode which is considered an important source of fossil fuels consumption and CO2 emissions. In order to make transport sector more sustainable, some strategies should be elaborated to reduce its energy consumption and gas emissions. In other terms, governments should apply a set of instruments, such as economic, fiscal, regulatory and technological instruments, to control driving factors of transport-related energy consumption and gas emissions.

Before any strategy, it's necessary to evaluate the sustainability degree of transport sector. Sustainable transport literature give us several indicators through them it's possible to measure energy demand and gas pollutants production associated to transport activity. Examples include transport intensity, transport energy intensity, transport energy emission

© 2012 Mraihi, licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2012 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

intensity, vehicle intensity and vehicle energy intensity. Other indicators, such as modal mix (road, rail, air and water shares), energy mix for every mode (gasoline, diesel, liquefied petroleum gas (LPG), electricity or other fuel types, such as bio-carburant ), rate of motorization, transport energy consumption share and annual growth of Vehicle Park are also used to diagnostic sustainability from transport sector. Determine the driving factors of transport-related energy consumption and pollutants gas emission growth is a one of main phases. It helps to choice the optimal strategy which corresponds to every responsible factor. With all types of intensities which are evoked, economic, demographic, urban and technological factors are taken as the main factors which can influence energy consumption and so gas emissions of transport sector. Moreover, examples of influencing factors include also average travelling distance, vehicle types share (personal cars, bus, heavy and light trucks), average vehicle age, driving condition, urbanization, urbanized kilometers and national road network.

Transport Intensity and Energy Efficiency: Analysis of Policy Implications of Coupling and Decoupling 273

of transport service (*tkm* or *pkm*). Amelioration of energy efficiency from transport sector implies reduction of energy intensity and so saving energy consumption for the sector.

Transport energy intensity has studied in several contexts. Examples include studies which interest to the relationship between economic growth and transport activity and transport energy consumption. This analyze is defined in two approaches. Firstly, the economic approach which aims to study the causality relationship between them and to analyse the expenditure in energy consumption. Secondly, the ecologic approach which aims to study the correlation between them in order to determine the coupling phenomena between them and to measure and analyse energy consumption and gas emissions. Moreover, examples include studies which decomposing the intensities in order to determine the influencing

More existing studies have focused on the causality and cointegration relationship between transport energy consumption, transport activity and some main factors. Their main objective is to determine the time tendencies of their trends, the sense of their causality, the degree of their cointegration and then the coupling problem (Meersman et Van De Voorde, 1999; Kulshreshtha et al*.*, 1999; Stead, 2001; Banister and Stead, 2003; Léonardi et

Recently, Akinboade, Ziramba and Kumo (2008) have used the co-integration techniques in order to analyze the long-run relationship among the variables which explicating the aggregate gasoline demand function over the period 1978-2005 in South Africa. The results confirm the existence of a co-integrating relationship. The estimation of the price and income elasticities of gasoline demand serves to develop appropriate energy policy. The estimated elasticities show that gasoline demand in South Africa is price and income inelastic. The important policy implication is the unreliability of the public transport system in South Africa. Yaobin (2009) explains cointegration relationship between transport energy consumption growth, population growth, economic growth and urbanization process for china over the period 1978 -2008. The results show a unidirectional Granger causality running from urbanization to energy consumption both in the long and short run. Lu, Lewis and Lin (2009) have estimated the development trends of the number of motor vehicles, vehicular energy consumption and CO2 emissions in Taiwan during 2007-2025. They have adopted simulation of different economic growth scenarios in order to explore the influence

Pongthanaisawan and Sorapipatana (2010) have analyzed the relationship between motorcycle and car ownerships and level of economic development for the case of Thailand. They study the impacts of this relationship on fuel consumption and greenhouse gas emissions. Using semi-parametric techniques, the authors have shown that economic development affects the ownership of private vehicles which and so fuel consumption and gas emissions. The important conclusion of their study is that the amelioration of public transport system leads to reducing the traffic mobility of private vehicles, promotion of the vehicle efficiency and so reducing fuel consumption and gas emissions. Yan and Crookes (2010) have forecasted the future trends of energy demand of road vehicle and emissions

factors of the transport-related energy consumption growth.

Baumgartner, 2004; Tanczos et Torok, 2007).

of economic growth on energy consumption.

More existing methods has presented by sustainable transport literature to examine the relationship between economic growth, transport activity and environmental impacts and to determine the driving factors. We distinguish three principal approaches: first, causality approach that based on time series methods, Kuznets Environmental Curve model and third, decomposing analysis method.

The aim of this chapter is to analyze the sustainability of transport activity especially through the energy efficiency indicator and different associated influencing factors. To this end, the rest of the chapter is structured as followed: section 2 presents an overview of works which have focused on transport energy efficiency. Section 3 describes methodologies applied to study energy consumption from transport sector with more interesting to decomposing analysis. Section 4 presents the driving factors and policy options to ameliorate transport energy efficiency. Section 5 concludes.

### **2. Transport energy saving: literature review**

Transport activity is strongly related to economic activities. Traffics realized permit to link markets of production and consumption through the satisfaction of persons and freight transport demand. However, transport services supply is often associated to many problems that affect economy, society and environment. Negative environmental externalities appear especially if economic activity and transport services are more coupled. In this context, *transport intensity* is often used to measure transport demand and to analyze negative consequences of the coupling relationship between economic growth and transport activity. It's defined as the ratio of gross mass movement to Gross Domestic Production (GDP). It can be measured separately for the passenger services (passenger kilometres, *pkm*) and freight (ton kilometres, *tkm*). Close relationship between the growth in transport and economic growth implied an increase of transport intensity and so an increase of transport-related energy consumption and gas emissions. Transport-related energy consumption is currently measured in the literature through *energy intensity* which calculated by the ratio of transport energy consumption to transport services supply. It indicates the demand of energy per unit of transport service (*tkm* or *pkm*). Amelioration of energy efficiency from transport sector implies reduction of energy intensity and so saving energy consumption for the sector.

272 Energy Efficiency – The Innovative Ways for Smart Energy, the Future Towards Modern Utilities

national road network.

third, decomposing analysis method.

ameliorate transport energy efficiency. Section 5 concludes.

**2. Transport energy saving: literature review** 

intensity, vehicle intensity and vehicle energy intensity. Other indicators, such as modal mix (road, rail, air and water shares), energy mix for every mode (gasoline, diesel, liquefied petroleum gas (LPG), electricity or other fuel types, such as bio-carburant ), rate of motorization, transport energy consumption share and annual growth of Vehicle Park are also used to diagnostic sustainability from transport sector. Determine the driving factors of transport-related energy consumption and pollutants gas emission growth is a one of main phases. It helps to choice the optimal strategy which corresponds to every responsible factor. With all types of intensities which are evoked, economic, demographic, urban and technological factors are taken as the main factors which can influence energy consumption and so gas emissions of transport sector. Moreover, examples of influencing factors include also average travelling distance, vehicle types share (personal cars, bus, heavy and light trucks), average vehicle age, driving condition, urbanization, urbanized kilometers and

More existing methods has presented by sustainable transport literature to examine the relationship between economic growth, transport activity and environmental impacts and to determine the driving factors. We distinguish three principal approaches: first, causality approach that based on time series methods, Kuznets Environmental Curve model and

The aim of this chapter is to analyze the sustainability of transport activity especially through the energy efficiency indicator and different associated influencing factors. To this end, the rest of the chapter is structured as followed: section 2 presents an overview of works which have focused on transport energy efficiency. Section 3 describes methodologies applied to study energy consumption from transport sector with more interesting to decomposing analysis. Section 4 presents the driving factors and policy options to

Transport activity is strongly related to economic activities. Traffics realized permit to link markets of production and consumption through the satisfaction of persons and freight transport demand. However, transport services supply is often associated to many problems that affect economy, society and environment. Negative environmental externalities appear especially if economic activity and transport services are more coupled. In this context, *transport intensity* is often used to measure transport demand and to analyze negative consequences of the coupling relationship between economic growth and transport activity. It's defined as the ratio of gross mass movement to Gross Domestic Production (GDP). It can be measured separately for the passenger services (passenger kilometres, *pkm*) and freight (ton kilometres, *tkm*). Close relationship between the growth in transport and economic growth implied an increase of transport intensity and so an increase of transport-related energy consumption and gas emissions. Transport-related energy consumption is currently measured in the literature through *energy intensity* which calculated by the ratio of transport energy consumption to transport services supply. It indicates the demand of energy per unit Transport energy intensity has studied in several contexts. Examples include studies which interest to the relationship between economic growth and transport activity and transport energy consumption. This analyze is defined in two approaches. Firstly, the economic approach which aims to study the causality relationship between them and to analyse the expenditure in energy consumption. Secondly, the ecologic approach which aims to study the correlation between them in order to determine the coupling phenomena between them and to measure and analyse energy consumption and gas emissions. Moreover, examples include studies which decomposing the intensities in order to determine the influencing factors of the transport-related energy consumption growth.

More existing studies have focused on the causality and cointegration relationship between transport energy consumption, transport activity and some main factors. Their main objective is to determine the time tendencies of their trends, the sense of their causality, the degree of their cointegration and then the coupling problem (Meersman et Van De Voorde, 1999; Kulshreshtha et al*.*, 1999; Stead, 2001; Banister and Stead, 2003; Léonardi et Baumgartner, 2004; Tanczos et Torok, 2007).

Recently, Akinboade, Ziramba and Kumo (2008) have used the co-integration techniques in order to analyze the long-run relationship among the variables which explicating the aggregate gasoline demand function over the period 1978-2005 in South Africa. The results confirm the existence of a co-integrating relationship. The estimation of the price and income elasticities of gasoline demand serves to develop appropriate energy policy. The estimated elasticities show that gasoline demand in South Africa is price and income inelastic. The important policy implication is the unreliability of the public transport system in South Africa. Yaobin (2009) explains cointegration relationship between transport energy consumption growth, population growth, economic growth and urbanization process for china over the period 1978 -2008. The results show a unidirectional Granger causality running from urbanization to energy consumption both in the long and short run. Lu, Lewis and Lin (2009) have estimated the development trends of the number of motor vehicles, vehicular energy consumption and CO2 emissions in Taiwan during 2007-2025. They have adopted simulation of different economic growth scenarios in order to explore the influence of economic growth on energy consumption.

Pongthanaisawan and Sorapipatana (2010) have analyzed the relationship between motorcycle and car ownerships and level of economic development for the case of Thailand. They study the impacts of this relationship on fuel consumption and greenhouse gas emissions. Using semi-parametric techniques, the authors have shown that economic development affects the ownership of private vehicles which and so fuel consumption and gas emissions. The important conclusion of their study is that the amelioration of public transport system leads to reducing the traffic mobility of private vehicles, promotion of the vehicle efficiency and so reducing fuel consumption and gas emissions. Yan and Crookes (2010) have forecasted the future trends of energy demand of road vehicle and emissions

under various strategies for reducing the impacts of china's road vehicles on energy resources and environment. These strategies have concerned on the fuel economy regulation, alternative fuels and vehicles, public and non-motorized transport and economic incentives. Rudra (2010) explores the causality relationship between transport infrastructure, energy consumption and economic growth in India over the period 1970-2007. He finds a unidirectional causality from transport infrastructure to energy consumption. This results mains that energy and transportation policies must be recognized. Marshall *et al*. (2011) explores the causal relationship between residential location and vehicle miles of travel, energy consumption and CO2 emissions in Chicago metropolitan area over the period 2007 - 2008. Reinhard *et al.* (2011) concluded that urban energy planning and urbanization management are strongly linked and must be coordinated to lead to sustainable energy development.

Transport Intensity and Energy Efficiency: Analysis of Policy Implications of Coupling and Decoupling 275

(2007) have studied the relationship between road transport, energy consumption and CO2 emissions for the case of Autriche. Sorrell *et al*. (2009) have found for the case of England during the period 1989-2004 that increasing of vehicle transportation capacity and reducing of vehicle average energy consumption have ameliorated the energy efficiency. Niovi *et al.* (2010) estimates the effect of the spatial structure of the economy and the degree of spatial concentration of activities on fuel demand by using decomposition analysis. The results

**3. Methodologies used for study of transport energy efficiency** 

**3.1. Causality and cointegration relationship between transport energy** 

An open question for relationship between transport activity, economic growth and environmental effects is the correlation between their trends. Negative environmental effect will be increasing when correlation between economic growth and transport is largely important. Exam of this correlation is important in so far as it provides a several instruments to elaborate an efficient transport policy. In literature, a large majority of studies have interested to determine separately correlation between first, transport activity and economic growth (coupling problem) and secondly transport activity and energy consumption and gas emission. In this chapter we propose a demarche which treats simultaneously both the tow problems. We attempt to include in our analysis dimension of sustainable transport for

In this context, a large number of studies have interested to the problem of coupling. An important number of institutional reports had elaborated like REDEFINE (1999) and SPRITE (2000) in Europe. Otherwise, several scientific studies have been elaborated in two directions. First, studies which estimate relationships between transport of passengers or goods and economic growth using previous traffic models (Meersman and Van De Voodre (1999) for the case of Belgium and Klushershtha and al. (1999) for India). Second, studies which have interested to aggregated indicators in order to estimate the coupling (Baum (2000) and Baum and Kurte (2002) have used the intensity of road transport to measure

In order to study the relationship between transport energy consumption, transport activity and economic growth, we should test the stationary of the series. To this end tests which are common use are Augmented Dickey-Fuller (ADF) tests (Dickey and Fuller, 1979) and Phillips-Perron (PP) tests (Phillips and Perron, 1988). The objective is to know if the series are stationary in levels or in some order of differentiation. If the integration of the two series is of the same order, we should test whether the two series are cointegrated over the same period. Analyze of

The Trace and maximum eigenvalue test provide us the information concerning the presence or nor of the cointegration. Consequently, we can estimate a vector error correction model (VECM) that incorporates variables and variation levels for information on the speed

cointegration between the series is often realized through method of Johansen (1988).

coupling for the case of Germany and Stead (2001) for the case of Europe).

show that urban density increases fuel consumption.

which actual policies transport have been taken.

**consumption, transport activity and economic growth** 

In order to study transport-related energy consumption, majority of works have used the decomposition method. It's one of the most effective applied tools used to investigate the factors influencing energy consumption and its environmental impacts. The intensity decomposition method dates back to studies undertaken in the 1980s. It has known an expansion with works interesting to evaluation of aggregate energy consumption caused especially by the preceding energy crisis. It had evoked especially in the industrial context (Howarth *et al.,* 1991, Parck 1992). However, in the 1990s and 2000s this technique has been generalized to be used and applied to other sectors such as transport sector. The main objective of this method is to identifying factors that influence directly or indirectly energy consumption. One of the important decomposition of energy efficiency is which had proposed by Kaya (1989) in the context of energy economy. Kolbs and Waker (1995) have used decomposition method to find determinants of energy consumption and greenhouse gas emissions1.

For the case of transport, several studies have been interested to decomposing energy consumption in transport sector in order to show the contribution of traffic in pollution. Schipper, Scholl and Price (1997) have decomposed energy intensity on three factors; transport activity (tone kilometre), structure of transport (types of modals) and intensity (energy used per unit of transport). They have concluded that best energy efficiency cannot compensate increasing of transport and modal share of road transport. Kveiborg and Fosgerau (2004) have decomposed the energy intensity of road transport for Denmark along the period 1981-1997. They have found that energy efficiency has been ameliorated by reducing of industrial production share and travelling distance through implantation of logistic platforms. Steer Davies Gleave (2003) have concluded, for the case of Germany, France, Spain, Italy and England during the period 1970-2000, that reducing of the road transport share has ameliorated the energy efficiency for the countries group. Steenhof *et al*. (2006) have used decomposition of energy intensity in order to examine the determinants of GES caused by freight transport in Canada. They have concluded that technical progress is inadequate solution if share of freight road transport increase for USA. Tanczos and Torok

<sup>1</sup> Liu and Ang (2007) have exposed a large majority of studies which used the method of intensity decomposition.

(2007) have studied the relationship between road transport, energy consumption and CO2 emissions for the case of Autriche. Sorrell *et al*. (2009) have found for the case of England during the period 1989-2004 that increasing of vehicle transportation capacity and reducing of vehicle average energy consumption have ameliorated the energy efficiency. Niovi *et al.* (2010) estimates the effect of the spatial structure of the economy and the degree of spatial concentration of activities on fuel demand by using decomposition analysis. The results show that urban density increases fuel consumption.
