The purpose of this paper is to identify the relations between transportation energy consumption and its impacted factors. We first analyze the current status of transportation energy consumption in China. Then, the logarithmic mean Divisia index (LMDI) technique is used to find the nature of the factors those influence the changes in transportation energy consumption. We find that: (1) In 2006, the transportation energy consumption increased by 7.63 times against that in 1980. (2) Up to 2006, the oil consumed by transportation accounted for 49.6% of that in the whole country, which almost equaled to the net oil import. (3) In the light of the increasing energy consumption intensity, the energy-utilization effectiveness of transportation sector has been declining gradually. (4) The transportation activity effect is the most important contributor to increase energy consumption in the transportation sector and the energy intensity effect plays the dominant role in decreasing energy consumption.
Transportation not only plays a major role in the sustainability of the earth but also they, themselves, must be sustained in order to continue to afford to all people access to the economic and social opportunities necessary for meaningful life. Therefore, there are two sides of energy use in transportation. One is about the large contribution made especially by highways transportation sources to pollution problems while the other is related to energy supply security for transportation sectors of each country [1].
The transportation sector accounts for a major share of energy consumption in China, especially the petroleum products. China has been a net oil importer since 1993, and has become the second largest oil consumer in the world since 2003 [2]. The energy consumption is likely to grow up further with economic development, population growth, rapid industrialization, urbanization, agricultural development, etc., which make China confront two great challenges: enhancing oil supply security and responding to environmental concerns of both local and global scales. Thus it is very necessary for China’s energy and environmental policy makers to investigate the driving forces governing energy consumption in the transportation sector.
Nowadays, various decomposition methods have being used to quantify the impact of different factors on the change of energy consumption and CO2 emissions, such as the econometric regression, the structural decomposition analysis (SDA) and the index decomposition analysis (IDA) and so on [3], [4], [5]. In the literature two well-known decomposition techniques, namely SDA and IDA, have been widely applied to analyze the driving forces. SDA is based on the input–output model in quantitative economics. Rose and Casler provided a review on its theoretical foundation and major features [6]. Wood used SDA to analyze Australia’s greenhouse gas emissions [7]. IDA uses index number concept in decomposition. Ang and Zhang and Sun gave respectively details on two kinds of IDA methodologies: Laspeyres index decomposition analysis and the Divisia index decomposition analysis [8], [9]. Each IDA can be applied in a period-wise or time-series manner. A period wise analysis compares indices between the first and the last year of a time period for a given country (or region, industry, etc.). However, the results of a period-wise decomposition are very sensitive to the choice of base year and final year and it does not show how the effects of the decomposed factors have evolved over the studied period. A time-series analysis involves yearly decomposition using time-series data, and its results show how the impacts of pre-defined explanatory factors have evolved over time. In any case, period-wise results can be derived from a time-series analysis, but not vice versa, of course.
However, there is no consensus among them as to which is the ‘best’ decomposition method [10]. Ang compared various index decomposition analysis methods, and concluded that the LMDI method was the preferred method, due to its theoretical foundation, adaptability, ease of use and result interpretation, along with some other desirable properties in the context of decomposition analysis [5]. However, due to the logarithmic terms in the LMDI formulae, complications arise when the data set contains zero values. Ang and Choi showed that the zero values may be replaced by a small number δ and converging results were obtained when δ approaches zero [11]. Wood and Lenzen argued that the above strategy was not necessarily robust because it would produce significant errors if applied in the decomposition of a data set containing a large number of zeros and/or small values [12]. At last, Ang gave eight strategies to handle zero values in LMDI decomposition approach [13].
Nowadays, some researchers have been devoted to investigate the decomposition analysis of the energy consumption in general and of the energy-related CO2 emissions in the transportation sector, such as Schipper et al. [14], [15], Lakshmanan and Han [16], Greening et al. [17], Mazzarino [18], Timilsina and Shrestha [19], [20]. To our knowledge so far only several studies have used systematically the decomposition technique on the passenger energy consumption in Chinese transportation sector. Based on Laspeyres index decomposition analysis, Li et al. analyzed the change of motorized passenger transport energy consumption in selected Chinese cities [21]; and Zhang and Mu utilized LMDI method to discuss this question [22]. The LMDI method was also used to analyze intercity passenger transportation energy consumption in China by Chang et al. [23].
This paper serves as a preliminary attempt to apply LMDI method to analyze the Chinese transportation sector over the period 1980–2006, to cast light upon the contribution of the factors which influence energy consumption. Because the transportation sector is very complex in China, and the transportation energy statistical data is scarce, especially for urban transportation. So this paper only consider intercity transportation. The transportation system in China is regarded consisting of five essential modes, i.e., highways, railways, waterways, civil aviation and pipeline transportation. Among such modes, highways, railways, waterways and civil aviation, are used for both passenger trips and freight-transportation. Pipeline transportation is only used for freight-transportation. Therefore, only five modes and nine sub-modes are considered in this paper. Highways transportation only includes the vehicle possessed by the conveyance of enterprise, which is being engaged in the operation of freight/passenger transportation.
The remainder of this paper is organized as follows. In the next section, we present a method to calculate energy consumption in the transportation sector, and then use the proposed LMDI approach to decompose the change of aggregate energy consumption in transportation sector over time. Section 3 discusses the related data used in this paper. The analysis of energy consumption in the transportation sector is presented in Section 4. In Section 5, the main results are reported. Finally, we conclude this study.
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