China has been the second CO2 emitter in the world, while the transportation sector accounts for a major share of CO2 emissions. Analysis of transportation sector CO2emissions is help to decrease CO2 emissions. Thus the purpose of this paper is to investigate the potential factors influencing the change of transport sector CO2 emissions in China. First, the transport sector CO2 emissions over the period 1985–2009 is calculated based on the presented method. Then the presented LMDI (logarithmic mean Divisia index) method is used to find the nature of the factors those influence the changes in transport sector CO2 emissions. We find that: (1) Transport sector CO2 emissions has increased from 79.67 Mt in 1985 to 887.34 Mt in 2009, following an annual growth rate of 10.56%. Highways transport is the biggest CO2 emitter. (2) The per capita economic activity effect and transportation modal shifting effect are found to be primarily responsible for driving transport sector CO2 emissions growth over the study period. (3) The transportation intensity effect and transportation services share effect are found to be the main drivers of the reduction of CO2 emissions in China. However, the emission coefficient effect plays a very minor role over the study period.
Next to the United States, China is the second source of CO2 emissions in the world. Thus, China is facing greater pressure to cut its CO2 emissions down. As a signatory to the United Nations Framework Convention on Climate Change (UNFCCC), the Chinese government announced its approval of the Kyoto Protocol in August 2002. As a non-Annex party, China would not be bound in the initial commitment period (2008–2012) to any quantitative restrictions on its GHG emission. Consequently, it would obligate to monitor and report to the Conference of Parties on the status of GHG emission sources and sinks, and identify measures to dampen growth of net emission in the future [1]. In 2009, Chinese government announced it would cut its CO2 emissions per unit of GDP by 40%–45% in 2020 from 2005.
The transport sector encompasses a plethora of activities relevant to the mobility of passengers and the movement of freight and as such it plays an important role in the economic activity of a society. However, the transport sector has also been identified as one of the major contributors to the depletion of fossil fuels, the degradation of the environment and the deterioration of human health [2]. 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 the transportation sectors of each country [3]. The energy consumption by transport is likely to grow up further with economic and population growth, rapid industrialization, urbanization and agricultural development which increase freight and passenger transport, and higher real incomes which stimulate leisure-related travel. Thus it is very necessary for China’s energy and environmental policymakers to investigate the driving forces governing energy-related CO2 emissions in the transport 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 [4], [5], [6]. 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 [7]. Wood used SDA to analyze Australia’s greenhouse gas emissions [8]. IDA uses index number concept in decomposition. Ang, Zhang and Sun gave, respectively, details on two kinds of IDA methodologies: Laspeyres index decomposition analysis and the Divisia index decomposition analysis [1], [9], [10]. 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. 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 [6]. However, due to the logarithmic terms in the LMDI formulas, 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 approached 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]. The detailed LMDI method is presented in Section 2.2.
Nevertheless, a few studies have identified factors affecting transport sector CO2 emissions. Scholl et al. examined how changed in transport activity, modal structure, CO2 intensity, energy intensity and fuel mix affect CO2 emissions from passenger transport in nine OECD countries between 1973 and 1992 [14]. In 1997, Schipper et al. also investigated the relative contribution of activity, modal structure, and energy intensity to changes in energy use and CO2 emissions from freight transport in ten industrialized countries from 1973 to 1992 [15]. Lakshmanan and Han attributed the change in transport sector CO2 emissions in the US between 1970 and 1991 to growth in people’s propensity to travel, population and GDP [16]. Transport sector CO2 emission growth was attributed to transportation activity, modal structure, modal energy intensity and fuel mix by Schipper et al. [17]. The Adaptive Weighted Divisia was applied to investigate factors affecting CO2 emissions from the freight sector of 10 OECD countries for the period 1970–1993 [18]. Mazzarino applied a comparative static approach highlight the main factors determining the variation of carbon dioxide emissions over the period 1980–1995 in India [19]. Lu et al. attributed changes in CO2 emissions from highway vehicles in Germany, Japan, South Korea and Taiwan during 1990–2002 to changes in emission coefficient, vehicle fuel intensity, vehicle ownership, population intensity and economic growth [20]. Timilsina and Shrestha utilized LMDI method to identify factors affecting CO2 emissions in 20 Latin American and Caribbean countries [21]. Similarly, Timilsina and Shrestha analyzed the potential factors influencing the growth of transport sector CO2 emissions in selected Asian countries during the 1980–2005 periods by decomposing annual emissions growth into components representing changes in fuel mix, modal shift, per capita GDP and population, as well as changes in emission coefficients and transportation energy intensity [22].
To our knowledge so far only one study has used systematically the decomposition technique on the transport sector CO2 emissions in China. Based on the IEA (International Energy Agency) data, Timilsina and Shrestha investigated the relative contribution of emission coefficient, fuel mix, modal mix, transportation energy intensity, per capita GDP and population to changes in CO2 emissions from transport in China from 1985 to 2005 [22]. Because the IEA data for transportation services (e.g., passenger kilometers, tonnes kilometers) are not available, that paper did not consider the affect of the changes of transportation services to CO2 emissions. This paper serves as a preliminary attempt to apply related data given by China Statistical Yearbook and Yearbook of China Transportation and Communications to calculate CO2 emissions by transport mode. The transport sector CO2 emissions can be expressed as an extended Kaya identity, and that can be decomposed into influenced factors by LMDI method, which is a useful tool to find the nature of the factors that influence the changes in transport sector CO2 emissions for the period 1985–2009.
Because of the scarcity of statistical data of urban transportation in China, this paper only considers intercity transportation consisting of five essential modes, i.e., highways, railways, waterways, civil aviation and pipeline transportation. The highways transport mode considers two fuel types: gasoline and diesel oil. There are three fuel types for railways transport, i.e. coal, diesel oil and electricity. Diesel fuel is used for waterways. Kerosene is only consumed by civil aviation mode. Pipeline transportation consumes two kind of fuel: fuel oil and natural gas.
The remainder of this paper is organized as follows. In the next section, we present a method to calculate CO2 emissions in the transport sector and use the proposed LMDI approach to decompose the change of aggregate CO2 emissions in the transport sector over time. Section 3 discusses the related data used in this paper. The main results are reported in Section 4. Finally, we conclude this study.
2. Data and methodology
2.1. Sources of data
In China, the data spanning from 1985 to 2009, used in this study, has been collected from various issues of the China Statistical Yearbook and Yearbook of China Transportation & Communications [23], [24]. The GDP and population data are in in 109 yuan (Billion Yuan) in constant 1978 price and in million, respectively. The transportation services are measured by tonne-km in this paper. For passenger-trips, person-km must be converted to tonne-km. The total transportation services of passenger and freight traffic is equal to the transportation services of passenger traffic divided by a conversion coefficient, plus that of freight traffic. The conversion coefficient is determined through experience in comparing revenues and expenditures per person-kilometer (moving one person 1 km) with those of moving one tonne of goods 1 km. This is to say transporting one tonne of goods 1 km is equivalent to transporting one passenger 1 km for railways. The conversion coefficients for other transport modes are is presented in Table 1.
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