برآورد صرفه جویی در انرژی و کاهش انتشار عامل دی اکسید کربن در چین بر اساس روش DEA توسعه یافته غیر شعاعی

In the process of setting operational targets to achieve sustainable development of economy, environment and natural resources, estimation of potential energy saving and potential CO2 emission reduction becomes extremely important. This estimation can be conducted based on the energy efficiency evaluation for different decision-making units (DMUs) by data envelopment analysis (DEA). Non-fossil energy is an important component of energy consumption in China, and it has great impacts on energy efficiency and energy-related carbon dioxide (CO2) emissions. This paper proposes a non-radial DEA model to evaluate regional energy efficiencies in China. In the proposed model, non-fossil energy is treated as a fixed input. Based on the model, a method of measuring potential energy saving and CO2 emission reduction for efficiency improvement is also presented. The proposed approaches are illustrated by using a regional dataset in China. Based on the application results, some implications for improving energy efficiency and reducing CO2 emissions in China are provided.

In past three decades, rapid economic growth has caused great energy consumption and serious environmental and ecological problems in China (Li and Oberheitmann, 2009). China has become the second largest energy-consuming country and the largest emitter of carbon dioxide (CO2) in the world. In 2009, the total energy consumption reached 3.07 billion tons of standard coal equivalent (SCE), which is about 5.37 times that of 1978 (Statistical Year Book of China in 2010). China′s gross domestic product (GDP) accounts for no more than 6.2% of the world′s total GDP, while its carbon emissions account for 20.85% of the world′s total carbon emissions (World Bank, 2009). To achieve sustainable developments of economy, environment and natural resources, Chinese government has in recent years implemented various strategies and policies, e.g., closing down backward production facilities, promoting the use of energy-saving technologies and making fiscal and tax policies for energy saving (Jiang et al., 2010 and Hou et al., 2011), to save energy consumption and to reduce carbon emissions. Particularly, on the 2009 Copenhagen conference, China announced to reduce the intensity of CO2 emissions per unit of GDP by 40–45% by 2020 compared to the level in 2005. Note that, these strategies and policies should be carried out across all regions in China. It is well known that CO2 emissions are largely attributed to burning fossil energy consumption. Therefore, improving energy efficiency has often been recognized as one of the most cost-effective ways to reduce CO2 emissions and to increase the security of energy supply (Ang et al., 2010 and Al-Mansour, 2011). Energy consumption structure in China is composed of fossil energy and non-fossil energy, which has three evident features. First, energy consumption exhibits an overdependence on coal, and little utilization of natural gas and non-fossil energy in China, as shown in Fig. 1.Fig. 1 describes energy consumption structure in China during 1978–2009 (the data is collected from Statistical Year Book of China in 2010). It can be observed in Fig. 1 that fossil energy (including coal, oil and natural gas) is the main source of energy consumption in China, while non-fossil energy (including hydro-power, nuclear power, wind power, solar and others) accounts for a very low proportion of total energy consumption. In 2009, coal, oil and natural gas respectively account for approximately 70.4%, 17.9% and 3.9% of the total energy consumption. Second, non-fossil energy has developed significantly in recent years. For example, since the renewable energy law was implemented in 2006, the increasing rate of wind power has been over 100% and China has taken the second position in the newly installed capacity in the world (Jiang et al., 2010). China has stated that by the year of 2020, 15% of primary energy consumption should come from non-fossil energy (Guo et al., 2011 and Wang et al., 2011). Third, there exist great disparities in energy structure among different regions in China. For instance, coal consumption in Shanghai accounts for about 50% of its total energy consumption; while in Anhui province, this proportion reaches 90% in 2008. In Fujian province, the percentages of fossil energy and hydropower in its total energy consumption are 84% and 15.8%, respectively (Wang et al., 2011). Fossil energy consumption rather than non-fossil energy consumption is a primary driver of CO2 emissions. Different energy structures (i.e., the percentages of energy sources in total energy consumption) result in different carbon emission structures (i.e., the percentages of CO2 emissions related to coal, oil and natural gas). Therefore, energy structure has significant impacts on regional energy efficiencies and CO2 emissions. This raises three important issues: (1) How to discriminate the effects of fossil energy and non-fossil energy on regional efficiencies and CO2 emissions? (2) How to measure the efficiency of each type of fossil energy? (3) How to measure potential energy saving and CO2 emission reduction? These issues need to be effectively addressed before making appropriate policies for energy saving and CO2 emission reduction in China. In the literature, various approaches have been explored to evaluate energy efficiency or environmental efficiency at macro economy level in recent years. These existing approaches can be generally classified as parametric and non-parametric methods (Sadjadi and Omrani, 2008). Parametric approaches such as stochastic frontier analysis (SFA) measure performance through estimation of a restrictive production or cost function. Therefore, deviations in function forms affect results of such methods. The non-parametric approaches, e.g., data envelopment analysis (DEA), evaluate performance based on a linear programming, which relies on construction of a piecewise linear combination of all observed inputs and outputs. A major advantage of the DEA approach is that it does not impose any functional form on the underlying technology (Zhang et al., 2011 and Choi et al., 2012). Thus, comparing to parametric approaches, DEA can effectively avoid model misspecification (Wei et al., 2007 and Chung, 2011). In addition, DEA can provide sufficient information for improving the efficiency of an inefficient decision making unit by slack and radial adjustments (Shi et al., 2010). With these methodological advantages, DEA has been widely applied to evaluate energy efficiencies or environmental efficiencies in recent years (Zhou et al., 2008). The existing studies on evaluating energy efficiency with CO2 emissions based on DEA approach can be mainly classified into three categories. The first one focuses on investigating the relationships among energy consumption, CO2 emissions and GDP growth for regions or counties, e.g., Ramanathan (2006), Lozano and Gutiérrez (2008) and Li et al. (2011). The second applies DEA approaches to compare efficiencies of energy or energy with carbon emissions for different regions (or countries), or to monitor the efficiency trends for regions or countries, e.g., Zhou et al. (2006), (2010), Chang and Hu (2010), Li (2010), Liou and Wu (2011), Zhang et al. (2011) and Wang et al. (2012). The third not only measures efficiencies for regions or countries, but also explores the potential targets of energy saving or carbon emission reduction by using DEA-based target setting approach, e.g., Hu and Wang (2006), Hu and Kao (2007), Zhou and Ang (2008), Shi et al. (2010), Guo et al. (2011), Lee et al. (2011) and Wei et al. (2012). The above studies have three outstanding features. The first is that, most of them treat energy consumption as an overall input variable in DEA models except Zhou and Ang (2008) who use non-radial measures for all energy inputs. The second is that in efficiency evaluation, the impact of non-fossil energy as an individual input on regional efficiencies is not taken into consideration. The third is that the targets setting for energy and CO2 emissions are obtained completely resting on DEA-based target setting approach, ignoring the effects of changes in energy structure on energy saving and CO2 emission reduction. One special case is Guo et al. (2011), who take energy structure adjustment into account in measuring CO2 emission reduction in China. However, it is not the same case as our problem. Thus it can be concluded that up till now there is no effective approach to simultaneously deal with the efficiency evaluation and estimation of potential energy saving and CO2 emission reduction issue in China. To reasonably evaluate regional energy efficiencies with CO2 emissions in China, the current paper proposes a non-radial DEA model based on environmental DEA technology (Färe and Primont, 1995 and Färe and Grosskopf, 2004). Since CO2 emissions are mainly generated from fossil energy consumption rather than non-fossil energy consumption, to improve the energy efficiency, it is better to decrease fossil energy consumption as much as possible but not to reduce the non-fossil energy consumption in real production. As a result, we in the proposed model take non-fossil energy as a fixed input. The rest of the paper is organized as follows. Section 2 introduces a methodology for estimating CO2 emissions in China, constructs a non-radial DEA model for evaluating energy efficiencies of regions, and presents a method based on the proposed model for measuring potential energy saving and CO2 emission reduction. In Section 3, we illustrate the proposed approaches by using regional dataset in China. Conclusions are described in Section 4.

The current paper proposes a non-radial DEA approach combining energy structure adjustment and DEA-based target setting together to measure energy saving and energy related carbon dioxide emission reduction in China. In the proposed approach, non-fossil energy incorporated as a fixed factor cannot be decreased in the efficiency optimization process. Based on the proposed model, a method for measuring potential energy-saving and CO2 emission reduction is developed. An application to regions in China is used to illustrate the proposed approaches. The application results demonstrate that the proposed approaches can be effectively applied to estimate potential reductions in energy consumption and its related CO2 emissions in China. Based on the application results, some conclusions can be drawn. First, since there are great disparities in potential energy savings and CO2 emission reductions in regions, policy making for the efficiency improvement should take these differences into consideration. Second, energy structure adjustment by decreasing coal consumption while increasing other energies’ supplies is an effective way to measure inefficiencies of coal dominated energy structure in China. Third, to effectively decrease CO2 emissions under the current production situation, reduction in coal consumption, the development of non-fossil energy and the slightly increasing supplies of oil and natural gas are required. Finally, energy structure adjustment combined with production technologies’ innovations (e.g., cleaning production) is a practical way to improve the performance of regions in China, especially in the case of coal′s reduction up to 20%. Note that, the above conclusions theoretically provide important information for China to draw up policies for reducing energy consumption and its related CO2 emissions. However, due to great unbalances of energy supplies, energy structures and economic structures of regions, it would be better for local governments to realize their targets for energy-savings and carbon reductions with their own policies. Therefore, considering all the above factors, some policy implications for energy-saving and CO2 emission reduction can be achieved as follows. • Energy structure adjustment by restricting coal supply and promoting other energies’ supplies is an effective way to reduce CO2 emissions for most of Chinese regions, except some regions like Shanghai and Hainan. The reason is that, coal consumptions of Shanghai and Hainan account for about 53% and 20.18% of their total energy consumptions, which cannot be decreased in the current energy supply situations. As for Hainan province, it is better to reduce oil supply while increasing non-fossil energy (e.g., nuclear power, wind power) supply to optimize its energy structure. • It is practical for the whole country to enhance its energy use efficiency to reduce energy consumption and its related CO2 emissions, especially for the regions such as Shanghai, the coal consumption proportions of which are about 50% of their total energy consumptions. This can be done by encouraging energy-saving technology innovations, closing down backward production companies, improving industrial structure, etc. It is noteworthy that policy making should consider the fact that the efficiencies of different energy inputs are different among regions. For example, the inefficiency of energy consumption in Hubei is mainly caused by coal consumption, while in Qinghai all energy inputs are inefficient. Particularly, in the east regions, more efforts should be made to improve coal and oil efficiencies, while in the west regions, it is important for them to focus on gas efficiency optimization. • Developing non-fossil energy is an important way to adjust the existing energy structure. However, this should be done according to local conditions. For example, nuclear power is the main supply of all non-fossil energies in China in the current state due to its huge capacity and lower generating cost (Jiang et al., 2010). The nuclear power plants are all located in the coastal regions (i.e., the east regions), which indicates that these regions should expand this energy supply efficiently. The biomass power has developed significantly in recent years, and the plants are mainly located in 14 regions, e.g., Henan, Inner Mongolia, Sichuan and Hebei. Wind power plants are mainly located in the north and east China regions; hydropower plants are situated in the regions along the bigger rivers in China (e.g., Hubei along the Yangtze River). These conditions imply that these regions may promote their local non-fossil energies to fulfill their adjustments of energy structure. Scenario analysis is used to estimate the potential reductions in energy consumption and its related CO2 emissions. Three specific scenarios are undertaken in this study, which can be further extended to other scenarios by changing parameters of energy structure adjustment. Furthermore, energy structure adjustment can also set different adjusting parameters for different areas or regions by considering their particular energy consumption situations. These issues may provide some interesting topics in future research.