This paper examines the driving forces for reducing China’s CO2 emission intensity between 1998 and 2008, utilizing the logarithmic mean divisia index (LMDI) technique. By first grouping the CO2 emissions into two categories, those arising from activities related to the electric power industry and those from other sources, emission intensity is further broken down into the effects of the CO2 emission coefficient, energy intensity of power generation, power generation and consumption ratio, electricity intensity of the gross domestic product (GDP), provincial structural change, and the energy intensity of the GDP for other activities. The decomposition results show that improvements in the energy intensity of power generation, electricity intensity of GDP, and energy intensity of GDP for other activities were mainly responsible for the success in reducing China’s CO2 emission intensity and that activities related to the electric power industry played a key role. It is also revealed that performance varied significantly at the individual province level. The provinces with higher emission levels contributed the most to China’s improvements in CO2 emission intensity.
China has made great progress in reducing its CO2 emission intensity. The Chinese government promised the world that by 2020 the CO2 emission intensity of its GDP would be reduced by 40–45% compared to 2005 levels. This is a responsible commitment and a difficult task, but also an achievable goal. China has long recognized that only a resource-saving and environmentally-friendly society can sustain long-term economic development. Guided by this belief, China has taken a multitude of steps to protect the environment in the last few years, including creating legal requirements related to emission reduction, restructuring industries, and improving energy efficiency. The effects of these efforts are obvious. China’s overall CO2 emission intensity fell from 4.1712 t/10,000 Yuan in 1998 to 2.947 t/10,000 Yuan in 2008, corresponding to a 29.14% decrease within this time span.
The electric power industry played an important role in reducing China’s CO2 emission intensity during this period. The industry used to be a cause of environmental degradation, especially greenhouse effects, characterized by lower energy efficiency and a higher portfolio of coal consumption. As a result, the Chinese government enacted a variety of plans to increase energy efficiency and reduce pollutant emissions for the electric power industry. The effects of these plans were obvious and profound. China’s aggregate energy intensity of power generation fell from 301.04 g(tce)/KWh in 1998 to 251.6935 g(tce)/KWh in 2008, corresponding to a 16.39% decrease during this period. The aggregate transmission and distribution losses dropped from 8.13% in 1998 to 6.79% in 2008, a decrease of 16.48%. These gains greatly promoted the reduction of China’s aggregate CO2 emission intensity of GDP during this period.
The goal of this paper is to find the importance of electric power industry in contributing to reduction of China’s overall CO2 emissions intensity during the last few years. To this end, we use the LMDI decomposing method to support our research, because it was proven to be a feasible tool in analyzing the change of energy intensity (Wachsmann et al. [1], Unander [2], Liu and Ang [3]) or CO2 emission intensity (Malla [4], Steenhof [5], Papagiannaki and Diakoulaki [6]). With the ever-increasing importance attached to greenhouse gas reduction, many scholars have researched the driving forces for changing CO2 emission intensity in different kinds of economies. Diakoulaki et al. [7], Lee and Oh [8], Lise [9], Lim et al. [10], and Paul and Bhattacharya [11] find that a fast-growing economy is the most important factor contributing to the increase of CO2 emissions in Greece, APEC countries, Turkey, India, and Korea, while İpek Tunç et al. [12], Hatzigeorgiou et al. [13], Ebohon and Ikeme [14], Zhang et al. [15], and Wang et al. [16] point to improving energy intensity as the most important factor for reducing the CO2 emissions of Turkey, Greece, sub-Saharan African countries, and China; Ma and Stern [17] revealed that a shift from biomass energy to commercial energy increases CO2 emissions by a magnitude comparable to that of the increase in emissions due to population growth.
Our work is different from the above research in the following respects. First, we mainly want to quantify the proportion the electric power industry contributes to China’s reduction of CO2emissions intensity. In this regard, the economy was divided into electric power industry activities and other activities. Based on this, the LMDI decomposing method is used to select factors that analyze the change of CO2 emission intensity and quantify the contribution of each factor. Second, we want to discover the driving forces for each explanatory factor. In order to achieve this goal, the LMDI method is further used to quantify the contribution of each explanatory factor to reducing the CO2 emission intensity of each of China’s mainland provinces. By comparing the difference in contributions of each province and analyzing their levels of economic development, energy structures, and other related factors, we found some explanations.
2. Methodology
2.1. LMDI
Policy makers and researchers have developed many methods to quantify the effects of different factors that contribute to the change of CO2 emission intensity or energy intensity. Since it is hard to point out which one is best, we use the logarithmic mean Divisa index (LMDI) for our study. The LMDI was proposed by Ang [18], because it is superior to other decomposing methods in (1) sound theoretical foundation, (2) adaptability, (3) ease of use, and (4) ease of result interpretation as discussed by Ang et al. [19]; it has been adopted by many studies. Inspired by their research, we also use LMDI to support our analysis in this research. Inspired by Subhes and Bhattacharyya [21] who used the LMDI to analyze the changes of GHG emission intensity in EU-15, we use this method to analyze the change of CO2 emission intensity across the 31 mainland provinces in China.
Generally speaking, the LMDI can be performed in two ways: namely, non-chaining methods and chaining methods. The non-chaining methods only compare the last year and the first year of the period in question, while the chaining methods research every year’s change compared to the previous year. The chaining method is used by many researchers because it can collect more information, as Ang et al. [19] point out. But we use the non-chaining method in this paper for the following reasons. Firstly, both China’s aggregate CO2emissions intensity and 31 mainland provinces’ are decomposed in this paper. If we perform the chaining method, all necessary data for the 31 mainland provinces will be collected which will lead to a very complex calculation process and results presentation. Secondly, we mainly want to understand the importance of the electric power industry in reducing China’s carbon emission intensity during the last few years, and which province did the best in this regard. Hence, to better elaborate our results, we chose the non-chaining method.
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