Research On The Influencing Factors Of Carbon Intensity And The Methods Of Carbon Dioxide Abatement

Chinese government has made a public commitment that China’s carbon dioxide intensity is to decrease 40%-45%. This objective connects the carbon dioxide emissions with economic development closely. As the biggest developing country in the world, it is a big challenge for China. In that case, how to realize the objective under the condition of normal economic development has become a major problem at present.To decrease the carbon intensity, it is necessary to make it clear that how China’s carbon intensity changes and what influence the changes. So this paper took carbon intensity as a sub-system of economics system. Firstly this paper calculated the CO2 emissions according to the IPCC guidelines and energy consumptions from 1978 to 2010, and then adopted Vector Error Correction Model(VECM) preceded by Johansen’s co-integration tests to analyze the long-run equilibrium and short-run fluctuation among China’s carbon intensity and the energy intensity, proportion of coal as well as the economical structure based on Kaya equations. Meanwhile the logarithmic mean Divisia index(LMDI) was used to study how the three contributors affect China’s carbon intensity and their contributions to the change of China’s carbon intensity. To understand clearly the relationship between China’s carbon intensity and economic development and the differences of that among China’s provinces, panel data and the environmental Kuznets curve(EKC) model were used and the tendency of carbon intensity were analyzed. Finally, after considering the differences of carbon emissions and economical development among China’s provinces, and under the “common but different” principle, this paper constructs the model which considers the responsibility and capacity as well as abatement potential, and this model has been adopted to allocate China’s deceasing objective of carbon intensity in 2020 to 30 provinces. Through the theoretical and empirical analysis as well as model improvement, the main conclusions in this paper indicated that:(1) Since the beginning of the opening reform, China’s carbon dioxide emissions based on energy consumption has witnessed a rapid increase, but the carbon intensity has been a downward trend. Meanwhile, it has been found that there existed differences among the provinces for both carbon emissions and carbon intensities. China’s carbon dioxide emissions have increased 438% from 1.415 billion tons in 1978 to 7.613 billion tons in 2010. China’s carbon intensity has decreased 1.16 t/104 Yuan from 9.33 t/104 Yuan in 1978 to 2.44 t/104 Yuan. The current situation of carbon emission and carbon intensity has provided basis and guidance for the realization of China’s carbon intensity objective and low-carbon economics.(2) The results of LMDI shown that energy intensity had played a major role in the decrease of carbon intensity, and energy structure was the secondary factor. The affecting direction of industrial structure on carbon intensity was not fixed, positive in some years and negative in other years. The results of complete decomposition model shown that 26 provinces promoted the decrease of China’s carbon intensity, but there were 4 provinces that hindered the decrease.(3) There exists a long-run equilibrium among China’s carbon intensity change, energy structure, energy intensity and economic structure. The increase of energy intensity has caused the largely increase of carbon intensity. Both the proportion of coal and the proportion of industry have the positive influences to China’s carbon intensity, but the strength was small. The carbon intensity has a strong positive reaction to a standard difference innovation of energy intensity through the analysis of impulse function. And the results of variance analysis shown that energy intensity had a constant impact to carbon intensity and the impact was stronger in short-run, which proved that it is practicable to decrease the carbon intensity through the decrease of energy intensity. Carbon intensity had an obvious reaction to the change of the change of energy structure and the reaction kept a long time. Carbon intensity had a weak reaction to the economical structure.(4) There were big differences among China in whole, eastern China, mid-China and western China, and there existed an upward trend from the east to the west. The results of EKC tests shown that China’s carbon intensities were strongly dependent on the history and that there existed the inverted “N” shaped EKC curve between carbon intensity and per capita GDP in China, “N” shaped EKC curve in eastern China and the inverted “U” shaped EKC curve in the middle and the west areas of China. The east China has experienced its first turning point and is to reach its second one in 2024, and the middle and west areas of China have both experienced their only turning point.(5) Under the principle of “common but different responsibility”, this paper adopts Wen-Jing Yi’s model which considers the responsibility and capacity as well as abatement potential at the same time. The model aims to allocate the China’s object of 40-45% decrease on carbon intensity to the provinces and meanwhile takes the provincial balanced development into consideration. Based on the literatures concerned, this study takes per-capita historical accumulated CO2 emissions as CO2 abatement responsibility, and adopts per capita GDP and provincial carbon intensity in 2010 to represent CO2 abatement capacity and CO2 abatement potential respectively. The different coefficients of three indexes represent different decision-making preferences. And this model has been adopted to allocate China’s deceasing objective of carbon intensity to 30 provinces. The results of provincial allocation show that there exist different abatement quota among 30 provinces and different province has different sensitivity to the decision-making preference. Comparing this paper’s allocating results of “Twelfth Five Year” plan with that of the State Council; it is found that this paper’s allotting results are more distinguishable. No matter in which case, certain provinces’ abatement quota are larger than others, such as Ningxia, Inner Mengolia, Tianjin, Shanghai and so on; While some provinces’ abatement quota are always small, such as Jiangxi, Hainai, Yunnan and Guangxi. Improving the model through adding two level indexes could make the allocating process more clear and do well to the provinces who can adopt specific emission abating measures.