Research On Energy Efficiency In China From The Perspective Of Low Carbon

Facing the severe situation of tight energy constraints, serious environmental pollution and ecosystem degradation, the traditional development mode to constantly consume energy and produce carbon dioxide has been difficult to sustain. From a global perspective, energy conservation, recycling, low-carbon has become a new development way,"green Industrial Revolution" has already begun. IEA (2010) points out that the potential for carbon emissions reduction by improving energy efficiency exceeds the electricity sector, becoming the largest source of emission reduction. As the rapid development of China, promoting the level of energy efficiency, strengthening energy conservation, supporting the development of low-carbon industry, making the formation of spatial pattern of saving energy and protecting environment are an important and effective way from the source to reverse the trend of ecological environment deterioration and to achieve the sustainable development of China’s green economy.In this paper, we integrate the various theories and method of energy efficiency, carbon emissions are taken into energy efficiency evaluation system, and based on economic growth theory, production theory, energy economics, environmental economics theory, we study the energy efficiency of carbon emission constraints in China from macro and meso level, thereby integrate of the three parts of the energy inputs, economic outputs and environmental pollution. According to the research, solve practical problems for the starting point, establish the appropriate economic model based on the actual available data, try to make comprehensive use of quantitative analysis method, the mathematical economic analysis and empirical analysis, systematically, comprehensively and scientifically evaluate the energy efficiency of China under low-carbon economy. The main contents and conclusions of this paper are as follows:1. Based on spatial dynamic panel data, we establish the spatial econometric model of energy efficiency of China and EKC expansion curve of CO2. The empirical results show that:there are significant spatial correlation characteristics between the neighboring provincial energy efficiency in China, there is a significant agglomeration effect and similarity. Relative to the geographical distance, the influence of economic distance to spatial correlation on regional economic activity is greater. Economic growth, technological progress and the opening degree to the outside world are negative regression coefficient of the energy intensity, and energy prices, the industrial structure and energy intensity has a significant positive correlation. China’s per capita carbon dioxide emissions and economic growth basically meet inverted U curve relationship of EKC assumption. Environmental Kuznets curve of China’s per capita carbon dioxide have dual influence between adjacent geographical and regional economic development. The economic turning point of per capita carbon dioxide is101276yuan. Combined with the actual level of development of China’s economy, China is currently on the left of the Kuznets curve of carbon dioxide.2. Using the non-radial, non-point of the SBM model, we measure total-factor energy relative efficiency and energy-saving emission potential reduction under the constraint of provinces carbon emissions. Compared with total factor energy efficiency without consideration of carbon emissions, total factor energy efficiency with consideration of the provinces of carbon emissions constraints is underestimated. The evaluations of provincial total factor energy efficiency show that Shanghai and Guangdong have been at the optimal production frontier in the period investigated. From the analysis of variance about regional total factor energy efficiency, the regional pattern of total factor energy efficiency in accordance with decreasing from east to west, and presents the trend of convergence. By the clustering analysis results, the provinces of the high efficiency area are the eastern coastal provinces. The provinces of the Medium efficiency are mostly in central regions and the northeast old industrial base, while most of provinces in western are low efficiency. Calculating and analyzing by the energy saving and emission reduction evaluation model, accordance with the regional comparison, the potential of energy-saving and emission-abating in western is the highest, followed by central and northeastern, eastern is minimum.3. Based on the sequence of DEA, directional distance function, environmental regulation intensity index, Malmqulist Luenberger index, we measure the dynamic changes of provincial total factor energy productivity, the decomposition variant and the cost of environmental regulation. The main conclusions are as follows:the green productivity is higher than traditional, green productivity shows W-type fluctuation trend, major turning point came in2005and2009. From the regional differences, the green highest productivity is in the eastern area, followed by the northeast, central, western lowest. The regional total factor energy efficiency exists the trend of convergence. The "innovator areas" mainly concentrate in the four provinces:Beijing, Shanghai, Guangdong and Hainan. After considering carbon emissions, China’s industrial structure has been optimized and adjusted, showing the promotion of the scale efficiency. Analyzing from the decomposition of the effect of economic growth, the driving force of national economic growth is mainly from the growth effects of the input factors, ratio of the average contribution of total factor productivity is only5%. The eastern region is in the green economy growth mode transition period from "extensive" to "intensive", and the northeast, central and western regions, economic growth mode is still "extensive"; intensity of environmental regulation has a significant positive correlation with the total factor energy productivity, which supports the hypothesis of the existence of Potter.4. Based on four-stage DEA and Bootstrapped DEA method, in the control of the effects of exogenous environmental variables and random shocks, we analyze the total factor energy efficiency and its decomposition variables of industrial enterprises above provincial domain scale. The main conclusions are as follows:the initial DEA model, four-stage DEA model and Bootstrapped DEA model to calculate the efficiency score have significant difference. Tobit regression model show that R&D investment is favorable factors to improve the total factor energy efficiency, and contribution proportion is the largest about reduction of CO2emission; environmental protection expenditures on the effect about the total factor energy efficiency are weak. The improvement in the level of nationalization is the adverse factors about the improvement of the total factor energy efficiency. Excluding the impact of environmental variable, the average pure technological efficiency of industrial enterprises has been improved, while the average technical efficiency, scale efficiency declined. Decreasing returns to scale provinces are adjusted for increasing returns to scale. After the bias correction by Bootstrapped DEA method in four-stage DEA efficiency scores, the total factor energy efficiency of all regions has declined.5. Based on the SBM directional distance function and Luenberger productivity index, we measure total factor energy efficiency and productivity and decomposition of variables under the constraint of36industries. The results show that:The green industries energy efficiency is higher than the traditional energy efficiency; in the measure of green energy efficiency, energy efficiency of manufacturing industry are the highest, The energy technology efficiency of power, gas and water production and supply industry is higher than the extractive industries; The optimal production frontier has been constantly shifting, green technology boundary is further away from the constant returns to scale technology; The analysis of the kernel density shows that:the cumulative distribution of total factor energy efficiency peaks gradually shifted to the right and the height significantly decreased, indicating that total factor energy efficiency of the overall industry has been improved. Influencing factors analysis show:the industry exist the economic scale of energy use; energy structure has significant inhibition impact on energy efficiency and productivity of the industry; capital deepening impact on the green energy efficiency in industry positively, while has a significant negative impact on the green productivity; between green energy efficiency, productivity and Marshall externalities show U-shaped relationship. This research framework does not support the pollution haven hypothesis.6. Shephard distance function based on the energy input in DEA introduced into LMDI decomposition model, we build six major industries of the Chinese energy consumption carbon emissions seven factor decomposition model. The results show that: industrial structure, economic output, population size, energy performance of the increase in carbon emissions has a certain stimulus, and the cumulative effect of economic output was the maximum contribution135%, Industrial structure, population size, energy efficiency of cumulative effect of contribution rate to carbon emissions were10.74%,9.39%,0.65%. Potential energy intensity of cumulative effects on the decreasing of carbon emissions had the maximum of54.6%contribution, the adjustment of the industrial energy intensity is larger, and the inhibitory effect of increased year by year; The effect of energy structure, energy technology progress cumulative contributions to China’s carbon emission reduction rates were0.2%and1.04%, contribution is weak and needs to be improved; finding from the study of industry level, the development of low-carbon is better about agriculture forestry animal husbandry and fishery, construction, wholesale and retail and catering industry, industrial, transportation, storage and postal industry is poor about low-carbon development, the industry has always been the main source of China’s carbon emissions.At present, China is in the transformation of economic growth mode by "extensive" to "intensive". While sustained economic growth, energy consumption, carbon emissions increasing are inevitable.In this paper, based on the quantitative analysis result, we propose the following policy recommendations.(1) To improve the TFP contribution to economic growth, changing the economic growth mode of energy and capital investment to mainly rely on technological progress, low carbon economy development mode.(2) To develop the modern industrial system, accelerating the adjustment of energy consumption structure. Pay attention to upgrade high energy consumption industry structure and enhance strategic emerging industries, the proportion of low carbon industry and the international competition strength.(3) To promote energy utilization of technology innovation, pushing forward the improvement of the pure technical efficiency and the continuous relocation of production frontier.(4) To promote the "catch-up effect" of the central and western regions, gradually narrowing the regional differences of the total factor energy efficiency.(5) To formulate carbon emissions regulation policy or incentive means according to local conditions, enchancing the supervision of the carbon market and expanding the pilot city of carbon emission trading.