School of Statistics and Applied Mathematics, Anhui University of Finance and Economics, Bengbu, China.
School of Business, Central South University, Changsha, China.
Environ Sci Pollut Res Int. 2018 May;25(14):13745-13759. doi: 10.1007/s11356-018-1574-5. Epub 2018 Mar 5.
Increased environmental pollution and energy consumption caused by the country's rapid development has raised considerable public concern, and has become the focus of the government and public. This study employs the super-efficiency slack-based model-data envelopment analysis (SBM-DEA) to measure the total factor energy efficiency of 30 provinces in China. The estimation model for the spatial interaction intensity of regional total factor energy efficiency is based on Wilson's maximum entropy model. The model is used to analyze the factors that affect the potential value of total factor energy efficiency using spatial dynamic panel data for 30 provinces during 2000-2014. The study found that there are differences and spatial correlations of energy efficiency among provinces and regions in China. The energy efficiency in the eastern, central, and western regions fluctuated significantly, and was mainly because of significant energy efficiency impacts on influences of industrial structure, energy intensity, and technological progress. This research is of great significance to China's energy efficiency and regional coordinated development.
由于国家的快速发展导致环境污染和能源消耗增加，引起了相当大的公众关注，并成为政府和公众关注的焦点。本研究采用超效率基于松弛的模型-数据包络分析（SBM-DEA）来衡量中国 30 个省份的全要素能源效率。区域全要素能源效率空间相互作用强度的估计模型基于威尔逊的最大熵模型。该模型用于使用 2000-2014 年 30 个省份的空间动态面板数据来分析影响全要素能源效率潜在值的因素。研究发现，中国各省份和地区之间的能源效率存在差异和空间相关性。东部、中部和西部地区的能源效率波动较大，这主要是因为产业结构、能源强度和技术进步对能源效率的影响显著。这项研究对中国的能源效率和区域协调发展具有重要意义。
