自然资源学报 ›› 2014, Vol. 29 ›› Issue (11): 1815-1825.doi: 10.11849/zrzyxb.2014.11.001

• 资源利用与管理 •    下一篇

基于DEA-ESDA的中国省际能源效率及其时空分异研究

杨宇, 刘毅   

  1. 中国科学院地理科学与资源研究所, 北京100101
  • 收稿日期:2014-02-05 修回日期:2014-03-18 出版日期:2014-11-20 发布日期:2014-11-20
  • 作者简介:杨宇(1984-),男,山东威海人,博士,助理研究员,研究方向为能源地理与区域发展。Email:yangyu@igsnrr.ac.cn
  • 基金资助:

    国家自然科学基金(41371141,41401132,41430636)。

Study on China's Energy Efficiency and Its Spatio-temporal Variation from 1990 to 2010 Based on DEA-ESDA

YANG Yu, LIU Yi   

  1. Institute of Geographic Sciences and Nature Resources Research, CAS, Beijing 100101, China
  • Received:2014-02-05 Revised:2014-03-18 Online:2014-11-20 Published:2014-11-20

摘要:

提高能源效率是节约能源、解决能源供需矛盾的有效途径之一,以国内最高能源效率为比较标准计算能源效率并分析其区域差异对能源投入要素的合理配置具有重要的参考价值。论文通过DEA-ESDA模型对1990、2000 和2010 年我国各省市能源效率进行分析,并探索其空间集聚状态以及冷热点区域格局的演化,得出结论如下:①我国各省市能源效率的地带性差异显著,呈现出从东部沿海向西部地区递减的趋势;②科技投入低和基本要素配置的不合理是我国部分省市能源效率较低的主要原因,大致可以分为能源投入松弛、人力资源投入松弛、资本投入松弛和多种要素投入松弛四类;③能源效率与纯技术效率的空间集聚状态基本一致,而规模效率受制于资源禀赋等原因,集聚状态相对较弱,纯技术效率对能源效率的影响越来越明显;④能源效率的冷热点格局亦呈现地带性分布,能源效率与纯技术效率的演化过程高度一致,而规模效率呈现出相异的特征。提高能源利用效率的空间配置应重视纯技术效率及其在空间上的协调。

关键词: 能源效率, 规模效率, 纯技术效率, DEA-ESDA

Abstract:

Improving energy efficiency is one of the important ways to solve the energy problem. This paper analyzes trajectories and geographies of energy efficiency in China. More specifically it examines the evolution and regional differences in 1990, 2000 and 2010 through the DEA model of the Total Factor Energy Efficiency (TFEE). And using the Exploratory Spatial Data Analysis (ESDA), the paper then discusses its spatial agglomeration as well as the cold and hot spots, and draws the following conclusions. 1) There is a substantial difference in the energy efficiency among provinces in China, with higher energy efficiency in the eastern region and lower in the western region. 2) Different factors contribute to the difference in energy efficiency among provinces in China. Generally speaking, the provinces can be divided into four main kinds which are energy input slack, human capital input slack, capital input slack as well as energy and human capital input slack. With the rapid growth of China's economy, energy input and human capital input are the most popular factors leading to low efficiency. 3) Spatial autocorrelation analysis demonstrates that there is significant agglomeration of energy efficiency, pure technical efficiency and scale efficiency amongst Chinese provinces. 4) The cold and hot spots in terms of energy efficiency in China have shown regional differences from coastal to inland, and gradual decline from east region to central and west regions. Viewed from the decomposition of energy efficiency, the spatial structure and evolution of energy efficiency and pure technical efficiency are basically identical, while the scale efficiency showed significant difference.

Key words: energy efficiency, scale efficiency, pure technical efficiency, DEA-ESDA

中图分类号: 

  • F416.2