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中国的节能政策有效吗?——基于SFA-Bayes分析框架的生态全要素能源效率测定

闫明喆 李宏舟 田飞虎

经济与管理研究2018,Vol.39Issue(3):89-101,13.
经济与管理研究2018,Vol.39Issue(3):89-101,13.DOI:10.13502/j.cnki.issn1000-7636.2018.03.008

中国的节能政策有效吗?——基于SFA-Bayes分析框架的生态全要素能源效率测定

Are the Energy Conserving Policies Effective?—Estimating Ecological Total Factor Energy Efficiency Based on SFA-Bayes Framework

闫明喆 1李宏舟 2田飞虎2

作者信息

  • 1. 东北财经大学产业组织与企业组织研究中心 大连 116025
  • 2. 东北财经大学产业组织与企业组织研究中心
  • 折叠

摘要

Abstract

The paper proposes a SFA-Bayes based framework with time series data and takes undesirable outputs into consideration,being an improved one when compared with the energy-to-GDP ratio and the DEA-SBM model.The empirical results obtained with panel data covering 29 Chinese provincial administrative regions during 2000—2013 show that the ecological total factor energy efficiency levels are relatively low,with an average value of 0.60,higher in the eastern area than in western and central areas.An improvement by 2 percent in efficiency score for the whole country and a higher improvement in western and central areas relative to the eastern area in technology progress are confirmed during the sample period,demonstrating that the energial conserving policy may be not as effective as seen in terms of the energy-to-GDP ratio.Further,it seems that location factors,when compared with industrial structure,have more impacts on ecological total factor energy efficiency,and when other things are equal,ecological total factor energy efficiency levels in the eastern area and in the third industry are relatively high.

关键词

生态全要素能源效率/非期望产出/SFA-Bayes/DEA-SBM/空间自相关性

Key words

ecological total factor energy efficiency/undesirable outputs/SFA-Bayes/DEA-SBM/spatial autocorrelation

分类

管理科学

引用本文复制引用

闫明喆,李宏舟,田飞虎..中国的节能政策有效吗?——基于SFA-Bayes分析框架的生态全要素能源效率测定[J].经济与管理研究,2018,39(3):89-101,13.

基金项目

教育部人文社会科学研究规划基金项目"准许成本加合理收益中导入效率改善因子的理论与落地化研究:以输配电行业为例"(17YJA790042) (17YJA790042)

大连市社科联(社科院)一般项目"基于前沿面分析框架的大连市城市供水业亏损原因分析及对策研究"(2016dlskyb054) (社科院)

经济与管理研究

OA北大核心CHSSCDCSSCICSTPCD

1000-7636

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