科研管理 ›› 2014, Vol. 35 ›› Issue (10): 128-135.

• 论文 • 上一篇    下一篇

基于生态足迹的我国居民间接能源消费研究

王兆华, 杨琳   

  1. 北京理工大学管理与经济学院, 北京 100081
  • 收稿日期:2013-03-20 修回日期:2014-02-26 出版日期:2014-10-25 发布日期:2014-10-23
  • 作者简介:王兆华(1974-),男(汉),山东省泰安市人,管理学博士,北京理工大学管理与经济学院教授,博士生导师。研究领域:资源与环境管理。
    杨琳(1988-),女(汉),陕西省韩城市人,北京理工大学管理与经济学院,博士研究生。研究领域:能源与环境管理。
  • 基金资助:

    本文受国家自然科学基金资助,基金项目批准号:71173017,起止时间:2012.1-2015.12;受国家“973”课题资助,基金编号:2012CB955703,起止时间:2012.1-2016.12。

An Analysis of Household Indirect Energy Consumption in China based on Ecological Footprint

Wang Zhaohua, Yang Lin   

  1. School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China
  • Received:2013-03-20 Revised:2014-02-26 Online:2014-10-25 Published:2014-10-23

摘要: 本文基于生态足迹这种可持续性地研究理念,利用生活方式分析法和净初级生产力法测算了2000-2010年城乡居民间接能源消费,然后运用改进的根据STIRPAT模型进行偏最小二乘回归。结果表明:对于城镇居民而言,间接能源生态足迹呈上升趋势,主要受消费结构和第三产业占比的影响;而对于农村居民来说,间接能源生态足迹逐年下降,主要受人均收入、消费结构和能源强度的影响。我国正处于工业化和城镇化的快速时期,随着居民生活水平的提高,消费方式的转变已经成为间接能源消费上升的重要拉动因素。

关键词: 间接能源消费, 生态足迹, STIRPAT模型, 影响因素

Abstract: Based on the concept of Energy Ecological Footprint (EEF),this paper utilizes Consumer Lifestyle Approach (CLA) and Net Primary Productivity (NPP) to quantify indirect energy consumption of China's urban and rural residents during the period of 2000-2010.According to STIRPAT (stochastic impacts by regression on population,affluence and technology) model,the influence factors on EEF are analyzed and the model is examined by partial least square regression.The results show that,the indirect energy use of EEF is on the rise in urban areas,and primarily influenced by tertiary industry and Engel coefficient;the indirect energy use of EEF declines in rural areas,and is primarily influenced by per capita income,Engel coefficient and energy intensity.As China is continuing industrialization and urbanization,consumption pattern changing has become an important factor to promote indirect energy consumption with resident living standard improving.

Key words: indirect energy consumption, ecological footprint, STIRPAT model, influencing factors

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