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中国科学院大学学报 ›› 2016, Vol. 33 ›› Issue (5): 641-650.DOI: 10.7523/j.issn.2095-6134.2016.05.010

• 环境科学与地理学 • 上一篇    下一篇

山西省煤炭消费需求影响因素的实证研究

董洁芳1,2,3, 王强2, 王兴民2,3, 张小雷2,3   

  1. 1 新疆大学资源与环境科学学院, 乌鲁木齐 830011;
    2 中国科学院新疆生态与地理研究所, 乌鲁木齐 830011;
    3 中国科学院大学, 北京 100049
  • 收稿日期:2015-07-08 修回日期:2016-04-11 发布日期:2016-09-15
  • 通讯作者: 王强
  • 基金资助:

    中国科学院新疆生态与地理研究所所长基金(Y475061001)资助

Empirical study on the influencing factors of coal consumption in Shanxi province

DONG Jiefang1,2,3, WANG Qiang2, WANG Xingmin2,3, ZHANG Xiaolei2,3   

  1. 1 College of Resource and Environment Sciences, Xinjiang University, Urumqi 830011, China;
    2 Xinjiang Institute of Ecology and Geography, Chinese Academy of Science, Urumqi 830011, China;
    3 University of Chinese Academy of Science, Beijing 100049, China
  • Received:2015-07-08 Revised:2016-04-11 Published:2016-09-15

摘要:

利用对数均值迪氏分解法和向量自回归模型,分别从流量及存量,静态与动态的角度对1988-2013年山西煤炭消费的影响因素进行实证分析.结果如下:1)经济增长是山西煤炭消费的主要驱动因素.2)技术的进步在短期内抑制煤炭消费,但长期存在“反弹效应”.3)人口规模对煤炭消费流量增长起到微弱促进作用,但人口的城镇化对煤炭消费存量的解释贡献率高达30.91%.4)产业结构的调整对于山西煤炭消费的影响非常微弱,方差分解显示其平均解释贡献率仅为3.98%.要降低煤炭消耗总量,山西省必须加快产业结构的调整,同时积极推动煤炭资源的清洁高效利用,努力实现经济增长与煤炭消费的脱钩.

关键词: 煤炭消费, 影响因素, 对数均值迪氏分解, 向量自回归模型, 山西省

Abstract:

In this study, the method of logarithmic mean Divisia index and vector autoregression model were used to analyze the influence factors of coal consumption in Shanxi province during 1988-2013. The results are as follows. 1) The wealth effect is the main driver of growth in total coal consumption. 2) In the short term technological progress suppresses coal consumption, but there exists "rebound effect" in the long run. 3) Population plays a weak role in promoting flow growth of coal consumption while the contribution rate of urbanization to coal consumption of the stock is up to 30.91%. 4) The impact of industrial structure adjustment on coal consumption in Shanxi is very weak, and variance decomposition shows that the average contribution rate is only 3.98%.

Key words: coal consumption, influence factor, LMDI, VAR Model, Shanxi province

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