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中国科学院大学学报 ›› 2015, Vol. 32 ›› Issue (3): 333-341.DOI: 10.7523/j.issn.2095-6134.2015.03.007

• 环境科学与地理学 • 上一篇    下一篇

基于因果和岭回归的新疆人口结构与CO2排放关系分析

霍金炜1,2, 杨德刚1, 汪菲1,2, 杨帆1,2, 张文彪1,2   

  1. 1. 中国科学院新疆生态与地理研究所, 乌鲁木齐 830011;
    2. 中国科学院大学, 北京 100049
  • 收稿日期:2014-04-16 修回日期:2014-07-22 发布日期:2015-05-15
  • 通讯作者: 杨德刚
  • 基金资助:

    中国科学院西部行动计划项目(KZCX2-XB3-01)资助

Analysis of the relationship between population structure and CO2 emissions in Xinjiang based on Granger causality and the ridge regression

HUO Jinwei1,2, YANG Degang1, WANG Fei1,2, YANG Fan1,2, ZHANG Wenbiao1,2   

  1. 1. Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China;
    2. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2014-04-16 Revised:2014-07-22 Published:2015-05-15

摘要:

利用新疆1958—2011年CO2排放量和人口数据,运用格兰杰因果检验和岭回归模型对新疆人口因素变化与CO2排放之间的因果关系和作用力进行实证分析,结果如下.1)在改革开放前,所有人口指标均与CO2排放呈现双向的因果联系;但改革开放后,农村居民消费与CO2排放失去因果关系.2)人口因素对CO2排放的推动作用在不同的发展阶段变化较大;随着改革开放政策的实施,生活方式发生较大变化,家庭户数与消费逐步取代就业结构和人口总量成为CO2排放最主要的推动因素.

关键词: 人口结构, 家庭户数, 格兰杰因果, 岭回归

Abstract:

With the data of CO2 emissions and population of Xinjiang from 1958 to 2011, we make empirical analysis on the causal relationship between the change in population factors and the CO2 emissions and on the acting force by using Granger causality test and the ridge regression model. The results are shown in the following. 1)Before the reform and opening up, the bidirectional causal relationship presents between all the relative indexes of population and CO2 emissions. However since the reform and opening up, the causal relationship between the countryside inhabitant cousumption and CO2 emissions disappears. 2)The promoting function of population factors on CO2 emissions is rather different in different stages of development. With the implementation of the reform and opening-up policy, the households and consumption become the main driving factos in place of employment structure and population gross.

Key words: population structure, number of households, granger causality, ridge regression

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