东北大学学报:自然科学版 ›› 2015, Vol. 36 ›› Issue (2): 297-300.DOI: 10.12068/j.issn.1005-3026.2015.02.031

• 管理科学 • 上一篇    下一篇

中国碳排放影响因素分析和趋势预测——基于STIRPAT 和GM(1,1)模型的实证研究

佟昕1, 陈凯1, 李刚1,2   

  1. (1.东北大学 工商管理学院,辽宁 沈阳110819; 2.东北大学 秦皇岛分校,河北 秦皇岛066004)
  • 收稿日期:2014-05-12 修回日期:2014-05-12 出版日期:2015-02-15 发布日期:2014-11-07
  • 通讯作者: 佟昕
  • 作者简介:佟昕(1975-),女,辽宁沈阳人,东北大学博士研究生; 陈凯(1961-),男,山西浑源人,东北大学教授,博士生导师.
  • 基金资助:
    教育部人文社会科学研究规划基金资助项目(12YJA790010); 教育部人文社会科学研究青年基金资助项目(11YJC790079); 河北省自然科学基金青年科学基金资助项目(G2012501013); 东北大学人文社会科学重点项目(XNR201307).

Influencing Factors Analysis and Trend Forecasting of China’s Carbon Emissions——Empirical Study Based on STIRPAT and GM(1,1) Models

TONG Xin1, CHEN Kai1, LI Gang1,2   

  1. 1. School of Business Administration, Northeastern University, Shenyang 110819, China; 2. Northeastern University at Qinhuangdao, Qinhuangdao 066004, China.
  • Received:2014-05-12 Revised:2014-05-12 Online:2015-02-15 Published:2014-11-07
  • Contact: TONG Xin
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摘要: 采用STIRPAT模型全面地对影响中国2000—2011年碳排放的因素进行分析,并利用灰色模型GM(1,1)预测了中国2012—2020年碳排放量.研究结果显示:城镇化率、经济增长、产业结构、能源价格、人口、能源结构和外贸强度对碳排放量有一定的促进作用,技术进步对碳排放量具有较强的抑制作用;其中对中国碳排放量增加影响较大的因素是人口和产业结构;根据GM(1,1)预测模型的结果,可以看出未来的减排压力还很大.因此,治理碳排放的政策应该综合考虑人口、产业结构和技术进步等影响因素.

关键词: 碳排放, 影响因素, 偏最小二乘回归法, STIRPAT模型, GM(1, 1)模型

Abstract: STIRPAT (stochastic impacts by regression on population, affluence, and technology) model is used to analyze the influencing factors of China’s carbon emissions, and gray model GM(1, 1)is applied to the prediction of emissions from 2012 to 2020. The analysis shows that urbanization,economy growth, industrial structure, energy prices, population, energy structure and foreign trade are the main factors to aggravate the emissions, while technology progress plays an important role in the inhibition of emissions. Among the factors mentioned, population and industrial structures are the two dominant factors. Based on the GM(1, 1), China’ carbon emission is predicted, showing the pressure of reducing carbon emissions is great. Therefore, the governance of carbon emission should synthetically consider the factors mentioned above.

Key words: carbon emission, influencing factor, partial least squares regression, STIRPAT model, GM(1, 1)model

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