资源科学 ›› 2021, Vol. 43 ›› Issue (6): 1153-1165.doi: 10.18402/resci.2021.06.08

• 碳排放 • 上一篇    下一篇

中国碳排放增长的多层递进动因——基于SDA和SPD的实证研究

张炎治(), 冯颖, 张磊()   

  1. 中国矿业大学经济管理学院, 徐州 221116
  • 收稿日期:2020-06-02 修回日期:2020-08-07 出版日期:2021-06-25 发布日期:2021-08-25
  • 通讯作者: 张磊,男,江苏徐州人,教授,主要研究方向为能源经济与管理。E-mail: mailing126@126.com
  • 作者简介:张炎治,男,河南巩义人,副教授,主要研究方向为资源环境经济学。E-mail: zyzcumt2003@163.com
  • 基金资助:
    国家自然科学基金项目(71874187)

Analysis on the progressive motivation of carbon emissions growth in China using structural decomposition analysis and structural path decomposition methods

ZHANG Yanzhi(), FENG Ying, ZHANG Lei()   

  1. School of Economics and Management, China University of Mining and Technology, Xuzhou 221116, China
  • Received:2020-06-02 Revised:2020-08-07 Online:2021-06-25 Published:2021-08-25

摘要:

研究碳排放增长的多维、多层动因,识别关键的增排产业链和增排路径,对于宏观减排政策制定和微观减排路径选择都具有较强的现实意义。本文基于非竞争型投入产出模型,利用结构分解和结构路径分解的分析方法,从总体、生产阶段、产业链3个层次对中国碳排放增长进行了递阶分解分析,识别出了2010—2015年中国碳排放增长的主要动因和路径。研究结论表明:①需求规模变化是中国总体、生产阶段、产业链碳排放增加的主导影响因素;②煤炭消费发挥着减排作用且贡献巨大,但其他能源的增排效应使煤炭的减排贡献大打折扣;③能源效率变化对各生产阶段的影响为正且呈递减趋势,最终需求结构变化对第一生产阶段的碳排放具有显著减排效应,对其他生产阶段具有轻微增排效应;④需求规模变化和直接消耗系数变化是多数增排产业链的最大影响因素,30条增排路径占中国2010—2015年碳排放增加量的25.7%,构成了碳排放增加的关键路径和动因。最后,从宏观和微观两方面提出了相应的减排政策建议。

关键词: 碳排放, 结构路径分解, 多层递进动因, 增排路径, 投入产出模型

Abstract:

Identifying the key driving factors for the growth of carbon emissions in China is of great practical significance. Based on the non-competitive input-output (I-O) model and using the structural decomposition analysis and structural path analysis methods, this study examined the key driving factors for the growth of carbon emissions in China from the macro, meso, and micro levels. The research results show that: (1) The change of final demand scale was the leading factor influencing the increase of carbon emissions at the three levels; (2) Coal consumption played an important role in carbon emissions reduction and contributed a great deal, but its emissions reduction effect was offset by the emissions increase of other energy sources; (3) The effect of energy efficiency on each production stage was positive and showed a decreasing trend. The change of final demand structure had a significant effect on carbon emissions reduction in production stage 1, and a slight increase effect in other production stages; (4) Changes in final demand scale and direct consumption coefficients were the primary influencing factors for most of the top 30 industrial chains with emissions increase effect. The top 30 critical paths accounted for 25.7% of the increase of carbon emissions in China from 2010 to 2015, which constituted the key driving factors for the growth of carbon emissions in the country. Finally, the article put forward some corresponding recommendations on emissions reduction from the macro and micro levels.

Key words: carbon emissions, structural path decomposition, progressive motivation, path increasing emissions, input-output model