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空间互动视角下CO2排放驱动因素及溢出效应——基于全球98个国家的数据分析
引用本文:吕天宇,曾晨,刘泽瑾,杨婧. 空间互动视角下CO2排放驱动因素及溢出效应——基于全球98个国家的数据分析[J]. 生态学报, 2020, 40(24): 8974-8987
作者姓名:吕天宇  曾晨  刘泽瑾  杨婧
作者单位:华中农业大学公共管理学院, 武汉 430070;华中农业大学公共管理学院, 武汉 430070;中国科学院地理科学与资源研究所, 北京 100101;武汉大学资源与环境科学学院, 武汉 430079
基金项目:国家重点研发计划项目(2016YFA0602502);国家自然科学基金项目(41771563);中国博士后基金特别资助(2019T12013)
摘    要:全球气候变暖已成为21世纪威胁人类可持续发展的严峻挑战,减少CO2排放是抑制气候变暖的重要路径。从全球碳减排的宏观视角出发,以98个国家为研究对象,基于总商品贸易和化石能源贸易的引力模型构建两种空间互动关系,利用扩展后的S-STIRPAT模型对2000、2005、2010年和2014年人均CO2排放的驱动机制和空间溢出效应展开实证分析,并基于发展差异视角进一步探究发达和欠发达国家CO2排放驱动机制异同。研究结果表明:(1)2000、2005、2010年和2014年人均CO2排放溢出效应呈增强态势。(2)城市化水平、人均GDP、能源强度对人均CO2排放产生显著正向影响,可再生能源使用率对人均CO2排放产生显著负向影响。(3)发展差异视角下,城市化水平、人均GDP和能源强度对欠发达国家影响更大,可再生能源使用率对发达国家影响更为显著。根据研究结果建议加强发达国家和欠发达国家低碳技术交流与合作,同时积极调整能源结构以减少CO2排放。

关 键 词:全球气候变化  碳排放  驱动机制  空间溢出效应  发达国家  欠发达国家
收稿时间:2020-01-07
修稿时间:2020-08-31

Driving factors and spillover effects of CO2 emissions from the perspective of spatial interaction: A case study of 98 countries worldwide
L,#; Tianyu,ZENG Chen,LIU Zejin,YANG Jing. Driving factors and spillover effects of CO2 emissions from the perspective of spatial interaction: A case study of 98 countries worldwide[J]. Acta Ecologica Sinica, 2020, 40(24): 8974-8987
Authors:L&#   Tianyu,ZENG Chen,LIU Zejin,YANG Jing
Affiliation:College of Public Administration, Huazhong Agricultural University, Wuhan 430070, China;College of Public Administration, Huazhong Agricultural University, Wuhan 430070, China;Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China
Abstract:Global warming has become a serious challenge that threatens human sustainable development in the 21st century. In the context of climate change, reducing greenhouse gas emission is important. In this study, we take 98 countries worldwide as the case study area and calculate the spatial interaction degree generated from the total merchandise and fossil energy trades through the gravity model. We then apply the spatial econometric model to explore the driving mechanism and spatial spillover effect in 2000, 2005, 2010, and 2014. We further explore the similarities and differences with respect to the driving mechanism of carbon emission between developed and underdeveloped countries. Results indicate that: (1) The spatial spillover effect increased in 2000, 2005, 2010, and 2014. (2) Urbanization, per capita GDP, and energy intensity had significant positive influences on per capita CO2 emissions, and the renewable energy use rate negatively influenced per capita CO2 emissions. (3) The contributions of urbanization, per capita GDP, and energy intensity were more powerful in underdeveloped countries than those in developed countries, whereas the use of renewable energy had a greater influence in developed countries than in underdeveloped countries. Accordingly, developed and underdeveloped countries should strengthen their cooperation and exchange of low-carbon technology. The energy structure should also be optimized to reduce CO2 emissions.
Keywords:global climate change  carbon emission  driving mechanism  spatial spillover effect  developed countries  underdeveloped countries
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