主管单位:中国科学技术协会
主办单位:中国地理学会
承办单位:华东师范大学

World Regional Studies ›› 2020, Vol. 29 ›› Issue (3): 512-522.DOI: 10.3969/j.issn.1004-9479.2020.03.2018507

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Spatiotemporal dynamics and influencing factors of provincial carbon emissions in China

Ying WANG(), Yanfen HE()   

  1. College of Urban and Environmental Science,Northwest University,Xi'an 710127, China
  • Received:2018-11-15 Revised:2019-02-27 Online:2020-05-30 Published:2020-06-12
  • Contact: Yanfen HE

中国省域二氧化碳排放的时空格局及影响因素

王瑛(), 何艳芬()   

  1. 西北大学城市与环境学院,西安 710127
  • 通讯作者: 何艳芬
  • 作者简介:王瑛(1993-),女,硕士研究生,主要研究方向为资源与环境可持续发展,E-mail:1412191347@qq.com
  • 基金资助:
    陕西省土地整治重点实验室项目(211927180168)

Abstract:

In this paper, the carbon dioxide emissions from 2000 to 2015 in the provinces of China were calculated, using the natural segment method, the carbon emissions in 2000, 2005, 2010 and 2015 were also classified respectively, to analysis of its spatial differentiation characteristics.Spatial autocorrelation analysis was used to reveal the spatial correlation of carbon emissions between neighboring provinces.On this basis, the LMDI method was used to decompose the factors affecting carbon emissions from the aspects of energy structure, energy intensity, economic development and population scale. The results showed that: In terms of time, China's total carbon emissions showed an overall upward trend, with only a 2% decline in 2014-2015. Except for Beijing, the carbon emissions of other provinces are increasing.In space, high-value carbon emissions have gradually spread from the Bohai Rim and the eastern coastal provinces to individual provinces in the central and western regions. The provincial carbon emissions were mainly characterized by high concentration and low agglomeration. The high concentration and centralization of carbon emissions were concentrated in Liaoning province,Hebei, Shandong, Shanxi and Jiangsu provinces, Beijing and Tianjin had a low concentration area with high carbon emissions. The eastern and central regions are more susceptible to energy structure, energy intensity, economic development and population scale than the western provinces. Economic development is driving the carbon emission. Energy intensity is inhibitory to carbon emissions. The energy structure has a positive and negative inhibitory effect on the carbon emissions of various provinces. Except for Guizhou province, the population scale of other provinces is positively driving carbon emissions.

Key words: carbon emission, spatiotemporal dynamics, spatial autocorrelation, LMDI

摘要:

通过测算全国30个省域2000—2015年的二氧化碳排放量,采用自然段点法,分别对2000年、2005年、2010年和2015年各省碳排放量进行分类,分析其空间分异化特征。采用空间自相关分析法揭示了相邻各省份碳排放量的空间关联性。在此基础上,运用对数均值迪氏分解法,从能源结构、能源强度、经济发展和人口规模等角度,对碳排放影响因素进行无残差分解。结果表明:1)时间上,我国碳排放总量整体呈上升趋势,在2014—2015年仅下降2%。除北京市外,其余各省份的碳排放量呈增长趋势;空间上,高值碳排放由环渤海及东部沿海省份逐步蔓延至中西部个别省份;2)各省域碳排放主要呈现高高集聚和低高集聚的特征,高高集聚稳定集中在辽宁、河北、山东、山西和江苏省,北京市和天津市与高碳排放的省份形成一个低高集聚区域;3)东、中部比西部省份更易受能源结构、能源强度、经济发展和人口规模等因素的影响。经济发展对碳排放是驱动作用,能源强度对碳排放是抑制作用,能源结构对各省份碳排放的影响有正向驱动和负向抑制作用,除贵州省外,其余省份人口规模对碳排放均是正向驱动作用。

关键词: 碳排放, 时空格局, 空间自相关, LMDI