SCIENTIA GEOGRAPHICA SINICA ›› 2018, Vol. 38 ›› Issue (5): 681-690.doi: 10.13249/j.cnki.sgs.2018.05.005

• Orginal Article • Previous Articles     Next Articles

Spatial Dependence Pattern of Carbon Emission Intensity in China’s Provinces and Spatial Heterogeneity of Its Influencing Factors

Xianzhao Liu1(), Changchun Gao2, Yong Zhang1, Dongshui Zhang1, Jinning Xie1, Yan Song1, Zhiqiang Wang1   

  1. 1. School of Resource, Environment and Safety Engineering, Hunan University of Science and Technology, Xiangtan 411201, Hunan,China
    2. School of Tourism and Resource Environment, Qiannan Normal University for Nationalities, Duyun 558000, Guizhou, China
  • Received:2017-08-20 Revised:2017-12-24 Online:2018-05-10 Published:2018-05-10
  • Supported by:
    Humanities and Social Sciences Foundation of Ministry of Education of China (14YJAZH050), National Social Sciences Foundation of China (17BGL138), Social Science Fund of Hunan Province (14YBA170)


The carbon emissions intensities of China’s thirty provinces caused by energy consumption were calculated according to the reference approach provided by IPCC. Exploratory spatial data analysis (ESDA), space-time transition measurement method and geographically weighted regression (GWR) model were employed to analyze the spatial dependence of provincial carbon emissions intensity and spatial heterogeneity of its driving factors from 1995 to 2015. The results were shown as follows: 1) There was a significant positive spatial correlation in carbon emissions intensity among provinces. Global spatial autocorrelation decreased first and then increased and last fluctuated slightly. The provinces with similar carbon intensity tended to be agglomerate, indicating that provincial carbon intensity had an obvious spatial dependence characteristics. 2) An uneven development pattern of carbon emission intensity existed in China's provinces. The provinces with H-H agglomeration were mainly distributed in the northwest of China, while the ones with L-L agglomeration mainly distributed in the southeast of China. 3) The spatial agglomeration of carbon intensity presented an overall trend of optimization, the provinces with H-H agglomeration decreased, while ones with L-L agglomeration increased. However, different provinces played different roles in the spatial agglomeration of carbon intensity. 4) The driving factors of carbon emissions intensity had obvious spatial heterogeneity among China’s provinces, and there was a positive correlation between the 4 explanatory variables and carbon intensity. The influence degree of 4 explanatory variables on carbon intensity was as follows: energy intensity>energy structure>industrial structure>per capita GDP. Different policies of carbon intensity reduction should be formulated according to the actual situation of each province. Therefore, in order to achieve regional differences in carbon emission reduction, it is necessary to take full account of the actual situation of carbon intensity in each province and the spatial differences of carbon intensity affected by different factors.

Key words: carbon emissions intensity, ESDA-GWR, spatial dependence, spatial heterogeneity

CLC Number: 

  • F119