Environmental Science and Pollution Research

, Volume 25, Issue 10, pp 9626–9635 | Cite as

Identification of the driving factors’ influences on regional energy-related carbon emissions in China based on geographical detector method

  • Xinlin Zhang
  • Yuan Zhao
Research Article


To investigate the influences of different factors on spatial heterogeneity of regional carbon emissions, we firstly studied the spatial-temporal dynamics of regional energy-related carbon emissions using global Moran’s I and Getis-Ord Gi and applied geographical detector model to explain the spatial heterogeneity of regional carbon emissions. Some conclusions were drawn. Regional carbon emissions showed significant global and local spatial autocorrelation. The carbon emissions were greater in eastern and northern regions than in western and southern regions. Fixed assets investment and economic output had been the main contributing factors over the study period, and economic output had been decreasing its influence. Industrial structure’s influence showed a decrease trend and became smaller in 2015. The results of the interaction detections in 2015 can be divided into two types: enhance and nonlinear, and enhance and bivariate. The interactive influences between technological level and fixed assets investment, economic output and technological level, population size and technological level, and economic output and economic development were greater than others. Some policy recommendations were proposed.


Carbon emissions Spatial heterogeneity Driving factors Geographical detector model Regions 


Funding information

The current work is supported by the National Natural Science Foundation of China (No. 41371518) and Scientific Research Innovation Projects of Graduate Students in Jiangsu Province (No. KYLX16_1272).


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Geography, Geomatics and PlanningJiangsu Normal UniversityXuzhouChina
  2. 2.School of Geographic ScienceNanjing Normal UniversityNanjingChina
  3. 3.Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and ApplicationNanjingChina
  4. 4.Ginling CollegeNanjing Normal UniversityNanjingChina

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