Identification of the driving factors’ influences on regional energy-related carbon emissions in China based on geographical detector method
- 87 Downloads
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.
KeywordsCarbon emissions Spatial heterogeneity Driving factors Geographical detector model Regions
- Anselin L (1988) Spatial econometrics: methods and models. Stud Oper Reg Sci 85:310–330Google Scholar
- Salahuddin M, Gow J, Ozturk I (2015) Is the long-run relationship between economic growth, electricity consumption, carbon dioxide emissions and financial development in Gulf Cooperation Council Countries robust? Renew Sust Energ Rev 51:317–326. https://doi.org/10.1016/j.rser.2015.06.005 CrossRefGoogle Scholar
- Wang JF, Li XH, Christakos G, Liao YL, Zhang T, Gu X, Zheng XY (2010) Geographical detectors-based health risk assessment and its application in the neural tube defects study of the Heshun region, China. Int J Geogr Inf Sci 24(1):107–127. https://doi.org/10.1080/13658810802443457 CrossRefGoogle Scholar
- Xu D, Gao L (2016) Research on the influence of forestry carbon storage incremental from forestry investment in fixed assets in China. For Econ 11:1–10Google Scholar