期刊
SUSTAINABILITY
卷 8, 期 3, 页码 -出版社
MDPI
DOI: 10.3390/su8030210
关键词
spatial spillover effects; industrial carbon emissions; geographically and temporally weighted regression; spatial Durbin panel data model
资金
- National science foundation of China [71473242, 41461118]
- National Basic Research 973 Program [2012CB955700]
- Chinese Academy of Sciences (CAS) [XDA05140300]
- IDE
- JETRO-ICCS
- Tsinghua Univ.-IGLCE
- Nagoya Univ.-EAEF
- Xian Jiaotong Univ.-CBS
- Fudan Univ. international joint project Tracing China's Greenhouse Gas and Pollution Sources in Global Value Chains (GVCs): Regional and Firm Heterogeneity
This paper proposes an extended Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model to investigate the factors driving industrial carbon emissions in China. In the first stage, a spatial Durbin model is applied to investigate the determinants of regional industrial carbon emissions. In the second stage, a geographically and temporally weighted regression is applied to investigate temporal and spatial variations in the impacts of these driving factors on the scale and intensity of regional industrial carbon emissions. The empirical results suggest that the provinces with low carbon emissions act as exemplars for those with high carbon emissions and that driving factors impact carbon emission both directly and indirectly. All of the factors were investigated, except energy intensity, energy price, and openness, significantly impact carbon emissions. Overall, the results suggest that spatial correlation, heterogeneity, and spillover effects should be taken into account when formulating policies aiming at reducing industrial carbon emissions. The paper concludes with relevant policy recommendations taking full account of the regional industrial carbon emissions, heterogeneity and spillover.
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