Spatial distribution and regional difference of carbon emissions efficiency of industrial energy in China

The three-stage super-efficiency slack-based measure and data envelopment analysis (SBM-DEA) model with undesirable outputs is used to calculate carbon emissions efficiency of industrial energy (CEEIE) of 30 provinces in China from 2000 to 2017. Then ArcGIS software is used to illustrate the spatial distribution of CEEIE, and Dagum Gini ratio is calculated to decompose the regional difference. The results show that the spatial distribution of CEEIE changes from disorder to order and provinces characterized with high or low CEEIE cluster in space over time. The total Dagum Gini coefficient indicates that the interprovincial difference in CEEIE across China is gradually expanding, which is mainly induced by the difference between regions. Our findings attach more importance to interregional integration policies for carbon emissions reduction in China.

Automated summary

The study utilized the SBM-DEA model and ArcGIS software to analyze the carbon emissions efficiency of industrial energy in 30 provinces of China from 2000 to 2017, showing a gradual widening of differences among provinces. This highlights the importance of interregional integration policies for carbon emissions reduction in China.