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

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Fang Liu ◽  
Lu Tang ◽  
Kaicheng Liao ◽  
Lijuan Ruan ◽  
Pingsheng Liu
Keyword(s):  
Spatial Distribution ◽  
Carbon Emissions ◽  
Regional Difference ◽  
Emissions Reduction ◽  
Data Envelopment ◽  
Undesirable Outputs ◽  
Carbon Emissions Reduction ◽  
Industrial Energy ◽  
The Difference ◽  
Over Time

AbstractThe 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.

scholarly journals Spatial Distribution and Regional Difference of Carbon Emissions Efficiency of Industrial Energy in China

2021 ◽  
Author(s):  
Fang Liu ◽  
Lu Tang ◽  
Kaicheng Liao ◽  
Lijuan Ruan ◽  
Pingsheng Liu
Keyword(s):  
Spatial Distribution ◽  
Carbon Emissions ◽  
Regional Integration ◽  
Regional Difference ◽  
Emissions Reduction ◽  
Data Envelopment ◽  
Carbon Emissions Reduction ◽  
Industrial Energy ◽  
The Difference ◽  
Over Time

Abstract 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 provincial difference in CEEIE across China is gradually expanding, which is mainly induced by the difference between regions. Our findings attach more importance to regional integration policies for carbon emissions reduction in China.

scholarly journals An Examination of Green Credit Promoting Carbon Dioxide Emissions Reduction: A Provincial Panel Analysis of China

2021 ◽  
Vol 13 (13) ◽  
pp. 7148
Author(s):  
Wenjie Zhang ◽  
Mingyong Hong ◽  
Juan Li ◽  
Fuhong Li
Keyword(s):  
Carbon Dioxide ◽  
Carbon Emissions ◽  
Carbon Dioxide Emissions ◽  
Industrial Structure ◽  
Emissions Reduction ◽  
Green Finance ◽  
Carbon Emissions Reduction ◽  
Negative Effect ◽  
Emissions Intensity ◽  
The Impact

The implementation of green finance is a powerful measure to promote global carbon emissions reduction that has been highly valued by academic circles in recent years. However, the role of green credit in carbon emissions reduction in China is still lacking testing. Using a set of panel data including 30 provinces and cities, this study focused on the impact of green credit on carbon dioxide emissions in China from 2006 to 2016. The empirical results indicated that green credit has a significantly negative effect on carbon dioxide emissions intensity. Furthermore, after the mechanism examination, we found that the promotion impacts of green credit on industrial structure upgrading and technological innovation are two effective channels to help reduce carbon dioxide emissions. Heterogeneity analysis found that there are regional differences in the effect of green credit. In the western and northeastern regions, the effect of green credit is invalid. Quantile regression results implied that the greater the carbon emissions intensity, the better the effect of green credit. Finally, a further discussion revealed there exists a nonlinear correlation between green credit and carbon dioxide emissions intensity. These findings suggest that the core measures to promote carbon emission reduction in China are to continue to expand the scale of green credit, increase the technology R&D investment of enterprises, and to vigorously develop the tertiary industry.

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