ArticlePloS one2021
Analysis of carbon emission performance and regional differences in China's eight economic regions: Based on the super-efficiency SBM model and the Theil index.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Spatiotemporal evolution regional differences and decoupling effects of greenhouse gas emissions from animal husbandry in Henan Province.Scientific reports · 2025Article
- Navigating the green shift with innovative techniques in petrochemical emission control.Scientific reports · 2024Article
- Spatiotemporal Evolution and Influencing Factors of Carbon Emission Efficiency in the Yellow River Basin of China: Comparative Analysis of Resource and Non-Resource-Based Cities.International journal of environmental research and public health · 2022Article
- Spatial Evolutionary Characteristics and Influencing Factors of Urban Industrial Carbon Emission in China.International journal of environmental research and public health · 2022Article
- The Spatiotemporal Distribution and Drivers of Urban Carbon Emission Efficiency: The Role of Technological Innovation.International journal of environmental research and public health · 2022Article
- Research on carbon emission differences decomposition and spatial heterogeneity pattern of China's eight economic regions.Environmental science and pollution research international · 2022Article
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Abstract
China's carbon emission performance has significant regional heterogeneity. Identified the sources of carbon emission performance differences and the influence of various driving factors in China's eight economic regions accurately is the premise for realizing China's carbon emission reduction goals. Based on the provincial panel data from 2005 to 2017, the super-efficiency SBM model and Malmquist model are constructed in this paper to measure regional carbon emission performance's static and dynamic changes. After that, the Theil index is used to distinguish the impact of inter-regional and intra-regional differences on different regions' carbon emissions performance. Finally, by introducing the Tobit model, the effect of various driving factors on carbon emission performance differences is analyzed quantitatively. The results show that: (1) There are significant differences in different regions' carbon emission performance, but the overall carbon emission performance presents an upward fluctuation trend. Malmquist index decomposition results show substantial differences in technology progress index and technology efficiency index in different regions, leading to significant carbon emission performance differences. (2) Overall, inter-regional differences contribute the most to the overall carbon emission performance, up to more than 80%. Among them, the inter-regional and intra-regional differences in ERMRYR contributed significantly. (3) Through Tobit regression analysis, it is found that residents' living standards, urbanization level, ecological development degree, and industrial structure positively affect carbon emission performance. On the contrary, energy intensity presents an apparent negative correlation on carbon emission performance. Therefore, to improve the carbon emission performance, we should put forward targeted suggestions according to the characteristics of different regional development stages, regional carbon emission differences, and influencing driving factors.
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