The decoupling analysis of CO2 emissions from power generation in Chinese provincial power sector

被引:35
|
作者
Wang, Juan [1 ,2 ]
Li, Ziming [1 ]
Wu, Tong [1 ]
Wu, Siyu [1 ]
Yin, Tingwei [1 ]
机构
[1] Tianjin Univ Finance & Econ, Coll Finance, Tianjin 300222, Peoples R China
[2] Tianjin Univ Finance & Econ, Lab Fintech & Risk Management, Tianjin 300222, Peoples R China
关键词
CO2; emissions; Decoupling analysis; LMDI method; Power sector; Spatial autocorrelation; CARBON EMISSIONS; DECOMPOSITION ANALYSIS; ELECTRICITY-GENERATION; ECONOMIC-GROWTH; INDUSTRY; LMDI; EFFICIENCY; ENERGY; LEVEL;
D O I
10.1016/j.energy.2022.124488
中图分类号
O414.1 [热力学];
学科分类号
摘要
The decoupling of CO2 emissions from power generation is of great significance for China to accomplish the commitments of carbon peak and carbon neutral. However, the related studies in China's power sector are still limited. This paper aims to explore the decoupling relationship between CO2 emissions and power generation of China's power sector as well as the driving factors of decoupling index at provincial level using Tapio model and LMDI method. The decoupling analysis shows that Heilongjiang, Beijing, Shanghai, Sichuan and Yunnan achieved decoupling and most provinces were in expansive coupling states from 2000 to 2019. The number of provinces in decoupling state during 2011-2015 was twenty-three and larger than periods of 2000-2005, 2006-2010 and 2016-2019. The decomposition analysis indicates that per capita GDP and population size were responsible for inhibiting the decoupling process for most provinces, while thermal power generation efficiency and electricity intensity promoted the decoupling. Specifically, coal-to-gas of Beijing, renewable energy utilization of Gansu and expansion in nuclear energy of Hainan contributed more to their decoupling. Besides, this paper also explores the regional agglomeration of decoupling index across provinces based on global and local Moran's I Index, demonstrating that the spatial autocorrelation was significantly positive during 2016-2019. (C) 2022 Elsevier Ltd. All rights reserved.
引用
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页数:18
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