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Article

Analysis of the Spatial and Temporal Evolution of China’s Energy Carbon Emissions, Driving Mechanisms, and Decoupling Levels

1
College of Economics and Management, Northeast Forestry University, Harbin 150040, China
2
College of Forestry, Northeast Forestry University, Harbin 150040, China
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(22), 15843; https://doi.org/10.3390/su152215843
Submission received: 3 October 2023 / Revised: 27 October 2023 / Accepted: 3 November 2023 / Published: 10 November 2023

Abstract

Excessive carbon emissions will cause the greenhouse effect and global warming, which is not conducive to environmental protection and sustainable development. In order to realize the goal of “carbon peak and carbon neutrality” as soon as possible, this paper utilizes the methodology provided by the IPCC to measure the carbon emissions and carbon intensity of China’s energy consumption. The classification method of carbon emission and the kernel density function method are used to explore the spatial and temporal evolution of regional carbon emissions. Based on the Log Mean Divided Index (LMDI) method, the drivers of China’s energy carbon emissions are measured. Based on the Tapio index function and the catch-up decoupling model, the decoupling status of Chinese provinces and the development gap with the benchmark provinces are examined. The results show that (1) China’s total energy carbon emissions show a “rising-declining-rising” trend from 2005 to 2021, and reach the first peak in 2013, totaling 1,484,984.406 million metric tons. China’s Hebei, Shanxi, and Shandong provinces have the highest energy carbon emissions. (2) China’s energy carbon emissions are influenced by multiple factors, and the contribution of each factor to energy carbon emissions is in the following order: economic development effect > energy intensity effect > energy structure effect > population size effect. (3) China’s catch-up provinces develop their economies at the expense of the environment and energy consumption.
Keywords: sustainable development; carbon peak and carbon neutrality; carbon emissions; Log Mean Divided Index (LMDI); catching-up decoupling; Tapio decoupling index sustainable development; carbon peak and carbon neutrality; carbon emissions; Log Mean Divided Index (LMDI); catching-up decoupling; Tapio decoupling index

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MDPI and ACS Style

Ji, J.; Li, C.; Ye, X.; Song, Y.; Lv, J. Analysis of the Spatial and Temporal Evolution of China’s Energy Carbon Emissions, Driving Mechanisms, and Decoupling Levels. Sustainability 2023, 15, 15843. https://doi.org/10.3390/su152215843

AMA Style

Ji J, Li C, Ye X, Song Y, Lv J. Analysis of the Spatial and Temporal Evolution of China’s Energy Carbon Emissions, Driving Mechanisms, and Decoupling Levels. Sustainability. 2023; 15(22):15843. https://doi.org/10.3390/su152215843

Chicago/Turabian Style

Ji, Jingyi, Chao Li, Xinyi Ye, Yuelin Song, and Jiehua Lv. 2023. "Analysis of the Spatial and Temporal Evolution of China’s Energy Carbon Emissions, Driving Mechanisms, and Decoupling Levels" Sustainability 15, no. 22: 15843. https://doi.org/10.3390/su152215843

APA Style

Ji, J., Li, C., Ye, X., Song, Y., & Lv, J. (2023). Analysis of the Spatial and Temporal Evolution of China’s Energy Carbon Emissions, Driving Mechanisms, and Decoupling Levels. Sustainability, 15(22), 15843. https://doi.org/10.3390/su152215843

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