Paper
6 February 2024 The influence factor decomposition and emission reduction path of power carbon emission in Jilin Province based on LMDI model
Zuoming Liu, Hongda Dong, Bing Liu, Haiyang Liu, Chenggang Li, Jingwen Yun, Xiaolong Yang
Author Affiliations +
Proceedings Volume 12979, Ninth International Conference on Energy Materials and Electrical Engineering (ICEMEE 2023); 129791Z (2024) https://doi.org/10.1117/12.3015829
Event: 9th International Conference on Energy Materials and Electrical Engineering (ICEMEE 2023), 2023, Guilin, China
Abstract
The power sector is one of the key areas to achieve the carbon peaking and carbon neutrality goals. Therefore, it is of great theoretical and practical significance to study the driving factors of carbon emission reduction in the power industry. Based on the data of Jilin Province from 2011 to 2021, this study adopts LMDI decomposition method to decompose ten driving factors affecting carbon emissions year by year and explores the main reasons for the change of carbon emissions in Jilin Province. The results show that per capita GDP and the ratio of electricity generation to consumption are driving factors of electric carbon emissions in Jilin Province, while the main inhibiting factors are industrial power intensity and coal consumption. In order to further promote the emission reduction of electric power carbon in Jilin Province, corresponding measures should be taken from the aspects of demand side management, clean energy development, industrial green upgrading and fuel conversion technology.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Zuoming Liu, Hongda Dong, Bing Liu, Haiyang Liu, Chenggang Li, Jingwen Yun, and Xiaolong Yang "The influence factor decomposition and emission reduction path of power carbon emission in Jilin Province based on LMDI model", Proc. SPIE 12979, Ninth International Conference on Energy Materials and Electrical Engineering (ICEMEE 2023), 129791Z (6 February 2024); https://doi.org/10.1117/12.3015829
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KEYWORDS
Carbon

Power consumption

Industry

Seaborgium

Analytical research

Carbon monoxide

Data modeling

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