LAPSE:2023.36589
Published Article
LAPSE:2023.36589
A Study of Carbon Emission Driving Factors of a Metal Chemical Enterprise in China Based on the LMDI Model
Li Tan, Zao Feng, Guangfa Zheng, Chaoqun Li
September 20, 2023
Abstract
The chemical industry is a typical high-carbon emitting industry, and achieving the goal of net zero emissions by 2050 is challenging. Therefore, metal chemical enterprises have to explore a special path of low-carbon development. This article conducted a case study on a Chinese metal chemical production enterprise with a processing scale of 28,000 t/year. Starting from the analysis of energy consumption carbon emissions, this article used available statistical data at the enterprise level to build a carbon emission estimation model for the enterprise combining different emission categories. Moreover, we also calculated the carbon emissions and carbon emission intensity of the enterprise from 2014 to 2022. Further quantitative analyses on the impact of production scale, energy efficiency, energy structure, and emission coefficient on carbon increment were also conducted using a logarithmic mean divisia index (LMDI) model. The results showed that the reduction in carbon emissions of the enterprise during the research period was due to the improvement of energy efficiency, while the production scale and energy structure served as important driving factors. Based on the results, this article proposes some policy suggestions on the future direction and focus of the enterprise’s carbon reduction work.
Keywords
carbon accounting, energy conservation and emission reduction, energy consumption, factor analysis
Suggested Citation
Tan L, Feng Z, Zheng G, Li C. A Study of Carbon Emission Driving Factors of a Metal Chemical Enterprise in China Based on the LMDI Model. (2023). LAPSE:2023.36589
Author Affiliations
Tan L: Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
Feng Z: Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China; Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology, Kunming 650500, China; Yunnan Internati [ORCID]
Zheng G: Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
Li C: Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
Journal Name
Processes
Volume
11
Issue
8
First Page
2230
Year
2023
Publication Date
2023-07-25
ISSN
2227-9717
Version Comments
Original Submission
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PII: pr11082230, Publication Type: Journal Article
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LAPSE:2023.36589
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https://doi.org/10.3390/pr11082230
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Sep 20, 2023
 
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Calvin Tsay
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