基于水泥企业电-碳关系的碳排放监测

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Abstract Based on the advantages of electrical data-instantaneity, accuracy and wide coverage, this paper establishes the electricity-carbon index for cement enterprises. By exploring the relationship between purchased electricity and total carbon dioxide emissions using the machine learning method, we formulate and investigate both single-sample daily monitoring models and multiple-sample annual monitoring models. The numerical study demonstrates that the Lasso model outperforms the other nine regression models deployed in the monitoring models, with a goodness of fit (R²) of 0.915 and 0.831. The results indicate that the critical factors influencing the electricity-carbon index of cement enterprises are the electricity emission factors in the clinker production and the proportion of electricity consumption in the clinker section, thus underscoring the importance of electrical data in carbon emission monitoring. The proposed model has the potential to significantly improve the efficiency and reduce the cost of carbon emission monitoring for cement enterprises.

Key words： carbon emission monitoring electricity-carbon relationship cement enterprises

Received: 06 December 2022

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X196

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	Articles by authors
	ZHANG Shu-han
	CHEN Hui
	WANG Bin
	YU Run-xin
	MA Zhi-ting
	MIAO Yu-han

Cite this article:

ZHANG Shu-han,CHEN Hui,WANG Bin等. Carbon emission monitoring based on analysis from “electricity-carbon” relationship of cement enterprises[J]. CHINA ENVIRONMENTAL SCIENCECE, 2023, 43(7): 3787-3795.

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http://www.zghjkx.com.cn/EN/ OR http://www.zghjkx.com.cn/EN/Y2023/V43/I7/3787

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