A Fast Screening Method of Key Parameters from Coal for Carbon Emission Enterprises

Weiye Lu (), Xiaoxuan Chen, Zhuorui Song, Yuesheng Li and Jidong Lu
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Weiye Lu: School of Electric Power, South China University of Technology, Guangzhou 510640, China
Xiaoxuan Chen: Guangdong Institute of Special Equipment Inspection and Research Shunde Branch, Foshan 528300, China
Zhuorui Song: Guangdong Institute of Special Equipment Inspection and Research Shunde Branch, Foshan 528300, China
Yuesheng Li: Guangdong Institute of Special Equipment Inspection and Research Shunde Branch, Foshan 528300, China
Jidong Lu: School of Electric Power, South China University of Technology, Guangzhou 510640, China

Energies, 2023, vol. 16, issue 22, 1-14

Abstract: During the process of determining carbon emissions from coal using the emission factor method, third-party organizations in China are responsible for verifying the accuracy of the carbon emission data. However, these verifiers face challenges in efficiently handling large quantities of data. Therefore, this study proposed a fast screening method that utilizes multiple linear regression (MLR), in combination with the stepwise backward regression method, to identify problematic carbon emission data for the lower calorific value (LCV) and carbon content (C) of coal. The results demonstrated the effectiveness of the proposed method. The regression models for LCV and C exhibited high R-squared (R 2 ) values of 0.9784 and 0.9762, respectively, and the root mean square error (RMSE) values of the validation set were 0.32 MJ/kg and 0.80% for LCV and C, respectively, indicating strong predictive capabilities. By analyzing the obtained results, the study established the optional error threshold interval for the LCV and C of coal as 2RMSE–3RMSE. This interval can be utilized as a reliable criterion for judging the quality and reliability of carbon emission data during the verification process. Overall, the proposed screening method can serve as a valuable tool for verifiers in assessing the quality and reliability of carbon emission data in various regions.

Keywords: screening; carbon dioxide emissions; coal; multiple regression; lower calorific value; carbon content (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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