Tracking the Carbon Emissions Using Electricity Big Data: A Case Study of the Metal Smelting Industry

Chunli Zhou, Yuze Tang (), Deyan Zhu and Zhiwei Cui
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Chunli Zhou: Guangxi Power Grid Co., Ltd., Nanning 530023, China
Yuze Tang: School of Applied Economics, Renmin University of China, Beijing 100872, China
Deyan Zhu: Guangxi Power Grid Co., Ltd., Nanning 530023, China
Zhiwei Cui: Guangxi Power Grid Co., Ltd., Nanning 530023, China

Energies, 2024, vol. 17, issue 3, 1-19

Abstract: Implementing real-time carbon emissions monitoring at the enterprise level enables the detailed breakdown of carbon neutrality goals for microcosmic enterprises, which is of paramount significance in ensuring the precision of policy formulations. Grounded in China’s historical electricity consumption and carbon emissions data, this study utilizes the network approach and input–output methods to compute and predict direct and indirect transmission coefficients of electricity consumption and carbon emissions in each province. We establish a methodology that enables the monitoring of real-time carbon emissions of enterprises based on corporate electricity consumption data. Using the metal smelting industry in Guangxi as an example, our findings are as follows: First, in 2020, the comprehensive carbon emissions of the ferrous metal smelting industry in Guangxi reached 58.84 million tons, marking a notable increase of 42.78% compared to emissions in 2014, indicating that emissions reductions are imperative. Second, significant regional variations in emission coefficients are observed, with values ranging from 14 g CO 2 /KWh to 940 g CO 2 /KWh among provinces. Meanwhile, the trends of direct carbon emissions and indirect carbon emissions are totally different, underscoring the importance of comprehensive carbon accounting in informing policy decisions. Third, through the carbon emissions real-time monitoring of 75 metal smelting industry enterprises using electricity big data, we identified that the distribution of emissions across industries, time periods, and regions is uneven. Overall, this method can optimize carbon emission measurement techniques to a higher spatiotemporal resolution and more microscopic monitoring subjects, providing essential numerical foundations for promoting carbon emissions reduction and sustainable development.

Keywords: carbon emissions monitoring; electricity big data; metal smelting industry (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: 2024
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