Development and empirical study of a carbon emission calculation model based on the electricity-carbon correlation

Abstract Energy activities are the primary source of carbon dioxide emissions. Real-time and accurate monitoring of their carbon emission dynamics is a crucial foundation for governments and enterprises in formulating carbon emission control measures. However, traditional accounting methods struggle to achieve high-frequency monitoring, and direct measurement approaches are cost-prohibitive. This results in a current lack of low-cost, high-frequency carbon emission calculation methods. Therefore, this study proposes an electricity-based carbon measurement (ECM) method. This method is predicated on the assumption of a stable relationship between electricity consumption and carbon emissions over a defined period and estimates emissions by leveraging power sector big data in conjunction with historical carbon emission inventory data. First, after explaining the basic principles of this method, the study systematically analyzes the related data requirements and demonstrates its feasibility. Subsequently, a calculation model is constructed and empirically tested using data from several provinces. The results show that, within the scope of our empirical validation and under conditions of a relatively stable electricity-carbon relationship, this method demonstrates good computational accuracy and application potential. Finally, a predictive analysis is performed based on 2021–2024 electricity-carbon data from an industrial park in Yunnan, and case studies of energy-intensive industries such as cement, steel, and electrolytic aluminum are used to demonstrate the potential utility of the method at both the enterprise and industry levels. This work provides a scientific basis to promote energy conservation, emission reduction, and green transformation of enterprises.