The Application of Support Vector Machine (SVM) in Industrial Carbon Accounting Prediction and Green Electricity Control Strategies

Abstract

In recent years, as global climate change has intensified, carbon emissions management in the industrial sector has become a critical area in addressing climate change. The application of intelligent algorithms in carbon emissions prediction and management offers new possibilities for implementing effective emission control strategies. This paper, based on the Support Vector Machine (SVM) model, explores its application in industrial carbon accounting, focusing on the interaction between carbon emissions prediction and optimization of control strategies. By analyzing the differences between predicted results and actual carbon emissions data, the paper proposes a series of emission control strategies driven by intelligent algorithms, and discusses them in the context of policy environments and production characteristics. The study shows that the SVM model demonstrates high accuracy in carbon emissions prediction, effectively supporting corporate carbon management decisions.