Research on User Carbon Emission Characterization Based on k-means Clustering Algorithm

In order to respond to the call of "double carbon" target and reduce greenhouse gas emissions, In this paper, the user-side carbon emission portrait technique is performed to characterize the user's carbon emission behavior. Firstly, the user carbon portrait label system is constructed considering the industry characteristics; then, the Pearson correlation coefficient is selected to characterize the degree of association between the 2 feature labels; furthermore, the k-means clustering algorithm is used to characterize the carbon footprint of the enterprise, and the comprehensive carbon reduction index is constructed to evaluate the degree of carbon reduction of the enterprise; finally, the validity of the proposed model is verified using the arithmetic examples.