Author(s)

Yunyan Peng, Hui Dong, Yongbo Sun

Corresponding Author

Yunyan Peng

Abstract

With the advancement of China's “goal of peak carbon dioxide emissions and carbon neutrality”, cities are areas with high energy consumption. Urban carbon emissions accounting and trend research have gradually become one of the hot spots of research. This paper selected the Tapio decoupling model and the K-means clustering algorithm, stared from both static and dynamic levels, and used SPSS software to conducted the clustering analysis of the peak carbon dioxide emissions trend of 34 sample cities in Northeast China. The results show that according to the trend of peak carbon dioxide emissions, cities in the Northeast region can be divided into three types: low-carbon potential cities, resource-dependent cities, and traditional industrial transformation cities. Except for provincial capitals, the average annual growth rate of carbon emissions in other cities has not changed significantly. Their economic growth is slow. The two kinds of city situations show weak negative decoupling and strong negative decoupling respectively, indicating that the development of carbon trends among cities is not balanced.

Keywords

City peak carbon dioxide emissions, K-means clustering algorithm, Tapio decoupling model, Clustering analysis, Peak carbon dioxide emissions trend