City classification for municipal solid waste prediction in mainland China based on K-means clustering

Abstract

Cities in mainland China are usually classified according to geographical locations. This traditional city classification system is limited to relative fixed factors, which lives out a gap in terms of the spatial differences of municipal solid waste (MSW). Developing a more comprehensive city classification system is essential for MSW generation prediction and waste management. In this study, six economic, social and climatic indicators that affect MSW generation: population, per capita GDP (PCGDP), environmental sanitation investment (ESI), average temperature, average precipitation, and average humidity, are selected. Weights were calculated for each indicator using a combination of CRITIC weight method and Pearson correlation coefficient prior to cluster analysis. The k-means clustering algorithm was used to classify all cities into four clusters, which differed significantly in the relationships between MSW generation and influencing factors. The results of Kruskal-Wallis test also show that cities in different clusters show different distributions in terms of the indicators selected. The cross-prediction results of the model further validate the reliability of the clustering results from a quantitative perspective. By establishing a city classification system, cities with similar relationships between MSW generation and influencing factors can be placed into one cluster. The model established in one certain city cluster can be used to predict the MSW generation for cities in the same cluster that lack historical data. This may also help to formulate appropriate regional policies according to different relationships between MSW generation and influencing factors, especially for the four city clusters in the mainland China.