A classification approach for urban metabolism using the CART model and its application in China

Abstract

Exploring resource utilization strategies for different types of cities is essential for achieving the sustainable development of cities. Urban metabolism is a systems-oriented approach to understanding the interrelationship between urban socioeconomic status and natural environment through resource utilization. Recognition and identification of urban metabolic types could help in formulating targeted urban resource utilization strategies. However, few studies have investigated the classification of urban metabolism. Therefore, this study combines material flow analysis with the classification and regression tree model to present a classification approach for urban metabolism that considers cities’ socioeconomic, demographic, and climatic conditions and resource consumption. This approach was tested in Chinese prefecture-level cities. The results showed that gross domestic product (GDP) per capita, population, population density, and climate types were significantly correlated with cities’ resource consumption indicators. GDP per capita and climate types were the most important threshold variables for classifying urban resource consumption levels. GDP per capita was the optimal split node for electricity, total energy, water, industrial minerals and metals, and total outputs, while climate types was the optimal split node for biomass, fossil fuels, and construction materials. The level of urban resource consumption in temperate climates was higher than in other climate types. Eight types of urban metabolism were identified in China that were closely related to urban socioeconomic conditions, scale grade, resource endowment, industrial level, and energy structure. The findings of this study provide a reference for determining the problem of urban critical resource consumption and formulating appropriate urban resource utilization strategies.