Toward integrated governance of urban CO2 emissions in China: Connecting the “codes” of global drivers, local causes, and indirect influences from a multi-perspective analysis

Abstract

Effective governance of factors that contribute to urban CO2 emissions is critical for decarbonizing our increasingly urbanized Earth. Existing studies have provided insightful understandings in many aspects, though in a piecemeal manner. Here we demonstrate a multi-method approach that can quantitatively identify and effectively connect the previously fragmented “codes” for cross-scale and cross-sectoral governance of urban CO2 emissions. We combined multivariate regression following the STIRPAT conceptual model, Geographically Weighted Regression (GWR), and GeoDector to determine the global drivers, local causes, and indirect influences of urban CO2 emissions in 187 Chinese cities. We found that urban expansion is a global driver contributing to Chinese urban CO2 emissions. In contrast, urban shape complexity and urban compactness are local causes of urban CO2 emissions. The effect of urban form factors is more remarkable for cities in Southwest China than other cities. Urban expansion is coupled with economic growth, resulting in the strongest synergistic effect on CO2 emissions in China. Our findings highlight that missing any one aspect of global drivers, local causes, and indirect influences in future studies of urban CO2 emissions—as commonly seen in the existing literature—would lead to potential risks of governance overlaps, gaps, and even conflicts.