Effects of Urban Form on Carbon Emissions in China: Implications for Low-Carbon Urban Planning

Carbon emissions are closely related to global warming. More than 70% of global carbon emissions have been generated in cities. Many studies have analyzed the effects of cities on carbon emissions, from the perspective of urbanization, economics, and land use, yet a detailed understanding of the relationship between urban form and carbon emissions is lacking due to the absence of a reasonable set of urban form metrics. The aim of this research is to explore the effects of urban form on carbon emissions through empirical research. By eliminating collinearity, we established a set of urban form landscape metrics comprising Class Area (CA), Mean Perimeter-Area Ratio (PARA-MN), Mean Proximity Index (PROX-MN), and Mean Euclidian Nearest Neighbor Distance (ENN-MN) representing urban area, complexity, compactness, and centrality, respectively. Through spatial autocorrelation analysis, the results show that there is a positive spatial autocorrelation of carbon emissions. The high-high agglomeration regions are located in the Beijing-Tianjin-Hebei and Yangtze River Delta, while the low-low agglomeration regions are concentrated in the Southwest and Heilongjiang Province. Based on a spatial error model, for the whole study area, CA, PARA-MN, and ENN-MN show a positive correlation with carbon emissions, but PROX-MN is the opposite. Based on ordinary least squares, PARA-MN in the Northeast and East, PROX-MN in the North and Mid-South, and ENN-MN in the North are significantly correlated with carbon emissions. These findings are helpful for low-carbon urban planning.