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

Abstract

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.

Keywords

• carbon emissions
• urban form
• spatial error model
• urban planning