Spatial Variations in the Impact of Urban Infrastructure on City-Level Carbon Emission Intensity in China: A Geographically Weighted Regression Approach

As urbanization accelerates globally, cities play a crucial role in shaping the environmental impacts of the built environment, particularly in terms of carbon emissions. This study provides a comprehensive analysis of the impact of urban development, with a focus on urban infrastructure, on carbon emission intensity (CEI) across Chinese cities, using both global and local regression models. The global regression model identifies per capita road area, economic growth, and industrial structure as key factors in reducing CEI. The Geographically Weighted Regression (GWR) model further reveals significant spatial heterogeneity in these impacts. Economic growth consistently shows a negative relationship with CEI across all cities, with the strongest effects in the northernmost regions. In contrast, per capita emissions have a consistently positive association with CEI, especially in northeastern cities. Interestingly, other infrastructure-related factors exhibit bidirectional effects depending on the region: for example, per capita road area increases CEI in western cities but reduces it in northeastern regions, while per capita green area raises CEI in eastern cities but decreases it in the west. These findings highlight the need for region-specific policy interventions to effectively manage urban growth and reduce carbon emissions within the built environment, thereby contributing to China's low-carbon economy goals and supporting global efforts to address climate change.