Investigating the impacts of urban agglomeration spatial structures on carbon emissions based on spatial networks of cities.

Quantifying the relationships between urban agglomeration spatial structures and carbon emissions yields insights for developing sustainable, low-carbon urban agglomerations. Previous studies characterizing urban agglomeration spatial structures usually rely solely on the attribution data of individual cities, but overlook inter-city linkages, i.e., the interactions between cities within urban agglomerations. However, the essence of urban agglomerations roots in the intimate connections between cities through the flows of people, logistics, information, etc. Therefore, it is crucial to accurately characterize the pattern of inter-city connections in order to completely express urban agglomeration spatial structures. However, previous studies have not explored this aspect in depth. To address this gap, this study innovatively utilizes train schedule data in 2010, 2015 and 2020 to measure inter-city connections and construct spatial networks of Chinese cities, thereby modeling the patterns of inter-city linkages. Subsequently, indicators characterizing urban agglomeration spatial structures were developed on the basis of spatial networks of cities. Panel data regression was then employed to investigate the relationships between these indicators and carbon emissions. Experimental results indicate that (1) monocentric spatial structure is conducive to reducing carbon emissions; (2) morphological polycentricity has a more significant effect on carbon emissions compared to functional polycentricity; and (3) network disparity exhibits a positive correlation with carbon emissions. These findings provide essential support for the formulation of carbon emission reduction policies at urban agglomeration level.