Enhancing carbon productivity is fundamental to achieving carbon neutrality while sustaining economic growth. Utilizing a comprehensive dataset of Chinese cities from 2010, 2015, and 2020, this study investigates the spatiotemporal patterns and underlying drivers of urban carbon productivity (UCP). Methods including kernel density estimation, spatial autocorrelation analysis, and the spatial Durbin model (SDM) are employed. The results reveal that China’s UCP has improved significantly overall, yet with increasing internal disparities among cities. The SDM decomposition indicates a fundamental shift in driving mechanisms. Green technological innovation has supplanted generalized R&D expenditure as the most dependable core driving force for improving local carbon productivity. Moreover, the economic development level also exerts positive spatial spillover effects in the later stage, which jointly contribute to the formation of a multi-centered pattern. Urban form metrics exert dual influences: urban compactness (ENNMN) shows a stable positive local effect, whereas urban fragmentation (PD) and urban sprawl (CONTAG) exhibit a paradoxical “local inhibition–neighborhood promotion” effect, highlighting intricate inter-city spatial interactions. The findings underscore the necessity for differentiated local practices, namely, policy must target differentiated city roles and manage spatial spillovers for synergistic regional green and sustainable transition.
Wang et al. (Thu,) studied this question.