LAUSR

In-depth investigation of the spatiotemporal driver patterns of city carbon emissions is vital toward establishing carbon neutrality, as such knowledge would aid policymakers in formulating differentiated emission reduction policies. Through developing a unique carbon emission dataset and applying a spatiotemporal logarithmic mean Divisia index decomposition approach, we explored the spatiotemporal drivers of CO2 emission for diverse cities in China categorized by economic structure and population size during 2002-2018. The results highlighted GDP per capita and industrial structure as the most positive and negative drivers, respectively, with the former overweighing the latter before 2016. Furthermore, the between-group differences of cities categorized using population size were higher than differences within groups, implying evident heterogeneity of carbon emissions. Emission related to within-differences in net primary productivity (NPP) constitutes the largest contributing factor promoting carbon emission in megacities and highly industrialized cities, whereas NPP between-differences in agricultural carbon intensity are predominantly associated with inhibiting emissions in large and highly commercialized cities. We therefore suggest that policymakers should optimize the industrial structure in highly industrialized cities and develop carbon sequestration in cities with high vegetation coverage through fiscal transfer for achieving carbon neutrality.