Spatial spillover effects of urbanization on carbon emissions in the Yangtze River Delta urban agglomeration, China

To achieve a win-win situation for both urbanization and carbon emissions reduction from a spatiotemporal perspective, we need to identify the salient links between urbanization and carbon emissions in different dimensions. Using 2008-2018 panel data on the Yangtze River Delta urban agglomeration, this paper constructs a Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model based on four dimensions of urbanization: population, economy, land, and ecology. Additionally, it uses a whole group of variables for reference, constructs a Spatial Durbin model (SDM) to estimate the spatial effect, and empirically investigates the spatial dependence of carbon emissions and the influence of various driving factors. The results show that (1) in the temporal dimension, the historical carbon emissions of the study area continue to increase. However, the extent to which they are doing so is slowing, the number of low carbon emissions areas has significantly decreased, the number of medium carbon emissions areas have significantly increased, the number of high and relatively high carbon emissions areas are relatively stable, and energy intensity continues to decline. (2) In the spatial dimension, Shanghai, Suzhou, and their surrounding cities have always been carbon emissions hotspots, high and relatively high carbon emissions areas are mainly concentrated in these cities. Low carbon emissions areas and cold spots are mainly distributed in Anhui Province. Medium carbon emissions areas show a great spatial and temporal evolution and are distributed in all provinces. (3) In the four dimensions of urbanization, per capita GDP will not only affect regional carbon emissions but also have a spatial spillover effect. For every 1% increase in the economic factors, carbon emissions in neighboring regions will increase by 0.38-0.43%. Population, economic, and technological factors have significant positive effects on carbon emissions, and economic factor is the most important factor. (4) In different dimensions of urbanization, there are obvious heterogeneities in the impacts of different factors on carbon emissions. Among them, the elasticity coefficient of per capita GDP and energy intensity is the smallest among the dimension of land urbanization, and the elasticity coefficient of the total population is the smallest among the dimension of population urbanization. Therefore, when formulating carbon emissions reduction policies, it is necessary to fully consider the spatial spillover effects, determine the optimal population size threshold, advocate for a low-carbon lifestyle, promote clean technology, and realize information exchange and policy interaction across regions from the perspective of holistic governance.