Exploring the pathway to carbon neutrality in China based on a dynamic spatial Durbin quantile regression model

Carbon neutrality is a critical pathway to achieving a sustainable future. Investigating the driving factors for carbon neutrality can provide empirical evidence to support ecosystem protection. Prior studies used mean regression to investigate carbon neutrality, concealing the heterogeneity of carbon neutrality. In this paper, we introduce a dynamic spatial Durbin quantile regression (DSDQR) model along with its estimation method, and derive the marginal effect formulas for independent variables at different quantiles. Then we apply this methodology to examine the impact mechanisms of environmental governance pressure, economic growth, and their interaction effects on carbon neutrality performance using Chinese provincial data spanning 2011-2022. Key findings include: (1) Temporal, spatial, and path dependencies in carbon neutrality performance are prevalent across nearly all provinces. (2) Environmental governance pressure exhibits an inhibitory short-term effect on carbon neutrality in provinces at medium and low quantiles, while it has a positive long-term impact in high quantile provinces. (3) Economic growth generally hinders carbon neutrality performance in most provinces. However, economic growth in high quantile provinces exerts a positive long-term influence on carbon neutrality performance after the COVID-19 pandemic. (4) The interaction between environmental governance pressure and economic growth demonstrates a significant positive short-term impact on carbon neutrality performance post-epidemic, yet it has a negative long-term effect in high quantile provinces. Finally, this article calls for differentiated decarbonization strategies based on provincial carbon neutrality development stages.