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

Area of Science:

• Environmental Science
• Economics
• Spatial Econometrics

Background:

• Carbon neutrality is crucial for a sustainable future, necessitating research into its driving factors.
• Previous studies using mean regression overlooked the heterogeneity in carbon neutrality drivers.
• Understanding these drivers is vital for effective ecosystem protection strategies.

Purpose of the Study:

• To introduce a novel dynamic spatial Durbin quantile regression (DSDQR) model.
• To examine the impact of environmental governance pressure, economic growth, and their interactions on carbon neutrality performance.
• To provide empirical evidence for developing differentiated decarbonization strategies.

Main Methods:

• Development and application of a dynamic spatial Durbin quantile regression (DSDQR) model.
• Derivation of marginal effect formulas for independent variables at different quantiles.
• Analysis of Chinese provincial data from 2011-2022.

Main Results:

• Carbon neutrality performance exhibits significant temporal, spatial, and path dependencies.
• Environmental governance pressure shows varied short-term (inhibitory) and long-term (positive) effects depending on quantile.
• Economic growth generally hinders carbon neutrality, except for a positive long-term impact in high-quantile provinces post-COVID-19.
• The interaction effect between governance pressure and economic growth is positive short-term but negative long-term in high quantiles.

Conclusions:

• Carbon neutrality performance is heterogeneous across provinces and development stages.
• Policy interventions must account for quantile-specific effects of environmental governance and economic growth.
• Differentiated decarbonization strategies are essential for achieving national carbon neutrality goals.