Analysis of the Factors Influencing the Spatial Distribution of PM2.5 Concentrations (SDG 11.6.2) at the Provincial Scale in China

Abstract

This study investigated the spatiotemporal characteristics and influencing factors of PM2.5 concentrations at the provincial scale in China. The findings indicate significant spatial autocorrelation, with notable high–high agglomerations in East and North China and mixed patterns in the northwest. The spatial Durbin model (SDM) with fixed effects, validated through comprehensive tests, was utilized to analyze data on 31 provincial scale regions from 2000 to 2020, addressing spatial autocorrelation and ensuring model reliability. The research delved into the effects of 21 variables on PM2.5 concentrations, identifying synergistic and trade-off effects among environmental and socioeconomic indicators. Environmental measures like vegetation protection and sulfur dioxide emission reduction correlate with lower PM2.5 levels, whereas economic growth and transport volume often align with increased pollution. The analysis reveals regional variances in these effects, suggesting the need for region-specific policies. The study underscores the intricate relationship between environmental policies, economic development, and air quality, advocating for an integrated approach to air quality improvement. It highlights the necessity of balancing industrial growth with environmental sustainability and suggests targeted, region-specific strategies to combat PM2.5 pollution effectively. This study offers crucial insights for policymakers, emphasizing that enhancing air quality requires comprehensive strategies that encompass environmental, economic, and technological dimensions to foster sustainable development.