Does financial deepening drive spatial heterogeneity of PM2.5 concentrations in China? New evidence from an eigenvector spatial filtering approach

Abstract To provide policymakers with a different perspective on reducing PM2.5 concentrations, this paper not only identifies the economic driving factors of PM2.5 concentrations in China but also investigates its spatial heterogeneity with special consideration of financial deepening. A random effect eigenvector spatial filtering (RE-ESF) approach with and without non-spatially varying coefficients (SNVC) is performed by using the provincial panel dataset over the period 2002–2016. The main findings are as follows: First, compared with the non-spatial pooled regression model, the RE-ESF and RE-ESF-SNVC models have increased the goodness-of-fit from 0.8854 to 0.9778 and 0.9834, respectively, indicating that the RE-ESF approach produces a better fit to the data. Second, the global results of the RE-ESF model show that a 1% increase in financial deepening will bring about 0.152% decrease in PM2.5 concentrations. Third, the local results of the RE-ESF-SNVC model indicate that the spatially varying coefficient of financial deepening ranges from −0.3215 to 0.101 with the median value of −0.1315, reflecting significant spatial heterogeneity of PM2.5 concentrations driven by financial deepening. These findings contribute to PM2.5 concentration reduction by identifying financial deepening as a significant economic driving factor, investigating its global and local impacts on PM2.5 concentrations, and providing policymakers with implications for developing appropriate financial policies, such as a more flexible province-specific reserve requirement policy supplemented by higher deposit rates and a cross-provincial allocation of bank credits.