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

To provide policymakers with a different perspective on reducing PM_2.5 concentrations, this paper not only identifies the economic driving factors of PM_2.5 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 PM_2.5 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 PM_2.5 concentrations driven by financial deepening. These findings contribute to PM_2.5 concentration reduction by identifying financial deepening as a significant economic driving factor, investigating its global and local impacts on PM_2.5 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.