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Abstract

Particulate Matter (PM2.5) pollution in China has been a primary concern for public health in recent years, which requires banks to appropriately control their credit supply to industries with high pollution, high energy consumption, and surplus capacity. For this reason, this paper examines economic determinants of PM2.5 concentrations and incorporates the spatial spillover effect of bank credit by employing the spatial Durbin model (SDM) under the stochastic impacts by regression on population, affluence and technology framework. Using China's provincial dataset from 1998 to 2016, the main findings are as follows: First, there is evidence in support of spatial dependence of PM2.5 concentrations and their inverted U-shaped relationship with economic growth in China. Second, PM2.5 concentrations in a province tend to increase as the level of its own urbanization increases, but they decrease as its own human capital and bank credit increase. Meanwhile, the level of neighboring urbanization positively influences a province's PM2.5 concentrations, whereas neighboring population size, industrialization, trade openness, and bank credit present negative impacts. Third, indirect effects of the SDM indicate significant and negative spatial spillover effect of bank credit on PM2.5 concentrations. These findings implicate policies on reforming economic growth, urbanization, human capital and bank credit to tackle PM2.5 pollution in China from a cross-provincial collaboration perspective.

Item Type:	Article
Authors/Creators:	Yang, Fan and Xu, Q and Li, K and Yuen, KF and Shi, W
Keywords:	Spatial Durbin model, PM<sub>2.5</sub> concentrations, bank credit, STIRPAT framework
Journal or Publication Title:	Environmental Pollution
Publisher:	Elsevier Ltd
ISSN:	1873-6424
DOI / ID Number:	https://doi.org/10.1016/j.envpol.2022.119639
Copyright Information:	Copyright 2022 Elsevier Ltd.
Related URLs:	Google Scholar: Shi, W UTAS Profile: Shi, W
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