School of Mathematics and Statistics, Central South University, Changsha, Hunan, 410083, PR China.
College of Economics and Management, Fujian Agriculture and Forestry University, Fuzhou, Fujian, 350002, PR China.
Environ Pollut. 2022 Sep 1;308:119639. doi: 10.1016/j.envpol.2022.119639. Epub 2022 Jun 23.
Particulate Matter (PM) 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 PM 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 PM concentrations and their inverted U-shaped relationship with economic growth in China. Second, PM 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 PM 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 PM concentrations. These findings implicate policies on reforming economic growth, urbanization, human capital and bank credit to tackle PM pollution in China from a cross-provincial collaboration perspective.
近年来,中国的颗粒物(PM)污染一直是公众健康的主要关注点,这要求银行适当控制对高污染、高能耗和产能过剩行业的信贷供应。基于此,本文采用随机人口、财富和技术影响回归框架下的空间杜宾模型(SDM),考察了 PM 浓度的经济决定因素,并纳入了银行信贷的空间溢出效应。本文利用中国 1998 年至 2016 年的省级数据集,主要发现如下:首先,有证据表明中国的 PM 浓度存在空间依赖性,且与经济增长之间存在倒 U 型关系。其次,一个省份的 PM 浓度随着自身城市化水平的提高而增加,但随着自身人力资本和银行信贷的增加而减少。同时,邻近地区的城市化水平对一个省份的 PM 浓度有正向影响,而邻近地区的人口规模、工业化、贸易开放度和银行信贷则有负向影响。第三,SDM 的间接效应表明,银行信贷对 PM 浓度具有显著的负向空间溢出效应。这些发现暗示,从跨省合作的角度出发,中国需要出台有关经济增长、城市化、人力资本和银行信贷改革的政策,以应对 PM 污染问题。