Exploring the real contribution of socioeconomic variation to urban PM2.5 pollution: New evidence from spatial heteroscedasticity

Abstract

Making cities safe, resilient and sustainable is one of the United Nations Sustainable Development Goals (SDGs). Health risk, productivity loss and climate change caused by air pollution obstacles the present urban sustainable development, especially people living in middle-and low-income countries areas are most affected. The spatial models (such as SAR and SEM) are often considered to examine the driven factors and the spatial spillover effect of PM2.5 concentrations. Given that these spatial models assume spatially dependent second-order moments of the dependent variable without considering the possible autoregressive conditional heteroscedasticity. This present study empirically examines the heterogeneous effects of economic development, secondary industry, FDI, population density, number of buses and urbanization on PM2.5 concentrations in 269 Chinese cities using the SAR, spARCH and SARspARCH, respectively. This newly proposed Spatial ARCH model is the first attempt to be applied to environmental research. The empirical results indicate that an increasing spatial correlation with PM2.5 concentration was observed among 269 cities during 2004–2016, and the most influential cities in high-high clustering are mainly located in North China. Furthermore, except for population density, the effects of other factors are heterogeneous on the time scale. Among those socioeconomic factors, population density shows the largest contribution to urban PM2.5 pollution, the effects of secondary industry, GDP and FDI may be overestimated in the absence of spatial neighbouring effects in mean or variance. The comparative analysis could provide new enlightenments for a deeper understanding of the socioeconomic impact on PM2.5 pollution.