The influential factors of urban PM2.5 concentrations in China: a spatial econometric analysis

• The influential factors of urban PM2.5 concentrations in China are investigated.
• Two spatial econometric models are used to control for the spatial correlation of air pollution.
• The EKC relationship between PM2.5 concentrations and economic development is verified.
• Vehicle population and the secondary industry influence urban PM2.5 concentrations positively.

Based on the data of PM2.5 concentrations and Air Quality Index of 73 Chinese cities in 2013, this study empirically investigates the socioeconomic influential factors of urban PM2.5 concentrations in China. Specifically, it examines whether and how the socioeconomic development indicators such as GDP per capita, industry and transport would affect the air quality. Due to the existence of spatial autocorrelation of air pollution, conventional regression techniques that ignore the spatial autocorrelation would yield biased and inconsistent estimation results. Therefore, in this study two spatial econometric models, namely Spatial Lag Model (SLM) and Spatial Error Model (SEM), are utilized to control for spatial effects. According to the estimation results, the relationship between PM2.5 concentrations and per capita GDP is inverted U-shaped, suggesting the existence of the inverted-U shaped Environmental Kuznets Curve (EKC) for air quality in China. In addition, the vehicle population and the secondary industry have significant and positive influences on urban PM2.5 concentrations. As a result, a series of comprehensive measures in both social and economic aspects as well as the regional coordination of environmental policies are needed to hold China's air pollution in check.