Spatio–Temporal Relationship and Evolvement of Socioeconomic Factors and PM_2.5 in China During 1998–2016

• Main Authors: Yi Yang, Jie Li, Guobin Zhu, Qiangqiang Yuan
• Format: Article
• Language: English
• Published: MDPI AG 2019-03-01
• Series: International Journal of Environmental Research and Public Health
• Subjects: PM_2.5 concentration; socioeconomic factors; Bivariate Moran’s I; spatial lag model
• Online Access: https://www.mdpi.com/1660-4601/16/7/1149

A comprehensive understanding of the relationships between PM_2.5 concentration and socioeconomic factors provides new insight into environmental management decision-making for sustainable development. In order to identify the contributions of socioeconomic development to PM_2.5, their spatial interaction and temporal variation of long time series are analyzed in this paper. Unary linear regression method, Spearman’s rank and bivariate Moran’s I methods were used to investigate spatio–temporal variations and relationships of socioeconomic factors and PM_2.5 concentration in 31 provinces of China during the period of 1998–2016. Spatial spillover effect of PM_2.5 concentration and the impact of socioeconomic factors on PM_2.5 concentration were analyzed by spatial lag model. Results demonstrated that PM_2.5 concentration in most provinces of China increased rapidly along with the increase of socioeconomic factors, while PM_2.5 presented a slow growth trend in Southwest China and a descending trend in Northwest China along with the increase of socioeconomic factors. Long time series analysis revealed the relationships between PM_2.5 concentration and four socioeconomic factors. PM_2.5 concentration was significantly positive spatial correlated with GDP per capita, industrial added value and private car ownership, while urban population density appeared a negative spatial correlation since 2006. GDP per capita and industrial added values were the most important factors to increase PM_2.5, followed by private car ownership and urban population density. The findings of the study revealed spatial spillover effects of PM_2.5 between different provinces, and can provide a theoretical basis for sustainable development and environmental protection.