Estimating the Impact of Urbanization on Air Quality in China Using Spatial Regression Models
AbstractUrban air pollution is one of the most visible environmental problems to have accompanied China’s rapid urbanization. Based on emission inventory data from 2014, gathered from 289 cities, we used Global and Local Moran’s I to measure the spatial autorrelation of Air Quality Index (AQI) values at the city level, and employed Ordinary Least Squares (OLS), Spatial Lag Model (SAR), and Geographically Weighted Regression (GWR) to quantitatively estimate the comprehensive impact and spatial variations of China’s urbanization process on air quality. The results show that a significant spatial dependence and heterogeneity existed in AQI values. Regression models revealed urbanization has played an important negative role in determining air quality in Chinese cities. The population, urbanization rate, automobile density, and the proportion of secondary industry were all found to have had a significant influence over air quality. Per capita Gross Domestic Product (GDP) and the scale of urban land use, however, failed the significance test at 10% level. The GWR model performed better than global models and the results of GWR modeling show that the relationship between urbanization and air quality was not constant in space. Further, the local parameter estimates suggest significant spatial variation in the impacts of various urbanization factors on air quality. View Full-Text
Share & Cite This Article
Fang, C.; Liu, H.; Li, G.; Sun, D.; Miao, Z. Estimating the Impact of Urbanization on Air Quality in China Using Spatial Regression Models. Sustainability 2015, 7, 15570-15592.
Fang C, Liu H, Li G, Sun D, Miao Z. Estimating the Impact of Urbanization on Air Quality in China Using Spatial Regression Models. Sustainability. 2015; 7(11):15570-15592.Chicago/Turabian Style
Fang, Chuanglin; Liu, Haimeng; Li, Guangdong; Sun, Dongqi; Miao, Zhuang. 2015. "Estimating the Impact of Urbanization on Air Quality in China Using Spatial Regression Models." Sustainability 7, no. 11: 15570-15592.