Abstract
China is experiencing severe environmental degradation, particularly air pollution. To explore whether air pollutants are spatially correlated (i.e., trans-boundary effects) and to analyse the main contributing factors, this research investigates the annual concentration of the Air Quality Index (AQI) and 13 polluting sectors in 30 provinces and autonomous regions across China. Factor analysis, the linear regression model and the spatial auto-regression (SAR) model are employed to analyse the latest data in 2014. Several important findings are derived. Firstly, the global Moran’s I test reveals that the AQI of China shows a distinct positive spatial correlation. The local Moran’s I test shows that significant high–high AQI agglomeration regions are found around the Beijing–Tianjin–Hebei area and the regions of low–low AQI agglomeration all locate in south China, including Yunnan, Guangxi and Fujian. Secondly, the effectiveness of the SAR model is much better than that of the linear regression model, with a significantly improved R-squared value from 0.287 to 0.705. A given region’s AQI will rise by 0.793% if the AQI of its ambient region increases by 1%. Thirdly, car ownership, steel output, coke output, coal consumption, built-up area, diesel consumption and electric power output contribute most to air pollution according to AQI, whereas fuel oil consumption, caustic soda output and crude oil consumption are inconsiderably accountable in raising AQI. Fourthly, the air quality in Beijing and Tianjin is under great exogenous influence from nearby regions, such as Hebei’s air pollution, and cross-boundary and joint efforts must be committed by the Beijing–Tianjin–Hebei region in order to control air pollution.
Highlights
With its rapid economic development and acceleration of industrialisation and modernisation, China needs enormous energy sources and metal and chemical products to sustain its rapidly growing socio-economic demands [1,2,3]
The spatial correlation coefficient is ρ = 0.793, consistent with the results of the global Moran’s I test (Table 9), which shows a distinct spatial correlation amongst dependent variables and indicates that a given region’s Air Quality Index (AQI) will rise by approximately 0.793% if the AQI of its ambient regions increase by 1%
Linear regression and spatial auto-regression (SAR) models were employed to analyse the data of annual concentration of AQI and 13 polluting sectors of 30 provinces and autonomous regions in 2014
Summary
With its rapid economic development and acceleration of industrialisation and modernisation, China needs enormous energy sources and metal and chemical products to sustain its rapidly growing socio-economic demands [1,2,3]. Grossman et al [7] demonstrated that the relationship between economic development and environmental quality generally follows the environmental Kuznets curve. This curve indicates the presence of a general process where environmental quality and economic development interact with each other. Economic development at initial stages leads to environmental degradations, which in turn inhibits economic development and causes it to slow down. The economy regains its high development speed. Considering this hypothesis, scholars have widely discussed the relationship between the economy and the environment. Ma and Zhang [8] stated that the economic development level of China remains far from the inflection point of environmental quality; in the future, economic development in China will intensify environmental pollution
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