Spatial Econometric Analysis of the Impact of Socioeconomic Factors on PM2.5 Concentration in China’s Inland Cities: A Case Study from Chengdu Plain Economic Zone

Abstract

Particulate matter with a diameter less than 2.5 µm (PM2.5), one of the main sources of air pollution, has increasingly become a concern of the people and governments in China. Examining the socioeconomic factors influencing on PM2.5 concentration is important for regional prevention and control. Previous studies mainly concentrated on the economically developed eastern coastal cities, but few studies focused on inland cities. This study selected Chengdu Plain Economic Zone (CPEZ), an inland region with heavy smog, and used spatial econometrics methods to identify the spatiotemporal distribution characteristics of PM2.5 concentration and the socioeconomic factors underlying it from 2006 to 2016. Moran’s index indicates that PM2.5 concentration in CPEZ does have spatial aggregation characteristics. In general, the spatial clustering from the fluctuation state to the stable low state decreased by 1% annually on average, from 0.190 (p < 0.05) in 2006 to 0.083 (p < 0.1) in 2016. According to the results of the spatial Durbin model (SDM), socioeconomic factors including population density, energy consumption per unit of output, gross domestic product (GDP), and per capita GDP have a positive effect on PM2.5 concentration, while greening rate and per capita park space have a negative effect. Additionally, those factors have identified spatial spillover effects on PM2.5 concentration. This study could be a reference and support for the formulation of more efficient air pollution control policies in inland cities.

Spatial Effect of Industrial Energy Consumption Structure and Transportation on Haze Pollution in Beijing-Tianjin-Hebei Region

“…The impact of coal burning and industrial production on pollution is essentially related to the industrial energy consumption structure [ 7 ], while automobile exhaust belongs to the transportation sector [ 8 ]. The distribution of PM 2.5 has unique spatial and temporal characteristics [ 9 , 10 ], and its transmission characteristics within and between regions have significant positive correlation [ 11 ]. This study combines the three factors of industrial energy consumption structure, economic development and transportation to discuss the haze problem [ 12 , 13 , 14 , 15 , 16 ], and attempts to analyze the problem from the perspective of space.…”

Spatial and temporal variations of PM2.5 concentrations in Chinese cities during 2015-2019

“…The study found a positive correlation between PM 2.5 pollution and economic development, and an imbalance in the level of economic development would lead to an unbalanced distribution of PM 2.5 concentrations (Ouyang et al 2019). Many studies have confirmed that there is a significant positive correlation between PM 2.5 pollution and population density (Chen et al 2018a;Yang et al 2020b). Population has also been shown to be the most important factor affecting the PM 2.5 concentration (Chen et al 2018a).…”

“…Nationwide, PM 2.5 pollution was most serious in the intersection area to the east of the Hu line and north of the Yangtze River, while the southeast coastal areas were found to have maintained good air quality, which was consistent with previous research results (Yan et al 2018;Chen et al2018b). The urban development level, population density, geographic location, and climatic conditions can affect PM 2.5 concentrations (Yang et al 2020b). The urban development level is largely attributed to the local economic conditions.…”

City Centrality, Migrants and Green Inovation Efficiency: Evidence from 106 Cities in the Yangtze River Economic Belt of China

“…To check the robustness of the empirical results, the spatial adjacency weight matrix is used to replace the spatial distance weight matrix to test the benchmark model and mediation effect test [48], this helps to more carefully observe the degree of influence of the independent variable on the dependent variable and its spatial spillover effect. WL is the spatial adjacency weight matrix (If city i and city j are adjacent, each element in the matrix is equal to 1, otherwise it is 0).…”