Examining the Effects of Socioeconomic Development on Fine Particulate Matter (PM2.5) in China’s Cities Based on Spatial Autocorrelation Analysis and MGWR Model

Abstract

Understanding the characteristics of PM2.5 and its socioeconomic factors is crucial for managing air pollution. Research on the socioeconomic influences of PM2.5 has yielded several results. However, the spatial heterogeneity of the effect of various socioeconomic factors on PM2.5 at different scales has yet to be studied. This paper collated PM2.5 data for 359 cities in China from 2005 to 2020, as well as socioeconomic data: GDP per capita (GDPP), secondary industry proportion (SIP), number of industrial enterprise units above the scale (NOIE), general public budget revenue as a proportion of GDP (PBR), and population density (PD). The spatial autocorrelation and multiscale geographically weighted regression (MGWR) model was used to analyze the spatiotemporal heterogeneity of PM2.5 and explore the impact of different scales of economic factors. Results show that the overall economic level was developing well, with a spatial distribution trend of high in the east and low in the west. With a large positive spatial correlation and a highly concentrated clustering pattern, the PM2.5 concentration declined in 2020. Secondly, the OLS model's statistical results were skewed and unable to shed light on the association between economic factors and PM2.5. Predictions from the GWR and MGWR models may be more precise than those from the OLS model. The scales of the effect were produced by the MGWR model's variable bandwidth and regression coefficient. In particular, the MGWR model's regression coefficient and variable bandwidth allowed it to account for the scale influence of economic factors; it had the highest adjusted R2 values, smallest AICc values, and residual sums of squares. Lastly, the PBR had a clear negative impact on PM2.5, whereas the negative impact of GDPP was weak and positively correlated in some western regions, such as Gansu and Qinghai provinces. The SIP, NOIE, and PD were positively correlated with PM2.5 in most regions. Our findings can serve as a theoretical foundation for researching the associations between PM2.5 and socioeconomic variables, and for encouraging the coequal growth of the economy and the environment.

Underlying Dynamics of PM2.5 Concentrations in China: Evidence Based on ARDL Approach

“…On the contrary, economic output, secondary industry, and FDI have heterogeneous impacts on PM 2.5 concentrations in time and their impacts may be overestimated unless considering the neighborhood among cities. Wang and Cao (2023), for the period 2005-2020, ascertained the negative association of PM 2.5 concentrations with general public budget revenue despite their positive association with economic output, secondary industry, and population density in 359 Chinese cities.…”

An Empirical Investigation on the Underlying Factors of Recently Declining Air Pollution in China

“…On the contrary, economic output, secondary industry, and FDI have heterogeneous impacts on PM 2.5 concentrations in time and their impacts may be overestimated unless considering the neighborhood among cities. Wang and Cao (2023), for the period 2005-2020, ascertained the negative association of PM 2.5 concentrations with general public budget revenue despite their positive association with economic output, secondary industry, and population density in 359 Chinese cities. Following a different strategy from these studies and including China in a wide range of panel groups of 60 countries over the period 2010-2019, Akça (2024) highlighted the e cient role of renewable energy sources in improving air quality through mitigating PM 2.5 concentrations.…”