Measuring the Impact of Industrial Pollution on Population in Beijing, China: An Empirical Analysis Based on the GWR Model

Industrial pollution is a detrimental byproduct of urban industrialization that has persisted over the past few decades, significantly impeding the sustainable development of cities. While previous research has confirmed the adverse effects of pollutants on human health, a comprehensive understanding of how pollutants impact urban populations still needs to be provided. Therefore, this study focuses on a critically affected urban center, Beijing, China, and conducts a high-precision research investigation. We employ a grid-based approach, dividing Beijing into 1 km × 1 km grids, to examine changes in various types of pollution gases (independent variables) and urban socio-economic factors (control variables) from 2015 to 2020. Utilizing these data, we construct a Geographic Weighted Regression (GWR) model to decipher the influence of these factors on population change rates. The findings reveal that the population distribution in Beijing from 2015 to 2020 was relatively stable, and the direction of population flow showed a significant correlation with urban circles and terrain, forming a spatial pattern of stable population in the central metropolitan area and population growth in the peripheral suburbs. According to the GWR model, O3 and NO3 are important pollution variables that affect population distribution. The correlation between these two variables and the population change rate has shown a positive impact on a large area in Beijing but also a negative effect on some areas. The concentrations of CO2 and SO2 tend to stabilize, with low correlation with population distribution, and spatial effects are also more dispersed. In addition, the correlation between OLD and population density is strong, while the relationship between GDP and population distribution is weak.