Investigating the spatially heterogeneous impacts of urbanization on city-level industrial SO2 emissions: Evidence from night-time light data in China

Abstract

The spatial distribution and the identification of the influential factors of industrial sulfur dioxide (SO2) emissions have received extensive attention. However, evidence is still lacking on the spatial impacts of urbanization on industrial SO2 emissions at the city scale in China. This work builds a Geography Weighted Regression (GWR) model on a Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) framework to investigate the spatially heterogeneous impacts of potential influencing factors on city-level industrial SO2 emissions from 288 prefecture-level cities in China. The results show that the GWR model significantly improved the goodness-of-fit of the model. The influence of night-time light intensity of the cities, as a proxy of the urbanization level, was calculated to be median (min, max): −0.505(-0.918, −0.413). The highest impacts of urbanization were observed in the Northeast and Southwest regions. Industrial influencing factors had generally promoted the growth of SO2 emissions, with higher positive impacts in western cities. We concluded that urbanization had a significant and negative effect on industrial SO2 emissions in most Chinese cities. It is necessary to formulate emission reduction and city development policies simultaneously based on the trade-offs between urbanization and air pollution control targets.

Keywords

• Spatial heterogeneity
• Geography Weighted Regression (GWR) model
• City-level SO2 emissions
• Urbanization
• STIRPAT framework