The contributions of socioeconomic indicators to global PM2.5 based on the hybrid method of spatial econometric model and geographical and temporal weighted regression

Abstract

PM2.5 pollution poses a negative effect on human health and economic growth. However, the major socioeconomic driving forces of global PM2.5 pollution during a long-term period remained unclear. In this study, we explored the potential association between socioeconomic indicators and the PM2.5 level worldwide using a spatial econometric model coupled with a geographical and temporal weighted regression (GTWR). The results suggested that renewable energy consumption ratio, per capita gross domestic production (GDP), per capita CO2 emission, urban population ratio, and fossil fuel consumption ratio were major factors responsible for the global PM2.5 pollution. The impacts of socioeconomic indicators on the PM2.5 level varied with the income-level and time. Fossil fuel consumption ratio, per capita CO2 emission, urban population ratio were major contributors for severe PM2.5 pollution in the developing countries (e.g., China and India). Further, these impacts have become more remarkable in recent years. Per capita GDP still played a crucial role on the PM2.5 pollution in India, indicating that energy-intensive industries were major contributors to its economic growth, thereby leading to the higher PM2.5 concentration in India. However, China has strode across the inflection of Environmental Kuznets Curve (EKC) as a whole and decreased the reliance on the secondary industries. Compared with the developing countries, the impacts of socioeconomic indicators on PM2.5 pollution in most of the developed countries remained relatively stable and weak, implicating that fossil fuel consumption and urbanization were not major contributors for local PM2.5 level. The findings of this study clarified major contributors for PM2.5 pollution, and provided scientific basis for mitigating the PM2.5 pollution.