Quantitative evaluation of PM2.5-related health economic losses and analysis of their driving factors in Chinese cities

Abstract

IntroductionGlobally, exposure to air pollution is estimated to cause millions of deaths annually as well as loss of healthy years of life (Li et al., 2021). PM 2.5 pollution has become a worldwide challenge, especially in developing countries such as China. PM 2.5 exposure not only affects the mortality rates of residents as well as outpatient and hospitalization rates for specific diseases but also increases cancer risk and death burden (Tan et al., 2018;Li et al., 2019). In addition, PM 2.5 pollution can cause economic losses by increasing health-related expenditures. Hence, evaluating the urban PM 2.5 -related health economic losses across China is of significance for environmentally sustainable and equitable decision-making developments.Various scholars have studied PM 2.5 -related health economic losses in China for different spatial scales. The average PM 2.5 -related economic loss was 0.3% (amended human capital: AHC) to 1% (value of statistical life: VSL) of the total gross domestic product (GDP) of 190 Chinese cities from 2014 to 2016 (Yang et al., 2018). In the Beijing-Tianjin-Hebei (BTH) region of China, the PM 2.5 economic loss was 6,081 million USD in 2017 (Wang et al., 2020). Most of the previous studies on this topic were mainly targeted at limited and individual areas or cities, such as 190 major cities in China (Yang et al., 2018), the BTH region (Wang et al., 2020), and Shanghai (Wu et al., 2017). However, owing to the spatial spillover effects of PM 2.5 and regional joint management requirements, it is important to systematically analyze the urban PM 2.5 -related health economic losses from the national-regional perspectives.Owing to regional differences in the economic, social, and environmental perspectives, the factors driving PM 2.5 -related health economic losses in different regions need to be analyzed further. The geographically weighted regression (GWR) is often used to identify such factors because of its flexibility in identifying relationships between different geospatial influences. Fu et al. ( 2014) used the GWR to analyze the factors influencing PM 2.5 health risks; the population, social, and economic factors accounted for about 45% of the PM 2.5 health risks. It is therefore helpful to identify

Quantitative Estimation of Urban PM2.5 Pollution Baseline and Meteorological Resource Endowment Using Machine Learning in Chinese Yangtze River Economic Belt

“…PM2.5 is one of the major pollutants in the atmosphere and already poses significant health risks to the population [1]. Especially in developing countries such as China, PM2.5 causes millions of deaths and results in economic losses due to health-related expenses [2]. In recent years, due to the successful implementation of China's air pollution policy, the overall concentration of PM2.5 has decreased.…”