Abstract

By collecting the panel data of 29 regions in China from 2008 to 2017, this study used the spatial Durbin model (SDM) to explore the spatial effect of PM2.5 exposure on the health burden of residents. The most obvious findings to emerge from this study are that: health burden and PM2.5 exposure are not randomly distributed over different regions in China, but have obvious spatial correlation and spatial clustering characteristics. The maximum PM2.5 concentrations have a significant positive effect on outpatient expense and outpatient visits of residents in the current period, and the impact of PM2.5 pollution has a significant temporal lag effect on residents’ health burden. PM2.5 exposure has a spatial spillover effect on the health burden of residents, and the PM2.5 concentrations in the surrounding regions or geographically close regions have a positive influence on the health burden in the particular region. The impact of PM2.5 exposure is divided into the direct effect and the indirect effect (the spatial spillover effect), and the spatial spillover effect is greater than that of the direct effect. Therefore, we conclude that PM2.5 exposure has a spatial spillover effect and temporal lag effect on the health burden of residents, and strict regulatory policies are needed to mitigate the health burden caused by air pollution.

Highlights

  • With the rapid development of China’s economy, the living standard and health of residents have been greatly improved, but ambient air pollution remains a serious problem

  • The main novelties and contributions of this paper were listed as follows: (1) This study explored the impact of PM2.5 exposure on the residents’ health burden, further enriching the research perspective of economic loss brought by air pollution

  • Due to the temporal lag effect of PM2.5 exposure on residents’ health burden [48], this study used the maximum of PM2.5 concentrations lags by one stage (PM2.5_max (-1)) as the independent variable to verify whether the temporal lag effect exists

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Summary

Introduction

With the rapid development of China’s economy, the living standard and health of residents have been greatly improved, but ambient air pollution remains a serious problem. Per capita outpatient visits increased from 3.7 times in 2008 to 6.0 times in 2018 (Per capita outpatient visits refers to the ratio of total number of outpatient visits to the total population) [19] This suggests that the Chinese government is increasingly investing in health expenditure to improve public health and make or become less the burden of residents. With the unprecedented economic development and urbanization in recent decades in China, energy consumption has increased significantly and PM2.5 pollution has become a serious problem [41]. (1) This study explored the impact of PM2.5 exposure on the residents’ health burden, further enriching the research perspective of economic loss brought by air pollution. Outpatient expense and outpatient visits were presented to measure the health burden, as well as the number of hospitalizations was selected to test robustness

Research Design
Dependent Variable
Independent Variable
Control Variable
Spatial Autocorrelation Test
Spatial Econometric Model
Model Test
Spatial Distribution
Spatial Autocorrelation Analysis
Empirical Analysis and Discussion
Alternative Independent Variable Estimation
Alternative Dependent Variable Estimation
Endogenous Test
Findings
Conclusions
Full Text
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