Spatial-Temporal Effects of PM2.5 on Health Burden: Evidence from China

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.

Analysis of the gap in enterprise access to renewable energy between rural and urban areas in Cameroon

“…Many countries have opted for cleaner and environmentally friendly energy production technologies to cope with the horrors of climate change. Although they constitute only a small part of total global primary energy, renewable energy sources generate less CO2 emissions than non-renewable sources, which constitute a significant share of total global primary energy (Zeng et al, 2019). In Cameroon, the consumption of renewable energies during the period 2000-2017 represents between 78% and 84.5% of global energy consumption (IEA et al, 2020).…”

Does Economic Policy Uncertainty Matter for Healthcare Expenditure in China? A Spatial Econometric Analysis

“…Where, d ij is the road distance between region i and region j. However, the above two spatial weight matrixes only reflect the geographical relationship between regions, but they cannot reflect the influence of other factors (27). Hence, we also construct the spatial economic weight matrix W3 as follows:…”

Spatial Effects of Environmental Pollution on Healthcare Services: Evidence from China

“…Song conducted research to estimate the health burden attributable to ambient PM2.5 across China by using the exposure-response model [ 11 ]. Zeng applied spatial autocorrelation analysis, hot spot analysis, and spatial empirical analysis to explore the spatial distribution of PM2.5 and described its relationship with healthcare services [ 12 ]. Wang revealed that water pollution was negatively associated with health outcomes by using a random-effects model, a random-effects logit model, and a mediator model [ 13 ].…”

“…Air pollution could spread over adjacent provinces with no regard for political boundaries [ 28 ]; water pollution upstream could cause destructive effects on downstream regions [ 29 ]; solid waste can contaminate soil without being limited by space [ 30 ]. To an extent, some studies have analyzed the spill-over effects of air pollution [ 12 ]; however, these spill-over effects have still not been fully considered, such as spill-overs due to water pollution and solid-waste pollution. Apart from that, specifically, there has been no detailed discussion about the relationship between outpatient services, inpatient services, and environmental pollution.…”