PM2.5 Pollution: Health and Economic Effect Assessment Based on a Recursive Dynamic Computable General Equilibrium Model
In: International Journal of Environmental Research and Public Health, 2019-12-13
Online
unknown
Zugriff:
At present particulate matter (PM2.5) pollution represents a serious threat to the public health and the national economic system in China. This paper optimizes the whitening coefficient in a grey Markov model by a genetic algorithm, predicts the concentration of fine particulate matter (PM2.5), and then quantifies the health effects of PM2.5 pollution by utilizing the predicted concentration, computable general equilibrium (CGE), and a carefully designed exposure&ndash
response model. Further, the authors establish a social accounting matrix (SAM), calibrate the parameter values in the CGE model, and construct a recursive dynamic CGE model under closed economy conditions to assess the long-term economic losses incurred by PM2.5 pollution. Subsequently, an empirical analysis was conducted for the Beijing area: Despite the reduced concentration trend, PM2.5 pollution continued to cause serious damage to human health and the economic system from 2013 to 2020, as illustrated by various facts, including: (1) the estimated premature deaths and individuals suffering haze pollution-related diseases are 156,588 (95% confidence intervals (CI): 43,335&ndash
248,914)) and six million, respectively
and (2) the accumulated labor loss and the medical expenditure negatively impact the regional gross domestic product, with an estimated loss of 3062.63 (95% CI: 1,168.77&ndash
4671.13) million RMB. These findings can provide useful information for governmental agencies to formulate relevant environmental policies and for communities to promote prevention and rescue strategies.
Titel: |
PM2.5 Pollution: Health and Economic Effect Assessment Based on a Recursive Dynamic Computable General Equilibrium Model
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Autor/in / Beteiligte Person: | Wu, Lingyan ; Liu, Xiaodong ; Yuan, Shuai ; Liu, Qi ; Chen, Keyao ; Wang, Guizhi ; Chen, Jibo |
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Zeitschrift: | International Journal of Environmental Research and Public Health, 2019-12-13 |
Veröffentlichung: | Multidisciplinary Digital Publishing Institute, 2019 |
Medientyp: | unknown |
ISSN: | 1660-4601 (print) |
DOI: | 10.3390/ijerph16245102 |
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