PM2.5 Pollution: Health and Economic Effect Assessment Based on a Recursive Dynamic Computable General Equilibrium Model

Chen, Keyao; Wang, Guizhi; Wu, Lingyan; Chen, Jibo; Yuan, Shuai; Liu, Qi; Liu, Xiaodong

Abstract

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–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): 43,335–248,914)) 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: 1,168.77–4671.13) 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.

• Journal Article Type: Article
• Acceptance Date: Dec 12, 2019
• Online Publication Date: Dec 13, 2019
• Publication Date: Dec 13, 2019
• Deposit Date: Feb 21, 2020
• Publicly Available Date: Feb 24, 2020
• Journal: International Journal of Environmental Research and Public Health
• Electronic ISSN: 1660-4601
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 16
• Issue: 24
• Article Number: 5102
• DOI: https://doi.org/10.3390/ijerph16245102
• Keywords: haze pollution; genetic algorithm; exposure-response model; computable general equilibrium model; health effects
• Public URL: http://researchrepository.napier.ac.uk/Output/2578165