PM 2.5 Pollution: Health and Economic Effect Assessment Based on a Recursive Dynamic Computable General Equilibrium Model

Keyao Chen, Guizhi Wang, Lingyan Wu, Jibo Chen, Shuai Yuan, Qi Liu and Xiaodong Liu
Additional contact information
Keyao Chen: National Climate Center, China Meteorological Administration, Beijing 100081, China
Guizhi Wang: School of Mathematics and Statistics, Nanjing University of Information Science & Technology, Nanjing 210044, China
Lingyan Wu: School of Mathematics and Statistics, Nanjing University of Information Science & Technology, Nanjing 210044, China
Jibo Chen: School of Mathematics and Statistics, Nanjing University of Information Science & Technology, Nanjing 210044, China
Shuai Yuan: School of Mathematics and Statistics, Nanjing University of Information Science & Technology, Nanjing 210044, China
Qi Liu: Shandong Beiming Medical Technology Ltd., Jinan 250000, China
Xiaodong Liu: School of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UK

IJERPH, 2019, vol. 16, issue 24, 1-17

Abstract: At present particulate matter (PM 2.5 ) 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 (PM 2.5 ), and then quantifies the health effects of PM 2.5 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 PM 2.5 pollution. Subsequently, an empirical analysis was conducted for the Beijing area: Despite the reduced concentration trend, PM 2.5 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.

Keywords: haze pollution; genetic algorithm; exposure-response model; computable general equilibrium model; health effects (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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