PM_2.5 Pollution: Health and Economic Effect Assessment Based on a Recursive Dynamic Computable General Equilibrium...

Area of Science:

• Environmental Science
• Public Health
• Economics

Background:

• Particulate matter (PM2.5) pollution poses a substantial threat to public health and China's economy.
• Effective prediction and quantification of PM2.5's health and economic impacts are crucial for policy development.

Purpose of the Study:

• To optimize a grey Markov model using a genetic algorithm for accurate PM2.5 concentration prediction.
• To quantify the health effects and long-term economic losses associated with PM2.5 pollution.
• To provide data-driven insights for environmental policy formulation and public health strategies.

Main Methods:

• Grey Markov model optimized with a genetic algorithm for PM2.5 prediction.
• Computable General Equilibrium (CGE) model integrated with an exposure-response model to assess health and economic impacts.
• Social Accounting Matrix (SAM) and recursive dynamic CGE model for long-term economic loss assessment.

Main Results:

• PM2.5 pollution in Beijing (2013-2020) resulted in an estimated 156,588 premature deaths and 6 million haze-related disease cases.
• Accumulated labor loss and medical expenditures due to PM2.5 negatively impacted regional GDP, with an estimated loss of 3062.63 million RMB.
• Despite decreasing concentration trends, PM2.5 pollution continues to inflict severe damage on human health and the economy.

Conclusions:

• PM2.5 pollution poses a significant and ongoing threat to public health and economic stability in China.
• The study's predictive and quantitative models offer valuable tools for assessing environmental policy effectiveness.
• Findings underscore the need for robust environmental policies and public health interventions to mitigate PM2.5 impacts.