Due to the lack of training data and effective haze disaster prediction model, the research on causality analysis and the risk prediction of haze disaster is mainly qualitative. In order to solve this problem, a nonlinear dynamic prediction model of Beijing haze disaster was built in this study. Based on the macroscopic evaluation of multiple influencing factors of haze disaster in Beijing, a causality model and flow diagrams of the Beijing crude oil consumption system, Beijing coal consumption

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... system, Beijing urban greening system and sulfur dioxide emission system in Hebei and Tianjin were established. The risk prediction of Beijing haze disaster was simulated at different conditions of air pollutant discharge level for the Beijing–Tianjin–Hebei region. Compared with the governance strategies of vehicle emission reduction, petrochemical production emission reduction, coal combustion emission reduction, greening and reducing dust and collaborative governance policy, the Beijing–Tianjin–Hebei cross-regional collaborative governance policy was more effective in controlling the haze disaster of Beijing. In the prediction, from 2011 to 2017, the air quality of Beijing changed from light pollution to good. By 2017, the PM2.5 of Beijing reduced to 75 µg/m3. From 2017 to 2035, the control effect of urban haze disaster for Beijing further strengthened. By 2035, the PM2.5 of Beijing reduced to 35 μg/m3. Finally, the PM2.5 of Beijing continued to reduce from 2035 to 2050. The speed of reduction for PM2.5 in Beijing slowed down. Meanwhile, the achievements of haze control in Beijing were consolidated. By 2050, the risk of haze disaster for Beijing was basically solved. The nonlinear dynamic prediction model in this study provides better promise toward the future control and prediction of global haze disaster under the condition of limited data.