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With ongoing urbanization, traffic congestion and the air pollution it induces are worsening. Using a system dynamics (SD) approach, this study constructed a driving-restriction policy model to explore the effects of different stages of policy implementation on variables such as traffic congestion, emissions, and parking demand. Medium- and long-term dynamic simulation showed that the effect of the policy was obvious in the initial stage but gradually weakened in the medium term, leading to a "fading" effect on traffic-congestion alleviation; a "rebound" effect was even observed at the end of the simulation. Thus, the policy will not effectively reduce traffic congestion in the long term and will induce a new demand for car purchases, resulting in paradoxical effects, which will aggravate parking demand, congestion, and pollution. Yet, it was also found that introducing penalty policies and an air pollution charging fee could weaken the paradoxical effects and compensate for some defects of the policy. Such strategies could help reduce emissions, traffic congestion, parking demand, the number of illegal trips, and the overall number of vehicle trips. These findings can provide not only a theoretical basis for further research but also practical guidance for policy improvement. [ABSTRACT FROM AUTHOR]

In order to improve the accuracy of forecasting crude oil prices, a new crude oil price forecasting method is introduced in the paper that is a combination of the FNN model and the stochastic time effective function—namely, the WT-FNN model. The FNN model keeps track of the historical values of crude oil prices and predicts future crude oil prices, and the stochastic time effective function gives greater weight to recent information and smaller weight to old information, thus making the prediction of crude oil prices more reasonable. We selected the daily data of Brent crude oil prices from 4 January 2000 to 30 September 2021 as research objects and then used the WT-FNN model to train and predict the research objects. By comparing it to the benchmark model, we found that the predictive effect of the WT-FNN model was better than the FNN model and the no-change model. The results also passed a robustness test. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic now affects the entire world and has many major effects on the global economy, environment, health, and society. Focusing on the harm COVID-19 poses for human health and society, this study used system dynamics to establish a prevention and control model that combines material supply, public opinion dissemination, public awareness, scientific and technological research, staggered work shifts, and the warning effect (of law/policy). Causal loop analysis was used to identify interactions between subsystems and explore the key factors affecting social benefit. Further, different scenarios were dynamically simulated to explore optimal combination modes. The main findings were as follows: (1) The low supervision mode will produce a lag effect and superimposed effect on material supply and impede social benefit. (2) The strong supervision mode has multiple performances; it can reduce online public opinion dissemination and the rate of concealment and false declaration and improve government credibility and social benefit. However, a fading effect will appear in the middle and late periods, and over time, the effect of strong supervision will gradually weaken (but occasionally rebound) and thus require adjustment. These findings can provide a theoretical basis for improving epidemic prevention and control measures. [ABSTRACT FROM AUTHOR]