Research on the Effectiveness of Policy Interventions in Illegal Wildlife Trade Based on System Dynamics and Logistic Regression Models

Abstract

This study is dedicated to assessing the long-term impact of policy interventions on the illegal wildlife trade system. To this end, we have constructed a System Dynamics model capable of simulating the complex interactions between key variables such as illegal trade demand (D), supply (S), enforcement strength (E), public awareness (A), and project intervention (P). By understanding the dynamic relationships among these variables, we can predict the effects of policy changes on the illegal wildlife trade market. Furthermore, we have employed a Logistic Regression model to quantify the probability of project success, considering multiple dimensions including increased enforcement efforts, enhanced public awareness, project interventions, and external factors. The combination of these two models not only enhances our comprehension of the dynamics of illegal wildlife trade but also provides policymakers with a scientific framework for evaluating and optimizing strategies. Parameter estimation based on historical data and expert opinions, along with sensitivity analysis of the models, enables us to provide a basis for the effective allocation of resources and precise policy adjustments. The findings indicate that reasonable policy interventions can significantly reduce the incidence of illegal wildlife trade while raising public awareness of wildlife conservation.