Logistic Regression Prediction Model for Illegal Wildlife Trade

Abstract

The aim of this study is to build a model to assess the success rate of the strategy to combat illegal wildlife trade, and to predict the probability of effective implementation of the strategy by quantifying the indicators of successful implementation of the project and building a probabilistic generative model. First, this paper defines successful implementation of the project as a 30% reduction in illegal wildlife trade, and considers the increase in public environmental awareness and the strength of law enforcement agencies as indicators of successful implementation. Based on these indicators, this paper establishes a probability-based model that combines a large amount of data and expert advice to improve the prediction of the probability of success using a Bayesian approach. Further, this paper hypothesises that the probability of strategy success is determined by enforcement efforts, the level of public environmental awareness, strategy interventions and external factors, and develops a logistic regression model for prediction. Multiple simplified datasets were used to demonstrate the effectiveness of model fitting, and the statsmodels library in Python was utilised to calculate the probability of success. The results of the sensitivity analysis show that increased public environmental awareness has a significant impact on the success of the strategy, the enforcement effort should be in the appropriate interval, and project interventions and external factors also have an impact on the probability of success. By analysing the sensitivity intervals and saturation points, decision-making guidance is proposed to rationally allocate resources to increase the probability of project success.