Research of Conservation Factors of Wildlife Trade Based on Regression Model

The problem of illegal wildlife trade has become an international problem of common concern around the world, and the existing research lacks inquiries on the underlying factors of this problem. In this paper, the ridge regression model, the ARIMA model and the multiple linear regression prediction model are constructed, and the latter two are weighted and optimized. The study finds that the increase of resource input and the increase of national interest play a positive role in combating illegal trade, while the volatility of power factors also has a significant impact on trading activities. The results of the optimized forecasts show that projects to combat wildlife trade have not only short-term effects, but also long-term positive impacts. After sensitivity analysis, the models established in this paper has a high robustness and can provide a reference for the world's anti-wildlife trade activities.