Research on Influencing Factors of Illegal Wildlife Trade Based on Multiple Linear Regression Model

This study aims to explore the influencing factors of illegal wildlife trade and assess the role of key indicators such as technology, law, and international cooperation in reducing this type of trade. By integrating Spearman's correlation analysis and multiple linear regression models, we conducted an in-depth analysis of China's wildlife trade data. Initially, Spearman's correlation analysis revealed significant positive correlations between education, public awareness, technology, law, and international cooperation with the reduction of illegal wildlife trade. Subsequently, we constructed a multiple linear regression model to quantify the specific impact of these indicators on the reduction of illegal wildlife trade. The model results indicated that technology and law are the main drivers in promoting the reduction of illegal trade, while international cooperation also plays an important role. Furthermore, we conducted sensitivity analysis to evaluate the impact of changes in model parameters on the predictive outcomes, ensuring the robustness of the model. This research provides a scientific basis for policymakers, emphasizing the importance of increasing technological investment, improving legal regulations, and deepening international cooperation in combating illegal wildlife trade. The findings not only contribute to the protection of biodiversity but also offer strategic guidance for achieving harmonious coexistence between humans and nature.