Study on Illegal Wildlife Trade Based on Linear Regression and Interpolated Prediction

Abstract

Against the backdrop of the current growing problem of illegal wildlife trade globally, this paper constructs a data-driven model that aims to significantly reduce the scale and impact of this trade. Through the collection and in-depth analysis of global statistics, we identified the Wildlife Conservation Society (WCS) as the ideal partner for the project. We made a strong argument that WCS has the influence and resources needed to implement the project. Using linear regression modeling and interpolated predictive modeling for inertia analysis, we demonstrated synergies between the project and WCS's goals. Based on the assessment of the power and resources required for WCS to implement the project, we propose a resource allocation model and discuss three policy options, namely mandatory, incentive, and cooperative policies, to ensure that the client has flexibility in the decision-making process. This study aims to make a significant contribution to global biodiversity conservation and to effectively address the threats to ecological balance posed by illegal wildlife trade.