Research on Trends in Illegal Wildlife Trade based on Comprehensive Growth Dynamic Model

TL;DR

This paper introduces a novel comprehensive growth dynamic model (CGDM) to forecast illegal wildlife trade trends, integrating economic and social factors, and demonstrates its effectiveness in predicting trade trajectories in China from 2024 to 2034.

Contribution

The paper develops a new integrated model combining logistic, power law, and Gaussian components to predict illegal wildlife trade dynamics.

Findings

Minimum trade point in 2027

Maximum trade point in 2029

Model shows robustness and generalizability

Abstract

This paper presents an innovative Comprehensive Growth Dynamic Model (CGDM). CGDM is designed to simulate the temporal evolution of an event, incorporating economic and social factors. CGDM is a regression of logistic regression, power law regression, and Gaussian perturbation term. CGDM is comprised of logistic regression, power law regression, and Gaussian perturbation term. CGDM can effectively forecast the temporal evolution of an event, incorporating economic and social factors. The illicit trade in wildlife has a deleterious impact on the ecological environment. In this paper, we employ CGDM to forecast the trajectory of illegal wildlife trade from 2024 to 2034 in China. The mean square error is utilized as the loss function. The model illuminates the future trajectory of illegal wildlife trade, with a minimum point occurring in 2027 and a maximum point occurring in 2029. The stability of contemporary society can be inferred. CGDM's robust and generalizable nature is also evident.