Addressing the complex dynamics of global illegal wildlife trade and its ecological security implications, this study applies a Vector Autoregression (VAR) model to analyze interactions between poaching activities and enforcement seizures while forecasting trade trends over 2023–2027. Using 2000–2022 data on African poaching incidents and global seizure volumes, we verify variable stationarity and Johansen cointegration to establish long-term equilibrium, subsequently developing a VAR(8) model. Key results: (1) Poaching and seizures exhibit bidirectional feedback, with seizures suppressing poaching short-term, while poaching drives long-term seizure growth; (2) Variance decomposition shows poaching explains 41.3% of seizure variability; (3) Exogenous variables demonstrate lagged decay effects; (4) Five-year projections indicate 4.2% annual seizure growth and 1.8% poaching reduction in Africa, confirming UAV surveillance efficacy. The study innovatively reveals nonlinear characteristics of illegal trade through multivariate dynamic modeling, transcending limitations of conventional single-factor analyses. We propose a four-dimensional "Technology-Law- Data-Society" collaborative governance framework: technologically integrating AI drones with blockchain traceability systems; legally designing transnational graduated penalty mechanisms; data-wise developing socio-ecological coupled expansion models; socially implementing VR education and community credit systems. These findings provide theoretical support for optimizing law enforcement resource allocation and establishing integrated "prediction-early warning-response" governance systems, demonstrating significant practical value in transitioning ecological security from passive reaction to proactive defense paradigms.

Fengjiao Xu. Research on Forecasting Model of Wildlife Trade Based on VAR Model. Academic Journal of Computing & Information Science (2025), Vol. 8, Issue 8: 43-50. https://doi.org/10.25236/AJCIS.2025.080807.

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