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Abstract Illegal wildlife trade (IWT) is a huge gray industry with a market valuation of billions of dollars. Not only does this trade pose a serious threat to the safety of human and animal life, it is also one of the major challenges facing biodiversity today and can lead to a range of serious problems including the spread of zoonotic pathogen and invasive alien species. Although policymakers have been able to respond relatively quickly, the current regulation and crackdown on illegal wildlife trade is still inadequate. Using the United States as an example, this study employs a statistical modeling approach that employs principal component analysis and hierarchical analysis to effectively delineate and assess power, resources, and interests in the global wildlife conservation enterprise. We conducted an in-depth analysis of the impact of multiple national indicators and policy assumptions on the illegal wildlife trade, and used ARIMA models to analyze trade data before and after the implementation of interventions to predict trends over the next 14 years. By visualizing sales data and active trade pairs over the years, this study provides a scientific basis for government decision-making. The results of the study show that wildlife trade is significantly affected by specific regional and national indicators. In addition, our proposed modeling framework reveals previously overlooked influences in the illegal versus legal wildlife trade and promotes the development and implementation of effective interventions to reduce the damage of illegal trade to wild populations. We expect this study to contribute to the development of global wildlife conservation and promote more international cooperation and policy innovation.
Wang et al. (Tue,) studied this question.