Research on Suppression Measures for Illegal Trade in Wildlife Based on Change Theory

Yandong Hu; Minglei Han; Yuxin Li

doi:10.62051/qjk1f597

Authors

Yandong Hu
Minglei Han
Yuxin Li

DOI:

https://doi.org/10.62051/qjk1f597

Keywords:

Illegal Wildlife Trading; Change Theory; Local Communities; Bayesian Game Models.

Abstract

The global illegal trade in wildlife is flourishing, causing serious consequences for biodiversity and communities. According to the International Criminal Police Organization, wildlife crime has become one of the largest and most valuable illegal activities in the world. This illegal trade has caused serious damage to biodiversity and communities, putting many species on the brink of extinction. To address this issue, this article proposes a series of response measures based on the theory of change: strengthening the suppression of illegal behavior, increasing management incentives, reducing the cost of coexisting with wildlife, and supporting alternative (non wildlife dependent) livelihoods. Within the framework of change theory, local communities are seen as key drivers of change. Through in-depth comparative analysis, selected case studies, and detailed data cross referencing, this article has gained a deeper understanding of the appropriateness of involving local communities in these change pathways and elaborated on the basic assumptions. In the specific practice of change, this study selected regulators (local communities) and regulated individuals (wildlife traders) as participants in the game, and constructed a Bayesian game model to study the strategic interaction between the two parties. Through this model, this article analyzes pure strategy Nash equilibrium and mixed strategy Nash equilibrium, and obtains the return matrix under different scenarios. The research results indicate that for regulatory agencies (local communities), controllable variables include penalties for misconduct by regulatory agencies (F1), penalties for trader violations (F2), and regulatory costs (C) (where F1 and F2 are completely controllable, and C is partially controllable, depending on the level of regulation). By adjusting these variables F1, F2, and C, illegal wildlife trade can be effectively reduced, thereby achieving sustainability and controllability.

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