Study on the Selection of International Wildlife Trade Partners Based on TOPSIS and Spatial Statistics

Abstract

Illegal wildlife trade has become one of the primary threats to global biodiversity conservation, as the exploitation of natural resources not only disrupts ecological balance but also profoundly impacts the economic and social structures of multiple countries. To effectively curb this cross-border issue, countries have intensified international collaboration and protective measures. In this context, this study combines the TOPSIS evaluation model and spatial statistical analysis to scientifically assess the collaboration potential of international organizations and examine the spatial patterns of global illegal wildlife trade. Using the TOPSIS model, the World Wide Fund for Nature (WWF) emerged as the ideal partner, with its expertise and resource support significantly enhancing project efficacy. Additionally, Moran's I algorithm was applied to analyze trade activities across 20 countries, revealing geographical clustering and interdependence between neighboring countries in illegal trade. This research offers a data-driven framework for collaboration and precise spatial intervention strategies to address the global wildlife trade crisis.