 Optimizing Wildlife Crime Intervention: A Modeling Approach for Project DesignFangyan Ma and Xue Chen GBP Proceedings Series, 2025, vol. 15, 19-27 Abstract: The rapid proliferation of digital platforms has not only transformed global commerce and communication but has also inadvertently facilitated the concealment, coordination, and expansion of illegal wildlife trade (IWT). This development presents a profound threat to global biodiversity, undermines conservation efforts, and poses challenges for law enforcement agencies. Addressing this growing concern requires innovative and proactive strategies capable of monitoring, predicting, and mitigating online wildlife trafficking. This paper proposes a comprehensive project framework specifically designed to significantly reduce online IWT by leveraging advanced algorithmic monitoring, data-driven analytics, and strategic organizational collaboration. To ensure the project targets the most effective implementing partner, we apply a fuzzy comprehensive evaluation model to systematically assess potential client organizations. Based on criteria encompassing organizational resources, investigative capacity, international influence, and policy execution ability, TRAFFIC emerges as the optimal partner to lead the initiative. This evaluation guarantees that the project is not only technically sound but also operationally feasible, with a high likelihood of producing measurable impact in curbing online IWT. A critical foundation for the project's rationale is established through a Pearson correlation analysis, which reveals a strong positive association between the prevalence of online IWT and overall IWT (r = 0.966, p < 0.01). This statistically significant relationship validates the focus on digital platforms as a strategic intervention point, demonstrating that targeted monitoring and intervention in the online sphere can meaningfully influence the broader patterns of wildlife trafficking. Building on this insight, we develop a linear regression prediction model to simulate and forecast the project's potential outcomes. The model indicates that the implementation of advanced algorithmic monitoring could lead to a substantial reduction in IWT cases over the next five years, offering quantitative evidence to support long-term planning and resource allocation. This predictive approach not only strengthens the evidence base for project design but also enables dynamic adjustment of monitoring strategies based on real-time data. Finally, the feasibility of the proposed framework is thoroughly assessed through stakeholder network analysis and sensitivity testing. This dual approach evaluates both the structural robustness of organizational partnerships and the system's responsiveness to varying conditions, ensuring that the project remains resilient under different operational scenarios. By integrating technological innovation, rigorous evaluation, and strategic collaboration, this study presents a practical, actionable, and data-supported five-year project proposal for TRAFFIC. Overall, the framework exemplifies a significant step toward mitigating online wildlife trafficking, demonstrating how intelligent monitoring solutions can enhance conservation outcomes, inform policy decisions, and strengthen global efforts against illegal wildlife trade. Keywords: illegal wildlife trade; algorithmic monitoring; fuzzy comprehensive evaluation; linear regression prediction; project feasibility (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:axf:gbppsa:v:15:y:2025:i::p:19-27 Access Statistics for this article More articles in GBP Proceedings Series from Scientific Open Access Publishing |
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