Combating Illegal Wildlife Trade through Big Data Monitoring and Management
- DOI
- 10.2991/978-94-6463-490-7_16How to use a DOI?
- Keywords
- Illegal wildlife trade; big data monitoring; management; PRI-AHP model; economic analysis; system dynamics modeling
- Abstract
Illegal wildlife trade is a persistent global issue that threatens biodiversity and ecological stability. Addressing this challenge requires innovative approaches, including leveraging big data for management solutions. This paper conducts a comprehensive evaluation and analysis of a management project aimed at combating illegal wildlife trade, with a focus on utilizing big data technology. Through the PRI-AHP model, economic analysis tools, and system dynamics modeling, the study identifies TRAFFIC as the optimal project management client, assesses financial feasibility and risk and predicts positive impacts on illegal trade activities, law enforcement, and monitoring system efficiency. This research provides valuable insights for decision-makers and emphasizes the potential of big data in wildlife conservation projects.
- Copyright
- © 2024 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Wenzhuo Wang AU - Jianing He AU - Shuo Liu PY - 2024 DA - 2024/08/31 TI - Combating Illegal Wildlife Trade through Big Data Monitoring and Management BT - Proceedings of the 2024 3rd International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID 2024) PB - Atlantis Press SP - 138 EP - 148 SN - 2589-4919 UR - https://doi.org/10.2991/978-94-6463-490-7_16 DO - 10.2991/978-94-6463-490-7_16 ID - Wang2024 ER -