Data Statistics Applications in Regulating the Balance of the Wildlife Trade

DOI

10.2991/978-94-6463-490-7_26How to use a DOI?

Keywords

Data Statistics Applications; Wildlife trade; Pareto Analysis; ADF test; Kendall consistency test

Abstract

The global wildlife trade is a massive industry, with over 36,000 protected species and profits exceeding $20 billion. While this trade brings huge profits to those involved, it also increases the risk of zoonotic diseases. To analyse the wildlife trade, we used the CITES trade database, which contains over 20 million records of trade. After data pre-processing, we got the result of the most traded wildlife species, Primates, and analysed the purpose of trade through Pareto Analysis Model. We also conducted an Augmented Dickey-Fuller Unit Root (ADF) test model to compare import and export quantities, finding that the import quantity was gentle, while the export quantity had fluctuations. We also created a line chart and a bubble map indicating unstable trade during a certain period. To determine if the wildlife trade is related to major infectious disease epidemics, we conducted a qualitative analysis, and a quantitative analysis as well as created two flow direction maps. Using the Kendall consistency test, our quantitative analysis found that the wildlife trade is indeed related to the epidemic of major infectious diseases. Afterward, we investigated the impact of the wildlife trade on the economy and society, building a hypothetical-deductive model. We found that while the volume of trade is not directly related to the economy, it does partially affect GDP growth. The Kendall consistency test showed a high degree of correlation between the economy, society, and trade. Eventually, we proposed some urgently needed measurements to regulate the balance of wildlife trade.

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.

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TY  - CONF
AU  - Zhanghao Chen
AU  - Yifei Chen
AU  - Xuan Gao
AU  - Xinyu Zhu
AU  - Jianfei Liu
PY  - 2024
DA  - 2024/08/31
TI  - Data Statistics Applications in Regulating the Balance of the Wildlife Trade
BT  - Proceedings of the 2024 3rd International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID 2024)
PB  - Atlantis Press
SP  - 238
EP  - 248
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-490-7_26
DO  - 10.2991/978-94-6463-490-7_26
ID  - Chen2024
ER  -