Study On the Current Status of Illegal Wildlife Trade Based on ARIMA And Logistic Regression Modelling

Authors

  • Zishuo Liu
  • Senyuan Ma
  • Yanli Zhang

DOI:

https://doi.org/10.54097/egzmgm30

Keywords:

Hierarchical analysis; ARIMA; Logistic regression.

Abstract

The purpose of this paper is to explore effective programs to address illegal wildlife trade. With increasing environmental problems, illegal wildlife trade threatens biodiversity and social stability. The government is identified as the client through hierarchical analysis and a comprehensive project program is proposed for a period of five years. The necessity of the project is analyzed through international cooperation, government capacity, human health and biodiversity, and the possible impacts of not implementing the project are demonstrated using ARIMA time series model. Further modeling of expected goals based on logistic regression methods to assess the success of the project. Promote the implementation of the project through cooperation and contribute to solving the problem of illegal wildlife trade. This study provides useful reference and practical guidance for taking effective measures to deal with IWT.

Downloads

Download data is not yet available.

References

Margulies J D, Bullough L A, Hinsley A, et al. Illegal wildlife trade and the persistence of “plant blindness” [J]. Plants, People, Planet, 2019, 1(3): 173-182.

Ceballos G, Ehrlich P R, Raven P H. Vertebrates on the brink as indicators of biological annihilation and the sixth mass extinction[J]. Proceedings of the National Academy of Sciences, 2020, 117(24): 13596-13602.

Parekh H, Yadav K, Yadav S, et al. Identification and assigning weight of indicator influencing performance of municipal solid waste management using AHP[J]. KSCE Journal of Civil Engineering, 2015, 19(1): 36-45.

Noor T H, Almars A M, Alwateer M, et al. Sarima: a seasonal autoregressive integrated moving average model for crime analysis in Saudi Arabia[J]. Electronics, 2022, 11(23): 3986.

Mohanty R P, Sahoo G, Dasgupta J. Identification of risk factors in globally outsourced software projects using logistic regression and ANN[J]. International Journal of Sup. Chain. Mgt, 2012, 1: 2-11.

Downloads

Published

26-06-2024

How to Cite

Liu, Z., Ma, S., & Zhang, Y. (2024). Study On the Current Status of Illegal Wildlife Trade Based on ARIMA And Logistic Regression Modelling. Highlights in Science, Engineering and Technology, 103, 282-289. https://doi.org/10.54097/egzmgm30