Research On the Strategy of Combating Illegal Wildlife Trade Based on Logistic Regression-ARIMA Model

Authors

  • Lanqi Guo
  • Ruiqi Mao
  • Mingrui Li

DOI:

https://doi.org/10.54097/x7mjf459

Keywords:

Illegal Wildlife Trade, Logistic Regression Model, Autoregressive Integrated Moving Average Model.

Abstract

In view of the continuous growth of the global illegal wildlife trade, this paper proposes a series of methods and strategies to combat the illegal wildlife trade and explores the feasibility and effectiveness of the implementation of the strategy by the United Nations Environment Programme (UNEP) based on logistic regression and ARIMA prediction model. The results of this paper show that the success rate of the strategy implemented by the UNEP is 71.429%, and it will achieve the effect of reducing the global illegal wildlife trade by nearly 5% per year after the implementation of the strategy. The data show that the strategy proposed in this paper can help the UNEP and other environmental regulators to combat illegal wildlife trade and provide evidence for the promotion of the strategy in the future.

Downloads

Download data is not yet available.

References

Scheffers B R, Oliveira B F, Lamb I, et al. Global wildlife trade across the tree of life[J]. Science, 2019, 366(6461): 71-76.

Herweijer C, Evison W, Mariam S, et al. Nature risk rising: Why the crisis engulfing nature matters for business and the economy[C]//World Economic Forum and PwC. http://www3. weforum. org/docs/WEF_New_ Nature_Economy_Report_2020. pdf. 2020.

Zhang Xiaoxue, Zhang Zuting, Liu Li, et al. Research and implementation of accessory adaptation model based on decision tree and logistic regression algorithm [J]. Chinese Journal of Rehabilitation Medicine, 2023,38 (8): 1108-1113.

Du Maokang, Peng Junjie, Hu Yongjin, and so on. A logistic regression matrix factorization recommendation algorithm that satisfies differential privacy [J]. Journal of Beijing University of Posts and Telecommunications, 2023,46 (3): 115-120.

Wang Chundong, Guo Ruyue. The Internet of Vehicles trust Management scheme based on logical regression and blockchain [J]. Computer Engineering and Application, 2024,60(1):281-288.

Wang Li, Cao Shiyi, Song Kaifang, et al. Application of ARIMA model in prediction of hand, foot and mouth disease epidemic in Jingzhou City[J].Chinese Journal of Social Medicine, 2023, 40(5):623-626

Liu Mingji, Tian Yanan, Zhang Liang, et alResearch on civil aviation turnover prediction based on Prophet-ARIMA model[J].Computer Technology and Development, 2022(002):032

Su Yanping, Sun Xiaowei, Gao Hanqing, et al.Evaluation of the effect of exponential smoothing model and ARIMA model in predicting the epidemic trend of pulmonary tuberculosis in Tongzhou District, Beijing[J].Medical Animal Control, 2023, 39(1):8-12

Jiang Xudong, Dong BaoliProduct demand forecasting method based on ARIMA-LSTM[J].Modelling and Simulation, 2023, 12(4):3498-3505.

Zhou Binbin, Huang Jiaxin, Geng Ranran, et alShort-term prediction of China's power generation based on gray-ARIMA coupling model[J].Science & Technology Industry, 2022, 22(12):382-387

Downloads

Published

22-07-2024

How to Cite

Guo, L., Mao, R., & Li, M. (2024). Research On the Strategy of Combating Illegal Wildlife Trade Based on Logistic Regression-ARIMA Model. Highlights in Business, Economics and Management, 38, 1-10. https://doi.org/10.54097/x7mjf459