Research On the Strategy of Combating Illegal Wildlife Trade Based on Logistic Regression-ARIMA Model
DOI:
https://doi.org/10.54097/x7mjf459Keywords:
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.
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