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Each year, a significant number of rare, protected, or endangered wildlife are illegally captured and trafficked, forming a vast illicit trade network that poses a serious threat to wildlife survival, potentially leading to species extinction and ecological imbalance. In response to this issue, we have launched a five-year project based on data analysis[1] aimed at reducing the scale and impact of illegal wildlife trade. The project will develop strategies through identifying target stakeholders, forecasting potential impacts, and evaluating the likelihood of project success.
We collected and analyzed illegal wildlife trade data from 2003 to 2021. The results indicate that China is one of the world's largest importers of illegal wildlife trade, thus selecting the Chinese government as the optimal target client. Concurrently, literature and data analysis underscore the impacts of illegal trade on natural environments and biodiversity, emphasizing the necessity for enhancing legal frameworks, strengthening enforcement, raising public awareness, and fostering international cooperation.
We employed a system dynamics model[2] to simulate the impact of project implementation on illegal wildlife trade, and established a logistic regression model[3] to analyze the likelihood of project success. Model predictions demonstrate a relatively high probability of success for the project. Additionally, sensitivity analysis was conducted, considering the influence of changes in factors such as public awareness and enforcement intensity on success probability, along with the potential impacts of external factors such as international cooperation and economic conditions.
In conclusion, this paper aims to assist in understanding and evaluating strategies to reduce illegal wildlife trade, providing a comprehensive framework based on empirical analysis. We selected the Chinese government as the optimal target client, believing in its significant role in global wildlife conservation and effective reduction of illegal wildlife trade through this project.
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