Author(s)

Hang Min ¹

Affiliation(s)

¹ School of International Business and Economics, Shanghai University of International Business and Economics, Shanghai, China

Corresponding Author

Hang Min

ABSTRACT

Illegal wildlife trade, a significant threat to global biodiversity, is estimated at $26.5 billion annually. This research focuses on the development and application of a comprehensive data analysis framework to combat this illicit trade. We employed a weighted scoring model to assess potential clients for collaboration, utilizing a multidimensional evaluation system informed by extensive literature review. The model allocated weights to key criteria such as resource capacity, political support, and management capabilities, leading to the selection of INTERPOL as the optimal partner. Subsequently, a multiple linear regression model was established to explore the correlation between illegal wildlife trade and other criminal activities, revealing a positive relationship with drug, arms, and human trafficking. To forecast the project's impact, an enhanced forecasting model was developed, incorporating intelligence collection and analysis to predict a decline in illegal wildlife trade cases over a five-year period. The sensitivity of the model to various factors was evaluated using the Monte Carlo simulation, which underscored the importance of operational efficiency and international cooperation in the project's success. This research demonstrates the potential of data-driven approaches to significantly influence and reduce illegal wildlife trade, offering valuable insights for future conservation efforts.

KEYWORDS

Illegal wildlife trade, data analysis, regression modeling, forecasting, Monte Carlo simulation

CITE THIS PAPER

Hang Min, Research on Combating Illegal Wildlife Trade Based on an Integrated Regression and Enhanced Forecasting Model. Information Systems and Economics (2024) Vol. 5: 141-147. DOI: http://dx.doi.org/10.23977/infse.2024.050319.

REFERENCES

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