Research of Conservation Factors of Wildlife Trade Based on Regression Model
DOI:
https://doi.org/10.54097/w61rdx33Keywords:
Principal Component Analysis, Ridge Regression, Multiple Linear Regression, ARIMA Model, Particle Swarm Optimization.Abstract
The problem of illegal wildlife trade has become an international problem of common concern around the world, and the existing research lacks inquiries on the underlying factors of this problem. In this paper, the ridge regression model, the ARIMA model and the multiple linear regression prediction model are constructed, and the latter two are weighted and optimized. The study finds that the increase of resource input and the increase of national interest play a positive role in combating illegal trade, while the volatility of power factors also has a significant impact on trading activities. The results of the optimized forecasts show that projects to combat wildlife trade have not only short-term effects, but also long-term positive impacts. After sensitivity analysis, the models established in this paper has a high robustness and can provide a reference for the world's anti-wildlife trade activities.
Downloads
References
[1] Jones C, Newsome D, et al. Understanding the conflicting values associated with motorized recreation in protected areas [J]. Ambio, 2016, (45): 323-330. DOI: https://doi.org/10.1007/s13280-015-0721-1
[2] Rytwinski T, Muir J M, Miller B R J, et al. What is the evidence that counter‐wildlife crime interventions are effective for conserving African, Asian and Latin American wildlife directly threatened by exploitation? A systematic map [J]. Ecological Solutions and Evidence, 2024, 5 (2). DOI: https://doi.org/10.1002/2688-8319.12104
[3] Amankwa J, Damnyag L, Banunle A. Determinants of forest law compliances among inhabitants of local communities surrounding tropical wildlife and forest reserve in Ghana [J]. Journal of Cleaner Production, 2024, (443): 964-974. DOI: https://doi.org/10.1016/j.jclepro.2024.140964
[4] Yue K K, Ruifang C, Chieh C W, et al. Towards effective wildlife protection law for marine species in China: A stakeholders’ perspective [J]. Frontiers in Marine Science, 2023, (10). DOI: https://doi.org/10.3389/fmars.2023.1055634
[5] Wang Yan. Research on Nature, Humans, and Power within the National Park System [D]. Central University for Nationalities, 2023, (03).
[6] MacGillivray Alexander O, et al. A functional regression analysis of vessel source level measurements from the Enhancing Cetacean Habitat and Observation (ECHO) database [J]. The Journal of the Acoustical Society of America, 2022, 152 (3): 1547-1547. DOI: https://doi.org/10.1121/10.0013747
[7] Yang Wei, Li Qiqiang. Overview of Particle Swarm Optimization Algorithm [J]. Chinese Engineering Science, 2004, (05): 87-94.
[8] Wu Zhaoliang, Jin Min. Research on residents' participation in community environmental governance: Based on the analysis of "situation-process-impact" [J]. Transactions of Beijing institute of Technology (Social Sciences), 2023, 25 (01): 55-66.
[9] Li Lingling, Xin Hao. Application of Time Series Model ARIMA in Data Analysis [J]. Fujian Computer, 2024, 40 (04): 25-29.
[10] Li Zhongsheng, Qin Hongxing. Optimized design of reliability growth planning based on sensitivity analysis [J]. Mechanical Engineering and Automation, 2021, (03): 46-49.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Highlights in Science, Engineering and Technology
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
