发现塑料贸易中价格越低体积越大这一反常模式，助力识别数据造假。
Pattern Recognition of Scrap Plastic Misclassification in Global Trade Data
- 基于逆价格-体积关系构建可解释机器学习模型
- 在联合国与企业级数据比对中达到0.9375准确率
- 适合海关监管与国际环保政策制定者使用
我们提出一种可解释的机器学习框架，用于识别传统方法难以发现的贸易数据异常。该系统分析贸易数据，发现一种新型逆价格-体积特征：报告体积随平均单价下降而上升。模型在大规模联合国数据与企业级数据比对中验证，准确率达0.9375，确认风险特征具有一致性。该可扩展工具为海关机构提供透明、数据驱动的方法，支持从常规检查转向基于优先级的查验策略，将复杂数据转化为可操作的智能信息，助力全球环境政策实施。
原文摘要 · Abstract (English)
We propose an interpretable machine learning framework to help identify trade data discrepancies that are challenging to detect with traditional methods. Our system analyzes trade data to find a novel inverse price-volume signature, a pattern where reported volumes increase as average unit prices decrease. The model achieves 0.9375 accuracy and was validated by comparing large-scale UN data with detailed firm-level data, confirming that the risk signatures are consistent. This scalable tool provides customs authorities with a transparent, data-driven method to shift from conventional to priority-based inspection protocols, translating complex data into actionable intelligence to support international environmental policies.
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