As the global economy transitions toward decarbonization, the aluminium sector has become a focal point for strategic resource management. While policies such as the Carbon Border Adjustment Mechanism (CBAM) aim to reduce emissions, they have inadvertently widened the price arbitrage between primary metal, scrap, and semi-finished goods, creating new incentives for market optimization. This study presents a unified, unsupervised machine learning framework to detect and classify emerging trade anomalies within UN Comtrade data (2020 to 2024). Moving beyond traditional rule-based monitoring, we apply a four-layer analytical pipeline utilizing Forensic Statistics, Isolation Forests, Network Science, and Deep Autoencoders. Contrary to the hypothesis that Sustainability Arbitrage would be the primary driver, empirical results reveal a contradictory and more severe phenomenon of Hardware Masking. Illicit actors exploit bi-directional tariff incentives by misclassifying scrap as high-count heterogeneous goods to justify extreme unit-price outliers of >$160/kg, a 1,900% markup indicative of Trade-Based Money Laundering (TBML) rather than commercial arbitrage. Topologically, risk is not concentrated in major exporters but in high-centrality Shadow Hubs that function as pivotal nodes for illicit rerouting. These actors execute a strategy of Void-Shoring, systematically suppressing destination data to Unspecified Code to fracture mirror statistics and sever forensic trails. Validated by SHAP (Shapley Additive Explanations), the results confirm that price deviation is the dominant predictor of anomalies, necessitating a paradigm shift in customs enforcement from physical volume checks to dynamic, algorithmic valuation auditing.

翻译：随着全球经济向脱碳转型，铝业已成为战略资源管理的焦点。尽管碳边境调节机制（CBAM）等政策旨在减少排放，却无意中扩大了原铝、废铝和半成品之间的价格套利空间，为市场优化创造了新的激励。本研究提出了一种统一的无监督机器学习框架，用于检测和分类联合国商品贸易统计数据库（2020年至2024年）中新出现的贸易异常。超越传统的基于规则的监测方法，我们应用了一个四层分析流程，结合了法证统计学、孤立森林、网络科学和深度自编码器。与可持续性套利为主要驱动因素的假设相反，实证结果揭示了一种矛盾且更为严重的硬件掩蔽现象。非法行为者利用双向关税激励，将废铝错误归类为高计数异质商品，以合理化每公斤超过160美元的极端单价异常值，这一高达1900%的溢价表明其属于贸易洗钱（TBML）而非商业套利。从拓扑结构上看，风险并非集中在主要出口国，而是集中在作为非法改道关键节点的高中心性影子枢纽中。这些行为者执行一种“空壳离岸”策略，系统性地将目的地数据压制为“未指定代码”，以破坏镜像统计并切断法证追踪。通过SHAP（沙普利加性解释）验证，结果确认价格偏差是异常的主要预测因子，这要求海关执法从物理量检查向动态算法化估值审计的模式转变。