2025/09/24 by Muhammad Sukri Bin Ramli, Ramli, Muhammad Sukri Bin
Business, Management and Accounting · Environmental Science · #FOS: Economics and business #General Economics (econ.GN) #Global Trade and Competitiveness #Global trade, sustainability, and social impact #Recycling and Waste Management Techniques
paper · pdf · doi:10.48550/arxiv.2509.21395
openalex publication_date 2025/09/24 · openalex created_date 2025/10/19 · openalex updated_date 2026/07/28
The global trade in electronic and electrical goods is complicated by the challenge of identifying e-waste, which is often misclassified to evade regulations. Traditional analysis methods struggle to discern the underlying patterns of this illicit trade within vast datasets. This research proposes and validates a robust, data-driven framework to segment products and identify goods exhibiting an anomalous "waste signature" a trade pattern defined by a clear 'inverse price-volume'. The core of the framework is an Outlier-Aware Segmentation method, an iterative K-Means approach that first isolates extreme outliers to prevent data skewing and then re-clusters the remaining products to reveal subtle market segments. To quantify risk, a "Waste Score" is developed using a Logistic Regression model that identifies products whose trade signatures are statistically similar to scrap. The findings reveal a consistent four-tier market hierarchy in both Malaysian and global datasets. A key pattern emerged from a comparative analysis: Malaysia's market structure is defined by high-volume bulk commodities, whereas the global market is shaped by high-value capital goods, indicating a unique national specialization. The framework successfully flags finished goods, such as electric generators (HS 8502), that are traded like scrap, providing a targeted list for regulatory scrutiny.