Forecasting Electronic Waste Using a Jaya-Optimized Discrete Trigonometric Grey Model

The growing use of electrical and electronic appliances, coupled with shorter product lifespans, has accelerated the rise in waste electrical and electronic equipment (WEEE). Accurate forecasting is essential for addressing environmental challenges, conserving resources, and advancing the circular economy (CE). This research employs a Trigonometry-Based Discrete Grey Model (TBDGM(1,1)) that integrates the Jaya algorithm and Least Squares Estimation (LSE) for parameter estimation. By leveraging Jaya’s parameter-free robustness and LSE’s computational efficiency, the model enhances prediction accuracy for small-sample and nonlinear datasets. WEEE data from Washington State (WA) in the USA and Türkiye are utilized to validate the model, demonstrating cross-context adaptability. To evaluate performance, the model is benchmarked against five state-of-the-art discrete grey models. For the WA dataset, additional benchmarking against methods used in prior e-waste forecasting literature enables a dual-layer comparative analysis, which strengthens the validity and practical relevance of the approach. Across evaluations and multiple performance metrics, TBDGM(1,1) attains satisfactory and competitive prediction performance on the WA and Türkiye datasets relative to comparator models. Using TBDGM(1,1), Türkiye’s e-waste is forecast for 2021–2030, with the 2030 amount projected at approximately 489 kilotones. The findings provide valuable insights for policymakers and researchers, offering a standardized and reliable forecasting tool that supports CE-driven strategies in e-waste management.