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

Zeynep Ozsut Bogar, Gazi Murat Duman, Askiner Gungor and Elif Kongar ()
Additional contact information
Zeynep Ozsut Bogar: Department of Industrial Engineering, Faculty of Engineering, Pamukkale University, Kınıklı Campus, Denizli 20160, Türkiye
Gazi Murat Duman: Department of Economics & Business Analytics, University of New Haven, Orange Campus, 584 Derby-Milford Rd, Orange, CT 06477, USA
Askiner Gungor: Department of Industrial Engineering, Faculty of Engineering, Pamukkale University, Kınıklı Campus, Denizli 20160, Türkiye
Elif Kongar: Department of Economics & Business Analytics, University of New Haven, Orange Campus, 584 Derby-Milford Rd, Orange, CT 06477, USA

Sustainability, 2025, vol. 17, issue 22, 1-23

Abstract: 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.

Keywords: circular economy; discrete grey model; electronic waste; forecasting methods; Jaya optimizer; trigonometric grey model (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/17/22/10073/pdf (application/pdf)
https://www.mdpi.com/2071-1050/17/22/10073/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:17:y:2025:i:22:p:10073-:d:1792027

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().