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Evaluation of Sustainable Waste-to-Material Pathways Using Fuzzy Cognitive Mapping

Authors

  • Anber Abraheem Shlash Mohammad ORCID Digital Marketing Department, Faculty of Administrative and Financial Sciences, University of Petra, Amman 11196, Jordan
  • Yogeesh Nijalingappa Email ORCID School of Engineering, Architecture and Interior Design, Amity University Dubai, Dubai P.O. Box 345019, United Arab Emirates; Department of Mathematics, Government First Grade College, Tumakuru 572101, India
  • Mohammed Almakki School of Engineering, Architecture and Interior Design, Amity University Dubai, Dubai P.O. Box 345019, United Arab Emirates
  • Asokan Vasudevan ORCID Faculty of Business and Communications, INTI International University, Nilai 71800, Malaysia; Business Administration and Management, Wekerle Business School, 1083 Budapest, Hungary
  • Natarajan Raja ORCID Department of Visual Communication, Sathyabama Institute of Science and Technology, Chennai 600119, India
  • Suleiman Ibrahim Mohammad ORCID Faculty of Business and Communications, INTI International University, Nilai 71800, Malaysia; Department of Business Administration, Business School, Al al-Bayt University, Mafraq 25113, Jordan
  • Tejas Bhushan N. B. ORCID Department of Chemistry, Regional Institute of Education (NCERT), Mysuru 570006, India

DOI:

https://doi.org/10.30564/jbms.v8i4.116362
Received: 11 November 2025 | Revised: 17 December 2025 | Accepted: 8 May 2026 | Published Online: 12 October 2026

Abstract

Waste-to-material (WtM) strategies are central to circular-economy planning, yet pathway selection is complicated by uncertain feedstock quality, policy variability, market fluctuations, and coupled operational trade-offs. Existing evaluations often treat key drivers independently, limiting their ability to represent feedback effects that shape long-run pathway performance. This study develops a fuzzy cognitive map (FCM) decision framework to compare four representative WtM pathways (mechanical recycling, chemical recycling, anaerobic digestion, and mineralization) using a system of interacting indicators: feedstock quality, collection rate, preprocessing efficiency, yield, energy intensity, market value, greenhouse-gas (GHG) impact, and circular uptake. For reproducibility, the resulting FCM adjacency structure is explicitly specified and all scenarios are parameterized following a defined set of node clamps to reflect policy push (feedstock expansion) and edge edits reflecting feedstock shock conditions. Pathway-specific environmental, economic and circularity indicators, as well as a comprehensive sustainability score with uncertainty bounds are reported for converged (steady-state) outputs. Results also indicate that pathway rankings and trade-off positions vary across scenarios, with policy developments leading to higher values of circular uptake and market value but lower values for preprocessing/yield (feedstock shocks) and GHG outcomes. The framework that we have proposed provides an interpretable and reproducible tool for WtM strategy selection under uncertainty, as well as a basis for policy-relevant sensitivity and robustness analysis.

Keywords:

Waste-to-Material Pathways,Fuzzy Cognitive Maps,Triangular Fuzzy Numbers,Monte Carlo Robustness,Scenario-Based Sustainability Scoring,Leverage Analysis,Circular Economy Decision Support

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2026 Vol.8 No.4 (In Progress)

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Copyright © 2026 Anber Abraheem Shlash Mohammad, Yogeesh Nijalingappa, Mohammed Almakki, Asokan Vasudevan, Natarajan Raja, Suleiman Ibrahim Mohammad, Tejas Bhushan N. B.

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