Application of an integrated IDOCRIW-Copeland MCDM framework for ranking agro-based natural fibers

The circular economy dictates a paradigm shift toward leveraging natural fibers as sustainable alternatives to conventional synthetic reinforcements. However, their inherent heterogeneity necessitates a systematic evaluative approach to ensure reliability. This study presents a hybrid Multi-Criteria Decision-Making (MCDM) framework executed within the Python environment using the pymcdm library to evaluate five natural fibers—Coir, Flax, Hemp, Jute, and Sisal—against a multidimensional matrix of nine physical, chemical, and mechanical indicators. The methodology integrates the IDOCRIW (Integrated Determination of Objective Criteria Weights) objective weighting, and six ranking algorithms (TOPSIS, VIKOR, MARCOS, WASPAS, EDAS, and COCOSO), further synthesized via the Copeland method to establish a consensus-based ranking. Spearman’s rank correlation analysis indicated a perfect positive alignment (ρ = 1.00) among TOPSIS, VIKOR, and EDAS, while exposing a distinct negative correlation along the COCOSO axis (from − 0.10 to -0.33), strongly justifying the Copeland meta-ranking aggregator. Under the specific criteria constraints, results indicate that Alternative Coir emerges as the leading candidate, demonstrating a favorable multi-dimensional equilibrium. Alternative Jute and Alternative Hemp capture the 2nd and 3rd positions, respectively. Ultimately, the framework presents a potential template that could assist in sustainable material screening, thereby reducing heuristic estimations in circular composite manufacturing.