ArticleBMC bioinformatics2026
A novel IVN-entropy based distance-driven MARCOS framework for evaluating and ranking global green hydrogen-producing countries.
Article in BMC bioinformatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundThe global transition toward carbon neutrality has intensified the need for reliable and data-driven methods to evaluate green hydrogen production across countries. Existing approaches to multi-criteria decision-making (MCDM) under uncertainty often struggle with the simultaneous treatment of data ambiguity, indeterminacy, and conflicting information. To address these limitations, this study develops a hybrid interval-valued neutrosophic (IVN) framework that combines entropy-based objective weighting with a distance-driven MARCOS (Measurement of Alternatives and Ranking according to Compromise Solution) model.
resultsThe proposed IVN-entropy-NC-SIVNDM MARCOS framework introduces a new normalized centroid-spread IVN distance measure (NC-SIVNDM) that captures the uncertainty and hesitancy inherent in expert or data-derived evaluations. Eight quantitative indicators such as covering production capacity, electrolyser deployment, investment maturity, cost efficiency, and energy intensity were used to assess ten leading green hydrogen-producing countries based on data from the international energy agency (IEA-global hydrogen review 2024). Objective entropy-based weights revealed that current low-emissions hydrogen production and installed electrolyser capacity were the most influential criteria. The proposed model produced consistent and discriminative country rankings, with sensitivity and comparative analyses confirming its robustness and reliability against perturbations in criteria weights.
conclusionsThe IVN-Entropy-NC-SIVNDM MARCOS model provides a transparent, replicable, and computationally efficient decision-support tool for benchmarking national performance in green hydrogen development. By integrating objective entropy weighting and a novel distance-based neutrosophic measure, the framework enhances ranking stability and interpretability under uncertainty. This methodology offers a valuable contribution to sustainable energy planning and may assist policymakers, investors, and researchers in guiding the global shift toward a green hydrogen economy and net-zero emission goals.
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