Small modular reactors (SMRs) promise reduced upfront costs, faster construction, and enhanced safety compared to traditional reactors. However, widespread adoption is hindered by challenges such as high capital costs, regulatory delays, supply chain inefficiencies, cybersecurity risks, nuclear waste management, and public skepticism. Despite qualitative studies highlighting these barriers, quantitative analyses remain scarce, necessitating systematic frameworks to model interdependencies and guide solutions. The goal of this study is to scrutinize SMR adoption challenges using a novel multi-criteria decision-making (MCDM) approach. Drawing on a literature review from Web of Science, Scopus, and various reports from international institutions, 13 key challenges were identified. A panel of 58 experts—academics, government officials, and cybersecurity specialists—provided inputs via pairwise comparisons. The methodology used in this study integrates Fermatean Fuzzy Interpretive Structural Modeling (FFISM) with the cross-impact matrix multiplication applied to classification (MICMAC) analysis. Fermatean fuzzy sets extend traditional ISM by accommodating higher uncertainty in expert judgments through expanded membership/non-membership degrees. Validation involved 10,000 simulations comparing FFISM to conventional fuzzy ISM. Results reveal a six-level hierarchy: licensing/regulatory constraints and lack of proven technology/FOAK units as top challenges, influencing linkage challenges such as supply chain effectiveness, cybersecurity risks, and waste management. Dependent challenges include perceived investment risk, cost estimation, and public opinion. Policy recommendations include risk-informed licensing to cut timelines, blockchain for traceability addressing fuel availability, and public-private partnerships with green bonds to mitigate risks. This research provides actionable strategies for policymakers and stakeholders to accelerate SMR deployment, strengthening nuclear energy's role in global decarbonization.
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