An Integrated Hybrid MCDM Framework for Renewable Energy Prioritization in Sustainable Development

Abstract

Switching to renewable energy is essential for reducing carbon emissions, enhancing energy security, and promoting sustainability. However, the decisionmaking process is complex, influenced by conflicting factors, uncertainties in expert judgments, and data variability. Existing methodologies often struggle to manage these uncertainties, leading to inconsistent outcomes. This study presents a decision-support framework that employs Linear Diophantine Fuzzy Sets (LiDFS) to more accurately model uncertainty and address these challenges. The Ranking Comparison (RANCOM) method is used to calculate subjective weights, offering a structured, expert-driven approach. In contrast, the Method based on the Removal Effects of Criteria (MEREC) determines objective weights, reducing bias and enhancing reliability. The Multi-Attribute Utility Theory (MAUT) method is then applied to systematically rank available options based on their overall utility. Sensitivity analysis evaluates the impact of weight variations on decision outcomes, while comparative analysis confirms the robustness of the proposed approach. This integrated framework provides a transparent and scientifically grounded methodology to support decision-makers in selecting optimal solutions within the renewable energy sector, effectively addressing uncertainty and conflicting priorities.