Subjective-objective median-based importance technique (SOMIT) to aid multi-criteria renewable energy evaluation

Accelerating the renewable energy transition requires informed decision-making that accounts for the diverse financial, technical, environmental, and social trade-offs across different renewable energy technologies. A critical step in this multi-criteria decision-making (MCDM) process is the determination of appropriate criteria weights. However, deriving these weights often solely involves either subjective assessment from decision-makers or objective weighting methods, each of which has limitations in terms of cognitive burden, potential bias, and insufficient contextual relevance. This study proposes the subjective-objective median-based importance technique (SOMIT), a novel hybrid approach for determining criteria weights in MCDM. By tailoring SOMIT to renewable energy evaluation, the method directly supports applied energy system planning, policy analysis, and technology prioritization under carbon neutrality goals. The practical utility of SOMIT is demonstrated through two MCDM case studies on renewable energy decision-making in India and Saudi Arabia. Using the derived weights from SOMIT, the technique for order preference by similarity to ideal solution (TOPSIS) method ranks the renewable energy alternatives, with solar power achieving the highest performance scores in both cases (e.g., 0.5725 in the India case). The main contributions of this work are five-fold: 1) the proposed SOMIT reduces the number of required subjective comparisons from the conventional quadratic order to a linear order; 2) SOMIT is more robust to outliers in the alternatives-criteria matrix (ACM); 3) SOMIT balances subjective expert knowledge with objective data-driven insights, thereby mitigating bias; 4) SOMIT is inherently modular, allowing both its individual parts and the complete approach to be seamlessly coupled with a wide range of MCDM methods commonly applied in energy systems and policy analysis; 5) a dedicated Python library, pysomit, is developed for SOMIT, providing an accessible and efficient tool to implement SOMIT in practical renewable energy evaluation and decision-support applications. • Proposes SOMIT, a median-based hybrid weighting method to assist MCDM. • SOMIT reduces the number of subjective pairwise comparisons to a linear scale. • SOMIT integrates subjective assessments with data-driven insights, mitigating bias. • SOMIT is applied to renewable energy evaluations in both India and Saudi Arabia. • Open-source Python libaray, pysomit, is developed for ease of use.