A Secondary-Data-Driven Decision Support Framework for Strategic Energy Investment Prioritization: An Explainable Multi-Criteria Application Across Countries

This study develops a secondary-data-driven decision support framework for prioritizing strategic energy investment readiness across countries. The empirical application covers 36 countries and 18 criteria grouped under macroeconomic feasibility, institutional capacity, energy security, sustainability and decarbonization, market and demand conditions, and technical resource potential. The study responds to a methodological gap in energy investment prioritization by moving from expert-only linguistic scoring toward a reproducible, explainable, stakeholder-sensitive, and validation-oriented multi-criteria decision support structure. In the implemented empirical model, public secondary data are transformed into a normalized country-level decision matrix, baseline readiness scores are calculated using equal weights, and entropy, Criteria Importance Through Intercriteria Correlation (CRITIC), and hybrid entropy-CRITIC configurations are used as objective weighting benchmarks. Large language model (LLM)-assisted extraction is used as a documented criterion-discovery and screening aid, not as an autonomous scoring, weighting, or ranking mechanism. The fuzzy component is specified as an uncertainty-sensitive extension and illustrated through panel-based fuzzy interval construction for selected time-series indicators, while the main baseline ranking is based on the latest available normalized data matrix. The results show meaningful cross-country variation, with top-10 readiness scores ranging from 0.675 to 0.537 and the lowest five scores ranging from 0.364 to 0.338. Persona-based results indicate that country priorities differ across public planners, private investors, grid operators, sustainability policymakers, and infrastructure funds, while robustness checks show broad ranking stability, with Spearman correlations between 0.892 and 0.986 and a median simulated-agent correlation of 0.932. Fairness and partial-correlation diagnostics suggest that the model captures multidimensional readiness rather than simply reproducing wealth or existing renewable capacity. Post-hoc validation against observable investment- and transition-related benchmarks further supports the convergent validity of the readiness index. The framework should therefore be interpreted as an early-stage country-level screening tool for strategic energy investment prioritization, not as a final project-level investment appraisal model.