Advanced Computational <scp>MCDA</scp> for Renewable Energy Resilience and Sustainable Resource Management in Climate‐Sensitive Regions

ABSTRACT The global shift toward renewable energy plays a crucial role in combating climate change, bolstering energy security, and promoting sustainable development. Yet, the resilience of renewable energy systems is still a largely overlooked area, particularly in developing nations, where socioeconomic and infrastructural limitations intensify existing obstacles. This research introduces an innovative analytical framework that combines Hierarchical Bayesian Networks (HBN) with Stochastic Multi‐Criteria Decision Analysis (MCDA) to assess renewable energy policies amid uncertainty. Drawing on expert input, the study pinpoints and prioritises key factors—environmental, economic, social, technological, and regulatory—customised to the specific circumstances of developing countries. Primary results highlight the value of decentralised energy setups, cutting‐edge storage solutions, and hybrid approaches in improving energy resilience. Additionally, the probabilistic methodology accounts for fluctuations in policy results, delivering reliable guidance for decision‐making involving multiple stakeholders. By providing practical strategies that emphasise resilience, equity, and sustainability, the framework enriches discussions on renewable energy shifts and supports international development objectives. Ultimately, this work establishes a standard for designing robust policies that tackle urgent energy issues in regions with limited resources.