Optimizing wind plant locations in Yemen: A geospatial model using full consistency method (FUCOM)

Yemen faces a severe energy crisis, necessitating a strategic shift toward sustainable wind power. Identifying suitable locations is a complex multi-criteria spatial problem, particularly in a fragile state where diverse environmental, social, and technical constraints intersect. This study develops a robust geospatial model to identify optimal wind farm sites in Yemen by integrating the Full Consistency Method (FUCOM) with Geographic Information Systems (GIS). The methodology defined seven key criteria, with weights determined via FUCOM based on a structured expert panel consensus achieved through a formal Delphi technique to minimize subjectivity and enhance decision-making accuracy. A comparative analysis against the Analytic Hierarchy Process (AHP) and Best-Worst Method (BWM) validated the model’s robustness, with FUCOM demonstrating superior consistency (with a consistency index X = 0.0000) and a high correlation with BWM weighting (Pearson’s r = 0.96). The resulting suitability map reveals that while optimal sites are scarce—with only 0.27 % of Yemen’s land (approximately 1425 km²) classified as ‘very highly suitable’—a significant 11.91 % (approximately 62,800 km²) is ‘highly suitable.’ Collectively, these high-potential zones represent 12.18 % of the nation’s territory, concentrated primarily along the eastern region and the strategic southern and western coastal regions. This area represents a substantial foundation for scalable wind energy development. These findings provide a critical, evidence-based foundation for policymakers, offering a transparent and reliable framework to prioritize wind energy investment, guide infrastructure planning, and enhance Yemen’s long-term energy security. • Integrated GIS with FUCOM for wind site selection in Yemen. • FUCOM showed superior consistency compared to AHP and BWM. • Wind speed was identified as the most critical siting factor. • Sensitivity analysis confirmed model stability and robustness. • The framework assists energy planning in developing nations.