Investment strategies for renewable energy technologies and harvesting systems in airport operations using spherical fuzzy MCDM models.

This study presents a novel evaluation framework for prioritizing investment strategies in sustainable airport energy systems by integrating advanced fuzzy decision-making techniques with artificial intelligence-based expert weighting. Specifically, it employs a hybrid Spherical Fuzzy CRITIC-RATGOS model to rank renewable energy alternatives based on economic feasibility, environmental impact, technological efficiency, scalability, and operational reliability. To address limitations associated with equal expert weighting, a Principal Component Analysis-driven dimension reduction technique is applied to calibrate expert influence based on professional background and consistency of evaluation. The model is applied to a real-world case study at Istanbul Airport, demonstrating that AI-optimized energy management, solar microgrids, and waste-to-energy conversion are the most promising investment alternatives. In contrast, although technologies such as piezoelectric harvesting show future potential, their current limitations reduce their immediate feasibility. Sensitivity analysis affirms the robustness and stability of the results across various weighting configurations. The proposed framework contributes to both theory and practice by offering a scalable, transparent, and replicable decision-support tool for airport authorities, policymakers, and energy planners aiming to align infrastructure development with global sustainability and decarbonization goals.