Applying Entropy Weighting and 2-Tuple Linguistic T-Spherical Fuzzy MCDM: A Case Study of Developing a Strategic Sustainability Plan for Istanbul Airport

Abstract

This study presents a novel sustainability plan tailored for Istanbul Airport, leveraging advanced decision-making methodologies to address the urgent need for sustainable practices in aviation. By integrating the entropy weighting and 2-tuple linguistic T-spherical fuzzy multi-criteria decision-making (MCDM) models, the study offers a comprehensive approach to evaluating and prioritizing sustainability criteria based on expert input from 12 professionals. The novelty of this research lies in its unique combination of advanced MCDM techniques with cutting-edge technologies, including IoT-enabled monitoring systems, digital twin models, blockchain-based sustainability reporting, and carbon capture initiatives, tailored specifically for large-scale airport operations. The study develops a phased implementation roadmap comprising three stages: (1) a short-term focus on energy efficiency and renewable energy infrastructure, achieving significant cost reductions within a 3-7.5-year payback period; (2) medium-term initiatives integrating IoT and digital twins to enhance operational efficiency; and (3) long-term measures incorporating carbon capture and blockchain for transparency and compliance. Key implementation steps include upgrading energy systems, deploying IoT sensors, creating digital replicas of airport infrastructure, and establishing regulatory and stakeholder collaboration frameworks. This research contributes a replicable framework for airports worldwide, bridging theoretical models with actionable solutions.