Adopting the circularity in airport supply chains: A rough multi-attribute decision-making framework

The accelerating depletion of natural resources, coupled with the escalating generation of waste, has necessitated a paradigm shift from linear to circular economic models. Within this context, the circular supply chain (CSC) has emerged as a pivotal strategy for enhancing environmental sustainability and bolstering economic resilience. Despite its recognized potential, the application of CSC principles within highly complex and rigorously regulated environments, such as airports, remains significantly underexplored. Airports function as intricate logistical ecosystems, generating diverse waste streams from operational activities, passenger traffic, and service providers. Yet, the scholarly investigation of circularity within this specific sector is notably limited. This study aims to address this research gap by systematically identifying and prioritizing the barriers to CSC implementation in airport operations, with a specific focus on the context of emerging economies, using Iran as a case study. To accommodate the inherent uncertainty and subjectivity of expert judgments, a hybrid Rough Multi-Criteria Decision-Making (Rough-MCDM) framework, integrating the Rough Best-Worst Method (RBWM) and the Rough MARCOS method, was employed. Through a comprehensive literature review, a systematic PRISMA-guided selection process, and focus group discussions, a total of 17 distinct challenges were identified and categorized into five key dimensions: Environmental, Strategic, Infrastructure, Partnership, and Economic. Furthermore, eight corresponding strategic solutions were formulated. The findings from the RBWM analysis reveal that Economic and Infrastructure barriers constitute the most formidable obstacles, achieving the highest priority weights of 0.351 and 0.234, respectively. The subsequent analysis suggests that the most effective mitigation strategies involve optimizing technological infrastructure and establishing specialized resource recovery units. A sensitivity analysis performed on the results demonstrates a correlation exceeding 90% across various prioritization scenarios, thereby confirming the high robustness and reliability of the findings. This research provides a robust, context-sensitive decision-making framework for policymakers and airport managers seeking to facilitate and manage circular transitions within aviation supply chains.