Abstract
In the face of rapid urbanization and the challenges posed by climate change, achieving sustainability and resilience in urban environments has become imperative. This paper explores a novel approach integrating Artificial Intelligence, Multi-Criteria Decision Making (MCDM), and q-rung orthopair fuzzy ExpoLogarithmic aggregation operators to address the complex dynamics of sustainable urban development. The paper introduces novel ExpoLogarithmic operations rooted in ExpoLogarithmic t-norms within the framework of q-rung orthopair fuzzy sets. By harnessing these operations, the paper proposes two robust aggregation operators under q-rung orthopair fuzzy sets: the q-rung orthopair fuzzy ExpoLogarithmic weighted average and the q-rung orthopair fuzzy ExpoLogarithmic weighted geometric. These aggregation operators demonstrate fundamental properties, including idempotency, monotonicity, and boundedness. To assess the effectiveness of these methods, a MCDM methodology utilizing the recommended aggregation operators is employed. This study integrates a practical demonstration in real-time, with a specific emphasis on enacting appropriate policies for sustainable urban innovation and resilience. Comprehensive analyses, comprising sensitivity, comparative, and performance evaluations of the approaches, are carried out. Additionally, a reflective discourse on the advantages and disadvantages of the proposed aggregation operators is provided alongside the analysis, underscoring the importance of this approach in bolstering city resilience and mitigating environmental impact.