Driven Decision Models in Sustainable Urban Ecosystems: A Multidisciplinary Perspective
DOI:
https://doi.org/10.66280/ijisi.v1i1.16Abstract
Artificial intelligence (AI) is increasingly positioned as a foundational capability for urban sustainability transitions, yet much of the discourse remains fragmented across technical optimization, sectoral “smart city” deployments, and policy narratives that understate sociotechnical risk. This paper develops a multidisciplinary, system-level account of AI-driven decision models in sustainable urban ecosystems, treating cities as coupled infrastructures, institutions, and communities operating under constraints of equity, robustness, legitimacy, and long-horizon ecological stewardship. We synthesize decision-model paradigms spanning predictive analytics, causal inference, control and planning, and learning-based policy optimization, and we connect them to the realities of urban data supply chains, governance regimes, and infrastructure interdependencies. Central contributions include an architectural framing that distinguishes advisory, automated, and autonomic decision loops; an analysis of structural trade-offs among efficiency, resilience, privacy, and distributive justice; and a governance-oriented view of model accountability that emphasizes auditability, contestability, and cross-jurisdictional interoperability. Through sectoral illustrations in mobility, buildings and energy, water, waste, air quality, and public health, we show how model performance is often dominated by data provenance, institutional incentives, and operational friction rather than algorithmic novelty. We conclude with forward-looking directions for trustworthy urban AI, including digital twins with causal grounding, rights-preserving data infrastructures, participatory evaluation, and procurement reforms that embed fairness and robustness as first-class requirements.
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