AI Governance, Urban Sustainability Transitions, and the Institutional Determinants of Smart City Transformation: A Comparative Cross-City Framework

Abstract

This study advances a governance-centred framework for understanding how artificial intelligence contributes to urban sustainability transitions. Drawing on systematic thematic analysis of 80 primary policy documents across five global cities—Singapore, Amsterdam, Barcelona, Seoul, and Toronto—together with a broader evidence base of 170 sources combining this primary corpus with the academic and policy literature cited throughout, the study argues that AI contributes to sustainable outcomes primarily by transforming governance structures rather than through technical optimisation alone. The study identifies four institutional conditions that determine whether the sustainability dividend of AI deployment is realised: regulatory coherence, multi-stakeholder participation, transparency and auditability of algorithmic decision-making, and adaptive governance capacity. A comparative framework is developed to assess AI governance maturity across urban sustainability domains, including energy management, climate adaptation, mobility, and green public procurement. The study further examines how AI governance systems address the accountability requirements arising from algorithmic opacity, using the South Tyrol and Estonia cases to identify the conditions under which distributed ledger technologies can strengthen audit trail mechanisms in urban sustainability governance. Findings suggest that governance design—not AI capability—is the primary determinant of sustainable transformation outcomes. The study contributes to the emerging interdisciplinary literature at the intersection of AI governance, smart city development, and organisational sustainability, offering a practitioner-relevant framework for policymakers, city managers, and business leaders engaged in AI-enabled green transformation.