AI-Enabled Climate-Resilient Infrastructure Governance: Predictive Design, Adaptive Capacity, and Institutional Transformation under Extreme Weather Risk

Abstract

Climate change has transformed the epistemological foundations of infrastructure planning by introducing deep uncertainty, non-linear risk, and compound extreme weather events that exceed the design assumptions of twentieth-century engineering paradigms. Floods, heatwaves, droughts, coastal surges, and cascading infrastructure failures now expose the limits of static safety margins, deterministic models, and siloed governance frameworks. In response, artificial intelligence has emerged not merely as a technical tool but as a socio-technical catalyst capable of reshaping how infrastructure systems are conceived, governed, financed, and adapted over time. This research article develops a comprehensive theoretical and empirical examination of AI-driven climate-resilient infrastructure design, positioning predictive analytics, machine learning, and adaptive decision systems as central to the future of climate adaptation governance. Building on recent advances in AI-enabled resilience planning, particularly the conceptual framework articulated by Bandela (2025), the article argues that AI fundamentally alters the temporal logic of infrastructure by shifting emphasis from ex post recovery toward anticipatory, learning-based adaptation.

The study synthesizes interdisciplinary scholarship spanning climate risk economics, urban planning, infrastructure finance, disaster risk reduction, and public administration. Rather than offering a narrow technical assessment, the article situates AI within broader debates on institutional capacity, public accountability, equity, and democratic legitimacy. The methodological approach is qualitative and interpretive, drawing on comparative policy analysis, institutional theory, and thematic synthesis of global adaptation practices documented across Europe, the Asia-Pacific region, and multilateral development institutions. Particular attention is given to how AI systems interact with national adaptation plans, fiscal frameworks, and local governance instruments, revealing both enabling conditions and structural constraints.

Findings indicate that AI-driven infrastructure resilience delivers value not only through improved hazard prediction but also by enhancing policy coherence, optimizing investment prioritization, and supporting adaptive governance under climate uncertainty. However, the research also identifies critical limitations, including data asymmetries, algorithmic opacity, fiscal path dependency, and uneven institutional readiness across jurisdictions. The discussion advances a conceptual model of AI-enabled adaptive infrastructure governance that integrates technical intelligence with social learning, legal frameworks, and participatory decision-making. By critically engaging with competing scholarly perspectives, the article contributes a nuanced understanding of AI as neither a technocratic panacea nor a neutral instrument, but as a transformative force whose impacts depend on governance design, ethical safeguards, and long-term institutional learning. The paper concludes by outlining future research pathways focused on longitudinal evaluation, equity impacts, and the co-evolution of AI systems and climate governance regimes.