AI-Enabled Climate-Resilient Infrastructure Governance: Integrating Predictive Intelligence, Fiscal Capacity, and Multilevel Policy for Extreme Weather Adaptation

Abstract

Climate change has transformed extreme weather from an episodic risk into a structural condition shaping infrastructure planning, fiscal stability, and governance capacity across jurisdictions. Traditional infrastructure design paradigms, grounded in historical climate baselines and static engineering assumptions, have proven increasingly inadequate in the face of compound, cascading, and non-linear climate hazards. In response, a growing body of scholarship and policy practice has turned toward artificial intelligence–enabled approaches to climate-resilient design, forecasting, and adaptive governance. This article advances an original, integrative research contribution by critically examining how AI-driven predictive systems can be embedded within infrastructure governance frameworks to enhance resilience against extreme weather events while accounting for fiscal constraints, institutional fragmentation, and socio-political complexity. Anchored in contemporary climate resilience literature and explicitly engaging with AI-driven climate-resilient design scholarship, the study synthesizes insights from urban resilience theory, public finance, technological foresight, and environmental governance.

The article develops a comprehensive analytical framework that conceptualizes AI not merely as a technical optimization tool, but as a governance technology capable of reshaping decision-making logics, temporal horizons, and accountability structures in infrastructure systems. Drawing on climate risk assessment platforms, subnational fiscal stress research, sustainable finance instruments, and European and North American policy experiences, the study demonstrates how AI-enabled predictive modeling can support anticipatory adaptation, prioritize infrastructure investments, and recalibrate resilience metrics beyond post-disaster recovery toward long-term adaptive capacity. Particular attention is paid to the role of AI in translating probabilistic climate forecasts into actionable infrastructure design choices, as well as to the political economy implications of algorithmic risk classification for bond markets, municipal creditworthiness, and intergovernmental transfers.

Methodologically, the article employs an interpretive, theory-building approach grounded in systematic literature analysis and comparative policy interpretation. Rather than generating new empirical datasets, it synthesizes existing evidence to construct a multi-layered explanatory model of AI-mediated climate resilience governance. The findings reveal that while AI-driven systems offer significant potential to enhance infrastructural robustness and flexibility, their effectiveness is contingent upon institutional learning, data governance quality, and equitable access to technological capacity. The analysis further highlights risks associated with technocratic overreach, data asymmetries, and the depoliticization of climate adaptation decisions.

The discussion situates these findings within broader debates on climate resilience, digital governance, and sustainable development, identifying critical tensions between predictive certainty and democratic accountability, efficiency and equity, and innovation and path dependency. The article concludes by outlining future research directions focused on ethical AI governance, cross-scale resilience coordination, and the integration of AI-driven climate intelligence into long-term infrastructure finance and planning regimes. Through its expansive theoretical elaboration and interdisciplinary synthesis, the study contributes to advancing climate-resilient infrastructure scholarship in an era of accelerating environmental uncertainty (Bandela, 2025; Gilmore et al., 2022).