Governing AI-Enabled Climate-Resilient Housing and Infrastructure Prioritization: A Caring Urban Governance Framework

Source abstract

Smart city governance increasingly relies on AI-enabled planning systems, digital twins, vulnerability scoring tools, and capital investment platforms to allocate climate-resilient housing and infrastructure investments. Yet existing smart-urbanism and adaptation frameworks do not adequately specify how such systems should encode well-being, equity, and climate uncertainty when translating urban data into ranked projects and funded portfolios. This paper develops the Caring Urban Governance Framework for AI-enabled urban prioritization through a structured scoping review and conceptual framework analysis integrating climate-risk decision-making under deep uncertainty, built-environment pathways affecting psychosocial well-being, and public-sector algorithmic accountability. The framework proposes a five-layer architecture linking urban form and infrastructure, climate exposure and environmental resources, psychosocial mediators of well-being, algorithmic design choices, and institutional governance, with explicit feedback loops. Its main outputs are an auditable decision architecture, eight mechanism-based propositions for empirical testing, an operational specification matrix for objective functions, equity constraints, robust logic, and documentation, and an analytical validation of construct clarity, coherence, literature congruence, and operationalizability. The analysis argues that aligning AI-enabled urban prioritization with SDG 11 requires treating well-being-supportive living conditions as a decision objective, constraining optimization with equity conditions, and institutionalizing auditability and contestability to reduce distributive and psychosocial harm in public investment planning.