AI‑ENHANCED GOVERNANCE FOR CLIMATE ADAPTATION AND RESILIENCE

The communities engaged in the early stages of climate change demand more precise, inclusive, and swift decisionmaking to handle floods, droughts, heatwave, and other disasters. Conventional governance procedures (often
fragmented, paper-based, reactive) have problems incorporating real-time information and scientific predictions with
local knowledge into logical adaptation approaches. The new dimension of decision support is that Artificial
Intelligence (AI) caters to the heterogeneous climate, socio-economic, and infrastructural data to convert them into
actionable insights to the local governments and community institutions. This paper will look at the role of AIenhanced governance systems in enhancing climate adaptation and resilience on a community scale, through better
risk assessment, early-warning system, participatory planning, and resource allocation.
This paper theorizes an adaptive framework of AI-managed climate adaptation system featuring machine learning
models, geospatial analytics, and decision analysis based on multi-criteria to be embedded in local planning processes.
The framework uses the simulated allocation of limited adaptation budgets across neighborhoods, prioritization of
climate risks, and transparent trade-off negotiation among stakeholders through back-dated, scenario-based data to
simulate how AI tools can be applied. We use examples of AI-enhanced flood-risk mapping, heat-vulnerability
scoring, and infrastructure-scheduling by reinforcement learning, which are integrated into communal governance
practices, including town-hall discussions, resilience committees, and participatory budgeting. The article also
addresses the issue of algorithmic transparency, equity issues, and the issue of institutional capacity in that the AI
systems can aid in making decisions, not to substitute the democratic process.