Climate Literacy Education in Intelligent Infrastructure: Data-Driven Learning Interventions and Community Engagement

The increasing frequency and intensity of climate-related hazards necessitate proactive urban resilience strategies informed by robust, data-driven models. This study examines the influence of data-driven approaches—including machine learning, geospatial analytics, and integrated climate risk models—on the design and implementation of climate policies in urban contexts. It highlights how these models enhance the capacity of policymakers to assess vulnerabilities, predict hazard impacts, and prioritise adaptive interventions for critical infrastructure, ecosystems, and vulnerable populations. The paper synthesises recent case studies from global cities, revealing that data-driven models facilitate evidence-based decision-making, optimise resource allocation, and foster stakeholder collaboration in urban climate governance. However, challenges such as data quality gaps, algorithmic biases, and socio-political constraints remain significant barriers to equitable and effective policy design. The findings underscore the need for interdisciplinary frameworks that integrate technical modelling, participatory planning, and ethical data governance to strengthen urban resilience in the face of accelerating climate risks. This research contributes to advancing sustainable, inclusive, and anticipatory climate policies tailored to the complex dynamics of urban systems.