AI-Based Climate Risk Assessment: Predictive Approaches for Resilient Environmental and Economic Planning

James Landay

AI-Based Climate Risk Assessment: Predictive Approaches for Resilient Environmental and Economic Planning

Author(s)	James Landay
Country	United States
Abstract	Climate change has intensified environmental, social, and economic risks, creating an urgent need for reliable climate-risk assessment and forward-looking planning. Conventional climate-risk assessment approaches frequently depend on historical observations, scenario modelling, expert assessment, and statistical forecasting. While these methods remain valuable, the increasing availability of satellite observations, sensor networks, geospatial information, socioeconomic datasets, and high-frequency environmental data has created new opportunities for Artificial Intelligence (AI)-based predictive assessment. This study examines the application of AI and predictive analytics to climate-risk assessment and evaluates their potential contribution to resilient environmental and economic planning. A qualitative and conceptual methodology based on secondary literature is adopted to examine AI applications in extreme-weather forecasting, flood and drought prediction, wildfire risk assessment, agricultural vulnerability, infrastructure resilience, and economic impact estimation. The analysis proposes an integrated framework connecting climate data acquisition, AI-based prediction, vulnerability assessment, risk mapping, scenario analysis, and adaptive planning. The study finds that AI can improve the speed, granularity, and responsiveness of climate-risk assessment by identifying complex patterns across large and heterogeneous datasets. However, challenges related to data quality, model uncertainty, explainability, computational requirements, geographical bias, cybersecurity, and unequal technological capacity remain significant. The study argues that AI should complement established climate science rather than replace physical climate models or expert judgment. Effective climate-resilient planning requires hybrid approaches combining AI, climate science, socioeconomic analysis, local knowledge, and institutional governance. The study concludes that responsible AI-based climate-risk assessment can strengthen anticipatory decision-making and support more resilient environmental and economic systems.
Keywords	Artificial Intelligence, Climate Risk, Predictive Analytics, Climate Resilience, Environmental Planning, Economic Planning, Machine Learning, Climate Adaptation, Risk Assessment, Sustainability.
Field	Engineering
Published In	Volume 5, Issue 3, May-June 2023
Published On	2023-06-02

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AI-Based Climate Risk Assessment: Predictive Approaches for Resilient Environmental and Economic Planning

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