Artificial intelligence and economic forecasts in climate risks management

The physical, financial, and systemic risks associated with climate change continue to increase across global economies. As the frequency and severity of climate-related risks intensify, it becomes increasingly difficult for standard forecasting tools to adequately capture complex ecological-economic interactions. This article examined the role of artificial intelligence (AI) and economic forecasting in managing climate risks. The study demonstrated that the identification and prediction of severe weather events are significantly improved through the use of AI-powered climate models. Explainable AI (XAI) provides additional transparency by enabling analysts and policymakers to better understand the factors influencing predictions generated by drought, flood, and heatwave models. When integrated with economic forecasting frameworks, AI enables comprehensive simulations of how climate challenges propagate through supply chains, labour markets, energy networks, and financial systems. Despite these advantages, AI-based modelling presents several limitations, including data scarcity, limited generalisability, and ethical concerns related to transparency, energy consumption, and inequality. The study concluded that integrating AI with economic forecasting provides a robust interdisciplinary framework for climate risk assessment, policy formulation, and adaptive strategy development. However, the effectiveness of this framework depends on improved governance structures, enhanced data infrastructure, and responsible technological implementation. The research has practical significance in enabling more accurate and early assessment of climate-related impacts on economic systems. The findings are particularly relevant for risk prediction and management in the energy, agricultural, and financial sectors