Artificial Intelligence for Climate Risk Prediction: A Data-Driven Framework for Sustainable Environmental Governance

Abstract

Climate change has intensified environmental risks, necessitating advanced predictive systems capable of supporting sustainable governance frameworks. This study examined the role of artificial intelligence (AI) in enhancing climate risk prediction and strengthening environmental decision-making processes. A quantitative, data-driven methodology was employed using panel data from climate-vulnerable economies covering the period 2005–2024. Machine learning models, including Random Forest, Support Vector Machine, and Long Short-Term Memory (LSTM) networks, were developed and compared with traditional regression techniques. The results indicated that AI capabilities significantly improved climate risk prediction accuracy, with deep learning models outperforming conventional statistical approaches. Prediction accuracy was found to mediate the relationship between AI capability and governance effectiveness, while explainable AI mechanisms positively moderate this relationship by enhancing transparency and policy usability. The findings demonstrated that integrating predictive analytics with governance frameworks strengthened early warning systems, adaptive planning, and institutional responsiveness. Despite the strong performance of AI models, challenges related to interpretability, data bias, and computational demands remained evident. The study concluded that a structured AI-driven framework provided a scalable and policy-oriented approach to climate risk management, contributing to resilience-building and sustainable development pathways in emerging and climate-sensitive regions.