Dynamic climate graph network and adaptive climate action strategy for climate risk assessment and low-carbon policy responses - Zhou, Fang; Shi, Yan; Zhao, Pengfei; Gu, Zhengzhao; Li, Ye (Frontiers in Environmental Science, 2025)
BackgroundThe increasing urgency to mitigate and adapt to climate change demands innovative methodologies capable of analyzing complex climate systems and informing policy decisions. Traditional climate action models often struggle with capturing intricate spatial-temporal dependencies and integrating multi-modal data, resulting in limited scalability and real-world applicability.MethodsTo address these challenges, we propose a novel framework that integrates the Dynamic Climate Graph Network (DCGN) with the Adaptive Climate Action Strategy (ACAS). DCGN utilizes graph-based learning to model spatial dependencies and temporal feature extraction to analyze evolving climate patterns. Multi-modal data fusion is employed to integrate meteorological, socio-economic, and geospatial information. ACAS builds upon DCGN’s predictive outputs by applying attention mechanisms and optimization under domain-specific constraints to prioritize high-impact regions and variables.ResultsEmpirical results demonstrate that the proposed framework consistently outperforms several state-of-the-art baselines across multiple benchmark datasets, achieving an average improvement of over 2.5% in F1 Score and AUC. These outcomes highlight the robustness, generalizability, and real-world applicability of our approach.ConclusionBy linking advanced machine learning techniques with interpretable and actionable climate policy insights, the integrated DCGN–ACAS framework provides a scalable and effective tool for climate risk assessment and low-carbon transition strategies. The proposed method offers promising implications for sustainable urban planning, environmental governance, and adaptive climate intervention.