Research and action on climate change(RACC)represent a complex global challenge that requires a systematic and multi-dimensional approach.Although progress has been made,persistent limitations in data processing,model...Research and action on climate change(RACC)represent a complex global challenge that requires a systematic and multi-dimensional approach.Although progress has been made,persistent limitations in data processing,modeling,and scenario evaluation continue to hinder further advances.Artificial Intelligence(AI)is emerging as a powerful tool to address these challenges by integrating diverse data sources,enhancing predictive modeling,and supporting evidence-based decision-making.Its capacity to manage large datasets and facilitate knowledge sharing has already made meaningful contributions to climate research and action.This paper introduces the RACC theoretical framework,developed through a systematic integration of the research paradigms of the three IPCC Working Groups(WGⅠ,WGⅡ,and WGⅢ).The RACC framework provides a comprehensive structure encompassing four key stages:data collection,scenario simulation,pathway planning,and action implementation.It also proposes a standardized approach for embedding AI across the climate governance cycle,including areas such as climate modeling,scenario development,policy design,and action execution.Additionally,the paper identifies major challenges in applying AI to climate issues,including ethical concerns,environmental costs,and uncertainties in complex systems.By analyzing AI-supported pathways for mitigation and adaptation,the study reveals significant gaps between current practices and long-term objectives—especially regarding content,intelligence levels,and governance structures.Finally,it proposes strategic priorities to help realize AI's full potential in advancing global climate action.展开更多
基金supported by the National Natural Science Foundation of China(72140007 and 42125503)supported by Tsinghua University(100008001)。
文摘Research and action on climate change(RACC)represent a complex global challenge that requires a systematic and multi-dimensional approach.Although progress has been made,persistent limitations in data processing,modeling,and scenario evaluation continue to hinder further advances.Artificial Intelligence(AI)is emerging as a powerful tool to address these challenges by integrating diverse data sources,enhancing predictive modeling,and supporting evidence-based decision-making.Its capacity to manage large datasets and facilitate knowledge sharing has already made meaningful contributions to climate research and action.This paper introduces the RACC theoretical framework,developed through a systematic integration of the research paradigms of the three IPCC Working Groups(WGⅠ,WGⅡ,and WGⅢ).The RACC framework provides a comprehensive structure encompassing four key stages:data collection,scenario simulation,pathway planning,and action implementation.It also proposes a standardized approach for embedding AI across the climate governance cycle,including areas such as climate modeling,scenario development,policy design,and action execution.Additionally,the paper identifies major challenges in applying AI to climate issues,including ethical concerns,environmental costs,and uncertainties in complex systems.By analyzing AI-supported pathways for mitigation and adaptation,the study reveals significant gaps between current practices and long-term objectives—especially regarding content,intelligence levels,and governance structures.Finally,it proposes strategic priorities to help realize AI's full potential in advancing global climate action.
