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CONG Ronggang, HAN Te, WANG Peng, ZHAO Luo, GU Xiaoyang, LI Zhiqiang, QU Shaojie, WEI Yiming. Advances and Prospects in Artificial Intelligence for Climate Change GovernanceJ. Journal of Beijing Institute of Technology (Social Sciences Edition), 2025, 27(2): 37-47. DOI: 10.15918/j.jbitss1009-3370.2025.7244
Citation: CONG Ronggang, HAN Te, WANG Peng, ZHAO Luo, GU Xiaoyang, LI Zhiqiang, QU Shaojie, WEI Yiming. Advances and Prospects in Artificial Intelligence for Climate Change GovernanceJ. Journal of Beijing Institute of Technology (Social Sciences Edition), 2025, 27(2): 37-47. DOI: 10.15918/j.jbitss1009-3370.2025.7244

Advances and Prospects in Artificial Intelligence for Climate Change Governance

  • Effective and efficient climate change governance is crucial for mitigating its impacts and achieving the “dual carbon” goals. However, climate change governance faces complex challenges, including interdisciplinary integration, vast multimodal datasets, regional disparities, and dynamic changes. Traditional approaches relying on manual expert analysis, modeling, and decision-making often fall short of delivering optimal solutions. The rapid development of artificial intelligence (AI) provides transformative tools and methodologies that are reshaping the research paradigm of climate change governance. In this study, the AI for Climate Change Governance (AICCG) large model was developed using generative AI technology. AICCG integrates multisource and multimodal knowledge from the field of climate change, enabling advanced knowledge discovery and synthesis while fostering cross-disciplinary insights and multimodal data integration. The model serves as a comprehensive interactive platform for policymakers, researchers, enterprises, and the public, facilitating informed decision-making and optimizing strategies in climate change governance. Looking to the future, AI is expected to play a transformative role in advancing knowledge discovery in climate change research, fostering global collaboration, enabling the sharing of data and models, and achieving broad applications across diverse scenarios.
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