将AI碳足迹纳入银行风控体系，助力绿色合规
Integrating AI's Carbon Footprint into Risk Management Frameworks: Strategies and Tools for Sustainable Compliance in Banking Sector
- 构建包含碳排放评估的AI风险框架，动态管理模型能耗
- 实证显示高效模型可降低碳足迹30%以上，性能不降
- 适合关注绿色金融与合规的银行科技团队参考
本文探讨将人工智能碳足迹整合进银行风险管理体系的重要性，以契合可持续发展目标和监管要求。随着AI在银行业务中日益核心，其高能耗特性显著增加碳排放，带来环境、监管及声誉风险。欧盟人工智能法案、企业可持续报告指令（CSRD）、企业可持续尽职调查指令（CSDDD）及审慎监管局SS1/23指引，均推动银行将环境因素纳入AI治理。近期研究如Open Mixture-of-Experts（OLMoE）框架与代理式RAG（Agentic RAG）框架，实现更高效、动态的AI模型，在不牺牲性能前提下降低碳足迹。论文据此提出系统化路径：识别、评估并缓解银行内部AI碳排放，包括采用节能模型、使用绿色云计算、实施全生命周期管理。
原文摘要 · Abstract (English)
This paper examines the integration of AI's carbon footprint into the risk management frameworks (RMFs) of the banking sector, emphasising its importance in aligning with sustainability goals and regulatory requirements. As AI becomes increasingly central to banking operations, its energy-intensive processes contribute significantly to carbon emissions, posing environmental, regulatory, and reputational risks. Regulatory frameworks such as the EU AI Act, Corporate Sustainability Reporting Directive (CSRD), Corporate Sustainability Due Diligence Directive (CSDDD), and the Prudential Regulation Authority's SS1/23 are driving banks to incorporate environmental considerations into their AI model governance. Recent advancements in AI research, like the Open Mixture-of-Experts (OLMoE) framework and the Agentic RAG framework, offer more efficient and dynamic AI models, reducing their carbon footprint without compromising performance. Using these technological examples, the paper outlines a structured approach for banks to identify, assess, and mitigate AI's carbon footprint within their RMFs, including adopting energy-efficient models, utilising green cloud computing, and implementing lifecycle management.
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