为公司碳排放报告中的AI推理服务提供可落地的四层核算方法。
Accounting for AI Inference in Corporate GHG Inventories: A Four-Tier Methodology for Scope 3 Category 1 Reporting
- 按数据可得性分四层：从直接能耗估算到支出法反推，兼顾精度与可行性。
- 实测显示一家欧洲企业AI推理年碳排不足1吨，远低于传统估算的10-40倍。
- 揭示碳排与用水的权衡关系，指导数据中心选址策略。
AI推理服务——包括API订阅、企业聊天工具及嵌入式AI功能的SaaS产品——明确属于企业可持续发展报告指令（CSRD）中的范围三类别1，要求自2024年起披露。然而目前尚无标准化方法纳入企业温室气体（GHG）清单。现有做法或完全忽略该类别，或采用面向整个信息通信技术（ICT）行业的经济投入产出（EEIO）因子，导致碳排放估算结果比基于物理数据的方法高10至40倍。本文提出一个四层框架，根据组织可获取的数据量级匹配估算精度：从基于使用量的直接物理估算（利用GPU能效基准和区域电网碳强度），到无使用数据时的支出驱动型EEIO法作为回退。排放因子源自同行评审的GPU能效基准（ML.ENERGY Leaderboard v3）、经验证的电网碳强度数据（EPA eGRID 2023；Ember 2023）以及公开的水效数据（Li et al., 2025）。应用于一家200人规模的欧洲企业，结果显示总排放低于1 tCO2e，表明合规挑战在于方法而非排放规模。此外，我们发现当前ESG工具未体现的水-碳权衡：瑞典以水电为主导的电网碳强度最低，但水足迹最高，对数据中心选址具有直接影响。
原文摘要 · Abstract (English)
AI inference services -- API subscriptions, enterprise chat tools, and SaaS products with embedded AI features -- fall unambiguously within Scope 3 Category 1 under the Corporate Sustainability Reporting Directive (CSRD), which requires disclosure for fiscal years starting January 2024. Yet no standardised methodology exists for including them in corporate GHG inventories. Current practice either omits the category entirely or applies a generic economic input-output (EEIO) factor calibrated to the ICT sector as a whole, overestimating AI inference emissions by 10-40x relative to physically derived alternatives. We propose a four-tier framework that matches estimation precision to the data organisations can realistically obtain, progressing from direct token-based physical estimation -- using GPU energy benchmarks and regional grid carbon intensities -- down to a spend-based EEIO fallback for services where no usage data exists. Emission factors are derived from peer-reviewed GPU energy benchmarks (ML.ENERGY Leaderboard v3), confirmed grid carbon intensities (EPA eGRID 2023; Ember 2023), and published water use effectiveness data (Li et al., 2025). Applied to a 200-person European firm, the framework yields a total below 1 tCO2e, illustrating that the compliance challenge is methodological rather than magnitude-driven. We further document a water-carbon trade-off that current ESG tools do not surface: Sweden's hydro-dominated grid delivers the lowest carbon intensity in our dataset but the highest water footprint, with direct implications for data centre location strategy.
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