Accounting for AI Inference in Corporate GHG Inventories: A Four-Tier Methodology for Scope 3 Category 1 Reporting
企業GHGインベントリにおけるAI推論の計上：スコープ3カテゴリ1報告のための4層手法 (AI 翻訳)
Guillermo Llopis
🤖 gxceed AI 要約
日本語
本論文は、AI推論サービス（API、エンタープライズチャット、SaaSのAI機能）のスコープ3カテゴリ1報告に関する実践的フレームワークを提案する。EEIOファクターによる過大評価（10～40倍）を指摘し、物理ベースの推定からスパンドベースまでの4段階手法を提示。200人企業での試算は1tCO2e未満とし、課題は規模ではなく方法論にあると論じる。また、水力主体のスウェーデンで水消費量が増加するカーボン・ウォーター・トレードオフを明らかにする。
English
This paper proposes a four-tier methodology for reporting AI inference services (API subscriptions, enterprise chat, SaaS with AI) in Scope 3 Category 1 under CSRD. It identifies 10-40x overestimation by generic EEIO factors and offers a framework from direct token-based physical estimation to spend-based fallback. Applied to a 200-person firm, total is below 1 tCO2e, showing the challenge is methodological. It also documents a water-carbon trade-off: Sweden's hydro grid has lowest carbon but highest water footprint.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本企業においても、CSRDと同様の開示義務はSSBJ基準で議論が進んでいる。AIサービス利用のスコープ3報告は未整備であり、本手法は実務上の参考となる。特に水-カーボンのトレードオフは、日本のデータセンター立地戦略にも示唆を与える。
In the global GX context
This paper addresses a gap in corporate GHG inventory methodology for AI services, relevant to global disclosure frameworks such as CSRD and ISSB. The water-carbon trade-off finding is important for data center location decisions globally, especially as AI adoption grows.
👥 読者別の含意
🔬研究者:Provides a novel methodological framework for AI inference emissions accounting and highlights the water-carbon trade-off for further study.
🏢実務担当者:Offers a practical four-tier approach for companies to comply with CSRD Scope 3 Category 1 reporting for AI services.
🏛政策担当者:Demonstrates the need for standardized guidance on AI inference emissions and the potential for double counting or overestimation if generic factors are used.
📄 Abstract（原文）
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.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.48550/arxiv.2606.10660first seen 2026-06-12 05:07:35
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