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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段階手法 (AI 翻訳)

Guillermo Llopis

📚 査読済 / ジャーナル2026-06-09#AI×ESGOrigin: Global経営インパクト: 調達リスク対象セクター: cross_sector
原典: https://www.semanticscholar.org/paper/bfac03f7c9d6e8e2669486d8afc13c3450495d90

🤖 gxceed AI 要約

日本語

AI推論サービス（API、チャットツール、SaaS組込AI）はCSRDのスコープ3カテゴリ1に該当するが、標準的な算定方法が存在しない。本論文は、トークン単位の物理推定から支出ベースのEEIOまで4段階の枠組みを提案し、従来のICT業界平均係数より10〜40倍の過大評価を是正する。欧州200人企業の事例で1tCO2e未満を示し、水と炭素のトレードオフも提示。

English

AI inference services fall under Scope 3 Category 1 under CSRD, yet lack standardized accounting. This paper proposes a four-tier framework from token-based physical estimation to spend-based EEIO, correcting 10-40x overestimates from generic ICT factors. A 200-person European firm case yields <1 tCO2e, and highlights a water-carbon trade-off in data center siting.

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

日本ではSSBJ開示が始まり、スコープ3算定の実務ニーズが高まる中、AIサービス利用の算定方法は未整備。本手法は日本企業のScope3対応に即応用可能で、データセンター立地戦略にも示唆を与える。

In the global GX context

With CSRD and ISSB requiring Scope 3 disclosure, this paper fills a methodological gap for AI services. It offers a practical, tiered approach that aligns with global reporting standards and highlights environmental trade-offs relevant for data center strategy worldwide.

👥 読者別の含意

🔬研究者:Provides a validated methodology for AI inference emissions, bridging AI and carbon accounting research.

🏢実務担当者:Offers a concrete framework to include AI services in Scope 3 inventories, reducing compliance risk.

🏛政策担当者:Highlights the need for standardized guidance on AI emissions in disclosure regulations.

📄 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.

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gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。