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カテゴリー1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サブスクリプション、チャットツール、AI機能搭載SaaSなど）のスコープ3カテゴリー1排出量を算定するための4層フレームワークを提案する。既存の産業連関法はAI推論の排出量を10～40倍過大評価することを示し、トークンベースの物理推定から支出ベースのフォールバックまで、データ可用性に応じた段階的アプローチを提供する。200人規模の欧州企業での適用例では総排出量1tCO2e未満と試算され、コンプライアンス上の課題は方法論にあることを明らかにした。さらに、スウェーデンの水力発電主体の系統では炭素強度が最も低いが水使用量が最大となる、水と炭素のトレードオフを指摘している。
English
This paper proposes a four-tier framework for including AI inference services (API subscriptions, enterprise chat tools, SaaS products) in Scope 3 Category 1 GHG inventories. It shows that current EEIO factors overestimate emissions by 10-40x compared to physically derived methods. The framework matches estimation precision to data availability, from direct token-based physical estimation using GPU benchmarks to spend-based EEIO fallback. Applied to a 200-person European firm, total emissions are below 1 tCO2e. The paper also documents a water-carbon trade-off, with Sweden's hydro grid having 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
日本でもSSBJ基準に基づくスコープ3開示が求められており、AIサービスの利用が増える中、本手法は日本企業にとっても実践的な指針となる。特にデータセンター立地戦略において、水と炭素のトレードオフは重要な考慮点である。
In the global GX context
Under CSRD and ISSB standards, companies face increasing pressure to report Scope 3 emissions comprehensively. This methodology fills a critical gap for AI inference services, which are often omitted or misestimated. The framework is practical and actionable for global firms.
👥 読者別の含意
🔬研究者:The four-tier framework and the underlying GPU energy benchmarks provide a foundation for further refinement of AI emission factors.
🏢実務担当者:Corporate sustainability teams can use the tiered approach to estimate Scope 3 emissions from purchased AI services with data they already have or can reasonably collect.
🏛政策担当者:The documented water-carbon trade-off highlights the need for integrated environmental metrics in data center regulation and energy policy.
📄 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://arxiv.org/abs/2606.10660first seen 2026-06-13 04:51:57
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