情報ギャップの解消:AIのScope 3排出量開示
Closing the Information Gap: AI Scope 3 Emissions Disclosure (原題)
Michael P. Vandenbergh, Ethan Ickovics Thorpe
🤖 gxceed AI 要約
日本語
AIデータセンターの電力需要と排出増に対し、公的規制の空白を民間開示制度で埋める改革を提案する論考。S&P500企業の2024・25年報告書のいずれもAI利用の炭素足跡を開示していないと指摘。GHGプロトコルScope 3の明確化、CDP質問項目追加、SBTiガイダンス、サプライチェーン契約拡充の4施策を提言し、AI企業の顧客である企業が削減を促す市場シグナルを強化すべきとする。
English
This essay proposes immediately implementable reforms to private carbon disclosure systems to drive down emissions from AI. It finds that none of the 2024-2025 sustainability reports of S&P 500 companies disclose the carbon footprint of their AI use. It recommends clarifying how GHG Protocol Scope 3 covers AI-driven emissions, adding CDP survey questions, issuing SBTi guidance, and expanding supply-chain contracting—leveraging corporate AI customers as a market signal for decarbonization.
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
SSBJ基準・有報開示がScope 3対応を迫る中、AI利用に伴う排出の算定・開示枠組みをどう整備するかは日本企業にも直結する論点。CDP・SBTi改革の動向は日本企業の回答実務やサプライチェーン要請に波及する。
In the global GX context
Directly engages the ISSB/TCFD disclosure architecture by targeting the GHG Protocol, CDP and SBTi—the private infrastructure underpinning Scope 3 reporting. It adds a novel angle to global disclosure scholarship: AI-use emissions as an emerging, currently undisclosed Scope 3 category that standard-setters and NGOs must address.
👥 読者別の含意
🔬研究者:AI利用排出をScope 3・開示制度の枠内でどう概念化・測定するかの研究課題を提示する。
🏢実務担当者:AI利用の炭素足跡をCDP・SBTi回答やサプライチェーン契約でどう開示・要請するかの実務指針になる。
🏛政策担当者:AI排出に対する公的規制の空白を民間開示制度で補う際の具体的改革メニューを提供する。
📄 Abstract(原文)
This Essay proposes straightforward, immediately implementable reforms to private carbon disclosures systems to motivate reductions in carbon emissions from Artificial Intelligence (AI). AI-driven data centers account for a rapidly increasing share of electricity demand, fossilfuel grid build out, and therefore carbon emissions, but federal, state, and local governments have struggled to develop a public governance response that balances the benefits of rapid development of AI with its carbon emissions. In recent years, non-governmental organizations (NGOs) have worked with corporations to fill gaps in public climate governance by developing the Greenhouse Gas (GHG) Protocol, the CDP (formerly Carbon Disclosure Project), the Science-Based Targets initiative (SBTi) and many supply chain contracting initiatives. This Essay argues that corporations and other organizations that use AI are a principal source of revenue for AI firms, and these corporate AI customers can motivate AI firms to reduce carbon emissions before longer-term regulatory reforms occur. Adequate information on corporate AI-related emissions is needed, but uncertainty about the disclosure obligations of AI-using organizations is a barrier, and the Essay finds that none of the 2024 and 2025 sustainability reports of S&P 500 companies discloses the carbon footprint of their AI use. The Essay proposes quickly resolving disclosure ambiguities and increasing the market signal for decarbonization through (1) clarification of how GHG Protocol Scope 3 standards include AI-driven emissions, (2) addition of questions to the CDP annual survey to gather and disclose information about AI usage and emissions by reporting organizations, (3) guidance on the inclusion of AI-driven emissions in the SBTi disclosure, commitment, and implementation process, and (4) expansion of efforts by NGOs to extend AI-related emissions disclosure and reduction requirements through supply chain contracting. The initiatives outlined in this Essay not only can drive corporate AI emissions reductions, but also emissions reductions from other organizational customers of AI firms, including state and local governments, public and private school systems, universities, religious and civic organizations, and civic and cultural organizations.
🔗 Provenance — このレコードを発見したソース
- openaire https://doi.org/10.2139/ssrn.7479462first seen 2026-11-05 04:24:23
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