世代間グリーンAIガバナンス：持続可能なAIシステムにおける環境説明責任の実装
Intergenerational Green Artificial Intelligence Governance: Operationalising Environmental Accountability in Sustainable Artificial Intelligence Systems (原題)
Shukla Parul, Jackson Simango Magoge, I. B. Chatterjee, Praygod J. Mbilinyi, Malavika Nandivelugu
🤖 gxceed AI 要約
日本語
AIのエネルギー消費・炭素排出・水使用・電子廃棄物などの環境負荷が法規制に十分組み込まれていないと指摘。ブラウン・ワイスの世代間衡平（選択・品質・アクセスの保全）を応用し、FLOPs/タスク、学習・推論あたりのCO2e、WUE、再エネ比率、ハード炭素、e-waste行き先などの指標からなる「世代間グリーンAI説明責任フレームワーク」を構築。モデル単位の環境開示、ライフサイクル評価、環境モデルカード、独立監査、持続可能な公共調達、衡平な国際協力からなる多中心型ガバナンスを提案する。
English
Argues AI's own environmental footprint—energy, carbon, water, hardware and e-waste—is poorly integrated into AI regulation. Applying Brown Weiss's intergenerational equity framework, it develops an Intergenerational Green AI Accountability Framework with indicators such as FLOPs per task, CO2e per training run and inference, WUE, renewable share, embodied carbon and e-waste destination. It proposes polycentric governance: model-level environmental disclosure, lifecycle assessment, environmental model cards, audits, sustainable procurement and equity-sensitive cooperation.
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・有報でのScope3開示が進む日本では、AI利用に伴う電力・炭素負荷を自社排出量やデータセンター調達の説明責任にどう組み込むかが実務課題となる。本稿の指標群はAIガバナンス指針や統合報告でのAI環境開示に示唆を与える。
In the global GX context
As ISSB/CSRD and SEC climate rules push Scope 3 and value-chain transparency, AI's compute-related emissions and water use are an emerging disclosure gap. The paper offers concrete, auditable indicators and model-card style disclosure that could feed into TCFD/ISSB-aligned reporting and green procurement standards.
👥 読者別の含意
🔬研究者:AIの環境負荷を世代間衡平の規範枠組みで捉え、測定可能な指標へ落とす研究設計の参考になる。
🏢実務担当者:AI調達・データセンター利用時の環境開示指標やモデルカード作成の実務的ひな形として活用できる。
🏛政策担当者:AI規制に環境開示・監査・公共調達要件を組み込む際の指標設計と多中心型ガバナンスの論拠を提供する。
📄 Abstract（原文）
ABSTRACT Artificial intelligence is increasingly promoted as a tool for sustainable development, climate governance and resource optimisation. Nevertheless, the environmental footprint of AI systems, including energy consumption, carbon emissions, water use, hardware production and electronic waste, remains insufficiently integrated into legal and regulatory frameworks. This article doctrinally examines Green AI through Edith Brown Weiss's tripartite framework of intergenerational equity comprising conservation of options, conservation of quality and conservation of access. It argues that current AI governance frameworks remain incomplete because they focus primarily on safety, bias, transparency and human oversight while treating environmental sustainability as a secondary or voluntary concern. The article develops an Intergenerational Green AI Accountability Framework that translates intergenerational equity into practical governance indicators, including FLOPs per task, CO 2 e per training run, CO 2 e per inference, kWh per inference, water usage effectiveness, renewable‐energy share, embodied hardware carbon, e‐waste destination and equitable access to green compute. It further proposes a polycentric governance architecture, such as model‐level environmental disclosure, lifecycle assessment, environmental model cards, independent audits, sustainable public procurement and equity‐sensitive international cooperation. The article concludes that AI should not be regarded as sustainable merely because it is deployed for climate or development objectives. It should be considered sustainable only where its own environmental costs are measured, disclosed, reduced and fairly governed.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1002/ail2.70038first seen 2026-10-26 04:40:01 · last seen 2026-10-31 04:41:03
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