消費前にAIデータセンター資源を統治する：ZEROフレームワーク
Governing AI Data-Centre Resources Before Consumption: The ZERO Framework (原題)
Kollia M
🤖 gxceed AI 要約
日本語
AIデータセンターの持続可能性評価を消費後から消費前に移すZEROフレームワークを提案。ハードな許容制約、ワークロード特性評価、地域資源状態評価、連邦比較、継続条件、監査可能な証拠を組み合わせ、実行前に多資源決定を要求する。合成ケーススタディで低炭素だが水クリティカルな施設を除外し、低影響の代替案を選択する論理を示す。
English
This Perspective proposes the ZERO framework to shift AI data-centre sustainability evaluation upstream, before resource consumption. It combines hard admissibility constraints, workload characterisation, local resource-state assessment, federated comparison, continuation conditions, and auditable evidence, requiring a multi-resource decision before execution. A synthetic case study demonstrates excluding a low-carbon but water-critical facility and selecting a lower-impact alternative.
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
日本ではデータセンターの電力・水消費が社会課題となっており、SSBJ開示や省エネ法対応に資する。ZEROは投資判断前の資源評価を促し、ESG投資家対応や地域共生にも貢献する。
In the global GX context
Globally, AI data-centre sustainability is a growing concern for investors and regulators. The ZERO framework offers a proactive governance approach that complements existing disclosure frameworks like TCFD and CSRD by addressing resource consumption before it occurs, potentially informing transition finance and climate risk assessments.
👥 読者別の含意
🔬研究者:Provides a novel framework for pre-consumption sustainability governance in AI data centres, with clear research agenda.
🏢実務担当者:Offers a structured approach for data-centre operators to integrate sustainability constraints into planning and procurement.
🏛政策担当者:Highlights the need for upstream resource governance in AI infrastructure, potentially informing regulatory standards.
📄 Abstract（原文）
<title>Abstract</title> <p>AI data-centre sustainability is usually evaluated after electricity, water and materials have already been committed. This Perspective develops ZERO as an operational governance framework that moves the decision boundary upstream. ZERO combines hard admissibility constraints, workload characterisation, local resource-state assessment, federated comparison, continuation conditions and auditable evidence. Its contribution is distinguished from efficiency metrics, carbon-aware scheduling, water-aware scheduling and circular recovery by requiring a multi-resource decision before execution. An illustrative computational case study shows how the framework can exclude a low-carbon but water-critical facility and select a lower-impact admissible alternative. The case study is deliberately synthetic and demonstrates decision logic rather than measured savings. The paper concludes with empirical evaluation requirements, limitations and a staged research programme.</p>
🔗 Provenance — このレコードを発見したソース
- Research Square https://doi.org/10.21203/rs.3.rs-10593841/v1first seen 2026-08-22 04:22:46 · last seen 2026-09-02 04:33:08
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