Carbon Reporting Practices in the NHS: Emissions and Omissions Relating to Artificial Intelligence (Preprint)
NHSにおける炭素報告の実践：人工知能に関連する排出と欠落（プレプリント） (AI 翻訳)
Duncan J. Reynolds
🤖 gxceed AI 要約
日本語
英国NHSにおけるAI導入の炭素排出が、必須のグリーンプラン報告で見落とされていることを指摘。現在のスコープ1〜3会計では、AIワークロードのエネルギー集約性、GPU等のライフサイクル排出、未調達の生成AIツールの使用による排出が欠落している。ChatGPTの利用だけで年間約349tCO2eの排出が推定され、報告ギャップを埋めるための具体的な対策（契約条項、スコープ3へのハードウェア排出係数組み込み、AIトラフィック監視）を提案する。
English
This paper reveals that AI-related carbon emissions are largely omitted from mandatory NHS Green Plan reporting. Current Scope 1-3 accounting misses emissions from AI workloads' energy intensity, hardware life-cycle (e.g., GPUs), and unprocured generative AI tools; ChatGPT alone may emit ~349tCO2e/year in primary care. Proposes contract clauses, Scope 3 hardware emission factors, and traffic monitoring to close gaps.
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算定の精緻化が課題。本論文のAI排出の可視化手法は、日本の医療機関や企業がAI導入のカーボン影響を評価する際の参考になる。特に、ハードウェアのライフサイクル排出や外部AIサービスの算定方法は、日本の開示実務に示唆を与える。
In the global GX context
Globally, this paper addresses a critical gap in climate disclosure: AI's carbon footprint is invisible in standard reporting frameworks like TCFD/ISSB. It offers practical accounting and procurement measures that can be adopted by any organization using AI, contributing to more accurate Scope 3 reporting and net-zero strategies.
👥 読者別の含意
🔬研究者:Provides a framework for quantifying AI-related emissions in organizational carbon accounting, highlighting specific gaps and proposing measurement methods.
🏢実務担当者:Offers actionable measures (contract clauses, emission factors, traffic monitoring) to improve AI carbon reporting and align with net-zero targets.
🏛政策担当者:Suggests policy interventions to mandate AI-specific carbon disclosure and include hardware life-cycle emissions in reporting standards.
📄 Abstract（原文）
<sec> <title>UNSTRUCTURED</title> Artificial intelligence (AI) is being rolled out across the UK National Health Service (NHS) to improve efficiency; yet, its carbon footprint is largely invisible within mandatory Green Plan reporting. This work shows where NHS carbon reporting omits AI-related emissions and proposes feasible accounting and procurement measures that allow trusts to assess whether AI adoption advances or undermines net zero. A review of NHS sustainability guidance, the Department for Environment, Food &amp; Rural Affairs conversion factors, and recent evidence on AI energy use shows that current Scopes 1-3 accounting omits substantial emissions at 3 points. First, a lack of granularity provides averages that can obscure the extreme energy intensity of certain AI workloads. Second, life-cycle emissions from specialized hardware (eg, graphics processing units) are often excluded unless trusts own the equipment, ignoring upstream manufacturing impacts. Third, widespread use of unprocured generative AI tools is unmeasured; extrapolating general practice survey data suggests that ChatGPT queries alone could release ≈ 349t CO₂e per year in primary care. To close these gaps, we propose three potential ways to help reduce these reporting gaps: (1) AI-specific carbon disclosure clauses in vendor contracts, (2) inclusion of cradle-to-grave emission factors for AI hardware in Scope 3 reporting, and (3) lightweight monitoring of external AI traffic (while recognizing potential ethical issues with this). Implementing these measures would give health care leaders a more accurate baseline against which to judge whether AI supports or undermines the NHS net-zero target. </sec>
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.2196/preprints.79174first seen 2026-08-02 17:05:29
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