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グリーンAIのパラドックス：AIの環境便益と大規模言語モデル（LLM）の炭素フットプリントの比較分析

The Green AI Paradox: A Comparative Analysis of AI's Environmental Benefits Vs. The Carbon Footprint of Large Language Models (LLMs) (原題)

Dr. Anjana Kumari, Arnav Raviraj, Arya

Zenodo (CERN European Organization for Nuclear Research)📚 査読済 / ジャーナル2026-10-21#AI×ESG経営インパクト: 調達リスク対象セクター: cross_sector
DOI: 10.5281/zenodo.23593595
原典: https://doi.org/10.5281/zenodo.23593595

🤖 gxceed AI 要約

日本語

AIは森林破壊追跡や気候モデリングで環境便益をもたらす一方、LLMの学習・運用に伴う膨大な電力消費と炭素排出が新たな環境負荷を生む「グリーンAIのパラドックス」を比較レビューする。EUのAI法やエネルギー効率指令は透明性報告を義務化するが、越境的な炭素会計基準の欠如と規制裁定により、地域の便益がLLM開発のグローバル排出で相殺されると指摘。アルゴリズム効率だけでなく、拘束力ある国際的なエネルギー基準とデジタルインフラの炭素会計の統一が必要と論じる。

English

This comparative literature and policy review contrasts the ecological benefits of environmental AI (deforestation tracking, biodiversity monitoring, climate modelling) with the growing carbon footprint and grid strain of training and running large language models. It argues that the absence of standardized cross-border carbon accounting for digital infrastructure, combined with regulatory arbitrage under frameworks like the EU AI Act and Energy Efficiency Directive, lets global LLM emissions neutralize local AI benefits. The authors call for binding international energy standards and harmonized digital carbon accounting to make 'Green AI' legally enforceable.

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Scope 3やデータセンター電力開示を検討する日本企業にとって、AI利用拡大に伴うデジタルインフラの排出算定と規制動向を整理する材料になる。EU規制の域外波及と規制裁定の論点は、日本がAI・データセンター政策とGXをどう整合させるかの示唆を含む。

In the global GX context

The paper speaks directly to the ISSB/CSRD-era gap in digital-infrastructure carbon accounting and to regulatory arbitrage concerns around the EU AI Act and Energy Efficiency Directive. It adds a legal-compliance lens to the AI×ESG disclosure debate, arguing that voluntary corporate climate pledges are insufficient without binding cross-border energy standards.

👥 読者別の含意

🔬研究者:AI×ESG研究において、LLMの炭素フットプリント算定と国際規制の整合性という未開拓の研究課題を提示する。

🏢実務担当者:AI導入・データセンター調達時に、電力由来排出の把握とEU等の透明性報告義務への対応を検討する際の論点整理に使える。

🏛政策担当者:デジタルインフラの炭素会計基準の国際調和と、AI規制における環境要件の実効性確保を政策設計に反映する必要を示す。

📄 Abstract（原文）

Artificial Intelligence (AI) is widely celebrated as a cornerstone of modern environmental governance, driving major breakthroughs in deforestation tracking, biodiversity monitoring, and climate modelling. However, this deployment creates a critical environmental paradox: the massive computational energy required to train and run generative Large Language Models (LLMs) generates significant carbon emissions and places unprecedented strain on local electrical grids. This paper presents a comparative literature and policy review that contrasts the measurable ecological benefits of environmental AI against the accelerating carbon footprint of its underlying digital infrastructure. Beyond the technical imbalance, this study evaluates how the rapid expansion of AI data centres intersects with international legal frameworks and environmental treaties. Nationally focused technological growth increasingly breaches global sustainability commitments. Currently, the lack of standardized, cross-border carbon accounting protocols for digital infrastructure undermines the emission reduction targets established under the Paris Agreement. Furthermore, while emerging regional regulations such as the European Union's AI Act and Energy Efficiency Directive pioneer mandatory transparency reporting for general-purpose AI models, they face severe international enforcement gaps. These gaps allow technology firms to engage in regulatory arbitrage, shifting resource-heavy model training to jurisdictions with loose environmental oversight and carbon-intensive energy grids. The findings indicate that without unified global compliance, the environmental benefits of local AI applications are routinely neutralized by the global emissions of LLM development. Ultimately, this paper argues that true "Green AI" cannot be achieved through algorithmic efficiency alone. It requires an international legal framework that harmonizes technological innovation with binding global energy standards, establishing a right to green digital infrastructure and turning voluntary corporate climate promises into legally enforceable global obligations.

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