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Responsible AI for Climate Action

気候アクションのための責任あるAI (AI 翻訳)

G. Silambarasan, Gautam Shivaraj

Advances in computational intelligence and robotics book seriesジャーナル2026-05-20#AI×ESGOrigin: Global
DOI: 10.4018/979-8-2600-2020-3.ch001
原典: https://doi.org/10.4018/979-8-2600-2020-3.ch001

🤖 gxceed AI 要約

日本語

AIは気候変動対策に有望だが、大規模モデルの訓練・運用による電力消費、水使用、電子廃棄物などの環境負荷も大きい。本論文はAIの二面性を批判的に検討し、ライフサイクル評価やリバウンド効果分析を統合した評価枠組みを提案する。

English

AI holds promise for climate action but involves significant environmental costs from energy, water, and e-waste. This chapter critiques AI's dual role and proposes an integrated evaluation framework using life-cycle assessment and rebound effect analysis.

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

日本では脱炭素DXが推進される中、AI導入に伴う環境負荷の定量評価は重要。本枠組みは企業のグリーンIT戦略やSSBJ情報開示の参考になり得る。

In the global GX context

As AI adoption accelerates globally, this framework helps assess its net climate impact, relevant for TCFD/ISSB reporting on technology-related emissions and transition risks.

👥 読者別の含意

🔬研究者:Provides a theoretical framework combining LCA and rebound effects for evaluating AI's climate impact.

🏢実務担当者:Offers guidance for sustainability teams to assess trade-offs in AI deployment.

🏛政策担当者:Highlights the need for regulation addressing AI's direct and indirect environmental impacts.

📄 Abstract（原文）

AI is increasingly promoted as a transformative enabler of climate mitigation and adaptation, offering capabilities in renewable energy optimization, disaster prediction, precision agriculture, and smart infrastructure management. However, the rapid expansion of AI systems introduces significant environmental trade-offs. Training and deploying large-scale models require substantial computational power, contributing to rising electricity demand, water-intensive cooling, and growing electronic waste from hardware production and disposal. Moreover, efficiency gains enabled by AI may generate rebound effects, where reduced operational costs stimulate increased consumption, potentially offsetting emission reductions. This chapter critically examines AI's dual role as both a climate solution and a sustainability risk. Drawing on sustainability theory, ecological modernization, rebound effect analysis, and life-cycle assessment, it proposes an integrated evaluation framework to assess direct, indirect, and induced emissions associated with AI deployment.

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gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。