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The Invisible Polluter: Is Artificial Intelligence Becoming One of the World's Biggest Environmental Threats?

見えない汚染者：人工知能は世界最大の環境脅威の一つになりつつあるのか？ (AI 翻訳)

A Joy and Yasmin Shaik F

Zenodoプレプリント2026-08-13#AI×ESGOrigin: Global経営インパクト: コスト削減対象セクター: cross_sector
DOI: 10.5281/zenodo.21922419
原典: https://zenodo.org/records/21922419
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🤖 gxceed AI 要約

日本語

本論文は、AI開発と大規模展開が環境に与える影響（エネルギー消費、炭素排出、水使用、電子廃棄物、鉱物採掘）を批判的に検討する。2024年のデータセンター電力消費は415TWhに達し、AIワークロードは2025年に50%急増、2030年には945〜1300TWhに達すると予測。グリーンAIの緩和戦略（アルゴリズム効率化、再生可能エネルギー調達、炭素認識スケジューリング、透明な報告基準）を評価し、即時のシステム的持続可能性介入を提言する。

English

This paper critically examines the environmental footprint of AI development and deployment, including energy, carbon, water, e-waste, and minerals. Data centre electricity hit 415 TWh in 2024, with AI workloads surging 50% in 2025, projected to reach 945-1,300 TWh by 2030. It evaluates Green AI mitigation strategies (algorithmic efficiency, renewable procurement, carbon-aware scheduling, transparent reporting) and calls for systemic interventions to align AI growth with environmental stewardship.

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、GX推進とAI戦略の両立が課題。SSBJ開示や有報での情報開示が進む中、AIの環境負荷を定量的に把握し、グリーンAIの実装を進めることは、日本企業の競争力とESG評価向上に直結する。本論文は、AI導入を検討する企業や政策立案者に具体的なデータと対策を提供する。

In the global GX context

Globally, this paper contributes to the growing discourse on AI's environmental impact, aligning with ISSB and CSRD disclosure requirements that increasingly expect companies to report on climate-related risks and opportunities, including those from AI operations. It provides a quantitative baseline and mitigation framework that can inform corporate sustainability strategies and policy development, especially as regulators scrutinize AI's energy and carbon footprint.

👥 読者別の含意

🔬研究者:Provides up-to-date quantitative benchmarks on AI's environmental footprint and a framework for evaluating Green AI strategies, useful for further research on sustainable AI.

🏢実務担当者:Offers actionable insights for corporate sustainability teams to assess and mitigate the environmental impact of AI deployments, including renewable procurement and efficiency measures.

🏛政策担当者:Highlights the need for policy interventions to ensure AI growth aligns with climate goals, providing data to support regulatory frameworks on AI energy use and reporting.

📄 Abstract（原文）

Artificial Intelligence (AI) has emerged as one of the most transformative technologies of the twenty-first century, reshaping industries, enhancing productivity, and enabling solutions to complex global challenges. Yet its environmental footprint — encompassing energy consumption, carbon emissions, water usage, electronic waste (e-waste), and mineral extraction — remains largely invisible to end users and policymakers alike. This paper critically examines the environmental implications of AI development and large-scale deployment, with particular emphasis on large language models (LLMs) and generative AI systems. Drawing on the most current quantitative benchmarks (IEA, 2025; UN University, 2025; Global E-Waste Monitor, 2024), we situate AI's resource demands within the broader context of global industrial energy consumption, updating prior analyses with 2024–2026 data. Data centre electricity consumption reached 415 TWh globally in 2024, with AI-specific workloads surging 50% in 2025 alone; by 2030, the sector is projected to consume 945–1,300 TWh annually. We further evaluate mitigation strategies under the emerging paradigm of Green AI — encompassing algorithmic efficiency, renewable energy procurement, carbon-aware scheduling, and transparent reporting standards. The study concludes that while AI is not yet among the largest absolute environmental threats, its compound growth trajectory demands immediate, systemic sustainability interventions to ensure technological advancement and environmental stewardship remain aligned.

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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。