The Environmental Cost of AI's Energy Use: Carbon, Water and Land Footprints
AIのエネルギー使用による環境コスト：炭素、水、土地のフットプリント (AI 翻訳)
Miriam Aczel, Sanaz Chamanara, Mir Matin, Aria Farsi, Tshilidzi Marwala, Kaveh Madani
🤖 gxceed AI 要約
日本語
本報告書は、AIシステムを動かすための電力消費が生み出す炭素、水、土地のフットプリントを定量化している。2025年のデータセンター消費電力は448TWh、うちAIが約20%を占め、2030年には945TWh、AIシェア40%に達する可能性がある。低炭素電力が自動的に低水・低土地フットプリントとは限らず、環境負荷が脆弱な地域に集中するリスクを指摘している。
English
This report quantifies the carbon, water, and land footprints of electricity used by AI systems. In 2025, data centers consumed 448 TWh, with AI accounting for ~20%; by 2030, total could reach 945 TWh with AI's share rising to 40%. It highlights that low-carbon electricity is not automatically low-water or low-land, and environmental burdens risk concentrating in vulnerable communities.
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
本報告書は、日本のAI・データセンター増加に伴う環境負荷を考える上で重要。SSBJ開示や投資家対応では、自社のAI利用に伴うScope2・3排出や水・土地影響の把握が求められる可能性があり、効率的な設計と透明性確保の必要性を示唆している。
In the global GX context
This paper provides critical data for global disclosure frameworks (TCFD, ISSB, CSRD) as companies increasingly need to report AI-related energy use and environmental footprints. It underscores the importance of location-based reporting and lifecycle assessment in AI's environmental impact.
👥 読者別の含意
🔬研究者:Researchers can use this study's quantitative estimates of AI's energy and resource footprints to inform lifecycle analysis and integrate environmental costs into AI design.
🏢実務担当者:Practitioners in data center operations and corporate sustainability can apply these findings to assess and mitigate the environmental impact of AI workloads, particularly in water-stressed regions.
🏛政策担当者:Policymakers should note the projected growth in AI energy demand and the risk of burden-shifting to vulnerable communities, calling for regulatory frameworks that mandate transparency and environmental justice safeguards.
📄 Abstract（原文）
The rapid global expansion of artificial intelligence is creating a new and largely underexamined sustainability challenge: the carbon, water and land footprints of the electricity required to power AI systems. While AI is increasingly presented as essential for innovation, economic growth, scientific discovery and climate action, its development depends on energy-intensive data centers, advanced chips, cooling systems, electricity grids, water resources, land and critical mineral supply chains that support AI hardware. This report shows that AI’s environmental costs depend not only on how much electricity is used, but also on where that electricity is generated and which energy sources power it. In 2025, data centers consumed an estimated 448 TWh of electricity, with AI workloads accounting for around 20% of this demand; by 2030, total data center electricity use could reach 945 TWh, while AI’s share could rise to 40%. Every kilowatt-hour used to train, deploy or operate AI carries carbon, water and land implications, and low-carbon electricity is not automatically low-water or low-land. As larger models, richer media outputs and everyday AI use scale rapidly, environmental burdens risk becoming concentrated in communities already facing water stress, land pressure, energy insecurity and limited governance capacity. Without transparent measurement, efficient design, lifecycle responsibility and environmental justice safeguards, the AI transition risks reproducing the uneven patterns of extraction and burden-shifting that have shaped earlier technological and energy transitions.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.53328/inr26rma002first seen 2026-06-22 04:51:36
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