Terawatts for Petabytes: Exploring the impact of AI data centres on Europe’s net zero goals
テラワット対ペタバイト：AIデータセンターが欧州のネットゼロ目標に与える影響の探求 (AI 翻訳)
Mohammad Hemmati, Vassilis M. Charitopoulos
🤖 gxceed AI 要約
日本語
AIの急速な拡大により追加の電力需要が欧州の電力システムに生じる。本研究では、空間的に明示的な最適化モデルを用いて、ハイパースケールデータセンターがエネルギーインフラ投資と排出経路に与える影響を評価。高成長シナリオでは、2050年までにAI関連電力需要が450TWh（欧州全体の7%）に達し、累積排出量が25 MtCO2増加する。追加容量は37-323 GW必要で、原子力・ガス・風力・太陽光・蓄電池への投資が含まれる。
English
The rapid expansion of AI is increasing electricity demand in Europe. This study uses a spatially explicit optimization model to assess how hyperscale data centers may reshape energy infrastructure investment and emissions trajectories. Under high-growth scenarios, AI-driven electricity demand could reach 450 TWh (7% of Europe's total) by 2050, increasing cumulative emissions by 25 MtCO2. Additional capacity requirements range from 37 to 323 GW, including investments in nuclear, gas, wind, solar, and battery storage.
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
日本でもAIデータセンターの拡大が電力需要増加要因として注目されており、再生可能エネルギーや原子力の利用拡大が政策課題となっている。本論文のモデル分析は、日本のエネルギー計画にも示唆を与える可能性がある。
In the global GX context
This paper is highly relevant for global climate policy as it quantifies the energy and emissions implications of AI data centers, a rapidly growing sector. It provides insights for infrastructure planning and net-zero pathway alignment, particularly for regions like Europe with ambitious decarbonization targets.
👥 読者別の含意
🔬研究者:The optimization model and scenario analysis offer a framework for integrating AI data center demand into energy system models.
🏢実務担当者:Data center operators and energy planners can use the findings to anticipate future capacity needs and investment strategies.
🏛政策担当者:Policymakers should consider AI-driven electricity demand in energy and climate planning to ensure net-zero targets remain achievable.
📄 Abstract（原文）
The unprecedented expansion of Artificial Intelligence is adding increasing electricity demand to Europe’s power system. While incumbent plans pursue a net-zero future by 2050, they fail to consider the implications of large-scale AI-based data centres. In this study, a spatially explicit optimisation model is developed to assess how hyperscale data centres may reshape energy infrastructure investment, and emissions trajectories, across different AI demand growth scenarios. The results indicate that, after 2030, AI capacity deployment increasingly shifts toward regions with the ability to expand nuclear and gas-based generation, as firm and flexible power sources are essential for supporting the deployment of high-capacity AI data centres. By 2050, AI-driven electricity demand under high growth scenarios may reach up to 450 TWh, corresponding to 7% of total Europe’s demand, with installed AI capacity reaching approximately 85 GW. This additional load leads to an increase of nearly 25 MtCO2 in cumulative emissions between 2030 and 2050. Our analysis indicates that, depending on the AI growth scenario, meeting AI-related electricity demand by 2050 requires between 37 and 323 GW of additional capacity across Europe, ranging from the pessimistic to the lift-off scenario, including investments in nuclear (2-12 GW), gas (2-7 GW), wind (13-100 GW), solar (20-134 GW), and battery storage (0-70 GW).
🔗 Provenance — このレコードを発見したソース
- crossref https://doi.org/10.69997/sct.164210first seen 2026-06-20 06:46:53 · last seen 2026-06-21 05:33:16
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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。