Comparative analysis of generative AI performance in nuclear energy for climate change mitigation
気候変動緩和のための原子力エネルギーにおける生成AI性能の比較分析 (AI 翻訳)
Kyung Bae Jang, Tae Ho Woo
🤖 gxceed AI 要約
日本語
本研究は、システムダイナミクス手法を用いて、生成AIが原子力エネルギーの貢献最適化と石炭・石油などの炭素集約型電源の脆弱性緩和に果たす役割を分析。Test1（低初期値）が他より優れたパフォーマンスを示し、感度分析でも変動が最大であった。AIがゼロ炭素電源の戦略的活用に向けた国際合意形成を促進する可能性を示唆。
English
This study uses a system dynamics approach to analyze how generative AI optimizes nuclear energy's contribution and mitigates vulnerabilities of carbon-intensive sources. Test 1 (low initial value) outperformed others, with the highest sensitivity. It suggests AI's capacity to facilitate international consensus on zero-carbon energy deployment.
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
日本では福島後の原子力再稼働が進む中、AIによる運転最適化や安全性向上への応用が期待される。本論文は韓国発の研究だが、日本の原子力政策やエネルギー転換にも示唆を与える。
In the global GX context
Globally, nuclear energy is gaining traction as a zero-carbon baseload source. This paper demonstrates AI's potential to enhance nuclear operations and support decarbonization pathways, contributing to discussions on technology-driven energy transition.
👥 読者別の含意
🔬研究者:Provides a system dynamics framework for modeling AI's impact on nuclear energy deployment, which can be extended to other low-carbon technologies.
🏢実務担当者:Offers a conceptual model for using generative AI to optimize nuclear plant operations, though practical validation is needed before implementation.
🏛政策担当者:Highlights AI's role in accelerating nuclear energy's contribution to climate goals, but emphasizes the need for international consensus-building.
📄 Abstract（原文）
Abstract Artificial intelligence (AI) is being strategically applied to the energy sector, leveraging the unique characteristics of nuclear power to effectively combat climate change. As a zero-carbon emission source, nuclear energy is increasingly vital for a sustainable future. Our study, utilizing an applied System Dynamics (SD) methodology, demonstrates the essential role of generative AI in optimizing nuclear energy’s contribution and mitigating the vulnerabilities of carbon-intensive sources like coal and oil. A comparative analysis between two scenarios, Test 1 and Test 2, reveals generative AI’s superior performance in Test 1, which began with a low initial value. In addition, in the comparison of the four tests, only Test1 shows a higher value than the other test cases, while the remaining test cases have similar values, so the comparison between Test1 and Test2 is valid. In the sensitivity analysis, the fact that Test1 has the largest standard deviation value means that the value of change is the largest. This finding underscores AI’s indispensable capacity to facilitate consensus-building among nations, making the strategic utilization of zero-carbon energy sources a more achievable and collaborative goal.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1007/s43621-026-04040-9first seen 2026-07-27 04:55:09
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