Next-Generation Nuclear Energy for Decarbonization, Energy Security, and Industrial Growth
次世代原子力エネルギー：脱炭素、エネルギー安全保障、産業成長のために (AI 翻訳)
Murali Krishna Pasupuleti
🤖 gxceed AI 要約
日本語
次世代原子力システムを社会技術インフラとして捉え、脱炭素とエネルギー安全保障への貢献を体系的に分析する。不確実性分析、統計モデリング、機械学習、政策ガバナンスを統合し、研究設計と評価手法を提供する。
English
This book develops a rigorous framework for studying advanced nuclear systems as socio-technical infrastructures. It integrates systems theory, uncertainty analysis, causal inference, and policy governance to examine how nuclear innovation can contribute to decarbonization and energy security across diverse regional contexts.
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
日本は原子力の活用をGX政策の柱の一つとして位置づけており（GX実現に向けた基本方針）、本稿の提供する社会技術的フレームワークは、安全性・制度的受容性・人材育成など日本の原子力政策課題に示唆を与える。
In the global GX context
Globally, nuclear energy is gaining renewed attention in the context of net-zero targets. This paper's integrated approach to advanced reactors, hydrogen, and industrial heat offers a comprehensive framework for policymakers and researchers seeking to evaluate nuclear's role in the energy transition.
👥 読者別の含意
🔬研究者:Provides a rigorous methodological framework for studying advanced nuclear systems as socio-technical infrastructures.
🏢実務担当者:Offers evaluation metrics and risk frameworks for decision-making on nuclear deployment in industrial and energy contexts.
🏛政策担当者:Highlights governance artifacts and institutional capacity requirements for successful nuclear policy.
📄 Abstract（原文）
Abstract Next-Generation Nuclear Energy for Decarbonization, Energy Security, and Industrial Growth develops a rigorous academic framework for studying advanced nuclear systems as socio-technical infrastructures rather than isolated power technologies. The manuscript integrates systems theory, uncertainty analysis, statistical explanation, causal inference, trustworthy machine learning, reproducible data engineering, and policy governance to examine how nuclear innovation may contribute to low-carbon industry, resilient electricity systems, strategic mobility, and national development. It emphasizes research design, evaluation logic, and governance artifacts that can support doctoral scholarship, industrial decision-making, and public policy in diverse regional contexts. The book avoids deterministic claims by treating deployment as contingent on safety evidence, institutional capacity, financing models, waste stewardship, safeguards, workforce readiness, and public legitimacy. Across five chapters, the analysis progresses from foundations and uncertainty to statistical modeling, machine learning, big data lifecycles, and applied sectoral strategy. The resulting manuscript offers conceptual models, evaluation metrics, methodological protocols, risk frameworks, and scalable implementation pathways for researchers, practitioners, and policymakers seeking disciplined approaches to nuclear-enabled decarbonization and energy security. Keywords next-generation nuclear energy, decarbonization, energy security, small modular reactors, advanced reactors, industrial heat, nuclear hydrogen, causal inference, statistical modeling, machine learning, trustworthy AI, digital twins, MLOps, risk governance, lifecycle assessment, nuclear safeguards, energy policy, industrial growth, reproducibility, public legitimacy, climate strategy, systems engineering, South Asia, Europe, Africa, Americas
🔗 Provenance — このレコードを発見したソース
- crossref https://doi.org/10.62311/nesx/rb-978-81-686373-5-1first seen 2026-05-14 22:05:09
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