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脱炭素化と持続可能な開発のインテリジェント支援ツールとしてのエネルギー転換におけるエキスパートシステム

Expert Systems in Energy Transition as a Tool for Intelligent Support of Decarbonization and Sustainable Development (原題)

Dariusz Sala, Alla Polyanska, Vladyslaw Psyuk

Energies📚 査読済 / ジャーナル2026-08-20#AI×ESGOrigin: EU経営インパクト: コスト削減対象セクター: cement
DOI: 10.3390/en19163916
原典: https://doi.org/10.3390/en19163916

🤖 gxceed AI 要約

日本語

本論文は、146件の文献の書誌計量分析により、エネルギー転換研究が技術中心から気候変動・再生可能エネルギー・政策へと進化したことを示す。さらに、デジタルツイン指向の技術経済意思決定支援フレームワーク（DTOTEDSF）を提案し、セメント産業の4つのCCSケーススタディに適用して実用性を実証した。

English

This paper uses bibliometric analysis of 146 publications to show the evolution of energy transition research from technical aspects to climate change, renewables, and policy. It proposes a Digital-Twin-Oriented Techno-Economic Decision-Support Framework (DTOTEDSF) and demonstrates its applicability through four industrial CCS case studies in the cement sector.

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

日本のGX実践では、CCSやデジタルツインを活用した脱炭素戦略の意思決定支援が注目されており、本フレームワークは国内のセメント産業やエネルギー企業の投資判断に示唆を与える。SSBJ開示や移行計画策定にも応用可能な分析基盤を提供する。

In the global GX context

Globally, this framework aligns with the growing need for intelligent decision-support tools in industrial decarbonization, particularly for CCS investments. It complements ISSB/CSRD disclosure requirements by providing a techno-economic basis for transition planning and carbon reduction strategies.

👥 読者別の含意

🔬研究者:Provides a bibliometric overview and a novel framework integrating techno-economic modeling with CCS case studies, useful for further DT development.

🏢実務担当者:Offers a structured approach for evaluating CCS projects and optimizing carbon-reduction strategies, applicable to cement and other hard-to-abate sectors.

🏛政策担当者:Highlights the evolution of energy transition research and the role of intelligent systems in supporting decarbonization policy.

📄 Abstract（原文）

The article explores the evolution of energy transition research through a bibliometric co-occurrence analysis of author keywords extracted from 146 scientific publications. The analysis reveals a shift from the technical aspects of energy systems and traditional decision-support approaches (2014–2018) towards climate change, decarbonization, renewable energy, investments, and energy policy (2018–2021). Recent studies (2022–2024) increasingly emphasize renewable energy, sustainable development, and intelligent decision-support systems, reflecting the growing digitalization of energy systems and the transition towards intelligent energy management. Based on these findings, the study develops a Digital-Twin-Oriented Techno-Economic Decision-Support Framework (DTOTEDSF) for optimizing and managing carbon-reduction strategies under dynamic energy transition conditions. Rather than representing a fully implemented digital twin (DT), the proposed framework constitutes the analytical foundation for its future development. It integrates techno-economic modeling, optimization, scenario analysis, and sensitivity assessment into a unified decision-support methodology. To demonstrate its practical applicability, the framework was applied to four industrial CCS case studies in the cement sector using publicly available technical and economic data. Its analytical core combines technical, economic, and optimization models to evaluate CCS performance under alternative operating conditions. Consequently, the proposed framework provides a methodological basis for the future implementation of fully operational DTs and contributes to the development of intelligent decision-support tools for industrial decarbonization and the sustainable energy transition.

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