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Prim-Lex Theory and Energy Transition: Eight-Dimensional Phase-Transition Governance from Fossil Fuels to Renewable Energy ——An Energy Transition Pathway Planning Framework Based on the Eight-Dimensional Framework of Prim-Lex Theory and the CLL Phase-Transition Early Warning Model

プリム・レックス理論とエネルギー転換:化石燃料から再生可能エネルギーへの八次元相転移ガバナンス——プリム・レックス理論の八次元フレームワークとCLL相転移早期警戒モデルに基づくエネルギー転換経路計画フレームワーク (AI 翻訳)

Shen Xiaowang

Zenodo (CERN European Organization for Nuclear Research)プレプリント2026-07-18#エネルギー転換対象セクター: cross_sector
DOI: 10.5281/zenodo.21426503
原典: https://doi.org/10.5281/zenodo.21426503

🤖 gxceed AI 要約

日本語

本論文は、エネルギー転換を相転移プロセスと捉え、プリム・レックス理論の八次元フレームワークとCLL相転移早期警戒モデルを適用した経路計画手法を提案する。IEAやIRENAのデータを用いて、新旧エネルギー並存の現状を分析し、転換の臨界点を特定するための定量化可能な意思決定ツールを提示する。

English

This paper proposes an energy transition pathway planning framework based on Prim-Lex Theory's eight-dimensional framework and the CLL phase-transition early warning model. It analyzes the current parallel expansion of fossil and renewable energy using IEA and IRENA data, offering a quantifiable decision-making tool to identify critical transition windows.

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政策(GX推進戦略、2050年カーボンニュートラル)において、エネルギー転換の段階的管理は重要課題。本モデルは、日本のエネルギー政策の進捗評価や、産業界の投資判断に応用可能な枠組みを提供する。

In the global GX context

Globally, the energy transition is a central theme in climate policy. This framework offers a novel approach to managing transition risks, complementing existing models like TCFD and transition finance frameworks by providing a structured method for identifying critical windows.

👥 読者別の含意

🔬研究者:Provides a new theoretical framework for modeling energy transition dynamics, potentially useful for further empirical testing.

🏢実務担当者:Offers a structured approach for corporate energy transition planning and risk assessment, though practical application requires further validation.

🏛政策担当者:Suggests a tool for monitoring energy transition progress and identifying critical intervention points, relevant for national energy policy design.

📄 Abstract(原文)

The global energy transition stands at a historic threshold, crossing from the “fossil fuel era” to the “renewable energy era.” According to the IEA World Energy Investment 2026 report, global energy investment is projected to reach $3.4 trillion in 2026, with clean energy investment at approximately $2.2 trillion and fossil fuel investment at approximately $1.2 trillion—clean energy investment now nearly double that of fossil fuels. IRENA data shows that global renewable energy capacity added 692 GW in 2025, reaching a total of 5,149 GW, accounting for 49% of global installed power capacity. However, coal investment has paradoxically risen to $180 billion, the highest since 2012; natural gas investment reached $330 billion, the highest in nearly a decade. This pattern of “parallel expansion of old and new energy” reveals that the energy transition is not a smooth linear substitution process, but a complex system evolution fraught with phase-transition risks. Based on the eight-dimensional framework of Prim-Lex Theory, this paper applies, for the first time, the CLL (Capital Market Condensation Level) phase-transition early warning model to energy transition pathway planning. This paper elaborates, dimension by dimension, the calculation principles and data collection methods for each of the eight dimensions in energy transition governance. Using three empirical cases——the global renewable energy “tripling” target, the paradoxical growth of coal investment, and the energy transition financing gap in developing countries——it demonstrates the standardized application pathway of the CLL phase-transition early warning model in identifying the “critical window” of energy systems, providing a quantifiable, programmable, and auditable mathematical language and decision-making tool for the transition of global energy governance from “passive response” to “active phase-transition management.”

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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。