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Sustainability of the European Energy System: The Evolution of the Energy Transition, Renewable Energy, and Energy Conservation

欧州エネルギーシステムの持続可能性：エネルギー転換、再生可能エネルギー、省エネの進化 (AI 翻訳)

Eugen Iavorschi, Laurențiu Dan Milici, Ioan Taran, Zvika Israeli

Sustainability📚 査読済 / ジャーナル2026-04-19#エネルギー転換Origin: EU
DOI: 10.3390/su18084046
原典: https://doi.org/10.3390/su18084046
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🤖 gxceed AI 要約

日本語

本論文は、欧州連合（EU）のエネルギー転換と再生可能エネルギー普及の現状を分析し、行動介入による省エネの可能性を探る。2023年にはEUの電力の約46%を再生可能エネルギーが占め、政策と投資が進展を支えた。一方、変動性の高い再生可能エネルギーの統合による系統安定化課題に対し、情報提供やリアルタイムフィードバック、動的価格設定などの行動的対策が有効であることを示す。これらの対策はピーク負荷低減や消費柔軟性向上に寄与し、蓄電システムと併せて系統安定化と気候中立目標達成に貢献する。

English

This paper analyzes the structural transformation of the EU energy mix, with renewables generating 46% of electricity in 2023. It examines operational challenges from rapid renewable integration and the role of behavioral interventions (information campaigns, real-time feedback, dynamic pricing) in enhancing energy system stability. Results show these interventions reduce peak loads and increase consumption flexibility, complementing energy storage for achieving EU sustainability goals.

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

EUのエネルギー転換政策は日本のGX戦略にも示唆を与える。行動介入による省エネは、日本の家庭部門における需要側対策として参考になる。ただし、日本の電力市場構造や再生可能エネルギー比率の違いに留意が必要。

In the global GX context

This paper provides empirical evidence on behavioral interventions for grid stability, relevant for global energy transition policy. While focused on the EU, its findings on demand-side flexibility and non-technological measures offer insights for regions integrating high shares of renewables, including Japan's ongoing energy transition.

👥 読者別の含意

🔬研究者:Energy transition researchers can use this paper's analysis of behavioral interventions and system integration challenges.

🏢実務担当者:Utility companies and energy managers can apply insights on demand response and dynamic pricing for grid stability.

🏛政策担当者:EU and other policymakers can leverage behavioral measures as cost-effective complements to storage for renewable integration.

📄 Abstract（原文）

Energy efficiency improvement represents a central strategic objective of the European Union (EU), essential for mitigating climate change and facilitating the transition toward a sustainable energy system. In 2023, renewable energy sources generated approximately 46% of the electricity produced in the EU, becoming the dominant component of the regional energy mix. This progress has been supported by coherent public policies, dedicated investment programs, and regulatory mechanisms aimed at accelerating the adoption of sustainable technologies. However, the existing literature highlights a research gap regarding the relationship between the dynamics of the European energy transition, the operational challenges generated by the rapid increase in the share of renewable energy sources, and the potential for energy savings in the residential sector through non-technological interventions. This paper analyzes the structural transformations of the European energy mix, the limitations of energy systems in the context of accelerated renewable energy integration, and the role of behavioral interventions in supporting the stability of the energy system. The study examines the dynamics of residential energy consumption, behavioral determinants of energy use, and the effectiveness of instruments such as information campaigns, real-time feedback, dynamic pricing, and demand response programs. The results indicate that these interventions can reduce peak loads, increase consumption flexibility, and alleviate pressure on energy networks under conditions of variable renewable energy generation. The integration of energy storage systems and the implementation of low-cost behavioral measures can act as complementary instruments for maintaining the dynamic stability of the energy system and for achieving the EU’s sustainability and climate neutrality objectives.

🔗 Provenance — このレコードを発見したソース

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。