Energy Transition Pathways Involving Fossil Fuels and Low‐Carbon Technologies and Sustainable Finance
化石燃料と低炭素技術を含むエネルギートランジション経路とサステナブルファイナンス (AI 翻訳)
Maoran He, Mohamed Djafar Henni, Kamel Si Mohammed, S. Makhmudov, Salim Bourchid Abdelkader
🤖 gxceed AI 要約
日本語
本稿は高頻度データを用いて、化石燃料と低炭素技術間の構造的相互連関を定量分析。VAR-GFEVDとDCC-GARCHにより、石炭・天然ガスがボラティリティの送信者、再生可能エネルギーが受信者であることを示す。ポートフォリオ分析では、多様化効果は技術分類ではなく経済的リンケージに依存。シナリオ分析では、高革新経路が2050年にシステムコスト45 USD/MWh、排出80%削減を達成し、システム連結性が低下することを予測。
English
This study quantitatively analyzes structural interactions between fossil fuels and low-carbon technologies using high-frequency data. Results show coal and natural gas as dominant volatility transmitters, while renewables are net receivers. Portfolio diversification benefits depend on economic linkages rather than technology classification. Scenario projections indicate high-innovation pathways achieve system costs ~45 USD/MWh by 2050 and 80% emission reductions, with reduced system connectedness.
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
日本はエネルギー安全保障と脱炭素の両立を模索しており、本論文の技術間の連関性やシナリオ分析は、電源構成の最適化や政策の安定性の重要性を示唆する。特に水素や蓄電池を含む分析は、日本の水素戦略や蓄電普及にも示唆を与える。
In the global GX context
This paper provides quantitative evidence on how innovation and policy stability reduce systemic risk in energy transitions. Its findings on volatility transmission and portfolio diversification are relevant for global investors and policymakers navigating the shift to low-carbon energy, aligning with TCFD/ISSB risk disclosure frameworks.
👥 読者別の含意
🔬研究者:Offers a comprehensive methodological framework (VAR-GFEVD, DCC-GARCH, portfolio optimization) for analyzing energy technology interactions and system costs.
🏢実務担当者:Portfolio and risk managers can use the correlation and diversification insights to optimize energy asset allocation and assess transition risks.
🏛政策担当者:Scenario projections underscore the role of innovation and stable policies in achieving cost-effective and resilient energy transitions.
📄 Abstract（原文）
This study provides a quantitative econometric analysis of structural interactions between fossil‐fuel and low‐carbon energy technologies using high‐frequency data for coal, natural gas, crude oil, solar photovoltaics (PVs), onshore wind, hydrogen, and battery storage. The analysis integrates VAR–GFEVD connectedness modeling, dynamic conditional correlation generalized autoregressive conditional heteroskedasticity (DCC–GARCH) dynamic correlation estimation, portfolio optimization, and scenario‐based projections to evaluate volatility transmission, dependency structures, and long‐term system evolution. The results show that fossil‐fuel technologies, particularly coal and natural gas, act as dominant volatility transmitters, while renewable technologies primarily function as net receivers of shocks. Dynamic correlations reveal persistent coupling between natural gas and solar PV, alongside gradual decoupling trends for wind technologies. Portfolio analysis indicates that diversification benefits depend on underlying economic linkages rather than simple technological classification. Regression results further demonstrate that system connectedness is jointly influenced by market volatility, technological cost dynamics, and policy‐related factors. Scenario projections indicate that high‐innovation pathways achieve the most favorable outcomes, system costs declining to ~45 USD/MWh by 2050, emission reductions approaching 80%, and system connectedness decreasing to around 50%–55%. These findings highlight the importance of technological innovation, renewable deployment, and policy stability in reducing systemic risk and enabling structurally resilient energy transitions.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1155/er/4859998first seen 2026-06-26 05:46:17
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