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Crude Oil Prices Forecasting in the Energy Transition Era: Evidence from Geopolitical and Technological Drivers

エネルギー移行期における原油価格予測：地政学的・技術的要因からの証拠 (AI 翻訳)

Asaad Sendi, Dalia Atif, Salim Bourchid Abdelkader, Kamel Si Mohammed

Energies📚 査読済 / ジャーナル2026-05-10#エネルギー転換Origin: Global
DOI: 10.3390/en19102302
原典: https://doi.org/10.3390/en19102302
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🤖 gxceed AI 要約

日本語

本研究は、エネルギー移行期における原油価格変動を分析し、ETS炭素価格、AI活動、EV市場、地政学的リスクが予測に与える影響を調査。重尾分布LSTMモデルを用いて、ETSが平均リターンに影響する一方、AIとEVはボラティリティに影響することを発見。結果は、エネルギー移行下では原油市場の予測可能性がリスク次元に集中することを示す。

English

This study analyzes crude oil return dynamics during the energy transition using a heavy-tailed LSTM model incorporating carbon allowance returns (ETS), AI, EV markets, and geopolitical risk. It finds that ETS significantly predicts mean returns, while AI and EV affect volatility. Out-of-sample results show limited mean predictability but improved tail risk forecasting, indicating that oil market predictability shifts to risk dimensions under transition dynamics.

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

本論文は日本固有の分析ではないが、炭素価格（ETS）やEV普及が原油市場に与える影響を示しており、エネルギー安全保障とGX政策の兼ね合いを考える日本の政策担当者やエネルギー関連企業にとって示唆に富む。

In the global GX context

This paper contributes to the global understanding of how climate policy signals (ETS) and technological innovation (AI, EV) affect oil price dynamics, which is crucial for risk management and investment strategies in the energy transition. It highlights the shift from mean to risk predictability in commodity markets.

👥 読者別の含意

🔬研究者:Provides a novel framework linking transition variables to oil price distribution and tail risk, useful for energy finance and climate policy research.

🏢実務担当者:Offers insights for energy traders and risk managers on which transition factors drive volatility and tail risk, aiding portfolio hedging.

🏛政策担当者:Demonstrates that carbon pricing (ETS) can influence oil returns, supporting the case for carbon markets as a tool for steering energy transition.

📄 Abstract（原文）

This study examines crude oil return dynamics in the context of the global energy transition, where decarbonization policies, technological innovation, and shifting energy demand increasingly influence market behavior. We propose a heavy-tailed distributional LSTM framework to jointly model the conditional mean, volatility, and tail risk of West Texas Intermediate (WTI) returns, incorporating key transition-related drivers: carbon allowance returns (ETS), artificial intelligence (AI) activity, electric vehicle (EV) market returns (SPKS), and geopolitical risk (GPR). Granger causality results show that ETS significantly predicts mean returns, reflecting the growing impact of climate policy signals, while AI and EV markets primarily affect volatility, indicating transmission through uncertainty channels. The model adopts a Student-t specification to capture heavy-tailed behavior and extreme price movements. Out-of-sample results reveal limited mean predictability but improved forecasting of return magnitude and tail risk. These findings highlight that, under energy transition dynamics, oil market predictability is increasingly concentrated in the risk dimension rather than in average returns.

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