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Time-Varying and Multi-Scale Dynamics Between Renewable Energy, Oil Prices, Climate Policy Uncertainty and CO2 Emissions

再生可能エネルギー、原油価格、気候政策の不確実性とCO2排出量の間の時間変動およびマルチスケールダイナミクス (AI 翻訳)

Elif Kaya, Mortaza Ojaghlou, Özge DEMİRKALE

Sustainability📚 査読済 / ジャーナル2026-04-20#再生可能エネルギーOrigin: Global
DOI: 10.3390/su18084093
原典: https://doi.org/10.3390/su18084093
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🤖 gxceed AI 要約

日本語

トルコを対象に、1987年から2024年までの四半期データを用いて、CO2排出量と原油価格、再生可能エネルギー導入、気候政策不確実性との時間周波数ダイナミクスを分析。ローリングウィンドウNARDLモデルとウェーブレットコヒーレンス分析を組み合わせ、非対称効果とマルチスケール伝達メカニズムを捉えた。原油価格下落は上昇よりも大きなCO2増加をもたらす負の非対称性が確認され、再生可能エネルギーは長期的に排出削減に寄与する。気候政策不確実性は短中期の排出経路に断片的な影響を与える。

English

This study analyzes the time-frequency dynamics between CO2 emissions and oil prices, renewable energy, and climate policy uncertainty in Turkey from 1987Q2 to 2024Q1 using a rolling-window NARDL and wavelet coherence analysis. Findings show persistent asymmetries: oil price decreases stimulate CO2 emissions more than increases reduce them. Renewable energy has a stable long-run negative relationship with emissions, concentrated over medium-to-long horizons. Climate policy uncertainty disrupts short-to-medium-term emission trajectories. The results highlight the need for temporally calibrated policies such as counter-cyclical carbon pricing and credible long-term frameworks.

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

本論文はトルコを対象としているが、時間領域と周波数領域を組み合わせた分析手法は日本のエネルギー・環境政策評価にも応用可能。特に、気候政策の不確実性が排出に与える影響をマルチスケールで捉える点は、日本の長期的な脱炭素戦略策定において示唆に富む。

In the global GX context

This paper adds to global GX scholarship by providing empirical evidence on the asymmetric and multi-scale effects of oil prices, renewable energy, and climate policy uncertainty on CO2 emissions. The methodological integration of rolling-window NARDL and wavelet coherence is relevant for policy evaluation in other countries. The finding that oil price decreases have a stronger emission-increasing effect than price increases offers insights for carbon pricing design.

👥 読者別の含意

🔬研究者:Time-frequency econometric methods (wavelet + NARDL) are valuable for analyzing non-linear, evolving energy-environment relationships.

🏢実務担当者:Corporate sustainability teams can use the insights on renewable energy's long-term emission reduction role to strengthen investment cases.

🏛政策担当者:The asymmetric oil price effect suggests that carbon pricing should be counter-cyclical to offset emission rebounds during price drops.

📄 Abstract（原文）

This study examines the time–frequency dynamics between CO2 emissions and their determinants—oil prices, renewable energy deployment, and climate policy uncertainty—in Türkiye from 1987Q2 to 2024Q1. We integrate a rolling-window Nonlinear Autoregressive Distributed Lag (NARDL) model with wavelet coherence analysis to capture evolving asymmetric effects and multi-scale transmission mechanisms. Our findings reveal pronounced, persistent asymmetries. Oil price decreases stimulate CO2 emissions substantially more than equivalent price increases reduce them, yielding a negative asymmetry effect. Renewable energy demonstrates a stable, negative long-run relationship with emissions, with wavelet analysis indicating this effect concentrates over medium-to-long-term horizons, underscoring its structural decarbonization role. Climate policy uncertainty exerts fragmented, episodic influences, disrupting short-to-medium-term emission trajectories. Rolling-window estimates confirm these asymmetric relationships shift markedly around structural breaks, including the 2001 domestic crisis and the 2008 global financial crisis. The study concludes that effective decarbonization requires temporally calibrated policies: counter-cyclical carbon pricing to offset oil price asymmetries, and credible long-term frameworks to sustain renewable energy investments. Methodologically, the results demonstrate the value of combining time-domain and frequency-domain techniques to diagnose complex, evolving interactions in the energy–environment nexus.

🔗 Provenance — このレコードを発見したソース

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