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Impact of artificial intelligence and energy-technology R&D on energy transition in the United States

米国における人工知能とエネルギー技術研究開発がエネルギー転換に与える影響 (AI 翻訳)

Seyed Alireza Athari, Berna Uzun, Mohamed Djafar Henni, Dilber Uzun Ozsahin, Adeola Praise Adepoju

Energy Reports📚 査読済 / ジャーナル2026-07-10#エネルギー転換Origin: US対象セクター: power
DOI: 10.1016/j.egyr.2026.109521
原典: https://doi.org/10.1016/j.egyr.2026.109521

🤖 gxceed AI 要約

日本語

米国の四半期データ（1980～2021年）にウェーブレット分位回帰を適用。AIは再生可能エネルギー消費に主に負の影響を与え、エネルギー技術R&Dは短期・高分位で効果的。金融発展が最大の正の要因で、貿易のグローバル化は中期的に寄与する一方、経済成長は在来型エネルギー構造下で再エネ消費を減らす。

English

Using wavelet quantile regression on US quarterly data (1980–2021), this paper finds that AI exerts a mostly negative effect on renewable energy consumption, while energy-technology R&D helps mainly at higher quantiles in the short run. Financial development is the strongest positive driver; trade globalization supports medium-term through clean supply chains, and economic growth tied to conventional energy reduces renewables. The authors offer targeted policy recommendations.

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

米国での実証だが、日本でもAI投資の電力消費増と再エネ導入の関係が論点となる。SSBJ対応で再エネ調達を検討する企業や政策立案者にとって、AIと再エネの負の関係やR&D・金融発展の重要性を示す知見は参考になる。

In the global GX context

For global GX scholarship, this adds US long-run evidence that AI expansion may initially crowd out renewable consumption, and that energy R&D and financial development are key enablers. It informs ISSB/CSRD-aligned transition planning by showing where policy and investment should be directed.

👥 読者別の含意

🔬研究者:Useful for those studying the macro-level interplay of AI, R&D, and renewable energy adoption with distributional methods.

🏛政策担当者:Relevant for designing R&D and financial-development policies to accelerate renewable transition.

📄 Abstract（原文）

As artificial intelligence reshapes the global economy, a critical question emerges: can digital transformation accelerate the renewable energy transition, or will it intensify pressure on clean-energy systems? To this end, this study explores the drivers of renewable energy consumption using a wavelet quantile-based approach and quarterly data from 1980Q1 to 2021Q4. The results show that artificial intelligence mostly exerts a negative effect on renewable energy consumption, especially in the short and long run. Energy-technology R&D improves renewable energy consumption mainly at higher quantiles in the short run, while its medium- and long-term effects are mixed. Financial development provides the strongest positive support, particularly at lower and higher quantiles. Trade globalization has mixed short-run effects but becomes more supportive in the medium term through technology transfer, renewable-energy imports, and clean supply chains. In contrast, economic growth mostly reduces renewable energy consumption in the medium and long run when tied to conventional energy structures. Robustness checks confirm that WQQR captures richer distributional patterns than WQR, while wavelet Granger causality reveals strong feedback linkages among the variables. Building on the empirical evidence, this study offers targeted and actionable policy recommendations.

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