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人工知能から再生可能エネルギー移行へ：グリーンイノベーションと制度的準備を通じた経路

From artificial intelligence to renewable energy transition: pathways through green innovation and institutional readiness (原題)

Anis Omri, Fadhila Hamza

Frontiers in Environmental Science📚 査読済 / ジャーナル2026-09-30#AI×ESGOrigin: Global対象セクター: power
DOI: 10.3389/fenvs.2026.1936215
原典: https://doi.org/10.3389/fenvs.2026.1936215
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🤖 gxceed AI 要約

日本語

新興国23カ国・2015〜2023年のパネルデータを用い、AIがグリーン技術革新（GTI）を媒介して再エネ移行を促進する経路を検証。サイバーセキュリティとESGパフォーマンスがこの関係を強化する調整要因として機能することを示した。AI単独では再エネ進展は達成できず、制度・ESG整備が不可欠と結論づける。

English

Using panel data from 23 emerging economies (2015–2023), this study shows AI advances the renewable energy transition partly through green technological innovation. Cybersecurity strengthens AI's effect on both innovation and renewables, while ESG performance amplifies the innovation-to-transition pathway. AI alone is insufficient; institutional and ESG readiness are essential.

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

日本ではSSBJ基準や有報でのサステナビリティ開示が進む中、AI投資とESG体制の相乗効果を示す本論文は、企業のDX×GX戦略立案や政策設計の参考になる。新興国中心の分析だが、制度準備の重要性は日本のGX推進にも示唆を与える。

In the global GX context

As global disclosure frameworks (ISSB, CSRD) increasingly link governance and technology readiness to transition planning, this paper provides empirical evidence that AI's climate contribution is conditional on cybersecurity and ESG institutions. It adds to the growing literature on digitalization as an enabler of energy transition in emerging markets.

👥 読者別の含意

🔬研究者:AI・グリーンイノベーション・ESGの相互作用を条件付きプロセスモデルで分析した実証手法が参考になる。

🏢実務担当者:AI導入とESG体制整備を同時に進めることが再エネ調達や脱炭素目標達成に有効である可能性を示唆。

🏛政策担当者:AI能力・サイバーセキュリティ・ESG制度を一体で強化する政策パッケージの必要性を裏付ける。

📄 Abstract（原文）

As emerging economies seek to decarbonize energy systems and advance the Sustainable Development Goals (SDGs), particularly SDG seven on affordable and clean energy, artificial intelligence (AI) is becoming a transformative capability, yet its contribution to the renewable energy transition remains insufficiently explained. This study examines how AI is associated with the renewable energy transition in 23 emerging economies from 2015 to 2023, with particular attention to the mechanisms and conditions that shape this relationship. It develops a conditional process framework in which green technological innovation (GTI) serves as the transmission mechanism, cybersecurity conditions the AI–GTI and AI–renewable energy transition relationships, together with environmental, social, and governance (ESG) performance conditions the GTI–renewable energy transition pathway. Hayes’s PROCESS macro estimates direct, mediating, moderating, and conditional indirect effects, while two-step System GMM verifies the consistency of the PROCESS-based findings. The results show a positive indirect association between AI and renewable energy transition through GTI, while the direct association remains significant, indicating partial mediation. Cybersecurity strengthens the contribution of AI to both GTI and renewable energy transition; it also increases the indirect AI–GTI–renewable energy transition effect. ESG performance strengthens GTI’s contribution to the renewable energy transition and, consequently, amplifies AI’s indirect effect through GTI. System GMM corroborates the direct, mediating, and moderating relationships. These findings indicate that AI alone cannot deliver renewable energy progress. Policymakers should strengthen AI capability, green innovation, cybersecurity readiness, and ESG-oriented institutions to help digital intelligence support scalable renewable energy outcomes and progress toward SDG 7.

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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。