低炭素エネルギー転換のための人工知能、技術革新、規制の質：所得グループ間の比較証拠
Artificial intelligence, technological innovation, and regulatory quality for low-carbon energy transition: comparative evidence across income groups (原題)
Fahrettin Pala, Emine Kaya, Esra Nur Akpınar, Abdulmuttalip Pilatin, Abdülkadir Barut, Abdel‐Aziz Ahmad Sharabati
🤖 gxceed AI 要約
日本語
本研究は、2002〜2019年の50か国のパネルデータを用いて、AI、再生可能エネルギー技術革新（RETI）、規制の質（RQ）が低炭素エネルギー転換に与える影響を所得グループ別に分析。AIは全所得層で転換を促進する一方、RQは高所得国で阻害、中低所得国で促進。RETIは高所得国でのみ効果的。
English
Using panel data from 50 countries (2002-2019), this study examines how AI, renewable energy technology innovation (RETI), and regulatory quality (RQ) affect low-carbon energy transition. AI promotes transition across all income groups, while RQ constrains it in high-income but stimulates it in middle/low-income countries. RETI is effective only in high-income countries.
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
日本のGX実践では、AIを活用したエネルギー効率化や系統最適化が重要。規制の質が高所得国で転換を阻害するという示唆は、日本の規制設計やSSBJ対応に示唆を与える。
In the global GX context
This study provides cross-country evidence on AI's role in energy transition, relevant to global climate policy and ISSB/TCFD frameworks. It highlights how regulatory quality and innovation capacity shape decarbonization pathways, informing differentiated policy approaches.
👥 読者別の含意
🔬研究者:AIとエネルギー転換の関係を所得グループ別に分析した実証手法と結果が参考になる。
🏢実務担当者:AI活用によるエネルギー効率化や再生可能エネルギー統合のビジネス機会を示唆。
🏛政策担当者:規制の質が国によって異なる影響を与えるため、政策設計に示唆を与える。
📄 Abstract（原文）
This study examines how artificial intelligence (AI), renewable energy technology innovation (RETI), and regulatory quality (RQ) shape the low-carbon energy transition across countries at different income levels. Using panel data from 50 high-, middle-, and low-income countries over the period 2002–2019, the low-carbon energy transition is measured by the share of renewable energy in total final energy consumption, which represents a key pathway for reducing fossil-fuel dependence and supporting carbon mitigation. The empirical analysis applies two-way fixed-effects and instrumental-variable (2SLS) models, complemented by several panel econometric and robustness tests. The findings show that AI significantly promotes the energy transition across all income groups by improving energy efficiency, system optimization, and the integration of renewable resources. Regulatory quality constrains the transition in high-income countries but stimulates it in middle- and low-income countries, indicating that the effectiveness of carbon-related regulatory frameworks varies according to countries’ institutional and developmental conditions. RETI consistently accelerates the transition in high-income countries, whereas its effects are weak or inconsistent in middle- and low-income countries, reflecting persistent disparities in technological capacity, financing, and innovation infrastructure. Overall, the results demonstrate that AI, technological innovation, and regulatory institutions jointly shape countries’ capacity to shift toward low-carbon energy systems. Although the study does not directly estimate carbon emissions, it identifies the institutional and technological mechanisms through which renewable energy expansion can contribute to decarbonization and carbon management objectives. The findings support differentiated policies for strengthening AI capabilities, facilitating clean-technology transfer, and aligning regulatory frameworks with national carbon-mitigation strategies.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1186/s13021-026-00497-3first seen 2026-08-22 04:52:27
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