Determinants of Green Energy Penetration in N-11 Countries: A Machine Learning Analysis
N-11諸国におけるグリーンエネルギー普及の決定要因：機械学習分析 (AI 翻訳)
Najabat Ali, Md Reza Sultanuzzaman
🤖 gxceed AI 要約
日本語
本研究はN-11諸国（2000-2022年）のグリーンエネルギー普及要因を機械学習（LASSO, Cross-Fit, SHAP）で分析。グリーン移行、ガバナンス品質、都市化が普及を促進する一方、FDIと産業成長は炭素集約型構造により抑制効果を持つ。政策提言として調整された投資戦略と制度強化の重要性を強調。
English
This study uses machine learning (LASSO, Cross-Fit, SHAP) to analyze determinants of green energy penetration in N-11 countries (2000-2022). It finds that green transition, governance quality, and urbanization promote penetration, while FDI and industrial growth have adverse effects due to carbon-intensive structures. Policy implications emphasize coordinated investment strategies and institutional strengthening.
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N-11諸国を対象としており、日本企業が新興国での再エネ投資を検討する際の参考になる。特にFDIの負の効果は、日本の海外投資の質的転換（グリーンFDI）の必要性を示唆。ただし直接的な日本政策への応用には限界がある。
In the global GX context
This paper provides empirical evidence on renewable energy determinants in fast-growing emerging economies, relevant for global energy transition strategies. The finding that FDI can hinder green energy penetration highlights the need for green FDI policies. For global audiences, it offers insights into balancing industrial growth with renewable adoption.
👥 読者別の含意
🔬研究者:機械学習を用いた再生可能エネルギー要因分析の手法として参考になる。
🏢実務担当者:新興国市場における再エネ投資戦略の立案に役立つ知見を提供。
🏛政策担当者:FDI規制や制度強化を通じた再エネ促進政策の設計に示唆を与える。
📄 Abstract（原文）
This study investigates the determinants of green energy penetration in the Next Eleven (N-11) economies over the period 2000–2022, with a particular focus on the roles of foreign direct investment (FDI), green transition, governance quality, industrial growth, and urbanization. The primary objective of the study is to assess how investment flows, structural transformation, and institutional capacity jointly shape the adoption of renewable energy in fast-growing emerging economies. To achieve this goal, the study employs a second-generation panel econometric and machine-learning framework that accounts for cross-sectional dependence, slope heterogeneity, and long-run equilibrium relationships. Specifically, cross-sectional dependence and slope homogeneity tests are conducted, followed by CADF and CIPS unit root tests and the Westerlund cointegration approach. Long-run effects are then estimated using Partialing-Out LASSO and Cross-Fit machine-learning estimators, complemented by SHAP analysis to interpret nonlinear and heterogeneous effects. The results indicate that green transition, governance quality, and urbanization significantly promote green energy penetration. In contrast, FDI and industrial growth exert adverse effects, reflecting carbon-intensive investment and production structures. The findings highlight the importance of coordinated investment strategies, institutional strengthening, and urban planning in accelerating renewable energy transitions in emerging economies. These results provide policy-relevant insights for achieving sustainable energy development while supporting long-term economic growth in the N-11 countries.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.3390/en19020541first seen 2026-05-15 20:07:00
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