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January 23, 2026Energies0 citationsOpen Access

Determinants of Green Energy Penetration in N-11 Countries: A Machine Learning Analysis

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NANajabat AliMSMd Reza Sultanuzzaman

Key Points

  • The study aims to assess the factors influencing the adoption of green energy in N-11 economies from 2000 to 2022.
  • Employed a second-generation panel econometric and machine-learning framework.
  • Conducted tests for cross-sectional dependence and slope homogeneity.
  • Utilized CADF and CIPS unit root tests, along with the Westerlund cointegration approach.
  • Estimated long-run effects using Partialing-Out LASSO and Cross-Fit machine-learning estimators.
  • Conducted SHAP analysis to interpret nonlinear and heterogeneous effects.
  • Green transition, governance quality, and urbanization promote green energy penetration.
  • Foreign direct investment and industrial growth have adverse effects on green energy adoption.
  • Findings highlight the significance of coordinated investment and institutional capacity for renewable energy transitions.

Abstract

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.

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Cite This Study

Ali et al. (2026) studied this question.

synapsesocial.com/papers/69731047c8125b09b0d1fff2https://doi.org/10.3390/en19020541
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