Asymmetric effects of renewable energy and artificial intelligence on green growth: evidence from G20 countries

This study investigates the impact of renewable energy (RE) and artificial intelligence (AI) on green growth in 18 G20 countries from 2000 to 2023, employing Cross-Sectional Pooled Mean Group ARDL (CS-PMG-ARDL) and Nonlinear ARDL (CS-PMG-NARDL) models to capture symmetric and asymmetric dynamics. The bounds test confirms cointegration (F = 28.27, p < 0.001), and the error correction term indicates stable long-run adjustment (ECT = −0.145, p < 0.001 in ARDL; ECT = −0.115, p = 0.024 in NARDL). Results reveal that renewable energy exerts a positive and significant long-run effect on green growth (0.101, p < 0.001), with asymmetric responses confirmed by Wald tests (short-run χ² = 4.102, p = 0.043; long-run χ² = 5.42, p = 0.020): positive RE shocks yield stronger benefits (0.012, p = 0.011) than the adverse effects of negative shocks, which are statistically weaker in the short run ( − 0.020, p = 0.111) and significant but smaller in the long run ( − 0.012, p = 0.015). AI shows a significant short-run impact (0.018, p < 0.001) but becomes insignificant in the long run (0.001, p = 0.806), suggesting its effects are conditional on institutional and technological contexts. Critically, GMM estimations highlight significant synergistic interaction effects: the RE×AI term is positive (0.007, p = 0.004), indicating that AI amplifies the marginal contribution of renewable energy to green growth, while the RE×CO 2 interaction is negative ( − 0.041, p < 0.001), underscoring that high emissions undermine renewable energy benefits. The models exhibit strong explanatory power (adjusted R² = 0.909 for ARDL; 0.999 for NARDL) and pass all diagnostic tests for instrument validity and absence of second-order autocorrelation. The study provides tailored policy recommendations for G20 nations, emphasizing integrated strategies that combine green AI applications, renewable energy expansion, and institutional reforms to foster resilient, low-carbon economic growth.

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Publication Details

Journal
Humanities and Social Sciences Communications
Published
2026-07-10
DOI
https://doi.org/10.1057/s41599-026-08130-x
Primary Topic
Energy, Environment, Economic Growth
Type
article
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Asymmetric effects of renewable energy and artificial intelligence on green growth: evidence from G20 countries

Sourour Guidara, Maha Bouattour, Kamel Helali, Olfa ZARRAD
Humanities and Social Sciences Communications
Energy, Environment, Economic Growth
article

Asymmetric effects of renewable energy and artificial intelligence on green growth: evidence from G20 countries

Sourour Guidara, Maha Bouattour, Kamel Helali, Olfa ZARRAD
article en

Abstract

This study investigates the impact of renewable energy (RE) and artificial intelligence (AI) on green growth in 18 G20 countries from 2000 to 2023, employing Cross-Sectional Pooled Mean Group ARDL (CS-PMG-ARDL) and Nonlinear ARDL (CS-PMG-NARDL) models to capture symmetric and asymmetric dynamics. The bounds test confirms cointegration (F = 28.27, p < 0.001), and the error correction term indicates stable long-run adjustment (ECT = −0.145, p < 0.001 in ARDL; ECT = −0.115, p = 0.024 in NARDL). Results reveal that renewable energy exerts a positive and significant long-run effect on green growth (0.101, p < 0.001), with asymmetric responses confirmed by Wald tests (short-run χ² = 4.102, p = 0.043; long-run χ² = 5.42, p = 0.020): positive RE shocks yield stronger benefits (0.012, p = 0.011) than the adverse effects of negative shocks, which are statistically weaker in the short run ( − 0.020, p = 0.111) and significant but smaller in the long run ( − 0.012, p = 0.015). AI shows a significant short-run impact (0.018, p < 0.001) but becomes insignificant in the long run (0.001, p = 0.806), suggesting its effects are conditional on institutional and technological contexts. Critically, GMM estimations highlight significant synergistic interaction effects: the RE×AI term is positive (0.007, p = 0.004), indicating that AI amplifies the marginal contribution of renewable energy to green growth, while the RE×CO 2 interaction is negative ( − 0.041, p < 0.001), underscoring that high emissions undermine renewable energy benefits. The models exhibit strong explanatory power (adjusted R² = 0.909 for ARDL; 0.999 for NARDL) and pass all diagnostic tests for instrument validity and absence of second-order autocorrelation. The study provides tailored policy recommendations for G20 nations, emphasizing integrated strategies that combine green AI applications, renewable energy expansion, and institutional reforms to foster resilient, low-carbon economic growth.

Humanities and Social Sciences Communications
University of Sfax (TN), Imam Mohammad ibn Saud Islamic University (SA)
Affordable and clean energy
Openalex Percentile: Top 3%
Energy, Environment, Economic Growth
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