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<p>This study examines whether artificial intelligence, natural resource extraction, and institutional quality promote green growth across G-20 nations from 2000 to 2023 within the framework of the United Nations Sustainable Development Goals. The second-generation econometric methods, including the Westerlund Bootstrap co-integration, Wavelet power spectrum and coherence analysis, Dumitrescu-Hurlin panel causality, and Driscoll &amp; Kraay robust methods, the study captures both log-and short-run dynamics. The empirical findings reveal a stable log-run co-integration relationship among the variables, while wavelet coherence analysis confirms that positive co-movements of institutional quality, artificial intelligence, and natural resource extraction with green growth across multiple time frame frequency bands. The robust analysis outcomes corroborate these results, indicating that improvements in institutional quality, artificial intelligence, and natural resource utilization significantly contribute to achieving Sustainable Development Goals 8 and 13. The Dumitrescu-Hurlin causality test results further indicate that unidirectional causality from the explanatory variables to green growth, except for environmental regulations, which exhibit bidirectional causality. This study recommends that policymakers should focus on strengthening institutional quality, expanding the application of artificial intelligence across various sectors, and promoting sustainable natural resource management to enhance green growth trajectories within the G-20 nations.</p>

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Do artificial intelligence, institutional quality, and natural resources encourage green growth in the G-20 states? An insight-based wavelet analysis under the SDGs framework

  • 期刊论文

  • 发表日期：

    2026年2月2日

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  • Xiaochun Luo,
  • Jianyu Liu,
  • Asad Amin,
  • Abbas Ali Chandio,
  • Adil Hameed Shah

摘要

This study examines whether artificial intelligence, natural resource extraction, and institutional quality promote green growth across G-20 nations from 2000 to 2023 within the framework of the United Nations Sustainable Development Goals. The second-generation econometric methods, including the Westerlund Bootstrap co-integration, Wavelet power spectrum and coherence analysis, Dumitrescu-Hurlin panel causality, and Driscoll & Kraay robust methods, the study captures both log-and short-run dynamics. The empirical findings reveal a stable log-run co-integration relationship among the variables, while wavelet coherence analysis confirms that positive co-movements of institutional quality, artificial intelligence, and natural resource extraction with green growth across multiple time frame frequency bands. The robust analysis outcomes corroborate these results, indicating that improvements in institutional quality, artificial intelligence, and natural resource utilization significantly contribute to achieving Sustainable Development Goals 8 and 13. The Dumitrescu-Hurlin causality test results further indicate that unidirectional causality from the explanatory variables to green growth, except for environmental regulations, which exhibit bidirectional causality. This study recommends that policymakers should focus on strengthening institutional quality, expanding the application of artificial intelligence across various sectors, and promoting sustainable natural resource management to enhance green growth trajectories within the G-20 nations.