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<p>This study examines the relationship between renewable energy use, industrialization, governance effectiveness, population growth, and technological innovation, and sovereign environmental, social, and governance (ESG) performance, used as a proxy for sustainable development, in China over the period 1996–2023. The analysis employs the Autoregressive Distributed Lag (ARDL) bounds testing approach to capture both short-run dynamics and long-run relationships among the variables. The findings indicate that population growth is negatively associated with ESG performance, whereas governance quality, renewable energy use, industrialization, and technological innovation are positively associated with improvements in ESG outcomes. To ensure robustness, the long-run estimates are validated using Fully Modified Ordinary Least Squares (FMOLS), Dynamic Ordinary Least Squares (DOLS), and Canonical Cointegrating Regression (CCR), and a series of diagnostic tests confirms the model’s stability and reliability. Granger causality results suggest unidirectional predictive relationships from renewable energy, industrialization, and population growth to ESG, as well as bidirectional predictive linkages between technological innovation and ESG. Overall, the findings highlight the importance of institutional quality, technological progress, and the energy transition in shaping China’s sovereign ESG performance.</p>

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Advancing sovereign ESG performance in China through renewable energy industrialization and government effectiveness

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月21日

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  • Mohammad Ridwan,
  • Jeremy Ko,
  • Afsana Akther,
  • S. Arafat Ayon,
  • Imran Hossain,
  • Abdullahi Sani,
  • Harry F. Lee,
  • Mahmoud Ibrahim M. Abdelmawgoud

摘要

This study examines the relationship between renewable energy use, industrialization, governance effectiveness, population growth, and technological innovation, and sovereign environmental, social, and governance (ESG) performance, used as a proxy for sustainable development, in China over the period 1996–2023. The analysis employs the Autoregressive Distributed Lag (ARDL) bounds testing approach to capture both short-run dynamics and long-run relationships among the variables. The findings indicate that population growth is negatively associated with ESG performance, whereas governance quality, renewable energy use, industrialization, and technological innovation are positively associated with improvements in ESG outcomes. To ensure robustness, the long-run estimates are validated using Fully Modified Ordinary Least Squares (FMOLS), Dynamic Ordinary Least Squares (DOLS), and Canonical Cointegrating Regression (CCR), and a series of diagnostic tests confirms the model’s stability and reliability. Granger causality results suggest unidirectional predictive relationships from renewable energy, industrialization, and population growth to ESG, as well as bidirectional predictive linkages between technological innovation and ESG. Overall, the findings highlight the importance of institutional quality, technological progress, and the energy transition in shaping China’s sovereign ESG performance.