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<p>The current study examines the impact of digitization and renewable energy utilization on greenhouse gas emissions. It utilizes models such as the pooled mean group auto-regressive distributed lag (PMG-ARDL) and the pooled mean group nonlinear auto-regressive distributed lag (PMG-NARDL) to assess the extent to which errors or random variations influence the data. It examines 20 European nations from 2000 to 2022. The ARDL model predicts a positive long-term relationship between wealth and emissions. GDP per capita and greenhouse gas emissions have a positive association of 0.24 percent. On the other hand, the NARDL model clearly shows that utilizing more renewable energy reduces emissions. Nevertheless, emissions will rise rapidly if more energy from renewable sources is employed. The long-term emissions of greenhouse gases decrease by 0.104% with each one-percentage-point rise in energy consumption generated from renewable sources. Reducing the amount of renewable energy utilized would ultimately lead to an increase in greenhouse gases, meaning that a 1% decrease in renewable energy would result in a 0.125% rise in greenhouse gas emissions. This investigation has verified the outputs of the PMG-ARDL model using the generalized method of moments (GMM) system method. This method confirms the PMG-ARDL model's finding by indicating that the consumption of energy from renewable sources significantly reduces greenhouse gas emissions. The research results also provide stakeholders, scholars, and policymakers with several actionable recommendations. The government must support infrastructure for sustainable energy sources.</p>

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The role of digitalization in the impact of renewable energy on sustainable development in the Eurozone

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月2日

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  • Olfa Zarrad,
  • Bilel Ben Mechichi,
  • Mariem Bouattour,
  • Kamel Helali

摘要

The current study examines the impact of digitization and renewable energy utilization on greenhouse gas emissions. It utilizes models such as the pooled mean group auto-regressive distributed lag (PMG-ARDL) and the pooled mean group nonlinear auto-regressive distributed lag (PMG-NARDL) to assess the extent to which errors or random variations influence the data. It examines 20 European nations from 2000 to 2022. The ARDL model predicts a positive long-term relationship between wealth and emissions. GDP per capita and greenhouse gas emissions have a positive association of 0.24 percent. On the other hand, the NARDL model clearly shows that utilizing more renewable energy reduces emissions. Nevertheless, emissions will rise rapidly if more energy from renewable sources is employed. The long-term emissions of greenhouse gases decrease by 0.104% with each one-percentage-point rise in energy consumption generated from renewable sources. Reducing the amount of renewable energy utilized would ultimately lead to an increase in greenhouse gases, meaning that a 1% decrease in renewable energy would result in a 0.125% rise in greenhouse gas emissions. This investigation has verified the outputs of the PMG-ARDL model using the generalized method of moments (GMM) system method. This method confirms the PMG-ARDL model's finding by indicating that the consumption of energy from renewable sources significantly reduces greenhouse gas emissions. The research results also provide stakeholders, scholars, and policymakers with several actionable recommendations. The government must support infrastructure for sustainable energy sources.