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Greening Automation: Policy Recommendations for Sustainable Development in AI-Driven Industries

Author

Listed:
  • Nicoleta Mihaela Doran

    (Department of Finance, Banking and Economic Analysis, Faculty of Economics and Business Administration, University of Craiova, 13 A.I. Cuza Street, 200585 Craiova, Romania)

  • Gabriela Badareu

    (Doctoral School of Economic Sciences, Faculty of Economics and Business Administration, University of Craiova, 13 A.I. Cuza Street, 200585 Craiova, Romania)

  • Marius Dalian Doran

    (Doctoral School of Economics and Business Administration, West University of Timisoara, 300223 Timisoara, Romania)

  • Maria Enescu

    (Department of Management, Marketing and Business Administration, Faculty of Economics and Business Administration, University of Craiova, 200585 Craiova, Romania)

  • Anamaria Liliana Staicu

    (Department of Finance, Banking and Economic Analysis, Faculty of Economics and Business Administration, University of Craiova, 13 A.I. Cuza Street, 200585 Craiova, Romania
    Filantropia Craiova Municipal Clinical Hospital, 1 Filantropiei Street, 200143 Craiova, Romania)

  • Mariana Niculescu

    (Department of Agricultural and Forestry Technologies, Faculty of Agriculture, University of Craiova, 13 A.I. Cuza Street, 200585 Craiova, Romania)

Abstract

This study delves into the dynamic relationship between artificial intelligence (AI) and environmental performance, with a specific focus on greenhouse gas (GHG) emissions across European countries from 2012 to 2022. Utilizing data on industrial robots, AI companies, and AI investments, we examine how AI adoption influences GHG emissions. Preliminary analyses, including ordinary least squares (OLS) regression and diagnostic assessments, were conducted to ensure data adequacy and model readiness. Subsequently, the Elastic Net (ENET) regression model was employed to mitigate overfitting issues and enhance model robustness. Our findings reveal intriguing trends, such as a downward trajectory in GHG emissions correlating with increased AI investment levels and industrial robot deployment. Graphical representations further elucidate the evolution of coefficients and cross-validation errors, providing valuable insights into the relationship between AI and environmental sustainability. These findings offer policymakers actionable insights for leveraging AI technologies to foster sustainable development strategies.

Suggested Citation

  • Nicoleta Mihaela Doran & Gabriela Badareu & Marius Dalian Doran & Maria Enescu & Anamaria Liliana Staicu & Mariana Niculescu, 2024. "Greening Automation: Policy Recommendations for Sustainable Development in AI-Driven Industries," Sustainability, MDPI, vol. 16(12), pages 1-17, June.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:12:p:4930-:d:1411413
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    References listed on IDEAS

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    2. Melguizo, Angel & Katz, Raúl & Jung, Juan, 2026. "Can AI grow green? Evidence of a Kuznets curve among AI, renewable energies and emissions," Energy Policy, Elsevier, vol. 208(C).

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