IDEAS home Printed from https://ideas.repec.org/p/hal/journl/hal-03459460.html
   My bibliography  Save this paper

Machine Learning-Based Modeling of the Environmental Degradation, Institutional Quality, and Economic Growth

Author

Listed:
  • Sami Ben Jabeur

    (ESDES - ESDES, Lyon Business School - UCLy - UCLy - UCLy (Lyon Catholic University), UR CONFLUENCE : Sciences et Humanités (EA 1598) - UCLy - UCLy (Lyon Catholic University))

  • Houssein Ballouk

    (CEREFIGE - Centre Européen de Recherche en Economie Financière et Gestion des Entreprises - UL - Université de Lorraine)

  • Wissal Ben Arfi

    (EDC - EDC Paris Business School)

  • Rabeh Khalfaoui

    (SU - Shaqra University, Saudi Arabia)

Abstract

This study was aimed at investigating the determinants of environmental sustainability in 86 countries from 2007 to 2018. The natural gradient boosting (NGBoost) algorithm was implemented along with five machine learning models to forecast the trends of CO2 emissions. In addition, the SHapley Additive exPlanation (SHAP) technique was used to interpret the findings and analyze the contribution of the individual factors. The empirical results indicated that the predictions obtained using NGBoost were more accurate than those obtained using other models. The SHAP value exhibited a positive correla- tion among the amount of CO2 emissions, economic growth, and opportunity entrepreneurship. A negative correlation was observed among the governance, personnel freedom, education, and pollution.

Suggested Citation

  • Sami Ben Jabeur & Houssein Ballouk & Wissal Ben Arfi & Rabeh Khalfaoui, 2021. "Machine Learning-Based Modeling of the Environmental Degradation, Institutional Quality, and Economic Growth," Post-Print hal-03459460, HAL.
    • Handle: RePEc:hal:journl:hal-03459460
    DOI: 10.1007/s10666-021-09807-0
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ben Jabeur, Sami & Ballouk, Hossein & Ben Arfi, Wissal & Sahut, Jean-Michel, 2023. "Artificial intelligence applications in fake review detection: Bibliometric analysis and future avenues for research," Journal of Business Research, Elsevier, vol. 158(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hal:journl:hal-03459460. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.