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

Sami Ben Jabeur (), Houssein Ballouk (), Wissal Ben Arfi and Rabeh Khalfaoui ()
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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

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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.

Keywords: CO2; Gross domestic product; Institutional conditions; Environmental degradation; NGBoost; SHAP value (search for similar items in EconPapers)
Date: 2021-11-24
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Published in Environmental Modeling & Assessment, 2021, 27, pp.953-966. ⟨10.1007/s10666-021-09807-0⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03459460

DOI: 10.1007/s10666-021-09807-0

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