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Article Dans Une Revue Environmental Modeling & Assessment Année : 2021

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

Résumé

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.

Dates et versions

hal-03459460 , version 1 (01-12-2021)

Identifiants

Citer

Sami Ben Jabeur, Houssein Ballouk, Wissal Ben Arfi, Rabeh Khalfaoui. Machine Learning-Based Modeling of the Environmental Degradation, Institutional Quality, and Economic Growth. Environmental Modeling & Assessment, 2021, 27, pp.953-966. ⟨10.1007/s10666-021-09807-0⟩. ⟨hal-03459460⟩
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