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

被引：11

作者：

Ben Jabeur, Sami ^{[1
]}

Ballouk, Houssein ^{[2
]}

Ben Arfi, Wissal ^{[3
]}

Khalfaoui, Rabeh ^{[4
]}

机构：

[1] Inst Sustainable Business & Org, ESDES, Confluence Sci & Humanites UCLY, 10 Pl Arch, F-69002 Lyon, France

[2] Univ Lorraine, CEREFIGE Lab, Nancy, France

[3] EDC Paris Business Sch, OCRE Res Lab, 74-80 Rue Roque Fillol,CS 10074, F-92807 Puteaux La Defense, France

[4] Fac Sci Econom & Gest Sfax, Lab Rech Econ & Gest LR18ES27, Sfax, Tunisia

来源：

ENVIRONMENTAL MODELING & ASSESSMENT | 2022年 / 27卷 / 06期

关键词：

CO2; Gross domestic product; Institutional conditions; Environmental degradation; NGBoost; SHAP value; FORECASTING CO2 EMISSIONS; ENERGY-CONSUMPTION; KUZNETS CURVE; GOVERNANCE; ENTREPRENEURSHIP; NEXUS;

D O I：

10.1007/s10666-021-09807-0

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

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 correlation among the amount of CO2 emissions, economic growth, and opportunity entrepreneurship. A negative correlation was observed among the governance, personnel freedom, education, and pollution.

引用

页码：953 / 966

页数：14

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