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

Jabeur, Sami Ben; Ballouk, Houssein; Arfi, Wissal Ben; Khalfaoui, Rabeh

doi:10.1007/s10666-021-09807-0

Cited by 11 publications

(3 citation statements)

References 47 publications

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“…A double machine learning model and time series clustering were shown to be effective in assessing and understanding climate change and lake response in China, and this study showed that precipitation was the most important feature for lake area growth rate (He et al, 2023 ). In another study aimed at assessing the determinants of environmental sustainability, ML was used to determine the factors associated with CO 2 emissions and their related correlations (Jabeur et al, 2022 ). The findings showed that advanced and interpretable machine learning models were successfully used to predict CO 2 emissions from large panel data which assists organizations and policymakers in understanding data such that they can develop energy reduction policies and improve environmental quality (Jabeur et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

“…In another study aimed at assessing the determinants of environmental sustainability, ML was used to determine the factors associated with CO 2 emissions and their related correlations (Jabeur et al, 2022 ). The findings showed that advanced and interpretable machine learning models were successfully used to predict CO 2 emissions from large panel data which assists organizations and policymakers in understanding data such that they can develop energy reduction policies and improve environmental quality (Jabeur et al, 2022 ). The versatility of ML has seen it outperform some traditional modelling approaches, for instance in the prediction of groundwater levels.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of individual and ensemble machine learning models for prediction of sulphate levels in untreated and treated Acid Mine Drainage

Hasrod,

Nuapia,

Tutu

2024

Environ Monit Assess

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Machine learning was used to provide data for further evaluation of potential extraction of octathiocane (S8), a commercially useful by-product, from Acid Mine Drainage (AMD) by predicting sulphate levels in an AMD water quality dataset. Individual ML regressor models, namely: Linear Regression (LR), Least Absolute Shrinkage and Selection Operator (LASSO), Ridge (RD), Elastic Net (EN), K-Nearest Neighbours (KNN), Support Vector Regression (SVR), Decision Tree (DT), Extreme Gradient Boosting (XGBoost), Random Forest (RF), Multi-Layer Perceptron Artificial Neural Network (MLP) and Stacking Ensemble (SE-ML) combinations of these models were successfully used to predict sulphate levels. A SE-ML regressor trained on untreated AMD which stacked seven of the best-performing individual models and fed them to a LR meta-learner model was found to be the best-performing model with a Mean Squared Error (MSE) of 0.000011, Mean Absolute Error (MAE) of 0.002617 and R2 of 0.9997. Temperature (°C), Total Dissolved Solids (mg/L) and, importantly, iron (mg/L) were highly correlated to sulphate (mg/L) with iron showing a strong positive linear correlation that indicated dissolved products from pyrite oxidation. Ensemble learning (bagging, boosting and stacking) outperformed individual methods due to their combined predictive accuracies. Surprisingly, when comparing SE-ML that combined all models with SE-ML that combined only the best-performing models, there was only a slight difference in model accuracies which indicated that including bad-performing models in the stack had no adverse effect on its predictive performance.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparison of individual and ensemble machine learning models for prediction of sulphate levels in untreated and treated Acid Mine Drainage

Hasrod,

Nuapia,

Tutu

2024

Environ Monit Assess

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“…The third paper, "Machine Learning-Based Modeling of the Environmental Degradation, Institutional Quality, and Economic Growth" by Jabeur et al, [3] provides a comprehensive investigation of the determinants of environmental sustainability through forecasting the carbon emission trends in 86 countries. Seven potential factors affecting CO 2 emissions are divided into three categories: economic environment, legislative environment, and environmental awareness.…”

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confidence: 99%

Foreword

Zaied

Nguyen

2022

Environ Model Assess

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According to scientific data, climate change and environmental deterioration have become severe issues threatening human society and sustainability. In particular, the Intergovernmental Panel on Climate Change (IPCC) found carbon emissions from fossil fuels and industry to be the dominant cause of global warming and climate change, accounting for 64% of the global net anthropogenic GHG emissions. 1 Countries worldwide must thus consider achieving carbon neutrality, or zero-carbon targets, as a strategic policy option to ensure sustainable economic growth. In this context, the 2015 Paris Climate Conference (COP21) constituted an essential advancement in tackling concerns related to climate change risks and launching a net-zero carbon global economy. However, it is important to recognize that most countries still need to put more effort into accelerating the transition to clean energy in order to achieve carbon neutrality.While climate change severely affects the environment, economic activities, welfare, and society, it is undoubtedly not a monotonic phenomenon. The long-term variability of future climate change, as well as necessary commitments and strategic actions involving adaptation and mitigation measures, would typically depend on forecast scenarios and the international coordination framework. This special issue on "Risks, Welfare, and Social Preferences in the Context of Climate Variability: Theory and Empirical Evidence" brings valuable contributions to the related literature as it was aimed at featuring high-quality research papers assessing, not only at national and international levels but also at

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Machine and Deep Learning: Their Roles in the Context of the Economic Growth Processes and Sustainability Assessment

Martinho

2023

SpringerBriefs in Applied Sciences and Technology

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

Cited by 11 publications

References 47 publications

Comparison of individual and ensemble machine learning models for prediction of sulphate levels in untreated and treated Acid Mine Drainage

Comparison of individual and ensemble machine learning models for prediction of sulphate levels in untreated and treated Acid Mine Drainage

Foreword

Machine and Deep Learning: Their Roles in the Context of the Economic Growth Processes and Sustainability Assessment

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