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AI for the Environmental Performance of Moroccan Public Organizations: A Comparative Analysis of Random Forests and XGBoost
Abstract
This study examines the application of Machine Learning (ML) to evaluate and predict the environmental performance of Moroccan public organizations. It compares the performance of Random Forests (RF), XGBoost, Support Vector Machines (SVM) and Partial Least Squares (PLS) regression on a dataset collected from 270 organizations between 2018 and 2022. Key variables include total solid waste production, energy consumption and the rate of achievement of National Sustainable Development Strategy (NSDS) targets. Our results, evaluated using accuracy, F1 score, R2 and mean square error (MSE), indicate that Random Forests offer better performance for predicting environmental performance. This research contributes to the literature on the application of AI to sustainable development in the public sector, and highlights the importance of environmental policies for optimizing resource management. Practical implications suggest that public organizations can use these models to guide their strategic decisions and improve their contribution to national sustainable development goals.
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