A paper titled “Modeling and Sustainability Implications of Harsh Driving Events: A Predictive Machine Learning Approach” authored by Antonis Kostopoulos, Thodoris Garefalakis, Eva Michelaraki, Christos Katrakazas and George Yannis has been published in Sustainability. This study addresses the complex task of predicting dangerous driving behaviors through a comprehensive analysis of over 356,000 trips, enhancing existing knowledge in the field and promoting sustainability and road safety. Findings indicate that Gradient Boosting and Multilayer Perceptron excel, achieving recall rates of approximately 67% to 68% for both harsh acceleration and braking events. The application of machine learning algorithms, feature selection, and k-means clustering offers a promising approach for improving road safety and reducing socio-economic costs through sustainable practices.