Sahbi Boubaker1,*, Sameer Al-Dahidi2, Faisal S. Alsubaei3
CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3583-3614, 2025, DOI:10.32604/cmes.2025.063957
- 30 June 2025
Abstract The transportation and logistics sectors are major contributors to Greenhouse Gase (GHG) emissions. Carbon dioxide (CO2) from Light-Duty Vehicles (LDVs) is posing serious risks to air quality and public health. Understanding the extent of LDVs’ impact on climate change and human well-being is crucial for informed decision-making and effective mitigation strategies. This study investigates the predictability of CO2 emissions from LDVs using a comprehensive dataset that includes vehicles from various manufacturers, their CO2 emission levels, and key influencing factors. Specifically, six Machine Learning (ML) algorithms, ranging from simple linear models to complex non-linear models, were applied under… More >