TY  - EJOU
AU  - Boubaker, Sahbi 
AU  - Al-Dahidi, Sameer 
AU  - Alsubaei, Faisal S. 

TI  - Modeling of CO<sub>2</sub> Emission for Light-Duty Vehicles: Insights from Machine Learning in a Logistics and Transportation Framework
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2025
VL  - 143
IS  - 3
SN  - 1526-1506

AB  - The transportation and logistics sectors are major contributors to Greenhouse Gase (GHG) emissions. Carbon dioxide (CO<sub>2</sub>) 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 CO<sub>2</sub> emissions from LDVs using a comprehensive dataset that includes vehicles from various manufacturers, their CO<sub>2</sub> 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 identical conditions to ensure a fair comparison and their performance metrics were calculated. The obtained results showed a significant influence of variables such as engine size on CO<sub>2</sub> emissions. Although the six algorithms have provided accurate forecasts, the Linear Regression (LR) model was found to be sufficient, achieving a Mean Absolute Percentage Error (MAPE) below 0.90% and a Coefficient of Determination (R2) exceeding 99.7%. These findings may contribute to a deeper understanding of LDVs’ role in CO<sub>2</sub> emissions and offer actionable insights for reducing their environmental impact. In fact, vehicle manufacturers can leverage these insights to target key emission-related factors, while policymakers and stakeholders in logistics and transportation can use the models to estimate the CO<sub>2</sub> emissions of new vehicles before their market deployment or to project future emissions from current and expected LDV fleets.
KW  - CO<sub>2</sub> emission; machine learning; modeling; prediction; performance metrics; light-duty vehicles; climate change; transportation and logistics

DO  - 10.32604/cmes.2025.063957