Open Access

ARTICLE

Modeling of CO₂ Emission for Light-Duty Vehicles: Insights from Machine Learning in a Logistics and Transportation Framework

Sahbi Boubaker^1,*, Sameer Al-Dahidi², Faisal S. Alsubaei³

1 Department of Computer and Network Engineering, College of Computer Science and Engineering, University of Jeddah, Jeddah, 21959, Saudi Arabia
2 Department of Mechanical and Maintenance Engineering, School of Applied Technical Sciences, German Jordanian University, Amman, 11180, Jordan
3 Department of Cybersecurity, College of Computer Science and Engineering, University of Jeddah, Jeddah, 23218, Saudi Arabia

* Corresponding Author: Sahbi Boubaker. Email:

(This article belongs to the Special Issue: Data-Driven Artificial Intelligence and Machine Learning in Computational Modelling for Engineering and Applied Sciences)

Computer Modeling in Engineering & Sciences 2025, 143(3), 3583-3614. https://doi.org/10.32604/cmes.2025.063957

Received 30 January 2025; Accepted 04 June 2025; Issue published 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 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 CO₂ 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 CO₂ 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 CO₂ emissions of new vehicles before their market deployment or to project future emissions from current and expected LDV fleets.

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