Abstract: Diesel-powered engines are major contributors to CO₂ emissions, driving environmental pollution and climate change. Predicting and mitigating these emissions is essential for improving fuel efficiency and minimizing environmental impact. Over a 10-month period (October 2022 to December 2023), this project aims to deliver actionable insights. This study applies machine learning models to analyse and predict CO₂ emissions using historical data. The models, including Linear Regression, Random Forest, and XGBoost, are trained on engine parameters, fuel traits, and operational data to generate accurate predictions. Key input variables, such as engine load, fuel consumption, and temperature, are processed to provide real-time emission estimates, categorized as low, moderate, or high. This approach enhances diesel engine efficiency and enables industries, researchers, and policymakers to make informed, data-driven decisions for reducing carbon footprints. Through AI-driven methods, the project advances sustainability by offering precise, actionable guidance for emission control and regulatory compliance. This initiative fosters decarbonization, balancing environmental responsibility with operational efficiency.
Keywords: CO₂ emissions, diesel engines, machine learning, emission prediction, fuel consumption, engine load, temperature, Linear Regression, Random Forest, XGBoost, sustainability, emission control, regulatory compliance.
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DOI:
10.17148/IJARCCE.2025.14357
