Analysis of Learning Algorithms for Predicting Carbon Emissions of Light-Duty Vehicles

research presents a comparative analysis of different learning methods developed for the prediction of carbon emissions from light-duty vehicles. With the growing concern over environmental sustainability, accurate prediction of carbon emissions is vital for developing effective mitigation strategies. The work assesses the performance of various algorithms trained on vehicle-specific data attributes to predict the emission patterns of a fuel type of different light duty models. This work uses two realtime petrol and diesel datasets collected by CariQ app and device. Canada government dataset is also used from the online repository for prediction of the vehicle emission. The evaluation is based on their predictive accuracy. The findings reveal insights into the effectiveness of different learning techniques in accurately estimating carbon emissions from vehicles, providing valuable guidance for policymakers and researchers in the field of environmental sustainability and transportation planning.