Machine Learning Assisted Experimental Investigation on Carbon Emissions of Diverse Fuels

In this article, an experimental investigation is done to study the impact of carbon dioxide (CO2) vehicle emissions on global climatic changes. The dataset for the analysis is taken from the Canadian Ministry of Transportation. Forecasting carbon emissions from automobiles is studied using five prominent ML models: regression models, decision trees, random forests, and neural networks. Variables like vehicle characteristics, fuel type, mileage, driving habits, and traffic conditions are considered for forecasting CO2 emissions. The study also discusses the possible advantages of including more variables in the predictive models, such as weather, road conditions, and vehicle fleet composition, which can improve forecast accuracy and make it possible for decision-makers to pinpoint specific emission-reduction measures. This research study highlights how machine learning could help stakeholders and policymakers make informed choices to develop low-carbon transportation networks.