###### Abstract  
Due to the alarming rate of climate change, fuel consumption and emission estimates are critical in determining the effects of materials and stringent emission control strategies. In this research, an analytical and predictive study has been conducted using the Government of Canada dataset, containing 4973 light-duty vehicles observed from 2017 to 2021, delivering a comparative view of different brands and vehicle models by their fuel consumption and carbon dioxide emissions. Based on the findings of the statistical data analysis, this study makes evidence-based recommendations to both vehicle users and producers to reduce their environmental impacts. Additionally, Convolutional Neural Networks (CNN) and various regression models have been built to estimate fuel consumption and carbon dioxide emissions for future vehicle designs. This study reveals that the Univariate Polynomial Regression model is the best model for predictions from one vehicle feature input, with up to 98.6% accuracy. Multiple Linear Regression and Multivariate Polynomial Regression are good models for predictions from multiple vehicle feature inputs, with approximately 75% accuracy. Convolutional Neural Network is also a promising method for prediction because of its stable and high accuracy of around 70%. The results contribute to the quantifying process of energy cost and air pollution caused by transportation, followed by proposing relevant recommendations for both vehicle users and producers. Future research should aim towards developing higher performance models and larger datasets for building APIs and applications. carbon dioxide emissions; light-duty vehicles; fuel consumption; regression models; machine learning; convolutional neural network; prediction model; estimation model; climate change +
Footnote †: journal: Medical and Computer Science  
Although many studies have introduced various machine learning models and techniques for the estimation of carbon dioxide emissions and fuel consumption, the trend focuses more on optimizing models rather than using vehicle metrics to analyze different vehicle types and brands [8; 10; 11]. Therefore, a comparative study of different types of vehicles and their effect on the environment has a significance for the vehicle market. Such research provides deep insights into understanding its environmental impacts. This identified gap is addressed by this research, that is, to provide an insight into vehicle fuel consumption and carbon dioxide emission through a series of rigorous data analytics and machine learning. It is worthwhile to note that the data analysis and machine techniques applied in this research are transferable to similar datasets. The following research objectives (RO) support the aim of this research. * **RO1:** To carry out a thorough systematic literature review of fuel consumption and carbon dioxide emissions for new light-duty vehicles for retail sale (use case: in Canada);
* **RO2:** To identify suitable datasets for analysis and implement the data preparation process;
* **RO3:** To utilize appropriate indicators to measure and analyze the sustainable impact of vehicles;
* **RO4:** To implement the following data analytics methodologies on the final dataset by addressing corresponding research questions (RQ). 1. **Level 1:** Descriptive Statistical Analysis * **RQ1.1** How do light-duty vehicles compare in terms of fuel consumption and CO\({}_{2}\) emission? * **RQ1.2** How have patterns of fuel consumption and emission of each vehicle type changed throughout the selected period? 2. **Level 2:** Inferential Statistical Analysis * **RQ2.1** Is there any particular distribution for fuel consumption in the city and the highway of vehicles in Canada? * **RQ2.2** Is there a notable difference in the performance of one specific vehicle (or fuel) type in comparison to the rest of the vehicle types in Canada?