## 6 Conclusions and Future Work  
In this research, an observational and predictive analysis has been performed using data from the Government of Canada, which includes 4973 light-duty vehicles observed between 2017 and 2021, to provide a comparative view of various brands and vehicle types in terms of fuel consumption and CO2 emissions before making applicable recommendations. Despite significant efforts that have been developed in the past [10; 19; 27], this research analyzes different vehicle types and brands using vehicle measurements, providing a deeper understanding of the vehicle market and its environmental effects. The proposed vehicle features and recommended prediction models in this study can be further used as a reference for vehicle manufactures and users to make relevant actions for reducing their environmental impacts. By using descriptive and inferential statistics methodologies, it is observed that the average total fuel consumption of light-duty vehicles is 10.86 L/100 km, and the average CO2 emission is 251.44 g/km. Different brands and vehicle features have been included in a rigorous, as well as comprehensive, analysis. Based on the findings, relevant recommendations have been made. Over the study period, some vehicle brands have been working towards optimizing their products with environmental awareness (such as Honda), while some are doing conversely (including Bugatti). Moreover, different machine learning and deep learning models have been built throughout this study for fuel consumption and CO2 emission prediction. Firstly, this study reveals that the Persistence model has outperformed the autoregression and optimized autoregression models for predictions from one input variable with vector autoregression. Additionally, the Univariate Polynomial Regression model (degree 5) attains a higher coefficient of determination, compared to the model itself with lower degrees and Linear Regression model. Secondly, for estimating total fuel consumption and CO2 emissions of vehicles from multiple inputs, the Multiple Linear Regression and Multivariate Polynomial Regression have been demonstrated to be the best models, compared to Logarithmic Regression (with Log and Exponential Transformation). Finally, it should be noted that Convolutional Neural Network is also promising for predicting in this field, with stable and high coverage of correct predicted values. Future research may gear towards developing higher performance models for predicting fuel consumption and CO2 emissions. Moreover, a larger dataset with more vehicle features should be studied for building a predictive model in vehicle design. Based on that, APIs and applications can be designed and constructed for predictions. Finally, vehicle consumers and producers can adopt the recommendations from the findings of this study to design, as well as implement appropriate action plans for reducing their environmental impacts. [PERSON] and [PERSON] contributed to conceptualization, software, validation, resources, and methodology; [PERSON] contributed to formal analysis, investigation, data curation, writing--original draft preparation, and visualization; [PERSON] contributed to writing--review and editing, project administration, and funding acquisition. All authors have read and agreed to the published version of the manuscript. This research and the APC were funded by European Commission grant numbers 612462-EPP-1-2019-1-SK-EPPKA2-KA and 610619-EPP-1-2019-1-FR-EPPKA1-JMD-MOB. Not applicable. The data used to analyze in this paper can be found in this link [[https://open.canada.ca/data/en/dataset/98f1a129-f628-4](https://open.canada.ca/data/en/dataset/98f1a129-f628-4) ce4-b24d-6f16 bf24 dd64]([https://open.canada.ca/data/en/dataset/98f1a129-f628-4](https://open.canada.ca/data/en/dataset/98f1a129-f628-4) ce4-b24d-6f16 bf24 dd64) (accessed on 30 November 2021). The authors declare no conflicts of interest. The following abbreviations are used in this manuscript: