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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. [ABSTRACT FROM AUTHOR]

Abstract: At the beginning of the 21st century imports of agricultural and food commodities have become a major part of many nations’ food baskets. Indeed the global food system has several merits for nations, businesses and individual consumers’ well-being. However, as increasing evidence suggests that we are approaching an era of climate change and scarcity of cheap energy sources the sustainability of that system must be examined. One part of any food commodity chain is its ‘food miles’ – the distance the commodity travels from point of production to point of consumption, the required energy and resulting emissions. This paper presents a 1 year ‘snapshot’ of Canada’s total import related food miles. It presents an analysis of the distance imported foods traveled from around the world to major points of consumption in Canada and documents the equivalent carbon dioxide emissions related to those imports. It presents both a macro scale picture of the equivalent emissions related to transportation of imported food and a micro scale picture which focuses on specific commodities consumed in various parts of the country. It then discusses policy implications for food sustainability. Overall the research highlights that about 30% of the agricultural and food commodities consumed in Canada are imported, resulting in ‘food miles’ of over 61billiontonneskm, leading to annual emissions of 3.3 million metric tonnes of CO2. Of the various agriculture and food commodities studied, fruits and vegetables had the highest food miles related emissions. [Copyright &y& Elsevier]