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The Road to Reducing Vehicle CO2 Emissions: A Comprehensive Data Analysis

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Computational Sciences and Sustainable Technologies (ICCSST 2023)

Abstract

In recent years, the influence of carbon dioxide (CO2) releases on the environment have become a major concern. Vehicles are one of the major sources of CO2 emissions, and their contribution to climate change cannot be ignored. This research paper aims to investigate the CO2 emissions of vehicles and compare them with different types of engines, fuel types, and vehicle models.

The study was carried out by gathering information about the CO2 emissions of vehicles from the official open data website of the Canadian government. Data from a 7-year period are included in the dataset, which is a compiled version. There is a total of 220 cases and 9 variables. The data is analyzed using statistical methods and tests to identify the significant differences in CO2 emissions among different Car Models. The results indicate that vehicles with diesel engines emit higher levels of CO2 compared to those with gasoline engines. Electric vehicles, on the other hand, have zero CO2 emissions, making them the most environmentally friendly option. Furthermore, the study found that the CO2 emissions of vehicles vary depending on the type of fuel used. The study also reveals that the CO2 emissions of vehicles depend on the model and age of the vehicle. Newer models tend to emit lower levels of CO2 compared to older models. In conclusion, this study provides valuable insights into the CO2 emissions of Cars and highlights the need to adopt cleaner and more sustainable transportation options.

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Correspondence to Kukatlapalli Pradeep Kumar .

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Madhurima, S., Mannooparambil, J.M., Kumar, K.P. (2024). The Road to Reducing Vehicle CO2 Emissions: A Comprehensive Data Analysis. In: Aurelia, S., J., C., Immanuel, A., Mani, J., Padmanabha, V. (eds) Computational Sciences and Sustainable Technologies. ICCSST 2023. Communications in Computer and Information Science, vol 1973. Springer, Cham. https://doi.org/10.1007/978-3-031-50993-3_24

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  • DOI: https://doi.org/10.1007/978-3-031-50993-3_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-50992-6

  • Online ISBN: 978-3-031-50993-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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