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Revealing Relevant Factors of Automobile Emissions Based on Linear Regression
- 1 Institute of Statistics, Southwestern University Of Finance And Economics, Chengdu, China
* Author to whom correspondence should be addressed.
Abstract
Global warming has emerged as a critical global concern, with the control of greenhouse gas emissions becoming a paramount priority for nations worldwide. Carbon dioxide (CO₂), a significant greenhouse gas, comprises a substantial portion of vehicle exhaust emissions. To effectively mitigate CO₂ emissions from automobiles, it is imperative to identify and analyze the key determinants influencing these emissions. In this paper, the collected data were fitted into a model through multiple linear regression in R. The variance inflation factor (VIF) detection method was used to detect multicollinearity, and the stepwise regression method was employed to eliminate multicollinearity in the model to study the related factors of CO₂ emissions from automobiles. The findings indicate that engine size, number of cylinders, and combined fuel consumption are primary factors affecting CO₂ emissions from vehicles. Among them, the combined fuel consumption is the most significant influencing factor. These results offer valuable insights for automotive engineers and researchers, guiding efforts to enhance vehicle design and reduce CO₂ emissions.
Keywords
Carbon Dioxide Emission, Multiple Linear Regression, Multicolinearity, Stepwise Regression
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Cite this article
Liu,Y. (2025). Revealing Relevant Factors of Automobile Emissions Based on Linear Regression. Theoretical and Natural Science,105,71-79.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 3rd International Conference on Mathematical Physics and Computational Simulation
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