Revealing Relevant Factors of Automobile Emissions Based on Linear Regression

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 (CO), a significant greenhouse gas, comprises a substantial portion of vehicle exhaust emissions. To effectively mitigate CO 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 CO emissions from automobiles. The findings indicate that engine size, number of cylinders, and combined fuel consumption are primary factors affecting CO 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 CO emissions.