Sustainable Transportation and Technology Linked to Carbon Emissions: A Cross-Sectional Data Analysis

This paper investigates the extent to which sustainable transportation variables reduce CO₂ emissions across the United States, using cross-sectional data from the fifty states in the year 2021. To examine the impact of green transportation linked with carbon emissions, this paper uses an empirical model to investigate various factors such as EVs per 1,000 people, average fuel prices, GDP per capita, and population. This analysis will compare states with different levels of sustainable transportation and be able to evaluate whether green transportation is effective with lower emissions using an ordinary least squares model. Additionally, this study includes machine learning to predict emissions per capita and help identify which variables are the most impactful in explaining differences within the U.S. through a Random Forest Model. The empirical results show that EVs per 1,000 people are statistically significant and negatively associated with carbon dioxide emissions per capita. The Random Forest Model also shows that EV adoption is the biggest predictor of emissions per capita. The findings from this study show that higher EV adoption states tend to have lower emissions per person, although the model does not prove that EV adoption is the only cause of lower emissions due to other state-level factors.