LIS 4317 Final Project

 Fuel Efficiency and Emissions Ratings in 2025 SmartWay Vehicles

Research Question:

How do SmartWay-certified vehicles compare to non-certified vehicles in terms of fuel efficiency and greenhouse gas emissions in model year 2025, and what role does fuel type play in these performance metrics?

Short Summary:

This project analyzes fuel efficiency and emissions scores among SmartWay-certified vehicles for Model Year 2025 using data from the U.S. Environmental Protection Agency (EPA). By comparing combined MPG performance, SmartWay labels, and greenhouse gas scores, the goal is to evaluate how well alternative fuel vehicles perform relative to traditional gasoline-powered counterparts.

Problem Description:

Amid growing environmental concerns and stricter emissions regulations, automotive manufacturers face increasing pressure to produce cleaner, more efficient vehicles. Consumers are also becoming more environmentally conscious, often turning to SmartWay certification labels to guide their vehicle purchases. This project assesses how effective these SmartWay certifications are at identifying higher-efficiency vehicles and investigates the relationship between vehicle fuel type and greenhouse gas performance.

Related Work:

The EPA’s Fuel Economy Guide and interactive dashboards on FuelEconomy.gov provide consumers with access to MPG comparisons, emissions scores, and vehicle classification tools. I was particularly inspired by the site's Compare Side-by-Side Tool, which offers a user-friendly platform for analyzing fuel efficiency and emissions data. My project builds upon the visual storytelling methods used by these tools, applying them to the latest available dataset for 2025 SmartWay vehicles.

Solution:

Distribution Analysis:

Code:

ggplot(df, aes(x = Fuel, y = Cmb.MPG, fill = Fuel)) +

  geom_boxplot() +

  labs(title = "Distribution of Combined MPG by Fuel Type",

       y = "Combined MPG", x = "Fuel Type") +

  theme_minimal()

This plot visualizes the spread and central tendencies (median, quartiles) of Combined MPG for each fuel type (e.g., Electricity, Gasoline, Hybrid).

Interpretation:

• Electric vehicles (EVs) usually show much higher MPG equivalents.

• Gasoline vehicles have a wider and generally lower distribution.

• Hybrids typically fall between EVs and gasoline in performance.

This directly addresses the research question on how fuel type impacts fuel efficiency. It visually demonstrates that EVs are more efficient, supporting the claim that fuel type plays a major role.

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Deviation Analysis:

Code:

df %>%

  group_by(SmartWay) %>%

  summarise(AvgMPG = mean(Cmb.MPG, na.rm = TRUE)) %>%

  ggplot(aes(x = SmartWay, y = AvgMPG, fill = SmartWay)) +

  geom_col() +

  labs(title = "Average Combined MPG by SmartWay Certification",

       x = "SmartWay Label", y = "Average Combined MPG") +

  theme_minimal()

This bar chart compares average Combined MPG between vehicles with different SmartWay certification levels (e.g., Elite, Yes, No).

Interpretation:

• Vehicles labeled "SmartWay Elite" or "SmartWay" show a higher average MPG than non-certified ones.

• This affirms that SmartWay certification is a useful indicator of better fuel efficiency.

It helps validate the effectiveness of the SmartWay label.

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Ranking: 

Code:

df %>%

  arrange(desc(Cmb.MPG)) %>%

  head(10) %>%

  ggplot(aes(x = reorder(Model, Cmb.MPG), y = Cmb.MPG, fill = Fuel)) +

  geom_col() +

  coord_flip() +

  labs(title = "Top 10 Most Fuel-Efficient Vehicles (2025)",

       x = "Vehicle Model", y = "Combined MPG") +

  theme_minimal()

This ranks the top 10 most fuel-efficient vehicles in the dataset.

Interpretation:

• The top slots are likely dominated by electric vehicles.

• Fuel type, model, and SmartWay certification status are visible.

This supports part of my analysis around highlighting best performers, making the case for how specific models and technologies are setting the standard.

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Correlation Analysis:

Code:

ggplot(df, aes(x = Cmb.MPG, y = Greenhouse.Gas.Score, color = Fuel)) +

  geom_point(size = 3, alpha = 0.7) +

  labs(title = "Combined MPG vs. Greenhouse Gas Score",

       x = "Combined MPG", y = "GHG Score") +

  theme_minimal()

This scatterplot visualizes the relationship between fuel economy (MPG) and emissions performance (GHG Score).

Interpretation:

• There is a positive correlation: as MPG increases, GHG Score improves.

• EVs and hybrids cluster in the top-right (best performance zone).

• Gasoline vehicles are more spread out and often lower-scoring.

This supports my hypothesis that more efficient vehicles emit less greenhouse gas and that fuel economy can predict emissions performance.

Summary:

This project explores the relationship between fuel type, emissions performance, and SmartWay certification using vehicle data from the U.S. Environmental Protection Agency’s 2025 SmartWay Vehicle List. The primary objective was to assess how SmartWay-certified vehicles compare to non-certified models in terms of fuel efficiency (measured by combined MPG) and greenhouse gas emissions. Using RStudio, I conducted a series of visual analyses—including distribution, deviation, ranking, and correlation—to uncover trends across fuel types (electric, hybrid, gasoline) and certification status. The findings show that electric vehicles consistently outperform other fuel types in fuel economy and emissions scores, while SmartWay-certified vehicles demonstrate higher average MPG than their non-certified counterparts. Through these insights, the project supports the conclusion that SmartWay labels are effective indicators of environmental performance and that alternative fuel technologies offer significant benefits in fuel efficiency and emissions reduction. This analysis reinforces the importance of data-driven consumer tools in guiding sustainable transportation choices.