Me, You, and CO2
A Look at CO2 Emissions on the Individual, Corporate, and Worldwide Scales
Naeha Regmi
Me
Introduction
Throughout my life, I have viewed climate change as a dark cloud of impending doom. If you turn back and acknowledge the ominous storm, you spiral into an existential crisis and fear to confront the consequences of your individual actions. In this project I did just that.
From October 9, 2023 to November 25, 2023 I kept track of my travels noting the date, destination, mode of transportation, type of transportation (public or private), distance traveled (mi), purpose, fuel type, and fuel amount. I then used the fuel type and amount to calculate how much CO2 (Kg) and Energy (GJ) I consumed with each trip. Also included in my dataset is the longitude and latitude of the destination as well as a cumulative sum of my CO2 emissions.
Below is a table explaining my dataset in greater depth.
| Variable | Type | Description | |
|---|---|---|---|
| Date | Date | Date | Date of trip |
| Destination | Destination | character | Destination of trip |
| Mode | Mode | factor | Mode of Transportation. 3 levels - Car, Bus, or Metro |
| Trans.Type | Trans.Type | factor | Type of Transportation. 2 levels - Public or Private |
| Distance | Distance | numeric | Total distance traveled during trip in miles |
| Purpose | Purpose | factor | Nature of the trip. 4 levels - Class, Shopping, Medical, or Leisure |
| Fuel.Type | Fuel.Type | factor | Type of fuel used in trip. Factor with 3 levels. Gas, Diesel, Electric |
| Fuel.Amt | Fuel.Amt | numeric | Amount of fuel used in trip in Gallons |
| CO2 | CO2 | numeric | Amount of CO2 emitted from trip in Kg |
| Lat | Lat | numeric | Latitude of destination |
| Long | Long | numeric | Longitude of destination |
| CO2Sum | CO2Sum | numeric | Cumulative total of CO2 emitted in Kg |
| Fuel_GJ | Fuel_GJ | numeric | Amount of Energy emitted from fuel usage in GJ |
With this data I want to explore the nature of my travel and quantify just how much I am affecting the environment within this sector of my life, as I often take weekend trips to visit my friends and family when conditions permit. Although this data set is quite small with only 39 observations spanning just over a month, I hope my vulnerability in sharing this data will encourage others to reflect on the situation at hand as well as their own impact while sparking discussions of meaningful methods to combat the issue of climate change. This is a conversation that all individuals should understand and take a part in.
Where Do I Go?
The first question I sought to answer was locations of where I frequent. Given that I prioritize visiting friends and family outside of Charlottesville, I depend on automated forms of transportation often.
This is plot is a map that depicts the major destinations of my travel. The point size represents the frequency in which I traveled to that location, therefore, the larger the point, the greater the frequency. It illustrates that the majority of my travel destinations have been to Washington, D.C. and back to Charlottesvile, VA as they are the largest points on the plot and relatively similar in size. The third most frequent destination is home in Baltimore, MD and lastly, the least frequent destination is Atlantic City, NJ.
Why Do I Travel?
Naturally, the next question I sought to answer was why I travel. Shown above is a frequency plot of the 4 classes of the categorical variable Purpose organized by their frequency in descending order. The nature of my travel is categorized as being for class, leisure, medical reasons, or shopping. The majority of my travel is for leisure with over 20 instances, making up of approximately half of my total data points. My least frequent travel purpose is medical at 1. Class and Shopping have the same frequency of travel purpose at 8.
From the first two plots, I can easily conclude that most of my travel during this period is to D.C. and back to Charlottesville and is primarily for leisure.
Consequences of my Travel
Now understanding the nature of my travel habits, I move onto confronting the consequences of my choices in travel. The plot above illustrates CO2 emissions in Kg for each trip in my data set between 10/09/23 and 11/25/23. It is color coded to the mode of transportation used in each trip to highlight how my choice to drive in my car, take the bus, or take the metro, add to the problem of climate change. Furthermore, there are labels for each data point that appear when hovered over. The labels specify the date, purpose, CO2 emitted (Kg), and Mode of Transport for each data point. Although this color palette may not be best suited for those with color blindness, because of the red and green tones, the interactive labels still allow users with CVD to understand the data. Here we can see that taking the metro is the most environmentally friendly option at 0 Kg of C02 emitted, followed by driving where the point with the maximum CO2 emitted in a trip occurred on 11/25/23 with 52.8Kg. The mode of transportation with the highest CO2 emitted data point was the bus (on 11/07/23) which is counter intuitive as public transportation is regarded to be better for the environment. However, it also has one of the lowest values of CO2 emissions in the data set at 2.89 Kg on 10/22/23.
The plot above seeks to explain why the datapoint from 11/07/23 is an outlier. It shows the impact distance has on CO2 emissions and also differentiates the mode of transportation with color. Here, we see that on 11/07/23, I traveled 306 miles whereas many of the other points are clustered around the origin. After a distance of 306 miles, the second highest distanced traveled was 168 miles on 11/25/23. This plot shows a very strong relationship between distance traveled and Kg of CO2 emitted, which is intuitive as greater distances require greater fuel consumption. I can conclude that although the mode of transportation is an important factor in determining my CO2 emissions, so to is the distance.
Is This a Normal Amount?
This plot is a double line graph of my cumulative emissions in a 39 day period compared against that of the national average. According to the EIA, the estimated national annual average of CO2 emitted per year is 14.4 metric tons which is equivalent to 14,400 Kg of CO2. Daily, this is approximately 40 Kg of CO2 contributed by Americans alone. The grey line is a cumulative total of 40 Kg of CO2 added per day and the coral line is my actual CO2 emissions from travel. The national average line is increasing at a consistent rate. My cumulative emissions were below the national average until 11/07 where it skyrocketed, making my cumulative emissions by the end of the time frame greater by approximately 500 Kg. Although the consistent increase of the national average line is unrealistic, this plot is still helpful in gauging how my personal CO2 emissions compare and contribute to the overall increase of CO2 in the atmosphere. With this information I am significantly more hesitant about going on long distance trips.
The World
How Much CO2 Has The U.S. Emitted Compared To The World?
The next visualization I created was a gif of annual CO2 emissions from 1750 to 2020 in the form of a double line graph. It is sourced from the Our World in Data CO2 and Greenhouse Gas Emissions Database which consists of environmental and economic metrics for all countries. The green line represents the United State’s CO2 emissions and it is compared against the CO2 emissions of the whole world, which is the coral color. Here, I wanted to see just how much the United States is contributing to the climate crisis relative to its peers just as I did in my previous plot. This plot shows that from 1750 to 1950, the US’s carbon footprint increased alongside the world’s. However, after 1950, their CO2 emissions diverge, with US’s increasing at a much slower rate, almost plateauing. On the other hand, worldwide CO2 emissions grew exponentially. With further research I found that the US is responsible for approximately 15% of total global emissions and is only second in the world with China at over 30%. This illustrates how collective action is necessary to combat climate change.
How Much Do Other Countries Emit? What Other Factors Contribute To Emissions?
This shiny app uses the same data that was used to create the previous plot however, this app allows the user to compare numeric data for all countries listed in the database. There are 3 select inputs, one for the country, one for the x axis variable, and one for the y axis variable. Upon the user’s choice, the app outputs a scatter plot as well as a table of the x and y values for the input country. I created this app to further compare CO2 emissions between countries while also acknowledging other variables that contribute to a larger carbon footprint such as population and gdp. With these visualizations, it is clear to see that one individual’s actions cannot offset the impact of the world, or even a given country. The scale is simply far too large to even compare the two.
If app does not appear please refer to link: https://naeharegmi.shinyapps.io/CO2_Econ/
Corporations
How Much CO2 Do Different Types of Vehicles Emit?
In this section, I focus my scope on the transportation industry as it is directly related to my personal travel habits and is a smaller scale than the global or national levels.
This visualization is a horizontal bar plot of CO2 emissions in Tg. Each bar represents a type of transportation and the graph is ordered in descending order. This data is sourced from the EPA’s Inventory of US Greenhouse Gas Emissions and Sinks from 1990 to 2020. Here we can see that the most emissions come from Light-Duty Trucks, followed by Medium/Heavy-Duty Trucks and Passenger Cars. For clarification, Light-Duty Trucks include pickup-trucks, SUVs, and Vans, Passenger Cars include sedans, coupes, and station wagons, and Medium/Heavy-Duty Trucks include delivery trucks, walk-in trucks, and tractors. Given that ground transport is shown as the highest category of CO2 emission and I personally drive a sedan, the Medium/Heavy-duty trucks and passenger car sources are highlighted. Specifically, I want to compare emissions of corporations, particularly private postal service companies laterally, as well as against my own to further quantify my individual impact.
How Do Private Postale Companies Compare Laterally?
This is a side by side stacked bar chart illustrating CO2 emissions in metric tons between Fedex and UPS across the years 2020, 2021, and 2022. It also shows the distribution of Scope 1, 2, and 3 emissions per company, per year. Scope 1 constitutes direct emissions, such as from travel, whereas Scope 2 and 3 are indirect emissions. Here, we see that holistically, FedEx emits much less CO2 than UPS per year. However, of their annual emissions, the majority is scope 1, which is the more directly reducible. UPS has a much more equal distribution of Scope 1 and Scope 3 emissions. With both FedEx and UPS, Scope 2 constitute the least of their emissions. According to UPS’s 2016 release, UPS trucks almost never turn left as a means to increase fuel efficeincy and minimize carbon footprint. However, FedEx has clearly implemented superior methods to reduce emissions. One of these initiatives is that FedEx has converted their entire parcel fleet to electric vehicles, which emit 0 CO2 as we saw with taking the metro in my data.
How do my emissions compare to a large company like UPS?
My last plot is a bar graph comparing how much energy I consumed vs how much UPS consumes in a 39 day in GJ solely from ground transport. This data is also sourced from UPS’ GRI report. This visualization highlights the significant difference in scale between an individual’s carbon footprint and that of a large corporation such as UPS, even for such a short time frame. The graph is interactive as it contains hover text over each individual bars with the exact Energy usage for that entity. Given the stark difference in scale, the plot allows the user to zoom in to see just how minimal a singular person’s CO2 emissions is relative to larger units.
Conclusion
I began this project to understand my personal travel habits as well as my impact on the environment as a way to encourage others to be cognizant of the environmental effects of their actions. After being confronted with how much CO2 I emitted from travel in this short period, i sought to compare my influence against my peers. I then sought to understand the bigger picture by exploring worldwide CO2 emissions. Lastly, I honed my focus on corporations who are much more influential than I but smaller in scale than the whole world. Although I initially sought to emphasize individual impact on the environment, I acknowledge that grassroots mobilization is not nearly enough to offset the climate situation at hand. It is illogical to simply omit the factors that are contributing to the issue however, true change can only happen with a much larger platform.