Reducing Carbon Emissions from High School Bus Transportation
In order to tackle the greatest challenge of our time, climate change, we must scrutinize all areas of human activity for waste, including the sports industry. In the U.S, it is estimated that 35,000 tonnes of CO2 is generated from sports events in the NFL, MLB, NBA, and NHL (Waste Management data). For example, the 2012 Super Bowl hosted in Indianapolis used roughly 15,000 megawatt hours of electricity. This is enough electricity to power nearly 1,400 average homes in the United States for one year. This amount of electricity consumed does not include the electricity used by the 111.3 million people that watched the game on television. This number increased to 115.5 million people for this past year’s Super Bowl. The excess amount of electricity used is not only for the Super Bowl. On a typical game day in Dallas, their AT&T Stadium consumes around more electricity than the whole country of Liberia.
The amount of trash generated at football events is a major contributor in harming the environment. The Tennessee Volunteers generate over 21 tons of waste per game. With college football teams playing about 7 home games a season, that results in roughly 147 tons of waste generated per season for Tennessee. UGA’s stadium is not as big as Tennessee’s but the amount of waste generated is definitely comparable.
Internationally, The Rio Olympics produced 4.5 million tonnes of CO2, as well as the 2010 and 2014 World Cup producing 2.75 and 2.8 million tonnes of CO2, respectively. Combined, those three events combined burned 11 billion pounds of coal. 85% of all sports-related greenhouse gas emissions come from transportation. Another big culprit is food, which, for example in the 2018 world cup, created 105,695 tonnes of CO2 emissions. Another big CO2 producer is the stadiums and infrastructure for the big sporting events. In the 2016 Olympics, 1.6 million tonnes of CO2 was generated from building the Olympic village. Although there are some major climate change issues in sports, teams and management are slowly making climate change their top priority. Notably, the goal for the 2024 Paris Olympics is to make it the greenest Olympics ever. Their goal is to cut carbon emissions in half compared to the 2012 and 2016 Olympics.
The MLB and MiLB also have a major CO2 issue and are actively trying to remove it. The amount of major and minor league teams, and the constant plane travel and bus trips, amount to a lot of CO2 emissions, something the MLB is trying to avoid. The main CO2 culprit for the MLB is the amount of travel. For example, the Brewers traveled as far as Miami and San Diego during their season, now multiply that by 162 games, and then multiply that by 30 teams. Many different teams/people have created proposals to help optimize schedules, to reduce travel.
Waste at Clubhouses and Stadiums
Another big culprit is clubhouses, and the amount of waste that causes. Cory Dickerson, a former MLB player, started a non-profit organization called Players for the Planet, which encourages players and organizations to reduce their CO2 emissions. Dickerson estimated that there are around 300,000 bottles thrown away every day from MLB and MILB teams, and there are 162 games.
Latin School of Chicago
While instituting change on a grand scale is important, grassroots efforts are also needed at every level. In order to understand how to affect change, I’ve chosen to evaluate a highschool sports initiative.
Transportation data
After doing an overview of the sports program at my school, we identified various activities in the athletic programs that could be a target for waste reduction. After careful review, I concluded that at present, I could affect improvement in transportation by reducing the number of bus rides annually for games and practices.
Data Set
I got a spreadsheet that put together all the bus rides to and from the school for the year 2018-2019 school year. The data set I received from the school was a google sheet that contained every bus ride during the 2018-2019 school year. In all, it contained 632 different bus rides. The diviors were, seasons (so fall, winter, spring), team (ie. Girls Varsity Volleyball), date, home/away, address, city, zip code, title (ie. Latin vs Parker), and location (ie. DePaul Prep). Although most of these divisors don’t help much, some are extremely helpful.
For example, we could see which teams were using the most resources. We could also see how many team players were on each bus. For some sports, the bus seats were underused because of smaller teams (tennis). However, one should note that the arrival of Covid-19 meant that the school had different rules to comply with. For example, only one player could sit on a bus seat, versus two historically.
Zip-Codes
I came up with many different variations on visualizing the data, but some of the main ones used zip codes as its main divisor, shown below. I also learned to use Tableau in searching for an optimal way to show zip code data.
Using this, I can calculate the distance between different zip codes. I hypothesized that the best way to reduce 10% of bus emissions was to share buses, and in order to do that, I need to calculate the zip codes that are near are on the way to the destinations. According to the data, if that is done enough, 10% of the CO2 emissions would be reduced.
These new visualizations were helpful to gain a new perspective on the data, and the chart that mapped out the dates were especially helpful. From this, I, with my advisor, came up with some questions.
How can we effectively calculate the distances between zip codes? Although it can be done manually, this is not a sustainable option
Research zip code distance calculators such as (https://www.youtube.com/watch?v=RIbwTuyE7aU)
How can we write a piece of code that can show zip codes that are near each other the same day?
What are good ways to calculate bus emissions per mile for on-going consideration for teams?
Hypothesis
Using the current charts, I was able to deduce that the best way to minimize bus usage at our school was sharing buses. I could use the dates, along with the zip codes, to figure out days where many buses were used, and see how that could’ve been optimized. For example, if one team was traveling to 60614 and another was traveling to 60610, they should just share a bus as they are both going to very similar spots. This, done enough times, should project around a 10% loss in total bus emissions.
Further Study
While a few attempts at creating code to create a solution have not been successful, I’m working on the manual option first. After proving my assertion, I will be working with the computer science department to help me further automate the solution.
