Booz Allen Hamilton

Analysis Background

Men’s Bracket I Women’s Bracket

For more information, please view the following links:

Press Release I Team Rankings by Tournament Carbon Footprint I Analysis Background

Ever wonder how big of a footprint sports teams are leaving on the environment? This analysis calculates the carbon footprint associated with the 2013 men’s and women’s college basketball tournaments. In particular, this model focuses on the greenhouse gas emissions associated with team and fan travel between each respective school and their tournament site. The same bracket framework is used as in the actual tournament, allowing users to enter their “picks” for all of the tournament games. Greenhouse gas emissions for each matchup are allocated to the two teams based on the travel required for the teams and their fans. As the team advances through the tournament bracket, the emissions are summed for each of the games that the team has played. The output of the model is the total carbon footprint of the selected bracket.

To conduct the analysis, Booz Allen borrowed elements of the Life Cycle Analysis (LCA) approach, an industry-accepted technique that assesses potential environmental impacts and helps translate this information into relative, understandable terms. Booz Allen performs more rigorous forms of Life Cycle Analysis for government and commercial clients that want to make decisions based on information about how various activities -- from major construction to employee travel -- might affect the environment.

The model compares the footprint for the bracket to the equivalent of two common environmental statistics -- the number of cars driven annually or the energy used for one year in a certain number of homes. For example, if the model calculates that there are 100,000 metric tonnes of carbon dioxide emissions for the bracket, this means that the greenhouse gas emissions is equivalent to the annual emissions from 20,800 cars (based on 11,500 miles driven per year) or the annual emissions associated with providing energy (electricity and heating) to 6,700 homes. In real-time, users can see how different picks alter the total footprint of the tournament bracket.

While it is easy to calculate the carbon footprint for a single team’s path to the championship, the objective of the Booz Allen model was to calculate the footprint of the entire tournament using a systems approach. The analysis factors in all possible outcomes from each tournament game and calculates the carbon footprint for each team as it travels on its journey through the tournament. The analysis ranks the different bracket combinations from lowest to highest carbon footprint and compares each footprint to common environmental statistics, such as the average home energy use and annual emissions from cars.

Given the size of the venues hosting the various games, fan travel represents a much more significant carbon footprint than team travel alone. This analysis accounts for emissions associated with both team and fan travel. In addition to fans affiliated with one school or another, the model also assumes that one-third of the fans attending the game are local to the venue and have no particular allegiance to either team that is playing. The distribution of fans at a particular game is difficult to predict and is almost certainly a function of how well the team is performing, the proximity to the venue, and number of alumni. For this analysis, our model assumes that higher seeds (i.e. one or two seeds) would have a larger turnout than low seeds (i.e. fifteen or sixteen seeds).

The distance between the school and the corresponding venue is calculated. If the distance is less than 350 miles, it is assumed that the transport mode is a coach bus. Distances larger than 350 miles necessitate air travel from the school to the venue. For simplicity, it was assumed that there are negligible greenhouse gas emissions (relative to those for long-distance travel) for the one-third of fans that are local to the venue.

The carbon footprint calculated for the bracket is based on the sum of life cycle greenhouse gas emissions associated with the particular travel mode. This means that the footprint consists of more than just the combustion emissions of a fuel (diesel for bus transport or jet fuel for air transport). The model also accounts for the upstream emissions associated with the production of those petroleum-based fuels from the extraction of crude oil through the refining and delivery of the finished fuel.

Other assumptions for the analysis are detailed below:

  • All distances are calculated using the great circle distance method.
  • The origin for all team and fan travel is assumed to be the location of the school.
  • All venues are assumed to have identical capacities of 20,000.
  • For the individual tournament winner footprint calculations, it was assumed that the rest of the bracket is optimized for each of the 68 scenarios.
  • Games played in rounds 2 and 3 take place at the same site. For those games, it is assumed that there is only a single instance of travel for the team and fans. That is, regardless if a team wins or loses the round 2 game, the fans remain for the round 3 game (no additional travel is required). The same assumption holds true for games played in rounds 4 and 5.

Contact Information

For technical questions about this analysis, please contact Joe Marriott at marriott_joe@bah.com

References

For more information, please view the following links:

Model Input

Reference

Airplane energy input and combustion emissions

ANL. (2012)

High-speed rail energy input

Chester, M., & Horvath, A. (2012)

Bus energy input and combustion emissions

Chester, M., Horvath, A., & Madanat, S. (2010)

Airplane combustion emissions

Dorbian, C. S., Wolfe, P. J., & Waitz, I. A. (2011)

US electricity grid composition

EIA. (2010) and (2012)

US electricity grid composition

EPA. (2008)

Greenhouse gas equivalency calculations

EPA. (2013)

Division 1 school coordinates

GeoCommons. (2008)

Diesel and jet fuel production emissions

NETL. (2008)

Life cycle emissions from power production

NETL. (2012 a-f)

Imports/exports of electricity to/from US

Statistics Canada. (2009) and (2011)

 

ANL. (2012). Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation (GREET) Model  Retrieved March 12, 2013, from http://greet.es.anl.gov/

Chester, M., & Horvath, A. (2012). High-speed rail with emerging automobiles and aircraft can reduce environmental impacts in California’s future. Environmental Research Letters, 7(3), 034012.

Chester, M., Horvath, A., & Madanat, S. (2010). Comparison of life-cycle energy and emissions footprints of passenger transportation in metropolitan regions. Atmospheric Environment, 44. doi: 10.1016/j.atmosenv.2009.12.012

Dorbian, C. S., Wolfe, P. J., & Waitz, I. A. (2011). Estimating the climate and air quality benefits of aviation fuel and emissions reductions. Atmospheric Environment. doi: 10.1016/j.atmosenv.2011.02.025

EIA. (2010). Electric Power Annual 2009. (DOE/EIA-0348(2009)). Washington, DC: Energy Information Administration Retrieved from http://www.eia.gov/cneaf/electricity/epa/epa_sum.html

EIA. (2012). Electricity Data  Retrieved September 20, 2012, from http://www.eia.gov/electricity/data.cfm#generation

EPA. (2008). The Emissions & Generation Resource Integrated Database (eGRID): U.S. Environmental Protection Agency  Retrieved June 12, 2012, from http://www.epa.gov/cleanenergy/energy-resources/egrid/index.html

EPA. (2013). Greenhouse Gas Equivalencies Calculator  Retrieved March 13, 2013, from http://www.epa.gov/cleanenergy/energy-resources/calculator.html

GeoCommons. (2008). NCAA, Division I Men's College Basketball Schools, USA, 2008 Retrieved March 13, 2013 from http://geocommons.com/overlays/1120

NETL. (2008). Development of Baseline Data and Analysis of Life Cycle Greenhouse Gas Emissions of Petroleum-Based Fuels. (DOE/NETL-2009/1346). Pittsburgh, PA: National Energy Technology Laboratory Retrieved from http://www.netl.doe.gov/energy-analyses/pubs/NETL%20LCA%20Petroleum-based%20Fuels%20Nov%202008.pdf

NETL. (2012a). Role of Alternative Energy Sources: Geothermal Technology Assessment. (DOE/NETL-2012/1531). Pittsburgh, PA: National Energy Technology Laboratory Retrieved from http://netl.doe.gov/energy-analyses/refshelf/PubDetails.aspx?Action=View&PubId=447

NETL. (2012b). Role of Alternative Energy Sources: Hydropower Technology Assessment. (DOE/NETL-2012/1519). Pittsburgh, PA: National Energy Technology Laboratory Retrieved from http://netl.doe.gov/energy-analyses/refshelf/PubDetails.aspx?Action=View&PubId=445

NETL. (2012c). Role of Alternative Energy Sources: Natural Gas Technology Assessment. (DOE/NETL-2012/1539). Pittsburgh, PA: National Energy Technology Laboratory Retrieved from http://www.netl.doe.gov/energy-analyses/refshelf/PubDetails.aspx?Action=View&PubId=435

NETL. (2012d). Role of Alternative Energy Sources: Nuclear Technology Assessment. (DOE/NETL-2012/1502). Pittsburgh, PA: National Energy Technology Laboratory Retrieved from http://netl.doe.gov/energy-analyses/refshelf/PubDetails.aspx?Action=View&PubId=441

NETL. (2012e). Role of Alternative Energy Sources: Solar Thermal Technology Assessment. (DOE/NETL-2012/1532). Pittsburgh, PA: National Energy Technology Laboratory Retrieved from http://netl.doe.gov/energy-analyses/refshelf/PubDetails.aspx?Action=View&PubId=449

NETL. (2012f). Role of Alternative Energy Sources: Wind Technology Assessment. (DOE/NETL-2012/1536). Pittsburgh, PA: National Energy Technology Laboratory Retrieved from http://netl.doe.gov/energy-analyses/refshelf/PubDetails.aspx?Action=View&PubId=451

Statistics Canada. (2009). Electric Power Generation, Transmission and Distribution. (57-202-X). Ottawa, Ontario, Canada: Minister of Industry Retrieved from http://www.statcan.gc.ca/pub/57-202-x/57-202-x2007000-eng.pdf

Statistics Canada. (2011). Energy Statistics Handbook Fourth Quarter 2010. (57-601-X). Ottawa, Ontario, Canada: Minister of Industry Retrieved from http://www.statcan.gc.ca/pub/57-601-x/57-601-x2010004-eng.pdf