Green Arena: Creating a Sustainable Future for the Super Bowl and the Olympics

Abstract

With the increasing global concern for environmental sustainability, major sports events such as the Super Bowl and the Olympics have brought significant social and economic benefits, but they are also accompanied by large amounts of energy consumption, waste and greenhouse gas emissions. At present, environmental factors have not been fully considered in the selection of the event's host location.

Our team aims to construct a host location selection model based on environmental sustainability. Through a quantitative model that integrates multiple methods, it systematically addresses four core research questions, helping event organizations ensure effectiveness while reducing environmental impact.

We identified 12 quantitative and qualitative sub-indicators, covering the environmental impact of the event, which involves five major environmental dimensions: energy consumption, greenhouse gas emissions, water resource usage, waste management, and traffic emissions. In addition, factors related to the characteristics of the host city have also been incorporated, such as climatic conditions, energy structure and public transportation coverage rate, etc. These factors together form a complete and solid foundation for environmental assessment.

With these 12 indicators, a fourth-order model (input layer, preprocessing layer, computing layer, and output layer) was constructed. The model balances the importance of the indicators through weighting methods, which combined Analytical Hierarchy Process (AHP) method, entropy weight method (EWM), and Criteria Importance Though Intercriteria Correlation (CRITIC), quantifies the distance between the city and the ideal environmental state by using Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), optimizes the uncertainty of the data through Monte Carlo simulation, and finally outputs the city ranking based on environmental factors. This model effectively eliminates the bias caused by subjective judgment and realizes the scientific quantification of environmental decision-making.

In terms of model application, the study applied it to 7 cities with hosting experience and 3 potential new cities respectively. Through NFL data and analogy methods, the ranking of preferred cities was obtained, and Inglewood was recommended as the top choice for the 2029 Super Bowl, with Seattle as an alternative. This verified the reliability and logical consistency of the model.

The model has been further expanded to adapt to multiple sports events such as the Olympic Games. Four specific driving factors, including long-term preparation energy consumption and international aviation emissions, have been added, and the model has been adjusted and optimized. Through this model to evaluate the candidate cities for the 2036 Olympics, Berlin performed outstandingly in terms of waste recycling and venue reuse rate and was recommended as the host city for the 2036 Olympics.

Finally, the sensitivity analysis verified the robustness of the model. Factors such as energy consumption, greenhouse gas emissions, and traffic emissions have a significant impact on the ranking of cities. Through the optimization of data uncertainty and its application in multiple scenarios, the model has demonstrated strong adaptability, providing scientific decision support for sports event organizers.