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2025 · Problem B — Environmental Impact of Sport Mega-Events

The problem in one paragraph

A consulting firm (COMAP — Consulting Organization Making Athletics Planet-positive) hires you to model the environmental footprint of sport mega-events. Using Super Bowl LIX (New Orleans 2025) as the baseline, design a model that picks the next Super Bowl host city based purely on environmental sustainability. Then extend the model to other mega-events (Olympics, World Cup, Formula 1).

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Requirements, restated

1. Investigate environmental impact using Super Bowl LIX as baseline. Identify the sustainability drivers (energy, water, waste, GHG emissions, transportation). Discuss how impact varies by location.
2. Build a model to pick the next Super Bowl host city based on environmental factors. Model can be qualitative or quantitative.
3. Apply to the 2029 Super Bowl. (a) From the previous host cities (Inglewood, Glendale, Las Vegas, New Orleans, Santa Clara, Atlanta), which should host again? (b) Pick three NFL cities that have never hosted and rank them.
4. Extend to (a) a single-sport championship (World Cup, etc.) or multi-sport event (Olympics); apply to that event. (b) Identify strategies cities can use to improve future-bid scores. (c) Compare single-game vs. multi-venue sustainability factors.
5. Write a 1–2 page letter to the NFL recommending the 2029 host city.

How to frame the problem

This is the prototypical multi-criteria decision-making (MCDM) problem. Score each candidate city on multiple environmental dimensions, weight the criteria, aggregate.

The factor catalogue (be honest about scope)

A solution outline

Step 1 — Baseline footprint

Use published Super Bowl LIX (and IX/LIV/LVIII) sustainability reports to anchor the model. Decompose total emissions into: spectator travel, broadcast operations, venue energy, food/waste, hospitality. The biggest single line item is typically spectator travel — that's often 60–80% of the footprint and it depends heavily on location.

Step 2 — Per-city scoring

For each candidate city $c$ and criterion $i$, compute a numeric score $r_{ci}$. Normalize across cities (vector or min-max). Combine weights from AHP (judgment) and the Entropy Weight Method (data spread) to get a hybrid weight vector $w$.

Step 3 — Aggregate

Two solid options:

• Weighted sum: $S_c = \sum_i w_i \cdot r_{ci}$. Simple, transparent, defensible.
• TOPSIS: closeness to ideal best. Better with mixed benefit/cost criteria.

Run both, see if they agree. Top teams in 2024 used both and reported the average.

Step 4 — Apply

Run on the 6 previously listed past hosts → recommend the most sustainable repeat. Then run on 3 never-hosted NFL cities (e.g., Seattle, Minneapolis, Denver — all have renewable-heavy grids and walkable downtowns) → recommend the most sustainable new option.

Step 5 — Extension to other events

For the Olympics, you add multi-venue complexity (cluster scoring) and longer event duration (higher cumulative impact). For Formula 1, add per-race travel between Grands Prix. Adjust weights to reflect what dominates impact in each event type.

Sensitivity analysis

The ranking is sensitive to weights. Run Monte Carlo over the weight vector (sample from a Dirichlet centered on your nominal weights) and report the distribution of rankings. Top teams used MC-TOPSIS here.

Pitfalls

• All weights uniform — judges read this as "team didn't engage with the criteria."
• No baseline numbers — assert "Super Bowl emits X tons CO₂" with a citation, then everything else is a delta from that.
• Ignoring spectator travel — it's typically the biggest factor; if your model misses it, the rankings will be wrong.
• Letter to NFL is a rehash — write it for a sports exec, not an academic.