Climate risks in motorsport: setting boundary conditions in Formula 1
Bibliographic record
Abstract
Research question The global nature of Formula 1 racing exposes the sport to diverse climate risks. We describe the process for setting evidence-based boundary conditions for climate-related hazards in motorsport, and then apply them to Formula 1.Research methods We assess risks for 25 Formula 1 racing circuits, including 24 venues in the 2024 racing schedule and one potential future site, by analysing historical weather data, air quality records, and flood risk projections to identify climate-related hazards that could impact race events.Results and findings The boundaries proposed herein are new and require further refinement; however, they allow us to draw some initial conclusions about the vulnerabilities of the F1 series in its current format. Our findings show that extreme heat is the most pervasive threat, affecting 19 of the 25 circuits. Flooding poses risks to 18 locations, poor air quality could compromise 10 sites, and heavy rainfall threatens seven venues. Singapore and Qatar emerged as the most vulnerable locations, while Austria shows lower climate risk profiles.Implications These results underscore the need for officials to consider adaptations to Formula 1’s race calendar and operations to address evolving environmental challenges. This study provides a foundation for developing climate adaptation plans in motorsport, potentially influencing practices in the wider automotive industry. As climate change intensifies, Formula 1’s response to these challenges may set precedents for the global sport and tourism industries facing similar environmental pressures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".