A life cycle assessment of the environmental impacts of small to medium sports events
Bibliographic record
Abstract
In the face of climate change and environmental concerns, sport event organizers have incorporated measures to improve environmental sustainability into their event planning. In 1994, the International Olympic Committee (IOC) added the environment as the third pillar of the Olympic Movement, alongside sport and culture, to signal its importance. However, event organizers don’t have a clear picture of the impacts of their events and are only beginning to use quantitative data as part of their planning process. The scientific literature and the event industry have recognized the need for theoretical and methodological work to better assess and understand the pattern of environmental impacts of events. The need is greatest for small to medium sized events. The goal of this research was to analyze the explanatory power and use-value of Life Cycle Assessment (LCA) to examine the environmental impacts and inform planning of small to medium events. Two case studies were conducted: the UBC Athletics & Recreation varsity 2011–2012 athletic season (UBC Athletics) held at the University of British Columbia over a one-year period, and the Special Olympics Canada 2014 Summer Games (SOC 2014) held over five days in Vancouver, British Columbia. LCA methodology was used to quantify and compare the environmental impacts in key organizational areas. The findings show that LCA has the potential to identify environmental impacts within small to medium sport events. They also show that impacts related to venues dominated across all environmental impact categories for UBC Athletics due to energy consumption and construction materials. Travel was the dominant contributor for SOC 2014 and was a major contributor for UBC Athletics – largely due to people travelling from out of town. The activities related to accommodation, materials, waste, communication and food were significantly smaller contributors to the overall environmental footprint. Sport organizers would benefit from applying LCA as a quantitative tool to rigorously identify areas of significant impact and target planning efforts accordingly, particularly for long distance travel and activities with significant energy use. Finally, I conclude that organizers need to be more aspirational in how they design events and leverage societal change to become environmentally sustainable.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".