The environmental footprint of academic and student mobility in a large research-oriented university
Bibliographic record
Abstract
Abstract Academic mobility for field work, research dissemination and global outreach is increasingly recognized as an important contributor to the overall environmental footprint of research institutions. Student mobility, while less studied, also contributes to universities’ environmental footprint. Université de Montréal (UdeM) is the largest university in Montréal, Canada. It has a research budget of 450M$, employs 1426 full-time professors, and has a total student population of 33 125 undergraduate and 12 505 graduate students. To assess the footprint of academic mobility at UdeM, we surveyed the research community (n = 703; including professors, research professionals and graduate students) about their travel habits. We also measured the contribution from travel undertaken by sports teams and international students as well as students engaged in study abroad and internships programs using data provided by the university. While the average distance travelled for work and research purposes by the UdeM community is around 8525 km/person, professors travel more than 33 000 km/person per year. We also estimated that the 5785 international students or students enroled in study abroad programs travel annually around 12 600 km/person. UdeM’s per capita annual travel-related C and N footprints vary, with international students generating for example 3.85 T CO2 and 0.53 kg N while professors generate 10.76 T CO2 and 2.19 kg N. Air travel emissions are the main contributors to these footprints. We provide insights into the distribution of travel-related environmental footprint within the university, the main reasons for travelling, the most frequent destinations, and the factors preventing researchers from reducing their travel-related environmental impact.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".