Travel behaviour and greenhouse gas emissions during the COVID-19 pandemic: A case study in a university setting
Bibliographic record
Abstract
The year 2020 was characterized by a marked shift in daily travel patterns due to the COVID-19 pandemic. While we know that overall travel decreased, less is known about modal shift among those who continued to travel during the pandemic or about the impact of these travel-behaviour changes on transport-related greenhouse gas emissions. Focusing on a university setting and drawing from a travel survey conducted in Fall 2020 in Montreal, Canada (n = 3358), this study examines modal shifts and quantifies greenhouse gas emissions at three time periods in the year 2020: pre-pandemic, early pandemic, and later pandemic. The pandemic resulted in a sharp reduction in travel to campus. Among those who continued to travel to campus (n = 1580), car-to-final destination mode share almost tripled at the start of the pandemic. The largest modal shift seen was the transition from walking, cycling, and transit, to driving at the beginning of the pandemic. Reductions in overall travel resulted in lower overall transport-related greenhouse gas emissions. However, if modal changes persist once students, staff, and academics return to campus, the transport carbon footprint is projected to increase above pre-pandemic levels. These results highlight the importance of putting in place policies that support a return to sustainable modes as universities and businesses reopen for in-person activities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".