Geographic versus institutional drivers of nitrogen footprints: a comparison of two urban universities
Bibliographic record
Abstract
Abstract Excess reactive nitrogen (N) is linked to a myriad of environmental problems that carry large social costs. Nitrogen footprint tools can help institutions understand how their direct and indirect activities are associated with N release to the environment through energy use, food, and transportation. However, little is known about how geographic context shapes the environmental footprints of institutions. Defining the system boundaries over which institutions are responsible and able to control individual drivers of N footprints is also a challenge. Here, we compare and contrast the circa 2017 N footprints for two research intensive universities located in Montréal, Canada, with a combined full-time equivalent campus population of ∼83 000. Our estimate of McGill University’s N footprint (121.2 t N yr−1) is 48% greater than Université de Montréal’s (74.1 t N yr−1), which is also reflected on a per capita basis (3.3 and 1.6 kg N capita−1 yr−1, respectively). Key institutional factors that explain the differences include McGill’s larger residential and international student populations, research farm, and characteristics of its on-campus fuel use. We use a series of counterfactual scenarios to test how shared urban geographic context factors lead to an effective reduction of the N footprints at both universities: the relatively small direct role of both institutions in food intake on campus (29%–68% reduction compared to a counterfactual scenario), energy from hydroelectricity (17%–21% reduction), and minimal car commuting by students (2%–3% reduction). In contrast, the near-zero N removal from the municipal wastewater system effectively increases the N footprints (11%–13% increase compared to a modest N removal and offset scenario). Our findings suggest that a shared geographic context of a dense city with plentiful off-campus housing, food options, and access to hydroelectricity shapes the absolute N footprints of Montréal’s two main universities more than the divergent institutional characteristics that influence their relative N footprints.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".