Developing ecological footprint scenarios on university campuses
Bibliographic record
Abstract
Abstract Purpose – The ecological footprint represents a simple way to assess the amount of materials consumed and waste produced by a given entity. The approach has been applied to countries, towns, households, and more recently university campuses. One of the challenges of using the ecological footprint at a university is the difficulty of determining how large the footprint should be. The authors have developed a calculator specific to the needs of a university campus, and applied it to the University of Toronto at Mississauga (UTM). Rather than focus on the overall size, the purpose of this paper is to instead create several scenarios to help communicate the relative impacts of alternative actions. Design/methodology/approach – An ecological footprint calculator appropriate to the campus was developed and applied to UTM. Three scenarios were then created: on‐campus electricity generation versus electricity purchased from the grid, current commuting patterns versus those expected if a student bus pass is adopted, and use of virgin office paper versus recycled office paper. Findings – The results of the calculator suggest that energy consumption represents the largest component of UTM's footprint, followed by commuting to campus. Practical implications – The relative benefits of on‐campus electricity generation, increasing public transit use, and the adoption of recycled paper are all highlighted through the scenario calculations. Originality/value – This paper presents a way to avoid the difficulty of determining how large a university's footprint should be through the use of an alternative scenario method, which provides an easy way to communicate the impacts of consumption decisions to a campus' community.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".