Bibliographic record
Abstract
Purpose – The purpose of this paper is to test the hypothesis that there are correlations between campus sustainability initiatives and environmental performance, as measured by resource consumption and waste generation performance metrics. Institutions of higher education would like to imply that their campus sustainability initiatives are good proxies for their environmental performance. Design/methodology/approach – Using data reported through the Association for the Advancement in Higher Education’s Sustainability Tracking and Rating System (AASHE STARS) framework, a series of univariate multiple linear regression models were constructed to test for correlations between energy, greenhouse gas (GHG), water and waste performance metrics, and credit points awarded to institutions for various campus sustainability initiatives. Findings – There are very limited correlations between institutional environmental performance and adoption of campus sustainability initiatives, be they targeted operational or coordination and planning best practices, or curricular, co-curricular or research activities. Conversely, there are strong correlations between environmental performance and campus characteristics, namely, institution type and climate zone. Practical implications – Institutional decision makers should not assume that implementing best practices given credit by AASHE STARS will lead to improved environmental performance. Those assessing institutional sustainability should be wary of institutions who cite initiatives to imply a certain level of environmental performance or performance improvement. Originality/value – This is the first paper to use data reported through the AASHE STARS framework to assess correlations between campus initiatives and environmental performance. It extends beyond previous research by considering energy, water and waste performance metrics in addition to GHG emissions, and it considers campus sustainability initiatives in addition to campus characteristics.
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 | 0.011 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".