From ecocity to ecocampus: Sustainability policies in university campuses
Bibliographic record
Abstract
Cities generate environmental impacts that have focused the global interest of scientists and authorities on the search for environment-friendly alternatives. The 'Ecocity' concept provides an innovative and sustainable vision of how to build and live in these settlements. Translating this vision to the university campus as a small-scale replica of a city is one of the challenges facing higher education institutions. Through teaching, research, outsourcing, association and university management, these institutions can promote and disseminate more advanced activities in sustainability. The purpose of this paper is to analyze the experiences in this area on two different campuses, one urban in a historic city and another suburban in the outskirts of a large city. The methodology adopts a qualitative method based on the technique of the focus group and in-depth interviews with academics and the 'Ecocampus' offices from two Spanish universities, one in an urban context and another in a suburban. The hypotheses indicate that sustainable policies in terms of setting, infrastructure, waste and water are best met by the suburban university. The sustainability efforts of the university in an urban environment stand out in energy-related indicators, transportation and education. In general, the paper suggests that higher institutions adapt their sustainability policies depending on the location of the campus; that is, in urban and suburban areas. The implications of this work addresses two perspectives, the first consists in sustainability policies of universities and the second in the contribution of these best practices to the environmental problems of the city. The originality of this study is to learn from the different experiences of sustainability policies of universities with different types of campuses and the influence of these in the development of cities.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".