Application of ESG factors in Higher Education Institutions: comparative documentary analysis between Brazilian and international universities
Bibliographic record
Abstract
Environmental, Social, and Governance (ESG) factors, initially applied exclusively within the business context, are beginning to be introduced into the environment of Higher Education Institutions (HEIs). Accordingly, this research aimed to investigate how ESG factors are applied in leading HEIs featured in the QS Sustainability Ranking, through a comparative analysis of institutional reports from both Brazilian and international universities. A qualitative, exploratory, and descriptive research approach was adopted. The research methods included a systematic literature review and documentary analysis of institutional reports and documents found on university websites. One of the main theoretical outcomes of this study is a conceptual framework, developed from the literature review, that compiles dozens of ESG factors applicable to HEIs. Based on the documentary analysis and the proposed framework, ESG factors were investigated in national HEIs (University of São Paulo – USP; University of Campinas – Unicamp; São Paulo State University – Unesp) and international HEIs (University of Toronto; University of California, Berkeley; The University of Manchester). The findings revealed a concentration of ESG initiatives in the environmental dimension, followed by the social dimension, with governance being the least addressed. The comparative analysis showed that the six universities demonstrated a similar performance in terms of the number of ESG factors addressed. However, governance initiatives were found to be more prevalent in foreign universities. It is believed that the results of this study may yield several contributions. The identified ESG factors can support the advancement of sustainability on university campuses, foster stronger relationships between HEIs and the community, and promote teaching and research related to this theme. This study contributes to the ESG and sustainability literature by offering a theoretical framework that can inform university actions, as well as presenting various examples of initiatives already implemented by top-ranked universities in a sustainability index. It is hoped that this work will promote the importance of expanding the application of ESG factors in HEIs, in synergy with businesses, the community, and government, aiming for increasingly widespread implementation of sustainable solutions.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".