A Review of Green Campus Management Sustainability with a Bibliometric Approach
Bibliographic record
Abstract
The contribution of universities to environmental conservation is realized through sustainable green campus management.Although many studies have investigated this initiative, a comprehensive analysis remains lacking.The lack of publications in journal database portals related to green campus management is evident.This study aims to provide a bibliometric and comprehensive review of the literature on green campus management.The bibliometric method utilizes the Scopus database as a reliable data source, analyzed with the assistance of VOSviewer and Excel.The bibliometric analysis indicates an increasing trend in research documents from 2010 to 2025.Trending keywords in green campus management include "renewable energy", "smart grid," and "sustainable campus."The leading countries in document outputs and citations are the United States, China, and Malaysia.The review of green campus management concepts can be grouped into three main areas: behavioral aspects, educational and learning equipment, and facilities and infrastructure that support the green campus initiative.Each area encompasses activities and programs that contribute to the success of the green campus concept.The findings of this review are intended to serve as a reference for higher education institutions in the implementation and development of a sustainable green campus concept.
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 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.064 | 0.089 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".