Overview of Green Accounting through Bibliometric Analysis
Bibliographic record
Abstract
This paper aims to provide an overview of green accounting and indicates authors, sources, and countries that significantly impact this area. Drawing on a sample of 360 papers from Scopus and Web of Science, we analyze the publication trends on green accounting from 1992 to the third quarter of 2024. VOSviewer is used to perform the bibliometric analysis to identify outstanding authors, studies, and countries. The results indicate that scholars such as Cairns, Bartelmus, and Peter have made significant contributions to this topic. The United States, the United Kingdom, China, and Indonesia emerge as the countries with the highest number of citations and co-authorships. Additionally, the study identifies key keywords and emerging research themes related to green accounting. Future researchers can utilize this work to explore trends and topics related to green accounting. The study also anticipates that the number of publications in this area will continue to rise.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.046 | 0.096 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".