Mapping the Literature on Sustainability Reporting: A Bibliometric Analysis Grounded in Scopus and Web of Science Core Collection
Bibliographic record
Abstract
Sustainability reporting has become an increasingly common practice among companies around the globe as around 90% of the world’s 250 largest companies from Fortune 500 prepare and publish its sustainability reporting. Aiming to help researchers to grasp the intellectual landscape of global research on sustainable reporting, we conducted a bibliometric analysis using CiteSpace software by applying evaluative and relational techniques to 928 articles published in 480 different journals in Scopus and 698 articles published in 374 different journals in Web of Science Core Collection from 1981 to 2020. Our findings indicate that the number of articles published in the field has increased rapidly, especially since 2009. We identified the leading countries (the United States, Australia, the United Kingdom, Germany, Spain, Canada, the Netherlands and Italy), the most prolific journals (Journal of Cleaner Production, Business Strategy and the Environment, Journal of Business Ethics), main journals categories (Business, Economics, Management and Finance, Environmental and Ecology and Science, Technology and Engineering), and the major research directions in the near future (sustainability reporting, corporate social responsibility, sustainable development, disclosure). From our findings we infer that the sustainability reporting research has just recently (2013-2019) gained traction in the literature. Moreover, our findings testify that a kind of bifurcation point has occurred is 2011 that manifests the maturity of the field of sustainability reporting. All this provides the reader with a high-view look at sustainable reporting as these quantitative findings complementing qualitative and providing valuable insights into the field.
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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 | 0.028 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.376 | 0.321 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".