Mapping the Landscape of Sustainability Reporting: A Bibliometric Analysis Across ESG, Circular Economy, and Integrated Reporting with Sectoral Perspectives
Bibliographic record
Abstract
Sustainability reporting has evolved into a multidimensional field encompassing Environmental, Social, and Governance (ESG) disclosure, integrated reporting (IR), and circular economy (CE) practices. This study aims to map the intellectual and thematic landscape of sustainability reporting research over the past decade, with a focus on sectoral differentiation. Drawing on bibliometric analysis of 1611 scientific articles indexed in Scopus, this research applies co-word analysis, thematic mapping, and bibliographic coupling to identify prevailing trends, conceptual clusters, and knowledge gaps. The results reveal a clear progression from fragmented debates toward a more integrated discourse combining ESG, IR, and CE frameworks. In the real economy, sustainability reporting demonstrates a mature operational focus, supported by standardized frameworks and extensive empirical evidence. In contrast, the banking sector exhibits emerging engagement with sustainability disclosure, while the public sector remains at an earlier stage of conceptual and practical development. Despite the increasing convergence of research streams, gaps persist in linking reporting practices to tangible sustainability outcomes, integrating digital innovations, and addressing social dimensions of circularity. This study concludes that further interdisciplinary and sector-specific research is essential to advance credible, comparable, and decision-useful reporting practices capable of supporting the transition toward sustainable and circular business models.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.018 | 0.044 |
| 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; 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".