Mapping the Global Research on Environmental, Social, Governance (ESG): A Bibliometric Analysis
Bibliographic record
Abstract
In the contemporary business landscape, firm evaluation increasingly extends beyond traditional financial indicators to encompass environmental sustainability, social responsibility, and governance practices. Environmental, Social, and Governance (ESG) factors have emerged as fundamental drivers of corporate values and sustainable financial performance, introduced through the UN Global Compact initiative’s Who Cares Wins report, and are now considered central to global corporate strategies, shaping accountability, transparency, and ethical business conduct. This study conducts a comprehensive bibliometric analysis to map the global ESG research landscape, examining the current state of knowledge, key trends, and future directions. Data were extracted from the Scopus database, comprising 3,177 articles published between 2008 and 2026, and analyzed using VOSviewer and Biblioshiny (R). The performance analysis evaluated annual scientific production, leading sources, authors, affiliations, countries, and most cited documents, while science mapping examined keyword occurrence, word clouds, co-authorship networks, thematic mapping, and thematic evolution. Results indicate that annual scientific production peaked in 2025 with 1,305 publications, with Sustainability emerging as the most influential journal. China (1,629 publications) and the USA (605 publications) led in productivity, while China (15,062 citations) and the USA (10,035 citations) dominated in citation impact. Keyword analysis identified “ESG,” “Sustainability,” and “Responsible Investment” as the most prominent themes. Overall, this study provides a structured overview of global ESG research, identifies influential contributors and sources, and highlights emerging thematic areas, offering a roadmap for scholars, practitioners, and policymakers aiming to advance ESG-focused research and implementation worldwide.
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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 | 0.014 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.214 | 0.293 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".