Sustainability Practices, Corporate Value, and Financial Risk: Is There an Academic Consensus? A Systematic Bibliometric Review
Bibliographic record
Abstract
This study presents a systematic review and bibliometric analysis of the relationship between sustainability practices—commonly framed within the environmental, social, and governance (ESG) framework—and both corporate value creation and financial risk mitigation. Our primary objective is to assess how ESG initiatives affect firm outcomes, with particular emphasis on risk reduction, a dimension less explored in the economic and financial literature. The search was conducted in the Web of Science database on 15 June 2024, using the keywords “ESG and Financial Risk” and “ESG and Valuation,” yielding 1074 initial records. After applying inclusion and exclusion criteria, we analyzed the final sample through descriptive and frequency-based methods. Findings reveal no clear consensus on the connection between ESG and value creation, with results varying across sectors, firm sizes, regions, and specific ESG components. In contrast, the evidence supporting the link between ESG practices and financial risk mitigation is stronger: 68% of the reviewed studies reported a positive relationship, while only 5% found negative effects. This review underscores the potential of sustainability as a risk-management mechanism and highlights research gaps that warrant deeper exploration. Limitations include heterogeneity of methodologies, metrics, and contexts among the studies reviewed.
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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 | 0.049 | 0.253 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.161 | 0.137 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 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".