The Impact of Environmental Accounting Information Disclosure on Financial Risk: The Case of Listed Companies in the Vietnam Stock Market
Bibliographic record
Abstract
This research study aims to assess the impact of environmental accounting information disclosure on financial risk within the context of Vietnam’s stock market. The data collection process involved 60 non-financial companies, carefully selected from both the pool of 100 Sustainable Companies listed in the “Programme on Benchmarking and Announcing Sustainable Companies in Vietnam (CSI)”, as organized by VBCSD, and companies outside this list. The data span a timeframe from 2018 to 2022. Afterward, we utilize regression models to assess relationships and employ the t-test to evaluate differences. The results indicate that environmental accounting information disclosure has an inverse effect on the financial risk of the current year and the following year. This implies that companies that are more transparent and proactive in reporting their environmental performance are likely to experience decreased financial risk. Furthermore, the results also show differences in financial risk between the group of companies within the “100 Sustainable Companies” list and the group of companies outside this list. This disparity underscores the potential financial benefits of being recognized as a sustainable company. Based on the findings, the research team has provided several recommendations to enhance environmental accounting information disclosure and awareness.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".