A Literature Study on the Impact of ESG Information Disclosure Quality on the Value of Listed Companies
Bibliographic record
Abstract
This paper reviews the existing literature on the impact of Environmental, Social, and Governance (ESG) information disclosure quality on the value of listed companies. In recent years, ESG has become an increasingly important indicator for investors to evaluate the long-term sustainable development capabilities of companies. High-quality ESG information disclosure helps reduce information asymmetry, enhancing investor confidence and improving capital market efficiency. The review of current research shows that ESG disclosure quality, based on well-established frameworks such as the Global Reporting Initiative (GRI), Sustainability Accounting Standards Board (SASB), and Task Force on Climate-related Financial Disclosures (TCFD), is significantly positively correlated with key financial indicators, including company valuation, cost of capital, and profitability. However, there are also critical viewpoints regarding ESG disclosure, such as inconsistent disclosure standards, the prevalence of "greenwashing" phenomena, and debates surrounding the financial relevance of ESG information. Despite these concerns, this paper underscores the necessity of establishing a unified global ESG information disclosure standard. Such standardization would enhance the consistency, transparency, and reliability of ESG data, providing investors with more accurate and valuable information for informed decision-making.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, 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".