Bibliographic record
Abstract
This study aims to investigate the correlations between ESG score and firm value. The paper verifies the hypothesis that there is a positive correlation between ESG score and firm performance, as indicated by levered free cash flow, ROE, current ratio, and quick ratio; also, the study aimed to investigate the relationship between ESG score and firm value improvement, as indicated by stock price of firm. The study applied linear regression to a panel data using Bloomberg ESG disclosure scores from a sample of 115 companies listed in Europe. The time under study was from 2016 to 2020. Findings suggest a positive and significant relationship between the variables. Research findings will help firms’ stakeholders to improve their awareness of the impact of ESG disclosure on the performance of the firm. The findings, which support the positive relationship between ESG and firm performance, can be used to supporting or even completing other studies with similar or same concept, after necessary adjustments have been made. Data used for this study need to be subjected to more statistical tests in order to establish a more robust validity and reliability. It is necessary to acquire further strengthened data and assume a variety of conditional situations. It is expected that subsequent studies can use larger samples and diversified by sector, a broader geographic base, and a multi-faceted analysis.
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 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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".