Voluntary carbon information disclosures, corporate-level environmental sustainability efforts, and market value
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<abstract> <p>Based on global 500 companies, this study examines whether the market incorporates the corporations' voluntary carbon emissions disclosures as part of their environmental sustainability efforts, thus increasing their market value. Proxies used to measure the corporations' ecological sustainability efforts include the choice of voluntary carbon disclosures, carbon emissions amounts, carbon intensity, and carbon disclosure quality. During the study period, those companies that chose to disclose their carbon information to the Carbon Disclosure Project (CDP), saw the market value their efforts towards environmental sustainability by increasing their market value. This study also compared the market value of disclosing and non-disclosing firms and found that non-disclosing companies had higher market value than did disclosing firms. However, this relationship was statistically insignificant. This study uses the more extensive data set, extended period, and more robust econometric approach (Difference GMM) and extends the boundaries of accounting research to incorporate environmental-related disclosures. Therefore, this most recent study can provide new insights to researchers, investors, and policymakers in the present context of environmental sustainability and business sustainability.</p> </abstract>
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.
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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 it