Voluntary carbon information disclosures, corporate-level environmental sustainability efforts, and market value
Bibliographic record
Abstract
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.
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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 | 0.003 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".