CDP (carbon disclosure project) emissions of CO <sub>2</sub> and environmental disclosure quality: the mediating role of media legitimacy and governance
Bibliographic record
Abstract
The paper assesses the relation between firms’ CO2 emissions as per the CDP (formerly known as Carbon Disclosure Project) platform and their environmental disclosure quality, as mediated by two monitoring mechanisms: media legitimacy (external) and firm governance (internal). Legitimacy theory as well as the monitoring dimension of agency theory underlie the empirical investigation. The sample comprises Canadian and U.S. firms, for which a governance disclosure score and media legitimacy information are available from Bloomberg. Relying on structural equations modelling, results are the following. First, CDP disclosing firms exhibit an environmental disclosure score that is twice as high as non-disclosing firms. Second, media legitimacy, expressed by the Janis-Fadner index, is significantly lower for CDP disclosing firms. This may explain why high CO2 emission firms are less inclined to disclose CDP emission data. Third, the direct effect of the decision to disclose CDP emissions of CO2 (full sample) on environmental disclosure quality is positive and significant. Consistent with our hypotheses, we observe an indirect (mediating) effect on this relationship of media legitimacy (external monitoring) and corporate governance (internal monitoring). This suggests that media legitimacy and corporate governance enhance the positive impact of the decision to disclose CDP emissions of CO2 on environmental disclosure quality. Fourth, the direct effect of CDP emissions of CO2 (reduced sample) and environmental disclosure quality is positive and significant. Also consistent with our hypotheses, we observe an indirect effect on this relationship from media legitimacy and corporate governance.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".