An Empirical Analysis of the Determinants of Greenhouse Gas Voluntary Disclosure in Australia
Why this work is in the frame
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Bibliographic record
Abstract
Based on a comprehensive theoretical framework, we investigate the determinants of greenhouse gas emission (GHG) voluntary disclosure of non GHG registered companies. Previous studies assessed the determinants of GHG voluntary disclosure of firms subject to environmental regulation, risk and liabilities. We also employ proxies of voluntary disclosure theory and agency theory in addition to the stakeholder theory and legitimacy theory used in prior studies. The content analysis for the period 2009 to 2011 shows a positive association between GHG voluntary disclosure, firm size and corporate governance. Further, firms with superior GHG performance are more likely to engage in discretionary disclosure, and listing status plays a significant role in GHG disclosure decision which suggests that stakeholders’ interests also determine disclosure decisions.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| 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 it