Market incidence of carbon information disclosure in the oil and gas industry: the mediating role of financial analysts and governance
Bibliographic record
Abstract
Purpose This study aims to assess the informativeness of carbon emission data for the stock markets and the mediating role played by financial analysts and the quality of the governance on this issue. Design/methodology/approach Relying on structural equation modelling, the authors assess the relation between embedded CO 2 disclosure or CO 2 emissions disclosure and the stock market valuation (Tobin Q), considering the mediating roles played by financial analysts (external monitoring) and corporate governance (internal monitoring). Findings Results based on a sample of North American firms in the oil and gas industry are the following. The disclosure of embedded CO 2 is negatively associated with a firm’s market value, but this association is mediated by analyst following and corporate governance. The disclosure of yearly CO 2 emissions is also negatively related to stock market value, while corporate governance mediates this negative impact, and analysts following does not. Considering that yearly CO 2 emissions represent short-term environmental risks, whereas embedded CO 2 represents long-term environmental risks, it appears important to consider embedded CO 2 when studying the impact of carbon disclosure on firm value. The authors also show that a firm’s environmental performance (measured by Carbon Disclosure Project – CDP) is positively associated with two mediating variables (i.e. analyst following and corporate governance). Originality/value The study results suggest that CO 2 emissions information is less relevant than embedded CO 2 in attracting financial analysts when they are assessing a firm’s value because it represents short-term environmental risks, whereas embedded CO 2 represents long-term environmental risks. Therefore, the authors consider important to include embedded CO 2 when studying the impact of environmental disclosure on a firm’s value.
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 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 | 0.011 | 0.090 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".