Corporate disclosure of CO<sub>2</sub> embedded in oil and gas reserves: stock market assessment in a context of global warming
Bibliographic record
Abstract
Purpose The authors assess the informativeness for stock markets of proven reserves of oil and gas, and embedded CO2 in those reserves. Design/methodology/approach Based on a two-step regression approach, the authors attempt to test the relationship between proven reserves, CO2 embedded in those reserves and the stock market value controlling for the selection bias (i.e. the decision of managers to disclose environmental information about embedded CO2). Findings Results, based on a sample of the US and Canadian firms are the following. Proven reserves increase the firm’s value, while embedded CO2 reduces the stock market value substantially. Furthermore, the decision of managers to disclose information about embedded CO2 is positively related to analyst following, share price volatility, firm size, and institutional ownership. Originality/value The current study assesses the long-term incidence of embedded CO2 (in oil and gas proven reserves) on firms’ stock market value, while most studies are focusing on yearly CO2 emissions.
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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 | 0.002 | 0.009 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".