Value Relevance of Climate Change Disclosure: An Empirical Study on The Oil & Gas Companies Listed on Toronto Stock Exchange (TSX)
Bibliographic record
Abstract
This study looks at how important it is for oil and gas companies which are listed on the Toronto Stock Exchange (TSX) to disclose information on climate change. I create a disclosure index by doing a content analysis of 58 firms' publicly accessible documents. As an independent variable, the Disclosure Index Score derived from the content analysis of 58 corporations is taken into consideration. As a stand-in for business value, the market to book assets ratio is employed. The relationship between corporate value and the degree of climate change disclosure is investigated in this study. According to empirical data, investors weigh how much information has been disclosed on climate change when determining a company's market value. Because it looks at the connection between climate change declarations and businesses' value, this study adds to the body of knowledge in environmental accounting. Practically speaking, the results of this investigation will give the Canadian Securities Administrator (CSA) an understanding of how disclosures about climate change are made and give them a framework for drafting associated disclosure requirements. Additionally, it should motivate Canadian oil and gas corporations to reveal their GHG emission reduction plans and strategies.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".