Climate Change Disclosure: An Empirical Study On The Oil & Gas Companies Listed on Toronto Stock Exchange (TSX)
Bibliographic record
Abstract
This research investigates the current state of disclosure on the climate change issues of the oil & gas companies listed on the Toronto Stock Exchange (TSX). Using a sample of 58 companies, I conduct a content analysis of their publicly available documents and develop a disclosure index. The study demonstrates that there is a significant association between the level of disclosure of climate change issues (disclosures index) and the board of director’s effectiveness (measured by Board Shareholder Confidence Index) for Canadian oil & gas companies. This study also explores the association between firms’ value and the level of climate change disclosure. The empirical evidence indicates that the investors take the extent of disclosures on climate changes into considerations when they assess the market value of the firms. This study contributes to environmental accounting literature because it examines the relationship between climate change disclosures and corporate governance. From a practical point of view, the outcome of this analysis will help Canadian Securities Administrator (CSA) to have insight into climate change disclosures practices and provides a frame of references for developing related disclosures requirement.
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 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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".