The evolution of corporate reporting on GHG emissions: A Canadian portrait
Bibliographic record
Abstract
This paper examines the evolution of the extent to which firms with a high greenhouse gases (GHG) emission impact complied with Chartered Professional Accountants (CPA) Canada guidelines on climate change disclosures, as well as the factors that influenced these disclosures. The sample is comprised of Canadian firms in the mining, energy, and chemical sectors. The study measures the influence of the firms’ political exposure and media visibility, their audit firm, the presence of an environment committee, their ownership structure, and their financial performance on their GHG emissions disclosures. Our findings show that these disclosures considerably evolved over the 10 year period from 2007 to 2017 and that this evolution was in the form of a leap rather than a slow and steady learning curve. We also confirmed the significant influence of the environment committee, political exposure, and media visibility on this evolution. Our empirical results corroborate the work of DiMaggio and Powell (1983), outlining the important role normative pressures play in voluntary GHG emissions disclosure firms make in order to secure the legitimacy conferred by society (Suchman, 1995)
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.017 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".