ACCOUNTING FOR GREENHOUSE GAS EMISSIONS: A COUNTER-ACCOUNT OF SUSTAINABILITY REPORTS
Bibliographic record
Abstract
The objective of this study is to analyze the quality of climate information disclosed by companies and the impression management strategies develop by them to justify or conceal the negative aspects of their performance. The study is based on a qualitative content analysis of the sustainability reports of 21 companies in the energy sector using Global Reporting Initiative (GRI) with application levels A + and A over a period of 5 years (n = 105). It contributes to the literature on climate disclosure and certification practices, in particular by demonstrating the ineffectiveness of the external assurance process in ensuring the quality and representativeness of the data. Significant non-compliance with GRI standards was identified in 90 of the 93 reports audited by a third party. In addition, 6 of the 21 companies surveyed were found to disclose information which became increasingly opaque over time due to concealing information concerning the measurement and methodology used. The study also permitted to identify four impression management strategies employed to justify certain information (by minimizing impacts, excuses and commitment) or conceal it (through strategic omissions and manipulation of figures). The study has important political and managerial implications, which put into question the possibility of stakeholders assessing, monitoring and comparing the climate performance of companies.
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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 | 0.028 | 0.154 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".