Issues in Sustainability Accounting Reporting
Bibliographic record
Abstract
In the U.S., sustainability accounting reporting is developing and becoming more prevalent in public companies. This paper reviews accounting literature and Dow 30 companies’ websites, presents a comprehensive view of the landscape of sustainability accounting reporting, and identifies seven issues of the reporting frameworks of sustainability accounting, i.e., (1) definitions, (2) measurements and disclosures, (3) motivations, (4) compliance, (5) enforcement, (6) standardization, and (7) the ultimate effect on reliability and comparability.An archival analysis approach is used to summarize and compare Dow 30 sustainability accounting reporting frameworks and information disclosed in 2015 annual reports and websites. The most popular framework is the Global Reporting Initiative (GRI) G4 Sustainability Reporting Guidelines. Some companies developed sustainability accounting reporting frameworks and others did not disclose any information regarding sustainability accounting reporting. Although the GRI framework is the most used, external assurance is present in only a few companies.
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 | 0.116 | 0.279 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.016 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.024 | 0.018 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".