Greenhouse gas emission rights in accounting – is a global benchmark needed?
Bibliographic record
Abstract
Purpose: The aim of the article is to verify accounting methods used to map the essence and specifics of greenhouse gas emission rights trading in corporate financial reporting. Methodology/approach: A literature review and an analysis of national and international environmental regulations and accounting guidelines were conducted for the United States, Canada, New Zealand, China, Japan, Germany, Great Britain, France, and Poland. The EU market for trading greenhouse gas emission allowances and the efforts made by the International Accounting Standards Board are presented separately. Findings: There is a regulatory gap in the recognition, measurement and disclosure of greenhouse gas emission rights in the financial statements. So far, no environmental accounting regu-lation (standard) of international importance has been adopted, although few of the proposals from national environmental organizations differ between jurisdictions. Practical implications: There is a need to fill the identified regulatory gap and improve financial reporting by establishing consistent and uniform principles for recognizing, measuring and presenting greenhouse gas emission rights. Originality/value: The article emphasizes the importance of the accounting information system in providing a coherent picture of the achievements of economic entities (including environmental performance) and identifies challenges for the scientific discipline of accounting in relation to the development of greenhouse gas emissions trading around the world.
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 | 0.066 | 0.134 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.019 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.016 | 0.027 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".