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Record W4317210746 · doi:10.5604/01.3001.0016.1304

Greenhouse gas emission rights in accounting – is a global benchmark needed?

2022· article· en· W4317210746 on OpenAlexaboutno aff
Monika Perlińska

Bibliographic record

VenueZeszyty Teoretyczne Rachunkowości · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsnot available
Fundersnot available
KeywordsGreenhouse gasAccountingEnvironmental accountingCarbon accountingEmissions tradingBusinessNational accountsFinancial accountingMark-to-market accountingEconomicsAccounting information system

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.743
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.003
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.014
GPT teacher head0.247
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2022
Admission routes1
Has abstractyes

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