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Record W4408990625 · doi:10.5430/ijba.v16n1p92

Accounting Choices and Financial Position in Brazilian Carbon Credit Markets

2025· article· en· W4408990625 on OpenAlexvenueno aff
Mariana S. F. A. Fregonesi, Maria Paula Vieira Cicogna, Maísa de Souza Ribeiro, Sílvio Hiroshi Nakao, Alessandra Segatelli

Bibliographic record

VenueInternational Journal of Business Administration · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicPrivate Equity and Venture Capital
Canadian institutionsnot available
FundersConselho Nacional de Desenvolvimento Científico e Tecnológico
KeywordsPosition (finance)Financial marketEconomicsBusinessFinanceFinancial systemAccounting

Abstract

fetched live from OpenAlex

In Brazil, two systems carbon credits coexist: the voluntary market for carbon credits in general, and the regulated market specifically for the fuel sector. This article aims to investigate the absence of accounting standards in voluntary and regulated carbon credit markets in Brazil, and its consequences on the reported financial position according to the economic interests of the agents involved in the markets. A positive and qualitative approach was applied to describe and analyze cases of carbon credit accounting practices of purchasing and selling companies. The cases studied recognized carbon credit assets as inventory, as intangible and as financial assets, although the characteristics of carbon credits do not qualify them under the current classifications. The results highlight different economic interests in voluntary and regulated markets that lead to the respective accounting choices linked to them. The research contributes to strengthening the scientific arguments on the accounting of carbon credits.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0020.004
Scholarly communication0.0040.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.249
Teacher spread0.241 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2025
Admission routes1
Has abstractyes

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