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Record W2750575850 · doi:10.1071/aj08058

Accounting and financial reporting considerations for oil and gas companies operating under Australia’s proposed Carbon Pollution Reduction Scheme*

2009· article· en· W2750575850 on OpenAlexaff
Nick Henry, Adam Cunningham

Bibliographic record

VenueThe APPEA Journal · 2009
Typearticle
Languageen
FieldEnergy
TopicGlobal Energy and Sustainability Research
Canadian institutionsPricewaterhouseCoopers (Canada)
Fundersnot available
KeywordsGreenhouse gasBusinessLegislationCash flowAccountingDeregulationCorporate governanceFinancePetroleum industryCarbon footprintCashFossil fuelNatural resource economicsEconomicsEngineeringWaste managementEnvironmental engineering

Abstract

fetched live from OpenAlex

The introduction of the Carbon Pollution Reduction Scheme (CPRS) is one of Australia’s most significant economic reforms since the deregulation of the Australian financial markets in the 1980s and will have a significant impact on companies across a number of sectors—in particular those in the oil and gas industry. Given the significant greenhouse gas emission footprint of the oil and gas industry in Australia, for many oil and gas companies the cost of buying carbon pollution permits and/or reducing emissions through targetted abatement programs is likely to be significant. From a strategic perspective, understanding how the proposed CPRS could affect future cash flows will be critically important. Financial markets have already begun to factor the potential cash flow impacts into valuations of companies likely to be directly impacted by the legislation. Public disclosure of the potential impacts of the CPRS is considered both an opportunity and threat for those companies exposed to it. The proposed CPRS will also pose significant governance, compliance and reporting challenges for those companies directly impacted by it. Measurement and reporting of emissions information will need to be subjected to the same level of control and rigour as other financial information. This paper will examine both the immediate and longer-term accounting and financial reporting considerations for oil and gas companies as a result of the CPRS, focussing on what companies need to be doing now to be prepared for the introduction of this legislation.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.399
Threshold uncertainty score0.943

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.054
GPT teacher head0.324
Teacher spread0.271 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations0
Published2009
Admission routes1
Has abstractyes

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