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Record W2513400335

The Economics of Financial Securities for Environmental Obligations and Their Impact in Royalty Revenues from Alberta Oil Sands in North America

2014· preprint· en· W2513400335 on OpenAlexaboutno aff
Fariz Guliyev

Bibliographic record

VenueMunich Personal RePEc Archive (Ludwig Maximilian University of Munich) · 2014
Typepreprint
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsnot available
Fundersnot available
KeywordsRevenueTaxable incomeBusinessOil sandsFinanceCommodityAsset (computer security)Order (exchange)Resource (disambiguation)West Texas IntermediateNatural resource economicsEconomicsAccountingAsphalt
DOInot available

Abstract

fetched live from OpenAlex

The use of natural resources comes with dramatic responsibilities for producers and resource \nowners. According to Alberta Environment and Sustainable Resources Development mining companies must plan for suspension, abandonment, remediation and surface reclamation of the territory they utilise. These companies, also known as Approval Holders, have choices as to which security types to use in order to satisfy their environmental liabilities. These choices have material impact in determining annual royalty and tax revenues collected by the government.Royalty regulation in Alberta allows Approval Holders to deduct their annual costs from revenues. QETs (Qualifying Environmental Trusts), unlike Letters of Credit, are allowed for such \ndeductions. As a result, when used by Approval Holders QETs shrink the royalty revenue materially, since its full value is tax and royalty deductible. However, Approval Holders cannot deduct QETs from taxable income if the mine field is no longer recoverable and the production of bitumen has stopped permanently. As time horizon of existing mine fields in Oil Sands shrinks and future commodity prices stay uncertain we expect that Approval Holders will make a quick use of QETs to \nreduce their taxable income in the near future. In this paper, we explain why oil sands operators have not used QETs as financial securities and which uncertainties should play critical roles in identifying negative revenue impacts. This report gives an analysis of such differences and suggests possible ways to avoid royalty revenue reductions from Oil Sands mine fields.

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)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.159
Threshold uncertainty score0.356

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0050.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.006
GPT teacher head0.167
Teacher spread0.161 · 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 designSimulation or modeling
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
Published2014
Admission routes1
Has abstractyes

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