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Record W3147827098 · doi:10.55016/ojs/sppp.v8i1.42552

A Primer on Alberta’s Oil sands Royalties

2015· article· en· W3147827098 on OpenAlexaffabout
Sarah Dobson

Bibliographic record

VenueThe School of Public Policy Publications · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicAmerican Environmental and Regional History
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsOil sandsPrimer (cosmetics)Petroleum engineeringBusinessGeologyGeographyArchaeologyChemistryAsphalt

Abstract

fetched live from OpenAlex

Fulfilling its campaign promise, the new NDP government announced a review of Alberta’s royalty framework in June 2015. The province receives royalty revenue from three main sources – natural gas, crude oil, and oil sands. Since the 2009-10 fiscal year the largest contributor to Alberta’s royalty revenues has been the oil sands. If you want a sense of how important oil sands royalties have been for Alberta’s finances, consider this: In the 2014–15 fiscal year, the government collected just over $5 billion from oil sands royalties. These royalties covered over 10 per cent of the province’s operational expenses of $48.6 billion in the same fiscal year. Over the last six fiscal years the oil sands have contributed an average of 10 per cent of revenues to provincial coffers. This makes oil sands royalties the fourth largest contributor behind personal income taxes (23 per cent), federal transfers (13 per cent) and corporate income taxes (11 per cent). But how many Albertans really understand how the royalty system works? What do we mean when we say “royalty”? How does the Alberta Government calculate royalties on oil sands producers? If the system is going to change, it’s important that Albertans understand how the current system works. That is what this paper is designed to do. For Albertans to properly judge the impact of new policy, they need a solid understanding of the current policy environment. We all know that oil prices have dropped and oil sands producers are losing profitability. As such, changes to the royalty system could have a deep and profound impact on the sector. Here are some of the issues this primer will study: • Pre-payout projects vs. post-payout projects, in other words, the classification of projects for royalty purposes based on whether the cumulative costs of a project exceed its cumulative revenues • Monthly payment of royalties vs. annual payment • Understanding the unit price of bitumen and how that price is applied • Gross vs. net revenues and the application of royalties • How the price of oil and the exchange rate between Canadian and U.S. dollars impact royalties • The historical and forecast contribution of oil sands royalties to Alberta’s finances Needless to say, a primer like this should be required reading for policymakers. It should also be required reading, however, for any Albertan who cares about the long-term benefit of the oil sands to Alberta’s revenue, and our financial future as a province.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.735
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.004

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 head0.034
GPT teacher head0.260
Teacher spread0.226 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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
Published2015
Admission routes2
Has abstractyes

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