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Record W6884642437 · doi:10.11575/prism/35954

Carbon Price Sensitivity Analysis On The Alberta Oil Sands: An Environmental And Economic Study

2016· other· en· W6884642437 on OpenAlexaboutno aff

Bibliographic record

VenuePRISM (University of Calgary) · 2016
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsGreenhouse gasBarrel (horology)Oil sandsEconomic impact analysisPetroleum industryCarbon footprintCarbon fibersAsphaltEnvironmental impact assessmentFossil fuel

Abstract

fetched live from OpenAlex

This paper provides an overview of the oil sands industry including the associated environmental impact, Alberta’s greenhouse gas (GHG) regulations, and the economics of bitumen production. The impact of the GHG regulations is examined using the emissions intensity of the oil sands industry relative to the provincial GHG reduction targets. The economic impact of GHG regulations on industry is analyzed by determining the average cost of compliance per barrel of bitumen produced. For comparison, the carbon pricing systems in British Columbia and Norway are discussed and the outcomes of the policies are provided as examples of the impact of higher carbon pricing and more stringent regulations. Norway’s relatively high carbon price is applied to Alberta and the economic implications are discussed. Through these analyses and findings, a number of policy recommendations for Alberta’s GHG regulations are provided to address the environmental issues of the industry while considering important economic factors.

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)0.002
metaresearch head score (Gemma)0.003
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.119
Threshold uncertainty score0.240

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.182
Teacher spread0.176 · 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
Published2016
Admission routes1
Has abstractyes

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