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Record W2593153651 · doi:10.1111/caje.12436

Leave it in the ground? Oil sands development under carbon pricing

2020· article· en· W2593153651 on OpenAlexaffvenue
Branko Bošković, Andrew Leach

Bibliographic record

VenueCanadian Journal of Economics/Revue canadienne d économique · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsOil sandsExternalityNatural resource economicsCarbon fibersClimate changeCarbon sequestrationFossil fuelCarbon cycleResource (disambiguation)Environmental scienceSocial costInvestment (military)Greenhouse gasCarbon priceEconomicsCarbon dioxideEcologyWaste managementMicroeconomicsEngineeringEcosystem

Abstract

fetched live from OpenAlex

Abstract We evaluate the impact of internalizing the carbon emissions externality on new oil sands projects. Using data from recent oil sands projects and estimates of both the social costs of carbon and carbon prices consistent with meeting global climate change targets, we estimate the potential impact of action on climate change on the economic viability of oil sands investments. Our results indicate that oil sands are a marginal resource before they incur any carbon costs. Incorporating carbon costs, we find that the viability of oil sands depends on the coverage of carbon pricing across the life cycle emissions from oil sands and on the equilibrium incidence of carbon prices on producers. We show an important interaction between resource royalties and carbon charges that implies that the impact carbon pricing depends on not only the stringency and coverage of the carbon price but also its point of application of a carbon price. Finally, we explore the potential for technological change to mitigate the impacts of carbon pricing on oil sands investment viability.

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)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.967
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0030.003
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.363
GPT teacher head0.201
Teacher spread0.162 · 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 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

Citations12
Published2020
Admission routes2
Has abstractyes

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Same venueCanadian Journal of Economics/Revue canadienne d économiqueSame topicClimate Change Policy and EconomicsFrench-language works237,207