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Record W4211192170 · doi:10.1080/02646811.2021.2012349

Canada’s carbon energy overhang

2022· article· en· W4211192170 on OpenAlexaboutno aff
Alastair R. Lucas

Bibliographic record

VenueJournal of Energy & Natural Resources Law · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsRenewable energyGreenhouse gasFossil fuelElectricityNatural resource economicsLegislationBusinessPetroleum industryGovernment (linguistics)Environmental protectionEconomyEconomicsEnvironmental sciencePolitical scienceEnvironmental engineeringWaste managementEngineering

Abstract

fetched live from OpenAlex

Canada’s historical legal framework for hydrocarbon development persists, creating a ‘carbon energy overhang’ that impedes the transition to a low-carbon economy on which Canadian governments have embarked. As a result, Canada remains a petro-state, the fourth largest global oil producer with the third largest reserves. There have been greenhouse gas emission reduction initiatives, particularly by the federal government, including a national carbon tax (that survived constitutional challenge), renewable energy initiatives, energy transition legislation establishing net zero goals, and a promised cap on oil and gas industry emissions. However, two major barriers remain, notably (1) the national economic importance of the oil and gas industry with its oil sands centrepiece, located mainly in the province of Alberta; and (2) very limited room to cut electricity generation emissions, with over 80 per cent of electricity already produced by renewables and nuclear, and a lack of interprovincial transmission interties with these renewable electricity (mainly hydro)-producing provinces that export large quantities of energy to the United States.

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.079
Threshold uncertainty score0.570

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0150.003
Scholarly communication0.0090.002
Open science0.0020.002
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0150.001

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 head0.003
GPT teacher head0.161
Teacher spread0.158 · 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 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

Citations3
Published2022
Admission routes1
Has abstractyes

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