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Record W1967595893 · doi:10.3138/cpp.36.4.409

Intensity-Based Climate Change Policies in Canada

2010· article· en· W1967595893 on OpenAlexaffvenueabout
Nic Rivers, Mark Jaccard

Bibliographic record

VenueCanadian Public Policy · 2010
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsGreenhouse gasEmissions tradingEconomicsGovernment (linguistics)Climate changeSystem dynamicsInternational economicsGeneral equilibrium theoryNatural resource economicsInternational tradeEnvironmental scienceMacroeconomicsComputer science

Abstract

fetched live from OpenAlex

To reduce greenhouse gas emissions from large industries the Canadian government proposed using a tradable emissions performance standard approach, where the intensity of emissions, rather than the absolute level, is regulated. Unlike a cap and trade system, an emissions performance standard does not guarantee a certain overall level of emission reductions, a fact that has led to significant criticism. However, because of the dynamics of performance standards, they may reduce concerns over reductions in international competitiveness in cases where a country has climate policies that are more aggressive than those of some of its trade partners. Likewise, a performance standard may mesh more efficiently with existing taxes and therefore cause less overall economic impact than an absolute cap and trade system. This paper considers the theoretical arguments for and against such a performance standard system and evaluates it in comparison to a cap and trade system using a dynamic general equilibrium model applied to Canada.

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)0.003
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.124
Threshold uncertainty score0.897

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0040.002
Scholarly communication0.0040.001
Open science0.0010.001
Research integrity0.0020.002
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 head0.088
GPT teacher head0.231
Teacher spread0.142 · 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
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

Citations40
Published2010
Admission routes3
Has abstractyes

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