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Record W1969310517 · doi:10.1504/ijgei.2005.006949

Canada's efforts towards greenhouse gas emission reduction: a case study on the limits of voluntary action and subsidies

2005· article· en· W1969310517 on OpenAlexaffabout
Nic Rivers, Mark Jaccard

Bibliographic record

VenueInternational Journal of Global Energy Issues · 2005
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsSubsidyKyoto ProtocolGreenhouse gasIncentiveClimate changeVoluntarism (philosophy)EconomicsEnergy policyClimate change mitigationGovernment (linguistics)BusinessPublic economicsInternational tradeNatural resource economicsEconomic policyInternational economicsRenewable energyMarket economyEngineering

Abstract

fetched live from OpenAlex

Canada has committed internationally to several agreements to limit climate change, most recently by ratifying the Kyoto Protocol in 2002. However, its domestic climate change policy is not reflective of these international commitments. In particular, federal government climate change policy over the last decade has emphasised noncompulsory policies such as voluntarism, information provision, and modest subsidies. These policies are designed primarily to engender minimal political resistance, and have been relatively ineffective in providing the incentives and regulatory structure for the dramatic technological and behavioural change required for significant greenhouse gas emissions reductions. Without a major change in direction towards more compulsory policies, it seems unlikely that Canada will achieve significant domestic greenhouse gas reductions over and beyond the Kyoto Protocol time frame. We suggest a more compulsory policy approach dominated by market-oriented regulations. When designed appropriately, this type of policy stimulates the development and commercialisation of new technologies without dramatically affecting prices of energy or goods.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.513
Threshold uncertainty score0.744

Codex and Gemma teacher scores by category

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

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 head0.080
GPT teacher head0.309
Teacher spread0.229 · 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 teacher head, 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

Citations13
Published2005
Admission routes2
Has abstractyes

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