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Record W1553306876

Impact of Canada's Voluntary Agreement on Greenhouse Gas Emissions from Light Duty Vehicles

2006· article· en· W1553306876 on OpenAlexaboutno aff
Nicholas P. Lutsey

Bibliographic record

VenueeScholarship (California Digital Library) · 2006
Typearticle
Languageen
FieldEnergy
TopicEnergy, Environment, and Transportation Policies
Canadian institutionsnot available
Fundersnot available
KeywordsGreenhouse gasCommitGovernment (linguistics)Fuel efficiencyTurnoverBusinessAutomotive industryEnvironmental economicsEngineeringNatural resource economicsEconomicsAutomotive engineering
DOInot available

Abstract

fetched live from OpenAlex

On April 5, 2005, a voluntary agreement between the automobile industry and government officials of Canada was reached to commit to greenhouse gas emission reductions through the year 2010. This report compares Canada's voluntary agreement with other voluntary and mandatory greenhouse gas reduction programs around the world in terms of what technologies are likely to be deployed and how much vehicle fuel consumption is likely to improve. It investigates various methods and measurement approaches for implementing the agreement, incorporating the potential effects of criteria pollutant emission reductions, fuel use modifications (including the effects of lower sulfur fuels and ethanol), and vehicle technology adoption (including mobile air conditioning systems). The findings of this study suggest that the decisions of the official MOU oversight committee on how to credit various existing automobile technology trends could substantially impact total emission reductions and the deployment of fuel efficiency technology in Canada. Based on the committee's determinations, Canada's voluntary agreement could result in substantially improved fuel economy, or it could have little or no effect. This analysis raises broader questions regarding the efficacy and effectiveness of voluntary agreements, relative to regulatory initiatives.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.163
Threshold uncertainty score1.000

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.193
Teacher spread0.186 · 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.

Study designObservational
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

Citations2
Published2006
Admission routes1
Has abstractyes

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