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Record W7019740688

The Impact of Bioenergy and Biofuel Policies on Employment in Canada

2022· report· en· W7019740688 on OpenAlexaboutno aff

Bibliographic record

VenueYork University Digital Library (York University) · 2022
Typereport
Languageen
FieldEconomics, Econometrics and Finance
TopicLabor market dynamics and wage inequality
Canadian institutionsnot available
Fundersnot available
KeywordsRenewable energyEnergy policyContext (archaeology)BioenergyClimate changeCorporate governanceGreenhouse gasBiofuel
DOInot available

Abstract

fetched live from OpenAlex

Environmental policy, particularly written to deal with climate change and the related issue of renewable or clean energy production, has the potential to change the capacity of businesses, states, and other organizations to provide employment opportunities. This paper reviews the development of environmental policy in Canada at the federal level as well as in two provinces (Ontario and British Columbia). Key policies include the Canadian renewable fuel standard (included in Bill C-30, the Clean Air Act of 2007) as well as Ontario’s Green Energy Act (Ontario Bill 150) and British Columbia’s Bioenergy Strategy. Our methodology describes employment associated with the bioenergy and biofuel sectors as concentric circles ranging from direct through indirect and temporary jobs, and describes forthcoming survey analyses that aim to quantify the impact that these policies have had on employment opportunities. We situate our findings within the context of an ambiguous climate or energy strategy at the national level, and discuss what may be at stake when labour issues are excluded from climate policy debates. The paper looks critically upon the strategic “greening”of economies, jobs and governance in Canada, while providing recommendations for future iterations of policy at the federal and provincial levels.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.894
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.022
GPT teacher head0.183
Teacher spread0.161 · 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 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

Citations0
Published2022
Admission routes1
Has abstractyes

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