MétaCan
Menu
Back to cohort
Record W4400712469 · doi:10.1787/7f94fc66-en

Estimates of household net carbon costs in 2030-31: Ontario

2023· other· en· W4400712469 on OpenAlexaboutno aff

Bibliographic record

VenueOECD economic surveys. Canada · 2023
Typeother
Languageen
FieldDecision Sciences
Topicdemographic modeling and climate adaptation
Canadian institutionsnot available
Fundersnot available
KeywordsNet (polyhedron)Carbon fibersEconomicsAgricultural economicsNatural resource economicsEnvironmental scienceMathematics

Abstract

fetched live from OpenAlex

Global price pressures beset Canada’s economy just as unemployment was nearing record lows amid a strong recovery from the pandemic. Policymakers face the challenge of reining in inflation without causing a recession. Strong revenues have reduced fiscal deficits even as the federal government has extended living-cost relief and announced measures to make housing and childcare more affordable. But multi-year spending commitments will make it hard to sustain budget improvements without improved tax and spending efficiency. Moreover, for Canada to escape years of weak investment and tepid productivity growth, reforms to improve the business climate are overdue. The challenge is to lift living standards with minimal environmental impact. Canada aims to eliminate its net greenhouse gas emissions by 2050. Achieving this in a resource-intensive economy requires strong incentives to phase out fossil-fuel use and encourage energy saving. To spur decarbonisation, the federal climate strategy deploys a mix of emissions pricing, green technology support and regulations. The focus should turn now to improving mitigation tools so that they work better together while addressing remaining barriers to low-cost abatement. Canada’s federal and sub-national governments must align efforts on delivering efficient and fair measures to reduce emissions and prepare communities for climate change.SPECIAL FEATURE : CANADA’S TRANSITION TO NET-ZERO EMISSIONS

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)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.242
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.085
GPT teacher head0.292
Teacher spread0.207 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueOECD economic surveys. CanadaSame topicdemographic modeling and climate adaptationFrench-language works237,207