MétaCan
Menu
Back to cohort
Record W1688038216

Carbon pricing and renewable energy innovation: A comparison of Australian, British and Canadian carbon pricing policies

2014· article· en· W1688038216 on OpenAlexaboutno aff
Karen Bubna-Litic, Natalie Stoianoff

Bibliographic record

VenueUTS ePRESS (University of Technology Sydney) · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsSubsidyGreenhouse gasInvestment (military)Carbon taxClean technologyNatural resource economicsElectricityRenewable energyOrder (exchange)Carbon priceBusinessEconomicsPublic economicsFinanceMarket economyEngineeringPolitical science
DOInot available

Abstract

fetched live from OpenAlex

© 2015, (publisher). All rights reserved. Introducing its now-abolished carbon price from July 2012, Australia argued that a price on carbon would reduce greenhouse gas emissions by improving energy efficiency and increasing investment in clean technology innovation. The United Kingdom has priced carbon since 2008 and is in the process of major electricity market reform with the aim of attracting £100 billion of infrastructure investment. British Columbia in Canada introduced a carbon tax in 2008, providing support for clean technology industries through a variety of allowances and operating subsidies.This article compares the United Kingdom, Canada and Australia, to assess the evidence base and policy experience of these jurisdictions in carbon pricing. In so doing, the article identifies what lessons can be learnt from these policy frameworks in order to promote investment in low-carbon innovation.

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)0.002
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.225

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.009
Science and technology studies0.0020.001
Scholarly communication0.0040.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.037
GPT teacher head0.213
Teacher spread0.176 · 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 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

Citations3
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueUTS ePRESS (University of Technology Sydney)Same topicClimate Change Policy and EconomicsFrench-language works237,207