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Record W4220724133 · doi:10.1787/8f030bcc-en

Carbon pricing and COVID-19

2022· report· en· W4220724133 on OpenAlexfundno aff
Daniel Nachtigall, Jane Ellis, Sofie Errendal

Bibliographic record

VenueOECD environment working papers · 2022
Typereport
Languageen
FieldEnergy
TopicEnergy, Environment, and Transportation Policies
Canadian institutionsnot available
FundersNational TreasuryEnvironment and Climate Change CanadaBundesministerium für Umwelt, Naturschutz, nukleare Sicherheit und VerbraucherschutzAustralian Government
KeywordsScope (computer science)Greenhouse gasCarbon priceClimate changeCoronavirus disease 2019 (COVID-19)Natural resource economicsAviationBusinessEnvironmental economicsEconomicsComputer scienceEngineeringEcology

Abstract

fetched live from OpenAlex

This paper assesses the role of carbon pricing in a sustainable recovery from COVID-19. It tracks the policy changes in carbon pricing within OECD and G20 countries between January 2020 and August 2021 of the COVID-19 pandemic. Carbon pricing as defined here includes emissions trading schemes, fossil fuel support and carbon, fuel excise or aviation taxes. The paper also highlights the need for the recovery to be sustainable and discusses the advantages, limitations and uses of carbon pricing therein. In addition, it describes additional challenges to as well as increased rationale for carbon pricing in the pandemic. It provides evidence on the effects of carbon pricing on the challenges and discusses carbon pricing design elements to help overcome those challenges. The paper concludes that there were more policy changes with an expected negative impact on climate. However, it is likely that the impact of the climate-positive changes – which are broader in coverage and scope - will outweigh the climate-negative changes.

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)0.001
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.050
Threshold uncertainty score0.168

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.005
Science and technology studies0.0010.001
Scholarly communication0.0050.003
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0500.005

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 head0.028
GPT teacher head0.247
Teacher spread0.219 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations8
Published2022
Admission routes1
Has abstractyes

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