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Record W6964593878 · doi:10.2870/965292

Emissions trading systems with different offsets provisions: implications for linking. Report for the Carbon Market Policy Dialogue

2022· book· en· W6964593878 on OpenAlexaboutno aff

Bibliographic record

VenueCadmus - EUI Research Repository (European University Institute) · 2022
Typebook
Languageen
FieldAgricultural and Biological Sciences
TopicForest Ecology and Biodiversity Studies
Canadian institutionsnot available
Fundersnot available
KeywordsEmissions tradingCarbon marketGreenhouse gasAlternative trading systemClimate policyEnvironmental policyCarbon offsetRegulatorAlgorithmic tradingPublic policy

Abstract

fetched live from OpenAlex

This report was prepared to inform the Carbon Market Policy Dialogue (CMPD) between the European Commission, as the regulator of the EU Emissions Trading System, and the regulatory authorities for the emissions trading systems (ETSs) of California, Quebec, China, New Zealand and Switzerland. The report deals with the implications of linking ETSs that differ from each other in the provisions on emission offsetting. The report provides a conceptual framework and summarizes the relevant scientific literature; it describes the current status of the six ETSs represented in the CMPD and, finally, it offers up a few ideas for discussion.

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)0.007
metaresearch head score (Gemma)0.012
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: Review · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.012
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.004
Science and technology studies0.0020.004
Scholarly communication0.0130.015
Open science0.0020.003
Research integrity0.0060.006
Insufficient payload (model declined to judge)0.0130.003

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 head0.069
GPT teacher head0.269
Teacher spread0.200 · 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
GenreReview

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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