MétaCan
Menu
Back to cohort
Record W7132946331

No Emissions Trading Scheme is an Island: Building a Global Linking Agreement from the "Bottom Up"

2014· dissertation· W7132946331 on OpenAlexaff
Susannah Rose Leslie

Bibliographic record

VenueTSpace · 2014
Typedissertation
Language
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTreatyEmissions tradingClimate changeGlobal warmingClimate policyCarbon taxGreenhouse gas
DOInot available

Abstract

fetched live from OpenAlex

The global climate change regime is at a crossroads. There remains little prospect states will agree to urgently needed binding commitments through a "top down" climate agreement, but the ability of national and sub-national policies to produce sufficient emission reductions in its place faces significant challenges. This paper proposes a possible alternative model for building a centralised climate regime from the "bottom up" through linking national and sub-national emissions trading schemes in stages under a global linking agreement. This model is argued as preferable to waiting for a global carbon price to develop from decentralised linkages, or for a "top down" linking agreement to emerge. The paper then considers how the legal design of such an agreement could best facilitate linking of different schemes, concluding that parties should agree to "low end" mutual recognition of allowances under an umbrella treaty structure, with less essential elements left to non-binding arrangements.

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)0.006
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0030.008
Scholarly communication0.0070.013
Open science0.0010.006
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0070.001

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 head0.095
GPT teacher head0.336
Teacher spread0.241 · 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 designTheoretical or conceptual
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
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueTSpaceSame topicClimate Change Policy and EconomicsFrench-language works237,207