MétaCan
Menu
Back to cohort
Record W2527632840 · doi:10.5539/jsd.v9n5p175

A Bottom-Up, Non-Cooperative Approach to Climate Change Control: Assessment and Comparison of Nationally Determined Contributions (NDCs)

2016· article· en· W2527632840 on OpenAlexvenueno aff
Carlo Carraro

Bibliographic record

VenueJournal of Sustainable Development · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsNegotiationGreenhouse gasControl (management)Climate changeBusinessEnvironmental resource managementEnvironmental economicsNatural resource economicsEnvironmental scienceEconomicsPolitical scienceEcology

Abstract

fetched live from OpenAlex

International negotiations on climate change control are moving away from a global cooperative agreement (at least from the ambition to achieve it) to adopt a bottom-up framework composed of unilateral pledges of domestic measures and policies. This shift from cooperative to voluntary actions to control GHG emissions already started in Copenhagen at COP 15 in 2007 and became a platform formally adopted by a large number of countries in Paris at COP 21. The new architecture calls for a mechanism to review the nationally determined contributions (NDCs) of the various signatories and assess their adequacy. Most importantly, countries’ voluntary pledges need to be compared to assess the fairness, and not only the effectiveness, of the resulting outcome. This assessment is crucial to support future, more ambitious, commitments to reduce GHG emissions. It is therefore important to identify criteria and quantitative indicators to assess and compare the NDCs.

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)0.046
metaresearch head score (Gemma)0.065
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.242

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0460.065
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.005
Science and technology studies0.0020.004
Scholarly communication0.0050.004
Open science0.0020.005
Research integrity0.0010.002
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 head0.061
GPT teacher head0.305
Teacher spread0.244 · 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
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

Citations10
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Sustainable DevelopmentSame topicClimate Change Policy and EconomicsFrench-language works237,207