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Record W4396241110 · doi:10.1038/s44168-024-00106-4

Supporting the Paris Agreement through international cooperation: potential contributions, institutional robustness, and progress of Glasgow climate initiatives

2024· article· en· W4396241110 on OpenAlexfundno aff
Takeshi Kuramochi, Andrew Deneault, Sander Chan, Sybrig Smit, Natalie Pelekh

Bibliographic record

Venuenpj Climate Action · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsnot available
FundersHORIZON EUROPE Framework ProgrammeBundesministerium für Wirtschaftliche Zusammenarbeit und EntwicklungRadboud UniversiteitYork UniversityUniversiteit UtrechtEuropean CommissionUniversity of Oxford
KeywordsRobustness (evolution)Climate changeRegional sciencePolitical scienceGeographyGeologyChemistryOceanography

Abstract

fetched live from OpenAlex

Abstract Many sector-level cooperative initiatives involving both national governments and non-state actors were launched around the 2021 Glasgow climate conference (COP26). However, there have been questions about whether and to what extent these initiatives could substantially contribute to achieving the Paris Agreement’s goal to limit global warming to 1.5 °C. To this end, this paper examines the prospects of the 14 Glasgow sector initiatives by investigating their aggregate mitigation ambition under current national signatories and the institutional robustness of each initiative. We find that the additional emission reduction ambition of the current national government signatories would, even if fully implemented, only fill about a quarter of the emissions gap in 2030 between the aggregate of existing national targets (nationally determined contributions: NDCs) and the required emission levels consistent with keeping warming below 1.5 °C, while the institutional robustness varied considerably across the initiatives. We also find that most national government signatories did not mention Glasgow initiatives in their updated NDCs submitted after COP26. Expansion of the national government participation, national government signatories’ incorporation of the initiatives’ goals into their updated NDCs by setting quantifiable domestic targets, and enhanced institutional capacity are key to successful emission reduction outcomes.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.931
Threshold uncertainty score0.583

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.062
GPT teacher head0.331
Teacher spread0.269 · 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 teacher head, 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

Citations13
Published2024
Admission routes1
Has abstractyes

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