One big conversation: the EU’s climate diplomacy across the international regime complex on climate change: the case of the Paris Agreement Negotiations
Bibliographic record
Abstract
The EU participates in many international fora related to climate change (e.g. UNFCCC, G20, Montreal Protocol), which collectively make up the international regime complex on climate change (IRCCC). For ambitious actors like the EU, the IRCCC presents the opportunity to use the other fora of the complex to facilitate reaching their objectives when negotiating multilateral agreements. Following empirical hints that the EU has considered using these different fora to such an end, this paper seeks to extend the study of EU climate diplomacy to the IRCCC. It addresses the following research question: How does the EU use the different fora of the IRCCC to achieve its objectives in the UNFCCC? To answer our research question, we examine the case study of the negotiations on the Paris Agreement, adopted within the UNFCCC. Using official documents, reports from media and observers, and semi-structured interviews with EU officials involved in the events, we find that the EU’s use different fora is influenced by the level of participants present and the level of abstraction of issues discussed. Along those lines, we develop three different sets of fora (climate-specific, high-level, and horizontal) that served different uses to EU climate diplomacy. As such, the findings not only establish the EU’s strategic use of fora across the IRCCC but also shed light on an under-studied aspect of EU climate diplomacy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.034 | 0.027 |
| Scholarly communication | 0.019 | 0.014 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".