The Kunming–Montreal Global Biodiversity Framework and the Paris Agreement need a joint work programme for climate, nature and people
Bibliographic record
Abstract
Abstract The global climate and biodiversity crises are closely intertwined, yet no policy instrument currently tackles both jointly. This is problematic, as the lack of international platforms for designing coordinated and coherent policy frameworks that align targets across the nexus of biodiversity and climate change poses risks to nature and people. Here, we argue that the successful implementation of both the Paris Agreement and the Global Biodiversity Framework (GBF) requires a joint work programme between the United Nations Framework Convention on Climate Change (UNFCCC) and the Convention on Biological Diversity (CBD). Such a joint work programme could fill the current global governance vacuum and address critical implementation gaps, while fostering innovation and synergies in climate and biodiversity action. Based on a review of existing work programmes under the UNFCCC and interagency and joint work programmes established with the CBD, we provide a concept of how this joint programme could be set up, ideas on the type of issues it should tackle and recommendations for successful implementation. Policy implications : The UNFCCC and CBD are unique platforms for providing the evidence to, and guiding the way through the changes we need, but higher levels of integration between the biodiversity and climate change agendas are needed to plug the implementation gap between the GBF and the Paris Agreement. The upcoming Conference of the Parties (COP) for the United Nations Framework Convention on Climate Change (COP29) and the Convention on Biological Diversity (COP16) present a clear policy window for the two conventions to introduce a formal governance structure, such as the one proposed in this contribution, that brings together ideas, people, organizations and processes necessary for joining the dots on how to both stabilize our climate and recover our nature.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".