The Kunming-Montreal Global Biodiversity Framework: what it does and does not do, and how to improve it
Bibliographic record
Abstract
The Kunming-Montreal Global Biodiversity Framework (GBF) marks one of the most ambitious environmental agreements of the 21st century. Yet despite the ambition, and the considerable change in approach since negotiating its predecessor (the 2025 Vision and Aichi targets), the many pressures, including working through a global pandemic mean that the final agreement, despite several years of delay, is weaker than might have been hoped for. The GBF provides a set of four goals, composed of 23 targets (and a series of supporting annexes) which explore the options for conservation, restoration and sustainable use of biodiversity, and the mobilisation of necessary resources to maintain life on Earth. In this perspective we systematically examine the composition of the GBF, exploring what the targets lack and what weaknesses exist in text. We also detail the link between the targets and the key indicators which can be used to track success toward fulfilling the targets. We offer key recommendations which could help strengthen the application of various targets, and show where the indicators could be improved to provide more detailed information to monitor progress. Furthermore, we discuss the association between targets and their indicators, and detail where indicators may lack the necessary temporal resolution or other elements. Finally, we discuss how various actors might better prepare for the successor to the GBF in 2030 and what has been learnt about the negotiating process, including lessons to help ensure that future agreements can circumnavigate issues which may have weakened the agreement.
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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 | 0.042 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.010 | 0.025 |
| Scholarly communication | 0.026 | 0.025 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".