Synergies between the Kunming-Montreal Global Biodiversity Framework and the Paris Agreement: the role of policy milestones, monitoring frameworks and safeguards
Bibliographic record
Abstract
The 2022 Kunming-Montreal Global Biodiversity Framework (GBF) and Paris Agreement (PA) are highly complementary agreements where each depends on the other’s success to be effective. The GBF offers a very specific framework of interim goals and targets that break down the objective of the Convention on Biodiversity (CBD) into a decade-spanning work plan. Comprised of 10 sections – including a 2050 vision and a 2030 mission, four overarching goals and 23 specific targets – the GBF is expected to guide biodiversity policy around the world in the coming years to decades. A similar set of global interim climate policy targets could translate the global temperature goal into concrete policy milestones that would provide policy makers and civil society with reference points for policy making and efforts to hold governments accountable. Beyond inspiring climate policy experts to convert temperature goals into policy milestones, GBF has the potential to strengthen the implementation of the PA at the nexus of biodiversity and climate (adaptation and mitigation) action. For example, the GBF can help to ensure that nature-based climate solutions are implemented with full consideration of biodiversity concerns, of the rights and interests of Indigenous Peoples and local communities, and with fair and transparent benefit sharing arrangements. In sum, the GBF should be mandatory reading for all climate policy makers.
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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 | 0.043 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.022 |
| Scholarly communication | 0.027 | 0.020 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.013 | 0.013 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".