The Post‐2020 Global Biodiversity Framework: How did we get here, and where do we go next?
Bibliographic record
Abstract
Abstract December 2022 finally saw the historic agreement of the Kunming‐Montreal Global Biodiversity Framework (KM‐GBF), a landmark framework that sets to halt and reverse global biodiversity loss by remedying the multifaceted drivers behind biodiversity declines around the planet. The KM‐GBF follows on from the Aichi targets, which aimed to prevent further biodiversity loss through a concerted effort between 2010 and 2020, but which were not successfully achieved. The KM‐GBF builds on the drivers of biodiversity losses rather than their outcomes and sets a suite of targeted and measurable actions to reconcile losses. Developing the framework faced considerable challenges, especially in the face of the coronavirus disease 2019 pandemic, and issues were often resolved at the very last moment. Consequently, compromises had to be made, useful elements were left out, or removed from the KM‐GBF to achieve consensus, and some will need to be reflected in other ways, or incorporated into indicators. The final agreed KM‐GBF includes 4 goals and 23 targets in addition to a package of annexes including a monitoring framework to set targets and benchmark progress. Particularly challenging issues included the flagship target of ‘30 × 30’ of protecting 30% of land, freshwater, coastal, and high‐sea in a representative way by 2030, which will require both new mechanisms and funding streams to enact effectively. Digital sequence information and funding mechanisms also presented major hurdles in the agreement of the KM‐GBF. Ultimately, the success of the new GBF depends on implementation and mainstreaming. New targets can only be achieved through the inclusion of all sectors, clear communication, and effective funding mechanisms to guide change and provide the means to implement it. Furthermore, while common but differentiated responsibility is crucial to implementation, impacts of inaction are disproportionate in developing economies, and more resources and support are needed to enable them to develop sustainably and meet targets. This highlights the urgent need for action if we are to achieve the new targets and secure a future for all life on earth.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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; both teacher heads agree on what is shown here.
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".