Implications of global distributive justice principles for implementation of the Kunming‐Montreal Global Biodiversity Framework
Bibliographic record
Abstract
In the era of the sixth mass extinction, reversing global biodiversity loss is of vital importance for life on Earth. In 2022, parties to the Convention on Biological Diversity (CBD) adopted the Kunming-Montreal Global Biodiversity Framework (GBF), a strategic plan with 23 action-oriented targets to be achieved by 2030. However, biodiversity action carries direct and indirect costs that are unevenly distributed globally. Moreover, parties to the CBD may differ relative to various aspects of bearing these costs. Although the GBF implicitly acknowledges its parties' common but different responsibilities for its implementation, what this means in practice is left open. We suggested a distributive justice framework to guide global sharing of the costs of biodiversity action, with a focus on specific GBF targets. We combined the contributor pays, beneficiary pays, and ability to pay principles from the normative-philosophical justice literature together with empirical information on trends in biodiversity degradation, benefits from resource exploitation, and livelihood levels in different countries to develop a distinct and comprehensive account of distributive justice in a global biodiversity policy context and to specify implications for target implementation. Our framework suggests that high-income countries should provide substantial financial resources for the implementation of GBF targets domestically and internationally. Moreover, these countries have a particularly high moral obligation to take action to reduce pressure on biodiversity-for instance, by reducing pollution or changing consumption patterns. Recent institutional innovations related to the funding, planning, monitoring, reporting, and review structures of the GBF hold promise for its just implementation, which ultimately depends on parties' political will.
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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 | 0.060 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.034 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".