Scaling Biodiversity Conservation Efforts: An Examination of the Relationship Between Global Biodiversity Targets and Local Plans
Bibliographic record
Abstract
Cities have a critical role to play in meeting global-scale biodiversity targets. Urban socio-ecological systems connect human and ecological well-being. The outsized impact of cities reaches well-beyond their geographic borders through cultural, ecological, and economic interactions. Although cities account for just 2% of the earth's surface, they host over half of the human population and are responsible for 75% of consumption. The Parties to the Convention on Biological Diversity (CBD) and others have acknowledged the important role cities can play in achieving global targets. In response, at least 110 cities have produced plans focused on biodiversity, but we do not know the extent to which these city plans align with global targets or what role they play in achieving these targets. Here, we explore the relationship between global biodiversity conservation targets and local biodiversity plans to identify how elements at the two scales align or diverge. We compared the CBD Strategic Plan 2011–2020 (Aichi Targets) with 44 local biodiversity plans (often called LBSAPs) from cities around the world. We analyzed more than 2,800 actions from the local plans to measure the relationship with these global targets. Our results show how local approaches to biodiversity conservation can inform post-2020 global frameworks to improve coordination between global and local scale processes. We identify actions particular to the local scale that are critical to conserve global biodiversity and suggest a framework for improved coordination between actors at different scales that address their respective roles and spheres of influence.
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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 | 0.012 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".