Understanding collaborative governance of biodiversity-inclusive urban planning: Methodological approach and benchmarking results for urban nature plans in 10 European cities
Bibliographic record
Abstract
The EU Biodiversity Strategy for 2030 aims to restore Europe's biodiversity for the benefit of people, climate and the planet. Target 14 mandates that cities with at least 20,000 inhabitants should develop an ambitious urban greening plan. The Kunming-Montreal Global Biodiversity Framework promotes biodiversity-inclusive urban planning through a stronger governance approach, that includes people and public participation across all levels and sectors. Recent policies encourage cities to develop urban greening plans, together with mechanisms for monitoring the progress toward EU and global biodiversity targets. This research proposes quality criteria against which EU cities could be evaluated while establishing ambitious urban nature plans An analytical framework was developed consisting of 30 criteria across six macro categories: urban biodiversity goals and targets, collaborative governance, institutional support, public participation, financing mechanisms, and monitoring and evaluation aspects. The framework was applied to assess urban green plans for a sample of ten cities with existing urban green or nature plan for at least 3 years. Policy and research experts were consulted on the selected criteria and the cities’ results. It emerges that public participation and collaborative governance are rarely considered integral part from the beginning of established plans except in a few cities despite EU guidelines advocating for adoption of co-creation approaches. Only 4 out of the 10 cities performed well across all the 6 macro categories. Moreover, 7 out of the 10 cities showed evidence on a lack of monitoring and evaluation, and financial mechanisms to promote urban greening and collaborative governance of biodiversity. These results can help local authorities to build ambitious, yet robust, urban greening plans and support national/regional authorities to monitor progress toward EU and global biodiversity policies.
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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 | 0.028 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.017 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".