Understanding collaborative governance of biodiversity-inclusive urban planning: Methodological approach and benchmarking results for urban nature plans in 10 European cities
Bibliographic record
Abstract
Abstract The EU Biodiversity Strategy for 2030 aims to ensure that Europe's biodiversity will be on the path to recovery for the benefit of people, climate and the planet. Specifically, the Target 14 from the strategy specifies that all cities with at least 20,000 inhabitants should have an ambitious urban greening plan. For this purpose, the European Commission advanced several mechanisms helping cities and larger urban areas unlocking funding for biodiversity; nonetheless a stronger need for an established governance framework that includes people and public participation across all levels and all sectors is needed as well as a stronger mechanism to monitor the progress of the target 14. Along these lines, this research was initiated as a way to understand the possible quality criteria against which several European cities could be evaluated while establishing their ambitious urban nature plans through three main research questions. Stemming from multiple database collections for the cities’ sampling methodology such as the green capital award, Urban Biodiversity Hub, Climate Neutral Cities Mission, the green City Accord (Nature/biodiversity priority area) and lastly the top-ranking cities in European Local Climate Adaptation Plans Initiative in the last 10 years, a sample of 10 cities was selected. To evaluate how ambitious these plans were, we developed an analytical framework of 30 criteria belonging to 6 macro categories focusing on urban biodiversity goals and targets, collaborative governance, institutional support, public participation, financing mechanisms, and monitoring and evaluation aspects. We propose and test this framework to evaluate the urban green plans of 10 lighthouse European cities having an existing Urban Nature plan for at least the last 3 years. During the evaluation process, several experts from different policy and research groups were consulted on the selected criteria and the cities’ results. It emerges that public participation and collaborative governance aspects are rarely considered as an integral part from the beginning of established plans except in few cities, nonetheless, several guidance by the EU calls on adopting more co-creation approaches. From the selected sample, only 4 out of 10 cities scored high across all the categorical analysis, namely: Hamburg, Utrecht, Thessaloniki and Barcelona. Meanwhile, the evidence on lack of monitoring and evaluation as well as financial mechanisms oriented towards advancing urban greening and collaborative governance of biodiversity is evident in 7 out of 10 cities. The results from this research can help local authorities to meet the EU target, and national/regional authorities to monitor the progress towards this commitment.
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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".