Actions, indicators, and outputs in urban biodiversity plans: A multinational analysis of city practice
Bibliographic record
Abstract
Urban biodiversity offers important benefits to residents and may be crucial to reaching global biodiversity conservation targets, but little research has been conducted on how cities actually plan for biodiversity. In this study, we conducted a mixed methods content analysis of biodiversity plans by 39 cities around the world to determine whether they measured their actions, how they did so (via quantitative indicators and qualitative outputs), and what topics these actions and measures covered. We based our analytical framework on the Singapore Index on Cities' Biodiversity (also known as the City Biodiversity Index), a widely applied 23-indicator index that helps cities track their progress in biodiversity planning. The Singapore Index groups its indicators into the following three core components: native biodiversity, ecosystem services, and governance and management. For actions and measures not classifiable by the Singapore Index, we inductively derived additional categories. Across all plans, we identified 2,231 actions, 346 indicators, and 444 outputs. We found that all of the plans included actions, while 82% included measures (67% included indicators and 72% included outputs). Only 29% of actions were associated with a measure. Overall, the plans covered all of the categories in the Singapore Index, particularly within the core components of native biodiversity and governance and management, though some plans had a narrower focus. The 20 additional urban biodiversity topics that were not covered by the Singapore Index framework included socioeconomic considerations, data collection, genetic diversity, urban agriculture and forestry, green infrastructure, human-wildlife conflicts, indigenous concerns, and citizen science. Indicators were the most common measures for native biodiversity and ecosystem service topics, while outputs were the most common measures for governance and management. Our results may inform the revision and development of urban biodiversity indicators in the post-2020 framework and of other initiatives that guide cities in contributing to local and global biodiversity goals.
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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) | 0.000 | 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 teacher head, 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".