Urban tree cover targets: The good, the bad and the SMART
Bibliographic record
Abstract
Urban tree cover (UTC) is a commonly used metric in policy and management activities, including urban forest resources assessment, equity and distribution, and ecosystem services modelling. Despite the well-established benefits associated with urban tree canopy, declining tree cover has catalysed many cities into setting UTC targets. In this short communication, we used an assessment of UTC targets set by 57 cities worldwide to discuss the merits and drawbacks of setting UTC targets and to inform recommendations for setting effective UTC targets. We found that UTC targets range in ambition, varying between 4% and 50%. To meet these targets, cities would have to increase their current UTC by between 0.47 and 23.3 percentage points within stated timelines of between 3 and 51 years. We found that cities with lower current UTC set ambitious targets, requiring relatively large annual increases in UTC. Moreover, cities in xeric or dry biomes set lower targets (< 20%) than cities in temperate or tropical biomes (> 25%). We found that setting UTC targets can provide a range of benefits, but achieving a UTC target at the expense of other indicators of urban forest structure and quality poses risks. We reflect on pathways to set specific, measurable, achievable, resourced, and time-bound UTC targets, while acknowledging the associated issues. This exploration of UTC targets will help ensure that UTC remains a useful metric for urban forest management and planning.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".