Aligning urban forest management actions with urban sustainability goals: A multi‐city expert elicitation
Bibliographic record
Abstract
Abstract City leaders are setting ambitious plans to achieve critical urban sustainability goals such as reducing urban heat, mitigating flooding during storms, and conserving biodiversity, and increasingly rely on urban forests as a key nature‐based solution to such challenges. Current paradigms of urban forest management typically prioritize goals like increasing tree canopy cover that are often viewed as proxies for increased ecosystem service provision, in a general sense. However, urban foresters, the professionals with responsibility to manage urban forests, are increasingly faced with the complex challenge of managing for new goals related to ecosystem services, biodiversity, or people–nature relationships, as cities increasingly set goals centered on such outcomes, without robust guidelines to follow. We ask: How can urban foresters align their street tree management actions with specific urban sustainability goals? We conducted a structured expert elicitation of urban forest professionals in three cities: Vancouver, British Columbia, Canada; Honolulu, Hawaiʻi, United States; and Washington, DC, United States. A socio‐ecological lens was used to examine urban foresters as agents of change in urban ecosystems. Participants assessed the impacts of 40 direct management actions on five goals: (1) canopy cover increase and tree risk reduction, (2) urban heat reduction, (3) people–nature relationships, (4) wildlife habitat, and (5) stormwater interception and infiltration. While certain actions (e.g., in the mature tree maintenance phase) were selected as needed to advance every goal, experts identified numerous actions which aligned with one or several goals, but not all. Preplanting actions, specifically site selection and species selection, presented the greatest opportunities to advance specific goals, suggesting that aligning this phase with city sustainability goals is critical. Participants were highly confident in being able to advance all goals through street tree management, but were more confident in being able to advance the goals of increasing canopy cover while reducing tree risk and of mitigating urban heat, possibly because these goals more closely align with traditional canopy cover goal setting. This research underscores the necessity of considering site‐level ecosystem management actions to advance strategic sustainability goals, while also revealing the complexity of the role and responsibilities of professionals who manage urban ecosystems.
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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 | 0.022 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".