Managing Urban Forest Public Values In A Changing Climate
Bibliographic record
Abstract
With more than half of the world’s population concentrated in urban areas, urban services are crucial for people’s lives. Some of these services are provided by urban trees, which are valued positively by most people. However, urban forest management (UFM) today faces a number of challenges, including accounting for the values of the public and climate change. These two are connected, since climate-driven biophysical changes will affect value provision and people’s urban forest values will determine the management direction by which we address the climate challenge. This study aims to understand how to incorporate public values and climate change in UFM by examining how people value the urban forest, how these values are managed, how urban forests are vulnerable to climate change, and how this vulnerability affects value provision. To address these questions, I review the urban forest values literature and reveal opportunities for research. Later I examine the content of 14 Canadian urban forest management plans and reveal that UFM today lacks detail in ecological and social themes. I argue that a management paradigm based on what the citizens consider important about urban forests may help deal with these shortcomings. I present urban forest values research from three Colombian cities (Bogotá, Cali, Pereira) using field tours, personal diaries, and focus groups. I then integrate this research with similar research in Canada to build a values typology that portrays how the public values the urban forest. I then review climate change in UFM and argue that climate change vulnerability assessments (CCVAs) are crucial for embracing climate adaptation in UFM. I present CCVA research in three Canadian urban forests (Halifax, London, Saskatoon) using an exploratory and expert-based method. I demonstrate that the survival of young trees and mal-adapted tree species are important sensitivity factors in urban forests. By mapping how urban forest vulnerability to climate change will affect value provision I argue that climate change is both a threat and an opportunity to bring specificity to ecological and social themes in UFM and to veer towards a UFM style that: plants more trees close to infrastructure and people; ensures tree survival by experimenting with different planting techniques and more-natural arrangements; embraces adaptive management and public engagement; and facilitates ecosystem transition without reducing values satisfaction.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".