Achieving the Urban Tree Trifecta: Scenario Modelling for Salubrious, Resilient, and Diverse Urban Forests in Densifying Cities
Notice bibliographique
Résumé
Abstract Background Urban forests can provide nature-based solutions (NBS) to complex climate-change challenges via the provision of ecosystem services such as shade and cooling that offset increased risks of chronic diseases and excess mortality. They also confer indirect health benefits by providing regulating ecosystem services that can facilitate climate-change mitigation efforts: increased shade can encourage shifts to lower-carbon transportation methods such as walking and cycling, for example. However, in order to ensure that urban forests are both resilient to threats and confer the maximum possible benefits, we must be able to project decades into the future in order to understand the implications of current urban forestry decisions. Methods This study outlines a framework for creating urban-forest scenario models and reports the results of a case study conducted to highlight the ways in which decisions made at each stage of the scenario-development process impact its outcomes and application. Our case study focused on a neighbourhood in Vancouver, Canada, that is simultaneously undergoing urban densification and aiming to significantly increase canopy cover by 2050. Three distinct aims were identified for the case study: maximizing public-health benefits, selecting climate-resilient tree species, and integrating planting across public and private lands to advance diversity. To achieve these aims, baseline information on the neighbourhood’s existing tree network was collected, entered into GIS software, and delineated based on a set of pre-identified characteristics. Next, a list of climate-adapted species was developed. This climate-adapted species list was then virtually “planted”across the neighbourhood, using a combination of machine-based and manual planting techniques. Finally, the resulting scenario model was quantitatively assessed to understand its composition and impacts. Results Our study demonstrates that a salubrious, resilient, and diverse urban forest can be created via a strategic program that complements extant trees in the public domain with planting programs along blue-green streets and on private property. Conclusions Achieving the urban tree trifecta will require collaboration among municipal departments and the development of a range of public and private initiatives, but it has the potential to maximize nature-based solutions in cities facing rapid shifts due to densification and climate change.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».