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Enregistrement W4415954892 · doi:10.1002/ppp3.70125

Urban forests as essential infrastructure for climate resilience and biodiversity: A call to policymakers

2025· article· en· W4415954892 sur OpenAlexaff
Manuel Esperón‐Rodríguez, Stefan K. Arndt, Michael Osei Asibey, Benno A. Augustinus, Albert Bach, Mónica Ballinas, Vı́ctor L. Barradas, David N. Barton, Jürgen Bauhus, Linda J. Beaumont, Jean‐François Bissonnette, Matthew Brookhouse, Pedro Calaza Martínez, Carlo Calfapietra, Tiago Capela Lourenço, Paloma Cariñanos, Matteo Clemente, Tenley M. Conway, Kees de Hoogh, Karen De Pauw, Morgane Dendoncker, Cynnamon Dobbs, Peter N. Duinker, Ujala Ejaz, Ana Alice Eleutério, Theodore A. Endreny, Diego Esperón Rodríguez, Claire Farrell, Rachael V. Gallagher, Zhengfei Guo, Nanamhla Gwedla, F. Richard Hauer, Martin Hermy, Peta Jeffries, Edith Juno, Rachel Knudten, Gervais Lee, Elizaveta Litvak, Stephen J. Livesley, Natalie Love, Gabriele Manoli, Renée M. Marchin, Alexander J.F. Martin, Pierre Masselot, Robert I. McDonald, Timon McPhearson, Christian Messier, Julie Messier, Rachel Morgain, Harini Nagendra, Mark Nieuwenhuijsen, Lukas G. Olson, Johan Östberg, Diane E. Pataki, Sebastian Pfautsch, Sally A. Power, Mohammad A. Rahman, Thomas B. Randrup, Peter B. Reich, Alessio Russo, Paul D. Rymer, Rossano Schifanella, P.K. Sen, Charlie M. Shackleton, Mahmuda Sharmin, Megan Shooter, Davide Siclari, Sivajanani Sivarajah, Johanna Deak Sjöman, Henrik Sjöman, Ingjerd Solfjeld, Annick St‐Denis, Jonah Susskind, Jens‐Christian Svenning, Christopher Szota, María Toledo‐Garibaldi, Olivier Villemaire‐Côté, Jess Vogt, Ariane Wagenknecht, Mingxuan Wan, Linsheng Wen, Subashini Anuradha Wijeratne, Geoffrey M. Williams, Nicholas Morrow Williams, Sylvia Wood, Hong Wu, Pengbo Yan, Yuyu Zhou, Carly D. Ziter, Jean-Bosco Benewinde Zoungrana, Mark G. Tjoelker

Notice bibliographique

RevuePlants People Planet · 2025
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueUrban Green Space and Health
Établissements canadiensConcordia UniversityUniversity of British ColumbiaUniversity of WaterlooCenter for Northern StudiesUniversité du Québec à MontréalUniversity of TorontoDalhousie UniversityUniversité du Québec en OutaouaisGeneral Electric (Canada)Centre de Géomatique du Québec
Organismes subventionnairesWestern Sydney UniversityVlaamse regeringFonds Wetenschappelijk OnderzoekDanmarks GrundforskningsfondNational Research FoundationAustralian Government
Mots-clésGreen infrastructureUrban forestEcosystem servicesWoodlandUrban climateUrban ecosystemClimate changeUrban forestryBiodiversityPopulation

Résumé

récupéré en direct d'OpenAlex

By 2050, nearly 70% of the global population will live in cities (UN, 2018), increasing the demand for urban green spaces. Urban areas are facing increasing risks from climate change, including heatwaves, flooding, wildfires, and growing social inequality, which challenges urban planning and design. Urban forests form the backbone of green infrastructure supporting resilient, equitable, and sustainable cities. Importantly, their cost-effective benefits advance sustainable development, climate action, and biodiversity conservation. Urban forests include all woody and understorey vegetation within and around dense settlements, from cultivated trees in streets, parks, and gardens to self-sustaining stands in remnant and peri-urban woodlands (FAO, 2016). As essential nature-based solutions (Cohen-Shacham et al., 2016), urban forests provide multiple ecosystem services. They help cool urban temperatures, reduce air pollution, enhance soil infiltration, slow stormwater runoff, buffer extreme weather, and support human health (Livesley et al., 2016). They contribute significantly to climate adaptation and moderately to mitigation by reducing the energy demand for cooling (McPhearson et al., 2023). Urban forests also enhance biodiversity by providing habitats and climate refugia at multiple scales (Alvey, 2006). Trade-offs in urban forest benefits, costs, and the impacts of policy interventions, such as those related to measurement, outcomes, or implementation, remain complex (Vogt et al., 2015), but the loss of canopy reduces air quality, biodiversity, and resilience to floods, droughts, pests, and extreme heat (Nowak, 2018). Canopy loss impairs recreation and impacts physical and mental health (Carrus et al., 2015). Because urban forests are inherently dynamic systems, the death or removal of large and mature trees should be anticipated through proactive planning for their replacement, including careful consideration of which species are selected and why. Unequal access drives social-environmental injustice and health inequities, which can be addressed through greenspace expansion, equitable distribution, and better management (Esperon-Rodriguez et al., 2025). Urban forests are increasingly at risk due to significant stewardship gaps. Despite the existence of international management standards, ongoing tree losses result from pests introduced through trade, climate and pollution stress, inadequate legal protections, rapid urban densification, and insufficient maintenance (Esperon-Rodriguez et al., 2022; Paap et al., 2017; Vogt et al., 2015). Planting alone cannot offset accelerated mature tree losses or replace the vital functions these trees provide over their shortened lifespans in urban environments. Closing the stewardship gap demands urgent investment, robust funding, and stronger policy to sustain diverse and resilient urban forests. Urban forests are among the most effective, equitable nature-based solutions available. When protected and resourced, they cool neighborhoods, manage stormwater, store carbon, support biodiversity, and improve health, especially in underserved communities. Yet mature tree loss outpaces replacement amid increasing climate and biological stresses. We urge COP30 policymakers to treat urban forests as essential and critical city infrastructure: safeguard mature trees, set and finance SMART canopy, diversity and access targets, mainstream urban forests in climate and biodiversity plans, and fund long-term operations, monitoring, nursery capacity, and biosecurity. Implementing this visionary action delivers cooler, healthier, more biodiverse, and more equitable cities now and for future generations. MER and MGT led the initiative and drafted the letter. All authors provided feedback, edited, and agreed with the content of the letter. All authors, except MER and MGT, are listed alphabetically. MER received funding from Western Sydney University's Research Theme Program. KDP was supported by the Research Foundation Flanders (FWO, grant 12A0L25N). RMM was supported by a Discovery Early Career Researcher Award (project DE200100649), funded by the Australian Research Council of the Australian Government. JCS received funding from the Danish National Research Foundation (grant DNRF173) and EARTHKEEPER (Global South Biodiversity Leadership Initiative). CS's contribution was funded by the National Research Foundation of South Africa (grant no 84379). We declare no conflict of interest. There are no data associated with the article.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,246
Score d'incertitude au seuil0,995

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,005
Tête enseignante GPT0,237
Écart entre enseignants0,232 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2025
Routes d'admission1
Résumé présentoui

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