Urban forests as essential infrastructure for climate resilience and biodiversity: A call to policymakers
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".