Global trends in urban forest irrigation: Environmental influences, challenges and opportunities for sustainable practices across 109 cities worldwide
Bibliographic record
Abstract
Urban forests are critical for climate adaptation and liveability, but effective irrigation management—key to their sustainability—remains poorly documented at the global scale. This study addresses this critical knowledge gap by analysing urban forest irrigation practices across 109 cities in 21 countries, offering one of the first global assessments of irrigation approaches, challenges, and opportunities. Using survey data, we examined water sources, irrigation frequency, constraints, and enabling conditions. Our results show that weather conditions were the leading factor influencing irrigation scheduling in 44% of cities, while 56% reported no formal water restrictions. Despite the importance of water conservation, 55% of respondents reported having no water usage monitoring systems, and 73% lacked financial incentives to promote water-efficient irrigation. A large majority (80%) did not use recycled wastewater, and 58% did not conduct water quality testing. Only 15% of cities regularly used water-efficient irrigation technologies, and 47% had no plans to implement smart systems. Over half (56%) rated their current irrigation practices as only moderately successful. Budget constraints and infrastructure limitations were the most frequently reported challenges, followed by climate change-related concerns. While environmental variables such as mean annual temperature and irrigation need influenced specific practices, local governance and institutional actions had stronger effects. Cities in the Global South reported distinct strategies and constraints compared to those in the Global North. Our findings provide actionable insights for climate-resilient urban water strategies and underscore the need for targeted policies, capacity-building, and efficient technologies to enhance urban forest sustainability worldwide.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".