Ecosystem services of urban food forests and their contributions to health and sustainability of North American cities: a narrative review
Bibliographic record
Abstract
This narrative review synthesizes the ecosystem services of urban food forests and their contributions to urban health and food sustainability in North American cities. The study selection and analysis processes were inspired by scoping review methodologies, with predefined inclusion criteria for empirical peer-reviewed studies on urban food forests in Canada and the United States. This review is based on 13 studies published between 2018 and 2023, using case studies, policy analyses and both quantitative and qualitative methods. The review identifies key ecosystem services of urban food forests - sustainable food production, climate regulation, water management, carbon sequestration and air quality improvement - which enhance environmental health. Urban food forests also promote food security, biodiversity, and provide recreational and educational spaces that foster community engagement, resilience and social equity. However, their implementation faces challenges, including public space governance, maintenance costs, regulatory barriers and ecological risks such as invasive species. The review highlights the importance of governance approaches that integrate ecological integrity, public health and community needs, alongside adaptive policies to support their development. Future research should prioritize longitudinal studies that evaluate long-term impacts and develop evidence-based practices for sustainable and equitable implementation of urban food forests.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".