Beyond food: A stochastic model to estimate the contributions of urban agriculture to sustainability
Bibliographic record
Abstract
In the first decades of the 21st century, urban agriculture has gained attention for its role in enhancing food security, particularly in developing nations. Additionally, it is commonly assumed that urban agriculture also has positive implications for other aspects of urban sustainability, such as mitigating runoff and creating job opportunities. However, the extent of these contributions has not been extensively quantified. This study aims to address this gap by presenting a stochastic model that quantifies the contributions of urban agriculture to urban sustainability, using Sant Feliu de Llobregat, a Mediterranean city, as a case study. We assessed eight indicators, including accessibility to green areas, food self-reliance, green surface area per capita, job creation, NO2 sequestration, runoff mitigation, urban heat island effect, and volunteer participation. These indicators were estimated across twelve different simulated scenarios using 1000 Monte Carlo simulations for each scenario, to account for uncertainties. The findings revealed that the contributions of urban agriculture are not straightforward, as they are influenced by factors such as garden typology and location. Although urban agriculture typically originates as a grassroots movement, it often receives administrative support. Therefore, strategic planning can be employed to maximize the contributions of urban agriculture to urban sustainability and minimize trade-offs between social and environmental benefits.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".