Urban foodprints (UF) – Establishing baseline scenarios for the sustainability assessment of high-yield urban agriculture
Bibliographic record
Abstract
allowing for significant water savings and year-round yields, controlled-Environment agriculture (cEa) is oftentimes portrayed as a sustainable alternative to conventional farming, and its practice in urban areas as a food, income and employment generator is expanding worldwide.particularly in today's fast growing cities, where economic strength is buying food security through imports, a largescale implementation of such practices should be further investigated as potential contributors -not only to food security but also to self-sufficiency -for the production of horticultural crops.however, further than quantifying the potential for food self-sufficiency of cities through urban cultivation, there is a crucial need for assessing the extent to which such scenarios are effectively more sustainable than existing supply chains.For that purpose, this paper presents the Urban Foodprints (UF) methodology, a fundamental preliminary step in the sustainability assessment of high-yield urban agriculture, consisting of collecting and integrating data on the existing supply chain, to be used as a baseline scenario in the environmental performance analysis.through the case of riyadh, saudi arabia, where harsh climatic conditions, a heavy reliance on food imports and a growing population constitute major threats to food security, the UF method is described and applied to the top four consumed horticultural cropswatermelon, tomato, onion and carrot.the environmental sustainability of high-yield urban agriculture in riyadh is subsequently assessed for tomato, as a comparison of the resulting city's current foodprint for the crop vs. a scenario of local production in cEa urban farms.results show that urban production in high-yield greenhouses has the potential to reduce global Warming potential (gWp) by 9%.however, while water savings contribute greatly to reducing irrigation-related emissions and food miles are considerably reduced, the energy needs of the greenhouses are significantly higher than the baseline.this outcome may be improved by enhancing the envelope of the farms to reduce overheating.
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 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 | 0.002 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".