Sprouting hydro-cities to feed urban communities: are they sustainable?
Bibliographic record
Abstract
Hydroponics systems grow plants in a nutrient solution using a growing medium instead of soil, providing an alternative approach to traditional gardening, particularly in urban areas. This study conducted a comprehensive life cycle assessment of hydroponics systems in Chicago to evaluate their potential, identify opportunities for improvements, and assess the benefits across various aspects. The evaluation used an existing hydroponics system in Chicago as a case study to compare a hypothetical scaled version of the hydroponics system and a scaled version of a community garden within the same area, serving the same number of users. Quantitative and semi-quantitative methods were used to assess the sustainability across the triple bottom line: environmental, social, and economic aspects. The environmental impact analysis was performed using life cycle assessment (SimaPro), while the social and economic sustainability were evaluated using a semi-quantitative approach. The results indicate that the hydroponics systems present greater sustainability challenges than community gardens, and the hydroponics system incorporated with LED lights was found to be more sustainable than the community garden. Furthermore, the analysis highlighted opportunities for improvement in hydroponic systems. Future research should assess differences between the two models more accurately in areas not fully captured by the current study.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".