Urban agriculture in the transition to low carbon cities through urban greening
Bibliographic record
Abstract
Urban agriculture presents an opportunity to extend food production to cities. This could enhance food security, particularly in developing countries, and allow for adaptation to growing urbanization. This review paper examines current trends in urban agriculture from a global perspective as a mitigation-adaptation approach to climate change adaptation in the midst of a growing world population. Employing vegetation as a carbon capture and storage system encapsulates a soft-engineering strategy that can be easily deployed by planners and environmental managers. In this review, urban agriculture is presented as a land-use solution to counteract the effects of urbanization, and as a means to establish a continuum between cities and the countryside. It espouses the usefulness of urban agriculture to enhance food security while sequestering carbon. As part of urban greening (including newer approaches, such as green roofs and gardens as well as more established forms of greening, such as forests and parks), urban agriculture offers traditionally rural services in cities, thereby contributing to food resources as well as working to alleviate pressing social issues like poverty. It also provides a way to reduce stress on farmland, and creates opportunities for employment and community-building. As part of greening, urban agriculture provides a buffer for pollution and improves environmental (and well as human) health and well-being. This review begins by addressing the physical factors of adopting urban agriculture, such as climate change and development, land use and degradation, technology and management, and experimental findings as well as human factors investigated in the published literature. As such, it presents an integrated approach to urban agriculture that is part of a social-ecological perspective.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".