Quantifying the foodshed: a systematic review of urban food flow and local food self-sufficiency research
Bibliographic record
Abstract
Abstract Cities are net consumers of food from local and global hinterlands. Urban foodshed analysis is a quantitative approach for examining links between urban consumers and rural agricultural production by mapping food flow networks or estimating the potential for local food self-sufficiency (LFS). However, at present, the lack of a coherent methodological framework and research agenda limits the potential to compare different cities and regions as well as to cumulate knowledge. We conduct a review of 42 peer-reviewed publications on foodsheds (identified from a subset of 829 publications) from 1979 to 2019 that quantify LFS, food supply, or food flows on the urban or regional scale. We define and characterize these studies into three main foodshed types: (1) agricultural capacity, which estimate LFS potential or local foodshed size required to meet food demands; (2) food flow, which trace food movements and embodied resources or emissions; and (3) hybrid, which combine both approaches and study dynamics between imports, exports, and LFS. LFS capacity studies are the most common type but the majority of cases we found in the literature were from cities or regions in the Global North with underrepresentation of rapidly urbanizing regions of the Global South. We use a synthetic framework with ten criteria to further classify foodshed studies, which illustrates the challenges of quantitatively comparing results across studies with different methodologies. Core research priorities from our review include the need to explore the interplay between LFS capacity and interregional food trade (both imports and exports) for foodsheds. Hybrid methodologies are particularly relevant to examining such dependency relationships in food systems by incorporating food flows into LFS capacity assessment. Foodshed analysis can inform policy related to multiple components of sustainable food systems, including navigating the social and environmental benefits and tradeoffs of sourcing food locally, regionally, and globally.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".