Local or global: A biophysical analysis of a regional food system
Bibliographic record
Abstract
Abstract Growing concern regarding environmental, social, economic and food quality outcomes of the modern global industrial food system as well as the implications of climate change on food security and food system sustainability have fomented interest in, and action to advance localized food systems. Environmental stewardship is an oft-touted benefit of food system localization. However, few studies have comparatively examined actual environmental benefits of local versus global supply systems and most focus on only one aspect (e.g., GHG emissions). The study reported here comparatively analyzes land, water, carbon and ecological footprints of a localized food supply and contemporary global food supply for the South-West British Columbia (Canada), bioregion (SWBC). The footprint family approach utilized allows measuring overall biophysical loads for the studied region. We quantified regional rates of reliance on imported biophysical services; measured the performances of specific food products grown locally in comparison with their imported counterparts; and identified those commodities that have better and worse local biophysical performances. For the SWBC bioregion, only 35% of the food consumed in the region is locally produced. Supplying the region's food demands requires 2 million hectares of land and 3 billion m 3 of water, generating approximately 2.8 million tons of CO 2 e, with an eco-footprint of 2.5 million gha. Examining a large number of commodities grown and consumed in the bioregion revealed that only some commodities grown locally have absolute or significant biophysical advantages, while the rest have very little to no local advantage. Our analysis challenges the notion that local food systems are necessarily more environmentally sustainable from a biophysical resource use perspective and therefore may not represent the most compelling argument(s) for food system localization. We call for better and more comprehensive comparative analysis of existing and desired food systems as a mean to advance sustainability.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".