Developing a Methodology for Estimating Transport-Related CO2 Emissions for Food Commodities
Bibliographic record
Abstract
There is a significant and growing interaction between the transport sector and the food sector as globalized markets continue to increase the demand for ‘food miles’ i.e. the number of miles a food item travels throughout its life cycle. The concept of ‘food miles’ has become interesting to the public and policy makers as a way to assess the relative carbon footprint of food choices. However, there is currently a lack of information available about the transport-related greenhouse gas emissions that would allow to accurately differentiate between food items. To help address these current knowledge gaps, this paper presents a transferable methodological approach to estimating the transport related CO2 emissions of 10 popular food commodities transported from the farm gate to the retailer. The methodology combines GIS, data from the scientific literature and detailed commodity specific data from personal communication with one of the largest food retailers in California. To travel from the farm gate to the retailer, the amounts of CO2 emissions varied amongst the 10 foods, ranging from 47 g CO2/kg oranges, to 78 g CO2/kg almonds. While California was used as a case study, this method would be replicable across other locations and food life cycle assessments.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".