Calculating the environmental impact for meals
Bibliographic record
Abstract
Aim: The aim of this work is to assess the environmental impact of various restaurant and quick service meals. It will also outline different methods of environmental impact assessment, highlighting key limitations and implications for standardisation. Method: The environmental impact of different meals was assessed using life cycle assessment techniques. The scope and boundary of this life cycle assessment were co-created based on stakeholder inputs and scientific evidence. This resulted in an agreed system boundary of farm-to-shelf for the ingredients included in each meal, i.e. environmental impacts resulting from transport between local distribution centre and restaurant or home were not included. Where required, secondary data was used as inputs for the assessment. Environmental impact was assessed using the global warming potential (GWP) metric and Envrioscore. Enviroscore is a 5-scale label that relativizes the environmental impact of a given product based on the Product Environmental Footprint methodology. This work applies Enviroscore to complete meals for the first time. Results: A variety of meals, including meat based and vegetarian dishes were assessed using the GWP and Enviroscore metrics. Meals were ranked based on their GWP and also based on an Enviroscore label, ranging from A (very low environmental impact) to E (very high environmental impact). Results of the GWP and Enviroscore were then compared to determine any discrepancies between the two impact assessment techniques. In general and as expected, vegetarian meals have a lower environmental impact than meat-based meals, however, this depends greatly on the means of production used for each ingredient assessed. In terms of impact assessment method, the Envirscore provides a more holistic approach with 13 different environmental impact metrics considered. However, there is much more data available to use when assessing the global warming potential. A detailed comparison of GWP vs Environscore when applied to a range of quick service meals will be presented. Conclusion: Both the global warming potential and Enviroscore are effective ways to assess the environmental impacts of meals. However, their impact will depend greatly on the availability of accurate and detailed information. A key limitation going forward will be the availability and quality of data used for meal inputs.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".