Eco-efficiency as a prioritization tool in the reduction of food waste in restaurants
Bibliographic record
Abstract
Striving towards eco-efficiency means creating more value while generating a product or service with a reduced environmental impact. This quest makes it possible to link objectives associated with both the environmental and the economic pillars of sustainable development. Eco-efficiency could therefore be an interesting tool to evaluate the impacts of food waste (FW) and the potential of various food waste reduction strategies (FWRSs). However, the use of eco-efficiency to assess the interest in implementing FWRSs has never been explored in the foodservice sector. This work firstly aims to carry out an in-depth analysis of the costs of the FW generation of an independent restaurant. Secondly, based on these costs data and on some previously documented environmental impact data with a life cycle assessment according to ISO 14045:2012, this work also aims to model and evaluate the performance of FWRSs from a perspective of improving a restaurant eco-efficiency. The impact of each FWRS on the eco-efficiency of the restaurant under study was measured by modeling their economic and environmental net benefits over three implementation periods (one week, one month and six months) and under scenarios of strong and weak adherence. The study identified the most eco-efficient FWRSs to be implemented to reduce FW in the studied restaurant. In addition, key factors affecting eco-efficiency were raised, namely the period following the implementation of FWRSs, the FW reduction rate between FWRSs affecting the same type of FW, the specificity of the FWRSs and their ability to limit the waste of vegetables, meat, sea products and food requiring significant processing time by the cooks. Thus, these elements will guide foodservice managers in adopting FWRSs aimed at reducing FW generated in their restaurant and at improving its eco-efficiency. In addition, this work proposes a new methodology intended for the scientific community to identify FWRSs that have a strong impact on a restaurant eco-efficiency.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".