Calculating the environmental impact for meals
Notice bibliographique
Résumé
Aim: <br/>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. <br/>Method: <br/>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. <br/>Results: <br/>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.<br/>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.<br/>Conclusion: <br/>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.<br/>
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».