Social life cycle assessment of calves in Mexico and identification of barriers in the use of a generic database
Notice bibliographique
Résumé
Abstract Purpose Social impacts regarding animal-based food are on the global agenda for sustainability development, especially due to reoccurring problems related to human rights, labor rights, decent work, and indifference to farm animal welfare. Social life cycle assessment (S-LCA) is considered an ideal tool for understanding social problems that may arise in the value chains of products and services. This study aims to (1) assess the social risks and opportunities associated with calf rearing using a generic database and (2) analyze the barriers of a generic database applied to S-LCA of animal-based food. Methods An S-LCA was carried out in the livestock sector, using midpoint indicators employing the Product Social Impact Life Cycle Assessment (PSILCA) database, based on 49 indicators. The functional unit was defined as producing 0.39 kg of live-weight calf in Mexico, a quantity corresponding to 1 USD necessary to assess the impacts with the PSILCA database. OpenLCA software version 1.10, 2020 was used to model the product system, incorporating foreground and background processes from the PSILCA database v.2. The results were analyzed to identify the critical indicators missing in the study, and the relevance of their inclusion was discussed. Results and discussion The highest impacts found are related to “trade unionism,” “certified environmental management system,” “sanitation coverage,” “public sector corruption,” and “drinking water coverage,” impacts that coincide with other studies of S-LCA in the agricultural sector. From the analysis of results, some limitations were identified in using the PSILCA database in animal-based food, such as the required granularity to discern slight differences between production systems, which can reduce understanding of the social implications in a differentiated way. Furthermore, indicators of the ethical treatment of animals and farm crime can be crucial in the agricultural sector in Latin America; therefore, these must be included in the social sustainability analysis of animal-based food. Conclusion The use of the PSILCA database highlighted key social risks associated with calf rearing in Mexico, specifically in relation to “safe and healthy living conditions” for the local community and “health and safety” for workers. However, the limitations of the PSILCA database, particularly its lack of granularity for the agricultural sector in the Latin American region, suggest the need for further interdisciplinary research. By integrating more region-specific knowledge and enhancing the database’s granularity, the evaluation of non-intensive livestock systems can be significantly improved, allowing for a more accurate representation of social sustainability in this context.
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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 | 0,001 | 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 | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».