An evaluation of mining camp food waste management practices in Canada : an overview
Notice bibliographique
Résumé
The mining industry is facing increasing scrutiny to demonstrate that sustainability considerations are taken into account in their operations. This research approaches food waste as an overlooked, yet important issue that can advance not only environmental responsibility but also social and economic concerns within the mining industry. The implications of food waste generation on greenhouse gases, use of energy, groundwater contamination, and landfill space required are also critical concerns for the mining industry in the context of sustainability. This study evaluates the efforts that food service supply companies in mining camps are implementing to limit their food waste and assesses whether the prevention of food waste is a priority for these companies. This study used an online survey as the primary methodology for data collection. The survey resulted in a sample of eight respondents. Survey responses were structured based on the type of food waste, quantity, and causes of the waste. These were then mapped into pre- and post-consumer phases of the food services. Results indicated that proactive action to prevent food waste was not a priority for the respondents. The findings showed that the "vegetable" category had the highest amount of food waste throughout the different phases. Six out of eight companies did not measure the quantity of wasted food. During pre-consumer phase, difficulty in assessing demand, and confusion over dates were the most frequently cited causes for wastage. At the same time, overproduction was the leading cause of wastage in the post-consumer phase. Further complimenting the survey findings, the potential for the re-utilization of food waste through the creation of compost and alternative energy sources, was evaluated. The improvements identified with regard to food handling practices and potential re-utilization, are significant. Suggestions regarding the prevention of food waste highlighted many ways in which foodservice supply companies, as well as the mining industry, could benefit. Within these benefits, there exist great opportunities to contribute to Sustainable Development Goals, especially SDG12.3. Increased efficiencies with respect to food waste would reduce the environmental footprint, lead to potential re-utilization of food waste for electricity and gas generation, and increase profitability.
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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) | 0,001 | 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 ».