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  • 标题:What gets measured gets managed – Or does it? Connection between food waste quantification and food waste reduction in the hospitality sector
  • 本地全文:下载
  • 作者:Mattias Eriksson ; Christopher Malefors ; Pieter Callewaert
  • 期刊名称:Resources, Conservation & Recycling: X
  • 电子版ISSN:2590-289X
  • 出版年度:2019
  • 卷号:4
  • 页码:1-8
  • DOI:10.1016/j.rcrx.2019.100021
  • 摘要:One innovation developed to tackle food waste in professional catering units is different versions of smart scales and softwares designed to simplify food waste quantification. The intention with this is to managing meal production more efficiently based on previous outcomes. However, quantification can be performed in different ways and having a catering unit quantify its food waste does not necessarily guarantee a reduction. Therefore this study sought to identify factors that could make food waste quantification more efficient in terms of waste reduction, and to determine the waste reduction payoff from more ambitious quantification set-ups. Data on 735 hotels, restaurants, and canteens in Europe, especially Sweden and Norway, that use a spreadsheet, a dedicated scale, or an internet-based service to track food waste were analyzed and parameters describing initial waste, number of guests and length, resolution, and completeness of quantification were determined. These parameters were then compared against the waste reduction achieved, in order to test their influence. It was found that 61% of the catering units studied had reduced their waste and that initial mass of waste per guest was the most influential factor for waste reduction. Catering units using more automated quantification tools recorded more data and reduced their food waste by slightly more, but also had a higher level of initial waste and therefore a greater opportunity for reduction. From this, it can be concluded that prioritizing catering units with the greatest waste volume could be an efficient strategy to reduce overall food waste in the most cost-efficient way.
  • 关键词:Food waste ; Hospitality sector ; Quantification ; Waste prevention ; Multiple linear regression ; Kitchen
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