Olano-Oteiza Begoña Untzizu, Amador-Cervera Manuel, Vargas-Viedma María Virginia, Alonso-Vicario Ainhoa
DeustoTech, Faculty of Engineering, University of Deusto, Av. Universidades, 24, Bilbao, 48007, Spain.
Deusto Research and Knowledge Transfer (DIT), University of Deusto, Av. Universidades, 24, Bilbao, 48007, Spain.
Sci Rep. 2025 Aug 12;15(1):29535. doi: 10.1038/s41598-025-12943-2.
Food loss and waste (FLW) demands urgent attention: over 58 million tonnes wasted annually in the EU, while 828 million people face hunger worldwide. SDG 12.3 targets a 50% reduction in FLW by 2030, requiring rigorous quantification. Governments must quantify FLW, especially at the consumption stage, where most of FLW is generated. However, the limited granularity of existing data emphasises the need for improved quantification methods that enable reliable comparisons and set solid basis to measure the impact of future FLW prevention actions. This paper proposes a FLW quantification methodology at municipal and regional scales aligned with the existing urban waste management requirements. The methodology involved experts and stakeholders to define the optimal FLW quantification framework and measurement method. It was validated in 6 Basque municipalities with a common waste characterisation matrix to compare waste fractions, generator types and waste collection systems. A cartographic analysis, from regional to street level, demonstrates how data granularity shapes FLW pattern interpretation. As in the case of Zamudio, where data at container level permits detailed insights. This approach enables improved waste collection and the design of ad-hoc FLW prevention actions (like economic instruments) according to the generator profile and type of FLW.
食物损失和浪费（FLW）问题亟待关注：欧盟每年浪费超过5800万吨食物，而全球有8.28亿人面临饥饿。可持续发展目标12.3旨在到2030年将食物损失和浪费减少50%，这需要进行严格的量化。各国政府必须对食物损失和浪费进行量化，尤其是在产生大部分食物损失和浪费的消费阶段。然而，现有数据的有限粒度凸显了改进量化方法的必要性，这些方法能够进行可靠的比较，并为衡量未来食物损失和浪费预防行动的影响奠定坚实基础。本文提出了一种在城市和区域尺度上与现有城市废物管理要求相一致的食物损失和浪费量化方法。该方法让专家和利益相关者参与进来，以确定最佳的食物损失和浪费量化框架及测量方法。它在6个巴斯克自治区的城市中得到验证，这些城市使用共同的废物特征矩阵来比较废物类别、产生者类型和废物收集系统。从区域到街道层面的制图分析表明了数据粒度如何影响对食物损失和浪费模式的解读。例如在萨穆迪奥，容器层面的数据能够提供详细的见解。这种方法有助于改进废物收集，并根据产生者概况和食物损失和浪费类型设计专门的食物损失和浪费预防行动（如经济手段）。
