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<p>Food waste (FW) remains a critical challenge on a global scale. While FW mitigation efforts have mostly focused on household at-home waste, there is a more recent issue to address FW generated in out-of-home dining contexts, where consumer behaviour plays a key role. This study, in alignment with the objectives of the Horizon 2020 LOWINFOOD project, adopts a consumer-centric approach to quantify FW at post-consumption stage with REGUSTO—a digital innovation leveraging food-sharing and doggy bag strategies to reduce out-of-home FW. Effectiveness of the two mitigation strategies are evaluated, and furthermore a verification of the data accuracy is pilot-run with AI technology. The findings indicate a remarkable 88% reduction in FW and significant variations in consumer engagement with different FW reduction practices. FW mitigation with food-sharing platforms out-perform doggy bags in the context of Central Italy, especially among the young and higher-educated workforce. Typical types of restaurants and food, additionally, moderate the effectiveness of those strategies, which provide valuable guidance for policymakers and business innovators seeking to develop tailored strategies for FW mitigation in out-of-home dining settings. Lastly, AI technology exhibits powerful capacity to extract information from real life food images, and a nearly 50% discrepancy of the food weight is discovered between AI estimation and consumer self-assessment—a potential overestimation of the mitigation results from the traditional survey-based data source.</p>

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Tracking post-consumption of restaurant food and leftovers: innovative digital solution and outcomes from REGUSTO

  • 期刊论文

  • 发表日期：

    2025年6月9日

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  • Mengting Yu,
  • Clara Cicatiello,
  • Camilla Comis,
  • Ludovica Principato,
  • Luca Secondi

摘要

Food waste (FW) remains a critical challenge on a global scale. While FW mitigation efforts have mostly focused on household at-home waste, there is a more recent issue to address FW generated in out-of-home dining contexts, where consumer behaviour plays a key role. This study, in alignment with the objectives of the Horizon 2020 LOWINFOOD project, adopts a consumer-centric approach to quantify FW at post-consumption stage with REGUSTO—a digital innovation leveraging food-sharing and doggy bag strategies to reduce out-of-home FW. Effectiveness of the two mitigation strategies are evaluated, and furthermore a verification of the data accuracy is pilot-run with AI technology. The findings indicate a remarkable 88% reduction in FW and significant variations in consumer engagement with different FW reduction practices. FW mitigation with food-sharing platforms out-perform doggy bags in the context of Central Italy, especially among the young and higher-educated workforce. Typical types of restaurants and food, additionally, moderate the effectiveness of those strategies, which provide valuable guidance for policymakers and business innovators seeking to develop tailored strategies for FW mitigation in out-of-home dining settings. Lastly, AI technology exhibits powerful capacity to extract information from real life food images, and a nearly 50% discrepancy of the food weight is discovered between AI estimation and consumer self-assessment—a potential overestimation of the mitigation results from the traditional survey-based data source.