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A machine learning approach to assess Sustainable Development Goals food performances: The Italian case

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  • Tommaso Castelli
  • Chiara Mocenni
  • Giovanna Maria Dimitri

Abstract

In this study, we introduce an innovative application of clustering algorithms to assess and appraise Italy’s alignment with respect to the Sustainable Development Goals (SDGs), focusing on those related to climate change and the agrifood market. Specifically, we examined SDG 02: Zero Hunger, SDG 12: Responsible Consumption and Production, and SDG 13: Climate Change, to evaluate Italy’s performance in one of its most critical economic sectors. Beyond performance analysis, we administered a questionnaire to a cross-section of the Italian populace to gain deeper insights into their awareness of sustainability in everyday grocery shopping and their understanding of SDGs. Furthermore, we employed an unsupervised machine learning approach in our research to conduct a comprehensive evaluation of SDGs across European countries and position Italy relative to the others. Additionally, we conducted a detailed analysis of the responses to a newly designed questionnaire to gain a reasonable description of the population’s perspective on the research topic. A general poor performance in the SDGs indicators emerged for Italy. However, from the questionnaire results, an overall significant interest in the sustainability of the acquired products from italian citizens.

Suggested Citation

  • Tommaso Castelli & Chiara Mocenni & Giovanna Maria Dimitri, 2024. "A machine learning approach to assess Sustainable Development Goals food performances: The Italian case," PLOS ONE, Public Library of Science, vol. 19(1), pages 1-20, January.
    • Handle: RePEc:plo:pone00:0296465
    DOI: 10.1371/journal.pone.0296465
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    References listed on IDEAS

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