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DOI10.1371/journal.pone.0296465
A machine learning approach to assess Sustainable Development Goals food performances: The Italian case
发表日期2024
ISSN1932-6203
起始页码19
结束页码1
卷号19期号:1
英文摘要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.
语种英语
WOS研究方向Science & Technology - Other Topics
WOS类目Multidisciplinary Sciences
WOS记录号WOS:001136266700113
来源期刊PLOS ONE
文献类型期刊论文
条目标识符http://gcip.llas.ac.cn/handle/2XKMVOVA/287296
作者单位University of Siena
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GB/T 7714
. A machine learning approach to assess Sustainable Development Goals food performances: The Italian case[J],2024,19(1).
APA (2024).A machine learning approach to assess Sustainable Development Goals food performances: The Italian case.PLOS ONE,19(1).
MLA "A machine learning approach to assess Sustainable Development Goals food performances: The Italian case".PLOS ONE 19.1(2024).
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