一种机器学习方法来评估可持续发展目标的食品表现:意大利案例
Tommaso Castelli1, Chiara Mocenni1, Giovanna Maria Dimitri1
1Department of Information Engineering and Mathematics, University of Siena, Siena, Italy.
PloS one
|January 2, 2024
概括
意大利在与气候和粮食有关的可持续发展目标 (SDGs) 中表现不佳,尽管公众对产品可持续性感兴趣. 本研究使用集群算法来评估意大利.
科学领域:
- 环境科学与政策
- 农业经济学 农业经济学
- 计算社会科学 计算社会科学
背景情况:
- 可持续发展目标 (SDGs) 对全球进步至关重要,气候变化和农业食品系统是重点关注的关键领域.
- 评估国家对可持续发展目标的表现,特别是对意大利等有影响力的经济体来说,需要强大的分析方法.
- 了解公众对可持续性的看法对于农业食品部门有效实施政策至关重要.
研究的目的:
- 评估意大利与特定可持续发展目标 (02,12,13) 的协调程度,重点关注气候变化和农业食品市场.
- 通过使用机器学习,将意大利的可持续发展目标绩效与其他欧洲国家进行比较.
- 测量消费者行为中对可持续性和可持续发展目标的公众意识和理解.
主要方法:
- 集群算法用于SDG绩效评估的应用.
- 无监督机器学习用于比较欧洲可持续发展目标分析.
- 关于消费者可持续发展意识的新问卷的管理和分析.
主要成果:
- 意大利在分析的关键可持续发展目标指标中表现普遍不佳.
- 机器学习评估将意大利与其他欧洲国家相比,在实现可持续发展目标方面处于相对较好的位置.
- 调查问卷数据显示,意大利公民对产品可持续性的公众兴趣很大.
结论:
- 意大利的可持续发展目标成绩与公众对可持续发展的兴趣之间存在着显著的差距.
- 创新的数据分析,包括机器学习,对于SDG评估是有效的.
- 需要进一步的研究和政策干预,以弥合公众意识和国家可持续发展目标绩效之间的差距.
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