The Role of Local Governance in Mitigating Student Food Waste: Integrating AI and Sociology for Enhanced School Per-formance

This study aims to explore the role of local governance in reducing student food waste behavior and enhancing school performance, integrating perspectives from artificial intel-ligence (AI) and sociology. A large-scale survey is conducted to collect extensive data on school cafeteria consumption behaviors and related activities, which is analyzed using a Backpropagation Neural Network (BPNN) model. The BPNN model effectively handles nonlinear relationships and reveals the complex interaction mechanisms between student behaviors and school performance. The results indicate a high positive correlation (0.75) between cafeteria size and student traffic fluctuations, suggesting that local governance should consider the impact of cafeteria size on student traffic when planning and con-structing school cafeterias to optimize resource allocation. Additionally, there is a positive correlation (0.8) between student traffic fluctuations and menu diversity, indicating that local policies can attract students and reduce food waste by adjusting menu diversity, thereby improving the efficiency of resource utilization in schools. Furthermore, psycho-logical and social factors such as personal values, lifestyle habits, social group pressure, and attitudes toward food significantly influence the level of food waste in school cafete-rias. This implies that local governance can change students' behaviors and reduce waste through education and community engagement initiatives. Besides analyzing food waste behaviors, this study also examines the impact of local governance on resource allocation, teacher development, and overall student behavior management. Based on the findings, the study proposes a series of local governance policy recommendations aimed at reduc-ing food waste and comprehensively enhancing school performance through optimized resource allocation, increased community involvement, and improved management effi-ciency.

本研究旨在探讨地方治理在减少学生食物浪费行为、提升学校办学绩效中的作用，融合人工智能（Artificial Intelligence）与社会学视角。本研究开展大规模调研，收集关于学校食堂消费行为及相关活动的多维度数据，并采用反向传播神经网络（Backpropagation Neural Network，BPNN）模型进行分析。该模型可有效处理非线性关联，揭示学生行为与学校办学绩效间的复杂交互机制。研究结果显示，食堂规模与学生客流量波动间存在高度正相关（相关系数为0.75），这表明地方治理在规划、建设学校食堂时，应考量食堂规模对学生客流量的影响，以优化资源配置。此外，学生客流量波动与菜单多样性间存在正相关（相关系数为0.8），这意味着地方政策可通过调整菜单多样性来吸引学生、减少食物浪费，进而提升学校资源利用效率。再者，个人价值观、生活习惯、群体社交压力及饮食态度等心理与社会因素，会对学校食堂的食物浪费程度产生显著影响。这表明地方治理可通过教育推广与社区参与举措，引导学生行为转变、减少食物浪费。除食物浪费行为分析外，本研究还探讨了地方治理对学校资源配置、教师队伍建设及学生整体行为管理的影响。基于上述研究发现，本研究提出一系列地方治理政策建议，旨在通过优化资源配置、扩大社区参与及提升管理效率，减少食物浪费并全面提升学校办学绩效。