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.

本研究旨在探究地方治理在减少学生食物浪费行为、提升学校办学效能中的作用，融合人工智能（Artificial Intelligence，AI）与社会学研究视角。本次研究开展大规模调研，收集学校食堂消费行为及相关活动的多维度详实数据，并采用反向传播神经网络（Backpropagation Neural Network，BPNN）模型开展数据分析。该模型可有效处理非线性关系，揭示学生行为与学校办学效能间的复杂交互机制。研究结果表明，食堂规模与学生客流波动间存在显著正相关关系（相关系数为0.75），这提示地方治理部门在规划、建设学校食堂时，需考量食堂规模对学生客流的影响，以此优化资源配置。此外，学生客流波动与菜单多样性间亦存在正相关关系（相关系数为0.8），说明地方政策可通过调整菜单多样性吸引学生、减少食物浪费，进而提升学校资源利用效率。进一步而言，个人价值观、生活习惯、社会群体压力及对食物的态度等心理与社会因素，会显著影响学校食堂的食物浪费水平。这意味着地方治理可通过教育引导与社区参与举措，改变学生行为、减少食物浪费。除针对食物浪费行为开展分析外，本研究还探究了地方治理对资源配置、教师发展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、增强社区参与及提升管理效能，减少食物浪费并全面提升学校办学效能。