通过政策、社会规范和循环经济实现家庭垃圾管理的数据驱动战略

Pamon Pumas , Maliwan Puangmanee , Pimpawat Teeratitayangkul , Warangkana Sintuya , Chayakorn Pumas
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引用次数: 0

摘要

本研究探讨了影响泰国清迈省基勒街道市生活垃圾分类做法的行为和社会因素。利用调查数据和混合方法,整合了相关分析、主成分分析和两阶段机器学习管道,并通过态度→意图→行为的验证性结构方程模型进一步验证,并映射到已建立的轻推分类法,该研究确定了分离行为最具影响力的预测因素。这些因素包括常规的有机废物分类、行为意向、情感承诺以及社区成员和地方当局的感知影响。在被检验的模型中,梯度增强回归的预测准确率最高(R2 = 0.782;MAE = 0.331),强调了它比传统方法更有效地捕捉复杂的非线性行为模式的能力。通过结合行为理论、社区衍生的见解和预测分析,这项工作为市政规划提出了一个新颖的、可转移的框架。它提供了实用的、符合ESG/可持续发展目标的战略,例如基于习惯的、同行支持的推动和人工智能驱动的监测系统,地方政府可以采用这些战略来设计基于证据的废物政策。本研究关注文献中经常被忽视的半城市环境,填补了一个关键的方法空白,并为扩大行为知情的废物管理干预措施绘制了一条可复制的途径。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Data-driven strategies for household waste management through Policy, social Norms, and circular economy
This study examines the behavioral and social factors influencing household waste separation practices in Keelek Subdistrict Municipality, Chiang Mai Province, Thailand. Drawing on survey data and a mixed‐methods approach that integrates correlation analysis, principal component analysis, and a two‐stage machine‐learning pipeline—further validated by confirmatory structural equation modeling of Attitude → Intention → Behavior and mapped onto an established nudge taxonomy—the research identifies the most influential predictors of separation behavior. These include routine organic waste sorting, behavioral intention, emotional commitment, and the perceived influence of community members and local authorities. Among the tested models, Gradient Boosting Regression yielded the highest predictive accuracy (R2 = 0.782; MAE = 0.331), underscoring its ability to capture complex non-linear behavioral patterns more effectively than traditional approaches. By uniting behavioral theory, community-derived insights, and predictive analytics, this work advances a novel, transferable framework for municipal planning. It offers practical, ESG/SDG–aligned strategies—such as habit-based, peer-supported nudges and AI-powered monitoring systems—that local governments can adopt to design evidence-based waste policies. Focusing on a semi-urban context often overlooked in the literature, this study fills a critical methodological gap and charts a replicable pathway for scaling behaviorally informed waste-management interventions.
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