{"title":"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","authors":"Pamon Pumas , Maliwan Puangmanee , Pimpawat Teeratitayangkul , Warangkana Sintuya , Chayakorn Pumas","doi":"10.1016/j.wmb.2025.100216","DOIUrl":null,"url":null,"abstract":"<div><div>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 (R<sup>2</sup> = 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.</div></div>","PeriodicalId":101276,"journal":{"name":"Waste Management Bulletin","volume":"3 3","pages":"Article 100216"},"PeriodicalIF":0.0000,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Data-driven strategies for household waste management through Policy, social Norms, and circular economy\",\"authors\":\"Pamon Pumas , Maliwan Puangmanee , Pimpawat Teeratitayangkul , Warangkana Sintuya , Chayakorn Pumas\",\"doi\":\"10.1016/j.wmb.2025.100216\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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 (R<sup>2</sup> = 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.</div></div>\",\"PeriodicalId\":101276,\"journal\":{\"name\":\"Waste Management Bulletin\",\"volume\":\"3 3\",\"pages\":\"Article 100216\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-05-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Waste Management Bulletin\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2949750725000458\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Waste Management Bulletin","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2949750725000458","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.