Exploring citizens’ cluster attitudes and importance-performance policy for adopting sustainable waste management practices

Abstract

This study aims to bridge the gap between governmental policymaking and citizens’ environmental attitudes by providing scientific advice to governments on how to encourage community engagement in sustainable waste management (SWM) programs. It seeks to integrate citizens’ class preferences, based on their environmental awareness, into SWM programs. The study employs comprehensive data analysis, including factor identification based on class attitudes, preference determination through environmental awareness, importance-performance analysis, and probit and logit models. Through this analysis, the study identifies the nexus between societal attitudes and environmental concerns as crucial for the success of SWM in metropolitan cities. The research consists of several stages of data analysis, such as identifying factors and classes based on citizen attitudes, analyzing preferences through an importance-performance analysis of SWM programs, and creating models using probit and logit methods. The analysis reveals two critical factors for the success of SWM in metropolitan cities: societal attitudes and environmental concerns. From the analysis, two distinct societal classes emerge: proactive ’Sustainable Supporters’ and more passive ’Indifferent Citizens’. The policy implications derived from the study suggest that while ’Sustainable Supporters’ can be further mobilized through an advanced SWM platform that emphasizes awareness, collection optimization, and waste processing, the ’Indifferent Citizens’ require strategies focused on bolstering digital SWM programs that simplify waste disposal and recycling. To encourage broader participation and ensure the effectiveness of SWM, governments and relevant organizations are advised to raise public awareness of SWM, improve infrastructural and technological capabilities in waste management, and formulate policies that unequivocally support SWM.