Publication:
Identifying green citizen typologies by mining household-level survey data

dc.contributor.authorPetriçli, Gülcan
dc.contributor.authorİnkaya, Tülin
dc.contributor.authorEmel, Gül Gökay
dc.contributor.buuauthorPETRİÇLİ, GÜLCAN
dc.contributor.buuauthorİNKAYA, TÜLİN
dc.contributor.buuauthorEmel, Gül Gökay
dc.contributor.orcid0000-0002-6260-0162
dc.contributor.researcheridAAH-5172-2021
dc.contributor.researcheridJCN-8103-2023
dc.contributor.researcheridAAH-2155-2021
dc.date.accessioned2024-10-28T06:18:41Z
dc.date.available2024-10-28T06:18:41Z
dc.date.issued2023-10-24
dc.description.abstractSome impactful but unfavorable results of rapid urbanization are human-nature disconnection, waste of energy/ water resources, and increased greenhouse gas emissions. To save the future of our planet, a transition to a more sustainable urban life is a must. However, there is no single sustainable city model because cities differ in terms of their assets. Hence, locally customized sustainable actions linked to global sustainability should be developed, such as a change in individual behaviors leads to a sustainable society, city, and country. This research investigated green citizen profiles and variables affecting the profiles in the context of environmental behavior and sustainability. For this purpose, survey research was done at the household level in a metropolis in Turkey. Measurement scales about environmental concern, human-nature connections, and sustainable consumption behavior were used for collecting data. A data analysis approach was proposed as the survey dataset contains mixed-type variables. It amalgamates statistics with machine learning algorithms, namely two-stage clustering with multilayered self-organizing maps, k-medoid clustering algorithm, factor analysis, permutational multivariate analysis of variance, principal component analysis and classification and regression trees algorithm. The results reveal that (i) five distinct profiles, namely unconscious greens, risky greens, economic greens, potential greens, and wasters are identified, none of which is entirely green; (ii) district, family life-cycle, household size, number of rooms, altruistic and biocentric environmental concerns are the most critical variables in distinguishing profiles; (iii) the proposed approach enables processing socio-demographic, psychographic, behavioral and consumption variables together.
dc.identifier.doi10.1016/j.rser.2023.113957
dc.identifier.eissn1879-0690
dc.identifier.issn1364-0321
dc.identifier.urihttps://doi.org/10.1016/j.rser.2023.113957
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S1364032123008158
dc.identifier.urihttps://hdl.handle.net/11452/47139
dc.identifier.volume189
dc.identifier.wos001102603600001
dc.indexed.wosWOS.SCI
dc.language.isoen
dc.publisherElsevier
dc.relation.bapKUAP(I) 2019/10
dc.relation.journalRenewable & Sustainable Energy Reviews
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectResidential energy-consumption
dc.subjectPro-environmental behavior
dc.subjectElectricity consumption
dc.subjectConsumer
dc.subjectDemand
dc.subjectModel
dc.subjectSegregation
dc.subjectSustainability
dc.subjectIntervention
dc.subjectProfiles
dc.subjectGreen citizen profiles
dc.subjectEnvironmental concern
dc.subjectNature relatedness
dc.subjectSustainable consumption behavior
dc.subjectSelf -organizing maps
dc.subjectMixed data
dc.subjectMachine learning
dc.subjectScience & technology
dc.subjectTechnology
dc.subjectGreen & sustainable science & technology
dc.subjectEnergy & fuels
dc.subjectScience & technology - other topics
dc.titleIdentifying green citizen typologies by mining household-level survey data
dc.typeArticle
dspace.entity.typePublication
relation.isAuthorOfPublication808f42f5-087b-47c3-89fb-3e5bd79a973f
relation.isAuthorOfPublication50789246-3e56-4752-a821-3ae9957be346
relation.isAuthorOfPublication.latestForDiscovery808f42f5-087b-47c3-89fb-3e5bd79a973f

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