Identifying green citizen typologies by mining household-level survey data

Gulcan Petricli, Tulin Inkaya and Gul Gokay Emel

Renewable and Sustainable Energy Reviews, 2024, vol. 189, issue PA

Abstract: Some 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.

Keywords: Green citizen profiles; Environmental concern; Nature relatedness; Sustainable consumption behavior; Self-organizing maps; Mixed data; Machine learning (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1016/j.rser.2023.113957

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