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Scientific Article details

Title Waste Management Analysis in Developing Countries through Unsupervised Classification of Mixed Data
ID_Doc 25036
Authors Caruso, G; Gattone, SA
Title Waste Management Analysis in Developing Countries through Unsupervised Classification of Mixed Data
Year 2019
Published Social Sciences-Basel, 8, 6
DOI 10.3390/socsci8060186
Abstract The increase in global population and the improvement of living standards in developing countries has resulted in higher solid waste generation. Solid waste management increasingly represents a challenge, but it might also be an opportunity for the municipal authorities of these countries. To this end, the awareness of a variety of factors related to waste management and an efficacious in-depth analysis of them might prove to be particularly significant. For this purpose, and since data are both qualitative and quantitative, a cluster analysis specific for mixed data has been implemented on the dataset. The analysis allows us to distinguish two well-defined groups. The first one is poorer, less developed, and urbanized, with a consequent lower life expectancy of inhabitants. Consequently, it registers lower waste generation and lower CO2 emissions. Surprisingly, it is more engaged in recycling and in awareness campaigns related to it. Since the cluster discrimination between the two groups is well defined, the second cluster registers the opposite tendency for all the analyzed variables. In conclusion, this kind of analysis offers a potential pathway for academics to work with policy-makers in moving toward the realization of waste management policies tailored to the local context.
Author Keywords cluster analysis; unsupervised classification; mixed data; circular economy; waste management
Index Keywords Index Keywords
Document Type Other
Open Access Open Access
Source Emerging Sources Citation Index (ESCI)
EID WOS:000683585100024
WoS Category Social Sciences, Interdisciplinary
Research Area Social Sciences - Other Topics
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