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Waste Management Analysis in Developing Countries through Unsupervised Classification of Mixed Data

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  • Giulia Caruso

    (Department of Philosophical, Pedagogical and Economic-Quantitative Sciences, University G. d’Annunzio Chieti-Pescara, Vle Pindaro n. 42, 65127 Pescara, Italy)

  • Stefano Antonio Gattone

    (Department of Philosophical, Pedagogical and Economic-Quantitative Sciences, University G. d’Annunzio Chieti-Pescara, Vle Pindaro n. 42, 65127 Pescara, Italy)

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 C O 2 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.

Suggested Citation

  • Giulia Caruso & Stefano Antonio Gattone, 2019. "Waste Management Analysis in Developing Countries through Unsupervised Classification of Mixed Data," Social Sciences, MDPI, vol. 8(6), pages 1-15, June.
    • Handle: RePEc:gam:jscscx:v:8:y:2019:i:6:p:186-:d:239380
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