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

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.

[1]  V. Zeller,et al.  Urban waste flows and their potential for a circular economy model at city-region level. , 2019, Waste management.

[2]  Peter L. Brooks,et al.  Visualizing data , 1997 .

[3]  S. Hellweg,et al.  Do We Have the Right Performance Indicators for the Circular Economy?: Insight into the Swiss Waste Management System , 2017 .

[4]  Marianthi Markatou,et al.  A semiparametric method for clustering mixed data , 2016, Machine Learning.

[5]  J M Fernández-González,et al.  Economic and environmental review of Waste-to-Energy systems for municipal solid waste management in medium and small municipalities. , 2017, Waste management.

[6]  Ron Kohavi,et al.  Supervised and Unsupervised Discretization of Continuous Features , 1995, ICML.

[7]  Tonio Di Battista,et al.  Clustering functional data on convex function spaces , 2016 .

[8]  Zhexue Huang,et al.  CLUSTERING LARGE DATA SETS WITH MIXED NUMERIC AND CATEGORICAL VALUES , 1997 .

[9]  Antonio Balzanella,et al.  Cluster Analysis: An Application to a Real Mixed-Type Data Set , 2018, Models and Theories in Social Systems.

[10]  P. Sopp.  Cluster analysis. , 1996, Veterinary immunology and immunopathology.

[11]  Manabu Ichino,et al.  Generalized Minkowski metrics for mixed feature-type data analysis , 1994, IEEE Trans. Syst. Man Cybern..

[12]  Hong Jia,et al.  Categorical-and-numerical-attribute data clustering based on a unified similarity metric without knowing cluster number , 2013, Pattern Recognit..

[13]  Michael Nelles,et al.  Waste Management in Germany – Development to a Sustainable Circular Economy? , 2016 .

[14]  Tonio Di Battista,et al.  Clustering functional data streams: Unsupervised classification of soccer top players based on Google trends , 2018, Qual. Reliab. Eng. Int..

[15]  Lipika Dey,et al.  A k-mean clustering algorithm for mixed numeric and categorical data , 2007, Data Knowl. Eng..

[16]  S. A. Gattone,et al.  A Micro-level Analysis of Regional Economic Activity Through a PCA Approach , 2019 .

[17]  Federica Cucchiella,et al.  A comparison of environmental and energetic performance of European countries: A sustainability index , 2017 .

[18]  O. Romero-Hernández,et al.  Maximizing the value of waste: From waste management to the circular economy , 2018 .

[19]  Francesca Fortuna,et al.  K-means clustering of item characteristic curves and item information curves via functional principal component analysis , 2018, Quality & Quantity.

[20]  Stefano Antonio Gattone,et al.  Cluster Analysis for mixed data: An application to credit risk evaluation , 2020 .

[21]  Evina Katsou,et al.  Municipal solid waste management and waste-to-energy in the context of a circular economy and energy recycling in Europe , 2017 .

[22]  Tonio Di Battista,et al.  Clustering dichotomously scored items through functional data analysis , 2016 .

[23]  William Hogland,et al.  Solid waste management challenges for cities in developing countries. , 2015, Waste management.

[24]  M. Gavrilescu,et al.  Solid Waste Management for Circular Economy: Challenges and Opportunities in Romania – The Case Study of Iasi County , 2018, Greening of Industry Networks Studies.

[25]  A. Giusti,et al.  Municipal solid waste management in Pudong New Area, China. , 2009, Waste management.

[26]  P. Rousseeuw.  Silhouettes: a graphical aid to the interpretation and validation of cluster analysis , 1987 .

[27]  J. Gower.  A General Coefficient of Similarity and Some of Its Properties , 1971 .

[28]  Tonio Di Battista,et al.  Cluster Analysis as a Decision-Making Tool: A Methodological Review , 2017, Decision Economics@DCAI.

[29]  T. Caliński,et al.  A dendrite method for cluster analysis , 1974 .

[30]  Stephen J Burnley,et al.  A review of municipal solid waste composition in the United Kingdom. , 2007, Waste management.

[31]  Joshua Zhexue Huang,et al.  Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values , 1998, Data Mining and Knowledge Discovery.

[32]  P. Schneider,et al.  Solid Waste Management in Ho Chi Minh City, Vietnam: Moving towards a Circular Economy? , 2017 .

[33]  WTE Incinerator Ash,et al.  Solid waste management , 2007 .

[34]  K. Komnitsas,et al.  Column leaching of low-grade saprolitic laterites and valorization of leaching residues. , 2019, The Science of the total environment.

[35]  Mahmood Zohoori,et al.  Municipal Solid Waste Management Challenges and Problems for Cities in Low-Income and Developing Countries , 2017 .

[36]  Francisco de A. T. de Carvalho,et al.  Fuzzy clustering of distribution-valued data using adaptive L2 Wasserstein distances , 2016, 1605.00513.

[37]  Fernando E. García-Muiña,et al.  The Paradigms of Industry 4.0 and Circular Economy as Enabling Drivers for the Competitiveness of Businesses and Territories: The Case of an Italian Ceramic Tiles Manufacturing Company , 2018, Social Sciences.