Exploring social determinants of municipal solid waste management: survey processing with fuzzy logic and self-organized maps.

In the present study, the establishment of decision-making criteria and a novel and robust interdisciplinary approach for systematically characterizing effects of uncertainties in social determinants of municipal solid waste management using an important fuzzy logic methodology is demonstrated. The primary goal is to highlight the social benefits of this waste management option such as job creation, hygiene and health protection, and working safety as well as to indicate certain side effects occurring during waste processing (odor and leachate production, social trust). The current research is based on a social survey in an agro-industrial region, Thessaly, Greece, and indicates a set of diversified key factors that are related to public acceptance of municipal waste management schemes. These features are input to Kohonen Self-Organized Maps (a special type of Artificial Neural Networks) for clustering residents according to their social perception and attitudes in terms of solid waste collecting and recycling. Both analyses highlight the environmental concern, social perception, hygiene and health, economic status, and lifestyle as the primary social determinants in affecting the public attitudes towards recycling. In both cases, these soft computing techniques seem to outperform the classical statistical and logical regression methodologies and become very promising in accurately predicting waste management practice and possibly other environmental behaviors.