Circular bio-economy via energy transition supported by Fuzzy Cognitive Map modeling towards sustainable low-carbon environment.

Several energy transition plans attempt to establish low-carbon practices towards a circular bio-economy in order to reduce greenhouse gas emissions. However, most actions only try to assuage the impacts of climate change without improving the resource flows generated by human activities. In this paper, we propose a semi-quantitative assessment of the impacts of biowaste-based energy transition by engaging all relevant social stakeholders' evaluation in the strategic plan.

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.