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. This holistic approach models a Decision Support System (DSS) to effectively evaluate the interplay of local and sectoral low-carbon actions. Regional energy alliances and stakeholders are used for participatory modeling to promote the buildup of the learning base of this DSS. The core pillar of the DSS involves the application of advanced features of soft computing for the development of a Fuzzy Cognitive Map (FCM) that elicits the inter-causalities of the critical factors affecting the energy transitions towards bio-economy options. The concepts participating in the map are established by experts, and their interrelations via a learning process that utilizes survey statistics. The strands of research include scenarios to highlight the effect of energy provision to urbanization and the increase of urban actors (social, technological, political) in influencing the decision making related to low-carbon policies. Particularly, we study a use case of a Greek region that, despite its munificent agricultural production, also disclosures a stimulated manufacturing economy sector. The proposed decision making tool uses analytics and optimization algorithms to guide competent authorities and decision makers to sustainable energy transitioning towards decarbonization.