Advanced Soft Computing for Smart Practices of Circular Economy in the Era of Covid-19

Dear Colleagues,

 The 2030 Agenda for Sustainable Development of the United Nations and the Paris Agreement on climate change, both adopted in 2015, represent two fundamental contributions to guide the transition towards an economic development model that has as its objective not only in profitability but also social progress and environmental protection. In this context, a crucial aspect is the more rational and sustainable management of natural resources, reducing the pollution and pressure due to the increase the demand for natural raw materials. This is a problem related to managing resources more efficiently while reducing waste, keeping as much as possible the value of products and materials. It is necessary to avoid the fact that everything that still intrinsically has a residual utility is not disposed in landfills but is recovered and reintroduced into the production cycles. These aspects constitute the essence of the circular economy, which aims through innovation technology and sustainable management to make economic activities more efficient and less impactful on the environment. Circularity is an essential part of a wider transformation of industry towards climate neutrality and long-term competitiveness. It can generate extra value and unlock economic opportunities.

The transition to a circular economy requires a coherent system of regulatory and economic tools and the involvement of all components of all stakeholders of the social system (enterprises, consumers/citizens, local authorities and NGOs). The EU launched the European Green Deal, a concerted strategy for a climate-neutral, resource-efficient and competitive economy. The European Green Deal provides an action plan to boost the efficient use of resources by moving to a clean and circular economy. The plan outlines the investments needed, and explains how to ensure an inclusive transition.

The health crisis caused by COVID-19 has had terrible consequences on the economy of almost every country in the world and, during the period of the pandemic, environmental emergencies have, of course, lost priority. Recently, EU leaders agreed on a comprehensive package that combines the multiannual financial framework (MFF) and an extraordinary recovery effort, Next Generation EU (NGEU). The package will help the EU to rebuild after the COVID-19 pandemic and will support investments in the Green Deal and sustainable development.

To increase the effectiveness of the transition to a circular economy, new tools based on soft computing can play an important role in the decision processes. Because of the vagueness and uncertainty of the available information, in many situations, decision makers often show a certain degree of hesitation in their decision-making processes. Soft computing can deal with this ambiguity and partial truth. Thus, it represents a valid tool to supply reasonable solutions to real life complex problems.

This Special Issue welcomes papers contributing to novel applications of soft computing for a better understanding of the relationship between the circular economy, uncertainty and vagueness. Relevant topics include, but are not limited, to current research about applications for sustainable development, sustainability evaluation, sustainable industrial systems, circular economy, resource use, resource efficiency, waste management, recycling, reuse and forms of material and energy recovery, treatment, environmental assessments and planning, policies and regulations, risk assessment analysis, risk management, green technologies, industrial ecosystems, industrial ecology and business model innovation, in the following areas:

• Multi-criteria, analytic network process,
• Fuzzy set, fuzzy inference, fuzzy multicriteria,
• Interval valued fuzzy sets,
• Interval valued intuitionistic fuzzy set,
• Intuitionistic fuzzy sets,
• Pythagorean fuzzy sets,
• Interval-valued Pythagorean fuzzy sets,
• Neutrosophic sets,
• Interval valued q-rung orthopair fuzzy sets,
• Hesitant fuzzy sets,
• Interval type-2 fuzzy sets,
• Spherical fuzzy sets,
• Neuro-fuzzy,
• Neural networks,
• Neurocomputing,
• Algorithm genetics,
• Evolution algorithms,
• Particle swarm optimization (PSO),
• Ant colony,
• Chaos theory,
• Artificial Intelligence (AI),
• Machine learning,
• Dynamic Systems,
• Approximate reasoning,
• Expert systems,
• Machine learning,
• Pattern recognition.

Prof. Fausto Cavallaro
Dr. Dragan Pamučar
Dr. Abbas Mardani
Dr. Jingzheng Ren
Guest Editors

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