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System Design and Analysis for Sustainability

Special Issue Information

Dear Colleagues,

Global initiatives such as the UN’s Paris Agreement and the EU’s Green Deal call upon us to reduce environmental degradation and adapt to climate change. In order to deal with these challenges, a transformation of the complete value-adding system is needed that goes beyond incremental improvements of sustainablity. Modern digital technologies, including big data, machine learning, the Internet of Things (IoT), and Blockchain, are very promising drivers and enablers of such a transformation. They can be used to design and implement new sustainable value chains. Several sustainable design approaches and solutions have been suggested in the scientific literature. Despite the extensive literature on system design and analysis on sustainability, a design framework that could tie the diverse design approaches is still lacking. It is, therefore, difficult to evaluate and compare design solutions. While there is a call for more sustainable and circular approaches, reference design approaches, such as the SCOR model, are originally based on the linear take–make–dispose model of resource utilization. Moreover, the existing frameworks are generally ad-hoc and not based on sound design principles.

In this Special Issue, we aim to gather contributions focusing on system design and analysis approaches for sustainable value chains. Possible topics to be addressed include the following:

  1. Secondary studies (systematic reviews, survey studies) on approaches and frameworks on designing sustainable systems such as SCOR, GRI, and ISO 14001;
  2. Requirements and evaluation criteria for sustainable system designs;
  3. Architecture design for sustainability that addresses national, regional, and global sustainability accords;
  4. Systems for sustainability compliance and monitoring;
  5. System-of-systems approaches for sustainability;
  6. System dynamics approaches for sustainable design;
  7. Data science and AI approaches for analysis of sustainable systems;
  8. Adoption of advanced technologies such as IoT, Blockchain, and cloud computing for sustainable design;
  9. Industrial experiences and case studies of designs of sustainable value chains, circular economy ecosystems, and circular production systems.

Dr. Ayalew Kassahun
Prof. Dr. Cagatay Catal
Prof. Dr. Bedir Tekinerdogan
Dr. Cor Verdouw
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • design frameworks
  • sustainable design cases
  • sustainability frameworks
  • sustainability metrics
  • sustainability requirements
  • systainable system-of-systems
  • big data analytics
  • machine learning

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Published Papers