Computational Methods and Data-Driven Techniques in Sustainability Science
A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Engineering and Science".
Deadline for manuscript submissions: closed (15 December 2023) | Viewed by 1761
Special Issue Editors
Interests: computational science; artificial intelligence; renewable energy
Special Issues, Collections and Topics in MDPI journals
Interests: sustainability transition; sustainable transportation; operations research
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Sustainability science has emerged as a new academic discipline that aims to understand and address challenges that threaten the future well-being of humanity, such as climate change, pollution, and depletion of energy and resources. Computer methods make great contributions to sustainability science by applying the techniques from applied mathematics, computer science, information science, statistics, and operations research fields. Particularly, with the explosive growth of data and fast development of artificial intelligence in recent years, data-driven techniques have been incorporated into computational science that boost the accuracy and efficiency of prediction. On one hand, solving the problems in sustainability science poses great challenges to both modelling and computing because it is a multidisciplinary field involving the complex interaction between human, societal, economic, and environmental needs. On the other hand, sustainability problems offer opportunities for the advancement of the state-of-the-art of computational science. Hence, consistent efforts should be made to enrich our knowledges in this research field.
The aim of this Special Issue is to bring together original research articles and review articles highlighting recent advances in mathematical models, numerical algorithms, statistical analysis, and data-driven techniques in sustainability science. In addition to engineering applications, we encourage submissions that investigate the computational social science for decision making concerning the management of resources, improvement of energy efficiency, maintenance of biological diversity, and other services in sustainability transition.
Dr. Haojie Lian
Dr. Xiao Lin
Dr. Wenjun Yuan
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
- sustainability
- computational science
- statistics
- artificial intelligence
- decision making
