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Integrating Data Envelopment Analysis with AI and Digital Tools for Sustainable Solutions

Special Issue Information

Dear Colleagues,

The integration of data envelopment analysis (DEA) with artificial intelligence (AI) and digital tools seeks to address pressing sustainability challenges and foster innovation. Consequently, this Special Issue focuses on experimental, computational, and theoretical studies promoting optimized and sustainable solutions across science, engineering, economics, management, and public policy. We encourage original works that integrate DEA with AI, machine learning, and data analytics to address complex issues of efficiency and sustainability, supporting the UN 2030 Agenda and Sustainable Development Goals (SDGs) while promoting open and replicable science.

Distinctive features of this Special Issue:

  • Manuscripts presenting research ideas and project proposals are welcome;
  • Electronic files, datasets, and software related to calculations, models, and experiments can be published as supplementary material;
  • Studies funded by public institutions that aim to create scientific and societal impact are particularly encouraged.

Subject Areas:

  • Advanced data envelopment analysis (DEA) methods for sustainability;
  • Integration of AI, machine learning, and data mining with DEA;
  • Application of ethics in the use of artificial intelligence;
  • Digital tools for efficiency assessment in industries and services;
  • Optimization models for renewable energy, resource use, and sustainable supply chains;
  • Applications of DEA and AI in healthcare, education, transportation, and public policy;
  • Evaluation and monitoring of sustainable development policies;
  • Predictive models for mitigating environmental and economic impacts;
  • AI-powered decision support systems for sustainable management;
  • Education, ethics, and governance in applying DEA and digital technologies;
  • Case studies and practical applications in businesses and governments.

The Special Issue aims to serve as a bridge between academic research and practical applications, promoting innovative and sustainable solutions that integrate scientific rigor with the use of emerging technologies.

Prof. Dr. Patricia Acosta-Vargas
Dr. Mario González-Rodríguez
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 250 words) can be sent to the Editorial Office for assessment.

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

  • data envelopment analysis (DEA)
  • artificial intelligence (AI)
  • machine learning
  • digital tools for sustainability
  • efficiency and optimization
  • Sustainable Development Goals (SDGs)
  • decision support systems
  • predictive modeling
  • big data analytics
  • renewable energy and resource management

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Sustainability - ISSN 2071-1050