Sustainable Applications of AI and Machine Learning in Digital Ecosystems
A special issue of Sustainability (ISSN 2071-1050).
Deadline for manuscript submissions: 30 April 2027 | Viewed by 85
Special Issue Editors
Interests: artificial intelligence (AI) issues; finance; natural language processing; deep learning architectures; neural networks and applications; metaheuristics; time-series models
Interests: game theory and applications; finance; neural networks and applications; time-series models; behavioral finance; sustainability; multicriteria decision making; differential geometry; health economics
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The rapid expansion of the Internet economy has fundamentally transformed how data is generated, processed, and leveraged across digital platforms. From e-commerce and financial technologies to smart mobility and digital governance, the integration of Artificial Intelligence (AI), machine learning (ML), and deep learning (DL) has become a cornerstone for enabling scalable, data-driven decision-making. However, this transformation poses critical sustainability challenges, including resource efficiency, fairness, transparency, and long-term economic and environmental impacts.
This Special Issue aims to explore innovative and sustainable applications of AI, ML, and DL in the Internet economy, with a particular focus on methodologies that balance predictive performance with robustness, interpretability, and responsible data use. Contributions are encouraged to address not only algorithmic advancements, but also the broader implications of AI deployment in digital ecosystems, including energy consumption, resilience to data limitations, and adaptability to real-world constraints.
We welcome interdisciplinary research that bridges data science, economics, operations research, and information systems, fostering solutions that support sustainable growth in digitally connected markets. Topics of interest include, but are not limited to:
- Machine learning and deep learning models for digital platforms (fintech, digital markets, and e-commerce);
- Sustainable and energy-efficient AI systems;
- Explainable and trustworthy AI in online environments;
- AI-driven resource allocation and platform efficiency;
- Applications of AI in smart cities, mobility, and digital infrastructures.
We look forward to receiving your contributions.
Dr. Domenico Santoro
Prof. Dr. Luca Grilli
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
- sustainable AI
- internet economy
- machine learning
- deep learning
- explainable AI
- trustworthy AI
- fairness-aware algorithms
- resource efficiency
- energy-efficient AI
- digital platforms
- fintech
- smart mobility
- time-series forecasting
- robust learning
- multi-objective optimization
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