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Artificial Intelligence and Emerging Data-Driven Solutions for Sustainable Systems

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: 31 January 2026 | Viewed by 1561

Special Issue Editor


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Guest Editor
Department of Computer Science, Faculty of Sciences and Techniques, Moulay Ismail University, Meknes 50050, Morocco
Interests: AI; software engineering; computer communications (networks); big data analysis; data mining
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue titled "Artificial Intelligence and Emerging Data-Driven Solutions for Sustainable Systems" explores the transformative role of AI and data-driven technologies in addressing critical sustainability challenges. It aims to showcase innovative methodologies and applications that integrate AI with tools such as IoT, blockchain, and big data to design efficient, resilient, and environmentally conscious systems. The scope spans diverse domains, including energy, agriculture, urban development, and healthcare, focusing on solutions that align with global sustainability goals.

This issue contributes to the existing literature by advancing AI-driven frameworks for optimizing resource use, predicting environmental changes, and enabling decentralized systems. It builds on prior research in AI for energy efficiency, IoT for smart systems, and big data for decision-making, introducing emerging concepts like hybrid AI models and real-time analytics. Moreover, it addresses the ethical and governance challenges of sustainable AI applications, providing a comprehensive perspective on their societal impacts.

By bridging technology and sustainability science, this Special Issue serves as a resource for researchers and practitioners seeking innovative solutions to global challenges. It advances the discourse on leveraging AI and data analytics for creating scalable, equitable, and sustainable systems that contribute to a better future.

The focus on sustainable systems situates this issue at the intersection of two key research domains: artificial intelligence and sustainability science. It builds upon and contributes to the existing literature in several ways:

AI and Sustainability: The existing works have demonstrated the potential of AI in energy efficiency, carbon footprint reduction, and natural resource conservation. This issue expands on these applications by addressing emerging challenges and integrating novel technologies such as hybrid AI frameworks.

Data-Driven Decision Making: Recent research emphasizes the importance of big data and machine learning for decision-making in sustainable systems. This Special Issue contributes to this body of knowledge by exploring more sophisticated data-driven approaches, including real-time analytics and edge computing.

IoT and Smart Systems: IoT-enabled smart systems have been extensively studied in the context of smart cities and agriculture. The Special Issue builds on this by exploring synergies between IoT and AI for enhanced sustainability outcomes.

Ethics and Social Implications: The current literature on AI ethics and governance often lacks a sustainability perspective. This issue aims to bridge this gap by incorporating ethical frameworks specifically tailored for AI applications in sustainability.

By addressing these themes, the Special Issue aims to advance the theoretical and practical understanding of how AI and data-driven solutions can contribute to sustainable development goals (SDGs) and pave the way for scalable, equitable, and environmentally sound systems. It seeks to be a valuable resource for researchers, practitioners, and policymakers working at the nexus of technology and sustainability.

Dr. Yousef Farhaoui
Guest Editor

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

  • AI-driven optimization of renewable energy systems
  • predictive analytics for resource management
  • sustainable urban and smart city development
  • circular economy frameworks enabled by data-driven tools
  • environmental monitoring and climate change mitigation using AI
  • blockchain for decentralized sustainable systems
  • ethical considerations and policy frameworks for sustainable AI applications

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Published Papers (1 paper)

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Research

20 pages, 2486 KiB  
Article
Adaptive Predictive Maintenance and Energy Optimization in Metro Systems Using Deep Reinforcement Learning
by Mohammed Hatim Rziki, Atmane E. Hadbi, Mohamed Khalifa Boutahir and Mohammed Chaouki Abounaima
Sustainability 2025, 17(11), 5096; https://doi.org/10.3390/su17115096 - 1 Jun 2025
Viewed by 905
Abstract
The rapid growth of urban metro systems requires novel strategies to guarantee operational dependability and energy efficiency. This article describes a new way to use deep reinforcement learning (DRL) to help metro networks with predictive maintenance that adapts to changing conditions and energy [...] Read more.
The rapid growth of urban metro systems requires novel strategies to guarantee operational dependability and energy efficiency. This article describes a new way to use deep reinforcement learning (DRL) to help metro networks with predictive maintenance that adapts to changing conditions and energy optimization. We used real-world transit data from the General Transit Feed Specification (GTFS) to model the maintenance scheduling and energy management problem as a Markov Decision Process. This included important operational metrics like peak-hour demand, train arrival times, and station stop densities. A custom reinforcement learning environment mimics the changing conditions of metro operations. Deep Q-Networks (DQNs) and Proximal Policy Optimization (PPO) sophisticated deep reinforcement learning techniques were used to identify the optimal policies for decreasing energy consumption and downtime. The PPO hyperparameters were additionally optimized using Bayesian optimization by implementing Optuna, which produces a far greater performance than baseline DQNs and basic PPO. Comparative tests showed that our improved DRL-based method improves the accuracy of predictive maintenance and the efficiency of energy use, which lowers operational costs and raises the dependability of the service. These results show that advanced learning and optimization techniques could be added to public transportation systems in cities. This could lead to more sustainable and smart transportation management in big cities. Full article
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