AI Meets Sustainability: A Special Issue on Real-World Applications
- Artificial Intelligence as a Driver of Sustainable Development
- “Predicting Sustainable Crop Yields: Deep Learning and Explainable AI Tools” (Contribution 1) addresses the critical theme of food security—a cornerstone of sustainable development. Its significance lies in moving beyond abstract machine learning models toward practical scenarios where decision transparency is essential for effective policy and management.
- “Digital Visualization of Environmental Risk Indicators in the Territory of the Urban Industrial Zone” (Contribution 2) explores the challenges of assessing and communicating environmental risks in complex urbanized landscapes. The study demonstrates how AI systems can collect and analyze data on air, soil, and water pollution and represent these findings through digital maps and risk indices.
- “Statistics Using Neural Networks in the Context of Sustainable Development Goal 9.5” (Contribution 3) focuses on the application of neural networks to monitor and evaluate progress toward Sustainable Development Goal 9.5, which emphasizes strengthening scientific research, modernizing industrial technologies, and expanding access to innovation. This article illustrates how AI contributes to more precise and evidence-based decision-making in public and sectoral policy.
- “Assessment of Water Hydrochemical Parameters Using Machine Learning Tools” (Contribution 4) concentrates on water quality control—a cornerstone of ecological sustainability. The study highlights how AI can be deployed to safeguard natural ecosystems. In the context of accelerating climate change and anthropogenic pressures, such monitoring systems are becoming indispensable elements of adaptive water resource management.
- “An Enhanced Particle Swarm Optimization Long Short-Term Memory Network Hybrid Model for Predicting Residential Daily CO2 Emissions” (Contribution 5) presents an example of research into processes where AI can serve as the foundation of climate-responsible urban policy. The study demonstrates how the technological integration of advanced AI methods with practical applications can directly support the fulfillment of climate commitments.
- Ethics and Responsibility in the Application of Artificial Intelligences
- Explainability: AI outcomes cannot remain a “black box,” especially when they pertain to human health, safety, or well-being [4].
- Fairness: Systems must not discriminate based on gender, age, ethnicity, or socioeconomic status.
- Data protection and privacy: Reliable safeguards must be in place when collecting and analyzing large datasets, including personal and biometric information [5].
- Inclusivity: Access to technologies and their benefits must be ensured for all stakeholders, regardless of income level or country of residence.
- Society 5.0: A Humanistic Model of the Technological Future
- Human-centeredness: all digital solutions must be designed for the benefit of people.
- Ethics and trust: technologies should inspire confidence through transparency and predictability.
- Environmental balance: AI should contribute to reducing anthropogenic pressures on nature.
- Inclusivity: access to tools and knowledge must be available to all.
- Artificial Intelligence for Real Life: Potential and Examples
- Challenges and Prospects
- Conclusions and Outlook
Author Contributions
Acknowledgments
Conflicts of Interest
List of Contributions
- Malashin, I.; Tynchenko, V.; Gantimurov, A.; Nelyub, V.; Borodulin, A.; Tynchenko, Y. Predicting Sustainable Crop Yields: Deep Learning and Explainable AI Tools. Sustainability 2024, 16, 9437. https://doi.org/10.3390/su16219437.
- Safarov, R.; Shomanova, Z.; Nossenko, Y.; Mussayev, Z.; Shomanova, A. Digital Visualization of Environmental Risk Indica-tors in the Territory of the Urban Industrial Zone. Sustainability 2024, 16, 5190. https://doi.org/10.3390/su16125190.
- Okulich-Kazarin, V. Statistics Using Neural Networks in the Context of Sustainable Development Goal 9.5. Sustainability 2024, 16, 8395. https://doi.org/10.3390/su16198395.
- Malashin, I.; Nelyub, V.; Borodulin, A.; Gantimurov, A.; Tynchenko, V. Assessment of Water Hydrochemical Parameters Using Machine Learning Tools. Sustainability 2025, 17, 497. https://doi.org/10.3390/su17020497.
- Hu, Y.; Wang, B.; Yang, Y.; Yang, L. An Enhanced Particle Swarm Optimization Long Short-Term Memory Network Hybrid Model for Predicting Residential Daily CO2 Emissions. Sustainability 2024, 16, 8790. https://doi.org/10.3390/su16208790.
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Al-Jumeily OBE, D.; Mustafina, J.; Jayabalan, M. AI Meets Sustainability: A Special Issue on Real-World Applications. Sustainability 2025, 17, 9148. https://doi.org/10.3390/su17209148
Al-Jumeily OBE D, Mustafina J, Jayabalan M. AI Meets Sustainability: A Special Issue on Real-World Applications. Sustainability. 2025; 17(20):9148. https://doi.org/10.3390/su17209148
Chicago/Turabian StyleAl-Jumeily OBE, Dhiya, Jamila Mustafina, and Manoj Jayabalan. 2025. "AI Meets Sustainability: A Special Issue on Real-World Applications" Sustainability 17, no. 20: 9148. https://doi.org/10.3390/su17209148
APA StyleAl-Jumeily OBE, D., Mustafina, J., & Jayabalan, M. (2025). AI Meets Sustainability: A Special Issue on Real-World Applications. Sustainability, 17(20), 9148. https://doi.org/10.3390/su17209148