Emerging Technologies and Intelligent Systems for Sustainable Development

1. Introduction

Sustainable development has become a global priority as societies face increasingly complex challenges related to climate change, resource scarcity, urbanization, food security, healthcare accessibility, industrial efficiency, and digital transformation. Emerging technologies, including artificial intelligence (AI), machine learning (ML), Internet of Things (IoT), big data analytics, edge computing, digital twins, and cybersecurity solutions, have significant potential to address these challenges by enabling data-driven decision making, intelligent automation, and optimized resource utilization [1,2,3,4].

This Special Issue, entitled “Emerging Technologies and Intelligent Systems for Sustainable Development”, was established to provide a multidisciplinary forum for researchers and practitioners working on innovative technologies that contribute to sustainable and resilient societies. The published contributions illustrate how intelligent systems can support sustainability objectives across critical sectors, including agriculture, industry, infrastructure, environmental monitoring, urban development, cybersecurity, and digital transformation.

This Special Issue includes six research articles, one review article, and one systematic review, covering a broad spectrum of emerging technologies and intelligent systems.

2. Overview of Published Contributions

The contributions published in this Special Issue demonstrate the growing convergence between intelligent technologies and sustainability-oriented applications.

2.1. Sustainable AI and Green Computing

A significant challenge facing modern AI systems is their increasing computational and energy requirements. Addressing this issue, ref. [5] proposed a sustainability-first framework for anomaly detection in Industrial Control Systems (ICSs). Their work combines cybersecurity protection with energy-efficient AI approaches, highlighting how green computing principles can reduce the environmental footprint of intelligent security systems while still maintaining high detection performance.

Similarly, ref. [6] explored TinyML solutions for sustainable edge intelligence under severe resource constraints. Their research demonstrates how lightweight machine learning models can support intelligent decision making on low-power devices, enabling practical deployment in sustainability-oriented applications while reducing both computational costs and energy consumption.

2.2. Smart Agriculture and Environmental Monitoring

Agriculture remains a critical domain for sustainable development. Ref. [7] designed and validated a solar-powered LoRa weather station capable of supporting environmental monitoring and agricultural decision making. Their low-cost and energy-autonomous solution demonstrates how IoT technologies can improve precision agriculture and facilitate data-driven farming practices.

In a complementary contribution, ref. [8] proposed interpretable deep learning models enhanced with attention mechanisms for detecting date palm diseases and pests. Beyond achieving high classification performance, the study critically addresses model interpretability, which is one of the key challenges of AI adoption in agriculture. The integration of explainable mechanisms allows agricultural stakeholders to better understand AI-generated predictions and supports more reliable decision making.

2.3. Digital Transformation and Sustainable Enterprises

Digital transformation has become a major driver of competitiveness and sustainability in modern economies. Ref. [9] investigated the digital maturity of manufacturing SMEs in Luxembourg, identifying both leaders and laggards across several dimensions of digital readiness. Their findings provide valuable insights into how digital capabilities contribute to organizational resilience, innovation, and long-term sustainable growth.

Sustainability reporting also plays a central role in responsible corporate governance. Using machine learning meta-regression techniques, ref. [10] examined the factors influencing sustainability reporting practices across different sectors. The study highlights the growing role of AI-driven analytics in evaluating and improving sustainability performance assessment.

2.4. Intelligent Infrastructure and Smart Cities

Urban sustainability increasingly relies on intelligent infrastructure systems. In a comprehensive review, ref. [11] analyzed the integration of AI into smart infrastructure management for sustainable urban planning. The review identifies three major application areas: predictive maintenance and energy optimization, intelligent transportation systems, and citizen participation frameworks. The findings emphasize the transformative role of AI in building resilient and sustainable cities.

2.5. Digital Twins and Industrial Sustainability

Digital twins are emerging as a powerful technology for improving industrial sustainability [12] conducted a systematic review of AI-driven digital twins in mining operations. Their work demonstrates how the integration of AI with real-time virtual representations can enhance operational efficiency, worker safety, environmental monitoring, and predictive maintenance in mining environments.

2.6. Intelligent Recognition Systems

This Special Issue also includes research on advanced intelligent recognition technologies. Ref. [13] proposed a robust face recognition framework combining MTCNN and Enhanced FaceNet with adaptive feature fusion techniques. Although primarily focused on computer vision performance, such intelligent recognition systems contribute to secure digital services and smart environments, which are essential components of sustainable digital transformation.

3. Emerging Trends and Research Opportunities

Collectively, the papers reveal several important research trends.

First, sustainability is increasingly becoming an explicit design objective rather than merely a secondary consideration. This is evident in contributions related to Green AI, TinyML, energy-efficient computing, and renewable-powered sensing infrastructures.

Second, explainability and trustworthiness emerge as critical requirements for intelligent systems. The growing adoption of AI in agriculture, healthcare, infrastructure, and industrial applications requires transparent and interpretable models capable of supporting human-centered decision making.

Third, the convergence of AI with IoT, digital twins, edge computing, and cybersecurity technologies is creating integrated ecosystems capable of addressing complex sustainability challenges through real-time monitoring, intelligent automation, and predictive analytics.

Finally, digital transformation remains a key enabler of sustainable development. Organizations, industries, and public institutions increasingly rely on data-driven technologies to improve efficiency, resilience, and competitiveness while supporting environmental and social objectives.

Future research is expected to further explore generative AI, large language models (LLMs), federated learning, quantum computing, autonomous systems, and Green AI approaches. However, important challenges remain regarding scalability, interoperability, energy consumption, privacy preservation, fairness, and regulatory compliance [14,15,16].

4. Conclusions

The articles published in this Special Issue demonstrate the significant contribution of emerging technologies and intelligent systems to sustainable development. From green computing and TinyML to smart agriculture, digital transformation, intelligent infrastructure, cybersecurity, and AI-driven digital twins, these collected works illustrate the range of innovative solutions currently being developed to address global sustainability challenges.

As Guest Editors, we sincerely thank all authors for their valuable contributions and the reviewers for their time, expertise, and constructive feedback. Their dedication has ensured the scientific quality and impact of this Special Issue. We also express our gratitude to the editorial team of Technologies for their continuous support throughout the publication process.

We hope that the research presented in this Special Issue will stimulate further interdisciplinary collaborations and inspire new scientific advances that contribute to a more sustainable, resilient, and intelligent future.