Digital Twin and AI-Driven Sustainability Excellence

Dear Colleagues,

The increased deployment of digital infrastructure in industry, such as Internet of Things (IoT), communication networks, and computational resources, has provided great opportunities to perform data-informed diagnosis, analysis, prediction, and optimization. With the help of both real-time data and historical data, artificial intelligence (AI) methods, particularly machine-learning-driven methods, have revolutionized the landscape of sustainability practices. For example, highly nonlinear relationships between process parameters and the final energy consumption behavior of complex engineering systems, such as manufacturing, construction, and building mechanical systems, can be properly modeled, enabling enhanced operational efficiency. Meanwhile, digital twin (DT) technology, which creates a virtual representation of industrial entities or processes with synchronized updates from its physical entities, can provide a great testbed for virtual verification, simulation, and optimization. The convergence of DT technology and AI has emerged as a powerful force driving innovation across various industries. This symbiotic relationship has paved the way for groundbreaking advancements in sustainability practices, revolutionizing how we approach environmental conservation, resource management, and societal well-being. However, DT and AI used for elevated sustainability still face many challenges, like the modeling of DT for sustainability services, the lack of standard practice for high-quality data acquisition, the scalability of DT across heterogeneous and hierarchical systems, and privacy and security issues.

This Special Issue aims to provide a platform for interdisciplinary dialogue, fostering collaborations, and disseminating impactful research that leverages DT and AI technologies to drive sustainability excellence. Both original research articles and reviews are welcome. The Special Issue will focus on, but is not limited to, the following topics:

• The theory and frameworks of DT for sustainability assessment and optimization;
• Machine-learning-assisted life cycle assessment and resource management;
• Benchmark testing design used for DT or AI in sustainability practices;
• Digital-twin-supported sustainable product design;
• Leveraging DT, AI, and IoT to improve system efficiency for digital transformation;
• Digital technologies used for real-time monitoring and adaptive control engineering systems;
• Digital twin and AI used for societal and economic sustainability;
• Applications of DT and AI for elevated sustainability in the fields of manufacturing, construction, building, smart cities, transportation, agriculture, etc.;
• Policy frameworks and governance models for the responsible use of DT and AI in sustainability initiatives.

We look forward to receiving your contributions.

Dr. Sheng Yang
Dr. Julia Zhao
Dr. Dahai Qi
Dr. Jingchao Jiang
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 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.

• digital twin
• artificial intelligence
• sustainability
• machine learning
• lifecycle assessment
• smart manufacturing
• smart cities
• digital transformation