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Sustainability
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Sustainable Circular Economy in Industry 4.0
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Sustainable Circular Economy in Industry 4.0

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Economic and Business Aspects of Sustainability".

Deadline for manuscript submissions: 20 November 2025 | Viewed by 2529

Special Issue Editors

Dr. George Lazaroiu

E-Mail Website
Guest Editor
Department of Economic Sciences, Spiru Haret University, 030045 Bucharest, Romania
Interests: smart city artificial intelligence of things; blockchain-based decentralized metaverse systems; digital twin simulation and modeling technologies; multisensory immersive extended reality algorithms
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Dan-Cristian Dabija

E-Mail Website
Guest Editor
1. Department of Marketing, Faculty of Economics and Business Administration, Babes-Bolyai University, Cluj-Napoca, Romania
2. Academy of Romanian Scientists, Bucharest, Romania
Interests: consumer behavior; sustainable marketing; retail marketing; international marketing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The focus of this Special Issue is on circular economy and Industry 4.0 research that can improve sustainability practices, efficiency, and performance in organizations through resource and technological support optimization in the multisensory immersive extended reality-based digital economy. The scope is to integrate research clarifying how smart product manufacturing, artificial intelligence algorithms, and big data processing and blockchain technologies enable sustained circular supply chain operations and performance, management practices, and product digitalization. The purpose is to show how Artificial Intelligence of Things, cloud computing services, and digital twin simulation and modeling technologies assist sustainable product management decision-making in resource planning and logistics, optimizing economic and environmental performance. This Special Issue aims to supplement, or relate to, the existing literature by integrating research, demonstrating how renewable energy and low-carbon technologies can be deployed in the organizational management and decision support of circular and sustainable smart production and manufacturing processes. This Special Issue will cover original research and systematic review articles on the above topics.

Dr. George Lazaroiu
Prof. Dr. Dan-Cristian Dabija
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.

Keywords

  • circular economy
  • Industry 4.0
  • sustainability practices
  • product and service digitalization
  • renewable energy and low-carbon technologies

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Published Papers (2 papers)

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29 pages, 9409 KiB  
Article
Sustain AI: A Multi-Modal Deep Learning Framework for Carbon Footprint Reduction in Industrial Manufacturing
by Manal Alghieth
Sustainability 2025, 17(9), 4134; https://doi.org/10.3390/su17094134 - 2 May 2025
Viewed by 1043
Abstract
The growing energy demands and increasing environmental concerns in industrial manufacturing necessitate innovative solutions to reduce fuel consumption and lower carbon emissions. This paper presents Sustain AI, a multi-modal deep learning framework that integrates Convolutional Neural Networks (CNNs) for defect detection, Recurrent Neural [...] Read more.
The growing energy demands and increasing environmental concerns in industrial manufacturing necessitate innovative solutions to reduce fuel consumption and lower carbon emissions. This paper presents Sustain AI, a multi-modal deep learning framework that integrates Convolutional Neural Networks (CNNs) for defect detection, Recurrent Neural Networks (RNNs) for predictive energy consumption modeling, and Reinforcement Learning (RL) for dynamic energy optimization to enhance industrial sustainability. The framework employs IoT-based real-time monitoring and AI-driven supply chain optimization to optimize energy use. Experimental results demonstrate that Sustain AI achieves an 18.75% reduction in industrial energy consumption and a 20% decrease in CO2 emissions through AI-driven processes and scheduling optimizations. Additionally, waste heat recovery efficiency improved by 25%, and smart HVAC systems reduced energy waste by 18%. The CNN-based defect detection model enhanced material efficiency by increasing defect identification accuracy by 42.8%, leading to lower material waste and improved production efficiency. The proposed framework also ensures economic feasibility, with a 17.2% reduction in operational costs. Sustain AI is scalable, adaptable, and fully compatible with Industry 4.0 requirements, making it a viable solution for sustainable industrial practices. Future extensions include enhancing adaptive decision-making with deep RL techniques and incorporating blockchain-based traceability for secure and transparent energy management. These findings indicate that AI-powered industrial ecosystems can achieve carbon neutrality and enhanced energy efficiency through intelligent optimization strategies. Full article
(This article belongs to the Special Issue Sustainable Circular Economy in Industry 4.0)
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25 pages, 5480 KiB  
Systematic Review
Integrating Circular Economy Principles in the New Product Development Process: A Systematic Literature Review and Classification of Available Circular Design Tools
by Benedetta Rotondo, Conny Bakker, Ruud Balkenende and Venanzio Arquilla
Sustainability 2025, 17(9), 4155; https://doi.org/10.3390/su17094155 - 4 May 2025
Viewed by 913
Abstract
Nowadays, the circular economy represents a promising strategy for achieving sustainable development through optimising resource efficiency, extending product lifespans, and reducing environmental impacts. Despite the growing interest in circular design practices, companies often face difficulties integrating these principles into their established New Product [...] Read more.
Nowadays, the circular economy represents a promising strategy for achieving sustainable development through optimising resource efficiency, extending product lifespans, and reducing environmental impacts. Despite the growing interest in circular design practices, companies often face difficulties integrating these principles into their established New Product Development (NPD) processes. This is mainly due to the overwhelming number of available design tools and methods, which are fragmented, challenging to navigate, overlap in functionality, and lack standardisation. This study provides a comprehensive mapping, classification, and analysis of 77 existing circular design tools identified through a systematic literature review and supplementary online searches. The tools were systematically categorised according to format, data type, industry sector, circular strategies, innovation focus, aims, and applicability across the NPD stages. The results indicate a predominance of physical, qualitative, and sector-agnostic tools, emphasising circularity integration within the Discover, Define, and Develop phases of the design process. This structured classification facilitates stakeholder navigation of existing resources, highlighting opportunities for more targeted, industry-specific tool development, consumer-oriented approaches, and the importance of considering Industry 4.0 technologies in circular design practice. Future research could address these gaps by developing customised frameworks, validating tool effectiveness through real industrial applications, and promoting deeper integration of circular design tools within NPD practices and business objectives. Full article
(This article belongs to the Special Issue Sustainable Circular Economy in Industry 4.0)
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