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Article

Digital Twin-Driven Low-Carbon Service Design and Modularization in Central Air Conditioning Ecosystems: A Multi-Criteria and Co-Intelligence Approach

by
Yong Cao
1,2,* and
Xinguo Ming
1
1
Department of Industrial Engineering and Management, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
2
GREE Electric Appliances, Inc. of Zhuhai, Zhuhai 519070, China
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(21), 9877; https://doi.org/10.3390/su17219877
Submission received: 1 September 2025 / Revised: 15 October 2025 / Accepted: 20 October 2025 / Published: 5 November 2025

Abstract

The urgent global mandate for carbon neutrality necessitates a shift from traditional product-centric models towards Digital Twin (DT)-driven low-carbon service solutions, particularly in Central Air Conditioning (CAC) systems. This paper proposes a novel DT-driven framework for systematic low-carbon service design and modularization in CAC ecosystems. The framework first facilitates a comprehensive demand analysis, informed by a three-dimensional Energy Scenario Intelligence model and quantified using robust multi-criteria methods. The framework then introduces a novel methodology for the quantitative analysis of co-intelligence relationships, which provides the foundation for an advanced service module generation and optimization approach that leverages an improved Girvan Newman algorithm and Interval Type-2 Fuzzy TOPSIS to handle high-level uncertainties. A key contribution is the explicit elucidation of DT’s pivotal role in enabling predictive and systemic low-carbon capabilities. The framework’s effectiveness was verified in an intelligent office building, achieving a 74.29% integrated energy saving rate and an annual carbon reduction of 618.5 tCO2. The findings offer valuable theoretical insights and a practical methodology for designing and implementing sustainable CAC service ecosystems.
Keywords: low-carbon service design; digital twin; central air conditioning ecosystems; co-intelligence approach low-carbon service design; digital twin; central air conditioning ecosystems; co-intelligence approach

Share and Cite

MDPI and ACS Style

Cao, Y.; Ming, X. Digital Twin-Driven Low-Carbon Service Design and Modularization in Central Air Conditioning Ecosystems: A Multi-Criteria and Co-Intelligence Approach. Sustainability 2025, 17, 9877. https://doi.org/10.3390/su17219877

AMA Style

Cao Y, Ming X. Digital Twin-Driven Low-Carbon Service Design and Modularization in Central Air Conditioning Ecosystems: A Multi-Criteria and Co-Intelligence Approach. Sustainability. 2025; 17(21):9877. https://doi.org/10.3390/su17219877

Chicago/Turabian Style

Cao, Yong, and Xinguo Ming. 2025. "Digital Twin-Driven Low-Carbon Service Design and Modularization in Central Air Conditioning Ecosystems: A Multi-Criteria and Co-Intelligence Approach" Sustainability 17, no. 21: 9877. https://doi.org/10.3390/su17219877

APA Style

Cao, Y., & Ming, X. (2025). Digital Twin-Driven Low-Carbon Service Design and Modularization in Central Air Conditioning Ecosystems: A Multi-Criteria and Co-Intelligence Approach. Sustainability, 17(21), 9877. https://doi.org/10.3390/su17219877

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