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

Yong Cao () and Xinguo Ming
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Yong Cao: Department of Industrial Engineering and Management, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Xinguo Ming: Department of Industrial Engineering and Management, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

Sustainability, 2025, vol. 17, issue 21, 1-29

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 (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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