Digital Twin and AI-Driven Carbon Management in Sustainable Construction and Urban Design

Authors

  • Xiaoxuan Wang

    Architectural Engineering School, Xiamen Nanyang University, Xiamen 361102, China

DOI:

https://doi.org/10.30564/jees.v8i1.12950
Received: 12 December 2025 | Revised: 6 January 2026 | Accepted: 8 January 2026 | Published Online: 27 January 2026

Abstract

The built environment and construction industry are another significant source of carbon emissions to the environment in the world, through the production of materials, construction activities, and the energy consumed during the lifecycle of an asset. These emissions are difficult to manage effectively because the data are not consolidated, the operating conditions are dynamic, and the traditional assessment tools are not able to support continuous and data-driven decisions. The new technologies, especially Digital Twins (DT) and artificial intelligence (AI), have some potential solutions, which will combine the lifecycle data and provide a predictive, adaptive carbon management in the building and urban systems. The given paper is a systematic review of the integration of DT and AI (DT–AI) into carbon management in operational construction and urban planning. Structured database searches and filters on the basis of DT-facilitated carbon monitoring, prediction, optimization, and operational control were used to identify peer-reviewed studies that were published within the last few years and filtered through to gather them. Three main functions of DT–AI systems are outlined in the review: predicting carbon emissions on the basis of data-driven models, optimizing low-carbon design and planning with multi-objective approaches, and providing intelligent control of the energy systems. Some of the major issues are data interoperability, model validation, and a lack of evidence of large-scale deployment. This study combines integrated DT–AI models and their contribution to lifecycle carbon management, unlike the previous reviews of either DT or AI alone. The paper ends with a conclusion and recommendations to create scalable, validated DT–AI solutions to accomplish carbon-neutral built environments.

Keywords:

Digital Twin; Artificial Intelligence; Carbon Management; Sustainable Construction; Urban Design

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How to Cite

Wang, X. (2026). Digital Twin and AI-Driven Carbon Management in Sustainable Construction and Urban Design. Journal of Environmental & Earth Sciences, 8(1), 177–201. https://doi.org/10.30564/jees.v8i1.12950

Issue

Vol. 8 , Iss. 1 (January 2026)

Article Type

Review

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Copyright © 2026 Xiaoxuan Wang

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This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.

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