Carbon emissions accounting and estimation of carbon reduction potential in the operation phase of residential areas based on digital twin

Anshan Zhang, Feiliang Wang, Huanyu Li, Bo Pang and Jian Yang

Applied Energy, 2024, vol. 376, issue PB, No S0306261924005385

Abstract: The carbon emissions of the construction industry have raised a lot of concern as it contributes about one third of global carbon emissions. Among them, the operation of residential areas is an important source of carbon emissions. However, due to the complexity of carbon emission sources, it is difficult to account carbon emissions in the operation phase of residential areas. The assessment of the carbon reduction potential for the transition to low carbon residential areas is also inadequate. In this study, carbon emission sources and carbon reduction methods in residential areas have been sorted out, while the carbon emission accounting methods and the estimation methods for carbon reduction potential have been proposed. Moreover, this article first proposes a theoretical framework based on digital twin (DT) for carbon emissions accounting and estimation of carbon reduction potential in the operation phase of residential areas. In this framework, building information modelling (BIM) and remote sensing (RS) image processing technology are integrated, and specific application processes are proposed to improve the intelligence level of carbon emission accounting and carbon reduction potential assessment. Additionally, the feasibility of this method is verified using a campus residential area as a case study. The case study indicates that the method proposed in this article has great potential for application. It also shows that the carbon sinks and new energy sources are the effective method to achieve low or even zero carbon residential areas. In this case, >60% of carbon emissions can be offset through the comprehensive use of various carbon reduction measures.

Keywords: Low-carbon residential area; Carbon emissions; Building information modelling; Digital twin; Building photovoltaics (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (2)

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DOI: 10.1016/j.apenergy.2024.123155

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