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A temporally dynamic model for regional carbon impact assessment based on city information modeling

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  • Su, Shu
  • Ju, Jingyi
  • Guo, Qiyue
  • Li, Xiaodong
  • Zhu, Yimin

Abstract

Cities consume a large amount of energy and discharge massive greenhouse gas emissions, which are key areas supporting low-carbon development and combating the global warming crisis. Conducting regional impact assessments has been a trending research topic, but not without its challenges; especially, foreground data acquisition and temporal dynamics consideration. This study integrates City Information Modeling (CIM) and Dynamic Life Cycle Assessment (DLCA) to develop a new regional carbon impact assessment model, including five parts: goal and scope definition, CIM module, foreground elementary flows analysis, dynamic assessment, and interpretation. In the integration model, CIM is used to extract the geometric and semantic data of the physical elements and geospatial information of built environment. Temporal variations in foreground elementary flows, background inventory datasets, characterization factors, and weighting factors were quantified and involved. A university campus was used to verify the CIM-DLCA integration model, and the impact results were visualized and analyzed from temporal and spatial dynamic perspectives. The influences and sensitivity of dynamic factors were quantified and compared. In addition, the uncertainty issues, data acquisition advantages, and CIM-DLCA integration strategies were discussed. Some valuable future research directions were proposed. This study takes an exploratory step and demonstrates that combining CIM with DLCA is both feasible and operable. This provides a theoretical foundation for intelligent assessment and can be used to promote low-carbon city management.

Suggested Citation

  • Su, Shu & Ju, Jingyi & Guo, Qiyue & Li, Xiaodong & Zhu, Yimin, 2023. "A temporally dynamic model for regional carbon impact assessment based on city information modeling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
    • Handle: RePEc:eee:rensus:v:173:y:2023:i:c:s1364032122009571
    DOI: 10.1016/j.rser.2022.113076
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    References listed on IDEAS

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