Prediction model of CO2 Emission for Residential Buildings in South Korea

Hyunseok Moon, Changtaek Hyun, Taehoon Hong

Research output: Contribution to journalArticlepeer-review

24 Citations (Scopus)


The emission of CO2 is becoming an increasingly important consideration in the construction industry, resulting in a significant amount of research actively being conducted to assess CO2 emission by using the completed designs of construction projects. However, studies on estimating CO2 emission of new construction projects remain few and far between. Thus, this study aimed at developing a model for estimating CO2 emission based on materials required in the construction and maintenance phases of residential buildings prior to design completion. Toward this end, the study used a case-based reasoning approach to predict the quantities of materials required in the construction and maintenance phases. Furthermore, through the life-cycle assessment approach, this study established a database of CO2 emission to calculate CO2 emission according to the construction materials. The study then proposed a method that could estimate CO2 emission by using the predicted quantities of materials and the established database. To validate the developed model, the study performed five case studies. Results showed that the error ratios of the prediction were -4.26, -5.06, 7.37, -4.32, and -2.94%, respectively, in each of the cases, and CO2 emissions of materials for residential buildings could be estimated prior to design completion. It is expected that the developed model will be a useful tool in the decision-making process on design alternatives that consider CO2 emission and the type of construction materials.

Original languageEnglish
Article number04014001
JournalJournal of Management in Engineering
Issue number3
Publication statusPublished - 2014 May 1

All Science Journal Classification (ASJC) codes

  • Building and Construction
  • Industrial relations
  • Engineering(all)
  • Strategy and Management
  • Management Science and Operations Research


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