Activity based prediction of on-site CO2 emissions containing uncertainty

Chulu Nam, Dongyoun Lee, Goune Kang, Hunhee Cho, Kyung In Kang

Research output: Contribution to conferencePaperpeer-review

1 Citation (Scopus)

Abstract

Recently, the importance of reducing embodied carbon has become clear. The construction stages, is the stage where the building production takes place, and a large quantity of embodied carbon is emitted. However, because this stage presents a variety of sources of uncertainty at building sites, it is difficult to compute and predict precise CO2 emissions. To solve this problem, existing research has estimated emissions amounts by considering the variability of the main materials' carbon emission factor, as well as the variability of the equipment's activity conditions. However, these approaches are unable to reflect uncertainty at activity level, leading to an underestimation of CO2 emissions. In this research, we perform an analysis by considering the uncertainty of CO2 emissions in the construction stage at activity level. In addition, from the results, we recognize the relevance of considering uncertainty for each activity. Therefore, we present a CO2 emission prediction method using a Monte Carlo simulation and confirm its effectiveness. We believe that the outcome of this research advocates for the necessity of considering the uncertainty in each activity and contributes to the prediction and management of on-site emissions.

Original languageEnglish
Pages1085-1092
Number of pages8
Publication statusPublished - 2017
Event34th International Symposium on Automation and Robotics in Construction, ISARC 2017 - Taipei, Taiwan, Province of China
Duration: 2017 Jun 282017 Jul 1

Other

Other34th International Symposium on Automation and Robotics in Construction, ISARC 2017
Country/TerritoryTaiwan, Province of China
CityTaipei
Period17/6/2817/7/1

Keywords

  • Activity based
  • CO
  • Monte Carlo simulation
  • Uncertainty

ASJC Scopus subject areas

  • Building and Construction
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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