Stochastic carbon emission estimation method for construction operation

    Chang-Yong Yi Affiliation
    ; Han-Seong Gwak Affiliation
    ; Dong-Eun Lee Affiliation


Low carbon construction is an important operation management goal because greenhouse gas (GHG) reduc­tion has become a global concern. Major construction resources that contribute GHG, such as equipment and labour, are being targeted to achieve this goal. The GHG emissions produced by the resources vary with their operating conditions. It is commendable to provide a statistical GHG emission estimation method that models the transitory nature of resource states at micro-scale of construction operations. This paper proposes a computational method called Stochastic Carbon Emission Estimation (SCE2) that measures the variability of GHG emissions. It creates construction operation models consisting of atomic work tasks, utilizes hourly equipment fuel consumption and hourly labourer respiratory rates that change according to their operating conditions classified into five categories, and identifies an optimal resource combi­nation by trading off eco-economic performance metrics such as the amount of GHG emissions, operation completion time, operation completion cost, and productivity. The study is of value to researchers because SCE2 fill in a gap to eco-economic operation modelling and analysis tool which considers operating conditions at micro-scale of construction operation having many stochastic work tasks. This study is also relevance to practitioners because it allows project man­agers to achieve eco-economic goals while honouring predefined constraints associated with time and cost.

First published online: 13 Jul 2016

Keyword : greenhouse gas emission, environmental impact, simulation, construction operation model, eco-economics

How to Cite
Yi, C.-Y., Gwak, H.-S., & Lee, D.-E. (2017). Stochastic carbon emission estimation method for construction operation. Journal of Civil Engineering and Management, 23(1), 137-149.
Published in Issue
Jan 19, 2017
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