Stochastic analysis of embodied carbon dioxide emissions considering variability of construction sites

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

doi:10.3390/su11154215

Stochastic analysis of embodied carbon dioxide emissions considering variability of construction sites

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

School of Civil, Environmental and Architectural Engineering

Research output: Contribution to journal › Article › peer-review

6 Citations (Scopus)

Abstract

The current method of estimating CO₂ emissions during the construction phase does not consider the variability that can occur in actual work. Therefore, this study aims at probabilistic CO₂ estimation dealing with the statistical characteristics in activity data of building construction work, focused on concrete pouring work and based on field data. The probabilistically estimated CO₂ emissions have some differences from CO₂ emissions measured by current deterministic methods. The results revealed that the minimum difference was 11.4%, and the maximum difference was 132.7%. This study also used Monte Carlo simulations to derive information on a probability model of CO₂ emissions. Results of the analysis revealed that there is a risk of underestimating emissions because the amount of emissions was estimated at a level that exceeds the 95% confidence interval of the simulation results. In addition, the probability that CO₂ emissions using the measured activities data were less than the estimated CO₂ emissions using the bill of quantity was 73.2% in the probability distribution model.

Original language	English
Article number	4215
Journal	Sustainability (Switzerland)
Volume	11
Issue number	15
DOIs	https://doi.org/10.3390/su11154215
Publication status	Published - 2019 Aug 1

Bibliographical note

Funding Information:
Acknowledgments: This research was supported by National Research Foundation of Korea grant number 2016R1A2B3015348 and Architecture & Urban Development Research Program funded by the Ministry of Land, Infrastructure and Transport of the Korean Government grant number 19AUDP-B121595-04.

Funding Information:
Funding: This research was funded by National Research Foundation of Korea grant number 2016R1A2B3015348 and Architecture & Urban Development Research Program funded by the Ministry of Land, Infrastructure and Transport of the Korean Government grant number 19AUDP-B121595-04.

Publisher Copyright:
© 2019 by the authors.

Keywords

CO emissions
Construction phase
Monte Carlo simulation
Stochastic analysis

ASJC Scopus subject areas

Geography, Planning and Development
Renewable Energy, Sustainability and the Environment
Environmental Science (miscellaneous)
Energy Engineering and Power Technology
Management, Monitoring, Policy and Law

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.3390/su11154215

Cite this

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title = "Stochastic analysis of embodied carbon dioxide emissions considering variability of construction sites",

abstract = "The current method of estimating CO2 emissions during the construction phase does not consider the variability that can occur in actual work. Therefore, this study aims at probabilistic CO2 estimation dealing with the statistical characteristics in activity data of building construction work, focused on concrete pouring work and based on field data. The probabilistically estimated CO2 emissions have some differences from CO2 emissions measured by current deterministic methods. The results revealed that the minimum difference was 11.4%, and the maximum difference was 132.7%. This study also used Monte Carlo simulations to derive information on a probability model of CO2 emissions. Results of the analysis revealed that there is a risk of underestimating emissions because the amount of emissions was estimated at a level that exceeds the 95% confidence interval of the simulation results. In addition, the probability that CO2 emissions using the measured activities data were less than the estimated CO2 emissions using the bill of quantity was 73.2% in the probability distribution model.",

keywords = "CO emissions, Construction phase, Monte Carlo simulation, Stochastic analysis",

author = "Dongyoun Lee and Goune Kang and Chulu Nam and Hunhee Cho and Kang, {Kyung In}",

note = "Funding Information: Acknowledgments: This research was supported by National Research Foundation of Korea grant number 2016R1A2B3015348 and Architecture & Urban Development Research Program funded by the Ministry of Land, Infrastructure and Transport of the Korean Government grant number 19AUDP-B121595-04. Funding Information: Funding: This research was funded by National Research Foundation of Korea grant number 2016R1A2B3015348 and Architecture & Urban Development Research Program funded by the Ministry of Land, Infrastructure and Transport of the Korean Government grant number 19AUDP-B121595-04. Publisher Copyright: {\textcopyright} 2019 by the authors.",

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language = "English",

volume = "11",

journal = "Sustainability (Switzerland)",

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AU - Lee, Dongyoun

AU - Kang, Goune

AU - Nam, Chulu

AU - Cho, Hunhee

AU - Kang, Kyung In

N1 - Funding Information: Acknowledgments: This research was supported by National Research Foundation of Korea grant number 2016R1A2B3015348 and Architecture & Urban Development Research Program funded by the Ministry of Land, Infrastructure and Transport of the Korean Government grant number 19AUDP-B121595-04. Funding Information: Funding: This research was funded by National Research Foundation of Korea grant number 2016R1A2B3015348 and Architecture & Urban Development Research Program funded by the Ministry of Land, Infrastructure and Transport of the Korean Government grant number 19AUDP-B121595-04. Publisher Copyright: © 2019 by the authors.

PY - 2019/8/1

Y1 - 2019/8/1

N2 - The current method of estimating CO2 emissions during the construction phase does not consider the variability that can occur in actual work. Therefore, this study aims at probabilistic CO2 estimation dealing with the statistical characteristics in activity data of building construction work, focused on concrete pouring work and based on field data. The probabilistically estimated CO2 emissions have some differences from CO2 emissions measured by current deterministic methods. The results revealed that the minimum difference was 11.4%, and the maximum difference was 132.7%. This study also used Monte Carlo simulations to derive information on a probability model of CO2 emissions. Results of the analysis revealed that there is a risk of underestimating emissions because the amount of emissions was estimated at a level that exceeds the 95% confidence interval of the simulation results. In addition, the probability that CO2 emissions using the measured activities data were less than the estimated CO2 emissions using the bill of quantity was 73.2% in the probability distribution model.

AB - The current method of estimating CO2 emissions during the construction phase does not consider the variability that can occur in actual work. Therefore, this study aims at probabilistic CO2 estimation dealing with the statistical characteristics in activity data of building construction work, focused on concrete pouring work and based on field data. The probabilistically estimated CO2 emissions have some differences from CO2 emissions measured by current deterministic methods. The results revealed that the minimum difference was 11.4%, and the maximum difference was 132.7%. This study also used Monte Carlo simulations to derive information on a probability model of CO2 emissions. Results of the analysis revealed that there is a risk of underestimating emissions because the amount of emissions was estimated at a level that exceeds the 95% confidence interval of the simulation results. In addition, the probability that CO2 emissions using the measured activities data were less than the estimated CO2 emissions using the bill of quantity was 73.2% in the probability distribution model.

KW - CO emissions

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KW - Monte Carlo simulation

KW - Stochastic analysis

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Stochastic analysis of embodied carbon dioxide emissions considering variability of construction sites

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

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