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Article

Uncertainty Analysis of Greenhouse Gas (GHG) Emissions Simulated by the Parametric Monte Carlo Simulation and Nonparametric Bootstrap Method

1
Department of Environmental and Safety Engineering, Ajou University, 206 Worldcup-ro, Yeongtong-gu, Suwon 16499, Korea
2
Environmental Regulation Compliance Office, Korea Institute of Industrial Technology, Hanshin Intervalley 24 East B/D 18F 322, Teheran-ro, Gangnam-gu, Seoul 06211, Korea
3
Division of Policy Research, Green Technology Center, 173, Toegye-re, Jung-gu, Seoul 04554, Korea
4
Office of Carbon Upcycling R&D strategy, Environment & Sustainable Resources Research Center, Korea Research Institute of Chemical Technology, 141 Gajeong-ro, Yuseong-gu, Daejeon 34114, Korea
*
Author to whom correspondence should be addressed.
Energies 2020, 13(18), 4965; https://doi.org/10.3390/en13184965
Received: 30 July 2020 / Revised: 7 September 2020 / Accepted: 18 September 2020 / Published: 22 September 2020
(This article belongs to the Special Issue Life Cycle Assessment of Environmental System)
Uncertainty of greenhouse gas (GHG) emissions was analyzed using the parametric Monte Carlo simulation (MCS) method and the non-parametric bootstrap method. There was a certain number of observations required of a dataset before GHG emissions reached an asymptotic value. Treating a coefficient (i.e., GHG emission factor) as a random variable did not alter the mean; however, it yielded higher uncertainty of GHG emissions compared to the case when treating a coefficient constant. The non-parametric bootstrap method reduces the variance of GHG. A mathematical model for estimating GHG emissions should treat the GHG emission factor as a random variable. When the estimated probability density function (PDF) of the original dataset is incorrect, the nonparametric bootstrap method, not the parametric MCS method, should be the method of choice for the uncertainty analysis of GHG emissions. View Full-Text
Keywords: uncertainty analysis; GHG emission factor; parametric Monte Carlo simulation; nonparametric bootstrap; R program uncertainty analysis; GHG emission factor; parametric Monte Carlo simulation; nonparametric bootstrap; R program
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MDPI and ACS Style

Lee, K.M.; Lee, M.H.; Lee, J.S.; Lee, J.Y. Uncertainty Analysis of Greenhouse Gas (GHG) Emissions Simulated by the Parametric Monte Carlo Simulation and Nonparametric Bootstrap Method. Energies 2020, 13, 4965. https://doi.org/10.3390/en13184965

AMA Style

Lee KM, Lee MH, Lee JS, Lee JY. Uncertainty Analysis of Greenhouse Gas (GHG) Emissions Simulated by the Parametric Monte Carlo Simulation and Nonparametric Bootstrap Method. Energies. 2020; 13(18):4965. https://doi.org/10.3390/en13184965

Chicago/Turabian Style

Lee, Kun M., Min H. Lee, Jong S. Lee, and Joo Y. Lee. 2020. "Uncertainty Analysis of Greenhouse Gas (GHG) Emissions Simulated by the Parametric Monte Carlo Simulation and Nonparametric Bootstrap Method" Energies 13, no. 18: 4965. https://doi.org/10.3390/en13184965

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