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Sustainability 2017, 9(9), 1522; doi:10.3390/su9091522

Uncertainty Analysis of a GHG Emission Model Output Using the Block Bootstrap and Monte Carlo Simulation

1
Department of Environmental and Safety Engineering, Eco-Product Research Institute, Ajou University, 206 Worldcup-ro, Yeongtong-gu, Suwon 16499, Korea
2
H.I.Pathway Co., Ltd., 10F #1006, ACE High-End Tower 10th, 30, Gasan Digital 1-ro, Geumcheon-gu, Seoul 08591, Korea
*
Author to whom correspondence should be addressed.
Received: 27 July 2017 / Revised: 18 August 2017 / Accepted: 23 August 2017 / Published: 26 August 2017
View Full-Text   |   Download PDF [929 KB, uploaded 26 August 2017]   |  

Abstract

Uncertainty analysis of greenhouse gas (GHG) emissions is becoming increasingly necessary in order to obtain a more accurate estimation of their quantities. The Monte Carlo simulation (MCS) and non-parametric block bootstrap (BB) methods were tested to estimate the uncertainty of GHG emissions from the consumption of feedstuffs and energy by dairy cows. In addition, the contribution to variance (CTV) approach was used to identify significant input variables for the uncertainty analysis. The results demonstrated that the application of the non-parametric BB method to the uncertainty analysis, provides a narrower confidence interval (CI) width, with a smaller percentage uncertainty (U) value of the GHG emission model compared to the MCS method. The CTV approach can reduce the number of input variables needed to collect the expanded number of data points. Future studies can expand on these results by treating the emission factors (EFs) as random variables. View Full-Text
Keywords: uncertainty analysis; GHG emission; contribution to variance; error propagation; Monte Carlo simulation; block bootstrap; dairy sector uncertainty analysis; GHG emission; contribution to variance; error propagation; Monte Carlo simulation; block bootstrap; dairy sector
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MDPI and ACS Style

LEE, M.H.; LEE, J.S.; LEE, J.Y.; KIM, Y.H.; PARK, Y.S.; LEE, K.M. Uncertainty Analysis of a GHG Emission Model Output Using the Block Bootstrap and Monte Carlo Simulation. Sustainability 2017, 9, 1522.

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