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

Min Hyeok Lee, Jong Seok Lee, Joo Young Lee, Yoon Ha Kim, Yoo Sung Park and Kun Mo Lee
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Min Hyeok Lee: Department of Environmental and Safety Engineering, Eco-Product Research Institute, Ajou University, 206 Worldcup-ro, Yeongtong-gu, Suwon 16499, Korea
Jong Seok Lee: Department of Environmental and Safety Engineering, Eco-Product Research Institute, Ajou University, 206 Worldcup-ro, Yeongtong-gu, Suwon 16499, Korea
Joo Young Lee: Department of Environmental and Safety Engineering, Eco-Product Research Institute, Ajou University, 206 Worldcup-ro, Yeongtong-gu, Suwon 16499, Korea
Yoon Ha Kim: Department of Environmental and Safety Engineering, Eco-Product Research Institute, Ajou University, 206 Worldcup-ro, Yeongtong-gu, Suwon 16499, Korea
Yoo Sung Park: H.I.Pathway Co., Ltd., 10F #1006, ACE High-End Tower 10th, 30, Gasan Digital 1-ro, Geumcheon-gu, Seoul 08591, Korea
Kun Mo Lee: Department of Environmental and Safety Engineering, Eco-Product Research Institute, Ajou University, 206 Worldcup-ro, Yeongtong-gu, Suwon 16499, Korea

Sustainability, 2017, vol. 9, issue 9, 1-12

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.

Keywords: uncertainty analysis; GHG emission; contribution to variance; error propagation; Monte Carlo simulation; block bootstrap; dairy sector (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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