文章基本信息

标题：Uncertainty Analysis of a GHG Emission Model Output Using the Block Bootstrap and Monte Carlo Simulation
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作者：Lee, Min Hyeok ; Lee, Jong Seok ; Lee, Joo Young 等
期刊名称：Sustainability
印刷版ISSN：2071-1050
出版年度：2017
卷号：9
期号：9
页码：1-12
出版社：MDPI, Open Access Journal
摘要：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.
关键词：uncertainty analysis; GHG emission; contribution to variance; error propagation; Monte Carlo simulation; block bootstrap; dairy sector