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

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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)

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.

Suggested Citation

  • Min Hyeok LEE & Jong Seok LEE & Joo Young LEE & Yoon Ha KIM & Yoo Sung PARK & Kun Mo LEE, 2017. "Uncertainty Analysis of a GHG Emission Model Output Using the Block Bootstrap and Monte Carlo Simulation," Sustainability, MDPI, vol. 9(9), pages 1-12, August.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:9:p:1522-:d:109912
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    References listed on IDEAS

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    1. Flysjö, Anna & Henriksson, Maria & Cederberg, Christel & Ledgard, Stewart & Englund, Jan-Eric, 2011. "The impact of various parameters on the carbon footprint of milk production in New Zealand and Sweden," Agricultural Systems, Elsevier, vol. 104(6), pages 459-469, July.
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    Cited by:

    1. Zhengping Liu & Wang Zhang & Hongxian Liu & Guohe Huang & Jiliang Zhen & Xin Qi, 2019. "Characterization of Renewable Energy Utilization Mode for Air-Environmental Quality Improvement through an Inexact Factorial Optimization Approach," Sustainability, MDPI, vol. 11(8), pages 1-19, April.
    2. Yoo-Sung Park & Sung-Mo Yeon & Geun-Young Lee & Kyu-Hyun Park, 2019. "Proposed Consecutive Uncertainty Analysis Procedure of the Greenhouse Gas Emission Model Output for Products," Sustainability, MDPI, vol. 11(9), pages 1-20, May.
    3. Kun Mo LEE & Min Hyeok LEE, 2021. "Uncertainty of the Electricity Emission Factor Incorporating the Uncertainty of the Fuel Emission Factors," Energies, MDPI, vol. 14(18), pages 1-14, September.
    4. Kun Mo Lee & Min Hyeok Lee & Jong Seok Lee & Joo Young Lee, 2020. "Uncertainty Analysis of Greenhouse Gas (GHG) Emissions Simulated by the Parametric Monte Carlo Simulation and Nonparametric Bootstrap Method," Energies, MDPI, vol. 13(18), pages 1-15, September.

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