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Proposed Consecutive Uncertainty Analysis Procedure of the Greenhouse Gas Emission Model Output for Products

Author

Listed:
  • Yoo-Sung Park

    (H.I.Pathway CO., LTD, Seoul 08591, Korea)

  • Sung-Mo Yeon

    (H.I.Pathway CO., LTD, Seoul 08591, Korea)

  • Geun-Young Lee

    (H.I.Pathway CO., LTD, Seoul 08591, Korea)

  • Kyu-Hyun Park

    (Department of Animal Resource Science, Kangwon National University, Chuncheon 24341, Korea)

Abstract

The study objective was to develop a method for an uncertainty analysis of the greenhouse gas (GHG) emission model output based on consecutive use of an analytical and a stochastic approach. The contribution to variance (CTV) analysis followed by the data quality analysis are the main feature of the procedure. When a set of data points of a certain input variable has a high CTV, but its data quality indicator (DQI) is good, then there is no need to iterate data collection of this input variable. This is because the DQI of this data set indicates that there is no room for the reduction of its variance, and the high variance must be its inherent attribute. Through the CTV analysis and data quality analysis, the identified input variables were selected as the input variables for the data from the iteration of data collection. The statistical parameters of the GHG emissions of the model were calculated using the Monte Carlo simulation (MCS). In the case study of a cattle dairy farm, the relative reduction in the CV value was 47.6%. In this study, a procedure was developed for the selection of the input variables for iteration of data collection to reduce their variance and subsequently reduce the uncertainty in the model output. The dairy cow case study showed that the uncertainty in the model output was decreased by the iteration of data collection, indicating that CTV analysis can be used to identify the input variables, contributing considerably to the uncertainty in the model output.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:9:p:2712-:d:230725
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    References listed on IDEAS

    as
    1. 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.
    2. Eric D. Williams & Christopher L. Weber & Troy R. Hawkins, 2009. "Hybrid Framework for Managing Uncertainty in Life Cycle Inventories," Journal of Industrial Ecology, Yale University, vol. 13(6), pages 928-944, December.
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    Cited by:

    1. 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.
    2. 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.

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