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Uncertainty Analysis of Greenhouse Gas (GHG) Emissions Simulated by the Parametric Monte Carlo Simulation and Nonparametric Bootstrap Method

Author

Listed:
  • Kun Mo Lee

    (Department of Environmental and Safety Engineering, Ajou University, 206 Worldcup-ro, Yeongtong-gu, Suwon 16499, Korea)

  • Min Hyeok Lee

    (Environmental Regulation Compliance Office, Korea Institute of Industrial Technology, Hanshin Intervalley 24 East B/D 18F 322, Teheran-ro, Gangnam-gu, Seoul 06211, Korea)

  • Jong Seok Lee

    (Division of Policy Research, Green Technology Center, 173, Toegye-re, Jung-gu, Seoul 04554, Korea)

  • Joo Young Lee

    (Office of Carbon Upcycling R&D strategy, Environment & Sustainable Resources Research Center, Korea Research Institute of Chemical Technology, 141 Gajeong-ro, Yuseong-gu, Daejeon 34114, Korea)

Abstract

Uncertainty of greenhouse gas (GHG) emissions was analyzed using the parametric Monte Carlo simulation (MCS) method and the non-parametric bootstrap method. There was a certain number of observations required of a dataset before GHG emissions reached an asymptotic value. Treating a coefficient (i.e., GHG emission factor) as a random variable did not alter the mean; however, it yielded higher uncertainty of GHG emissions compared to the case when treating a coefficient constant. The non-parametric bootstrap method reduces the variance of GHG. A mathematical model for estimating GHG emissions should treat the GHG emission factor as a random variable. When the estimated probability density function (PDF) of the original dataset is incorrect, the nonparametric bootstrap method, not the parametric MCS method, should be the method of choice for the uncertainty analysis of GHG emissions.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:18:p:4965-:d:417310
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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. Chang, Ching-Chih & Chung, Chia-Ling, 2018. "Greenhouse gas mitigation policies in Taiwan's road transportation sectors," Energy Policy, Elsevier, vol. 123(C), pages 299-307.
    3. 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.
    4. Marie Laure Delignette-Muller & Christophe Dutang, 2015. "fitdistrplus : An R Package for Fitting Distributions," Post-Print hal-01616147, HAL.
    5. Delignette-Muller, Marie Laure & Dutang, Christophe, 2015. "fitdistrplus: An R Package for Fitting Distributions," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 64(i04).
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    Cited by:

    1. Erica Hargety Kimei & Devotha G. Nyambo & Neema Mduma & Shubi Kaijage, 2024. "Review of Sources of Uncertainty and Techniques Used in Uncertainty Quantification and Sensitivity Analysis to Estimate Greenhouse Gas Emissions from Ruminants," Sustainability, MDPI, vol. 16(5), pages 1-15, March.
    2. Michel Noussan & Matteo Jarre, 2021. "Assessing Commuting Energy and Emissions Savings through Remote Working and Carpooling: Lessons from an Italian Region," Energies, MDPI, vol. 14(21), pages 1-19, November.
    3. Jian Zhang & Jingyang Liu & Li Dong & Qi Qiao, 2022. "CO 2 Emissions Inventory and Its Uncertainty Analysis of China’s Industrial Parks: A Case Study of the Maanshan Economic and Technological Development Area," IJERPH, MDPI, vol. 19(18), pages 1-14, September.
    4. Georgiana Moiceanu & Mirela Nicoleta Dinca, 2021. "Climate Change-Greenhouse Gas Emissions Analysis and Forecast in Romania," Sustainability, MDPI, vol. 13(21), pages 1-21, November.
    5. 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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