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

Lee, Minhyeok; Lee, Jong Seok; Lee, Joo Young; Kim, Yoon Ha; Park, Yoo Sung; Lee, Kun Mo

doi:10.3390/su9091522

Cited by 16 publications

(11 citation statements)

References 17 publications

(35 reference statements)

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“…To compare the uncertainty obtained from different uncertainty analysis methods, the ratio between the half-width of the 95% confidence interval and the mean of the model output, WSF, was used [40,47]. The ratio, termed U, represents the percentage uncertainty of the model output and is defined as U =…”

Section: Comparison Of the Uncertaintymentioning

confidence: 99%

“…Thus, it is a futile attempt to find the parametric distribution of the AWARE CF data where the maximum value is 100. This is because the estimation of the parametric probability distribution may not capture extreme values [47] such as a maximum AWARE CF value of 100. There was no maximum CF value in August; however, in the case of June, 20% (9 years out of 45 years) of the CF data shows a maximum value of 100 (Table S1).…”

Section: Statisticmentioning

confidence: 99%

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Uncertainty Analysis of the Water Scarcity Footprint Based on the AWARE Model Considering Temporal Variations

Lee

Chun

et al. 2018

Water

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The purpose of this paper is to compare the degree of uncertainty of the water scarcity footprint using the Monte Carlo statistical method and block bootstrap method. Using the hydrological data of a water drainage basin in Korea, characterization factors based on the available water remaining (AWARE) model were obtained. The uncertainties of the water scarcity footprint considering temporal variations in paddy rice production in Korea were estimated. The block bootstrap method gave five-times smaller percentage uncertainty values of the model output compared to that of the two different Monte Carlo statistical method scenarios. Incorrect estimation of the probability distribution of the AWARE characterization factor model is what causes the higher uncertainty in the water scarcity footprint value calculated by the Monte Carlo statistical method in this study. This is because AWARE characterization factor values partly follows discrete distribution with extreme value on one side. Therefore, this study suggests that the block bootstrap method is a better choice in analyzing uncertainty compared to the Monte Carlo statistical method when using the AWARE model to quantify the water scarcity footprint.

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Section: Comparison Of the Uncertaintymentioning

confidence: 99%

Section: Statisticmentioning

confidence: 99%

Uncertainty Analysis of the Water Scarcity Footprint Based on the AWARE Model Considering Temporal Variations

Lee

Chun

et al. 2018

Water

Self Cite

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“…The contribution analysis for GHG emission is not a suitable method for finding key issues about the uncertainty of the results [7]. Therefore, it is a reasonable choice to identify significant input variables that require iteration of data collection through the CTV analysis and DQR.…”

Section: Rank Of the Variancementioning

confidence: 99%

“…According to previous studies, the contribution of greenhouse gas (GHG) emissions in the dairy sector is estimated to be 3-5% of the global GHG missions. In Korea, various efforts are being made to reduce GHG emissions in the dairy sector and there is a growing demand for accuracy [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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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.

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“…Previously, there were research efforts focusing on a variety of complexities and uncertainties in energy and environmental systems management [5][6][7][8][9][10][11]. Among them, stochastic programming methods are widely devised and analyzed to provide reliable assistance for decision making.…”

Section: Introductionmentioning

confidence: 99%

Characterization of Renewable Energy Utilization Mode for Air-Environmental Quality Improvement through an Inexact Factorial Optimization Approach

et al. 2019

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Energy-related environmental problems have been hot spot issues in regional energy system sustainable development. Thus, comprehensive planning of energy systems management is important for social and economic development, as well as environmental sustainability. In addition, uncertainties and complexities, as well as their potential interactions pose a great challenge for effective management in energy and environmental system. This study proposes a stochastic factorial energy systems management model to conduct uncertainties and risks in the energy systems, as well as handle their interaction effects among different environmental policies. The developed method can not only tackle uncertainties expressed as probability distributions and even interval values, but also be applied to determine decision alternatives associated with multiple economic penalties if the formulated environmental policy targets are violated. Meanwhile, by introducing the factorial technology, it can analyze a parameter’s impact on the system and their coordination effect. To verify the feasibility and effectiveness of the proposed method, the developed model was applied to a hypothetical case study for energy structure optimization under considering energy supply, SO2 emissions reduction, and environmental quality requirements. Multiple facilities, related environmental pollutants, and energy demand levels were taken into account. Moreover, the key factors of the system and their interaction effect were discovered. The results indicated that the developed method can resolve meritorious uncertainties in decision-making and analysis, generate effective management programming under multi-levels of the proposed energy and environmental systems. The method can be used for supporting the adjustment for allocating fossil fuels and renewable energy resources, analyzing the tradeoff between conflicting economic and environmental objectives and formulating the local policies.

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

Cited by 16 publications

References 17 publications

Uncertainty Analysis of the Water Scarcity Footprint Based on the AWARE Model Considering Temporal Variations

Uncertainty Analysis of the Water Scarcity Footprint Based on the AWARE Model Considering Temporal Variations

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

Characterization of Renewable Energy Utilization Mode for Air-Environmental Quality Improvement through an Inexact Factorial Optimization Approach

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