Parameter Uncertainty Analysis of the Life Cycle Inventory Database: Application to Greenhouse Gas Emissions from Brown Rice Production in IDEA

Abstract

Abstract:The objective of this paper is to develop a simple method for analyzing the parameter uncertainty of the Japanese life cycle inventory database (LCI DB), termed the inventory database for environmental analysis (IDEA). The IDEA has a weakness of poor data quality because over 60% of datasets in IDEA were compiled based on secondary data (non-site-specific data sources). Three different approaches were used to estimate the uncertainty of the brown rice production dataset, including the stochastic modeling approach, the semi-quantitative DQI (Data Quality Indicator) approach, and a modification of the semi-quantitative DQI approach (including two alternative approaches for modification). The stochastic modeling approach provided the best estimate of the true mean of the sample space and its results were used as the reference for comparison with the other approaches. A simple method for the parameter uncertainty analysis of the agriculture industry DB was proposed by modifying the beta distribution parameters (endpoint range, shape parameter) in the semi-quantitative DQI approach using the results from the stochastic modeling approach. The effect of changing the beta distribution parameters in the semi-quantitative DQI approach indicated that the proposed method is an efficient method for the quantitative parameter uncertainty analysis of the brown rice production dataset in the IDEA.

Managing the Uncertainty and Accuracy of Life Cycle Assessment Results for the Process of Beverage Bottle Moulding

“…In practice, data uncertainty assessment procedures come down to one type of analysis, e.g., the semi-quantitative DQI approach [ 20 ], stochastic modelling with the use of the MC simulation [ 22 , 48 ], sensitivity analysis [ 10 , 65 ]. Combinations of several methods are not common, due to the significant input of labour and time required and a great number of data than need to be entered and collected [ 62 , 72 ].…”

“…In the simplest terms, it can be assumed that individual DQIs are equivalent. In many works, however, there are methods for determining the weight of individual indicators [ 12 , 14 , 20 ]. In this work, we adopted weights according to Maurice et al [ 12 ]: 0.25 in relation to the geographical and technological scope and 0.167 in relation to the other three DQIs.…”

“…In the MC simulation the results obtained on the basis of LCA were used. The log-normal distribution, which is commonly used in analyzing data uncertainty, was used to estimate the value of the impact category in the Monte Carlo method [ 20 , 62 ]. Distribution parameters, i.e., population mean µ and standard deviation σ were adopted respectively: µ as the mean value of the impact category obtained as a result of data uncertainty analysis, σ as the standard deviation of the impact category obtained as a result of data uncertainty analysis.…”

“…Usually, the method is subject to a qualitative approach [ 18 , 19 ]. The semi-quantitative approach adopts a numerical rating system, and the assigned quality rating is processed to obtain a single data quality score based on a probability distribution [ 20 , 21 ].…”

“…The existing literature on the theoretical foundations of the mathematical computational structure for performing uncertainty analysis in LCA is still hardly practiced [ 32 ]. However, there are various techniques for uncertainty analysis, such as: the theory of possibilities, e.g., [ 33 ], fuzzy theory, e.g., [ 34 , 35 ], data quality indicators, e.g., [ 9 , 14 , 15 , 16 , 17 ] and expert opinions, e.g., [ 36 , 37 ] or a combination of two or more techniques, e.g., [ 20 , 30 , 31 , 38 ]. Despite the wide range of approaches available, the analysis of uncertainty in LCA in the scientific and business process of bottle production remains largely unexplored.…”