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.
Highlights
Life cycle assessment (LCA) has been used widely for assessing the environmental impact of a product
Based on the comparison between the two approaches, we proposed a simple method for the parameter uncertainty analysis of the agricultural dataset in the inventory database for environmental analysis (IDEA) by modifying the semi-quantitative data quality indicator (DQI) approach
Key parameters derived from the result of the contribution analysis were determined by the percent contribution of the parameter to the total GHG emissions of the brown rice production
Summary
Life cycle assessment (LCA) has been used widely for assessing the environmental impact of a product. Oftentimes, an LCA result is reported as a single value of the environmental impact for a given functional unit [1,2]. Concepts and types of uncertainties had been defined previously in risk assessment and policy analysis fields. Types of uncertainties were classified into data, model, and completeness in the risk assessment field [3]. In the policy analysis area, uncertainty was classified into scenario, parameter, and model uncertainty [4]. Of the three types of uncertainties (parameter, scenario, and model) in LCA, parameter uncertainty a more significant effect on the results, and is the most frequently addressed and commonly recognized form of uncertainty compared to that of the model or choices [5,6]. A parameter uncertainty analysis is chosen for the qualification of uncertainty from the datasets of the life cycle inventory (LCI) database (DB) because parameter uncertainty is a more significant issue than others in the LCI DB
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