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

Sustainability

Chun-Youl Baek¹  Kyu-Hyun Park²  Kiyotaka Tahara³ 

[1] Center for Resources Information & Management, Korea Institute of Industrial Technology, Gangnam-gu, Seoul 06211, Korea;Department of Animal Industry Convergence, Kangwon National University, 1 Kangwondaehak-gil, Chuncheon 24341, Korea;National Institute of Advanced Industrial Science and Technology (AIST), 16-1 Onogawa, Tsukuba Ibaraki 305-8569, Japan;

关键词: semi-quantitative DQI approach;    GHG emissions;    parameter uncertainty analysis;    brown rice production;    inventory database for environmental analysis (IDEA);   

DOI  :  10.3390/su10040922

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown