引用本文:莫 华,张天柱.生命周期清单分析的数据质量评价[J].环境科学研究,2003,16(5):55-58.
MO Hua,ZHANG Tian-zhu.Data Quality Assessment of Life Cycle Inventory Analysis[J].Reserrch of Environmental Science,2003,16(5):55-58.]
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生命周期清单分析的数据质量评价
莫 华,张天柱
作者单位
莫 华 清华大学,环境科学与工程系,北京,100084 
张天柱 清华大学,环境科学与工程系,北京,100084 
摘要:
生命周期清单分析(LCI)数据质量的分析方法可概括为两类:采用诸如地理代表性、数据年代或数据获取方式等一系列指标来表示;根据不确定性来综合反映LCI质量.笔者在分析了这两类方法各自所存在缺陷的基础上,提出了将这两者相结合的评价方法:采用5个独立的反映数据质量的指标,根据系统各单元各数据属性对各指标从1~5进行打分,形成数据的质量指标向量元素.根据数据质量向量元素的算术平均在总指标范围中所占的百分数将质量指标向量转化为对应的综合数据质量指标(DQI),继而根据DQI可得出每个数据的随机分布,以便进行清单结果的不确定性随机模拟.最后将方法应用于钢铁生产生命周期清单数据中.
关键词:  生命周期评价  数据质量  清单分析  不确定性分析  
DOI:
分类号:
基金项目:国家自然科学基金资助项目(50278043);清华大学基础研究基金资助项目(JC2000005)
Data Quality Assessment of Life Cycle Inventory Analysis
MO Hua,ZHANG Tian-zhu
Abstract:
The approaches proposed for assessing the life cycle inventory analysis(LCI)data quality during a few last years can be classified into two main categories.The first uses a data quality indicator(DQI) such as geographic representation,age of data or data acquisition method.The second represents the overall LCI quality in terms of uncertainty.Both approaches have critical drawbacks. A method is presented that enables combining the data quality indicator with data uncertainty.Five independent data quality indicators are suggested as necessary and sufficient to describe attributes of data quality that influence the reliability of the result.The attributes are coded on a similar scale of 1~5 in order to create a vector element of quality index.The measure of arithmetic average of the data quality vector components as a percentage of the total quality range attainable is equivalent measure for the aggregate DQI that represents the total quality associated with the data element.Once the input DQIs are determined for a given LCI models,each input data element can be transformed to a random variable based on a representative probability distribution and used in stochastic LCA modeling.The method is used to the production of steel to demonstrate the application feassible.
Key words:  life cycle assessment  data quality  inventory analysis  uncertainty analysis