Framework for modelling data uncertainty in life cycle inventories
Framework for modelling data uncertainty in life cycle inventories
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DOI：
10.1007/bf02978728
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发表时间：
2001-01-01
影响因子：
4.8
通讯作者：
de Beaufort, ASH
中科院分区：
文献类型：
--
作者：
Huijbregts, MAJ;Norris, G;de Beaufort, ASH
虽然有不同的技术可用于估计和表达不确定性，并将不确定性传播到最终的模型结果，但在生命周期清单（LCI）中，对数据不确定性进行建模并不是常见的做法。为了澄清和促进在常见LCI实践中使用数据不确定性评估，SETAC“数据可用性和质量”工作组提出了LCI中数据不确定性评估的框架。数据不确定性分为两类：(1)缺乏数据，进一步指定为完全缺乏数据（数据缺口）和缺乏代表性数据，以及(2)数据不准确。填补数据空白可以通过投入产出模型，使用类似产品或产品主要成分的信息，并应用质量守恒定律来完成。使用的数据和需要的数据之间缺乏时间、地理和进一步的技术相关性，可以通过对非代表性数据应用不确定性因素来解释。随机建模是一种很有前途的方法，它可以通过蒙特卡罗模拟来实现。
Modelling data uncertainty is not common practice in life cycle inventories (LCI), although different techniques are available for estimating and expressing uncertainties, and for propagating the uncertainties to the final model results. To clarify and stimulate the use of data uncertainty assessments in common LCI practice, the SETAC working group 'Data Availability and Quality' presents a framework for data uncertainty assessment in LCI Data uncertainty is divided in two categories: (1) lack of data, further specified as complete lack of data (data gaps) and a lack of representative data, and (2) data inaccuracy. Filling data gaps can be done by input-output modelling, using information for similar products or the main ingredients of a product, and applying the law of mass conservation. Lack of temporal, geographical and further technological correlation between the data used and needed may be accounted for by applying uncertainty factors to the non-representative data. Stochastic modelling, which can be performed by Monte Carlo simulation, is a promising technique to deal with data inaccuracy in LCIs.