Stochastic and epistemic uncertainty propagation in LCA
Stochastic and epistemic uncertainty propagation in LCA
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DOI：
10.1007/s11367-013-0572-6
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发表时间：
2013-08-01
影响因子：
4.8
通讯作者：
Christensen, Thomas H.
中科院分区：
文献类型：
--
作者：
Clavreul, Julie;Guyonnet, Dominique;Christensen, Thomas H.
在进行不确定性传播时，大多数LCA从业者选择用单一的概率分布来表示不确定性，并使用随机方法来传播它们。然而，当面对稀缺的信息或专家判断（认知不确定性）时，单一概率分布的选择往往显得任意。在过去的几十年里，可能性理论已经发展到解决这个问题。本研究的目的是提出一种方法，结合概率和可能性理论，以一致的方式表示随机和认知的不确定性，并将其应用于LCA。一个案例研究是用来显示所提出的方法进行的不确定性传播，并比较它使用概率和可能性theors.Basic知识的概率论进行传播首先回顾，其次是一个详细的描述，认知不确定性表示使用模糊区间。所使用的传播方法是概率分布的蒙特卡罗分析和模糊区间的α-切割的优化。该方法独立随机集（Independent Random Set，IRS）将随机抽样的过程推广到概率分布和模糊区间，从而使同时使用这两种表示成为可能。结果突出了概率表示和可能性表示之间的根本区别：虽然蒙特卡罗分析产生单个概率分布，IRS方法产生由上分布和下分布限定的概率分布族。这两个界限之间的距离是属于某些参数的信息的不完整性的结果。在真实的情况下，这两个界限之间的距离过大可能会促使决策者增加关于某些关键参数的信息库，以减少不确定性。这样的决定不可能从基于主观的纯粹概率计算中得出。（假设）分布（尽管缺乏信息），因为没有办法区分，在计算结果的可变性，什么来自真正的随机性，什么来自不完全的信息。所提出的方法提供了将焦点放在信息上的优点，而不是先验地决定如何表示它。如果如果信息是丰富的，那么纯统计的表示模式就足够了，但是如果信息是稀缺的，那么它可能更好地由可能性分布来传达。
When performing uncertainty propagation, most LCA practitioners choose to represent uncertainties by single probability distributions and to propagate them using stochastic methods. However, the selection of single probability distributions appears often arbitrary when faced with scarce information or expert judgement (epistemic uncertainty). The possibility theory has been developed over the last decades to address this problem. The objective of this study is to present a methodology that combines probability and possibility theories to represent stochastic and epistemic uncertainties in a consistent manner and apply it to LCA. A case study is used to show the uncertainty propagation performed with the proposed method and compare it to propagation performed using probability and possibility theories alone.Basic knowledge on the probability theory is first recalled, followed by a detailed description of epistemic uncertainty representation using fuzzy intervals. The propagation methods used are the Monte Carlo analysis for probability distribution and an optimisation on alpha-cuts for fuzzy intervals. The proposed method (noted as Independent Random Set, IRS) generalizes the process of random sampling to probability distributions as well as fuzzy intervals, thus making the simultaneous use of both representations possible.The results highlight the fundamental difference between the probabilistic and possibilistic representations: while the Monte Carlo analysis generates a single probability distribution, the IRS method yields a family of probability distributions bounded by an upper and a lower distribution. The distance between these two bounds is the consequence of the incomplete character of information pertaining to certain parameters. In a real situation, an excessive distance between these two bounds might motivate the decision-maker to increase the information base regarding certain critical parameters, in order to reduce the uncertainty. Such a decision could not ensue from a purely probabilistic calculation based on subjective (postulated) distributions (despite lack of information), because there is no way of distinguishing, in the variability of the calculated result, what comes from true randomness and what comes from incomplete information.The method presented offers the advantage of putting the focus on the information rather than deciding a priori of how to represent it. If the information is rich, then a purely statistical representation mode is adequate, but if the information is scarce, then it may be better conveyed by possibility distributions.