Using Standard Statistics to Consider Uncertainty in Industry-Based Life Cycle Inventory Databases (7 pp)
Using Standard Statistics to Consider Uncertainty in Industry-Based Life Cycle Inventory Databases (7 pp)
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DOI：
10.1065/lca2005.05.211
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发表时间：
2005-05
期刊：
影响因子：
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通讯作者：
H. Sugiyama;Y. Fukushima;M. Hirao;S. Hellweg;K. Hungerbühler
中科院分区：
文献类型：
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作者：
H. Sugiyama;Y. Fukushima;M. Hirao;S. Hellweg;K. Hungerbühler
目标、范围和背景决策者需要有关其行动可能产生的结果范围的信息。因此，为了将生命周期评估（LCA）开发为决策工具，生命周期清单（LCI）数据库应提供不确定性信息。应根据 LCI 数据库的特点适当选择纳入不确定性的方法。例如，在收集大量最新过程数据的基于行业的生命周期清单数据库中，统计方法可能有助于量化不确定性。然而，在实践中，对于哪些统计方法对于获得所需参数最有效仍然缺乏了解。从行业角度来看的另一个问题是过程数据的保密性。本文的目的是提出一种将不确定性信息与基于行业的生命周期清单数据库中的统计方法相结合的程序，同时保留单个数据的机密性。方法所提出的考虑基于行业的数据库中的不确定性的程序有两个组成部分：适合分散单元过程数据的连续概率分布，以及库存流之间的排序相关系数。使用统计方法（例如拟合优度统计或基于经验的方法）选择概率分布的类型。使用最大似然估计来估计概率分布的参数。计算库存项目的排名顺序相关系数，以保留数据的相互依赖性。这种概率分布和排序相关系数可用于蒙特卡罗模拟，以便将LCA结果中的不确定性量化为概率分布。结果与讨论对聚对苯二甲酸乙二醇酯（PET）化学回收系统的技术选择进行了案例研究。与传统焚烧技术相比，基于二氧化碳减排量对三种工艺进行了评估。为了说明所提议程序的应用，对生命周期清单流量的不确定性进行了假设。显示了概率分布和排序相关系数的应用，并进行了敏感性分析。讨论了假设案例研究结果的潜在用途。结论和展望该案例研究说明了如何在 LCA 中使用 LCI 数据库中的不确定性信息。由于案例研究中没有实际的散射单元过程数据，因此 LCA 结果的不确定性分布是假设的。然而，采用拟议程序的优点已经得到说明：根据 LCA 结果的重要性做出更明智的决策成为可能。通过这个说明，作者希望鼓励数据库开发人员和数据供应商将不确定性信息纳入 LCI 数据库中。
Goal, Scope and BackgroundDecision-makers demand information about the range of possible outcomes of their actions. Therefore, for developing Life Cycle Assessment (LCA) as a decision-making tool, Life Cycle Inventory (LCI) databases should provide uncertainty information. Approaches for incorporating uncertainty should be selected properly contingent upon the characteristics of the LCI database. For example, in industry-based LCI databases where large amounts of up-to-date process data are collected, statistical methods might be useful for quantifying the uncertainties. However, in practice, there is still a lack of knowledge as to what statistical methods are most effective for obtaining the required parameters. Another concern from the industry's perspective is the confidentiality of the process data. The aim of this paper is to propose a procedure for incorporating uncertainty information with statistical methods in industry-based LCI databases, which at the same time preserves the confidentiality of individual data.MethodsThe proposed procedure for taking uncertainty in industry-based databases into account has two components: continuous probability distributions fitted to scattering unit process data, and rank order correlation coefficients between inventory flows. The type of probability distribution is selected using statistical methods such as goodness-of-fit statistics or experience based approaches. Parameters of probability distributions are estimated using maximum likelihood estimation. Rank order correlation coefficients are calculated for inventory items in order to preserve data interdependencies. Such probability distributions and rank order correlation coefficients may be used in Monte Carlo simulations in order to quantify uncertainties in LCA results as probability distribution.Results and DiscussionA case study is performed on the technology selection of polyethylene terephthalate (PET) chemical recycling systems. Three processes are evaluated based on CO2 reduction compared to the conventional incineration technology. To illustrate the application of the proposed procedure, assumptions were made about the uncertainty of LCI flows. The application of the probability distributions and the rank order correlation coefficient is shown, and a sensitivity analysis is performed. A potential use of the results of the hypothetical case study is discussed.Conclusion and OutlookThe case study illustrates how the uncertainty information in LCI databases may be used in LCA. Since the actual scattering unit process data were not available for the case study, the uncertainty distribution of the LCA result is hypothetical. However, the merit of adopting the proposed procedure has been illustrated: more informed decision-making becomes possible, basing the decisions on the significance of the LCA results. With this illustration, the authors hope to encourage both database developers and data suppliers to incorporate uncertainty information in LCI databases.