Methods for global sensitivity analysis in life cycle assessment
Methods for global sensitivity analysis in life cycle assessment
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DOI：
10.1007/s11367-016-1217-3
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发表时间：
2017-07-01
影响因子：
4.8
通讯作者：
de Boer, Imke J. M.
中科院分区：
文献类型：
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作者：
Groen, Evelyne A.;Bokkers, Eddie A. M.;de Boer, Imke J. M.
在生命周期评估（LCA）中量化环境影响所需的输入参数可能是不确定的，例如，由于时间的变化或未知的排放因子的真实值。环境影响的不确定性可以通过全球敏感性分析进行分析，以更深入地了解产出差异。本研究旨在（1）深入了解和（2）比较生命周期评价中的全局敏感性分析方法，重点关注清单阶段，评价了5种量化对产出方差贡献的方法：平方标准化回归系数、平方斯皮尔曼相关系数、关键问题分析、Sobol指数和随机平衡设计。为了能够比较全球敏感性方法的性能，构建了两个案例研究：一个小的假设的案例研究，描述电力生产的输入参数和一个大的案例研究，描述东北大西洋渔业生产系统的一个小的变化是敏感的。构造了具有相对较小和较大输入不确定性的输入参数。从抽样设计、输出方差、解释方差和输入参数对输出方差的贡献四个方面对灵敏度方法进行了比较，其中抽样设计的评价与灵敏度方法的计算量有关。关键问题分析不使用抽样，是最快的，而Sobol的方法必须产生两个抽样矩阵，因此是最慢的。总输出方差（II）导致每种方法的输出方差大致相同，但关键问题分析除外，该分析低估了方差，特别是对于高输入不确定性。小输入不确定性的解释方差（III）和方差贡献（IV）最佳量化的平方标准化回归系数和主要Sobol指数。对于较大的输入不确定性，斯皮尔曼相关系数和Sobol指数表现最好。然而，这种比较仅基于两个案例研究，大多数全局敏感性分析方法表现得同样好，特别是对于相对较小的输入不确定性。当受限于LCA中环境影响的量化表现为线性的假设时，平方标准化回归系数、平方斯皮尔曼相关系数、Sobol指数或关键问题分析可用于全局敏感性分析。选择哪一种方法取决于现有数据、数据不确定性的程度和研究的目的。
Input parameters required to quantify environmental impact in life cycle assessment (LCA) can be uncertain due to e.g. temporal variability or unknowns about the true value of emission factors. Uncertainty of environmental impact can be analysed by means of a global sensitivity analysis to gain more insight into output variance. This study aimed to (1) give insight into and (2) compare methods for global sensitivity analysis in life cycle assessment, with a focus on the inventory stage.Five methods that quantify the contribution to output variance were evaluated: squared standardized regression coefficient, squared Spearman correlation coefficient, key issue analysis, Sobol' indices and random balance design. To be able to compare the performance of global sensitivity methods, two case studies were constructed: one small hypothetical case study describing electricity production that is sensitive to a small change in the input parameters and a large case study describing a production system of a northeast Atlantic fishery. Input parameters with relative small and large input uncertainties were constructed. The comparison of the sensitivity methods was based on four aspects: (I) sampling design, (II) output variance, (III) explained variance and (IV) contribution to output variance of individual input parameters.The evaluation of the sampling design (I) relates to the computational effort of a sensitivity method. Key issue analysis does not make use of sampling and was fastest, whereas the Sobol' method had to generate two sampling matrices and, therefore, was slowest. The total output variance (II) resulted in approximately the same output variance for each method, except for key issue analysis, which underestimated the variance especially for high input uncertainties. The explained variance (III) and contribution to variance (IV) for small input uncertainties were optimally quantified by the squared standardized regression coefficients and the main Sobol' index. For large input uncertainties, Spearman correlation coefficients and the Sobol' indices performed best. The comparison, however, was based on two case studies only.Most methods for global sensitivity analysis performed equally well, especially for relatively small input uncertainties. When restricted to the assumptions that quantification of environmental impact in LCAs behaves linearly, squared standardized regression coefficients, squared Spearman correlation coefficients, Sobol' indices or key issue analysis can be used for global sensitivity analysis. The choice for one of the methods depends on the available data, the magnitude of the uncertainties of data and the aim of the study.