Variance-based global sensitivity analysis and beyond in life cycle assessment: an application to geothermal heating networks
Variance-based global sensitivity analysis and beyond in life cycle assessment: an application to geothermal heating networks
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DOI：
10.1007/s11367-021-01921-1
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发表时间：
2021-05-11
影响因子：
4.8
通讯作者：
Trutnevyte, Evelina
中科院分区：
文献类型：
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作者：
Jaxa-Rozen, Marc;Pratiwi, Astu Sam;Trutnevyte, Evelina
在生命周期评价（LCA）中，全局敏感性分析日益取代人工敏感性分析。基于方差的全局敏感性分析通过估计所谓的Sobol指数来识别有影响的不确定模型输入参数，Sobol指数表示每个参数对模型输出方差的贡献。然而，在分析非正常模型输出时，这种技术可能是不可靠的，并且它不能告诉分析人员可能与决策相关的模型输入或输出的具体值。我们展示了三种新兴方法，它们建立在基于方差的全局敏感性分析的基础上，可以为典型LCA应用中的不确定性提供新的见解，这些应用呈现非正态输出分布、环境影响之间的权衡以及模型输入之间的相互作用。为了识别有影响的模型输入、权衡和决策相关的相互作用，我们实施了基于分布的全局敏感性分析（PAWN技术）、光谱聚类和场景发现（患者规则归纳方法：PRIM）技术。我们之所以选择这些技术，是因为它们适用于一般的蒙特卡罗采样和常用的LCA软件。我们将这些技术与基于方差的Sobol指数进行比较，使用先前发表的地热供暖网络的LCA案例研究。我们评估了三种设计方案在不确定性下的八种环境影响，涵盖不同的地热生产温度和供热网络配置。结果基于PAWN分布的敏感性指数与Sobol指数识别的影响模型参数基本一致。然而，一些差异突出了我们分析中获得的非正态分布上的Sobol指数的潜在误导性解释，其中方差可能没有意义地描述不确定性。光谱聚类突出了在环境影响之间表现出不同权衡的模型结果组。与二阶Sobol相互作用指数相比，PRIM提供了与这些不同计算影响组相关的输入值组合的更精确信息。PAWN指数、光谱聚类和PRIM具有计算优势，因为它们在相对较小的样本量（n = 12,000）下产生稳定的结果，而不像Sobol指数（二阶指数n = 100,000）。我们建议将这些新技术添加到LCA的全局敏感性分析中，因为无论模型输出的分布如何，它们都能更精确地了解不确定性。与基于方差的全局敏感性分析相比，基于PAWN分布的全局敏感性分析提供了输入敏感性的计算效率评估。聚类和情景发现的结合使分析人员能够精确地识别与环境影响的不同结果相关的输入参数或不确定性的组合。
Purpose Global sensitivity analysis increasingly replaces manual sensitivity analysis in life cycle assessment (LCA). Variance-based global sensitivity analysis identifies influential uncertain model input parameters by estimating so-called Sobol indices that represent each parameter's contribution to the variance in model output. However, this technique can potentially be unreliable when analyzing non-normal model outputs, and it does not inform analysts about specific values of the model input or output that may be decision-relevant. We demonstrate three emerging methods that build on variance-based global sensitivity analysis and that can provide new insights on uncertainty in typical LCA applications that present non-normal output distributions, trade-offs between environmental impacts, and interactions between model inputs. Methods To identify influential model inputs, trade-offs, and decision-relevant interactions, we implement techniques for distribution-based global sensitivity analysis (PAWN technique), spectral clustering, and scenario discovery (patient rule induction method: PRIM). We choose these techniques because they are applicable with generic Monte Carlo sampling and common LCA software. We compare these techniques with variance-based Sobol indices, using a previously published LCA case study of geothermal heating networks. We assess eight environmental impacts under uncertainty for three design alternatives, spanning different geothermal production temperatures and heating network configurations. Results In the application case on geothermal heating networks, PAWN distribution-based sensitivity indices generally identify influential model parameters consistently with Sobol indices. However, some discrepancies highlight the potentially misleading interpretation of Sobol indices on the non-normal distributions obtained in our analysis, where variance may not meaningfully describe uncertainty. Spectral clustering highlights groups of model results that present different trade-offs between environmental impacts. Compared to second-order Sobol interaction indices, PRIM then provides more precise information regarding the combinations of input values associated with these different groups of calculated impacts. PAWN indices, spectral clustering, and PRIM have a computational advantage because they yield stable results at relatively small sample sizes (n = 12,000), unlike Sobol indices (n = 100,000 for second-order indices). Conclusions We recommend adding these new techniques to global sensitivity analysis in LCA as they give more precise as well as additional insights on uncertainty regardless of the distribution of the model outputs. PAWN distribution-based global sensitivity analysis provides a computationally efficient assessment of input sensitivities as compared to variance-based global sensitivity analysis. The combination of clustering and scenario discovery enables analysts to precisely identify combinations of input parameters or uncertainties associated with different outcomes of environmental impacts.