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.

中文翻译：

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基于方差的全局敏感性分析以及生命周期评估中的超越：在地热供热网络中的应用

目的

全局敏感性分析在生命周期评估（LCA）中逐渐取代了人工敏感性分析。基于方差的全局敏感性分析通过估计所谓的Sobol指数来识别有影响力的不确定模型输入参数，这些指数代表每个参数对模型输出方差的贡献。但是，此技术在分析非正常模型输出时可能不可靠，并且不会将可能与决策相关的模型输入或输出的特定值告知分析人员。我们演示了三种基于方差全局敏感性分析的新兴方法，这些方法可以提供有关典型LCA应用程序中不确定性的新见解，这些应用程序显示非正态输出分布，环境影响之间的权衡以及模型输入之间的相互作用。

方法

为了确定有影响力的模型输入，折衷和决策相关的交互作用，我们实现了基于分布的全局敏感性分析（PAWN技术），频谱聚类和方案发现（患者规则归纳方法：PRIM）的技术。我们选择这些技术是因为它们适用于通用的蒙特卡洛采样和通用的LCA软件。我们使用以前发布的LCA地热供热网络案例研究，将这些技术与基于方差的Sobol指数进行了比较。我们评估了三种设计方案在不确定性下的八种环境影响，这些设计方案涵盖了不同的地热生产温度和供热网络配置。

结果

在地热供热网络的应用案例中，基于PAWN分布的灵敏度指标通常会确定与Sobol指标一致的影响模型参数。但是，某些差异突出了我们分析中获得的非正态分布上Sobol指数的潜在误导性解释，其中方差可能无法有意义地描述不确定性。频谱聚类突出显示了在不同环境影响之间呈现不同折衷的模型结果组。与二阶Sobol交互作用指标相比，PRIM然后提供有关与这些不同的已计算影响组相关的输入值组合的更精确信息。PAWN索引，频谱聚类和PRIM具有计算优势，因为它们在相对较小的样本量下可产生稳定的结果（n  = 12,000），不同于Sobol指数（ 二阶指数n = 100,000）。

结论

我们建议将这些新技术添加到LCA中的全局灵敏度分析中，因为无论模型输出的分布如何，它们都可以提供更精确的信息以及对不确定性的更多见解。与基于方差的全局灵敏度分析相比，基于PAWN分布的全局灵敏度分析可提供对输入灵敏度的计算有效评估。聚类和场景发现的组合使分析人员能够准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。