Purpose

Understanding uncertainty is essential in using life cycle assessment (LCA) to support decisions. Monte Carlo simulation (MCS) is widely used to characterize the variability in LCA results, be them life cycle inventory (LCI), category indicator results, normalized results, or weighted results. In this study, we present a new method to decompose MCS results into underlying contributors using the logarithmic mean Divisia index (LMDI) decomposition method with a case study on natural gas focusing on two impact categories: global warming and USETox human health impacts.

Methods

First, after each run of MCS, the difference in simulated and deterministic results is decomposed using the LMDI decomposition method, which returns the contribution of each factor to the difference of the run. After repeating this for 1000 MCS runs, the statistical properties of the contributions by each factor are analyzed. The method quantifies the contribution of underlying variables, such as characterization factors and LCI items, to the overall variability of the result, such as characterized results.

Results

The method presented can decompose the variabilities in LCI, characterized, normalized, or weighted results into LCI items, characterization factors, normalization references, weighting factors, or any subset of them. As an illustrative example, a case study on natural gas LCA was conducted, and the variabilities in characterized results were decomposed into underlying LCI items and characterization factors. The results show that LCI and characterization phases contribute 65% and 35%, respectively, to the uncertainty of the characterized result for global warming. For the human health impact category, LCIs and characterization factors contribute 32% and 68%, respectively, to the overall uncertainty. In particular, methane emissions in LCI contributed the most to the overall uncertainties in global warming impact, while the characterization factor of chromium was identified as the main driver of the overall uncertainties in human health impact of natural gas. 

Conclusions and discussion

Using this approach, LCA practitioners can decompose the overall variability in the results to the underlying contributors under the MCS setting, which can help prioritize the parameters that need further refinement to reduce overall uncertainty in the results. The method reliably estimates the uncertainty contributions of the variables with large variabilities without the need for large computational resources, and it can be applied to any stage of an LCA calculation including normalization and weighting, or to other fields than LCA such as material flow analysis and risk assessment.

中文翻译：

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将LCA结果的不确定性分解为影响因素的方法

目的

了解不确定性对于使用生命周期评估（LCA）来支持决策至关重要。蒙特卡罗模拟（MCS）被广泛用于表征LCA结果的可变性，无论是生命周期清单（LCI），类别指标结果，归一化结果还是加权结果。在这项研究中，我们提出了一种使用对数平均Divisia指数（LMDI）分解方法将MCS结果分解为潜在贡献者的新方法，并以天然气为案例研究，重点研究了两种影响类别：全球变暖和USETox对人类健康的影响。

方法

首先，在每次运行MCS之后，使用LMDI分解方法分解模拟结果和确定性结果中的差异，该方法将每个因素的贡献返回给运行差异。在重复进行1000次MCS运行之后，将分析每个因素的贡献的统计特性。该方法量化了基础变量（例如表征因子和LCI项目）对结果（例如特征化结果）的整体可变性的贡献。

结果

提出的方法可以将LCI中的差异，特征化，标准化或加权的结果分解为LCI项，特征因子，归一化参考，加权因子或它们的任何子集。作为示例，对天然气LCA进行了案例研究，并将表征结果的差异分解为潜在的LCI项目和表征因素。结果表明，LCI和特征化阶段分别对全球变暖的特征化结果的不确定性贡献了65％和35％。对于人类健康影响类别，生命质量指数和特征因子分别对总体不确定性贡献32％和68％。特别是，LCI中的甲烷排放量是造成全球变暖影响总体不确定性的最大因素， 

结论与讨论

使用这种方法，LCA练习者可以将结果的整体可变性分解为MCS设置下的潜在贡献者，这可以帮助确定需要进一步优化的参数的优先级，以减少结果的整体不确定性。该方法无需大量的计算资源即可可靠地估算出具有较大变异性的变量的不确定性贡献，并且该方法可应用于LCA计算的任何阶段（包括归一化和加权），或应用于LCA以外的其他领域，例如物料流分析和风险评估。