Method to decompose uncertainties in LCA results into contributing factors
Method to decompose uncertainties in LCA results into contributing factors
复制标题
将 LCA 结果的不确定性分解为影响因素的方法
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
S. Suh
中科院分区：
文献类型：
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作者：
Yuwei Qin;S. Suh
了解不确定性对于使用生命周期评估 (LCA) 支持决策至关重要。蒙特卡罗模拟 (MCS) 广泛用于表征 LCA 结果的变异性，无论是生命周期清单 (LCI)、类别指标结果、标准化结果还是加权结果。在本研究中，我们提出了一种新方法，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，并以天然气为例进行案例研究，重点关注两个影响类别：全球变暖和 USETox 对人类健康的影响。首先，每次运行 MCS 后，使用 LMDI 分解方法对模拟结果和确定性结果的差异进行分解，该方法返回每个因素对运行差异的贡献。重复 1000 次 MCS 运行后，分析每个因素贡献的统计特性。该方法量化了基础变量（例如特征因子和 LCI 项目）对结果（例如特征结果）的总体变异性的贡献。所提出的方法可以将 LCI 中的变异性、特征化、标准化或加权结果分解为 LCI 项目、特征化因子、标准化参考、加权因子或其任何子集。作为一个说明性的例子，我们进行了天然气生命周期评估的案例研究，并将特征结果的变异性分解为基础生命周期清单项目和特征因素。结果表明，LCI 和表征阶段分别对全球变暖表征结果的不确定性贡献了 65% 和 35%。对于人类健康影响类别，LCI 和特征因素对总体不确定性的影响分别为 32% 和 68%。特别是，LCI中的甲烷排放对全球变暖影响的总体不确定性贡献最大，而铬的特征因子被确定为天然气对人类健康影响的总体不确定性的主要驱动因素。使用这种方法，LCA 从业者可以将结果的总体变异性分解为 MCS 设置下的潜在贡献者，这有助于优先考虑需要进一步细化的参数，以减少结果的总体不确定性。该方法无需大量计算资源即可可靠地估计变化较大的变量的不确定性贡献，并且可以应用于包括归一化和加权在内的LCA计算的任何阶段，或应用于LCA以外的其他领域，例如物料流分析和风险评估。
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. 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. 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. 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.