Bren School of Environmental Science and Management, University of California, Santa Barbara, California, United States of America.
PLoS One. 2018 Dec 19;13(12):e0209474. doi: 10.1371/journal.pone.0209474. eCollection 2018.
In life cycle assessment (LCA), performing Monte Carlo simulation (MCS) using fully dependent sampling typically involves repeated inversion of a technology matrix for a sufficiently large number of times. As the dimension of technology matrices for life cycle inventory (LCI) databases grows, MCS using fully dependent sampling is becoming a computational challenge. In our previous work, we pre-calculated the distribution functions of the entire LCI flows in the ecoinvent ver. 3.1 database to help reduce the computation time of running fully dependent sampling by individual LCA practitioners. However, it remains as a question whether the additional errors due to the use of pre-calculated uncertainty values are large enough to alter the conclusion of a comparative study, and, if so, what is the odds of such cases. In this study, we empirically tested the probability of altering the conclusion of a comparative LCA due to the use of pre-calculated uncertainty values. We sampled 10,000 random pairs of elementary flows of ecoinvent LCIs (ai and bi) and ran MCSs (1) using pre-calculated uncertainty values and (2) using fully dependent sampling. We analyzed the distribution of the differences between ai and bi (i.e., ai-bi) of each run, and quantified the probability of reversing (e.g., ai > bi became ai < bi) or moderating the conclusion (e.g., ai > bi became ai ≈ bi). In order to better replicate the situation under a comparative LCA setting, we also sampled 10,000 random pairs of elementary flows from the processes that produce electricity, and repeated the same procedure. The results show that no LCIs derived using pre-calculated uncertainty values constitute large enough differences from those using fully dependent sampling to reverse the conclusion. However, in 5.3% of the cases, the conclusion from one approach is moderated under the other approach or vice versa. When elementary flow pairs are sampled only from the electricity-producing processes, the probability of moderating the conclusions increases to 10.5%, while that of reversing the conclusions remains nil. As the number of unit processes in LCI databases increases, running full MCSs in a PC-environment will continue to be a challenge, which may lead some LCA practitioners to avoid uncertainty analysis altogether. Our results indicate that pre-calculated distributions for LCIs can be used as a proxy for comparative LCA studies in the absence of adequate computational resources for full MCS. Depending on the goal and scope of the study, LCA practitioners should consider using pre-calculated distributions if the benefits of doing so outweighs the associated risks of altering the conclusion.
在生命周期评估 (LCA) 中，使用完全依赖抽样进行蒙特卡罗模拟 (MCS) 通常需要对技术矩阵进行足够多次的重复反转。随着生命周期清单 (LCI) 数据库的技术矩阵维度的增加，使用完全依赖抽样的 MCS 成为了一个计算挑战。在我们之前的工作中，我们预先计算了 ecoinvent ver. 3.1 数据库中整个 LCI 流的分布函数，以帮助减少单个 LCA 从业者运行完全依赖抽样的计算时间。然而，由于使用预计算的不确定性值而导致的额外误差是否大到足以改变比较研究的结论，以及如果是这样，这种情况发生的可能性有多大，仍然是一个问题。在这项研究中，我们通过实证检验了由于使用预计算的不确定性值而改变比较 LCA 结论的可能性。我们随机抽取了 10000 对 ecoinvent LCI 的基本流 (ai 和 bi)，并进行了 MCS 模拟：(1) 使用预计算的不确定性值，(2) 使用完全依赖抽样。我们分析了每个运行的 ai 和 bi 之间的差异 (即 ai-bi) 的分布，并量化了反转 (例如，ai > bi 变为 ai < bi) 或缓和结论 (例如，ai > bi 变为 ai ≈ bi) 的概率。为了更好地复制比较 LCA 环境下的情况，我们还从发电过程中随机抽取了 10000 对基本流，并重复了相同的过程。结果表明，使用预计算的不确定性值得出的 LCI 没有足够大的差异来反转结论。然而，在 5.3%的情况下，一种方法的结论会被另一种方法缓和，反之亦然。当仅从发电过程中抽取基本流对时，结论缓和的概率增加到 10.5%，而结论反转的概率仍然为零。随着 LCI 数据库中的单元过程数量的增加，在 PC 环境下运行完整的 MCS 将继续成为一个挑战，这可能导致一些 LCA 从业者完全避免不确定性分析。我们的结果表明，在没有足够的计算资源进行完整的 MCS 的情况下，LCIs 的预计算分布可以作为比较 LCA 研究的替代。根据研究的目标和范围，如果这样做的好处大于改变结论的相关风险，LCA 从业者应该考虑使用预计算的分布。
