Consequential life cycle assessment (C-LCA) aims to assess the environmental consequences of a decision. It differs from traditional LCA because its inventory includes all the processes affected by the decision which are identified by accounting for causal links (physical, economic, etc.). However, C-LCA results could be quite uncertain which makes the interpretation phase harder. Therefore, strategies to assess and reduce uncertainty in C-LCA are needed. Part of uncertainty in C-LCA is due to spatial variability that can be reduced using regionalization. However, regionalization can be complex and time-consuming if straightforwardly applied to an entire LCA model. The main purpose of this article is to prioritize regionalization efforts to enhance interpretation in C-LCA by assessing the spatial uncertainty of a case study building on a partial equilibrium economic model. Three specific objectives are derived: (1) perform a C-LCA case study of alternative transportation scenarios to investigate the benefits of implementing a public policy for energy transition in France by 2050 with an uncertainty analysis to explore the strength of our conclusions, (2) perform global sensitivity analyses to identify and quantify the main sources of spatial uncertainty between foreground inventory model from partial equilibrium economic modeling, background inventory model and characterization factors, (3) propose a strategy to reduce the spatial uncertainty for our C-LCA case study by prioritizing regionalization. Results show that the implementation of alternative transport scenarios in compliance with public policy for the energy transition in France is beneficial for some impact categories (ICs) (global warming, marine acidification, marine eutrophication, terrestrial acidification, thermally polluted water, photochemical oxidant formation, and particulate matter formation), with a confidence level of 95%. For other ICs, uncertainty reduction is required to determine conclusions with a similar level of confidence. Input variables with spatial variability from the partial equilibrium economic model are significant contributors to the C-LCA spatial uncertainty and should be prioritized for spatial uncertainty reduction. In addition, characterization factors are significant contributors to the spatial uncertainty results for all regionalized ICs (except land occupation IC). Ways to reduce the spatial uncertainty from economic modeling should be explored. Uncertainty reduction to enhance the interpretation phase and the decision-making should be prioritized depending on the goal and scope of the LCA study. In addition, using regionalized CFs in C-LCA seems to be relevant, and C-LCA calculation tools should be adapted accordingly.

中文翻译：

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优先区域化以加强对后续生命周期评估的解释：使用部分均衡经济模型应用于替代交通场景

结果生命周期评估 (C-LCA) 旨在评估决策的环境后果。它与传统的 LCA 不同，因为它的清单包括所有受决策影响的过程，这些过程通过考虑因果关系（物理、经济等）来确定。然而，C-LCA 结果可能非常不确定，这使得解释阶段更加困难。因此，需要在 C-LCA 中评估和减少不确定性的策略。C-LCA 的部分不确定性是由于空间可变性，可以使用区域化来减少。但是，如果直接应用于整个 LCA 模型，区域化可能会很复杂且耗时。本文的主要目的是通过评估基于部分均衡经济模型的案例研究的空间不确定性，优先考虑区域化工作，以增强 C-LCA 中的解释。得出三个具体目标：(1) 对替代交通情景进行 C-LCA 案例研究，以调查到 2050 年在法国实施能源转型公共政策的好处，并通过不确定性分析来探索我们结论的强度，(2 ) 执行全局敏感性分析，以从部分均衡经济模型、背景清单模型和特征因素中识别和量化前景清单模型之间空间不确定性的主要来源，(3) 为我们的 C-LCA 案例研究提出减少空间不确定性的策略通过优先区域化。结果表明，根据法国能源转型公共政策实施替代交通方案有利于某些影响类别 (IC)（全球变暖、海洋酸化、海洋富营养化、陆地酸化、热污染水、光化学氧化剂形成、和颗粒物形成），置信水平为 95%。对于其他 IC，需要降低不确定性以确定具有类似置信度的结论。来自部分均衡经济模型的具有空间变异性的输入变量是 C-LCA 空间不确定性的重要贡献者，应优先考虑减少空间不确定性。此外，特征因子是所有区域化 IC（土地占用 IC 除外）空间不确定性结果的重要贡献者。应该探索减少经济模型中空间不确定性的方法。根据 LCA 研究的目标和范围，应优先考虑降低不确定性以加强解释阶段和决策。此外，在 C-LCA 中使用区域化 CF 似乎是相关的，并且 C-LCA 计算工具应该相应地进行调整。