Prospective life cycle assessment (LCA) needs to deal with the large epistemological uncertainty about the future to support more robust future environmental impact assessments of technologies. This study proposes a novel approach that systematically changes the background processes in a prospective LCA based on scenarios of an integrated assessment model (IAM), the IMAGE model. Consistent worldwide scenarios from IMAGE are evaluated in the life cycle inventory using ecoinvent v3.3. To test the approach, only the electricity sector was changed in a prospective LCA of an internal combustion engine vehicle (ICEV) and an electric vehicle (EV) using six baseline and mitigation climate scenarios until 2050. This case study shows that changes in the electricity background can be very important for the environmental impacts of EV. Also, the approach demonstrates that the relative environmental performance of EV and ICEV over time is more complex and multifaceted than previously assumed. Uncertainty due to future developments manifests in different impacts depending on the product (EV or ICEV), the impact category, and the scenario and year considered. More robust prospective LCAs can be achieved, particularly for emerging technologies, by expanding this approach to other economic sectors beyond electricity background changes and mobility applications as well as by including uncertainty and changes in foreground parameters. A more systematic and structured composition of future inventory databases driven by IAM scenarios helps to acknowledge epistemological uncertainty and to increase the temporal consistency of foreground and background systems in LCAs of emerging technologies.

中文翻译：

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当背景重要时：在预期生命周期评估中使用综合评估模型中的方案

前瞻性生命周期评估（LCA）需要处理关于未来的认识论上的巨大不确定性，以支持对技术进行更强大的未来环境影响评估。这项研究提出了一种新颖的方法，该方法可基于综合评估模型（IAM），IMAGE模型的方案，系统地更改预期LCA中的背景过程。使用ecoinvent v3.3在生命周期清单中评估了IMAGE的全球一致场景。为了测试该方法，在2050年之前使用六个基准和缓解气候情景，仅改变了内燃机车辆（ICEV）和电动车辆（EV）的预期LCA中的电力部门。此案例研究表明，电力变化背景对于电动汽车的环境影响可能非常重要。也，该方法表明，电动汽车和ICEV的相对环境性能随时间推移比以前假设的更为复杂和多方面。取决于产品（EV或ICEV），影响类别以及所考虑的情景和年份，未来发展带来的不确定性表现为不同的影响。通过将这种方法扩展到电力背景变化和移动性应用之外的其他经济领域，以及将不确定性和前景参数的变化包括在内，可以实现更强大的预期LCA，尤其是对于新兴技术。由IAM场景驱动的未来库存数据库的更系统和结构化的组成有助于确认认识论上的不确定性并增加新兴技术LCA中前台和后台系统的时间一致性。