Summary 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.

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The environmental benefits of the circular economy (CE) are often taken for granted. There are, however, reasons to believe that rebound effects may counteract such benefits by increasing overall consumption or "growing the pie." In this study, we focus on two main rebound mechanisms: (1) imperfect substitution between "re-circulated" (recycled, reused, etc.) and new products and (2) re-spending due to economic savings. We use the case study of smartphone reuse in the US to quantify, for the first time, rebound effects from reuse. Using a combination of life cycle assessment, sales statistics, consumer surveying, consumer demand modeling, and environmentally-extended input-output analysis, we quantify the magnitude of this rebound effect for life-cycle greenhouse gas emissions. We find a rebound effect of 29% on average, with a range of 27-46% for specific smartphone models. Moreover, when exploring how rebound might play out in other regions and under different consumer behavior patterns, we find that rebound effects could be higher than 100% (backfire effect). In other words, we estimate that about one third, and potentially the entirety, of emission savings resulting from smartphone reuse could be lost due to the rebound effect. Our results thus suggest that there are grounds to challenge the premise that CE strategies, and reuse in particular, always reduce environmental burdens.

Interpretation of comparative Life Cycle Assessment (LCA) results can be challenging in the presence of uncertainty. To aid in interpreting such results under the goal of any comparative LCA, we aim to provide guidance to practitioners by gaining insights into uncertainty-statistics methods (USMs). We review five USMs—discernibility analysis, impact category relevance, overlap area of probability distributions, null hypothesis significance testing (NHST), and modified NHST–and provide a common notation, terminology, and calculation platform. We further cross-compare all USMs by applying them to a case study on electric cars. USMs belong to a confirmatory or an exploratory statistics’ branch, each serving different purposes to practitioners. Results highlight that common uncertainties and the magnitude of differences per impact are key in offering reliable insights. Common uncertainties are particularly important as disregarding them can lead to incorrect recommendations. On the basis of these considerations, we recommend the modified NHST as a confirmatory USM. We also recommend discernibility analysis as an exploratory USM along with recommendations for its improvement, as it disregards the magnitude of the differences. While further research is necessary to support our conclusions, the results and supporting material provided can help LCA practitioners in delivering a more robust basis for decision-making.

a b s t r a c tThe term eco-innovation has been coined to label those innovations expected to reduce the life cycle environmental burdens resulting from their use. Claims of environmental superiority are usually supported by technology-oriented analyses, such as product-level life cycle assessment. However, the environmental superiority of an innovation depends not only on its technical characteristics but also on technologyedemand interactions. In this article, such interactions are incorporated through the concept of the environmental rebound effect. Using the Dynamic IPAT-Life cycle assessment with Environmental Rebound effect or DILER model, environmental superiority claims of seven alleged transport ecoinnovations were evaluated by comparing alternative macro-level scenarios (with and without innovation) for Europe. The results support the claims of environmental superiority of only three out of seven studied innovations. That is, a majority of innovations actually induced increases in various environmental pressures. Such increases can be attributed mostly to the influence of generally noteworthy environmental rebound effects. The magnitude of the rebound effect is found to be highly correlated with two variables: the total change in effective income resulting from the use of the innovation and the difference between the environmental pressures per monetary unit of the studied innovations and that of the rest of consumption. The article contributes to the literature by (a) applying a comprehensive approach to the rebound effect and its relationship with the eco-innovation concept, (b) by calculating original rebound estimates of specific transport innovations and assessing these in absolute terms, as well as by (c) obtaining novel insights into the drivers behind the rebound effect. The counterintuitive results of this study also invite to re-assess the use of technology-oriented tools for guiding environmental policy. Other policy implications of this study relate to the relevance of transport cost differences, the targeted promotion of actual eco-innovations and its combination with broader policies as well as the achievement of higher quality mobility.