The objective of this paper is to reveal to what degree biobased jet fuels (biojet) can reduce greenhouse gas (GHG) emissions from the U.S. aviation sector. A model of the supply and demand chain of biojet involving farmers, biorefineries, airlines, and policymakers is developed by considering factors that drive the decisions of actors (i.e., decision-makers and stakeholders) in the life cycle stages. Two kinds of feedstock are considered: oil-producing feedstock (i.e., camelina and algae) and lignocellulosic biomass (i.e., corn stover, switchgrass, and short rotation woody crops). By factoring in farmer/feedstock producer and biorefinery profitability requirements and risk attitudes, land availability and suitability, as well as a time delay and technological learning factor, a more realistic estimate of the level of biojet supply and emissions reduction can be developed under different oil price assumptions. Factors that drive biojet GHG emissions and unit production costs from each feedstock are identified and quantified. Overall, this study finds that at likely adoption rates biojet alone would not be sufficient to achieve the aviation emissions reduction target. In 2050, under high oil price scenario assumption, GHG emissions can be reduced to a level ranging from 55 to 92%, with a median value of 74%, compared to the 2005 baseline level.

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The production of lithium-ion batteries (LIBs) has increased in capacity by almost eight fold in the past ten years due to growing demand for consumer electronics and electric-drive vehicles. The social and environmental implications of increased lithium demand is significant not only in the context of policy initiatives that are incentivizing electric vehicle adoption, but also because electric vehicle adoption is part of the vision of sustainability transitions that are being put forth in a variety of contexts. Any evidence that suggests that the externalities of the technology uptake are not being addressed would directly counter the intent of such initiatives. For LIBs to be fully sustainable, it is imperative that impacts along life cycle stages be adequately addressed, including lithium mineral extraction. This study investigates how the scope and focus of research in this area are changing and what drives their evolution. Based on a bibliometric analysis, we evaluate the state of research on the issues of lithium mineral extraction, use, and their impacts. The article identifies research hotspots and emerging research agendas by mapping the evolution of research focus and themes. Our analysis finds that research on the socio-environmental impacts of lithium extraction at local level has been very limited. We discuss some research directions to address the knowledge gaps in terms of specific research topics, methodologies, and broader system perspectives.

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Sometimes experts, decisionmakers, and analysts are confronted with policy problems that involve deep uncertainty. Such policy problems occur when (1) the future is not known well enough to predict future changes to the system, (2) there is not enough knowledge regarding the appropriate model to use to estimate the outcomes, and/or (3) there is not enough knowledge regarding the weights stakeholders currently assign to the various criteria or will assign in the future. This paper presents an MCDA approach developed to deal with conditions of deep uncertainty, which is called Exploratory Multi-Criteria Decision Analysis (EMCDA). EMCDA is based on exploratory modelling, which is a modelling approach that allows policy analysts to explore multiple hypotheses about the future world (using different consequence models, different scenarios, and different weights). An example of a policy problem that can benefit from this methodology is decision making on innovations for improving traffic safety. In order to improve traffic safety, much is expected from Intelligent Speed Adaptation (ISA), an in-vehicle system that supports the driver in keeping an appropriate speed. However, different MCDA studies on ISA give different results in terms of the estimates of realworld safety benefits of ISA and the willingness of stakeholders (e.g. the automotive industry) to supply ISA. The application of EMCDA to the implementation of ISA shows that it is possible to perform an MCDA in situations of deep uncertainty. A full analysis taking into account the complete uncertainty space shows that the best policy is to make mandatory an ISA system for young drivers (less than 24 years of age) that restricts them from driving faster than the speed limit. Based on different assumptions, the analysis also shows that ISA policies should not target older drivers.

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