The challenge of assessing emerging technologies with life cycle assessment (LCA) has been increasingly discussed in the LCA field. In this article, we propose a definition of prospective LCA: An LCA is prospective when the (emerging) technology studied is in an early phase of development (e.g., small-scale production), but the technology is modeled at a future, more-developed phase (e.g., large-scale production). Methodological choices in prospective LCA must be adapted to reflect this goal of assessing environmental impacts of emerging technologies, which deviates from the typical goals of conventional LCA studies. The aim of the article is to provide a number of recommendations for how to conduct such prospective assessments in a relevant manner. The recommendations are based on a detailed review of selected prospective LCA case studies, mainly from the areas of nanomaterials, biomaterials, and energy technologies. We find that it is important to include technology alternatives that are relevant for the future in prospective LCA studies. Predictive scenarios and scenario ranges are two general approaches to prospective inventory modeling of both foreground and background systems. Many different data sources are available for prospective modeling of the foreground system: scientific articles; patents; expert interviews; unpublished experimental data; and process modeling. However, we caution against temporal mismatches between foreground and background systems, and recommend that foreground and background system impacts be reported separately in order to increase the usefulness of the results in other prospective studies. Keywords:case study emerging technology industrial ecology life cycle assessment (LCA) prospective technological changeConflict of interest statement: The authors have no conflict to declare.

One promising future bulk application of graphene is as composite additive. Therefore, we compare two production routes for in-solution graphene using a cradle-to-gate lifecycle assessment focusing on potential differences in energy use, blue water footprint, human toxicity, and ecotoxicity. The data used for the assessment is based on information in scientific papers and patents. Considering the prospective nature of this study, environmental impacts from background systems such as energy production were not included. The production routes are either based on ultrasonication or chemical reduction. The results show that the ultrasonication route has lower energy and water use, but higher human and ecotoxicity impacts, compared to the chemical reduction route. However, a sensitivity analysis showed that solvent recovery in the ultrasonication process gives lower impacts for all included impact categories. The sensitivity analysis also showed that solvent recovery is important to lower the blue water footprint of the chemical reduction route as well. The results demonstrate the possibility to conduct a life cycle assessment study based mainly on information from patents and scientific articles, enabling prospective life cycle assessment studies of products at early stages of technological development.

Nanocellulose is a bionanomaterial with many promising applications, but high energy use in production has been described as a potential obstacle for future use. In fact, life cycle assessment studies have indicated high life cycle energy use for nanocellulose. In this study, we assess the cradle-to-gate environmental impacts of three production routes for a particular type of nanocellulose called cellulose nanofibrils (CNF) made from wood pulp. The three production routes are (1) the enzymatic production route, which includes an enzymatic pretreatment, (2) the carboxymethylation route, which includes a carboxymethylation pretreatment, and (3) one route without pretreatment, here called the no pretreatment route. The results show that CNF produced via the carboxymethylation route clearly has the highest environmental impacts due to large use of solvents made from crude oil. The enzymatic and no pretreatment routes both have lower environmental impacts, of similar magnitude. A sensitivity analysis showed that the no pretreatment route was sensitive to the electricity mix, and the carboxymethylation route to solvent recovery. When comparing the results to those of other carbon nanomaterials, it was shown that in particular CNF produced via the enzymatic and no pretreatment routes had comparatively low environmental impacts.

Managing the potential environmental risks of nanoparticles requires methods to link nanoparticle properties with macro-scale risks. This study outlines challenges in exposure modeling of nanoparticles in aquatic environments, such as the role of natural organic matter, natural colloids, fractal dimensions of agglomerates, coatings and doping of particles, and uncertainties regarding nanoparticle emissions to aquatic environments. The pros and cons of the exposure indicators mass concentration, particle number concentration, and surface area are discussed. By applying colloid chemistry kinetic equations describing particle agglomeration and sedimentation for the case of titanium dioxide nanoparticles, a limited exposure assessment including some of the factors mentioned is conducted with particle number concentration as the exposure indicator. The results of the modeling indicate that sedimentation, shear flows, and settling are of less importance with regard to particle number based predicted environmental concentrations. The inflow of nanoparticles to the water compartment had a significant impact in the model, and the collision efficiency (which is affected by natural organic matter) was shown to greatly affect model output. Implications for exposure modeling, regulation and science are discussed. A broad spectrum of scientific disciplines must be engaged in the development of exposure models where nano-level properties are linked to macro-scale risk.

(323 words)36 Rockström et al. (2009aRockström et al. ( , 2009b have warned that humanity must reduce anthropogenic impacts 37 defined by nine planetary boundaries if "unacceptable global change" is to be avoided.

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Exposure assessment of engineered nanoparticles (ENPs) is a challenging task mainly due to the novel properties of these new materials and the complexity caused by a wide range of particle characteristics, ENP-containing products and possible environmental interactions. We here present a research agenda in which we propose to face the complexity associated with ENP exposure assessment through informed and systematic simplifications. Exposure modelling is presented as a method for addressing complexity by identifying processes dominant for the fate of ENPs in the environment and enabling an iterative learning process by studying different emission and fate scenarios. Furthermore, the use of models is important to highlight most pressing research needs. For this reason, we also strongly encourage improved communication and collaboration between modellers and experimental scientists. Feedback between modellers and experimental scientists is crucial in order to understand the big picture of ENP exposure assessment and to establish common research strategies. Through joint research efforts and projects, the field of ENP exposure assessment can greatly improve and significantly contribute to a comprehensive and systematic risk assessment of ENPs.

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