Summary: 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are available for prospective modeling of the foreground system: scientific articles; patents; expert interviews; unpublished experimental data; and process modeling.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.

Purpose: Lignin extraction in pulp mills and biorefineries are emerging technologies. Lignin is always the product of a multi-output process. Assessing such processes using life cycle assessment (LCA) requires the environmental impacts to be divided between the co-products of the system, referred to as allocation. This article explores different allocation approaches for lignin and illustrates the influence of the choice of allocation approach on the climate impact in a case study. Method: Ten different applicable allocation methods were found in literature and two more were developed. Lignin production in a Kraft pulp mill using the LignoBoost process for lignin extraction was selected as a study object for the case study, and due to limited data availability only climate impact was considered. A cradle-to-gate LCA was done for the study object, and all of the twelve allocation approaches were applied; for eight of the methods, factors that strongly influence the results were identified and varied. Finally, the results were put in the context of cradle-to-grave LCAs from literature for different possible uses of lignin to give an indication of how important the choice of allocation approach can be when assessing lignin as a substitute for other raw materials. Results and discussion: Results show that all allocation approaches tested were applicable to the special case of lignin, but each one of them comes with inherent challenges. Factors that often have a large impact on the results are (1) market and price of different outputs; (2) what is seen as the main product or the driver of the system or system changes; (3) what the surrounding system looks like and hence what other products will be displaced by outputs. These factors can be particularly challenging in prospective studies as such studies are future-oriented and consider systems that do not yet exist. Finally, the results show that the choice of allocation could have a significant influence on the climate impact on the cradle-to-grave climate impact of the final product. Conclusions: We recommend for LCAs of lignin-based technologies that allocation methods are very carefully selected based on the goal and scope of the study and that when relevant, several methods are applied and factors are varied within them in a sensitivity analysis. In particular, the driver(s) of the system’s existence or of changes to it, sometimes reflected in market prices of outputs, should be carefully considered.

Biomass pyrolysis is considered as a thermochemical conversion system that is performed under oxygen-depleted conditions. A large body of literature exists in which thermodynamic equilibrium (TE) and kinetic approaches have been applied to predict pyrolysis products. However, the reliability, accuracy and predictive power of both modeling approaches is an area of concern. To address these concerns, in this paper, two new simulation models based on the TE and kinetic approaches are developed using Aspen Plus, to analyze the performance of each approach. Subsequently, the results of two models are compared with modeling and experimental results available in the literature. The comparison shows that, on the one hand, the performance of the TE approach is not satisfactory and cannot be used as an effective way for pyrolysis modeling. On the other hand, the results generated by the new model based on the kinetic approach suggests that this approach is suitable for modeling biomass pyrolysis processes. Calculation of the root mean square error (RMS), to quantify the deviation of the model results from the experiment results, confirms that this kinetic model presents superior agreement with experimental data in comparison with other kinetic models in the literature. The acquired RMS for the developed kinetic method in this paper varies within the span of 1.2 to 3.2 depending on temperature (400–600 °C) and various feedstocks (pine spruce sawdust, bagasse, wood bark, beech wood and paddy straw).