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Global environmental problems have urged the need for developing sustainable technologies. However, new technologies that enter the market have often higher economic costs and potentially higher environmental impacts than conventional technologies. This can be explained by learning effects: a production process that is performed for the first time runs less smooth than a production process that has been in operation for years. To obtain a fair estimation of the potential of a new technology, learning effects need to be included. A review on the current literature on learning effects was conducted in order to provide guidelines on how to include learning effects in prospective technology assessment. Based on the results of this review, five recommendations have been formulated and an integration of learning effects in the structure of prospective technology assessment has been proposed. These five recommendations include the combined use of learning effects on the component level and on the end product level; the combined use of learning effects on the technical, economic and environmental level; the combined use of extrapolated values and expert estimates; the combined use of learning-by-doing and learning-by-searching effects and; a tier-based method, including quality criteria, to calculate the learning effect. These five complementary strategies could lead to a clearer perspective on the environmental impact and cost structure of the new technology and a fairer comparison base with conventional technologies, potentially resulting in a faster adoption and a shorter time-to-market for sustainable technologies. • Learning effects quantify the principle "practice makes perfect" for technologies. • A review on learning effects in prospective technology assessment was performed. • Learning effects have mainly been used for investment costs of energy technologies. • Based on best practices, guidelines on the use of learning effects were formulated. • These guidelines enable including learning effects for a wide range of technologies. [ABSTRACT FROM AUTHOR]

The valuable properties of carbon steel slag are currently underexploited. To date, research mainly focusses on valorising a single property of the slag. In this study an ex-ante life cycle assessment (LCA) was applied to evaluate the environmental profile of a novel technological pathway aimed at the extraction of chromium from carbon steel slag in combination with high quality valorisation of the residual matrix material. A comparison with current practice was made, not only by calculating the environmental impact of the lab scale observations, but more importantly by estimating the impact on an industrial scale. Practical guidance on ex-ante LCA is limited, so this study contributes by incorporating simulations on thermodynamic behaviour, complemented with empirical calculation rules and including information derived from similar technologies to perform the upscaling. These principles of ex-ante LCA were applied to the lab results of two consecutive research iterations. Substantial improvements of the environmental profile were observed: ex-ante results turned out to be a factor 20 lower compared to the results from the lab observations after the first iteration and had decreased by a factor 2 compared to the small pilot scale of the second iteration. All upscaled results are better than those from the worst case reference scenario (landfill). Based on the experience gained after this iterative research cycle, a practical recommendation is that at a low technology readiness level using more simple calculation rules in combination with a flowsheet based on elementary design principles for processes at an industrial scale is a more efficient way of modelling compared to a fully-fledged process design from the start. • Ex-ante LCA was applied to emerging carbon steel slag treatment technologies. • Two research iterations enable different ex-ante methods and techniques. • Ex-ante environmental impact is a factor 20 lower compared to lab observations. • All upscaled results are better compared to the worst case reference scenario. • Heat and material recovery reduces environmental impact with 38–45%. [ABSTRACT FROM AUTHOR]