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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]

A future bio-based economy envisions the transformation of the petrochemical industry into using biomass such as wood (waste) as a major resource. The early-stage evaluation of a biorefinery project requires the optimization of the lay-out of the supply chain considering the spatio-temporal variability of the availability of feedstock and the techno-economical characteristics of the biorefinery process. Therefore, the presented methodology was developed combining three models: (1) a forest management and planning tool providing a detailed prediction on the wood resource availability as well as the harvested feedstock quantity and cost with respect to location and time, (2) a techno-economic assessment model of the biorefinery process (e.g., species-specific conditions, capacity, CAPEX, OPEX), and (3) a strategic supply chain optimization model combining the insights of (1) and (2) into a spatio-temporal explicit supply chain analysis. The developed methodology has been evaluated through a case-study on the emerging reductive catalytic fractionation (RCF) biorefining in the Flanders region (EU) and shows that the most economically interesting configuration is one large biorefinery with a yearly wood chip intake of 150 kton. The biorefinery location reflects the available feedstock distribution in Flanders and is suggested to be situated best in the most forested region. The proposed methodology proved to be dynamic and robust: (1) input data and technical calculations can easily be adapted or updated; (2) the methodology can be applied to a broad range of applications beyond the scope of the biorefinery, to different feedstock choices; (3) the impact of the biorefinery location on e.g. energy balance, CO 2 emissions, and financial balance can be assessed. [Display omitted] • Integration of resource availability, techno-economic analysis, supply chain design. • Supports designing new bio-based value chains at low Technology Readiness Level. • Case study on emerging reductive catalytic fractionation in Flanders. • Region features and time variations impact process feasibility and chain layout. [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]

A new reductive catalytic fractionation biorefinery process (RCF) is currently being developed transforming wood into high-value end-products. RCF is considered to be in the pilot stage with a technology readiness level of 5–6. Apart from the RCF-process characteristics, the economic feasibility also depends on the investment decisions that are made upstream and downstream within the wood value chain, increasing the level of uncertainty. Two investment options within the value chain are considered: an option to invest in harvesting equipment and an option to invest in the RCF. To understand the impact of multiple sources of uncertainty on the decision to invest in an innovative RCF-driven wood value chain, an analytical two-factor real options model is presented, accounting for correlated cost and price uncertainties. Two different scenarios, separated and united investments in harvesting equipment and RCF, are analyzed. In both scenarios, market uncertainty postpones investment in comparison to the traditional NPV approach. When both investments are considered separately, the investment in RCF is expected to be earlier than the investment in harvesting equipment. When both investment decisions are united, the probability of investment increases. The study reveals that RCF has the potential to stimulate investments from different investors, –upstream and midstream–, within the wood value chain. Besides, the introduced real options model proofs its ability to assess the economic feasibility of innovative technologies (e.g RCF) individually or within the value chain, taking into account multiple sources of uncertainty. [Display omitted] • An analytical real options model with multiple-correlated uncertainties. • Investments are postponed under market uncertainties. • An united investment is more favorable than two separated investments. • RCF has the potential to stimulate investments within the wood value chain. [ABSTRACT FROM AUTHOR]

Carbon capture and utilization (CCU) technologies capture CO2 waste emissions and utilize them to generate new products (such as fuels, chemicals, and materials) with various environmental, economic, and social opportunities. As most of these CCU technologies are in the R&D stage, their technical and economic viability are examined with less attention to the social aspect which is an important pillar for a holistic sustainability assessment. The lack of systematic social impact research is mainly due to the difficulty of identifying and quantifying social aspects through the entire life cycle of products. We will fill this gap for CCU technologies and identify the main social indicators. A multi-criteria decision making tool: technique for order of preference by similarity to ideal solution (TOPSIS) was applied to empirically determine which indicators are more relevant for assessing the social impact of a company operating CCU activities within a European context. First, seeing that social impact categories are linked to key stakeholder groups, we considered workers, consumers, and local communities as relevant stakeholders. Second, the main social impact categories and their potential performance indicators associated to each group of stakeholders were listed using the United Nations Environment Program/Society of Environmental Toxicology and Chemistry (UNEP/SETAC) guidelines. In the third step, an online questionnaire was distributed to identify the main social categories and indicators for CCU, to which 33 European CCU experts responded. Finally, a modified TOPSIS was applied to rank the indicators based on their relevance. We found that the indicators related to "end of life responsibility" and "transparency" within a CCU company achieved the highest rank affecting the consumers group, whereas "fair salary" and "equal opportunities/discriminations" were determined as the most relevant impact categories for the workers. For the local community group, "secure living conditions" and "local employment" received the highest priority from the experts' point of view. Furthermore, "health and safety" considerations were identified as one of the most important criteria affecting all three groups of stakeholders. The ranking list of the main social indicators identified in our study provides the basis for the next steps in the social sustainability assessment of CCU technologies; that is, data collection and impact assessment. Our outcomes can also be used to inform the producers regarding the most and least relevant social aspects of CCU so that the potential social impacts caused by their production activities can be improved or prevented. [ABSTRACT FROM AUTHOR]

[Display omitted] • Techno-economic assessment framework including technology learning. • Calculation procedure for minimum learning rates to reach economic feasibility. • Economic feasibility of corn stover bioethanol plants in Belgium studied by 2030. • Single- and multi-component learning curves studied for different component types. • Minimum learning rates for large-scale plant close to typical literature values. Lignocellulosic biomass is the most abundant source of renewable biomass and is seen as a high-potential replacement for petroleum-based resources. The conversion technologies to advanced biofuels are still at a low maturity level, thus allowing for future cost reductions through technological learning. This fact is barely considered in state-of-the-art techno-economic assessments and a structured approach to account for technological learning in techno-economic assessments is needed. In this study, a framework for techno-economic assessments of advanced biofuels, integrating learning curves, is proposed. As a validation of this framework, the economic feasibility of the valorization of corn stover for the production of second-generation bioethanol in Belgium is studied. Process flowsheet simulations in Aspen Plus are developed, with an emphasis on the comparison of four different pretreatment technologies and two plant capacities at 156 dry kt biomass/y and 667 dry kt/y. The dilute acid pretreatment model of the large-scale biorefinery required the lowest minimum learning rate to reach an economically feasible biorefinery by 2030, being 3.9%, almost half as the one calculated for the smaller scale plant. This learning rate seems to be achievable based on learning rates commonly estimated in literature. We conclude that there is a potential for advanced ethanol production in Belgium under the current state of technology for large-scale biorefineries, which require additional biomass imports, when accounting for future cost reductions through learning. [ABSTRACT FROM AUTHOR]

Economic and environmental impact assessments are increasingly being adopted in the design and implementation of emerging systems. However, their emerging nature leads to several assessment challenges that need to be addressed to ensure the validity and usefulness of results in understanding their potential performance and supporting their development. There is the need to (i) account for spatial and temporal variability to allow a broader perspective at an early stage of development; (ii) handle uncertainties to systematically identify the critical factors and their interrelations that drive the results; (iii) integrate environmental and economic results to support sound decision-making based on two sustainability aspects. To address these assessment challenges, this study presents an alternative approach with the following corresponding features: (i) multiple scenario development to conduct an exploratory assessment of the systems under varying conditions and settings, (ii) global sensitivity analysis to identify the main critical factors and their interrelations, and (iii) trade-off and eco-efficiency analysis to integrate the economic and environmental results. The integrated approach is applied to a case study on plasma gasification for solid waste management. The results of the study highlight how the approach allows the identification of the dynamic relations between project settings and surrounding conditions. For example, the choice of gasifying agent largely depends on the background energy system, which dictates the impacts of the process energy requirement and the savings from the substituted energy of the syngas output. Based on these findings, the usefulness and validity of the proposed integrated approach are discussed in terms of how the key assessment challenges are addressed and how it can provide guidance for the development of emerging systems. [Display omitted] • An integrated ex-ante environmental and economic assessment framework is defined. • The exploratory approach allows addressing uncertainties in the ex-ante assessment. • Global sensitivity analysis allows the fine-grained analysis of the system. • A case study on plasma gasification is conducted. • The framework supports the sustainable development of emerging technologies. [ABSTRACT FROM AUTHOR]

A new lignin-first biorefinery with a reductive catalytic fractionation process, which targets the valorization of the lignin and the carbohydrate fraction into higher value end-products, is currently being designed. To identify the various R&D drivers for projects with a low technology readiness level (TRL), we developed an integrated techno-economic assessment (TEA) that directly integrates the results of lab studies with economic costs and benefits. Furthermore, different linkages are made to upstream wood availability and downstream demand to understand its fit into existing wood value chains. By making the relations across the wood value chain explicit within the integrated TEA, we find that the scale of the plant, the feedstock-specific output quantities, and output prices highly determine the economic feasibility. Furthermore, this detailed analysis reveals the importance of assessing different types of feedstock. If only virgin wood is available as feedstock, minimum capacity levels between 190 and 234 kilotons per year are needed for the investment to be profitable. Waste wood proves to be the most profitable feedstock with an NPV of M€ 59 and an IRR of 26%. Using only waste wood as feedstock makes the investment profitable at a lower capacity level of 80 kilotons per year and economic shocks can be absorbed. Based on these results we show that an integrated and detailed TEA is indispensable to define future development paths for early-stage, innovative technologies. Image 1 • Integrated TEA with multiple interlinkages within the wood-based value chain. • Use of a unique set of lab-based data for birch, poplar, pine and waste wood (type B). • Capacity level and feedstock type are crucial factors to reach profitability. • Waste wood (type B) is the most profitable feedstock. [ABSTRACT FROM AUTHOR]