Bioprocesses based on (ligno-)cellulosic biomass are highly prone to batch-to-batch variations. Varying raw material compositions and enzyme activities hamper the prediction of process yields, economic feasibility and environmental impacts. Commonly, these performance indicators are averaged over several experiments to select suitable process designs. The variabilities in performance indicators resulting from variable process inputs are often neglected, causing a risk for faulty performance predictions and poor process design choices during scale-up. In this paper, a multi-scale variability analysis framework is presented that quantifies the effects of process input variations on performance indicators. Using the framework, a kinetic model describing simultaneous saccharification and ethanol fermentation was integrated with a flowsheet process model, techno-economic analysis and life cycle assessment in order to evaluate a wheat straw-based ethanol biorefinery. Hydrolytic activities reported in the literature for the enzyme cocktail Cellic® CTec2, ranging from 62 to 266 FPU·mL 1, were used as inputs to the multi-scale model to compare the variability in performance indicators under batch and multi-feed operation for simultaneous saccharification and fermentation. Bioprocess simulations were stopped at ethanol productivities ≤ 0.1 g·L-1·h-1. The resulting spreads in process times, hydrolysis yields, and fermentation yields were incorporated into flowsheet, techno-economic and life cycle scales. At median enzymatic activities the payback time was 7%, equal to 0.6 years, shorter under multi-feed conditions. All other performance indicators showed insignificant differences. However, batch operation is simpler to control and well-established in industry. Thus, an analysis at median conditions might favor batch conditions despite the disadvantage in payback time. Contrary to median conditions, analyzing the input variability favored multi-feed operation due to a lower variability in all performance indicators. Variabilities in performance indicators were at least 50% lower under multi-feed operation. Counteracting the variability in enzymatic activities by adjusting the amount of added enzyme instead resulted in higher uncertainties in environmental impacts. The results show that the robustness of performance indicators against input variations must be considered during process development. Based on the multi-scale variability analysis process designs can be selected which deliver more precise performance indicators at multiple system levels.

Industrial symbiosis (IS) involves transferring waste materials and/or energy flows between stakeholders to enhance resource efficiency and reduce environmental impacts. The success of these transactions depends on supply–demand matching, technical feasibility of waste integration into industrial processes, economic savings, and compliance with legal and environmental regulations. This paper presents a methodology for the technoeconomic assessment of IS projects, integrating material flow cost accounting (MFCA) and cost–benefit analysis (CBA) incorporating CAPEX and OPEX considerations. MFCA, traditionally used to identify hidden costs from inefficiencies, is adapted here to assess resource utilisation across industry networks. The methodology is applied to two real-world demo cases: a novel fertiliser production process in Escombreras (Spain), where IS focuses on process optimisation and by-product valorisation, and an IS process design in Frövi (Sweden), where CO₂ and residual energy flows are exchanged between industrial sectors. The results demonstrate the potential of MFCA-CBA integration to enhance decision making in IS implementation. In Spain, process optimisation led to a 50% reduction in operating costs, whereas, in Sweden, CO₂ reutilisation resulted in a 30% increase in resource efficiency. These findings highlight the economic and environmental benefits of IS and provide insights into cost allocation and pricing strategies.

This systematic review examines the intersection of systems thinking and learning theory in addressing rural–urban challenges in light of increasing global urbanization. We explore how different dimensions of systems thinking—ontological (how we understand systems) and epistemological (how we think about systems)—align with single-, double-, and triple-loop learning in rural–urban research from 2014 to 2024. Through a rigorous screening process of the peer-reviewed literature, we analyze how theoretical frameworks manifest in research approaches, methodological choices, and learning outcomes. Our findings reveal promising developments and persistent gaps in current approaches, and suggest pathways for more integrated theoretical and methodological frameworks. We also highlight the need for studies that develop knowledge and practices that support collective learning and joint trajectories towards sustainability from a cross-sectorial perspective in rural–urban geographies. This synthesis contributes to discussions on how to effectively address complex challenges at the rural–urban interface while advancing both theoretical understanding and practical application.