Diversifying supply chains through reshoring and friendshoring is increasingly proposed as a key strategy for supply security and resilience. Quantitative analyses characterizing to what extend diversification shield countries from supply disruptions remain however scarce. In this paper, we present a methodology to assess the supply risk exposure of countries in different supply diversification scenarios – business-as-usual, reshoring, friendshoring. For each scenario, the propagation of three types of upstream disruptions – supply shortage, export restriction, bilateral trade conflict – is simulated. A fragility ratio metric is introduced to quantify the potential downstream shortages caused by these disruptions. A novel friendshoring modelling approach is also proposed. It consists in determining risk-optimized trade relations based on criteria such as supply concentration and UN voting similarity. The Python-based model is tested on the case of diversified photovoltaics supply chains, e.g., if the US, EU, and India increase domestic production from polysilicon to module. Beyond building up manufacturing capacities, choosing between vertical integration and trade is highly determinant in risk exposure. Each diversification scenario shows pros and cons depending on the country and process considered. Overall, this paper underlines the need for supply risk research to nuance diversification recommendations. It would be particularly helpful to improve indicators accounting for a region's technical and economic ability to supply a given product, and to realistically model the challenges of reshoring.

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Tin is an important metal for society with a high risk of supply disruptions. It is, therefore, classified as a critical material in many parts of the world. An exception is the European Union, for which tin was classified as a non-critical material in 2023. However, there are many discrepancies in the literature regarding the definitions and values of the indicators used to determine tin criticality in general, and recycling indicators in particular. Values for end-of-life recycling rate (EoL RR) range between 20% and 75%, and values for end-of-life recycling input rate (EoL RIR) range between 11% and 32%. In this paper, we critically assess the circularity and criticality indicator values for tin and calculate new values using material flow analysis. The new values for tin recycling indicators are lower than those used in most previous research, with a global EoL RR of 16% and an EoL RIR of 11% in 2017. Based on the updated recycling values, combined with a highly concentrated supply, high import reliance, and difficult substitution, we argue that the European Union should classify tin as a critical material. This reclassification can lead to more policy attention for tin, which can help reduce the impact of future supply disruptions and increase the resilience of the European and global tin supply chains.

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The world is facing a growing neodymium demand, creating the need for developing a recycling system to handle future waste flows. Recycling technologies are emerging, but the recycling system around them can only be established with knowledge about available end-of-life (EoL) products. Therefore, this study quantified neodymium waste in European countries using material flow analysis, and assessed the recyclability of major EoL products. For 2019, we find a waste flow of 7.7 kt Nd, consisting mostly of NdFeB magnets. HDDs represent a large current waste flow, while the demand for magnets in industrial applications is increasing. In the future, electric vehicle motors and wind turbines likely provide a source of neodymium with good recyclability. Consequently, there will be different product groups that determine the future waste volumes. To manage the changing waste flows, a neodymium recycling system should be developed with the product properties of future waste flows in mind. Meanwhile, the recyclability of products can be improved by addressing bottlenecks in the recycling chain.@en

Life cycle assessment (LCA) databases and software evolve. We analyzed to which extent software and evolving life cycle inventory databases affect the comparison of technology alternatives, using a comparative LCA on permanent magnets as a case study, with two selected software tools: CMLCA and Brightway LCA. We migrated the system models from the CMLCA to Brightway LCA software and alternated between the ecoinvent database versions 2.2 and 3.1 to 3.6 in the system background. When using ecoinvent v3.6 instead of v2.2, the change of the indicator results ranged from (Formula presented.) to 283%. The evolution of the ecoinvent database impacted the absolute amounts of the characterized results and the relative performance between alternatives. The impact category with the highest variability was ionizing radiation, which even showed a ranking inversion with ecoinvent v3.4. In contrast, the impact of using CMLCA or Brightway was negligible because the same data and modeling assumptions caused percentage differences below 0.4%. During the semi-automated data migration to Brightway, we identified 23 environmental flows in the CMLCA model that were not paired with their corresponding characterization factors in the published study of reference. This error had led to an underestimation of 63% in the photochemical oxidation indicator of one of the alternatives. This underestimation relates to an interoperability issue regarding the nomenclature of environmental flows in software alternatives and is a matter of data implementation rather than an issue intrinsic to the selected software. Finally, we identified improvement opportunities for the transparency and reusability of LCA models. This article met the requirements for a Gold-Gold JIE data openness badge described at http://jie.click/badges.

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