The mining sector, like other sectors of the economy, is under increasing pressure to adopt circular economy (CE) principles across its value chains and core operations. This paper offers a critical and conceptually grounded contribution to understanding how CE can support systemic transformation in one of the most resource-intensive and path-dependent sectors of the global economy. It examines the structural and institutional conditions that shape the adoption of CE in mining and identifies key tensions that constrain or enable transformative change. In parallel, the paper explores emerging pathways informed by technological innovation, shifts in production routines, and the rise of new circular business models. These insights are synthesised into a multi-level framework that captures the dynamic interactions between micro-, meso-, and macro-level processes shaping CE transitions. In addition to offering a diagnostic perspective, the framework outlines concrete action points for advancing systemic change.

Aiming to reach circularity and resource efficiency, the metal industry pushes towards recycling secondary products instead of producing from primary material. This increases the use of scrap, which can bring about several benefits but could also come at the expense of the materials' quality and the potential loss of valuable resources. The above is mainly a result of the likely accumulation of unwanted elements throughout the recycling process, which may dissolve and become difficult to extract from the metal's melt, turning into what is known as tramp elements. This study focuses on the opportunity to limit the accumulation of unwanted elements before they end up in the molten solution. Taking an exploratory approach with the use of observation and expert interviews, this study examined where and how unwanted elements enter the recycling system. Eight element types were identified and categorised after the intentionality of their addition and their desirability in the end product. Thereafter, this article proposes a taxonomy based on the way in which these are present inside the furnace before melting, suggesting that the manner elucidates the entry points where impurities are introduced in the recycling stream. By introducing a taxonomy, this study aims to pave the way for developing strategies and research on how to minimise or prevent the presence of these elements in recycled metals, thereby increasing the quality of the recycling process.

While steel is the most recycled material, it is often downcycled due to the accumulation of unwanted elements. These not only diminish its quality but also represent a resource loss. Previous research has looked into the causes behind the accumulation of tramp elements in the metal and the resource loss stemming from current recycling processes. Nevertheless, these rarely consider the complex interactions between industrial practices, resource flows and system constraints, nor examine time delays in the system. This exploratory study is the first attempt to use Causal Loop Diagrams to bring together and map these interdependencies, incorporating time delays to better understand the accumulation of unwanted elements and its impact on the future of the steel recycling industry. The findings suggest that persisting with current practices risks creating accumulations of overly-contaminated, unusable scrap, thereby jeopardising the future of metal recycling.

The global expansion of solar energy presents a paradox: while it is a key sustainable technology, a comprehensive waste management strategy for decommissioned solar panels remains insufficient. Previous studies have examined this issue, yet waste volume estimations remain incomplete due to the exclusion of early waste streams and the failure to account for temporal fluctuations in key variables. This study addresses these gaps by employing System Dynamics Modelling (SDM) to capture a more nuanced understanding of the heterogeneity of decommissioned panels. The findings reveal significant discrepancies between projections from conventional static models and those generated by the developed model, underscoring the need for more adaptive forecasting methods that account for temporal variations and the evolving characteristics of decommissioned panels. Furthermore, this paper highlights the inefficiencies of uniform waste management approaches, emphasizing the need for differentiated strategies based on panel characteristics. Crucially, the findings challenge the recycling-centric paradigm by exposing the overlooked potential of functional discarded panels, advocating for circular strategies that prioritize reuse and secondary markets.

Uncertainties with respect to the chemical composition of scrap limit its suitability as an input to recycling. This study offers an alternative approach in dealing with this concern and explores the hypothetical case where this uncertainty is nonexistent. The effect of fully knowing the scrap composition is simulated using an optimization software adopted to scrap-based, stainless-steel production. Through the systematic implementation of this information-driven model in the studied cases, the results suggest that with access to perfect information, recycling incentives can be realized. Essentially, the steel scraps’ consumption increased since it was possible to select and combine scrap quantities with varying composition profiles to achieve the targeted product compositions. This also meant that elements already in the scrap were allocated in a manner that was less dependent on pure alloy additions. Being able to demonstrate the value of information on scrap composition could rationalize upgrades on current scrap management systems.

The current nature of steel design and production is a response to meet increasingly demanding applications but without much consideration of end-of-life scenarios. The scrap handling infrastructure, particularly the characterization and sorting, is unable to match the complexity of scrapped products. This is manifested in problems of intermixing and contamination in the scrap flows, especially for obsolete scrap. Also, the segmentation of scrap classes in standards with respect to chemical compositions is based on tolerance ranges. Thus, variation in scrap composition exists even within the same scrap type. This study applies the concept of expected value of perfect information (EPVI) to the context of steel recycling. More specifically, it sets out to examine the difference between having partial and full information on scrap composition by using a raw material optimization software. Three different scenarios with different constraints were used to appraise this difference in terms of production and excess costs. With access to perfect information, production costs decreased by 8–10%, and excess costs became negligible. Overall, comparing the respective results gave meaningful insights on the value of reestablishing the compositional information of scrap at the end of its use phase. Furthermore, the results provided relevant findings and contribute to the ongoing discussions on the seemingly disparate prioritization of economic and environmental incentives with respect to the recycling of steel.

Sustainable entrepreneurship, that is, venturing with the aim of contributing to a shift of practices towards environmental and social sustainability, is an increasingly prominent phenomenon. This article investigates how sustainability ventures orient between dual – commercial and environmental – logics when conducting the legitimation work necessary to secure their first major partnership with an incumbent firm. Specifically, we study multiple cases of partnerships on food waste reduction. This setting is characterised by limited tension between the two logics, which implies that ventures are not forced into hybridity. We find some indications that ventures are able to draw on both types of logic to legitimate their ventures. However, the dominant pattern is that sustainability ventures tend to orient their legitimation work around a salient founding logic. Our analysis suggests that this pattern can be attributed partly to organisational imprinting, but also to legitimation work in this context being inherently logic-specific to a significant degree. This seems to be particularly true for ventures with a salient environmental logic. .

This paper is concerned with the management of multi-plant manufacturing networks. Two key concepts in this domain are plant roles and plant autonomy in the context of operations strategy decision-making. We investigate the relationship between these two concepts and their impact on plant performance. We use data from 102 manufacturing plants belonging to multi-plant networks. The results suggest a relationship between plant roles and operations strategy decision-making structures. Plants with high levels of decision-making autonomy typically have high levels of production, supply chain, and development competences, while plants with a low level of decision-making autonomy are primarily those with only production site competences. Integrated structures for operations strategy decision-making, which include both the network level and the plant level, exist for all plant types and are thus not restricted to plants with a certain set of site competences. In accounting for both the plant type and decision-making structure, we were unable to detect any significant differences between groups in terms of performance effects. Instead, it seems that the fit between plant type and decision-making structure is important and that choosing the right type of operations strategy decision-making structure moderates the performance of plants with low site competence levels.