The steel industry is a major contributor to CO2 emissions, accounting for 7 of global emissions. The European steel industry is seeking to reduce its emissions by increasing the use of electric arc furnaces (EAFs), which can produce steel from scrap, marking a major shift towards a circular steel economy. Here, we show by combining trade with business intelligence data that this shift requires a deep restructuring of the global and European scrap trade, as well as a substantial scaling of the underlying business ecosystem. We find that the scrap imports of European countries with major EAF installations have steadily decreased since 2007 while globally scrap trade started to increase recently. Our statistical modelling shows that every 1,000 tonnes of EAF capacity installed is associated with an increase in annual imports of 550 tonnes and a decrease in annual exports of 1,000 tonnes of scrap, suggesting increased competition for scrap metal as countries ramp up their EAF capacity. Furthermore, each scrap company enables an increase of around 79,000 tonnes of EAF-based steel production per year in the EU. Taking these relations as causal and extrapolating to the currently planned EAF capacity, we find that an additional 730 (SD 140) companies might be required, employing about 35,000 people (IQR 29,000-50,000) and generating an additional estimated turnover of USD 35 billion (IQR 27-48). Our results thus suggest that scrap metal is likely to become a strategic resource. They highlight the need for a massive restructuring of the industry's supply networks and identify the resulting growth opportunities for companies.

Stakeholder trust is central to advancing sustainable development, yet it is increasingly strained as corporate sustainability commitments expand faster than verifiable performance. This study examines how authenticity and transparency in corporate sustainability practices influence the formation and maintenance of stakeholder trust under conditions of scrutiny and institutional accountability. Using longitudinal evidence from European publicly listed firms and incorporating both comparative and contextual analytical perspectives, the research shows that trust grows when organizations communicate candidly, report measurable progress, and reinforce their commitments through credible assurance. Trust is shaped not by the volume of sustainability reporting but by the sincerity and consistency of alignment between commitment and demonstrable action. The findings highlight the role of institutional safeguards that encourage honest disclosure and responsible conduct as firms navigate sustainability transitions. This work advances the understanding of sustainable development governance by clarifying how credible communication and transparent practices can reinforce accountability and strengthen legitimacy. The insights offer practical direction for organizations and policymakers working to foster sustainability cultures grounded in integrity and long-term trust.

The escalating climate crisis, coupled with intensifying stakeholder demands and regulatory pressures, has placed sustainability at the forefront of global supply chain management. sustainable supply chain management (SSCM) has emerged as a transformative field, integrating economic, environmental, and social dimensions to address critical challenges in achieving a sustainable future. This study presents a rigorous systematic review and bibliometric analysis of SSCM research from 2000 to 2023, offering novel insights into its evolution, geographical contributions, and key thematic areas such as green supply chain practices, closed-loop systems, ethical considerations, and advanced sustainability metrics. By uncovering critical research gaps and emerging trends, including the adoption of advanced technologies, circular economy frameworks, and collaborative governance models, this review provides a strategic blueprint for advancing both academic inquiry and practical implementation. The findings emphasize the pivotal role of SSCM in driving systemic change, fostering corporate resilience, and catalyzing the transition toward a net-zero and resource-efficient global economy. This work serves as a cornerstone for academics, policymakers, and industry leaders to collectively reimagine supply chains as engines of sustainable transformation.

Rare earth elements (REEs) are critical to a wide range of clean and high-tech applications, yet global trade dependencies expose countries to vulnerabilities across production networks. Here, we construct a multi-tiered input-output trade network spanning 168 REE-related product codes from 2007-2023 using a novel AI-augmented statistical framework. We identify significant differences between dependencies in upstream and intermediate (input) products, revealing that exposure and supplier concentration are systematically higher in input products, while systemic trade risk is lower, suggesting localized vulnerabilities. By computing network-based dependency indicators across countries and over time, we classify economies into five distinct clusters that capture structural differences in rare-earth reliance. China dominates the low-risk, high-influence cluster, while the EU and US remain vulnerable at intermediate tiers. Regression analyses show that high exposure across all products predicts future export strength, consistent with import substitution. However, high systemic trade risk in input products like magnets, advanced ceramics or phosphors, significantly impedes the development of comparative advantage. These results demonstrate that the structure of strategic dependencies is tier-specific, with critical implications for industrial resilience and policy design. Effective mitigation strategies must move beyond raw material access and directly address country-specific chokepoints in midstream processing and critical input production.

Scope 3 emissions are the largest and most complex component of corporate carbon footprints, presenting major challenges for decarbonization. This study analyzes a panel dataset of publicly listed European companies (2002–2023) using advanced econometric methods to assess how Scope 3 emission reduction strategies affect Environmental, Social, and Governance (ESG) performance. We employ Panel Autoregressive Distributed Lag (ARDL) models to capture both short- and long-term dynamics, Method of Moments Quantile Regression (MMQR) to examine effects across ESG performance levels, and Necessity Condition Analysis (NCA) to identify critical thresholds for high ESG outcomes. Our results show that proactive investments in Scope 3 emission reduction and enhanced supply chain transparency significantly improve ESG performance, with their impact intensifying among higher-performing firms. MMQR reveals that transparency and investment are especially influential at advanced ESG tiers, while NCA indicates that no single factor is strictly necessary, but combinations of high investment and transparency are critical for superior sustainability outcomes. These findings highlight the need for integrated strategies - balancing financial commitments, operational transparency, and sustainable sourcing - to achieve measurable and lasting ESG gains. The study provides actionable insights for managers and policymakers, emphasizing that addressing Scope 3 emissions is essential not only for regulatory compliance but also for building resilience and competitive advantage in a low-carbon economy. By clarifying dynamic pathways and necessary conditions, this research offers a robust empirical foundation for integrating decarbonization into core corporate strategy.

PurposeThis study investigates the impact of smart supply chain construction (SSCC) on corporate performance (CP) in European firms from 2015 to 2023. It examines how resilience and dynamic capabilities contribute to enhancing CP through improved operational efficiency and adaptability.Design/methodology/approachA mixed-methods approach was adopted, combining Difference-in-Differences (DID), necessity condition analysis (NCA) and fuzzy-set qualitative comparative analysis (fsQCA). The analysis draws on a comprehensive dataset of publicly listed European companies, identifying causal relationships and critical configurations influencing CP.FindingsThe findings reveal that SSCC significantly enhances CP by strengthening operational resilience, fostering dynamic capabilities, and optimizing resource allocation. Key drivers include proactive and reactive resilience capabilities, R&D investments and robust resource utilization. Configurations involving smart technologies and effective resource management emerged as essential enablers of superior performance.Originality/valueThis study uses a rarely applied multi-method empirical framework to examine SSCC's transformative potential and integrates the resource based view (RBV) and dynamic capabilities theory. It highlights the interplay of resilience, innovation and resource management in boosting CP while addressing critical gaps in smart supply chain research. It provides empirical insights and actionable strategies for businesses and policymakers to enhance resilience and corporate performance in dynamic markets.

PurposeComplexity has been called the 21st-century supply chain (SC) challenge. Most SC managers view it as a necessary evil, ever-present, costly and tough to manage, and few prioritize it. Still, anecdotes suggest some leverage it to drive operational excellence. This study aims to explore how they do it, delving into the development of a complexity management capability, under what circumstances it emerges and its effect on competitiveness.Design/methodology/approachTo better understand why, and how, companies develop (or not) a distinctive SC complexity management capability, this study employed an inductive study of 10 leading European companies, each operating a complex SC.FindingsAlthough SC complexity raises costs, increases disruptions and makes decision-making difficult, few companies have made complexity management a priority. Among those, most focus on reducing or absorbing complexity to improve operational excellence. A few invest to develop a distinctive SC complexity management capability. They manage complexity for market success. The interaction among competitive pressures, managerial attitudes and investments delineate a dynamic capability development process.Research limitations/implicationsDespite extensive research on complexity drivers, the tools used to manage SC complexity and the impact of SC complexity on performance, the interplay among factors that promote, or hinder, the development of an SC complexity capability continues to be poorly understood. By mapping the complexity capability development process, this study explicates a more nuanced approach to managing SC complexity that can yield a competitive edge.Practical implicationsSC complexity prevails because the dynamic, iterative complexity capability development process is overlooked. Managers can use the complexity capability roadmap to assess the cost/benefits of pursuing a distinctive complexity management capability more accurately.Originality/valueThis study demystifies the development of a complexity management capability, showing how some companies develop the capability to distinguish between value-added and value-dissipating complexity and thus become empowered to leverage SC complexity for competitive advantage.

Integrating green supply chain strategies and circular economy (CE) practices holds substantial potential for promoting environmental sustainability and reducing CO2 emissions. This study investigates the synergy between green supply chain practices, circular economy, and economic growth (RGDP) impacts on carbon emissions in 13 selected European Union (EU) countries, using a comprehensive panel dataset from 2000 to 2022. We employ both linear and nonlinear panel ARDL models, along with causality tests, to examine how CO2 emissions respond to changes in green supply chain management (GSCM), real GDP (RGDP), and various recycling practices, including bio-waste, municipal waste, and packaging waste. Our findings reveal that GSCM practices significantly reduce carbon emissions in the long run, while economic growth (RGDP) and municipal waste generation correlate positively with increased CO2 emissions. Interestingly, the nonlinear ARDL model highlights that only recycling packaging waste (RWP) exhibits a positive long-run effect on reducing emissions. Additionally, the method of moments quantile regression (MMQR) analysis indicates that the impact of GSCM is more pronounced at higher quantiles of CO2 emissions, whereas the effect of RGDP on emissions remains inconsistent. These results underscore the crucial need to adopt and enhance green supply chain practices within a circular economy framework to achieve substantial carbon emission reductions, holding significant implications for carbon emissions policies in the selected EU countries.

Recent events exemplified the fragility of national and international supply networks (SNs), leading to significant supply shortages of essential goods, such as food and medicines. Severe disruptions propagating along complex SNs can expose entire regions or countries to these risks. A lack of data and quantitative methodology has hitherto prevented an empirical quantification of the vulnerabilities of populations created by SN disruptions. Here, we propose supply network stress-testing (SNST) as a new data-driven methodology to quantify product-level supply losses of administrative districts that result from cascading supply disruptions between establishments. We demonstrate SNST on a large fraction of the Austrian food SN - composed of the pork production network from farms to meat processors and the distribution network of large food retailers - containing 23,001 establishments, 44,730 supply links, and 116 administrative districts. We rank all establishments with respect to their systemic criticality for the population using a novel systemic risk index, $ ESRI_i<^>{crit} $ ESRIicrit. We identify 28 facilities that - in case of failure - are expected to cause severe supply shortages to up to 20% of the population. SNST enables governments and industry to stress-test national supply networks of critical goods, to identify their weak spots and make them more resilient to future crises.

Globalization has had undesirable effects on the labor standards embedded in the products we consume. This paper proposes an ex-ante evaluation of supply chain due diligence regulations, such as the EU Corporate Sustainable Due Diligence Directive (CSDDD). We construct a full-scale network model derived from structural business statistics of 30 million EU firms to quantify the likelihood of links to firms potentially involved in human rights abuses in the European supply chain. The 900 million supply links of these firms are modeled in a way that is consistent with multiregional input-output data, EU import data, and stylized facts of firm-level production networks. We find that this network exhibits a small world effect with three degrees of separation, meaning that most firms are no more than three steps away from each other in the network. Consequently we find that about 8.5% of EU companies are at risk of having child or forced labor in the first tier of their supply chains, about 82.4% are likely to have such offenders at the second tier and more than 99.1% have such offenders at the third tier. We also profile companies by country, sector, and size for the likelihood of having human rights violations or child and forced labor violations at a given tier in their supply chain, revealing considerable heterogeneity across EU companies. Our results show that supply chain due diligence regulations that focus on monitoring individual buyer-supplier links, as currently proposed in the CSDDD, are likely to be ineffective due to a high degree of redundancy and the fact that individual company value chains cannot be properly isolated from the global supply network. Rather, to maximize cost-effectiveness without compromising due diligence coverage, we suggest that regulations should focus on monitoring individual suppliers.

Extant research highlights how resilient organizations effectively cope with supply chain disruptions to enhance firm performance. Yet, it remains unclear how an organization's most basic resource—that is, its individual employees—facilitates such resilience. Through a qualitative study that includes 44 interviews across four manufacturing companies, we identify critical individual attributes and show how these attributes contribute to organizational resilience. Our findings advance a framework of cognitive, emotional, and behavioral attributes through which employees facilitate resilient outcomes. Our empirically driven model enriches theory about resilience within the supply chain management domain and elevates the importance of managerial approaches associated with human resource management activities, which affect organizations' repositories of the employee attributes that foster resilience.

Why does the world seem to suddenly be fighting over antibiotics? For one thing, production is increasingly concentrated in two countries - China and India. For another, an entire industry seems surprised by the rapid increase in demand for drugs after the decline during the Covid-19 pandemic. Our results suggest three broad policy recommendations. First, health policies should invest into improvements of the demand tracking, planning, and forecasting infrastructure. Health policies should focus on shortages of drugs for which substitutes are also unavailable. Second, the health system seeks to guarantee the sufficient provision of drugs at low prices. The international division of labour that has emerged seems to provide price efficiency, but supply security risks have become increasingly evident. This implies a refocusing of policies towards greater supply resilience. Third, the market structure should not only mitigate supply risks, but the market design should ideally internalise supply security risks, thereby rendering ad-hoc policy interventions obsolete.

Existing research suggests a link between a country’s infrastructure quality and its economic performance. However, no country can invest in all types of infrastructure across the country. Therefore, it is critical to identify and assess multiple indicators of infrastructure for regions within a country and focus on improving infrastructure as identified by the weak indicators. Research investigating the determinants of regional wealth and economic growth is limited. In this comprehensive research including all 35 regions in Austria, we began by evaluating the relationship between logistics-related infrastructure and regional gross domestic product. In the process, other indicators, and mediators such as knowledge infrastructure, business attractiveness emerged as impacting gross domestic product. The findings help to better understand the relative importance of diverse logistics indicators influencing regional economic development and provide insights for policy decision-making.

Knowledge graphs (KGs) have in recent years gained a large momentum both in academic research and in business applications. They have become a bridge between databases, artificial intelligence (AI), data science, the (semantic) web, linked data, and many other areas. In particular, in declarative AI, they have become a bridge between logic-based reasoning, and machine learning-based reasoning. Languages for KGs on the one hand, and systems for KGs – i.e., Knowledge Graph Managament System (KGMS) – on the other hand, have garnered increasing attention. Of particular importance are language and system extensions – such as probabilistic reasoning, numeric reasoning, etc. – supporting various real-world applications, and the business applications that can be built using such extensions. In this work, we give an overview of the Vadalog language and system, a KGMS. We focus on three areas: (1) a basic overview, including an introduction to dependencies, the Datalog and Vadalog languages, (2) the extensions of the system, including arithmetic and aggregation, real-world data interfaces, temporal reasoning, and machine learning, and (3) the business applications, including: corporate governance, media intelligence, supply chains, collateral eligibility, hostile takeovers, smart anonymization, and anti-money laundering.