Social sustainability is largely challenged by unequal exchange within global supply chains (GSCs). Unequal exchange, commonly understood as the net appropriation of value from the Global South to the Global North, arises from power asymmetries in the world economy. As a consequence of the power asymmetries, the distributions of social costs and benefits across countries along GSCs are largely imbalanced. This intensifies regional disparities and obstructs global progress toward social sustainability. However, a comprehensive understanding of the unequal social consequences across multiple dimensions for countries along GSCs is still lacking. This study examines the degrees and patterns of unequal distribution between social benefits and costs along GSCs at the country and sector levels in 2019. To this end, we propose a scale-free GSC Theil index based on the multi-regional input–output framework to measure how far a GSC deviates from an egalitarian state where workers’ benefits align with their social costs. Our results reveal pronounced degrees of unequal distribution between labor compensation and job-quality-related social costs along GSCs. Across sectors, we find labor compensation is more misaligned with social costs associated with vulnerable employment , low-skilled jobs , and gender inequality in high-value GSC sectors. In contrast, in labor-intensive sectors, the unequal distribution is more pronounced for social costs associated with occupational health damage and working hours . Across regions, developing countries, despite suffering from a large global share of social costs, are confined to positions with limited labor compensation, whereas developed regions externalize these costs and capture more social benefits. • Distributions of social benefits and costs are highly unequal across regions in GSCs. • Greater unequal distribution occurs between social benefits and job-quality costs. • Key GSCs contributing to unequal distribution differ across social dimensions. • Developed regions secure more social benefits in GSCs while offshoring social costs. • Developing regions in low-value GSC positions bear greater shares of social costs.

Mango is traditionally classified as a climacteric fruit, in which ethylene and abscisic acid (ABA) play central roles in ripening. Mango fruit is cultivated only in tropical and some subtropical regions, therefore, to meet global demand, cold storage is widely used during transport, followed by a ripening period. This study evaluated the effects of simulated commercial transit cold storage (9 °C, > 85% relative humidity [RH]) used during the sea freight supply chain from the Southern hemisphere to the UK, by comparing the two main mango fruit cultivars (‘Keitt’ and ‘Kent’) imported into the UK market. Fruit were ripened with and without prior cold storage (control) and the synthesis and catabolism of key ripening regulators (ABA and its metabolites) as well as associated changes in quality attributes (internal colour, firmness, individual sugar content) were investigated. Cold storage delayed firmness loss, extending the postharvest life from 10 to 12 days to 29 days. ABA accumulated throughout ripening in control fruit but was depleted in cold-stored fruit for both cultivars, indicating its central role in mango ripening; cold storage fundamentally altered ABA dynamics by suppressing accumulation and modifying spatial distribution and conjugation patterns in a cultivar and pulp dependent manner. Additionally, sugar metabolism was altered, likely affecting taste, and meaning that, in cold-stored fruit, sugar content no longer correlated with total soluble solids (TSS). These findings highlight ABA as a potential target for postharvest management and underscore the need for integrated quality indices beyond TSS to improve fruit evaluation and reduce losses in global supply chains. • Cold storage depletes abscisic acid and disrupts normal ripening progression. • Sugar metabolism is altered, decoupling sugars from soluble solids content. • No chilling injury symptoms were observed after storage at 9 °C.

Logistical agility at the Strait of Hormuz: A framework for managing sustained geopolitical disruption risks in global supply chains

Traditional computer vision-based quality perception lacks depth information, limiting its application to reliable food quality management in the post-harvest supply chain. Three-dimensional (3D) reconstruction technology captures detailed surface geometry and internal structural information for reliable non-destructive quality inspection. Combined with emerging artificial intelligence (AI) technologies, 3D data-driven adaptive management brings potential for next-generation post-harvest quality management. This review analyzed 90 major related studies during 2015–2025, covering high-throughput 3D in-line inspection, high-resolution tomography, portable 3D sensing, and AI-driven 3D reconstruction technologies. Post-harvest supply chain application scenarios are mainly distributed in post-harvest processing (33 studies), manufacturing (29 studies), distribution (15 studies), and consumption (13 studies). Among them, fruit and vegetable products are the most intensively researched, highlighting the suitability and potential benefits of 3D reconstruction for these types of products. Besides, multiple 3D reconstruction technologies have been validated for postharvest evaluation, with X-ray CT dominating postharvest processing, manufacturing, and distribution, and portable RGB imaging devices dominating application in consumption. Besides, relevant 3D reconstruction analysis is evolving from geometry-driven to AI-enhanced analysis and management, highlighting the growing role of 3D reconstruction technology in intelligent, traceable, and sustainable post-harvest supply chain quality management. In the future, by integrating digital twins, IoT, and blockchain technologies, it is expected to build a transparent, tamper-proof quality traceability and control system across the global food supply chain. • 3D reconstruction significantly improves non-destructive food quality inspection. • AI integration expands 3D reconstruction applications in food supply chains. • Applications span post-harvest, processing, logistics, and consumer evaluation. • Enables automation in grading, manufacturing, cold chain, and dietary assessment. • Future trends include sensor fusion, XR, and digital twin for food quality control.

ABSTRACT We examined how strategic asset seeking, global value chain upgrading, and high control overseas governance drive the internationalization intensity of emerging market lead firms. Using 2010–2023 panel data from 60 Chinese clean energy equipment manufacturers, we estimated a log-linear model based on the Objective-Trajectory-Governance framework. Results reveal that these three dimensions significantly promote internationalization intensity, though effects vary significantly across product types, geographic regions, and ownership structures. Mechanism tests link the three dimensions to technological innovation, supply chain optimization, and cost reduction. We contribute by empirically operationalizing this framework, demonstrating its boundary conditions, and providing novel insights into how latecomers transition into global leaders.

Global supply chains are increasingly exposed to multi-source uncertainties, ranging from geopolitical tensions to climate extremes, making the accurate and interpretable prediction of disruptions an urgent operational priority. Existing predictive models often rely on either shallow statistical learners, which struggle with high-dimensional interactions, or deep neural networks, which trade off interpretability for marginal performance gains. To address this gap, we propose an interpretable machine learning framework that couples a feature-attention mechanism with a gradient-boosted decision tree ensemble for early warning of shipment-level disruption events. First, a dedicated attention module is trained to assign importance weights to 14 heterogeneous risk factors, generating an interpretable feature ranking that highlights pivotal signals such as lead-time volatility and geopolitical risk. The reweighted features are then fed into a gradient boosting classifier, which effectively captures non-linear patterns and interaction effects. Evaluated on a publicly available dataset of 5000 international freight records available on Kaggle, the proposed framework achieves an AUC of 0.8213 (±0.0002 over three independent runs), matching the best-performing baseline (standard gradient boosting, 0.8212 ± 0.0001) and surpassing logistic regression (0.777), random forest (0.806), and a standalone feature-attention network (0.805). The attention module preserves full predictive accuracy while adding an interpretability layer that conventional black-box implementations lack. Notably, the framework preserves the predictive accuracy of gradient boosting while enhancing interpretability through attention-based feature ranking and dual-perspective importance analysis, achieving a precision of 0.770 and a balanced F1-score of 0.781. The convergence of attention-based interpretability and ensemble learning efficiency provides supply chain managers with a transparent decision-support tool—distinguishing genuine “tipping points” from “false alarms” and enabling targeted risk mitigation under deep uncertainty.

ABSTRACT Global trade has shifted from the traditional exchange of finished goods to the widespread integration of global value chains (GVCs). The expansion of GVCs has important implications for both developed and developing economies. We construct a framework to explain how GVC participation affects GDP per capita and how these effects differ across countries at different income levels by analysing types of GVC activities and the mediating role of total factor productivity (TFP). We use advanced econometric methods, including fixed-effects models, two-stage least squares estimators, system generalized method of moments estimators, and fixed-effects quantile regression, to provide detailed insights into the differential effects of GVC participation. Our results show that the impact of GVC participation on economic growth varies by income level. For low-income countries, backward GVC participation significantly affects GDP per capita, while forward GVC participation has significant effects only for upper-middle- and high-income countries. We also find that TFP mediates the impact of GVC participation on GDP per capita.

Value capture in the emerging medical cannabis global value chain: insights from South Africa.

Global supply chains have demonstrated acute vulnerability to systemic disruptions, as evidenced by the cascading effects of the COVID-19 pandemic, the Suez Canal blockage of 2021, and escalating geopolitical trade tensions. This case study-based research investigates the supply chain resilience strategies employed by four multinational firms across the automotive, pharmaceutical, electronics, and fast-moving consumer goods (FMCG) sectors during the period 2020 to 2024. Applying Yin's (2018) multiple-case study design, the research analyzes how firms implemented supply chain resilience capabilities including redundancy, flexibility, visibility, and collaboration in response to major disruptions. Cross-case analysis reveals that firms achieving superior recovery performance combined proactive redundancy investment with dynamic supply chain reconfiguration capabilities and digital visibility infrastructure. Single-source dependency emerged as the most consistent predictor of prolonged disruption impact, while collaborative supplier relationships accelerated recovery across all four cases. The study develops a Disruption-Recovery-Resilience (DRR) cycle model and offers practical implications for supply chain strategists in disruption-prone operating environments.

Abstract In the last two decades, rising greenhouse gas (GHG) emissions have driven into global warming. Efforts like expanding renewable energy, produce an increased metal demand, especially copper, which also emits GHGs. Trying to mitigate this, the European Union established the Emissions Trading System (EU-ETS) to regulate and price industrial GHG emissions. This study quantifies the GHG emissions associated with copper production and examines the impact of the EU-ETS and Carbon Border Adjustment Mechanism (CBAM) on copper costs across different temporal and geographical scenarios, concretely across the supply chain of four different countries. A Life Cycle Carbon Footprint assessment was conducted using the Environmentally Extended Multi-Regional Input-Output Analysis with the EXIOBASE database. A sensitivity analysis was also conducted. The results showed that Poland, despite having the lowest emissions, was the most penalized under the EU-ETS, while China, with one of the highest GHG emissions, faced minimal costs. This discrepancy arises as cost from the EU-ETS are based on an allocation principle, which benefits non-EU countries and EU countries that primarily import copper. The study suggests that incorporating copper into the CBAM could help balance emissions and costs by accounting for the GHG emissions of imported products. The sensitivity analysis revealed that countries with higher import levels, such as Spain and Germany, have less control over GHG emission costs by altering their electricity mix. This emphasises the need for coordinated global targets to effectively mitigate emissions.

Driven by digitalization, infrastructure expansion, and the clean energy transition, global mining has grown rapidly over the past two decades, intensifying land-use pressure and sharpening trade-offs between resource extraction and biodiversity conservation. However, spatially explicit assessments of mining-related biodiversity impacts and the tracing of these impacts along mining supply chains remain limited. Here, we present a spatially explicit assessment framework that links local biodiversity intactness loss, expressed as mean species abundance, to global potential species loss, and we couple this framework with a multiregional input-output model to trace consumption-driven impacts along global mining supply chains. We find that biodiversity loss impacts associated with global mining land use are nearly twice as high as those of previous global estimates. Hotspots in Indonesia, New Caledonia, Australia, Brazil, and Peru account for 57% of the global mining-related biodiversity impacts. Coal, precious metals, nickel, iron, and copper extraction together contribute 82% of the total impact. Due to international trade, 77% of mining-related biodiversity footprints occur outside the countries of final consumption, with demand from China, Europe, Japan, and the United States accounting for 58% of the total footprints. Our results improve the transparency of biodiversity impacts embedded in global mining supply chains and support hotspot-oriented biodiversity conservation and supply chain governance.

The Belt and Road Initiative (BRI), the world's largest ongoing infrastructure endeavor, tackles urgent development needs in underserved regions. However, its construction phase, especially material extraction and processing, may have far-reaching climate implications that remain poorly known. Here, we present the first global, project-level assessment of greenhouse gas (GHG) emissions embodied in the construction of over 700 individual BRI projects (2008-2024) across 105 countries. By integrating a detailed project data set with a physically grounded global supply chain model that captures material-specific sourcing patterns, we estimate 134 Mt of CO2 equiv emissions, around half of which occur outside the project host countries. This reveals the previously unquantified global supply chain reach of the BRI. Depending on operational performance, BRI renewable energy projects may achieve emission reductions comparable in scale to the construction-phase emissions within 2 years. Our results underscore the need and opportunity to embed cleaner material sourcing and sustainability assessment into BRI and other transnational infrastructure efforts.

ABSTRACT As the global apparel industry navigates increasing sourcing complexity, digital transformation, and evolving tariff structures, apparel firms are under growing pressure to recruit managerial talent capable of coordinating cross-functional operations and global supply chain demands. Against this backdrop, this study investigates employer expectations for managerial roles in Bangladesh’s apparel industry, the world’s second-largest exporter of ready-made garments. Drawing on Kunz’s (1995) Behavioural Theory of Apparel Firms, this study analysed 508 managerial job advertisements using content analysis, point-biserial correlation, and one-way ANOVA. Findings show that professional experience, communication skills, and digital proficiency are consistently prioritised across managerial categories, while technical educational degrees are not significantly associated with broader job responsibilities. Significant differences also emerged across job categories, with finance, marketing, and executive management roles exhibiting greater breadth of responsibilities than merchandising and operations. The study offers implications for recruitment, workforce development, and curriculum alignment in export-oriented and globally integrated apparel industries.

ABSTRACT This study uses the novel measure of aggregate trade restrictions (MATR) to explore the dynamic effects of trade restrictions on global value chain (GVC) positions across 146 countries (1990 to 2015). Employing the local projection method, this study finds that the aggregate MATR persistently and negatively affects GVC downstreamness but not upstreamness. Decomposition analyses on the five MATR dimensions show diverse outcomes: import restrictions reduce upstreamness, while invisible transfer restrictions increase it. Finally, heterogeneity analyses show that the effects of trade restrictions vary significantly across countries and sectors. These findings offer actionable insights for policymakers on balancing protectionism and global integration.

This research investigates the critical determinants of Foreign Direct Investment (FDI) in Latin America within the context of the post-pandemic global supply chain reconfiguration. As multinational corporations increasingly adopt "nearshoring" and "green-shoring" strategies to mitigate geopolitical risks, Latin America has emerged as a strategic destination. However, the transition from a commodity-dependent investment model to high-value-added sectors is hindered by significant structural constraints. This study analyzes the interplay between three primary pillars: infrastructure, energy, and talent. Utilizing a mixed-methods approach combining econometric panel data analysis (2014–2024) with a Comparative Qualitative Analysis (QCA) the research identifies the specific bottlenecks that deter high-quality capital. The findings reveal that while the region possesses a comparative advantage in renewable energy potential, aging electrical grids and "transmission gaps" prevent the full realization of green-shoring opportunities. Furthermore, the "digital divide" and deficiencies in 5G deployment act as ceilings for Industry 4.0 integration. Most critically, a profound STEM "skills gap" and the misalignment between academic curricula and industrial needs limit the domestic "knowledge spillover" effect, trapping several nations in low-skill manufacturing enclaves. The results suggest that geographical proximity to the North American market is insufficient without a synchronized upgrade of physical and intangible assets. The study concludes that institutional stability and a triple-helix collaboration (state-industry-academia) are essential to dismantle these bottlenecks. Ultimately, the research provides a strategic roadmap for policymakers to harness FDI as a catalyst for sustainable economic growth and technological upgrading in the region.

Understanding how domestic and foreign-invested firms across China's provinces participate in domestic and international production networks has become increasingly important in both scientific research and policy analysis. However, existing data infrastructures, most notably input-output (IO) datasets, does not adequately support this line of inquiry. Conventional inter-country IO (ICIO) databases treat China as a single, homogeneous economy and therefore overlook substantial variation at the provincial level. Meanwhile, China's inter-provincial IO tables provide no direct linkages between individual provinces and their foreign trading partners. Moreover, firm ownership heterogeneity is largely absent in most available IO datasets. To address these limitations, we construct a new ICIO dataset that integrates China's inter-provincial IO tables-disaggregated by domestic and foreign-invested firms-with the OECD's Activities of Multinational Enterprises-ICIO data and detailed Chinese customs statistics, all within a globally consistent accounting framework. The resulting database comprises 26 sectors, 107 regions, and 2 ownership categories for the benchmark years 2007, 2012, and 2017. These data offer a new foundation for empirical work in economics, environmental assessments, network analyses, and their interdisciplinary applications, particularly for studies examining how firms with different ownership structures across Chinese provinces engage in global supply chains.

The COVID-19 pandemic revealed a poor level of preparedness by the manufacturing sector in building resilient operations and logistics framework to respond to severe global supply chain disruptions. This research focuses on the effectiveness of data-driven innovation strategies in mitigating the impacts on humanitarian operations of Covid-19 related supply chain interruptions. The review is supported by the outcome of a major conceptual issues and uncovered two main topics of debate notably (i) the significant role of big data analytics and Artificial Intelligence in boosting firm performance by untangling supply chain disruptions and (ii) the paucity of research in this emerging area of literature. Consequently, this paper proposes a conceptual model for humanitarian supply chain operations to highlight how big data analytics and AI technologies could be deployed by firms to gain a competitive advantage by responding promptly and effectively to severe disruptions akin to those experienced during recent COVID-19 pandemic.

Nickel is a critical metal with broad applications in stainless steel and nickel-based alloys. In recent years, its rapidly growing use in secondary batteries has become particularly important, underscoring its strategic role in enabling low-carbon, green, and sustainable societal development. As high-grade nickel sulfide resources become increasingly scarce and global demand for nickel accelerates-fueled by the expansion of clean energy technologies-there is a pressing need to develop environmentally benign extraction strategies for low-grade nickel sulfide ores to safeguard the stability and sustainability of the global nickel supply chain. Here we report a low-temperature predominantly solid-state process for extracting nickel from unconventional, low-grade ultramafic ores, estimated to contain approximately 45 million tonnes of untapped nickel. The method leverages cheap metallic iron as a nickel getter offering a sustainable pathway for high-value nickel recovery aligned with decarbonized metal production. The process produces ferronickel alloys (16-24% nickel) with several key advantages, including rapid processing time ( ~ 3 hours), low operational temperatures ( < 950 °C), and the elimination of SO2 emissions. Temperature, atmosphere, and iron addition were tailored to create favorable thermodynamic conditions within the reactor, enabling selective partitioning of nickel into metallic alloys while effectively sequestering sulfur as stable solid sulfide phases. Controlled tuning of alloy particle size and morphology facilitates efficient separation from gangue, yielding a ferronickel which can be converted to battery-grade nickel through conventional refining. This method, verified to mini-plant scale broadens the technological landscape of nickel extraction and contributes to a more equitable and resilient global nickel supply chain.

Carbon risk and corporate supply chain resilience.

Trade openness amplifies water-saving benefits of global photovoltaic supply chains.