Search Results (369)

Vietnam’s exports expanded dramatically from $14.5 billion in 2000 to $405 billion in 2024, elevating the country to the world’s 22nd largest exporter despite persistent global shocks. This paper introduces the application of the Causal Machine Learning Approach to Resilience Estimation (CLARE) to industry-level trade analysis, utilizing a comprehensive panel of 97 HS2 sectors from 2000 to 2024 (2425 observations) drawn from UN COMTRADE and WITS databases. We implement Double Machine Learning to estimate causal effects of the Global Financial Crisis (2008–2009) and COVID-19 pandemic (2020–2021) on export growth. Results reveal stark industry disparities: electrical machinery (HS85) exhibits exceptional resilience, fueled by 72% high-technology content and low product concentration, while knitted apparel (HS61) proves highly vulnerable. Fixed effect regressions substantiate core hypotheses: a 10-percentage-point increase in high-tech share elevates the resilience index by 0.031 points (approximately 4.1% relative to the sample mean); a one-standard-deviation reduction in product HHI (0.14 units) yields a 0.026-point gain (3.6% relative); and each additional FTA contributes 0.047 points (approximately 6.2% relative), with all estimates significant at conventional levels. Robustness encompassing alternative learners, detrended outcomes, and synthetic controls upholds findings. Policy recommendations center on accelerating high-tech global value chain integration—targeting semiconductors and electric vehicles—while optimizing CPTPP and EVFTA utilization (currently 35%) and mitigating US–China market concentration (45% of exports). These insights chart pathways for Vietnam’s Vision 2045 high-income ambition amid intensifying geopolitical and climate risks, providing a replicable framework for other export-reliant emerging economies. Full article

This study examines the dynamic interlinkages among energy, food, and metal commodity markets under geopolitical tensions using daily data from January 2022 to July 2025. The empirical framework integrates correlation analysis, Granger causality tests, and a Vector Error Correction Model (VECM) to capture both short- and long-run transmission mechanisms, with robustness assessed through impulse response functions, forecast error variance decomposition, and a Diebold–Yilmaz connectedness analysis across three structurally distinct geopolitical event windows. The results reveal asymmetric and sector-specific transmission patterns in which geopolitical risk significantly influences key commodity prices—particularly WTI crude oil, wheat, copper, and aluminium—confirming its role as a primary external shock driver. WTI emerges as the dominant transmitter of shocks, while industrial metals exhibit strong internal connectedness. Critically, gold’s role proves to be conditional and context-dependent: within an integrated energy–food–metal network under geopolitical stress, it functions primarily as a net receiver and passive absorber of macroeconomic uncertainty rather than as a systemic transmitter, a finding that complements, rather than contradicts, its established safe-haven role in financial asset pricing frameworks. These findings are subject to limitations, including reliance on futures price data and a linear VECM framework that may not fully capture nonlinear or regime-dependent dynamics. Full article

Surface water systems are increasingly exposed to multiple pressures generated by climate variability, intensified water resource exploitation, and evolving geopolitical dynamics. This study provides a novel contribution by identifying critical threshold effects and non-linear interactions that influence nitrate concentrations through an integrated information systems framework. It develops an integrated information-system-based analytical framework that combines hydrological, climatic, geopolitical, and strategic indicators to shape the broader contextual framework within which hydrological and climatic pressures operate, rather than serving as direct predictors. Considering the nitrate concentration in rivers as a key parameter of water quality, the paper goes beyond univariate analysis of nitrite concentration, examining its relationship with four explanatory variables: the Water Exploitation Index Plus (WEI+), the number of heat stress days (Heat_Stress), the Geopolitical Risk Index (GPR), and a proxy variable representing the presence of strategic infrastructure (Nuclear_State) using a Reduced Error Pruning Tree (REPTree) decision tree algorithm with 10-fold cross-validation. The results indicate that climatic stress emerges as the primary predictor, with a critical threshold of approximately 7.83 heat stress days, beyond which nitrate concentrations increase significantly. Under conditions of high climatic stress and intensive water exploitation (WEI+ ≥ 67.39), predicted nitrate levels exceed 20 mg/L and can reach extreme values of up to 58.82 mg/L. In contrast, low hydrological pressure (WEI+ < 0.39) combined with moderate climatic stress is associated with very low nitrate concentrations, around 2.75 mg/L. The model demonstrates strong predictive performance, with a correlation coefficient of 0.976, a Mean Absolute Error (MAE) of 0.593, a Root Mean Squared Error (RMSE) of 2.046, and a Receiver Operating Characteristic (ROC) area exceeding 0.94 for classification tasks. While geopolitical and strategic variables do not act as direct predictors, they contribute to shaping the contextual framework influencing water resource management and environmental vulnerability. Overall, the study highlights the non-linear and systemic nature of water quality dynamics and demonstrates the effectiveness of decision tree-based models within integrated information systems for supporting environmental monitoring and decision-making under conditions of climate stress and geopolitical uncertainty. Full article

This paper provides a comparative analysis of the economic crises in Argentina and Lebanon to derive policy-relevant lessons for the design of IMF-supported adjustment programs in fragile economies. Using a structured comparative case study approach, the study examines crisis dynamics, policy responses, and socioeconomic outcomes across both countries, with particular attention given to exchange rate collapse, banking sector distress, public debt, inflation, unemployment, and poverty. The findings suggest that programs centered primarily on macroeconomic stabilization and fiscal austerity, without adequate attention to institutional capacity, social protection, and debt restructuring, risk deepening economic contraction and social vulnerability. The Argentine experience shows that IMF-supported adjustment in institutionally fragile environments may fail to restore confidence or deliver sustainable recovery when reform sequencing is weak and complementary domestic policies are absent. For Lebanon, where the crisis is deeper and compounded by governance failures and geopolitical instability, IMF engagement appears necessary but insufficient on its own. The paper concludes that a sustainable recovery requires a hybrid strategy combining external financial support with country-specific reforms, including exchange rate unification, banking sector restructuring, debt resolution, stronger governance, and targeted social protection. Full article

As artificial intelligence (AI) systems become increasingly autonomous, scalable, and embedded in critical digital infrastructure, AI safety has emerged as a significant consideration for cybersecurity, system reliability, and institutional trust. Advances in large language models and agentic systems expand the threat surface to include misalignment, large-scale misuse, opaque decision-making, and cross-border risk propagation, while existing debates remain fragmented across technical, ethical, and geopolitical domains. This paper conducts a structured comparative analysis of AI safety perspectives from ten influential thinkers, examining them across five dimensions and reframing their insights through a cybersecurity lens spanning national governance, industry standards, and firm-level design. Building on this synthesis, the study proposes a layered control architecture that organizes technical safeguards, governance mechanisms, and human oversight into a defense-in-depth structure. The framework is conceptual and theory-building, intended to clarify system-level security reasoning and support future empirical refinement across diverse institutional contexts. Full article

This paper proposes an adaptive decarbonization model for the Russian non-ferrous metallurgy sector. The model accounts for the specific structure of the national energy balance (with nuclear and hydropower accounting for up to 40%), existing technological constraints, and regulatory risks, including the EU Carbon Border Adjustment Mechanism (CBAM). Based on a comparative analysis of key companies (RUSAL, Norilsk Nickel, and UMMC), an algorithm for the sequential assessment of decarbonization priorities is developed. Its core element is an integrated urgency indicator, which enables the ranking of enterprises according to their sensitivity to carbon-related restrictions. The model aims to minimize potential financial losses arising from external carbon taxation while leveraging the structural competitive advantages of the Russian energy system. The priority in decarbonization in Russia is determined not by the absolute level of technological development or the current carbon intensity of production, but by the degree of exposure to external regulatory and market risks combined with the ability to adapt. It is proven that in the current geopolitical and economic realities, the successful decarbonization of Russian non-ferrous metallurgy is impossible either as exclusively technological modernization or as a passive reaction to external regulatory pressure. The findings indicate that directly adopting international decarbonization strategies developed for the EU and North America (such as the EU Green Deal and CBAM) is ineffective due to fundamental differences in raw material bases, climatic conditions, and logistics. Full article

Background: This study explores sourcing risk in supply chains by identifying key risk categories, trends, and management strategies. It responds to increased vulnerabilities exposed by recent global disruptions such as the COVID-19 pandemic and geopolitical conflicts. Methods: The research applies a Systematic Literature Network Analyses (SLNA) combined with textual analysis to examine 687 peer-reviewed publications over the past three decades using the PRISMA protocol. Citation network analysis, keyword co-occurrence mapping, and main path analysis were conducted to map intellectual developments. Additionally, textual analysis using the Semantic Brand Score (SBS) approach revealed thematic relevance, novelty, and impact. Results: A shift exists from foundational supplier optimization models to resilience-building/strengthening, ethical sourcing, and technology-enabled strategies. Responsible sourcing and modern slavery were found to be the most innovative and underexplored areas. Research on sector-specific challenges, particularly for small and medium-sized enterprises, remains limited. Conclusions: Sourcing risk has become a systemic challenge requiring resilience, ethics, and data-driven coordination across supply networks. Full article

Understanding how geopolitical risk propagates through shipping networks to impact shipping supply chain resilience (SSCR) is essential for advancing global maritime governance reform. This study examines the systemic effects of geopolitical risk on SSCR using cross-border panel data derived from international shipping networks and identifies the transmission mechanisms operating through critical logistics nodes. The results indicate that geopolitical risk exerts a significant and persistent negative impact on SSCR, with significant multidimensional heterogeneity. Mechanism analysis shows that SSCR is undermined through three channels: logistics infrastructure disruption, increased freight rate volatility, and reduced customs clearance efficiency. Node-level evidence further reveals consistently negative effects across most critical logistics nodes. Logistics infrastructure disruption is particularly pronounced in ports. Logistics nodes along Indian Ocean routes exhibit more pervasive effects through the freight rate volatility channel, while reduced customs clearance efficiency represents a common transmission channel across most nodes. Full article

Cross-border equipment manufacturers in Shandong are under growing pressure to maintain supply chain continuity and long-term sustainability amid geopolitical uncertainty and industrial restructuring. Using quarterly data for 149 listed firms from 2001Q1 to 2024Q3, this study develops an interpretable early-warning model for firms’ relative vulnerability. Because firm-level disruption events are not consistently observable, vulnerability is proxied by return-on-assets underperformance relative to the industry median. We compare a multilayer perceptron (MLP) with logistic regression, decision tree, random forest, XGBoost, and LightGBM, and then use Shapley additive explanations (SHAP) to interpret the selected model. Under the study’s F1-oriented early-warning objective, the multilayer perceptron achieves the highest observed F1 score (the harmonic mean of precision and recall) in our evaluation setting, whereas XGBoost performs slightly better on threshold-independent ranking metrics. The interpretation results show that stronger profitability is associated with lower predicted vulnerability, policy-backed export demand with greater stability, and India-related geopolitical risk with higher predicted vulnerability. These findings suggest that interpretable early-warning tools may help support continuity-oriented operations, resilience investment, and sustainability-oriented industrial upgrading in export-dependent manufacturing regions. Full article

Climate change disproportionately threatens low- and middle-income countries, yet integrated assessments combining socio-economic fragility with physical hazards remain limited. This study quantifies multi-dimensional climate vulnerability and derives optimized adaptation policies for six representative nations (Bangladesh, Colombia, Kenya, Morocco, Pakistan, Vietnam) by fusing socio-economic indicators with climate risk data (2000–2024). A computational framework integrating unsupervised learning, dimensionality reduction, and predictive modeling was employed. Principal Component Analysis synthesized eight indicators into a Compound Vulnerability Score (CVS), while K-Means and DBSCAN identified distinct vulnerability regimes. XGBoost quantified driver importance, and Graph Neural Networks captured systemic interconnections. XGBoost identified projected drought risk (31.2%), precipitation change (18.1%), and poverty headcount (14.3%) as primary drivers. Graph networks demonstrated significant risk amplification in African nations (Morocco SRS: 0.728–0.874; Kenya SRS: 0.504–0.641) versus damping in Asian countries. A Reinforcement Learning (RL) agent was trained using Deep Q-Networks with experience replay to optimize intervention portfolios under budget constraints. The RL policy achieved a 23% reduction in systemic risk compared to uniform allocation baselines, generating context-specific priorities: drought management for Morocco (score 50) and Pakistan (40); poverty alleviation for Kenya (40); coastal protection for Bangladesh (40); agricultural resilience for Vietnam (35); and institutional capacity building for Colombia (50). In conclusion, socio-economic fragility non-linearly amplifies climate hazards, with poverty and drought risk constituting critical vulnerability multipliers. The AI-driven framework demonstrates that targeted interventions in high-sensitivity systems maximize systemic risk reduction. This integrated approach provides a replicable, evidence-based foundation for strategic adaptation finance allocation in an increasingly uncertain climate future. Full article

This paper examines whether climate, geopolitical and economic policy uncertainty indices improve Value-at-Risk (VaR) and Expected Shortfall (ES) forecasts for green and brown stocks. We extend the Realized-ES-CAViaR framework by incorporating physical and transition climate risk, geopolitical risk and economic policy uncertainty indices alongside a high-low range volatility estimator. Using daily data for the iShares Global Clean Energy ETF (ICLN) and the iShares Global Energy ETF (IXC) over the period January 2012–December 2024, we evaluate alternative model specifications at the 1% and 2.5% risk levels through backtesting procedures, strictly consistent scoring rules and the Model Confidence Set methodology. Results reveal a pronounced asymmetry in the predictive content of risk indices across asset classes and quantile levels. Transition climate risk dominates tail-risk forecasting at the 1% level for both asset classes, while geopolitical risk and economic policy uncertainty emerge as the leading factors at the 2.5% level for green and brown stocks, respectively. These findings highlight the heterogeneous channels through which uncertainty shocks propagate into financial tail-risk, with direct implications for risk management and regulatory oversight during the low-carbon transition. Full article

Global supply chains face increasing vulnerability to disruptions from geopolitical tensions, natural disasters, and demand shocks. The global trade network for railway vans, critical for transcontinental freight transport, remains understudied despite its foundational role in global logistics. This study addresses the gap in understanding how the railway vans trade network structure evolves and responds to different types of shocks, moving beyond static analyses to capture dynamic vulnerabilities. Using UN Comtrade data (2013–2024), multi-level network analysis examined structural evolution at macroscopic, mesoscopic, and microscopic scales. Three risk propagation models simulated supply disruption, demand shock, and cooperation disruption scenarios to assess systemic vulnerabilities. The network transformed from a polycentric to core-periphery structure, with China dominating exports (67 partners in 2024) and Germany leading European integration. Supply disruptions from Romania and Czechia affected up to 114 countries under low risk absorption capacity (α = 0.1), while demand shocks from the USA impacted 53 countries. The disruption of strategic trade links, such as China–Australia, triggered severe systemic risks. The systemic criticality of risk sources varies by shock type, requiring context-specific resilience strategies. The findings guide policymakers in identifying critical vulnerabilities and designing targeted interventions for enhancing supply chain resilience in infrastructure sectors. Full article

As a key node in the global supply chain, the resilience of ports is crucial for coping with multiple risks such as increasingly frequent climate change, operational accidents, and geopolitics, and ensuring the smooth flow of trade and sustainable development. This paper takes Ningbo-Zhoushan Port, which leads the world in throughput, as the research object, aiming to construct a comprehensive port resilience assessment model. Through the system dynamics method, the smart port system is deconstructed into three interrelated subsystems: meteorology, production, and economic-politics, and a simulation model including a causal relationship diagram and a system flow diagram is established accordingly. The model is verified to be effective and robust through historical data testing and sensitivity analysis. By setting different scenarios, this paper quantitatively analyzes the impact of single and compound risk shocks such as extreme weather, production accidents, and tariff policies on port throughput, and classifies port resilience into three levels: strong, medium, and weak. The research results show that Ningbo-Zhoushan Port shows strong resilience to the above-mentioned single risks. Even when the risk parameters are increased by 100%, the change rate of port throughput is less than the historical average annual change rate by 5.06%. However, in the extreme scenario of multiple risk couplings, the decline in port throughput is more significant, highlighting the importance of coping with compound risks. Further strategy simulation reveals that accelerating the economic development of the hinterland, increasing investment in port infrastructure, increasing the frequency of equipment maintenance, expanding the proportion of high-quality employees, and strengthening public facility management for accurate risk prediction are all effective ways to enhance port resilience. This research provides a scientific decision-making support tool for port managers, and the proposed resilience enhancement strategies have important theoretical and practical significance for ensuring the long-term stable operation of ports and the sustainable development of the regional economy. Full article

The European Union is increasingly confronted with a convergence of sustainability, democratic, and security-related challenges that affect the conditions for long-term transformation. While sustainable development and democratic resilience are often discussed separately, their interdependence has become more visible in the context of geopolitical instability, geoeconomic competition, hybrid threats, and growing societal polarization. This article examines the relationship between sustainable development and democratic resilience in the European Union and analyses how external pressures shape both agendas. The study employs a qualitative, concept-driven research design that combines the analysis of EU strategic and policy documents, a structured review of relevant scholarly literature, and triangulation with selected sustainability and governance indicators. The findings suggest that the implementation of sustainable development goals depends not only on regulatory and economic capacity, but also on social cohesion, public trust, and the resilience of democratic institutions, which together shape the legitimacy, continuity, and political feasibility of long-term transformative policies. At the same time, energy dependence, supply-chain vulnerabilities, technological dependencies, and information threats increasingly constrain the EU’s sustainability agenda. In response, the article proposes the concept of Sustainable Democratic Security as an analytical framework linking sustainability governance, democratic resilience, and strategic-security capacity. The article contributes to the literature by conceptualising these dimensions as mutually conditioning components of a common governance framework and by outlining their implications for integrated EU policymaking under conditions of geopolitical and geoeconomic pressure. Full article

The escalation of geopolitical tensions has forced global manufacturers to reconfigure their supply chains. While Digital Transformation (DT) is widely touted as a primary driver of resilience, traditional variance-based research often assumes a symmetric, linear relationship that applies universally across firms. This study challenges this assumption through the lens of Complexity Theory. Viewing supply chains as Complex Adaptive Systems (CASs), we employ Fuzzy-Set Qualitative Comparative Analysis (fsQCA) on a stratified sample of 928 manufacturers in a geopolitical high-risk zone (Taiwan). We identify equifinal pathways to Organizational Resilience, revealing a fundamental asymmetry between organizational types. The results suggest that while large enterprises rely on a resource-intensive strategy—which we term the “Digital Fortress” configurational metaphor (combining high digital maturity and agility as a core condition)—SMEs can achieve high resilience through an “Agile Dodger” configuration, leveraging operational agility and niche positioning without necessitating high digital maturity. This study contributes to the systems literature by mapping the “topology of resilience” and offering tailored configurational pathways that complement traditional variance-based perspectives in volatile ecosystems. Full article