Search Results (1,272)

This study examines risk spillovers among China’s green financial markets using the TVP-VAR-BK model. An Artificial Intelligence (AI) technology attention index is constructed and combined with an AI industry development index to capture AI shocks. The TVP-VAR-SV model is then employed to examine how these AI shocks interact with risk spillovers among China’s green financial markets. The results reveal that: (1) China’s green financial markets exhibit noticeable risk spillovers, mainly driven by short-term risk spillover, with considerable heterogeneity in markets’ roles as net risk transmitters and receivers across frequency horizons. (2) Risk spillovers are highly time-varying, increasing during periods of geopolitical tensions, public health crises, and industrial policy adjustments, but weakening as external conditions stabilize. Risk spillovers are predominantly short-term, except during the rapid development of generative AI, when noticeable long-term effects emerge. (3) The posterior mean responses of risk spillovers among China’s green financial markets to AI technology attention and AI industry development shocks exhibit time-varying characteristics and frequency-dependent heterogeneity, with predominantly positive and dynamic patterns. However, the 90% posterior credible intervals include zero during some periods, and these findings should therefore be interpreted as indicative dynamic patterns rather than conclusive evidence of effects credibly different from zero. Full article

Amid the profound restructuring of global value chains, supply chain risk has mainly been linked to visible shocks such as pandemics, wars, and geopolitical conflict. Much less is known, however, about whether exchange rate volatility can become a source of operational instability within firms. We examine this question using Chinese A-share listed firms from 2007 to 2021. We construct an industry-level exchange rate volatility measure by combining ADB input–output tables with bilateral real exchange rate volatility, and measure firms’ perceived and disclosed supply chain disruption risk from the MD&A sections of annual reports using a word-embedding approach. We find that higher industry-level exchange rate volatility is associated with a significant increase in firms’ perceived and disclosed supply chain disruption risk. The mechanism evidence indicates that this effect operates through both supply-side operating frictions and demand-side pressure: higher industry-level exchange rate volatility reduces inventory turnover and weakens overseas revenue realization. The effect is weaker in industries with longer backward production length but stronger among firms facing tighter financing constraints. It is also stronger among firms located in more open regions, firms with overseas-experienced executives, and firms with greater export intensity, but weaker among manufacturing firms. These findings extend research on the real effects of exchange rate volatility by showing how industry-level exchange rate uncertainty can materialize as firm-level perceived and disclosed supply chain disruption risk and undermine the operational continuity and long-term economic sustainability of internationally connected supply chains. Full article

Critical raw materials (CRMs) sit at the intersection of industrial strategy, climate policy, sustainable development, and geopolitics. This paper reviews how the concept has evolved since the 1939 US Strategic and Critical Materials Stock Piling Act, tracing the gradual addition of economic, energy, technological, environmental, and sustainability criteria to the original military framing. It then compares the methodologies developed by the European Union and the United States over the last fifteen years. The EU has published five consecutive lists between 2010 and 2023; the US has produced parallel frameworks through the U.S. Geological Survey and the Department of Energy, each weighing supply risk, economic importance, environmental impact, and clean energy relevance differently. The 2023 EU list is examined in detail, including the addition of copper and nickel on strategic rather than quantitative grounds. Convergences and divergences across the three frameworks are discussed, alongside their alignment with the United Nations Sustainable Development Goals—particularly SDG 7, SDG 9, SDG 12, and SDG 13—and the progressive integration of circular economy targets, recycling indicators, and environmental impact considerations into criticality assessment. The analysis shows that sustainability has become a structural axis of CRM policy rather than a peripheral concern. The review provides the methodological background for a companion paper that applies the EU methodology to resource-rich Latin American countries (Chile, Brazil, and Peru). Full article

Macroeconomic forecasting has become increasingly challenging in environments characterized by structural breaks, nonlinear dynamics, and elevated economic uncertainty. Traditional econometric forecasting models frequently experience substantial predictive deterioration during periods of financial crises, geopolitical instability, and rapidly evolving macroeconomic conditions due to their reliance on assumptions of parameter stability and linear economic relationships. In response to these limitations, this study proposes an integrated causal machine learning framework designed to improve macroeconomic forecasting performance under structural instability and uncertainty. The proposed framework combines structural break detection techniques, machine learning algorithms, causal inference methodologies, and Explainable Artificial Intelligence (XAI) tools within a unified empirical architecture. More specifically, the study integrates Bai–Perron structural break analysis, Markov-Switching regime identification, Double Machine Learning (DML), Causal Forest estimation procedures, and SHAP-based explainability techniques. The empirical analysis employs a U.S. macroeconomic time-series dataset covering major crisis episodes, including the 2008 Global Financial Crisis, the COVID-19 pandemic, and the 2022 inflation shock. The dataset combines inflation, monetary, financial, energy-market, and uncertainty indicators obtained from publicly available U.S. macroeconomic databases. The empirical findings demonstrate that causal machine learning models significantly outperform conventional econometric frameworks such as VAR and TVP-VAR models, as well as standard machine learning algorithms including Random Forest (RF), XGBoost, and LSTM networks. The Double Machine Learning framework generates the strongest forecasting performance across all forecasting horizons, economic regimes, and robustness specifications. The results further reveal that macroeconomic relationships are highly regime-dependent and strongly influenced by uncertainty indicators, financial volatility, oil price shocks, and monetary policy dynamics. Explainability analysis additionally shows that uncertainty measures and energy market variables become dominant drivers of inflation forecasts during crisis periods characterized by elevated instability. The study contributes to the growing literature on macroeconomic forecasting by bridging econometric forecasting theory, causal inference methodologies, machine learning techniques, and explainable artificial intelligence within a unified forecasting framework. The findings provide important implications for central banks, policymakers, and financial institutions seeking more adaptive, transparent, and robust forecasting systems under uncertain macroeconomic environments. Full article

As critical geopolitical and migratory hubs in Eastern Europe, the Azov and Donbas regions represent an epidemiological melting pot for HIV-1 trafficking, exacerbating the global trend of rising dolutegravir (DTG) resistance through the cross-border dissemination of drug-resistant strains. This study presents a comprehensive molecular epidemiological analysis of HIV-1 in these regions in 2025 (N = 1666), focusing on drug resistance and cross-border transmission networks using phylogenetic and molecular network approaches. The study cohort was predominantly female (53.33%) and had heterosexual transmission (78.77%). Most patients (87.64%) received antiretroviral therapy (ART). Sub-subtype A6 predominated, with the radiation’s origin traced to September 1994. Molecular network analysis identified the study area as a significant node, demonstrating viral exports towards the Russian Federation and Belarus, alongside multiple imports from Ukraine, Poland, and Russia. The overall resistance prevalence was 4.49% to integrase strand transfer inhibitors (INSTIs), 2.49% to protease inhibitors (PIs), 12.19% to nucleoside reverse transcriptase inhibitors (NRTIs), and 16.07% to non-nucleoside reverse transcriptase inhibitors (NNRTIs). Surveillance drug resistance mutations in treatment-naive individuals stood at 0.89% (INSTIs), 4.90% (PIs), 4.90% (NRTIs), and 8.82% (NNRTIs). Crucially, intermediate or high-level DTG resistance and key mutations (G118R, R263K, and Y143R) were detected in individuals without prior DTG exposure. This 4.49% integrase inhibitor resistance cannot be considered low; combined with intense cross-border viral dissemination, it may indicate the formation of a stable pool of resistant variants, potentially posing a risk of dolutegravir-based regimen failure and highlighting the need for enhanced regional molecular surveillance. Full article

Amid intensifying global geopolitical conflicts and increasingly frequent market fluctuations, enhancing the resilience of agricultural sustainable development has become a core issue for ensuring food security and promoting rural revitalization. Digital infrastructure offers a crucial pathway for addressing the pain points in agricultural development and improving resilience levels. Using panel data from 30 Chinese provinces over the period 2013–2024, this paper employs the entropy method to measure agricultural sustainable development resilience and the level of digital infrastructure development. It empirically examines the empowering effect and mechanism of digital infrastructure on agricultural sustainable development resilience through FE two-way models, mediation models, and other econometric approaches. The findings are as follows: (1) digital infrastructure can enhance agricultural sustainable development resilience in the long term; (2) digital inclusive finance plays a significant partial mediating role, and this empowering effect exhibits a double-threshold characteristic; (3) marketization exerts a positive moderating effect; (4) the empowering effect is more pronounced in the central region. Accordingly, this paper proposes policy recommendations including optimizing the regional layout of digital infrastructure, implementing threshold-based differentiated policies for digital inclusive finance, and improving market-oriented supporting mechanisms, thereby providing practical support for international academic research on enhancing agricultural sustainable development resilience. Full article

Against the backdrop of deepening global digital economic integration and intensifying geopolitical competition, cross-border data flow regulation has become a core institutional arrangement for coordinating development and security. Based on panel data from 31 Chinese provinces from 2015 to 2025, this study proposes an adapted provincial DFRI and a comprehensive resilience index, which together provide an operational foundation for examining the relationship between data regulation and industrial chain resilience across Chinese regions. It empirically examines the effects, transmission mechanisms, and configurational pathways through which regulation influences industrial chain resilience. The results show that cross-border data flow regulation significantly enhances the resilience of the core industrial chains of the digital economy. The balance between efficiency and security plays a key mediating role in the process through which regulation affects industrial chain resilience. Moreover, the regulatory effect exhibits significant heterogeneity in the level of digital economic development. The coefficient of REG is higher in provinces whose digital economy development index exceeds the sample mean than in provinces at or below the sample mean. Further, fuzzy-set qualitative comparative analysis (fsQCA) identifies four equivalent pathways through which regulatory improvement, infrastructure development, innovation investment, digital trade, and industrial compatibility jointly drive high resilience. The findings provide empirical evidence and policy implications for constructing regionally differentiated data governance systems and enhancing the resilience of China’s core digital economy industrial chains. Full article

Growing geopolitical instability has led to increasing concerns about the psychological impact of war beyond directly affected populations, highlighting the need for culturally appropriate instruments to assess fear of war. In this study, we evaluate the psychometric properties of the Turkish version of the Fear of War Scale (FOWARS) and examine its associations with sociodemographic characteristics and personality traits. A total of 643 adults across two independent samples (sample 1 = 360; sample 2 = 283) completed the FOWARS, the Tolerance of Uncontrollability Questionnaire, the Next Big Five Inventory (BFI-2), the Depression Anxiety Stress Scale-21 (DASS-21), and a sociodemographic information form. Confirmatory factor analysis supported the original two-factor structure of the FOWARS, with the Turkish version demonstrating satisfactory structural validity, convergent validity, discriminant validity, criterion-related validity, strong internal consistency, and measurement invariance across gender. Fear of war was negatively associated with tolerance of uncontrollability and positively associated with depression, anxiety, and stress. In addition, fear of war was significantly associated with gender, socioeconomic status, agreeableness, neuroticism, and conscientiousness. These findings expand the understanding of the individual determinants of fear of war, underscoring the importance of developing targeted interventions for vulnerable populations. Full article

This study examines the impact of the Russian–Ukrainian war on global financial stability, focusing on currency crises, exchange-rate dynamics, and economic vulnerability during 2003–2024 with an outlook for subsequent years. The objective is to assess how geopolitical shocks, combined with global monetary tightening, influenced the frequency and intensity of currency crises across developed and emerging economies. The study applies a quantitative comparative methodology based on a modified Exchange Market Pressure Index (EMPI) using monthly IMF data on exchange rates, reserves, interest rates, and depreciation dynamics. Currency crises are identified through threshold-based criteria, enabling cross-country and temporal comparison. A conceptual framework explains how geopolitical risk affects currency markets, financial stability, and macroeconomic performance. The findings show that crisis episodes were more frequently concentrated around the Great Recession, the COVID-19 pandemic, and the Russian–Ukrainian war. Emerging economies were more vulnerable, experiencing stronger capital outflows, sharper currency depreciation, and more frequent crises, while developed economies were affected mainly through inflation and energy price shocks. The war intensified financial fragmentation, increased safe-haven flows toward the US dollar, gold, and Swiss franc, and raised systemic risks in debt, banking, and corporate sectors. The study concludes that differentiated macroeconomic strategies, stronger external buffers, and enhanced international financial coordination are necessary to reduce risks and preserve currency stability. Full article

The Levelized Cost of Energy (LCOE) is the standard metric for evaluating renewable energy project economics; however, conventional formulations inadequately represent the dynamic effects of logistics performance, supply chain disruptions, geopolitical risk, and institutional constraints on project costs. This study addresses this gap by proposing the Integrated Levelized Cost of Energy (I-LCOE), a conceptual framework designed for macro-level renewable energy planning and policy analysis. An interpretivist qualitative research design was adopted, combining a systematic literature review with twelve semi-structured interviews involving renewable energy, logistics, regulatory, and academic experts from the MENA and GCC regions. Thematic analysis identified five recurring challenges: limited knowledge management, weak integration of logistics within conventional LCOE models, fragmented sustainability metrics, stakeholder coordination inefficiencies, and reliance on tacit knowledge. The findings indicate that transportation delays, customs bottlenecks, infrastructure limitations, and geopolitical disruptions generate dynamic risk premiums that are insufficiently reflected in existing macro-level cost assessment approaches. In response, the study develops the four-layer I-LCOE framework, supported by an operational proxy variable mapping framework, a comparative assessment against established uncertainty methods, and an integrated digital knowledge management platform. The proposed framework provides a structured approach for incorporating logistics-induced uncertainty into renewable energy cost assessment, supporting more informed strategic planning, investment prioritization, and policy development aligned with Sustainable Development Goal 7 and the Paris Agreement. Full article

The European Union operates in a highly fragmented international environment shaped by geopolitical rivalry, geoeconomic competition, technological disruption, and growing uncertainty regarding critical external dependencies. These developments have elevated economic security and resilience to the centre of European policymaking, challenging assumptions that traditionally linked economic openness with stability, prosperity, and sustainable development. The purpose of this article is to examine how geopolitical and geoeconomic fragmentation is reshaping the European Union’s approach to economic and technological resilience and to analyse the implications of this transformation for long-term competitiveness and sustainable development. Employing a qualitative research design based on policy analysis and systematic document analysis, supported by secondary statistical evidence from European and international institutional sources, the study investigates three interconnected dimensions of resilience: technological resilience, resource resilience, and competitiveness resilience. The findings indicate that the European Union is moving from an efficiency-oriented model of globalisation towards a resilience-oriented governance framework focused on managing sensitive dependencies in critical technologies, strategic resources, and supply chains. The analysis further demonstrates that resilience cannot be sustained through risk reduction measures alone. Its long-term effectiveness depends on productivity growth, innovation performance, technological capabilities, industrial strength, and investment capacity. The article concludes that competitiveness constitutes a foundational condition for durable resilience and that economic security increasingly functions as an enabling factor for sustainable development in a more contested global economic environment. Full article

This paper investigates how restrictive monetary policy impacts long-term strategic infrastructure financing under conditions of geopolitical fragmentation and supply-side inflationary pressures. The analysis focuses on Poland, a medium-sized European economy exposed to energy transition requirements, post-pandemic supply chain disruptions, and regional geopolitical instability. Using quarterly data for 2005Q1–2024Q2, the study employs a Structural Vector Autoregression (SVAR) model to evaluate the transmission of monetary policy shocks to inflation dynamics and sectoral credit allocation. The research examines bank lending directed toward infrastructure-intensive sectors, including energy, utilities, transport, and strategic industrial investments. Empirical results demonstrate that while monetary tightening contributes to a statistically significant reduction in inflationary pressures over the medium term, it simultaneously triggers unintended consequences. Specifically, higher policy rates are associated with a persistent contraction in long-term infrastructure-related credit volumes. Impulse response analysis reveals that this decline in strategic infrastructure financing is disproportionately stronger and more enduring than the drop observed in aggregate corporate lending. These findings highlight asymmetric monetary transmission effects across investment categories. Consequently, the paper suggests implementing targeted macroprudential and liquidity-support instruments to protect strategic sectors without compromising inflation stabilization objectives. Full article

Contemporary sustainable development is affected by geopolitical competition, geoeconomic fragmentation and growing strategic uncertainty. These developments challenge conventional assumptions on which sustainability governance is based and raise questions about the relationship between sustainable development, economic security and strategic autonomy. This article examines how geopolitical and geoeconomic transformations are reshaping sustainable development within the European Union and assesses their implications for the future evolution of the European development model. The analysis is based on a qualitative analysis of policies and documents in key EU strategic frameworks, legislative instruments, sustainability reports and the academic literature. Particular attention is paid to the European Green Deal, the European Economic Security Strategy and the Europe 2026 Sustainable Development Report. Sustainable development now depends not only on environmental and social outcomes but also on resilience, secure access to key resources and technologies and the ability to adapt to external disruptions. Economic security and strategic autonomy do not emerge as competing priorities but as enabling conditions for the implementation of sustainability goals. This article argues that sustainability governance in the European Union is undergoing a process of geopoliticisation, in which environmental, economic and strategic considerations are now closely intertwined. Its main contribution is the creation of a framework of sustainable strategic resilience that conceptualises these interactions as mutually reinforcing dimensions of contemporary EU governance. Full article

This paper presents a systemic analysis of the global energy transition, framing it as the central thermodynamic and economic driver of the modern polycrisis. The primary research objective is to demonstrate how current decarbonization strategies, lacking a holistic view, inadvertently trigger nonlinear amplifying feedback loops that exacerbate geopolitical and market volatility. Methodologically, the research introduces a systemic control dashboard, operationalizing the anthropological concept of contact zones into measurable techno-ecological and socio-economic interfaces. This framework is preferable to existing socio-technical approaches because it operationally couples rigid physical limits (e.g., thermodynamics) with volatile socio-behavioral friction, eliminating the blind spots of siloed analyses. The main contribution of this study lies in its systemic validation: it proves that the energy transition operates as the master control node of the polycrisis and successfully identifies eight high-leverage control levers (comprising sixteen Key Performance Indicators) that govern the friction between physical boundaries and societal acceptance. Regarding policy implications, the findings demonstrate that securing a Social License to Operate (SLO) requires actively managing these specific interface tensions—such as balancing renewable deployment against baseload inertia—rather than relying on isolated cost–benefit metrics. Ultimately, this approach equips energy planners with a robust methodology to minimize systemic risk and guide the transition toward a resilient, low-entropy steady state. Full article

Enhancing supply chain resilience has become crucial for sustainable manufacturing development as firms face repeated disruptions from pandemics, geopolitical shocks, logistics bottlenecks, and climate-related uncertainty. As digital finance alleviates corporate financing constraints and improves information transmission across supply chains, it may strengthen supply chain resilience, thereby supporting the sustainable development of manufacturing firms. Accordingly, this paper examines this effect using panel data consisting of 21,060 firm-year observations of Chinese A-share listed manufacturing firms spanning the period 2011–2023. It combines the proxy of digital finance, which is the city-level Baidu search index for digital finance, with the entropy-weighted and firm-level supply chain resilience index to assess its sustainability. Based on the fixed-effects model, digital finance positively affects the resilience of manufacturing companies’ supply chains and, by extension, promotes the sustainable development of manufacturing supply chains. We also find that digital finance improves manufacturing supply chain resilience by enhancing information transparency, resolving maturity mismatches between investment and financing, and mitigating financial risks. This impact is larger in poorly developed traditional financial regions, firms with poor governance, and in growing companies. Policy recommendations center on advancing digital supply chain finance, strengthening data governance, and improving risk management systems to reinforce supply chain resilience and promote the long-term sustainable development of manufacturing firms. Full article