Search Results (260)

Special forces operators are increasingly being utilized as the weapon of choice by many governments on the geopolitical stage. Given the specialized and high-risk nature of special forces operations, it is important to understand the differences that exist when comparing the psychological traits of these groups to regular military forces. An understanding of these traits is crucial when looking to select, develop, and support the most appropriate individuals to succeed in these roles. While previous research has painted a clear picture relating to personality differences between special forces operators and the wider military forces, there is still little research that has explored the psychological traits that both influence and determine performance. As a result, the aim of this study was to explore the perceptions of former United Kingdom (UK) special forces operators regarding the psychological traits they believed were crucial for success as a special forces operator in the UK military. Participants in this study were 20 former UK special forces operators, each having transitioned from active service to civilian life within the previous five years. Data were collected and analyzed using reflexive thematic analysis. Results suggested a specific profile of UK special forces operators composed of nine specific factors: resilience, adaptability, self-belief, perseverance, emotional regulation, self-control, drive, humility, and stubbornness. With the last two relatively novel compared with relevant research in similar populations. These findings can help to underpin the development of special forces-specific programs of support and development. Full article

The South China Sea (SCS) is a critical fishery region facing sustainability challenges due to overexploitation, geopolitical tensions, and inadequate monitoring. Traditional monitoring methods, such as AIS and VMS, have limitations due to data gaps and vessel deactivation. We developed an improved remote sensing algorithm using VIIRS nighttime light observations (2013–2022) to detect and classify lit fishing boats in the SCS. The study introduces a Two-Dimensional Constant False Alarm Rate (2D-CFAR) algorithm integrated with morphological analysis, which enhances boats’ detection accuracy. The classification of fishing boat types was based on light power thresholds derived from spatial entropy analysis, where distinct clustering patterns indicated three operational categories: small interfering lights (<1.2–3.7 kW), small-to-medium-sized lit fishing boats (1.2–3.7 to 28.6–43.2 kW), and large lit fishing boats (>28.6–43.2 kW). Our findings reveal a 4.4-fold dominance of small-to-medium-sized lit fishing boats over large lit fishing boats. China’s summer fishing moratorium effectively reduces large lit fishing boats activity by 85%, yet small-to-medium-sized lit fishing boats, primarily from neighboring countries like Vietnam, persist, exploiting this period illegally. Spatially, small-to-medium-sized lit fishing boats concentrate in the central SCS, southeast Vietnam, and Nansha Islands, while large lit fishing boats target upwelling zones near Hainan and Guangdong. Moreover, a new fishing hotspot emerged in eastern SCS, reflecting intensified resource and geopolitical competition. Light intensity analysis reveals rapid growth in contested areas (10% annually, p < 0.01), underscoring ecological risks. These findings highlight the limitations of unilateral policies and the urgent need for regional cooperation to curb illegal, unreported, and unregulated (IUU) fishing. Our algorithm offers a robust tool for monitoring fishing dynamics, providing quantitative insights into vessel distribution, policy impacts, and resource-driven patterns. This supports evidence-based fisheries management and biodiversity conservation in the SCS, adaptable to other marine regions facing similar challenges. Full article

Using high-frequency financial data, this study investigates volatility spillovers between five renewable energy subsectors (wind, solar, geothermal, bioenergy, and fuel cells), five conventional energy markets (oil, gas, coal, uranium, and gasoline), and carbon emissions for five industrial sectors (power, industry, ground transportation, domestic aviation, and residential) based on a Diebold–Yilmaz VAR-based spillover framework. The results document that the industry and power sectors are the key players in the transmission effects of carbon shocks. In contrast, the reverse is true for the residential and aviation sectors. For renewable energy, fuel cells, and geothermal power, strong forward linkages appear to significantly reduce carbon emissions, while reverse linkages that increase carbon emissions in response to shocks in clean-energy and carbon-intensive industries are relatively high for coal and oil. We also find that the total volatility connectedness exceeds 84%, indicating significant systemic risk transmission. The clean-energy subsectors, particularly wind and solar, now compete in fossil-fuel markets during geopolitical crises. Applying the DCC-GARCH t-copula method to assess portfolio hedging strategies, we find that fuel cell and geothermal assets are the most effective in hedging against volatility in fossil-fuel prices. In contrast, nuclear and gas assets provide benefits from diversification. These results underscore the growing strategic importance of clean energy in mitigating sector-specific emission risks and fostering resilient energy systems in alignment with the United States’ net-zero carbon goals. Full article

In a few years, most investment firms will deploy Generative AI (GenAI) and large language models (LLMs) for reduced-cost stock trading decisions. If GenAI-run investment decisions from most firms are heavily coordinated, they could all give a “sell” signal simultaneously, triggering market crashes. Likewise, simultaneous “buy” signals from GenAI-run investment decisions could cause market bubbles with algorithmically inflated prices. In this way, coordinated actions from LLMs introduce systemic risk into the global financial system. Existing risk analysis for GenAI focuses on endogenous risk from model performance. In comparison, exogenous risk from external factors like macroeconomic changes, natural disasters, or sudden regulatory changes, is understudied. This research fills the gap by creating a framework for measuring exogenous (systemic) risk from LLMs acting in the stock trading system. This research develops a concrete, quantitative framework to understand the systemic risk brought by using GenAI in stock investment by measuring the covariance between LLM stock price predictions across three industries (technology, automobiles, and communications) produced by eight large language models developed across the United States, Europe, and China. This paper also identifies potential data-driven technical, cultural, and regulatory mechanisms for governing AI to prevent negative financial and societal consequences. Full article

This study investigates the nonlinear, regime-dependent, and frequency-specific interdependencies between rare earth element (REE) markets and key global critical sectors, including artificial intelligence, semiconductors, clean energy, defense, and advanced manufacturing, under varying levels of geopolitical and financial uncertainty. The main objective is to assess how REE markets transmit and absorb systemic risks across these critical domains. Using a mixed-methods approach combining Quantile-on-Quantile Regression (QQR), Continuous Wavelet Transform (CWT), and Wavelet Transform Coherence (WTC), we examine the dynamic connections between two REE proxies, SOLLIT (Solactive Rare Earth Elements Total Return) and MVREMXTR (MVIS Global Rare Earth Metals Total Return), and major sectoral indices based on a dataset of daily observations from 2018 to 2025. Our results reveal strong evidence of asymmetric, regime-specific risk transmission, with REE markets acting as systemic amplifiers during periods of extreme uncertainty and as sensitive receptors under moderate or localized geopolitical stress. High co-volatility and persistent low-frequency coherence with critical sectors, especially defense, technology, and clean energy, indicate deeply embedded structural linkages and a heightened potential for cross-sectoral contagion. These findings confirm the systemic relevance of REEs and underscore the importance of integrating critical resource exposure into global supply chain risk strategies, sector-specific stress testing, and national security frameworks. This study offers relevant insights for policymakers, risk managers, and institutional investors aiming to anticipate disruptions and strengthen resilience in critical industries. Full article

This study investigates the asymmetric responses of AI and ESG Exchange Traded Funds (ETFs) to geopolitical and financial uncertainty, with a focus on resilience across market regimes. The NASDAQ-100 and MSCI ESG Leaders indices are used as proxies for thematic ETFs, and their dynamic interlinkages are examined in relation to volatility indicators (VIX, GPR), alternative assets (Bitcoin, Ethereum, gold, oil, natural gas), and safe-haven currencies (CHF, JPY). A daily dataset spanning the 2016–2025 period is analyzed using Quantile-on-Quantile Regression (QQR) and Wavelet Coherence (WCO), enabling a granular assessment of nonlinear, regime-dependent behaviors across quantiles. Results reveal that ESG ETFs demonstrate stronger downside resilience under extreme uncertainty, maintaining stability even during periods of elevated geopolitical and financial risk. In contrast, AI-themed ETFs tend to outperform under moderate-risk conditions but exhibit greater vulnerability during systemic stress, reflecting differences in asset composition and investor risk perception. The findings contribute to the literature on ETF resilience and cross-asset contagion by highlighting differential behavior patterns under varying uncertainty regimes. Practical implications emerge for investors and policymakers seeking to enhance portfolio robustness through thematic diversification during market turbulence. Full article

Energy raw materials are the basis of the economic system. From this emerges the need to examine in more detail how various uncertainty indices interact with the dynamic of spillover connectedness among energy markets. The TVP-VAR model is used to investigate connectedness among US, European, and Indian oil and gas markets and the S&P carbon allowances Eua index. Following this, the wavelet decomposition technique is used to capture the dynamic correlations between uncertainty indices (GPR and VIX) and connectedness indices. First, the results indicate that energy market spillovers are time-varying and crisis-sensitive. Second, the time–frequency dependence among uncertainty indices and connectedness indices is more marked and can change with the occurrence of unexpected events and geopolitical conflicts. The VIX index shows a positive dependence on total dynamic connectedness in the mid-long-term, while the GPR index has a long-term effect only after 2020. The analysis of the interdependence among the connectedness of each market and the uncertainty indices is more heterogeneous. Political tensions and geopolitical risks are, therefore, causal factors of energy prices. Given their strategic and economic importance, policy makers and investors should establish a risk warning mechanism and try to avoid the transmission of spillovers as much as possible. Full article

Economic stability in emerging market economies is increasingly shaped by the interplay between global financial integration, domestic monetary dynamics, and asset price fluctuations. Yet, early detection of financial market disruptions remains a persistent challenge. This study constructs a Financial Stress Index (FSI) for Pakistan, utilizing monthly data from 2005 to 2024, to capture systemic stress in a globalized context. Using Principal Component Analysis (PCA), the FSI consolidates diverse indicators, including banking sector fragility, exchange market pressure, stock market volatility, money market spread, external debt exposure, and trade finance conditions, into a single, interpretable measure of financial instability. The index is externally validated through comparisons with the U.S. STLFSI4, the Global Economic Policy Uncertainty (EPU) Index, the Geopolitical Risk (GPR) Index, and the OECD Composite Leading Indicator (CLI). The results confirm that Pakistan’s FSI responds meaningfully to both global and domestic shocks. It successfully captures major stress episodes, including the 2008 global financial crisis, the COVID-19 pandemic, and politically driven local disruptions. A key understanding is the index’s ability to distinguish between sudden global contagion and gradually emerging domestic vulnerabilities. Empirical results show that banking sector risk, followed by trade finance constraints and exchange rate volatility, are the leading contributors to systemic stress. Granger causality analysis reveals that financial stress has a significant impact on macroeconomic performance, particularly in terms of GDP growth and trade flows. These findings emphasize the importance of monitoring sector-specific vulnerabilities in an open economy like Pakistan. The FSI offers strong potential as an early warning system to support policy design and strengthen economic resilience. Future modifications may include incorporating real-time market-based metrics indicators to better align the index with global stress patterns. Full article

This study examines the comparative impact of energy policy uncertainty, geopolitical risk, and R&D expenditures on renewable and fossil fuel consumption in China from 2002m1 to 2022m12, using Fourier ADL, fully modified and dynamic ordinary least squares methods. The analysis aims to clarify how these key factors shape the country’s energy transition. The results show that energy policy uncertainty significantly promotes renewable energy but has no significant impact on fossil fuel consumption. Geopolitical risk increases the adoption of renewables, while fossil fuel consumption decreases, reflecting concerns about energy security. R&D expenditure contributes to the growth of both renewable and fossil fuel consumption, indicating a dual investment path in China’s energy strategy. These findings underscore the importance of consistent energy policies, reduced reliance on imported fossil fuels, and targeted R&D investment to support China’s transition to a low-carbon energy future. To effectively promote renewable energy and reduce dependence on fossil fuels, China should stabilize its energy policy environment, redirect R&D funding to clean technologies, and treat geopolitical risks as a strategic driver to accelerate domestic renewable energy capacity and energy self-sufficiency. Full article

Amid an increasingly complex and uncertain global landscape, geopolitical tensions and frequent trade frictions have emerged as critical external risks threatening the economic stability and sustainable development of Chinese cities. Enhancing cities’ economic resilience has become a key challenge in advancing China’s high-quality development agenda. As a major national strategic initiative, the Belt and Road Initiative (BRI) is expected to offer new development opportunities and pathways for risk mitigation, particularly for cities situated along its domestic routes. This paper examines whether and how the BRI affects the economic resilience of these cities and further explores the moderating role of local governance capacity in policy implementation. To this end, an empirical strategy combining the entropy weighting method and the difference-in-differences (DID) approach is employed to systematically assess the impact of the BRI on urban economic resilience at the city level. The key findings are as follows: (1) The findings show that the BRI has an enhancing effect on the economic resilience of cities along the routes, but governance is very weak, and urban resilience improves by 0.0045 units on average. Our findings imply that, while the BRI appears to be on the correct path, enhanced governance is necessary to implement city-specific planning approaches effectively. (2) The results of the moderating effect indicate that local governance capacity significantly amplifies the impact of the BRI on urban economic resilience, underscoring the critical role of institutional strength in the policy transmission process. (3) The heterogeneity analysis reveals significant regional disparities in policy effectiveness: while the BRI significantly improves economic resilience in eastern and central cities, it exerts a suppressive effect in western regions. This divergence is closely associated with variations in local governance capacity. In contrast, cities with stronger governance capabilities are more likely to experience positive outcomes, as confirmed by the significant moderating effect of local governance capacity. This study contributes to the growing literature on the spatial implications of national development strategies by empirically examining how the BRI reshapes urban economic resilience across regions. It offers important policy insights for enhancing the spatial governance of cities, particularly in aligning strategic infrastructure investment with differentiated local capacities. The findings also provide a valuable reference for land-use planning and regional development policies aimed at building resilient urban systems under conditions of global uncertainty. Full article

This paper analyzes the volatility relationship between the Romanian BET index and the U.S. SFT-500 index during the period 2019–2024, with a particular focus on the impact of political and geopolitical shocks. The study investigates whether financial markets in emerging economies react symmetrically or asymmetrically to external shocks originating from mature markets, especially during periods of political uncertainty. The research period includes four major systemic events: the COVID-19 pandemic, the military conflict in Ukraine, the 2024 U.S. presidential elections, and the 2024 Romanian elections, all of which generated significant volatility in global markets. The methodological approach combines time series econometrics with the Impulse Indicator Saturation (IIS) technique to identify structural breaks and outliers, without imposing exogenous assumptions about the timing of events. The econometric model includes autoregressive and lagged exogenous variables to estimate the influence of the SFT-500 index on the BET index, while IIS variables capture unanticipated political and economic shocks. Additionally, a Fractionally Integrated GARCH (FIGARCH) specification is applied to model the persistence of volatility over time, capturing the long-memory behavior often observed in emerging markets like Romania. The results confirm a statistically significant but partial synchronization between the two markets, with lagged and contemporaneous effects from the SFT-500 index on the BET index. Volatility in Romania is markedly higher and longer-lasting during domestic political episodes, confirming that local factors are a primary source of market instability. For investors, this underscores the need to embed political risk metrics into emerging market portfolios. For policymakers, it highlights how stronger institutions and transparent governance can dampen election- and crisis-related turbulence. Full article

Water, an indispensable resource for life and not a complete substitute, is indispensable for energy production, industry, agriculture, and ecosystem sustainability. In particular, the limited and unequal distribution of freshwater reserves makes water a strategic power element on a global scale, making competition inevitable. Increasing water demand and decreasing water resources increase regional and global security risks, causing water to go beyond being a vital natural resource and become a determining factor in diplomacy, conflict, and the balance of power. This study aimed to examine the relationship between freshwater resources and geopolitical risk between 1961 and 2021 using the ARDL model. All models had long-run relationships between water resources and geopolitical risk. In the long-run, a 1% decrease in water resources increased geopolitical risk by 0.37% in Chile, 0.30% in Colombia, 0.46% in the Netherlands, 0.42% in Thailand, 0.44% in Ukraine, and 0.29% in Venezuela. The adjustment rates for the prior period imbalances were estimated to be 0.75% in Switzerland, 0.68% in Chile, 0.28% in Colombia, 0.45% in the Netherlands, 0.86% in Thailand, 0.14% in Ukraine, and 0.59% in Venezuela. Full article

The current global energy crisis is one of the most pressing challenges of the 21st century, it highlights the fragility of an old power system based on fossil fuels, geopolitical dependencies and often the precariousness and age of equipment and installations, affecting the economy, security and social stability on a national, regional and world scale. The risk of blackout thus becomes not only a technological threat, but a symbol of the need for a paradigm shift. The energy future must be sustainable, collaborative and adaptable—to guarantee not only the continuity of services with electricity, but also the stability of modern society. This paper provides an intrinsic interdisciplinary analysis on the causes, implications and possible solutions related to major imbalances in contemporary power systems, emphasizing the growing risk of blackout (large power outages). The main causes of crises are analyzed interdisciplinary, such as: insecurity in the functioning of the National Power System, terrorist attack on the National Power System, extreme weather condition, natural calamity, energy insecurity and political/military insecurity. The paper highlights the interdependence between energy infrastructure and energy security, as well as the vulnerability of power grids to cyberattacks, natural disasters and consumer pressures. In addition, socio-economic, technological and political issues are addressed, providing an integrated view of the phenomenon. Finally, national, regional and bilateral mitigation, limitation and restoration (resilience) procedures and measures are proposed in the event of an electricity crisis—blackout. Full article

The manufacturing supply chain has been exposed to natural disasters and geopolitical risks whose impacts, such as disruptions in the supply of materials and parts, can be devastating. In recent years, the data space has become more widely implemented, and it is expected to be used as a platform for widespread collaboration between companies. This article discusses how companies participating in the manufacturing supply chain cooperate to recover from disruption and mitigate risks using a data space platform and a flexible manufacturing system. Employing enterprise architecture modeling, we explore a comprehensive strategy for enhancing the resilience of a data space-based manufacturing supply chain. The proposed strategy adopts a comprehensive approach to addressing physical security and cybersecurity risks from a security perspective. By combining enterprise architecture modeling with the Unified Architecture Framework and conducting a scenario-based simulation, we discovered that an alternative manufacturing process with a flexible method in the data space can be a key security control measure for mitigating the risk associated with parts supply. The results of the alternative manufacturing simulation show that flexible manufacturing using BJT and MIM methods elicits better performance in terms of parts production volume and cost compared with conventional methods. The proposed method and the findings of this study contribute to consolidating a profound understanding of security and the mitigation of disruptive situations in a data space-based manufacturing supply chain. Full article

This study examines the impact of geopolitical risk (GPR) on the connectedness dynamics among the sovereign bonds of the emerging seven (E7) and the Group of Seven (G7) countries. Initially, a quantile-based vector-autoregressive (Q-VAR) connectedness approach is used to calculate the total connectedness index (TCI) among sovereign bonds under different market states. Then, the impact of GPR on the TCI at the median and tails is estimated to examine if GPR affects the TCI among sovereign bonds. Using daily yields from 30 January 2012, to 17 June 2024, the findings show that the GPR is one of the significant determinants of the TCI among sovereign bonds during normal and extreme market conditions. Other determinants of the TCI include yields on Treasury bills (T-bills), the exchange rate, and the financial market volatility index. The impact of GPR on the TCI varies significantly during different GPR episodes and bond market conditions. The effect of GPR on the TCI among sovereign bonds yields is higher during war times and when bond yields are average. These findings can be utilized by investors seeking to achieve international diversification and policymakers aiming to mitigate the effects of heightened geopolitical risk on financial stability. Furthermore, GPR can be used as an early signal tool for systematic tail risk spillovers among sovereign bonds. Full article