Search Results (6,565)

Grounded in the theoretical frameworks of safe haven and hedging asset theory, alongside wavelet-based time-frequency analysis, this study investigates the dynamic interconnectedness between three major cryptocurrencies and six global stock markets spanning both developed and emerging economies. Unlike prior wavelet studies that rely predominantly on graphical interpretation, this paper advances the literature by complementing graphical outputs with numerical results, offering a more rigorous and reproducible analytical foundation. Using daily price data from January 2018 to October 2024, the study applies both univariate and multivariate wavelet techniques to capture return co-movements across multiple time horizons. The univariate analysis reveals significant variance in stock returns concentrated at high frequencies, particularly over 2–4-day cycles, with pronounced fluctuations during the COVID-19 pandemic. In emerging markets such as Nigeria, additional volatility is attributed to political instability and macroeconomic crises. The multivariate analysis further demonstrates that observed co-movements between cryptocurrencies and stock markets are largely driven by interdependence rather than contagion. The paper’s findings are relevant to portfolio diversification strategies across both developed and emerging markets for investors combining stock and cryptocurrency assets. Full article

The present study investigates extreme co-movements and financial contagion across a broad set of global financial markets, including ten developed and emerging stock market indices, commodities (gold and oil), and cryptocurrencies (Bitcoin), over the period from January 2007 to May 2023. In the context of the increasing interconnectedness of global financial markets, it is imperative to comprehend the propagation of systemic shocks across asset classes for the purpose of effective risk management. In order to achieve this objective, a Copula-GARCH framework is employed, in which the Student’s t-copula is selected for its superior ability to capture nonlinear dependence and tail co-movements. The analysis compares dependence structures during the pre-crisis and the COVID-19 crisis periods. The present study diverges from the majority of previous research in its utilisation of a combined approach, integrating Copula-GARCH modelling with wavelet analysis. This novel method is employed to collectively examine tail dependence and multi-scale contagion dynamics, thereby facilitating a more comprehensive evaluation of financial interconnectedness during periods of market stress. The empirical evidence indicates significant and largely symmetric tail dependence across the majority of market pairs. This finding suggests the presence of stronger co-movements during periods of extreme market conditions, a phenomenon that was particularly evident throughout the course of the global pandemic. The robustness of these findings is further confirmed by wavelet analysis, which provides a multi-scale perspective on shock transmission across markets. The results demonstrate that financial contagion intensified during the pandemic, with important implications for international portfolio diversification and risk management. Furthermore, the role of gold as a potential safe-haven asset during periods of severe financial stress is highlighted, providing valuable insights for investors and policymakers. Full article

Recreational fisheries contribute significantly to global economies through tourism, employment, and ecosystem service valorization. However, a systematic understanding of their economic magnitude and regional disparities remains fragmented. This study conducts a systematic review of 112 peer-reviewed articles published between 1991 and 2024, synthesizing the economic evidence across three key domains: consumer behavior, resource management, and ecological interactions. Consistent with previous research, our systematic synthesis documents a pronounced imbalance in the existing evidence base: studies from high-income countries more frequently report established management frameworks (e.g., bag limits, catch-per-unit-effort monitoring) and robust economic valuation mechanisms (e.g., Willingness to Pay estimation), whereas low- and middle-income countries face significant barriers in infrastructure and resource quantification. We identify that non-market valuation methods, particularly the travel cost method and consumer surplus analysis, are critical for internalizing the environmental value of fisheries. Furthermore, while ecological restoration demonstrates clear positive feedback loops for local economies, the sector exhibits high vulnerability to external shocks, such as the COVID-19 pandemic. This review highlights the urgent need to bridge the data gap in developing regions and suggests that transferring successful adaptive management models from developed nations is essential for achieving equitable and sustainable growth in the global recreational fishery economy. Full article

Research on industrial location criteria has grown since the first industrial revolution, revealing key factors for choosing sites for industrial parks. However, macro-elements like economy, culture, and technology continuously reshape these criteria. Most existing studies focus on specific factors, indicating a need for a comprehensive synthesis. This study employs systematic literature review (SLR) methodology following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. to analyze scientific information and aim to summarize essential criteria for selecting industrial zones. Following the screening and eligibility assessment process, 182 full-text studies were reviewed, with 60 recent publications emphasized for synthesizing current evidence, complemented by 56 supplementary references to support theoretical and contextual analysis. Findings include: (1) the need for frequent updates to criteria in response to global economic and scientific trends; (2) traditional factors like natural resources, transportation systems, costs, and population density remain significant; and (3) of the identified criteria organized into six main evaluation factors, public health emerged as an important one, especially highlighted by the COVID-19 pandemic. Given the potential for project failures due to overlooked criteria, regularly reviewing and updating industrial location considerations is essential and can serve as a valuable resource for future research and practice. Full article

Companion animals have increasingly become central to human emotional life, yet the literature linking living with companion animals, human–animal interaction, and mental health remains fragmented. This study mapped global research trends on companion animals and mental health from 2000 to 2024 using bibliographic records retrieved from the Web of Science Core Collection (WoSCC). A final dataset of 930 peer-reviewed articles was analyzed with Bibliometrix (RStudio), CiteSpace, and VOSviewer to characterize publication growth, national and institutional collaboration, influential authors and journals, co-cited references, keyword clusters, and citation bursts. Results show a strong nonlinear increase in output after 2015, with the United States as the dominant contributor and Australia and the WoSCC country node labelled England (distinct from Scotland) as important international bridges. Core knowledge clusters center on human–animal interaction, animal-assisted intervention, older adults, child/adolescent development, loneliness, attachment, and post-traumatic stress. Recent burst terms and references indicate a shift from general companion-animal caregiving and physiological stress buffering toward intervention design, vulnerable populations, and pandemic-era mental health. Overall, the field has matured into an interdisciplinary research area with growing methodological sophistication, but future work should strengthen cross-cultural evidence, longitudinal designs, standardized outcome measures, and explicit assessment of animal welfare. Full article

Influenza remains a persistent global health threat, accounting for an estimated one billion infections and 350,000–600,000 deaths worldwide annually. Beyond substantial mortality and morbidity, influenza imposes major socio-economic costs through healthcare utilisation and productivity losses, amounting to tens of billions of dollars each year. Current seasonal vaccination strategies are constrained by rapid antigenic evolution, which necessitates frequent vaccine reformulation and contributes to suboptimal vaccine efficacy across seasons. These drawbacks highlight the need for a universal influenza vaccine (UIV) that protects against all seasonal and potential pandemic influenza strains via a single dose. This review evaluates the feasibility of a UIV as an alternative to a seasonal influenza vaccine (SIV) by consolidating evidence from current UIV candidates in preclinical and clinical development across a diverse range of platforms and target epitopes. Preclinical studies and clinical trials have demonstrated the safety and tolerability of multiple candidates, as well as their ability to elicit broadly cross-reactive immune responses. However, several challenges remain regarding the long-term efficacy of these vaccines. In particular, pre-existing host immunity can shape subsequent vaccine responses, with immune imprinting introducing biases toward previously encountered influenza strains, potentially reducing vaccine effectiveness. In addition, the genetic plasticity of influenza viruses, driven by ongoing mutations and genetic reassortment, raises concerns about the ability of a single vaccine formulation to maintain broad and durable protection. Overall, several UIV candidates have shown promising early results. Although ecological and immunological barriers remain, a UIV could become a viable alternative to, or eventual replacement, for SIVs. Full article

In May 2025, a new and welcome Pandemic Treaty was signed. The success of this treaty for the management of future pandemics depends on a re-evaluation of the importance of previous well-established treaty-based international human rights norms in controlling health crises. During the COVID-19 pandemic, public health responses globally were characterised by poor preparation, uncertainty, and hasty and sometimes perverse decisions. International human rights norms, especially health rights, and other treaty obligations were honoured more in their breach than their observance. The lessons learned from public health strategies that had integrated international human rights norms into the control and management of the HIV/AIDS pandemic were either ignored or forgotten. Early public health attempts to control the HIV/AIDs pandemic were hobbled by data breaches, travel restrictions, compulsory reporting, stigma, and misinformation about how the virus spread. This was replaced by a more successful human rights-based approach (HRBA) that used health rights indicators to identify groups susceptible to the disease but difficult to reach with conventional public health policies. The efficacy of health rights indicators and HRB methodology to remove barriers to treatment and suppress pandemics should have been seriously considered in strategies to control COVID-19. The article claims that failure to do this meant that more lives were lost than necessary and more people were left with serious long-term health effects. This article explores the practical significance of the international human rights legal framework, especially health rights, as trialled during the HIV/AIDS pandemic, for the management of COVID-19 and other pandemics. Full article

The present study examined the dynamics of equity-market integration among India and five major global economies: China, Hong Kong SAR, Japan, the United Kingdom and the United States. Daily data were analysed for the period from January 2002 to December 2025. This study employs Johansen co-integration and the Granger causality test, along with a DCC-GARCH model and the Diebold–Yilmaz connectedness approach, to estimate time-varying conditional correlations across crisis regimes. The findings reveal a single long-run co-integrating relationship in the pre-COVID-19 period (2002–2019) that weakens to none when the post-COVID-19 period (2020–2025) is investigated in isolation, suggesting that the intense early-pandemic coupling became moderated as monetary-policy cycles diverged. The Granger causality test showed that the United States consistently and unidirectionally drives the Indian market, while India’s pre-crisis role as a transmitter to Asian markets fades after the pandemic. The DCC-GARCH indicated that India’s conditional correlations with selected economies rose sharply during the 2008 and 2020 crises, peaking with Hong Kong SAR (0.64). The DY connectedness framework reinforced this pattern. Systemwide connectedness rose sharply during both crises, exceeding 57%, compared to roughly 45% in calmer phases. The United States emerged as the key net transmitter of shocks, and India acted as a net receiver. Full article

This study examines the determinants of conditional volatility in energy-sector equity returns and their implications for sustainable energy resilience, energy security, and investment stability. Using a multi-stage econometric framework, the analysis investigates how global financial, commodity, and macroeconomic shocks are transmitted to volatility dynamics across heterogeneous energy companies. The dataset includes domestic and international firms, enabling a comparative assessment of volatility behavior and risk-transmission mechanisms under different market and institutional conditions. The empirical framework combines ARMA models for return dynamics, EGARCH/GARCH specifications for conditional volatility estimation, and OLS regressions with HAC standard errors to identify key determinants of volatility, including market indices, commodity prices, exchange rates, and major geopolitical and economic events. The findings reveal strong volatility persistence across all assets and asymmetric responses to market shocks in most cases. Global market conditions, particularly lagged MSCI World returns, significantly affect volatility, whereas commodity effects related to oil, gas, and coal remain heterogeneous across firms. Event-based regressors show that systemic shocks, including the COVID-19 pandemic and the European energy crisis, increase volatility, although geopolitical effects depend on firm-specific exposure. The results contribute to the sustainability literature by linking energy-sector financial volatility with market resilience, energy security, and stable investment conditions for the energy transition. Full article

European energy markets have experienced significant instability as a result of consecutive global systemic shocks, particularly the COVID-19 pandemic and the geopolitical conflict in Ukraine. This paper analyses the development of crude oil and natural gas prices between 2019 and 2024 and discusses their implications for sustainable logistics, energy security and supply chain resilience in Europe. The study is based on secondary data from internationally recognized sources, including the International Energy Agency, OPEC, Eurostat, the European Council, the World Bank and the U.S. Energy Information Administration. An event-based comparative approach supported by descriptive price-change calculations was applied to distinguish between the pandemic-related demand shock and the geopolitical supply-side shock after 2022. The results show that crude oil prices declined from approximately 64 USD/barrel in 2019 to 41 USD/barrel in 2020, representing a decrease of about 3f5.9%, mainly in connection with reduced mobility, lower transport activity and industrial slowdown during the COVID-19 pandemic. In contrast, crude oil prices increased to approximately 100 USD/barrel in 2022, representing an increase of about 143.9% compared to 2020, coinciding with geopolitical uncertainty and supply-side pressures. The European natural gas market appeared particularly vulnerable to the 2022 crisis because of supplier dependence, pipeline infrastructure constraints and reduced Russian gas flows. EU natural gas demand declined by 55 billion m³, or 13%, in 2022, indicating the effect of high prices, energy savings and crisis adaptation. The findings suggest that crude oil shocks are mainly related to transport costs and freight rates, while natural gas shocks may influence energy-intensive production, warehousing, cold chains and broader supply chain stability. The study highlights the need for energy diversification, renewable and low-carbon energy development, energy efficiency and more resilient logistics strategies. Full article

Recently, the Internet of Things (IoT) has evolved into the Artificial Intelligence of Things (AIoT) through its combination with artificial intelligence (AI) technology and has become capable of providing intelligent services in all industrial sectors. Globally, energy consumption within buildings is continuously increasing alongside the advancement of IT and AI technologies. Since this increase is attributed to various causes—ranging from large-scale climate change to small-scale indoor environmental factors (air quality) and health factors—research aimed at reducing indoor energy consumption is actively underway. In particular, in the home environment where people spend a significant portion of their day, maintaining indoor air quality (IAQ) is critical for health, and energy conservation in heating, ventilation, and air conditioning (HVAC) systems is essential. In Korea, the number of single-person households is increasing and was expected to reach 36.1% of all households by 2024, leading people to live in increasingly smaller homes. This study aimed to verify residents using contactless facial recognition to prevent pandemics such as COVID-19 and to provide comfortable indoor air quality. Resident facial recognition was performed by identifying residents’ faces in images captured by the Pi camera using OpenCV’s Haar feature-based cascade classifier. Indoor air quality measurements were conducted in four indoor locations, measuring various environmental factors (PM_2.5, CO₂, etc.) based on environmental sensors and the IoT. Furthermore, to manage indoor air quality, AI was utilized based on the measurement data to classify the four spaces, with a success rate of 96%. Additionally, considering the indoor area of the experimental environment (97 m²), it was confirmed that operating a 70 W air purifier only when the resident is indoors can reduce power consumption by approximately 33–75% compared to running it 24 h a day. Full article

Background/Objectives: Telehealth (TH) and Telemedicine (TM) can significantly benefit individuals who live far from healthcare institutions and those with limited financial resources by improving access to essential health services. This review explores the attitudes and determinants affecting the use of TH and TM, focusing on the MENA region with insights from the global context. Methods: A narrative review was created using a compilation of scientific articles published between 2020 and 2025 from PubMed and Scopus. Studies were screened to be relevant with the determinants, attitudes or use of TH and TM. Forty one sources met the eligibility criteria and covered evidence from the MENA region with supporting background from global developments. Results: In the MENA region, although high perceived effectiveness is recorded, the use of TH and TM remains below optimal levels. The majority of the studies reviewed suggested that participants have positive attitudes towards TH and TM. Nevertheless, some studies suggest low awareness and limited utilization of available TH and TM services. Determinants affecting TH and TM included pandemic-based, socioeconomic, cultural, financial, health literacy, and infrastructure-based factors, and those relating to physician implementation. Disparities in utilization of these services were evident among individuals with low digital literacy, older individuals and low-income workers. Conclusions: TH and TM are observed to increase the availability and accessibility of preventative and treatment-based health services, especially in regions facing geographic limitations. The findings exhibit the disparities in institution and uptake of these services due to socioeconomic, geographic and cultural aspects. Interventions at the system level, supported by evidence such as multilingual digital platforms, investments in digital infrastructure and coverage in insurance, need to be implemented to ensure equitable utilization of these services in the MENA region. Full article

The COVID-19 pandemic severely disrupted tourism globally, with considerable variation in impact and recovery across regions. This study examines how tourism demand, revenue, employment, and transport evolved in the Visegrád Four (V4) countries—Czechia, Hungary, Poland, and Slovakia—between 2015 and 2025, assessing recovery speed and sectoral resilience across nine indicators. The modelled window varies by indicator: 2015–2023 for revenue indicators, 2015–2024 for visitor-flow indicators, and 2015–2025 for employment and air passenger indicators. Annual data from Eurostat and the Hungarian Central Statistical Office were analysed using descriptive comparative methods, linear mixed-effects models, and interrupted time series analysis with counterfactual projections. All indicators showed sustained pre-pandemic growth followed by a statistically significant 2020 shock. International demand fell roughly twice as sharply as domestic demand, and air passenger traffic suffered the largest absolute collapse. Revenue recovered fastest, surpassing pre-pandemic levels by 2023–2024 in all countries, while guest-volume recovery is projected only by 2025–2027. Part-time employment remained below 2015 levels in three of four countries despite volume and revenue recovery, indicating structural labour market adjustment. Slovakia consistently showed non-significant post-shock recovery slopes in seven of nine indicators, emerging as the region’s weakest performer. These findings indicate that COVID-19 produced uneven, revenue-led recovery across V4, and the employment paradox and Slovakia’s underperformance underscore the need for diversified, evidence-based tourism crisis management. 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

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