Search Results (53)

The COVID-19 pandemic has caused market turmoil and economic distress. To understand the effect of the pandemic on the U.S. financial systemic risk, we analyze the explanatory power of detailed COVID-19 data on three market-based systemic risk measures (SRMs): Conditional Value at Risk, Distress Insurance Premium, and SRISK. In the time-series dimension, we use the Dynamic OLS model and find that financial variables, such as credit default swap spreads, equity correlation, and firm size, significantly affect the SRMs, but the COVID-19 variables do not appear to drive the SRMs. However, if we focus on the first wave of the COVID-19 pandemic in March 2020, we find a positive and significant COVID-19 effect, especially before the government interventions. In the cross-sectional dimension, we run fixed-effect and event-study regressions with clustered variance-covariance matrices. We find that market capitalization helps to reduce a firm’s contribution to the SRMs, while firm size significantly predicts the surge in a firm’s SRM contribution when the pandemic first hits the system. The policy implications include that proper market interventions can help to mitigate the negative pandemic effect, and policymakers should continue the current regulation of required capital holding and consider size when designating systemically important financial institutions. Full article

The investment–saving relationship has been the subject of much debate. On the one hand, there is the conventional mainstream neoclassical school of thought that advocates for the idea that saving determines investment. On the other hand, heterodox economists (mainly in the post-Keynesian/structuralist tradition) posit an inverse relationship between these variables. This article empirically investigates the direction of causality in order to contribute to the existing literature on the topic. To this end, two Granger panel tests are applied to a dataset of 106 countries over the period from 1980 to 2023. The econometric techniques used are effective in accounting for both cross-sectional dependence and heterogeneity in the data. In summary, our findings align with the theoretical models that posit bidirectional causality as the most probable explanation of the mechanism driving investment and saving. More specifically, they are consistent with post-Keynesian (demand-led) assumptions describing an open economy operating below its maximum potential growth rate within a current account solvency constraint. Full article

The transition to sustainable, innovation-driven economies has become a global imperative, particularly for resource-dependent regions like the Middle East, where environmental challenges, fossil fuel reliance, and economic diversification pressures intersect. In this context, green innovation plays a pivotal role in mitigating environmental degradation while supporting long-term economic growth. This study examines the short-term and long-term drivers of green innovation across 13 Middle Eastern countries from 1990 to 2023, with a focus on environmental governance, environmental pollution, economic growth, and natural resource abundance. Using a balanced panel dataset, this study applies Frees, Friedman, and Pesaran CSD tests to address cross-sectional dependency and second-generation unit root tests for data stationarity. Both first- and second-generation cointegration tests confirm long-run relationships among variables. The empirical analysis employs the cross-sectional autoregressive distributed lag (CS-ARDL) model, alongside Pooled Mean Group (PMG-ARDL), Average Mean Group (AMG), and Common Correlated Effects CCEMG estimators, ensuring robustness. The findings indicate that, in the long term, environmental governance, economic growth, population size, and natural resource abundance significantly promote green innovation, with respective coefficients of 0.3, 0.01, 0.02, and 0.4. Conversely, human development and environmental pollution exert a negative influence on green innovation, particularly over the long term. These results suggest that, while economic and governance factors drive innovation, human capital development may prioritize immediate growth over sustainability, and pollution may hinder long-term innovation. Enhancing environmental governance, accelerating renewables, using strategic resource revenue for green projects, integrating green growth, and regional collaboration can position Middle Eastern economies as green innovation leaders. Full article

This study investigates the environmental effects of agricultural trade, renewable energy use, and economic growth in a panel of 14 selected countries for the period 2000–2021. Per capita CO₂ emissions are modeled as the dependent variable using a second-generation panel data method, the Augmented Mean Group (AMG) estimator, which accounts for cross-sectional dependence and slope heterogeneity. The analysis reveals that the share of renewable energy in total energy consumption significantly reduces carbon emissions, emphasizing the role of green energy policies in environmental improvement. In contrast, economic growth is found to increase emissions, indicating the validity of only the initial phase of the Environmental Kuznets Curve (EKC) hypothesis. Additionally, agricultural imports—and in certain cases, exports—exert upward pressure on emissions, likely due to logistics and production-related externalities embedded in the trade process. Group-specific results highlight distinct dynamics across countries: while renewable energy adoption plays a stronger role in emission mitigation in developing economies, trade composition and production technology drive environmental outcomes in developed ones. The findings underscore the need to redesign trade and energy strategies with explicit consideration of environmental externalities to align with long-term sustainability objectives. Full article

The chemical industry plays a pivotal role in the health of the world’s economies despite facing significant criticism for its contribution to environmental degradation, particularly in pollution management and sustainable development. This paper investigates the key factors motivating executives in chemical companies to engage in corporate social responsibility (CSR), including regulatory pressure, profit maximization, stakeholder demands, and environmental concerns. Data were collected through a cross-sectional survey of over 400 executives worldwide, and structural equation modelling (SEM) was employed to test four hypotheses examining the relationships among various variables. The findings indicate that regulatory pressure positively influences CSR adoption, although a profit-maximization orientation negatively moderates this relationship. This suggests that companies with an excessive focus on profits are less likely to engage in meaningful CSR activities beyond mere compliance. Additionally, unmet stakeholder needs drive environmental commitment, highlighting that managers and executives are responsive to the environmental expectations of consumers, society, and investors. In turn, environmental commitment strongly correlates with implementing pollution-prevention mechanisms, emphasizing the role of intrinsic motivations in promoting authentic CSR practices. This research expands on prior studies of CSR in high-impact industries by proposing a more integrated theoretical framework, drawing from Institutional Theory, Stakeholder Theory, and the Theory of Planned Behavior. Practical implications underscore the value of incentives that encourage firms to make substantial CSR commitments without jeopardizing profitability. Limitations of the study include its cross-sectional design, which calls for longitudinal research to understand causation better. Future studies could also explore additional industries to produce findings applicable across various sectors. Full article

Background: Inappropriate antibiotic use drives antimicrobial resistance and remains a global concern. Evidence suggests antibiotic use may be higher among malaria-negative patients compared to malaria-positive ones, but uncertainty persists, particularly in regions with varying malaria prevalence. This study measured antibiotic residuals in three Tanzanian regions with varying malaria epidemiology and analyzed factors influencing their presence. Methods: A cross-sectional household survey was conducted in 2015, covering a population of 6000 individuals across three regions of Tanzania. Dried blood spot samples from a subset of participants were analyzed using broad-range tandem mass spectrometry to detect residual antibiotics. Risk factors associated with antibiotic presence, including household healthcare-seeking behaviors, malaria testing, and other relevant variables, were evaluated. Results: The overall prevalence of residual antibiotics in the study population was 14.4% (438/3036; 95% CI: 11.4–15.8%). Stratified by malaria transmission intensity, antibiotic prevalence was 17.2% (95% CI: 12.9–17.2%) in Mwanza (low), 14.6% (95% CI: 10.6–15.0%) in Mbeya (moderate), and 11.2% (95% CI: 7.9–11.6%) in Mtwara (high). Trimethoprim was the most frequently detected antibiotic (6.1%), followed by sulfamethoxazole (4.4%) and penicillin V (0.001%). Conclusions: Residual antibiotic prevalence did not directly correlate with malaria endemicity but was influenced by healthcare practices, including co-prescription of antibiotics and antimalarials. The higher antibiotic use in malaria-negative cases highlights the need for improved diagnostics to reduce unnecessary use and mitigate antimicrobial resistance in malaria-endemic areas. Full article

To achieve higher enthalpy and pressure, the technique of variable cross-section drive is effectively combined with the heating of light gas to enhance the intensity of the incident shock wave. A study was conducted to predict the impact of variable cross-sections on the performance of high-temperature shock tube flow using a shock tube with a 2.6:1 diameter ratio between the driver and driven sections. The driver section was filled with a helium–argon gas mixture (mass ratio of 1:9), while the driven section contained dry air. Under total pressure conditions of 14.5 MPa and total temperature of 3404 K, as well as total pressure of 45 MPa and total temperature of 4845 K in the driver section, corresponding to driven section pressures of 10 kPa and 80 kPa, the results of chemical non-equilibrium numerical simulations were compared to experimental measurements of the incident shock Mach number and total pressure. The results indicated the following: First, after adding the contraction–expansion nozzle, the incident shock accelerated through the contraction section and reflected within the contraction section. Strong oscillations occurred during the flow, with increasing intensity as the throat size decreased. Second, without the nozzle, the shock velocity increased and then decreased. However, with the nozzle, the Mach number was highest near the nozzle exit and gradually decreased thereafter. Third, the presence of the nozzle led to the formation of a distinct fan-shaped wavefront, accompanied by significant variations in flow variables such as pressure, temperature, and Mach number in the region. This phenomenon was attributed to the interaction between the shock wave and the nozzle geometry, which altered the flow dynamics. Finally, as the throat size decreased, the intensity of the incident shock also decreased. After reflecting at the end of the shock tube, the total pressure in the driven section also decreased. The numerical simulations employed a multi-component, multi-temperature chemical non-equilibrium model, validated against experimental data, to accurately capture the complex flow behavior and wave interactions within the shock tube. Full article

Background. Food addiction is often linked to overeating and difficulty in controlling eating habits. At the same time, food addiction is often associated with intense eating-related thoughts and emotional eating behaviors. However, despite extensive research on food addiction, the psychological processes that contribute to these outcomes have not been fully examined. Consequently, this study aims to fill that gap by investigating the influence of eating-related thoughts, as well as emotional eating behaviors that may precede episodes of uncontrolled eating. Methods. A cross-sectional design was used. A sample of 467 individuals was enrolled from the general population. Participants completed a battery of self-report questionnaires. A sequential mediation analysis with latent variables (i.e., structural equation modeling; SEM) using 5000 bootstrap samples and observed variables was performed. Results. The proposed model provides good fit indices. Indeed, food addiction predicts uncontrolled eating behaviors through eating-related thoughts (p < 0.001), which were also significantly associated with the emotion-driven eating patterns (p < 0.001), revealing a fully mediated model explaining 61.6% of the outcome variance (R² = 0.616). Discussion. The findings underscore the critical influence of cognitive factors (i.e., eating-related thoughts) in driving maladaptive coping mechanisms like emotional eating. Moreover, emotional eating may act as a precursor to behaviors associated with overeating, which are often rooted in food addiction. Conclusions. Recognizing the central role of thoughts and emotions can help clinicians develop more targeted psychological interventions for those experiencing food addiction symptoms. Full article

Motor efficiency presents a trade-off between low-speed and high-speed regions. Additionally, the cross-sectional area of hairpin motors employing rectangular wires is larger than that of round wires, thereby amplifying AC copper losses. As the operating speed increases, the AC copper loss also becomes more pronounced; therefore, efficiently determining the optimal design point considering these characteristics is essential. This study optimizes the efficiency of an electric vehicle (EV) simulation is conducted using MATLAB 2024, and the main operating points according to the driving cycle are selected. For the EV simulation to select the main operating points, the driving cycle of the multi-cycle test method, which is used for measuring domestic driving range, is considered to enhance the validity of the operating points. The efficiency optimization of the main operating points was performed considering the AC copper loss, and essential parameters such as the torque ripple and total harmonic distortion of the back-electromotive force were incorporated as constraints. Furthermore, the predictive performances of the 11 metamodels were compared to identify the most suitable metamodel for the output and design variables. Subsequently, the selected metamodel was integrated with four optimization algorithms to optimize the design. Full article

Climate change is a major global challenge affecting migration patterns, particularly in coastal communities vulnerable to sea-level rise, flooding, and extreme weather. Pakistan, with its extensive coastline and diverse environmental conditions, faces significant climate-induced migration issues, especially in Karachi, Thatta, Gwadar, Badin, and Muzaffargarh. This study aims to investigate the impact of climate change on migration patterns in these five selected regions of Pakistan. By analyzing climate variables and socio-economic factors, the research seeks to provide a localized understanding of how climate change drives population movements. A cross-sectional survey design was employed to gather data from 350 participants across these regions. Stratified random sampling ensured representation from each area, and data were collected using a structured questionnaire administered online. Statistical analyses included multiple linear regression, logistic regression, and structural equation modeling (SEM). This study found a strong positive relationship between climate change variables (sea level rise, temperature increases, and flooding) and migration patterns. Both direct impacts of climate change and indirect socio-economic factors influenced the likelihood of migration. The SEM analysis revealed that climate awareness partially mediates the relationship between climate change and migration. In conclusion, climate change significantly drives migration in Pakistan’s coastal communities, with both direct environmental impacts and socio-economic conditions playing crucial roles. Enhanced climate awareness and comprehensive adaptation strategies are essential. Policies should focus on climate resilience through infrastructure improvements, early warning systems, and socio-economic support programs. Strengthening education and economic opportunities is vital to build community resilience and effectively manage climate-induced migration. Full article

Conventional methods for process monitoring often fail to capture the causal relationships that drive outcomes, making hard to distinguish causal anomalies from mere correlations in activity flows. Hence, there is a need for approaches that allow causal interpretation of atypical scenarios (anomalies), allowing to identify the influence of operational variables on these anomalies. This article introduces (CaProM), an innovative technique based on causality techniques, applied during the planning phase in business process environments. The technique combines two causal perspectives: anomaly attribution and distribution change attribution. It has three stages: (1) process events are collected and recorded, identifying flow instances; (2) causal learning of process activities, building a directed acyclic graphs (DAGs) represent dependencies among variables; and (3) use of DAGs to monitor the process, detecting anomalies and critical nodes. The technique was validated with a industry dataset from the banking sector, comprising 562 activity flow plans. The study monitored causal structures during the planning and execution stages, and allowed to identify the main factor behind a major deviation from planned values. This work contributes to business process monitoring by introducing a causal approach that enhances both the interpretability and explainability of anomalies. The technique allows to understand which specific variables have caused an atypical scenario, providing a clear view of the causal relationships within processes and ensuring greater accuracy in decision-making. This causal analysis employs cross-sectional data, avoiding the need to average multiple time instances and reducing potential biases, and unlike time series methods, it preserves the relationships among variables. Full article

In accordance with the United Nations Sustainable Development Goals agenda for decent and sustainable economic growth highlighted in the UNSDGs-8, several economies over the years have been on the quest for drivers for decent and sustainable economic growth, of which the Arab bloc is no exception. To this end, the present study draws strength from the classical growth model while exploring the dynamic nexus between oil production and economic growth while accounting for other key growth drivers like gross capital formulation accumulation, labour, trade openness, and financial development for a balanced panel of selected Arab economies. To operationalise the study objectives, the present study leverages second-generational panel econometric approaches. The econometrics techniques applied circumvent the cross-sectional dependency and slope heterogeneity in the sampled bloc. For co-integration analysis, the Westerlund’s panel co-integration test affirms a long-run equilibrium relationship between the study’s outlined variables. Furthermore, for long-run estimates, the present study leverages the common correlated effects mean group (CCEMG) methodology and the augmented mean group (AMG) method for robustness and soundness of the results and coefficients. The present study corroborates the trade-induced growth hypothesis in the entire panel at a p < 0.001 statistical level, which resonates with the mercantilism school of thought. Additionally, the present study also affirms the Solow–Swan hypothesis, where gross capital formation accumulation and labour drive economic growth. Interestingly, the panel bloc shows that oil production is a key driver to the nation’s economic growth, at a p < 0.05 statistical level. However, from a policy standpoint, there are policy suggestions for diversification of the Arab economies to move from a mono-economy dependent on oil production to other sectors like service, industry, and manufacturing, which require labour, capital accumulation, and more. Further policy caveats are outlined in the concluding section. Full article

This study explores the multifaceted factors influencing student learning motivations and educational outcomes. Utilizing a diverse student body from Franklin University Switzerland, the study emphasizes the impact of internal factors, such as the psychological state of flow and a self-reported love of learning, alongside GPA and student cohort influences like year of study, academic discipline, country of origin, and academic travel. Through a cross-sectional survey of 112 students, the study evaluates how these factors correlate with and diverge from each other and student GPAs, aiming to dissect the influences of intrinsic motivations, demographic variables, and educational experiences. Our analysis revealed significant correlations between students’ self-reported love of learning, experiences of flow, and academic performance. Conversely, academic travel did not show a significant direct impact, suggesting that while such experiences are enriching, they do not necessarily translate into a greater love of learning, flow, or higher academic achievement in the short term. However, demographic factors, particularly discipline of study and country of origin, significantly influenced the students’ love of learning, indicating varied motivational drives across different cultural and educational backgrounds. This study provides valuable insights for educational policymakers and institutions aiming to cultivate more engaging and fulfilling learning environments. Full article

Decision-making styles are a habit-based propensity that drive behavior and affect daily life. Rational and intuitive decision-making styles have been associated with good mental health. However, the underlying mechanisms are not clear. In the last decade, high basal levels of heart rate variability (HRV) have been proposed as an index of health and emotional control, and this could be one of the variables involved in the effects of decision making on health. Therefore, the aim of this study is to analyze the capability of decision-making styles to predict resting HRV. A cross-sectional study was conducted in a sample of 199 (119 women) young university students, and a resting ECG was recorded to extract frequency domain HRV variables. Subsequently, participants completed sociodemographic data and the General Decision-Making Style questionnaire (GDMS). Results showed that the intuitive style predicted high-frequency HRV, while the avoidant style predicted less low-frequency HRV. This study presents new data on the relationship between decision-making style and HRV, suggesting that the intuitive style has a cardioprotective effect, while the avoidant style is related to lower HRV, which has been associated with health vulnerability. In conclusion, this study contributes to the understanding of HRV and its potential as a biomarker for cognitive styles that may improve health. Full article

This article aims to better understand the mechanisms that connect climate change perceptions and general willingness to engage in pro-environmental behavior using Spanish cross-sectional data (N = 403) that included 102 members of environmental organizations. To do this, we first developed and validated the General Willingness for Environmental Behavior Scale (GWEBS), which includes the classical approach of voluntarily doing new actions but also actions implying not doing things (degrowth) and actions forced by social constraints. The exploratory and confirmatory factor analysis showed a good fit for the one-factor structure, which had adequate validity based on their relationship with other variables. Additionally, the GWEBS distinguished between women and men, left- and right-oriented people, and people who belonged to pro-environmental groups and people who did not. In the second place, we tested the parallel mediator role of eco-anxiety and trust in science in the relationship between climate change perceptions and the GWEBS. The results showed that eco-anxiety fully mediated and trust in science partially mediated such a relationship, making them crucial in terms of mobilizing the intention to act according to perceptions. This study contributes to understanding the psychological mechanisms that eventually drive pro-environmental behaviors and provides a clear direction for future research. Full article