Search Results (858)

Egypt, the world’s second-largest wheat importer, has been working hard to narrow the gap between its domestic wheat production and consumption. However, these efforts have been hampered by water scarcity and the negative impact of climate change on wheat production. This study seeks to analyze the influence of climatic and technical factors on wheat production in Egypt over the long and short term. Using Egypt-specific data from 1961 to 2022 and employing the Autoregressive Distributed Lag (ARDL) model and Granger-causality, the study examines the impact of factors such as harvested area, fertilizers, technology, CO₂ emissions, seasonal temperature and precipitation patterns (winter and spring) on wheat production in Egypt. The empirical results indicate that the harvested area, level of technology, and average winter temperature significantly and positively impact wheat production. Precisely, a 1% increase in these factors leads to a 1.08%, 1.49%, and 6.89% increase in wheat production, respectively. Conversely, a 1% rise in CO₂ emissions, average spring temperature, and precipitation reduced wheat production by 1.76%, 0.52%, and 0.054%, respectively. The Granger causality results indicate a bidirectional causal relationship between wheat production and harvested area. Furthermore, the technology level exhibits a significant causal influence on wheat production, cultivated area, and CO₂ emissions, highlighting its pivotal role in both the wheat production process and its environmental impact. In conclusion, this study is crucial for Egypt’s future food security. By identifying the key climatic and non-climatic factors that impact wheat production, policymakers can gain valuable insights to address climate change and resource limitations. Improving domestic production through technological advancements, effective resource utilization, and climate-resilient practices will ensure a sustainable food supply for Egypt’s expanding population in the face of global uncertainties. Full article

This study provides a comprehensive empirical examination of the drivers and dynamics of Juglar cycles in China from 1981 to 2024. We develop a unified framework that integrates investment, institutional, productivity, and structural factors, and employ a Vector Error Correction Model to analyze the long-run equilibrium and short-run adjustment mechanisms linking fixed asset investment (FAI), government fiscal expenditure (GFE), total factor productivity (TFP), industrial structure upgrading (ISU), and gross domestic product (GDP). Our results confirm a stable cointegration relationship and identify FAI as the most influential long-run driver of output, with a 1% increase in FAI leading to a 0.88% rise in GDP. Industrial upgrading also exerts a positive long-run influence on growth, whereas government spending exhibits a significant negative effect, potentially indicating crowding-out or efficiency losses. In the short run, we find unidirectional Granger causality from FAI to GDP, suggesting that changes in investment contain meaningful predictive power for future output fluctuations. Furthermore, impulse response and variance decomposition analyses illustrate the temporal evolution of these effects, highlighting that the contribution of TFP gains importance over the medium term. Overall, this study deepens our understanding of business cycle transmission mechanisms in emerging economies and offers valuable insights for policymakers seeking to balance investment-driven growth with structural reforms for sustainable and robust economic development. Full article

This study proposes a novel causality-structured matrix long short-term memory (C-mLSTM) model for significant wave height (SWH) forecasting. The framework incorporates a two-stage causal feature selection methodology using cointegration testing and Granger causality testing to identify long-term stable causal relationships among variables. These relationships are embedded within the C-mLSTM architecture, enabling the model to effectively capture both temporal dependencies and causal information within the data. Furthermore, the model integrates Bayesian optimization (BO) and twin delayed deep deterministic policy gradient (TD3) algorithms for synergistic optimization. This combined TD3-BO approach achieves an 11.11% improvement in the mean absolute percentage error (MAPE) on average compared to the base model without optimization. For 1–24 h SWH forecasts, the proposed TD3-BO-C-mLSTM outperforms the benchmark models TD3-BO-LSTM and TD3-BO-mLSTM in prediction accuracy. Finally, a Shapley additive explanations (SHAP) analysis was conducted on the input features of the BO-C-mLSTM model, which reveals interpretability patterns consistent with the two-stage causal feature selection methodology. This research demonstrates that integrating causal modeling with optimization strategies significantly enhances time-series forecasting performance. Full article

Wastewater-based epidemiology is a robust, scalable, cost-effective, and high-performing tool to monitor and predict SARS-CoV-2 trends. We aimed to investigate whether this approach could be applied to influenza A/B viruses and respiratory syncytial virus (RSV) in Marseille, southern France. Wastewater concentrations of SARS-CoV-2, influenza A/B viruses, and RSV in Marseille were monitored by qPCR between January 2021 and October 2024. These concentrations were compared with the diagnosis numbers for the three viruses collected at public hospitals in Marseille, using cross-correlation analyses. The Granger causality test was used to determine whether wastewater concentrations can predict the number of clinical cases. SARS-CoV-2 and influenza virus concentrations in wastewater preceded the rise in the incidence of patient diagnoses by a lag of five days and nine/ten days, respectively. In contrast, for RSV, the rise in incidence of clinical cases preceded that of wastewater concentrations. We conclude that wastewater-based epidemiology is a powerful tool to monitor the level of circulation of these viruses independently of tests carried out on people. It enables earlier alerts than monitoring patients for SARS-CoV-2 and influenza symptoms. However, for RSV, it does not provide an early warning, and clinical data-based surveillance appears to be more suitable. Full article

This study examines the dynamic relationship between the gold markets of India and the United States from 2005 to 2025. Recognising gold’s role as a hedge and safe-haven during market uncertainty, we employ the Autoregressive Distributed Lag (ARDL) model to assess long-term co-integration and apply the Toda–Yamamoto causality test to evaluate directional influences. Additionally, the Generalised Autoregressive Conditional Heteroskedasticity (GARCH) (1, 1) model is applied to examine volatility spillovers. Results reveal no long-term co-integration between the two markets, suggesting they function independently over time. However, unidirectional causality is observed from the U.S. to the Indian gold market, and the GARCH model confirms bidirectional volatility transmission, indicating interconnected short-run dynamics. These findings imply that gold market shocks in one country may affect short-term pricing in the other, but not long-term trends. From a portfolio diversification and risk management perspective, investors may benefit from allocating assets across both markets. This study contributes a novel empirical framework by integrating ARDL, Toda–Yamamoto Granger causality, and GARCH(1, 1) models over a two-decade period (2005–2025), incorporating post-COVID market dynamics. The combination of these methods, applied to both an emerging (India) and developed (U.S.) economy, provides a comprehensive understanding of gold market interdependence. In doing this, the paper offers valuable insights into causality, volatility transmission, and diversification potential. The econometric rigour of the study is enhanced through residual diagnostic tests, including tests of normality, autocorrelation, and other heteroscedasticity tests, as well as VAR stability tests. These ensure strong inference and model validity; more specifically, they are pertinent to the analysis of financial time series. Full article

Background: Hemiplegic cerebral palsy (HCP) is a motor dysfunction disorder resulting from perinatal developmental brain injury, predominantly impairing upper limb function in children. Nonetheless, there has been insufficient research on the brain activation patterns and inter-brain coordination mechanisms of HCP children when performing motor control tasks, especially in contrast to children with typical development(CD). Objective: This cross-sectional study employed functional near-infrared spectroscopy (fNIRS) to systematically compare the cerebral blood flow dynamics and brain network characteristics of HCP children and CD children while performing upper-limb mirror training tasks. Methods: The study ultimately included 14 HCP children and 28 CD children. fNIRS technology was utilized to record changes in oxygenated hemoglobin (HbO) signals in the bilateral prefrontal cortex (LPFC/RPFC) and motor cortex (LMC/RMC) of the subjects while they performed mirror training tasks. Generalized linear model (GLM) analysis was used to compare differences in activation intensity between HCP children and CD children in the prefrontal cortex and motor cortex. Finally, conditional Granger causality (GC) analysis was applied to construct a directed brain network model, enabling directional analysis of causal interactions between different brain regions. Results: Brain activation: HCP children showed weaker LPFC activation than CD children in the NMR task (t = −2.032, p = 0.049); enhanced LMC activation in the NML task (t = 2.202, p = 0.033); and reduced RMC activation in the MR task (t = −2.234, p = 0.031). Intragroup comparisons revealed significant differences in LMC activation between the NMR and NML tasks (M = −1.128 ± 2.764, t = −1.527, p = 0.025) and increased separation in RMC activation between the MR and ML tasks (M = −1.674 ± 2.584, t = −2.425, p = 0.031). Cortical effective connectivity: HCP group RPFC → RMC connectivity was weaker than that in CD children in the NMR/NML tasks (NMR: t = −2.491, p = 0.018; NML: t = −2.386, p = 0.023); RMC → LMC connectivity was weakened in the NMR task (t = −2.395, p = 0.022). Conclusions: This study reveals that children with HCP exhibit distinct abnormal characteristics in both cortical activation patterns and effective brain network connectivity during upper limb mirror training tasks, compared to children with CD. These characteristic alterations may reflect the neural mechanisms underlying motor control deficits in HCP children, involving deficits in prefrontal regulatory function and compensatory reorganization of the motor cortex. The identified fNIRS indicators provide new insights into understanding brain dysfunction in HCP and may offer objective evidence for research into personalized, precision-based neurorehabilitation intervention strategies. Full article

The COVID-19 pandemic and the implementation of strict lockdown measures have significantly impacted various dimensions of the global economy. This study examines the impact of COVID-19 and lockdown stringency on exchange rate volatility in India using three core variables, i.e., COVID-19 cases, the lockdown stringency index, and exchange rate volatility. To achieve the above objectives, we have employed advanced econometric techniques, such as wavelet coherence and a hybrid non-parametric quantile causality framework, on the dataset spanning from 30 December 2020 to 24 January 2022. Robustness is assessed using Troster–Granger causality in quantiles and Breitung–Candelon Spectral Causality tests. The wavelet coherence analysis indicates that the initial outbreak of COVID-19 increased the exchange rate volatility, while the enforcement of stringent lockdowns in the later phases helped reduce this volatility. Similarly, the hybrid quantile causality results indicate that both COVID-19 cases and lockdown measures possess predictive power over exchange rate fluctuations. The robustness checks confirm these findings and establish a causal relationship between the pandemic, policy responses, and currency market behaviour. This study helps clarify the complex, nonlinear dynamics between pandemic-related variables and exchange rate volatility in emerging markets. Based on the aforementioned result, it is recommended that policymakers implement targeted lockdown strategies coupled with timely monetary interventions (such as foreign exchange reserve management or interest rate adjustments) to mitigate volatility and maintain currency stability during future pandemic-induced shocks. Full article

Background: The cerebral compliance (or compensatory reserve) index, RAP, is a critical yet underutilized physiological marker in the management of moderate-to-severe traumatic brain injury (TBI). While RAP offers promise as a continuous bedside metric, its broader cerebral physiological context remains partly understood. This study aims to characterize the burden of impaired RAP in relation to other key components of cerebral physiology. Methods: Archived data from 379 moderate-to-severe TBI patients were analyzed using descriptive and threshold-based methods across three RAP states (impaired, intact/transitional, and exhausted). Agglomerative hierarchical clustering, principal component analysis, and kernel-based clustering were applied to explore multivariate covariance structures. Then, high-frequency temporal analyses, including vector autoregressive integrated moving average impulse response functions (VARIMA IRF), cross-correlation, and Granger causality, were performed to assess dynamic coupling between RAP and other physiological signals. Results: Impaired and exhausted RAP states were associated with elevated intracranial pressure (p = 0.021). Regarding AMP, impaired RAP was associated with elevated levels, while exhausted RAP was associated with reduced pulse amplitude (p = 3.94 × 10⁻⁹). These two RAP states were also associated with compromised autoregulation and diminished perfusion. Clustering analyses consistently grouped RAP with its constituent signals (ICP and AMP), followed by brain oxygenation parameters (brain tissue oxygenation (PbtO₂) and regional cerebral oxygen saturation (rSO₂)). Cerebral autoregulation (CA) indices clustered more closely with RAP under impaired autoregulatory states. Temporal analyses revealed that RAP exhibited comparatively stronger responses to ICP and arterial blood pressure (ABP) at 1-min resolution. Moreover, when comparing ICP-derived and near-infrared spectroscopy (NIRS)-derived CA indices, they clustered more closely to RAP, and RAP demonstrated greater sensitivity to changes in these ICP-derived CA indices in high-frequency temporal analyses. These trends remained consistent at lower temporal resolutions as well. Conclusion: RAP relationships with other parameters remain consistent and differ meaningfully across compliance states. Integrating RAP into patient trajectory modelling and developing predictive frameworks based on these findings across different RAP states can map the evolution of cerebral physiology over time. This approach may improve prognostication and guide individualized interventions in TBI management. Therefore, these findings support RAP’s potential as a valuable metric for bedside monitoring and its prospective role in guiding patient trajectory modeling and interventional studies in TBI. Full article

Concerns about ensuring a sustainable environment are growing, attracting major attention from policy professionals worldwide. Therefore, this study investigates the nonlinear impacts of fiscal consolidation on CO₂ emissions in 17 OECD countries from 1978 to 2020. To probe the short- and long-term connections across various quantiles of CO₂ emissions, we adopted panel QARDL frameworks. The Granger non-causality test was used to investigate the variables’ association with CO₂ emission. The study’s main findings confirm the overall beneficial effect of fiscal consolidation on carbon emissions. It reduces CO₂ emissions at almost all quantiles in the short run. By contrast, in the long run, the effect is positive at lower quantiles and turns negative at upper quantiles. Furthermore, a causality analysis identified a bidirectional causal relationship between fiscal consolidation and CO₂ emissions, confirming the existence of mutual influence. While Keynesian theory links fiscal consolidation to economic recession, our findings support the non-Keynesian view, showing that such policy can foster economic growth and thereby contribute to reducing CO₂ emissions in the short run. Thus, OECD countries are orienting public spending and carbon taxation toward environmentally friendly practices while ensuring environmental protection and deficit reduction. Nonetheless, the identified mixed effect in the long run highlights the need for sustained consolidation policies by enhancing expenditure efficiency and adopting targeted taxation measures to achieve lasting emission reductions and support the transition to cleaner energy, even when emissions are relatively low. Full article

This paper investigates the information transmission performance of GIFT Nifty futures using high-frequency data, a novel area of study given their recent introduction. We employ Johansen cointegration tests, Granger causality tests, GARCH models, Hasbrouck’s Information Share (IS) model, and Gonzalo–Granger’s Component Share (CS) model to assess market integration, volatility, and price discovery dynamics. Our findings reveal significant bidirectional Granger causality and cointegration between the GIFT Nifty futures price and the Nifty index price, indicating a stable long-term equilibrium. Additionally, the GARCH model captures substantial volatility, reflecting the market’s responsiveness to new information. The IS and CS models confirm that the GIFT Nifty futures play a crucial role in the price discovery process, leading the Nifty index. This research is timely, within eight months of the first anniversary of GIFT Nifty futures trading since its launch. The findings highlight the information transmission performance and importance of the GIFT Nifty futures in enhancing market stability and transparency, offering valuable insights into market behaviour, integration, and forecasting abilities. Full article

Soaring housing prices worldwide are compromising housing affordability, potentially leading to significant economic, social, and health repercussions. Understanding the price discovery process within the real estate market is therefore crucial for policymakers. While the relationship between land and housing prices in urban residential markets has been widely examined in the literature, the results are often context-specific, leaving the question of whether the land market leads the housing market or vice versa open to debate. Saudi Arabia, with its rapidly growing real estate market, evolving demographics and urbanization trends, and transformative regulatory reforms, presents a compelling context for revisiting the land–housing nexus. This study examines the long-term relationship between land and housing markets and investigates the short-term price dynamics with the ultimate goal of understanding the price formation in the housing market. The study dataset comprises quarterly time-series price indices published by the General Authority for Statistics (GASTAT) in Saudi, representing the nation-wide price movements of residential lands and villas from 2014Q1 to 2025Q1. The study employs the Johansen cointegration method and the Granger causality testing. The results of cointegration analysis confirm a significant long-run equilibrium relationship between the two markets, while the error correction model reveals that both land and housing prices adjust to restore this equilibrium. Granger causality test results show a unidirectional relationship, where land prices predict future housing prices, consistent with the neoclassical rent theory. These findings reinforce the long-term, intrinsic link between land and housing markets observed in prior studies. The dynamics in the Saudi market are likely shaped by rapid urbanization that intensified speculation in the land market, and also the prevalence of self-building enabled by government-supported financing. This study underscores the importance of striking a delicate balance between supply and demand side policies in the real estate market while monitoring the impact of these policies on housing affordability. Full article

There is a lack of empirical studies investigating the individual and combined effects of environmental policy stringency, energy transition, and green technologies on greenhouse gas emissions in the context of BRICS–T countries. To address this gap in the literature, the article presents empirical evidence from panel quantile, Driscoll–Kraay, and Ridge regression models for examining energy and climate action within the framework of Sustainable Development Goals 7 and 13 in BRICS–T economies during the period 1995–2020. The main findings obtained from the analyses show that environmental policy stringency, as well as the combined effect of environmental policy stringency with green technology, reduce ecological deformation. On the other hand, energy transition, green technology, primary energy consumption, and the combined effect of energy transition and environmental policy stringency have been shown to increase emissions. Dumitrescu–Hurlin Granger causality findings indicate that all variables exhibit two–way causality relationships reflecting the feedback effect. The results highlight that countries should focus on implementing stricter environmental regulations, promoting green innovation, adopting comprehensive fiscal and environmental policies, accelerating the transition from conventional to clean energy, and strengthening policy measures to achieve long-term ecological goals. Full article

Middle-income economies contribute significantly to global CO₂ emissions as they pursue economic development, creating an urgent need to understand emission drivers. This article investigates the impact of renewable energy use, financial development, and industrialization on CO₂ emissions in 71 middle-income countries (32 upper-middle income, 39 lower-middle income) between 2002 and 2020. We used the advanced Generalized Method of Moments Panel Vector Autoregression (GMM-PVAR) approach to address endogeneity and reveal complex relationships among the variables. Our findings revealed that renewable energy utilization had no substantial influence on emissions reduction in either upper- or lower-middle-income countries, challenging conventional policy assumptions. Financial development consistently reduces emissions across both income groups (−0.08% and −0.06%, respectively). Industrialization has heterogeneous effects, increasing emissions by 2.03 percent in upper-middle-income countries and with no effect in lower-middle-income countries. Granger causality tests illustrated a bidirectional relationship connecting CO₂ emissions and financial development, whereas no causal link was found between CO₂ emissions and renewable energy use. These findings prove the importance of coordinated policies that strengthen financial systems and sustainable industrial practices. Full article

This study employed quarterly data spanning from 2005 to 2024 to investigate the factors affecting China’s tea exports to Malaysia using demand theory. The Autoregressive Distributed Lag (ARDL) approach and Granger causality test were applied to examine the long-run and short-run impacts of key variables, including the prices of China’s tea and coffee imported by Malaysia, Malaysia’s GDP, Malaysia’s tea production, and the international oil price. The ARDL bounds testing confirmed the existence of a long-run equilibrium among these variables. The empirical findings revealed that an increase in the price of China’s tea significantly reduced export volumes, whereas Malaysia’s GDP exerted a strong positive influence. The price of coffee exhibited a significantly negative effect, suggesting an unconventional substitution relationship with tea. Both Malaysia’s domestic tea production and the international oil price imposed downward pressures on China’s tea exports. Furthermore, the Granger causality analysis indicated that the price of China’s tea, the price of coffee, and Malaysia’s GDP all exerted short-run effects on China’s tea exports to Malaysia. These findings contribute to the export demand literature and offer implications for policies aiming to enhance bilateral tea trade between China and Malaysia. Full article

Despite the close linkage between carbon markets and fossil fuel markets, minimal research has investigated their co-movement dynamics during times of heightened geopolitical instability and public health crises, including the COVID-19 pandemic, Israeli–Palestinian conflict, and the Russia–Ukraine war. Most studies use conditional mean regression models for testing linear Granger causality, which falls short in assessing time-varying causal relationships. This paper employs a time-varying Granger causality framework to examine the dynamic linkages between fossil fuel markets and carbon markets across multiple time horizons. This methodology enables the evaluation of causal relationships that evolve over time, providing deeper insights into how the carbon market interacts with traditional fossil fuel markets. The study examines causal linkages among carbon, coal, and oil prices from 2 January 2018 to 11 July 2025, using data from Wind Database. The findings reveal a short-lived yet highly significant bidirectional causality between the carbon and fossil fuel markets during the COVID-19 period, whereas a sustained and highly significant bidirectional causal relationship emerges after the onset of the Russia–Ukraine war. During the outbreak of the Israeli–Palestinian conflict, this linkage continued without major disruptions or directional shifts. Furthermore, the recursive evolution approach, based on variable sub-window sizes, detects additional evidence of significant bidirectional causal relationships among carbon, coal, and oil prices. These discoveries can serve as valuable inputs for investors and policymakers, enabling them to make informed decisions that protect their interests and ensure market stability. Additionally, coal prices showed greater persistence than oil prices in these bidirectional causal links. Full article