Search Results (941)

Effective water resource management requires understanding the causal mechanisms driving water quality dynamics, yet extracting actionable insights from complex multivariate monitoring data remains a persistent challenge. This study presents CEcBaN (CCM-ECCM-Bayesian Networks), a decision-support tool that integrates Convergent Cross Mapping (CCM) for detecting dynamical coupling, Extended CCM (ECCM) for identifying temporal lags and causal directionality, and Bayesian network (BN) modeling for probabilistic scenario-based inference. The tool was designed to enable managers and researchers without programming expertise to reconstruct causal networks from routine monitoring data, distinguish direct from indirect effects, and evaluate intervention scenarios. CEcBaN was validated using four synthetic datasets with known causal structures, achieving superior specificity (0.83) and edge count accuracy (25% error) compared to Transfer Entropy (0.47 specificity, 139% error), Granger causality (0.82, 39% error), and the PC algorithm (0.83, 46% error). Application to Lake Kinneret, Israel, demonstrated the tool’s utility across three water quality challenges: (1) nitrogen cycling, where the nitrification pathway was reconstructed and seasonal stratification was identified as a key modulator (accuracy 0.931); (2) thermal dynamics, where a transition from atmosphere-driven to internally regulated heat transfer during stratification was revealed (2.1-fold increase in coupling strength); and (3) cyanobacterial bloom prediction, where prior phytoplankton community composition provided a 4–6-week early warning window (accuracy 0.846). CEcBaN advances causal inference in water resource management by making these analytical methods accessible through an intuitive interface. Full article

The presence of non-stationary features poses a major challenge to root cause diagnosis in industrial processes, as they can distort fault propagation paths inferred through causal testing. To address this issue, an adaptive moving-window dual-test Granger causality framework is proposed for non-stationary industrial processes. First, a dual non-stationary test mechanism, which integrates the Augmented Dickey–Fuller and Kwiatkowski–Phillips–Schmidt–Shin tests, is developed to assess the stationarity of process variables. Next, an adaptive moving-window strategy is designed to adjust window lengths based on the non-stationarity test results. Time series are then segmented according to the selected windows, and a vector error-correction model is fitted to provide a robust basis for causal testing. Subsequently, Granger causality tests are conducted within each window to capture the true causal relationships among variables. Finally, window-wise scores are aggregated to identify the root cause and infer the fault propagation path. The proposed framework is evaluated on the Tennessee Eastman Process, and the results demonstrate that it effectively improves the accuracy of root cause diagnosis. Full article

Short-term fluctuations in climate patterns (climate variability) often indicate long-term climate change (CC) trends, which are a global threat to our planet today. CC is speeding up the terrestrial water cycle and potentially affecting groundwater availability, a major component of that cycle. Considering that terrestrial water cycle intensity (WCI) and groundwater level (GWL) are indicators of CC and groundwater availability, respectively, this study explored the dynamic relationship between WCI and GWL anomalies (WCIAs and GWLAs, respectively) in an arid region, based on an innovative approach to statistical correlation and causation analysis. Pearson correlation (r) assessed the strength and direction of a contemporaneous linear relationship between both variables; a cross-correlation function (CCF) determined the dynamic nature of those relationships considering monthly lags up to a predetermined maximum of 12 months; and Granger causality (GC) tests assessed the statistical significance of past values of the lead variable in enhancing the prediction of future values of the lagged variable. A contemporaneous linear relationship between both variables was mostly absent but appeared at various lags. At these lags, the strongest correlations were dominantly negative, with GWLA leading WCIA, as supported by the GC tests. This trend suggests that the intensification of the water cycle reflects a decline in past groundwater levels, necessitating immediate water management actions in the affected areas. Full article

Although evidence exists for android-induced spontaneous facial mimicry, the timing and temporal precedence (Granger causality) of this effect remain uncertain. We used the Facial Action Coding System (FACS) to analyze simultaneous dyadic facial video recordings of participants observing android Nikola’s negative (frowning) and positive (smiling) dynamic facial expressions. Principal component analysis of Nikola’s expressions indicated that, in addition to the action units (AUs) 04 (brow lowerer) and 12 (lip-corner puller), AUs 25 (lips part) and 26 (jaw drop) contributed significantly to Nikola’s facial expressions. Cross-correlation analysis revealed AU04 mimicry of negative expressions and AU12 mimicry of positive expressions from 400 ms onwards. AU25 and AU26 mimicry occurred faster, starting at around 200 ms. Multilevel vector autoregression incorporated the android and participant AUs and quantified the temporal evolution of the Granger causality for the first time. In addition to paired android–human AU04, 12, 25, and 26 effects, significant Granger causality was found between different android–human AU combinations, such as from android AU04 to participant AU25 in the negative condition, and from android AU25 to participant AU12 in the positive condition. These results suggest that the spontaneous facial responses to Nikola’s expressions involved not only motor copying, but also higher-level goal emulation and motor planning in the mirror mechanism, supporting the reliability of the social function of android facial expressions. Cross-correlation and Granger causality analysis can be valuable when further investigating behavioral matching in real-life contexts. Full article

This study examined the ‘too central to fail’ concept in the South African equity sector. We employed the Granger causality framework and PageRank algorithm to generate the centrality scores of the sectors on the Johannesburg Stock Exchange under extreme market conditions. Using the realized volatilities of sectoral returns for the full sample period (3 January 2006–31 December 2021), as well as during the global financial crisis (GFC), European debt crisis (EDC), COVID-19 pandemic, and US–China trade war sub-periods, we analyzed the sectors’ interconnections and calculated each sector’s centrality score across the entire sample and under different extreme market conditions. This allowed us to rank sectors relative to their centrality scores. The results indicate that, in the full sample, the insurance sector has the highest PageRank centrality score, suggesting it is too central to fail. This implies that the insurance sector acts as a systemic receiver of risks and provides stability within the network of sectors. However, the sub-period analyses reveal that General Industrial and Automobiles emerged as the key sectors with the highest PageRank centrality scores, and shocks from other sectors can disproportionately affect these industries during crisis periods. Underperformance in these sectors could have destabilizing effects on the South African economy. The findings have significant implications for regulators and policymakers, portfolio and fund managers, local and international investors, and researchers in the field of finance. Full article

Wetlands in semi-arid regions are critical for ecological resilience but are increasingly degraded. Ecological water consumption (EWC), reflecting wetland water demand, is essential for understanding wetland sustainability. This study investigated the spatiotemporal dynamics of marsh wetland EWC in the Bashang Plateau, China, from 1986 to 2021, and identified its main driving forces. A Random Forest model was used to downscale GLASS evapotranspiration (ET) product from 0.05° to a 250 m monthly resolution, showing good agreement with flux measurements (RMSE = 21.94 mm, R² = 0.83). Marsh wetland EWC was estimated using the downscaled ET and land cover data, and Granger causality analysis was applied to explore driving mechanisms. Results indicate that the marsh wetland area declined by 74% (from 552.81 to 143.69 km²) while forestland expanded by 217%. Correspondingly, marsh wetland EWC decreased by 67.2%, from 125 to 41 million m³. Precipitation and surface water area were identified as direct drivers of marsh wetland EWC decline, whereas groundwater table, forest EWC, and cropland EWC acted as indirect drivers. While cropland water use has been widely reported as an important factor, results suggest that increased forest EWC associated with large-scale afforestation contributed considerably to groundwater table decline, thereby influencing marsh wetland EWC. Full article

This investigation assessed the neurovascular coupling response through integrated assessments of neuronal function [electroencephalography (EEG)], microvascular oxygenation concentrations [functional near-infrared spectroscopy (fNIRS)], and arterial responses [transcranial Doppler ultrasound (TCD)]. The NVC response was assessed in 113 participants (86 females, aged 19–40 years) during visual (“Where’s Waldo?”) and motor (finger tapping) tasks. Block-averaged, time–frequency power was computed from the EEG data, while hemodynamic response functions were obtained from the fNIRS and TCD metrics. Granger causality assessed the predictiveness between EEG-fNIRS-TCD waveforms for each participant and was converted into a percentage of individuals displaying a significant value. Linear models were computed to determine the influence of sex, concussion history, young adulthood age, cardiorespiratory fitness, and mental health/learning disabilities on NVC parameters. During the initial 10 s of task onset, unidirectional predictiveness was weak to very strong for EEG-TCD (range: 47–83%) and fNIRS-TCD (44–92%) relationships; however, very weak to weak predictiveness was seen for the E0EG-fNIRS (0–29%) relationship for both tasks. Aside from known sex-, age-, and fitness-based influences on baseline/peak hemodynamic values (p < 0.050), the addition of concussion history and mental health/learning disabilities had minimal influence on NVC responses (p > 0.050). The findings demonstrated a unidirectional feedforward mechanism from the neuronal and microvasculature to the upstream arteries during task onset. Full article

This study investigates directional causality between Bitcoin and gold across different market conditions. Rather than relying on mean-based dependence, we examine how causal effects vary across return quantiles, investment horizons, and market regimes. To address this question, we apply a Causal–Frequency–Quantile–Regime (CFQR) framework. The approach combines frequency-domain Granger causality, quantile-based non-causality tests, and endogenous regime classification within a unified setting. Macroeconomic controls are included to reduce omitted variable bias. Statistical inference relies on bootstrap procedures with false discovery rate correction to account for multiple testing. Using daily data from 2013 to 2025, we find that the full-sample directional dominance between Bitcoin and gold is generally weak after multiple testing adjustments. However, under stress regimes, the causal relationship of gold to Bitcoin becomes more pronounced at longer investment horizons. Under normal conditions, causal effects remain unstable and fragmented. Economic effects are modest. Variance-based hedging gains are limited, while downside risk measures show moderate improvement during stress periods. Overall, the evidence suggests that gold does not serve as a universal hedge for Bitcoin, but may exert conditional informational influence during high-uncertainty states. The CFQR framework provides a structured way to identify such state-dependent causal patterns. Full article

Rapid agricultural expansion threatens water security in one of the world’s largest hydroelectric systems, the Itaipu dam, located on the Brazil–Paraguay border. Yet regional hydrological responses to land cover change and climate variability remain insufficiently characterized at management-relevant scales. The Upper Paraná River Basin (UPRB), which sustains agriculture, hydropower, and municipal water supply across both countries, exemplifies this challenge as accelerating cropland conversion raises concerns about long-term water availability. This study investigates hydrological transitions and their statistical associations with land cover changes in the Itaipu study region from 2002 to 2023. We integrate GRACE/GRACE-FO (Gravity Recovery and Climate Experiment Follow-On), Terrestrial Water Storage Anomalies (TWSAs), MODIS (Moderate Resolution Imaging Spectroradiometer) land cover, CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) precipitation, and LandScan population density using Pettitt’s breakpoint test and Mann–Kendall trend analysis to detect temporal breakpoints and quantify co-variability between hydrology and land surface dynamics. Together, these methods identify a significant basin-wide shift in TWSAs in mid-2009, with storage increases of 151.6 cm at Itaipu and 103.1 cm at Yguazú Reservoir. Over the study period, cropland expanded from 13.5% to 37.9% of total land cover, while savanna declined from 28.1% to 24.2%. After 2009, correlations between land cover and TWSAs strengthened substantially, particularly for wetlands (r = 0.88), croplands (r = 0.73), and savannas (r = −0.81; all p < 0.001), indicating strong coupling between landscape transformation and basin-scale storage variability. Principal Component Analysis shows land use change explains 39–41% of TWSA variance, exceeding hydroclimatic contributions. Granger causality analysis reveals bidirectional coupling between wetlands and water storage at Itaipu, while cropland and savanna dynamics exert predictive influence on downstream hydrology in the Yguazú basin. Water balance decomposition further indicates a post-2009 regime shift, with residual storage transitioning from −10.6 to +4.7 and 78% greater runoff generation per unit precipitation, consistent with reduced infiltration capacity. Together, these findings underscore intensifying land–water feedback and the need for adaptive watershed management under expanding agriculture and climate variability. Full article

Background/Objectives: This study investigates the effects of multi-sensory stimulation on brain functional connectivity in patients with disorders of consciousness (DOC). DOC patients experience prolonged loss of consciousness due to brain injury, posing significant challenges for rehabilitation. Methods: In the study, visual, olfactory, and visual–olfactory (V-O) combined stimulation were applied to DOC patients while their EEG signals were recorded. A brain functional network was constructed based on the conditional Granger causality (CGC) method to analyze its topological characteristics. Results: The results revealed that the strength of brain functional connectivity in Minimally Conscious State (MCS) patients was significantly higher than that in Vegetative State (VS) patients, indicating a strong correlation between the intensity of synergistic activity in brain functional connectivity and the level of consciousness. Furthermore, V-O combined stimulation significantly enhanced brain functional connectivity compared to single-modality stimulation. The selection of different stimulation also differentially affected brain functional connectivity, with olfactory stimulation exhibiting high pleasure, arousal, and dominance (Self-Assessment Manikin) values demonstrating unique advantages in reducing individual variability and improving global efficiency. Conclusions: The study provides a theoretical foundation for the application of multi-sensory stimulation in the rehabilitation of DOC patients. V-O stimulation not only enhances information transmission in brain regions corresponding to visual and olfactory processing under single-modality stimulation but also increases the intensity of information transfer to other brain regions; this may serve as a reference for understanding the effects of multi-sensory stimulation on brain networks. Full article

This paper explores how climate risks affect the spillover between cryptocurrency and precious metals markets, given the increased interplay between climate-related threats and financial markets. The dynamic spillovers of the cryptocurrency and precious metals markets are analyzed initially by the TVP-VAR-DY model. Subsequently, it investigates how transition risk and physical risk affect these spillovers using quantile Granger causality (QGC), quantile–quantile regression (QQR), and wavelet quantile regression (WQR), with a particular focus on the differences in the results across the pre- and during-COVID-19 periods. The results show that climate risks significantly affect the spillovers in the cryptocurrency and precious metals markets, and these effects are heterogeneous in nature. Specifically, it is found that, under normal market conditions, both TRI and PRI have the effect of strengthening the spillovers. However, in extreme market states, their influences weaken because of investor distraction. In addition, at extremely low levels of climate risk, both TRI and PRI tend to intensify spillovers, and the impact of PRI is more pronounced. Moreover, during the COVID-19 crisis, climate risks seemed to have a limited effect in the short run, while they were more sustainable in the long run. These findings offer crucial implications for mitigating climate-related systemic risks and fostering a resilient, sustainable financial ecosystem amidst global decarbonization efforts. Full article

This study investigates the impact of sustainability-related uncertainty (SRU)—captured via the Sustainability-related Uncertainty Index in equal-weighted (ESGUI_EQ) and GDP-weighted (ESGUI_GDP) forms—on the volatility of green financial assets, focusing on decentralized finance (DeFi) protocols and Environmental, Social, and Governance (ESG)-focused Exchange-Traded Funds (ETFs). Employing a fuzzy logic framework, complemented by 3D surface visualization, Rule Viewer analysis, diagnostic validation, and Granger causality tests, the study uncovers non-linear, asymmetric, and time-varying responses of these assets to sustainability ambiguity. Empirical results reveal a structural divergence: DeFi protocols amplify volatility due to fragmented governance, speculative investor behavior, and sensitivity to policy-driven signals, often exhibiting bidirectional predictive feedback with SRU, whereas ESG ETFs maintain stability through diversification, regulatory oversight, and rigorous ESG screening, primarily absorbing sustainability shocks. These findings extend sustainable finance theory by integrating governance, technology, and policy dimensions, and illustrate the value of fuzzy logic combined with Granger causality in modeling complex, ambiguous markets. From a practical standpoint, the study provides actionable guidance for investors, fund managers, and policymakers, emphasizing the importance of technology-informed governance, standardized ESG disclosures, regulatory sandboxes, and continuous monitoring of SRU. Full article

In recent times, countries have made it a point of duty to promote economic activities that will foster a sustainable environment following the Sustainable Development Goals (SDGs). One of the measures employed to achieve these goals, especially SDGs 7 and 13, is the adoption of renewable energy consumption. As a result, this research investigates the effect of financial globalization (FGLO) and technological innovation (TECH) on renewable energy consumption (RENC) in Nigeria from 1990 to 2020 using the Autoregressive Distributed Lag (ARDL) model. In addition, this research contributes to the existing literature by employing the Frequency Domain Causality Approach to capture long- and short-run causal relationships. Also, control variables such as financial development (FIND) and economic growth (GDP) were employed. The ARDL result is as follows: (1) The bounds test confirms a long-run association between the variables. (2) In the long-run, TECH and FIND spur RENC, while GDP reduces RENC. In addition, FGLO has a positive long-run coefficient, but the evidence is not strong enough to claim a clear long-run effect. (3) In the short-run, FGLO and FIND increase RENC, while GDP reduces RENC. Furthermore, the Frequency Domain Causality results confirm that TECH Granger causes RENC in the medium and long term, FIND Granger causes RENC in the medium and long term, while GDP Granger causes RENC in the short, medium, and long term. Based on these results, policies are recommended. Full article

Assessing the quality of the environment is absolutely essential. However, there has been a lack of research evaluating the impact of the dimensions of Supply Chain Digitalization on the environment. Therefore, this research examines the impact of Supply Chain Digitalization (SCD), Green Innovation (GIN), Financial Globalization (FIG), and economic growth (GDP) on Greenhouse Gases (GHGs) in Saudi Arabia from 2000Q1 to 2022Q4, employing the Autoregressive Distributed Lag (ARDL) model, Frequency Domain Causality (FDC) approach, and Multiple Quantile-on-Quantile (MQQ) analysis. Saudi Arabia is considered a key player in SCD due to its strategic location in global trade, the alignment of its Vision 2030 economic diversification goals, and its significant investment in digital infrastructure and skills. The ARDL outcomes showed that in the short run, SCD and GDP increase GHGs, while GIN and FIG reduce GHGs. In the long run, SCD and GDP drive GHGs, while GIN reduces GHGs. The FDC test shows that SCD and FIG Granger-cause GHGs in the long term, while GDP Granger-causes GHGs in the short and long term. The MQQ analysis confirms that at lower quantiles, the combined effect of SCD, GIN, FIG, and GDP on GHGs tends to be negative. However, at middle and higher quantiles, the effect becomes positive. Based on these results, policies are recommended. Full article

The integration of renewable energy sources (RESs) into electric power systems introduces new challenges for system operation, reliability, and emergency management. Causal analysis, as a powerful data analysis tool, can reveal the interactions and influences between components in the power system, thus supporting the design, operation and optimization of the system. This review examines causal analysis methods applied to electric power systems with high-RES penetration, highlighting their effectiveness in identifying interactions among system components, detecting potential risks, and supporting operational decision-making. Key system properties, including safety, efficiency, flexibility, survivability, and reliability, are discussed in the context of high renewable integration. The review also analyzes lessons from systemic accidents and explores strategies to mitigate risks associated with excessive RES penetration. Finally, directions for future research are outlined, emphasizing real-time monitoring, advanced causal modeling, and methods to enhance the resilience of modern power systems. Full article