Search Results (1,221)

This study investigates the effects of financial market instability, national security, and foreign direct investment (FDI) on economic growth in Sub-Saharan Africa from 1991 to 2023, while examining the moderating role of institutional quality. The study employs a panel autoregressive distributed lag (ARDL) model as the baseline estimator, complemented by fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) for robustness analysis. The results reveal that financial market instability constrains economic growth primarily through sovereign bond market instability and vulnerability to earnings manipulation, whereas stock price volatility exerts no significant long run effect. Improved peace conditions and domestic military expenditure promote growth, while terrorism undermines economic performance. Government effectiveness and control of corruption enhance growth, whereas foreign direct investment exerts a negative long-run effect in the absence of supportive domestic conditions. The interaction results show that institutional quality significantly conditions the effects of financial market conditions, national security, and foreign investment on growth. Stronger institutions reduce the adverse consequences of financial and security-related disturbances and improve the growth-enhancing capacity of foreign capital. The FMOLS and DOLS estimates largely confirm the baseline results. The study concludes that institutional quality constitutes a critical transmission mechanism through which financial stability, national security, and foreign investment influence long run economic growth in Sub-Saharan Africa. Full article

Manufacturing sectors and ecosystems can improve their innovation performance through digital platforms, connected intelligence, and organisational settings that enable collaboration among experts and ecosystem members. The convergence of skills and capabilities distributed across humans, organisations, communities, and AI agents creates intelligent environments that can support ecosystemic and transformative innovation. To examine this hypothesis, we follow a three-stage methodology. First, we develop a modelling framework based on a vector autoregressive model, in which a weighted matrix representing directed binary couplings among human, collective, and machine intelligence drives the transition of a manufacturing ecosystem from a baseline innovation state to a more advanced one. Second, we present the SmartGreenEcos experiment, which develops an intelligent environment adapted to a specific manufacturing ecosystem. The experiment demonstrates the feasibility of the model’s abstract architecture by implementing digital platforms, e-services, and AI agents that facilitate inter-company collaboration, experimentation, and innovation. Third, we use simulations and analyse the eigenvalues and eigenvectors of the weighted matrix to examine the internal dynamics of intelligent environments and identify key thresholds and drivers of change. The results of this three-stage methodology provide insights into the design of intelligent environments and the interaction parameters through which connected intelligence can improve innovation performance. Full article

The rapid expansion of the digital currency market and the growing role of stablecoins as potential intermediaries have brought its interconnectedness with traditional financial markets to the forefront of global financial research. Using daily data from 4 January 2021 to 30 September 2025, this study constructs a variable system with the price indices of USDT and USDC as core digital currency proxies, alongside traditional financial asset indices for stocks, bonds, and gold derived via the entropy weight method. We employ a comprehensive set of econometric techniques, including static correlation analysis, vector autoregression (VAR), impulse response functions, and extreme-event shock tests, to systematically investigate the interdependence structure, risk spillover dynamics, time-varying co-movements, and structural changes between the two markets during extreme risk episodes. The findings reveal an overall weak and asymmetric bidirectional spillover relationship between the cryptocurrency and traditional financial markets. Volatility in the digital currency market is found to be largely endogenous, with a limited capacity to transmit shocks externally. Conversely, traditional financial markets—particularly the equity market—exert a more pronounced influence on the digital currency market. Critically, under the impact of extreme risk events, the cross-market linkages exhibit structural breaks; the direction and intensity of correlation can strengthen significantly or even reverse, demonstrating a clear state-dependency. This research provides empirical evidence for understanding the functional role of digital assets within the macro-financial system, their risk transmission pathways, and their implications for systemic financial stability. The findings offer valuable theoretical and practical insights for financial regulators in designing robust cross-market risk prevention frameworks and for investors seeking to optimize asset allocation strategies. Full article

Climate change is becoming an increasingly important source of economic and health vulnerability in developing countries. This study examines the dynamic relationship between climate change and poverty across 22 countries in the Middle East and North Africa (MENA) region from 2000 to 2023. Poverty is captured through two indicators: household consumption expenditure, the monetary dimension, and infant mortality, the non-monetary dimension. Methodologically, the analysis relies on a panel autoregressive distributed lag (panel ARDL) model, estimated using the Pooled Mean Group (PMG) and Mean Group (MG) approaches. The results reveal a long-run relationship among climatic variables, macroeconomic factors, and poverty-related indicators. In the long run, precipitation is associated with a decline in household consumption expenditure, while temperature is associated with higher infant mortality, indicating a deterioration in both monetary and health-related well-being under changing climatic conditions. In the short run, rising temperatures are also associated with lower household consumption expenditure, revealing the immediate vulnerability of living standards to climate shocks. In addition, GDP per capita is associated with higher household consumption and lower infant mortality, while education is associated with lower health-related poverty. Inflation appears to exacerbate poverty, whereas the positive association between health expenditure and infant mortality suggests reverse causality or inefficiencies in the allocation of health resources. These findings highlight the need to articulate climate adaptation strategy, macroeconomic stability, education investment, and improved efficiency of health spending in order to achieve sustainable reduction in poverty in the MENA region. Full article

Central heating systems often rely on uniform regulation, making it difficult to meet the differentiated comfort demands of users and frequently leading to overheating. This paper proposes an end-to-end, data-driven framework that systematically couples density-adaptive clustering with dynamic response modeling for precision heating control. First, an adaptive DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm is developed, which automatically determines its parameters via k-distance graph initialization, differential evolution optimization, and hierarchical clustering post-processing. Without requiring a pre-set cluster number, it consistently identifies four typical daily room temperature fluctuation patterns. Validated on 120-day data from a residential community in Luoyang, the first four clusters cover over 80% of users, and the clustering quality approaches that of manually optimized conventional methods. Second, multi-input ARX (Autoregressive with Exogenous Inputs) models are built for the representative user of each cluster to characterize dynamic responses to supply water temperature, flow rate, and outdoor temperature. Rolling prediction for the entire community achieves an RMSE of 0.24 °C and an R² of 0.93. Finally, a differentiated regulation strategy combining main-cluster supply temperature control and small-cluster flow compensation is designed. Simulation results demonstrate that this strategy drives the room temperatures of all clusters significantly toward the 20 °C comfort target, with a marked reduction in standard deviation. The primary contribution of this study lies in the construction of a reproducible, closed-loop pipeline—from raw room temperature data to demand-based regulation logic—offering a quantitative basis for central heating systems transitioning from passive balancing to data-driven, classified control. Full article

This paper evaluates the macroeconomic and financial determinants of the money demand of India from 1996: Q1 to 2024: Q4. The paper utilizes the Autoregressive Distributed Lags (ARDL) bounds testing framework and an Error Correction Model (ECM) to estimate the long-run equilibrium and the short-run dynamics of monetary aggregates, narrow money (M1) and broad money (M3). The empirical findings confirm a stable, singularly cointegrated relationship between real money balances (M1, M3), real income (GDP), opportunity cost (91-Day Treasury Bill Rate), and equity wealth (BSE Sensex). Since M1’s income elasticity is 0.53 and M3’s is 0.98, the traditional transaction motives dominate both M1 and M3. The interest rate exerts a negative substitution effect on M1; however, M3 remains structurally safeguarded against short-term fluctuations. Equity market valuations exhibit statistical insignificance across all variable specifications, indicating that the equity market fluctuations do not systematically destabilize long-run money demand. The ECM results reveal a short-term adjustment speed of 17.92% and 8.37% per quarter for M1 and M3, respectively. These findings establish that M3 acts as a robust and stable measure of the Reserve Bank of India’s long-term monetary targeting, driven predominantly by the fluctuations in real fundamental macro variables. Full article

Accurate forecasting of electricity prices is essential for efficient operation and decision-making in deregulated power markets, particularly in the Indian electricity market, characterized by high volatility and dynamic pricing. This study presents a comparative analysis of statistical, machine learning, and deep learning methods for electricity price prediction in the Indian market, with a focus on feature dimensionality. The evaluated models include autoregressive-integrated-moving average, seasonal autoregressive-integrated-moving average, seasonal autoregressive-integrated-moving average with exogenous variables, categorical boosting, random forest, long short-term memory, bidirectional long short-term memory, and a hybrid convolutional neural network-bidirectional long short-term memory model. Historical data available on the Indian Energy Exchange webpage are deployed in this study. The models are analyzed for varying input vector sizes with features that include date, type of day, day of the week, previous day, month, and year market prices. The results indicate improved predictive performance of all model while increasing the input feature dimensionality from five to seven. The results indicate that while increasing the feature size from five to seven increases the prediction accuracy, the gains become marginal beyond seven, emphasizing the importance of feature relevance over feature quantity. From a theoretical perspective, the study highlights the dominance of short-term temporal dependencies in MCP prediction and provides empirical evidence for the point of diminishing returns in feature expansion. From a practical standpoint, the results endorse the choice of computationally efficient and interpretable models for real-world deployment. Full article

The nexus between fiscal decentralization (FD) and the renewable energy transition remains theoretically and empirically inconclusive, particularly in developing economies characterized by fiscal constraints. This study examines the relationships between the revenue and expenditure dimensions of FD and the renewable energy share in Türkiye over the period 1975–2024 within an extended STIRPAT framework, controlling for GDP per capita, trade openness, and urban population. Fourier-based econometric techniques are employed to allow for potential smooth structural changes. Long-run relationships are examined using the Fourier Autoregressive Distributed Lag (FADL) cointegration test, while long-run coefficients are estimated using Fully Modified Ordinary Least Squares (FMOLS). Dynamic Ordinary Least Squares (DOLS) and Canonical Cointegrating Regression (CCR) are additionally employed to assess the robustness of the long-run findings, and predictive causality is examined using the Fourier Toda–Yamamoto causality test. The results confirm a long-run cointegrating relationship among the variables. The baseline FMOLS estimates indicate that both revenue and expenditure decentralization are negatively and statistically significantly associated with the renewable energy share, with revenue decentralization exhibiting a larger coefficient in absolute magnitude. The negative signs are preserved across FMOLS, DOLS, and CCR, although statistical significance is not maintained under DOLS, indicating some estimator sensitivity. The causality analysis further provides evidence of unidirectional predictive causality from expenditure decentralization to the renewable energy share, while no statistically significant predictive causality is detected between revenue decentralization and the renewable energy share. Overall, the findings suggest that greater FD does not necessarily correspond to a higher renewable energy share and that its relationship with the renewable energy transition may depend on the broader fiscal and institutional context. The results offer policy-relevant evidence for Türkiye and may also provide useful insights for developing economies with similar fiscal and institutional characteristics. Full article

This study investigated the dynamic volatility connectedness among financial technology (FinTech), green indices, and global uncertainty metrics between June 2018 and May 2025. The research was conducted to understand how technological innovation and sustainability indices interact with systemic risk during periods of extreme global stress, such as the COVID-19 pandemic, the Russia–Ukraine conflict, and the market disruptions of early 2025. The analysis employed a time-varying parameter vector autoregression (TVP-VAR) framework to capture time-varying interdependencies and risk spillovers across multiple market regimes. Key findings indicated that total dynamic connectedness intensified significantly during crisis events, with major spikes occurring during the 2020 pandemic onset and the 2025 shocks possibly related to the “DeepSeek” AI disruption and the US tariff announcements. FinTech indices and green assets consistently functioned as net transmitters of shocks, while uncertainty indices, particularly the VIX, served as net recipients. Notably, the Alternative Finance Index (AFI) exhibited regime-dependent behaviour, transitioning from a transmitter to a recipient during the COVID-19 pandemic. These results imply that innovative and sustainable sectors have evolved into systemic drivers of global market sentiment rather than mere recipients of external shocks. The findings provide critical insights for stakeholders in financial markets, helping them to rethink their current approaches and prevent financial losses amid market upheaval. Full article

Energy transitions reshape not only energy supply and demand but also the broader socio-technical, environmental, and economic conditions that influence the resilience of European economies. This study examines whether selected energy and environment-related indicators are associated with employment-rate dynamics in 26 European countries over the period 2005–2022. Harmonised Eurostat indicators from the Sustainable Development Goals monitoring framework are used as empirical proxies for system-level characteristics, rather than as normative measures of SDG implementation. Employment rate by citizenship is treated as an observable indicator of labour-market adjustment within the broader process of sustainable energy-system transformation. The empirical analysis applies a dynamic panel-data model with autoregressive and distributed lag components, estimated using weighted least squares. The results indicate that employment-rate dynamics are associated with energy demand, import dependency, energy productivity, household energy conditions, transport structure, recycling capacity, and environmental pressure. Import dependency shows a negative long-term association, whereas final energy consumption is positively associated with employment-rate dynamics. The study contributes to energy-sustainability research by interpreting labour-market adjustment as one dimension of a resilient and just energy transition. Full article

Climate change poses increasing risks to global agricultural productivity, making understanding of its effects on crop production essential. This study analyses how rising temperatures and accumulating atmospheric CO₂ reshape the productive dynamics of wheat, rice, corn, oats, barley, potato, sugar cane, and sunflower production during 2000–2023 using Vector Autoregression, Granger causality, impulse response functions, forecast error variance decomposition, and threshold regression. Granger causality analysis identified a statistically significant predictive relationship between temperature and wheat production, whereas no statistically significant predictive effects were detected for rice, corn, oats, barley, potato, sugar cane, or sunflower production. Impulse response functions showed differentiated crop responses, with corn exhibiting temporary positive adjustments, rice predominantly negative responses, and wheat delayed structural shifts following temperature shocks. Forecast error variance decomposition further revealed substantial differences in climate sensitivity across crops, indicating that sunflower and sugar cane production were largely driven by their own dynamics, whereas rice, corn, potato, oats, and barley were comparatively more influenced by external climatic and economic shocks. Threshold regression identified crop-specific non-linear responses for six crops, whereas oats and barley did not reject the null hypothesis of linearity once critical atmospheric CO₂ concentrations were exceeded, confirming that the relationship between temperature and agricultural production changes across climatic regimes. These findings emphasise that climate change does not affect all crops uniformly and support the adoption of crop-specific adaptation strategies, including climate-responsive cultivation practices, targeted irrigation, drought-resistant varieties, and sustainable land management. The study provides evidence-based insights to support climate adaptation policies and strengthen the resilience of global agricultural production systems. Full article

Small and medium-sized enterprises (SMEs) face increasing cyber threats, while existing cyber resilience approaches remain largely conceptual or provide static assessments with limited predictive capability. This study develops a dynamic mathematical framework for analysing and forecasting cyber resilience in SMEs. Building upon a previously developed conceptual model, the framework formalises the interactions among company security, cyber risk, cybersecurity capability, incident response and recovery, and digital maturity using normalised state variables, bounded nonlinear difference equations, and autoregressive forecasting. The theoretical analysis establishes boundedness of the state variables, equilibrium existence, and local stability of the proposed dynamic system. The framework further integrates computational resilience metrics, a Dynamic Resilience Index (DRI), scenario analysis, and sensitivity analysis within a unified analytical structure. An illustrative simulation demonstrates the computational implementation of the framework by generating resilience trajectories, supporting conditional forecasting, and comparing alternative cybersecurity scenarios. The study concludes that cyber resilience can be represented as a dynamic and measurable organisational capability. The proposed framework provides a transparent and extensible mathematical basis for continuous resilience monitoring, predictive analysis, and evidence-based cybersecurity decision-making in resource-constrained SMEs. Full article

With the large-scale integration of distributed photovoltaic generation (PV) into modern distribution networks, the inherent stochasticity and volatility of renewable energy outputs have imposed non-negligible impacts on the secure and economic operation of power systems. Conventional probabilistic power flow (PPF) methods are limited in accurately modeling source–load uncertainty and, more importantly, in capturing complex nonlinear and time-varying dependence among multiple renewable energy sources. To address these issues, this paper proposes a novel PPF calculation framework based on advanced source-load modeling and time-varying D-vine Copula. Firstly, an enhanced finite mixture Beta model and a Gaussian cluster mixture model are developed to characterize the uncertainty of PV output and load demand, respectively. Secondly, a time-varying D-vine Copula model based on the generalized autoregressive score framework is constructed. And a two-stage regularized profile likelihood estimation method is proposed to estimate correlation parameters, capturing the dynamic nonlinear dependence among multiple PV generators. Finally, the de-randomized Sobol sequence-based Quasi-Monte Carlo method is adopted to perform stochastic power flow calculation. Simulation results on a real-world 129-bus distribution system in East China verify the accuracy and effectiveness of the proposed method. Full article

The temperature field of a high-voltage direct-current (HVDC) cable governs its current rating and insulation lifetime and must therefore be predicted accurately across diverse operating conditions. Finite-element (FE) simulation is accurate but too costly for repeated evaluation, whereas data-driven reduced-order models (ROMs) often extrapolate poorly beyond the training-current range. This paper proposes a physics-structured POD–neural ROM to address this limitation. Specially, proper orthogonal decomposition (POD) compresses the three-dimensional temperature-rise field into a few modal coefficients, which are predicted from the operating conditions by a neural network. The key innovation is to embed the Joule-heating law directly into the architecture: the leading coefficient is represented as a current-squared factor multiplied by a learned current-independent shape. This construction guarantees the correct current scaling of the dominant mode, including its zero-current limit and extrapolation beyond the training range. On FE data for an eight-layer cross-linked polyethylene cable, the model achieves 2.4% mean relative error under current extrapolation and remains below 5% at twice the maximum training current, outperforming Gaussian-process, dynamic-mode-decomposition, autoregressive, and black-box baselines. The full field is evaluated in approximately one millisecond per condition, with a cost independent of the training-set size. Controlled ablations show that the improvement arises from structurally enforcing the scaling law rather than merely supplying $I^2$ as an input feature. Embedding known physical scaling into a surrogate architecture therefore provides a principled route to reliable extrapolation. Full article

Reliable navigation in GPS-denied environments remains a critical challenge for autonomous vehicles (AVs), particularly in complex indoor and urban settings. GPS-based localization systems often fail under these conditions, highlighting the need for resilient multimodal solutions. In this article, we present a radar-assisted tracking system that integrates LiDAR and inertial measurements within a sensor-fusion architecture to achieve robust navigation. The principal methodological contribution is a unified tracking and prediction framework that combines Bayesian state estimation with learning-based temporal prediction, enabling accurate tracking while continuously forecasting the slave robot’s short-term future state from mapping observations generated by the master robot, with a typical end-to-end perception-to-action latency of 20–60 ms. The communication and prediction forecasting module operates with an update interval below 35 ms, enabling real-time cooperative robotic operation. Sensor data are fused through a pipeline incorporating Gaussian Mixture Models (GMMs) for post-processing, which helps mitigate the limitations associated with individual sensors during edge processing. Moreover, Kalman filtering is employed to mitigate sensor noise and drift, thereby improving state estimation accuracy through trajectory smoothing. The fused spatiotemporal information is subsequently exploited by a Convolutional Recurrent Neural Network (CRNN) coupled with a Nonlinear Autoregressive model with eXogenous Inputs (NARX) to model the robot’s motion dynamics and provide short-horizon state prediction. Through simulations and real-world indoor experiments conducted in GPS-denied environments, we validate the system’s ability to provide accurate and continuous pose estimation with low localization errors. Experimental results show that the proposed framework achieves root-mean-square errors of 0.12 m, 0.15 m, and 0.28 m along the X, Y, and Z axes, respectively, while maintaining sub-meter maximum position deviations throughout the evaluated trajectories. These results confirm that the proposed framework provides reliable localization and predictive state estimation for cooperative robotic navigation in indoor GPS-denied environments. Future work will investigate outdoor validation and extend the framework to additional data-driven decision-making models for future robotic services. Full article