Search Results (904)

This paper investigates the symmetric and asymmetric effects of renewable and non-renewable energy on Greece’s economic growth within an extended Cobb–Douglas production function for 1990–2022. The study is motivated by the rising role of renewable energy and the need to determine whether the energy–growth nexus is linear or nonlinear, an issue of central importance for policy. The Brock–Dechert–Scheinkman (BDS) test confirms the nonlinearity of the variables, while Zivot–Andrews unit root tests with structural breaks capture crisis-related disruptions. The Wald test indicates that renewable energy has an asymmetric long-run relationship with growth, whereas non-renewables exert symmetric effects. To model these dynamics, the Nonlinear Autoregressive Distributed Lag (NARDL) framework is applied. Results show that in the long run, positive shocks to renewable energy enhance growth, while both positive and negative shocks to non-renewables have symmetric impacts. In the short run, only non-renewable energy shocks significantly affect growth. Asymmetric causality analysis reveals a bidirectional relationship between positive renewable shocks and growth, suggesting a virtuous cycle of renewable expansion and economic performance. The study contributes by providing the first systematic evidence for Greece on the nonlinear energy–growth nexus, advancing empirical modeling with NARDL and break-adjusted tests, and highlighting the heterogeneous growth effects of renewable versus non-renewable energy. Full article

South Asia faces the dual challenge of sustaining rapid economic growth while managing severe ecological pressures. This study explores the relationship between Ecological Footprint (EF), Financial Development (FD), Economic Growth (GDP), Foreign Direct Investment (FDI), and Political Stability (PS) in SAARC countries from 2000 to 2020. Using a Panel Vector Autoregression (PVAR) combined with a Vector Error Correction Model (VECM), the analysis captures both short-run dynamics and long-run equilibrium relationships, addressing endogeneity among variables. Results reveal that EF negatively correlates with FD, GDP, and FDI, while showing a positive association with PS. Cointegration tests using dynamic and fully modified ordinary least squares confirm long-term relationships between the variables. Impulse response functions illustrate how shocks to one variable affect others over time, highlighting complex interactions. Granger causality tests suggest limited short-term causal links, reflecting the multifaceted nature of these relationships. This research is particularly relevant as SAARC countries face the dual challenge of sustaining rapid economic growth while mitigating ecological pressures. The study advances the literature by explicitly integrating political stability into the environmental–economic nexus, a factor often overlooked in earlier regional analyses. By providing empirical evidence on the joint role of economic, financial, and political drivers of ecological sustainability, the paper contributes both to academic debate and to the design of more balanced policy frameworks for sustainable development in South Asia. Full article

Strategic mergers and acquisitions (M&A) can support sustainable business models by enabling firms to adapt their capabilities and competitive positions as conditions change. This study examines how target listing status (public vs. private) and acquirer size shape short-term shareholder wealth in Korean SMEs (Small- and medium-sized enterprise), and links announcement reactions to subsequent operating outcomes. Using an event study and multivariate regressions on 155 M&A announcements by KOSDAQ-listed SMEs (Korean Securities Dealers Automated Quotations) (2016–2020), we find that smaller acquirers earn significantly higher announcement-period cumulative abnormal returns (CAR)—i.e., smaller firm size is positively associated with superior market-adjusted performance around M&A events. Although acquisitions of privately held targets and diversifying deals show higher unadjusted means, their effects become statistically insignificant once firm fundamentals and size are controlled for. To connect M&A strategy with business-model sustainability, we operationalize sustainability as the alignment between short-term market expectations (CAR) and realized operating performance over 1–2 years, measured by return on operating cash flow (ROCF); medium-term checks indicate that the short-run “size effect” attenuates, underscoring the role of execution and scale in longer-run outcomes. Overall, the evidence highlights the primacy of firm-specific fundamentals, strategic fit, and integration capacity in guiding M&A decisions that advance both near-term performance and longer-term resilience. The Korean SME setting—marked by concentrated ownership, resource constraints, and a chaebol-influenced market and policy environment—provides a stringent context for these tests. Full article

This paper investigates how renewable electricity production, energy consumption, and economic openness influence economic growth in the Gulf Cooperation Council (GCC) countries from 2008 to 2023. Using annual panel data for six countries—Saudi Arabia, UAE, Qatar, Bahrain, Kuwait, and Oman—we apply both the Pooled Mean Group (PMG) and Dynamic Fixed Effects (DFEs) estimators to explore short-run dynamics and long-run equilibrium relationships. These methods are preferred because they balance flexibility with efficiency where PMG allows country differences in short-run dynamics, while DFE provides robustness under small-sample conditions, making them more suitable than the Mean Group (MG) estimator or standard Fixed Effects (FE) models for our short panel of six countries. The results show that traditional electricity consumption significantly supports economic growth in the long run, while renewable energy, despite its potential, has yet to show a statistically significant growth-enhancing effect, likely due to its currently small share in the energy mix. Foreign direct investment and trade openness display mixed impacts, with their significance varying across models. Short-run dynamics highlight the importance of energy efficiency and infrastructure readiness in shaping how energy translates into growth. Overall, the findings suggest that while energy remains central to GCC economies, the transition to renewables must be better aligned with broader development and investment strategies. These insights are highly relevant for policymakers navigating the twin goals of energy diversification and sustainable economic growth under Vision 2030 agendas. Full article

Background/Objectives: Declining physical activity (PA) and cardiorespiratory fitness (CRF) in children are global public health concerns, particularly in populations experiencing urbanization and economic transition. This study investigated the effects of a school-based intervention on PA, CRF, and cardiovascular disease (CVD) risk factors in children aged 6–12 years from marginalized communities in Gqeberha, South Africa. Methods: A cluster randomized controlled trial was conducted in four schools, with participants randomly assigned to one of the following four arms: (i) PA and multi-micronutrient supplementation (MMNS); (ii) PA and placebo; (iii) MMNS; or (iv) placebo (control). A total of 1151 children were assessed at baseline (T1), 1003 at post-intervention (T2), and 549 at follow-up (T3). PA was measured using accelerometers. Secondary outcomes included CRF (20 m shuttle-run) and CVD risk factors (i.e., anthropometry, blood pressure, glycated hemoglobin [HbA1c], and lipid profile). Mixed linear models adjusted for baseline characteristics were used. Results: None of the interventions significantly improved daily PA. From T1 to T2, the MMNS arm significantly increased CRF, while PA + MMNS reduced HbA1c. However, MMNS alone increased triglycerides, and PA + placebo increased low-density lipoprotein (LDL). From post-intervention (T2) to follow-up (T3), the MMNS arms significantly reduced blood pressure. Yet, the PA + MMNS arm increased body fat percentage and decreased high-density lipoprotein (HDL). Conclusions: While MMNS showed promise for improving fitness and blood pressure and PA + MMNS reduced HbA1c, adverse metabolic changes emerged. The results should be interpreted with caution due to the short intervention span and COVID-19 disruptions during the second year of the intervention. Full article

In the context of low carbon, achieving accurate forecasting of electrical energy is critical for power management with the continuous development of power systems. For the sake of improving the performance of load forecasting, an improved MNet-Atten electric vehicle charging load forecasting based on composite decomposition and the evolutionary predator–prey and strategy model is proposed. In this light, through the data decomposition theory, each subsequence is processed using complementary ensemble empirical mode decomposition and filters out high-frequency white noise by using singular value decomposition based on matrix operation, which improves the anti-interference ability and computational efficiency of the model. In the model construction stage, the MNet-Atten prediction model is developed and constructed. The convolution module is used to mine the local dependencies of the sequences, and the long term and short-term features of the data are extracted through the loop and loop skip modules to improve the predictability of the data itself. Furthermore, the evolutionary predator and prey strategy is used to iteratively optimize the learning rate of the MNet-Atten for improving the forecasting performance and convergence speed of the model. The autoregressive module is used to enhance the ability of the neural network to identify linear features and improve the prediction performance of the model. Increasing temporal attention to give more weight to important features for global and local linkage capture. Additionally, the electric vehicle charging load data in a certain region, as an example, is verified, and the average value of 30 running times of the combined model proposed is 117.3231 s, and the correlation coefficient PCC of the CEEMD-SVD-EPPS-MNet-Atten model is closer to 1. Furthermore, the CEEMD-SVD-EPPS-MNet-Atten model has the lowest MAPE, RMSE, and PCC. The results show that the model in this paper can better extract the characteristics of the data, improve the modeling efficiency, and have a high data prediction accuracy. Full article

The two groups of EU economies, the euro area and the non-euro area, are statistically analyzed taking into account the fulfillment of the euro area financial criteria and economic performance over the period 2000–2023. Compliance with financial criteria, economic performance, and their significant influencing factors are presented comparatively for the two groups of countries. The long-run equilibrium between economic growth and its factors is identified by econometric approaches with the error correction model (ECM) and autoregressive distributed lag (ARDL) models for the two data panels. In the short term, economic shocks are taken into account to compare their different influences on economic growth within the two groups of countries. The GMM system is used to model economic convergence at the EU level over the period under review. Comparisons between GDP growth and its theoretical values from econometric models have led to interesting conclusions regarding the existence and characteristics of economic convergence at the group and EU level. EU countries outside the euro area have higher economic growth rates than euro area economies over the period 2000–2023. In the long run, investment brings a higher increase in economic development in EU countries outside the euro area than in euro area countries. Economic shocks have been felt more deeply on economic growth in the euro area than in the non-euro area. The speed of adjustment towards long-run equilibrium in econometric models is slower for non-euro area economies than in the euro area over a one-year period. At the level of the European Monetary Union, change policies have a faster impact on economic development and a faster speed of adjustment towards equilibrium. Full article

Industrial trademark examination enables users to apply for and manage their trademarks efficiently, promoting industrial and commercial economic development. However, there still exist many challenges, e.g., how to customize a blockchain-based crowdsourcing method for interactive trust evaluation, how to decentralize the functionalities of a centralized entity to nodes in a blockchain network instead of removing the entity directly, how to design a protocol for the method and prove its security, etc. In order to overcome these challenges, in this paper, we propose the Blockchain-based Crowdsourcing Interactive Trust Evaluation (Block-CITE for short) method to improve the efficiency and security of the current industrial trademark management schemes. Specifically, Block-CITE adopts a dual-blockchain structure and a crowdsourcing technique to record operations and store relevant data in a decentralized way. Furthermore, Block-CITE customizes a protocol for blockchain-based crowdsourced industrial trademark examination and algorithms of smart contracts to run the protocol automatically. In addition, Block-CITE analyzes the threat model and proves the security of the protocol. Security analysis shows that Block-CITE is able to defend against the malicious entities and attacks in the blockchain network. Experimental analysis shows that Block-CITE has a higher transaction throughput and lower network latency and storage overhead than the baseline methods. Full article

Forecasting outcomes in high-stakes competitive spectacles like the Olympic Games, World Cups, and professional league championships has grown increasingly vital, directly impacting strategic planning, resource allocation, and performance optimization across a multitude of fields. However, accurate forecasting remains challenging due to complex, nonlinear interactions inherent in high-dimensional time-series data, further complicated by socioeconomic indicators, historical influences, and host-country advantages. In this study, we propose a comprehensive forecasting framework integrating structured time-series modeling with ensemble learning. We extract key structural features via two novel indices: the Advantage Index (measuring a competitor’s dominance in specific areas) and the Herfindahl Index (quantifying performance outcome concentration). We also evaluate host-country advantage using a Difference-in-Differences (DiD) approach. Leveraging these insights, we develop a dual-branch predictive model combining an Attention-augmented Long Short-Term Memory (Attention-LSTM) network and a Random Forest classifier. Attention-LSTM captures long-term dependencies and dynamic patterns in structured temporal data, while Random Forest handles predictions for unrecognized contenders, addressing zero-inflation issues. Extensive stability and comparative analyses demonstrate that our model outperforms traditional and state-of-the-art methods, exhibiting strong resilience to input perturbations, consistent performance across multiple runs, and appropriate sensitivity to key features. Our key contributions include the development of a novel integrated forecasting framework, the introduction of two innovative structural indices for competitive dynamics analysis, and the demonstration of robust predictive performance that bridges technical innovation with practical strategic application. Finally, we transform our modeling insights into actionable strategic insights. This translation is powered by interpretable feature importance rankings and stability analysis that rigorously validate the robustness of key predictors. These insights apply across multiple dimensions—encompassing advantage assessment, resource distribution, strategic simulation, and breakthrough potential identification—providing comprehensive decision support for strategic planners and policymakers navigating competitive environments. 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

Forecasting national energy demand is challenging under climate variability and macroeconomic uncertainty. We assess whether hybrid Prophet–(Q)LSTM models that integrate climate and economic predictors improve long-horizon forecasts for The Netherlands. This study covers 2010–2024 and uses data from ENTSO-E (hourly load), KNMI and Copernicus/ERA5 (weather and climate indices), Statistics Netherlands (CBS), and the World Bank (macroeconomic and commodity series). We evaluate Prophet–LSTM and Prophet–QLSTM, each with and without stacking via XGBoost, under rolling-origin cross-validation; feature choice is guided by Bayesian optimisation. Stacking provides the largest and most consistent accuracy gains across horizons. The quantum-inspired variant performs on par with the classical ensemble while using a smaller recurrent core, indicating value as a complementary learner. Substantively, short-run variation is dominated by weather and calendar effects, whereas selected commodity and activity indicators stabilise longer-range baselines; combining both domains improves robustness to regime shifts. In sustainability terms, improved long-horizon accuracy supports renewable integration, resource adequacy, and lower curtailment by strengthening seasonal planning and demand-response scheduling. The pipeline demonstrates the feasibility of integrating quantum-inspired components into national planning workflows, using The Netherlands as a case study, while acknowledging simulator constraints and compute costs. Full article

This paper examines how emerging markets balance the seemingly conflicting objectives of higher employment generation and improved environmental quality, with particular attention to the pivotal roles of trade openness and natural resource endowments. Utilizing a balanced panel dataset from 20 emerging economies that include all BRICS nations (except Ethiopia) and 10 other major emerging economies such as Mexico, Malaysia and Thailand spanning 1991–2020, the analysis applies the cross-section augmented autoregressive distributed lag (CS-ARDL) model to estimate both short- and long-run relationships. The findings indicate that increased trade openness and greater natural resource rents do not intensify the employment–environment trade-off; instead, they may facilitate simultaneous improvements in both areas, resulting in a win–win scenario. The results show that effective trade and resource management policies can reduce conflicts between unemployment and environmental issues, benefiting both the economy and the environment. This study also highlights the importance of integrated policies that connect trade liberalization, resource governance, and sustainability, offering useful guidance for emerging economies aiming for the Sustainable Development Goals (SDGs). 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

In free-running ship simulations, PID control gains for rudder and propeller revolution are often selected based on empirical experience without a standardized procedure, leading to inconsistent results under varying operational conditions. This study examined PID control gains by implementing a simulation framework using STAR-CCM+. The Ziegler–Nichols tuning method was applied to derive control gains, and their behavior was analyzed across different wave conditions (calm, short, medium, and long waves), PID period condition, ship speeds (low and design speeds), and scale ratios. The simulations showed that the PID gains derived under moderate wave conditions provided stable and reliable control performance across various sea states. Furthermore, the influence of scale ratio changes on the control performance was evaluated, and a non-dimensional scaling formula for PID coefficients was proposed to enhance applicability across different model sizes. Validation against experimental data confirmed the reliability of the simulation setup. These findings offer a systematic guideline for selecting the PID control gains for free-running simulations, promoting improved accuracy and stability under diverse environmental and operational conditions. This research contributes to developing standardized practices for maneuvering performance evaluations in realistic maritime environments. Full article

Short-season (90 d) produce packing operations may run double shifts with no clean breaks in between. This practice can result in produce contamination from food contact surfaces that are not cleaned and sanitized. Our study examined the survival of Salmonella and Listeria monocytogenes on polycarbonate, polypropylene, polyvinyl chloride (PVC), rubber, and stainless steel surfaces that contact produce in operations that have a short packing season. Coupons were spot-inoculated with five-strain cocktails of rifampicin-resistant Salmonella or L. monocytogenes (~7 log CFU/coupon), stored at 22 °C and 45–55% relative humidity, and enumerated at 0, 0.06, 0.25, 1, 2, 3, 7, 10, 14, 21, 30, 60, and 90 d. Significant differences were evaluated (p ≤ 0.05), and survival was modeled using linear and biphasic models. Salmonella reductions varied significantly by surface type, with rubber showing the greatest survival, followed by stainless steel at 90 d. In contrast, Salmonella concentrations on polycarbonate, polypropylene, and PVC were below the limit of detection at 90 d. L. monocytogenes reductions were not significantly different across materials at 90 d. Biphasic models better fit the inactivation of both pathogens. These findings highlight the importance of clean breaks and focusing interventions where pathogens demonstrate greater persistence in short-season packinghouses. Full article