Search Results (1,656)

In developing countries, sustainable development strategies are increasingly shifting toward a green economy that integrates economic, social, and environmental dimensions. Despite the growing importance of green economic growth, comparative empirical studies examining its determinants in Turkey and Romania remain limited. This study investigates the dynamic relationships between environmentally sustainable growth, carbon emissions, life expectancy, renewable energy consumption, education, and technological innovation in Turkey and Romania over the period 1980–2023. Using annual time series data, the analysis applies the Augmented Dickey–Fuller and Zivot–Andrews unit root tests to examine stationarity and potential structural breaks. The empirical framework is based on the Autoregressive Distributed Lag (ARDL) bounds testing approach, which allows the estimation of both long-run equilibrium relationships and short-run dynamics. The results provide partial evidence of long-run relationships among the variables. Although the ARDL bounds test results fall within the inconclusive region, the negative and statistically significant error correction terms indicate that deviations from long-run equilibrium are corrected over time. The findings also reveal heterogeneous short-run causal interactions across the two countries, suggesting that the drivers of environmentally sustainable growth differ between Turkey and Romania. Overall, the results highlight the importance of country-specific policy frameworks, institutional structures, and energy transition pathways in promoting green economic growth. Full article

With increasing demand for wellness tourism, identifying the key factors influencing emotional perceptions is essential for optimizing destination planning and management. Although Anhui Province has experienced rapid growth in wellness tourism destinations in recent years, scientific understanding of tourists’ emotional perceptions and their driving mechanisms has lagged behind this rapid expansion, a gap that can be addressed by integrating big data with spatial analysis to provide a scientific perspective for optimizing destination planning and informing regional wellness tourism policy. To address this gap, this study conducts a sentiment analysis of wellness bases in Anhui Province using user-generated content (UGC) data. Sentiment scores were quantified via SnowNLP, while kernel density, time-series, and multivariate statistical analyses were applied to examine spatial distributions, temporal dynamics of sentiments and review volumes, and emotional driving factors. The results indicate a spatial pattern of higher density in the south, lower density in the north, and dual-core agglomeration, closely linked to natural resource endowments. Temporally, sentiment scores rise in spring and summer and decline in winter, while review volumes peak in spring and autumn. Overall regression analyses reveal a significant positive effect of green coverage and a negative effect of accommodation prices. In the typological analysis, sentiment scores of Forest Wellness Bases (FWBs) relate to green coverage and negative ions, while Hydrological Wellness Bases (HWBs), Traditional Chinese Medicine Wellness Bases (TCMWBs), and Wellness Towns (WTs) are driven by the combined effects of facility services, locational price, and ecological environment. These findings provide a scientific basis for the sustainable development and differentiated management of wellness tourism destinations. Full article

This study investigates the forward discount puzzle by examining the dynamic relationships among excess returns arising from interest rate parity deviations, interest rate differentials, and the USD/JPY exchange rate. The empirical analysis employs correlation analysis, the Autoregressive Distributed Lag (ARDL) cointegration test, and variance decomposition together with impulse response functions derived from a Toda–Yamamoto augmented Vector Autoregressive (VAR) model, using data spanning January 2001 to September 2025. The correlation results indicate that the spot exchange rate is negatively related to both the swap rate and the interest rate differential. Impulse response analysis shows that the USD/JPY rate responds positively to swap rate shocks in the medium to long run, while responding negatively to interest rate differential shocks in the short run. Variance decomposition results are consistent with the impulse response analysis and underscore the dominant bilateral linkage between the exchange rate and the swap rate. The long-run ARDL estimates further reveal that the swap rate is positively associated with dollar appreciation, whereas both the interest rate differential and relative output are negatively related. Overall, although short-run arbitrage appears temporarily, the cointegration and dynamic results provide robust evidence that the forward discount puzzle persists for a substantial period rather than interest rate parity holding. Full article

Sustainable urban development increasingly depends on understanding how digital activity is distributed across space and time, yet the spatiotemporal dynamics of the urban digital landscape remain poorly mapped by conventional data sources. This study uses Beijing as an empirical testbed, applying a multi-dimensional analytical framework to massive mobile network traffic data to decode the metabolic rhythms, distributional laws, and functional organization of the urban digital landscape. The results reveal three findings. First, the urban digital landscape exhibits a sleepless trapezoidal temporal rhythm characterized by continuous saturation without a midday trough and a quantifiable weekend activation lag, indicating that digital metabolism is structurally decoupled from physical mobility patterns. Second, digital traffic follows a skew-normal distribution consistent with a 20/70 rule of spatial polarization, in which the top 20% of super-connector nodes sustain approximately 70% of total urban digital flow, yielding a Gini coefficient of 0.68 as a measurable indicator of infrastructure inequality and systemic vulnerability. Third, four distinct functional prototypes are identified—ranging from continuously active metropolitan cores to inverse-tidal ecological peripheries—empirically validating Beijing’s polycentric transformation through the lens of digital flows. These findings demonstrate that large-scale mobile network traffic data offers a replicable and structurally distinct lens for sustainable urban digital governance, supporting resilient network planning, equitable allocation of digital resources, and evidence-based monitoring of urban functional transformation in rapidly growing megacities. Full article

Background/Objectives: Successful interception of moving targets requires combining predictive control, which anticipates future target states, and reactive control, which compensates for ongoing sensory discrepancies. How these components evolve over time and are distributed across gaze and manual behavior remains unclear. We aimed to explore the time-resolved dynamics of predictive control during continuous interception and to dissociate eye and hand contributions. Methods: Human participants intercepted a moving target in a two-dimensional arena using a joystick while eye movements were recorded. Target speed was systematically varied, and visual information was selectively reduced by occluding either the target or the user-controlled cursor. Predictive control was assessed using two complementary metrics: a geometric strategy index capturing moment-to-moment spatial lead or lag relative to target motion, applied separately to gaze and manual trajectories, and root mean square error (RMSE) computed relative to current and forward-shifted target positions to quantify predictive alignment. Results: Successful interception was characterized by structured, speed-dependent transitions between predictive and reactive control rather than a fixed strategy. Predictive alignment emerged early and was dynamically reweighted as temporal constraints increased. Gaze and manual behavior showed complementary but partially dissociable predictive signatures. Occluding the target decreased predictive alignment, whereas occluding the user-controlled cursor had comparatively minor effects, indicating strong reliance on internal state estimation rather than continuous visual feedback of the effector. Conclusions: Predictive and reactive control are continuously and dynamically reweighted during interception. Their interaction unfolds within single trials and depends on target dynamics and sensory availability. These findings provide quantitative evidence for time-resolved coordination between anticipatory and feedback-driven control mechanisms in goal-directed behavior. Full article

Current sustainability discourse promotes sustainable yard practices as a means for residents to contribute to urban environmental health and soil conservation. Social–ecological research suggests that yard practices are shaped by multiscale social drivers, including social contagion, whereby visible expressions of individuality in front yard design are copied by nearby neighbors. This study evaluated residential areas within the Río Piedras Watershed (RPWS) in the San Juan metropolitan area to assess evidence of social contagion in front yard configuration and vegetation structure, and to examine whether these variables were associated with socio-demographic and economic characteristics when spatial effects were considered. A total of 6858 front yards across six highly urbanized sites were analyzed using Google Earth Street View imagery. Housing lot sizes were quantified, and yards were classified into eight landscape configurations based on green and gray cover elements. Woody vegetation structures, including trees, shrubs, and palms, were also quantified to generate estimates of functional diversity and a front yard quality index. Significant differences in yard characteristics were observed among sites. Spatial analyses revealed significant clustering at distances of 65–80 m, particularly for front yard configuration, while clustering of woody vegetation density was weaker. Local clustering patterns and the distribution of outliers varied across sites. Spatial lag models indicated that lot area positively influenced yard configuration and quality, and the density and diversity of woody vegetation. While socio-economic variables were not significant predictors of yard quality, their effects cannot be discarded. Overall, results are consistent with social contagion processes but also highlight neighborhood design as a key driver of clustering, alongside widespread conversion of green to paved front yards, with implications for soil and green infrastructure loss as well as environmental and human health in the RPWS. Full article

The global transport sector stands at a critical juncture where economic growth imperatives intersect with urgent environmental sustainability challenges. This paper investigates the impact of air freight transport, digitalisation, energy consumption, economic growth, and regulatory quality on CO₂ emissions in Gulf Cooperation Council (GCC) countries. Despite the region’s strategic importance in global air freight networks and rapid digital transformation, empirical evidence on how these factors collectively influence environmental sustainability remains limited. GCC countries provide a unique context for examining the digitalisation–transport–environment nexus. Using panel data from six GCC member states spanning 1999–2022, this study employs a second-generation autoregressive distributed lag (CS-ARDL) model to analyse short- and long-run relationships while accounting for cross-sectional dependence and heterogeneity. The empirical model designates CO₂ emissions as the dependent variable, while the digitalisation indicator, air freight transport, and energy consumption serve as principal explanatory variables. The empirical findings indicate that energy consumption and economic growth are significant drivers of CO₂ emissions in GCC countries, while digitalisation is associated with lower emissions. Regulatory quality exhibits a weaker but non-negligible negative influence. Moreover, air freight transport does not display a significant long-run effect on emission in the GCC context. These findings are robust across multiple panel estimators. The research provides evidence-based guidance for GCC national vision programmes, green aviation initiatives, and digital transformation strategies, contributing to a sustainable development discourse in resource-rich economies. Full article

The integration of intangible cultural heritage (ICH) and tourism development (TD) is regarded as a crucial national strategy for China’s sustainable development, as their synergistic relationship is considered pivotal for regional progress. A coupling coordination evaluation system was constructed. Kernel density estimation, entropy method, coupling coordination degree (CCD) and relative development degree (RDD) models, and a tobit model were employed to examine the spatiotemporal characteristics and influencing factors of ICH–TD integration in Sichuan Province. Key findings are as follows: (1) Sichuan is endowed with abundant ICH resources characterized by high heritage value and diverse typologies. However, the distribution is skewed toward traditional skills, exhibiting notable regional disparities. ICH demonstrates a “single-core, belt-shaped and multi-cluster” pattern, which is centered on Chengdu, extends along a north–south high-density belt, and forms several secondary high-density clusters. (2) Temporally, the CCD demonstrates a sustained upward trend, whereas the RDD transitions from ICH-lagged to TD-lagged. Spatially, the number of high coordinated cities increases annually, expanding radially from regional centers, while central-eastern regions consistently outperform the west. (3) Regarding influencing factors, comprehensive economic strength, distribution of industrial structure, overall level of urbanization, and transportation accessibility exert significant positive effects on the CCD, with comprehensive economic strength demonstrating the strongest influence. This study contributes to the theoretical understanding of ICH–TD synergy and provides policy-relevant guidance for integration. Full article

Regulatory bodies have observed an increase in environmental issues due to firms’ interactions with the environment. Nonetheless, reconciliation actions are emerging, driven by the pursuit of sustainable development goals. This study investigated the impact of financial development and foreign direct investment on ecological footprints in sub-Saharan African nations, while examining the mediating role of regulatory quality and control for corruption. The research was motivated by the growing environmental degradation in the region amid growing capital inflows and financial market expansion. Using panel data of 18 sub-Saharan African countries between 1996 and 2023, sourced from the World Bank database and World Governance Indicators, we employed an Autoregressive Distributed Lag model to assess the short- and long-run relationships among ecological footprint, financial development, foreign direct investment, and key institutional factors. Results from the baseline model show that financial development significantly increases ecological footprints, while the effect of foreign direct investments is insignificant in the absence of institutional factors. However, when mediating variables are introduced, foreign direct investment significantly worsens ecological footprint, and regulatory quality and control for corruption show strong moderating effects, confirming the pollution haven hypothesis. Also, all control variables (trade openness, gross domestic product per capita, government expenditure, and population density) show significant outcomes with environmental sustainability. The findings underscore the importance of institutional factors in shaping sustainable foreign direct investment flows and financial systems. These research findings offer policy pathways for aligning investment strategies with sustainability goals in sub-Saharan Africa. Recommendations include strengthening the nation’s institutional framework, linking foreign direct investment to environmental compliance and promoting green finance policies across the region. Full article

Utilising data from the selected companies listed on the Johannesburg Stock Exchange and using the Panel Autoregressive Distributed Lag (ARDL) specifically employing the Pooled Mean Group approach, this study examines the cointegrating and causal relationships among firm liquidity, performance and firm leverage. The results reveal a negative and significant long-run and short-run relationship between profitability and leverage. Conversely, higher leverage is found to diminish firm performance, consistent with trade-off theory implications regarding financial distress costs. On liquidity, results revealed a bidirectional long-run relationship among liquidity, leverage, and firm value as measured by Tobin’s Q. Also, liquidity plays a pivotal moderating role, where firms with stronger liquidity and profitability exhibit reduced reliance on external debt, highlighting the interplay between financial health and capital structure decisions. Additionally, a positive bidirectional relationship between Tobin’s Q and leverage suggests that growth opportunities and market valuation influence firms’ debt utilisation. The error correction terms confirm stable long-run equilibrium and moderate adjustment speeds. These results contribute to the understanding of optimal capital structure by integrating liquidity and performance factors and provide practical insights for corporate financial management and policy formulation. Full article

This study investigates the cointegrating relationships among financial inclusion, institutional quality, and economic growth in 20 Sub-Saharan African nations from 2008 to 2024. Employing the Pooled Mean Group (PMG) estimator in an Autoregressive Distributed Lag (ARDL) panel, the analysis showed a significant and favourable long-term association between economic growth, financial inclusion and institutional quality. In particular, regardless of the proxy for economic growth, the long-term association between financial inclusion and economic growth is positive and statistically significant. Similarly, institutional quality demonstrates a favourable and significant long-run linkage to economic growth, suggesting that improvements in institutional frameworks are related to sustained economic expansion. In contrast, short-run dynamics differs. There is a short-term correlation between institutional quality and economic growth but not between financial inclusion and economic growth. These findings show the importance of institutional quality as a catalyst for economic growth in the region. Consequently, the study recommends that governments in Sub-Saharan Africa should prioritise setting up strong institutions and policies to foster financial inclusion, which has a correlation with sustainable economic growth. This is crucial for both overall economic development and the creation of job opportunities. Full article

This study examines the short-run dynamics and long-run determinants of bilateral trade between Saudi Arabia (KSA) and Jordan during the period of 1995–2024 using the autoregressive distributed lag (ARDL) bounds testing approach. Employing a country-pair time series framework, the analysis examines how economic growth, foreign direct investment (FDI), inflation differentials, and crude oil prices affect trade volume between the two countries over time. The ARDL bound test confirms the presence of long run cointegration among the variables. The Long-run results suggest that crude oil prices, inflation differential, and FDI exert positive and statistically significant effects on bilateral trade, while Saudi economic growth and FDI show negative long-run effects, suggesting that Saudi’s economy structural characteristic and domestic absorption may decrease the demand for Jordanian’s products in the long-run. The short-run results reveal a negative and statistically significant error-correction term, confirming convergence toward long-run equilibrium with approximately 32.16% of deviations corrected each year, implying a moderate speed of adjustment following economic shocks. In the short-run, economic growth, FDI, Inflations differentials, and oil prices exert significant but mixed effect on trade volume, with oil prices emerging as the most influential determinant. Several variables displayed lagged responses due to adjustment costs, production constraints, and contractual rigidities between the two countries. Overall, the findings contribute new time-series evidence on the macroeconomic drivers of bilateral trade between an oil-exporting economy such as Saudi Arabia and a neighboring non-oil-exporting partner like Jordan, offering policy insights for strengthening trade integration and economic cooperation. Full article

Attaining education sustainability is indeed important as it ensures the overall economic sustainability of countries and it is directly connected with the United Nations Sustainable Development Goals (SDG-4). However, the literature evidence on the determinants of education sustainability is indeed very scarce and largely inconclusive, particularly in the case of the Kingdom of Saudi Arabia (KSA). Accordingly, this research paper focuses on exploring the determinants of education sustainability by focusing on the role of government education spending. The paper utilized annual time series data for the period 1991–2023 and applied the time series cointegration technique of “Autoregressive Distributed Lag (ARDL)” to assess the long-run and short-run impact of government education expenditures on education sustainability in KSA Our results based on the ARDL approach demonstrated that government expenditures have casted a positive influence on education sustainability both in the long run and short run in the case of KSA. Similarly, we found that trade openness, which is the main determinant of economic performance, has positively contributed to education sustainability in the long run and short run in KSA. On the other hand, the unemployment rate has worsened education sustainability both in the long and short run. The results further demonstrated a negative short-run impact that FDI has on education sustainability, suggesting structural or sectoral dynamics that need further empirical investigation. Moreover, GDP per capita has improved education sustainably only in the long run while its short-run impact is insignificant. Our results offer important policy implications for the policymakers of KSA to attain education sustainability and contribute to the overall economic sustainability, which is aligned with the Vision 2030 of KSA. Full article

Given the context of the COVID-19 pandemic disrupting global logistics, coupled with the Russia–Ukraine war causing global energy price changes, examining both the linear and nonlinear associations between shipping cost and oil price is crucial in a global context. This study empirically exhibits the association among Global Commodity Prices Index (GPI), Oil Price (OP), Gold Future Price (GFP), and Baltic Dry Index (BDI) by employing Linear Autoregressive Distributive Lag (ARDL) as well as Nonlinear Autoregressive Distributive Lag (Nonlinear ARDL) from January 2003 to January 2023. The findings indicate that the influence of OP on BDI has a negative impact in the long run and a positive impact in the short run. Furthermore, the OP has an asymmetric effect on BDI in both the long and short terms. Finally, the predictive performance of the NARDL model outperforms the ARDL model in forecasting OP and BDI. The empirical findings derived from the ARDL and NARDL algorithms offer valuable insights for policymakers in designing public policies and for investors in portfolio construction. Full article

Seasonal hypoxia in bottom waters of the Bohai Sea poses an escalating threat to marine ecosystems, yet monitoring it via satellite remote sensing continues to be challenging due to the inaccessibility of bottom layers. However, surface bio-optical signals do not instantaneously reflect variation in bottom-water dissolved oxygen (DO); instead, a distinct temporal lag exists between surface biological activity and its influence on bottom DO. Leveraging this insight, an inversion framework was established, integrating multi-source remote sensing data with decision tree-based machine learning models to estimate bottom-water DO concentration. We evaluated multiple lag intervals for satellite-derived bio-optical variables and adopted a 14-day lag as representative of the delayed impact of surface processes on bottom DO. An optimized feature set selected via a genetic algorithm (GA) was used to train the XGBoost model, which achieved high predictive performance (R² = 0.86, RMSE = 0.79 mg/L, MAPE = 8.89%). Interpretability analysis identified the sea surface temperature as the dominant driver of bottom-water DO variation in the Bohai Sea. The framework successfully reproduced the spatiotemporal variability in bottom DO from 2022 to 2024 in the Bohai Sea and captured the locations of summer hypoxic zones. Further analysis demonstrated that incorporating physically based bottom-layer variables substantially enhances model accuracy (R² = 0.89, RMSE = 0.68 mg/L, MAPE = 7.85%), underscoring their critical role in regulating bottom-water DO concentrations. Building on the established inversion framework and integrating extended in situ and satellite observations, we reconstruct the long-term temporal distribution of bottom DO in the Bohai Sea from 2014 to 2025, revealing the considerable potential of satellite data for monitoring bottom-water DO conditions in coastal seas. Full article