Search Results (248)

Ocean wave evolution reflects delayed wind forcing, nonlinear wind–wave interactions, and wave persistence, which complicate multi-horizon significant wave height (SWH) forecasting. This study analyzes wind–wave lag relationships and wave memory characteristics in the South China Sea and the Bay of Bengal using ERA5 data from 2022 to 2024, leading to region-specific input windows of 12 h and 21 h. Based on these findings, SSA-STLSTM-LT integrates Wind–Wave Lag Attention, ST-LSTM, a Temporal Transformer, and spatial self-attention within a direct six-horizon forecasting framework. Feature ablation further identifies a nine-variable input configuration. Under a common ERA5-based evaluation protocol, the model achieved horizon-averaged RMSE values of 0.098 m and 0.065 m in Regions A and B, respectively. It obtained the lowest MAE and RMSE across all six horizons in Region A. In Region B, it achieved the lowest RMSE from 3 to 24 h, remained comparable to the best model at 48 h, and was less accurate than the LT-enhanced recurrent baselines at 72 h. Independent altimeter validation further showed favorable observational consistency relative to PredRNN-LT and TrajGRU-LT, with smaller differences in Region B. The results indicate strong but region- and horizon-dependent forecasting advantages. Full article

Artificial intelligence (AI) is increasingly regarded as a key instrument for sustainability governance, but its role may differ across risk domains. Rather than functioning solely as an anticipatory tool, AI may also develop reactively in response to risk shocks. Using monthly Chinese data from January 2013 to December 2023, this study examines the dual role of AI in sustainability governance by analyzing its time–frequency relationships with climate-related government response and infectious disease-related market attention. The continuous wavelet transform and partial wavelet coherence are employed, with the World Uncertainty Index controlled. The results show that AI-sector development is positively associated with both risk-related signals, but the lead–lag patterns differ substantially. In the climate-response domain, the relationship shifts from risk-signal precedence in 2015–2016 to AI-sector precedence in short-term bands after 2020, suggesting that AI-related capacity may have become increasingly embedded in anticipatory climate governance. In contrast, infectious disease-related attention mainly precedes AI-sector movements, especially during 2017–2018 and the COVID-19 period, indicating a more reactive role of AI in public health risk contexts. These findings do not provide causal evidence that AI directly reduces physical climate risks or epidemiological burdens. Instead, they reveal the dual role of AI as both a response to sustainability-related risk shocks and a potential contributor to forward-looking governance capacity. This study contributes to AI and sustainability governance research by clarifying the conditions and boundaries under which AI shifts from reactive crisis response toward anticipatory risk preparedness. Full article

Background/Objectives: Clear cell renal cell carcinoma (ccRCC), the predominant malignant subtype of kidney cancer, is the leading cause of death among renal cell carcinoma patients. Although a subset of ccRCC patients benefit from select immune checkpoint inhibitors (ICIs), prognosis remains poor. While PD-1 and PD-L1 have been extensively studied, the prevalence and distribution of other immune checkpoints (ICs) and their relationship with epithelial-to-mesenchymal transition (EMT) remain poorly characterised. Here, we investigated the interplay between twenty ICs and EMT markers and assessed their combined prognostic relevance in ccRCC patients. Methods: Transcriptomic profiling and integrated bioinformatic analyses were performed, including differential expression, correlation analyses, survival analyses, forest plot analyses, ROC curve evaluation, and OncoPrint visualisation, complemented by analysis of single-cell RNA sequencing data, immunohistochemistry, and multiplex secretory IC (LegendPlex) assays. Results: Transcriptomic profiling of over 500 ccRCC tumours versus normal kidney tissue revealed dysregulation of ICs, particularly LAG3 and NT5E. Notably, expression of ICs, including LAG3 and NT5E, was associated with poor overall survival in 415 ccRCC patients. ICs that synergised with the EMT phenotype provided improved prognostic discrimination compared to individual ICs. Correlation analyses, single-cell RNA sequencing, and immunohistochemistry demonstrated an association between EMT-associated tumours and expression of LAG3 and NT5E. ROC analysis indicated modest prognostic performance of LAG3 and NT5E. Conclusions: Collectively, this study identifies an EMT–IC axis in ccRCC and demonstrates its relevance to tumour biology and patient outcomes, highlighting LAG3 and NT5E as potential prognostic markers and therapeutic targets that warrant further investigation. Full article

The Xizang–Sichuan region is rich in tourism resources, yet its complex geography and lagging transportation infrastructure have resulted in pronounced spatial disparities in tourism development. From a sustainability perspective, such disparities can lead to a ‘rich-get-richer’ cycle: over-tourism and ecological stress in high-accessibility cores, versus underdevelopment and resource idling in low-accessibility peripheries. To systematically examine the spatial structure of tourism in this region, this study uses counties as basic units and integrates the Analytic Hierarchy Process (AHP) with an improved gravity model for tourism network potential accessibility (TNPA) to assess regional tourism development levels and the network-based accessibility among counties. A comprehensive evaluation framework was established across three dimensions: tourism resource endowment, service capacity, and socio-economic support. Travel times between county centers were obtained from the Amap API, and a TNPA index was computed to reflect each county’s potential for tourism interaction with the rest of the region. Results indicate that resource endowment dominates the evaluation, with scenic quality as the critical factor. TNPA exhibits a pronounced core–periphery differentiation, with Chengdu and surrounding areas forming a high-value network core, while most counties in Xizang show extremely low network potential. Equity analysis reveals a significant imbalance in the distribution of network accessibility between Sichuan and Xizang. Under the baseline setting, the population-weighted Gini coefficient, coefficient of variation, and Theil index reach 0.677, 1.724, and 0.884, respectively, indicating that tourism network potential remains highly concentrated in a limited number of counties. The population-weighted mean TNPA of Sichuan is also far higher than that of Xizang, revealing a distinct interregional accessibility gap. The development–TNPA mismatch analysis further identifies counties where tourism development foundations are relatively strong but network integration remains weak. Robustness checks indicate that the high-development–low-TNPA pattern is not simply an artefact of the median-based classification, with the most evident cases mainly concentrated in Aba and Garze in western Sichuan and a few counties in Xizang. The study highlights the asymmetric relationships among resource endowment, network accessibility, and equity, providing a scientific basis for optimizing cross-regional tourism cooperation and transportation corridors in the Xizang–Sichuan region. Full article

This study investigates the long-run and short-run relationships between capital market development, foreign direct investment, trade openness, and real GDP per capita in Romania over 2003–2024, employing the Autoregressive Distributed Lag (ARDL) bound testing approach, complemented by lag-augmented VAR Granger-causality analysis and a comprehensive set of diagnostic and stability tests. The bounds tests strongly reject the null of no cointegration, confirming a long-run relationship that remains robust under finite-sample critical values. The causality analysis demonstrates a supply-leading mechanism from the equity market to real economic activity, while economic growth in turn Granger-causes both market liquidity and trade openness, pointing to demand-following dynamics for these channels. The analysis shows that foreign direct investment, market liquidity, and trade openness exert positive and significant short-run effects; yet their long-run coefficients are negative, significantly for FDI (foreign direct investments), capturing an asymmetry between immediate output gains and durable structural contribution that is characteristic of emerging European economies. The error-correction term is positive, demonstrating that real GDP (gross domestic product) per capita does not adjust back toward the long-run relationship in the conventional sense, but, instead, it behaves as a forcing variable that leads the financial and trade channels rather than being led by them. All in all, the findings describe an economy with functional short-run transmission channels, but limited long-run structural anchoring, with direct relevance for Sustainable Development Goals 8 and 17 and Romania’s ongoing OECD accession. Full article

Background: Heat exposure is a leading climate-related health threat, yet whether the heat–disease burden relationship is moderated by national adaptive capacity remains poorly quantified at the global level. We examined associations between heat exposure and cause-specific disability-adjusted life year (DALY) burden across climate vulnerability strata and projected future burden to 2050 under IPCC AR6 warming scenarios. Methods: We constructed a country–year panel spanning 187 countries and 34 years (1990–2023) by merging ERA5 reanalysis temperature data; GBD 2023 DALY rates for cardiovascular diseases (CVD), chronic kidney disease (CKD), and chronic respiratory diseases (CRD); ND-GAIN adaptive-capacity scores; and WHO GHO health system indicators. Countries were stratified into adaptive-capacity tertiles (Low: n = 63; Medium: n = 62; High: n = 62). We used two-way fixed-effects panel regression with country-clustered standard errors, a formal Chow test of slope equality, lagged exposure models, and a benefit-of-adaptation counterfactual. Future DALY burden was projected to 2030, 2045, and 2050 using country-specific ERA5 warming trends scaled to IPCC AR6 SSP scenario multipliers. Findings: The heat–CVD dose–response was 26 times larger in Low versus High adaptive-capacity countries (β = −346.2 vs. −13.1 DALY years per 100,000 per °C). The Chow test confirmed statistically significant slope heterogeneity across tertiles for all three outcomes (CVD: F = 22.0, p < 0.0001; CKD: F = 14.9, p < 0.0001; CRD: F = 9.4, p < 0.0001). CKD burden rose 47·8% globally between 1990 and 2023, with the strongest within-country heat–CKD association in Medium adaptive-capacity countries (β = −61.5, p < 0.0001). These findings were robust to lagged exposure specifications. Under SSP5-8.5 by 2050, Low adaptive-capacity countries face a projected CVD DALY rate change 23 times larger than High adaptive-capacity countries (−16.2% vs. −0.7%). Upgrading Low adaptive-capacity countries to High tertile standards would avert 15.6% of projected CVD DALY burden under SSP5-8.5 by 2050. Conclusions: Adaptive capacity substantially moderates the health consequences of heat exposure. The quantified benefit of adaptation investment—expressed as averted DALY burden—provides a direct metric for health-system strengthening and climate adaptation financing, particularly in low-income settings facing the steepest projected burden increases. These results position adaptive capacity as a critical social determinant of planetary health, linking Earth-system boundary transgression to inequitably distributed human disease burden across the global community. Full article

This study investigates the frequency of Tibetan Plateau vortices (TPVs) and their statistical relationship with global sea surface temperature (SST) anomalies. The results show that TPV frequency exhibits pronounced seasonal and interannual variability. Annual TPV frequency generally ranges from 50 to 70 events, with short-lived TPVs, particularly those lasting two days, accounting for the majority of occurrences. TPV activity is most active during summer and relatively weak during autumn and winter. Lagged correlation analyses reveal that the North Atlantic exhibits the strongest statistical linkage with TPV frequency among all global ocean basins. After removing the linear trends, the maximum correlation occurs when North Atlantic SST anomalies lead TPV frequency anomalies by approximately two months, indicating a robust lagged relationship between the two variables. Further circulation analyses suggest that North Atlantic SST anomalies are closely associated with large-scale atmospheric circulation anomalies over the North Atlantic–Eurasian sector prior to TPV-active months. Anomalous geopotential height and wind fields at 500 hPa, together with upper-level wind anomalies at 200 hPa, indicate significant adjustments of the Eurasian midlatitude circulation and upper-level westerly jet associated with North Atlantic SST variability. During TPV-active months, enhanced upper-level divergence, strengthened upward motion, and intensified cyclonic anomalies emerge over the Tibetan Plateau, providing favorable dynamical conditions for TPV formation and development. Overall, the results reveal a statistically robust linkage between North Atlantic SST anomalies and TPV frequency variability and provide new insight into the associated large-scale circulation background over the Tibetan Plateau. Full article

Dam seepage is a critical issue affecting the safe operation of reservoir dams, making the monitoring and early warning of abnormal seepage conditions particularly important. Currently, analyses of dam seepage primarily focus on using finite element methods to invert seepage conditions and employ regression analysis and classical machine learning methods to predict seepage. However, there has been limited analysis of the relationships among various influencing factors. The subjectivity of input factors in seepage safety monitoring models, the imprecision of factor relationships, and the randomness of parameter selection can all lead to uncertainty in model predictions. Therefore, to identify the primary factors influencing reservoir seepage issues, we took a specific reservoir project as an example and employed stepwise regression analysis and Granger causality tests to comprehensively examine the relationships between reservoir water level, rainfall, seepage pressure at various locations, and seepage pressure around the dam. Based on this analysis, the key influencing factors for seepage pressure around the dam were identified. The results indicate that reservoir water level and seepage pressure influence the seepage pressure around the dam. The stepwise regression method can comprehensively screen for potential influencing factors. Meanwhile, GCT utilizes time lag characteristics to further narrow the range of influencing factors. These two methods significantly narrow the scope of screening for factors affecting seepage pressure around the dam. These two methods can be used to narrow down the range of factors influencing seepage pressure around the dam, reduce interference from these factors, scientifically eliminate spurious correlations and redundant variables, and efficiently and reliably detect and provide early warnings of abnormal dam seepage. Full article

In dollarised economies, the absence of autonomous monetary policy shifts the burden of macroeconomic adjustment onto the banking system, where deposits and credit constitute the principal channel through which liquidity is conveyed to commercial activity. The literature has documented this relationship using aggregate national data, yet its behaviour at the monthly provincial scale remains underexplored for Latin America, particularly in fully dollarised economies and over recent periods marked by severe shocks. This article addresses that gap for Ecuador using a monthly panel of its 24 provinces over 2019–2025, estimated as a Panel VAR by two-step GMM, with monthly sales declared to the Internal Revenue Service used as a high-frequency indicator of provincial economic activity. The pandemic is incorporated as an exogenous control. The theoretical framework combines the supply-leading hypothesis, the credit-channel literature on transmission lags arising from financial frictions, and financial intermediation theory on liquidity and asset transformation. The system exhibits a predominantly supply-leading dynamic: deposits and credit retain predictive capacity over provincial sales, with no robust evidence of reverse feedback. Transmission speed is heterogeneous across channels. Deposits affect sales with a one-period lag, whereas credit requires an additional period—a pattern consistent with the differential role of each channel in banks’ asset-transformation function. The provincial-scale evidence for a dollarised economy shows that the macroeconomic relevance of financial intermediation depends on the heterogeneous transmission speeds of its components, with implications for territorial policy. Full article

The COVID-19 pandemic caused an unprecedented disruption to global tourism, exposing the structural vulnerability of tourism-dependent economies and leading to a sharp decline in international mobility. This study examines post-pandemic tourism recovery in Kazakhstan, focusing on the association between travel expenditure and tourism demand, and on regional disparities in recovery patterns. The empirical strategy combines time-series econometric modelling, infrastructure index construction, and regional cluster analysis. Using annual data for 2000–2023, a parsimonious autoregressive distributed lag (ARDL) model is applied to estimate both short-run dynamics and long-run relationships. The results show a positive and statistically significant association between tourism demand and travel expenditure, supporting the interpretation of expenditure-related recovery dynamics. They also indicate persistence in demand and a significant negative association with the coronavirus disease 2019 (COVID-19) shock. The error correction mechanism indicates rapid adjustment toward long-run equilibrium. Infrastructure and regional analyses highlight substantial post-pandemic capacity expansion alongside pronounced spatial disparities, with activity concentrated in major urban centres. The findings suggest that recovery is closely associated with demand-side dynamics and accompanied by persistent structural and regional constraints, highlighting the need for coordinated policies that combine demand stimulation, infrastructure development, and balanced regional growth. Full article

The temporal variations of evapotranspiration (ET) and its controlling factors occur across time scales ranging from seconds to decades, with significant differences in the lag effects of ET drivers under varying water conditions. Therefore, identifying the dominant time scales of the relationships between ET and its controlling factors under varying water conditions is crucial for optimizing irrigation strategies of crops grown in a greenhouse. In our study, we utilized two years of continuous lysimeter observations of greenhouse tomato ET, and applied two water treatments: well-irrigated (0.9E_pan, E_pan is the cumulative pan evaporation) and deficit-irrigated (0.5E_pan). Wavelet transform technology served as the core method to systematically examine the temporal variations of ET and its controlling factors. Observations indicated that the power spectra of ET featured pronounced peaks at daily and seasonal scales. The cospectra between ET and soil water content for greenhouse tomato revealed strong temporal correlation at 2~5 day scales, confirming the regulatory effect of irrigation cycles on ET. Moreover, ET variations were largely synchronous with net radiation, with ET lagging net radiation but leading vapor pressure deficit and air temperature at daily scales. In addition, significant disparities in phase angles between ET and individual meteorological variables were identified under 0.9E_pan and 0.5E_pan water conditions. Partial wavelet coherence revealed that net radiation was the primary meteorological driver of greenhouse tomato ET across multiple time scales, particularly at the daily scale, followed by vapor pressure deficit. These findings provide scientific evidence for selecting appropriate ET models at different time scales and offer valuable insights for optimizing water-saving irrigation for crops grown in greenhouses. Full article

Achieving sustainable development requires decoupling economic growth from environmental degradation. In this context, this study examines the effects of tourism arrivals on CO₂ intensity and energy intensity, two key indicators of environmental sustainability aligned with SDGs 7 and 13. Panel autoregressive distributed lag (ARDL) and nonlinear ARDL models are employed using a balanced panel of 54 countries over the period 1996–2023. In addition, Wald tests for long-run asymmetry, dynamic multiplier analysis, and Dumitrescu–Hurlin causality tests are applied. The results confirm the existence of stable long-run relationships between tourism arrivals and both CO₂ intensity and energy intensity. In the symmetric framework, tourism growth is associated with significant long-run reductions in CO₂ and energy intensity, while short-run effects are negative and significant only for CO₂ intensity. In the asymmetric framework, positive tourism shocks generate stronger and more persistent reductions in both intensity measures, whereas negative shocks lead to weaker environmental efficiency gains. Moreover, the Wald test shows the existence of long-run asymmetry between positive and negative tourism shocks. In addition, the dynamic multiplier analysis confirms that environmental intensity adjusts gradually over time following tourism shocks. Finally, Dumitrescu–Hurlin causality tests indicate bidirectional Granger causality relationships between tourism arrivals and environmental intensity indicators. The findings are robust to dynamic endogeneity, the COVID-19 shock, and country heterogeneity. Overall, the findings indicate that tourism arrivals contribute to lowering long-term environmental intensity, consistent with relative decoupling and the goals of sustainable tourism development. Full article

Wastewater-based epidemiology has emerged as a valuable complementary tool for population-level monitoring. This study evaluated the early warning value of wastewater surveillance for monitoring SARS-CoV-2 and its correlation with COVID-19 infection trends. From May 2024 to December 2025, 526 wastewater samples were collected from five treatment plants. Spearman correlation and a quasi-Poisson generalized additive model (adjusting for wastewater temperature) were used to assess relationships between SARS-CoV-2 RNA concentration, the number of reported cases, and lag associations. Wastewater viral loads (copies/mL) significantly correlated with reported cases. Wastewater temperature was positively correlated with both viral concentrations and case numbers. A significant lagged association was observed for the N gene, with relative risk peaking at a 10-day lag. Although the ORF1ab gene was not significant for most lag periods, its temporal trend was consistent with that of the N gene. Wastewater surveillance of SARS-CoV-2, particularly targeting the N gene, can effectively predict COVID-19 infection dynamics with a 10-day lead time, thereby supporting wastewater surveillance as an early warning tool for public health monitoring. Full article

This study examines the determinants of energy consumption in South Africa over the period 1980–2023 using a multivariate time-series framework. Unlike conventional studies that focus primarily on the energy–growth nexus, this analysis incorporates financial development, industrialization, and population growth to provide a more comprehensive understanding of energy demand dynamics. The Autoregressive Distributed Lag (ARDL) approach is employed to estimate both short-run and long-run relationships. Unit root tests confirm that all variables are integrated of order one, justifying the application of the ARDL bounds testing approach. The results reveal the existence of a stable long-run relationship between energy consumption and its determinants. Industrialization and population growth emerge as the most significant drivers of energy demand in both the short and long run, reflecting South Africa’s energy-intensive economic structure and rising demographic pressures. Financial development is found to have a positive and statistically significant effect, suggesting that improved access to credit stimulates energy consumption through increased investment and economic activity. In contrast, economic growth exhibits a positive but statistically insignificant long-run effect, indicating partial decoupling between output growth and energy demand. The error correction term is negative and statistically significant, confirming convergence to long-run equilibrium. Causality analysis further indicates that energy consumption is primarily driven by macroeconomic factors rather than acting as a leading indicator. The findings underscore the importance of industrial energy efficiency, population-responsive energy planning, and targeted financial support for sustainable energy investment. This study contributes to the literature by providing a comprehensive, country-specific analysis and offers policy-relevant insights for enhancing energy security and supporting sustainable economic development in South Africa. Full article

This study examines selected factors influencing white maize price movements in South Africa over the period 1994–2024. Given the importance of white maize for food security, understanding the drivers of producer price dynamics is essential for effective policy formulation and managing price stability. Annual time-series data are analysed using an Autoregressive Distributed Lag (ARDL) modelling framework, complemented by bounds testing, an error-correction model, Toda–Yamamoto causality and structural break tests. The bounds test confirms the existence of a stable long-run cointegrating relationship between maize prices and the selected explanatory variables. In the short run, imports and fuel prices exert significant upward pressure on maize producer prices, while lagged fuel prices and rainfall reduce prices. In the long run, imports and fuel prices remain statistically significant determinants, whereas maize production, exports, the exchange rate, and rainfall are insignificant. Complemented with the structural break tests that identify regime shifts in the early 2000s, 2012, and 2021, causality results indicate that imports, rainfall and fuel prices lead to Granger causality in maize producer prices. Collectively the findings reinforce the conclusion that white maize prices in South Africa are governed by long-run structural relationships, while short-run price movements reflect temporary adjustments rather than permanent shifts in market fundamentals. An integrated, long-horizon analysis that jointly incorporates climatic, trade, and macroeconomic determinants within an ARDL framework is provided by the study. Therefore, the findings have important implications for climate-risk management, transport cost containment, trade and price-stabilisation policies. Full article