Search Results (552)

A robust analysis of water use in major food production systems is crucial for improving their productivity and sustainability in water-scarce arid and semi-arid regions like Punjab (India) facing the depletion of groundwater resources. This study aimed to assess blue water use and blue water productivity in dairy farming systems across different farm sizes in Punjab. Comprehensive monitoring and assessment of water use over a full year (from July 2022 to June 2023) was conducted on 24 dairy farm sheds in Punjab, revealing significant variability in their blue water use (measured in L per adult animal per day) and blue water productivity quantified as kg of fat- and protein-corrected milk (FPCM) produced per m³ of the blue water consumed. The variability was influenced by factors such as livestock species, farm size (medium with 15–25 livestock, large with 25–100 livestock, and commercial with >100 livestock), bathing and servicing routines, and energy use patterns. The average dairy livestock total blue water consumption varied from 112 ± 14 to 131 ± 19 L per adult animal per day, with 20–40% higher livestock drinking water and about six times higher livestock bathing and serving water used during the summer months. Interestingly, a large share (45%) of the average total blue water consumption is contributed by indirect water consumption via the use of energy (electricity and diesel) in dairy farm sheds. Dairy milk blue water productivity was quantified higher, ranging from 154 ± 11 to 225 ± 59 kg FPCM per m³ in buffalo- and crossbred cattle-based dairy farm sheds. However, indigenous cattle showed a lower blue water productivity ranging from 56 to 97 kg FPCM per m³, reflecting their lower milk yields and limited use of intensified management practices. The state-level water scarcity footprint (WSF) of Punjab dairy farm sheds was quantified at 4870 million m³ world-eq, which showed a significant spatial variation among Punjab districts. However, the results of this study offer novel seasonally and spatially disaggregated benchmarks of blue water consumption, blue water productivity, and the water scarcity footprint of Punjab’s dairy farming sheds. This new information is crucial for the development of locally calibrated and validated models for improving the water productivity and sustainability of dairy farming across Punjab and other similar arid and semi-arid regions in Southeast Asian countries. Full article

This study presents a structured methodology for mid-term electricity demand forecasting in the Greek interconnected power system, incorporating climate-sensitive and socio-economic variables. A set of linear regression models was developed to produce forecasts at both monthly and daily resolutions, aiming to balance accuracy with transparency and computational efficiency. Monthly demand was modeled using macro-trend variables such as GDP, population, and energy prices, while daily demand was approached through a disaggregated modeling structure, assigning a distinct regression model to each day of the week. Temperature effects were introduced at both levels using cooling and heating degree days, estimated based on seasonal weather forecasts provided by 51 meteorological models. The modeling approach developed shows a high predictive value. The monthly electricity demand forecast over a six-month horizon exhibits a mean absolute percentage error and a maximum error of approximately 1.4% and 3.9%, respectively, when actual meteorological data are employed, and 3.7% and 8.5%, respectively, when seasonal meteorological forecasts are used for the entire year 2022, in which it has been tested. Adjusting the model for projecting, the monthly peak load in the same time horizon, presents less accurate yet satisfactory results, with a mean and maximum error of 2.9% and 9.6%, respectively, when actual meteorological data are used, and 5.3% and 12.9%, respectively, when seasonal meteorological forecasts are employed. Full article

Poland ranks among the world’s leading exporters of goose meat and edible offal, yet domestic consumption remains minimal, revealing a structural imbalance between production and internal demand. This study aims to provide a comprehensive economic assessment of Poland’s foreign trade in goose meat and offal during 2020–2024, examining export specialization, price dynamics, and market resilience. Using official data from the Central Statistical Office (GUS), Eurostat, UN Comtrade, and the National Bank of Poland (NBP), trade flows were disaggregated by CN product codes, destination countries, and unit prices to identify key structural patterns. Results indicate that export volumes remained largely limited by price responsiveness despite sharp price increases and exchange rate fluctuations, confirming stable foreign demand. Exports were heavily concentrated in Germany, which absorbed over 70% of the total trade value, while domestic consumption stayed below 0.5 kg per capita annually. These findings demonstrate both the competitiveness and the fragility of Poland’s export-oriented trade model, characterized by dependence on a single market and limited domestic integration. The study concludes that long-term food system resilience requires diversification of export destinations, stimulation of domestic demand, and stronger alignment with sustainability goals. A forthcoming second part will address environmental impacts and consumer awareness. Full article

Short-form video (SFV) platforms have become a central part of older adults’ digital lives, yet their psychological implications remain theoretically contested. Drawing on social empowerment theory, Self-Determination Theory, attachment theory, and the displacement hypothesis, this study examined whether different sources of social support—family, peer, and community—exert differential effects on life satisfaction through SFV engagement and social connectedness. Survey data were collected from 385 community-dwelling Chinese older adults (mean age = 70.6 years) and analyzed using bootstrapped serial mediation models with 5000 resamples. The results revealed clear source differentiation, as family support most strongly predicted SFV engagement and showed the largest total association with life satisfaction, consistent with a social empowerment mechanism. Community participation showed a weaker but still positive association with engagement, whereas peer support was unrelated to engagement. Across pathways, higher SFV engagement was associated with lower social connectedness, while greater social connectedness was associated with higher life satisfaction. However, none of the chained indirect effects reached significance, suggesting that social support influenced life satisfaction primarily through direct rather than serially mediated pathways. These findings demonstrate the importance of disaggregating social support by source and contribute to a more precise framework for understanding older adults’ digital well-being. Full article

Accurate demand forecasting and adaptive load balancing are critical for maintaining stability and efficiency in modern smart power grids. This study proposes a hybrid deep learning (DL) framework, termed Transformer-Generative Adversarial Network-Gated Recurrent Unit (Transformer-GAN-GRU), which integrates global attention-based temporal modeling, generative data augmentation, and sequential refinement into a unified architecture. The proposed framework captures both long- and short-term dependencies while improving representation of imbalanced demand patterns. The model is evaluated on three heterogeneous benchmark datasets, namely Pecan Street, the reliability test system-grid modernization laboratory consortium (RTS-GMLC), and the reference energy disaggregation dataset (REDD). Experimental results demonstrate that the proposed model consistently outperforms state-of-the-art baselines, achieving a maximum accuracy (Acc) of 99.49%, a recall of 99.67%, and an area under the curve (AUC) of 99.83%. In addition to high predictive performance, the framework exhibits strong stability, fast convergence, and low inference latency, confirming its suitability for real-time deployment in smart grid environments. Full article

Airport stand allocation research has traditionally focused on optimizing assignments within fixed infrastructure configurations, while strategic decisions regarding stand category composition remain underexplored. This study investigates how different proportional distributions of stand categories affect system-level performance under high traffic demand at international airports. A discrete-event simulation model implemented in MATLAB evaluates fifteen infrastructure configurations with varying distributions of small, medium, and large stands, classified according to the ICAO Annex 14. The model employed a first-come–first-served allocation logic to isolate infrastructure-driven effects from algorithmic decision-making. System throughput was measured through acceptance and rejection rates, disaggregated by aircraft stand category. Acceptance rates ranged from 33% to 92% across tested configurations, demonstrating pronounced sensitivity to stand composition. Balanced configurations consistently outperformed asymmetric alternatives. Insufficient stand availability in any single category led to concentrated rejection patterns and non-linear performance degradation; excess capacity in unconstrained categories could not compensate for shortfalls in constrained ones. Proportionality across stand categories is identified as a critical determinant of infrastructure robustness. The proposed simulation framework provides a computationally efficient tool for early-stage (pre-operational planning phase) infrastructure screening, supporting informed strategic capacity decisions prior to detailed operational optimization. Full article

Though separate research has found that early experiences, parental beliefs, and cognitive skills all influence science learning, science remains an underexamined domain compared to math and reading, despite its policy and societal implications. We integrate and expand on previous research by examining culturally relevant variables in different subgroups of Asian American kindergartners (N = 894). Guided by the Opportunity-Propensity Model of Achievement, we conducted a multi-group path analysis with science scores as the outcome, and propensity (self-regulation, social skills, and prior knowledge), opportunity (e.g., parent and child reading, TV-watching routine), and antecedent variables (e.g., poverty, SES, number of siblings and close grandparents, parental expectations, primary language at home, immigrant status) as predictors. We expected that propensity and opportunity variables would mediate the effects of antecedent variables. We conducted a multi-group path analysis, in which we examined differences between subgroups (China, India, Vietnam, Other East, Other Southeast, Other). Although we did not find heterogeneity in science achievement among subgroups, we found various direct and indirect effects at the subgroup level. Findings suggest that Asian American children may generally benefit from enhanced self-regulatory skills and prior knowledge, though some subgroups may benefit specifically from having fewer TV-watching rules and non-structured activities. We also recommend further disaggregation and reporting of data to better support learners. Full article

This study examines the relationship between banks’ ESG performance and core risk and balance sheet indicators in the Turkish banking sector. Using an unbalanced panel of eight banks listed on Borsa Istanbul over the period 2008–2023, we estimate bank fixed-effects models with one-year-lagged ESG measures and controls and report Driscoll–Kraay standard errors. Two complementary specifications are employed: one based on the composite ESG score and another based on its environmental (E), social (S), and governance (G) pillars. The findings suggest that the composite ESG score is positively associated with non-performing loans and capital adequacy, while its relationship with liquidity risk and net balance sheet position/equity is less stable across specifications. When the ESG pillars are examined separately, substantial heterogeneity emerges across the E, S, and G dimensions. In particular, the environmental score is negatively associated with capital adequacy, whereas the social score is negatively associated with net balance sheet position/equity. Governance-related results appear weaker and more sensitive to specification choice. Overall, the findings indicate that ESG does not operate through a uniform risk channel in banking and should be interpreted as associational rather than causal. The study contributes evidence from an emerging-market banking system and highlights the importance of disaggregated ESG analysis. Full article

The identification of senior residential concentrations requires geospatial methods that combine fine-scale population modeling with robust uncertainty assessment. This study introduces NORC-SIMCLUST, a framework that integrates dasymetric disaggregation of senior households with density-based clustering and stability confidence measures derived from simulation runs and parameter sweeps. The method creates synthetic microdata by allocating census block senior household counts to residential parcels using housing-unit information, then estimates cluster stability through repeated simulations. By addressing data sparsity and spatial analysis pitfalls inherent in aggregated areal approaches, our work improves reliability and enables the detection of both horizontal and vertical NORCs—an underexplored geospatial challenge. A case study in Colorado Springs, USA, demonstrates enhanced detection reliability and confidence assessment compared to conventional heuristics. This work advances geospatial analytics for aging-in-place research and planning by providing a scalable, reproducible pipeline for demographic simulation, spatial clustering, and uncertainty analysis. Full article

Non-communicable diseases (NCDs) are the leading causes of mortality in Serbia, with cardiovascular diseases (CVDs) accounting for a substantial share of premature mortality. In alignment with Sustainable Development Goal (SDG) Target 3.4, which aims to reduce premature mortality from NCD by one-third by 2030 relative to 2015, this study forecasts changes in CVD mortality counts at the municipal level in Serbia. Time-series data for the period 2005–2022 were analyzed within a spatio-temporal forecasting framework implemented in the Space Time Pattern Mining toolbox in ArcGIS Pro (Version 3.1). Three established forecasting models (Curve Fit Forecast, Exponential Smoothing, and Forest-based) were applied, and the most accurate model for each municipality was selected using location-specific municipality-level validation. The results reveal pronounced spatial variation: approximately half of the municipalities (51.2%) are forecasted to experience a decline in CVD mortality counts by 2030, while others are expected to show increases or no statistically significant change. Forecasted differences range from a 15.1% decrease to a 13.9% increase across municipalities, indicating heterogeneous spatial trajectories and suggesting that achieving SDG Target 3.4 may remain challenging without targeted interventions across municipalities where mortality reductions are not forecasted. Although the study does not introduce new forecasting methods, it provides a novel spatially disaggregated application of multi-model forecasting to support municipality-level monitoring of SDG 3.4. The results underscore the need for geographically differentiated public health policies and demonstrate the value of spatial forecasting approaches for supporting equitable and targeted health planning. Full article

Non-Intrusive Load Monitoring (NILM) represents a powerful approach for energy disaggregation, which enables detailed insights into energy consumption patterns without requiring extensive sensor deployment. While significant advances have been achieved in residential NILM applications, commercial and industrial buildings remain largely underexplored despite their substantial contribution to global energy consumption. This study addresses this gap by developing and evaluating multiple artificial intelligence approaches for energy disaggregation across residential, commercial, and industrial buildings under a unified experimental protocol. We implement and compare several AI-based models, including Vision Transformer (ViT), Variational Autoencoder (VAE), Random Forest (RF), and custom architectures inspired by TimeGPT and Prophet, alongside traditional baseline methods. The proposed framework is validated using three benchmark datasets representing residential (AMPds), commercial (COmBED), and industrial (IMDELD) environments. Experimental results demonstrate that architecture–load interactions, rather than model complexity alone, are the primary determinants of disaggregation accuracy: the ViT-small configuration achieves superior performance for complex industrial loads with R² values exceeding 0.94, Random Forest proves most effective for finite-state commercial HVAC systems with R² up to 0.97, and the Prophet-inspired model excels in capturing seasonal patterns in residential appliances. These findings provide evidence-based guidelines for selecting appropriate AI models based on load characteristics, signal-to-noise ratio, and building type, contributing to the practical deployment of NILM in heterogeneous building environments. Full article

Timely assessment of macroeconomic conditions is essential because official gross domestic product (GDP) statistics are released with substantial delays and are often revised. This study examines whether high-frequency highway traffic volumes, disaggregated by vehicle type, improve short-term GDP nowcasting in the Republic of Korea. Using nationwide expressway traffic data from 328 toll plazas over the period from September 2008 to September 2025, we integrate traffic series with conventional macroeconomic indicators into a mixed-frequency dynamic factor model and evaluate pseudo-real-time nowcasting performance against official quarterly GDP releases. Time-series diagnostics indicate that traffic volumes contain short-horizon predictive information for GDP and satisfy stationarity requirements after appropriate transformation. In the full evaluation sample, the macro-only benchmark records an RMSE of 1.0258 and an MAE of 0.8716. Adding aggregated traffic changes these metrics only marginally (RMSE = 1.0269, MAE = 0.8696), whereas the model augmented with the heaviest freight class (Vehicle Type 6) performs best, lowering RMSE to 1.0179 and MAE to 0.8652. During the COVID-19 period, forecast accuracy deteriorates across specifications: aggregated traffic increases RMSE and MAE to 1.3456 and 1.2096 relative to the macro-only benchmark (RMSE = 1.3082, MAE = 1.2020), while Vehicle Type 6 lowers MAE to 1.1683 but still records a higher RMSE of 1.3198. These findings show that aggregate mobility measures add limited value, whereas freight-oriented vehicle-type disaggregation provides the most informative highway traffic signal for real-time GDP nowcasting. Full article

Climate-related economic losses across Europe have evolved from isolated environmental shocks to persistent, structurally embedded fiscal risks, posing a direct challenge to the long-term fiscal sustainability of European states. This study presents an empirical framework for diagnosing and quantifying this transformation across 38 European countries between 1980 and 2023. Combining regime-switching time-series models with a two-part panel design, we identify temporal shifts and spatial asymmetries in loss exposure. Our findings reveal the emergence of a high-loss regime from the early 2000s, alongside a widening inequality in national vulnerability, with countries such as France, Germany, Italy, and Spain bearing a disproportionate burden. This concentration raises critical questions about the sustainability and equity of current EU risk-sharing frameworks. The two-part model further disaggregates the probability of experiencing losses from their conditional magnitude, enabling country-level estimates of expected annual losses. These results highlight the limitations of current fiscal instruments, which remain reactive and fail to align with the spatial and temporal dynamics of climate risk. We argue for a shift from climate loss management to climate loss governance, underpinned by predictive analytics, differentiated policy tools, and a reorientation of EU fiscal solidarity mechanisms. By quantifying, measuring, and spatially disaggregating climate-related fiscal exposure, this study contributes directly to the sustainability agenda: it demonstrates that climate losses are no longer exogenous disruptions but endogenous features of the European economic landscape that must be integrated into sustainable development planning, fiscal governance, and EU-level adaptation policy. Full article

The Logistics Performance Index (LPI) is a widely used benchmarking tool for assessing national logistics capabilities. However, its role in sustainability-oriented research remains unclear. This study reconceptualizes the LPI as a multidimensional analytical framework for examining the structural associations between logistics performance and sustainability outcomes. Using cross-country data from 2023, the analysis evaluates the alignment of the six disaggregated LPI dimensions with economic (GDP per capita), social (Human Development Index), and environmental (CO₂ emissions) indicators across approximately 120 countries. The analysis applies an integrated framework combining linear models, ensemble learning techniques, explainable artificial intelligence (SHAP), and clustering analysis to assess the consistency and interpretability of these relationships. The results indicate that logistics performance is more strongly aligned with economic and social outcomes than with environmental indicators. Infrastructure quality, tracking and tracing, and timeliness emerge as key logistics dimensions associated with higher income levels and human development. In contrast, the moderate alignment observed for CO₂-related outcomes highlights the influence of broader structural factors, such as energy systems and industrial composition, beyond logistics performance. Clustering analysis further reveals distinct logistics–environmental configurations, underscoring substantial heterogeneity in sustainability trajectories among countries with similar logistics capabilities. Overall, these findings establish the LPI as a system-level lens for diagnosing logistics–sustainability relationships and for designing context-sensitive policies aligned with the Sustainable Development Goals (SDGs), particularly SDGs 8, 9, 11, and 13. Full article

Non-Intrusive Load Monitoring (NILM) systems are increasingly applied in residential and commercial environments to disaggregate energy consumption without requiring additional hardware sensors. The integration of Machine Learning (ML) techniques has enhanced the accuracy and efficiency of load identification and classification in smart meter-based systems. This study presents a systematic review and meta-analysis aimed at identifying, classifying, and quantitatively evaluating ML models applied to NILM. Searches were conducted in the IEEE Xplore and Scopus databases, restricted to peer-reviewed publications from 2017 to 2024. Thirty studies met the eligibility criteria and were included in the quantitative synthesis using a random-effects meta-analysis model (DerSimonian–Laird estimator). The primary effect measure was the F1-score. Statistical analyses were performed using R (version 4.5.0) and Python (version 3.10.0), including heterogeneity assessment and subgroup analyses according to model type. Hybrid models, such as SVDT-KNN-MLP, LE-CRNN, and RBFNN-MOGA, achieved the highest pooled F1-scores, although supported by a limited number of studies. Traditional approaches, including CNN, KNN, and Random Forest, demonstrated consistently strong performance and broader validation, whereas Boosted Trees and RNN-based models showed lower or more variable results. Substantial heterogeneity was observed across studies, highlighting the need for dataset standardization, reproducible evaluation frameworks, and further validation of emerging hybrid architectures in diverse operational scenarios. This study contributes by providing a quantitative synthesis of machine learning models applied to NILM using a structured PRISMA-based methodology and subgroup analysis by model architecture. Unlike previous narrative reviews, this work integrates scientometric analysis with meta-analytic performance aggregation, offering a consolidated and comparative evidence base for future NILM research. Full article