Search Results (2,642)

This research paper examines the spatial and time variation of demographic dependency in Europe in a 30-year horizon of the evolution of the demographic dividend regarding the economic dependency ratio (ADR1). We used the Curve Fit Forecast tool to estimate the trends of ADR1 in each of the EU Member States using data on Eurostat projections and a sophisticated geostatistical analysis tool developed in ArcGIS Pro 3.2.2. The findings indicate that the dependency in all countries has increased significantly in a statistically significant manner as the Gompertz function has appeared as the best curve in a third of the cases. It is an S-shaped asymptotic behaviour of this function that effectively describes the nonlinear patterns of acceleration and saturation of demographic ageing. As indicated in the analysis, the European regions are increasingly moving apart, with the southern and eastern nations such as Romania demonstrating the most alarming decline in ADR1. These trends highlight the need to reform labour market policies and social protection mechanisms to an ageing population. The paper combines the curve-fitting, descriptive statistics (median, skewness, interquartile range (IQR)) with time clustering (value, correlation, and Fourier) to provide an effective, replicable approach to early warning and policy prioritisation. Overall, the results highlight the importance of integrating predictive spatial modelling and demographic economics to support anticipatory and evidence-based policy decisions. The proposed approach proves to be a robust and transferable framework, applicable to a wide range of socio-economic phenomena characterised by inertia and structural change. Future research should extend the analysis to subnational levels, incorporate additional explanatory variables, and develop scenario-based simulations, including multivariate Gompertz-type models, to further enhance both predictive accuracy and policy relevance in the context of emerging structural labour scarcity. Full article

We estimate Oregon’s mass timber-related market value and economic contribution using two complementary valuation strategies and two IMPLAN implementations. Although mass timber includes CLT, glulam, nail-laminated timber, dowel-laminated timber, mass plywood panels, and structural composite lumber products, the empirical market-value estimates are centered primarily on CLT- and MPP-related evidence because these products have the most consistently available Oregon-specific data. Market value is inferred from production-based approaches, including facility capacity, Oregon’s share of U.S. output, and tracer-product scaling, and from demand-based approaches, including harvest routing, construction floor area, and U.S. demand allocation. These direct values are then entered into industry contribution analysis (ICA) for Oregon’s Engineered Wood Member and Truss Manufacturing sector and into analysis-by-parts (ABP) using a custom mass timber spending pattern. During 2018–2023, production-based estimates were larger and more variable than demand-based estimates, bracketing a plausible scenario range rather than providing a single point estimate. In 2022 price scenarios, all price-exposed cases scale proportionally with assumed panel prices. When identical direct values are used, ABP produces larger total employment and output effects than ICA because it routes more activity through upstream supplier industries. Output-per-worker sensitivity affects only direct employment in ABP. Forward scenarios for 2030 and 2035 indicate substantially larger total effects under ABP than ICA, but these estimates are conditional scenarios rather than forecasts. The framework provides a transparent basis for policy, investment, supplier-development, and workforce-planning discussions in an emerging industry with incomplete product-level data. Full article

Freezing rain is a high-impact winter weather phenomenon that acts as a major disturbance agent in forest ecosystems, causing canopy damage, stem breakage, tree mortality, and long-term changes in forest structure and functioning. Although ice storms have been studied for decades, research on freezing rain impacts on forests remains fragmented across multiple disciplines, and few studies have attempted an integrated synthesis that simultaneously combines climatological, ecological, and methodological perspectives. In this study, we present a systematic and integrative review of the scientific literature on freezing rain and forests, combining a large-scale bibliometric analysis with an in-depth qualitative synthesis. A total of 241 publications retrieved from the Scopus and Web of Science databases were analyzed following PRISMA guidelines. The bibliometric assessment examined publication trends, geographic distribution, institutional contributions, research domains, and keyword networks. The qualitative review synthesized current knowledge on freezing rain climatology, forest damage mechanisms, species-specific vulnerability, major ice storm events, detection and modeling approaches, and ecological consequences. Results reveal a strong increase in scientific output over the last two decades, dominated by research from North America and northern Europe. Ice accretion intensity emerges as the primary driver of forest damage, while species traits, crown architecture, tree size, stand structure, topography, and exposure strongly modulate damage severity. Freezing rain affects a wide range of forest types worldwide and triggers both immediate structural damage and long-term ecological effects, including altered successional dynamics and reduced forest productivity. Recent methodological advances—including passive remote sensing (e.g., optical satellite data), active remote sensing (e.g., LiDAR), experimental ice storm simulations, reanalysis datasets, and machine learning approaches—have significantly improved detection, monitoring, and forecasting capabilities. Despite these advances, major research gaps remain, particularly regarding long-term ecosystem recovery, trait-based vulnerability, socio-economic impacts, and future freezing rain regimes under climate change. This review highlights freezing rain as an increasingly important but underappreciated forest disturbance and underscores the need for interdisciplinary research and adaptive management strategies in ice-prone regions. Full article

Scientific assessment of energy conservation, emissions reduction, public health externalities, and economic costs is crucial for the sustainable development of new energy vehicles (NEVs). Despite minimal emissions during the operational phase of NEVs, the production process of energy, such as electricity and hydrogen, contributes to pollution across the full supply chain, shifting environmental and health burdens to upstream sectors and raising concerns about the overall societal benefits. To address this, we apply a full-chain life cycle assessment (FC-LCA) framework that integrates emissions from vehicle production, energy supply, and end-of-life stages, while simultaneously quantifying health-related mortality attributable to key pollutants. By incorporating upstream energy production structure and downstream industry emissions, this approach captures the complete energy supply chain and enables a systematic comparison between NEVs and conventional vehicles. We further employed and compared ARIMA, LSTM, and Bi-LSTM models to forecast future vehicle demand and defined different forecasting scenarios for China’s passenger vehicle sector. Results provide policy-relevant insights for decision-makers to make informed policy choices concerning the widespread implementation of NEVs in a sustainable manner. Full article

As artificial intelligence (AI) increasingly reshapes the global technological and economic landscape, understanding and forecasting national AI competitiveness has become an important yet challenging task. Unlike conventional Analytic Hierarchy Process (AHP)–Entropy-based evaluation methods and machine learning approaches that treat indicators as isolated or weakly connected features, this study proposes an integrated framework that explicitly represents inter-indicator dependencies as a structured global topology. Based on an Input–Process–Output–Environment (IPOE) system with 24 indicators for 10 major economies during 2016–2025, AHP–Entropy, XGBoost, Design of Experiments (DOE), and Bayesian networks are combined to identify dependency pathways among indicators. These structural relations are embedded into a graph neural network (GNN) for competitiveness assessment, while a Dynamic GNN-ARIMA module is developed to project future competitiveness trajectories under limited samples. Building on these projections, a multi-objective fund allocation optimization model is constructed and solved via the NSGA-II algorithm to reduce policy volatility while maintaining future AI competitiveness with a strategic investment of RMB 500 billion. Results show that the U.S. remains the clear leader, followed by China, while mid-tier economies show noticeable reshuffling. Under the Min-Variance strategy with the investment, China is projected to significantly narrow the gap with the United States, reaching a comparable level of competitiveness. Full article

Artificial intelligence development offers new solutions for enhancing corporate carbon performance and is crucial for promoting sustainable business practices. This study investigates the dynamic impact of artificial intelligence (AI) policy on corporate carbon performance using time series panel data of Chinese A-share listed companies from 2010 to 2024. Leveraging the staggered establishment of the National New Generation Artificial Intelligence Innovation Development Pilot Zones as a quasi-natural experiment, we develop a multi-period difference-in-differences framework with time-varying treatment. Our time series-based identification strategy addresses serial correlation and time-varying confounding factors through robust clustering and event study specifications. The findings reveal that AI policy significantly improves corporate carbon performance, a conclusion that remains robust after rigorous endogeneity tests, placebo checks, and counterfactual analyses. Using dynamic panel models, this study traces the temporal evolution of policy effects and demonstrates that AI exerts indirect effects through three time-lagged pathways: micro-level technological diffusion, future industry development, and the progressive accumulation of digital infrastructure and computing resources. Heterogeneity analysis reveals differentiated impacts across micro- and macro-levels, providing granular insights for forecasting heterogeneous treatment effects. By integrating panel time series econometrics with causal inference, this study contributes to the literature on corporate carbon performance while expanding analytical frameworks for understanding AI’s enabling effects. The findings offer policy insights and empirical benchmarks for forecasting green transition trajectories, with direct implications for green finance and sustainable economic development. Full article

Despite the many advantages of renewable energy sources, the stochastic nature of their generation creates a mismatch between electricity production and demand timing. Without appropriate storage solutions, surplus energy remains unused. Although battery energy storage systems are increasingly applied to improve the flexibility and reliability of power systems, there is still a research gap in forecasting the optimal power and storage capacity of solar power plant–battery energy storage system energy complexes operating in parallel with the grid under short-term forecasting conditions, particularly when economic aspects such as partial leasing of storage capacity are considered. Therefore, the development of energy complexes based on solar power plants with the integration of battery energy storage systems, as well as the development of corresponding computational models, becomes critical for ensuring the stability, flexibility, reliability, and efficiency of power systems. Battery energy storage systems are widely used due to their availability, high response speed, significant energy density, and sufficient power capacity; however, their cost remains relatively high. This paper proposes a methodology and a calculation model for determining the optimal forecasted capacity and the rational storage requirements of an energy complex consisting of a solar power plant and a battery energy storage system operating in parallel with the grid at constant power under short-term forecasting conditions (day-ahead or longer). The proposed approach makes it possible to minimise the costs of energy companies associated with the short-term lease of part of a battery energy storage system when they do not own one, or, if such a system is available, to lease out its unused capacity and obtain corresponding profits. The validation of the computational model uses a dataset of hourly daily power outputs of solar power plants in the Integrated Power System of Ukraine for 2018. Statistical analysis of the obtained results shows that the probability of occurrence of maximum deviations for the optimal capacity of the energy complex (5.4%), as well as for the power and capacity of the battery energy storage system (13% and 18%, respectively), does not exceed 0.05 during the year. The results confirm that the proposed methodology provides a reliable basis for determining optimal parameters of solar power plant–battery energy storage system energy complexes and enables economically efficient use of storage capacity through short-term leasing mechanisms. Although the proposed methodology is applied using solar power plant generation data for the national power system as a whole, it can also be used for individual solar power plants located in different regions and countries with different climatic conditions. Certainly, the calculated coefficients differ, but the methodology itself and the sequence of its application remain the same. Full article

Background: Varicella burden is closely linked to national socioeconomic development, yet systematic analyses of its non-linear relationship with the Socio-demographic Index (SDI) are lacking. This study aims to elucidate this relationship and inform equitable, context-specific strategies. Methods: Based on data from the Global Burden of Diseases 2021 study, we analyzed global trends (1990–2021) in the incidence, prevalence, mortality, and disability-adjusted life-years (DALYs) of varicella. Joinpoint regression was used to identify trend transition points, and an autoregressive integrated moving average (ARIMA) model was applied to forecast the disease burden through 2035. Analyses were conducted, and countries and territories were stratified into five SDI groups: high (SDI > 0.81), high–middle (0.70–0.81), middle (0.61–0.69), low–middle (0.46–0.60), and low (SDI < 0.46). These approaches aimed to systematically elucidate the socioeconomic gradient of the varicella burden and to specifically investigate its potential non-linear relationship with SDI. Results: From 1990 to 2021, global age-standardized mortality and DALYs declined by −45.71% (95% UI: −48.32% to −42.95%) and −36.15% (95% UI: −39.04% to −33.01%), respectively, while incidence and prevalence rates slightly increased. A significant U-shaped relationship emerged between burden and SDI, with rates highest in low- and high-SDI regions. The rise in high-SDI regions was driven by increasing incidence and prevalence from 1996 to 2015. Projections to 2035 indicate continued global decline but persistent disparities. Conclusions: The varicella burden follows a U-shaped socioeconomic gradient. Rising incidence in high-SDI regions highlights that economic development and routine pediatric vaccination alone are insufficient. Precision strategies tailored to SDI levels—closing adult immunity gaps in high-SDI, sustaining gains in middle-SDI, and expanding vaccine access in low-SDI regions—are essential. Full article

Ginseng Alternaria leaf and stem blight, caused by Alternaria panax, imposes substantial yield and economic losses to the ginseng cultivation industry. Current diagnostic methods for ginseng diseases primarily rely on pathogen isolation from infected tissues, a procedure that is laborious, time-consuming, and inherently low in sensitivity. This study has therefore developed a rapid, specific and sensitive SYBR Green-based quantitative real-time PCR (qPCR) assay for detecting A. panax in plants, seeds, and soil. The developed qPCR assay exhibited high sensitivity and repeatability, with a detection limit of 0.074 fg/μL of target amplicon DNA (0.619 ng/μL of genomic DNA) and a coefficient of variation below 2%. In artificially inoculated tissues (leaves, stems and seeds), Ct values decreased progressively with increasing incubation time, reflecting pathogen proliferation. Analysis of field-collected leaves and stems showed a strong overall correlation between Ct values and visual disease grades. Surveying of ginseng-growing areas revealed that A. panax was detected in asymptomatic leaves and stems at rates of 12.12% and 14.29%, respectively, and in 14.46% of soil samples and 23.73% of seed samples. This qPCR assay presented here provides a robust tool for forecasting early disease, tracking the primary inoculum of the pathogen and its transmission chains, and screening of both ginseng seed lots and candidate soils for ginseng Alternaria leaf and stem blight prior to planting. Full article

Accurate wind speed forecasting is essential for the efficient integration of wind energy into power systems, as it directly influences generation scheduling, grid stability, and energy market operations. Forecast errors can lead to significant economic losses, including increased balancing costs, inefficient dispatch of backup generation, and penalties in electricity markets. However, wind behaviour is highly complex due to the influence of synoptic weather systems, terrain variability, and turbulence, which makes accurate prediction particularly challenging. This paper proposes a hybrid modelling framework that combines a linear regression mean model with Gaussian process (GP) residual modelling to improve forecast accuracy. Monitoring stations were grouped based on geographic coordinates and elevation, with cluster validation using the Hopkins statistic and silhouette analysis. The results show that for high-elevation inland stations (cluster 2), GP residual modelling improves forecast accuracy by up to 16.3%. In contrast, for low-elevation coastal stations (cluster 1), the GP approach does not yield improvements, indicating that its effectiveness depends strongly on the underlying wind regime. Full article

High-frequency volatility and extreme tail risks in offshore wind power pose severe challenges to grid stability and economic operation. Conventional storage planning often relies on deterministic profiles or static allocation rules, failing to capture the non-stationary temporal dynamics of marine wind resources. To bridge this gap, this paper proposes a closed-loop framework that integrates ultra-short-term probabilistic forecasting with dynamic hybrid energy storage optimization. A novel Dual-Channel Residual Network is developed to provide well-calibrated predictive uncertainty quantification, which explicitly drives a Prediction-Guided Dynamic Hybrid Storage Optimization Framework. By dynamically coordinating lithium-ion batteries and liquid air energy storage based on evidential predictive variance, the proposed approach achieves superior synergy between short-term power response and long-duration energy shifting. Case studies on an offshore wind farm validate that the framework significantly reduces the Levelized Cost of Energy and loss-of-load risks while enhancing frequency regulation capabilities compared to state-of-the-art benchmarks. Full article

Drought in Angola is a recurrent and cyclical natural phenomenon that poses significant environmental, economic, and social challenges, affecting water resources, agriculture, ecosystems, livestock, and vulnerable communities. This study investigates the temporal evolution and spatial behavior of drought in the provinces of Cunene, Huila, and Namibe over the period 1980–2024. Drought conditions were assessed using the Standardized Precipitation Index (SPI) and the Standard Precipitation–Evapotranspiration Index (SPEI) at multiple time scales. Trends were evaluated using the Modified Mann–Kendall test and Sen’s slope estimator, while drought intensity was analyzed using run theory. The results reveal a clear intensification of drought conditions in the last decade, characterized by an increase in frequency and intensity, particularly after 2010. Extreme drought events were identified in the early 1980s, the mid-1990s, and more recently in 2019 and 2021. Despite some regional variability, the three provinces exhibit consistent temporal patterns, with drought events generally occurring simultaneously over the study period. These findings highlight the increasing pressure on water and environmental systems and underscore the need for improved drought monitoring and forecasting approaches to support more effective adaptation and decision-making. Full article

Under the dual pressures of global carbon emission reduction and production cost control, energy-intensive industrial enterprises are in urgent need of a balanced low-carbon operation strategy that reconciles economic benefits, environmental performance and production continuity. To address the limitations of existing methods in multi-dimensional objective balancing, this paper proposes a dynamic multi-objective optimization framework for industrial electricity consumption, integrating high-precision load forecasting and optimal scheduling. For load forecasting, an improved dual-gate optimization temporal attention long short-term memory (DGO-TA-LSTM) model is developed, which is modeled based on the one-year hourly electricity operation data (8760 samples) of a high-energy industrial enterprise in southern China, and its performance is verified via three standard metrics—the mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE)—compared with five mainstream baseline models. On this basis, when taking time-varying electricity-carbon factors and time-of-use electricity prices as dual guiding signals, a three-objective optimization model minimizing electricity cost, carbon emissions and load deviation is constructed, which is solved by the Non-Dominated Sorting Genetic Algorithm II (NSGA-II), with the Improved Gray Target Decision-Making (IGTD) method introduced to select the optimal compromise solution. Case study results show that the proposed scheme achieved a $1.9$% reduction in electricity cost and a 30% reduction in carbon emissions compared with the unoptimized strategy, providing a feasible and scalable low-carbon operation path for industrial enterprises. Full article

Croatia hosts one of the most intensive recreational boating activities in the Mediterranean, with over 134,600 registered vessels along 5835 km of Adriatic coastline. This paper presents an AI-driven simulation framework for evaluating electrification pathways for the Croatian recreational vessel fleet. A key contribution is the explicit treatment of the AIS data gap: recreational vessels in Croatia are not required to carry AIS transponders, so synthetic operational profiles calibrated from manufacturer specifications and verified economic data are used instead. Six machine learning architectures are compared for vessel energy demand forecasting, with a proposed Transformer-based model achieving the best simulated performance. Fleet-weighted Monte Carlo simulation across three electrification scenarios suggests that an AI-optimised hybrid configuration can, subject to use intensity, reduce per-vessel CO₂ emissions by up to 56.8% relative to conventional engines. Techno-economic analysis shows payback periods ranging from over 15 years for low-use private owners to 7–9 years for charter operators, supporting targeted incentive design. The framework is intended to be transferable to other Mediterranean coastal regions facing comparable data and operational constraints. Full article

Arid agricultural systems face increasing sustainability challenges due to water scarcity, climate variability, and structural resource constraints. Although Artificial Intelligence (AI) is widely promoted as a key enabler of sustainable agriculture, empirical evidence on its long-term effects on agriculture-related Sustainable Development Goals (SDGs), particularly in arid regions, remains limited. This study investigates the role of AI in supporting sustainable agricultural development in Saudi Arabia’s Ha’il region. Using annual data from 1995 to 2025, AI adoption—proxied by SDG9 indicators that reflect AI-enabling digital infrastructure and innovation readiness rather than observed on-farm AI deployment—is examined in relation to a composite Sustainable Agricultural Development Goals index (SADGH), which integrates SDG2 (food security), SDG6 (water management), SDG8 (economic performance), SDG12 (responsible production), SDG13 (climate action), and SDG15 (land sustainability). Econometric analysis based on a Vector Error Correction Model (VECM) reveals a stable long-run relationship between AI adoption and agricultural sustainability, with approximately 32% of short-term disequilibrium corrected annually. In the short run, AI adoption is positively associated with food security, economic performance, and land sustainability, while water- and climate-related indicators adjust more gradually. Dynamic analyses suggest that AI-related shocks may generate cumulative effects over time. In addition, deep learning models using Long Short–Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures are applied within an exploratory framework to capture potential nonlinear dynamics and generate indicative forecasts. The GRU model shows lower prediction errors; however, results should be interpreted with caution, given the limited sample size. Overall, the findings suggest that AI may contribute to sustainable agricultural development in arid regions, while highlighting the need for further research based on larger datasets. Full article