Search Results (708)

Big L days are days when the lunar semidiurnal variation M₂ in the ionosphere is strongly enhanced by a factor of 2 or more. The worldwide network of ground-based receivers for the Global Navigation Satellite System (GNSS) has monitored the ionospheric total electron content (TEC) since 1998. The derived world maps of TEC are provided by the International GNSS Service (IGS) and allow the study of the characteristics of big L days in TEC. In the data analysis, the signal of the lunar semidiurnal variation M₂ in TEC is separated from the solar semidiurnal variation S₂ by means of windowing in the spectral domain. The time series of the M₂ amplitude often shows enhancements of M₂ (big L days) a few days after sudden stratospheric warmings (SSWs). The M₂ amplitude can reach values of 8 TECU. The M₂ composite of all SSWs from 1998 to 2024 shows that the M₂ amplitude is enhanced after the central date of the SSW. Regions in Southern China and South America show stronger effects of big L days. Generally, the effects of big L days on TEC show latitudinal and longitudinal dependencies. Full article

The transition to renewable energy is essential for mitigating climate change and promoting sustainable development, particularly in Latin America and the Caribbean (LAC). Despite its vast potential, the region faces structural and economic challenges that hinder a sustainable energy transition. Understanding scientific production in this field is key to shaping policy, investment, and technological progress. The primary objective of this study is to conduct a large-scale, data-driven analysis of renewable energy research in LAC, mapping its thematic evolution, collaboration networks, and key research trends over the past three decades. To achieve this, machine learning-based topic modeling and network analysis were applied to examine research trends in renewable energy in LAC. A dataset of 18,780 publications (1994–2024) from Scopus and Web of Science was analyzed using Latent Dirichlet Allocation (LDA) to uncover thematic structures. Network analysis assessed collaboration patterns and regional integration in research. Findings indicate a growing focus on solar, wind, and bioenergy advancements, alongside increasing attention to climate change policies, energy storage, and microgrid optimization. Artificial intelligence (AI) applications in energy management are emerging, mirroring global trends. However, research disparities persist, with Brazil, Mexico, and Chile leading output while smaller nations remain underrepresented. International collaborations, especially with North America and Europe, play a crucial role in research development. Renewable energy research supports Sustainable Development Goals (SDGs) 7 (Affordable and Clean Energy) and 13 (Climate Action). Despite progress, challenges remain in translating research into policy and addressing governance, financing, and socio-environmental factors. AI-driven analytics offer opportunities for improved energy planning. Strengthening regional collaboration, increasing research investment, and integrating AI into policy frameworks will be crucial for advancing the energy transition in LAC. This study provides evidence-based insights for policymakers, researchers, and industry leaders. Full article

Australia’s renewable energy transition will be dominated by solar and wind power, yet their contrasting variability necessitates hybrid integration with storage to ensure reliability. This study uses Australian reanalysis data, BARRA (Bureau of Meteorology Atmospheric High-Resolution Regional Reanalysis for Australia), to quantify solar (global horizontal irradiance, GHI) and wind (wind power density, WPD) resources by examining their availability, variability, synergy, episode length, and lulls. The novelty of this work is the use of rarely examined metrics such as variability, availability, episode length, and extended lull events (Dunkelflaute) with a high-resolution and 29-year duration reanalysis dataset. The results show that solar is the more reliable resource, with high daytime availability and relatively short lulls. Wind, despite being abundant in coastal regions, is highly intermittent, characterized by a skewed distribution, low availability, and extended periods of lulls. Synergy metrics demonstrate significant complementarity, with combined solar–wind synergy reducing deficits in single resources, while joint non-synergy events define critical system vulnerabilities. Importantly, hybrid systems limit maximum joint lulls, which are far shorter than wind-only extremes, thereby reducing the scale of long-duration storage required. These findings underscore that, while solar provides a stable baseline supply and wind contributes spatial diversity, hybrid systems supported by batteries offer a resilient pathway. Synergy and non-synergy statistics provide essential parameters for optimally sizing storage to withstand rare but severe shortfalls, ensuring a reliable, utility-scale renewable future for Australia. Full article

The land demands of ground-mounted PV systems raise concerns about competition with agriculture, particularly in regions with limited productive farmland. Agrivoltaics, which integrates solar energy generation with agricultural use, offers a potential solution. While agrivoltaics has been extensively studied, less is known about its feasibility and impacts in complex temperate maritime climates such as Aotearoa New Zealand, in particular, the effects of PV-induced shading on ground-level light availability and vegetation. This study modelled the spatial and seasonal distribution of ground-level irradiation and Photosynthetic Photon Flux Density (PPFD) beneath fixed-tilt PV arrays at the Tauhei solar farm in the Waikato region. It quantifies and maps PPFD to evaluate light conditions and its implications for vegetation growth. The results reveal significant spatial and temporal variation over a year. The under-panel ground irradiance is lower than open-field GHI by 18% (summer), 22% (spring), 16% (autumn), and 3% (winter), and this seasonal reduction translates into PPFD gradients. This variation supports a precision agrivoltaic strategy that zones land based on irradiance levels. By aligning crop types and planting schedules with seasonal light profiles, land productivity and ecological value can be improved. These findings are highly applicable in Aotearoa New Zealand’s pasture-based systems and show that effective light management is critical for agrivoltaic success in temperate maritime climates. This is, to our knowledge, the first spatial PPFD zoning analysis for fixed-tilt agrivoltaics, linking year-round ground-light maps to crop/pasture suitability. Full article

Promoting economic prosperity, social justice, and ecological sustainability requires the rapid decarbonization of our global energy system in favor of renewable sources of energy. Recent news analysis estimates that 15% of counties across the US have banned wind turbines, solar fields, and other green energy developments. We answer two overarching research questions: (1) How do regulations of wind facilities vary at the county level? And (2) what factors appear to explain the variation in local wind regulations? We created a GIS database of energy regulations for all 105 counties in Kansas, a top state for wind potential and a recent hotbed of local actions. We coupled descriptive statistics, mapping, and regression modeling to describe the variation in local policy approaches and identify factors driving the variation. We find counties using at least five different policy approaches to enable or block wind regulations. Factors driving variation include a combination of infrastructure capacity, demographic characteristics that shape local planning capacity, and the apparent reliance on large farming operations for local economic output but not partisan voting patterns or underlying wind capacity. Our findings provide vital insights for policymakers at the federal, state, and local levels, as well as providing a foundation for future scholarship on planning for a just energy future. Full article

Urban areas are increasingly challenged by the combined effects of climate change, rapid population growth, and high energy demand. The integration of renewable energy systems, such as photovoltaic (PV) panels, and nature-based solutions, such as green roofs, represents a key strategy for sustainable urban development. This study evaluates the spatial potential for PV and green roof implementation in Iași, Romania, using moderate to high-resolution geospatial datasets, including the ALOS AW3D30 Digital Surface Model (DSM) and the Copernicus Urban Atlas 2018, processed in ArcMap 10.8.1 and ArcGIS Pro 2.6.0. Solar radiation was computed using the Area Solar Radiation tool for the average year 2023, while roof typology (flat vs. pitched) was derived from slope analysis. Results show significant spatial heterogeneity. The Copou neighborhood has the highest PV-suitable roof share (73.6%) and also leads in green roof potential (46.6%). Integrating PV and green roofs can provide synergistic benefits, improving energy performance, mitigating urban heat islands, managing stormwater, and enhancing biodiversity. These findings provide actionable insights for urban planners and policymakers aiming to prioritize green infrastructure investments and accelerate the local energy transition. Full article

Ecosystems are nonlinear systems that can shift between multiple stable states. Ecosystem service bundles (ESBs) integrate the supply and trade-offs of multiple services, yet the conditions for achieving high-supply and balanced states remain unclear from a nonlinear, threshold-based perspective. In this study, six representative ecosystem services in Fujian Province were quantified, and ESBs were identified using a Self-Organizing Map (SOM). By integrating the Multiclass Explainable Boosting Machine (MC-EBM) with the API interpretable algorithm, we propose a framework for exploring ESB driving mechanisms from a nonlinear, threshold-based perspective, addressing two key questions: (1) Which factors dominate ESB formation? (2) What thresholds of these factors promote high-supply, balanced ESBs? Results show that (i) the proportion of water bodies, distance to construction land, annual solar radiation, annual precipitation, population density, and GDP density are the primary driving factors; (ii) higher proportions of water bodies enhance and balance multiple services, whereas intensified human activities significantly reduce supply levels, and ESBs are highly sensitive to climatic variables; (iii) at the 1 km × 1 km grid scale, optimal threshold ranges of the dominant factors substantially increase the likelihood of forming high-supply, balanced ESBs. The MC-EBM effectively reveals ESB formation mechanisms, significantly outperforming multinomial logistic regression in predictive accuracy and demonstrating strong generalizability. The proposed approach provides methodological guidance for multi-service coordination across regions and scales. Corresponding land management strategies are also proposed, which deepen understanding of ESB formation and offer practical references for enhancing ecosystem service supply and reducing trade-offs. Full article

Conventional grain monitoring systems often rely on isolated data points (e.g., point-based temperature measurements), limiting holistic condition assessment. This study proposes a novel Mechanism and Data Driven (MDD) framework that integrates physical mechanisms with real-time sensor data. The framework quantitatively analyzes solar radiation and external air temperature effects on silo boundaries and introduces a novel interpolation-optimized model parameter initialization technique to enable comprehensive grain condition perception. Rigorous multidimensional validation confirms the method’s accuracy: The novel initialization technique achieved high precision, demonstrating only 1.89% error in Day-2 low-temperature zone predictions (27.02 m² measured vs. 26.52 m² simulated). Temperature fields were accurately reconstructed (≤0.5 °C deviation in YOZ planes), capturing spatiotemporal dynamics with ≤0.45 m² maximum low-temperature zone deviation. Cloud map comparisons showed superior simulation fidelity (SSIM > 0.97). Further analysis revealed a 22.97% reduction in total low-temperature zone area (XOZ plane), with Zone 1 (near south exterior wall) declining 27.64%, Zone 2 (center) 25.30%, and Zone 3 20.35%. For dynamic evolution patterns, high-temperature zones exhibit low moisture (<14%), while low-temperature zones retain elevated moisture (>14%). A strong positive correlation between temperature and relative humidity fields; temperature homogenization drives humidity uniformity. The framework enables holistic monitoring, providing actionable insights for smart ventilation control, condensation risk warnings, and mold prevention. It establishes a robust foundation for intelligent grain storage management, ultimately reducing post-harvest losses. Full article

The availability of properly analyzed energy resource potential data is a prerequisite in energy planning and development. However, this was sparsely applied in Ethiopia’s renewable energy turnkey project development strategies. This study focuses on developing a solar energy resource map of Tigray to accelerate the expansion of solar energy to improve electricity access through on-grid and off-grid development schemes. This study uses monthly sunshine hour data from sixteen meteorological stations, measured at a 2 m height, and average yearly solar radiation data from twenty-two satellite stations, validated by solar radiation data and measured at three sites at 10 and 30 m heights. The solar energy potential was analyzed by taking relevant atmospheric and meteorological factors to produce solar radiation components. Accordingly, the average annual solar radiation of Tigray was found to be 6.1 kWh/m²/day and 5.3 kWh/m²/day based on meteorological and satellite data, respectively. The meteorological result gave a closer estimate to Ethiopia’s ESMAP Global Solar result of 5.83 kWh/m²/day. Finally, monthly and annual average solar radiation maps of the region were developed using ArcGIS10.5. The study’s results could contribute to assisting various solar energy developers in preparing better solar energy development plans to alleviate the chronic energy poverty of the region. Full article

The Denoising Wavelet Transform Convolutional Long Short-Term Memory Network (DWTConvLSTM) is a novel ionospheric total electron content (TEC) spatiotemporal prediction model proposed in 2025 that can simultaneously consider high-frequency and low-frequency features while suppressing noise. However, it also has flaws as it only considers unidirectional temporal features in spatiotemporal prediction. To address this issue, this paper adopts a bidirectional structure and designs a bidirectional DWTConvLSTM model that can simultaneously extract bidirectional spatiotemporal features from TEC maps. Furthermore, we integrate a lightweight attention mechanism called Convolutional Additive Self-Attention (CASA) to enhance important features and attenuate unimportant ones. The final model was named CASA-BiDWTConvLSTM. We validated the effectiveness of each improvement through ablation experiments. Then, a comprehensive comparison was performed on the 11-year Global Ionospheric Maps (GIMs) dataset, involving the proposed CASA-BiDWTConvLSTM model and several other state-of-the-art models such as C1PG, ConvGRU, ConvLSTM, and PredRNN. In this experiment, the dataset was partitioned into 7 years for training, 2 years for validation, and the final 2 years for testing. The experimental results indicate that the RMSE of CASA-BiDWTConvLSTM is lower than those of C1PG, ConvGRU, ConvLSTM, and PredRNN. Specifically, the decreases in RMSE during high solar activity years are 24.84%, 16.57%, 13.50%, and 10.29%, respectively, while the decreases during low solar activity years are 26.11%, 16.83%, 11.68%, and 7.04%, respectively. In addition, this article also verified the effectiveness of CASA-BiDWTConvLSTM from spatial and temporal perspectives, as well as on four geomagnetic storms. Full article

This study evaluates two approaches for detecting solar photovoltaic (PV) installations across agricultural areas, emphasizing their role in supporting sustainable energy monitoring, land management, and planning. Accurate PV mapping is essential for tracking renewable energy deployment, guiding infrastructure development, assessing land-use impacts, and informing policy decisions aimed at reducing carbon emissions and fostering climate resilience. The first approach applies deep learning-based semantic segmentation to high-resolution RGB orthophotos, using the pretrained “Solar PV Segmentation” model, which achieves an F1-score of 95.27% and an IoU of 91.04%, providing highly reliable PV identification. The second approach employs multitemporal pixel-wise spectral classification using Sentinel-2 imagery, where the best-performing neural network achieved a precision of 99.22%, a recall of 96.69%, and an overall accuracy of 98.22%. Both approaches coincided in detecting 86.67% of the identified parcels, with an average surface difference of less than 6.5 hectares per parcel. The Sentinel-2 method leverages its multispectral bands and frequent revisit rate, enabling timely detection of new or evolving installations. The proposed methodology supports the sustainable management of land resources by enabling automated, scalable, and cost-effective monitoring of solar infrastructures using open-access satellite data. This contributes directly to the goals of climate action and sustainable land-use planning and provides a replicable framework for assessing human-induced changes in land cover at regional and national scales. Full article

A temporal and dynamic approach to the environmental impact of electricity production is necessary to accurately determine its impact. This study aimed to assess the environmental impacts of domestic electricity generation technologies in Slovenia and Croatia using a production-based dynamic life cycle assessment approach for 2020–2024. Hourly resolved actual generation per production type from the ENTSO-E Transparency platform was used and mapped to the Ecoinvent electricity generation datasets. The results showed lower impacts in the climate change category, which correlated with periods of higher renewable contributions. The relative standard deviation values were 21.6% and 18.6% for Slovenia and Croatia, respectively. A higher average impact on resource use, minerals and metals was observed in the Croatian electricity production mix. In Slovenia, significant fluctuations in solar power generation led to a high coefficient of variation of 90.5% in the resource use, minerals and metals impact category, with higher values observed in summer owing to the seasonality of photovoltaic generation. Conversely, Croatia exhibited substantial hourly variability in wind power generation (6.0–629.3 MW), with a relative standard deviation of 18.9%. The results highlight the potential for optimizing the operation of flexible appliances and electric vehicle charging based on real-time emission intensity, contributing to lower environmental impacts through smarter energy use. Full article

With the continuous advancement of on-orbit services and space intelligence sensing technologies, the efficient and accurate identification of spacecraft components has become increasingly critical. However, complex lighting conditions, background interference, and limited onboard computing resources present significant challenges to existing segmentation algorithms. To address these challenges, this paper proposes a lightweight spacecraft component segmentation framework for on-orbit applications, termed M4MLF-YOLO. Based on the YOLOv5 architecture, we propose a refined lightweight design strategy that aims to balance segmentation accuracy and resource consumption in satellite-based scenarios. MobileNetV4 is adopted as the backbone network to minimize computational overhead. Additionally, a Multi-Scale Fourier Adaptive Calibration Module (MFAC) is designed to enhance multi-scale feature modeling and boundary discrimination capabilities in the frequency domain. We also introduce a Linear Deformable Convolution (LDConv) to explicitly control the spatial sampling span and distribution of the convolution kernel, thereby linearly adjusting the receptive field coverage range to improve feature extraction capabilities while effectively reducing computational costs. Furthermore, the efficient C3-Faster module is integrated to enhance channel interaction and feature fusion efficiency. A high-quality spacecraft image dataset, comprising both real and synthetic images, was constructed, covering various backgrounds and component types, including solar panels, antennas, payload instruments, thrusters, and optical payloads. Environment-aware preprocessing and enhancement strategies were applied to improve model robustness. Experimental results demonstrate that M4MLF-YOLO achieves excellent segmentation performance while maintaining low model complexity, with precision reaching 95.1% and recall reaching 88.3%, representing improvements of 1.9% and 3.9% over YOLOv5s, respectively. The mAP@0.5 also reached 93.4%. In terms of lightweight design, the model parameter count and computational complexity were reduced by 36.5% and 24.6%, respectively. These results validate that the proposed method significantly enhances deployment efficiency while preserving segmentation accuracy, showcasing promising potential for satellite-based visual perception applications. Full article

This article aims to report and analyze the main findings of a study on how constraints affect the engagement and creativity of non-designers in co-creation activities. It focuses particularly on identifying the limits and potentials of using a physical interface to address tectonic and renewable energy aspects within the design process. To explore these issues, this study adopted a qualitative case study approach, combining co-design charrettes mediated by a physical interface with a mapping process used as the primary analytical and evaluative framework. The interface allows users to anticipate the structural behavior and construction aspects of small roundwood structures from the Brazilian Caatinga biome, as well as the operation of solar energy systems—all without prior technical training. Despite its limitations, this study offers three main contributions: (a) it demonstrates that interfaces and charrettes can include non-designers in technical design processes; (b) it highlights the pedagogical, technical, and political potential of these tools in democratizing architectural decisions; and (c) it emphasizes the value of constraints as generative elements in creative processes—a topic still underexplored in the co-design literature. Full article

Sustainable intensification requires metrics that are able to capture both economic performance and the often-hidden environmental inputs that support agriculture. Emergy analysis (EmA) meets this need by converting all inputs—free environmental flows and purchased goods/services—into a common unit (solar emjoules, sej). We conducted a PRISMA-documented bibliometric review of EmA in agroecosystems (Web of Science + Scopus, 2000–2022) using Bibliometrix and synthesized farm-scale indicators (ELR, EYR, ESI, %R). Our results show output has grown but is concentrated in a few countries (China, Italy and Brazil) and journals, with farm-level assessments dominating over regional and national assessments. Across cases, mixed crop–livestock systems tend to show lower environmental loading (ELR) and higher sustainability (ESI) than crop-only or livestock-only systems. %R is generally modest, indicating continued reliance on non-renewables, with fertilizers (crops) and purchased feed (livestock) identified as recurrent drivers. Thematic mapping reveals well-developed niche clusters but no single motor theme, consistent with the presence of incongruous baselines, transformities and boundaries that limit comparability. We recommend adoption of the 12.1 × 10²⁴ sej yr⁻¹ baseline, transparent transformity reporting and multi-scale designs that link farm diagnostics to basin and national trajectories. Co-reporting with complementary sustainability assessment methods (such as LCA and carbon footprint), along with appropriate UEV resources, would increase its reputation among policymakers while preserving EmA’s systems perspective, converting dispersed case evidence into cumulative knowledge for circular, resilient agroecosystems. Full article