Search Results (971)

Driving wheel slippage in agricultural tractors is influenced by soil moisture, density, and penetration resistance. These surface variations reflect post-tillage composition, enabling dynamic mapping via Remotely Piloted Aircraft (RPAs). This study evaluated ballast recommendations based on soil surface data and slippage percentages, correlating added wheel weights at different speeds for a tractor-reversible plow system. Six 94.5 m² quadrants were analyzed for slippage monitored by RPA (Mavic3M-RTK) pre- and post-agricultural operation overflights and soil sampling (moisture, density, penetration resistance). A 2 × 2 factorial scheme (F-test) assessed soil-surface attribute correlations and slippage under varying ballasts (52.5–57.5 kg/hp) and speeds. Results showed slippage ranged from 4.06% (52.5 kg/hp, fourth reduced gear) to 11.32% (57.5 kg/hp, same gear), with liquid ballast and gear selection significantly impacting performance in friable clayey soil. Digital Elevation Model (DEM) and spectral indices derived from RPA imagery, including Normalized Difference Red Edge (NDRE), Normalized Difference Water Index (NDWI), Bare Soil Index (BSI), Green–Red Vegetation Index (GRVI), Visible Atmospherically Resistant Index (VARI), and Slope, proved effective. The approach reduced tractor slippage from 11.32% (heavy ballast, 4th gear) to 4.06% (moderate ballast, 4th gear), showing clear improvement in traction performance. The integration of indices and slope metrics supported ballast adjustment strategies, particularly for secondary plowing operations, contributing to improved traction performance and overall operational efficiency. Full article

This study is designed to investigate the dynamic risk transmission processes between clean energy ETFs and ESG indices in the BRICS countries—Brazil, India, China, and South Africa—while excluding Russia due to the lack of consistent data availability during the study period, which coincides with the Russia–Ukraine conflict. The analysis is conducted on daily data obtained from DataStream, spanning from 27 October 2021 to 5 January 2024. By applying a time-varying parameter vector autoregression (TVP-VAR) modeling framework, we considered examining the global market conditions and economic shocks’ effects on these indices’ interconnectedness, including COVID-19 and geopolitical tensions. In this context, clean energy ETFs turned out to stand as net shock transmitters throughout volatile market spans, while ESG indices proved to act as net receivers. Moreover, we undertook to estimate both of the minimum variance and minimum connectedness portfolios’ hedging efficiency and performance. The findings highlight that introducing clean energy indices into investment strategies helps boost financial outcomes while maintaining sustainability goals. Indeed, the minimum connectedness portfolio consistently delivers superior risk-adjusted returns across varying market circumstances. In this respect, the present study provides investors, regulators, and policymakers with practical insights. Investors may optimize their portfolios by integrating clean energy and ESG indexes, useful for achieving financial and sustainability aims. Similarly, regulators might apply the findings to establish reliable green investment norms and strategies. Thus, this work underscores the crucial role of dynamic portfolio management in optimizing risk and return in the globally evolving green economy. Full article

Rapid urbanization has resulted in widespread habitat loss and fragmentation, threatening global biodiversity. Urban parks serve as essential refuges for wildlife within cities, particularly for birds, which are sensitive indicators of ecosystem health and habitat quality. In recent years, numerous Chinese cities have begun integrating biodiversity-friendly design approaches into new park development. However, the effectiveness of these strategies remains insufficiently evaluated. This study assesses the ecological performance of newly built parks by examining 11 recently constructed parks (within the past decade) and 9 historical parks in Zhengzhou, China’s high-density urban area. Monthly bird surveys were conducted across all 20 parks from May to December 2020, covering breeding, post-breeding, and overwintering seasons. Our findings reveal that new parks significantly outperformed old parks in bird abundance, species richness, Shannon diversity index, and functional diversity. Analysis of environmental variables at both local (within-park) and landscape (1-km buffer) scales showed that habitat diversity and multi-layered vegetation structure were the most influential local factors promoting bird diversity, while green space connectivity was the primary landscape-scale contributor. Notably, neither park area nor age significantly predicted diversity patterns. Based on these results, we propose three key planning strategies: (1) enhancing habitat diversity within parks to support species from various ecological niches; (2) implementing multi-layered vegetation planting to provide diverse food resources and nesting opportunities; (3) improving green space connectivity to facilitate species movement and population persistence within urban environments. These findings provide valuable insights for designing more effective biodiversity-friendly urban green spaces. Full article

Efficient water management is critical for sustainable crop production in arid and semi-arid regions. This study investigated the effects of two irrigation regimes—25% and 50% Management Allowable Depletion (MAD) and two planting patterns (single-row and double-row) on evapotranspiration (ET) partitioning, water use efficiency (WUE), and water footprint (WF) in drip-irrigated faba bean (Vicia faba L.). Field data were combined with a leaf area index (LAI)-based model to estimate the relative contributions of transpiration (T) and evaporation (E) to total ET. The highest grain yield (6171 kg ha⁻¹) and the lowest blue (570 m³ ton⁻¹) and green (68 m³ ton⁻¹) water footprints were recorded under the 25% MAD with double-row planting. This treatment also achieved the highest proportion of transpiration in ET (70%), indicating a shift toward productive water use. In contrast, the lowest-performing treatment (50% MAD, single-row) had the highest total water footprint (792 m³ ton⁻¹) and the lowest transpiration share (44%). Although high-density planting slightly reduced WUE based on transpiration, it improved overall water efficiency when total input (ETc) was considered (1.57 kg m⁻³ for total input WUE, 4.17 kg/m⁻³ for T-based WUE). These findings highlight the importance of integrating irrigation scheduling and planting pattern to improve both physiological and agronomic water productivity. The approach offers a practical strategy for sustainable faba bean production in water-scarce environments and supports climate-resilient irrigation planning aligned with Iraq’s National Water Strategy. Full article

This study assesses the biometeorological conditions in the Ancient Olive Grove Campus of the University of West Attica, near central Athens, to evaluate human thermal comfort in an urban green space. Hourly calculations of the Universal Thermal Climate Index (UTCI) and HUMIDEX were performed using BioKlima 2.6, based on data from the campus meteorological station (DAVIS Vantage Pro 2), covering July 2022 to April 2024. Results show diverse thermal comfort-discomfort conditions, with more extremes in warmer months. The study highlights how microclimatic factors influence thermal perception, supporting efforts to design climate-adaptive, user-friendly urban environments with historical and ecological value. Full article

This study examines industrial digitalization, with a particular focus on the transformation from Industry 4.0 to Industry 5.0. The research is based on a database of 1441 Scopus-indexed articles, which forms the basis of a systematic literature review and bibliometric network analysis. The articles were ranked using Global Citation Score (GCS), followed by Co-Coupling Network (CCN) within VosViewer, the method to create arrays. The arrays were analyzed based on the connection strengths of the citations in them. Next, we performed Burst Detection using the CiteSpace app. Finally, the most relevant keywords, determined in the Burst Detection, were used for Co-Occurrence Network (CONK), with which we could create new arrays and analyze them. By connecting the various, fragmented scientific findings, our results highlight that digital twins, artificial intelligence, supply chain resilience and the Internet of Things are the focus of Industry 4.0, i.e., the technological side is dominant. In contrast, Industry 5.0 places employees at the center. It also emphasizes the analysis of human–machine interaction and the importance of green digital sustainability. The results provide a comprehensive picture of how decision-makers, researchers, and professionals can interpret a changing mindset and apply it as practical advice. Full article

Unmanned aerial vehicle (UAV)-based light detection and ranging (LiDAR) technology enables the acquisition of high-precision three-dimensional point clouds of the Earth’s surface. These data serve as a fundamental input for applications such as digital terrain model (DTM) construction and terrain analysis. Nevertheless, accurately extracting ground points in densely vegetated areas remains challenging. This study proposes a point cloud filtering method for the separation of ground points by integrating elevation frequency histograms and a multi-feature Gaussian mixture model (GMM). Firstly, local elevation frequency histograms are employed to estimate the elevation range for the coarse identification of ground points. Then, GMM is applied to refine the ground segmentation by integrating geometric features, intensity, and spectral information represented by the green leaf index (GLI). Finally, Mahalanobis distance is introduced to optimize the segmentation result, thereby improving the overall stability and robustness of the method in complex terrain and vegetated environments. The proposed method was validated on three study areas with different vegetation cover and terrain conditions, achieving an average $OA$ of 94.14%, $IoUg$ of 88.45%, $IoUng$ of 88.35%, and F1-score of 93.85%. Compared to existing ground filtering algorithms (e.g., CSF, SBF, and PMF), the proposed method performs well in all study areas, highlighting its robustness and effectiveness in complex environments, especially in areas densely covered by low vegetation. Full article

The acquisition of plant ecological indicators, such as leaf area index and leaf area density values, typically relies on labor-intensive field sampling and measurements, which are often time-consuming and hinder large-scale application. As different plant ecological indicators are closely related to plants’ geometric characteristics, the development of dynamic correlation and prediction methods for relevant indicators has become an important research topic. However, existing 3D plant models are mainly used for visualization purposes, which cannot accurately reflect the plant’s growth process or geometric characteristics. This study presents a workflow for parametric 3D plant modeling and ecological indicator analysis, integrating dynamic plant modeling, indicator calculation, and microclimate simulation. With the established plant model, a method for calculating and analyzing ecological indicators, including the leaf area index, leaf area density, aboveground biomass, and aboveground carbon storage, was then proposed. A method for exporting the model-generated data into ENVI-met v.5.0 to simulate the microclimate environment was also established. Then, by taking Daijia Lake Park as an example, this study utilized site planting construction drawings and field survey data to perform parametric modeling of 21,685 on-site trees from 65 species at three different growth stages using Blender v.4.0 and The Grove plugin v.10. The generated plant model’s accuracy was then verified using the 3D IoU ratio between the models and on-site scanned point cloud data. Plant ecological indicators at various stages were then extracted and exported to ENVI-met for microclimate analysis. The workflow integrates the simulation of plant growth dynamics and their interactions with environmental factors. It can also be used for scenario-based predictions in planting design and serves as a basis for urban green space monitoring and management. Full article

This study is motivated by the rising global demand for sustainable development and the increasingly important role of foreign institutional investors in shaping corporate behavior in emerging markets. It aims to investigate whether and how qualified foreign institutional investors (QFIIs) influence the Environmental, Social, Governance (ESG) performance of Chinese listed companies. Using panel data from Chinese A-share listed firms between 2009 and 2022, this study employs a two-way fixed-effects model to examine the impact of QFII shareholding on corporate ESG performance and its underlying mechanisms. The findings reveal that QFIIs significantly enhance ESG performance, primarily through promoting green technology innovation, green investment, and green expenses. Furthermore, a composite index of information transparency is developed to investigate its moderating effect, uncovering a substitution effect: QFIIs’ marginal governance impact diminishes in highly transparent firms. Notably, the mediation analysis reveals that QFIIs enhance ESG performance through multiple environmental investment pathways—green innovation, green investment, and green expenses—while the moderating effect of information transparency suggests that QFIIs exert greater influence in less transparent firms. This research advances the theoretical understanding of foreign institutional investors’ influence on sustainability in emerging markets and provides actionable insights for policymakers seeking to align foreign capital with green transition goals. Full article

Assessing energy security in the context of sustainable development, as well as the current geopolitical climate, is a highly important, timely, and complex challenge. Addressing this issue, this paper introduces a new multi-barrier methodological approach to evaluation based on the Multi-Barrier Energy Security System (MBEES) model. This model incorporates five barriers (dimensions) influencing energy security. The MBEES model, along with the developed methodology, was applied to assess the energy security of the EU-27 countries for the period of 2014–2023, in line with EU policy objectives such as Fit for 55 and the Green Deal. The Criteria Importance Through Intercriteria Correlation and Entropy methods, combined with the Laplace criterion, were employed to determine the weights of the model’s sub-indicators. This multi-criteria decision-making (MCDM) approach enabled a synthetic overall evaluation of both the general energy security status of the EU-27 countries and the performance of each barrier examined. The study also identified the weakest elements (barriers) within national energy systems that could potentially threaten their stability and resilience. This identification is essential for effective energy risk management and for enhancing the resilience of energy systems against disruptions. Due to its broad scope—covering availability, self-sufficiency, diversification, energy efficiency, energy costs, as well as environmental and social aspects—the study delivered a comprehensive evaluation of energy security in the EU-27 during the examined period. The findings reveal significant spatial and temporal variations in energy security levels among the EU-27 countries. Scandinavian and Western European nations achieved the highest scores, whereas Central, Eastern, and Southern European countries showed lower MBEES index values, reflecting persistent structural, social, and environmental vulnerabilities. The results hold strong potential for practical application, offering guidance for EU policymakers in aligning national strategies with overarching policy frameworks such as REPowerEU and the European Green Deal. Full article

Differentiation of various crops in small-scale crops is important for food security and economic development in many rural communities. Despite being the oldest and simplest classification technique, thresholding continues to gain popularity for classifying complex images. This study aimed to evaluate the effectiveness of a multilevel thresholding technique in differentiating various crop types in small-scale farms. Three (3) types of crops were identified in the study area, and these were cabbage, maize, and sugar bean. Analytical Spectral Devices (ASD) spectral reflectance data were used to detect subtle differences in the spectral reflectance of crops. Analysis of ASD reflectance data revealed reflectance disparities among the surveyed crops in the Green, red, near-infrared (NIR), and shortwave infrared (SWIR) wavelengths. The ASD reflectance data in the Green, red, and NIR were then used to define thresholds for different crop types. The multilevel thresholding technique was used to classify the surveyed crops on the unmanned aerial vehicle (UAV) imagery, using the defined thresholds as input. Three (3) other machine learning classification techniques were also used to offer a baseline for evaluating the performance of the MLT approach, and these were the multilayer perceptron (MLP) neural network, radial basis function neural network (RBFNN), and the Kohonen’s self-organizing maps (SOM). An analysis of crop cover patterns revealed variations in crop area cover as predicted by the MLT and selected machine learning techniques. The classification results of the surveyed crops revealed the area covered by cabbage crops to be 7.46%, 6.01%, 10.33%, 7.05%, 9.48%, and 7.04% as predicted by the MLT on Blue band, MLT on Green band, MLT on NIR, MLP, RBFNN, and SOM, respectively. The area covered by maize crops as predicted by the MLT on Blue band, MLT on Green band, MLT on NIR, MLP, RBFNN, and SOM were noted to be 13.62%, 26.41%, 12.12%, 11.03%, 12.19% and 15.11%, respectively. Sugar bean was noted to occupy 57.51%, 43.72%, 26.77%, 27.44%, 24.15%, and 16.33% as predicted by the MLT on Blue band, MLT on Green band, MLT on NIR, MLP, RBFNN, and SOM, respectively. Accuracy assessment results generally showed poor crop pattern prediction with all tested classifiers in categorizing the surveyed crops, with the kappa index of agreement (KIA) values of 0.372, 0.307, 0.488, 0.531, 0.616, and 0.659 for the MLT on Blue band, MLT on Green band, MLT on NIR, MLP, RBFNN, and Kohonen’s SOM, respectively. Despite recommendations by recent studies, we noted that the MLT was noted to be unsuitable for classifying complex features such as spectrally overlapping crops. Full article

Conventional methods for testing steroids and hormones (SHs) in environmental samples are exhaustive, complex, and score poorly in sustainability matrices. Therefore, this study evaluates the automated sample preparation approach using the modular Biomek i7 Workstation for the analysis of 27 SHs in wastewater. Method development involved optimizing Ultra Performance Liquid Chromatography–Tandem Mass Spectrometry (UPLC-MS/MS) parameters, preparing wastewater matrix blank, and assessing extraction efficiency using three solid phase extraction (SPE) cartridges. Extraction efficiency trials showed suitability in the order of Hydrophilic–Lipophilic Balance (HLB) > Mixed-Mode Cation Exchange (MCX) > Mixed-Mode Anion Exchange (MAX). The method demonstrated specificity for all targeted SHs, with Cholesterol showing a maximum interfering peak of 17.71% of the quantification limit (LOQ). The method met matrix effect tolerance of ±20% for 26 SHs, while Epi Coprostanol (34.92%) showed signal enhancement >20%. The 8-point calibration curve plotted using automated extraction demonstrated acceptable linearity across the tested range. Spiked studies at low (LQC), middle (MQC), and higher (HQC) quality control (QC) levels (n = 6, repeated on three separate occasions) demonstrated % RSD values within 20% and recoveries ranging from 71.54% to 115.00%. The method met validation criteria, showing reliability in Intra-Laboratory Comparison (ILC) and Blind Testing (BT). The method outperformed the conventional approach in greenness assessment (Complex Modified Green Analytical Procedure Index) and practicality evaluation (Blue Applicability Grade Index), offering an effective and sustainable protocol for environmental testing laboratories. Full article

Urban green space (UGS) is crucial for enhancing ecosystem services (ESs), offering both ecological and social benefits. The multifunctional and synergistic development of UGS is essential for addressing ecological security challenges and meeting the demand for high-quality urban living. In densely urbanized areas, optimizing green space scale is essential for maximizing its multifunctionality. This study focuses on the Taihu Lake region in China, assessing six ESs. A self-organizing map (SOM) was employed to identify five distinct ecosystem service bundles (ESBs), while redundancy analysis (RDA) explored how green space scale characteristics influence ESs within each bundle. The results indicate that ESs exhibit significant spatial heterogeneity, with the ESBs showing two typical patterns in terms of synergistic-tradeoff relationships. The green ratio (GR) is the primary driver, with largest patch index (LPI) acting as the secondary factor, while other indicators’ effects vary across ESBs. This study systematically examines the pathways through which UGS scale characteristics influence ESs under multiple scenarios, adopting the ESB perspective. It proposes a tiered UGS scale regulation framework aimed at achieving synergistic, multi-value outcomes. Such a framework has strong potential to enhance both the ecological performance and spatial efficiency of UGS allocation. The findings contribute a novel approach to resolving multifunctional integration challenges in high-density urban settings and providing valuable insights for landscape planning and management. Full article

Accurate river flow velocity estimation is critical for flood risk management and sediment transport modeling. This study proposes an artificial intelligence (AI)-based framework that integrates optical flow analysis and deep learning to estimate flow velocity from charge-coupled device (CCD) camera videos. The approach was tested on a field dataset from Yufeng No. 2 stream (torrent), consisting of 3263 ten min 4 K videos recorded over two months, paired with Doppler radar measurements as the ground truth. Video preprocessing included frame resizing to 224 × 224 pixels, day/night classification, and exclusion of sequences with missing frames. Two deep learning architectures—a convolutional neural network combined with long short-term memory (CNN+LSTM) and a three-dimensional convolutional neural network (3D CNN)—were evaluated under different input configurations: red–green–blue (RGB) frames, optical flow, and combined RGB with optical flow. Performance was assessed using Nash–Sutcliffe Efficiency (NSE) and the index of agreement (d statistic). Results show that optical flow combined with a 3D CNN achieved the best accuracy (NSE > 0.5), outperforming CNN+LSTM and RGB-based inputs. Increasing the training set beyond approximately 100 videos provided no significant improvement, while nighttime videos degraded performance due to poor image quality and frame loss. These findings highlight the potential of combining optical flow and deep learning for cost-effective and scalable flow monitoring in small rivers. Future work will address nighttime video enhancement, broader velocity ranges, and real-time implementation. By improving the timeliness and accuracy of river flow monitoring, the proposed approach supports early warning systems, flood risk reduction, and sustainable water resource management. When integrated with turbidity measurements, it enables more accurate estimation of sediment loads transported into downstream reservoirs, helping to predict siltation rates and safeguard long-term water supply capacity. These outcomes contribute to the Sustainable Development Goals, particularly SDG 6 (Clean Water and Sanitation), SDG 11 (Sustainable Cities and Communities), and SDG 13 (Climate Action), by enhancing disaster preparedness, protecting communities, and promoting climate-resilient water management practices. Full article

Since the 18th National Congress of the Communist Party of China in 2012, green and low-carbon development has become a national strategic priority. This study constructs a 39-indicator evaluation system grounded in the DPSIRM framework, which includes six interlinked subsystems. A key innovation lies in incorporating the Digital Inclusive Finance Index as a driver of green transitions and using Baidu search indices for “environmental protection” and “carbon dioxide” as proxies for public awareness. Using a projection pursuit model optimized by simulated annealing, we assess green low-carbon development across 30 Chinese provinces from 2011 to 2021. Temporal and spatial patterns are analyzed via kernel density estimation and Moran’s I, while Theil Index decomposition quantifies regional disparities. Results: First, substantial variations exist among Chinese provinces in both subsystem performance and integrated green low-carbon development levels, and response subsystems have the greatest influence on the overall development level. Second, over time, the gaps in green, low-carbon development between provinces have become more pronounced. Third, geographically, a distinct east-to-west declining gradient characterizes the regional clustering patterns of green low-carbon development. Fourth, the Theil Index for green, low-carbon development exhibits an overall trend of fluctuating increase, indicating that the overall gap in green, low-carbon development is gradually widening, with within-group disparities as the primary cause. This research enhances understanding of China’s green and low-carbon development, actively promoting global sustainable development and environmental improvement. Full article