Search Results (441)

Forestry carbon sinks play a crucial role in mitigating climate change and protecting ecosystems, significantly contributing to the development of carbon trading systems. Remote sensing technology has become increasingly important for monitoring carbon sinks, as it allows for precise measurement of carbon storage and ecological changes, which are vital for forecasting carbon prices. Carbon prices fluctuate due to the interaction of various factors, exhibiting non-stationary characteristics and inherent uncertainties, making accurate predictions particularly challenging. To address these complexities, this study proposes a method for predicting carbon trading prices influenced by multiple factors. We introduce a Decomposition (DECOMP) module that separates carbon price data and its influencing factors into trend and cyclical components. To manage non-stationarity, we propose the KAN with Multi-Domain Diffusion (KAN-MD) module, which efficiently extracts relevant features. Furthermore, a Wave-MH attention module, based on wavelet transformation, is introduced to minimize interference from uncertainties, thereby enhancing the robustness of the model. Empirical research using data from the Hubei carbon trading market demonstrates that our model achieves superior predictive accuracy and resilience to fluctuations compared to other benchmark methods, with an MSE of 0.204% and an MAE of 0.0277. These results provide reliable support for pricing carbon financial derivatives and managing associated risks. Full article

As Chinese open-pit mines progressively transition to deeper operations, challenges such as rising stripping ratios, declining slope stability, and environmental degradation have become increasingly pronounced. The sustainability of traditional open-pit mining models faces substantial challenges. Underground mining, offering higher resource recovery rates and minimal environmental disruption, is emerging as a pivotal technological pathway for the green transformation of mining. Consequently, the transition from open-pit to underground mining has emerged as a central research focus within mining engineering. This paper provides a comprehensive review of key technological advancements in this transition, emphasizing core issues such as mine development system selection, mining method choices, slope stability control, and crown pillar design. A typical case study of the Anhui Xinqiao Iron Mine is presented to analyze its engineering approaches and practical experiences in joint development, backfilling mining, and ecological restoration. The findings indicate that the mine has achieved multi-objective optimization of resource utilization, environmental coordination, and operational capacity while ensuring safety and recovery efficiency. This offers a replicable and scalable technological demonstration for the green transformation of similar mines around the world. Full article

The Mediterranean region, identified by scientists as a ‘climate hot spot’, is experiencing warmer and drier conditions, along with an increase in the intensity and frequency of extreme weather events. One such extreme phenomena is droughts. The recent wildfires in this region are a concerning consequence of this phenomenon, causing severe environmental damage and transforming natural landscapes. However, droughts involve a two-way interaction: On the one hand, climate change and various human activities, such as urbanization and deforestation, influence the development and severity of droughts. On the other hand, droughts have a significant impact on various sectors, including ecology, agriculture, and the local economy. This study investigates drought dynamics in four Mediterranean countries, Greece, France, Italy, and Spain, each of which has experienced severe wildfire events in recent years. Using satellite-based Earth observation data, we monitored drought conditions across these regions over a five-year period that includes the dates of major wildfires. To support this analysis, we derived and assessed key indices: the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Normalized Difference Drought Index (NDDI). High-resolution satellite imagery processed within the Google Earth Engine (GEE) platform enabled the spatial and temporal analysis of these indicators. Our findings reveal that, in all four study areas, peak drought conditions, as reflected in elevated NDDI values, were observed in the months leading up to wildfire outbreaks. This pattern underscores the potential of satellite-derived indices for identifying regional drought patterns and providing early signals of heightened fire risk. The application of GEE offered significant advantages, as it allows efficient handling of long-term and large-scale datasets and facilitates comprehensive spatial analysis. Our methodological framework contributes to a deeper understanding of regional drought variability and its links to extreme events; thus, it could be a valuable tool for supporting the development of adaptive management strategies. Ultimately, such approaches are vital for enhancing resilience, guiding water resource planning, and implementing early warning systems in fire-prone Mediterranean landscapes. Full article

Bamboo gene science has witnessed significant advancements over the past two decades, driven by breakthroughs in gene cloning, marker-assisted breeding, sequencing, gene transformation, and gene editing technologies. These developments have not only enhanced our understanding of bamboo’s genetic diversity and adaptability but also provided critical tools for its genetic improvement. Compared to other crops, bamboo faces unique challenges, including its long vegetative growth cycle, environmental dependency, and limited genetic transformation efficiency. Then, the launch of China’s “Bamboo as a Substitute for Plastic” initiative in 2022, supported by the International Bamboo and Rattan Organization, has opened new opportunities for bamboo gene science as well as for bamboo production systems. This policy framework has spurred research into bamboo genetic regulation, fiber-oriented recombination, and green separation technologies, aiming to develop sustainable alternatives to plastic. Future research directions include overcoming bamboo’s environmental limitations, improving genetic transformation efficiency, and deciphering the mechanisms behind its flowering. By addressing these challenges, bamboo genetic science can enhance its economic and ecological value, contributing to global sustainability goals and the “dual-carbon” strategy. Full article

Accurate and efficient estimation of microalgae cell concentration is critical for applications in hydrochemical monitoring, biofuel production, pharmaceuticals, and ecological studies. Traditional methods, such as manual counting with a hemocytometer, are time-consuming and prone to human error, while automated systems are often costly and require extensive training data. This paper presents a low-cost, automated approach for estimating cell concentration in Chlorella vulgaris suspensions using classical computer vision techniques. The proposed method eliminates the need for deep learning by leveraging the Hough circle transform to detect and count cells in microscope images, combined with a conversion factor to translate pixel measurements into metric units for direct concentration calculation (cells/mL). Validation against manual hemocytometer counts demonstrated strong agreement, with a Pearson correlation coefficient of 0.96 and a mean percentage difference of 17.96%. The system achieves rapid processing (under 30 s per image) and offers interpretability, allowing specialists to verify results visually. Key advantages include affordability, minimal hardware requirements, and adaptability to other microbiological applications. Limitations, such as sensitivity to cell clumping and impurities, are discussed. This work provides a practical, accessible solution for laboratories lacking expensive automated equipment, bridging the gap between manual methods and high-end technologies. Full article

The remote sensing classification of urban functional zones provides scientific support for urban planning, land resource optimization, and ecological environment protection. However, urban functional zone classification encounters significant challenges in accuracy and efficiency due to complicated image structures, ambiguous critical features, and high computational complexity. To tackle these challenges, this work proposes a novel XT-SECA algorithm employing a strengthened efficient channel attention mechanism (SECA) to integrate the feature-extraction XGBoost branch and the feature-enhancement Transformer feedforward branch. The SECA optimizes the feature-fusion process through dynamic pooling and adaptive convolution kernel strategies, reducing feature confusion between various functional zones. XT-SECA is characterized by sufficient learning of complex image structures, effective representation of significant features, and efficient computational performance. The Futian, Luohu, and Nanshan districts in Shenzhen City are selected to conduct urban functional zone classification by XT-SECA, and they feature administrative management, technological innovation, and commercial finance functions, respectively. XT-SECA can effectively distinguish diverse functional zones such as residential zones and public management and service zones, which are easily confused by current mainstream algorithms. Compared with the commonly adopted algorithms for urban functional zone classification, including Random Forest (RF), Long Short-Term Memory (LSTM) network, and Multi-Layer Perceptron (MLP), XT-SECA demonstrates significant advantages in terms of overall accuracy, precision, recall, F1-score, and Kappa coefficient, with an accuracy enhancement of 3.78%, 42.86%, and 44.17%, respectively. The Kappa coefficient is increased by 4.53%, 51.28%, and 52.73%, respectively. Full article

The rapid advancement of artificial intelligence is transforming agriculture by enabling data-driven plant disease monitoring and decision support. Soil-borne mosaic wheat virus (SBWMV) is a soil-transmitted virus disease that poses a serious threat to wheat production across multiple ecological zones. Due to the regional variability in environmental conditions and symptom expressions, accurately evaluating the severity of wheat soil-borne mosaic (WSBM) infections remains a persistent challenge. To address this, the problem is formulated as large-scale group decision-making process (LSGDM), where each planting plot is treated as an independent virtual decision maker, providing its own severity assessments. This modeling approach reflects the spatial heterogeneity of the disease and enables a structured mechanism to reconcile divergent evaluations. First, for each site, field observation of infection symptoms are recorded and represented using intuitionistic fuzzy numbers (IFNs) to capture uncertainty in detection. Second, a Bayesian graph convolutional networks model (Bayesian-GCN) is used to construct a spatial trust propagation mechanism, inferring missing trust values and preserving regional dependencies. Third, an enhanced spectral clustering method is employed to group plots with similar symptoms and assessment behaviors. Fourth, a feedback mechanism is introduced to iteratively adjust plot-level evaluations based on a set of defined agricultural decision indicators sets using a multi-granulation rough set (ADISs-MGRS). Once consensus is reached, final rankings of candidate plots are generated from indicators, providing an interpretable and evidence-based foundation for targeted prevention strategies. By using the WSBM dataset collected in 2017–2018 from Walla Walla Valley, Oregon/Washington State border, the United States of America, and performing data augmentation for validation, along with comparative experiments and sensitivity analysis, this study demonstrates that the AI-driven LSGDM model integrating enhanced spectral clustering and ADISs-MGRS feedback mechanisms outperforms traditional models in terms of consensus efficiency and decision robustness. This provides valuable support for multi-party decision making in complex agricultural contexts. Full article

Urban growth often surpasses the actual needs of the population, leading to inefficient land use and long-term environmental challenges. This study provides an integrated perspective on urban landscape transformation by linking socio-demographic dynamics with ecological consequences, notably vegetation loss and increased impervious surfaces. The study area is Năvodari Administrative-Territorial Unit (ATU), a coastal tourist city located along the Black Sea in Romania. By integrating geospatial datasets such as Urban Atlas and Corine Land Cover with population- and construction-related statistics, the analysis reveals a disproportionate increase in urbanized land compared to population growth. Time-series analyses based on the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI) from 1990 to 2022 highlight significant ecological degradation, including vegetation loss and increased built-up density. The findings suggest that real estate investment and tourism-driven development play a more substantial role than demographic dynamics in shaping land use change. Understanding urban expansion as a coupled social–ecological process is essential for promoting sustainable planning and enhancing environmental resilience. While this study is focused on the coastal city of Năvodari, its insights are relevant to a broader international context, particularly for rapidly developing tourist destinations facing similar urban and ecological pressures. The findings support efforts toward more inclusive, balanced, and environmentally responsible urban development, aligning with the core principles of Sustainable Development Goal 11, particularly Target 11.3, which emphasizes sustainable urbanization and efficient land use. Full article

Biofloc technology (BFT), traditionally centered on feed supplementation and water purification in aquaculture, harbors untapped multifunctional potential as a sustainable resource management platform. This review systematically explores beyond conventional applications. BFT leverages microbial consortia to drive resource recovery, yielding bioactive compounds with antibacterial/antioxidant properties, microbial proteins for efficient feed production, and algae biomass for nutrient recycling and bioenergy. In environmental remediation, its porous microbial aggregates remove microplastics and heavy metals through integrated physical, chemical, and biological mechanisms, addressing critical aquatic pollution challenges. Agri-aquatic integration systems create symbiotic loops where nutrient-rich aquaculture effluents fertilize plant cultures, while plants act as natural filters to stabilize water quality, reducing freshwater dependence and enhancing resource efficiency. Emerging applications, including pigment extraction for ornamental fish and the anaerobic fermentation of biofloc waste into organic amendments, further demonstrate its alignment with circular economy principles. While technical advancements highlight its capacity to balance productivity and ecological stewardship, challenges in large-scale optimization, long-term system stability, and economic viability necessitate interdisciplinary research. By shifting focus to its underexplored functionalities, this review positions BFT as a transformative technology capable of addressing interconnected global challenges in food security, pollution mitigation, and sustainable resource use, offering a scalable framework for the future of aquaculture and beyond. Full article

Land desertification represents a significant and sensitive global ecological issue. In the Inner Mongolia region of China, soil desertification and salinization are widespread, resulting from the combined effects of extreme drought conditions and human activities. Using Gaofen 5B AHSI imagery as our data source, we collected spectral data for seven distinct land cover types: lush vegetation, yellow sand, white sand, saline soil, saline shell, saline soil with saline vegetation, and sandy soil. We applied Particle Swarm Optimization (PSO) to fine-tune the Wavelet Packet (WP) decomposition levels, thresholds, and wavelet basis function, ensuring optimal spectral decomposition and reconstruction. Subsequently, PSO was deployed to optimize key hyperparameters of the Random Forest algorithm and compare its performance with the ResNet-Transformer model. Our results indicate that PSO effectively automates the search for optimal WP decomposition parameters, preserving essential spectral information while efficiently reducing high-frequency spectral noise. The Genetic Algorithm (GA) was also found to be effective in extracting feature bands relevant to land desertification, which enhances the classification accuracy of the model. Among all the models, integrating wavelet packet denoising, genetic algorithm feature selection, the first-order differential (FD), and the hybrid architecture of the ResNet-Transformer, the WP-GA-FD-ResNet-Transformer model achieved the highest accuracy in extracting soil sandification and salinization, with Kappa coefficients and validation set accuracies of 0.9746 and 97.82%, respectively. This study contributes to the field by advancing hyperspectral desertification monitoring techniques and suggests that the approach could be valuable for broader ecological conservation and land management efforts. Full article

Amidst global efforts toward sustainable development, this research addresses underexplored academic dimensions by evaluating the transformative potential of intelligent, sustainable architecture. Employing bibliometric techniques and Citespace 6.4.R1, we analyze two decades (2005–2024) of the Web of Science literature to identify patterns and challenges. Findings demonstrate rising scholarly output, dominated by themes like energy-efficient design, Building Information Modeling integration, and circular economy principles in urban contexts. While Europe and North America lead research activity, systemic limitations persist—including duplicated methodologies, fragmented institutional networks, and incompatible smart technologies. This study advocates for three strategic priorities: fostering interdisciplinary innovation to break homogeneity, establishing cross-sector collaboration frameworks, and accelerating industry–academia knowledge transfer. Intelligent, sustainable architecture emerges as a dual solution—technologically enabling carbon-neutral construction practices while redefining human-centric spatial quality. This dual advantage positions the International Sustainability Alliance as critical infrastructure for achieving UN Sustainable Development Goals, reconciling ecological responsibility with evolving societal demands for resilient, adaptive built environments. Full article

According to the “dual-carbon” goal, the solution to achieving balanced regional development and industrial structural optimization while promoting sustainable development goals lies in the synergistic evolution mechanism of carbon emission efficiency and green technological innovation. Using provincial panel data from China from 2000 to 2022, this study calculates industrial structural optimization coefficients for the “advanced,” “rationalization,” and “ecology” dimensions. The impact of green technological innovation on industrial structural optimization is experimentally explored using panel regression, threshold effect, and mediating effect methodologies, based on the constraint perspective of carbon emission efficiency. The findings show that (1) the optimization of the regional industrial structure is successfully driven by both carbon emission efficiency and green technological innovation; (2) the impact of green technological innovation on industrial structural optimization is positively regulated by carbon emission efficiency, which also helps to achieve sustainable economic transformation; and (3) this moderating effect exhibits significant regional heterogeneity and U-shaped nonlinear characteristics, in the order of “central > west > east”. This study reveals how green technological innovation affects industrial structural optimization under the constraint of carbon emission efficiency. It offers reference recommendations for the creation of sustainable development policies in the future. Full article

Accurate identification of non-food crops underpins food security by clarifying land-use dynamics, promoting sustainable farming, and guiding efficient resource allocation. Proper identification and management maintain the balance between food and non-food cropping, a prerequisite for ecological sustainability and a healthy agricultural economy. Distinguishing large-scale non-food crops—such as oilseed rape, tea, and cotton—remains challenging because their canopy reflectance spectra are similar. This study proposes a novel phenology-aware Vision Transformer Model (PVM) for accurate, large-scale non-food crop classification. PVM incorporates a Phenology-Aware Module (PAM) that fuses multi-source remote-sensing time series with crop-growth calendars. The study area is Hunan Province, China. We collected Sentinel-1 SAR and Sentinel-2 optical imagery (2021–2022) and corresponding ground-truth samples of non-food crops. The model uses a Vision Transformer (ViT) backbone integrated with PAM. PAM dynamically adjusts temporal attention using encoded phenological cues, enabling the network to focus on key growth stages. A parallel Multi-Task Attention Fusion (MTAF) mechanism adaptively combines Sentinel-1 and Sentinel-2 time-series data. The fusion exploits sensor complementarity and mitigates cloud-induced data gaps. The fused spatiotemporal features feed a Transformer-based decoder that performs multi-class semantic segmentation. On the Hunan dataset, PVM achieved an F1-score of 74.84% and an IoU of 61.38%, outperforming MTAF-TST and 2D-U-Net + CLSTM baselines. Cross-regional validation on the Canadian Cropland Dataset confirmed the model’s generalizability, with an F1-score of 71.93% and an IoU of 55.94%. Ablation experiments verified the contribution of each module. Adding PAM raised IoU by 8.3%, whereas including MTAF improved recall by 8.91%. Overall, PVM effectively integrates phenological knowledge with multi-source imagery, delivering accurate and scalable non-food crop classification. Full article

This article introduces two enhanced techniques: the Natural Transform Iterative Method (NTIM) and the Optimal Auxiliary Function Method (OAFM). These approaches provide a close approximation for solving fractional-order Navier–Stokes equations, which are widely employed in domains such as biology, ecology, and applied sciences. By comparing the solutions derived from these methods to exact solutions, it is clear that they provide accurate and efficient outcomes. These findings highlight the straightforward yet effective use of these methodologies in modeling engineering systems. Navier–Stokes equations have numerous practical uses, including analyzing fluid flow in pipelines and channels, predicting weather patterns, and constructing aircraft and vehicles. Full article

Amidst the dual challenges of escalating ecological environmental pressures and economic transformation globally, green innovation emerges as a pivotal pathway toward achieving high-quality sustainable development. To elucidate how digitalization and novel productive forces synergistically drive the green transition, the research utilizes panel data from 30 provincial-level administrative regions in China spanning 2009 to 2022, constructing a green innovation efficiency measurement frame-work grounded in the Super Slack-Based Measure (Super-SBM)model, alongside a novel productive forces evaluation system based on the triad of laborers, labor objects, and means of production. Employing spatial difference-in-differences and double machine learning methodologies within a quasi-natural experimental design, the research investigates the causal mechanisms through which digital empowerment and novel productive forces influence regional green innovation efficiency. The findings reveal that both digital empowerment and novel productive forces significantly enhance regional green innovation efficiency, exhibiting pronounced positive spatial spillover effects on neighboring regions. Heterogeneity analyses demonstrate that the promotive impacts are more pronounced in eastern provinces compared to central and western counterparts, in provinces participating in carbon trading relative to those that do not, and in innovation-driven provinces versus non-innovative ones. Mediation analysis indicates that digital empowerment operates by fostering the aggregation of innovative talent and elevating governmental ecological attentiveness, whereas new-type productivity exerts its influence primarily through intellectual property protection and the clustering of high-technology industries. The results offer empirical foundations for policymakers to devise coordinated regional green development strategies, refine digital transformation policies, and promote industrial structural optimization. Furthermore, this research provides valuable data-driven insights and theoretical guidance for local governments and enterprises in cultivating green innovation and new-type productivity. Full article