Search Results (3,352)

Oil spill simulation models are essential for predicting the oil spill behavior and movement in marine environments. In this study, we comprehensively reviewed a large and diverse body of peer-reviewed literature obtained from Scopus and Web of Science. Our initial analysis phase focused on examining trends in scientific publications, utilizing the complete dataset derived after systematic screening and database integration. In the second phase, we applied elements of a systematic review to identify and evaluate the most influential contributions in the scientific field of oil spill simulations. Our analysis revealed a steady and accelerating growth of research activity over the past five decades, with a particularly notable expansion in the last two. The field has also experienced a marked increase in collaborative practices, including a rise in international co-authorship and multi-authored contributions, reflecting a more global and interdisciplinary research landscape. We cataloged the key modeling frameworks that have shaped the field from established systems such as OSCAR, OIL-MAP/SIMAP, and GNOME to emerging hybrid and Lagrangian approaches. Hydrodynamic models were consistently central, often integrated with biogeochemical, wave, atmospheric, and oil-spill-specific modules. Environmental variables such as wind, ocean currents, and temperature were frequently used to drive model behavior. Geographically, research has concentrated on ecologically and economically sensitive coastal and marine regions. We conclude that future progress will rely on the real-time integration of high-resolution environmental data streams, the development of machine-learning-based surrogate models to accelerate computations, and the incorporation of advanced biodegradation and weathering mechanisms supported by experimental data. These advancements are expected to enhance the accuracy, responsiveness, and operational value of oil spill modeling tools, supporting environmental monitoring and emergency response. Full article

The water-yield function in the Yellow River Basin (YRB) of China for maintaining the basin’s ecological water balance plays a crucial role. Understanding its spatiotemporal variation and the underlying drivers in the basin is crucial for the management, utilization, and development of water resources. Thus, we used the InVEST model to explore its spatiotemporal dynamics across multiple scales (“basin–county–pixel”). Then, we integrated socio-economic and natural factors to elucidate the driving forces and spatial heterogeneity of water-yield dynamics. Our findings indicated that water-yield trends increased in 71.76% of the YRB, and significant water-yield increases were detected in 13.9% of the basin over the past 40 years. A phase-wise comparison revealed a shift in water yield from a decreasing trend in the first two decades to a significant increasing trend in the last two decades. Hotspot analysis revealed that hotspots of increasing water-yield trends have shifted from the downstream section of the basin toward the southwest, while hotspots of decreasing water-yield trends first concentrated in the basin’s southern part and then disappeared. Both natural and socioeconomic factors have exerted positive and negative impacts on water-yield dynamics. Among them, the dynamics of water yield have been predominantly driven by natural variables. Full article

Water is essential for ecological balance, environmental sustainability, and food security, particularly in arid regions where effective water management increasingly depends on farmer cooperation. The Samarkand region of Uzbekistan, known for its favorable climate and leading role in grape production, is facing rising drought conditions. This study explores the factors influencing grape farmers’ willingness to collaborate on water management in the districts of Ishtikhan, Payarik, and Kushrabot, which together produce 75–80% of the region’s grapes. A quantitative survey of 384 grape-producing households was conducted across 19 county citizens’ gatherings (38.7% of such gatherings), and structural equation modeling was employed to analyze a framework consisting of four dimensions: norms, environmental concerns, economic barriers, and the intention to adopt sustainable practices. The results indicate that norms and environmental concerns positively influence collaboration, suggesting a collective orientation toward sustainability. In contrast, economic barriers such as high costs and limited financial capacity significantly hinder cooperative behavior. Furthermore, a strong individual intention to adopt sustainable practices was associated with a greater likelihood of collaboration. These findings highlight the critical drivers and constraints shaping collective water use in agriculture and suggest that targeted policy measures and community-led efforts are vital for promoting sustainable water governance in drought-prone regions. Full article

Against the backdrop of rapid urbanization in western China, which has triggered remarkable land-use changes and habitat degradation, Chengdu, as a developed city in China, plays a demonstrative and leading role in the economic and social development of China during the transition period. Therefore, integrated modeling approaches are required to balance development and conservation. This study responds to this need by conducting a scenario-based assessment of urbanization-induced land-use changes and regional habitat quality dynamics in Chengdu (1990–2030), using the FLUS-InVEST model. By integrating remote sensing-derived land-use data from 1990, 1995, 2000, 2005, 2010, 2015, and 2020, we simulate future regional habitat quality under three policy scenarios: natural development, ecological priority, and cropland protection. Key findings include the following: (1) From 1990 to 2020, cropland decreased by 1917.78 km², while forestland and built-up areas increased by 509.91 km² and 1436.52 km², respectively. Under the 2030 natural development scenario, built-up expansion and cropland reduction are projected. Ecological priority policies would enhance forestland (+4.2%) but slightly reduce cropland. (2) Regional habitat quality declined overall (1990–2020), with the sharpest drop (ΔHQ = −0.063) occurring between 2000 and 2010 due to accelerated urbanization. (3) Scenario analysis reveals that the ecological priority strategy yields the highest regional habitat quality (HQ_mean = 0.499), while natural development results in the lowest (HQ_mean = 0.444). This study demonstrates how the FLUS-InVEST model can quantify the trade-offs between urbanization and regional habitat quality, offering a scientific framework for balancing development and ecological conservation in rapidly urbanizing regions. The findings highlight the effectiveness of ecological priority policies in mitigating habitat degradation, with implications for similar cities seeking sustainable land-use strategies that integrate farmland protection and forest restoration. Full article

Enhancing market integration levels is crucial for advancing sustainable regional collaborative development and achieving ecological protection and high-quality development goals within the Yellow River Basin, fostering a balance between economic efficiency, social equity, and environmental resilience. This study analyzed the retail price data of goods from prefecture-level cities in the Yellow River Basin from 2010 to 2022, employing the relative price method to measure the market integration index. Additionally, it examined the temporal and spatial evolution patterns and driving factors using the Dagum Gini coefficient and panel regression models. The results indicate the following. (1) The market integration index of the Yellow River Basin shows a fluctuating upward trend, with an average annual growth rate of 9.8%. The spatial pattern generally reflects a situation where the east is relatively high and the west is relatively low, as well as the south being higher than the north. (2) Regional disparities are gradually diminishing, with the overall Gini coefficient decreasing from 0.153 to 0.104. However, internal differences within the downstream and midstream areas have become prominent, and contribution rate analysis reveals that super-variable density has replaced between-group disparities as the primary source. (3) Upgrading the industrial structure and enhancing the level of economic development are the core driving forces, while financial support and digital infrastructure significantly accelerate the integration process. Conversely, the level of openness exhibits a phase-specific negative impact. We propose policy emphasizing the need to strengthen development in the upper reach of the Yellow River Basin, further improve interregional collaborative innovation mechanisms, and enhance cross-regional coordination among multicenter network nodes. Full article

Agriculture is the foundational industry for human survival, profoundly impacting economic, ecological, and social dimensions. In the face of global challenges such as rapid population growth, resource scarcity, and climate change, achieving technological innovation in agriculture and advancing smart farming have become increasingly critical. In recent years, deep learning and computer vision have developed rapidly. Key areas in computer vision—such as deep learning-based image processing, object detection, and multimodal fusion—are rapidly transforming traditional agricultural practices. Processes in agriculture, including planting planning, growth management, harvesting, and post-harvest handling, are shifting from experience-driven methods to digital and intelligent approaches. This paper systematically reviews applications of deep learning and computer vision in agricultural growth management over the past decade, categorizing them into four key areas: crop identification, grading and classification, disease monitoring, and weed detection. Additionally, we introduce classic methods and models in computer vision and deep learning, discussing approaches that utilize different types of visual information. Finally, we summarize current challenges and limitations of existing methods, providing insights for future research and promoting technological innovation in agriculture. Full article

Dark skies, a vital natural and cultural resource, have been increasingly threatened by light pollution due to rapid urbanization, leading to ecological degradation and biodiversity loss. As a key strategy for sustainable regional development, dark sky parks (DSPs) not only preserve nocturnal environments but also enhance livability by balancing urban expansion and ecological conservation. This study develops a novel framework for evaluating DSP suitability, integrating ecological and socio-economic dimensions, including the resource base (e.g., nighttime light levels, meteorological conditions, and air quality) and development conditions (e.g., population density, transportation accessibility, and tourism infrastructure). Using the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) as a case study, we employ Delphi expert consultation, GIS spatial analysis, and multi-criteria decision-making to identify optimal DSP locations and prioritize conservation zones. Our key findings reveal the following: (1) spatial heterogeneity in suitability, with high-potential zones being concentrated in the GBA’s northeastern, central–western, and southern regions; (2) ecosystem advantages of forests, wetlands, and high-elevation areas for minimizing light pollution; (3) coastal and island regions as ideal DSP sites due to the low light interference and high ecotourism potential. By bridging environmental assessments and spatial planning, this study provides a replicable model for DSP site selection, offering policymakers actionable insights to integrate dark sky preservation into sustainable urban–regional development strategies. Our results underscore the importance of DSPs in fostering ecological resilience, nighttime tourism, and regional livability, contributing to the broader discourse on sustainable landscape planning in high-urbanization contexts. Full article

Dams play a vital role in securing water and electricity supplies for households and industry, and they contribute significantly to flood protection. Regular monitoring of dam deformations holds fundamental socio-economic and ecological importance. Traditionally, this has relied on time-consuming in situ techniques that offer either high spatial or temporal resolution. Persistent Scatterer Interferometry (PSI) addresses these limitations, enabling high-resolution monitoring in both domains. Sensors such as TerraSAR-X (TSX) and Sentinel-1 (S-1) have proven effective for deformation analysis with millimeter accuracy. Combining TSX and S-1 datasets enhances monitoring capabilities by leveraging the high spatial resolution of TSX with the broad coverage of S-1. This improves monitoring by increasing PS point density, reducing revisit intervals, and facilitating the detection of environmental deformation drivers. This study aims to investigate two objectives: first, we evaluate the benefits of a spatially and temporally densified PS time series derived from TSX and S-1 data for detecting radial deformations in individual dam segments. To support this, we developed the TSX2StaMPS toolbox, integrated into the updated snap2stamps workflow for generating single-master interferogram stacks using TSX data. Second, we identify deformation drivers using water level and temperature as exogenous variables. The five-year study period (2017–2022) was conducted on a gravity dam in North Rhine-Westphalia, Germany, which was divided into logically connected segments. The results were compared to in situ data obtained from pendulum measurements. Linear models demonstrated a fair agreement between the combined time series and the pendulum data ($R^2$ = 0.5; MAE = 2.3 mm). Temperature was identified as the primary long-term driver of periodic deformations of the gravity dam. Following the filling of the reservoir, the variance in the PS data increased from 0.9 mm to 3.9 mm in RMSE, suggesting that water level changes are more responsible for short-term variations in the SAR signal. Upon full impoundment, the mean deformation amplitude decreased by approximately 1.7 mm toward the downstream side of the dam, which was attributed to the higher water pressure. The last five meters of water level rise resulted in higher feature importance due to interaction effects with temperature. The study concludes that integrating multiple PS datasets for dam monitoring is beneficial particularly for dams where few PS points can be identified using one sensor or where pendulum systems are not installed. Identifying the drivers of deformation is feasible and can be incorporated into existing monitoring frameworks. Full article

Skipjack tuna constitutes a crucial fishery resource in the Western and Central Pacific Ocean (WCPO) purse seine fishery, with high economic value and exploitation potential. It also serves as an essential subject for studying the interaction between fishery resource dynamics and marine ecosystems, as its resource abundance is significantly influenced by marine environmental factors. Skipjack tuna can be categorized into unassociated schools and associated schools, with the latter being predominant. Overfishing of the associated schools can adversely affect population health and the ecological environment. In-depth exploration of the spatial distribution responses of these two fish schools to environmental variables is significant for the rational development and utilization of tuna resources and for enhancing the sustainability of fishery resources. In sparsely sampled and complex marine environments, geographic similarity methods effectively predict tuna resources by quantifying local fishing ground environmental similarities. This study introduces geographical similarity theory. This study focused on 1° × 1° fishery data (2004–2021) released by the Western and Central Pacific Fisheries Commission (WCPFC) combined with relevant marine environmental data. We employed Geographical Convergent Cross Mapping (GCCM) to explore significant environmental factors influencing catch and variations in causal intensity and employed a Geographically Optimal Similarity (GOS) model to predict the spatial distribution of catch for the two types of tuna schools. The research findings indicate that the following: (1) Sea surface temperature (SST), sea surface salinity (SSS), and net primary productivity (NPP) are key factors in GCCM model analysis, significantly influencing the catch of two fish schools. (2) The GOS model exhibits higher prediction accuracy and stability compared to the Generalized Additive Model (GAM) and the Basic Configuration Similarity (BCS) model. R² values reaching 0.656 and 0.649 for the two types of schools, respectively, suggest that the geographical similarity method has certain applicability and application potential in the spatial prediction of fishery resources. (3) Uncertainty analysis revealed more stable predictions for unassociated schools, with 72.65% of the results falling within the low-uncertainty range (0.00–0.25), compared to 52.65% for associated schools. This study, based on geographical similarity theory, elucidates differential spatial responses of distinct schools to environmental factors and provides a novel approach for fishing ground prediction. It also provides a scientific basis for the dynamic assessment and rational exploitation and utilization of skipjack tuna resources in the Pacific Ocean. Full article

In mining areas with high groundwater levels, intensive coal mining has led to the accumulation of substantial surface water and significant alterations in regional landscape patterns. Reconstructing the ecological security pattern (ESP) has emerged as a critical focus for ecological restoration in coal mining subsidence areas with high groundwater levels. This study employed the patch-generating land use simulation (PLUS) model to predict the landscape evolution trend of the study area in 2032 under three scenarios, combining environmental characteristics and disturbance features of coal mining subsidence areas with high groundwater levels. In order to determine the differences in ecological network changes within the study area under various development scenarios, morphological spatial pattern analysis (MSPA) and landscape connectivity analysis were employed to identify ecological source areas and establish ecological corridors using circuit theory. Based on the simulation results of the optimal development scenario, potential ecological pinch points and ecological barrier points were further identified. The findings indicate that: (1) land use changes predominantly occur in urban fringe areas and coal mining subsidence areas. In the land reclamation (LR) scenario, the reduction in cultivated land area is minimal, whereas in the economic development (ED) scenario, construction land exhibits a marked increasing trend. Under the natural development (ND) scenario, forest land and water expand most significantly, thereby maximizing ecological space. (2) Under the ND scenario, the number and distribution of ecological source areas and ecological corridors reach their peak, leading to an enhanced ecological network structure that positively contributes to corridor improvement. (3) By comparing the ESP in the ND scenario in 2032 with that in 2022, the number and area of ecological barrier points increase substantially while the number and area of ecological pinch points decrease. These areas should be prioritized for ecological protection and restoration. Based on the scenario simulation results, this study proposes a planning objective for a “one axis, four belts, and four zones” ESP, along with corresponding strategies for ecological protection and restoration. This research provides a crucial foundation for decision-making in enhancing territorial space planning in coal mining subsidence areas with high groundwater levels. Full article

Urban expansion has a profound impact on both society and the environment. In this study, VOSviewer 1.6.16 and CiteSpace 6.3.R1 were used to conduct a bibliometric analysis of 2987 articles published during the period of 1992–2022 from the Web of Science database in order to identify the research hotspots and trends of urban expansion and its driving factors. The number of articles significantly increased during the period of 1992–2022. The spatiotemporal characteristics and driving forces of urban expansion, urban growth models and simulations, and the impacts of urban expansion were the main research topics. The rate of urban expansion showed regional differences. Socioeconomic factors, political and institutional factors, natural factors, path effects, and proximity effects were the main driving factors. Urban expansion promoted economic growth, occupied cultivated land, and affected ecological environments. Big data and deep learning techniques were recently applied due to advancements in information techniques. With the increasing awareness of environmental protection, the number of studies on environmental impacts and spatial planning regulations has increased. Some political and institutional factors, such as subsidies, taxation, spatial planning, new development strategies, regulation policies, and economic industries, had controversial or unknown impacts. Further research on these factors and their mechanisms is needed. A limitation of this study is that articles which were not indexed, were not included in bibliometric analysis. Further studies can review these articles and conduct comparative research to capture the diversity. Full article

This study explores the role of community-centered farm-based hospitality in promoting sustainable rural development, with a focus on South Tyrol, Italy. A survey of 461 local residents assessed perceptions of agritourism’s impact on agricultural heritage, environmental sustainability, and community well-being. Factor analysis identified two main constructs—Agroheritage Sustainability and Empowered Eco-Tourism—which together capture the multifaceted benefits of agritourism. Agroheritage Sustainability reflects the preservation of traditional farming practices, cultural landscapes, and intergenerational knowledge, emphasizing the role of tourism in maintaining cultural identity and preventing land abandonment. Empowered Eco-Tourism highlights the socio-economic benefits of sustainable tourism, including community empowerment, environmental stewardship, and the creation of new economic opportunities. The study’s findings indicate that local residents view agritourism as a holistic approach that supports rural livelihoods while preserving cultural heritage and promoting ecological resilience. The analysis further supports the potential of farm-based hospitality as a model for sustainable rural development, aligning closely with EU policies and global best practices. The Roter Hahn initiative in South Tyrol serves as a practical example of this approach, demonstrating the value of certification programs in enhancing transparency, quality, and sustainability. These insights provide valuable guidance for policymakers and tourism developers seeking to promote sustainable rural tourism globally. The contribution of this research lies in its empirical validation of a dual-construct model that links community engagement with agroecological and cultural sustainability, offering a transferable framework for evaluating agritourism as a lever for sustainable rural development in diverse regional contexts. Full article

The escalating threat of climate change has intensified the global urgency to accurately predict carbon dioxide (CO₂) emissions for sustainable development, particularly in developing economies experiencing rapid industrialization and globalization. Traditional Extreme Learning Machines (ELMs) offer rapid learning but often yield unstable performance due to random parameter initialization. This study introduces a novel hybrid model, Red-Billed Blue Magpie Optimizer-tuned ELM (RBMO-ELM) which harnesses the intelligent foraging behavior of red-billed blue magpies to optimize input-to-hidden layer weights and biases. The RBMO algorithm is first benchmarked on 15 functions from the CEC2015 test suite to validate its optimization effectiveness. Subsequently, RBMO-ELM is applied to predict Indonesia’s CO₂ emissions using a multidimensional dataset that combines economic, technological, environmental, and globalization-driven indicators. Empirical results show that the RBMO-ELM significantly surpasses several state-of-the-art hybrid models in accuracy (higher R²) and convergence efficiency (lower error). A permutation-based feature importance analysis identifies social globalization, GDP, and ecological footprint as the strongest predictors underscoring the socio-economic influences on emission patterns. These findings offer both theoretical and practical implications that inform data-driven Artificial Intelligence (AI) and Machine Learning (ML) applications in environmental policy and support sustainable governance models. Full article

Changes in habitat suitability are critical indicators of the ecological impacts of climate change. Syndiclis Hook. f., a rare and endangered genus endemic to montane limestone and cloud forest ecosystems in China, holds considerable ecological and economic value. However, knowledge of its current distribution and the key environmental factors influencing its habitat suitability remains limited. In this study, we employed the MaxEnt model, integrated with geographic information systems (ArcGIS), to predict the potential distribution of Syndiclis under current and future climate scenarios, identify dominant bioclimatic drivers, and assess temporal and spatial shifts in habitat patterns. We also analyzed spatial displacement of habitat centroids to explore potential migration pathways. The model demonstrated excellent performance (AUC = 0.988), with current suitable habitats primarily located in Hainan, Taiwan, Southeastern Yunnan, and along the Yunnan–Guangxi border. Temperature seasonality (bio7) emerged as the most important predictor (67.00%), followed by precipitation of the driest quarter (bio17, 14.90%), while soil factors played a relatively minor role. Under future climate projections, Hainan and Taiwan are expected to serve as stable climatic refugia, whereas the overall suitable habitat area is projected to decline significantly. Combined with topographic constraints, population decline, and limited dispersal ability, these changes elevate the risk of extinction for Syndiclis in the wild. Landscape pattern analysis revealed increased habitat fragmentation under warming conditions, with only 4.08% of suitable areas currently under effective protection. We recommend prioritizing conservation efforts in regions with habitat contraction (e.g., Guangxi and Yunnan) and stable refugia (e.g., Hainan and Taiwan). Conservation strategies should integrate targeted in situ and ex situ actions, guided by dominant environmental variables and projected migration routes, to ensure the long-term persistence of Syndiclis populations and support evidence-based conservation planning. Full article

Against the background of intensifying human–land contradictions, evaluation of land use benefits and identification of obstacles are crucial for sustainable land management and socioeconomic development. Taking Henan Province as an example, this research employed the entropy weight method and TOPSIS model to assess the land use benefits across its cities from 2011 to 2020, a period of rapid land use transformation, analyzed their spatiotemporal evolution, and identified key obstacles via an obstacle degree model. The results showed the following. (1) The social land use benefits consistently exceeded the ecological and economic benefits, with steady improvements observed in both the individual and comprehensive benefits. Spatially, the benefits showed a “one city dominant” pattern, decreasing gradually from the central region to the south, north, east, and west, with this spatial gradient further intensifying over time. (2) Economic factors were the primary obstacles, with significantly higher obstruction degrees than social or ecological factors. The main obstacles were the general budget revenue of government finance per unit land area, domestic garbage removal volume, and total retail sales of social consumer goods per unit land area. (3) The policy implications focus on strengthening regional differentiated development by leveraging Zhengzhou’s core role to boost the land-based economic benefits, integrating social–ecological strengths with agricultural modernization, and promoting “core–periphery linkage” to narrow gaps through targeted industrial and infrastructure strategies. This study could provide region-specific insights for sustainable land management in agricultural provinces undergoing rapid urbanization. Full article