Search Results (948)

Arable land green and low-carbon utilization (ALGLU) is an important pathway to safeguard food safety and achieve the green transformation and progress of agriculture, playing a crucial role in promoting agricultural ecological protection and economic sustainability. This study takes the Yangtze River Delta region (YRD), where rapid urbanization is most typical, as the study area. On the basis of fully considering the carbon sink function of arable land, the study measures the green and low-carbon utilization level of arable land in the region using the Super-slack and based measure (Super-SBM) model, and analyzes its spatial and temporal evolution using the spatial autocorrelation model, the center of gravity, and the standard ellipsoid model, and then analyzes its impact with the help of the geographic detector and the geographically weighted regression model. We analyzed the multifactor interaction and spatial heterogeneity of the factors with the help of the geodetector and geographically weighted regression model. Results: (1) The ALGLU in the YRD has shown a fluctuating upward tendency, increasing from 0.7307 in 2012 to 0.8604 in 2022, with a growth rate of 17.75%. The phased changes correspond to national agricultural development policies and the stages of socio-economic development. (2) There are significant spatial differences in the level of ALGLU in the YRD, with high levels distributed in the southwest of Jiangsu, northern Zhejiang, and northwest Anhui, while low levels are distributed in the southwest of the YRD. Positive spatial autocorrelation exists in the level of ALGLU in the YRD. The spatial transfer trends of the gravity and standard deviation ellipses essentially align with changes in the spatial pattern. (3) The level of ALGLU in the YRD is affected by many factors, with the intensity of interaction effects far exceeding that of individual factors. When considering single-factor effects, precipitation, topography, and farmers’ income levels are important factors influencing the level of ALGLU. In scenarios involving multiple-factor interactions, agricultural policies become the primary focus of interaction effects. Furthermore, the driving effects of influencing factors exhibit spatial heterogeneity, with significant differences in the direction and extent of driving effects of each factor in different cities. This study can provide valuable insights for future ALGLU in the YRD and regional sustainable development. Full article

Urbanization in the 21st century has reshaped carbon stock distributions through the expansion of cities. By using the PLUS and InVEST models, this study predicts land use and carbon stocks in Wuhan in 2050 using three future scenarios. Employing local Moran’s I, we analyze carbon stock clustering under these scenarios, and the Getis–Ord Gi* statistic identifies regions with significantly higher and lower carbon-stock changes between 2020 and 2050. The results reveal a 2.5 Tg decline in Wuhan’s carbon stock from 2000 to 2020, concentrated from the central to the outer city areas along the Yangtze River. By 2050, the ecological conservation scenario produced the highest carbon stock prediction, 77.48 Tg, while the economic development scenario produced the lowest, 76.4 Tg. High-carbon stock-change areas cluster in the north and south, contrasting with low-change area concentrations in the center. This research provides practical insights that support Wuhan’s sustainable development and carbon neutrality goals. Full article

Gully erosion is a serious environmental threat, compromising soil health, damaging agricultural lands, and destroying vital infrastructure. Pinpointing regions prone to gully erosion demands careful selection of an appropriate machine learning algorithm. This choice is crucial, as the complex interplay of various environmental factors contributing to gully formation requires a nuanced analytical approach. To develop the most accurate Gully Erosion Susceptibility Map (GESM) for India’s Raiboni River basin, researchers harnessed the power of two cutting-edge machine learning algorithm: Extreme Gradient Boosting (XGBoost) and Random Forest (RF). For a comprehensive analysis, this study integrated 24 potential control factors. We meticulously investigated a dataset of 200 samples, ensuring an even balance between non-gullied and gullied locations. To assess multicollinearity among the 24 variables, we employed two techniques: the Information Gain Ratio (IGR) test and Variance Inflation Factors (VIF). Elevation, land use, river proximity, and rainfall most influenced the basin’s GESM. Rigorous tests validated XGBoost and RF model performance. XGBoost surpassed RF (ROC 86% vs. 83.1%). Quantile classification yielded a GESM with five levels: very high to very low. Our findings reveal that roughly 12% of the basin area is severely affected by gully erosion. These findings underscore the critical need for targeted interventions in these highly susceptible areas. Furthermore, our analysis of gully characteristics unveiled a predominance of V-shaped gullies, likely in an active developmental stage, supported by an average Shape Index (SI) value of 0.26 and a mean Erosivness Index (EI) of 0.33. This research demonstrates the potential of machine learning to pinpoint areas susceptible to gully erosion. By providing these valuable insights, policymakers can make informed decisions regarding sustainable land management practices. Full article

The green and low-carbon transformation of land use (GLTLU) is a pressing global issue that requires urgent attention. The digital economy has emerged as a new driver for the GLTLU. However, current research mainly focuses on the measurement and environmental effects of the digital economy, with less exploration of how the digital economy influences the spatial effects and regulatory mechanisms of GLTLU, particularly regarding the differential impacts and specific mechanisms at the regional level. This study uses panel data from 283 cities in China from 2011 to 2019, employing the spatial Durbin model (SDM) and the panel threshold model to examine the spatial and regulatory mechanisms of the digital economy’s impact on GLTLU. The findings reveal that digital economy promotes GLTLU not only within cities but also in surrounding regions. Robustness analyses support this conclusion. Notably, the digital economy’s positive impact on GLTLU in surrounding areas is confined to the central region of China. In contrast, the Yangtze River Delta urban agglomeration experiences a significant negative impact on GLTLU in nearby regions due to the digital economy. The study also identifies that the positive spatial spillover effect of the digital economy on GLTLU reaches its peak at a distance of 450 km. Additionally, the digital economy’s ability to promote GLTLU is contingent upon financial agglomeration levels exceeding 9.1728. Moreover, the local government’s emphasis on the digital economy and intellectual property protection enhances the digital economy’s impact on GLTLU. The promotion effect is maximized when these factors surpass the thresholds of 27.8054 and 3.5189, respectively. Overall, this study contributes to the understanding of how the digital economy influences sustainable land development, highlighting the critical role of regional factors and regulatory mechanisms in amplifying the digital economy’s positive effects on GLTLU. Full article

Synthetic aperture radar (SAR) technology has become an important means of flood monitoring because of its large coverage, repeated observation, and all-weather and all-time working capabilities. The commonly used thresholding and change detection methods in emergency monitoring can quickly and simply detect floods. However, these methods still have some problems: (1) thresholding methods are easily affected by low backscattering regions and speckle noise; (2) changes from multi-temporal information include urban renewal and seasonal variation, reducing the precision of flood monitoring. To solve these problems, this paper presents a new flood mapping framework that combines semi-automatic thresholding and change detection. First, multiple lines across land and water are drawn manually, and their local optimal thresholds are calculated automatically along these lines from two ends towards the middle. Using the average of these thresholds, the low backscattering regions are extracted to generate a preliminary inundation map. Then, the neighborhood-based change detection method combined with entropy thresholding is adopted to detect the changed areas. Finally, pixels in both the low backscattering regions and the changed regions are marked as inundated terrain. Two flood datasets, one from Sentinel-1 in the Wharfe and Ouse River basin and another from GF-3 in Chaohu are chosen to verify the effectiveness and practicality of the proposed method. Full article

Microplastic pollution is a pervasive issue, with remarkably high concentrations observed even in the most remote locations such as Arctic sea ice and snow. The reason for such large microplastic abundances in sea ice is still speculative and applies mainly to saline or freshwater conditions. In this study, we investigated seasonal ice core samples collected in March 2021 from the northern Baltic Sea (Gulf of Bothnia) for their microplastic distributions. The Baltic Sea is characterized by low salinity and can be ice-covered for up to six months annually. Microplastics were analyzed in the melted ice samples using an adsorption technique and Raman microscopy to identify their abundances, colors, shapes, and sizes to calculate their masses. Due to the strong dynamic of the ice layer and the repeated melting and freezing processes during the ice formation, no discernible trends in microplastic abundances, masses, or polymer types were observed throughout the ice core length. The average microplastic abundance (±SD) in the Baltic Sea ice was determined to be 22.3 ± 8.6 N L⁻¹, with 64.9% of the particles exhibiting a particulate shape and 35.1% having a fibrous shape. The most prevalent polymer type was polyethylene terephthalate (PET), accounting for 44.4% of all polymers. This is likely due to the high proportion of PET fibers (93.8%). The majority of particle-shaped microplastics were identified as polyethylene (PE; 37.2%), followed by PET (17.2%), polyvinyl chloride (PVC; 15.9%), and polypropylene (PP; 15.9%). No correlations were found between microplastic concentrations and proximity to land, cities, industries, or rivers, except for PP mass concentrations and particle sizes, which correlated with distances to industries in Luleå, Sweden. Full article

Environmental-economic analysis is an evolving field that seeks to situate the human economy within environmental systems through its consumption of environmental resources and cycling of resources and waste products back into the environment. Environmental accounting has seen increased focus in recent years as national and regional governments look to better track environmental flows to aid in policy development and evaluation. This study outlines a conceptual environmental-economic framework founded on network science principles. An empirical study operationalizes portions of the framework and highlights the need for further research in this area to develop new data sources and analytic methods. We demonstrate a spatial mismatch between the location of water-intensive industries and the natural location of water resources (i.e., lakes, rivers, and precipitation), which climate change is likely to exacerbate. We use eigenvector centrality to measure differences in the US economy according to economic trade flow and five associated environmental flow accounts (land use, water consumption, energy use, mineral metal use, and greenhouse gas production). Population normalization helps to identify low-population counties that play a central role in the environmental-economic system as a function of their natural resources. Full article

Carbon storage (C-storage) is a critical indicator of ecosystem services, and it plays a vital role in maintaining ecological balance and driving sustainability. Its assessment provides essential insights for enhancing environmental protection, optimizing land use, and formulating policies that support long-term ecological and economic sustainability. Previous research on C-storage in the Yellow River Basin has mainly concentrated on the spatiotemporal fluctuations of C-storage and the investigation of natural influencing factors. However, research combining human activity factors to explore the influences on C-storage is limited. In this paper, based on the assessment of the spatiotemporal evolution of C-storage in the region along the Middle and Lower Yellow River (MLYR), the influences of anthropogenic and natural factors on C-storage were explored from the perspective of sustainable development. The findings reflected the relationship between socio-economic activities and the ecological environment from a sustainable development perspective, providing important scientific evidence for the formulation of sustainability policies in the region. We noticed the proportion of arable land was the highest, reaching 40%. The increase of construction land because of the fast urbanization mainly came from arable land and grassland. During the past 15 years, the cumulative loss of C-storage was 71.17 × 10⁶ t. The high-value of C-storage was primarily situated in hilly areas, and the area of C-storage hotspots was shrinking. The aggregation effect of low-value C-storage was strengthening, while that of high-value C-storage was weakening. The dominant factors (q > 0.5) influencing the spatiotemporal variation of C-storage in the region along the Middle Yellow River (MYR) were temperature and precipitation, while the primary factor in the region along the Lower Yellow River (LYR) was temperature. Overall, meteorological factors were the main determinants across the entire study area. Additionally, compared to the MYR, anthropogenic factors had a smaller impact on the spatiotemporal evolution of C-storage in the LYR, but their influence has been increasing over time. Full article

Under the influences of climate change and human activities, habitat quality (HQ) in inland river basins continues to decline. Studying the spatiotemporal distributions of land use and HQ can provide support for sustainable development strategies of the ecological environment in arid regions. Therefore, this study utilized the SD-PLUS model, InVEST-HQ model, and Geodetector to assess and simulate the land-use changes and HQ in the Tarim River Basin (TRB) at multiple scales (county and grid scales) and scenarios (SSP126, SSP245, and SSP585). The results indicated that (1) the Figure of Merit (FoM) values for Globeland 30, China’s 30 m annual land-cover product, and the Chinese Academy of Sciences (30 m) product were 0.22, 0.12, and 0.15, respectively. A comparison of land-use datasets with different resolutions revealed that the kappa value tended to decline as the resolution decreased. (2) In 2000, 2010, and 2020, the HQ values were 0.4656, 0.4646, and 0.5143, respectively. Under the SSP126 and SSP245 scenarios, the HQ values showed an increasing trend: for the years 2030, 2040, and 2050, they were 0.4797, 0.4834, and 0.4855 and 0.4805, 0.4861, and 0.4924, respectively. Under SSP585, the HQ values first increased and then decreased, with values of 0.4791, 0.4800, and 0.4766 for 2030, 2040, and 2050, respectively. (3) Under three scenarios, areas with improved HQ were mainly located in the southern and northern high mountain regions and around urban areas, while areas with diminished HQ were primarily in the western part of the basin and central urban areas. (4) At the county scale, the spatial correlation was not significant, with Moran’s I ranging between 0.07 and 0.12, except in 2000 and 2020. At the grid scale, the spatial correlation was significant, with clear high- and low-value clustering (Moran’s I between 0.80 and 0.83). This study will assist land-use planners and policymakers in formulating sustainable development policies to promote ecological civilization in the basin. Full article

Rapidly changing climate issues and increasingly severe carbon emissions are great challenges to the carbon peaking and carbon neutrality strategy. Analyzing the impact of future land use changes on carbon emissions can provide an important basis and reference for scientifically constructing a low-carbon and sustainable territorial spatial planning, as well as realizing the goal of the dual-carbon strategy. Based on land use data, agricultural production activity data, and energy consumption statistics, this study simulated the land use changes of the Yangtze River Delta region (YRDR) from 2030 to 2060 under the natural development (ND) scenario and sustainable development (SD) scenario by using the Patch-generating Land Use Simulation (PLUS) model and analyzed the impacts of future land use changes on carbon emissions. The results showed that: (1) The land use simulation results obtained by using the PLUS model under the sustainable development scenario were highly consistent with the actual land use with an OA value of 97.0%, a Kappa coefficient of 0.952, and a FoM coefficient of 0.403; (2) Based on the simulated land use under the SD scenario from 2030 to 2060, the quantity of construction land was effectively controlled, and the spatial distributions of cropland and forests were found to dominate in the north and south of the Yangtze River, respectively; (3) Anhui Province was the major contributor (accounted for 49.5%) to the net carbon absorption by cropland while Zhejiang Province was the major contributor (accounted for 63.3%) to the net carbon absorption by forest in the YRDR during the period 2020–2060 under the SD scenario; (4) Carbon emissions from construction land were the main source of carbon emissions from land use in the YRDR during the period 2020–2060 with proportions higher than 99% under both the ND and SD development scenarios. These findings underscore the urgent need for the government to take measures to balance the relationships between cropland and ecological protection and economic development, which provides a reference for the optimization of land use structure and policy formulation in the future. Full article

The rapid expansion of built-up land, a hallmark of accelerated urbanization, has emerged as a pivotal factor contributing to regional climate change and the degradation of ecosystem functions. The decline in ecosystem service value (ESV) has consequently garnered significant attention in global sustainable development research. The Shandong Peninsula urban agglomeration is crucial for promoting the construction of the Yellow River Economic Belt in China, with its ecological status increasingly gaining prominence. This study investigated the ESV response to land use/cover change (LUCC) through the elasticity coefficient in order to analyze the degree of disturbance caused by land use activities on ecosystem functions in the Shandong Peninsula urban agglomeration. This analysis was based on the examination of LUCC characteristics and ESV from 1990 to 2020. The findings reveal that (1) the Shandong Peninsula urban agglomeration experienced a continuous increase in the proportion of built-up land from 1990 to 2020, alongside a highly complex transfer between different land use types, characterized by diverse transfer trajectories. The most prominent features were noted to be the rapid expansion of built-up land and the simultaneous decline in agricultural land. (2) The analysis of four landscape pattern indices, encompassing Shannon’s diversity index, indicates that the continuous development of urbanization has led to increased fragmentation in land use and decreased connectivity. However, obvious spatial distribution differences exist among different districts and counties. (3) The ESV was revised using the normalized difference vegetation index, revealing a slight decrease in the total ESV of the Shandong Peninsula urban agglomeration. However, significant differences were observed among districts and counties. The number of counties and districts exhibiting low and high ESVs continuously increased, whereas those with intermediate levels generally remained unchanged. (4) The analysis of the elasticity coefficient reveals that LUCC exerts a substantial disturbance and influence on ecosystem services, with the strongest disturbance ability occurring from 2000 to 2010. The elasticity coefficient exhibits obvious spatial heterogeneity across both the entire urban agglomeration and within individual cities. Notably, Qingdao and Jinan, the dual cores of the Shandong Peninsula urban agglomeration, exhibit markedly distinct characteristics. These disparities are closely related to their development foundations in 1990 and their evolution over the past 30 years. The ESV response to LUCC displays significant variation across different time periods and spatial locations. Consequently, it is imperative to formulate dynamic management policies on the basis of regional characteristics. Such policies aim to balance social and economic development while ensuring ecological protection, thereby promoting the social and economic advancement and ecological environment preservation of the Shandong Peninsula urban agglomeration. Full article

Human activities exert a profound influence on land use and land cover, and these changes directly influence habitat quality and ecosystem functioning. In the Gansu–Qinghai contiguous region of the upper Yellow River, habitat quality has undergone substantial transformations in recent years due to the synergistic impacts of natural processes and human intervention. Therefore, evaluating the effects of land use changes on habitat quality is crucial for advancing regional sustainable development and improving the worth of ecosystem services. In response to these challenges, we devised a two-pronged approach: a land use simulation (FLUS) model and an integrated valuation of ecosystem services and trade-offs (InVEST) model, leveraging remote sensing data. This integrated methodology establishes a research framework for the evaluation and simulation of spatial and temporal variations in habitat quality. The results of the study show that, firstly, from 1980 to 2020, the habitat quality index in the Gansu–Qinghai contiguous region of the upper Yellow River decreased from 0.8528 to 0.8434. Secondly, our predictions anticipate a decrease in habitat quality, although the decline is not pronounced across all scenarios. The highest habitat quality values were projected under the EP (Ecology Priority) scenario, followed by the CLP (Cultivated Land Priority) scenario, while the BAU (Business as Usual) scenario consistently yielded the lowest values in all three scenarios. Finally, the ecological land, including forest land and grassland, consistently occupied areas characterized by high habitat quality. In contrast, Construction land consistently appeared in regions associated with low habitat quality. The implementation of conservation measures emerges as a crucial strategy, effectively limiting the expansion of construction land and promoting the augmentation of forest land and grassland cover. This approach serves to enhance overall habitat quality. These outcomes furnish a scientific foundation for the judicious formulation of future land-use policies and ecological protection measures. Full article

Predation is an important factor limiting bird populations and is usually the main factor influencing nest survival. In riverine habitats, flooding poses an additional significant challenge. Our study aimed to elucidate the influence of nest location and incubation timing on the survival of common sandpiper nests in a large, semi-natural, lowland river. The survey was carried out in central Poland on the Vistula River, in 2014–2015, 2021, and 2023, along two river sections 2 km and 10 km in length. The nest survival rate was 27%, which is twice as low as that reported on small upland rivers, with flooding being an additional factor causing losses on the Vistula River. Our research showed that mammalian and avian predation accounted for 51% of losses and flooding for 49% of losses. The negative impact of floods on nest survival decreased as the breeding season progressed between May and July, while the chances of being depredated increased during the same period. Nests placed under shrubs were less likely predated than nests located in grass. Moreover, locating the nest in proximity to water increased nesting survival and in fact, more nests found in our study were situated close to the water’s edge. Full article

This study introduces a spatial layout framework for the multi-objective optimization of low-impact development (LID) measures at an urban watershed scale, targeting the mitigation of urban flooding and water pollution exacerbated by urbanization. The framework, tailored for the Dahongmen area within Beijing’s Liangshui River Watershed, integrates the storm water management model (SWMM) with the nondominated sorting genetic algorithm II (NSGA-II). It optimizes LID deployment by balancing annual costs, volume capture ratio of rainfall, runoff pollution control rate, and the reduction in heat island potential (HIPR). High-resolution comprehensive runoff and land use data calibrate the model, ensuring the realism of the optimization approach. The selection of optimal solutions from the Pareto front is guided by weights determined through both the entropy weight method and subjective weight method, employing the TOPSIS method. The research highlights the positive, nonlinear correlation between cost and environmental benefits, particularly in reducing heat island effects, offering vital decision-making insights. It also identifies a critical weight range in specific decision-making scenarios, providing a scientific basis for rational weight assignment in practical engineering. This study exemplifies the benefits of comprehensive multi-objective optimization, with expectations of markedly improving the efficacy of large-scale LID implementations. Full article

The thermal conditions of transitional (ranging from warm to cold) coldwater streams impact the ranges and resource availabilities for biota inhabiting these lotic systems. With ongoing climate change and increasing land modifications, thermal boundaries may shift, altering thermal transition zones and their biotic communities. The objective of this study was to investigate the condition of trout across three forks of the Whitewater River catchment, located in southeastern Minnesota, and to investigate factors influencing fish community composition and distribution. Each fork was characterized into three separate sections: headwater (coolwater), middle (warmwater), and lower (coldwater). Springs were identified throughout each fork, with greatest concentrations in the lower sections of each fork. Using single-pass electrofishing, we sampled 61 sites across the three forks in the Whitewater River system (North = 21 sites, Middle = 19, South = 21), and catch statistics were used to calculate diversity, trout abundance, and trout condition. In general, diversity increased, and trout were healthier but less abundant in middle and headwater sections, whereas diversity decreased slightly, trout condition decreased, and trout abundance increased in lower reaches, with changes differing somewhat among forks. Canonical correlation analysis highlighted strong significant correlations showing that Simpson diversity and trout condition increase going upstream, with high non-trout abundance, while trout catch rates decrease and width narrows. The Whitewater River is a catchment exhibiting transitional temperature-pattern characteristics with generally low fish community diversity and trout conditions that range from thin, normal, and robust. Dominated by a changing landscape (agriculture) and intensifying climate change, we may begin to see stream temperatures increase along with species diversity. Understanding how spring temperature influences species composition and distribution can bring potential stressors to light, increasing our understanding of thermal conditions and helping to mitigate the negative impacts from land use and climate change. Full article