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Symbiotic relationships between enterprises help mitigate resource and environmental impacts of industrial activities via exchanging waste or by‐products as material inputs among each other. However, the emergence of such symbiotic relationships under complex driving factors across different geographical scales remains hitherto not well understood. Here, we provide an analytic framework including a random forest model and Shannon index, to systematically describe and explain the scale effects of driving factors underlying the symbiotic relationships. Based on a questionnaire survey for 324 enterprises in Chun'an, a typical industrial city in eastern China, we applied this analytical framework. The results show that, first, the quantity of symbiotic relationships exhibits an inversely proportional function across various geographical scales. Second, there exist significant differences in the dominant factors at different scales. Finally, the diversity of importance of factors and the emergence of symbiotic relationships exhibit a consistent trend of fluctuation, providing evidence for the explanatory potential of our proposed analytical framework for the driving mechanisms of emergence. We find that when enterprises are simultaneously affected by multiple driving factors with potent forces (referred to as the diversity of importance), symbiotic behaviors are more likely to occur. Moreover, our results suggest that fostering symbiotic relationships necessitates considering the variations in driving factors across different scales comprehensively and formulating targeted promotional measures tailored to the specific driving factors of different enterprise types. Our proposed framework would help to maximize industrial symbiosis potentials in a specific region. [ABSTRACT FROM AUTHOR]

Finding appropriate measures to drive industrial agglomeration is particularly urgent for Africa, which has been facing a decline in industrial production activities. Therefore, this study contributed by developing a multi-level spatial analysis framework that explored the global driving mechanisms and local heterogeneity of industrial agglomeration in Africa through various critical factors. The findings are as follows: (1) From 2009 to 2019, the industry in Africa has always been highly concentrated in a few countries, but the degree of agglomeration has been declining, with some countries in the East and West African regions showing a significant increase in the level of industrial agglomeration and a high potential for development. (2) Industrial agglomeration in African countries was driven by several factors, of which GDP per capita, highway network density, and electricity supply were the strongest and most consistent drivers. The impact strength of factors varied considerably across regions. (3) The drivers didn’t act independently and directly on the dependent variable, but were the product of synergy after the interaction between the two factors. Synergies between access to electricity and GDP per capita and other factors dominated the pattern of industrial agglomeration in Africa. [ABSTRACT FROM AUTHOR]

Biodiversity loss will lead to a serious decline for ecosystem services, which will ultimately affect human well-being and survival. Monitoring the spatial and temporal dynamics of grassland biodiversity is essential for its conservation and sustainable development. This study integrated ground monitoring data, Landsat remote sensing, and environmental variables in the Three Rivers Headwater Region (TRHR) from 2000 to 2021. We established a reliable model for estimating grassland species diversity, analyzed the spatial and temporal patterns, trends of change, and the driving factors of changes in grassland species diversity over the past 22 years. Among models based on diverse variable selection and machine learning methods, the random forest (RF) combined stepwise regression (STEP) model was found to be the optimal model for estimating grassland species diversity in this study, which had an R2 of 0.44 and an RMSE of 2.56 n/m2 on the test set. The spatial distribution of species diversity showed a pattern of abundance in the southeast and scarcity in the northwest. Trend analysis revealed that species diversity was increasing in 80.46% of the area, whereas 16.59% of the area exhibited a decreasing trend. The analysis of driving factors indicated that the changes in species diversity were driven by both climate change and human activities over the past 22 years in the study area, of which temperature was the most significant driving factor. This study effectively monitors grassland species diversity on a large scale, thereby supporting biodiversity monitoring and grassland resource management. [ABSTRACT FROM AUTHOR]

Ecological environmental protection and poverty alleviation are of great significance for the study of human–land relationship coordination and sustainable development, and they have also been a focus of attention in China in the past few decades. In this study, we chose 13 contiguous poverty-stricken areas in China as the study area. Using MODIS Leaf Area Index (LAI) data from 2000 to 2020, the spatial–temporal changes in greenness were obtained using the Bayesian spatial–temporal model (BYM). Spatial autocorrelation was used to identify the spatial distribution of poverty using socio-economic statistical data. Driving factors, including natural factors, poverty factors, and the Grain for Green Policy (GTGP), and their influence on greenness were analyzed by using the Geodetector model for detecting spatial differentiation and factors' interactions. The results showed the following: (1) In 13 contiguous poverty-stricken areas (CPSAs) in China, 59% of the area presented an increasing trend of greenness. (2) In 2000, the high poverty levels with larger MPI values were widely distributed. After 20 years, the overall MPI value was lower, except in some northwest regions with increased MPI values. The spatial autocorrelation of poverty, which relates to the mutual influence of poverty in adjacent areas, also decreased. (3) In the study area, 65.24% of the regions showed strong synergistic effect between greening progress and poverty reduction in the interaction between poverty status and green development. With the improvement of greenness level, the positive correlation between poverty alleviation and ecological environment improvement has become increasingly close. (4) The impacts of interaction factors with the highest q values changed from temperature interacting with precision to regional division interacting with the Grain for Green Policy. The conclusions are that from 2000 to 2020, the impact of natural factors, geographical division, and poverty status on greenness has shown a decreasing trend; The effect of the Grain for Green Policy is gradually increasing; At the same time, the interaction and overlapping effects between the Grain for Green Policy and poverty were increasing. Taking into account the needs of ecological environment, poverty alleviation, and rural revitalization, this research provides valuable reference for formulating and implementing relevant policies based on the actual situation in different regions to promote harmonious coexistence between human-land relationship. [ABSTRACT FROM AUTHOR]

Five national-level urban agglomerations in China, namely the Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), middle reaches of the Yangtze River (MRYR), Pearl River Delta (PRD), and Chengdu-Chongqing (CY), have undergone rapid economic development, accompanied by a surge in carbon emissions. Notably, there exists a gap in existing research that hinders comprehensive comparative studies on the carbon emissions of these urban agglomerations during the 12th and 13th Five-Year Plan periods, respectively. In this study, we comparatively analyze the spatiotemporal characteristics and spatial clustering characteristics of carbon emissions and their driving factors across the five urban agglomerations during the 12th and 13th Five-Year Plan periods using spatial autocorrelation and multiple spatial econometric models. The main results are as follows: firstly, the total carbon emissions across the YRD are the highest, while the average carbon emissions in BTH are higher than those across other urban agglomerations. Secondly, during the 12th Five-Year Plan period, the main related socioeconomic factors for carbon emissions of the BTH, PRD, YRD, MRYR and CY are per capita GDP, general public budget expenditure, urbanization rate, population density, and industrial structure, respectively. Thirdly, during the 13th Five-Year Plan period, industrial structure have a close link with carbon emissions across BTH and MRYR; the carbon emissions across PRD have close correlations with urbanization rate and general public budget expenditures; across YRD and CY, the key associated driver was the general public budget expenditures. All in all, these findings offer valuable insights for shaping effective emission reduction policies tailored to the unique characteristics of each urban agglomeration. [ABSTRACT FROM AUTHOR]

This study employs Landsat images from 1995, 2009, and 2022, utilizing Google Earth Engine and Random Forest algorithm in R software for land use and land cover change analysis in Dar es Salaam Metropolitan City. Results show a substantial shift, notably in bushland and forest, with a 14.57% and 2.9% decline, respectively. Drivers of change include urban (14.87%) and agricultural (4.47%) growth. Overall, 64.3% of land cover changed, primarily transitioning from bushland to agriculture (25.7%) and forest to agriculture (9.2%). Qualitative insights underscore unregulated urban expansion, informal settlements, migration, human activities, and inadequate planning as significant contributors, aiding sustainable urban governance. [ABSTRACT FROM AUTHOR]

The surface area of Poyang Lake, the largest freshwater lake in China, has decreased substantially in recent decades due to multiple potential factors, including the change in inflow into the lake, flow rate regulation of the Three Gorges Reservoir (TGR), shifts in the Yangtze River stage-discharge relationship, and sand mining in the lake. Here, the daily surface area of Poyang Lake is estimated using Landsat image data and water-level measurements. Multiple regression models are then used to establish the relationship between the surface area and the predictor variables of inflow into the lake, Yangtze River water level, and cumulative sand mining. The contributions of each driving factor to the reduction of Poyang Lake's surface area are quantified by scenario comparison method. The results reveal that the mean annual surface area of Poyang Lake decreased by 388 km2 (approximately 15% of the total surface area), from 2,585 km2 in 1980–2002 to 2,197 km2 in 2003–2016. In terms of overall contribution, sand mining is identified as the most important factor in the surface area reduction, followed by the shift in the Yangtze River stage-discharge relationship and the TGR flow rate regulation. In terms of monthly scale impacts, sand mining reduced the surface area by more than 260 km2 each month. The shift in the Yangtze River stage-discharge relationship reduced the surface area by 16–156 km2 during the dry season (November–May) of the Yangtze River and increased the surface area by 33–84 km2 during the flood season (June–October). The TGR flow rate regulation has the opposite effects: it reduced the surface area by 109–172 km2 during the impoundment period from September to October and increased it by 19–68 km2 during the release period from December to June. [ABSTRACT FROM AUTHOR]

In the context of rapid ageing, the scale and distribution of social care resources for older adults in China are undergoing significant changes. Based on the macroscopic samples of the National Statistical Yearbooks from 2011 to 2021, this study examines the regional disparities, dynamic evolution, and drivers of social care provision for older adults in China. The results reveal that while the overall level of social care provision for older adults has consistently improved, significant regional disparities persist. The most abundant social care resources have long been concentrated in the economically prosperous coastal regions. Although overall disparities have shown a fluctuating downward trend over time, the absolute gaps across certain economic regions continue to widen. Furthermore, this study identifies several driving factors behind senior social care provision, including regional economic conditions, government preferences for welfare fiscal expenditure, regional consumption patterns, and urbanisation rates. However, the increase in local financial autonomy has a negative impact on the provision of social care for older adults. The findings highlight the importance of developing a more scientific fiscal oversight mechanism, creating region‐specific policies, and addressing the needs of older migrants to achieve the goal of equalising social care provision for older adults. [ABSTRACT FROM AUTHOR]

Sloping farmland (SpF) is not only an important space for food production and supply in China's hilly areas, but also a major source of soil erosion. Thus, it is important to achieve a healthy balance between regional food security and environmental protection. Yangtze River Economic Belt (YREB), an important grain production base where SpF concentrated in China, is also faced with serious soil erosion. However, research at the macro scale on the spatiotemporal change of SpF and its driving forces in YREB is still lacking. To bridge the gap, we first analyzed the long-term evolution characteristics of SpF in 1069 counties in the YREB and then explored the driving mechanism of SpF changes during 1980–2020. Results showed that the SpF in the YREB continuously decreased during the study period, with a total area decreasing by 26,300 km2. SpF was primarily concentrated in the upper reaches of the YREB while SpF use dynamic degree varied significantly with the most active change in the lower reaches, reaching to a maximum of 0.324%. The spatial gravity of SpF distribution relocated 20.15 km towards the southwest. As for the driving factors, the socioeconomic factors contributed greater to SpF changes in the whole YREB and its subregions. The intensity of human activities is the most crucial, with factor contribution rate constantly above 0.76. The interactive detection revealed that the prevailing interaction format was primarily bi-enhanced, supplemented with nonlinear-enhanced, which amplified the role of different factors after interacting with them. The pair-wise interaction involving socioeconomic factors had a more potential effect on SpF changes compared to those between physical geography and locational factors. The influence of the intensity of human activities on SpF changes is greatly enhanced after interacting with any factor. It dominated SpF changes in the YREB and its interaction with GDP played an important role at all times. These findings can enlighten differential management strategies of SpF use and ecological conservation in the YREB. [ABSTRACT FROM AUTHOR]

Variations in the wind speed intensity significantly impact evapotranspiration, water cycle processes, air quality and wind utilization. Previous studies have focused primarily on changes in mean wind speed, with little research on variations in different wind speed intensities. In this paper, we defined five wind speed indices to quantify the changes in different wind speed intensities and analyzed their associations with atmospheric circulation based on daily wind speed data collected from 601 meteorological stations across China from 1960 to 2018. The wind speed indices we defined include the annual mean wind speed, the annual maximum daily mean wind speed, the number of heavy wind days, the number of gentle breeze days and the number of light breeze days. The results showed that from 1960 to 2018, the annual mean wind speed, the annual maximum daily mean wind speed, the number of heavy wind days and the number of gentle breeze days exhibited significant decreasing trends (P < 0.05). The number of light breeze days exhibited a significant increasing trend (P < 0.001) in China during the same period. Large-scale atmospheric circulation patterns were one of the main factors affecting the changes in wind speed intensity. The Arctic Oscillation (AO) and the West Pacific Subtropical High Intensity Index (WPSHI) were significantly negatively correlated with the annual mean wind speed, the annual maximum daily mean wind speed, the number of heavy wind days and the number of gentle breeze days (P < 0.01), and the Asian Polar Vortex Intensity Index (APVI) was extremely significantly positively correlated with these four wind speed indices (P < 0.001). This suggests that monitoring and analyzing these atmospheric circulation indices can enable more accurate predictions of wind speed. These findings will provide information for climate change forecast, air pollution risk assessments and wind energy utilization. [ABSTRACT FROM AUTHOR]

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Mariculture is a key practice to promote the supply-side reform of fishery, and it is of strategic significance to explore the causes and paths of its high-level development. Based on the TOE (Technology-Organization-Environment) theoretical framework and the configuration methodology, this paper adopts the dynamic qualitative comparative analysis (QCA) method and panel data from 10 coastal provinces and cities in China from 2013 to 2021 to explore the configuration effects of six antecedents, namely, the intensity of technology promotion, investment in scientific research, personnel specialization, industry intensification, nearshore water quality, and offshore pollution discharge, along temporal and spatial dimensions, on the level of mariculture development. The results show that (1) individual driving factors do not constitute the necessary conditions for a high level of mariculture industry development, but the necessity of the three conditions—research funding, industry intensification and nearshore seawater quality—shows a general increasing trend; and (2) the results of the path analysis show that a total of seven configuration paths for a high level of development are generated, which can be further classified into "organization-led and technology synergistic", "technology-organization-environment multiple-driven type", and "technology-environment dual-driven type". Based on the panel data, this study explores the impact of spatial and temporal changes in factor combinations on the development level of mariculture and provides a theoretical basis and practical insights for the development of locally adapted execution pathways. [ABSTRACT FROM AUTHOR]

During the development of the Chengdu–Chongqing Urban Agglomeration (CCUA) in China, PM2.5 pollution severely threatened public health, presenting a significant environmental challenge. This study employs a novel spatial interpolation method known as High Accuracy Surface Modeling (HASM), along with the geographical detector method, local and regional contributions calculation model, and the Hybrid Single–Particle Lagrangian Integrated Trajectory model to analyze the seasonal spatial distribution of PM2.5 concentrations and their anthropogenic driving factors from 2014 to 2023. The transport pathway and potential sources of seasonal PM2.5 concentrations were also examined. The results showed the following: (1) HASM was identified as the most suitable interpolation method for monitoring PM2.5 concentrations in the CCUA; (2) The PM2.5 concentrations exhibited a decreasing trend across all seasons, with the highest values in winter and the lowest in summer. Spatially, the concentrations showed a pattern of being higher in the southwest and lower in the southeast; (3) Industrial soot (dust) emissions (ISEs) and industry structure (IS) were the most important anthropogenic driving factors influencing PM2.5 pollution; (4) The border area between the eastern part of the Tibet Autonomous Region and western Sichuan province in China significantly contribute to PM2.5 pollution in the CCUA, especially during winter. [ABSTRACT FROM AUTHOR]

Conflicts between different types of land use, driven by rapid urbanization, are altering ecosystem services supply–demand balances (ESDB), and the reduction of ESDB will threaten regional sustainable development and human welfare. However, there are few studies on the interrelationships and their drivers between land use conflicts (LUCs) and ESDB from a coupling perspective, especially in different main functional areas. Therefore, this study focused on Southwest China. Firstly, the coupling coordination degree model was employed to measure the ESDB‐LUCs relationship and analyze its dynamic changes from 1990 to 2020. Then, the RDA method was used to explore the driving factors of the ESDB‐LUCs relationship in different functional areas (i.e., urban development area (UDA), agricultural development area (APA), and ecological protection area (EPA)). The results show that the LUCs index displayed a downward trend in the SW during 1990–2020, with a decreasing distribution pattern from the northeast to the southwest. The ESDB index exhibited a downward and then upward trend, with an increasing distribution mode from the northeast to the southwest. There was a spatial dependence between LUCs and ESDB. The type of coupling coordination between LUCs and ESDB was dominated by moderate coordination, with the index showing a spatial pattern of UDA > APA > EPA. Among these, the proportion of cropland and the proportion of urban land were the main factors influencing the degree of coordination of the UDA (explanation rate > 80%), showing positive and negative effects, respectively. The proportion of forestland and the proportion of cropland were the main factors influencing the APA and EPA (explanation rate > 70%), with negative and positive effects, respectively. Therefore, mitigating the conflict between cropland and urban land, cropland and forest land is essential to achieve ecosystem balance in the SW. [ABSTRACT FROM AUTHOR]

Predicting the dissolved oxygen concentration and identifying its driving factors are essential for improved prevention and management of anoxia in estuaries. However, complex hydrodynamic conditions and the limitations in traditional methods result in challenges in the identification of the driving factors for the low dissolved oxygen (DO) phenomenon. The objective of our study is to develop a robust deep learning model using four-year in situ data collected from an automatic water quality monitoring station (AWQMS) in an estuary, for accurate identification and quantification of the driving factors influencing DO levels. Mitigations in hypoxia were observed during the initial two years, but a subsequent decline in DO concentrations was witnessed recently. The periodicity of DO concentrations in the Pearl River Estuary reduced with the increase in the hypoxic intensity. Maximal information coefficient (MIC) and extreme gradient boosting (XGBoost) were employed to determine the significance of input variables, which were subsequently validated by using the long- and short-term memory networks (LSTMs). The driving factors contributing to the hypoxia problem were shown as temperature, pH, conductivity, and NH4+-N concentrations. Notably, the evaluation index values of the hybrid model are MAPE = 0.0887 and R2 = 0.9208, which have been improved compared with the LSTM model by about 99.34% in MAPE reduction and 16.56% in R2 improvement, indicating that the MixUp-LSTM model was capable of effectively capturing nonlinear relationships between DO and other water quality indicators. Based on existing literature, three traditional statistical methods and four machine learning models were also performed to compare with the proposed MixUp-LSTM model, which outperformed other models in terms of prediction accuracy and robustness. Overall, the successful identification of the driving factors for the deoxygenation phenomenon would have important implications for the governance and regulation of low DO in estuaries. [ABSTRACT FROM AUTHOR]

The Yellow River Basin (YRB) is an important grain and energy production base in China. However, the sustainable development of the YRB is constrained by water scarcity. Identifying the key factors influencing water use changes through the lens of the Water–Energy–Food Nexus (WEF-Nexus) is essential for sustainable resource use in the YRB. This study analyzed the spatial and temporal changes in water use in the energy and food sectors from 2000 to 2020 and identified the key factors influencing water use changes based on the generalized Divisia index method. Then, the water use change induced by the WEF-Nexus was evaluated. The results indicated that (1) the scale of energy production is the key factor leading to increased water use, with a total contribution of 21.345 billion m3 from 2000 to 2020. The intensity of energy water use is the key factor in inhibiting increased water use, reducing regional water use by 18.853 billion m3. (2) In the past 20 years, the changes in water use by energy and food production in the midstream and downstream regions showed nonsignificant downward trends and upward trends in the upstream region. Thus, the stability and sustainability of water use downstream have strengthened, while the water use sustainability upstream and the stability of water use midstream should further increase. This paper will support sustainable water use in the YRB. [ABSTRACT FROM AUTHOR]

The urban functional zone, serving as a bridge to understanding the complex interactions between human spatial activities and surface thermal environmental changes, explores the driving force information of its internal temperature changes, which is crucial for improving the urban thermal environment. However, the impacts of the current urban functional zones on the thermal environment, based on the delineation of human activities, have yet to be sufficiently investigated. To address the issue, we constructed a two-factor weighted dominant function vector model of "population heat--land use scale" to identify urban functional zones. This model is based on multisource data and considers the perspective of urban functional supply and demand matching. We then analyzed the spatial differentiation and driving factors of the relationship between urban functional zones and the surface thermal environment using the random forest algorithm, bivariate spatial autocorrelation, geographical detectors, and geographically weighted regression models. The results showed that there are significant differences in the Land Surface Temperature among different urban functional zones in the central urban area of Lanzhou. Among these, the life service zone has the greatest impact on the surface thermal environment, followed by the industrial zone and catering service zone, while the green space zone has the least impact. The surface thermal environment exhibits high-high clusters in localized spatial clustering patterns with life service, industrial, catering service, and residential zones. In contrast, it tends to exhibit low-high clusters with green spaces. Significant spatial clustering and dependence exist between various functional zones and the surface thermal environment. The land cover types characterized by the Normalized Difference Bare Land and Building Index, the vegetation coverage represented by the Fraction of Vegetation Cover, and the density of industrial activities indicated by the Industrial POI Kernel Density Index are the main drivers of the surface thermal environment in the various functional zones of the central urban area of Lanzhou, and all exhibit significant spatial heterogeneity. [ABSTRACT FROM AUTHOR]

Groundwater nitrate contamination poses a potential threat to human health and environmental safety globally. This study proposes an interpretable stacking ensemble learning (SEL) framework for enhancing and interpreting groundwater nitrate spatial predictions by integrating the two-level heterogeneous SEL model and SHapley Additive exPlanations (SHAP). In the SEL model, five commonly used machine learning models were utilized as base models (gradient boosting decision tree, extreme gradient boosting, random forest, extremely randomized trees, and k-nearest neighbor), whose outputs were taken as input data for the meta-model. When applied to the agricultural intensive area, the Eden Valley in the UK, the SEL model outperformed the individual models in predictive performance and generalization ability. It reveals a mean groundwater nitrate level of 2.22 mg/L-N, with 2.46% of sandstone aquifers exceeding the drinking standard of 11.3 mg/L-N. Alarmingly, 8.74% of areas with high groundwater nitrate remain outside the designated nitrate vulnerable zones. Moreover, SHAP identified that transmissivity, baseflow index, hydraulic conductivity, the percentage of arable land, and the C:N ratio in the soil were the top five key driving factors of groundwater nitrate. With nitrate threatening groundwater globally, this study presents a high-accuracy, interpretable, and flexible modeling framework that enhances our understanding of the mechanisms behind groundwater nitrate contamination. It implies that the interpretable SEL framework has great promise for providing valuable evidence for environmental management, water resource protection, and sustainable development, particularly in the data-scarce area. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive review of building lifecycle carbon emissions (CEBL) and reduction approaches, analyzing over 300 recent publications and engaging in in-depth discussion of more than 100 key studies. The review systematically examines CO2 emissions across all stages of a building's lifecycle, from material production and transportation to construction, operation, demolition, and material recycling. While existing research highlights the significance of operational energy efficiency, this review reveals critical research gaps in quantifying transportation emissions, accounting for on-site equipment emissions during construction, and addressing the unique characteristics of non-residential buildings. Furthermore, the paper underscores the urgent need for improved construction waste management practices, especially in developing countries where landfilling remains prevalent. For the building carbon reduction approaches, promising building carbon emission reduction approaches include leveraging carbon dioxide sinks, implementing integrated energy systems, integrating building-integrated photovoltaics (BIPV), and enacting effective policy interventions are separately discussed. By revealing data and theoretical limitations within current research, this review calls for more integrated and context-specific approaches to CEBL assessment, paving the way for a more sustainable built environment. [ABSTRACT FROM AUTHOR]

Dryland lakes are indispensable to regional water resource systems. Ebinur Lake, the largest saline lake in Xinjiang Uygur Autonomous Region, is vital for regional biodiversity and environmental stability but has been facing the predicament of gradual shrinkage in recent decades. In this study, we proposed a new dual-index method for Landsat (-5, -7, -8, and -9) data to extract water with the combinations of the normalized difference water index (NDWI) and the modified NDWI for turbid waters (NDWIturbid). The dual-index method showed a high overall accuracy of 96.36% for Ebinur Lake. Landsat series images from 1992 to 2023 were employed to acquire the water areas of Ebinur Lake. The results showed that, over the past three decades, the area of Ebinur Lake exhibited a fluctuating decreasing trend, with an average lake area of 568.74 ± 152.43 km². The northwest intermittent water areas showed significant changes, and there was a close connection between the northwest and core water areas. Seasonally, the lake area decreased from spring to autumn. River inflow, driven by rainfall and human activities, was the primary factor affecting the inter/inner annual changes in Ebinur Lake. Furthermore, due to the valley effects, wind was found to be a critical factor in the diurnal changes in the water areas. This study should deepen the understanding of the variations of Ebinur Lake and benefit local water resource management. [ABSTRACT FROM AUTHOR]

Salinity is an essential parameter for evaluating water quality and plays a crucial role in maintaining the stability of lake ecosystems, particularly in arid and semi-arid climates. Salinity responds to changes in climate and human activity, with significant impacts on water quality and ecosystem services. In this study, Sentinel-2A/B Multi-Spectral Instrument (MSI) images and quasi-synchronous field data were utilized to estimate lake salinity using machine learning approaches (i.e., XGB, CNN, DNN, and RFR). Atmospheric correction for MSI images was tested using six processors (ACOLITE, C2RCC, POLYMER, MUMM, iCOR, and Sen2Cor). The most accurate model and atmospheric correction method were found to be the extreme gradient boosting tree combined with the ACOLITE correction algorithm. These were used to develop a salinity model (N = 70, mean absolute percentage error = 9.95%) and applied to eight lakes in Inner Mongolia from 2016 to 2024. Seasonal and interannual variations were explored, along with an examination of potential drivers of salinity changes over time. Average salinities in the autumn and spring were higher than in the summer. The highest salinities were observed in the lake centers and tended to be consistent and homogeneous. Interannual trends in salinity were evident in several lakes, influenced by evaporation and precipitation. Climate factors were the primary drivers of interannual salinity trends in most lakes. [ABSTRACT FROM AUTHOR]

Vegetation dynamics are critical to the terrestrial carbon and water cycle, with China recognized as one of the largest contributors to global greening due to significant variations in forest coverage. However, distinguishing the effects of vegetation changes from those of climate factors on vegetation productivity remains challenging. This study conducted a comprehensive analysis of vegetation productivity in Northwest China over the past two decades, focusing on the spatiotemporal patterns and drivers of gross primary production (GPP) within ecological restoration areas. Using trend analysis and ridge regression models, we assessed the relative contributions of climate factors and vegetation coverage changes to GPP dynamics. The results revealed a significant increase in both the GPP and vegetation coverage in Northern China from 2001 to 2020, with GPP rising by 6.7 g C m−2 yr−1 and forest coverage increasing by 0.08% per year. A strong positive correlation (r = 0.9) was observed between vegetation coverage changes and GPP. The increase in GPP was driven by both climate factors and changes in forest coverage, with climate factors contributing 61.0% and vegetation coverage changes contributing 39.0%. Among the climate factors, radiation, temperature, and precipitation contributed 15.4%, 6.4%, and 39.2%, respectively. The study highlights the critical role of ecological restoration efforts, particular in regions like the Less Plateau and Inner Mongolian Plateau, in enhancing vegetation productivity. These findings provide valuable insights for addressing desertification and inform strategies for ecological restoration and sustainable development in Northern China. [ABSTRACT FROM AUTHOR]

Rainfall interception loss (Ei) is a difficult to study and poorly understood flux compared to transpiration and soil evaporation. The influence of climate and vegetation on Ei is not well known at continental‐to‐global and annual‐to‐decadal scales. Here, we use a long‐term multi‐product approach to examine the global trends in Ei, and further utilize a recently developed and validated dataset to isolate the relative contributions of precipitation, vegetation and evaporative demand. At decadal timescales, increasing Ei is largely driven by global vegetation greening through an increase in the intercepting surface and storage capacity, while its inter‐annual variations are mainly controlled by changes in precipitation, largely related to El Niño/Southern Oscillation. Increasing evaporative demand, driven by atmospheric warming, also positively contributes to the global rise in Ei. This study provides new perspectives for further understanding the impacts of climate change on the terrestrial hydrological cycle. Plain Language Summary: Rainfall interception loss is the volume of rain that gets caught by plants before reaching the ground and evaporated back into the atmosphere. It is among the least understood components of the global water cycle. In our research, we used satellite data over a long time (from 1981 to 2020) and a recently developed global model to study how rainfall interception has changed in time and space. We discovered that globally, more rain is being caught by vegetation over the years. This increase happens because our planet is greening, increasing the surface over which rain can be intercepted. On the other hand, changes in how much it rains dominate the year‐to‐year differences in interception loss. At the same time, as the atmosphere gets warmer, water can evaporate faster from vegetation, which adds to the growing trend in interception loss. These results match with the expectation of an intensified water cycle over the continents. Key Points: Rainfall interception loss exhibits increasing trends globallyIts multi‐decadal trends are driven by vegetation greening and warming, whereas interannual variations are controlled by precipitationENSO regulates rainfall interception loss largely through its influence on precipitation dynamics [ABSTRACT FROM AUTHOR]

Using Landsat TM/OLI remote sensing images and social statistical data from 1995, 2000, 2005, 2010, 2015, and 2020, construction land information in Beijing's main urban area was extracted with ArcGIS 10.4.1 and other software. Based on calculations of the expansion speed, expansion intensity, fractal dimension, and elasticity coefficient, the spatiotemporal expansion characteristics of the urban area of Beijing were analyzed to reveal the laws and driving forces of urban expansion in Beijing. The results showed that the urban construction land area in Beijing expanded by a factor of 0.53 from 1995 to 2020, and its expansion speed and intensity gradually slowed. The overall expansion trend is that the central urban area remains basically unchanged, while the peripheral areas are rapidly expanding, showing a trend of rapid growth first and then stable growth, and the urban layout is basically stable. The urban expansion of Beijing has led to increasingly complex, tortuous, and unstable boundaries. Overall, the center of gravity of Beijing is moving toward the northeast, and the elasticity coefficient of urban expansion is 1.67 times that of a reasonable coefficient. The intensity and direction of urban expansion in Beijing are most significantly related to population mobility. Research on the expansion of Beijing lies the foundation for the integration and coordinated planning of resources in the various districts of Beijing and provides a basis for its sustainable development. [ABSTRACT FROM AUTHOR]

Grasping how scale influences the interactions among ecosystem services (ESs) is vital for the sustainable management of multiple ESs at the regional level. However, it is currently unclear whether the actual ES interactions and their driving mechanisms are consistent across different spatial and temporal scales. Therefore, using the Lishui River Basin of China as a case study, we analyzed the spatial and temporal distribution of five key ESs across three scales (grid, sub-watershed, and county) from 2010 to 2020. We also innovatively used Pearson correlation analysis, Self-organizing Mapping (SOM), and random forest analysis to assess the dynamic trends of trade-offs/synergies among ESs, ecosystem service bundles (ESBs), and their main socio-ecological drivers across different spatiotemporal scales. The findings showed that (1) the spatial distribution of ESs varied with land use types, with high-value areas mainly in the western and northern mountainous regions and lower values in the eastern part. Temporally, significant improvements were observed in soil conservation (SC, 3028.23–5023.75 t/hm2) and water yield (WY, 558.79–969.56 mm), while carbon sequestration (CS) and habitat quality (HQ) declined from 2010 to 2020. (2) The trade-offs and synergies among ESs exhibited enhanced at larger scales, with synergies being the predominant relationship. These relationships remained relatively stable over time, with trade-offs mainly observed in ES pairs related to nitrogen export (NE). (3) ESBs and their socio-ecological drivers varied with scales. At the grid scale, frequent ESB flows and transformations were observed, with land use/land cover (LULC) being the main drivers. At other scales, climate (especially temperature) and topography were dominant. Ecosystem management focused on city bundles or downstream city bundles in the east of the basin, aligning with urban expansion trends. These insights will offer valuable guidance for decision-making regarding hierarchical management strategies and resource allocation for regional ESs. [ABSTRACT FROM AUTHOR]

The distribution characteristics of the microbial community in rhizosphere soils of different altitudinal gradients were explored to uncover ecological factors affecting microbial community composition. In this study, the community variations of bacteria and fungi in the rhizosphere soil of Chrysanthemum indicum L. were analyzed. Samples were distributed along an altitudinal gradient of 300–1500 m above sea level in the Fuling watershed of the Three Gorges Reservoir area, China. The analysis was conducted using Illumina MiSeq high-throughput sequencing and bioinformatics analyses. Through correlation analysis with ecological factors, the altitude distribution pattern and driving factors of soil microbial diversity in the mountainous and hilly region of Chongqing were explored. According to the results, the richness and diversity of rhizosphere soil bacteria increased with altitude, while fungi were the richest and most diverse at an altitude of 900 m. The composition of the microbial community differed among different altitudes. Actinobacteria, Proteobacteria, Acidobacteriota, Chloroflexi, Bacteroidota, Ascomycota, unclassified_k_Fungi, Basidiomycota, and Mortierellomycota dominated the microbial community in rhizosphere soil. Correlation analysis showed that the distribution of rhizosphere soil microbial communities correlated with soil ecological factors at different altitudes. Moisture, pH, total nitrogen, total potassium, available potassium, urease, and catalase were significantly positively correlated with rhizosphere soil bacterial α-diversity, while their correlations with fungi were not significant. Variation partition analysis showed that the combined effects of soil physical and chemical factors, enzyme activity, and microbial quantity regulated bacterial community structure and composition. Their combined contributions (19.21%) were lower than the individual effects of soil physical and chemical factors (48.49%), enzyme activity (53.24%), and microbial quantity (60.38%). The effects of ecological factors on fungal communities differed: While the soil physical and chemical factors (44.43%) alone had a clear effect on fungal community structures, their combined contributions had no apparent effect. The results of this study not only contribute to a deeper understanding of the impact mechanism of altitude gradient on the diversity of rhizosphere soil microbial communities, but also provide a scientific basis for the protection and management of mountainous and hilly ecosystems. It lays a foundation for the future exploration of the relationship between microbial communities and plant–soil interactions. [ABSTRACT FROM AUTHOR]

A series of empirical analytical tools have been adopted to investigate the driving mechanisms of surface urban heat islands (SUHI) on a global scale, among which spatial heterogeneity is yet to be fully elucidated. In this study, we investigated the spatial non-stationarity of the driving factors concerning surface properties, climate conditions, and urbanization processes for global long-term SUHI. First, the potential impact on SUHI was explored using global ordinary least squares regression. Geographically weighted regression (GWR) and multi-scale GWR (MGWR) from local perspectives were employed for comparison. The results show that the MGWR has the highest goodness of fit at 0.87, 0.73, 0.90, 0.74, 0.85, and 0.76 for annual day/night (AD/AN), summer day/night (SD/SN), and winter day/night (WD/WN) scales, respectively. Although both global and local schemes exhibit similar influencing magnitudes and signs on the SUHI, the MGWR is better at capturing spatial non-stationarity. Globally, for AD, AN, SD, SN, WD, and WN, the coefficients of the urban-rural vegetation index difference (ΔEVI) and surface albedo difference (ΔWSA), urban mean precipitation (MAP), wind speed (WS), population density (PD), and urban area (UA) are −0.50, +0.30, +0.16, +1.31, −0.03, and +0.03, respectively, at daytime, and −0.38, −0.33, −0.39, −0.10, +0.18, and +0.08, respectively, at night-time. Given the spatial heterogeneity of multiple factors, ΔEVI exhibits a strong mitigation effect on the SD SUHI especially in arid zones. The negative influence of ΔWSA on night-time SUHI demonstrates a strong latitudinal disparity and greater sensitivity in the equatorial zone. The positive correlations between MAP and AD/SD SUHIs have evident latitudinal and longitudinal variations. The mitigation effect of WS displayed distinct coastal amplification, especially in WD. In contrast, the PD and UA presented prominent positive impacts on night-time SUHI with less seasonal contrast. [ABSTRACT FROM AUTHOR]

With global change and urban expansion, the city's vulnerable to climate-induced disasters is increasing significantly. Addressing this challenge has become a global priority and there is an urgent need to improve the resilience and adaptability. We focused on the climate adaptability of cities in the Pearl River Delta (PRD) in the southeastern China, and employed the entropy-weighting method and TOPSIS model to assess city's adaptability to climate in three levels, exposure, sensitivity, and adaptation. Then, we applied the Obstacle and Geodetector model to identify the challenges of the cities and elucidate the primary drivers of the changes in climate adaptability from 2000 to 2020. This study shows a significant increase in climate adaptation within the PRD region over the past two decades, especially for economy-prosperous cities such as Shenzhen and Guangzhou that show significant improvement. Spatially, central cities are more adaptable than western cities. The density of urban drainage pipes, doctors per 1000 people, and GDP per capita are the main obstacles. The explanatory power of the number of invention patents and fixed investments persists in surpassing that of the heat index and other factors. This interaction underscores the imperative for integrated strategies aimed at fostering both socio-economic development and climate adaptability. It emphasizes the need to tailor urban planning approaches to the specific characteristics of cities in different locations and stages of development, thereby enhancing their capacity to adapt to climate change. [ABSTRACT FROM AUTHOR]

High-quality development is the primary task of comprehensively building a socialist, modern country, as well as the primary task of building urban agglomerations in China. Based on the five development concepts, this paper used the entropy method to measure the High Quality Development Index (HQDI) of the five major urban agglomerations. The results showed that the HQDI of the five major urban agglomerations shows a fluctuating upward trend. First, using the Dagum Gini coefficient to explore the sources of HQDI development differences in urban agglomerations, we found that the main source of HQDI differences in urban agglomerations was interregional differences, while intra-regional differences were not important. Second, kernel density estimation was used to test the dynamic evolution trend of HQDI within urban agglomerations. There was a polarisation phenomenon in the HQDI of urban agglomerations, such as the Pearl River Delta urban agglomeration and the Chengdu-Chongqing urban agglomeration. But overall, the degree of imbalance had decreased. Third, using geographic detectors to examine the driving factors of HQDI in urban agglomerations, we found that the main driving forces for improving HQDI in urban agglomerations were economic growth, artificial intelligence technology and fiscal decentralisation. All the interaction factors had greater explanatory power for the spatial differentiation of HQDI, which can be divided into two types: two-factor improvement and non-linear improvement. This study is conducive to improving and enriching the theoretical system for evaluating the high quality development of urban agglomerations, and provides policy references for promoting the high quality development of urban agglomerations. [ABSTRACT FROM AUTHOR]

[Objective] This study aims to comprehend the spatial distribution patterns of arable soil nutrients in various geomorphic conditions of the Loess Plateau, to unveil the driving factors behind these changes, and to provide the critical insights for precision management in the regional agriculture and the construction of agricultural projects. [Methods] Based on the 2020 soil nutrient data from the Loess Plateau in Shaanxi, the Kriging interpolation method was employed to analyze the spatial patterns of soil nutrients across four different geomorphic types. The driving factors behind these nutrient spatial variations was explored by using the geodetector model. [Results] (1) In the Loess Plateau, spanning from the Great Wall's sandy lands, through the hilly and gully regions of northern Shaanxi, the loess table lands in northern of Weihe River, to the Guanzhong Plain, the average contents of soil organic matter (SOM), total nitrogen (TN), available phosphorus (AP), and available potassium (AK) exhibited a gradual increasing trend. Specifically, the SOM content averaged 10.20, 10.08, 15.28, and 18.78 g/kg, respectively, across these regions. TN averaged 0.63, 0.66, 0.98, and 1.19 g/kg. AP level averaged 12.99, 13.10, 16.97, and 27.24 mg/kg, and AK averaged 117.72, 149.94, 217.27, and 252.83 mg/kg, respectively. This trend indicated a consistent increase from the sandy lands to the plains. (2) In the loess area of Shaanxi, the duration of sunshine had most significant factor influencing overall nutrient contents, followed by annual temperature and fertilizer use. The distribution of nutrients in the windy area was predominantly affected by soil bulk density, whereas in the hilly and gully areas, plateau areas, and plains, fertilizer use was the primary influencing factor. The interaction of various factors provided a more comprehensive explanatory of nutrient dynamics than individual factor alone. [Conclusion] To optimize soil health, it is advisable to increase nutrient input and enhance soil fertility retention in sandy areas and hilly and gully areas. In contrast, the focus should be on improving nutrient utilization efficiency for plateau and plain areas. Additionally, in the process of farmland, it is crucial to tailor human activities to suit the environmental characteristics and specific conditions of different geomorphic regions. [ABSTRACT FROM AUTHOR]

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Plant diversity is the basis for human survival and development, directly affecting the function and stability of urban ecosystems. Its distribution pattern and causes have been a central issue in ecological and landscape gardening research. Rapid urbanization in Zhengzhou City has led to the fragmentation of urban green spaces and damage to ecosystems, seriously affecting urban biodiversity conservation. Understanding the distribution pattern of plant diversity in the region and its relationship with environmental factors is crucial for maintaining and enhancing urban plant diversity. Plant data from 178 sample plots in the built-up area of Zhengzhou City were collected and combined with environmental factors, and the characteristics of plant diversity, richness patterns, and their main environmental explanations in Zhengzhou City were explored. Results showed that there were 596 plant species belonging to 357 genera and 110 families in the study area. There were five dominant families and four dominant genera. Four distinct spatial patterns of plant diversity were identified along the urban–rural gradient. Urbanization factors such as GDP per capita, house prices, and imperviousness within 500 m from the patch significantly influenced plant diversity. There was an imbalance between the spatial pattern of plant diversity and application of urban landscape greening in Zhengzhou City. Future studies should focus on the application of native plants, curb plant homogenization, and reduce anthropogenic interference, which are conducive to protecting and enhancing urban plant diversity. These results can provide a basis for understanding the distribution pattern and influence mechanism of urbanization factors on plant diversity and serve as a reference for policymakers and planners of plant diversity conservation in Zhengzhou City. [ABSTRACT FROM AUTHOR]

Assessing vegetation changes in alpine arid and fragile ecosystems is imperative for informed ecological restoration initiatives and adaptive ecosystem management. Previous studies primarily employed the Normalized Difference Vegetation Index (NDVI) to reveal vegetation dynamics, ignoring the spatial heterogeneity alterations caused by bare soil. In this study, we used a comprehensive analysis of NDVI and its spatial heterogeneity to examine the vegetation changes across the Three-River Headwaters Region (TRHR) over the past two decades. A random forest model was used to elucidate the underlying causes of these changes. We found that between 2000 and 2022, 9.4% of the regions exhibited significant changes in both NDVI and its spatial heterogeneity. These regions were categorized into six distinct types of vegetation change: improving conditions (62.1%), regrowing conditions (11.0%), slight degradation (16.2%), medium degradation (8.4%), severe degradation (2.0%), and desertification (0.3%). In comparison with steppe regions, meadows showed a greater proportion of improved conditions and medium degradation, whereas steppes had more instances of regrowth and slight degradation. Climate variables are the dominant factors that caused vegetation changes, with contributions to NDVI and spatial heterogeneity reaching 68.9% and 73.2%, respectively. Temperature is the primary driver of vegetation dynamics across the different types of change, with a more pronounced impact in meadows. In severely degraded steppe and meadow regions, grazing intensity emerged as the predominant driver of NDVI change, with an importance value exceeding 0.50. Notably, as degradation progressed from slight to severe, the significance of this factor correspondingly increased. Our findings can provide effective information for guiding the implementation of ecological restoration projects and the sustainable management of alpine arid ecosystems. [ABSTRACT FROM AUTHOR]

Against the backdrop of globalization and ecological civilization, this study aims to analyze the patterns of system coupling coordination development in the Yangtze River Basin under the interacting influences of population growth, ecological conservation, energy utilization, and digital economic development. Using a multisource model, this paper explores the state of coordinated development, spatial–temporal evolution characteristics, and influencing factors in the Yangtze River Basin from 2011 to 2020. The results indicate the following: (1) The overall degree of coupling coordination in the Yangtze River Basin shows better performances in the eastern coastal areas compared to the central and western regions. Over time, the spatial autocorrelation of coupling and coordination increases, exhibiting a significant spatial clustering trend. (2) The Moran's I index increased from 0.327 to 0.370, with high–high clusters primarily distributed in economically developed coastal provinces, while low–low clusters were observed in remote provinces in the central and western regions, revealing regional development imbalance issues. (3) The driving force analysis shows that green coverage and GDP are the core factors influencing the spatial differentiation of coupling coordinated development. Factors such as the urbanization rate, nighttime light index, and energy consumption had significant impacts in certain years but are generally considered minor factors. The results of this study not only contribute to understanding the dynamic mechanisms of regional coupling and development but also provide a scientific basis for formulating regional coordinated development policies, promoting the achievement of win–win goals of economic growth and ecological civilization in the Yangtze River Basin and similar regions. [ABSTRACT FROM AUTHOR]

Clarifying the complex relationships among ecosystem services (ESs) and their driving mechanisms is essential for effective ecosystem management and enhancing human welfare. Nonetheless, the current research on these issues still remains limited; therefore, further theoretical exploration is required. This study aims to quantitatively illustrate the trade-off strength of ESs and investigate the spatiotemporal heterogeneity connections between these relationships and various anthropogenic and natural factors in Baiyangdian basin, China, integrating InVEST, RMSE, geographical detector and MGWR methods. From 2000 to 2020, the total water yield (WY) and nutrient export (NE) increased, while the total carbon storage (CS) and habitat quality (HQ) decreased slightly. The trade-offs of ESs showed spatiotemporal heterogeneity. The most serious trade-off occurred between regulating services (CS and NE) and supporting services (HQ) in 2000, which was mainly distributed in the densely forested and grassed western and northern regions of the basin. The trade-off intensities of half of the pairwise ESs in 2020 increased, with the strengthened areas mainly located in the southeast of the watershed where built-up lands are concentrated. Various factors dominated the trade-offs among ESs, with the interactive effects of multiple drivers being more significant than those of individual factors. Land use type, vegetation cover and precipitation have the most pronounced effect on the trade-offs among ESs. The findings of this study may suggest and advocate for spatial ecological strategies to enhance the integrated and holistic advancement of various ESs and also serve as a reference for regional ecosystem governance and the attainment of sustainable growth. [ABSTRACT FROM AUTHOR]

Analyzing the spatiotemporal changes and influencing factors of carbon emissions generated by land use is of great importance for improving land use structure and promoting regional low-carbon economic development. This study, based on remote sensing and statistical yearbook data from 1995 to 2020, calculated the carbon emissions from land use in Jiangxi Province, China. Multiple spatial analysis methods and the logarithmic mean Divisia index were used to elucidate the spatiotemporal evolution and driving factors of carbon emissions, and the findings revealed the following: (1) The spatiotemporal changes in land use in Jiangxi Province during 1995–2020 were substantial as forest land accounted for 65% of the entire land area, while construction land increased by 98.1%. Cultivated land decreased the most, followed by forest land. (2) There was a fourfold rise in carbon emissions in Jiangxi Province, driven primarily by construction land, and northern areas produced higher carbon emissions compared with central and southern regions. Forest land was the main carbon sink. (3) Economic development (257.36%) and the impact of the proportion of construction land (211.31%) were the primary factors contributing to the increase in carbon emissions from land use, while other factors had inhibitory effects. This study transformed the macroscale low-carbon development strategy of cities into targeted local policies, and the research theories and methods adopted could provide scientific reference for other regions in urgent need of carbon reduction worldwide. [ABSTRACT FROM AUTHOR]

Water resources, energy, and food are essential for the development of society, and they are strongly interdependent. The coupling and coordination relationships of the water–energy–food (WEF) system are important for regional resource security and high-quality development. The Yellow River Irrigation Area in Shandong Province, China, is a grain production base and has a substantial impact on national food security. To examine the water, energy, and food subsystem dynamics in this area, an evaluation system for the WEF system was established. A comprehensive weighting method based on game theory was employed to determine index weights. TOPSIS was used to assess the development level of the WEF system. A coupling coordination degree model was used to analyze the evolution of the coupling coordination degree of the WEF system from 2000 to 2020, and a GWR model was constructed to explore the spatial heterogeneity of its driving factors. The findings indicated that the development level of the WEF system in the study area was moderate, with a gradual upward trend. The coupling coordination degree fluctuated between 0.62 and 0.739. The GWR model revealed that temperature had an overall negative effect on the coupling coordination degree, with the greatest impact on the central irrigation area; the slope and NDVI had a negative effect, with increasing intensity from the southwest to the northeast; and rainfall had an overall positive effect, with the greatest impact on the irrigation area near the estuary in the northeast. Overall, the building area ratio had a negative effect on the coupling coordination degree, with exceptions in some areas. These research outcomes provide theoretical support for sustainable agricultural development in the Yellow River irrigation areas of Shandong Province and methodological reference data for studying collaborative resource utilization in irrigation regions. [ABSTRACT FROM AUTHOR]

Soil erosion has been recognized as a critical environmental issue worldwide. While previous studies have primarily focused on watershed-scale soil erosion vulnerability from a natural factor perspective, there is a notable gap in understanding the intricate interplay between natural and socioeconomic factors, especially in the context of spatial heterogeneity and nonlinear impacts of human-land interactions. To address this, our study evaluates the soil erosion vulnerability at a provincial scale, taking Hubei Province as a case study to explore the combined effects of natural and socio-economic factors. We developed an evaluation index system based on 15 indicators of soil erosion vulnerability: exposure, sensitivity, and adaptability. In addition, the combination weighting method was applied to determine index weights, and the spatial interaction was analyzed using spatial autocorrelation, geographical temporally weighted regression and geographical detector. The results showed an overall decreasing soil erosion intensity in Hubei Province during 2000 and 2020. The soil erosion vulnerability increased before 2000 and then. The areas with high soil erosion vulnerability were mainly confined in the central and southern regions of Hubei Province (Xiantao, Tianmen, Qianjiang and Ezhou) with obvious spatial aggregation that intensified over time. Natural factors (habitat quality index) had negative impacts on soil erosion vulnerability, whereas socioeconomic factors (population density) showed substantial spatial variability in their influences. There was a positive correlation between soil erosion vulnerability and erosion intensity, with the correlation coefficients ranging from -0.41 and 0.93. The increase of slope was found to enhance the positive correlation between soil erosion vulnerability and intensity. [ABSTRACT FROM AUTHOR]

Urbanization research is essential for the sustainable use of regional land resources and ecological environment protection. The expansion process and driving factors of urban construction land at different scales in the middle reaches of the Yellow River (MRYR) have not been comprehensively elucidated. In this study, we explored the distribution pattern of urban construction land on different slope gradients at different scales and analyzed its influencing factors. The main findings were as follows: (1) There has been significant expansion of urban construction land in the MRYR over the past 20 years. Spatial heterogeneity was observed in the regional urban construction land expansion process among different geomorphic regions. (2) The urban construction land in the MRYR was expanded vertically to areas with slopes of >5°, particularly in 2005–2010. Significant slope climbing of urban construction land was observed in the loess hilly-gully and rocky mountain areas. (3) In MRYR, 68.45% of the counties were categorized as the slope-climbing types, including 37.38% high-slope-climbing types. (4) The regional population density and economic development level were closely associated with regional urban construction land area variability. (5) The climbing process of regional urban construction can effectively alleviate farmland encroachment and pressure on the regional ecological environment. The urban expansion of the metropolitan distribution areas in the Plain region (such as Xi'an, Taiyuan) had a relatively significant impact on the local carbon storage. [ABSTRACT FROM AUTHOR]

Desertification has had a significant impact on the ecological environment of the Yellow River Basin (YRB) in China. However, previous studies on the evaluation of the ecological environment quality (EEQ) in the YRB have paid limited attention to the indicator of desertification. It is of great significance to incorporate the desertification index into the spatiotemporal assessment of the EEQ in the YRB in order to protect the ecological environment in the region. In this study, based on multi-source remote sensing data from 91 cities in the YRB, this article proposes a desertification remote sensing ecological index (DRSEI) model, which builds upon the traditional Remote Sensing Ecological Index (RSEI) model, to analyze the spatiotemporal changes in the EEQ in the YRB from 2001 to 2021. Furthermore, using the geographic detector (GD), and geographically and temporally weighted regression (GTWR) model, the study assesses the impact of human and natural factors on the EEQ in the YRB. The research findings indicate that: (1) Compared to the traditional RSEI, the improved DRSEI shows a decreasing trend in the evaluation results of the EEQ. Among the 24 cities, the change in DRSEI exceeds 0.05 compared to RSEI, accounting for 26.37% of the YRB. The remaining 67 cities have changes within a range of less than 0.05, accounting for 73.63% of the YRB. (2) The results of the GD for individual and interactive effects reveal that rainfall and elevation have significant individual and interactive effects on the EEQ. Furthermore, after the interaction with natural factors, the explanatory power of human factors gradually increases over time. The spatial heterogeneity results of GTWR demonstrate that rainfall has a strong direct positive impact on the EEQ, accounting for 98.90% of the influence, while temperature exhibits a more pronounced direct inhibitory effect, accounting for 76.92% of the influence. Human activities have a strong negative impact on the EEQ and a weak positive impact. [ABSTRACT FROM AUTHOR]

The restoration of mining wastelands, particularly in karst regions contaminated by heavy metals, is an environmental challenge in need of urgent attention. Soil microbes play a vital role in nutrient cycling and ecosystem recovery, yet the long-term evolution of soil microbial communities in such settings remains poorly understood. This study explored the dynamics and influencing factors of soil microbial communities during 35 years of natural restoration in abandoned manganese (Mn) mine areas in Guangxi Province, China. The results revealed that the concentrations of Mn, Cd, Zn, and Cu were significantly (p < 0.05) reduced by 80.4–85.3%, 55.3–70.0%, 21.0–38.1%, and 29.4–49.4%, respectively, in the mid-late restoration periods (R19 and R35) compared with R1. The α diversities of the bacterial and fungal communities significantly increased in the middle–late restoration periods (R19 and R35), indicating increased microbial diversity as restoration progressed. The bacterial community structure exhibited more pronounced changes than did the fungal community structure, with significant shifts observed in dominant phyla such as Proteobacteria, Actinobacteria, Acidobacteriota, and Ascomycota. Notably, the relative abundances of Rhizobiales, Burkholderiales, and Hypocreales increased gradually with succession. Co-occurrence network analysis revealed that bacterial interactions became stronger over time, whereas interactions between bacteria and fungi weakened. Mantel tests and partial least squares path modeling (PLS‒PM) identified soil pH, heavy metals (Mn, Cd, Zn, and Cu), and nutrients (SOM and TN) as key drivers shaping the microbial community composition. These factors were more strongly correlated with bacterial communities than with fungal communities, underscoring the different responses of microbial groups to environmental changes during natural restoration. These findings enhance our understanding of the ecological processes governing microbial community succession in heavy metal-contaminated soils undergoing natural restoration. [ABSTRACT FROM AUTHOR]

Vibrio spp. is a group of heterotrophic bacteria that are ubiquitous in marine habitats, with various ecological and clinical importance. This study investigated the environmental factors that regulate Vibrio spp. dynamics in various tropical marine habitats, including nearshore (an estuary and a coastal beach) and offshore transects located northwest and southeast of Peninsular Malaysia, while focusing on the distribution of attached and free-living Vibrio spp., population growth, and community composition. The results showed that > 85% of the Vibrio spp. in nearshore waters occurred in attached form and correlated positively to total suspended solids (TSS) and Chlorophyll a (Chl a) concentrations. On the other hand, Vibrio spp. growth rates were positively correlated to dissolved organic carbon (DOC) concentrations, but negatively correlated to total bacterial counts, likely due to resource competition. In addition, high-throughput sequencing of 16S rRNA V3-V4 region showed that Vibrio spp. in these tropical waters contributed < 1 − 18% of the whole bacterioplankton community, and the six major Vibrio spp. taxa were V. alginolyticus group, V. brasiliensis, V. caribbeanicus, V. hepatarius group, V. splendidus group and V. thalassae. db-RDA (cumulative variance explicated = 93.53%) further revealed the influence of TSS, DOC, and dissolved organic nitrogen (DON) to the Vibrio spp. community profiles. The study highlighted the importance of suspended solids (TSS and Chl a) and dissolved organic nutrients (DOC and DON) towards Vibrio spp. dynamics in tropical marine waters. [ABSTRACT FROM AUTHOR]

Habitat quality (HQ) serves as a pivotal metric for assessing biodiversity and ecosystem health, with alterations in land use driven by human activities posing direct implications on HQ and ecological sustainability within river basins. Prior research on HQ has predominantly centered on historical land use changes, neglecting the comprehensive consideration of future land use transformations and ecological zoning strategies' influence on HQ. Consequently, this investigation simulates potential land use shifts in the Min River Basin across various future scenarios, leveraging the integration of PLUS and InVEST models, quantitatively dissects HQ's responsiveness to these changes and delves into the spatial differentiation dynamics underlying these responses, while also exploring the drivers behind such differentiation. Synergizing with the Human Footprint Index (HFI), the study devises a rational ecological zoning plan tailored to the region and outlines targeted control measures for each zone. The results of the study showed that: 1) the east-central part of the Min River Basin was subject to a greater degree of human interference, and the trend of interconversion between grassland, forest land, and cropland was relatively significant, with construction land mainly originating from the transfer of cropland; 2) from 2000 to 2020, average HQ scores for priority protected zones, priority recovery zones, and appropriate development zones stood at 0.9372, 0.2697, and 0.6098, respectively, accompanied by a rise in the proportion of low and moderate HQ areas to 15% and 17%; (3) DEM and Slope were the main drivers affecting HQ, and their explanatory power reached 0.519 and 0.426, respectively; (4) in comparison to a natural development scenario (ND), the planning protection scenario (PP) offers greater promise for ecological preservation and sustainable development within the Min River Basin. The research results can provide technical support for the ecological restoration of land resources and the development and protection of national land space in watershed areas. [ABSTRACT FROM AUTHOR]

In the intricate landscape of the global food system, a nuanced understanding of dynamic evolution patterns and driving mechanisms of food trade network is essential for advancing insights into the African food trade and maintaining the food security of Africa. This paper constructs a framework for analyzing the food trade network from a comparative perspective by comparing and analyzing the evolution of food trade networks in China, the United States, Russia, the European Union, and African countries. The development trend of food trade between China, Russia, the United States, the European Union, and African countries is relatively good. China, the United States, Russia, and the European Union export far more food to African countries than they import, and bilateral food trade plays an important role in alleviating food supply shortages in Africa. The food trade networks between China, the United States, Russia, the European Union, and African countries exhibit a butterfly-shaped structure centered in Africa, and the overall intensity of bilateral trade linkages is gradually increasing. France has the greatest control over the food trade network between China, the United States, Russia, the European Union, and African countries, and the influence of the United States on the food trade network between China, the United States, Russia, the European Union, and African countries is increasing. China's independence in the food trade network between China, the United States, Russia, the European Union, and African countries is enhanced, but its control ability is limited. The impact of differences in total population, differences in food production, and geographical borders on the trade network between China, the United States, the European Union, and African countries tends to decrease, while the influence of differences in the proportion of agricultural employment, differences in the arable land available for food production, and institutional distance tends to increase. [ABSTRACT FROM AUTHOR]

Land Use and Land Cover Change (LUCC) represents the interaction between human societies and the natural environment. Studies of LUCC simulation allow for the analysis of Land Use and Land Cover (LULC) patterns in a given region. Moreover, these studies enable the simulation of complex future LUCC scenarios by integrating multiple factors. Such studies can provide effective means for optimizing and making decisions about the future patterns of a region. This review conducted a literature search on geographic models and simulations in the Web of Science database. From the literature, we summarized the basic steps of spatiotemporal dynamic simulation of LUCC. The focus was on the current major models, analyzing their characteristics and limitations, and discussing their expanded applications in land use. This review reveals that current research still faces challenges such as data uncertainty, necessitating the advancement of more diverse data and new technologies. Future research can enhance the precision and applicability of studies by improving models and methods, integrating big data and multi-scale data, and employing multi-model coupling and various algorithmic experiments for comparison. This would support the advancement of land use spatiotemporal dynamic simulation research to higher levels. [ABSTRACT FROM AUTHOR]

Alpine natural heritage sites hold significant value due to their unique global resources. Studying land cover changes in these areas is crucial for maintaining and preserving multiple their values. This study takes Kalajun-Kuerdening, one of the components of Xinjiang Tianshan World Natural Heritage Site, as an example to analyze land cover changes and their driving factors in alpine heritage sites. Highlights include: (1) Between 1994 and 2023, Forest and Grassland increased by 55.96 km2 and 18.16 km2, with notable forest growth from 2007 to 2017. Trends in Forest changes align with forest protection policies, and a substantial amount of Bareland converted to Grassland indicates an increase in vegetation cover. (2) Elevation, precipitation, temperature, and evapotranspiration are key drivers of land cover changes, as validated by Random Forest algorithm and Geodetector model. (3) Favorable conditions for Grassland to Forest transition include annual precipitation between 275 and 375 mm, annual temperature between −2 and 3 °C, annual evapotranspiration between 580 and 750 mm, elevation between 1800 and 2600 m, and aspect between 0 to 110° and 220 to 259.9°. Continuous monitoring of land cover changes and their driving factors in mountain heritage sites contributes to the protection of the ecological environment and provides data and information support for addressing climate change, resource management, and policy making. [ABSTRACT FROM AUTHOR]

Simple Summary: Environmental factors varying widely across altitudinal gradients contribute significantly to the intricate and diverse distribution patterns observed in insect communities. Investigating the diversity patterns in the grasshopper community along altitudinal gradients in Xizang is crucial for understanding broader trends in insect diversity. This study revealed a strong effect of altitude on grasshopper community diversity distribution in Xizang, showing that grasshopper species richness, Margalef richness index, Shannon–Wiener index, and Simpson dominance index decreased with an increase in altitude. The results of Pearson correlation analysis and hierarchical partitioning showed that temperature, moisture, and soil properties are closely related to the altitude distribution patterns of grasshopper communities. The key factors driving changes in grasshopper community diversity along altitudinal gradients include the mean annual temperature range, precipitation in the coldest season, and precipitation in the driest month. To summarize, the interplay between elevation and environmental variables significantly influences grasshopper community structure, distribution patterns, and diversity. To determine the grasshopper species composition, altitudinal distribution patterns, and their main drivers, we conducted a study in Xizang using 33 sample plots ranging from 600 to 4100 m. Grasshoppers were collected from August to October during 2020–2022 using sweep nets. A total of 1159 grasshoppers from six families, 28 genera, and 44 species were identified, with Omocestus cuonaensis and Aserratus eminifrontus as the dominant species, comprising 30.03% and 10.26% of total grasshoppers, respectively. The results showed that species richness and the Margalef richness index of grasshopper communities decreased significantly (p < 0.05) with increasing altitude, peaking at 1100–1600 m and lowest values at 2600–3100 m. Similarly, the Shannon–Wiener index and Simpson dominance index also decreased significantly (p < 0.05) with an increase in altitude, showing the highest and lowest values at 600–1100 m and 3100–3600 m, respectively. The Jaccard similarity coefficients among grasshopper communities varied from 0 to 0.40 across altitudinal gradients, indicating different degrees of dissimilarity. The results of Pearson correlation analyses showed that the Shannon–Wiener index, species richness, Margalef richness index, and Simpson dominance index of grasshopper communities were significantly negatively correlated with the temperature factors and soil pH, but they were significantly positively correlated with the moisture factors. Hierarchical partitioning identified annual mean temperature–daily difference, precipitation in the coldest season, and driest month precipitation as the primary factors explaining variance in grasshopper community diversity in Xizang. These findings provided greater insights into the mechanisms underlying insect community structure, distribution patterns, and diversity in Xizang ecosystems, including implications for the effects of global warming on insect communities. [ABSTRACT FROM AUTHOR]

In light of the growing demand for green and low-carbon development, the advancement of low-carbon agriculture in alignment with China's specific national circumstances is imminent. Given this urgency, the accounting of non-CO2 greenhouse gas (GHG) emissions in China's agricultural system is still in the process of continuous research and improvement. Therefore, in this paper, we present an account of agricultural non-CO2 GHG emissions in Southwest China from 1995 to 2021, based on the carbon emission coefficient method. Furthermore, we explore the extent of the influence of the drivers and the relationship with economic development, utilizing the Stochastic Impact of Regression of Population, Affluence, and Technology (STIRPAT) model and the Tapio model. We observe a general trend of increasing and then decreasing non-CO2 GHG emissions from agriculture in the Southwest region, with a pattern of higher in the center and lower in the east and west. Economic, demographic, structural, and technological levels show different degrees of impact in different provinces, favoring the development of targeted agricultural planning policies in each region. For the majority of the study period, there was a weak or strong decoupling between economic growth and GHG emissions. Finally, recommendations are made to promote low-carbon agricultural development in Southwest China, providing a database and policy support to clarify the GHG contribution of the agricultural system. [ABSTRACT FROM AUTHOR]

Forest–grassland ecotone (FGE) has essential ecological and economic value. Unfortunately, it is impacted greatly by environmental changes and anthropogenic disturbance, and is considered one of the most severely threatened biomes in China. To protect Chinese FGE, identifying its exact boundary and exploring its landscape structure dynamic are badly needed, especially on nationwide scale at one‐year temporal resolution. Here, we mapped the annual FGE distribution of China from 1990 to 2020, investigated its changing trends of area, location and landscape patterns, and revealed the underlying driving factors. Our results showed that FGE area over the 31 years totaled 1 011 870 km2, covering about 10.54% of China's land. The FGE area first increased from 1990 and peaked in 1999, and then kept decreasing until 2020. The FGE gravity center has moved accumulatively 590.15 km over the 31 years, with the net moving distance of 228.76 km southwestward. From 1990 to 2020, forest area increased continuously while grassland and cropland area decreased, but these three landscape types had been dominating the FGE. The increase in forest area was largely converted from grassland. The decline in grassland mainly resulted from its conversion into cropland and forest. Meanwhile, the conversion of cropland to grassland supplemented grassland loss to a certain extent. At landscape level, the total area with decreased fragmentation is larger than that with increased fragmentation. Returning Farmland to Grassland Project and land reclamation were primary drivers for changes of fragmentation in the northern and middle part of the FGE, while temperature and precipitation were primary drivers in southern part. Our results will improve the understanding into the dynamic trends of distribution and pattern of FGE at nationwide scale, and thus help to optimize the designing of ecological projects and protective schemes for FGE as a unique and integral biome. [ABSTRACT FROM AUTHOR]