Search

The identification and quantification of the ecological risks, sources and distribution of heavy metals in purple soils are essential for regional pollution control and management. In this study, geo-accumulation index (Igeo), enrichment factor (EF), pollution index (PI), potential ecological risk index (RI), principal component analysis (PCA) model and geographical detector (GD) were combined to evaluate the status, ecological risk, and sources of heavy metals (HMs) in soils from a typical purple soil areas of Sichuan province. The results showed that the average contents of As, Cd, Cr, Cu, Hg, Ni, Pb and Zn in purple soil were 7.77, 0.19, 69.5, 27.9, 0.077, 30.9, 26.5 mg/kg and 76.8 mg/kg, and the Igeo, EF and RI of topsoil Hg and Cd in designated area was the highest, and the average contents of Hg and Cd in topsoil were obviously greater than respective soil background value in Sichuan province and purple soil. The hot spots for the spatial distribution of 8 HMs were mainly focused in the southwest and northeast of the designated area, and there were also significant differences for 8 HMs distribution characteristics in the profile soil. Cu comes from both anthropogenic and natural sources, Zn, Ni and Cr mainly come from natural sources, but As, Pb, Hg and Cd mainly derived from human activities. GD results showed that soil texture (X18), altitude (X4), total nitrogen (TN), clay content (X3), sand content (X2) and silt content (X1) had the greatest explanatory power to 8 HMs spatial differentiation.This study provides a reference for understanding the status and influencing factors of HM pollution in typical purple soil, and lays a theoretical foundation for the environmental treatment of purple soil in China. [ABSTRACT FROM AUTHOR]

Habitat quality is an important basis for human well-being and the achievement of sustainable development. Based on land-use data for the Bosten Lake Basin in 2000, 2005, 2010, 2015, and 2022, the PLUS and InVEST models are applied in this study to predict and analyze land-use changes and explore the spatial and temporal evolution characteristics of the region's habitat quality. Additionally, we use a geographic detector model to reveal the drivers of spatial variation in habitat quality. The results show that: (1) Land use in Bosten Lake Basin is dominated by grassland and bare land, with an area share of 93.21%. Habitat quality shows a trend of degradation followed by improvement, with a spatial pattern of high in the northwest and low in the southeast. (2) Habitat quality in 2030 increased from 2022 in all cases, with a mean of 0.354 for the natural development scenario, a maximum of 0.355 for the ecological development scenario, and a minimum of 0.353 for the economic development scenario. (3) The main drivers affecting habitat quality in the Bosten Lake watershed are DEM, mean annual precipitation (MAP), and GDP per capita. X1∩X4 (0.50) and X4∩X10 (0.51) are the interaction factors with the largest dominant effect in 2000, 2010 and 2020, respectively. [ABSTRACT FROM AUTHOR]

For a long time, human activities have been prohibited in ecologically protected areas in the Ebinur Lake Wetland National Nature Reserve (ELWNNR). The implementation of total closure is one of the main methods for ecological protection. For arid zones, there is a lack of in-depth research on whether this measure contributes to ecological restoration in the reserve. The Normalized Difference Vegetation Index (NDVI) is considered to be the best indicator for ecological monitoring and has a key role to play in assessing the ecological impacts of total closure. In this study, we used Sentinel-2, Landsat-8, and Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing data to select optimal data and utilized Sen slope estimation, Mann-Kendall statistical tests, and the geographical detector model to quantitatively analyze the normalized difference vegetation index (NDVI) dynamics and its driving factors. Results were as follows: (1) The vegetation distribution of the Ebinur Lake Wetland National Nature Reserve (ELWNNR) had obvious spatial heterogeneity, showing low distribution in the middle and high distribution in the surroundings. The correlation coefficients of Landsat-8 and MODIS, Sentinel-2 and MODIS, and Sentinel-2 and Landsat-8 were 0.952, 0.842, and 0.861, respectively. The NDVI calculated from MODIS remote sensing data was higher than the value calculated by Landsat-8 and Sentinel-2 remote sensing images, and Landsat-8 remote sensing data were the most suitable data. (2) NDVI indicated more degraded areas on the whole, but the ecological recovery was obvious in the localized areas where anthropogenic closure was implemented. The ecological environment change was the result of the joint action of man and nature. Man-made intervention will change the local ecological environment, but the overall ecological environment change was still dominated by natural environmental factors. (3) Factors affecting the distribution of NDVI in descending order were as follows: precipitation > evapotranspiration > land use type > elevation > vegetation type > soil type > soil erosion > slope > temperature > slope direction. Precipitation was the main driver of vegetation change in ELWNNR. The synergistic effect of the factors showed two-factor enhancement and nonlinear enhancement, and the combined effect of the driving factors would increase the influence on NDVI. [ABSTRACT FROM AUTHOR]

Nanling Mountain region is a typical southern hilly region, which plays an important ecological and environmental protection role in China's overall land protection pattern. Based on the remote sensing image data of Longnan City in Nanling Mountain region in 2013, 2018 and 2023, this paper interpreted the land use type and analyzed the land use transfer situation by using land use transfer flow, and a land use transfer matrix. At the same time, based on the remote sensing ecological index (RSEI) model, the ecological environmental quality of Longnan City from 2013 to 2023 was retrieved. The temporal and spatial response model of the ecological environmental quality to land use transfer in Longnan City from 2013 to 2023 was discussed based on spatial autocorrelation and a geographical detector. The results show that from 2013 to 2023, the decrease of forest land (16.23 km2) and the increase of construction land (13.25 km2) were the main land use transfers in Longnan City. The ecological environment indexes of Longnan City in 2013, 2018 and 2023 were 0.789, 0.917 and 0.872, respectively, showing a trend of "first rising and then decreasing". The ecological environmental quality in the north of Longnan City was significantly lower than that in the south, and the poor ecological quality area appeared in and around the northern main urban area, showing a trend of "inward contraction". Forest land, garden land, grassland, cultivated land and water area have a positive impact on ecological environmental quality, while traffic land, construction land and other land have a negative impact on ecological environmental quality. The response of ecological environmental quality to different land use transfer modes is related to the change of the overall ecological environmental quality. The interaction between land use and land cover change (LUCC) and other factors had a great impact on the evolution of ecological environmental quality in Longnan City. [ABSTRACT FROM AUTHOR]

Clarifying the spatiotemporal distribution and influencing factors of mosque architecture in China's Hehuang region has significant positive implications for the overall protection and development of the region's architectural cultural heritage. This study utilizes field surveys and acquires POI data of traditional mosques built before 1993 in the region to analyze the distribution characteristics of mosques, aiming to explore future development trends of these religious structures. It also investigates the influencing factors, with the goal of emphasizing the primary and secondary factors affecting mosque distribution. The study finds the following: (1) Mosques are generally centered around the Huangshui Valley, displaying a "central clustering, peripheral dispersal" distribution pattern, forming a spatial structure of "two cores, one belt, multiple points", with distinct differentiation and overall uneven distribution. (2) Mosques are primarily situated at elevations between 2147 and 2764 m; on slopes less than 15°, in sunny and gentle slopes; within 20 km from rivers; within 14 km from roads; in areas receiving 400–500 mm annual rainfall; and within temperature ranges of 5.54–10.22 °C. (3) The study also finds that the spatial distribution of mosques is profoundly influenced by both natural geographical factors and human environmental factors. The better the natural location, the larger and denser the population, the richer the cultural resources, the higher the level of economic development, and the greater the concentration of Hui people, the more numerous and concentrated the mosques. (4) Population factors are the dominant factors for the clustered distribution of traditional mosques in the Hehuang area. Since the construction of mosques in the region is closely related to the number of Hui people and the proportion of Muslim adherents, areas with a high concentration of mosques also have relatively larger populations of Hui people. Temperature, precipitation, altitude, rivers, and roads are foundational factors for traditional mosques in the Hehuang area, influencing mosque distribution as external factors. [ABSTRACT FROM AUTHOR]

A series of ecological and environmental issues often occur in the coal resources and large-scale exploitation activities. The Qinling Mountains have been the most vulnerable ecological areas in China. This study aims to explore the temporal and spatial evolution of mine habitat quality in the northern foot of the Qinling Mountains for regional ecological protection and sustainable development. A systematic investigation was implemented to clarify the impact of human activities on the ecological environment of mining areas under small river basins. The land use pattern was extracted from the Xiangyu mining area in the Feng River basin, according to the remote sensing images in 1985, 2002, 2010, and 2022. The InVEST model and geographical detector were used to quantitatively analyze the habitat quality and driving factors. The results showed that the land use in the Xiangyu mining area was mainly from the dry land to the forest and industrial land from 1985 to 2022, where the transfer area of forest reached 18 245 505.71 m², indicating the remarkable project performance of returning farmland to forest and grassland. Large ranges of bare and cultivated land were occupied by the development of the urban economy after 2010, where the industrial land was expanded rapidly. The habitat quality index decreased from 0.50 to 0.49, whereas, the standard deviation increased from 0.06 to 0.09, indicating the decreasing habitat quality and the ever-expanding spatial difference. Medium and low habitat quality were concentrated mainly in the areas with bare and construction land. In addition, the overall average degree of habitat degradation decreased from 0.48 to 0.47, but the rapid expansion of industrial land continued to affect the surrounding forests. There was a high spatial consistency in the distribution of habitat quality, habitat degradation, and land use patterns. The geographical detector showed that the influence degree of driving factors on habitat quality was ranked in the descending order of the land use (0.82), DEM (0.40), temperature (0.31), precipitation (0.30), population (0.29), NDVI (0.26), GDP (0.22), slope (0.12), and aspect (0.11). Among them, land use was the main cause of the fragile ecological environment. The coupled influence of natural with human factors on the ecological environment was greater than that of single factors. The interaction between land use and natural factors showed a double-factor enhancement, illustrating habitat quality was the result of natural factors and human factors. Research on mining areas should focus mainly on the land use policy and the impact of natural factors on engineering activities. Timely measures should be taken after high-intensity engineering activities, such as industrial construction and mining areas. The vegetation can be reconstructed to restore the water quality in the watershed. This finding can provide the scientific reference for the evolution of the ecological environment in similar mining areas, thus promoting the optimization of landscape patterns and the construction of ecological civilization. [ABSTRACT FROM AUTHOR]

China's low-carbon development path will make significant contributions to achieving global sustainable development goals. Due to the diverse natural and economic conditions across different regions in China, there exists an imbalance in the distribution of carbon emissions. Therefore, regional cooperation serves as an effective means to attain low-carbon development. This study examined the pattern of carbon emissions and proposed a potential joint emission reduction strategy by utilizing the industrial carbon emission intensity (ICEI) as a crucial factor. We utilized social network analysis and Local Indicators of Spatial Association (LISA) space-time transition matrix to investigate the spatiotemporal connections and discrepancies of ICEI in the cities of the Pearl River Basin (PRB), China from 2010 to 2020. The primary drivers of the ICEI were determined through geographical detectors and multi-scale geographically weighted regression. The results were as follows: 1) the overall ICEI in the Pearl River Basin is showing a downward trend, and there is a significant spatial imbalance. 2) There are numerous network connections between cities regarding the ICEI, but the network structure is relatively fragile and unstable. 3) Economically developed cities such as Guangzhou, Foshan, and Dongguan are in the center of the network while playing an intermediary role. 4) Energy consumption, industrialization, per capita GDP, urbanization, science and technology, and productivity are found to be the most influential variables in the spatial differentiation of ICEI, and their combination increased the explanatory power of the geographic variation of ICEI. Finally, through the analysis of differences and connections in urban carbon emissions under different economic levels and ICEI, the study suggests joint carbon reduction strategies, which are centered on carbon transfer, financial support, and technological assistance among cities. [ABSTRACT FROM AUTHOR]

Rapid urbanisation has generated numerous environmental consequences, particularly regarding open space fragmentation. Open space fragmentation is the transformation of open space from a state of homogeneity, integration, and continuity to a state of heterogeneity, division, and incoherence. Nevertheless, one main obstacle to understanding this issue is how to address the spatial heterogeneity of the impact of urbanisation on open space fragmentation. Thus, this paper provides a comprehensive framework for the mechanistic associations between open space fragmentation and urbanisation in Hong Kong's built-up area. The results illustrate that both open space fragmentation index and urbanisation index values are noticeably higher in dense urban areas. Land urbanisation, represented by the percentage of construction land in total land, has the highest explanatory power for spatial differentiation in open space fragmentation, followed by social and population urbanisation factors. Furthermore, the relational interrelations of open space fragmentation drivers are the bivariate and nonlinear enhancement interactions. Social urbanisation and land urbanisation have the strongest bivariate enhancement interaction for the use fragmentation form and the largest nonlinear enhancement interaction for the internal fragmentation form. Based on initial urban planning, open space fragmentation is an adaptation outcome of population, land, and social urbanisation factors, and this self-organisation phenomenon has been further emphasised in the historical process. These insights significantly enrich our understanding of how urbanisation affects open space fragmentation and provide valuable guidance for better open space strategies. [ABSTRACT FROM AUTHOR]

Copyright of Bulletin of Soil & Water Conservation is the property of Bulletin of Soil & Water Conservation Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Bulletin of Soil & Water Conservation is the property of Bulletin of Soil & Water Conservation Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Exploring the characteristics of vegetation dynamics and quantitatively analyzing the potential drivers and the strength of their interactions are of great significance to regional ecological environmental protection and sustainable development. Therefore, based on the 2000–2022 MODIS NDVI dataset, supplemented by climatic, topographic, surface cover, and anthropogenic data for the same period, the Sen+Mann–Kendall trend analysis, coefficient of variation, and Hurst exponent were employed to examine the spatial and temporal characteristics and trends of NDVI in Hubei Province, and a partial correlation analysis and geographical detector were used to explore the strength of the influence of driving factors on the spatial differentiation of NDVI in vegetation and the underlying mechanisms of interaction. The results showed that (1) the mean NDVI value of vegetation in Hubei Province was 0.762 over 23 years, with an overall increasing trend and fluctuating upward at a rate of 0.01/10a (p < 0.005); geospatially, there is a pattern of "low east and high west"; the spatial change in NDVI shows a trend of "large-scale improvement in the surrounding hills and mountains and small-scale degradation in the middle plains"; it also presents the spatial fluctuation characteristics of "uniform distribution in general, an obvious difference between urban and rural areas, and a high fluctuation of rivers and reservoirs", (2) the future trend of NDVI in 70.76% of the region in Hubei Province is likely to maintain the same trend as that of the 2000–2022 period, with 70.78% of the future development being benign and dominated by sustained improvement, and (3) a combination of partial correlation analysis and geographical detector analysis of the drivers of vegetation NDVI change shows that land cover type and soil type are the main drivers; the interactions affecting the distribution and change characteristics of NDVI vegetation all showed two-factor enhancement or nonlinear enhancement relationships. This study contributes to a better understanding of the change mechanisms in vegetation NDVI in Hubei Province, providing support for differentiated ecological protection and project implementation. [ABSTRACT FROM AUTHOR]

Population exposure is a dominant representation of rainstorm hazard risks. However, the refined precipitation data in temporal resolution and the comparison of exposure to different rainstorm events remain relatively unexplored. Hourly precipitation data from 165 meteorological stations w used to investigate the spatiotemporal evolution of population exposure to different rainstorm scenarios in the prefecture-level cities for different periods and age groups. The Geographical Detector was adopted to quantitatively analyze the influencing factors and contribution rates to changes in population exposure during each period. The results revealed that population exposure to persistent rainstorms and abrupt rainstorms was low in the center and high in the surrounding areas, and the high exposure value decreased significantly in the 2010s. Additionally, as the duration of rainstorm events increased, the center of the high-value area of population exposure shifted southward. The distribution of population exposure was closely related to the age structure, demonstrating strong consistency with the distribution of different age groups. Except for abrupt rainstorms, the contribution rates of the average land GDP and urbanization rate to the exposure of all rainstorm scenarios increased significantly. This implies that the main factors influencing population exposure have shifted from meteorological to socioeconomic factors. [ABSTRACT FROM AUTHOR]

To clearly describe the structural characteristics of China's rare earth innovation network (REIN), this study used data on China's rare earth patents since 2001 in the Incopat global patent database to analyze the structural characteristics of China's REIN from 2001 to 2020 at the provincial and municipal levels using social network analysis. The study finds the following: (1) The overall characteristics of the network show that the connectivity and diversification of China's REIN is increasing. (2) The network spatial pattern analysis shows that the REIN exhibits a radial spatial pattern centered on Beijing at both the provincial and municipal levels. (3) Research on network nodes shows that the network is dense in eastern regions and sparse in western regions, showing distinct characteristics of coastal and resource agglomeration. Not all regions with high levels of economic development play leading roles in the REIN. Small and medium-sized cities with abundant rare earth resources also play important leading roles due to their high levels of rare earth innovation. At the provincial and municipal levels, the REIN consists of four subgroups, among which there is not significant factional diversity. (4) In terms of influencing factors, economic development level, technology absorptive capacity, innovation output level, infrastructure, and market leadership have significant impacts at the provincial and municipal levels. In addition, government leadership, industrial structure, and education level have significant impacts at the municipal level. The study clarified China's technological accumulation in the field of rare earths and provided practical guidance for the innovative development of China's rare earth industry. [ABSTRACT FROM AUTHOR]

It is necessary to further investigate the spatial considerations, temporal characteristics, and drivers of change affecting the beginning and end of soil freezing and thawing, including the maximum depth of the seasonal freezing (MDSF) and the active layer thickness (ALT) in Northeast China. Hourly soil temperature, among other data, from 1983–2022 were investigated, showing a delay of about 6 days in freezing. In contrast, thawing and complete thawing advanced by about 26 and 20 d, respectively. The freezing period and total freeze-thaw days decreased by about 29 and 23 days, respectively. The number of complete thawing period days increased by about 22 days, while the MDSF decreased by about 25 cm. The ALT increased by about 22 cm. Land Surface Temperature (LST) is the main factor influencing the beginning and end of soil freezing and thawing, MDSF and ALT changes in Northeast China; air temperature, surface net solar radiation, and volumetric soil water content followed. The influence of the interacting factors was greater than the single factors, and the interactive explanatory power of the LST and surface net solar radiation was highest when the soil started to freeze (0.858). The effect of the LST and the air temperature was highest when the soil was completely thawed (0.795). LST and the volumetric soil water content interacted to have the first explanatory power for MDSF (0.866) and ALT (0.85). The results of this study can provide scientific reference for fields such as permafrost degradation, cold zone ecological environments, and agricultural production in Northeast China. [ABSTRACT FROM AUTHOR]

Abstract The Hemiptera insects are the largest incomplete metamorphosis insect group in Insecta and play a vital role in ecosystems and biodiversity. Previous studies on the spatial distribution of Hemiptera insects mainly focused on a specific region and insect, this study explored the spatial distribution characteristics of Hemiptera insects in China (national scale), and further clarified the dominant factors affecting their spatial distribution. We used spatial autocorrelation analysis, hot spot analysis, and standard ellipse to investigate the spatial distribution characteristics of Hemiptera insects in China. Furthermore, we used geographic detectors to identify the main factors affecting their spatial distribution under China's six agricultural natural divisions and explore the influencing mechanism of dominant factors. The results show that: (i) The spatial differentiation characteristics of Hemiptera insects in China are significant, and their distribution has obvious spatial agglomeration. The Hu Huanyong Line is an important dividing line for the spatial distribution of Hemiptera insects in China. From the city scale, the HH type (high‐high cluster) is mainly distributed on both sides of the Hu Huanyong Line. (ii) The hot spots of Hemiptera insects are mainly distributed in southwest China, along the Qinling Mountains, the western side of the Wuyi Mountains, the Yinshan Mountains, the Liupanshan Mountains, the Xuefeng Mountains, the Nanling Mountains, and other mountainous areas. (iii) Under agricultural natural divisions, the influence of natural environmental factors on the spatial distribution of Hemiptera insects is obviously different. Temperature and precipitation are the dominant factors. Natural factors and socio‐economic factors have formed a positive reinforcement interaction mode on the spatial distribution of Hemiptera insects. These can provide the decision‐making basis for biodiversity conservation and efficient pest control.

Shrub encroaching on grasslands threatens grassland ecosystems and negatively affects human land use and livelihood. However, existing research on this subject is limited, having focused on small areas, thus hindering a comprehensive understanding of the diverse patterns of large-scale shrub invasions into grasslands. In recent decades, a significant encroachment of shrubs into Xilin Gol grassland has been observed in China, resulting in considerable damage to the grasslands. Therefore, this study aimed to investigate the rapid expansion of shrub-encroached grassland (SEG) from 1990 to 2020 in the Xilin Gol League, Inner Mongolia Autonomous Region, China, using long-term Landsat images. The spatial distribution characteristics of the new SEG areas were analyzed. The main factors driving the shrub encroachment in the study area were identified using a geographic detector. Our results revealed a continuous expansion of SEG in the study area over the past three decades, marked by an increase of 48.76 × 103 km2 and an average annual gain rate of 9.41 %. The increased SEG was primarily owing to grassland transition. The sprawl of the SEG was the most prominent in the western and northern regions of the study area, extending to gentle slopes of approximately 5° and low-middle elevations between 600–1300 m above mean sea level. Shrub encroachment was primarily influenced by surface temperature, wind speed, and relative humidity, with elevation, slope, and precipitation exerting relatively weaker influences. Rather than being driven by a singular factor, the expansion was a result of the combined influence of various factors. This study provides a valuable case study for understanding shrub invasion dynamics in arid and semi-arid regions globally.

With the escalation of global climate change and human activity, geohazards become increasingly frequent which cause severe casualties and property losses to local communities. To alleviate this situation and provide scientific guidance for risk reduction, it is imperative to address some of the basic questions related to geohazards, including: i) how to detect active geohazards (AGs) rapidly and automatically over a wide area; ii) how to determine the region with a high level of hazard activity; iii) what are the primary conditioning factors (CFs) of AGs; and iv) do factors operate independently or are they interconnected. To tackle these issues, we propose a universally applicable framework for wide area automated detection of AGs. The framework is based on multi-source Earth observations which capture surface deformation ranging from millimeters to meters. Our study has focused on the Hexi Corridor (HXC) in Gansu Province, China, covering an area of 210,000 km2 with a length of 1100 km. First, we construct an AGs database for the HXC with high automatic and rapid update capabilities, including a total of 4492 AGs (3652 active landslides and 840 land subsidence areas). Second, using the Geographic Detectors method, we determine the primary CFs including elevation, land surface temperature, and precipitation. We find that faults exert greater control over very slow-moving landslides, but are less effective over slow-moving landslides. Third, we analyzed the interactive effects of dual CFs on geohazard actives. Any interaction effect of dual CFs contributes to the bivariate enhancement of geohazard activity. This study significantly enhances the capabilities of the wide area automated detection of AGs, and provides a crucial dataset for hazard prediction and mitigation along the HXC.

Study region: The study area is located in Guizhou province, the centre of the karst region in southwest China. Study focus: As a typical karst region, Guizhou has long faced challenges with water scarcity. Quantifying water use efficiency (WUE) is crucial for the efficient use of water resources and sustainable management. This study examined the spatio-temporal evolution patterns and its driving factors on WUE in Guizhou using Theil-Sen's slope, partial correlation methods, and the geographic detector model. New hydrological insights for the region: (1) The multi-annual mean WUE of non-karst region (NKR, 2.75 gC kg−1 H2O yr−1) is higher than that of karst region (KR, 2.10 gC kg−1 H2O yr−1) during 2001–2020. (2) The rate of increase of WUE was three times higher in KR than in NKR. (3) Precipitation and temperature were identified as the key factors influencing WUE. Interactions between each environmental factors were observed to have both bi-variate and non-linear enhancement effects. Notably, in the NKR, soil moisture, vegetation type and leaf area index were the dominant factors influencing WUE, with interactions between environmental factors had both enhanced and weakened effect on WUE. (4) The synergistic effects of these interactions exceeded the individual or cumulative effects of the two environmental factors. These findings contribute to our understanding of the environmental factors driving changes in WUE in the KR.

Study region: Loess Plateau (LP), China Study focus: Evapotranspiration (ET) is crucial in the terrestrial hydrological cycle and serves as a vital indicator of water resource availability. However, the interactions between ET and vegetation, climate, and soil on the LP have been left unexplored, especially during the growing season and regarding ET thresholds for different land-use types. Therefore, we used ridge regression and geographic detector model to analyze ET response to fractional vegetation coverage (FVC), temperature, precipitation, land surface temperature (LST), and temperature vegetation dryness index (TVDI). Additionally, the elastic coefficient was used to determine the ET thresholds for dominant factors in different land-use types. New hydrological insight for the region: The study reveals FVC as the dominant factor driving ET changes, while also highlighting the synergistic effects of temperature, precipitation, LST, and TVDI with FVC. The synchronization of ET and FVC change trends in vegetation unchanged and restored areas underscores the significance of vegetation cover in regulating regional water cycles. Furthermore, the study provides valuable insights into the regional water cycle dynamics and offers a certain level of guidance for water management strategies tailored to different land-use types.

[Objective] Soil erosion is a prominent ecological problem in southwestern Hubei Province. A reasonable assessment of the vulnerability of soil erosion in this area and exploration of its driving mechanisms are prerequisites for effective soil and water conservation measures. [Methods] The effects of natural and socio-economic factors on soil erosion in southwest Hubei were analyzed from the perspective of the human-land coupling system. The evaluation index system of soil erosion vulnerability in southwest Hubei was constructed from three aspects: exposure, sensitivity, and adaptability. The comprehensive weighting method was used to determine the index weight. [Results] (1) From 2010 to 2020, the vulnerability of soil erosion in southwest Hubei generally showed a trend of decreasing first and then increasing. The vulnerability of soil erosion with strong and above intensity showed a fragmented distribution pattern, which was mainly concentrated in the central and southern parts of southwest Hubei and the western part of Yichang municipal district in the east. (2) The area with the highest sensitivity in the study area was concentrated at an altitude of 800 ~ 1 500 m, and the sensitivity was the largest at a slope of 25° ~ 35°, and showed a decreasing trend to both sides. When the slope was greater than 15°, the area with higher and higher sensitivity increased sharply; (3) Soil erosion vulnerability was higher than soil erosion intensity at higher and above soil erosion levels, and lower than soil erosion intensity at lower and below soil erosion levels. There was a synergistic trend between soil erosion vulnerability and soil erosion intensity. (4) The distribution pattern of soil erosion vulnerability was caused by a multi-factor synergy. The analysis of the geographical detector showed that slope, vegetation coverage, education quality, and urbanization rate have strong explanatory power for soil erosion vulnerability. [Conclusion] In the future, it is necessary to attach great importance to the protection of vegetation coverage in areas with a slope>15°, and to enhance the awareness of local residents about soil and water conservation by improving the quality of local education and strengthening the publicity of soil and water conservation.

[Objective] Snowmelt erosion is one of the main forms of soil erosion in the black soil area of Northeast China, which plays an important role in land degradation in the area. The erosivity of snowmelt runoff is the key factor for calculating the erosion amount of snowmelt runoff, so it is of great significance to study the erosivity of snowmelt runoff. The erosivity of snowmelt runoff is mainly affected by snow depth, radiation intensity and warming rate, and its main driving forces include snowmelt rate and surface runoff. [Methods] The erosivity of daily average snowmelt runoff in the past 31 years was calculated, the temporal and spatial distribution characteristics of snowmelt runoff erosivity in the black soil area of Northeast China were analyzed, and the main influence factors of snowmelt runoff erosivity in each region were analyzed by using geographic detectors. [Results] The daily average snowmelt runoff erosivity in the three black soil subregions from 1990 to 2020 showed a trend of first increasing and then decreasing, and gradually stabilized in recent years, and the erosivity of daily average snowmelt runoff for many years was 0.01 (MJ·mm)/(hm2·h·a). Spatially, the erosivity of multi-year daily average snowmelt runoff was in the range of 0~0.21 (MJ·mm)/(hm2·h·a), showing a pattern of small in the middle and large in the surrounding areas. The influence factors of snowmelt runoff erosivity in spatial were as follows, the snow depth was large in the north and small in the south, the solar radiation intensity decreased from west to east, and the warming rate decreased from north to south. The influence factor of snowmelt runoff erosivity were analyzed by ground detector, and it was found that the warming rate had the strongest effect in the Mengdong black soil subregion, while the snow depth had the strongest effect in the Songnen black soil subregion and the Sanjiang black soil subregion. [Conclusion] Analyzing the spatiotemporal characteristics and influence factors of snowmelt runoff erosivity in the black soil region of Northeast China from 1990 to 2020 had certain theoretical and practical significance for deepening the study of snowmelt runoff erosivity characteristics and snowmelt erosion prevention in the study area.

To explore the human-land relationship between traditional Tujia settlements and the natural environment, by applying the geographic information systems (GIS), this study constructs a suitability evaluation system for human settlements of the Tujia ethnic minority based on 12 indexes, including elevation, slope, aspect, landform, relief degree of land surface (RDLS), river, vegetation, soil, road, the ancient Sichuan salt route, temperature, and precipitation. It applies the geographical detector (Geodetector) to analyze the driving force of the settlement distribution, and the spatial pattern of human settlements suitability and the spatial relationship between the suitability and the settlement distribution are determined by fuzzy comprehensive evaluation method. The results reveal that among the 12 indexes, RDLS exercises the most significant impact on the spatial distribution of settlements, followed by the elevation, while slope has the most minor importance, and therefore the suitability for farming remained Tujia ancestors' primary concern when selecting the settlement locations. In addition, there appears to be a strong interaction between the indicators and the spatial distribution of the settlements, with the interaction effect of any two indicators being greater than that of one, suggesting that the ancestors selected the sites of their settlements based on comprehensive consideration. Moreover, the settlements are primarily located in more suitable areas with low hills and flat relief; however, some cluster in less suitable areas considering the source of income.

Understanding the driving mechanisms behind surface urban heat island (SUHI) effects is essential for mitigating the degradation of urban thermal environments and enhancing urban livability. However, previous studies have primarily concentrated on central urban areas, lacking a comprehensive analysis of the entire metropolitan area over distinct time periods. Additionally, most studies have relied on regression analysis models such as ordinary least squares (OLS) or logistic regression, without adequately analyzing the spatial heterogeneity of factors influencing the surface urban heat island (SUHI) effects. Therefore, this study aims to explore the spatial heterogeneity and driving mechanisms of surface urban heat island (SUHI) effects in the Guangzhou-Foshan metropolitan area across different time periods. The Local Climate Zones (LCZs) method was employed to analyze the landscape characteristics and spatial structure of the Guangzhou-Foshan metropolis for the years 2013, 2018, and 2023. Furthermore, Geographically Weighted Regression (GWR), Multi-scale Geographically Weighted Regression (MGWR), and Geographical Detector (GD) models were utilized to investigate the interactions between influencing factors (land cover factors, urban environmental factors, socio-economic factors) and Surface Urban Heat Island Intensity (SUHII), maximizing the explanation of SUHII across all time periods. Three main findings emerged: First, the Local Climate Zones (LCZs) in the Guangzhou-Foshan metropolitan area exhibited significant spatial heterogeneity, with a non-linear relationship to SUHII. Second, the SUHI effects displayed a distinct core-periphery pattern, with Large lowrise (LCZ 8) and compact lowrise (LCZ 3) areas showing the highest SUHII levels in urban core zones. Third, land cover factors emerged as the most influential factors on SUHI effects in the Guangzhou-Foshan metropolis. These results indicate that SUHI effects exhibit notable spatial heterogeneity, and varying negative influencing factors can be leveraged to mitigate SUHI effects in different metropolitan locations. Such findings offer crucial insights for future urban policy-making.

Investigating the evolution and drivers of multifunctional land use is essential for sustainable land management and regional biological conservation. This research focuses on the Hehuang Valley, where we developed an “ecological-social-production” evaluation system for assessing land use multifunctionality from the perspective of multifunctional landscape. Leveraging Geographic Information System technologies, we conducted a quantitative analysis of spatiotemporal variations in multifunctional land use across the valley in recently twenty years. Correlation coefficients were employed to identify trade-offs and synergies among various land use functions. Additionally, geographical detector and grey relational analysis models were utilized to pinpoint the factors influencing spatiotemporal changes in land use functions during the specified period. The results showed that: (1) During the period, the overall multifunctionality of land use in the Hehuang Valley exhibited an increasing trend. The economic production function of the land showed the highest growth, while the ecological and social functions showed lower growth. (2) In most areas of the Hehuang Valley, there was a positive correlation between social and economic production functions and a negative correlation between social and ecological functions, as well as between economic production and ecological functions. (3) Natural conditions were the main factors of spatial variation of land use comprehensive functions, but human factors, including land use intensity and the rate of farmland conversion to non-agricultural uses, were the primary drivers of temporal changes in multifunctional land use. The findings provide valuable references and scientific support for policymakers in optimizing land use and multifunctional landscape conservation.

Urban agglomerations significantly alter the regional thermal environment. It is urgent to investigate the evolution and influence mechanisms of urban agglomeration heat island intensity from a regional perspective. This study is supported by Google Earth Engine long-term MODIS data series. On the basis of estimating surface urban heat island intensity (SUHI) in the Yangtze River Delta urban agglomeration from 2001 to 2020 based on the suburban temperature difference method, the causes of heat islands in the urban agglomeration were analyzed by using geographical detector analysis. Additionally, the heat island proportion (PHI) and SUHI indicators were used to compare and analyze the changing characteristics of the urban heat island effect of ten representative cities. The research reveals the following: (1) The average SUHI of the study area increased from 0.11 °C in 2001 to 0.29 °C in 2020, with an average annual increase rate of 0.009 °C. (2) According to the results of the geographical detector analysis, SUHI was influenced by several driving factors exhibiting obvious seasonal variations. (3) SUHI difference between cities is significant in the summer (1.52 °C), but smallest in the winter; the PHI difference between cities is larger in the autumn (46.7%), while it is smaller in the summer. The research findings aim to effectively serve the formulation of collaborative development plans for the Yangtze River Delta urban agglomeration.

Surface albedo plays a pivotal role in the Earth’s energy balance and climate. This study conducted an analysis of the spatial distribution patterns and temporal evolution of albedo, normalized difference vegetation index (NDVI), normalized difference snow index snow cover (NSC), and land surface temperature (LST) within the Qilian Mountains (QLMs) from 2001 to 2022. This study evaluated the spatiotemporal correlations of albedo with NSC, NDVI, and LST at various temporal scales. Additionally, the study quantified the driving forces and relative contributions of topographic and natural factors to the albedo variation of the QLMs using geographic detectors. The findings revealed the following insights: (1) Approximately 22.8% of the QLMs exhibited significant changes in albedo. The annual average albedo and NSC exhibited a minor decline with rates of −0.00037 and −0.05083 (Sen’s slope), respectively. Conversely, LST displayed a marginal increase at a rate of 0.00564, while NDVI experienced a notable increase at a rate of 0.00178. (2) The seasonal fluctuations of NSC, LST, and vegetation collectively influenced the overall albedo changes in the Qilian Mountains. Notably, the highly similar trends and significant correlations between albedo and NSC, whether in intra-annual monthly variations, multi-year monthly anomalies, or regional multi-year mean trends, indicate that the changes in snow albedo reflected by NSC played a major role. Additionally, the area proportion and corresponding average elevation of PSI (permanent snow and ice regions) slightly increased, potentially suggesting a slow upward shift of the high mountain snowline in the QLMs. (3) NDVI, land cover type (LCT), and the Digital Elevation Model (DEM, which means elevation) played key roles in shaping the spatial pattern of albedo. Additionally, the spatial distribution of albedo was most significantly influenced by the interaction between slope and NDVI.

In the 21st century, the rapid urbanization process has led to increasingly severe urban heat island effects and other urban thermal environment issues, posing significant challenges to urban planning and environmental management. This study focuses on Zhengzhou, China, utilizing Landsat remote sensing imagery data from five key years between 2000 and 2020. By applying atmospheric correction methods, we accurately retrieved the land surface temperature (LST). The study employed a gravity center migration model to track the spatial changes of heat island patches and used the geographical detector method to quantitatively analyze the combined impact of surface characteristics, meteorological conditions, and socio-economic factors on the urban heat island effect. Results show that the LST in Zhengzhou exhibits a fluctuating growth trend, closely related to the expansion of built-up areas and urban planning. High-temperature zones are mainly concentrated in built-up areas, while low-temperature zones are primarily found in areas covered by water bodies and vegetation. Notably, the Normalized Difference Built-up Index (NDBI) and the Normalized Difference Vegetation Index (NDVI) are the two most significant factors influencing the spatial distribution of land surface temperature, with explanatory power reaching 42.7% and 41.3%, respectively. As urban development enters a stable stage, government environmental management measures have played a positive role in mitigating the urban heat island effect. This study not only provides a scientific basis for understanding the spatiotemporal changes in land surface temperature in Zhengzhou but also offers new technical support for urban planning and management, helping to alleviate the urban heat island effect and improve the living environment quality for urban residents.

In recent years, China’s urbanization has accelerated, significantly impacting ecosystems and the carbon balance due to changes in urban land use. The spatial patterns of CO2 emissions from urban land are essential for devising strategies to mitigate emissions, particularly in predicting future spatial distributions that guide urban development. Based on socioeconomic grid data, such as nighttime lights and the population, this study proposes a spatial prediction method for CO2 emissions from urban land using a Long Short-Term Memory (LSTM) model with added fully connected layers. Additionally, the geographical detector method was applied to identify the factors driving the increase in CO2 emissions due to urban land expansion. The results show that socioeconomic grid data can effectively predict the spatial distribution of CO2 emissions. In the Yangtze River Economic Belt (YREB), emissions from urban land are projected to rise by 116.23% from 2020 to 2030. The analysis of driving factors indicates that economic development and population density significantly influence the increase in CO2 emissions due to urban land expansion. In downstream cities, CO2 emissions are influenced by both population density and economic development, whereas in midstream and upstream city clusters, they are primarily driven by economic development. Furthermore, technology investment can mitigate CO2 emissions from upstream city clusters. In conclusion, this study provides a scientific basis for developing CO2 mitigation strategies for urban land within the YREB.

Since China’s reform and opening up in 1978, the reclamation and abandonment of cropland in Xinjiang have become significant features of the land use change in the arid land of Northwest China. However, the spatiotemporal changes and driving mechanisms of cropland reclamation and abandonment over long time periods are still unclear, but this is crucial in understanding cropland changes in inland arid land, providing important insights for land management and agricultural development. Based on 40 years of remote sensing data on resources and the environment, this study examines the spatiotemporal characteristics of cropland reclamation and abandonment in Xinjiang over four periods since 1980. Additionally, it uses an optimal parameter geographical detector model to quantify the driving factors for each period. The results indicate that cropland reclamation experiences a “slow decrease–rapid increase” trend, forming a “V-shaped” pattern, while abandonment shows a “rapid decrease–slow decrease–slow increase” trend, forming a “U-shaped” pattern. These trends can be divided into three periods: 1980–1990 (unstable growth), 1990–2010 (stable growth), and 2010–2020 (growth with constraints). The movement pattern of cropland reclamation’s center of gravity is “slightly southeast–slightly northeast–southwest”, whereas the abandonment’s center of gravity shifts “northeast–southwest–northeast”. Further analysis reveals that the impact of agricultural technological investment and infrastructure on cropland reclamation has increased, while the influence of natural environmental factors has decreased. Although climate and water resources remain key factors in cropland abandonment, the influence of economic and social factors has gradually diminished, and the impact of agricultural mechanization has steadily risen.

Examining the spatiotemporal dynamics and determinants of land urbanization is critical for promoting healthy urban development and the rational use of land resources. Based on the dataset consisting of land use change data and selected factors in 2010 and 2020, this study used visual analysis to reveal the spatiotemporal dynamics of land urbanization across prefecture-level cities in China. Meanwhile, the driving forces underlying land urbanization were examined by using geographical detector technique. Following are the findings: 1) we find that there exist notable spatial variances in land urbanization across prefecture-level cities. Currently, the differentiation in land urbanization between the northern and southern cities is more pronounced than that between the coastal and inland cities, or between the eastern and western cities. Prefecture-level cities located in central and western China have experienced the most rapid growth in land urbanization. Conversely, the growth rate in northeastern China is the lowest, while the velocity in eastern China remains relatively stable. By using spatial autocorrelation analysis, this study reveals that the land urbanization level in prefecture-level cities has significant spatial agglomeration. 2) We further find that land urbanization in China is influenced by factors related to urban land supply and demand, and urban population growth, economic growth, land financial and political incentive have greater impact on land urbanization than other factors. 3) We also find that the impacts of determinants on China's land urbanization vary over time, the explanatory power of economic development increased, while the explanatory power of state forces declined. We argue that integrating the supply and demand factors of land urbanization can provide a more comprehensive understanding of the driving mechanisms underlying land urbanization in China and other transitional countries, and help decision-makers in these countries formulate more detailed and specific land urbanization policies. [ABSTRACT FROM AUTHOR]

As a new concept for systematically evaluating ecosystem services, Gross Ecosystem Product (GEP) provides an effective means to comprehensively reveal the overall status of the ecosystem, the impact of economic activities on the ecological environment, and the effectiveness of ecological protection efforts. GEP accounting has been conducted in various regions; however, GEP's application in natural reserves still requires further exploration. Taking the Qinling Mountains as the research area, this paper aims to assess the relationship between GEP and economic development on the basis of the GEP accounting system. The results indicated that: (1) From 2010 to 2020, GEP tended to increase continuously and exhibited a distribution pattern with high value regions in the east and west, and low value regions in the north and south. (2) Over the years, the coupling coordination degree between GEP and GDP was in a consistent upward trend. In 2020, a good coupling coordination state between GEP and GDP was achieved in most districts and counties. (3) With the relative development between GEP and GDP, the social economy of most districts and counties lagged behind GEP in 2010. The number of districts and counties lagging in GEP in 2020 increased, while the number of regions with a balanced development of GEP and GDP was still relatively discouraging. (4) In general, elevation, contagion, temperature, population density, and precipitation were the main drivers of coupling coordination degree between GEP and GDP. If the relationship between economic development and ecological environmental protection can be reasonably balanced, it will further promote the sustainable development of nature reserves, and provide a scientific basis for sustainable policy-making in other similar areas. [ABSTRACT FROM AUTHOR]

The pollution risk of potentially toxic elements (PTEs) in soil is a common concern in the fields of environmental science and agriculture. The spatial distribution characteristics, pollution risk assessment and genetic sources of PTEs in soil in high geological background areas are crucial to the safe use of land. Based on the data for five PTEs in 921 surface soil samples from typical high geological background areas in SW China, we discuss their spatial distribution characteristics using geostatistics, the semivariance function and its model. We carry out pollution risk assessment using the influence index of comprehensive quality (IICQ) and analyse their genetic sources using the UNMIX receptor model. The spatial distribution of element content has a high spatial coupling with the stratigraphic spread, which is affected by external random factors, resulting in a weakened autocorrelation. The IICQ indicates that soil is dominated by mild and moderate pollution, accounting for 42.24 and 44.95%, respectively, with cadmium contributing up to 63.00% to the IICQ. Chromium and cadmium are controlled by natural sources, accounting for 83.98 and 69.39%, respectively. Arsenic and mercury have anthropogenic sources, accounting for 68.7 and 44.0%, respectively. Lead is controlled by both lead–zinc mining and geological background, accounting for 47.7 and 36.4%, respectively. The UNMIX model demonstrates that the spatial variability of PTEs is influenced by anthropogenic factors, such as coal combustion emissions, which account for 30.1% of the pollution sources. Soil parent material sources contribute 49.4%, followed by lead–zinc mining and smelting activities at 20.5%. [ABSTRACT FROM AUTHOR]

Copyright of Bulletin of Soil & Water Conservation is the property of Bulletin of Soil & Water Conservation Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Arid Land Geography is the property of Chinese Academy of Sciences, Xinjiang Institute of Ecology & Geography and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Global warming and extreme climate events (ECEs) have grown more frequent, and it is essential to investigate the influences of ECEs on vegetation in the Yellow River Basin (YRB) and other environmentally fragile areas. This study was based on data from 86 meteorological stations in the YRB for the period 2000–2020. Twenty-five extreme climate indices (ECIs) were chosen, encompassing four dimensions: extreme value, intensity, duration, and frequency. The trend analysis approach was used to examine the spatiotemporal characteristics of extreme climate conditions. Additionally, geographical detectors and Pearson correlation analysis methods were employed to quantitatively assess the influence of ECEs on the Normalized Difference Vegetation Index (NDVI). The Multiscale Geographically Weighted Regression (MGWR) method was adopted to analyze the regression of twenty-five ECIs. The findings revealed the following: (1) Over the last 21 years, there has been a distinct rise in both the extreme precipitation indices (EPIs) and the extreme temperature indices (ETIs). (2) The spatial distribution of the NDVI throughout the year displayed the characteristic of being high in the south and low in the north. The annual NDVI demonstrated a noteworthy increase at a rate of 0.055/decade, with the enhancement encompassing an extensive area of 87.33%. (3) The investigation revealed that EPIs, including PRCPTOT, R10mm, CWD, R95p, and CDD, had explanatory values surpassing 0.4. This implied that the intensity, frequency, and duration of extreme precipitation played pivotal roles in steering vegetation alterations in the YRB. (4) The correlation between the EPIs and vegetation was greater than the ETIs. Grassland meadows exhibited greater sensitivity to precipitation than woody plants. The EPIs (excluding CDD and SDII) and the ETIs (TXn) displayed a substantial positive correlation with the NDVI in regions hosting grasslands, broadleaf forests, and shrubs. Desert vegetation and cultivated plants were less affected by ECEs. This study underscores the importance of the interplay between extreme climate and vegetation in the YRB. Additionally, it provides a scientific basis for formulating environmental safeguarding strategies. [ABSTRACT FROM AUTHOR]

This study uses green patent data from 264 cities in China between 2006 and 2020 to examine the evolution of spatial patterns in urban green technology innovation (GTI) across the country and identify the underlying driving factors. Moran's I index, Getis-Ord Gi* index, standard deviation ellipse, and geographical detector were used for the analysis. The findings indicate an increase in the overall level of GTI within Chinese cities. Provincial capitals, cities along the eastern coast, and planned cities emerge as the prominent "highlands" of GTI, whereas the "lowlands" of GTI predominantly lie in the western and northeastern regions, forming the spatial pattern of "hot in the east and center of the country, cold in the northwest and the northeast." The distribution center of gravity of GTI is toward the southwest of China. The distribution pattern is in the "northeast-southwest" direction, which is characterized by "diffusion," followed by "agglomeration." Differences in economic development have the highest determining power on the spatial differentiation of GTI in Chinese cities, whereas differences in environmental regulation and industrial structure have the lowest degree of relative influence. The interaction between any two factors contributes to an amplified explanatory power in understanding the differences in GTI. [ABSTRACT FROM AUTHOR]

Copyright of Arid Zone Research / Ganhanqu Yanjiu is the property of Arid Zone Research Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Introduction: This study aims to investigate the factors influencing ecosystem service trade-offs/synergies (TOSs) in major agricultural production areas in the southern hilly region and to propose optimization strategies to promote ecosystem sustainability in agricultural areas. Methods: The study used a geographical detector to analyze the determinants influencing the ecosystem service trade-offs as well as correlation analysis, geographically weighted regression (GWR), and a geographical detector to analyze the spatial and temporal evolution of ecosystem service and TOS relationships from 2000 to 2020 in Hunan Province. Results: The results showed that the comprehensive value of ecosystem services in Hunan Province showed an increasing trend from 2000 to 2020. With spatial heterogeneity, the areas with high values were mainly distributed in the hilly areas in the west, south, and east of Hunan Province, and the areas with low values were mainly distributed in the Dongting Lake Plain and the Xiangzhong Hilly Basin. There was a trade-off relationship between food production (FP) and all other ecosystem services, of which FP has the strongest trade-off effect with habitat quality (HQ). The synergy effect between HQ, water yield (WY), carbon storage (CS), and soil conservation (SC) shows an increasing trend. Gross domestic product (GDP) and SLOPE are the dominant factors for the strength of trade-offs between food supply and other ecosystem services, and Digital Elevation Model (DEM) and Normalized Difference Vegetation Index (NDVI) are the dominant factors for the strength of synergy effects among ecosystem services. The strength of TOS effects of ecosystem services is determined by interactions or co-influences between the two services rather than by a single component. Discussion: The results of this study can provide a reference basis for the enhancement of ecosystem services and the sustainable planning of agricultural landscapes in the southern hilly areas. [ABSTRACT FROM AUTHOR]

Land use multifunctionality research is important for the efficient use of land resources and the resolution of land use conflicts. With the use of methods such as the technique for order of preference by similarity to ideal solution (TOPSIS) model, coupling coordination model, and geographical detector, the land use multifunctionality level, spatiotemporal coupling, and influencing factors in the Sichuan Province of China from 2000 to 2020 were systematically analyzed in this paper. It was revealed that, from 2000 to 2020, the comprehensive land use functionality in Sichuan Province was continuously improved with increasing economic, social, and ecological functionality levels. The comprehensive land use functionality in each city (prefecture) exhibited a positive development trend. The coupling coordination degree of the land use multifunctionality in Sichuan Province has been continuously improved, undergoing an evolutionary process from the brink of disarray to barely coordinated, then to primary coordination, and finally to medium coordination. The spatial differentiation of land use multifunctionality coupling coordination among cities (prefectures) was notable, showing center–periphery spatial distribution characteristics. The average slope and employed population density exhibited the highest explanatory power for the spatial differences in land use multifunctionality coupling coordination. The interaction between any two factors exerted a greater impact than any single factor on the spatial differentiation of land use multifunctionality coupling coordination. Based on the regional development characteristics, region-specific strategies should be adopted to enhance the land use multifunctionality level in Sichuan Province. [ABSTRACT FROM AUTHOR]

Soil conservation (SC) plays a vital role in preventing soil erosion and ensuring ecological security. While current research on SC primarily focuses on historical spatiotemporal variations, there remains a dearth of sufficient simulation research exploring future development scenarios. In this study, simulations were applied to the source of Yellow River (SYR), a representative ecologically fragile area. Satellite remote sensing and product data, including precipitation, soil, land use/cover, DEM, and SPOT/VEGETATION NDVI, were utilized. The historical and future evolutionary trends of SC in the SYR were quantitatively assessed using the Revised Universal Soil Loss Equation (RUSLE) and trend analysis method, and the geographical detector was employed to explore the forces driving spatial differentiations in SC. The results demonstrated that: (1) 2000–2020, the spatial heterogeneity of SC in the SYR was characterized by the distribution of "gradually decreasing from Southeast to Northwest", demonstrated a trend of "increasing, decreasing, and then increasing". (2) Under the diverse development scenarios, the trend of SC change in the SYR was predominantly rising, and the natural change scenario (NCS) > ecological conservation scenario (ECS) > economic expansion scenario (EES). (3) Slope was the most important single driver affecting the spatiotemporal differentiation of SC, and the interaction of slope with average annual precipitation, and NDVI on the spatiotemporal heterogeneity of SC had the strongest explanatory ability. The results can serve as a scientific basis for regional SC and ecological protection and construction of the SYR. [ABSTRACT FROM AUTHOR]

The evaluation of water resources management performance (WRMP) can provide guidance for water resources management. This paper constructs a scientific WRMP evaluation index system based on "water resources–water environment–water ecology". Secondly, the game variable weight matter–element extension model is appropriately introduced to dynamically evaluate the WRMP level of the provinces (cities) in the YREB from 2012 to 2021, and Arcgis is used to analyze the spatial and temporal variations in the performance level of each sub-system. Lastly, a geographical detector model is used to explore the main factors influencing the WRMP in the Yangtze River Economic Balt (YREB). The main findings are as follows: (1) The overall provincial WRMP level in the YREB has been improving from 2012 to 2021, and the performance of water resource utilization (WRU) and water environment treatment (WET) are high in the east and low in the west, while the performance of water ecological protection (WEP) shows a trend of continuous improvement. (2) Compared with the model without variable weight modification, the game variable weight matter–element extension model can reflect the influence of the measured value of the index on the evaluation result as much as possible. (3) The top eight factors that have a greater impact on the WRMP level are the industrial water conservation rate, water resource development and utilization rate, water resource sustainability index, sewage diameter ratio, urban water penetration rate, industrial wastewater treatment completion rate, ecological construction and protection of the year to complete the investment in GDP, and the water ecological carrying capacity growth rate. The interaction types of each influence factor are nonlinear enhancement and two-factor enhancement. [ABSTRACT FROM AUTHOR]

In order to provide reference for solving the problems of agricultural water shortage and water environment pollution in Zhejiang, an agricultural water use efficiency index was constructed, and an ultra-efficiency SBM model was used to measure the agricultural water use efficiency in Zhejiang Province from 2010 to 2020. The kernel density estimation method was used to analyze the spatio-temporal evolution law of agricultural water use efficiency. Finally, the main influencing factors of agricultural water use efficiency were analyzed using a geographical detector. The agricultural water use efficiency in Zhejiang Province increased from 1.153 in 2010 to 1.661 in 2020, with an average annual growth of 3.71% and an average of 1.535 in recent years, which is always in a state of effective utilization. Over the years, the overall trend of fluctuation rose, stability was strong, and the influence of pure technical efficiency was great. Although the agricultural water use efficiency in northern Zhejiang is higher than that in southern Zhejiang, the regional differences are decreasing. Economic development level, the government′ s emphasis on agricultural water use, agricultural planting structure, and water resource abundance were the main factors influencing the spatial differentiation of agricultural water use efficiency in Zhejiang Province. At the same time, the spatial variation of agricultural water use efficiency is not the result of a single factor, but of the nonlinear enhancement under joint action of many factors. Therefore, Zhejiang Province needs to further promote the efficiency of agricultural water use by improving farmers′ income level, increasing agricultural financial investment, promoting water-saving irrigation technology, and improving farmers′ consciousness of water-saving. [ABSTRACT FROM AUTHOR]

This study was conducted to analyze the changing trends of vegetation resilience and climate change resistance in Fujian Province from 2000 to 2020, and to explore the driving factors and response mechanisms of vegetation resilience at multiple scales. An enhanced vegetation index (EVI) derived from MOD13Q1 product was utilized to establish a multivariate linear regression model with climate indices to analyze trends in vegetation resilience and resistance. The impact mechanisms of climate, topography, and road factors on vegetation resilience were analyzed at multiple scales using a geographic detector and multiple logistic regression models. It was found that the changing trend of vegetation resilience exhibited notable spatial heterogeneity, with enhanced trends mainly observed in the northern region. Overall, vegetation resistance to precipitation showed a weakening trend, while resistance to potential evapotranspiration weakened in the northern region but strengthened in other areas. The results of geographic detector factor analysis indicate that vegetation resilience is associated with multiple factors across different scales, including precipitation, temperature, topography (elevation and slope), as well as the kernel density of national, provincial, and township-level roads. Multiple logistic regression results indicate that between 2000 and 2020, the positive impacts of temperature, precipitation, and slope on vegetation recovery have strengthened, while the positive influences of elevation, population, and gross domestic product(GDP) on vegetation degradation have gradually weakened. From 2010 to 2020, regions with higher railway density exhibited a weaker promoting effect on vegetation recovery, while areas with higher provincial-level road density demonstrated a stronger inhibiting effect on vegetation recovery. Overall, vegetation in Fujian Province is undergoing positive succession, with its responsiveness to human activities and topographic factors gradually diminishing. Meteorological and road-related factors are becoming more prominent in influencing vegetation dynamics. [ABSTRACT FROM AUTHOR]

Copyright of Geography & Geographic Information Science is the property of Geography & Geo-Information Science Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The anthropogenic disturbance caused by irrational urbanization impacts the ecological security of cities. The study of the relationship between urban spatial form (USF) and urban ecological resilience (UER) can offer guidance for sustainable urban development. We select the UER evaluation indexes and construct a DRSIR model based on the causal relationship among the natural, social, and economic dimensions of cities. We also carried out empirical research on the impact of USF on UER in the city cluster located in the Yangtze River midstream (YRM) through the geographical and temporal weighted regression (GTWR) model and geographical detector for four time cross-sections: 2005, 2010, 2015, and 2020. The following findings were obtained: (1) The computing results for UERIs of cities in the YRM noticeably and consistently increased during the study period, exhibiting a spatial distribution pattern with high values in the southeast, second-highest in the northwest, and low values in the center. (2) The regression coefficients of USFIs and UERIs displayed significant spatial and temporal variations in the YRM during the study period. (3) CA, AWMPFD, PD, and AI showed a positive correlation with UERIs, while PD exhibited a negative correlation with UERIs. (4) LPI and AI emerged as the primary drivers of spatial heterogeneity in UERIs, and the interaction between these two factors significantly enhanced their impact on UERIs compared to individual influencing factors. Exploring the correlation between UER and USF can be utilized to provide urban management recommendations that are suitable for the level of urban development, which will contribute to the achievement of sustainable development in the YRM. [ABSTRACT FROM AUTHOR]

Rural settlement is the basic spatial unit for compact communities in rural area. Scientific exploration of spatial-temporal differentiation and its influencing factors is the premise of spatial layout rationalization. Based on land use data of Liangshan Yi Autonomous Prefecture (hereinafter referred to as Liangshan Prefecture) in Sichuan Province, China from 1980 to 2020, compactness index, fractal dimension, imbalance index, location entropy and the optimal parameters-based geographical detector (OPGD) model are used to analyze the spatial-temporal evolution of the morphological characteristics of rural settlements, and to explore the influence of natural geographical factors, socioeconomic factors, and policy factors on the spatial differentiation of rural settlements. The results show that: (1) From 1980 to 2020, the rural settlements area in Liangshan Prefecture increased by 15.96 km2. In space, the rural settlements are generally distributed in a local aggregation, dense in the middle and sparse around the periphery. In 2015, the spatial density and expansion index of rural settlements reached the peak. (2) From 1980 to 2020, the compactness index decreased from 0.7636 to 0.7496, the fractal dimension increased from 1.0283 to 1.0314, and the fragmentation index decreased from 0.1183 to 0.1047. The spatial morphological structure of rural settlements tended to be loose, the shape contour tended to be complex, the degree of fragmentation decreased, and the spatial distribution was significantly imbalanced. (3) The results of OPGD detection in 2015 show that the influence of each factor is slope (0.2371) > traffic accessibility (0.2098) > population (0.1403) > regional GDP (0.1325) > elevation (0.0987) > poverty alleviation (0). The results of OPGD detection in 2020 show that the influence of each factor is slope (0.2339) > traffic accessibility (0.2198) > population (0.1432) > regional GDP (0.1219) > poverty alleviation (0.0992) > elevation (0.093). Natural geographical factors (slope and elevation) are the basic factors affecting the spatial distribution of rural settlements, and rural settlements are widely distributed in the river valley plain and the second half mountain area. Socioeconomic factors (traffic accessibility, population, and regional GDP) have a greater impact on the spatial distribution of rural settlements, which is an important factor affecting the spatial distribution of rural settlements. Policy factors such as poverty alleviation relocation have an indispensable impact on the spatial distribution of rural settlements. The research results can provide decision-making basis for the spatial arrangement of rural settlements in Liangshan Prefecture, and optimize the implementation of rural revitalization policies. [ABSTRACT FROM AUTHOR]

Vegetation greenness change is the result of the combination of natural and anthropogenic factors. Understanding how these factors individually and collectively affect vegetation dynamics and whether their spatial heterogeneity has any effect on vegetation greenness change is the crucial investigation area. Previous studies revealed the distinct characteristics of spatial and temporal heterogeneity in the impact factors influencing vegetation greenness change across various regions, often assuming a linear contribution mechanism between vegetation greenness change and these drivers. However, such a simplistic assumption fails to adequately capture the real-world dynamics of vegetation greenness change. Thus, this study firstly used geographical detector (Geodetector) to quantitatively measure the contribution of each factor to vegetation greenness change considering spatial heterogeneity in the Yangtze River Economic Belt (YREB) during the growing season from 2000 to 2020, then selecting significant factors from numerous drivers with the recursive feature elimination algorithm combined with a random forest model (RFE-RF), which is able to reduce redundant features in the data and prevent overfitting. Finally, four stable impact factors and the spatial heterogeneity of some factors contributing to vegetation greenness change were identified. The results show that approximately 83% of the regional vegetation has shown an overall increasing trend, while areas undergoing rapid development predominantly experienced a decline in greenness. Single factor screened by Geodetector with the explanatory power greater than 10% for vegetation greenness change included temperature (Tem), population density (PD), the land-use/land-cover (LULC), DEM, wind speed, and slope. The RFE-RF method identified precipitation (Pre) and CO2 emissions as additional influential factors for vegetation greenness change, in addition to the first four factors mentioned previously. These findings suggest that the four stable factors consistently influence vegetation greenness change. Combined with the principles of the algorithms and the above results, it was found that the spatial heterogeneity of wind speed and slope has an effect on vegetation greenness change, whereas the spatial heterogeneity of Pre and CO2 emissions has minimal effect. [ABSTRACT FROM AUTHOR]

With the rapid advancement of global technology, high-tech industries have become key drivers for the economic growth of many nations and regions. This study delves into the spatiotemporal dynamics and determinants influencing China's high-tech sector from 2007 to 2021. The key findings include the following: (1) Nationally, the high-tech sector has been a cornerstone for China's GDP growth over the preceding 15 years. The expansion rate of the high-tech domain consistently outpaces the broader economy. In particular, since 2015, the percentage of high-tech industries' GDP has surged to approximately 42%. (2) At the provincial level, the spatial representation of the high-tech sector's GDP predominantly leans towards the east and the south, revealing pronounced spatial autocorrelation. Nevertheless, the demarcations between east and west and between north and south are progressively diminishing. (3) Regarding influential determinants, R&D internal expenditure, operating revenue, and industry agglomeration have been instrumental in spearheading innovation and bolstering growth within the high-tech realm. These insights are invaluable for comprehending the evolutional nuances of China's high-tech industry and devising pertinent policy measures. [ABSTRACT FROM AUTHOR]

Identifying the regional differences and drivers for carbon reduction of construction and demolition waste (C&DW) recycling is essential to combat climate change. This study aims to calculate the carbon reduction potential for C&DW recycling from 2006 to 2021 in China and investigates the spatial differences and driving factors of carbon reduction potential for C&DW waste by combining the Theil index, Gini coefficient, and geographic detector methods. The carbon reduction potential for C&DW recycling in China was "high in the east and low in the west" overall level, with an average annual growth rate of 6.27%. The overall differences in carbon reduction potential for C&DW recycling are decreasing, mainly due to intraregional differences and inter-provincial differences in Northeast China. The population size, urbanization rate, and technological effect are the key factors influencing carbon reduction potential for C&DW recycling. There are two types of interactions between influencing factor pairs: nonlinear enhancement and two-factor enhancement. This study's results can guide policymakers to devise relevant, regionally specific policies. [ABSTRACT FROM AUTHOR]

During the last decades, the association between the quality of the urban living environment and individual subjective well-being (SWB) has received wide attention in many metropolitan areas across the world. However, few studies have focused on the effects of the quality of the urban living environment on SWB from a human perception perspective and concerned the underdeveloped areas such as resource-based cities. Drawing on a large-scale social survey data set collected from the City Health Examination Evaluation (CHEE) in China, this paper employed the Geographical Detector method to reveal the association between the CHEE and individual subjective well-being in four resource-based cities in China (Ganzhou, Huangshi, Jingdezhen, and Luoyang). The results showed that the SWB and CHEE of the respondents in the resource-based cities are generally good, with mean evaluation scores of 4.15 and 4.27, respectively. The factor detector results indicated that the eight subdimensions of the CHEE are all significantly associated with individual SWB in the resource-based cities, with the q-statistic values in the following decreasing order: life amenities > urban management > urban vitality > diversity and inclusiveness > security and resilience > convenient transportation > urban landscape and scenery > ecological livability. Furthermore, individual socioeconomic attributes, such as gender, hukou, house tenure, car ownership, age, household income, and occupation, were also significantly associated with individual SWB. The interaction factor results revealed that there are bilinear enhancing and nonlinear enhancing effects on individual SWB after the interaction between the subdimensions in the CHEE. Regarding city types, the average evaluation scores of the CHEE and SWB in the declining cities, mature cities, and regenerating cities decreases in turn, and the subdimensions of the CHEE had heterogeneous effects on individual SWB across city types. The findings in this study could provide insights into improving individual SWB in China's resource-based cities by enhancing the quality of the urban living environment. [ABSTRACT FROM AUTHOR]