To assess the impact of future climate change on geological hazard zoning in the Ili River Basin, Xinjiang, China, climate data from different scenarios of the Coupled Model Intercomparison Project Phase 6 (CMIP6) were selected to analyze climate change characteristics under various shared socioeconomic pathway (SSP) scenarios from 2021 to 2040. The information quantity-random forest model was employed to conduct the geological hazard assessment and generate a prediction map. The results indicate that: (1) High and extremely high hazard areas are primarily concentrated in northern Yining County, southern Nilka County, and northern Xinyuan County in the middle mountainous hilly regions; debris flow hazard areas are mainly located in southern Zhaosu County, the northern region of Keguqin Mountain in Huocheng County, Hejing County, and the middle-to-high mountainous areas in eastern Nilka County. (2) From 2021 to 2040, the Ili River Basin is projected to experience a general increase in temperature and precipitation, with a maximum annual average temperature rise of approximately 1.53 ℃ and a maximum precipitation increase of about 19.3 mm. (3) Under future SSP126, SSP245, SSP370, and SSP585 scenarios, high-hazard areas for landslides and rockfalls are expected to expand. The severity of landslides in southern Yining County, northern Xinyuan County, and southwestern Nilka County, as well as debris flows in northern Khorgas City and Yining County, is anticipated to worsen, with maximum increases of 17.31% and 8.77%, respectively. The findings of this study provide valuable insights for future disaster prevention and mitigation efforts in the Ili River Basin.

The Loess Plateau of China has been experiencing an increase in extreme climate events due to global warming. Understanding the spatiotemporal characteristics of extreme precipitation events in this region is crucial for disaster prevention. This study analyzes daily precipitation data from 111 meteorological stations across the Loess Plateau, spanning the years 1960 to 2023. Using detrended fluctuation analysis (DFA), we established thresholds for extreme precipitation events and examined their spatiotemporal characteristics through the Mann-Kendall test and other methods. The findings reveal the following. (1) Extreme precipitation thresholds at meteorological stations vary between 27.4 mm and 89.1 mm, with 54% of the stations exceeding a threshold of 50 mm. The average threshold values across different ecological regions range from 35.0 mm to 59.6 mm, exhibiting a gradient that is lower in the northwest and higher in the southeast. (2) The amount and intensity of extreme precipitation events increase from 10.6 mm·a^-1 and 33.0 mm·d^-1 in the northwest to 71.5 mm·a^-1 and 133.0 mm·d^-1 in the southeast, respectively. The frequency of their occurrence increases from 0.3 d·a^-1 in the north to 0.8 d·a^-1 in the south. The number of extreme precipitation days closely aligns with heavy rain days, particularly in the loess hilly gully B2 sub-region. (3) The loess tableland gully, earth-rocky mountainous, and river valley plain regions are identified as high-risk areas for extreme precipitation events and should be prioritized for disaster prevention and control. (4) Over the past 64 years, extreme precipitation events have shown distinct interannual variability, with an overall increase observed, particularly in July and August. (5) In the last decade, the loess tableland gully and loess hilly gully regions have seen increased precipitation amounts and frequencies of extreme events. By contrast, the declining trend of extreme precipitation events in the sandy land and irrigated agricultural regions has slowed, whereas both the earth-rocky mountainous and river valley plain regions experienced a sudden spike in extreme precipitation events in 2020. This study serves as a reference for disaster prevention and mitigation regarding extreme precipitation events across the different ecological regions of the Loess Plateau.

This study analyzes daily temperature data from 38 meteorological stations in Xizang, China, covering the period from 1981 to 2023. A new method for seasonal division in Xizang was employed to categorize the four seasons, and the regional climate changes, temporal shifts in the start dates of each season, and trends in seasonal changes were examined. The results show the following. (1) Areas with four distinct seasons in Xizang are primarily found along the Yarlung Zangbo River and in Nyingchi City, whereas regions with less distinct seasonal variations (including areas without a summer season) are mainly situated in the western and northern parts of Xizang as well as in the high-altitude regions of the Himalayas. (2) In Xizang, the beginning of spring and summer tends to occur earlier, whereas the onset of autumn and winter tends to be delayed. Notably, the start date of spring was significantly earlier in 2000, whereas the onset of autumn and winter was significantly delayed in 2003 and 1995, respectively. (3) Regarding the timing of seasonal starts, the first empirical orthogonal function (EOF1) for spring and autumn exhibited a pattern of “northwest low and southeast high in spring, and middle high and both sides low in autumn”. In spring, the second EOF (EOF2) presented a contrasting distribution pattern of “northwest positive and southeast negative”, whereas in autumn, EOF2 showed an opposite spatial distribution pattern characterized by “southwest positive and northeast negative”. The EOF1 in winter revealed a “high in the north and low in the southwest” pattern, whereas the EOF2 shared similarities with that of spring’s EOF2. (4) Looking ahead, we anticipate that the start dates of spring and summer will be delayed, whereas those for autumn and winter will be advanced.

Building resilience in urban agglomerations has become a key focus in urban risk governance research. This study utilizes panel data from 15 cities in the urban agglomeration on the northern slope of Tianshan Mountains, Xinjiang, China from 2010 to 2022. Based on the DPSR model, an urban resilience evaluation index system is constructed, encompassing four dimensions (driving force, pressure, state, and response) analyzed from a dynamic perspective. The spatial and temporal evolution of urban resilience is assessed using the entropy method and kernel density estimation. Furthermore, the primary influencing factors are identified through geographical detectors, and future development trends are predicted using a grey prediction model. The results indicate that: (1) From 2010 to 2022, the resilience of the urban agglomeration on the northern slope of Tianshan Mountains has significantly improved, forming a spatial pattern characterized by a “core-edge” structure. The western part of Urumqi City has emerged as a high-resilience area, whereas the periphery of the urban agglomeration exhibits lower resilience. (2) When analyzing the factors influencing urban resilience, the response system has consistently exerted a strong impact. Over time, the influence of environmental regulation and ecological pollution indicators has increased. Specifically, the total production value of large-scale industrial enterprises, carbon dioxide emissions, the number of utility model patents, the digital financial inclusion index, and general public budget expenditures have demonstrated a strong long-term effect on urban resilience in this region. (3) For the period 2023—2027, dynamic predictions suggest that the resilience of the urban agglomeration will improve significantly, with cities in the urban agglomeration exhibiting steady growth. Spatially, Urumqi City will continue to lead in resilience levels, further widening interregional disparities. Consequently, the “core-edge” spatial pattern within the region will become more pronounced. These findings provide a theoretical reference for the development planning of urban agglomeration on the northern slope of Tianshan Mountains and the enhancement of urban resilience, thereby strengthening cities’ ability to adapt to, recover from, and sustainably develop in response to various disturbances.

The systematic analysis of the factors influencing urban ecological resilience and improvement in its levels are essential for achieving sustainable urban development and fostering “resilient cities”. Based on panel data from 281 prefecture-level cities of China collected between 2005 and 2021, this study constructed evaluation indexes across three dimensions: resistance, adaptability, and resilience. The spatial and temporal evolution of urban ecological resilience were characterized, and spatial and temporal geographically weighted regression models were applied. In addition, a global trend analysis was conducted to explore the spatial and temporal heterogeneity of influencing factors. The results showed the following. (1) From 2005 to 2021, the ecological resilience level of Chinese cities increased from 0.0207 to 0.0248, with an average annual growth rate of 1.140%. Over this period, the center of gravity for ecological resilience shifted along a northeast-southwest direction. The difference in ecological resilience levels initially expanded but then narrowed. (2) Economic development, industrial structure, topographical factors, environmental regulations, and human capital demonstrated positive effects on urban ecological resilience. However, the influence of these factors exhibited obvious spatial and temporal heterogeneity, with their effects varying significantly in terms of magnitude and direction across different regions. (3) The impact of influencing factors on urban ecological resilience exhibited a greater variation along the east-west direction compared to the north-south direction. The ecological resilience was observed to be most sensitive to changes in industrial structure along the east-west direction, while environmental regulation had the greatest sensitivity along the north-south direction.

As the ninth largest industry cluster in Xinjiang, the high-quality development of the tourism economy plays a leading role in the region’s modernization in China. This study constructs a seven-dimensional measurement system to evaluate the high-quality development of the tourism economy based on three perspectives: quality, efficiency, and tourist experience. The level of high-quality development in Xinjiang’s tourism economy from 2008 to 2023 is measured, employing geographic information systems spatial visualization to analyze its spatial and temporal evolution characteristics. In addition, the study identifies obstacle sources using the obstacle degree model and explores influencing factors through factor analysis and gray correlation analysis. The results indicate the following. (1) The initial level of high-quality development of the tourism economy in Xinjiang is low, but it has improved rapidly since the introduction of the “tourism to develop Xinjiang” strategy in 2018. (2) Significant spatial and temporal differences in the level of high-quality development are observed within Xinjiang’s tourism economy. The overall trend shows strong development in the north and weaker development in the south, with the east and west exhibiting coherence. This is specifically reflected in the cities of Urumqi, the Ili Kazakh Autonomous Prefecture, the Altay Prefecture, and the Kashgar Prefecture, demonstrating a “four-wheel drive” evolution and a “core of three points” symbiotic development pattern. (3) The overall high-quality development of the tourism economy in Xinjiang is primarily constrained by regional innovation capacity. Regions with lower levels of high-quality development face challenges due to a lack of tourism market attractiveness and underdeveloped cultural and tourism resources. (4) Key factors affecting the high-quality development of Xinjiang’s tourism economy include information services, economic support, and tourism transportation, whereas resource elements and tourism services are considered secondary factors. Notably, factors such as increased Internet accessibility, rising disposable incomes, higher fixed-asset investment in the tertiary industry, and expanded highway infrastructure significantly affect the high-quality development of Xinjiang’s tourism economy.

Water, energy, and food are critical components of ecosystem services, influencing the supply-demand balance and high-quality regional development. Using the InVEST model, supply-demand matching degree (SDMD), Pearson correlation analysis, and other methods, this study quantifies the spatiotemporal characteristics of four ecosystem services (water production, carbon sequestration, soil conservation, and food provision) in the urban agglomeration on the northern slope of the Tianshan Mountains, Xinjiang, China from 2000 to 2020 and explores the spatial heterogeneity of supply-demand matching at different scales. The results indicate that: (1) The overall supply of food provision services in the study area exhibited an upward trend, whereas the supply of water production, carbon sequestration, and soil conservation services declined. The demand for all four services increased. (2) The supply of water production and food provision services was higher in the west and lower in the east, while demand was concentrated in densely populated areas in a point-like distribution. Carbon sequestration services had higher supply levels in the southwest and central regions and lower levels in the northeast. The supply and demand of soil conservation services formed a point-like high-value distribution in the Tianshan Mountains, with their spatial distribution roughly aligned. (3) The SDMD of soil conservation and food provision services increased, whereas the SDMD of water production and carbon sequestration services decreased. SDMD exhibited spatial heterogeneity across different scales, with the most significant variations observed at the grid scale. (4) The spatial differentiation of ecosystem services was evident, highlighting the need for zoning control, classification optimization, and hierarchical governance. These strategies can provide scientific support for high-quality economic and social development, ecological protection, and the restoration of the urban agglomeration on the northern slope of the Tianshan Mountains.

The Mu Us Sandy Land, located in the agro-pastoral ecotone of northern China, has a fragile ecological environment highly susceptible to agricultural development. This study employs long-term multivariate remote sensing data to analyze the spatio-temporal patterns of newly cultivated and abandoned farmland in the region from 1964 to 2020 and their subsequent impacts on desertification. The results indicate that (1) From 1964 to 2020, changes in the area of newly cultivated and abandoned farmland in the Mu Us Sandy Land can be categorized into three stages. Between 1964 and 1986, the extent of both newly cultivated and abandoned farmland was relatively high, with abandoned farmland significantly exceeding other periods. The average annual abandoned farmland area was 2.89 times that of 1986—2020, and newly cultivated farmland in pastoral areas was notably greater than that in agricultural areas. Between 1986 and 2007, both newly cultivated and abandoned farmland remained relatively low and stable, with newly cultivated farmland slightly exceeding abandoned farmland. From 2007 to 2020, the area of newly cultivated farmland expanded rapidly, with an average annual increase 3.24 times that of 1964—2007, while the abandoned farmland area remained relatively low. (2) Significant spatial and temporal differences exist between newly cultivated and abandoned farmland. From 1964 to 1986, newly cultivated farmland hotspots were widely distributed in the pastoral areas of central and western Mu Us Sandy Land. Between 1971 and 1986, a few concentrated cultivation hotspots emerged in the agricultural areas of eastern Mu Us Sandy Land. From 2007 to 2020, newly cultivated farmland hotspots were mainly concentrated in the east, central agricultural areas, and the southern region. (3) Between 1964 and 1986, large-scale farmland cultivation and abandonment, driven by policy factors, led to severe land desertification in the Mu Us Sandy Land. The area of fixed sandy land surrounding abandoned farmland decreased by 99.9%, while the area of shifting sandy land increased by 358.2%. From 2007 to 2020, no significant trend of desertification was observed around newly cultivated farmland; however, the degree of desertification surrounding newly cultivated farmland showed an increasing trend. Future agricultural and animal husbandry management policies, as well as desertification prevention and control plans, should carefully balance agricultural development with the preservation of fragile sandy ecosystems. Additionally, attention should be given to the potential desertification risks associated with land reclamation and abandonment.

The spatiotemporal characteristics and influencing factors of actual evapotranspiration (ET) in the northeastern Qinghai-Xizang Plateau are crucial for the effective management of regional water resources and the ecological environment. Using Qinghai Province, located in this region, as the study area, this study analyzed actual ET data (MOD16 ET) from 2001 to 2020 to explore the spatiotemporal patterns, variation trends, and influencing factors over the past 20 years. The results indicate the following: (1) The average annual actual evapotranspiration in Qinghai Province from 2001 to 2020 was 260 mm·a^-1, showing a fluctuating increasing trend. The fluctuation period also showed an increasing trend, with an average interannual change rate of 2%. Areas where actual ET increased accounts for 69.69% of the total area, and areas where it decreased accounted for 16.51%. Among them, the Qilian Mountains area and the eastern part of the river source ecological zone exhibited an increasing trend in actual ET. The seasonal variation of actual ET in Qinghai Province was significant, with the maximum in summer, the minimum in winter, and similar values in spring and autumn. (2) The average actual ET in Qinghai Province from 2001 to 2020 showed a spatial distribution characteristic of being low in the northwest and high in the southeast. There were large differences in actual ET among various ecological zones in Qinghai Province, with the Three River Source area and the Qilian Mountains area exhibiting the largest actual ET distribution, and the Qaidam Basin ecological zone had the smallest actual ET. The actual ET of the main vegetation cover types was ranked as follows: shrubland>forest land>grassland>arable land. (3) The fluctuating changes in actual ET in Qinghai Province from 2001 to 2020 were basically consistent with temperature variations. The increase in actual ET largely corresponded to the increasing trend of precipitation fluctuations, but the peak lagged behind changes in precipitation. (4) Actual ET was positively correlated with annual average temperature, annual total precipitation, sunshine duration, and average wind speed in 73%, 56%, 43%, and 44% of the total study area, respectively. Temperature and precipitation were the primary controlling factors of actual ET, whereas sunshine duration and wind speed also exerted notable influences. There were significant regional differences in the factors affecting the changes in actual evapotranspiration.

Effective management of agricultural water pollution is essential for addressing the water crisis and promoting sustainable regional development. This study introduces the agricultural grey water footprint intensity (AGWFI), which integrates the agricultural grey water footprint and local economic development levels to reflect agricultural pollution levels. It calculates the AGWFI for 112 prefecture-level cities in the Yellow River Basin of China from 2012 to 2021, thoroughly analyzing the spatial patterns and trends of AGWFI in the region. In addition, a quantile regression method is employed to examine the influencing factors. The findings reveal the following. (1) From 2012 to 2021, AGWFI significantly decreased across the Yellow River Basin and its upper, middle, and lower reaches, with a more significant reduction in the upper reaches compared to the middle and lower reaches. (2) The AGWFI displayed a distribution pattern characterized by high values in the west and low values in the east; the Gini coefficient for AGWFI in the basin and its upper, middle, and lower reaches was notably high and increasing, with intra- and inter-regional disparities as primary sources. Furthermore, the transfer of AGWFI primarily occurred between adjacent levels. (3) The level of agricultural economic development negatively affected the overall AGWFI in the Yellow River Basin and its upper, middle, and lower reaches. By contrast, the output share of the primary industry and the utilization rate of agricultural water resources positively influenced AGWFI. These research findings can serve as scientific references for the development of targeted agricultural water pollution management strategies in the Yellow River Basin.

Exploring the impact of territorial spatial ecological protection and restoration on the economic development of China’s poverty-alleviated regions provides valuable insights for promoting sustainable economic development and rural revitalization in these areas during the post-poverty era. Using the double-difference propensity score matching (PSM-DID) model, this study analyzed economic panel data from 58 national-level poverty-alleviated regions involved in the first and second batches of national integrated protection and restoration projects for mountains, rivers, forests, farmlands, lakes, grasslands, and deserts (the “Shanshui Project”) during China’s 13^th and 14^th Five-Year Plans. These were compared with data from 182 non-participating poverty-alleviated regions that had similar natural, social, and economic conditions from 2010 to 2020. This study assesses the impact of ecological protection and restoration on the economic development of these regions. The results indicate the following: (1) Under otherwise unchanged conditions, the economic development rate of poverty-alleviated regions implementing territorial spatial ecological protection and restoration increased significantly by 0.0329 compared to regions that did not implement such measures. This conclusion remains robust after balance tests, parallel trend tests, and placebo tests. (2) In the implementation of spatial ecological protection and restoration, both short-term and long-term economic development effects should be considered. Realizing the value of ecological products is a key driver of sustainable economic development in poverty-alleviated regions. To achieve this, increased policy support is recommended for this region, with a focus on improving systems for realizing the value of ecological products. This approach would enable these areas to successfully transform “green waters and mountains” into “golden mountains and silver mountains”.

The Guanzhong Plain urban agglomeration is the largest urban cluster in the water-scarce region of northwest China. Exploring the coordination among water resources, the socio-economic system, and the ecosystem is crucial for regional water governance and sustainable development. This study examines 11 prefecture-level cities within the Guanzhong Plain urban agglomeration. By establishing a comprehensive evaluation index system for the coordinated development of water resources, socio-economic, and ecological systems, the comprehensive evaluation indices of these three systems from 2006 to 2020 are calculated. The coupling coordination model is employed to measure and analyze their coordination, while the obstacle degree function is used to identify the primary constraints affecting the coupling coordination degree. The results indicate that: (1) From 2006 to 2020, the development levels of water resources, socio-economic, and ecological systems across the cities in the Guanzhong Plain urban agglomeration exhibited an overall upward trend with distinct spatial gradation. Cities with higher water resource system levels include core and surrounding cities, while those with higher socio-economic system levels are predominantly core cities. Prefecture-level cities with higher ecological system levels are either core or peripheral cities. (2) The coupling coordination degree of the Guanzhong Plain urban agglomeration improved from a state of slight imbalance in 2006 to basic coordination by 2020. Spatially, the coupling coordination followed a hierarchical pattern of “core cities>surrounding cities>peripheral cities”. (3) Significant differences exist in the primary obstacles affecting the coupling coordination degree across the 11 prefecture-level cities. Based on the detection results of obstacle factors, the coordinated development of the water-socio-economic-ecosystems in the study area can be categorized into three types: comprehensive development constrained by water resources (core cities), ecological environment lagging behind (surrounding cities), and socio-economic lagging behind (peripheral cities).

The promotion of low-carbon agricultural development necessitates in-depth research into the spatiotemporal heterogeneity of agricultural carbon emission efficiency and its influencing factors. This will facilitate the acceleration of Xinjiang’s agricultural economic development while driving the green transformation of agricultural production. This study focused on 14 prefectures and cities in Xinjiang from 2000 to 2020 to assess agricultural carbon emission efficiency. The analysis was conducted using the SBM model of nonexpected output and the Malmquist index. The spatial characteristics of agricultural carbon emission efficiency were further examined using the spatial autocorrelation model, and the Tobit model was applied to explore factors influencing efficiency. The findings suggested the following. (1) From 2000 to 2020, the agricultural carbon emission efficiency in Xinjiang followed a “slow-fast-slow” development pattern, with significant inter-regional disparities. (2) In 2000, the Tacheng Prefecture exhibited a low-high agglomeration pattern in case of agricultural carbon emission efficiency. By 2007, the Changji Hui Autonomous Prefecture transitioned to a high-high agglomeration pattern. Further, by 2014, Turpan City and the Changji Hui Autonomous Prefecture were both exhibited high-high agglomeration. In 2020, the Bayingol Mongolian Autonomous Prefecture, Hami City, and Changji Hui Autonomous Prefecture were situated in a low-high agglomeration. Thus, a general decline in regions exhibiting high-high agglomeration and an increase in those with low-high agglomeration was observed. (3) The extent of arable land scale and the overall advancement of the agricultural economy positively affected the agricultural carbon emission efficiency. Further, the agricultural industry structure, crop cultivation structure, and effective irrigation rate negatively affected the agricultural carbon emission efficiency. Thus, this study highlights the spatial and temporal heterogeneity of agricultural carbon emission efficiency and its influencing factors in Xinjiang. The findings are expected to provide theoretical support and empirical evidence for the sustainable development of agriculture in arid areas.

This study used in-situ land surface temperature observation data (from 2017 to 2019) from seven stations in the Heihe River Basin, northern Gansu Province, China, to evaluate the errors of four land surface temperature products: the Fengyun-3C visible and infrared radiometer (FY-3C VIRR) land surface temperature product, the Terra moderate resolution imaging spectroradiometer (MOD11A1/MOD11C3) land surface temperature product, the European Center for medium-range weather forecasts fifth-generation land surface reanalysis dataset (ERA5-LAND), and the China Meteorological Administration land data assimilation system (CLDAS-V2.0). Bias (BIAS), root mean square error (RMSE), correlation coefficient (CC), and ratio of standard deviation (RSD), were employed as statistical metrics for analyzing errors across different temporal scales. The results indicated the following. (1) All four land surface temperature products exhibited a general spatial pattern of higher temperature in the south and lower temperature in the north. However, the FY-3C VIRR and MOD11A1 products exhibited finer spatial details. (2) The FY-3C VIRR daytime land surface temperature product demonstrated relatively lower BIAS and RMSE values, indicating higher accuracy. Further, the MOD11A1 daytime land surface temperature product yielded the highest CC values, ranging across 0.957-0.987. However, it also produced larger errors. This was attributed to the tendency of the MOD11A1 daytime product to overestimate temperatures. (3) The MOD11A1 nighttime land surface temperature product outperformed the FY-3C VIRR, ERA5-LAND, and CLDAS-V2.0 nighttime products in terms of accuracy. Among these, the CLDAS-V2.0 nighttime product exhibited the largest errors. (4) For the FY-3C VIRR, MOD11A1, and ERA5-LAND products, the nighttime land surface temperature accuracy surpassed those of their respective daytime products. Conversely, the CLDAS-V2.0 daytime land surface temperature product exhibited higher accuracy than its nighttime counterparts.

Accurately quantifying the accessibility of kindergarten education is essential for evaluating the spatial allocation efficiency of resources, particularly in underrepresented areas of the education system. This study focuses on the main urban area of Lanzhou City, Gansu Province, China, utilizing data on kindergarten points of interest, school-age children, available places, and road grades. Using kernel density analysis and a multi-level-multi-travel mode Gaussian accessibility algorithm, we examine the agglomeration characteristics and accessibility of kindergartens at various levels. We also employ a spatial regression model and bivariate spatial autocorrelation to investigate the factors affecting accessibility distribution. The findings reveal that: (1) Kindergartens are distributed in “one core” and “four center” patterns, with density decreasing from east to west. Notably, densely populated areas in Xigu District are independently clustered. The classifications of provincial-level, municipal-level, district-level standard kindergartens, and general kindergartens predominantly exhibit the spatial distribution characteristics of a “single core”. (2) The accessibility of all kindergartens displays a spatial bias trending eastward, and “south-to-north”, and high accessibility areas are concentrated in Chengguan District and Qilihe District. The accessibility of kindergartens, based on their grades and quality, shows a “multi-center” structure, with general kindergartens reflecting the highest accessibility values, indicating their significant role in serving local enrollment. (3) Factors such as school-age population, family economic status, and kindergarten enrollment quotas influence school accessibility. By contrast, road network density and bus stop availability negatively affect accessibility, although this correlation is not significant. Kindergarten education fees also have a negative effect on accessibility.

Rainfall-type landslides are the most frequent and impactful geological hazards in Qinghai Province, China; however, studies on the specific rainfall characteristics that trigger these landslides remain limited. This paper analyzes 339 rainfall-type landslides that occurred between 2016 and 2023 by examining precipitation conditions on the day of occurrence and 1-10 days prior. The objective is to identify the disaster characteristics of rainfall-type landslides and their triggering precipitation conditions. The study reveals the following: (1) Rainfall-type landslides are more frequent in the east than in the west, with Xining City and Haidong City accounting for 76.1% of occurrences. The highest incidence is observed in Xining City’s Huangzhong District, Minhe County, and Ledu District. (2) Landslide frequency correlates with rainfall distribution—years with higher rainfall tend to experience more landslides, with August being the peak month for occurrences. (3) Light to moderate rainfall serves as the foundational condition for landslides, while heavy rainfall or greater acts as the triggering factor. When the cumulative rainfall of 10 days exceeds 40 mm, the probability of landslide occurrence increases significantly. (4) Rainfall-type landslides exhibit a certain lag effect relative to rainfall. The probability of occurrence is highest on the day following two consecutive days of heavy rainfall or greater, as well as on the third day after rainfall ends. In addition, landslide probability increases again approximately five days after rainfall ceases. The findings of this study provide a scientific basis for understanding the occurrence mechanisms of rainfall-type landslides and developing forecasting and early warning models.

The normalized difference vegetation index (NDVI) is a crucial indicator for assessing the stability of the ecological environment. The ecological environment in northwest China’s arid and semi-arid regions is fragile, and analyzing the spatio-temporal changes and driving forces of NDVI is critical for effective vegetation restoration in this area. This study utilizes a variety of datasets, including temperature, precipitation, evapotranspiration, elevation, soil, and nighttime lighting indices. Employing methodologies such as the coefficient of variation, Theil-Sen median trend analysis, the Mann-Kendall significance test, a geographic detector, and the multiscale geographically weighted regression (MGWR) model, we explored the spatiotemporal variation characteristics and driving factors of NDVI in the arid and semi-arid regions of northwest China from 2003 to 2022. The results showed that (1) NDVI exhibited an overall increasing trend from 2003 to 2022, ranging from 0.1974 to 0.2464. The minimum NDVI value was recorded in 2009, whereas the maximum occurred in 2018. (2) Overall, NDVI levels in most areas of the arid and semi-arid region of northwest China are relatively low, displaying a spatial distribution pattern of “high in the east and west, low in the middle”. (3) Most areas experience low stability in NDVI changes, with the central region showing strong stability and the eastern and western regions displaying weak stability. (4) An increasing trend in NDVI was observed across most areas, with only a few regions showing a decline. (5) The spatiotemporal variation of NDVI in the arid and semi-arid region of northwest China is influenced by both natural and human factors, with soil type serving as the primary driving factor. The interplay of various factors also affects the region. MGWR model analysis confirmed that soil type has the most significant effect on NDVI, with air temperature, potential evapotranspiration exerting negative effects, whereas precipitation, and nighttime light index have positive effects.

Scientifically measuring economic resilience in the Yellow River region is essential for enhancing regional development quality and sustainability. This study constructed a comprehensive indicator system to evaluate economic resilience across the Yellow River region from 2003 to 2022, examining three critical dimensions: Resistance, resilience, and transformation. We employed the Theil index to quantify regional disparities and the Moran’s index to analyze spatial correlation patterns. Our analysis revealed four key findings: (1) Regional performance: The Yellow River region demonstrated stable economic operation with strong overall resilience during the study period, though a significant gap persists compared to national economic resilience levels. Among the dimensional subsystems, resilience scored highest while transformation scored lowest. (2) Spatial distribution: Economic resilience exhibits a clear center-periphery structure at the prefecture level. Higher resilience areas are concentrated in the Shandong Peninsula, provincial capitals in central and western China, and mineral-rich cities. Lower resilience characterizes Qinghai, Ningxia, most of Gansu, Shaanxi, Henan, western Inner Mongolia, and western Shandong. (3) Temporal trends: The overall difference in economic resilience across the Yellow River region showed a decreasing trend over most of the study year. Regional differences contributed more than 75% to overall inequality, highlighting the need for coordinated intra-regional strategies. (4) Spatial correlation: Positive Moran’s index values throughout the study period indicate significant autocorrelation in economic resilience. Local spatial clustering revealed predominant “low-low” agglomeration (concentrated in Qinghai, Gansu, and Ningxia) and “high-high” agglomeration (primarily in Shandong).

Considering the global climate change trends, understanding the impact of drought on vegetation is crucial for ecological protection and sustainable development. This study analyzes the spatiotemporal variation of vegetation and its response to meteorological drought within the Tianshan Mountains in Xinjiang, China during the growing season (April-October) for the period of 2001—2023. Using the meteorological drought composite index (MCI) and normalized difference vegetation index (NDVI), we examine the trends in vegetation dynamics and drought conditions. The results indicate the following. (1) The NDVI values in Tianshan Mountains exhibited a slow increasing trend overall; however, a decline was observed since 2019. (2) The MCI values fluctuated but generally exhibited a slow decreasing trend, with a significant intensification of drought conditions in Xinjiang’s Tianshan Mountains region since 2020, particularly in summer, when MCI values dropped sharply. (3) A moderate positive correlation between MCI and NDVI suggested that meteorological drought significantly affected vegetation growth. Vegetation coverage exhibited notable seasonal and spatial heterogeneity, while drought severity followed a gradient—mildest in the west, intensifying in the central region, and most severe in the east. (4) The vegetation response to meteorological drought varied across different regions, with the most pronounced impact observed in the Wuqia County in the southwest. The results provide a scientific foundation for understanding climate change in Tianshan Mountains, assessing ecological responses to meteorological drought, and formulating effective drought mitigation strategies.

Identifying the spatiotemporal characteristics and influencing factors of agricultural carbon emissions is essential for reducing uncertainty in carbon emission reduction target accounting and implementing accurate policies to achieve those targets. Based on agricultural carbon emission estimates in Gansu Province, China, from 2014 to 2022, this study employed the Moran’s I index to analyze the spatiotemporal differentiation characteristics of carbon emissions. The dynamic evolution trends were analyzed using the kernel density estimation method, and a geographically weighted regression model was constructed to identify the factors influencing agricultural carbon emissions. The results showed the following. (1) From 2014 to 2022, the agricultural carbon emissions in Gansu Province tended to decline. However, emissions from animal husbandry, identified as the primary source, exhibited an upward trend. The order of carbon emissions by region from highest to lowest was as follows: Hexi oasis agricultural area>Longdong-Longzhong Loess Plateau dry farming area>alpine pastoral area> Longnan Mountain rain-fed agricultural area. (2) The spatial agglomeration of total agricultural carbon emissions was weak and concentrated within the Hexi Oasis agricultural area. Notably, Jiuquan City and Zhangye City exhibited high-low agglomeration, while Jinchang City exhibited low-high agglomeration. Other regions exhibited no significant agglomeration patterns. (3) The carbon emission intensity across all four agricultural regions declined over the study period, and regional differences in the emission intensity gradually narrowed. (4) Per capita gross agricultural product, agricultural industrial structure, and total population demonstrated a significant role in reducing agricultural carbon emission. The disposable income of rural residents, amount of agricultural fertilizer used, and total power of agricultural machinery caused an increase in the carbon emissions. Finally, it was recommended to improve livestock and poultry varieties, implement moderate-scale breeding practices, and ensure accurate operation and management across the entire industrial chain to increase emission reduction efforts in animal husbandry. Furthermore, minimizing the use of agricultural materials such as fertilizers and plastic films, along with promoting the application of green technologies, is essential to achieving agricultural carbon reduction goals.