This study establishes a framework to assess greenspace supply-demand disparities related to thermal discomfort from the perspective of population mobility across urban functional zones (UFZs). High-resolution greenspace maps and location-based service (LBS) datasets for Nanjing, China, were combined with Spearman correlation analyses and a four-quadrant model to elucidate associations and matching patterns between greenspace exposure and thermal comfort. The findings indicate that population fluctuations affect the availability of actual greenspace, with correlations to thermal discomfort showing significant temporal variations among different UFZs. During morning workday hours, commercial zones have a significantly higher representation in Quadrant II (82.26%) compared to non-workdays (70.86%), which is characterized by high population density, low greenspace exposure, and pronounced thermal discomfort. In contrast, residential and public service zones maintain consistently high and stable proportions in Quadrant I across all periods. This spatial mismatch is primarily caused by differences in available greenspace quantities and population mobility. Planning adjustments should focus on ensuring sufficient greenspace provision in key areas during peak population mobility periods to mitigate thermal discomfort. Minimizing residents’ staying time in thermally uncomfortable zones, implementing time-specific greenspace access, and strategically increasing greenspace coverage are essential for improving the mismatch between greenspace supply and demand.

Morphology, the study of shapes or forms, when applied to tourism, emphasizes the multifarious spatial practices between morphological elements and tourism activities. However, existing literature on morphology in the context of tourism usually only focuses on a single or a limited number of study areas, overlooking common or even universal patterns across various tourism destinations. To address this gap, we utilize geospatial big data and present a case study on the morphology of 406 “AAAAA”-rated scenic areas in China. A framework based on “points”, “lines”, “planes”, and “solids” was designed to systematically organize and analyze morphological elements across scenic areas. The findings provide valuable insights for tourism planning and development, such as the co-occurrence of dense road networks and fragmented landscapes within scenic areas, as well as the resource- context-influenced (cultural or natural) associations between morphological features and tourism indicators. This research provides valuable strategic guidance for more effective and informed tourism development while acknowledging the trade-offs between generalizability and local specificity.

A low-carbon lifestyle presents new opportunities for sustainable urban development. While previous studies have verified the impact of the built environment and socio- economic status (SES) on low-carbon lifestyles, they have primarily focused on direct effects. At present, there is still a lack of analysis on the interaction effects on low-carbon lifestyles, and limited attention has been given to the peer effect in low-carbon lifestyles, especially in the context of residential differentiation. Therefore, we take Zhengzhou city as the case area and first calculate the low-carbon lifestyle of 1485 families from three dimensions: low-carbon action (A), low-carbon interest (I) and low-carbon opinion (O). We then analyze the direct and interactive impacts of the built environment and SES on low-carbon lifestyles and explore the peer effect. Our findings indicate that families with higher SES have higher levels of low-carbon interest and low-carbon opinion, but relatively low levels of low-carbon action. This suggests an interest-action bias in the low-carbon lifestyles of high-SES families. POI density, road network density and accessibility positively affect low-carbon lifestyles—that is, residents living in areas with well-developed infrastructure and convenient transportation tend to be green in their daily behavior. The peer effect influences low-carbon action, interest, and opinion by 54.6%, 34.9%, and 16%, respectively, indicating that the peer effect is most evident in low-carbon action. That is, the peer effect is more obvious in low-carbon action. In addition, the built environment affects the low-carbon lifestyles of different SES groups. Land-use mix positively increases low-carbon action and low-carbon interest among high-SES groups but reduces low-carbon opinion. Road network density positively affects the low-carbon action of high-SES groups and the low-carbon interest and low-carbon opinion of low-SES groups. This study explores low-carbon lifestyles from a situational perspective, providing a practical basis for policies aimed at accelerating a transition to sustainable living.

This study proposes a framework for the concept of “new quality productive forces” in the ice and snow economy (ISE) as a strategic response to global climate change and the demands of technological and industrial transformation for high-quality development. These new quality productive forces in the ISE have developed alongside the zonal distribution of natural resources, strictly adhere to ecological principles, and integrate value transformation mechanisms specific to ice and snow resources. Their development is projected to generate multiple benefits across ecological, economic, and social dimensions. The new quality productive forces in the ISE are characterized by technology-driven resource development, synergistic integration across the entire ice and snow industry value chain, and a focus on high-quality, green growth. Grounded in geography and economics, the new quality productive forces in the ISE link scientific innovation, the reallocation of productive factors, and industrial upgrading within the context of resource constraints. Furthermore, they expand the growth potential of the ISE by fostering new production relations through digital, intelligent, and green integration, while advancing low-carbon, sustainable development under the guiding principle that “ice and snow landscapes are also mountains of gold and silver.” For China’s ISE, these new quality productive forces emphasize rigorous resource protection, balanced human-environment relationships, a resilient integrated supply chain framework, and an efficient “dual circulation” economic model. Practical strategies include integrating production factors, optimizing spatial resource allocation, fostering industrial synergy, and adapting production relations, all aimed at advancing the sustainable and high-quality development of China’s ISE.

Rapid urbanization in China has led to spatial antagonism between urban development and farmland protection and ecological security maintenance. Multi-objective spatial collaborative optimization is a powerful method for achieving sustainable regional development. Previous studies on multi-objective spatial optimization do not involve spatial corrections to simulation results based on the natural endowment of space resources. This study proposes an Ecological Security-Food Security-Urban Sustainable Development (ES-FS- USD) spatial optimization framework. This framework combines the non-dominated sorting genetic algorithm II (NSGA-II) and patch-generating land use simulation (PLUS) model with an ecological protection importance evaluation, comprehensive agricultural productivity evaluation, and urban sustainable development potential assessment and optimizes the territorial space in the Yangtze River Delta (YRD) region in 2035. The proposed sustainable development (SD) scenario can effectively reduce the destruction of landscape patterns of various land-use types while considering both ecological and economic benefits. The simulation results were further revised by evaluating the land-use suitability of the YRD region. According to the revised spatial pattern for the YRD in 2035, the farmland area accounts for 43.59% of the total YRD, which is 5.35% less than that in 2010. Forest, grassland, and water area account for 40.46% of the total YRD—an increase of 1.42% compared with the case in 2010. Construction land accounts for 14.72% of the total YRD—an increase of 2.77% compared with the case in 2010. The ES-FS-USD spatial optimization framework ensures that spatial optimization outcomes are aligned with the natural endowments of land resources, thereby promoting the sustainable use of land resources, improving the ability of spatial management, and providing valuable insights for decision makers.

Human activities have significantly impacted the land surface temperature (LST), endangering human health; however, the relationship between these two factors has not been adequately quantified. This study comprehensively constructs a Human Activity Intensity (HAI) index and employs the Maximal Information Coefficient, four-quadrant model, and XGBoost- SHAP model to investigate the spatiotemporal relationship and influencing factors of HAI-LST in the Yellow River Basin (YRB) from 2000 to 2020. The results indicated that from 2000 to 2020, as HAI and LST increased, the static HAI-LST relationship in the YRB showed a positive correlation that continued to strengthen. This dynamic relationship exhibited conflicting development, with the proportion of coordinated to conflicting regions shifting from 1:4 to 1:2, indicating a reduction in conflict intensity. Notably, only the degree of conflict in the source area decreased significantly, whereas it intensified in the upper and lower reaches. The key factors influencing the HAI-LST relationship include fractional vegetation cover, slope, precipitation, and evapotranspiration, along with region-specific factors such as PM_2.5, biodiversity, and elevation. Based on these findings, region-specific ecological management strategies have been proposed to mitigate conflict-prone areas and alleviate thermal stress, thereby providing important guidance for promoting harmonious development between humans and nature.

Green roofs play a vital role in promoting sustainable urban development and achieving carbon neutrality by enhancing carbon sequestration, oxygen release, and efficiency of land use. Despite these benefits, living roof coverage in China remains limited. To address the challenges in policy formulation, operational monitoring, and the absence of multi-scale retrofit strategies supported by robust assessment methods, this study develops a comprehensive evaluation framework. The framework integrates vector data, building age information, and point-of-interest (POI) data, and applies an optimized Prophet model to classify six major climate zones. This approach facilitates the selection of appropriate plant species and substrates while quantifying the potential for carbon sequestration and oxygen release. An assessment of 90 cities reveals approximately 1.3861 billion square meters of rooftop area suitable for green roof implementation, with an estimated annual carbon sequestration potential of 67.30 million tons and oxygen release of 30.36 million tons. Commercial buildings contribute significantly, comprising 65% of the total suitable area. Climate zones 2 and 3 exhibit the most favorable outcomes. The current study provides a reliable quantitative reference for evaluating the carbon sequestration and oxygen release capacities of green roofs and supports the formulation of effective retrofit policies.

With the rapid advancement of global socio-economy and mounting environmental and ecological risks, China faces challenges in ensuring its food security and sustainable development, which further affects global food trade and security. This study aims to identify the supply‒demand match between cropland supply and food consumption and to evaluate sustainable cropland zoning in multiple scenarios and multidimensional assessments. This study uses ecological, environmental and socioeconomic data to quantify diverse food demand patterns into corresponding cropland demands, further mapping the spatio-temporal characteristics of China’s cropland supply‒demand matches. By utilizing shared socioeconomic pathways (SSPs), this study delineates multiple scenarios to determine the supply‒demand of cropland across different Chinese regions from 2030 to 2050. On the basis of ecological, geographical and socioeconomic datasets, this study constructs a multidimensional and multiscenario framework for sustainable agricultural zoning from 2030 to 2050 and proposes a future sustainable agricultural development strategy for each region in different periods. The results indicate that between 2002 and 2022, there was a significant gap between cropland supply and demand. Moreover, an obvious spatial mismatch is observed between cropland supply and demand across various Chinese regions. From 2030 to 2050, there is a noticeable shift in the spatial distribution of cropland supply and demand, with the supply‒demand match becoming more strained and varying considerably under different development scenarios. With significant differences between different development scenarios, different regions will have to adopt different development strategies at different periods. This study proposes a multiscenario and multidimensional simulation framework for future agricultural sustainable zoning, which aims to provide scientific insights and policy improvements to promote sustainable agricultural development.

Understanding the characteristics and driving factors behind changes in vegetation ecosystem resilience is crucial for mitigating both current and future impacts of climate change. Despite recent advances in resilience research, significant knowledge gaps remain regarding the drivers of resilience changes. In this study, we investigated the dynamics of ecosystem resilience across China and identified potential driving factors using the kernel normalized difference vegetation index (kNDVI) from 2000 to 2020. Our results indicate that vegetation resilience in China has exhibited an increasing trend over the past two decades, with a notable breakpoint occurring around 2012. We found that precipitation was the dominant driver of changes in ecosystem resilience, accounting for 35.82% of the variation across China, followed by monthly average maximum temperature (Tmax) and vapor pressure deficit (VPD), which explained 28.95% and 28.31% of the variation, respectively. Furthermore, we revealed that daytime and nighttime warming has asymmetric impacts on vegetation resilience, with temperature factors such as Tmin and Tmax becoming more influential, while the importance of precipitation slightly decreases after the resilience change point. Overall, our study highlights the key roles of water availability and temperature in shaping vegetation resilience and underscores the asymmetric effects of daytime and nighttime warming on ecosystem resilience.

Exploring the spatial heterogeneity of ecosystem services (ESs) and their driving factors under various interaction patterns is essential for informing sustainable development policies. Using Northeast China as a case study, this research investigates eight key ESs, including water yield (WY), carbon storage (CS), food provision (FP), habitat quality (HQ), soil conservation (SC), wind-break and sand-fixation (WS), water purification (WP) and aesthetic landscape (AL). The study examines the complexity of ESs from three dimensions: individual ES, ES pairs and ES bundles, and further evaluates their spatial heterogeneity and socio- ecological drivers. The results indicate that the spatial distribution of ESs remained relatively stable from 2000 to 2020. During this period, WY and FP increased significantly, CS and HQ remained relatively unchanged. SC, WS and AL followed an “increase-decrease-increase” trend, and WP exhibited a “decrease-increase” fluctuation. Overall, synergistic relationships among ES Pairs were more prevalent than trade-offs. Notably, CS showed trade-offs with over 70% of the other ESs, while HQ exhibited trade-offs with SC, WS, WP, and AL. The FP-HQ synergy bundle, primarily located in the Greater Hinggan Mountains and eastern regions, emerged as the dominant ES bundle. Ecological factors—such as solar radiation, temperature, slope, DEM, and NDVI—exerted a stronger influence on ES patterns than social factors like GDP and population density. Furthermore, these ecological drivers had a greater impact on individual ESs compared to ES pairs or ES bundles. These findings offer valuable insights for policymakers to understand the complex interrelationships among ESs and to design more effective and regionally tailored management strategies.

Existing studies have mostly focused on sustainable intensification (SI) in agricultural systems, while neglecting the integrated analysis of SI for the land space utilization system (LSUS). This has resulted in a lack of systematic solutions in balancing sustainable resource utilization and environmental protection. This study reviewed SI’s conceptual framework and evaluation, identified the gaps, and proposed an analytical framework of SI with clear logic and modeling processes for LSUS. Key findings include: (1) Resource competition and ecosystem pressures have highlighted the need to extend traditional agriculture-focused SI to LSUS and establish a clear quantitative evaluation framework for SI; (2) SI for LSUS refers to a system state in which a specific sub-system produces its dominant functions with resource savings, reduced environmental impact, efficient function output, and stable/enhanced function provision, while sub-systems evolve in a coordinated and orderly manner; (3) The assessment framework of SI for LSUS clarifies modeling processes, suggested indicators, methods and scale hierarchy system to help policymakers identify SI priorities across scales, informing strategies to balance agricultural, socioeconomic, and ecosystem goals. This study overcomes the limitations of traditional SI, providing crucial insights for tracking SI performance and identifying barriers in LSUS to enlighten the sustainable land use and management practices.

In drylands, biocrusts play crucial roles in regulating ecosystem functions. The study was conducted in the hilly rangelands of the semi-arid northern Negev of Israel, where we assessed the visual, morphological, spectral, and soil properties of livestock trampling routes and inter-route spaces in northern and southern facing hillslopes. Overall, both hillslope aspects were visually similar, whereas the ground surface of the routes was brighter (74.4% were characterized as having a ‘light’ color) than the inter-route spaces (86.8% were characterized as having a ‘dark’ color). These observations were supported by morphological identification of biocrust composition, which was dominated by cyanobacteria (67%) in the routes, and by mixed cyanobacteria/moss (56%) in the inter-routes. Mean Normalized Difference Vegetation Index (NDVI) was 24% higher in the inter-routes, while the mean Brightness Index (BI) was 12% higher in the routes. At the same time, the mean Crust Index (CI) was identical in the two microhabitats. Soil quality index (SQI), calculated based on the (pedoderm) soil properties of the two microhabitats, was 6% greater in the inter-routes than in the routes. This study suggests that recurrent trampling exacerbates soil compaction and shearing along the routes, thus preventing the successional development of complex biocrust compositions.

Achieving conservation goals in natural habitats requires a balanced approach that integrates both sustainable community development and nature conservation, rather than completely excluding human activities from wilderness areas. However, limited understanding exists regarding locals’ willingness to participate (WTP) in the construction and stewardship of national parks as well as their driving factors behind this willingness. To identify the key drivers that promote locals’ WTP in national parks, we investigated local residents’ participation willingness and embedded an additional structure perceived value (PV) into the Theory of Planned Behavior (TPB) model, analyzing the data by using structural equation modeling. Local communities were slightly willing to participate in Changtang National Park and conservation in general; interestingly, nomads’ willingness was stronger than settlers’. Perceived behavioral control (PBC) exhibited the most significant impact on WTP, with particular emphasis on the livelihood risks associated with grasslands. PV indirectly influenced WTP by affecting attitude (ATT), personal/social norms (PSN), and PBC, while it did not have a direct impact on WTP. For settlers and nomads, different variables influence their varying levels of willingness to engage in park participation. These results deepen our understanding of community willingness to participate and differences in drivers of WTP between settlers and nomads, contributing to relevant knowledge to inform seeking a balance between sustainable community development and nature conservation.

The Qingtongxia Irrigation District in Ningxia is an important hydrological and ecological region. To assess its ecological environment quality from 2001 to 2021 across multiple scales and identify driving factors, a modified remote sensing ecological index (MRSEI) was developed by incorporating evapotranspiration. Spatial and temporal patterns were analyzed using the coefficient of variation, spatial autocorrelation, and semi-variogram methods, while influencing factors were explored via the optimal parameter geographical detector model. The MRSEI’s first principal component loadings and rankings aligned with those of RSEI (average contribution: 81.31%), effectively reflecting spatiotemporal variations. At sub-irrigation district and landscape scales, ecological quality was slightly lower than at the district level but remained stable. Moderate and good ecological grades accounted for 36.28% and 33.38% of the area, respectively, at the district scale, and the moderate grade reached 70.48% on smaller scales. Spatial heterogeneity intensified with decreasing scale, and human activity lost explanatory power below a 5 km range. Human factors mainly drove ecological differentiation at the district scale, while natural factors dominated at finer scales. The MRSEI offers a novel tool for ecological assessment in arid/semi-arid areas and supports scale-adapted ecological protection strategies.

To address soil salinization's significant impact on human production and livelihood in arid regions, especially in high-salinity areas like salt lake regions, this study used multi-source remote sensing data to extract 52 surface factors. Combined with measured soil salinity data, correlation analysis, multicollinearity testing, and projection importance analysis identified eight dominant factors. Subsequently, four machine learning algorithms were applied for modeling, and the optimal models were selected to study the spatiotemporal variation of soil salinization. The results indicate that the average soil salt content in the study area was 20.74% in 2020. LST (land surface temperature) can effectively identify areas with high salinity, such as saline-alkali land and salt flats. Among inversion models, the GBDT (gradient boosting decision trees) model demonstrated the highest predictive ability and minimal errors. The optimal inversion results revealed that soil salinization distribution was influenced by topographic elevation, distance from Qarhan Salt Lake, and river network density. Over the past 21 years, there was significant fluctuation in soil salinity observed in the concentrated area of grassland within the groundwater overflow zone, indicating strong variation in salinization. This fluctuation correlates with changes in groundwater levels in the groundwater overflow zone, which are influenced by temperature variations that determine the amount of snow and ice meltwater, and the precipitation in the upstream area. This study enhances understanding of soil salinization and its drivers in extremely arid salt lake regions.

The intricate network of bilateral trade relationships among Pacific Rim countries (PRCs), along with the associated embodied carbon flows plays a pivotal role in shaping global carbon emission patterns and dynamics. This study employs a multi-regional input- output analysis and a symbiotic degree model to explore the symbiotic effects of trade-embodied carbon flows between China and PRCs. We show that between 2009 and 2021, China’s trade-embodied carbon exports to PRCs surged from 214.20 million tons to 614.80 million tons, driven largely by mechanical and electronic equipment. The share of the United States, Japan, and South Korea in China’s total embodied carbon exports to PRCs has declined, whereas Southeast Asian countries have emerged as the primary source of China’s embodied carbon imports. The degree of symbiosis in trade-embodied carbon between China and PRCs shifted from negative to positive, indicating a gradual trend toward positive asymmetric symbiosis. Moreover, China’s role in regional trade-embodied carbon flows has transitioned from passive to active, with its influence particularly pronounced in countries such as Vietnam, Thailand, Japan, South Korea, and Russia.

The amplitude of the annual temperature cycle (ATC) is a crucial component of Earth’s climate and profoundly influences its phenology and ecosystem dynamics. However, most previous studies on ATC amplitude have been confined to the post-industrial instrumental period. Although a few studies have reconstructed ATC amplitudes over the past few centuries using proxy data, these efforts have been limited to regional scales, leaving the global profile of ATC amplitude from the pre- to post-industrial periods poorly understood. Here, leveraging rigorous evaluation and screening of monthly mean air temperature data derived from eleven CMIP5/CMIP6 models spanning the last millennium, combined with grid-based weighted averaging, we produced reliable ATC amplitude series for global and hemispheric land areas since 850 CE. Our analysis reveals a significant reduction in ATC amplitude since the 1860s across global and Northern Hemispheric lands, whereas the Southern Hemisphere has been relatively stable. The unprecedented decline in ATC amplitude since the late 19th century stands in stark contrast to the modest increases observed during the Medieval Climate Anomaly (ca. 1000-1300 CE) and the Little Ice Age (ca. 1400-1850 CE). These findings, particularly the distinct shift in ATC amplitude between the pre- and post-industrial periods, provide an early global fingerprint of anthropogenic forcing on climate change.

This study investigates climate- and human-induced hydrological changes in the Zavkhan River-Khyargas Lake Basin, a highly sensitive arid and semi-arid region of Central Asia. Using Mann-Kendall, innovative trend analysis, and Sen’s slope estimation methods, historical climate trends (1980-2100) were analyzed, while land cover changes represented human impacts. Future projections were simulated using the MIROC model with Shared Socioeconomic Pathways (SSPs) and the Tank model. Results show that during the past 40 years, air temperature significantly increased (Z=3.93***), while precipitation (Z=-1.54*) and river flow (Z=-1.73*) both declined. The Khyargas Lake water level dropped markedly (Z= -5.57***). Land cover analysis reveals expanded cropland and impervious areas due to human activity. Under the SSP1.26 scenario, which assumes minimal climate change, air temperature is projected to rise by 2.0℃, precipitation by 21.8 mm, and river discharge by 1.61 m³/s between 2000 and 2100. These findings indicate that both global warming and intensified land use have substantially altered hydrological and climatic processes in the basin, highlighting the vulnerability of western Mongolia’s water resources to combined climatic and anthropogenic influence.

A comprehensive understanding of urbanization impacts on landscape dynamics, eco-environmental consequences, and advancements in human habitation is paramount for effectively advancing urbanization-related sustainable development goals. This study predicted the urbanization process within the Hohhot-Baotou-Ordos-Ulanqab (HBOU) region and its projected implications for ecology, human settlement, and energy consumption in 2020-2050 using multi-source data and models under Shared Socioeconomic Pathways (SSPs). The results revealed that the HBOU region’s urban area grew by 624.66 km² between 1990 and 2020. By 2050, it is expected to reach 1793.49±169.30 km², mainly expanding into cropland (58.95%) and natural ecological land (31.79%). Urban greening is projected to enhance, with the highest urban green space (UGS) predicted under SSP1 (32.42%). Under this scenario, the per capita urban area (PCUA) and per capita urban green space area (PCUGA) are projected to reach 172.66 and 55.63 m²/person in 2050, respectively. Furthermore, the ecological and energy utilization impacts are anticipated to decrease by 3.99% to 37.52% relative to alternative scenarios. Our projections suggest that limiting urbanization area in the HBOU region to 1500-1600 km² would significantly enhance the settlement environment and mitigate ecological and energy consumption effects. These results guide urban strategies balancing ecology, energy use, and habitation in arid regions.

Rapid regional population shifts and spatial polarization have heightened pressure on cultivated land—a critical resource demanding urgent attention amid ongoing urban-rural transition. This study selects Jiangsu province, a national leader in both economic and agricultural development, as a case area to construct a multidimensional framework for assessing the recessive morphological characteristics of multifunctional cultivated land use. We examine temporal dynamics, spatial heterogeneity, and propose an integrated zoning strategy based on empirical analysis. The results reveal that: (1) The recessive morphology index shows a consistent upward trend, with structural breaks in 2007 and 2013, and a spatial shift from “higher in the east and lower in the west” to “higher in the south and lower in the north.” (2) Coordination among sub-dimensions of the index has steadily improved. (3) The index is expected to continue rising in the next decade, though at a slower pace. (4) To promote coordinated multidimensional land-use development, we recommend a policy framework that reinforces existing strengths, addresses weaknesses, and adapts zoning schemes to current spatial conditions. This research offers new insights into multifunctional cultivated land systems and underscores their role in enhancing human well-being, securing food supply, and supporting sustainable urban-rural integration.

Ecosystems along the eastern margin of the Qinghai-Tibet Plateau (EQTP) are highly fragile and extremely sensitive to climate change and human disturbances. To quantitatively assess climate-induced ecosystem responses, this study proposes a Climate-Induced Productivity Index (CIPI) based on the Super Slack-Based Measure (Super-SBM) model using remote sensing data from 2001 to 2020. The results reveal persistently low CIPI values (0.47-0.53) across major ecosystem types, indicating widespread vulnerability to climatic variability. Among these ecosystems, forests exhibit the highest CIPI (0.55), followed by shrublands (0.54), croplands (0.53), grasslands (0.51), and barelands (0.43). The Theil index analysis further demonstrates significant intra-group disparities, suggesting that extreme climatic events amplify CIPI heterogeneity. Moreover, the dominant environmental drivers differ among ecosystem types: the Palmer Drought Severity Index (PDSI) primarily constrains grassland productivity, solar radiation (SRAD) strongly influences shrub and cropland systems, whereas subsurface factors exert greater control in forested regions. This study provides a quantitative framework for evaluating climate-ecosystem interactions and offers a scientific basis for long-term ecological monitoring and security planning across the EQTP.

The thawing of ice-rich permafrost leads to the formation of thermokarst landforms. Precise mapping of retrogressive thaw slumps (RTSs) is imperative for assessing the degradation and carbon exchange of permafrost at both local and regional scales on the Tibetan Plateau (TP). However, previous methods for RTSs mapping rely on a large number of samples and complex classifiers with low automation level or unnecessary complexity. We propose an automatic mapping network (AmRTSNet) for producing decimeter-level RTSs maps from GaoFen-7 images based on deep learning. Both the quantitative metrics and qualitative evaluations show that AmRTSNet trained in the Beiluhe offers significant advantages over previous methods. Without further fine-tuning, we conducted RTSs automatic mapping based on AmRTSNet in the Wulanwula, Chumarhe, and Gaolinggo. Over 141,312 ha on the TP have been automatically mapped, comprising 926 RTS regions with a total RTS area of 2318.72 ha. The average statistics of the mapped RTSs show low roundness (0.38), moderate rectangularity (0.61), and high convexity (0.79). About 90% of the RTSs are smaller than 6 ha. The average aspect ratio is 2.18. RTSs are unevenly distributed in belt-like aggregations with dominant density peaks. RTSs often concentrate in hillslopes and along lateral streams, with more dense areas more likely to have larger RTSs.

Intermittent rivers and ephemeral streams (IRES), also known as non-perennial river segments (NPRs), have garnered attention due to their significant roles in watershed hydrology and ecosystem services, especially in the context of climate change and escalating human activities. Recent advances in machine learning (ML) techniques have significantly improved the analysis of dynamic changes in IRES. Various ML models, including random forest (RF), long short-term memory (LSTM), and U-Net, demonstrate clear advantages in processing complex hydrological data, enhancing the efficiency and accuracy of IRES extraction from remote sensing data. Furthermore, hybrid ML approaches enhance predictive performance in complex hydrological scenarios by integrating multiple algorithms. However, ML methods still face challenges, including high data dependence, computational complexity, and scalability issues with models. This review proposes an IRES monitoring framework that combines satellite data with ML algorithms, integrating remote sensing technologies such as optical imaging and synthetic aperture radar, and evaluates the advantages and limitations of different ML methods. It further highlights the potential of integrating multiple ML techniques and high-resolution remote sensing data to monitor IRES dynamics, conduct ecological assessments, and support sustainable water management, offering a scientific foundation for addressing environmental and anthropogenic pressures.

Existing studies on the Regional Comprehensive Economic Partnership (RCEP) mainly focused on institutional features, macro-economic impacts, and trade-network structures, while its geographic attributes and their implications remain underexplored. Taking the RCEP as a case, this paper examines how the FTA reshapes China’s trade geography and validates these effects with an enhanced GTAP model, providing an empirical basis for advancing trade-geography theory. Key findings include: (1) RCEP significantly reduces regional trade costs. After full implementation of the agreement, the average tariffs among member countries will decrease to 40.5% of the pre-implementation level, while import and export trade facilitation levels improve by 34.3% and 29.6%, respectively. However, these improvements exhibit marked regional disparities. (2) RCEP asymmetrically promotes China’s foreign trade growth, with stronger import stimulation than export expansion, alongside significant product-specific variations. (3) The agreement reshapes China’s trade geography, driving a 7.66% increase in intra-RCEP trade while reducing extra-RCEP trade by 0.80%. (4) The restructuring of China’s trade patterns under RCEP emerges from the complex interplay of trade creation, diversion, and crowding-out effects. Accordingly, China should further harmonize regional tariff schedules, enhance trade-facilitation mechanisms, strengthen industrial competitiveness and expand multilateral partnerships.

Landslides pose a formidable natural hazard across the Qinghai-Tibet Plateau (QTP), endangering both ecosystems and human life. Identifying the driving factors behind landslides and accurately assessing susceptibility are key to mitigating disaster risk. This study integrated multi-source historical landslide data with 15 predictive factors and used several machine learning models—Random Forest (RF), Gradient Boosting Regression Trees (GBRT), Extreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost)—to generate susceptibility maps. The Shapley additive explanation (SHAP) method was applied to quantify factor importance and explore their nonlinear effects. The results showed that: (1) CatBoost was the best-performing model (CA=0.938, AUC=0.980) in assessing landslide susceptibility, with altitude emerging as the most significant factor, followed by distance to roads and earthquake sites, precipitation, and slope; (2) the SHAP method revealed critical nonlinear thresholds, demonstrating that historical landslides were concentrated at mid-altitudes (1400-4000 m) and decreased markedly above 4000 m, with a parallel reduction in probability beyond 700 m from roads; and (3) landslide-prone areas, comprising 13% of the QTP, were concentrated in the southeastern and northeastern parts of the plateau. By integrating machine learning and SHAP analysis, this study revealed landslide hazard-prone areas and their driving factors, providing insights to support disaster management strategies and sustainable regional planning.

Addressing poverty among the elderly is essential for achieving sustainable development objectives. Despite considerable research progress on elderly poverty, studies that explore this topic from the perspective of spatial changes in urban-rural differences are scarce. This study aims to fill this gap by utilising data from the Chinese Longitudinal Healthy Longevity Survey covering the years 2011, 2014 and 2018. This research applies the Alkire- Foster and Geodetector methods and investigates the spatial variations and determinants of urban-rural differences in elderly poverty (URDEP). Results reveal that rural elderly individuals experience higher poverty levels than their urban counterparts. In addition, URDEP is lower in developed areas and more pronounced in less-developed regions. This study identifies several factors that influence spatial changes in URDEP, including urban-rural income gap, economic level and quality of community elderly care services. These findings offer valuable insights into reducing elderly poverty and enriching the understanding of mechanisms driving spatial changes in URDEP from a geographical perspective.

Balancing urbanization with ecological carrying capacity is essential for sustainable urban development. Traditional land use prediction and urban growth boundary (UGB) delineation methods often overlook ecological assessments and fail to address policy conflicts. This study proposes an integrated model combining urban spatial suitability (USS) and ecological carrying capacity (ECC) evaluations with cellular automata (CA) model to improve simulation accuracy and support scenario-based UGB delineation. First, we identify spatial variations in urban development potential under different scenarios by adjusting the weights of USS and ECC. Then, a multi-objective planning model is used to optimize the future land-use structure, maximizing overall benefits. Finally, the development potential and optimized land allocation are incorporated into the CA model to simulate future land use and delineate UGB for each scenario. Results show that integrating USS and ECC evaluations improves simulation accuracy, with the Kappa coefficient increasing from 0.836 (with only USS evaluation) to 0.908 and overall accuracy reaching 94.1%. While the economic development scenario yields the highest economic benefits, a stronger emphasis on ECC produces more compact and spatially organized urban forms, characterized by higher aggregation and lower fragmentation. This framework provides a robust basis for multi-scenario urban simulation and offers valuable guidance for the scientific UGB delineation.

Transboundary rivers, traversing multiple national borders, integrate sovereign states into a unified ecological system, complicating water resource governance amid rising global water scarcity and geopolitical tensions. Consequently, transboundary river governance exemplifies the public resource dilemma. This study, framed by constructivist international relations theory, examines the Lancang-Mekong River Basin as a case study, using data from multiple sources and socioeconomic indicators to explore the evolution of collective identity among riparian countries and its influencing factors. Key findings include: (1) The collective identity of riparian countries evolved in three phases: emergence (1971-1991), formation (1992-2014), and development (2015-2022). During this process, basin governance evolved from limited mechanisms to a more comprehensive, basin-wide system, with an expanded issue range and an increasing number of cooperation agreements. Cooperative attitudes transitioned from broadly positive to differentiated, ultimately aligning more favorably. (2) Economic interdependence is critical to the formation of collective identity among riparian countries, while diplomatic alignment enhances cooperation. (3) Extreme weather events and political globalization exert dual effects on collective identity formation: extreme weather fosters cooperation but also prioritizes domestic recovery, complicating agreements and expanding issues. Political globalization has facilitated institutionalization and normalization of cooperation, though external involvement has deepened divisions in cooperative attitudes. This study contributes to theoretical perspectives on transboundary river governance and supports collective action in global environmental governance.

Accurate prediction of flood events is important for flood control and risk management. Machine learning techniques contributed greatly to advances in flood predictions, and existing studies mainly focused on predicting flood resource variables using single or hybrid machine learning techniques. However, class-based flood predictions have rarely been investigated, which can aid in quickly diagnosing comprehensive flood characteristics and proposing targeted management strategies. This study proposed a prediction approach of flood regime metrics and event classes coupling machine learning algorithms with clustering-deduced membership degrees. Five algorithms were adopted for this exploration. Results showed that the class membership degrees accurately determined event classes with class hit rates up to 100%, compared with the four classes clustered from nine regime metrics. The nonlinear algorithms (Multiple Linear Regression, Random Forest, and least squares-Support Vector Machine) outperformed the linear techniques (Multiple Linear Regression and Stepwise Regression) in predicting flood regime metrics. The proposed approach well predicted flood event classes with average class hit rates of 66.0%-85.4% and 47.2%-76.0% in calibration and validation periods, respectively, particularly for the slow and late flood events. The predictive capability of the proposed prediction approach for flood regime metrics and classes was considerably stronger than that of hydrological modeling approach.

This paper provides a comprehensive reflection on the evolution of globalization research in the Pearl River Delta (PRD), considering the current international context and national strategies. It identifies several challenges in existing studies, such as the ambiguity of globalization patterns and the insufficient representativeness of key indicators. In response to these challenges, this paper draws upon the theory of strategic coupling to propose a new theoretical framework for analyzing globalization in latecomer regions. Based on the concepts of spatial stickiness and locational advantages, this paper further develops a two-dimensional quantitative indicator matrix. Using the PRD as a case study, it conducts empirical measurements and analysis, leading to three main conclusions. First, the theory of strategic coupling proves well-suited for analyzing the globalization of latecomer regions, exemplified by the PRD. It offers a more systematic, clearer, and more robust explanatory framework compared to traditional measurement methods. Second, the empirical analysis from the PRD reveals that the pattern of regional globalization does not follow a simple linear growth or cyclical model. Instead, it exhibits a circuitous, complex, and upward spiral, unfolding along an S-shaped evolutionary trajectory. Third, through comparisons of the eastern and western shores, as well as segmented city analyses, this study finds that locational advantages significantly shape the evolutionary pattern of globalization. This influence is not only apparent during the region’s initial take-off phase but also plays a more profound role in shaping its subsequent developmental trajectory. This study makes a distinctive contribution to both the theoretical understanding of globalization in latecomer regions and the practical field of regional economic development in China. Additionally, it introduces a novel measurement approach for studying regional globalization.