Climate change significantly affects the arid/humid processes and patterns in China, directly impacting management decisions related to adaptive agriculture and water resources management, desertification control, and spatial ecological restoration. However, current studies primarily focus on changes in arid/humid climate variables, lacking quantitative characterization of the dynamic evolution of areal systems and their nonlinear responses. Based on the data of national meteorological stations from 1961 to 2020, we systematically quantified the nonlinear response of arid/humid patterns to climate change. The results revealed that 6.98% of eco-geographical arid/humid regions underwent type shifts over the past six decades, with 4.95% transitioning toward wetter conditions. Humid and semi-arid regions expanded significantly while sub-humid and arid regions contracted significantly. In the late 1990s, trends of the humid and sub-humid region shifted. Humid region contraction in northern China was driven primarily by precipitation decline, whereas the Tibetan Plateau responded to increasing potential evapotranspiration. During the same period, the retreat rate of the arid region slowed, linked to intensified aridification in the west part of northern China and a decelerating wetting trend in northwest China, both primarily driven by precipitation trends. Our study reveals the nonlinear response of the arid/humid patterns under climate change, providing a scientific basis for the improvement of regional climate resilience.

Promoting the synergistic governance of pollution control (PC) and carbon reduction (CR) in the agricultural sector was an important way for the Chinese government to implement the “dual carbon” initiative and respond to climate change. Based on the data of China’s crop production from 31 provincial-level regions from 1997 to 2022, this paper constructs a framework consisting of spatiotemporal evolution, synergy effect measurement, differences in contributions across regions, and influencing factors analysis to reveal the relationship between agricultural PC and CR. The results showed that the annual growth rates of pollutant emissions and carbon emissions were 1.85% and 0.79%, respectively. However, the annual decline rates of their emission intensities were 3.14% and 4.32%, respectively. This indicated that China’s actions to reduce pollution and carbon emissions in agriculture have achieved good results, that the effect of PC was weaker than that of CR and had an obvious “policy node effect.” Simultaneously, the synergy between PC and CR evolved from “basic coordination” to “basic imbalance.” The contribution of inter-regional differences was relatively large, while intra-regional differences were smaller, highlighting the importance of reducing regional disparities in promoting the synergistic governance of PC and CR. The basic conditions, industrial structure, input intensity, and development potential of agricultural development were key factors in widening the coupling coordination gap between PC and CR, and the influence of these significant factors exhibited clear spatiotemporal heterogeneity. These findings have provided important evidence for understanding China’s agricultural environmental governance strategies and could offer experiential insights for developing countries in advancing the coordinated governance of agricultural PC and CR.

Ensuring national food security amidst rapid population growth and increasing extreme weather events remains a critical global challenge. However, the extent to which agricultural modernization in China enhances grain yield and contributes to food security remains unclear. Therefore, using panel data from 327 Chinese cities (2013-2021), this study employs spatial econometric models to analyze the spatial spillover effects of agricultural modernization level (AML) on grain yield and to reveal regional heterogeneity across nine major agricultural zones. The results showed a cumulative grain yield increase of 23.7 million tons, with peak productivity concentrated along the Hu Line and declining eastward and westward. AML also exhibited a steady increase but a clear spatial gradient, decreasing from coastal to inland regions, with the highest level observed in Southern China (SC). A key finding was that a 1% increase in AML directly raised local grain yield by an average of 4.185%, accompanied by significant positive spillover effects on neighboring regions. Regional variations revealed distinct patterns: the direct effects of AML were more pronounced in southern and eastern zones, while spillover effects dominated in northern and western zones. The largest positive direct impact of AML on grain yield was observed in the SC (8.499%), while Middle-Lower Yangtze Plain ranked second but exhibited the strongest positive spatial spillover effect (4.534%). These findings highlight the critical role of agricultural modernization in promoting grain production and provide a solid basis for optimizing regional agricultural systems, ensuring food security, and advancing sustainable agriculture.

Urban agglomerations, representing a high-level organizational form of urbanization, play an increasingly vital role in promoting sustainable development. These regions attract substantial population inflows due to their robust economic foundations and advanced public service facilities. To assess this dynamic, an evaluation index system for urban sustainable development goals (SDGs) was constructed based on the United Nations SDGs framework. Using three representative urban agglomerations of Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), and Chengdu-Chongqing (CY) in China as case studies, this research explores the realization of SDGs since the construction of the urban agglomerations and its coupling with population changes by combining multifactorial analysis and the coupling coordination degree model. Results reveal that SDG scores in these cities have increased by an average of 25.33% since 2005. Scores in central cities are significantly higher than the average, and the gap between cities is narrowing. However, there are still trade-offs among some of the goals. Additionally, the process of SDGs realization in core cities with large populations is largely coordinated with population growth. The findings provide a reference for urban agglomerations to adopt cross-regional collaborative governance measures to achieve the SDGs.

The development of human settlements (HS) in coastal cities is an integral component and a vital pathway toward building a strong marine power. It is also an essential requirement for achieving the coordinated development of HS systems in these cities. In this study, we constructed an indicator system to analyze the coupling coordination degree (CCD) of HS systems in coastal cities in the Bohai Rim region of China (CCBRR). This study is based on five systems and employs methods such as the entropy weight method, CCD model, spatial trend surface analysis, and geographic detector to examine comprehensively the spatial and temporal patterns of CCD in 17 CCBRR during the period 2011-2022, as well as to explore their influencing factors. The findings are as follows: (1) Temporally, the CCD is high and exhibits a slow increasing trend, with distinct stage characteristics. (2) Spatially, the distribution of CCD reveals a “one core, many strengths” structural pattern. (3) Moreover, socioeconomic factors are the dominant force driving the CCD of the internal HS systems in the CCBRR. (4) Finally, we constructed a coupling coordination driving mechanism for HS in the CCBRR with the aim of providing scientific references and path choices for the high-quality and coordinated development of the CCBRR along with the implementation of the new quality productive forces regionalization.

Understanding the scale-dependent dynamics of ecosystem services (ESs) and their socio-ecological drivers is essential for sustainable development. While many studies rely on static or single-scale approaches, this research employs an integrated multi-temporal (2000−2020) and multi-scale (grid, county, and landscape levels) framework to investigate China’s Central Asian frontier, a representative dryland region. We quantified six ESs: habitat quality (HQ), net primary productivity (NPP), carbon sequestration (CS), water yield (WY), soil conservation (SC), and grain production (GP). Furthermore, we explored their interrelationships and identified the drivers influencing these services across different spatial scales. Our results revealed divergent ES trajectories: the declining HQ (−0.03 a⁻¹), NPP (−0.43 t km⁻² a⁻¹), and SC (−3.41 t ha a⁻¹) contrasted with rising WY (+2.33 mm a⁻¹), GP (+0.06 t km⁻² a⁻¹), and CS (+0.02 t km⁻² a⁻¹). The ES relationships were predominantly synergistic, while HQ-WY exhibited a trade-off (grid: −0.03; county: −0.02; landscape: −0.03) at temporal dimension but a synergistic relationship (grid: 0.45; county: 0.92; landscape: 0.92) at spatial dimension. As spatial scale increased, SC-CS shifted from synergy (grid: 0.001) to trade-off (county: −0.01; landscape: −0.005) in the temporal dimension, while all trade-off relationships in the spatial dimension were transformed into synergies. Key drivers of ES relationships varied with spatial scale: fraction vegetation coverage (FVC) and leaf area index (LAI) at the grid scale, annual precipitation (MAP) and soil moisture (SMA) at the county scale, and population density (POP), gross domestic product (GDP), and silt content (Silt) at the landscape scale. Based on the multi-scale findings, the study divides northern Xinjiang into Grain Priority Region, Ecological Priority Region, and Desert Containment Region, and proposes tailored management recommendations, offering a flexible framework for balancing ecological and socioeconomic needs.

Rising global change intensifies water scarcity in China’s vital Yellow River Basin grain region, which mounts the need for precise spatial water management. In this study, we investigated the irrigation water demand for seven major crops in cities at the prefecture level between 2000 and 2019. Using Logarithmic Mean Divisia Index (LMDI) decomposition and k-means clustering, we quantified how yield, area, water use efficiency, and cropping patterns affect water demand and identified five irrigation development clusters. Key water-saving areas were identified by tracking transitions among clusters, and NSGA-II was applied to optimize crop structure. The results revealed that the total irrigation demand in the Yellow River Basin averaged 50.09 billion m³/year, with wheat accounting for 54.7%. The increase in yield and area increased demand by 15.2 and 5.5 billion m³, respectively, which was partly offset by changes in water use efficiency and cropping pattern (-7.0 and -1.8 billion m³, respectively). Regions in the upper reaches, particularly within the Lanzhou-Toudaoguai section, were identified as critical for water conservation. Optimization of the cropping structure in key regions can reduce annual irrigation water demand by 280 million m³, which accounts for 4.9% of the total demand in these areas, with minimal impact on crop production. This study provides a spatially explicit basis for targeted water conservation strategies in water-scarce agricultural regions.

As an essential component of terrestrial carbon sinks, lake sediments store vast quantities of both organic carbon (OC) and inorganic carbon (IC). However, the spatiotemporal relationship between the OC and IC in sediments and their responses to climate change remains unclear, which hinders the comprehensive understanding of carbon dynamics in lake ecosystems. This study systematically analyzes the spatiotemporal dynamics of carbon burial across the Tibetan Plateau using surface sediments from 119 lakes and sediment cores from four representative lakes. Results show that OC burial dominates in humid and dry sub-humid zones, whereas IC burial prevails in arid and semi-arid regions. This distribution reflects the influences of lake and catchment productivity and water chemistry on OC and IC patterns. Sediment cores confirm that these factors have consistently affected lake carbon burial over the past century. Specifically, in humid and dry sub-humid zones, increased precipitation enhances watershed productivity and sedimentation, promoting coupled OC and IC burial. In arid and semi-arid regions, wind-driven dust supplies nutrients and alters water chemistry, also driving coupled OC and IC burial. Based on these findings, the carbon sink capacity of lake sediments on the Tibetan Plateau is projected to increase under the “warming and wetting” trend.

Research into the location and development of rice paddies after the collapse of Neolithic cultures is of crucial importance. This study explores the phytolith assemblages and soil micromorphologies of potential rice paddy relics found at the Xingang Site (3556-3360 cal. a BP) in the Taihu Lake Plain, Lower Yangtze River, offering insights into these issues. The discriminant function of the phytolith assemblage distinguished six out of 19 samples in the suspected paddy field area as wild rice fields, while the rest were non-rice fields. Soil micromorphology indicated that the alleged paddy field area experienced repeated dry and wet conditions, with signs of plant growth but no evidence of human activity, suggesting it was not an artificially managed paddy field. These findings suggest the area during the Shang Dynasty consisted of abandoned paddies from the post-Neolithic era. The proportion of rice bulliform phytoliths with ≥9 fish-scale decorations (35%-47%) was significantly lower at the Xingang Site (marginal area) during the Shang Dynasty compared to periods like Qianshanyang-Guangfulin (4300-3900 a BP) (central area), suggesting that diminished population density in marginal areas after the Neolithic collapse likely led to paddy field abandonment. Additionally, the collapse of the Liangzhu social structure, along with a rice-farming economy that lacked strong resource competitiveness, may have also contributed to this phenomenon. This study provides an empirical example of rice paddy locations following the Neolithic collapse in the Lower Yangtze River, enhancing our understanding of the decline of the Liangzhu civilization.

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.

Understanding the evolution and mechanisms of livestock industry agglomeration provides valuable policy insights for reconciling growing meat demand with constrained resource endowments. This study analyzes the spatial agglomeration of livestock industry at the county level across China from 2000 to 2022 using the localization quotient and Moran’s I. An interpretable machine learning approach is employed to test hypotheses concerning the driving mechanisms underlying the spatial distribution of livestock industry. The results show that the agglomeration of China’s livestock industry is intensifying, with the agro-pastoral transitional zone (APTZ) emerging as a prominent agglomeration area and distinct agglomeration patterns observed within the zone as well as in its eastern and western regions. Proximity to markets has become an increasingly important determinant of livestock industry agglomeration in China. This market-driven shift has heightened the demand for agricultural feed, prompting the livestock industry to relax its dependence on local natural resource endowments and gradually relocate eastward. Regionally, the agglomeration within the APTZ is shaped by the joint effects of natural and social factors. Natural factors dominate agglomeration dynamics in the western regions of the zone, whereas social factors are more influential in its eastern regions.

Assessing the carbon sink potential of marine aquaculture is critical to fostering sustainable marine economic development and achieving carbon neutrality. This study evaluates the carbon sink potential of four nearshore aquaculture systems in China: floating raft, net cage, pond, and tidal flat. China’s coastal aquaculture shows a dramatic potential range from −5401.28×10⁴ t to 84.65×10⁴ t, acting as both a carbon sink and a source. Floating raft (11.19× 10⁴ t to 105.65×10⁴ t) and tidal flat (42.83×10⁴ t to 114.35×10⁴ t) are net carbon sinks. In contrast, net cage (−427.39×10⁴ t to −4.26×10⁴ t) and pond (−5027.91×10⁴ t to −131.09×10⁴ t) are significant net carbon sources. This heterogeneity is driven by differences in species, feed inputs, energy consumption, and management practices. The results highlight the need for targeted low-carbon technologies in high-emission systems to maximize carbon sequestration and mitigate their environmental impacts. This study provides a scientific basis for optimizing carbon management and offers insights for global sustainable aquaculture and carbon neutrality.

Land use conflicts (LUCs) pose a major challenge to urbanization, and their effective regulation is essential for promoting sustainable regional land use. However, the influence of urban development on conflicts has often been overlooked. This study developed an index system from three dimensions—agricultural production, residential life, and ecological security—and quantified LUCs in China using spatial statistics and a coupling relationship matrix. It further explored the spatial relationships between conflict types and urban built-up areas (UBA) through accessibility analysis, and applied regression analysis to reveal the spatial evolution of conflicts from an urban-scale perspective. The results showed that agricultural-construction conflicts were concentrated in the eastern plains, while agricultural-ecological conflicts prevailed in the mountainous areas in the western region. Spatial distribution of the distance from conflicts to UBA (DCU) exhibited a clear east-west gradient, being closer in the east (less than 20 km) and farther in the west. Between 2000 and 2020, LUCs moved progressively closer to UBA, except in the ecologically fragile western region. For all urban hierarchies except small cities, the average distance was below 10 km; megacities exhibited the shortest DCU, roughly half that of small cities. Moreover, LUCs displayed significant hierarchical scale effects: as urban size increased, distance tended to decrease in a non-linear pattern, with the steepest decline occurring in central China. Land management authorities should work to curb sprawling urban development. Overall, this study provides new insights into the spatial evolution of LUCs and contributes to more sustainable land use management.

Understanding the impacts of human activities on the plateau’s living environment is essential for advancing modernization pathways that promote harmony between humanity and nature. However, studies on the dynamic interactions between human activities and the living environment on the Qinghai-Xizang Plateau (QXP) remain limited, with a paucity of quantitative relationship analyses. This study established an assessment framework to evaluate human influences on the living environment in QXP, using data on typical human activities, ecological conditions, and human settlements. Within this framework, the spatial analysis methods and the coupling coordination model were used to examine the spatio-temporal characteristics and relationship of human activities and living environment on the QXP from 2000 to 2020. The geographical detector model was then applied to identify the key factors influencing the plateau’s human living environment. Subsequently, the four-quadrant analysis model was adopted to assess human influences on the living environment. The results indicate that the human activity intensity (HAI) on the QXP remained relatively low yet increased by 15.41% from 2000 to 2020. Spatially, the human living environment quality (LEQ) improved from northwest to southeast, with 61.14% of the areas remaining stable and 18.47% experiencing slight improvement. The analysis of coupling coordination revealed a continuous improvement between the HAI and LEQ, with the areas of high and relatively high coordinated types increasing by more than 9%. Precipitation and urban-rural construction were identified as the primary factors influencing changes in the LEQ. The interaction between the HAI and LEQ was strengthening, with 40.44% classified as coordinated development type and 38.35% as development-environment conflict type. These findings provide valuable insights for enhancing the resilience of human settlements and promoting green development across the plateau.

Establishing a Regional Marine Innovation Ecosystem (RMIE) is crucial for advancing China’s maritime power strategy. Concurrently, developing a competitive RMIE serves as a strategic lever to enhance the global competitiveness of China’s marine science sector. However, research on the competitiveness of RMIE is limited. To this end, this study constructs an evaluation index system based on ecological niche theory to assess the competitiveness of RMIE in China from 2008 to 2020. The findings indicate generally fluctuating upward trends in RMIE’s competitiveness, with Shandong, Jiangsu, and Guangdong showing relatively strong positions. Notably, there are significant intra-regional imbalances and inter-regional asynchrony in RMIE’s competitiveness across China’s three major marine economic circles. Recognizing that forecasting RMIE competitiveness can inform policy formulation, this paper proposes a systematic multivariate grey interval prediction model that incorporates spatial proximity effects. This model effectively captures the interval and uncertainty characteristics of RMIE’s competitiveness while considering spatial relationships among regions. Results from comparative analysis, robustness tests, and sensitivity analysis demonstrate its superior applicability and forecasting accuracy. Additionally, interval forecasts and scenario analyses suggest that RMIE competitiveness will maintain stable growth, although unbalanced and unsynchronized development is likely to persist. Overall, the approach developed for evaluating and forecasting RMIE competitiveness offers valuable insights for effective policy formulation.

Urban spatial morphology (USM) optimization is critical to balancing biodiversity conservation and sustainable urbanization. However, previous studies predominantly focused on the socio-economic efficiency and static ecological metrics and rarely addressed the dynamic USM optimization across spatial scales. Here, we developed a multi-level ecological network (MEN) framework to resolve the tension between urban expansion and ecological integrity. By integrating the cost-weighted distance analysis with a hierarchical network transmission mechanism, we established a cross-scale spatial optimization system, which coordinated the regional ecological corridors and local habitat patches. Comparative experiments with conventional single-scale approaches and scenario simulations using the PLUS model show that the MEN framework had superior performance in three dimensions: (1) spatial governance: the primary-level network (peri-urban natural reserves) effectively contained urban sprawl, and the secondary-level network (intra-urban green corridors) mitigated habitat fragmentation and improved the built-environment; (2) scenario robustness: the model maintained an optimal compactness-loose balance in multiple development pathways; (3) landscape metrics: patch fragmentation decreased by 18.25%, and the internal landscape richness improved by 10.66% compared to the scenario without USM optimization. The findings provide new insight to establish a hierarchical ecological optimization framework as a nature-based spatial protocol to reconcile metropolitan growth with landscape sustainability.

The concept of land use multifunctionality (LUMF) is fundamental to national spatial zoning, with its main functions evolving in response to socio-economic development. In this dynamic, maintaining alignment between the actual and the planned main functions is essential for effective spatial planning. This study centers on Sichuan province, where the entropy weight-TOPSIS model was utilized to delineate the spatiotemporal distribution of LUMF and a mechanical equilibrium model was applied to evaluate functional coordination. This approach facilitated an examination of the congruence between the actual and planned main functions. Discrepancy was identified and subsequently excluded from the analysis. Building on this foundation, the propensity score matching-difference-in-differences (PSM-DID) model was employed to quantitatively assess the policy impacts within the matched areas. Key Findings: (1) From 2010 to 2020, the average increase in urban development functions was 10.4, with a decrease in intensity from the urban periphery outward; ecological conservation functions experienced an increase of 1.53, while agricultural functions exhibited a slight decline. (2) Overall, functional coordination showed improvement across 80.32% of the studied areas. However, coordination diminished in key development areas, whereas it was strengthened in agricultural and ecological areas. (3) In 28 counties, a divergence between planned and actual functions was observed, primarily characterized by a focus on agricultural production in key development areas. (4) The planning processes bolstered urban development and ecological conservation functions but exerted limited influence on the major agricultural production areas. The findings provide empirical support for spatial coordination, land allocation, and policy development.

Climate change and anthropogenic activities have profoundly affected coastal systems, making geomorphological research a critical focus for coastal protection and sustainable development. In this study, a comprehensive classification of beach states around Hainan Island is conducted for the first time by utilizing the Ω-RTR model and geological control modes. Six distinct classic beach states ranging from dissipative to reflective are identified: barred dissipative beaches or no-barred dissipative beaches (BD or NBD), barred beaches (B), low-tide terrace or low-tide bar with rip (LTTR or LTBR), and reflective state (R). Among these, the BD and B types are predominant on Hainan Island. Notably, the beach states are subject to multiple factors, such as hydrodynamic forcings, geomorphic features and underlying substrates, and exhibit remarkable spatiotemporal variability. During extreme events, hydrodynamic forcings impact beach states more substantially than geological and geomorphic features do, leading to a more homogeneous distribution of beach states. Under normal circumstance, beach states are predominantly controlled by geological and geomorphic features. Coastal geological and geomorphic features have a pronounced influence on beach morphology and stability. For example, hard substrates underpin wide and stable dissipative beaches, whereas softer substrates lead to narrower, erosion-prone beaches. Three geological control modes are identified, namely, gently sloping hard substrates with dissipative beaches, moderately sloping hard substrates with seasonally variable reflective beaches, and steeply sloping soft substrates with dynamic sandbar-dominated beaches. These findings highlight the necessity of integrating geological settings in tandem with hydrodynamic forcings into coastal management practices. A dual- mode strategy is proposed: maintaining geomorphic self-organization on hard-substrate coasts under normal conditions and implementing hybrid engineering-ecological measures (e.g., artificial sand replenishment and vegetation restoration) on erosion-prone soft substrates.

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.

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.

Securitization of economies has received much attention in urban and regional studies, as how the city economy has connected with the capital markets has been increasingly important for a city’s development. This study develops a modified Buffett indicator, calculating the ratio of total market capitalization of listed companies to the gross domestic product (GDP) of the city in which these companies are headquartered, to measure city securitization rates (CSR). Drawing on this indicator, this study maps the CSR of all prefecture-and-above level cities in China. It is found that cities with high CSR in China are mainly financial centers and some resource-based cities, while most other cities’ CSR are quite low. The regression results show that city’s innovation capacity and political status are positively and significantly associated with the CSR in China. In addition, being close to financial centers can significantly improve the CSR of cities in the eastern region of the country. With the growing financialization of societies, the modified Buffett indicator has a potential to explore the city economy from the perspective of its connection with capital markets in future research.

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.

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.

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.

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.

Precipitation events, which follow a life cycle of initiation, development, and decay, represent the fundamental form of precipitation. Comprehensive and accurate detection of these events is crucial for effective water resource management and flood control. However, current investigations on their spatio-temporal patterns remain limited, largely because of the lack of systematic detection indices that are specifically designed for precipitation events, which constrains event-scale research. In this study, we defined a set of precipitation event detection indices (PEDI) that consists of five conventional and fourteen extreme indices to characterize precipitation events from the perspectives of intensity, duration, and frequency. Applications of the PEDI revealed the spatial patterns of hourly precipitation events in China and its first- and second-order river basins from 2008 to 2017. Both conventional and extreme precipitation events displayed spatial distribution patterns that gradually decreased in intensity, duration, and frequency from southeast to northwest China. Compared with those in northwest China, the average values of most PEDIs in southeast China were usually 2-10 times greater for first-order river basins and 3-15 times greater for second-order basins. The PEDI could serve as a reference method for investigating precipitation events at global, regional, and basin scales.

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.

Aeolian deposits across the Yarlung Zangbo River Basin on the southern Tibetan Plateau record the landscape and atmospheric evolution of Earth’s Third Pole. The complex mountain-basin system exhibits nonlinear responses to climate forcing, complicating the interpretation of its high-altitude environmental dynamics. Investigating the magnetic enhancement mechanism of aeolian deposits offers an opportunity to decipher climate signals. Our analysis of three aeolian sections from the basin indicates that magnetic minerals are predominantly low-coercivity ferrimagnetic minerals, and grain sizes fine from upper to lower reaches due to climate shifts from arid to humid. Magnetic enhancement in the upper reaches primarily originates from dust input, while dust input and pedogenesis contribute variably over time in the middle and lower reaches. Similar complex patterns occur in the Ili basin, a mountain-basin system in northwestern China. They differ from the Chinese Loess Plateau, where long-distance-transported dust is well-mixed and the pedogenic enhancement model is applied, and desert peripheries where short-distance dust is transported and the dust input model is applied. We summarize the magnetic enhancement mechanisms in various settings and offer a new framework for applying magnetic techniques in paleoclimate reconstruction within global mountain-basin systems, which highlights the need for caution in interpreting their magnetic susceptibility records.

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.