The transpiration-to-evapotranspiration ratio (T/ET) is a crucial indicator of the carbon-water cycle and energy balance. Despite the marked seasonality of warming and greening patterns, the differential responses of T/ET to environmental changes across the seasons remain unclear. To address this, we employed a model-data fusion method, integrating the Priestley-Taylor Jet Propulsion Lab model with observational datasets, to analyze the seasonal trends of T/ET in China’s terrestrial ecosystems from 1981 to 2021. The results showed that T/ET significantly increased in spring, summer, and autumn, with growth rates of 0.0018 a^-1 (p<0.01), 0.0024 a^-1 (p<0.01), and 0.0013 a^-1 (p<0.01), respectively, whereas the winter trends remained statistically insignificant throughout the study period. Leaf area index dynamics were identified as the primary driver of the increase in T/ET during summer, accounting for 79% of the trend. By contrast, climate change was the main contributor to the rising T/ET trends in spring and autumn, accounting for 72% and 77% of the T/ET increase, respectively. Additionally, warming is pivotal for climate-driven changes in T/ET trends. This study elucidated seasonal variations in T/ET responses to environmental factors, offering critical insights for the sustainable management of ecosystems and accurate prediction of future environmental change impacts.

Regular quantitative assessments of regional ecological environment quality (EEQ) and driving force analyses are highly important for environmental protection and sustainable development. Northern China is a typical climate-sensitive and ecologically vulnerable area, however, the changes in EEQ in this region and their underlying causes remain unclear. Traditional evaluations of EEQ rely primarily on the remote sensing ecological index (RSEI), which lacks assessments of indicators such as greenness (NDVI), humidity (WET), heat (LST), and dryness (NDBSI). To address these issues, this study employs the principal component analysis method and the Google Earth Engine to construct an RSEI suitable for long-term and large-scale applications and analyzes the spatio-temporal variations in the RSEI, NDVI, WET, NDBSI, and LST. Additionally, geographical detectors are utilized to analyze the driving factors affecting EEQ. The results indicate the following. (1) The RSEI shows a fluctuating upward trend, with an average value of 0.4566, indicating a gradual improvement in EEQ. The EEQ exhibited significant spatial heterogeneity, with a pattern of lower values in the west and higher values in the east. (2) The NDVI and WET exhibit fluctuating increasing trends, indicating improvements in both indices. The NDBSI shows a fluctuating decreasing trend, whereas the LST presents a fluctuating increasing trend, suggesting an improvement in the NDBSI and a slight deterioration in the LST. NDVI and WET demonstrate a spatial pattern characterized by low values in the west and high values in the east. NDBSI and LST demonstrate a spatial pattern characterized by low values in the east and high values in the west. (3) Land use types and precipitation are the primary driving factors influencing the spatial differentiation of the EEQ. The explanatory power of these driving factors significantly increases under their interactions, particularly the interaction between land use types and other driving factors. This study fills the gap in existing EEQ evaluations that analyze only the RSEI without considering the NDVI, WET, NDBSI, and LST. The findings provide new insights for EEQ assessments and serve as a scientific reference for environmental protection and sustainable development.

Globalization has resulted in a notable rise in the flow of high-skilled talent from emerging countries to developed nations. Current research on transnational talent flow mainly focuses on the destination countries, with less attention given to the perspective of the sending countries, particularly lacking a dynamic discussion on its impact on technological evolution in the origin countries. Based on the OECD REGPAT database, this paper aims to explore how talent groups migrating to developed countries facilitate the return of knowledge and technology to emerging countries and achieve breakthroughs in their technological evolution paths, while further discussing the potential mechanisms involved. The findings of this paper are as follows: (1) The technological development of emerging countries is a path-dependent process, where countries often branch into new technologies related to their preexisting knowledge base. Consequently, knowledge feedback from high-skilled talents increases the likelihood of sending countries developing unrelated technologies. (2) The mobility of talents across borders fosters more international collaborations and citations for patents that are unrelated to the local knowledge base, thus enriching the technological paths of sending countries. (3) The mobility of high-skilled talents primarily affects complex technologies, which have significant economic effects that encourage imitation by other countries. However, the effect on novel technologies is less significant due to their strong geographical stickiness. In general, this paper addresses the gaps in existing research on talent outflow and the technological evolution of origin countries, providing empirical evidence for the positive role of transnational talent mobility in the technological catch-up of emerging nations. Besides, it offers recommendations for talent export, import, and innovation policy formulation in these countries.

Understanding the evolutionary trends and driving factors behind extreme hourly precipitation (EHP) in typical urban agglomerations is crucial for predicting and preventing rapid floods. We collected hourly precipitation datasets from 31 observation stations in the Central Yunnan Urban Agglomeration (CYA) spanning from 2004 to 2020. Urban and rural observations were dynamically classified based on impervious surface fraction. Linear (Granger) and nonlinear causal methods(convergent cross-mapping and Liang-Kleeman information flow) were used to identify the causal impact mechanisms of large-scale circulation, environment and urbanization on EHP. Moreover, geo-detector further reveals the spatial influence of these factors and their interactions on EHP. Our findings revealed that EHP mainly occurred in the afternoon and at midnight. Also, the frequency and intensity of EHP in the CYA significantly (p≤0.05) increased from 2004 to 2020, especially in urban areas. The increasing rate in urban areas was higher than that in rural areas. However, the duration of EHP/hourly total precipitation exhibited a significant/nonsignificant decreasing trend with no significant difference between urban and rural areas. Causality tests and geo-detector indicated that EHP was impacted by natural variability and urbanization. Large-scale circulation indices such as the Pacific Decadal Oscillation, El Niño-Southern Oscillation, and Indian Ocean Dipole nonlinearly influenced EHP. Additionally, urban landscape layout, vegetation, and population variation may strengthen EHP by changing environmental factors such as temperature and relative humidity. Interactions exist between these factors and influence EHP, although large-scale circulation remains the dominant influence. With global climate warming and rapid urbanization in the CYA, the frequency and intensity of EHP may further amplify in the future.

While algorithms have been created for land usage in urban settings, there have been few investigations into the extraction of urban footprint (UF). To address this research gap, the study employs several widely used image classification method classified into three categories to evaluate their segmentation capabilities for extracting UF across eight cities. The results indicate that pixel-based methods only excel in clear urban environments, and their overall accuracy is not consistently high. RF and SVM perform well but lack stability in object-based UF extraction, influenced by feature selection and classifier performance. Deep learning enhances feature extraction but requires powerful computing and faces challenges with complex urban layouts. SAM excels in medium-sized urban areas but falters in intricate layouts. Integrating traditional and deep learning methods optimizes UF extraction, balancing accuracy and processing efficiency. Future research should focus on adapting algorithms for diverse urban landscapes to enhance UF extraction accuracy and applicability.

The application of ecosystem services (ES) theories in land consolidation is a confusing issue that has long plagued scholars and government officials. As the upgraded version of traditional land consolidation, comprehensive land consolidation (CLC) emphasizes ecological benefits, but it does not achieve the expected effect during the pilot phase. This study first proposed a theoretical analysis framework based on ES knowledge to answer the three key questions of why, where, and how to implement CLC better. Taking mountainous counties as the study area, we found that ES trade-offs/synergies, bundles, and drivers were significantly affected by scale effects. ES knowledge can play a crucial role in designing multi-scale CLC strategies regarding the objective, zoning, intensity, and mode. Specifically, mitigating the significant trade-offs between recreational opportunities, food production, and other ES is the top priority of CLC. Land consolidation zoning based on the ES bundles analysis is more rational and can provide the scientific premise for designing locally adapted CLC measures. Land consolidation can be classified into high-intensity direct intervention and low-intensity indirect intervention modes, based on the major drivers of ES. These findings help narrow the gap between ES and CLC practices.

Ecosystem services in urban agglomerations are the environmental conditions under which human survival and development are sustained. Quantitative assessment of ecosystem services and complex interactions can contribute positively to the achievement of the Sustainable Development Goals (SDGs) for urban agglomerations. However, studies on the future contribution of multi-scenario ecosystem services to the SDGS are lacking. We propose novel integrated modeling framework that integrates the CLUES, InVEST, SOM, and GWR approaches to address the complex relationship between ecosystem services over a long “past-present-future” time series. We construct a novel ecosystem service bundle-based approach for measuring urban agglomerations progress towards achieving ecologically relevant sustainable development goals at multiple scales. In the future scenario, the water yield (WY), habitat quality (HQ), and soil conservation (SC) show similar spatial patterns, with comparable spatial grids, while carbon stock (CS) remains predominantly unchanged and the ecological protection scenario (EPS) improves more significantly. The high-synergy regions are mainly distributed in bundle 4, and most of the trade-off regions appear in bundles 1 and 2. Over the last 30 years, all but the water-related SDGs are declining in bundle 1 of the two urban agglomerations, which are 15% higher in the Guangxi Beibu Gulf (GBG) than in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA). From 2020 to 2035, the three scenarios demonstrate that the optimization of the SDGs progresses most effectively under the future ecological protection scenario (EPS). In particular, bundles 3 and 4 are significantly improved. This critical new knowledge can be used in sustainable ecosystem management and decision-making in urban agglomerations.

China is the world’s largest carbon dioxide (CO₂) emitter and a major trading country. Both anthropogenic and natural factors play a critical role in its carbon budget. However, previous studies mostly focus on evaluating anthropogenic emissions or the natural carbon cycle separately, and few included trade-related (import and export) CO₂ emissions and its contribution on global warming. Using the CarbonTracker CT2019 assimilation dataset and China trade emissions from the Global Carbon Project, we found that the change trend of global CO₂ flux had obvious spatial heterogeneity, which is mainly affected by anthropogenic CO₂ flux. From 2000 to 2018, carbon emissions from fossil fuels in the world and in China all showed an obvious increasing trend, but the magnitude of the increase tended to slow down. In 2018, the radiative forcing (RF) caused by China’s import and export trade was ‒0.0038 W m^‒2, and the RF caused by natural carbon budget was ‒0.0027 W m^‒2, offsetting 1.54% and 1.13% of the RF caused by fossil fuels that year, respectively. From 2000 to 2018, the contribution of China’s carbon emission from fossil fuels to global RF was 11.32%. Considering China’s import and export trade, the contribution of anthropogenic CO₂ emission to global RF decreased to 9.50%. Furthermore, taking into account the offset of carbon sink from China’s terrestrial ecosystems, the net contribution of China to global RF decreased to 7.63%. This study demonstrates that China’s terrestrial ecosystem and import and export trade are all mitigating China’s impact on global anthropogenic warming, and also confirms that during the research process on climate change, comprehensively considering the carbon budget from anthropogenic and natural carbon budgets is necessary to systematically understand the impacts of regional or national carbon budgets on global warming.

In recent years, the uncontrollable risks of urban production-living-ecological (PLE) space have increased sharply, making resilience enhancement essential for sustainable urban development. Based on the social-ecological system (SES) theory, this study constructs an assessment framework for urban PLE space resilience by analyzing its inherent characteristics. The central urban area of Ganzhou city is taken as a case study to evaluate urban PLE space resilience and diagnose its obstacles. The results are as follows: The PLE space resilience in the central urban area of Ganzhou exhibits gradations and substantial spatial differentiation. The ecological space resilience in the study area was the highest, followed by that of production space, while living space resilience was the lowest. The primary factors influencing PLE space resilience are concentrated in the dimensions of robustness and adaptability. In particular, the robustness of the PLE space is relatively low. Based on these results, targeted spatial resilience governance strategies for the PLE space have been proposed. These strategies serve as theoretical and technical references for the study area. By adopting the PLE space perspective, this paper enriches resilience research and provide theoretical support for sustainable urban development.

The agro-pastoral ecotone in northern China (APENC) is an ecologically fragile region with a variable climate and unbalanced socioeconomic development. Identifying the spatial range and transitional dynamics of the APENC is crucial for understanding the delicate balance between regional ecology, the economy, and society. The human-Earth system provides a comprehensive research framework in which human activities and the natural environment are viewed as interdependent and dynamically interactive. Guided by the principles of human-Earth system science, in this study, the boundaries of the APENC are identified by integrating core parameters, including water, land, climate, ecology, and human factors. Raster-based spatial data analysis is employed to examine the spatial and temporal evolution of the APENC from 1990 to 2020. The APENC extends from northeast to southwest along the central axis of northern China, displaying trends of contraction and fragmentation over time, with its centre of gravity shifting closer to the Hu Huanyong Line. The peripheral areas exhibit heightened sensitivity to environmental and ecological changes, highlighting the region’s vulnerability to external pressures. In this study, management strategies grounded in sustainable development principles are proposed, a framework for integrating ecological changes with socioeconomic strategies is established, and actionable guidance for policy- makers to promote sustainable development in this fragile and dynamic region is provided.

Drought significantly constrains vegetation growth and reduces terrestrial carbon sinks. Currently, the spatiotemporal patterns and mechanisms of the differential impacts of soil and meteorological droughts on vegetation productivity remain inadequately understood. In this study, we analyzed soil moisture (SM), vapor pressure deficit (VPD), and gross primary productivity (GPP) to investigate their spatiotemporal patterns and the combined effects on GPP over China. The results revealed that: (1) Soil drought and meteorological drought generally exhibited temporally synchronous trends across China. (2) GPP was predominantly affected by the combined and synchronous effects of both SM and VPD, although their effects displayed directional variability differences in certain regions. (3) SM demonstrated a greater relative importance on GPP than VPD across more than half of the regions in China, whereas deciduous broadleaf forests were the only vegetation type primarily affected by VPD. (4) Under the lag effects, both SM and VPD exhibited bidirectional Granger causality with GPP, with the interaction between VPD and GPP proving more pronounced than that of SM. Our research provides valuable insights into the mechanisms through which SM and VPD influence GPP, contributing to improved predictions vegetation productivity and implementing ecological restoration.

Agriculture, significantly impacted by climate change and climate variability, serves as the primary livelihood for smallholder farmers in South Asia. This study aims to examine and evaluate the factors influencing smallholder farmers’ adaptive capacity (AC) in addressing these risks through surveys from 633 households across Nepal, India, and Bangladesh. The findings reveal that AC is influenced by various indicators categorized under eight principal factors. The first three factors, which explain about one-third of the variance in each country, include distinct significant indicators for each nation: in Nepal, these indicators are landholding size, skill-development training, knowledge of improved seed varieties, number of income sources, access to markets, and access to financial institutions; in India, they encompass access to agricultural-input information, knowledge of seed varieties, access to markets, access to crop insurance, changing the sowing/harvesting times of crops, and access to financial services; in Bangladesh, the key factors are access to financial institutions, community cooperation, changing the sowing/harvesting times of crops, knowledge of improved seed varieties, and access to agricultural-input information. Notably, indicators such as trust in weather information, changing sowing/harvesting times of crops, and crop insurance were identified as important determinants of AC, which have been overlooked in previous studies.

The uneven distribution of medical resources has led to increasingly frequent patient mobility; however, the interaction between this phenomenon and the healthcare supply-demand relationship remains underexplored. The present study constructed the 2023 Cross-City Patient Mobility Network in China using one million patient mobility data records obtained from online healthcare platforms. We applied urban network analysis to uncover mobility patterns and used the coupling coordination degree model to assess healthcare supply-demand relationships before and after patient mobility. Explainable machine learning further revealed the impact of supply-demand coupling on patient mobility. The results indicated the following: Patient mobility followed administrative boundaries, although megacities serve areas beyond provincial borders; The scale of healthcare supply and demand displayed a multi-centric spatial pattern with a general decline from east to west, and these characteristics of demand distribution were further solidified by patient mobility; Cities with low supply-demand coupling and undersupply experienced patient outflows, while cities with high coupling and oversupply attracted them. In turn, patient mobility helped balance healthcare supply and demand, optimising the coupling relationship across cities. Thus, this research not only provides a methodological reference for understanding the interaction between patient mobility and healthcare systems but also offers empirical insights for public health policy.

Social networks are vital for building the livelihood resilience of rural households. However, the impact of social networks on rural household livelihood resilience remains empirically underexplored, and most existing studies do not disaggregate social networks into different dimensions, which limits the understanding of specific mechanisms. Based on 895 household samples collected in China’s Dabie Mountains and structural equation modeling, this paper explored the pathway to enhance livelihood resilience through social networks by disaggregating it into five dimensions: network size, interaction intensity, social cohesion, social support, and social learning. The results indicate that: (1) Livelihood assets, adaptive capacity and safety nets significantly contribute to livelihood resilience, whereas sensitivity negatively affects it. Accessibility to basic services has no significant relationship with livelihood resilience in the study area. (2) Social networks and their five dimensions positively impact livelihood resilience, with network support having the greatest impact. Therefore, both the government and rural households should recognize and enhance the role of social networks in improving livelihood resilience under frequent disturbances. These findings have valuable implications for mitigating the risks of poverty recurrence and contributing to rural revitalization.

Cropland suitability analysis is a vital tool for ensuring food security and sustainable agriculture, coordinating ecological space with human activity space on the Qinghai-Tibet Plateau (QTP). However, there are few studies on complete and accurate cropland suitability assessments on the QTP, let alone on identifying key potential areas for cropland development. We used a novel assessment model to generate a 30-m cropland suitability map for the QTP. The identification of areas with cropland development potential and the evaluation of potentially available cropland were further integrated into a unified analytical framework. We found that only 10.18% of the study area is suitable for large-scale and permanent cropland. Moreover, approximately 72.75% of the existing cropland was found to be distributed in suitable or marginally suitable areas. Considering the trade-offs related to irrigation water supply convenience, approximately 1.07% of the study area was identified as having high potential for cropland development. Four key potential areas were further identified: the Shannan Valley, the Nyingchi Valley, the Zanda Valley, and the Gonghe Basin. These areas boast abundant potentially available cropland resources and ecological resettlement capacities, which leads us to recommend strategic priorities for comprehensive land consolidation and water development. This study has practical significance for optimizing land resource allocation and guiding decision-making related to ecological migration on the QTP.

Research on urban health constitutes an important issue in the field of health geography and also a strong propeller of the Healthy China Initiative. As the main form that realizes new-type urbanization, urban agglomerations should become the primal sites for the construction of a “Healthy China”. The evaluation of healthy cities’ development in urban agglomerations has both theoretical and practical values. Based on the concept of urban health and its evaluation models, this paper developed an evaluation framework for healthy cities that involved multiple data sources. With 19 urban agglomerations in China as the research subjects, we used CRITIC weighting and geographical detectors to examine the geographies of healthy cities and their influencing factors in 2010 and 2020. The results were fourfold. Firstly, the urban health level of China significantly increased from 2010 to 2020, and the comprehensive health index developed towards a positive skewed distribution, along with a shift from “low in the hinterland - high in the coastal areas” to a “multipolar” pattern led by the coastal and southwest urban agglomerations. Secondly, among various dimensions of urban health, the healthy environment index became improved with narrowed regional differences; while the health services index was still polarized; health collaboration was upgraded with a strengthened intercity health network; the healthy population index slightly declined and converged to the middle. Thirdly, urban health in China has initially demonstrated the characteristics of a H-H pattern in the Yangtze River Delta and Chengdu- Chongqing regions, as well as L-L clusters in the northern urban agglomerations, the narrowed regional differences, and increasing coordination within each urban agglomeration. Fourthly, the geographical detector found that economy, urbanization and the human capital were significant external factors that affected urban health development. The explanatory power of technological innovation and opening to the outside world were also increasing. The development of healthy cities is yet to be transformed into regional health integration.

Uncovering the evolution process of rural revitalization level (RRL) in China and elucidating the complex driving mechanism hold significant implications for implementing rural revitalization strategy and advancing rural modernization. This study analyzes the spatio-temporal evolution of China’s RRL from 2002 to 2022 and reveals its complex driving mechanism. The results show that China’s RRL steadily increased from 0.1083 to 0.4463, and the provincial RRL exhibited the characteristic of decreasing successively in the eastern region, the central region, and the western region. The overall differences of RRL are shrinking, and intra-group differences contribute almost 1/3 of the overall variation, more than the contribution of inter-group differences. Although the influencing factors show nonlinear characteristics, on the whole, economic level and human capital exhibit positive effects, while relief degree, urbanization, industrialization, and opening degree exhibit negative effects. Farmland resources and investment intensity exhibit the characteristics of positive effect and negative effect equilibrium. At the regional scale, influencing factors exhibit significant spatio-temporal heterogeneity. In the future, to achieve comprehensive rural revitalization, it is vital to implement systemic policy measures, such as enhancing industrial competitiveness, supplementing rural talents, and optimizing the relations between urban and rural areas as well as between industry and agriculture.

Changes in production-living-ecological spaces (PLES) profoundly affect the global carbon cycle, further challenging socio-ecological system sustainability. However, the impacts of PLES changes on carbon balance have been insufficiently discussed under a spatial heterogeneity perspective, resulting in an inadequate understanding of green development. This paper quantified the dynamics of PLES using the transfer matrix method and assessed the carbon balance computed by the ecological support coefficient of carbon emissions (ESC) in Shandong province from 2000 to 2020. The impacts of PLES changes on ESC were further investigated using a geographically weighted regression model. On this basis, carbon balance zones were delineated through cluster analysis. The results indicated that both production and ecological spaces decreased while the living space increased during 2000-2020. Carbon emissions increased and its sequestration decreased. As a result, ESC initially increased and then decreased, exhibiting apparent spatial clustering. The impact of different PLES transfer changes on ESC varies across county sites, with production→living space having the most significant impact on regional ESC and ecology→living space having the most negligible impact. Finally, six types of carbon balance zones were established to reduce carbon emissions. The findings are expected to support policy implementations for reducing carbon emissions and optimizing territorial development through low-carbon land use.

Carbon fluxes are essential indicators assessing vegetation carbon cycle functions. However, the extent and mechanisms by which climate change and human activities influence the spatiotemporal dynamics of carbon fluxes in arid oasis and non-oasis area remains unclear. Here, we assessed and predicted the future effects of climate change and human activities on carbon fluxes in the Hexi Corridor. The results showed that the annual average gross primary productivity (GPP), net ecosystem productivity (NEP), and ecosystem respiration (Reco) in the Hexi Corridor oasis increased by 263.91 g C·m^-2·yr^-1, 118.45 g C·m^-2·yr^-1 and 122.46 g C·m^-2·yr^-1, respectively, due to the expansion of the oasis area by 3424.84 km² caused by human activities from 2000 to 2022. Both oasis and non-oasis arid ecosystems in the Hexi Corridor acted as carbon sinks. Compared to the non-oasis area, the carbon fluxes contributions of oasis area increased, ranging from 10.21% to 13.99% for GPP, 8.50% to 11.68% for NEP, and 13.34% to 17.13% for Reco. The contribution of the carbon flux from the oasis expansion area to the total carbon flux change in the Hexi Corridor was 30.96% (7.09 Tg C yr^-1) for GPP, 29.57% (3.39 Tg C yr^-1) for NEP and 32.40% (3.58 Tg C yr^-1) for Reco. The changes in carbon fluxes in the oasis area were mainly attributed to human activities (oasis expansion) and temperature, whereas non-oasis area was mainly due to climate factors. Moreover, the future increasing trends were observed for GPP (64.99%), NEP (66.29%) and Reco (82.08%) in the Hexi Corridor. This study provides new insights into the regulatory mechanisms of carbon cycle in the arid oasis and non-oasis area.

An appropriate spatial structure of a power battery supply network is crucial for the specialization and scale development of key components in new energy vehicles, accelerating the transformation and upgrade of the industry. This paper investigates the cooperative relationships among supply chain enterprises from the perspective of complex networks. Employing methodologies such as the gravity model and Moran’s I analysis, it explores the spatial structural characteristics and correlation patterns of the power battery supply network in China and discusses the influencing factors using the quadratic assignment procedure, revealing the mechanisms behind the differences in the spatial distributions of the power battery supply network. The results indicate that the distribution of power battery enterprises is densely concentrated in the eastern and southern regions, whereas the western region has a sparse distribution. The spatial supply network consists of a four-tier linkage system, encompassing 135 prefecture-level cities, with Chongqing, Shanghai, Nanjing, and other cities particularly prominent. Overall, the degree of agglomeration is low, with coastal cities dominating the landscape and inland cities serving as complementary regions. Most areas are characterized as insignificant or low-high regions, and the regional linkage effect of core cities is not pronounced. There is a notable lack of significance and high spatial heterogeneity. Four types of factors—spatial factors, market factors, agglomeration economies, and innovation levels—jointly influence and shape the spatial structure of the power battery supply network.

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.

In research on the legendary Xia Dynasty of ancient China, the famous archaeological site of Erlitou and its culture are the most debated topics. A key question is whether this ancient culture is truly related to the Xia Dynasty. This study combines traditional literature (Xia Xiao Zheng), archaeological evidence (on alligators), and climate simulation (of autumn rains) to demonstrate that the ancient Chinese phenological calendar, Xia Xiao Zheng, likely originated in the same region as the Erlitou culture. A logical explanation of these findings is that both Xia Xiao Zheng and the Erlitou culture are indeed closely related to the Xia Dynasty.

Situated in the semi-arid regions of North China, the Nihewan Basin documents the fluvio-lacustrine sequence and Pleistocene archaeological sites, offering an excellent opportunity to investigate human adaptation to environmental change in East Asia, especially in North China. However, paleoenvironmental datasets from Middle Pleistocene archaeological sites are not fully understood. Focusing on the evidence from the 0.63-0.49 Ma-old site of Jijiazhuang (Nihewan Basin, North China), this paper presents the results of various environmental indicators from the site context. Moreover, it explores the links between hominin behavioral adaptations and ecological variability during the extra-long interglacial period in North China. Sedimentological features of the excavated section indicate that the site was formed in the margin of the Nihewan paleolake. Based on well-constructed pollen, sediment grain size, color reflectance, and major geochemical element analyses, five stages of environmental changes were identified during site formation. This study indicates that hominins occupied the site at the early part of Stage 2, when the Nihewan paleolake had a relatively low water level and the climate was temperate, with strong weathering intensity dominated by wooded grassland landscapes. In conclusion, the results suggest that the extra-long duration of interglacial or mild stadial climate events (MISs 15-13) in the Northern Hemisphere may have provided favorable conditions for increased technological innovations and adaptive strategies among Middle Pleistocene hominins in the Nihewan Basin even in North China.

Dryland regions face complex interactions between urbanization and ecological changes, where effective coordination is essential for enhancing sustainability and resilience. However, most studies concentrate on the national or provincial scales, with insufficient research on county-level coordination, limiting the ability to provide targeted policies from a precise perspective. This study addresses this gap by analyzing 39 counties within the Hohhot-Baotou-Ordos-Yulin Urban Agglomeration (HBOYUA), a typical dryland urban cluster in China. We use daytime and nighttime remote sensing images to track the spatio-temporal evolution of urbanization and ecological conditions from 1992 to 2023. A novel quantitative framework based on an improved coupling coordination degree (CCD) is proposed to assess their coordination relationship. The results reveal that: (1) Urbanization and ecological quality both exhibited fluctuating upward trends, with spatial heterogeneity increasing for urbanization and decreasing for the eco-environment. Regions with better ecological conditions had higher urbanization levels. (2) The overall coordinated level improved from imbalance (0.36) to low-level coordination (0.55), although its spatial distribution remained uneven, with central urban areas showing higher CCD than surrounding counties. (3) Socioeconomic factors exerted greater effects on CCD than natural factors, with GDP and land surface temperature (LST) playing a significant role in interaction analysis. (4) In western arid regions, urbanization did not necessarily harm ecosystems; instead, ecological conditions improved alongside urbanization. This research offers targeted and valuable references for county and city governments in resource allocation and sustainable development. The proposed methodology is also adaptable for urban resilience studies in other regions.

Medicinal plant diversity (MPD) is an indispensable part of global plant diversity, serving as the foundation for human survival by offering remedies and preventive measures against diseases. However, factors such as overexploitation, competition from invasive alien species, and climate change, threaten the habitats of medicinal plants, necessitating a comprehensive understanding of their spatial distribution and suitable habitats. We leveraged a decade of field survey data on medicinal plant distribution in the Yinshan Mountains, combined with spatial analysis, species distribution modeling, and the Carnegie Ames Stanford Approach (CASA) to explore the MPD spatial distribution and suitable habitats. Spatial analysis revealed that the central and eastern parts of Yinshan Mountains were the primary MPD hotspots, with no cold spots evident at various spatial scales. As the spatial scale decreased, previous non-significant regions transformed into hotspots, with instances where large-scale hotspots became insignificant. These findings offer valuable guidance for safeguarding and nurturing MPD across diverse spatial scales. In future climate change scenarios within the shared socioeconomic pathways (SSP), the habitat suitability for MPD in the Yinshan Mountains predominantly remains concentrated in the central and eastern regions. Notably, areas with high net primary productivity (NPP) values and abundant vegetation coverage align closely with MPD habitat suitability areas, potentially contributing to the region’s rich MPD.

With the continuous evolution of urban surface types, the impact of the urban heat island effect on the human population has intensified. Investigating the factors influencing urban thermal environments is crucial for providing theoretical support to urban planning and decision-making. In this study, Shenyang was selected to comprehensively analyse multiple factors, including topography, human activity, vegetation and landscape. Moreover, we used the random forest algorithm to explore nonlinear factors influencing land surface temperature (LST) over four years in the study area. The results revealed that from 2005 to 2020, the total areas with sub-high and high-temperature zones in northern Shenyang steadily increased. The area ratio of these zones increased from 20.18% in 2005 to 24.86% in 2020. Additionally, significant and strong correlations were observed between LST and variables such as the enhanced vegetation index (EVI), normalised difference vegetation index (NDVI), population density, proportion of cropland and proportion of impervious land. In 2010, proportion of impervious land exhibited the strongest correlation with LST at the 5 km scale, reaching 0.852 (p<0.01). The 4 km grid scale was identified as the optimal grid size for this study, while the 2 km grid performed the worst. In 2020, NDVI emerged as the most significant factor influencing LST. These findings provide valuable guidance for improving urban planning and developing sustainable strategies.

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.

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.

Population growth leads to increased utilization of water resources. One of these resources is groundwater, which has steadily declined each year. The depletion of these resources brings about various environmental challenges. The present study aimed to explore the relationship between groundwater fluctuations and land subsidence in the Malayer Plain, Iran, focusing on quantifying subsidence resulting from groundwater extraction. Using Sentinel-1 satellite data (2014-2019) and monthly piezometric measurements (1996-2018), the analysis revealed an average deformation velocity of -6.3 cm yr^-1, with accumulated subsidence of -32 cm over the 2014-2019 period. The maximum subsidence rate reached 10.3 cm yr^-1 in areas of intensive agricultural activity. A wavelet-PCA spatiotemporal analysis of groundwater fluctuations identified critical multi-scale patterns strongly correlated with subsidence trends. Regression analysis between subsidence rates and groundwater fluctuations at various wavelet decomposition levels explained 75% of the variance (R² = 0.75), indicating that intermediate-scale groundwater declines were the primary drivers of subsidence. Furthermore, land use analysis using Landsat data (1999-2021) revealed a 6230-ha increase in irrigated farmland, contributing to heightened groundwater extraction and subsidence rates. These findings highlight the critical need for sustainable groundwater management to mitigate the risks of continued subsidence in the region.