As global biodiversity continues to decline and ecosystems degrade, mountains are often regarded as crucial refuges for numerous species due to their unique montane environments and relatively unfragmented landscapes. The conservation of mountain biodiversity is a key component of the United Nations Sustainable Development Agenda. Gaining insight into the distribution of montane species and identifying priority conservation areas are essential for effective action. However, such efforts have been relatively limited in China. In this study, we evaluated the contribution of mountains to biodiversity conservation within the country. Our findings indicate that China’s mountains support a remarkable percentage of the country’s wildlife. They include 95 % of mammal species, 85 % of bird species, 89 % of amphibian species, 85 % of reptile species, and 80 % of higher plant species. These areas harbor over 90 % of China’s natural ecosystem subclasses, despite constituting only 65 % of the total land area. Approximately a quarter of important sites for mountain biodiversity are covered by protected areas, but some key regions remain unprotected. It is recommended that protection be prioritized in the southeastern Qinghai-Xizang Plateau, the Hengduan Mountains and the Southeastern China Hills, with a focus on narrowly distributed ecosystems, to achieve the biodiversity target and vision.

Geography is shifting from static description to a feedback-driven, adaptive discipline integrating sensing, prediction, comparison, and continuous self-improvement. This transformation underlies Intelligent Geography (IG), where artificial intelligence (AI), big data analytics, and high-performance computing (HPC) converge to enhance spatial understanding and guide intelligent decisions in complex systems. The discipline’s historical stages—descriptive, experimental, theoretical, quantitative, GIScience, and information geography—form the foundation for an overarching adaptive framework. In this framework, diverse geospatial data streams seamlessly feed real-time models whose predicted outputs are compared with observed conditions to iteratively refine predictions. A hallmark of IG is embedding domain theory into AI workflows, producing predictive models that self-adjust to new data or control system behavior. Applications such as smart traffic management, climate-responsive urban planning, and disaster-resilient digital twins illustrate the sensing–prediction–adaptation/learning cycle in practice for complex changing systems. We examine the enabling roles of HPC, deep learning, and geographic large models in implementing feedback loops, and address persistent challenges in data integration, interpretability, and governance. We conclude with a vision of IG as an evolving socio-technical ecosystem that through adaptation and self-learning turns spatial data into adaptive, actionable knowledge that assists in intelligent decision-making, whether it is for AI systems or human ones.

The share of wind and solar energy in global energy mix is rising rapidly. Despite their great potential for reducing carbon emissions, poorly planned wind and solar farms may encroach on socio-ecologically sensitive areas, threatening biodiversity and Indigenous people’s traditional land uses. However, these potential risks associated with wind and solar farm development worldwide are poorly understood. Here, we evaluate the potential biodiversity and Indigenous risks from wind and solar energy development by examining the extent to which global wind and solar farms are situated within or adjacent to socio-ecologically sensitive areas. Our analysis revealed that 13,699 wind and solar farms or 14.4 % of the farms’ total footprint area are within protected areas, critical habitats, and Indigenous people’s lands, occupying a total of 26,840 km² of those socio-ecologically sensitive areas. Wind and solar farms overlap with the distribution ranges of 2,310 threatened amphibians, birds, mammals, and reptiles, accounting for 36.3 % of the world’s 6,362 threatened vertebrate species. The encroachment of solar and wind farms on sensitive areas mostly occurs in economically developed countries with substantial wind and solar energy facilities, while many developing countries in the tropics tend to have a higher proportion of such farms situated within sensitive areas. Compared to wind farms, solar farms pose greater risks to biodiversity and Indigenous people’s lands. These findings provide valuable insights into the socio-ecological risks of wind and solar energy development and highlight the urgent need for strategic planning to mitigate the risks.

Achieving Sustainable Development Goals (SDGs) requires place-based solutions that reconcile global aspirations with local realities. Landscapes and regions represent a pivotal scale domain—large enough to capture cross-boundary ecological and socioeconomic processes, yet sufficiently grounded to enable context-sensitive understanding and governance. Landscape sustainability science offers a robust framework for bridging the global-local divide in SDG implementation. Rooted in the long-standing convergence between ecology and geography—tracing back to Humboldt’s unity of nature—landscape sustainability science advances a spatially explicit, systems-oriented approach guided by the principles of strong sustainability. Here we present the landscape sustainability science framework, structured around the core triad of landscape pattern, ecosystem services, and human wellbeing, and operationalized through dual feedback loops and the analysis–adaptation–assessment cycle. Our assessment shows that landscape sustainability science contributes directly to eight SDGs and indirectly to six others, offering actionable strategies for climate resilience, sustainable land management, and inclusive landscape governance. By helping to spatialize, localize, and operationalize global sustainability targets, landscape sustainability science provides a pragmatic pathway to advance the SDGs in diverse socioecological contexts. If global sustainability is to be achieved, we must think and act like a landscape.

Quantitative studies on the national-scale effects of extreme climatic events on soil organic carbon (SOC) remain scarce, thus limiting our understanding of SOC dynamics. This study utilized 4515 publicly available soil samples to quantify the impacts of 19 extreme climatic indices (ECIs) on ΔSOC reservoirs in China through a hybrid space-for-time and meta-analysis approach. Overall, 16/19 ECIs were negatively correlated with ΔSOC, with the minimum temperature of the coldest night (TNn) showing the strongest negative correlation. Notably, topographic factors played a pivotal role in the modeling process, contributing an average of 25 %, followed by ECIs. Under the influence of the ECIs, SOC exhibited spatial variation. Extreme heat resulted in the greatest SOC losses in cold regions, such as North China, with average reductions of > 5 %, whereas its impact was weaker in South China, with SOC losses of ∼3 %. Extreme cold and wet indices promoted SOC accumulation in the Northeast China, with increases of ∼3 %, but showed a weaker response in the humid region, where the SOC increased by only 1 %. At the national scale, the impacts of extreme climatic events on SOC in the 0–20 cm ranged from −2.36 Pg to 2.34 Pg. Different ecosystems responded variably, with forest and grassland ecosystems being more sensitive to ECIs, potentially due to higher organic matter inputs and greater ecosystem complexity. In contrast, bare land exhibited weaker responses due to limited vegetation cover and organic inputs. These findings provide valuable insights into SOC dynamics at national scale during extreme climatic events.

Meeting China’s burgeoning food demand while safeguarding the resources and environmental long-term development is a critical challenge for the sustainable food systems of this century. China’s accelerated food imports have far-reaching implications for global resource allocation and environmental development. Hence, detailed information regarding China’s food trade resource-environmental impacts is imperative for the design of effective policies that promote environmental mitigation and resource conservation. This study estimated the spatial transfers of virtual water trade (VWT), virtual land trade (VLT), and virtual GHG emission trade (VGT) embodied in China’s food trade. Findings indicate that the VWT, VLT, and VGT transfers embodied in China’s food trade increased by 10.4 %, 9.8 %, and 15.2 % annually. It is more important to mention that virtual water import (VWI) and virtual land import (VLI) saved 119.5 × 10⁹m³ of global water resources and 29.5 Mha of land resources, respectively, but virtual GHG emission import (VGI) increased global 13 Mt CO₂-eq GHG emissions. The divergent impacts of China’s food import on global food sustainability stem from variations in virtual water content, yields and emission intensities. Moreover, significant differences in sustainability scores were found among the top 15 importing countries, indicating that China’s food trade contributes to the deepening of global food system sustainability. This study highlights the need for a multifaceted approach that considers the various environmental impacts of food trade. China is therefore encouraged to fully integrate the benefits of resource and environmental conservation into its sustainable food trade strategy, restructuring the food system to ensure the long-term nourishment of its large population.

Safe and just operating spaces (SJOS) are influenced by complex cross-scale interactions and cascading effects spanning global, regional, and local landscape scales. However, existing SJOS research has often focused on single-scale assessments, overlooking the impacts of multiscale interactions and within-region heterogeneity on urban SJOS. To address this gap, we developed a cross-scale framework for assessing urban SJOS, explicitly incorporating top-down influences from upper-level constraints and bottom-up effects from lower-level heterogeneity. This approach was applied to China’s five major metropolises to examine the states and cross-scale dynamics influencing urban SJOS between 1990 and 2020. Our findings reveal that the SJOS of China’s metropolises were primarily influenced by factors at national and local landscape scales, with weaker influences from the global and continental scales. A persistent trade-off between social justice and environmental safety was identified across spatiotemporal scales. For instance, Chongqing in southwestern China lagged behind the eastern four metropolises in social performance but exhibited stronger environmental safety due to its extensive natural landscapes, which mitigated the anthropogenic impacts of urban centers. Regional issues, such as the overshoot of PM_2.5 and ecological footprints (EF), were primarily driven by the bottom-up accumulation of localized pressures, while the overshoot of CO₂ was attributed to national policy constraints and the universal exceedance of safe thresholds across scales. Addressing urban sustainability requires avoiding adverse cascading effects from other levels by emphasizing landscape heterogeneity within metropolises and fostering coordinated collaboration across scales, particularly at the regional landscape and national levels.

The Qinghai–Xizang Plateau is a primary water supply region in Asia. The Lhasa River Basin is the political, economic, and cultural core area and main cultivation area of Qinghai–Xizang Plateau and is considered ecologically fragile. With uneven spatial and temporal distribution of water resources, mismatched supply and demand may accentuate differences in distribution and affect the security of regional water resources. This study employed system dynamics (SD) to measure the supply and demand of water supply services and analyzed the correlation between supply and beneficiary areas by evaluating the supply and demand overlap. Moreover, the 2030 supply–demand relationship was predicted, the pattern of sustainable development of the basin is discussed, and optimization suggestions are proposed. The range of water supply service beneficiary areas in the Lhasa River Basin shows an increasing trend from 2005 to 2020. The spatial distribution of water supply in 2030 is predicted to be the same as that in 2020, while the total amount of water supply is expected to decrease. By 2030, the largest proportion of water demand will be industry, followed by agriculture, forestry, and animal husbandry. Overall, there is a mismatch between water supply and demand services in the Lhasa River Basin, and it is essential to develop a reasonable water resource management and allocation policy as well as an optimized ecological management strategy for the basin through integrated planning. Here, we provide suggestions for the sustainable development and ecological environmental protection of the Lhasa River Basin.

Ecological restoration is considered an important way to mitigate ecosystem degradation and improve regional nature’s contributions to people (NCPs). Ecological planning is a prerequisite for ecological restoration and the prevention of future ecological risks. However, few studies have focused on integrating ecological plans within the framework of Sustainable Development Goals (SDGs) and shared socioeconomic pathways (SSPs). In this study, taking the Qinghai‒Xizang Plateau (QXP) as a case, we assessed ecological restoration priorities based on NCPs under various SDGs and SSP scenarios. Specifically, the land use demand was predicted using system dynamics (SD) and cellular automata (CA) models between 2030 and 2060 under SDG-SSP scenarios. In addition, habitat maintenance (NCP1), climate regulation (NCP4), and water quantity regulation (NCP6) were assessed based on the predicted land use. Finally, priority areas for ecological restoration were identified using a zonation model. The results indicated that the grassland, forest, and cultivated areas will increase in the SDGs and SSPs scenarios, respectively. The high-value NCP areas are mainly located in the southeast part of the QXP, accounting for 45.16 % of the study area. In addition, the ecological restoration area involves grassland, cultivated and bare land. In the single-objective scenario, NCP1, NCP4, and NCP6 can be improved by 30.29 %, 28.75 % and 25.63 %, respectively, through the restoration of 15.33 % of the priority areas identified in 2015. When shifting to a multi-objective cooperative optimum, NCP1, NCP4 and NCP6 can be improved 35.79 % by restoring 54.96 % of the priority areas. This study provides insight into how SDGs and SSPs can contribute to ecological restoration for mitigating ecosystem degradation under SDG-SSP scenarios.

Ecological restorations (ERs) have been widely implemented in recent decades to enhance ecosystem stability. However, the extent of their impacts on ecosystem stability and the underlying mechanism remain poorly understood. This study developed a comprehensive framework for ecosystem stability assessment by integrating the temporal stability of ecosystem service (ES) provision, ecological resistance, and ecological resilience. Additionally, ER intensity was quantified using vegetation index trends, while the pathways and magnitudes of key factors driving ecosystem stability were identified by partial least squares structural equation modeling. Using the Jialing River Basin as a case study, our results revealed that forests exhibited the highest ecosystem stability due to their enhanced capacity to maintain temporal stability of ES provision and ecological resilience. However, farmlands demonstrated the strongest ecological resistance, followed by forests and grasslands. ER projects were primarily implemented in northern and southern farmland regions characterized by low ecological resilience. Pathway analysis identified that favorable climates significantly enhanced the temporal stability of ES provision, and rugged topography improved the ecological resistance. However, fragmented landscape patches disrupted stable ES provision by reducing ecological connectivity, and socioeconomic development diminished both resistance and resilience through land-use intensification. Notably, ERs improved ecological resilience, which in turn elevated overall ecosystem stability. Our results indicated that the proposed framework provides a systematic approach for comprehensive ecosystem stability evaluation and offers critical insights for developing region-specific ER strategies.

The Sustainable Development Goals (SDGs) represent a solemn commitment by United Nations member states, but achieving them faces numerous challenges, particularly armed conflicts. Here, we analyzed the impact of armed conflict on SDG progress and its driving mechanism through causal inference methods and machine learning technique. The results show that between 2000 and 2021, armed conflicts slowed overall SDG progress by 3.43 %, equivalent to a setback of 18 years. The Middle East was the most affected region, with a 6.10 % slowdown in progress. The impact of different types of conflict varies across specific goals: interstate conflicts primarily affect SDG 5 (Gender Equality) and SDG 7 (Affordable and Clean Energy), while intrastate conflicts have a larger impact on SDG 4 (Quality Education) and SDG 9 (Industry, Innovation and Infrastructure). Additionally, SDG 15 (Life on Land) is severely affected by both types of conflict, with long-term consequences. As armed conflicts increase, the development progress would regress rapidly in a non-linear manner. To achieve the SDGs by 2030, it is crucial not only to prevent conflicts but also to proactively address and mitigate their impacts on development.

The future increased frequency and intensity of heat waves (HWs) across China will exacerbate adverse effects on society and the environment. However, changes in socioeconomic exposure remain underexplored. In this study, climate model outputs from the Coupled Model Intercomparison Project Phase 6 (CMIP6), together with population and gross domestic product (GDP) projections were used to investigate projected heat stress and socioeconomic exposure across China and its eight subregions under four shared socioeconomic pathway (SSP) scenarios (SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5) over three periods (2021–2040, 2051–2070, and 2081–2100). Our results indicate a consistent upward trend in the Universal Thermal Climate Index (UTCI) across all scenarios, with intensifying increases over time, peaking at > 6 °C. This suggests a continuous increase in the number of extreme heat events (EHEs) in China. Population exposure to EHEs across the four UTCI thresholds (> 26 °C, > 32 °C, > 38 °C, and > 46 °C) shows an increasing trend. Projections indicate a ∼14-fold increase nationwide, 500-fold increase in Northwest China (NWC), and a 1000-fold in Southwest China (SWC2) under SSP5–8.5 by 2081–2100 compared with current levels. The eastern and southeastern regions, especially the Yangtze River and Pearl River Delta, show significant GDP exposure increases under SSP3–7.0 and SSP5–8.5. Population exposure is mainly driven by climatic effects under severe scenarios, whereas GDP exposure is influenced by interaction effects, particularly under SSP5–8.5 and during the 2090s. This study’s findings offer actionable insights for targeted adaptation in China’s diverse geographies.

Photovoltaics play an essential role in supporting the unprecedented growth of renewable energy transition as well as facing a series of trade risks due to complex international dynamics and intermittent trade disruptions. By combining complex network modeling and shock propagation analysis, the spatial-temporal evolution of photovoltaic supply chains worldwide was depicted, and the potential trade risks under different scenarios were elucidated in this study. The results show that the trade patterns of photovoltaic supply chains have evolved significantly, particularly characterized by the rise of China, Malaysia, Vietnam, and Thailand. The complexity of photovoltaic supply chains increases significantly with the addition of more nodes and edges in the networks. The vulnerability of critical photovoltaic supply chains tends to intensify with the increasing concentration of global supply chains in a geographic sense. The interruption of trade ties between China and Vietnam may lead to the most drastic impact on photovoltaic supply chains, followed by trade disruptions between Southeast Asia and North America. By unveiling the spatial-temporal network evolution and potential trade disruption of global photovoltaic supply chains, it is practical to propose rational and feasible strategies that consider the geographical diversification and international cooperation of photovoltaic supply chains worldwide.

State farms, although a minority in China’s agricultural sector, play a critical role in regions like Heilongjiang, leading national food production. However, how state farms (SFs) and rural household farms (RFs) respond to food policies, especially the 2017 soybean subsidy policy (post-Sino–U.S. trade war) and the 2019 soybean revitalization policy, remains unclear. This study examines changes in cropping patterns on SFs and RFs in Heilongjiang from 2013 to 2022 using annual crop maps. We find that SFs, with larger and more clustered fields, responded more effectively to the soybean policies: soybean acreage recovery (2019–2021) reached 91.51 % of pre-trade war levels for RFs and 98.2 % for SFs; following the revitalization policy, maize-soybean rotations were implemented four times in 62.3 % of SFs and 45.4 % of RFs. These results highlight the influence of global trade and agricultural policies on cropland management, providing critical insights into sustainable practices and food security across different agricultural systems.

Livestock management plays a crucial role in environmental protection, food security, and sustainable livelihoods worldwide. However, comprehensive research on its microeconomic dimensions remains limited. Here, we used piecewise structural equation modeling to identify key drivers of livestock management among rural smallholders, focusing on livestock stocking rates (LSR) and livestock offtake rates (LOR). Data were collected via semi-structured questionnaires and household head interviews in 54 villages in northern Xizang between 2018 and 2020 (n = 549). Our findings revealed pronounced spatial heterogeneity in livestock management, with households in alpine meadows showing the highest LSR (2.14 standardized sheep units per hectare, SSU· ha⁻¹) and the lowest LOR (9 %), in contrast to households in desert steppe areas (0.27 SSU· ha⁻¹ and 15 %, respectively). Across northern Xizang, five grouped environmental factors—climatic conditions, natural resource endowment, market conditions, demographics, and household income—jointly explained 66 % and 20 % of the variance in LSR and LOR, respectively. Biophysical factors had a greater influence than socioeconomic ones, though demographic variables and market conditions were also positively correlated with LSR and LOR, respectively. Given the consistently low LOR among species (9 %–15 %), with marked differences between yaks and sheep (5 %) and goats (2 %), targeted policies are needed to encourage herders to adopt circular economy practices to balance ecological conservation with economic growth. This study highlights an underutilized livestock economy in high-altitude pastoral communities and clarifies the interplay of biophysical and socioeconomic factors in herders’ decision-making. The findings offer valuable insights for refining policy frameworks related to livestock and environmental management in rural China and beyond.

Large-scale urban land expansion in mountainous and hilly areas (UEMH) has significantly altered the terrain in many Chinese cities, leading to various environmental and urban challenges. Despite its importance, there is limited nation-scale research that reveals the amount and the spatial variations of UEMH-induced terrain alteration. This research integrates the Global Annual Urban Dynamics dataset, the Global Basic Landform Unit dataset, the TanDEM-X DEM Change Map (DCM) dataset, Baidu Points-of-Interest (POI), and other auxiliary datasets to conduct a comprehensive analysis of terrain alteration induced by UEMH in China from 2012 to 2020. The results indicate that the country-wide UEMH-induced terrain alteration reached approximately 13 billion m³, which is about 100 times the volume of Hangzhou West Lake, and terrain alteration volume in over 300 counties exceeded 10 million m³. The Southwest and Southeast regions, which are ecologically sensitive and critical, feature the greatest alteration in terms of area and volume. The most significant terrain alteration in terms of intensity is observed in the Southwest and Hengduan Mountain Area. Additionally, there are significant spatial variations in the contributions of different urban functional zones to terrain alteration. Our findings indicate that urban land expansion in mountains and hills has significantly altered terrain in some regions of China, necessitating customized urban planning strategies for better managing mountainous urban land expansion and governance policies to address the geological, ecological, and urban development challenges.

High-altitude peatlands (HAPs; defined as > 1,500 m) provide important ecosystem services including soil carbon (C) storage. However, temperatures in high-altitude regions have been rising rapidly in recent decades, while HAPs are increasingly affected by human activities such as intensive drainage and grazing. Collectively, climate change and land management may strongly affect the HAP C cycle. Here, we synthesise current global progress on the HAP C cycle, focussing on the impacts of climate change and land management. Warming increased both ecosystem respiration (ER) and methane (CH₄) emissions (26 %–86 %), while impacts on net ecosystem exchange (NEE) of CO₂ were still unclear. However, short-term drought decreased ER and CH₄ emissions (7 %–96 %), along with NEE (12 %–52 %). Snow, permafrost, and glacier decline may also impact the C cycle in HAPs, although a limited number of studies have been conducted. Grazing and vegetation degradation impacts on HAP C cycling were related to grazing and degradation intensity, while generally decreasing soil organic C stocks (3 %–51 %). Moving from shallower to deeper WTLs stimulated ER (9 %–812 %), while reducing CH₄ emissions (13 %–100 %), with variable effects on NEE (-53 %–700 %). Restoration by rewetting began to reverse the trend of drainage. We highlight several knowledge gaps, including limited understanding of climate change and land-management effects on gross primary productivity and dissolved organic carbon, while there is still limited knowledge of regional differences in HAP C cycling. Future research should focus on the interaction of land-use and climate change in HAPs, including HAP restoration, which may help future conservation of these valuable ecosystems.

Promoting positive urban–rural interactions is a key strategy for addressing rural decline and advancing regional sustainable development. This study examines the impacts of urban–rural interactions on rural development and explores their mechanisms for advancing sustainability within urban agglomeration areas. Using the Chang–Zhu–Tan (CZT) urban agglomeration as a case study, with an indicator system to measure urban–rural interactions and rural sustainable development, we analyze the mediating effects of resource flows in the process of urban–rural interactions driving rural sustainability through a mediation model. The results show that spatial connectivity, industrial convergence, and social integration between urban and rural areas positively contribute to the economic and social sustainability of rural areas. However, urban–rural spatial connectivity and social integration may negatively impact on rural environment. In this process, capital, technology, and labor play significant mediating roles, whereas the influence of land is less pronounced. Based on these findings, we propose several recommendations for strategically leveraging the benefits of urban–rural interactions across various social-ecological contexts while mitigating their drawbacks.

Extensive changes in land cover and energy use resulting from urbanization lead to an imbalance in urban thermal conditions, making cities more susceptible to the impacts of climate change. Nature-based solutions (NbS) that leverage the cooling effect of green spaces to mitigate urban heat are gaining attention as a way to improve urban sustainability in the face of climate change. The study evaluated the urban-scale application of NbS’s impacts on heat mitigation capacity, air temperature, cooling energy, carbon emissions, and carbon sequestration, as well as the resulting economic benefits using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Urban Cooling Model (UCM). Green roofs as building adaptations, land use adaptations such as the expansion of urban parks and roadside green space, forest restoration, and multiple adaptations, which are combinations of building and land use adaptations, were considered applicable NbS. Cool roofs were also studied to compare their effects with other urban green infrastructure. The results showed that simultaneously implementing the multiple adaptation methods is the most effective if the applicable areas are sufficient. Considering the implemented area ratio, urban parks are the most effective single adaptive measure, with energy savings of 14.75, 8.63, and 1.98 times higher than those of 100 % green roofs, cool roofs, and 20 % roadside green space expansions, respectively. Restoring forests (21.29 km²) can yield 4.7 times higher energy savings than installing 100 % green roofs (62 km²). In contrast, deforestation loses more energy and carbon than cool roofs can save. This study can help provide an appropriate strategy for achieving urban carbon neutrality by reducing carbon emissions and increasing carbon sequestration through NbS in addition to relieving urban temperatures.

Temporal dynamics in soil organic carbon (SOC) play a crucial role in the global carbon cycle. How warming affects SOC change has been widely studied at the site scale, mainly through short-term manipulative experiments. Decades-long SOC dynamics in ecosystems can be complicated, particularly as real-world warming rates varied on decade-scale. However, the lack of long-term repeated observations on whole-profile SOC limits our understanding of SOC dynamics across large regions. Herein, we reconstructed 45 years of SOC dynamics (1970–2014) in topsoil (0–30 cm) and subsoil (30–100 cm) using 10,639 soil profiles from forest and cropland across the contiguous United States, and investigated their relations with key dynamic environments (e.g., climate, vegetation and nitrogen). We further examined the spatial pattern of SOC stock changes at a finer scale (∼2 km) using machine learning techniques. Our results revealed ecosystem-dependent, two-stage changes of SOC stock, characterized by continental-scale halts in SOC loss following warming deceleration since the late 1990s. This shift led to an overall increase in SOC stock of 1.41 % in forest and 1.14 % in cropland within the top 1-meter over 45 years. Temperature was the primary factor related to topsoil SOC losses, whereas soil water content may primarily control subsoil SOC change. Notably, a threshold effect of warming rates on SOC loss was identified in both topsoil and subsoil. These findings provide new insights into long-term whole-profile SOC dynamics at a large scale, offering valuable implications for carbon sequestration to support sustainable development in different ecosystems.

Soil erosion is the primary factor causing the loss of soil resources and land degradation. Clarifying the current status of soil erosion in China and the characteristics of future changes under different pathways of development is important to the global management of soil resources, food security, and ecosystem services. We used the revised universal soil loss equation and the most recent and reliable soil and environmental data to characterize soil erosion in China at present and under typical Shared Socioeconomic Pathways and Representative Concentration Pathways (i.e., SSP1–2.6 and SSP5–8.5) in the medium- and long-term future (2050 and 2100). The current average rate of soil erosion in China was 14.78 t ha^-1 yr^-1, with a total amount of about 14.0 Pg yr^-1. The amount of total erosion increased by 5.0 %, 10.8 %, 9.9 %, and 25.9 % for scenarios 2050_SSP1–2.6, 2050_SSP5–8.5, 2100_SSP1–2.6, and 2100_SSP5–8.5, respectively, compared to the baseline amount in 2010. The contribution of climate change and land use to the increase in erosion ranged from 9.5 % to 31.5 % and -6.95 % to -1.78 %, respectively, with the contribution of climate change about 2.36- to 7.54-fold larger than the contribution of land use. Converting arable barren land into forest and grassland or adopting conservation tillage practices for farmland, could nevertheless effectively offset the increase in erosion under the four future scenarios. This study provides data and a scientific basis for managing soil erosion in China and provides a useful reference for conserving global land resources and formulating policies to cope with climatic and environmental changes.

Optimizing landscape patterns and management measures would be an effective strategy for the agro-pastoral transitional zone in northern China (ATNC) to adapt to future climate change. Existing studies generally focus on cropland or pasture, and thus there is a lack of comprehensive understanding of the landscape composition and configuration in complex agro-pastoral transitional zone. In this study, Ansai County in the ATNC was chosen as an experimental area. Four typical agroecosystem services (AESs), food provision (FP), soil carbon (SC), soil retention (SR) and water yield (WY) from 1980 to 2020, were simulated by spatially integrating a model of the agricultural system using the Environmental Policy Integrated Climate (EPIC) combined with geographic information systems technology. The impacts of different crop types, pasture configurations, and tillage practices on AESs under future climate scenarios were assessed in the context of agro-pastoral transition. Finally, the optimal landscape pattern configuration and management measures were identified through single-objective and multi-objective optimization models. The results showed that under historical scenarios, implementing policies such as converting cropland to pastureland improved SC and SR but reduced FP and WY. Compared to traditional and reduced tillage, no-till practices benefited the enlargement of AESs and the agricultural ecosystem. Notably, future climate change generally negatively affected AESs, especially under the Shared Socioeconomic Pathway (SSP5–8.5) climate scenario. The combination of planting corn and no-till measures would be ideal for optimizing the agricultural ecosystem in Ansai County. For the fragile ATNC, we should advocate conservation agriculture and policies converting cropland to pastureland to mitigate the adverse impacts of climate changes. This study establishes a replicable framework to address landscape management in complex agropastoral systems and offers solutions for climate-resilient land management in global dryland transitional zones, contributing to the realization of regional ecosystem sustainability.

Although the Vietnamese Mekong Delta (VMD) is recognised as one of the world’s most vulnerable deltas, scholars have yet to provide an integrated diagnosis linking locally driven pressures to actionable pathways for halting its rapid elevation loss. The VMD—39,000 km² that feeds 18 million people—is sinking because four pressures act in concert: upstream dams have already cut sediment delivery by 70 %–83 % (projected 96 % if all planned projects proceed), mean sea level is rising 1.5–2 cm/yr, river-bed sand mining now removes about 3 Mm³/yr and deepens channels by up to 15 cm/yr, and groundwater withdrawals of approximately 2.5 Mm³/day have accelerated land-surface subsidence from smaller than 3 cm/yr in 2006–2010 to peaks of 5–6 cm/yr today. Scenario modelling shows that halving pumping would stabilize aquifer heads and cut subsidence by about 50 % within a decade, while provincial sand-quota cuts of 30 %–50 % would slow bed incision and ease salinity intrusion, reducing the irrigation deficits that drive further pumping. While the large-scale causes of subsidence (dams, sea level rise, sand mining, groundwater extraction) are well recognized, actionable, local-level management solutions to immediately slow subsidence and salinity intrusion—independent of slow international negotiations—have been underexplored and under-implemented. Because dam and climate remedies rely on slow transboundary negotiations, we target the more practical local pressures—sand mining and groundwater extraction—by first tightening sand-mining licenses, enforcing tiered groundwater tariffs, and scaling up rain- and surface-water alternatives, buying time for longer-term basin and climate agreements. These locally actionable measures can significantly reduce subsidence and provide a scalable model for sustaining deltas around the world.

Coastal salt marshes provide critical ecological services, including carbon sequestration. However, the landscape patterns, driving factors, and carbon dynamics associated with salt marsh losses and gains remain insufficiently explored, which is vital for effective restoration. This study conducted a comprehensive analysis of these aspects across China, with a focus on species-specific differences. Based on historical salt marsh data, landscape analysis was applied for identifying the spatiotemporal characteristics of changes. XGBoost algorithm was used for driving factor analysis. Carbon dynamics derived from salt marsh changes were estimated with statistical calculation. Our results indicated that the distribution patterns of salt marshes, as indicated by mean center, ellipse area, and landscape indices, varied significantly from 1985 to 2019, particularly between 2005 and 2010. Native species, such as Phragmites australis and Suaeda salsa, experienced significant losses with a 72 % reduction, while exotic species Spartina alterniflora showed substantial gains with a 680-fold. Human disturbances emerged as the primary driver of these changes, with mean temperature and precipitation influencing certain regions and years. Overall, salt marsh changes resulted in a net emission of 68.1 Mt CO₂, with the highest emission in Shandong and linked to the loss of Phragmites australis. Conversely, carbon sequestration equivalent to 11.1 Mt CO₂ mainly resulted from the expansion of Spartina alterniflora, with Shanghai contributing the most. This species-specific and site-specific analysis of landscape patterns, drivers, and carbon dynamics in China could enhance our understanding of salt marsh changes and offer valuable insights for targeted restoration efforts at both local and national levels.

Antibiotic resistance genes (ARGs) are increasingly recognized as a global public health threat, with glaciers acting as reservoirs for ARGs transported via atmospheric pathways. Warming climate accelerates glacier melting, releasing ARGs into downstream environments, posing ecological health and sustainable aquatic ecosystem development challenges. However, the distribution profiles of ARGs and their risks in glaciers from the polar region remain unclear. This study used 294 metagenomic sequences to investigate the distribution and risks of ARGs in glaciers across the Qinghai-Xizang Plateau, Antarctica, and the Arctic regions and compared them with adjacent and anthropogenically impacted environments. Among the three glacier regions studied, the Qinghai-Xizang Plateau exhibited the highest abundance of ARGs, whereas Antarctica displayed the lowest. ARG abundance in adjacent environments was comparable to that in the glaciers of the Qinghai-Xizang Plateau, but in the anthropogenically impacted environment, it was significantly higher than in glaciers. A shared resistome was identified in glaciers, dominated by bacitracin, multidrug, and macrolide-lincosamide-streptogramin (MLS) resistance genes. The bacA gene, which is related to bacitracin resistance, was the most common subtype, indicating that it is naturally present in microbial communities of glaciers. Risk assessments showed that 74.1 %–78.9 % of ARGs were low-risk in the Qinghai-Xizang Plateau and polar glaciers, indicating minimal human influence. However, 7.3 %–8.0 % were classified as high-risk, posing potential threats through horizontal gene transfer (HGT) and the spread of multidrug-resistant pathogens. These findings highlight the need to monitor ARGs in glacier environments, as climate change accelerates glacier melting and subsequent release of ARGs into downstream ecosystems.

Water stress is expected to intensify due to escalating atmospheric and surface dryness under global warming. Despite extensive research indicate that intensified dryness exacerbates water constraints on ecosystems, the dynamics and underlying mechanisms of surface water stress (SWS) under climate change remain poorly understood. In this study, we use annual evaporative stress as the surface water stress index (WSI) and provide a comprehensive analysis of historical and projected global terrestrial SWS, covering its characteristic changes, driving factors, and impacts on vegetation. Our results show a significant declining trend in WSI during 1982–2014 (-0.0033/decade, p < 0.01), indicating the enhancement of SWS concurrent with a rapid expansion of water stress intensified areas at a rate of 1.85 %/decade (p < 0.01). Using the Budyko-Penman budget framework, we found that the intensification of SWS was primarily driven by an increase in vapor pressure deficit (VPD) and a decrease in precipitation. Furthermore, the intensification of SWS contributed to a decline in vegetation growth, with the extent of areas experiencing increased vegetation water deficit expanding rapidly at a rate of 1.38 % per decade (p < 0.01). In the future, SWS is projected to escalate, with the proportion of areas experiencing intensified SWS increasing from 6.3 % to 24.3 % by the end of the century under the SSP5–8.5. Our study provides a comprehensive analysis of the drivers of SWS under climate change and its impacts on ecosystems, offering valuable scientific insights for the effective management of water resources.

Although Vegetation Restoration Programs (VRPs) on the Loess Plateau, China, have significantly improved the region’s ecological condition, their impact on the local economy and agriculture remain unclear. Here we used the difference-in-differences analysis to quantify the effects of the VRPs on population, economic, and agricultural aspects. Results suggest that the implementation of the VRPs increased mean county-based Gross Domestic Product by 148 % and per capita grain production by 30 %, but decreased rural labor resources by 11 %. VRPs promoted the transfer of population to the secondary industry and increased the income of local farmers. We predict that grain production will likely start to decline when the restoration area exceeds approximately 55 % of the total county area in the future. Our study suggests that while VRPs on the Loess Plateau are economically sustainable, their expansion beyond a certain threshold could jeopardize agriculture.

Forest biomass carbon storage (BC) plays a critical role in mitigating climate change. However, the spatiotemporal patterns and stability of BC growth in China remain unclear. Using the latest BC maps (2002–2021) and multi-source remote sensing data, we analyzed the spatiotemporal dynamics of BC and applied resilience indicators to reliably assess its stability. Our results show that while China’s long-term BC has continued to increase, the risk of BC losses has also intensified, particularly in old forests (>70 years), where approximately half exhibit a declining trend. Moreover, BC dynamics do not consistently align with resilience changes. About 53.4 % of forests display weakening resilience, directly reducing BC accumulation rates by 23.1 % and amplifying interannual variability. Alarmingly, 10.4 % of forests (BC-, resilience-), predominantly high-BC-density forests (mean: 28.3 tC/ha), face an extremely high risk of carbon loss (carbon emissions: -118 Tg C). We further found that the accelerating effect of resilience weakening on BC losses significantly outweighs the promoting effect of resilience enhancement on BC accumulation (-17.79 ± 4.72 Mg/ha vs. 11.47 ± 3.42 Mg/ha). Our study highlights that China’s BC growth is characterized by unstable components and faces substantial loss risks. In future efforts to enhance forest carbon sinks, greater attention should be paid to changes in forest resilience to improve the stability of biomass carbon sinks and achieve sustainable, long-term carbon sequestration.

Due to its impact on cereal yields, vegetation growth, animal wellbeing, and human health, considerable attention has been paid to diurnal temperature range, focusing on the temporal dimension of surface air temperature. However, the characteristics of spatial temperature range and its response to climate change remain unclear, despite its importance to various natural and societal activities. Here, we proposed a daily spatial temperature range (DSTR, difference between spatial maximum and minimum temperature, STmax and STmin) indicator to measure the maximum spatial temperature range within a given region over a day. We analyzed the spatiotemporal pattern of DSTR and its trend under climate change at four scales (global, hemispheric, national, and provincial), with the following main results: (1) DSTR was scale dependent, provincial pattern of which were mainly related to sensible and latent heat fluxes. (2) The key regions affecting DSTR and temporal distribution at different scales were mapped out. (3) Under climate change, DSTR significantly decreased globally, hemispherically, and in several Chinese provinces due to the greater warming of STmin than STmax. The influence of latent heat flux and solar shortwave radiation was larger at global/hemispheric scales, while the albedo was a more critical driver at provincial scale. For the first time, we proposed the DSTR indicator and emphasized the importance of exploring spatial temperature heterogeneity. This spatial information is important to optimize relevant societal activities, and the response of DSTR to climate change has further led to the consideration of the relationship between DSTR and extreme events, biodiversity, etc.

Maintaining community stability has profound positive impacts on the ecological functions and sustainable utilization of grassland ecosystems. Numerous studies have explored how community stability responds to climate change and its relationship with plant species diversity. Nevertheless, the impact and underlying mechanisms of belowground ecosystem multifunctionality (BGEMF) on community stability along a precipitation gradient in alpine grasslands remain poorly understood. To address this knowledge gap, we conducted field surveys from 2015 to 2020, measuring plant species diversity, annual net primary productivity (ANPP), and soil physicochemical properties across 79 sites in alpine grassland ecosystems on the Qinghai-Xizang Plateau. Our findings highlight both plant species diversity (standardized total effect: 32 %) and BGEMF (standardized total effect: 75 %) had an indirect effect on stability viaregulating mean ANPP within alpine grasslands. Furthermore, mean annual precipitation substantially impacted both plant species diversity and BGEMF, subsequently affecting community stability. However, temperature had a strong negative regulatory effect on species diversity, the mean and variability of ANPP. Thus, we emphasized the pivotal role of plant species diversity and BGEMF in shaping community stability, and stated the imperative need for species conservation and BGEMF improvement to sustain alpine ecosystems in the face of ongoing climate change.

Although geography’s role in advancing the Sustainable Development Goals (SDGs) is widely recognised, a comprehensive quantitative synthesis of its intellectual contributions has been absent. This study fills that critical research gap through a large-scale bibliometric analysis. Drawing from 122 core geography journals (Web of Science, 2010–2024), we employed three-level search criteria (SDGs, sustainability and SDG indicators) to identify a final corpus of 70,122 relevant articles. We then combined publication trend analysis, co-citation and collaboration networks, and keyword co-occurrence mapping to systematically delineate research foci, contributions, and future directions. Our findings reveal six major thematic research clusters: (1) climate change impacts and governance; (2) agricultural landscape and environmental sustainability; (3) resilience and adaptive capability in social-ecological systems; (4) land use change and metacoupling impacts; (5) urban growth and transport accessibility; and (6) biodiversity and ecosystem services. The SDG overlap analysis highlights strong linkages among environmental SDGs, while revealing that SDG 1 (No Poverty) and SDG 10 (Reduced Inequalities) are more isolated. Overall, geography supports the SDGs across four key dimensions: (1) providing spatial data analysis for assessment; (2) conducting regional studies for localisation; (3) applying human-environment interaction research to advance synergies; and (4) strengthening science-policy interface efforts for achievement. To maximise its future impact, this study calls for the geography community to develop a dedicated methodological framework for SDG analysis, proactively contribute to shaping the post-2030 agenda, advance holistic integrated approaches, and prudently harness the power of artificial intelligence to accelerate sustainability transitions.

Elderly individuals disproportionately face heat exposure risk compared to other demographic groups, with projected amplification in the future. The vast disparities between Global North and South countries necessitate a comprehensive understanding of the underlying factors influencing future heat exposure vulnerabilities. Here, we use factor decomposition method to quantify the contribution of climate change, population, and aging to heat exposure risk under four shared socioeconomic pathways (SSP) (SSP126, SSP245, SSP370, SSP585) from 2000 to 2100 at 20-year intervals. Results demonstrate a projected global escalation in heat exposure risk by 16 and 76 times under SSP126 and SSP585, respectively, with the North generally suffering lower risk than the South. Climate change emerges as a pivotal driver of future heat exposure risk in the North while aging notably influences the South. Despite climate change is projected to reduce heat exposure risk by up to 10 % in the North under SSP1-2.6 by the end of the 21st century, aging remains a critical risk factor.

The land, water, energy use, and greenhouse gas (GHG) emissions involved in agricultural production are intrinsically linked. However, quantitative characterization and scenario simulations of these elements’ inherent interrelationships remain scarce. We developed a land-water-energy-GHG (LWEG) nexus framework for the North China Plain (NCP). The framework identifies the mutual feedback in the life cycle of agricultural production among the four factors. We applied the framework to assess the agricultural GHG mitigation potential for winter wheat, summer maize, and rice in NCP municipalities. The results showed that cropping structure optimization reduced GHG emissions by 1.96 Mt CO₂e. Controlling indirect energy consumption in upstream processes of crop production and reducing on-site energy use reduced the volume and intensity per unit area of agricultural GHG emissions. Because of the synergies between land, water, and energy, nexus management, which combines multiple measures of groundwater management, fertilizer, and energy control, has substantial GHG mitigation potential. The nexus management scenario produced a total GHG of 159.51 Mt CO₂e, a decrease of 15.38 % from the baseline scenario. This study quantifies the LWEG nexus within agricultural production processes and identifies agricultural management practices that integrate water, energy conservation, and emissions mitigation contributing to the Sustainable Development Goals.

Urbanization develops with the goal of establishing improved and more sustainable habitats for residents. Environmental and social performance must be simultaneously monitored to ascertain whether regions are progressing towards or deviating from the safe and just space (SJS) in urbanization. Despite relevant studies, the absence of indicators that bridge ecological preservation and human well-beings renders dual monitoring challenging. This study bridged the gap by exploring the interactions between urbanization, ecosystem services (ESs), and basic water, energy, and food (WEF) needs within the SJS framework across China and its provinces. By quantifying the minimum and actual demands for freshwater withdrawal, carbon emissions, phosphorus emissions, and land use, as well as the supply of ESs into unified biophysical indicators, we found that: (1) China can meet the basic WEF needs for all from 2000 to 2020, but only water and land provisioning ESs can operate within the SJS. Carbon emissions surpassed the sequestration capacity in 2010, while phosphorus purification ES has consistently been unsafe. (2) The SJS performance in terms of ecological and social fulfilment exhibited scale differences and undergone changes with urbanization. Overall, no province in China can consistently operate within all SJSs. (3) In the process of urbanization, improvements in ecological protection and production practices in most provinces expanded the size of SJS, but the continuous increase in total demand failed to steer regions toward safer spaces. Our framework emphasized the common but differentiated pathways that regions at varying stages of urbanization navigate to achieve safety and justice. It also provides an applicable solution for regions aiming to pursue urban growth while maintaining ecological conservation and social justice, ultimately achieving sustainable development.

The inequality of socio-economic resources has threatened individual well-being and urban sustainability. However, the inequality in different resource allocation scenarios is still unclear, and the accessibility distance to resources has not been considered. We developed a large-scale, long-term, and multi-perspective quantitative evaluation framework of inequality in the dividing-resource and sharing-resource scenarios over the past 31 years (1992–2022) within 15-minute cities. This framework is informed by patterns of urban development and the spatial distribution of resources and population. The results from 334 Chinese cities demonstrate the differences in inequality between developed and developing cities. When individuals share resources within 15-minute accessibility distance, inequality is lower in developed cities relative to developing cities due to more spatially balanced resources, with a decreasing trend over the past 31 years. However, due to the uneven spatial distribution of the population in developed cities, inequality among individuals has increased when resources are divided within 15-minute accessibility distance. We suggest that the government avoid policy lagging and reduce inequality by rationalizing the spatial configuration of socio-economic resources. Developed cities could adopt policies to direct the overpopulation of city centers outward, and developing cities should care about resources for suburban citizens.

Sudden earth’s surface anomalies caused by natural and anthropogenic factors pose significant threats to ecological sustainability and the safety of human life and property, highlighting the urgent need for their immediate monitoring and early warning. Satellite remote sensing is the most effective means for large-scale earth’s surface anomaly detection. However, constrained by traditional observation paradigms, satellite payload limitations, and other physical factors, current remote sensing detection faces two major challenges: “inability to observe quickly” and “inability to observe effectively”. To solve these problems, we have researched immediate remote sensing detection of sudden earth’s surface anomalies. Its core concept is to deploy the entire detection process on satellites, enabling on-orbit immediate detection of earth’s surface anomalies based on a single image through the integrated “positioning, navigation, timing, remote sensing, communication (PNTRC)” intelligent constellation and edge computing technologies. Subsequently, the detection results are transmitted directly to the subscriber mobile terminal through the BeiDou Navigation Satellite System (BDS). The immediate remote sensing of sudden earth’s surface anomalies emphasizes the continuous capture and immediate feedback of geographic processes, overcoming the longstanding reliance of traditional geography on “slow variables”. Its significance lies not only in the improvement of data acquisition efficiency but also in promoting the transformation of geography from a “descriptive science” to a “predictive science”.

Vegetation restoration (VR) is critical for enhancing the resilience of fragile ecosystems, yet its impact on landscape ecological risk (LER) remains uncertain. The VR project on the Loess Plateau in Shaanxi Province (LPSX) was taken as a case study to address ecological and environmental challenges, including soil erosion and land degradation. This study used multi-source data, including land cover, fractional vegetation cover, and nighttime light. It employed landscape pattern analysis, spatio-temporal correlation analysis, and causality analysis to assess the impacts. This study found a generally positive relationship between VR and the mitigation of LER in LPSX, though spatial and temporal variations exist from 2000 to 2020. Localized VR significantly influenced 17.66 % to 27.03 % of the study area. Positive effects were mainly observed in sandy and gully-hilly regions, showing an upward fluctuating trend that peaked at 21.91 % in 2010. After 2010, negative effects in the Fen-Wei Plain, Qinling Mountains, and Liupan Mountains outweighed the positive effects and continued to expand. Urbanization had a broader impact on LER distribution compared to VR. The findings indicate that future VR projects should focus on the spatial pattern of restoration and its associated eco-social effects to ensure sustainable development.

Water is an indispensable resource for agricultural production. However, its value in agriculture remains largely unknown. This oversight results in agriculture water value being seldom integrated into water pricing, thereby restricting the information available for water allocation decisions. In this study, we estimated irrigation water value over the last 30 years on the north slope of the Tianshan Mountains, where agriculture is largely dependent on irrigation water supply. Using a data-parsimonious biophysical framework with a function of crop growth and water-demanding dynamics, we estimate the additional net economic benefit of irrigated agriculture relative to rainfed conditions for three major crops at the county level. Our results reveal that mean irrigation water values were 0.27, 0.32, and 0.16 USD m^–3 for cotton, maize, and wheat, respectively, which were 2.0 − 3.2 times higher than global estimates. The value of irrigation water significantly increased over time, primarily driven by rising crop prices and improved water use efficiency. Our findings indicate that farmers in arid regions with water limitations may favor crops with high irrigation water use efficiency. Wheat is suggested to be spatially reallocated in light of the economic benefit, given its relatively low output price and water use efficiency. Irrigation water value was more sensitive to precipitation than air temperature by lowering crop prices and narrowing the gap between rain-fed and irrigated yields. The inclusion of irrigation water value in planning could lead to more efficient use of water resources and support decisions regarding irrigation investments, water use rights, and, ultimately, food sustainability.