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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.