With the global expansion of protected areas (PAs) and increasing involvement of indigenous communities, understanding their impacts on indigenous peoples is crucial. This study evaluates the extent to which China’s national cultural ecological protection areas (CEPAs) safeguard indigenous culture, using land-use disturbance as a key metric to assess impacts on cultural keystone species (CKS). We employ a multi-step evaluation framework that reclassifies land use, identifies environment-dependent CKS, and analyzes land-use dynamics by comparing disturbances before and after CEPAs establishment. Our results reveal that, despite overall improvements in land conditions, over 36 % of CEPAs are in land disturbance threat or warning status. All of these sites are indigenous CEPAs, indicating a disproportionate disturbance burden on indigenous communities. Notably, traditional medicinal practices are particularly vulnerable. These findings underscore the urgent need for policies aligning ecological diversity with cultural diversity to support the global commitment to expand PAs to over 30 % of Earth’s land and oceans by 2030.

Global warming and socioeconomic development are expected to exacerbate human exposure to heat stress, but the extent and inequality of such changes remain unclear. Here, we quantified the future population exposure to heat stress (PEHS) under different Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs) scenarios using a novel decomposition framework that separates the contributions of climate change, population change, and their interaction. Results show that global PEHS will increase substantially during the 21st century, with low-latitude regions experiencing the largest absolute increases, and high-latitude regions facing the largest relative increases. Globally, projected increases in PEHS under SSP3-7.0 are roughly three times those under SSP1-2.6, with low latitudes contributing about 70 %-75 % of the global total. SSP1-2.6 most effectively limits future heat exposure, with the highest risks in low-latitude developing regions, underscoring the need for low-emission pathways and targeted population and urbanization management. The findings highlight the urgent need for both climate mitigation and population adaptation strategies to address the growing and uneven heat exposure risks worldwide.

With the rapid advancement of Artificial Intelligence (AI) technologies, its applications have become increasingly widespread across various aspects of geography, offering unprecedented analytical capabilities across disciplinary boundaries. Despite this revolutionary potential, a comprehensive understanding of the current research landscape and development trajectory of AI in geographical sciences remains limited. To fill this gap, we conducted a large-scale systematic review based on 400,000 geographical publications published from 1990 to 2023. We utilized large language model (LLM) prompt engineering, topic modeling and other natural language processing techniques to analyze the publications. Our findings reveal that AI applications constitute 8.1 % of geographical research, with publication volume having increased 20-fold over three decades. Both China and the United States have been the leading contributors to AI-driven geographical studies, together accounting for 62.78 % of all publications in this field. Notably, more than half of the studies used traditional machine learning methods. Among the various geographical topics, remote sensing applications and spatial data analysis emerged as the most extensively explored areas using AI techniques, with image feature extraction being the topic with the deepest level of adoption and most significant ongoing impact of AI methods. This systematic review provides critical insights into the integration trajectory of AI within geographical sciences, establishing a foundation for identifying emerging research opportunities and enhancing our understanding of AI’s transformative role in advancing geographical knowledge.

Global climate change is a pressing environmental challenge. Climate-induced migration highlights the severe impact of unsuitable climatic conditions. However, current research methods are limited in their ability to assess climate suitability for residents in high-altitude areas. In this study, we assess climate suitability across the Qinghai-Xizang Plateau from 1979 to 2018 and project future changes using four different Shared Socioeconomic Pathway (SSP) climate scenarios by constructing the Climate Suitability Index (CSI). The findings reveal a notable increase in CSI from 0.32 to 0.36 from 1979 to 2018. The primary factors contributing to the increased climate suitability are increasing annual mean precipitation (61.42 %) and decreasing solar radiation (17.22 %) from 1979 to 2018. Furthermore, the study forecasts a continued enhancement of climate suitability across all SSP scenarios, with SSP585 demonstrating the greatest improvement, followed by SSP370, SSP245, and SSP126. Although low oxygen levels at high altitudes remain a challenge, the overall improvement in climate suitability offers hope for people living at high altitudes to cope with climate change.

Compared with traditional energy sources, wind power has a lower environmental impact. However, emissions are still generated across the life cycle of wind turbines, from production to recycling. As wind power rapidly develops and deployment increases, these impacts are becoming increasingly evident. A comprehensive understanding of these impacts is crucial for sustainable development. Based on the harmonization of previous detailed life cycle assessment (LCA) studies, this study develops a simplified LCA model that estimates the life cycle environmental impacts of wind turbines based on their nominal power. Using this simplified LCA model, we assess the global warming potential (GWP), acidification potential (AP), and cumulative energy demand (CED) of wind power at the regional scale for 2022 and under three future scenarios (high-power wind turbine promotion, reduced wind curtailment, and a comprehensive development scenario). The results indicate that in 2022, the life cycle GWP, AP, and CED of wind power in western China were 10.76 g CO₂ eq/kWh, 0.177 g SO₂ eq/kWh, and 17.6 kJ/kWh, respectively. Scenario simulations suggest that reducing wind curtailment is the most effective approach for reducing emissions in Inner Mongolia, Gansu, Qinghai, Ningxia, and Xinjiang, producing average decreases of 8.64 % in GWP, 8.39 % in AP, and 9.26 % in CED. In contrast, for Guangxi, Chongqing, Sichuan, Guizhou, Yunnan, Xizang, and Shaanxi, the promotion of high-power wind turbines provides greater environmental benefits than reducing curtailment, producing average decreases of 3.45 %, 3.09 %, and 4.29 % in GWP, AP, and CED, respectively. These findings help clarify the environmental impact of wind power across its life cycle at the regional scale and provide theoretical references for the direction of future wind power development and the formulation of related policies.

Great Lakes Regions (GLRs) in China often confront landscape fragmentation, wetland degradation, and ecological resilience (ER) losses owing to extensive and intensive urbanization. In GLRs, however, the ER responses to urbanization remain unclear. This study explored the spatiotemporal evolution of ER and urbanization in five GLRs in China to analyze the ER dynamic patterns along center−lakeside−periphery gradient. The Spatial Durbin Model (SDM) and Panel Threshold Model (PTM) were combined to reveal the spillover and threshold effects of urbanization in five GLRs. The results indicate that the ER in five GLRs declined with a rate of 21 % from 2000 to 2020. There was a clear “center-periphery” contraction trend with low ER areas primarily spreading to human activity-concentrated regions such as lakesides, riversides, and road networks. Driven by economic and land urbanization, the average urbanization level increased from 0.06 to 0.13, where lakesides, riversides, and road networks were key areas undergoing expansion. The urbanization showed a noticeable negative spatial spillover effect on ER. Away from central lakes, the negative impacts on ER exhibited a two-phase decrease with the threshold of 81 km. This study contributes to the understanding of human-environment interactions by examining the ecological resilience response process of GLRs under the impact of urbanization. Based on a multidimensional “center−lakeside−periphery” analytical model, this study provides a strategic framework for ecological construction in GLRs in China, promoting sustainable development and adaptive capacity in vulnerable areas.

Amid accelerating global land degradation, establishing high-efficiency ecological restoration principles and frameworks is crucial. Here, we explore the application of threshold effects in the ecological restoration process based on field experiments and globally available experimental data from 173 sites. Combining data integration analysis and meta-analysis, we collectively verified the universality of threshold effects in grasslands. The global grasslands’ average nitrogen application threshold is 3.78 g·m⁻²·yr⁻¹, while the threshold value of degraded grassland (3.65 g·m⁻²·yr⁻¹) is lower than that of nondegraded grassland (5.90 g·m⁻²·yr⁻¹). The low nitrogen-driven thresholds are affected by degradation status, climate (precipitation and temperature), and other site conditions, but not fertilization forms. Independent experiments further demonstrated that an increase in soil moisture content can lead to the disappearance of nitrogen threshold effects, revealing that ecological threshold effects are influenced by ecosystem stress factors. Following the significant increase in plant biomass triggered by the nitrogen threshold, the ecosystem undergoes systemic improvement. Soil organic carbon, urease activity, soil microbial diversity, and other soil properties are significantly enhanced. Soil nitrogen cycle-related microbial communities and soil physicochemical attributes are significantly activated. The results indicate that a threshold response pattern may develop before nitrogen saturation is reached, and low nitrogen input can boost productivity and improve the plant-soil-microbe system. Our findings reveal a nonprogressive path of restoration in degraded ecosystems, and thus, restoration based on threshold effects can offer an efficient and safe solution to combat ecological degradation.

Sustained and spatially explicit monitoring of the United Nations 2030 Agenda for Sustainable Development is critical for effectively tracking progress toward the global Sustainable Development Goals (SDGs). Although land cover information has long been recognized as an essential component for monitoring SDGs, a standardized scientific framework for identifying and prioritizing land cover related essential variables does not exist. Therefore, we propose a novel expert- and data-driven framework for identifying, refining, and selecting a priority list of Essential Land cover-related Variables for SDGs (ELcV4SDGs). This framework integrates methods including expert knowledge-based analysis, clustering of variables with similar attributes, and quantified index calculation to establish the priority list. Applying the framework to 15 specific SDG indicators, we found that the ELcV4SDGs priority list comprises three main categories, type and structure, pattern and intensity, and process and evolution of land cover, which are further divided into 19 subcategories and ultimately encompass 50 general variables. The ELcV4SDGs will support detailed spatial monitoring and enhance their scientific applications for SDG monitoring and assessment, thereby guiding future SDG priority actions and informing decision-making to advance the 2030 SDGs agenda at local, national, and global levels.

Understanding the complex interactions between human activities and ecosystem functions is a prerequisite for achieving sustainable development. Since the implementation of the “Grain for Green” Project in 1999, ecosystem functions in China’s Loess Plateau have significantly improved. However, intensified human activities have also exacerbated the pressures on the region’s fragile ecological environment. This study investigates the spatiotemporal variations in the human activity intensity index (HAI) and net ecosystem benefits (NEB) from 2000 to 2020, using expert-based assessments and an enhanced cost-benefit evaluation framework. Results indicate that HAI increased by 16.7 % and 16.6 % at the grid and county levels, respectively. NEB exhibited pronounced spatial heterogeneity, with a total increase of USD 36.2 trillion at the grid scale. At the county level, the average NEB rose by 75 %. The degree of trade-off was higher at the grid scale than at the county scale, while the synergistic areas initially expanded and then declined at both scales. Key areas for improvement and regions of lagging development were identified as priority zones for ecological management and spatial planning at both spatial resolutions. This study offers scientific insights and practical guidance for harmonizing ecological conservation with high-quality development in ecologically vulnerable regions.

Unequal virtual water transfer may aggravate local water scarcity risk. However, the quantitative confirmation of a clear geographic convergence between virtual water transfer and water scarcity risk remains undetermined. We present an analytical framework that reveals the spatial matching between global water scarcity risk and virtual water trade inequality. This framework integrates a three-dimensional water scarcity risk assessment, hybrid input-output analysis, pollution trade term construction, and geographic convergence identification. The framework is applied to 123 countries for long-term validation from 1991 to 2021. We show that despite global improvements in water efficiency and security, countries exceeding the maximum water vulnerability threshold have increased by 50 %. South Asia is the largest net exporter of virtual water. Central Asia exhibits the most pronounced virtual water trade inequality. To achieve the same economic growth, Central Asia needs to pay several times the local water consumption costs of developed regions (15.9 − 83.6 times, 2021). In the past 30 years, the average geographic convergence index exceeded 0.8. Countries facing severe water scarcity also exhibit pronounced inequalities in virtual water trade, indicating that a significant geographic convergence relationship exists. Effectively responding to this unsustainable relationship necessitates balancing both domestic resource risk management and global virtual water trade regulation.

Accurate water budget closure is critical for sustainable water resource management facing increased pressures from climate change and human activities. Although error reduction methods for individual water balance components have advanced, persistent biases remain due to the independent development of datasets, impacting basin scale water budget balance. In this research, we analyzed the mathematical origin of the bias between water budget components and developed a new basin-scale water balance calibration method that redistributes errors across components while enforcing water balance constraints. Validation confirms systematic improvements, with reduced RMSE (Precipitation: -2.29 mm/month; ET: -1.34 mm/month) and increased R² against in situ observations. Applied to the Jinghe River Basin (2000−2019), the calibrated data reveal declining precipitation (-1.70 mm/year) and evapotranspiration (-1.84 mm/year) alongside slightly increasing runoff (0.20 mm/year in basin depth), signaling a drying trend. Land cover changes—marked by cropland loss (-3,497 km²) and forest (+720 km²) and grassland (+2,776 km²) expansion—reflect improved water consumption requirements by ecosystem, raising concerns for water retention and ecosystem stability. The method is particularly effective for ungauged basins with sparse ground data and underscores the need for integrated land-water management to enhance long-term resilience.

Ukraine, as one of the world’s largest agricultural producers and exporters, plays a critical role in global food security. It is essential to understand the spatiotemporal dynamics and drivers of productive cropland in Ukraine, particularly in the context of the 2022 Russia-Ukraine conflict. We provide the first comprehensive assessment of both conflict- and non-conflict-related factors that influenced the distribution and productivity of Ukraine’s cropland from 2013 to 2023. In addition, we propose a novel method using machine learning models to isolate the impact of conflict on cropland. Our findings reveal that, prior to the conflict, the spatial pattern of Ukraine’s mean cultivation rate was primarily shaped by natural factors—such as climate, soil properties, and elevation—whereas socio-economic factors (e.g., GDP and population size) exerted a weaker influence. Interannual dynamics in productive cropland area were largely driven by climate variability. The onset of conflict in 2022 dramatically altered this landscape, with nearly half of the cropland grid cells experiencing a conflict-induced reduction. Notably, almost half of the interannual reduction in productive cropland in 2022 was attributed to climate change. Remarkably, in 2023, the return of displaced populations and favorable climatic conditions in many oblasts contributed to a positive trend in cropland reclamation. Despite this, the total area of productive cropland in 2023 remained below expected levels, due to ongoing conflict and localized droughts. Finally, we highlight the urgent need to adopt a two-pronged approach that addresses both the immediate impacts of conflict and the ongoing threats posed by climate change to ensure the resilience and sustainability of agricultural systems in post-conflict areas.

International trade serves as a crucial pathway for enhancing global food security and equality amid severe food crises worldwide. Under globalization, economic development has profoundly influenced food trade, while disparities in food purchasing power among different economic development groups have led to uneven food security outcomes. However, the varying contributions of international trade to food security across these groups remain to be quantitatively elucidated. This study categorized countries into four economic development groups—high, high-medium, medium-low, and low—and examined changes in their food security scores from 2010 to 2019. The cross-group contributions of international trade to food security across these groups were compared. The results revealed that the food security score of the high economic development group was 9.22 times higher than that of the low economic development group. From 2010 to 2019, the high economic development group exhibited a significant upward trend in food security scores, whereas the low economic development group showed a significant decline. Moreover, international trade contributed significantly to both cross-group and within-group food security in the high economic development group, while its contribution to the low economic development group remained negligible. These findings demonstrated that international trade has further widened the food security gap between the high and low economic development groups, and its limited contribution to the low economic development group has failed to reverse the declining trend in their food security scores. This study quantified the divergent impacts of international trade on food security across economic development groups, providing valuable insights for optimizing global food trade policies—particularly in addressing the food security challenges faced by low econominc development group.

Projections of future urban land change are essential for a range of sustainability assessments, including those related to biodiversity loss, carbon emissions, and agricultural land conversion. However, to what extent and where current projections agree or disagree remains unknown. Here, we systematically compare existing global projections that are consistent with the Shared Socioeconomic Pathways. We find that the total global urban land area is expected to increase by 112% between 2020 and 2100 (averaged across all projections), with a coefficient of variation of 0.81. This variation is mostly caused by the selection of the underlying drivers that are included in the different models. Regionally, the highest average growth rates are found in sub-Saharan Africa (+679% to +730%), while this region also has the highest variation across projections (coefficient of variation ranging from 2.02 to 2.18). When ranking scenarios within a study from the highest to the lowest projected increase in urban land, rankings are relatively similar for regions in the Global North, but not for regions in the Global South. The large disagreement across projections can lead to high uncertainties in assessments of future urban land change impacts, which can undermine the effectiveness of long-term planning, policymaking, and resource management decisions.

Large-scale afforestation and forest conservation policies have been widely implemented in Southwest China over past decades. These efforts have significantly protected the remaining long-established forests in the region and greatly expanded forested areas. Utilizing nearly 30 years of satellite time-series data, we reveal that the region’s enhanced carbon sequestration (3 × 10¹² g·C annually) is primarily driven by crucial changes in forest structure and age, occurring alongside a nearly 120 % increase in forested land area. We observe that dense forests maintain a rapid growth rate of approximately 2.5 % annually for carbon sequestration in the initial years after establishment. However, this growth rate decelerates with increasing apparent forest age. Meanwhile, the densification (modeled as an increasing forest probability) rate of forests reaches its peak growth during the 10-20 year period, sustaining a high annual growth rate of about 1.8 %. We also find that improvements in forest structure, particularly the increasing of forest canopy density and apparent forest age coupled with a notable reduction in forest fragmentation, are also the main driving factors for the enhanced carbon sequestration capacity. Based on these findings, we conclude that forest restoration policies in Southwest China have been successful not only in facilitating large-scale forest growth in Southwest China but, more critically, in promoting the structural maturation (e.g., densification and reduced fragmentation) that is essential for enhancing the region’s carbon sink capacity and its resilience.

Land degradation, coupled with climate change impacts, poses serious threats to global land health and human well-being. Participatory scenario planning (PSP) has become a key tool for exploring these interconnected challenges; however, its progress and effectiveness remain underexplored. This study reviews 46 papers, using PRISMA guidelines, to investigate how PSP supports sustainable land management and climate resilience. We document how PSP applications have evolved from a biophysical focus to one addressing broader environmental, societal, and economic challenges. Disparities in how participants engage across PSP phases document the need for more equitable and meaningful participation. Clustering future scenarios reveals the complex interconnections among ecological, social, and economic factors underpinning land management and climate resilience, underscoring the need for inclusive and integrated strategies. From the emerging trends, we identify opportunities to advance PSP implementation, including early engagement of decision-makers, balanced representation and equitable power dynamics, meaningful participation, cross-disciplinary collaboration, integration of human-nature relationships, and regular revision of future pathways. Overall, our review highlights PSP’s potential to co-create inclusive, equitable scenarios and actionable pathways towards sustainable and resilient land use futures.

The growing demand for land to accommodate renewable energy infrastructure has intensified competition with biodiversity conservation, agriculture, and ecosystem services. In Portugal, electricity system decarbonisation relies heavily on utility-scale solar energy (USSE) facilities, yet the spatial extent of land transformation associated with photovoltaic development has not been systematically assessed. This study provides an assessment of the land occupancy of USSE facilities and associated land use and land cover (LULC) changes in continental Portugal over the past two decades, as well as their spatial relationship with areas designated for land and nature conservation. A geospatial database of USSE installations (≥1 MW) was developed through the integration of multiple data sources using geographic information systems (GIS). The geometric consistency of spatial features was ensured through harmonisation and validation procedures involving GIS-based corrections supported by Sentinel-2 satellite imagery. Spatial overlay analyses were conducted with multitemporal LULC datasets and with land-use planning constraints, including areas classified for nature conservation, ecological reserves, and agricultural reserves. The results indicate that USSE deployment has been predominantly located in the southern regions of Portugal, although the location of planned projects indicates a northward shift. The implementation of USSE facilities has been mainly associated with LULC changes in forest land, agricultural areas, pastures and shrubland. Spatial overlaps were observed with areas classified within the national ecological and agricultural reserves. These patterns may be indicative of growing land-use conflicts, but the extent to which these developments align with land-use planning objectives and conservation priorities requires further examination.

Resilience plays a crucial role in maintaining desirable ecosystem states and is a key objective of sustainable ecosystem management. This study synthesizes the concepts and measurement approaches of terrestrial ecosystem resilience and expounded on its spatio-temporal changes and influencing factors based on the literature over the past 50 years. Arid regions exhibited the lowest levels of spatial resilience, and the global ecosystem resilience showed a downward trend. In the focal regions, ecological resilience in Amazonian and Southeast Asian rainforest regions declined primarily driven by human activities such as deforestation and cropland expansion. Precipitation and temperature exerted bidirectional influences the resilience of ecosystems, indicating that ecosystem responses to climatic factors were non-monotonic. Evidence concerning anthropogenic factors such as land management and deforestation on ecosystem resilience were predominantly negative. Overall, this study provides a comprehensive synthesis of large scale terrestrial ecosystem resilience assessments, offering valuable insights for ecosystem protection and restoration policy development.

Endowed with opportunities from both land and ocean, coastal areas attract expanding human populations and economic activities. At the same time, they face growing societal and environmental pressures from both the above river catchments and the bordering sea due to climate change, ecosystem degradation, and expansion of built-up areas. Despite the accumulation of human population, economic activities, and environmental impacts, we lack social-ecological systems analysis on water-related risks to world’s coastal human population. To address this research gap, we analyze the spatial extent of six globally important water stressors to people within the world’s coastal zone (100 km from the coastal line) and classify this zone globally into 12 groups by distance from the coastline and elevation from the mean sea level. Adopting the approaches of the UN Sendai Framework and IPCC, we produce risk maps from the stressor maps by multiplying them with population exposure and vulnerability. For most risks, geographical hotspots are the Chinese coast, Bay of Bengal, Gujarat, and the Island of Java. The analysis reveals fundamental differences between water stressors and related risks, often mixed in scholarly literature. Both manifest specific geographic patterns and latitudinal profiles. Our study highlights the importance of high-resolution spatial analysis of vulnerability, exposure, and risks posed by water related stressors in the world’s coastal zone, in a manner prompted by key policy bodies to promote policy design and shared responsibility for managing stress-prone areas.