In the context of social and economic transformation in rural China, ecosystem disservices have emerged frequently. This study reveals the spatiotemporal patterns, hazards and driving factors of wild boar damage from 2000 to 2021 by using the meta-analysis and collecting 733 typical human and wild boar conflicts. In this period, the number, spatial scope and hazard degree of wild boar damage incidents showed an increasing trend, and the number of provincial-level regions, prefecture-level cities and districts (counties) involved increased from 18, 41 and 67 to 25, 147 and 399, respectively. Wild boar damage incidents were concentrated in Chongqing municipality and central and western Hubei province before 2005, and then expanded to the Sichuan Basin, Loess Plateau, middle-lower reaches of Yangtze River and mountainous areas such as Changbai Mountains after 2015. The main manifestations were destroying crops, infringing poultry and causing casualties, especially the destruction of crops and farmland abandonment, accompanied by a rapid increase in casualties, accounting for 23.66% of the damage incidents. Meanwhile, the spreading trend and harmfulness of wild boar damage is a typical phenomenon of ecosystem disservices. The aggravation of this phenomenon is the result of ecological restoration, hunting ban policy, unclear boundary between agricultural land and ecological land, strong viability of wild boar and lack of natural enemies. This has posed an obvious threat to the use of abandoned farmland, the improvement of farmers’ livelihood and the maintenance of regional ecological security. It is urgent to formulate a policy of controlling the number of wild boars and establish a compensation mechanism for the loss by wild boars.

Spatial-temporal scales effects are general among human-nature interactions. However, the laws and mechanisms of the interaction between humans and the environment at different spatial-temporal scales remain to be identified. The Hexi Corridor in Northwest China is located in the eastern section of the Silk Road and is one of the world’s first long-distance cultural exchange centers. Here we present a comprehensive dataset of the Hexi Corridor, including changes in environments, population, wars, famines, settlements, and ancient oases from the Neolithic to the historic period. Results show that humans adapt to climate change on the millennium scale by choosing corresponding production methods. Environmental change, civilization evolution, and dynasty replacement interrelate on the decadal and centennial scales. Social crises are closely linked to extreme weather events on the interannual scale. On the basis of these results, we find similar time scale effects in the world’s major ancient civilizations. We do so by analyzing their processes of civilization evolution.

Karst depressions are common negative topographic landforms formed by the intense dissolution of soluble rocks and are widely developed in Guizhou province. In this work, an inventory of karst depressions in Guizhou was established, and a total of approximately 256,400 karst depressions were extracted and found to be spatially clustered based on multidistance spatial cluster analysis with Ripley's K function. The kernel density (KD) can transform the position data of the depressions into a smooth trend surface, and five different depression concentration areas were established based on the KD values. The results indicated that the karst depressions are clustered and developed in the south and west of Guizhou, while some areas in the southeast, east and north have poorly developed or no clustering. Additionally, the random forest (RF) model was used to rank the importance of factors affecting the distribution of karst depressions, and the results showed that the influence of lithology on the spatial distribution of karst depressions is absolutely dominant, followed by that of fault tectonics and hydrological conditions. The research results will contribute to the resource investigation of karst depressions and provide theoretical support for resource evaluation and sustainable utilization.

Situated in the hinterland of Eurasia, Central Asia is characterized by an arid climate and sparse rainfall. The uneven spatial distribution of water and land resources across the region has pressured economic and social development. An accurate understanding of Central Asia's water resources carrying capacity (WRCC) is vital for enhancing the sustainability of water resources utilization and guiding regional economic and social activities. This study aims to facilitate the sustainability of water resources utilization by evaluating the region's WRCC from the viewpoints of economic and technological conditions and social welfare. A concise yet effective model with relatively fewer parameters was established by adopting water resources data from the Food and Agriculture Organization (FAO) and socioeconomic data from the World Bank. The results indicated that the WRCC of all five Central Asian countries showed an increasing trend with improved water use efficiency from 1995 to 2020. Kazakhstan's WRCC was significantly higher than the other four countries, reaching 54.03 million people in 2020. The water resources carrying index (WRCI) of the five Central Asian countries varied considerably, with the actual population sizes of Turkmenistan and Uzbekistan highly overloaded. Although there has been a decrease in Central Asian countries’ WRCI between 1995 and 2020, water resources utilization problems in the region remain prominent. Based on the water resources carrying capacity evaluation system, to increase available water resources and improve production water use efficiency are key to address these issues. In light of this, this study offers practical and feasible solutions at the policy level: (1) The implementation of signed multilateral agreements on transboundary water resources allocation must proceed through joint governmental efforts. (2) Investments in advancing science and technology need to be increased to improve water use efficiency in irrigation systems. (3) The output of water-intensive crops should be reduced. (4) The industrial structure could be further optimized so that non-agricultural uses are the primary drivers of gross domestic product (GDP) growth.

Yield forecasting can give early warning of food risks and provide solid support for food security planning. Climate change and land use change have direct influence on regional yield and planting area of maize, but few studies have examined their synergistic impact on maize production. In this study, we propose an analysis framework based on the integration of system dynamic (SD), future land use simulation (FLUS) and a statistical crop model to predict future maize yield variation in response to climate change and land use change in a phaeozem region of central Jilin province, China. The results show that the cultivated land is likely to reduce by 862.84 km² from 2030 to 2050. Nevertheless, the total maize yield is expected to increase under all four RCP scenarios due to the promotion of per hectare maize yield. Among the scenarios, RCP4.5 is the most beneficial to maize production, with a doubled total yield in 2050. Notably, the yield gap between different counties will be further widened, which necessitates the differentiated policies of agricultural production and farmland protection, e.g., strengthening cultivated land protection and crop management in low-yield areas, and taking adaptation and mitigation measures to coordinate climate change and production.

Effective soil particle size composition can more realistically reflect the particle size sorting process of erosion. To reveal the individual contributions of rainfall intensity and slope to splash erosion, and to distinguish the enrichment ratio of each size and the critical size in splash, loessial soil collected on the Loess Plateau in May 2019 was tested under different rainfall intensities (60, 84, 108, 132, 156 mm h^-1) and slopes (0°, 5°, 10°, 15°, 20°). The results demonstrated that 99% of splash mass was concentrated in 0-0.4 m. Rainfall intensity was the major factor for splash according to the raindrop generation mode by rainfall simulator nozzles. The contributions of rainfall intensity to splash erosion were 82.72% and 93.24%, respectively in upslope and downslope direction. The mass percentages of effective clay and effective silt were positively correlated with rainfall intensity, while the mass percentages of effective very fine sand and effective fine sand were negatively correlated with rainfall intensity. Opposite to effective very fine sand, the mass percentages of effective clay significantly decreased with increasing distance. Rainfall intensity had significant effects on enrichment ratios, positively for effective clay and effective silt and negatively for effective very fine sand and effective fine sand. The critical effective particle size in splash for loessial soil was 50 μm.

We propose a theoretical framework for assessing the ecological benefits provided by key national ecological projects in China over the past 20 years. A dataset consisting of six primary indicators and nine secondary indicators of ecosystem structure, ecosystem quality, and ecosystem services for 2000-2019 was generated using ground survey and remote sensing data. Ecological benefits were quantitatively evaluated following the implementation of these projects in China. Areas with medium, relatively high, and high degrees of ecological restoration accounted for 24.1%, 11.9%, and 1.7% of the national land, respectively. Degrees of ecological restoration were higher in areas with greater numbers of ecological projects. Areas with relatively and absolutely high degrees of ecological restoration were mainly concentrated in the Loess Plateau, the farming-pastoral zone of northern China, the Northeast China Plain, and an area spanning the borders of Sichuan, Yunnan, Guizhou, Chongqing, and Hunan. The relative contributions of climatic factors and ecological projects to changes in vegetation net primary productivity were 85.4% and 14.6%, respectively, and the relative contributions of climatic factors and ecological projects to changes in water erosion modulus were 69.5% and 30.5%, respectively. The restoration potential of national vegetation coverage was 20%, and the restoration potential percentage of forest and grassland vegetation coverage was 6.4% and 23%, respectively. Climatic conditions can inhibit ecological restoration. Areas with relatively high and high degrees of ecological restoration were mainly distributed in areas with an average annual temperature greater than 0°C and annual precipitation greater than 300 mm. Therefore, the limitations associated with climate conditions require consideration during the implementation of national ecological projects. The implementation of combined measures should be emphasized, and the benefits of ecological investment funds should be maximized.

Land subsidence is a geohazard phenomenon caused by the lowering of land elevation due to the compression of the sinking land soil body, thus creating an excessive constraint on the safe construction and sustainable development of cities. The use of accurate and efficient means for land subsidence prediction is of remarkable importance for preventing land subsidence and ensuring urban safety. Although the current time-series prediction method can accomplish relatively high accuracy, the predicted settlement points are independent of each other, and the existence of spatial dependence in the data itself is lost. In order to unlock this problem, a spatial convolutional long short-term memory neural network (ConvLSTM) based on the spatio-temporal prediction method for land subsidence is constructed. To this end, a cloud platform is employed to obtain a long time series deformation dataset from May 2017 to November 2021 in the understudied area. A convolutional structure to extract spatial features is utilized in the proposed model, and an LSTM structure is linked to the model for time-series prediction to achieve unified modeling of temporal and spatial correlation, thereby rationally predicting the land subsidence progress trend and distribution. The experimental results reveal that the prediction results of the ConvLSTM model are more accurate than those of the LSTM in about 62% of the understudied area, and the overall mean absolute error (MAE) is reduced by about 7%. The achieved results exhibit better prediction in the subsidence center region, and the spatial distribution characteristics of the subsidence data are effectively captured. The present prediction results are more consistent with the distribution of real subsidence and could provide more accurate and reasonable scientific references for subsidence prevention and control in the Beijing-Tianjin-Hebei region.

Glaciers in the Tianshan Mountains are an essential water resource in Central Asia, and it is necessary to identify their variations at large spatial scales with high resolution. We combined optical and SAR images, based on several machine learning algorithms and ERA-5 land data provided by Google Earth Engine, to map and explore the glacier distribution and changes in the Tianshan in 2001, 2011, and 2021. Random forest was the best performing classifier, and the overall glacier area retreat rate showed acceleration from 0.87%/a to 1.49%/a, while among the sub-regions, Dzhungarsky Alatau, Central and Northern/Western Tianshan, and Eastern Tianshan showed a slower, stable, and sharp increase rates after 2011, respectively. Glacier retreat was more severe in the mountain periphery, low plains and valleys, with more area lost near the glacier equilibrium line. The sustained increase in summer temperatures was the primary driver of accelerated glacier retreat. Our work demonstrates the advantage and reliability of fusing multisource images to map glacier distributions with high spatial and temporal resolutions using Google Earth Engine. Its high recognition accuracy helped to conduct more accurate and time-continuous glacier change studies for the study area.

The degradation of ecosystem structure and function on the Qinghai-Tibet Plateau is the result of a combination of natural and anthropogenic factors, with landscape change driven by global change and human activities being one of the major ecological challenges facing the region. This study analyzed the spatiotemporal characteristics of ecosystem services (ESs) and landscape patterns in eastern Qinghai province (EQHP) from 2000 to 2018 using multisource datasets and landscape indices. Three ecosystem service bundles (ESBs) were identified using the self-organizing map (SOM), and changes in ecosystem structure and function were analyzed through bundle-landscaped spatial combinations. The study also explored the interactions between ESs and natural and human factors using redundancy analysis (RDA). We revealed an increase in total ecosystem service in the EQHP from 1.59 in 2000 to 1.69 in 2018, with a significant change in landscape patterns driven by the conversion of unused land to grassland in the southwest. Forestland, grassland, and unused land were identified as important to the supply of ESs. In comparison to human activities, natural environmental factors were found to have a stronger impact on changes in ESs, with vegetation, meteorology, soil texture, and landscape composition being the main driving factors. However, the role of driving factors within different ESBs varied significantly. Exploring the response of ecosystem services to changes in landscape patterns can provide valuable insights for achieving sustainable ecological management and contribute to ecological restoration efforts.

Climate change and human activities have profoundly altered ecosystem services in the Yellow River Basin (YRB) since the Grain for Green project was implemented, but have not been accurately revealed on a year-by-year scale. This study combined the InVEST model to reveal the year-by-year changes in the water-related ecosystem services (WRESs) in YRB during 1990-2020, including water yield, soil conservation and water purification services. The trade-off/synergy of WRESs and impacts of land management measures on WRESs were assessed fully. The results showed that from 1990 to 2020, cropland and barren land were considerably converted to forest and grassland in YRB. WRESs were continuously improved as a result of increase of water yield and reductions of soil export and nitrogen export, at rates of +1.11 mm·yr^-1, -0.23 t·km^-2·yr^-1 and -1.01 kg·km^-2·yr^-1, respectively. We found that in YRB water purification service showed trade-off relationships with soil conservation and water yield services in recent decades, and water yield and soil conservation maintained a synergitic effect. Additionally, the revegetation measures showed a potential of enhancing soil conservation and water purification, but reducing water yield. This study provided a thorough understanding of WRESs dynamics and a valuable reference for the ecological restoration practices.

Under the background that economy and urbanization of China are gradually entering the stage of high-quality development, clarifying the influence of economic development on urban livability is of significant academic and practical value. In this paper, regarded as one “factor”, livability was introduced into the research framework of production function, and a theoretical model of the impact of economic development on urban livability was established. Based on the panel data of 40 cities in China from 2005 to 2019, the System GMM, panel threshold model and other methods were further adopted to carry out an empirical analysis. The results show that: (1) The livability level of large and medium-sized cities in China from 2005 to 2019 has been rising generally, but they present obvious characteristics of dimensional and spatial differentiation. (2) In general, economic development has an inhibiting effect on the improvement of urban livability, but this logical effect shows obvious heterogeneity in different time periods and diverse city scales. This inhibitory effect is more significant for the cities before entering the new normal phase of economy, and large-scale municipalities and economically-developed provincial capitals (namely Class-A cities). (3) There are significant threshold effects in the impact of economic development on urban livability, where the threshold variables are income level and economic development. With the increase of city dwellers’ income, this effect presents an inverted N-shaped nonlinear feature. When the development of economy makes the average wage of employees between 60,000 and 80,000 yuan, economic development can significantly improve urban livability. Also, there is a significant single threshold inhibitory effect when economic development is taken as a threshold variable. However, its negative impact shows a law of diminishing marginal efficiency. In addition, a similar threshold effect is found in smaller-scale Class-B cities. The findings of this research can provide some insights for urban planners and policymakers in both China and vast developing countries to understand better the relationship between economic development and urban livability. Finally, according to the research findings, we proposed the corresponding policy enlightenment from both “macro guidance” and “micro action”.

Rural decline is a global issue accompanied by the regional imbalanced development and dysfunction in rural areas. Coordinated interaction among production, living, and ecological functions is essential for the sustainability of rural regional systems. Based on the framework of “element-structure-function”, an indicator system was constructed to explore the evolution characteristics and driving factors of rural regional functions in the farming-pastoral ecotone of northern China (FPENC) using the models of entropy-based TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), revised vertical and horizontal comparison, and GeoDetector. The results indicated a gradual synergy of rural production, living, and ecological functions during the period 2000-2020. Improvements were observed in production and living functions, and higher ecological function was found in Hebei, Inner Mongolia, Liaoning, and Shaanxi. However, conflicts between ecological function and production and living functions were evident in Shanxi, Gansu, and Ningxia. The spatial structure played a dominant role in determining rural production, living, and ecological functions, with ratios of 38%, 56%, and 84%, respectively. Land and industry emerged as the main driving factors influencing the evolution of rural regional functions. Notably, combined interactions of rural permanent population and primary industry output (0.73), grassland area and tertiary industry output (0.58), and forest area and tertiary industry output (0.72) were responsible for the changes observed in rural production, living, and ecological functions, respectively. The findings suggest that achieving coordinated development of rural regional functions can be accomplished by establishing differentiated rural sustainable development strategies that consider the coupling of population, land, and industry in FPENC.

The leaf area index (LAI) shows a significant increasing trend from global to regional scales, which is known as greening. Greening will further enhance photosynthesis, but it is unclear whether the contribution of greening has exceeded the CO₂ fertilization effect and become the dominant factor in the gross primary productivity (GPP) variation. We took the Yangtze River Delta (YRD) of China, where cropland and natural vegetation are significantly greening, as an example. Based on the boreal ecosystem productivity simulator (BEPS) and Revised-EC-LUE models, the GPP in the YRD from 2001 to 2020 was simulated, and attribution analysis of the interannual variation in GPP was performed. In addition, the reliability of the GPP simulated by the dynamic global vegetation model (DGVM) in the area was further investigated. The research results showed that GPP in the YRD had three significant characteristics consistent with LAI: (1) GPP showed a significant increasing trend; (2) the multiyear mean and trend of natural vegetation GPP were higher than those of cropland GPP; and (3) cropland GPP showed double-high peak characteristics. The BEPS and Revised-EC-LUE models agreed that the effect of LAI variation (4.29 Tg C yr^-1 for BEPS and 2.73 Tg C yr^-1 for the Revised-EC-LUE model) determined the interannual variation in GPP, which was much higher than the CO₂ fertilization effect (2.29 Tg C yr^-1 for BEPS and 0.67 Tg C yr^-1 for the Revised-EC-LUE model). The GPP simulated by the 7 DGVMs showed a huge inconsistency with the GPP estimated by remote sensing models. The deviation of LAI simulated by DGVM might be a potential cause for this phenomenon. Our study highlights that in significant greening areas, LAI has dominated GPP variation, both spatially and temporally, and DGVM can correctly simulate GPP only if it accurately simulates LAI variation.

Rural decline has become a global problem. To address this issue, the division of rural functions and identification of driving factors are important means of rural revitalization. Taking the town area as a unit, this study conducts a division and evolution analysis of rural regional functions in Jiangsu province in coastal China by constructing an evaluation system using the spatial econometric model to diagnose endogenous and exogenous driving factors of rural multifunction formation. The results show that the functions of agricultural supply and ecological conservation have decreased, while the functions of economic development and social security have increased. Agricultural production functions are concentrated in northern and central Jiangsu. The economic development function is mainly based on industrial development, and is the strongest in southern Jiangsu. Social security functions are concentrated in suburban area, county centers, and key towns. High-value areas of ecological conservation are concentrated along lakes, the coast, and hilly areas of southern Jiangsu. The multifunctional development of villages and towns is affected by endogenous and exogenous factors, including economic geographic location, natural resources, economic foundation, human capital, traffic conditions, market demand, infrastructure, and environmental governance. Natural factors have a significant impact on the supply of agricultural products and the formation of ecological conservation functions. The effects of socioeconomic factors on these four functions differ significantly. This study expands the theory of rural development functions, the classification and zoning paradigm, and the quantitative study of driving mechanisms. The results provide a reference for practical value and policy significance for the reconstruction of rural functions and rural revitalization.

Changes in surface temperature extremes have become a global concern. Based on the daily lowest temperature (TN) and daily highest temperature (TX) data from 2138 weather stations in China from 1961 to 2020, we calculated 14 extreme temperature indices to analyze the characteristics of extreme temperature events. The widespread changes observed in all extreme temperature indices suggest that China experienced significant warming during this period. Specifically, the cold extreme indices, such as cold nights, cold days, frost days, icing days, and the cold spell duration index, decreased significantly by −6.64, −2.67, −2.96, −0.97, and −1.01 days/decade, respectively. In contrast, we observed significant increases in warm extreme indices. The number of warm nights, warm days, summer days, tropical nights, and warm spell duration index increased by 8.44, 5.18, 2.81, 2.50, and 1.66 d/decade, respectively. In addition, the lowest TN, highest TN, lowest TX, and highest TX over the entire period rose by 0.47, 0.22, 0.26, and 0.16°C/decade, respectively. Furthermore, using Pearson’s correlation and wavelet coherence analyses, this study identified a strong association between extreme temperature indices and atmospheric circulation factors, with varying correlation strengths and resonance periods across different time-frequency domains.

The mass elevation effect (MEE) is a thermal effect, in which heating produced by long wave radiation on a mountain surface generates atmospheric uplift, which has a profound impact on the hydrothermal conditions and natural geographical processes in mountainous areas. Based on multi-source remote sensing data and field observations, a spatial downscaling inversion of temperature in the Tianshan Mountains in China was conducted, and the MEE was estimated and a spatio-temporal analysis was conducted. The GeoDetector model (GDM) and a geographically weighted regression (GWR) model were applied to explore the spatial and temporal heterogeneity of the study area. Four key results can be obtained. (1) The temperature pattern is complex and diverse, and the overall temperature presented a pattern of high in the south and east, but low in the north and west. There were clear zonal features of temperature that were negatively correlated with altitude, and the temperature difference between the internal and external areas of the mountains. (2) The warming effect of mountains was prominent, and the temperature at the same altitude increased in steps from west to east and north to south. Geomorphological units, such as large valleys and intermontane basins, weakened the latitudinal zonality and altitudinal dependence of temperature at the same altitude, with the warming effect of mountains in the southern Tianshan Mountains. (3) The dominant factors affecting the overall pattern of the MEE were topography and location, among which the difference between the internal and external areas of the mountains, and the absolute elevation played a prominent role. The interaction between factors had a greater influence on the spatial differentiation of mountain effects than single factors, and there was a strong interaction between terrain and climate, precipitation, the normalized difference vegetation index (NDVI), and other factors. (4) There was a spatial heterogeneity in the direction and intensity of the spatial variation of the MEE. Absolute elevation was significantly positively correlated with the change of MEE, while precipitation and the NDVI were dominated by negative feedback. In general, topography had the largest effect on the macroscopic control of MEE, and coupled with precipitation, the underlying surface, and other factors to form a unique mountain circulation system and climate characteristics, which in turn enhanced the spatial and temporal heterogeneity of the MEE. The results of this study will be useful in the further analysis of the causes of MEE and its ecological effects.

Global extreme hydrological events pose considerable challenges to the sustainable development of human society and river ecology. Land use/cover change (LUCC) is a visible manifestation of human activity and has caused substantial alterations in extreme hydrological regimes across rivers worldwide. The Jinsha River lies upstream of the Yangtze River and its hydrological variability has had profound socioeconomic and environmental effects. In this study, we developed Hydrological Simulation Program-FORTRAN (HSPF) and land-use simulation models of the entire watershed to simulate the effects of LUCC on hydrological extremes and quantify the inter-relationships among them. The main land-use changes between 1995 and 2015 were those associated with cropland, forest land, and grassland. Between 2015 and 2030, it is estimated that the coverage of forest land, grassland, construction land, and unused land will increase by 0.64%, 0.18%, 69.38%, and 45.08%, respectively, whereas that of cropland, water bodies, and snow- and ice-covered areas will decline by 8.02%, 2.63%, and 0.89%, respectively. LUCC has had irregular effects on different hydrological regimes and has most severely altered stream flows. The responses of hydrological extremes to historical land-use change were characterized by spatial variation. Extreme low flows increased by 0.54%-0.59% whereas extreme high flows increased by 0%-0.08% at the lowest outlet. Responses to future land-use change will be amplified by a 0.72%-0.90% reduction in extreme low flows and a 0.08%-0.12% increase in extreme high flows. The hedging effect caused by irregular changes in tributary stream flow was found to alleviate the observed flow in mainstream rivers caused by land-use change. The extreme hydrological regimes were affected mainly by the net swap area transferred from ice and snow area to forest (NSAIF) and thereafter to cultivated land (NSAIC). Extreme low flows were found to be positively correlated with NSAIF and NSAIC, whereas extreme high flows were positively correlated with NSAIC and negatively correlated with NSAIF.

The two concepts of “liudong renkou (floating population or FP)” and “renkou liudong (mobility of the floating population or MOFP)”, along with relevant data based on these two concepts, have long been used extensively in China’s research and policy making, playing a central role in Chinese studies of migration. Unlike the concepts of “migrant” and “migration” in the international literature, which are focused on people’s spatial mobility, “liudong renkou” and “renkou liudong” are identified and measured by the separation of one’s place of residence from one’s place of household registration (hukou), an approach inconsistent with relevant international practices. By analyzing various census data and data from the China Migrant Dynamic Survey (CMDS), this article examines the validity and reliability of these two concepts and the data based on them in the international context, revealing that they have become increasingly invalid and unreliable for the purpose of measuring migration events since China’s reform and opening up in the late 1970s. The results further demonstrate that these two concepts and the data based on them have become increasingly detached from real migration events and processes. They may become invalid by overestimating the volume of the mobile population, ineffective due to systematic omission of certain mobile populations (such as urban-urban migrants), or misleading as to the changing direction of migration flows. In addition, data on the floating population cannot be used to calculate migration rates and are not comparable in the international context. The concepts of “liudong renkou” and “renkou liudong” and data based on these two concepts may still need to be used in China for a long period of time due to the continuing existence of the hukou system and its roles in the provision of public services, social welfare and social security. However, we argue that concepts, measurements, and methods of data collection in research on migration in China should be gradually shifted to and focused on migrations as spatial events; further, transition data, based on an individual’s residence five years ago and one year ago, should be gradually adopted as the main data source and included in the short form of future censuses; additionally, migration event data based on population registration and administrative records should be used more fully, so that China’s migration research can be conducted on the solid basis of valid and reliable data sources.

Climate change is manifesting rapidly in the form of fires, droughts, floods, resource scarcity, and species loss, and remains a global risk. Owing to the disaster risk management, there is a need to determine the Dead Fuel Index (DFI) threshold of the fire occurrence area and analyze the spatio-temporal variation of DFI to apply prevention measures efficiently and facilitate sustainable fire risk management. This study used the MODIS Burned Area Monthly L3 (MCD64A1), Landsat Global Burned Area (BA) products, and MODIS Surface Reflectance 8-Day L3 (MOD09A1) data from 2001 to 2020 to calculate the values of the DFI in the study area before the occurrence of fire. The results showed that: (1) The inversion of the meadow steppe DFI values in the fire area was distributed in the range of 14-26, and the fire rate was the highest in the range of 20-22. The inversion of the typical steppe DFI values in the fire area was distributed in the range of 12-26, and the fire rate was the highest in the range of 16-22. (2) Areas with high fire DFI values included Khalkhgol, Matad, Erdenetsagaan, Bayandun, Gurvanzagal, Dashbalbar in Mongolia, and scattered areas of the Greater Khingan Mountains (forest edge meadow steppe area), East and West Ujumqin Banner, and Xin Barag Right Banner. The highest fire probability of fire occurred during October and April. (3) The DFI values were sensitive to changes in altitude. The results of this study may provide useful information on surface energy balance, grassland carbon storage, soil moisture, grassland health, land desertification, and grazing in the study area, especially for fire risk management.

There is a great uncertainty in generation and formation of non-point source (NPS) pollutants, which leads to difficulties in the investigation of monitoring and control. However, accurate calculation of these pollutant loads is closely correlated to control NPS pollutants in agriculture. In addition, the relationships between pollutant load and human activity and physiographic factor remain elusive. In this study, a modified model with the whole process of agricultural NPS pollutant migration was established by introducing factors including rainfall driving, terrain impact, runoff index, leaching index and landscape intercept index for the load calculation. Partial least squares path modeling was applied to explore the interactions between these factors. The simulation results indicated that the average total nitrogen (TN) load intensity was 0.57 t km^-2 and the average total phosphorus (TP) load intensity was 0.01 t km^-2 in Chengdu Plain. The critical effects identified in this study could provide useful guidance to NPS pollution control. These findings further our understanding of the NPS pollution control in agriculture and the formulation of sustainable preventive measures.

Transpiration (Tc) is a critical component of the global water cycle. Soil moisture (SM) and vapor pressure deficit (VPD) are key regulators of Tc, and exploring their contributions to changes in Tc can deepen our understanding of the mechanisms of water cycling in terrestrial ecosystems. However, the driving roles of VPD and SM in Tc changes remain debated because of the coupling of SM and VPD through land-atmosphere interactions which restrict the quantification of the independent effects of SM and VPD on Tc. By decoupling the correlations between SM and VPD using a novel binning approach, this study analyzed the dominant drivers of vegetation transpiration in subtropical China from 2003 to 2018 based on multi-source data, including meteorological reanalysis, remotely sensed soil moisture, transpiration, and land cover data. The results show that Tc first increased and then remained stable with an increase in SM across the study area but changed slightly with increasing VPD. Overall, the relative contribution of SM to the change in Tc was approximately five times that of VPD. The sensitivities of Tc to SM and VPD differed among vegetation types. Although the sensitivity of Tc to SM was greater than that of VPD for all four vegetation types, the thresholds of Tc in response to SM were different, with the lowest threshold (approximately 35%) for the other forests and the highest threshold (approximately 55% ) for short wood vegetation. We infer that this is associated with the differences in ecological strategies. To verify the reliability of our conclusions, we used solar- induced chlorophyll fluorescence (SIF) data as a proxy for Tc based on the tight coupling between photosynthesis and transpiration. Consistent results were obtained by repeating the analyses. The results of this study, in which the impacts of SM and VPD on Tc were decoupled, are beneficial for further understanding the critical processes involved in water cycling in terrestrial ecosystems in response to climate change.

Since the Bonn 2011 conference, the “water-energy-food” (WEF) nexus has aroused global concern to promote sustainable development. The WEF nexus is a complex, dynamic, and open system containing interrelated and interdependent elements. However, the nexus studies have mainly focused on natural elements based on massive earth observation data. Human elements (e.g., society, economy, politics, culture) are described insufficiently, because traditional earth observation technologies cannot effectively perceive socioeconomic characteristics, especially human feelings, emotions, and experiences. Thus, it is difficult to simulate the complex WEF nexus. With the development of earth observation sensor technologies and human activity perception methods, geographical big data covering both human activities and natural elements offers a new opportunity for in-depth WEF nexus analysis. This study proposes a five-step framework by leveraging geographical big data mining to dig for the hidden value in the data of various natural and human elements. This framework can enable a thorough and comprehensive analysis of the WEF nexus. Some application examples of the framework, major challenges, and possible solutions are discussed. Geographical big data mining is a promising approach to enhance the analysis of the WEF nexus, strengthen the coordinated management of resources and sectors, and facilitate the progress toward sustainable development.

The transition of human societies from high mobility to sedentary lifestyles had a profound impact on subsistence, technology, and the origin of civilization. Sedentism was influenced by various factors such as climate change, population growth, resource pressure, and technological innovation. The Tibetan Plateau, due to its alpine and hypoxic conditions, is an ideal region to study human adaptation to extreme environments. However, the prehistoric process of sedentism on the Tibetan Plateau is unclear and the chronological sequence and driving mechanism of sedentism on the Tibetan Plateau are still controversial. Previous studies have focused on the diffusion of agriculture from low to high elevation areas, with little attention given to the role of animal resources in sedentism. Seasonality analysis using animal remains is crucial in determining whether a site was occupied year-round. To establish the seasonal calendar of animal resource utilization, it is recommended to create a database of skeletal morphology, whole genome, and proteome of contemporary Tibetan Plateau fauna to aid in the identification of animal remains from archaeological sites. Thus, intricate web of human-animal-environment relationship and the role of animal resources in human sedentism on the Tibetan Plateau can then be evaluated.

Urban-rural integration is an advanced form resulting from the future evolution of urban-rural relationships. Nevertheless, little research has explored whether urban and rural areas can move from dual segmentation to integrated development from a theoretical or empirical perspective. Based on the research framework of welfare economics, which offers an appealing paradigm to frame the underlying game between cities and villages, this study clarifies the ideal state of urban-rural integration. It then proposes a series of basic assumptions, and constructs a corresponding objective function and its constraints. Moreover, it assesses the possibility of seeing the transmutation from division to integration between urban and rural areas with continuous socio-economic development. The authors argue that the ideal state of urban-rural integration should be a Pareto-driven optimal allocation of urban-rural resources and outputs, and the maximization of social welfare in the entire region. Based on a systematic demonstration using mathematical models, the study proposes that urban and rural areas can enter this ideal integrated development pattern when certain parameter conditions are met. In general, this study demonstrates the theoretical logic and scientific foundations of urban-rural integration, enriches theoretical studies about urban-rural relationships, and provides basic theoretical support for large developing countries to build a coordinated and orderly urban-rural community with a shared future.

In recent years, the phenomenon of abandonment of cultivated land in mountainous areas has occurred frequently, and the problem of abandonment has become a focus of attention of government agricultural departments and academic circles. However, few studies have paid attention to the impact of differences in labor transfer on the abandonment behavior of farmers’ terraced fields. Based on this, this paper takes the terraced fields of Hunan, Fujian and Jiangxi provinces as the research area, combined with data from 1438 farmer households, and uses the Probit and Tobit models to analyzed the impact of the difference in the quantity, distance and quality of labor off-farm transfer on the decision-making and scale of terraced field abandonment of farmer households. The results show that: (1) The greater the quantity of labor transfer, the farther the transfer distance and the higher the quality of non-agricultural employment, can promote the decision of farmers to abandon terraced fields; (2) With the deepening of the degree of non-agricultural transfer, the scale of abandoned terraced fields by farmers in hilly and mountainous areas will also expand; (3) The distance and quality of labor transfer can strengthen the impact of labor transfer quantity on farmers’ decision to abandon land. To alleviate the phenomenon of abandoned terraced fields, the government should pay attention to the labor substitution role of agricultural service outsourcing and mechanization in mountainous areas. The government should actively promote the transformation of terraced fields into mechanized farming, improve the cultivated land transfer market, and encourage farmers to transfer terraced fields.

Accurately identifying the dominant factor of karst ecosystem services (ESs) is a prerequisite for the rocky desertification control. However, the explanatory power of environmental factors on the spatial distribution of ESs is affected by scaling, and the quantitative research on the scale effect still needs to be further strengthened. This study used the geographical detector to access the explanatory power of environmental factors on soil erosion and water yield at different spatial resolutions, and then explored its differences in three geomorphological-type areas. Results showed that slope and vegetation coverage were the dominant factors of soil erosion, and the interactive explanatory power between the two factors was stronger. Affected by the universality of topographic relief and landscape fragmentation in the study area, the explanatory power of slope and land use type on soil erosion was optimal at low resolution. Precipitation, elevation, and land use type were the dominant factors for the spatial heterogeneity of water yield, and the interaction between precipitation and land use type explained more than 95% of water yield. The spatial variability of elevation in different geomorphological-type areas affected its optimal explanatory power, specifically, in the terrace and hill-type areas, the spatial variability of elevation was weak, its explanatory power was optimal at high resolution. While in the mountain-type areas, the spatial variability of elevation was strong, and its explanatory power was optimal at low resolution. This study quantitatively identified the optimal explanatory power of ES variables through multi-scale analysis, which aims to provide a way and basis for accurate identification of the dominant factors of karst mountain ESs and zoning optimization.

Elucidating the distribution of the grazing pressure requires an understanding of the grazing activities. In this study, we analyzed the grazing behavior of yaks in Three-River- Source Region (TRSR) and identified the main factors influencing the distribution of grazing intensity (GI) using trajectory data and remote sensing datasets. Our results revealed that a semi-resident transhumance strategy is employed in this region. The average grazing time (GT) of four GPS collars over the year was 11.84 h/day (N6), 11.01 h/day (N11), 9.25 h/day (N18), and 11.61 h/day (N24). GT was generally higher in warm seasons (summer and autumn) than in cold seasons (spring and winter). The average daily moving speed was found to be closely related to the pasture size of different herders and the seasons. Geodetector analysis identified the distance to camp (DOC) as the most important single factor influencing the distribution of GI, explaining up to 52% of the GI variations. However, relying solely on this factor may not accurately depict the actual GI distribution. When pairwise factors interacted, the explanatory power of the model increased, ranging from 34.55% to 63.26%. Our study highlights the importance of considering multiple factors when predicting grazing intensity, as grazing activities tend to cluster near settlements, but other factors may also be influential.

Despite recent progress in assessing future population exposure, few studies have focused on the exposure of certain vulnerable groups, such as working people. Working in hot environments can increase the heat-related risk to human health and reduce worker productivity, resulting in broad social and economic implications. Based on the daily climatic simulations from the Coupled Model Intercomparison Project phase 6 (CMIP6) and the age group-specific population projections, we investigate future changes in working-age population exposure to heat extremes under multiple scenarios at global and continental scales. Projections show little variability in exposure across scenarios by mid-century (2031-2060), whereas significantly greater increases occur under SSP3-7.0 for the late century (2071-2100) compared to lower-end emission scenarios. Global exposure is expected to increase approximately 2-fold, 6-fold and 16-fold relative to the historical time (1981-2010) under SSP1-2.6, SSP2-4.5 and SSP3-7.0, respectively. Asia will have the largest absolute exposure increase, while in relative terms, the most affected region is Africa. At the global level, future exposure increases are primarily caused by climate change and the combined effect of climate and working-age population changes. Climate change is the dominant driver in enhancing future continental exposure except in Africa, where the main contributor is the combined effect.

Social upgrading does not inherently follow economic upgrading; rather, they present a complex interplay. This paper, focusing on China, utilizes the coupling coordination degree and a panel regression model to shed light on the intricate interaction between social upgrading and economic upgrading. It is found that the coupling coordination degree of social and economic upgrading in China has improved from 0.33 to 0.49 since the mid-1990s, undergoing a shift from a stage of slight imbalance to low-level coordination. However, significant regional disparities are present in terms of economic upgrading, social upgrading, and their coupling coordination degree. Developed areas exhibit a higher degree of coupling coordination compared to less developed regions, indicating a connection between the coupling coordination degree and the level of economic growth. Economic globalization, public governance, and the legal environment positively impact the coupling coordination between social and economic upgrading, while economic privatization and corporate violations of law exert negative effects. The paper concludes with policy discussions for enhancing the coupling coordination between social and economic upgrading.