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

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.

Exploring the spatial heterogeneity of ecosystem services (ESs) and their driving factors under various interaction patterns is essential for informing sustainable development policies. Using Northeast China as a case study, this research investigates eight key ESs, including water yield (WY), carbon storage (CS), food provision (FP), habitat quality (HQ), soil conservation (SC), wind-break and sand-fixation (WS), water purification (WP) and aesthetic landscape (AL). The study examines the complexity of ESs from three dimensions: individual ES, ES pairs and ES bundles, and further evaluates their spatial heterogeneity and socio- ecological drivers. The results indicate that the spatial distribution of ESs remained relatively stable from 2000 to 2020. During this period, WY and FP increased significantly, CS and HQ remained relatively unchanged. SC, WS and AL followed an “increase-decrease-increase” trend, and WP exhibited a “decrease-increase” fluctuation. Overall, synergistic relationships among ES Pairs were more prevalent than trade-offs. Notably, CS showed trade-offs with over 70% of the other ESs, while HQ exhibited trade-offs with SC, WS, WP, and AL. The FP-HQ synergy bundle, primarily located in the Greater Hinggan Mountains and eastern regions, emerged as the dominant ES bundle. Ecological factors—such as solar radiation, temperature, slope, DEM, and NDVI—exerted a stronger influence on ES patterns than social factors like GDP and population density. Furthermore, these ecological drivers had a greater impact on individual ESs compared to ES pairs or ES bundles. These findings offer valuable insights for policymakers to understand the complex interrelationships among ESs and to design more effective and regionally tailored management strategies.

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.

Wetlands are important natural resources for humans and play an irreplaceable ecological function in the terrestrial ecosystem. To curb the continued loss of wetlands globally, international organizations and many countries have taken a series of major conservation and restoration measures. This work reviews these wetland conservation and restoration measures, interprets China’s wetland conservation and restoration management policies, and proposes that future research on wetland resources in China should be conducted from the aspects of international frontiers and national strategic plans, socioeconomics, and smart services. The results show that the 27 International Wetlands Days from 1997 to 2023 provided new goals and tasks for the protection and management of wetlands. The important topics and outcomes of the 14 Conferences of the Contracting Parties to the Convention on Wetlands from 1980 to 2022 provided new directions and new challenges for wetland development. In the future, we should enhance wetland ecological functions, promote sustainable wetland development, and overcome the technical bottleneck of fragile wetland ecosystem restoration. From 1992 to 2022, China embarked on a new phase of wetland protection and restoration. The overall experience of wetland protection and restoration in China has been formed through national strategic deployment, legal policy establishment, and project planning and implementation. The needs to provide for and plan the long-term protection of wetlands at the national level, to innovate restoration and management techniques and application systems, and to effectively address the complex issues of wetland protection and restoration through collaborative division of labor among multiple departments were emphasized. Research on the future trends of wetlands should be directed towards the exploration and practice of the United Nations Sustainable Development Goals and several international conventions in support of sustainable wetland development. Wetland protection, restoration, and management services should be promoted for national strategic needs and local, high-quality social and economic development. In addition, research on cross-integration and academic innovation should be enhanced for disciplinary development, global supervision, comprehensive assessment, and smart decision making.

Glaciers are considered to be ‘climate-sensitive indicators’ and ‘solid reservoirs’, and their changes significantly impact regional water security. The mass balance (MB) from 2011 to 2020 of the Qiyi Glacier in the northeast Tibetan Plateau is presented based on field observations. The glacier showed a persistent negative balance over 9 years of in-situ observations, with a mean MB of −0.51 m w.e. yr⁻¹. The distributed energy-mass balance model was used for glacier MB reconstruction from 1980 to 2020. The daily meteorological data used in the model were from HAR v2 reanalysis data, with automatic weather stations located in the middle and upper parts of the glacier used for deviation correction. The average MB over the past 40 years of the Qiyi Glacier was −0.36 m w.e. yr⁻¹ with the mass losses since the beginning of the 21st century, being greater than those in the past. The glacier runoff shows a significant increasing trend, contributing ~81% of the downstream river runoff. The albedo disparity indicates that the net shortwave radiation is much higher in the ablation zone than in the accumulation zone, accelerating ablation-area expansion and glacier mass depletion. The MB of the Qiyi Glacier is more sensitive to temperature and incoming shortwave radiation variation than precipitation. The MB presented a non-linear reaction to the temperature and incoming shortwave radiation. Under future climate warming, the Qiyi Glacier will be increasingly likely to deviate from the equilibrium state, thereby exacerbating regional water balance risks. It is found that the mass losses of eastern glaciers are higher than those of western glaciers, indicating significant spatial heterogeneity that may be attributable to the lower altitude and smaller area distribution of the eastern glaciers.

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.

In the context of global warming, escalating water cycles have led to a surge in drought frequency and severity. Yet, multidecadal fluctuations in drought and their multifaceted influencing factors remain inadequately understood. This study examined the multidecadal changes in drought characteristics (frequency, duration, and severity) and their geographical focal points within China’s north-south transitional zone, the Qinling-Daba Mountains (QDM), from 1960 to 2017 using the Standardized Precipitation Evapotranspiration Index (SPEI). In addition, a suite of eight scenarios, correlation analysis, and wavelet coherence were used to identify the meteorological and circulation factors that influenced drought characteristics. The results indicate the following: (1) From 1960 to 2017, the QDM experienced significant interdecadal variations in drought frequency, duration, and severity, the climate was relatively humid before the 1990s, but drought intensified thereafter. Specifically, the 1990s marked the period of the longest drought duration and greatest severity, while the years spanning 2010 to 2017 experienced the highest frequency of drought events. (2) Spatially, the Qinling Mountains, particularly the western Qinling Mountain, exhibited higher drought frequency, duration, and severity than the Daba Mountains. This disparity can be attributed to higher rates of temperature increase and precipitation decrease in the western Qinling Mountain. (3) Interdecadal variations in droughts in the QDM were directly influenced by the synergistic effects of interdecadal fluctuations in air temperature and precipitation. Circulation factors modulate temperature and precipitation through phase transitions, thereby affecting drought dynamics in the QDM. The Atlantic Multidecadal Oscillation emerges as the primary circulation factors influencing temperature changes, with a mid-1990s shift to a positive phase favoring warming. The East Asian Summer Monsoon and El Niño-Southern Oscillation are the main circulation factors affecting precipitation changes, with positive phase transitions associated with reduced precipitation, and vice versa for increased precipitation.

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

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

The Yellow River Basin (YRB) is a vital ecological zone in China owing to its sensitive and fragile environment. Under the long-term influence of climate changes and artificial factors, the relationship between precipitation, vegetation, and surface water in the YRB has changed drastically, ultimately affecting the water resources and environmental management. Therefore, we applied multivariate statistical analysis to investigate the precipitation, normalized difference vegetation index (NDVI), and surface water changes in the YRB from 2000 to 2021. Furthermore, we attempted to clarify the ecological effects of precipitation by explaining the relationship between precipitation and vegetation in terms of the time-lag relationship using the Integrated Multi-satellite Retrievals for Global Precipitation Measurement algorithm, Moderate Resolution Imaging Spectroradiometer, and hydrological databases. Precipitation, vegetation, and area of surface water in the YRB showed increasing trends from 2000-2021 (e.g., 7.215 mm/yr, 0.004 NDVI/yr, and 0.932 km²/yr, respectively). The water level in the upper reaches of the YRB showed a downward trend, whereas that in the middle and lower reaches exhibited an upward trend. Changes in precipitation had a positive effect on vegetation and surface water in the YRB, with correlation coefficients of 0.63 and 0.51, respectively. The responses of NDVI and surface water elevation to precipitation were heterogeneous and delayed, with the majority showing a lag time of approximately ≤ 16 days. Moreover, the lag times of Longyangxia Lake and Ngoring-Co Lake were 0 and 8 days, respectively. We showed that precipitation variability can effectively explain vegetation improvement and increases in surface water elevation, while providing a proven scenario for predicting the surface water and vegetation productivity under the influence of climate change.

The Qinba Mountains are climatically and ecologically recognized as the north- south transitional zone of China. Analysis of its phenology is critical for comprehending the response of vegetation to climatic change. We retrieved the start of spring phenology (SOS) of eight forest communities from the MODIS products and adopted it as an indicator for spring phenology. Trend analysis, partial correlation analysis, and GeoDetector were employed to reveal the spatio-temporal patterns and climatic drivers of SOS. The results indicated that the SOS presented an advance trend from 2001 to 2020, with a mean rate of -0.473 d yr^-1. The SOS of most forests correlated negatively with air temperature (TEMP) and positively with precipitation (PRE), suggesting that rising TEMP and increasing PRE in spring would forward and delay SOS, respectively. The dominant factors influencing the sensitivity of SOS to climatic variables were altitude, forest type, and latitude, while the effects of slope and aspect were relatively minor. The response of SOS to climatic factors varied significantly in space and among forest communities, partly due to the influence of altitude, slope, and aspect.

In this study, the interplay between ecosystem services and human well-being in Seni district, which is a pastoral region of Nagqu city on the Qinghai-Tibet Plateau, is investigated. Employing the improved InVEST model, CASA model, coupling coordination model, and hierarchical clustering method, we analyze the spatiotemporal patterns of ecosystem services, the levels of resident well-being levels, and the interrelationships between these factors over the period from 2000 to 2018. Our findings reveal significant changes in six ecosystem services, with water production decreasing by 7.1% and carbon sequestration and soil conservation services increasing by approximately 6.3% and 14.6%, respectively. Both the habitat quality and landscape recreation services remained stable. Spatially, the towns in the eastern and southern areas exhibited higher water production and soil conservation services, while those in the central area exhibited greater carbon sequestration services. The coupling and coordination relationship between ecosystem services and human well-being improved significantly over the study period, evolving from low-level coupling to coordinated coupling. Hierarchical clustering was used to classify the 12 town-level units into five categories. Low subjective well-being townships had lower livestock breeding services, while high subjective well-being townships had higher supply, regulation, and support ecosystem services. Good transportation conditions were associated with higher subjective well-being in townships with low supply services. We recommend addressing the identified transportation disparities and enhancing key regulatory and livestock breeding services to promote regional sustainability and improve the quality of life for Seni district residents, thus catering to the diverse needs of both herdsmen and citizens.

Since China’s reform and opening-up in 1978, rapid urbanization has coincided with a surge in carbon emissions. Statistical, geospatial, and time-series analysis methods were utilized to examine the dynamic relationship between urbanization and carbon emissions over the past 43 years; elucidate the mechanisms through which dimensions of urbanization, such as population, land, economy, and green development, impact carbon emissions at various stages; and further explore the heterogeneity among cities of different scales. The analysis reveals that 2001 and 2011 represent significant turning points in China’s carbon emission growth “S” curve. The phase of rapid carbon emissions growth is associated with an increase in the urbanization rate from 40% to 50%, a shift in industrial structure from being dominated by secondary industry to tertiary industry, and a decrease in urban population density from 19,600 to 16,000 people per square kilometer of built-up area. Regions northeast of the “Bayannur-Ningde Line” have experienced rapid increases in carbon emissions, with large and medium-sized cities being the primary contributors nationwide. The TVP-VAR results indicate that higher urbanization rates have short-term carbon and mid- to long-term carbon-reducing effects. Population concentration in large cities facilitates short- to mid-term carbon reduction, whereas intensive urban development, industrial upgrading, and the promotion of clean energy use have sustained carbon-reducing effects. Carbon emissions exhibit path dependence. Increased urbanization rates in mega-cities and super-cities result in carbon-increasing effects, whereas the optimization of industrial structures exerts an inhibitory effect on carbon emissions in medium-sized and large cities. The changes in impulse response values of various variables are influenced by the developmental trajectory of Chinese cities from “small to large and then to agglomerations.” These recommendations indicate the necessity for differentiated emission reduction strategies contingent on the specific regions and types of cities in question.

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

Reference crop evapotranspiration (ET₀) is essential for determining crop water requirements and developing irrigation strategies. In this study, ET₀ was calculated via the FAO-56 Penman‒Monteith model, and the spatiotemporal variations in ET₀ over China from 1960 to 2019 were analyzed. We then quantified the contributions of five driving factors (air temperature, wind speed, relative humidity, sunshine hours, and CO₂ concentration) to the ET₀ trends via a detrending experiment. The results revealed that nationwide ET₀ showed no significant (p>0.05) decreasing trend from 1960 to 2019, with a trend of -8.56×10^-2 mm a^-2. The average temperature and wind speed were identified as the dominant factors affecting ET₀ trends at the national scale. The contributions of the driving factors to the ET₀ trends were ranked in the following order: average temperature (21.3%) > wind speed (-15.63%) > sunshine hours (-11.99%) > CO₂ concentration (6.36%) > relative humidity (3.58%). Spatially, the dominant factors influencing the ET₀ trends varied widely. In the southeastern region, average temperature and sunshine hours dominated the trends of ET₀, whereas wind speed and average temperature were the dominant factors in the northwestern region. The findings provide valuable insights into the dominant factors affecting ET₀ trends in China and highlight the importance of considering different driving factors in calculating crop water requirements.

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

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

Flood susceptibility modeling is crucial for rapid flood forecasting, disaster reduction strategies, evacuation planning, and decision-making. Machine learning (ML) models have proven to be effective tools for assessing flood susceptibility. However, most previous studies have focused on individual models or comparative performance, underscoring the unique strengths and weaknesses of each model. In this study, we propose a stacking ensemble learning algorithm that harnesses the strengths of a diverse range of machine learning models. The findings reveal the following: (1) The stacking ensemble learning, using RF-XGB- CB-LR model, significantly enhances flood susceptibility simulation. (2) In addition to rainfall, key flood drivers in the study area include NDVI, and impervious surfaces. Over 40% of the study area, primarily in the northeast and southeast, exhibits high flood susceptibility, with higher risks for populations compared to cropland. (3) In the northeast of the study area, heavy precipitation, low terrain, and NDVI values are key indicators contributing to high flood susceptibility, while long-duration precipitation, mountainous topography, and upper reach vegetation are the main drivers in the southeast. This study underscores the effectiveness of ML, particularly ensemble learning, in flood modeling. It identifies vulnerable areas and contributes to improved flood risk management.

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

Construction land expansion is a key driver of urbanization and industrialization, yet it poses the risk of losing farmland and cascading impacts on food supply. The spatial characteristics of farmland occupied by construction land and its association with grain yield in China were unclear. We analyzed the characteristics of farmland converted into construction land, and its relationship with grain yield in China for 2000-2020. Construction land increased in area in central and western regions of China, and farmland decreased in area in southeastern China. The expansion of construction land in the North China Plain, Northeast China Plain, and the Loess Plateau, occurred at the expense of farmland. Except the southeast coast of China, grain yield increase was only weakly dependent on farmland area. Patterns in which farmland was converted into construction land and grain-yield change were highly coupled in southeastern coastal China, Sichuan Basin, Shandong Peninsula, and the Hu Huanyong Line. It should be noted that expansion in construction land area does have some influence on grain production; ultimately it is greatly affected by yield per unit area.

To understand the spatio-temporal variability of precipitation (P) in the Third Pole region (centered on the Tibetan Plateau-TP), it is necessary to quantify the interannual periodicity of P and its relationship with large-scale circulations. In this study, Morlet wavelet transform was used to detect significant (p<0.05) periodic characteristics in P data from meteorological stations in four climate domains in the Third Pole, and to reveal the major large-scale circulations that triggered the variability of periodic P, in addition to bringing large amounts of water vapour. The wavelet transform results were as follows. (1) Significant quasi- periodicity varied from 2 to 11 years. The high-frequency variability mode (2 to 6 years quasi-periods) was universal, and the low-frequency variability mode (7 to 11 years quasi-periods) was rare, occurring mainly in the westerlies and Indian monsoon domains. (2) The majority of periods were base periods (53%), followed by two-base periods. Almost all stations in the Third Pole (95%) showed one or two periods. (3) Periodicity was widely detected in the majority of years (84%). (4) The power spectra of P in the four domains were dominated by statistically significant high-frequency oscillations (i.e., with short periodicity). (5) Large-scale circulations directly and indirectly influenced the periodic P variability in the different domains. The mode of P variability in the different domains was influenced by interactions between large-scale circulation features and not only by the dominant circulation and its control of water vapour transport. The results of this study will contribute to better understanding of the causal mechanisms associated with P variability, which is important for hydrological science and water resource management.

The joint study of agriculture and rural areas is of great significance for safeguarding agricultural development, revitalizing rural areas, and enhancing farmers’ well-being. This paper aims to assess the spatiotemporal evolution characteristics of the coupling and coordination degree of agricultural resilience and rural land use efficiency and their dynamic transfer law and driving mechanisms, based on panel data of 31 provinces (municipalities and autonomous regions) in China from 2010 to 2020. The results showed: (1) Good coupling and coordination of agricultural resilience and rural land use efficiency, with reduced temporal differentiation degrees between regions; (2) Significant spatial autocorrelation between the overall coupling and coordination degrees of agricultural resilience and rural land use efficiency, forming cold spot and hot spot spatial patterns in the western and eastern parts, respectively, with a central transition area; (3) A spillover effect of the dynamic transfer process, with a manifested specific law as “club convergence”, “Matthew effect”, and progressive development characteristics; (4) The key roles of the natural, social, economic, and policy indicators in the coupling and coordination development process of agricultural resilience and rural land use efficiency. However, the selected indicators showed substantial spatial differences in their influences on the coupling and coordination process between provinces.

The Liaohe River Valley was one of the key centers of the origination and development of agriculture in northern China during the Holocene. To understand the long-term interaction among the evolutions of climate, agriculture, and human activities, it is essential to quantitatively reconstruct the spatiotemporal changes in regional prehistoric human land use. In this study, known archaeological sites and a prehistoric land use model (PLUM) were combined to reconstruct changes in land use in the Liaohe River Valley during 8-2 ka BP from a quantitative perspective. The land use area experienced two stages of increase (during 8-5 ka BP and after 4 ka BP) and one stage of decrease (during 5-4 ka BP); these periods were characterized by spatial expansions and contractions. The land use intensity level differed significantly in the western and eastern parts of the valley before 4 ka BP, but the situation changed as the distribution center of the human activity shifted to the southern part of the valley around 5-4 ka BP. Overall, the spatial and temporal changes in the land use areas in both the western and eastern parts of the valley responded well to variations in precipitation during 8-2 ka BP, which potentially provides useful insights into understanding the responses of human activity to future climate change.

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

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

The variation of land surface temperature (LST) has a vital impact on the energy balance of the land surface process and the ecosystem stability. Based on MDO11C3, we mainly used regression analysis, GIS spatial analysis, correlation analysis, and center-of -gravity model, to analyze the LST variation and its spatiotemporal differentiation in China from 2001 to 2020. Furthermore, we employed the Geodetector to identify the dominant factors contributing to LST variation in 38 eco-geographic zones of China and investigate the underlying causes of its pattern. The results indicate the following: (1) From 2001 to 2020, the LST climate average in China is 9.6℃, with a general pattern of higher temperatures in the southeast and northwest regions, lower temperatures in the northeast and Qinghai-Tibet Plateau, and higher temperatures in plains compared to lower temperatures in mountainous areas. Generally, LST has a significant negative correlation with elevation, with a correlation coefficient of -0.66. China’s First Ladder has the most pronounced negative correlation, with a correlation coefficient of -0.76 and the lapse rate of LST is 0.57℃/100 m. (2) The change rate of LST in China during the study is 0.21℃/10 a, and the warming area accounts for 78%, demonstrating the overall spatial pattern a “multi-core warming and axial cooling”. (3) LST’s variation exhibits prominent seasonal characteristics in the whole country. The spatial distribution of average value in winter and summer differs significantly from other seasons and shows more noticeable fluctuations. The centroid trajectory of the seasonal warming/cooling area is close to a loop shape and displays corresponding seasonal reverse movement. Cooling areas exhibit more substantial centroid movement, indicating greater regional variation and seasonal variability. (4) China’s LST variation is driven by both natural influences and human activities, of which natural factors contribute more, with sunshine duration and altitude being key factors. The boundary trend between the two dominant type areas is highly consistent with the “Heihe-Tengchong Line”. The eastern region is mostly dominated by human activity in conjunction with terrain factors, while the western region is predominantly influenced by natural factors, which enhance/weaken the change range of LST through mutual coupling with climate, terrain, vegetation, and other factors. This study offers valuable scientific references for addressing climate change, analyzing surface environmental patterns, and protecting the ecological environment.

The transboundary influence of environmental change is a critical issue in the Lancang-Mekong region. As the largest river-connected lake in the lower Mekong, the ecological change and influence of Tonle Sap Lake have received widespread attention and discussion, especially after 2008, when the hydrological regime of the Lancang-Mekong River mainstream underwent distinct changes. However, the linkage and coupling mechanism between the lake riparian environment and mainstream water level change are still unclear. In this study, the interannual spatiotemporal changes in land cover in the Tonle Sap Lake riparian zone (TSLRZ) and their relationship with mainstream water levels were analysed. The results showed that the expansion of farmland was the most notable change in 1988-2020. After 2008, the land cover changes intensified, manifested as accelerated farmland expansion, intensified woodland fragmentation and significant water body shrinkage. Furthermore, the responses of the water body, degraded land, wasteland and grassland areas to the mainstream water levels weakened after 2008. Evidently, the land cover changes in the TSLRZ in the last 30 years were less related to the mainstream water level change than to local reclamation and logging. These results can offer a new scientific basis for the transboundary influence analysis of hydrological change.

This study uses green patent data from 264 cities in China between 2006 and 2020 to examine the evolution of spatial patterns in urban green technology innovation (GTI) across the country and identify the underlying driving factors. Moran’s I index, Getis-Ord G_i^* index, standard deviation ellipse, and geographical detector were used for the analysis. The findings indicate an increase in the overall level of GTI within Chinese cities. Provincial capitals, cities along the eastern coast, and planned cities emerge as the prominent “highlands” of GTI, whereas the “lowlands” of GTI predominantly lie in the western and northeastern regions, forming the spatial pattern of “hot in the east and center of the country, cold in the northwest and the northeast.” The distribution center of gravity of GTI is toward the southwest of China. The distribution pattern is in the “northeast-southwest” direction, which is characterized by “diffusion,” followed by “agglomeration.” Differences in economic development have the highest determining power on the spatial differentiation of GTI in Chinese cities, whereas differences in environmental regulation and industrial structure have the lowest degree of relative influence. The interaction between any two factors contributes to an amplified explanatory power in understanding the differences in GTI.

The metropolitan area is a crucial spatial element in promoting new urbanization in China. It possesses theoretical and empirical value in the determination of the evolutionary patterns and development trends necessary for regional integration and high-quality development. This study focused on Nanjing Metropolitan Area, the first national metropolitan area in China, and established three development scenarios by combining ecological-economic spatial conflict (EESC) zones and national key ecological functional areas. These scenarios simulate the spatial distribution of future land use and land cover change (LUCC) using the development zone planning function of the patch generation land use simulation (PLUS) model. The results show that: (1) Between 2000 and 2020, the most prominent characteristics of land use change were largely the massive expansion of built-up land and the significant decline of farmland, while changes in the area of ecological land were less evident. (2) EESC areas experienced significant changes over the past 20 years, but the overall level of conflict was low. Ecological land was the main land use type in the lowest-conflict area, while built-up land was the main land use type in the highest-conflict area. (3) From 2030 to 2050, further expansion of built-up areas is expected, with continued decrease of farmland. The regulation of these land use changes can be achieved through different development zone plans. The economic development scenario had the largest built-up land area, while the ecological protection scenario had the largest farmland area. This study simulates the spatial pattern changes in the metropolitan area based on spatial conflict patterns and national main functional area planning process, providing a scientific reference for future spatial planning and management.