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.

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.

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.

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.

Resilience studies have long been a focal point in the fields of geography, social science, urban studies, and psychology. Recently, resilience studies from multiple disciplines have scrutinized resilience at an individual scale. As one important behavior in the daily life of human beings, travel behavior is characterized by spatial dependence, spatiotemporal dynamics, and group heterogeneity. Moreover, how to understand the interaction between travel behavior (or demand) and transportation supply and their dynamics is a fundamental question in transportation studies when transportation systems encounter unexpected disturbances. This paper refines the definition of travel behavior resilience based on fundamental theories from multiple disciplines, including ecology, transportation engineering, and psychology. Additionally, this paper proposes a conceptual theoretical framework of travel behavior resilience based on the dynamic equilibrium characteristics of transportation supply and demand. In general, travel behavior has three stages of variation, namely, dramatic reduction, rapid growth, and fluctuation recovery, which have helped capture the travel behavior resilience triangle. Then, we construct a corresponding evaluation methodology that is suitable for multiscale and multidimensional perspectives. We emphasize that the evaluation of travel behavior resilience should be process-oriented with temporal continuity or capture the inflection points of travel behavior. Using multisource big data such as mobile phone signaling data and smart card data, this paper reviews empirical studies on travel behavior resilience, exploring its spatial heterogeneity and group differences. With location-based analysis, we confirm that people show greater travel behavior resilience in places where people engage in various socioeconomic activities. The group-based analysis shows that age and socioeconomic attributes of mobility groups significantly affect travel behavior resilience. Travel behavior resilience can be one pillar, offering geographic perspectives in resilience studies. In the future, the study of travel behavior resilience at multiple scales and from multiple perspectives can explore the spatial heterogeneity of transportation re-equilibrium and travel modal differences, contributing to urban spatial structure studies. Studying travel behavior resilience can provide scientific and technological support for urban management and resilient city construction.

Polder is a type of irrigation field unique to the lower Yangtze River of China. It is a product of long-term and ingenuous human modifications of wetland landscapes. In the pre-Qin Period, 3000 years ago, the poldered area of eastern Wuhu city was once a large lake called the ancient Danyang wetland. The introduction of agricultural civilization and polder technology to the area during the Wu and Yue Kingdoms period gradually transformed it into an agricultural area. With an accelerating rate of land reclamation under a changing late-Holocene regional climate, the ancient Danyang wetland became an aquatic system strongly influenced by intensifying anthropogenic activities. In this study, based on field survey data, historical documents, and remote-sensing and archaeological data, we reconstructed the spatial distribution of the polder landscape over the last 3000 years and identified their structural diversity. We found that polder landscapes began to emerge in the Spring and Autumn Period, land reclamation intensified in the Three Kingdoms and developed rapidly in the Song Dynasty before eventually reaching the peak from the Ming and Qing Dynasties. The relocation of historical sites to low-altitude areas also marked the expansion of poldered fields from the center of the wetland to the southeast and northwest. The development and evolution of the polder landscape are related to regional climate conditions, changing social and economic statuses, and the development of agricultural technology in the Song Dynasty and succeeding periods.

Reducing carbon emissions from the transport sector is essential for realizing the carbon neutrality goal in China. Despite substantial studies on the influence of urban form on transport CO₂ emissions, most of them have treated the effects as a linear process, and few have studied their nonlinear relationships. This research focused on 274 Chinese cities in 2019 and applied the gradient-boosting decision tree (GBDT) model to investigate the nonlinear effects of four aspects of urban form, including compactness, complexity, scale, and fragmentation, on urban transport CO₂ emissions. It was found that urban form contributed 20.48% to per capita transport CO₂ emissions (PTCEs), which is less than the contribution of socioeconomic development but more than that of transport infrastructure. The contribution of urban form to total transport CO₂ emissions (TCEs) was the lowest, at 14.3%. In particular, the effect of compactness on TCEs was negative within a threshold, while its effect on PTCEs showed an inverted U-shaped relationship. The effect of complexity on PTCEs was positive, and its effect on TCEs was nonlinear. The effect of scale on TCEs and PTCEs was positive within a threshold and negative beyond that threshold. The effect of fragmentation on TCEs was also nonlinear, while its effect on PTCEs was positively linear. These results show the complex effects of the urban form on transport CO₂ emissions. Thus, strategies for optimizing urban form and reducing urban transport carbon emissions are recommended for the future.

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.

Emphasis on future environmental changes grows due to climate change, with simulations predicting rising river temperatures globally. For Poland, which has a long history of thermal studies of rivers, such an approach has not been implemented to date. This study used 9 Global Climate Models and tested three machine-learning techniques to predict river temperature changes. Random Forest performed best, with R²=0.88 and lowest error (RMSE: 2.25, MAE:1.72). The range of future water temperature changes by the end of the 21st century was based on the Shared Socioeconomic Pathway scenarios SSP2-4.5 and SSP5-8.5. It was determined that by the end of the 21st century, the average temperature will increase by 2.1°C (SSP2-4.5) and 3.7°C (SSP5-8.5). A more detailed analysis, divided by two major basins Vistula and Odra, covered about 90% of Poland’s territory. The average temperature increase, according to scenarios SSP2-4.5 and SSP5-8.5 for the Odra basin rivers, is 1.6°C and 3.2°C and for the Vistula basin rivers 2.3°C and 3.8°C, respectively. The Vistula basin’s higher warming is due to less groundwater input and continental climate influence. These findings provide a crucial basis for water management to mitigate warming effects in Poland.

Water use efficiency (WUE), as a pivotal indicator of the coupling degree within the carbon-water cycle of ecosystems, holds considerable importance in assessment of the carbon-water balance within terrestrial ecosystems. However, in the context of global warming, WUE evolution and its primary drivers on the Tibetan Plateau remain unclear. This study employed the ensemble empirical mode decomposition method and the random forest algorithm to decipher the nonlinear trends and drivers of WUE on the Tibetan Plateau in 2001- 2020. Results indicated an annual mean WUE of 0.8088 gC/mm∙m² across the plateau, with a spatial gradient reflecting decrease from the southeast toward the northwest. Areas manifesting monotonous trends of increase or decrease in WUE accounted for 23.64% and 9.69% of the total, respectively. Remarkably, 66.67% of the region exhibited trend reversals, i.e., 39.94% of the area of the Tibetan Plateau showed transition from a trend of increase to a trend of decrease, and 26.73% of the area demonstrated a shift from a trend of decrease to a trend of increase. Environmental factors accounted for 70.79% of the variability in WUE. The leaf area index and temperature served as the major driving forces of WUE variation.

Urban sprawl has been a prevailing phenomenon in developing countries like China, potentially resulting in significant carbon dioxide (CO₂) emissions from the transport sector. However, the impact of urban sprawl on transport CO₂ emissions (TCEs) is still not fully understood and remains somewhat rudimentary. To systematically investigate how urban sprawl influences TCEs, we employ panel regression and panel threshold regression for 274 Chinese cities (2005-2020), and obtain some new findings. Our results affirm that the degree of urban sprawl is positively associated with TCEs, and this holds true in different groups of city size and geographical region, while significant heterogeneity is observed in terms of such impact. Interestingly, we find urban sprawl nonlinearly impacts TCEs—with an equal increase in urban sprawl degree, TCEs are even lower in cities with larger population size and better economic condition, particularly in East China. Furthermore, the low-carbon city pilot policy shows potential in mitigating sprawl’s impact on TCEs. Drawing on our findings, we argue that to achieve the target of TCEs reduction in China by curbing urban sprawl, more priority should be placed on relatively small, less developed, and geographically inferior cities for cost-efficiency reasons when formulating future urban development strategies.

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.

Land consolidation (LC) stands as a globally recognized strategy for rural development. In China, it has evolved towards comprehensive land consolidation (CLC) to support the rural revitalization initiative. However, there are ongoing challenges in understanding CLC’s specific pathway and mechanism, particularly its role in stimulating rural endogenous development. This study aims to investigate the localization process of international experiences, examine the pathway of CLC, and scrutinize its mechanism in rural development from a novel perspective of neo-endogenous development. Field research and semi-structured interviews were conducted in Nanzhanglou village, renowned for its early adoption of CLC practices inspired by German experiences since 1988. Overall, key findings underscore the advantages of CLC in spatial restructuring, industrial development, and human capital enhancement in rural areas. Additionally, international experiences emerge as crucial exogenous forces, primarily by knowledge embedding, which catalyzes rural neo-endogenous development via the “resource-engagement-identity-endogenous” mechanism. Collectively, by introducing a neo-endogenous theoretical framework, this study offers valuable insights into the CLC implementation in China and beyond, and emphasizes the positive impact of knowledge embedding as an exogenous force in promoting rural neo-endogenous development to address existing research gaps. Recommendations for sustainable rural development involve enhancing rural planning practicality, governance capacity, and local leadership, while prioritizing agricultural modernization and increasing investments in education and vocational training to ensure that villagers benefit from industrial development.

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 response to issues such as incomplete segmentation and the presence of breakpoints encountered in extracting debris-flow fans using semantic segmentation models, this paper proposes a local feature and spatial attention mechanism to achieve precise segmentation of debris-flow fans. Firstly, leveraging the spatial inhibition mechanism from neuroscience theory as a foundation, an energy function for the local feature and spatial attention mechanism is formulated. Subsequently, by employing optimization theory, a closed-form solution for the energy function is derived, which ensures the lightweight nature of the proposed attention mechanism algorithm. Finally, the performance of this algorithm is compared with other mainstream attention mechanism algorithms embedded in semantic segmentation models through comparative experiments. Experimental results demonstrate that the proposed method outperforms both the original models and mainstream attention mechanisms across various classic models, effectively enhancing the performance of network models in debris-flow fan segmentation tasks.

The urban heat island (UHI) is an environmental problem of wide concern because it poses a threat to both the human living environment and the sustainable development of cities. Knowledge of the spatiotemporal characteristics and the driving factors of UHI is essential for mitigating their impact. However, current understanding of the UHI in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) is inadequate. Combined with data (e.g., land surface temperature and land use.) acquired from the Google Earth Engine and other sources for the period 2001-2020, this study examined the diurnal and seasonal variabilities, spatial heterogeneities, temporal trends, and drivers of surface UHI intensity (SUHII) in the GBA. The SUHII was calculated based on the urban-rural dichotomy, which has been proven an effective method. The average SUHII was generally 0-2°C, and the SUHII in daytime was generally greater than that at night. The maximum (minimum) SUHII was found in summer (winter); similarly, the largest (smallest) diurnal difference in SUHII was during summer (winter). Generally, the Mann-Kendall trend test and the Sen’s slope estimator revealed a statistically insignificant upward trend in SUHII on all time scales. The influence of driving factors on SUHII was examined using the Geo-Detector model. It was found that the number of continuous impervious pixels had the greatest impact, and that the urban-rural difference in the enhanced vegetation index had the smallest impact, suggesting that anthropogenic heat emissions and urban size are the main influencing factors. Thus, controlling urban expansion and reducing anthropogenic heat generation are effective approaches for alleviating surface UHI.

Floodplain lakes are important water storage areas in lowland regions that often undergo geomorphologic evolution, and timely topographic data are generally unavailable. In this study, to assess the impacts of lakebed deformation on hydraulic performance in Dongting Lake, a set of semi-empirical methods was proposed to establish performance graphs (PGs) using only hydrological data. These methods were used to evaluate the changes in water level, storage capacity, and flood detention ability in Dongting Lake caused by topographic adjustment after the Three Gorges Reservoir impoundment. These methods showed that PGs can effectively simulate the water level and outflow processes of Dongting Lake with Nash-Sutcliffe efficiency coefficients (NSEs) above 0.9. A comparison of the estimated water level and discharge using PGs from different periods suggested that bed erosion in Dongting Lake caused water level decreases of 0.18 m and 0.32 m during the flood and dry seasons, respectively. Because the magnitude of erosion at high elevations in the lake is small, the impacts of bed adjustment on the storage capacity and flood detention ability are not currently significant. This study showed that the hydraulic performance of a floodplain lake can be evaluated independently of topographic data under the condition of no reverse flows or negative water surface slopes.

Land-use and land-cover change (LUCC) simulations are powerful tools for evaluating and predicting future landscape dynamics amid rapid human‒nature interactions to support decision-making. However, existing models often overlook spatial heterogeneity and temporal dependencies when modeling LUCC at both the macro and microscales. In this paper, we propose a new model, a self-calibrated convolutional neural network-based cellular automata (SC-CNN-CA) model, which integrates macro- and microspatial characteristics to simulate complex interactions among land-use types. The SC-CNN-CA model incorporates a self-calibration module using Gaussian functions to capture macrotrend such as urban sprawl while accounting for microlevel land-use interactions such as neighborhood effects. The results indicated that (1) the neighborhood effect between agricultural land and urban land tended to “increase followed by a decrease.” (2) Urban sprawl in Wuhan was highly compact, with a relatively high intensity of urban expansion at distances between 11.96 km and 24.44 km. (3) Compared with the other CA models tested, the SC-CNN-CA model demonstrated superior performance, achieving an overall accuracy of 84.12% and a figure of merit of 20.20%. This new model can enhance our understanding of historical LUCC trajectories and improve predictions of spatially explicit information for efficient land resource and urban management.

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.

In recent years, the uncontrollable risks of urban production-living-ecological (PLE) space have increased sharply, making resilience enhancement essential for sustainable urban development. Based on the social-ecological system (SES) theory, this study constructs an assessment framework for urban PLE space resilience by analyzing its inherent characteristics. The central urban area of Ganzhou city is taken as a case study to evaluate urban PLE space resilience and diagnose its obstacles. The results are as follows: The PLE space resilience in the central urban area of Ganzhou exhibits gradations and substantial spatial differentiation. The ecological space resilience in the study area was the highest, followed by that of production space, while living space resilience was the lowest. The primary factors influencing PLE space resilience are concentrated in the dimensions of robustness and adaptability. In particular, the robustness of the PLE space is relatively low. Based on these results, targeted spatial resilience governance strategies for the PLE space have been proposed. These strategies serve as theoretical and technical references for the study area. By adopting the PLE space perspective, this paper enriches resilience research and provide theoretical support for sustainable urban development.

It is essential to map the cropping patterns when investigating the mechanisms and impacts of climate change. However, the long-term evolution of cropping patterns remains poorly understood. This study collected hundreds of records of cropping intensity and crop combinations from local gazetteers and other relevant articles for the North China Plain (NCP) over the past 300 years. Then, we analyzed the evolutionary characteristics and drivers in terms of climate change and advances in agricultural technology. From the Qing Dynasty to the 1950s, one harvest per year (1H1Y) was the dominant pattern in the northern NCP, and three harvests in two years (3H2Y) was the dominant pattern in Henan and Shandong provinces. The 1H1Y crops were cereals and sorghum. The 3H2Y crop combinations were spring maize, winter wheat, and beans. In the 1960s and 1970s, the cropping intensity in much of the NCP was two harvests per year (2H1Y) or a mix of the 2H1Y and 3H2Y patterns. In the 1980s, the cropping intensity in the NCP was dominated by 2H1Y. Since the 1960s, the 2H1Y crop compositions have been winter wheat−summer maize in Shandong, Henan, and Hebei provinces, while winter wheat−rice dominated north of the Huaihe River. The 3H2Y summer crop changed from beans to maize/cereals over time. Climate warming was not the dominant factor driving the evolution of cropping intensity in the NCP. Advances in agricultural production conditions and reforms in production relations have promoted the rapid development of multiple cropping since the 1950s.

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.

Globalization has resulted in a notable rise in the flow of high-skilled talent from emerging countries to developed nations. Current research on transnational talent flow mainly focuses on the destination countries, with less attention given to the perspective of the sending countries, particularly lacking a dynamic discussion on its impact on technological evolution in the origin countries. Based on the OECD REGPAT database, this paper aims to explore how talent groups migrating to developed countries facilitate the return of knowledge and technology to emerging countries and achieve breakthroughs in their technological evolution paths, while further discussing the potential mechanisms involved. The findings of this paper are as follows: (1) The technological development of emerging countries is a path-dependent process, where countries often branch into new technologies related to their preexisting knowledge base. Consequently, knowledge feedback from high-skilled talents increases the likelihood of sending countries developing unrelated technologies. (2) The mobility of talents across borders fosters more international collaborations and citations for patents that are unrelated to the local knowledge base, thus enriching the technological paths of sending countries. (3) The mobility of high-skilled talents primarily affects complex technologies, which have significant economic effects that encourage imitation by other countries. However, the effect on novel technologies is less significant due to their strong geographical stickiness. In general, this paper addresses the gaps in existing research on talent outflow and the technological evolution of origin countries, providing empirical evidence for the positive role of transnational talent mobility in the technological catch-up of emerging nations. Besides, it offers recommendations for talent export, import, and innovation policy formulation in these countries.

Balanced development and the reduction of inequality are central objectives of the United Nations Sustainable Development Goals (SDGs). This study explores the use of Nighttime Light (NTL) brightness and the Nighttime Light Development Index (NLDI) as indicators of socioeconomic development in urban centers, focusing on six Indian cities. It examines the correlation between these indices and socioeconomic inequality across affluent neighborhoods, urban slums, downtown areas, and general urban areas in 2015, 2018, and 2021. The results reveal that lighting brightness in affluent areas can be lower than that in bustling downtowns, due to factors such as lower residential density. This challenges the conventional assumption that higher NTL necessarily indicates greater prosperity. This study further confirmed significant developmental disparities between well-lit downtowns and poorly illuminated peripheral slum areas, as reflected by lower NLDI scores. Notably, the results uncover a phenomenon termed “same value but different spectrum” based on a careful examination of NLDI values of urban centers and their corresponding curves. This suggests that NLDI alone may not fully capture the complexity of urban development, and that underlying development trajectories, along with on-the-ground realities, must be further examined. The findings emphasize the importance of applying NLDI for urban internal analyses. In addition, the study highlights the necessity for nuanced urban planning and targeted policy interventions specifically tailored to the unique conditions of different urban areas.

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.

Research on urban health constitutes an important issue in the field of health geography and also a strong propeller of the Healthy China Initiative. As the main form that realizes new-type urbanization, urban agglomerations should become the primal sites for the construction of a “Healthy China”. The evaluation of healthy cities’ development in urban agglomerations has both theoretical and practical values. Based on the concept of urban health and its evaluation models, this paper developed an evaluation framework for healthy cities that involved multiple data sources. With 19 urban agglomerations in China as the research subjects, we used CRITIC weighting and geographical detectors to examine the geographies of healthy cities and their influencing factors in 2010 and 2020. The results were fourfold. Firstly, the urban health level of China significantly increased from 2010 to 2020, and the comprehensive health index developed towards a positive skewed distribution, along with a shift from “low in the hinterland - high in the coastal areas” to a “multipolar” pattern led by the coastal and southwest urban agglomerations. Secondly, among various dimensions of urban health, the healthy environment index became improved with narrowed regional differences; while the health services index was still polarized; health collaboration was upgraded with a strengthened intercity health network; the healthy population index slightly declined and converged to the middle. Thirdly, urban health in China has initially demonstrated the characteristics of a H-H pattern in the Yangtze River Delta and Chengdu- Chongqing regions, as well as L-L clusters in the northern urban agglomerations, the narrowed regional differences, and increasing coordination within each urban agglomeration. Fourthly, the geographical detector found that economy, urbanization and the human capital were significant external factors that affected urban health development. The explanatory power of technological innovation and opening to the outside world were also increasing. The development of healthy cities is yet to be transformed into regional health integration.

The uneven distribution of medical resources has led to increasingly frequent patient mobility; however, the interaction between this phenomenon and the healthcare supply-demand relationship remains underexplored. The present study constructed the 2023 Cross-City Patient Mobility Network in China using one million patient mobility data records obtained from online healthcare platforms. We applied urban network analysis to uncover mobility patterns and used the coupling coordination degree model to assess healthcare supply-demand relationships before and after patient mobility. Explainable machine learning further revealed the impact of supply-demand coupling on patient mobility. The results indicated the following: Patient mobility followed administrative boundaries, although megacities serve areas beyond provincial borders; The scale of healthcare supply and demand displayed a multi-centric spatial pattern with a general decline from east to west, and these characteristics of demand distribution were further solidified by patient mobility; Cities with low supply-demand coupling and undersupply experienced patient outflows, while cities with high coupling and oversupply attracted them. In turn, patient mobility helped balance healthcare supply and demand, optimising the coupling relationship across cities. Thus, this research not only provides a methodological reference for understanding the interaction between patient mobility and healthcare systems but also offers empirical insights for public health policy.

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.

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.