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

The 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.

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.

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.

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 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Random forest model is the mainstream research method used to accurately describe the distribution law and impact mechanism of regional population. We took Shijiazhuang as the research area, with comprehensive zoning based on endowments as the modeling unit, conducted stratified sampling on a hectare grid cell, and systematically carried out incremental selection experiments of population density impact factors, optimizing the population density random forest model throughout the process (zonal modeling, stratified sampling, factor selection, weighted output). The results are as follows: (1) Zonal modeling addresses the issue of confusion in population distribution laws caused by a single model. Sampling on a grid cell not only ensures the quality of training data by avoiding the modifiable areal unit problem (MAUP) but also attempts to mitigate the adverse effects of the ecological fallacy. Stratified sampling ensures the stability of population density label values (target variable) in the training sample. (2) Zonal selection experiments on population density impact factors help identify suitable combinations of factors, leading to a significant improvement in the goodness of fit (R²) of the zonal models. (3) Weighted combination output of the population density prediction dataset substantially enhances the model’s robustness. (4) The population density dataset exhibits multi-scale superposition characteristics. On a large scale, the population density in plains is higher than that in mountainous areas, while on a small scale, urban areas have higher density compared to rural areas. The optimization scheme for the population density random forest model that we propose offers a unified technical framework for uncovering local population distribution law and the impact mechanisms.

High-level investment facilitation is crucial for China’s overseas free economic zones (COFEZs) to attract and retain investment, mitigate business interruption risks, and foster a virtuous cycle. While research on investment facilitation in COFEZs has mainly focused on summarizing and examining the investment facilitation measures adopted by typical national-level examples of COFEZs, relatively little attention has been paid to investigating the overall level and general problems of investment facilitation across COFEZs. This study expands the scope of case investigations by taking 60 COFEZs as samples. It constructs a comprehensive evaluation indicator system which includes four dimensions: industrial infrastructure, social infrastructure, business support services, and seamless administrative supervision. By employing content analysis and regression analysis, this study identifies the characteristics and influencing factors of investment facilitation level in COFEZs. The results show that the overall level of investment facilitation in COFEZs is currently low. Specifically, COFEZs exhibit higher levels of investment facilitation in processing and manufacturing types and in Europe, while those in trade and logistics types and in Africa are relatively poor. Industrial infrastructure and business support services contribute more significantly to the overall scores of investment facilitation in COFEZs compared to social infrastructure and seamless administrative supervision. The investment facilitation level in COFEZs is essentially the result of a series of behaviors by developers and host governments, and it is affected by a combination of developers’ perceptions of investment facilitation and the social environment in which developers and host governments promote investment facilitation. This study offers a new perspective on understanding COFEZs and contributes to the sustainable development of COFEZs.

The internal technological innovation (IT) and external technological cooperation (ET) of a city are crucial drivers for its green development (GD). Although previous studies have extensively explored the effect of IT on GD, IT, ET and GD have not been integrated into the same framework to explore their relationship. Using panel data of 13 cities in the Beijing-Tianjin-Hebei urban agglomeration, this study revealed the spatio-temporal evolution of GD and analyzed the effects of IT and ET on GD from the perspective of baseline impact, spatial effect and synergy effect. Empirical results demonstrate that the level of urban GD has upgraded and the difference in GD between cities has been narrowed though it decreases from the middle to both ends. IT significantly promotes the growth of GD while ET has an inverted U-shaped effect on GD. Under the influence of spatial spillover, IT has a U-shaped effect on the GD of neighboring cities while the effect of ET on neighboring GD is not significant. Additionally, the interaction between IT and ET has not been effective, leading to an insignificant synergy effect on GD. These findings will provide reference for taking rational advantage of IT and ET to facilitate urban GD.

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.

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.

The Yangtze River Economic Belt (YREB) is a pivotal contributor to China’s economic growth, particularly as the nation undergoes a green transformation. Achieving synergistic reductions on pollution and carbon emissions is deemed crucial for this transition. This paper examines the spatial and temporal changes in the synergy of pollution and carbon reduction in the YREB and delves into the underlying mechanisms. Our findings indicate that while the synergy in the YREB is increasing, it manifests disparities across regions, with the lower reaches outperforming the middle and upper ones. Enterprise behavior, government guidance, and regional endowments influence this synergy. Cities in the YREB must strategically plan their urban scale, curb population overgrowth, recalibrate their industrial structures, curtail energy consumption, and enhance policy efficacy. Distinct regions should prioritize various objectives: the lower reaches should hasten scientific advancements and technological innovations; the middle reaches should foster innovation and industrial upgrades; and the upper reaches should prioritize rural and urban land intensification.

Due to water conflicts and allocation in the Lancang-Mekong River Basin (LMRB), the spatio-temporal differentiation of total water resources and the natural-human influence need to be clarified. This work investigated LMRB’s terrestrial water storage anomaly (TWSA) and its spatio-temporal dynamics during 2002-2020. Considering the effects of natural factors and human activities, the respective contributions of climate variability and human activities to terrestrial water storage change (TWSC) were separated. Results showed that: (1) LMRB’s TWSA decreased by 0.3158 cm/a. (2) TWSA showed a gradual increase in distribution from southwest of MRB to middle LMRB and from northeast of LRB to middle LMRB. TWSA positively changed in Myanmar while slightly changed in Laos and China. It negatively changed in Vietnam, Thailand and Cambodia. (3) TWSA components decreased in a descending order of soil moisture, groundwater and precipitation. (4) Natural factors had a substantial and spatial differentiated influence on TWSA over the LMRB. (5) Climate variability contributed 79% of TWSC in the LMRB while human activities contributed 21% with an increasing impact after 2008. The TWSC of upstream basin countries was found to be controlled by climate variability while Vietnam and Cambodia’s TWSC has been controlled by human activities since 2012.

Green development is a critical component of sustainable tourism, which prioritizes a comprehensive, ecologically-friendly, and people-oriented approach to development. This study presents a case study of the Beijing-Tianjin-Hebei (BTH) urban agglomeration from 2001 to 2021 to analyze the spatio-temporal evolution characteristics and influencing factors of tourism green development efficiency (TGDE). The study defines the concept of tourism green development and constructs an evaluation system, which is used to explore the internal differences and spatial patterns of TGDE within the urban agglomeration. The methodological approach includes the SBM-Undesirable model, kernel density estimation, Markov chain, and spatial gravity model. The findings indicate that the TGDE in the BTH urban agglomeration is generally favorable, displaying a temporal phase of “rising-declining-rising.” However, the study observes lower TGDE in tourism node cities compared to tourism regional center cities and tourism core hub cities. The non-equilibrium degree of each region indicates significant spatio-temporal evolution patterns and internal differences among the three regions, with a spatially decreasing distribution of “core hub-regional center-node city.” The TGDE in the urban agglomeration experienced an evolutionary trend of “first decreasing and then increasing” with apparent endogenous evolution characteristics. The linkage pattern of green development efficiency in the tourism industry between cities is relatively stable. Furthermore, neighboring cities generally exhibit a higher spatial connectivity strength of green development efficiency in the tourism industry compared to non-neighboring cities. Economic development level, industrial structure, and science and education level are identified as key factors that affect TGDE. However, the study finds that the factors influencing TGDE in tourism core hub cities, tourism regional center cities, and tourism node cities differ somewhat. Economic development level, industrial structure, science and education level, openness, and government regulation impact TGDE in tourism core hub cities and tourism regional center cities, while economic development level, industrial structure, and tourism resource endowment are the primary factors affecting TGDE in tourism node cities. The study provides policymakers and tourism practitioners with valuable insights into enhancing the green development of the tourism industry in the BTH urban agglomeration and other similar regions. Corresponding policy recommendations based on the results are proposed to improve the TGDE of the tourism industry in these regions, promote sustainable tourism development, improve the quality of life of local residents, and protect the ecological environment.