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

Cities are the key areas for human beings to achieve sustainable development goals in the future. Estuarine cities are a special type of coastal city in urgent need of a clear definition. This paper proposed that estuarine cities are cities developed on the coast where rivers and oceans meet and defined four connotations, including the intersection of rivers and marine water systems, the coordinated development of land and oceans, the location advantages of connecting rivers and seas, and the important fragility of the ecological environment. We used HydroSHEDS, OSM, GPW, and urban socioeconomic statistics and selected 50 estuarine cities with large rivers as representatives to summarize the main geographical basis and socioeconomic characteristics. Cities are primarily found in low-altitude, flat regions with average annual temperatures that mainly vary from 10°C to 25°C, relatively abundant precipitation, and extensive biological resources. There are substantial variations in the socioeconomic features of estuarine cities. We proposed eight development patterns, including open and inclusive city spirit, high-quality livable cities, high-quality development driven by innovation, integration of internal and external communication with ports and cities, construction of an international financial center, ecological environment protection and restoration, active promotion of cultural tourism, and positive international exchanges.

Construction land is the leading carrier of human activities such as production and living. Evaluating the construction land suitability (CLS) on the Qinghai-Tibet Plateau (QTP) holds significant implications for harmonizing the relationship between ecological protection and human activity and promoting population and industry layout optimization. However, no relevant studies provide a complete CLS assessment of the QTP. In this study, we developed a model-based CLS evaluation framework coupling of pattern and process to calculate the global CLS on the QTP based on a previously developed CLS evaluation model. Then, using the land-use data of 1990, 2000, 2010, and 2020, we examined the adaptability of existing construction land (ECL) to the CLS assessment result through the adaptability index and vertical gradient index and further analyzed the limitations of maladaptive construction land. Finally, we calculated the potential area of reserve suitable construction land. This article includes four conclusions: (1) The highly suitable, suitable, moderately suitable, marginally suitable, and unsuitable CLS classes cover areas of 0.33×10⁴ km², 10.42×10⁴ km², 18.06× 10⁴ km², 24.12×10⁴ km², and 205.29×10⁴ km², respectively. Only approximately 11% of the study area on the QTP is suitable for large-scale permanent construction land, and approximately 79.50% of the area is unsuitable under current economic and technological conditions. (2) The ECL adaptability index is 85.16%, 85.93%, 85.18%, and 78.01% during 1990-2020, respectively, with an average adaptability index exceeding 80% on the QTP. The ECL distribution generally conforms to construction land suitable space characteristics but with a significant spatial difference. (3) From 1990 to 2020, the maladaptive ECL was dominated by rural settlement land, transport land, and special land, with a rapidly increasing proportion of urban and other construction land. The maladaptive ECL is constrained by both elevation and slope in the southern Qinghai Plateau, the Hengduan Mountains, and the Qilian Mountains. In contrast, elevation is significantly more limiting than slope in the northern Tibet Plateau, the Gangdis Mountains, and the Himalayan Mountains. (4) The potential area of reserve suitable construction land is 12.41×10⁴ km², accounting for 4.81% of the total land area of the QTP, and the per capita area is 9928 m². Regions of Qaidam Basin, Gonghe Basin, and Lhasa-Shannan Valley have the richest and most concentrated land resource of reserve suitable construction land. The research results provide spatial decision support for urban and rural settlement planning and ecological migration on the QTP.

Identification of the spatial mismatch between land use functions (LUFs) and land use efficiencies (LUEs) is essential to regional land use policies. However, previous studies about LUF-LUE mismatch and its driving factors have been insufficient. In this study, we explored the spatiotemporal mismatch of LUFs and LUEs and their influencing factors from 2000 to 2018 in the Middle Reaches of the Yangtze River (MRYR). Specifically, we used Spearman correlation analysis to reveal the trade-off relationship between LUFs and LUEs and determine the direction of the influencing factors on the LUF-LUE mismatch, adopted spatial mismatch analysis to measure the imbalance between LUFs and LUEs, and used the geographical detector model to analyze the factors influencing this spatial mismatch. The results showed that production function (PDF), living function (LVF), ecological function (ELF), agricultural production efficiency (APE), urban construction efficiency (UCE), and ecological services efficiency (ESE) all displayed significant spatial heterogeneity. The high trade-off areas were widely distributed and long-lasting in agricultural space and urban space, while gradually decreasing in ecological space. Wuhan and Changsha showed high spatial mismatch coefficients in urban space, but low spatial mismatch coefficients in agricultural space. Hunan generally presented high spatial mismatch coefficients in ecological space. Furthermore, the interaction of the proportion of cultivated area and transportation accessibility exacerbated the mismatch in agricultural space. The interaction effects of capital investment and technology innovation with other factors have the most intense impact on the mismatch in urban space. The internal factor for cultivated area interacts with other external factors to drastically affect ecological spatial mismatch.

Urban shrinkage has attracted the attention of many geographers and urban planners in recent years. However, there are fewer studies on vacant housing in shrinking cities. Therefore, this study combines multi-source remote sensing images and urban building data to assess the spatiotemporal variation patterns of housing vacancy in a typical shrinking city in China. The following points were obtained: (1) We developed an evaluation model to identify vacant residential buildings in shrinking cities by removing the contribution of nighttime lights from roads and non-residential buildings; (2) The residential building vacancy rate in Fushun city significantly increased from 2013 to 2020, resulting in a significant high-value clustering effect. The impact of urban shrinkage on vacant residential buildings was higher than that on vacant non-residential buildings; (3) The WorldPop population data demonstrated consistent spatial distribution and trend of population change in Fushun with the residential building vacancy rate results, suggesting good reliability of the constructed evaluation model in this study. Identifying housing vacancies can help the local government to raise awareness of the housing vacancy problem in shrinking cities and to propose reasonable renewal strategies.

The carbon cycle of terrestrial ecosystems is influenced by global climate change and human activities. Using remote sensing data and land cover products, the spatio-temporal variation characteristics and trends of NEP in the Yangtze River Delta from 2000 to 2020 were analyzed based on the soil respiration model. The driving influences of ecosystem structure evolution, temperature, rainfall, and human activities on NEP were studied. The results show that the NEP shows an overall distribution pattern of high in the southeast and low in the northwest. The area of carbon sinks is larger than that of the carbon sources. NEP spatial heterogeneity is significant. NEP change trend is basically unchanged or significantly better. The future change trend in most areas will be continuous decrease. Compared with temperature, NEP are more sensitive to precipitation. The positive influence of human activities on NEP is mainly observed in north-central Anhui and northern Jiangsu coastal areas, while the negative influence is mainly found in highly urbanized areas. In the process of ecosystem structure, the contribution of unchanged areas to NEP change is greater than that of changed areas.

The taiga vegetation in Western Siberia has been seriously threatened by climate warming in recent decades. However, how vegetation in different growing states and climate conditions responds to climate changes differently is still unclear. Here we explore the vegetation activity trends in Western Siberia taiga forests using the annual rate of change in leaf area index (LAI) during 1982-2018 so as to answer two questions: (1) how did climate warming affect taiga vegetation activity in the recent last decades? (2) Did the growing state of taiga forest affect its response to climate warming? Our results revealed that climate warming promoted taiga vegetation activity in Western Siberia before 2000. However, continuous warming caused excessive evapotranspiration and led to decreased vegetation activity after 2000. Moreover, the intensity of vegetation growth response to warming was positively related to canopy height and LAI, indicating that both the positive and negative effects of warming were more significant in taiga forests in better growing state. Since these forests generally have higher productivity and play more important roles in ecosystem functioning (e.g., carbon sink and biodiversity conservation), our results highlight their vulnerability to future climate change that need more research attention.

Current ecosystem models used to simulate global terrestrial carbon balance generally suggest that terrestrial landscapes are stable and mature, but terrestrial net primary productivity (NPP) data estimated without accounting for disturbances in species composition, environment, structure, and ecological characteristics will reduce the accuracy of the global carbon budget. Therefore, the steady-state assumption and neglect of elevation-related changes in forest NPP is a concern. The Qilian Mountains are located in continental climate zone, and vegetation is highly sensitive to climate change. We quantified aboveground biomass (AGB) and aboveground net primary productivity (ANPP) sequences at three elevations using field-collected tree rings of Picea crassifolia in Qilian Mountains of Northwest China. The results showed that (1) There were significant differences between AGB and ANPP at the three elevations, and the growth rate of AGB was the highest at the low elevation (55.99 t ha^-1 10a^-1). (2) There are differences in the response relationship between the ANPP and climate factors at the three elevations, and drought stress is the main climate signal affecting the change of ANPP. (3) Under the future climate scenario, drought stress intensifies, and the predicted decline trend of ANPP at the three elevations from mid-century to the end of this century is -0.025 t ha^-1 10a^-1, respectively; -0.022 t ha^-1 10a^-1; At -0.246 t ha^-1 10a^-1, the level of forest productivity was significantly degraded. The results reveal the elevation gradient differences in forest productivity levels and provide key information for studying the carbon sink potential of boreal forests.

Digital elevation model (DEM) plays a fundamental role in the study of the earth system by expressing surface configuration, understanding surface process, and revealing surface mechanism. DEM is widely used in analysis and modeling in the field of geoscience. However, traditional DEM has the defect of single attribute, which is difficult to support the research in earth system science oriented to geoscience process and mechanism mining. Hence, realizing the value-added data model on the basis of traditional DEM is necessary to serve digital elevation modeling and terrain analysis under the background of a new geomorphology research paradigm and earth observation technology. A theoretical framework for value-added DEM that mainly includes concept, connotation, content, and categories, is constructed in this study. The relationship between different types of value-added DEMs as well as the research significance and application category of this theoretical framework are also proposed. The following are different methods of value-added DEMs: (1) value-added methods of DEM space and time dimensions that emphasize the integration of the ground and underground as well as coupling of time and space, (2) attribute-based value-added methods composed of material (including underground, surface, and ground) and morphological properties, and (3) value-added methods of features and physical elements that consider geographical objects and landform features formed by natural processes and artificial effects. The digital terrace, slope, and watershed models are used as examples to illustrate application scenarios of the three kinds of value-added methods. This study aims to improve expression methods of DEMs under the background of new surveying and mapping technologies by adding value to the DEM at three levels of dimensions, attributes, and elements as well as support knowledge-driven digital geomorphological analysis in the era of big data.

In the context of climate change and human activities, flood disasters in arid mountainous areas have become increasingly frequent, and seriously threatened the safety of people’s lives and property. Rapid and accurate flash flood inundation modelling is an essential foundational research area, which can aid in the reduction of casualties and the minimization of disaster losses; however, this modelling is also very difficult, and models need to be urgently developed to address flash flood forecasting and warnings. The objective of this study is to construct a numerical modelling method for flash floods in drylands. Based on a 2D high-resolution flood numerical model (FloodMap-HydroInundation2D), we hindcasted the dynamic process of flash flooding and show the spatio-temporal characteristics of flash flood inundation for the “8·18” flash flood disaster that occurred in Datong county, Qinghai province. The results showed that the model output effectively agreed with the observed inundation after the event in terms of both spatial extent and temporal process. Extensive flooding mainly occurred between 00:00 and 01:00 on August 18, 2022. Qingshan, Hejiazhuang and Longwo villages were affected most heavily. We further conducted model sensitivity analysis and found that the model was highly sensitive to both roughness and hydraulic conductivity in drylands, and the effect of hydraulic conductivity was more pronounced. Our study confirmed the good performance of our model for the simulation of flash flooding in arid areas and provides a potential method for flash flood assessment and management in arid areas.

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.

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.

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.

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.

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.

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.

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.

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.

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.

The spatiotemporal variation in the altitudinal belts in the Kunlun Mountains reflects the natural geographical environment of the arid areas and is essential for constructing a dynamic change system of altitudinal belts in central and western China. In this study, we compiled the altitudinal belt spectra for three typical peaks (Amne Machin Mountain, Muztag Mountain, and Kongur Peak from east to west) using the Google Earth Engine (GEE), Landsat, and NASADEM. Considering historical vegetation surveys, we analyzed spatiotemporal patterns in the Kunlun Mountains for 2000, 2010, and 2020. The results reveal that 1) the altitudinal belt spectra of the Kunlun Mountains exhibited a trend of complexity, simplicity, and complexity, with the number of altitudinal belts decreasing from six to three and then increasing to four from east to west. The dominant belt transitions from an alpine meadow belt to alpine desert steppe and montane desert steppe belts, excluding the nival belt. 2) The upper limits of altitudinal belts are higher on sunny slopes than on shady slopes in the Kunlun Mountains, with an average altitudinal difference of 77.90 m. 3) From 2000 to 2020, there was a widening trend in vegetation and desert belts, accompanied by glacier shrinkage. The range of glacier belts (nival and subnival) decreased by an average of 64 m, resulting in a 64 m rise in the upper limit of the alpine desert steppe belt in the Kunlun Mountains. This suggests drying and warming trends at high altitudes in the Kunlun Mountains in the study period.

Against the backdrop of global warming, the dynamics of glaciers and their water resources have significant implications for hydrological processes in the arid regions of Northwest China. The Aksu River, which is an essential inland river enriched by substantial meltwater contributions, plays a pivotal role in the economic, ecological, and social development of the region. Based on 231 water samples collected during the period of intense glacial ablation in 2023, this study conducted a comprehensive analysis of the hydrochemical and stable isotopic characteristics of the Little Kurgan glacial basin in the Aksu River source region. A Piper diagram classified the hydrochemical type of the river water as Calcium- Bicarbonate. Analysis based on a Gibbs diagram indicated that rock weathering is the predominant factor affecting the hydrochemical properties within the studied basin. Through application of principal component analysis and end-member mixing analysis, it was determined that the glacier meltwater contribution to runoff was 67%, 61%, and 55% in July, August, and September, respectively. The findings of this study reveal that glacier meltwater is the principal component of the river water, and highlight the critical impact of alterations in glacier ablation on the hydrological cycle within the Aksu River source region, which is vitally important for sustainable water resource management.

Potential Natural Vegetation (PNV) represents the climax of vegetation succession in a natural environment, free from significant disturbances. The reconstruction of PNV is widely used to study climate-vegetation relationships and predict future vegetation distributions. However, fine-scale PNV maps with high accuracy are still rare in biodiversity hotspots due to the complexity of ecosystems and limited field observations. In this study, we mapped the spatiotemporal distribution of 16 PNV types using adequate field and literature data, and an improved Comprehensive and Sequential Classification System (CSCS) approach under current (2005-2016) and future (2021-2080) climate scenarios in Yunnan province, Southwest China. We found that 1) from T0 (2005-2016) to T3 (2021-2080), regions with cold alpine PNV types, such as mid-mountain humid evergreen broad-leaved forests (EBLF), are projected to experience more significant temperature increases compared to regions with warm PNV types, like tropic rainforests and monsoon rainforests. High-emission scenarios (SSP585) are expected to result in temperature increases approximately 2℃ higher than low-emission scenarios (SSP126). Precipitation is projected to increase for water-deficient PNV types (e.g., monsoon rainforest and semi-humid EBLF) but decrease for humid PNV types (e.g., rainforest and mountain mossy EBLF). The SSP370 scenario predicts a slightly smaller increase in precipitation compared to other scenarios. 2) All PNV types are expected to shift to higher latitudes (by an average of 0.86°) and higher elevations (by an average of 454 m) by T3, based on their current niches. Alpine PNV types are more sensitive to climate change and are projected to shift more prominently than other types. For example, mountain mossy EBLF is expected to move 1.78° northward, while mid-mountain moist EBLF is projected to rise by 578 m. 3) Cold PNV types are likely to be replaced by warm types both in latitude and altitude. Semi-humid EBLF is projected to shrink the most, by 57,984 km² (51.5% of its present range), while monsoon EBLF is expected to expand the most, by 44,881 km² (64.7% of its present range). The suitable habitat for cold-temperate sclerophyllous EBLF and temperate shrublands may disappear entirely in Yunnan. Given the over-estimate of the projected PNV shift without accounting for the lag effects, these findings are still useful in planning future conservation and management efforts, which should prioritize PNV types experiencing drastic changes in temperature (e.g., mid-mountain moist EBLF), precipitation (e.g., mountain mossy EBLF), and distribution area (e.g., semi-humid EBLF and cold-temperate sclerophyllous EBLF).

While sustainability is widely recognized as a necessary path for development and climate change mitigation, there is no consensus on this concept’s goals and future aspirations. Advocates of a green economy believe that economic growth is a prerequisite for long-term progress and the modernization of society. This entails gradually transitioning to a more sustainable economy and addressing carbon emissions. Therefore, there is a need for the scientific community to investigate how different forms of modernization affect carbon emissions. This study examines the impact of modernization on carbon emissions in China, the world’s largest developing economy, focusing on five indicators of sustainable modernization: ecological modernization, agricultural modernization, digitalization, industrialization, and urbanization. The study analyzes data from 31 Chinese provincial-level regions between 2005 and 2020, using the GeoDetector technique to explore the effects of these indicators on carbon emissions. The spatial analysis reveals a distinct “core-periphery” structure of carbon emissions. The findings demonstrate that ecological modernization and digitalization contribute to reducing emissions. On the other hand, industrialization and urbanization have a positive influence on carbon emissions. Interestingly, agricultural modernization initially increases carbon emissions in the short term but has a diminishing effect in the long term.

The special economic zone (SEZ) is an important place-based policy adopted by the Chinese government to simulate regional and urban growth, and existing studies mainly focus on the impacts of SEZs on local economic outcomes and productivity. This paper establishes the linkage between SEZ and urban spatial structure based on time-series nighttime light images spanning 2000 to 2020 in China. Through a set of time-varying difference-in- differences (DID) regressions at the county level, we find that the introduction of national SEZs has a significant negative impact on monocentricity, while provincial SEZs need to operate for 7 years before they have a substantial impact on spatial structure. However, the average effect masks great heterogeneity with respect to the characteristics and geographic location of zones. SEZs characterized by higher research and development (R&D) intensity, larger scale, and longer establishment duration have more pronounced effects on spatial structure. Geographically, the effects peak when SEZs are 5-15 km away from existing centers, and the effects of SEZs are mainly observed in urban areas and top-tier cities.

Knowledge spillover via collaboration is essential to innovation, with proximity being a vital factor. Nevertheless, little consensus has been achieved on which form of proximity is more critical for innovation. Instead of reaching a definitive conclusion, we highlight the potential of addressing the argument through the lens of innovation heterogeneity. This work thus contributes to current literature by integrating two forms of innovation, radical and incremental, into the discourse of geographical and organizational proximity in knowledge spillover via collaboration. Utilizing a dataset of patents from China’s listed firms between 2001 and 2017, we first categorize radical and incremental innovation according to the characteristics of knowledge combination, encompassing the familiarity of combined knowledge and maturity of combination ways. We further investigate the heterogenous effects of intra-region and intra-group knowledge spillovers, linked to geographical and organizational proximity in collaboration, on radical and incremental innovation. Empirical findings demonstrate that innovation relies on knowledge spillover both within groups and within regions. Moreover, intra-region spillover is essential for fostering radical innovation, while intra-group spillover only facilitates incremental innovation. Our findings provide both theoretical and practical implications, suggesting that multilocational enterprises should enhance their collaborator selection to leverage diverse knowledge spillovers, thereby fostering radical and incremental innovation in distinct ways.

The ecological function of land use is the basis for developing an ecological civilization and realizing sustainable development. This paper may help guide the coordination of economic development and ecological development in China’s coastal border areas. Based on theoretical analysis, this paper studies the spatiotemporal evolution of the functional spaces and the ecological function transitions of land use in the Beibu Gulf Economic Zone (BGEZ) by analyzing patterns, processes, and factors by applying eco-environmental quality index, grid subdivision, kriging interpolation, barycenter model, and Geodetector. This paper constructs a theoretical framework of ecological function transitions of land use based on the research idea of “system-pattern-process-factor”, and carries out empirical research. Some conclusions can be drawn as follows: (1) The ecological space in the BGEZ has continuously decreased, converting mainly into agricultural production space and industrial-mining production space. The production space has expanded slowly. The area of living space in the BGEZ has increased rapidly. (2) The ecological function of land use in the BGEZ has continued to weaken, especially in the southern part of the BGEZ. The “high - sub-high” quality zones of ecological function are retreating to the north. (3) There were more deterioration transition areas than optimization transition areas of ecological function in the BGEZ. The former were mostly located in the central and southern urbanized areas of the BGEZ, whereas the latter were mostly located in the edge zones of district and county units. (4) As for the driving factors of the deterioration transitions of land use ecological function in the BGEZ, the significant trend of “de-ecologization” of the land cover/land use structure was the dominant driving factor; the interactions among the “natural-socioeconomic-managerial” systems were the main recessive factor. The natural system played a fundamental role, and the driving force of natural factors was the strongest. The industrialization, urbanization, and GDP increment in the socioeconomic system and the policy positioning of development intensity in the managerial system played a significant role. The interactions among natural factors, road construction, and industrial non-agricultural transformation had a “fuze” effect on stimulating driving forces.

Research on rural transformation provides a scientific framework for understanding the process and mechanism of rural development. Deepening the study of rural transformation at the micro level can help reveal the universal model and regulatory path of rural human‒land interactions and provide a reference for rural sustainable development. This study constructs a theoretical framework for rural transformation based on the theory of the human-land relationship areal system and selects Majiabian village in Suide county as an example to explore the process and mechanism of typical rural transformation in the loess hilly-gully region through semi-structured interviews and remote sensing image interpretation. The results show that the development of Majiabian village from 1980-2022 can be divided into three stages, i.e., agricultural decentralization under the orientation of local urbanization (1980-1996), rural hollowing under the orientation of rapid urbanization (1997-2012), and agricultural specialization under the orientation of urban‒rural integration (2013-2022), which correspond to the three transition states of SDS₂, RDS, and HDS, respectively. Under the long-standing urban‒rural dual structure, the siphoning effect and other negative impacts of rapid industrialization and urbanization have caused Majiabian village to deteriorate. Fortunately, with the joint efforts of government policies, village elites, and grassroots organizations, Majiabian village has achieved the process of transformation from decline to revitalization. The experience of Majiabian village provides valuable insights for the transformation and revitalization of general villages across the country. We propose that the capacity for sustainable development in such villages can be enhanced in five ways: strengthening policy support, fostering new agricultural business entities, promoting the two-way free flow of factors between urban and rural areas, strengthening rural social governance, and reinforcing the systematic research and practice of geographic engineering.

Land use and land cover change (LUCC) process exhibits spatial correlation and temporal dependency. Accurate extraction of spatiotemporal features is important in enhancing the modeling capabilities of LUCC. Cellular automaton (CA) models, recognized as powerful tools for simulating dynamic LUCC processes, are traditionally applied in LUCC, focusing on time-slice driving factor data, often neglecting the temporal dimension. However, the transformer architecture, a highly acclaimed model in machine learning, has been rarely integrated into CA models for the simulation of dynamic LUCC processes. To fill this gap, we proposed a novel spatiotemporal urban LUCC simulation model, namely, transformer-convolutional neural network (TC)-CA. Based on CA models that involve the utilization of a convolutional neural network (CNN) for extracting latent spatial features, TC-CA extends this paradigm by incorporating a transformer architecture to extract spatiotemporal information from temporal driving factor data and temporal spatial features. The evaluation results with Wuxi city as a study area indicated the advantage of our proposed TC-CA against random forest-CA, conventional CNN-CA, artificial neural network-CA, and transformer-CA. Compared with the three non-transformer-based CAs, the TC-CA improved the figure of merit by up to 2.85%-8.14%. This study contributes a fresh spatiotemporal perspective and transformer approach to the field of LUCC modeling.

In recent years, tourism has emerged as a significant driver of economic development in China’s border regions. The study utilizes various methods, such as the super-efficiency SBM model, spatial variability, cold and hot spot analysis, and Geo-Detector approach, to measure and describe the spatial and temporal evolution patterns of land border tourism efficiency and its influencing factors. The findings reveal that the Dai autonomous prefecture of Xishuangbanna has the highest border tourism efficiency of 1.6207, while Ngari prefecture has the lowest tourism efficiency with a value of only 0.0365 at the prefecture level during the period 2010-2019. The southwest and northwest regions of China are high- and low-level agglomeration areas respectively, indicating varying levels of border tourism development. Additionally, the study identifies an upward trend in China’s border tourism efficiency from 2010-2019. The southwest region emerges as a hotspot and the most active region, while the northwest and northeast regions are considered cold spots with ample room for improvement. Furthermore, the density of transportation facilities, national vulnerability, cultural proximity, the number of border ports, and market opportunity are crucial factors influencing the spatial and temporal pattern of border tourism efficiency in China.

The efficient development of the urban economy is a major concern of scholars in the fields of geography and urban science. In the context of globalization, informatization, industrialization, and urbanization, the external relationships of China’s cities are experiencing the joint action of urban scale hierarchies and connection networks (“hierarchy-network”). However, under the interactive effect of the two, the mechanism of urban economic efficiency (UEE) is unclear. Therefore, based on Baidu migration data, the regionalization with dynamically constrained agglomerative clustering and partitioning (REDCAP) method, and a spatial simultaneous equation model, this paper analyzes the UEE spatial pattern and mechanism in China. The results indicate that: (1) the urban economy has a superlinear relationship with the population size. However, the benefit of this superlinear growth is in marginal decline. (2) The UEE shows a pattern of differentiation between China’s eastern, then central, and then western region. Also, local differences are found within the three major sub-regions. (3) The increase of urban network centrality can promote UEE, while the impact of urban scale is negative. (4) There is regional heterogeneity of the interactive effect of “hierarchy-network” on UEE. This study reveals the influencing mechanism of UEE and also provides policy implications for the development of UEE.

Pseudo Human Settlements (PHS) are a fundamental element in human settlements geography, serving as an innovative frontier in the exploration of human-land relationships. Since entering the information age, PHS have emerged as a new catalyst for people’s lives and urban development. Based on the Baidu Index, cold hot spot analysis and the Pearson correlation coefficient method were used to evaluate the spatiotemporal variation characteristics of the development of the quality of PHS at different levels in the three provinces of Northeast China (TPNC) during 2011-2022 and to characterize the influence of the system and factors. The results indicated that: 1) temporally, PHS exhibits significant fluctuations, with an overall pattern of rapid increase followed by a gradual decline; 2) spatially, PHS is marked by regional differentiation, with “three-core” dominance and a “cluster-like” distribution; 3) systematically, the five major PHS systems generally exhibit an ascending and then a descending trend; 4) in terms of influence, the socialization system serves as the core influence of PHS, with WeChat, JD.COM, and others are identified as the core influencing factors of subsystems. The findings of this study can provide scientific guidance for diversifying approaches to human settlements, promoting sustainable urban development, and revitalizing Northeast China.

A comprehensive understanding of village development patterns and the identification of different village types is crucial for formulating tailored planning for rural revitalization. However, a model for large-scale village classification to support tailored rural revitalization planning is still lacking. This study aims to develop a large-scale village classification model using the Gaussian Mixture Models to support tailored rural revitalization efforts. Firstly, we propose a multi-dimensional index system to capture the diverse features of massive villages. Secondly, the GMM clustering algorithm is applied to identify distinct village types based on their unique features. The model was employed to classify the 25,409 villages in Hubei province in China into four classes. Villages in these classes exhibit discernible differences in spatial distribution, topography, location, economic development level, industrial structure, infrastructure, and resource endowment. In addition, the GMM-based village classification model demonstrates a high level of agreement with evaluations made by planning experts, confirming its accuracy and reliability. In the empirical study, our model achieves an overall accuracy of 95.29%, signifying substantial concordance between the classifications made by planning experts and the results generated by our model. Based on the identified features, tailored paths are proposed for each village class for rural revitalization efforts.

Promoting the green transformation of agricultural clusters represents an effective strategy to address pressing issues related to agricultural resources and environmental concerns. However, existing literature provides limited insights into the internal mechanisms and pathways for achieving green transformation of agricultural clusters. To address the challenges in international research on the collaborative green transformation of entire agricultural value chains, a theoretical analysis framework is constructed in this study, which is characterized by “point-line-plane three-layer embeddedness and four-force interaction,” positioning green innovation as a pivotal entry point. Through social network analysis, this study examines the processes and mechanisms underlying the collaborative green transformation of agricultural clusters and proposes viable pathways for implementation using the Shouguang vegetable industrial cluster as a case study. The research findings are as follows: (1) The green transformation of agricultural clusters includes the green transformation of cluster behavior actors (point), formation of a green innovation network (line), construction of a green environment (plane), and embedded integration and coordinated transformation of the three. Green innovations generated by leading enterprises, universities, and research institutions serve as the foundation for this transformation, whereas farmers’ adoption of these innovations forms the basis, and government policies provide regulatory environment to ensure successful implementation. The transformation is realized through green collaborative innovation and governance, achieving the “three-layer embeddedness.” (2) Under the influence of four driving forces, namely, market-driven mechanisms, environmental regulations, green innovation, and multidimensional proximity, actors at various levels form and embed green innovation networks that are integrated into regional environments through institutional constraints. This results in a “five-in-one” system of collaborative green innovation and governance encompassing enterprises, industries, technologies, institutions, and spatial dimensions, which constitute the internal mechanisms for the green transformation and upgrading of agricultural clusters. (3) Building on the “three-layer and four-force” framework, this study proposes pathways for achieving the green transformation of agricultural clusters, thereby providing theoretical insights and policy recommendations for developing countries to foster green agricultural clusters and enhance their agricultural sectors’ international competitiveness.

Windbreak and sand fixation services (SR) provided by grasslands are a joint result of climate change and human activities. Series of grassland protection measurements have been successively implemented on Inner Mongolia grasslands since 2000, but their effects on SR remains unclear. Based on satellite-derived vegetation dynamics and the Revised Wind Erosion Equation (RWEQ) model, this paper developed a method for quantitatively separating the impact of human activities on SR and revealed the contribution of human activities to SR in the Inner Mongolia grasslands from 2000 to 2020. In 2020, the actual sand fixation (SR_A) of Inner Mongolia grasslands was 12.50 t·ha^-1, spatially characterized as lower in the eastern and western parts, which was dominated by the sparse vegetation coverage and the low potential wind erosion respectively, while higher in the central part, due to the grassland vulnerability. The human-driven sand fixation (SR_H) of Inner Mongolia grasslands changed from -1.28 t·ha^-1 to -0.14 t·ha^-1 from 2000 to 2020, indicating human activities inhibited SR, but the inhibition was gradually weakened. In semidesert and meadow steppes, the SR_H changed from -3.00 t·ha^-1 to 0.00 t·ha^-1 and -0.16 t·ha^-1 to 0.00 t·ha^-1, respectively, which showed that the effect of human activities changed from inhibition to promotion. However, it should be noted that human activities still inhibited the SR in typical steppes. The results implicated that grassland ecological protection should pay much more attention to reasonable use of vulnerable typical steppes. Future grassland use requires quantitative evaluation on the effects of human activity for precise monitoring and sustainable management.

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.

The classification of Chinese traditional settlements (CTSs) is extremely important for their differentiated development and protection. The innovative double-branch classification model developed in this study comprehensively utilized the features of remote sensing (RS) images and building facade pictures (BFPs). This approach was able to overcome the limitations of previous methods that used only building facade images to classify settlements. First, the features of the roofs and walls were extracted using a double-branch structure, which consisted of an RS image branch and BFP branch. Then, a feature fusion module was designed to fuse the features of the roofs and walls. The precision, recall, and F1-score of the proposed model were improved by more than 4% compared with the classification model using only RS images or BFPs. The same three indexes of the proposed model were improved by more than 2% compared with other deep learning models. The results demonstrated that the proposed model performed well in the classification of architectural styles in CTSs.

Mapping floods is crucial for effective disaster management. This study focuses on flood assessment in northern Morocco, specifically Tangier, Tetouan, and Larache. Due to the lack of a comprehensive flood inventory map, we used unsupervised learning techniques, such as K-means clustering and fuzzy logic algorithms, to predict flood-prone areas. We identified nine conditioning factors influencing flood risk: elevation, slope, aspect, plan curvature, profile curvature, land use, soil type, normalized difference vegetation index (NDVI), and topographic position index (TPI). Using Landsat-8 imagery and a Digital Elevation Model (DEM) within a Geographic Information System (GIS), we analyzed topographic and geo-environmental variables. K-means clustering achieved silhouette scores of 0.66 in Tangier and 0.70 in Tetouan, while the fuzzy logic method in Larache produced a Davies-Bouldin Index (DBI) score of 0.35. The maps classified flood risk levels into low, moderate, and high categories. This research demonstrates the integration of machine learning and remote sensing for predicting flood-prone areas without existing flood inventory maps. Our findings highlight the main factors contributing to flash floods and assess their impact, enhancing the understanding of flood dynamics and improving flood management strategies in vulnerable regions.

Previous studies have extensively explored the critical influence of the built environment on land values, but the non-linear relationship has yet to be fully revealed. This study aims to uncover the non-linear relationship between land values and the five built environment dimensions using machine learning algorithms and Shapley Additive exPlanation (SHAP). The results highlight that the Gradient Boost Decision Tree (GBDT) outperforms eXtreme Gradient Boosting (XGBoost), Ordinary Least Squares (OLS), and Multiscale Geographically Weighted Regression (MGWR) in land value estimation, exhibiting higher R² and lower Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The results illustrate that density and destination accessibility are the dominant factors, contributing 32.48% and 37.38% to land value variation, respectively. We observed that the top three factors affecting land values are the built-floor area ratio, the number of floors and the number of restaurants. Additionally, the results revealed the non-linear relationship between the built environment and land values, suggesting that maintaining built environment features at optimal thresholds may increase land values. Neglecting interaction effects may lead to bias in determining relationships between land values and the built environment. This study contributes to the literature by providing non-linear and threshold identification evidence in land value determinants, offering valuable insights for urban planners and real estate managers.

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.