The study is based on the maximum value composite MODIS NDVI data of growing seasons (GNDVI) from 2000 to 2020 in the Three River Headwater Region (TRHR). It uses trend analysis and spatial analysis methods to quantify changes in grassland greenness in the region. Additionally, correlation and partial correlation analyses are applied to explore the relationship between temperature, precipitation, and GNDVI at different temporal and spatial scales. The results of the study show that: 1) From 2000 to 2020, the overall trend of grassland GNDVI in the TRHR has increased, with 77.53% of pixels showing an increasing trend. Among these, 33.95% of pixels show a significant increase (P<0.1). On the other hand, a decrease is observed in some areas, with 22.47% of pixels showing a decreasing trend, and 3.03% of these showing a significant decrease (P<0.1). 2) The pixels with a significant increase in GNDVI are mainly found at elevations of 4500-5000 m and on north-facing slopes with a gradient of 2°-6°. Conversely, the pixels with a significant decrease in GNDVI are primarily located at elevations of 4500-5000 m and on south-facing slopes with a gradient of 6°-15°. 3) Overall, in the TRHR, GNDVI shows the strongest correlation with temperature and precipitation during the growing season. The correlation with the minimum temperature during the growing season (R=0.79, P<0.001) is stronger than with precipitation (R=0.66, P<0.001) and average temperature (R=0.55, P<0.001). The relationship between monthly climate factors at the grid scale and GNDVI shows that the interannual fluctuations of GNDVI are most strongly correlated with precipitation and minimum temperature in July. Spatially, the eastern GNDVI is primarily influenced by precipitation, while the western GNDVI is mainly driven by temperature.

Based on the key research perspective of ‘the difference of status and role of the same city in different scale spaces’, which has been generally ignored in existing urban network studies, this paper takes the population flow between Chinese cities as the analysis path based on Tencent migration big data, and establishes a multi-scale network analysis model covering three levels: metropolitan area, urban agglomeration and national. By analyzing and comparing the multi-scale pattern and cross-scale changes of this intercity population flow network features, the special cities are explored from the multi-scale perspective, and the reasons for the emergence of special cities are initially explained. On the basis of describing the overall pattern of intercity population flow network at multi-scale, the study not only compared the static pattern of the geometric, quantitative and directional characteristics of the intercity population flow network, but also focused on the change trend of the above three characteristics s in the process of the scale expansion of the metropolitan area-urban agglomeration and urban agglomeration-national network. 1) The ‘core-edge’ characteristics of intercity population flow network in China are obvious at the ‘national level’ and ‘metropolitan area level’, while in the ‘urban agglomeration level’, there is a relatively balanced urban community. 2) The regional economic center cities mostly absorb people from the national level network and transport it to the lower level network. 3) In intercity population flow network at multi-scale, the cities with the strongest Closeness Centrality in the same city community are generally stable, while the cities with the strongest Weighted Degree Centrality change more, which reflects that the ‘population mobility scheduling’ ability of each city in the intercity population flow network is more susceptible to the impact of the spatial scale. 4) The population flow between most cities is more active in the metropolitan circle level network and the national level network, but some cities link a wider population base, more convenient circulation path, and are subject to stronger push and pull forces in the urban agglomeration-level network, which explains why the intercity flow activity of these cities also peaks in the urban agglomeration-level network. In the perspective of multi-scales, the differences in status and role of the same city in different scales of space are very obvious. Taking a multi-scales research perspective in urban network studies can help understand the full picture of each city’s role, which is of great value for understanding the network characteristics of cities and formulate the relevant planning and management policies for the coordinated development of urban areas.

Around 2011, the growth rate of China’s economy slowed down, and the fundamentals of the Chinese economy underwent substantial changes. Economic development began to enter a new normal. With the increasingly acute drawbacks of the factor driven economic development model, relying on innovation-driven to shape new driving forces and advantages for development, and achieving the transformation of new and old driving forces for economic growth, has become the key for China to break the shackles of factors and achieve high-quality economic development. As an important bearing space for China to shape new development advantages, cities have already become an important position of innovation-driven development strategy. The improvement of urban innovation-driven level provides a powerful source of power for achieving the goal of Chinese path to modernization. This article is based on the theory of innovation value chain, with technological innovation as the core to construct an urban innovation-driven system. The SBM model of unexpected output super efficiency is used to measure the input-output efficiency of the transformation and diffusion stage of scientific and technological achievements in the urban innovation-driven system, indirectly characterizing the level of urban innovation-driven, identify the spatiotemporal evolution characteristics of innovation-driven level in 284 prefecture level and above cities in China from 2003 to 2017 using the Global Moran’s I and hot spot analysis method, and further analyze the spatiotemporal heterogeneity of factors influencing urban innovation-driven level using the spatiotemporal geographically weighted regression model (GTWR model).The results show that: 1) The overall innovation-driven level of Chinese cities showed a slow growth trend from 2003 to 2017, with an average annual growth rate of 1.32%, fluctuating from 0.307 to 0.369, showing a clear two-stage characteristic. The growth momentum of innovation-driven levels in north China, northeast China, and northwest China is insufficient. The insufficient ability to transform and diffuse scientific and technological achievements, as well as the enormous pressure on carbon reduction, have become the main reasons for the slow growth of innovation-driven level in Chinese cities. 2) The spatial distribution pattern of urban innovation-driven levels has evolved from “high in the west and low in the east” to “high in the south and low in the north”. Correspondingly, the spatial distribution pattern of urban innovation-driven cold and hot spots has evolved from “cold in the east and hot in the west” to “hot in the south and cold in the north”. The spatial distribution of urban innovation-driven growth clusters exhibits a clear “core-edge” feature, which is highly correlated with the spatial distribution of urban clusters, and most provincial capitals/municipalities are regional growth poles. 3) The spatiotemporal evolution of China’s urban innovation-driven level from 2003 to 2017 is the result of a combination of factors, mainly driven by urban affluence and government intervention tendency in the early period, and relying on urban affluence and industrial development level in the later period. In addition, the effect, action intensity and fluctuation direction of each factor on level of urban innovation-driven vary in different regions and periods.

Using panel regression models with time and entity fixed effects and cointegration analysis, the article investigates both internal and external driving factors: 1) Green transportation technology innovation in China is primarily propelled by progress in road transport and enabling technologies in transport, which account for the largest shares, at 62.8% and 51.5%, respectively. 2) The key innovators of innovation has shifted from predominantly individual to enterprise, with firms representing the largest proportion at 82.4%. 3) The spatial distribution of green transportation technology innovation demonstrates notable differentiation and growing concentration, with the Pearl River Delta and Yangtze River Delta emerging as key innovation hubs. Shenzhen has surpassed Shanghai in two fundamental domains: road transport and enabling technologies in transport. 4) External factors such as urban comprehensive transportation accessibility and research and development (R&D) investment universally promote urban green transportation technology innovation at the national scale. In the eastern region, R&D investment and urban comprehensive transportation accessibility exert a stronger positive influence; in the central region, urban scale and R&D investment are the principal driving forces; and in the western region, urban scale, urban transportation logistics industrial location entropy, foreign direct investment, and governmental environmental regulations all contribute to promoting innovation. Compared with green technology innovation, green transportation technology innovation differs significantly in terms of innovation thresholds, the role of foreign investment, environmental regulations, and environmental conditions. Additionally, within green transportation’s internal technological system, innovation in enabling technologies in transport significantly spurs innovation across other categories. This study provides references and insights for the formulation of policy regulation measures tailored to the local context of green transportation technology innovation and development at the national and regional levels.

Based on daily precipitation data from 1970 to 2020, we analyzed the spatio-temporal variation of precipitation seasonality index (PSI) in south and north Qinling Mountains. Then, the empirical orthogonal function (EOF) analysis is performed to identify the leading spatial patterns of PSI in the study region. More specially, we discussed the relationship between the leading spatial patterns of PSI and sea surface temperature anomaly (SSTA). The results show that: 1) The change of PSI in south and north of the Qinling Mountains was mainly synchronous variation over the past 51 years. Before 1997, it could be observed one peak (dry) periods (1975—1986) and two valley (wet) periods (1970—1975 and 1987—1996) of PSI variation. After 1997, the precipitation showed markedly seasonality with a long drier season in 1997—2015, which indicated the dry climate is becoming the normal condition for China’s south-north transitional geographical zone. 2) Spatially, the single type of precipitation seasonality is clearly seen over most regions (61.3% of the study area) and the combined type of precipitation seasonality (32.7% of the study area) does not prevail. In detail, for the single type, the eastern part of Hanjiang River Basin and western part of Daba Mountains (28.3% of the study area) are mainly controlled by a longer wet season. Moreover, precipitation seasonality with the dry−wet balance accounted for 22.9% of the study area, which located in the west of Jialing River Basin, Hanzhong Basin, Ankang Basin and the middle of Guanzhong Plain. 3) This study investigates the first leading spatial patterns of the interannual variability of PSI in the south and north Qinling Mountains. The positive phase of the first leading mode (EOF1) showed characterized by positive PSI anomalies for the whole region. The positive phase of EOF1 was significantly associated with the negative phase of North Atlantic Oscillation (NAO) from pre-winter to spring, as well as the transition from El Niño in pre-winter to La Nina in summer.

Planting industry in China is in a critical period of transitioning from a production-oriented to a quality-oriented presently. Therefore, it is of great significance to analyze the spatiotemporal differentiation and driving mechanisms of ecological efficiency in planting industry, such as achieving agricultural quality and efficiency improvement, promoting its economic ecological coordinated development, and enhancing people’s well-being. DEA-SBM model, carbon emission model, non-point pollution method, spatial analysis technology of GIS and geographical detector model were used in this study, and the conclusions were as follows: 1) From 2010 to 2020, 3 trends of increasing, decreasing, and stabilizing coexisted in the input, and an upward trend in the expected output, while increasing and decreasing trends in non-expected output. The administrative units with low levels of ecological efficiency for planting industry continuously transformed to higher levels, and the hierarchical structure was optimizing; 2) The ecological efficiency for planting industry was higher in the east and lower in the west, and the “upward” and “unchanged” regions alternated from east to west. The changes of ecological efficiency in the east-west and north-south were mild, there were multiple core areas and had a “center-periphery” feature; 3) The ecological efficiency for planting industry in the study area was influenced by multiple factors. Natural conditions are the foundation to affect its pattern and evolution, agricultural technologies are the driving forces, the impact of agricultural economic development has 2 sides, and the product market plays a decisive role; 4) In the future, the planting industry in the study area should focus on the issue of carbon emissions, improve the utilization efficiency of agricultural chemicals, strengthen environmental education and pay attention to the radiation and driving role of the central areas. This study has used indicators such as geographical indications of agricultural products and green foods that reflect the contemporary characteristics of the planting industry to improve the existing evaluation system. It helps to improve the research methods and techniques, provide scientific basis for optimizing agricultural policies, and assist in the strategies of rural revitalization and agricultural high-quality development.

To explore the variations of aeration zone soil specific yield under the condition of deep buried groundwater, The southern edge of Gurbantunggut Desert was taken as the research area by field in-situ observation.The complete specific yield under the condition of zero surface flux, the average releasing specific yield under the condition of evapotranspiration and the average charging specific yield under the condition of lateral leakage recharge were determined, and the effects of groundwater depth, infiltration and evapotranspiration on specific yield were discussed. Results showed that: 1) It is feasible to determine the soil specific yield under the condition of deep buried groundwater by the zone of aeration section water content method. 2) Under the condition of zero surface flux, the complete specific yield μ increases with the increase of groundwater depth H. When the groundwater depth exceeds the maximum rising height of capillary water, the change of complete specific yield is small and can be approximately regarded as a constant. 3) The average groundwater depth of interdune land in the southern edge of Gurbantunggut Desert is 8.80 m. The complete specific yield under the condition of zero surface flux is 0.36, the average releasing specific yield under the condition of deep buried groundwater evaporation is 0.13, and the average charging specific yield under the condition of lateral leakage recharge is 0.17. The results of this study can provide a new idea for the determination of soil specific yield under the condition of deep buried groundwater.

With the advancement of industrialization, globalization, and informatization, various subsystems within rural areas are constantly exchanging material and energy, and the population, land, and industry are important components and core elements of rural cultural, resources, and economic systems, respectively. Among them, the population is an important support for the development of rural industries, the land is the basic carrier for the development of rural industries and the lives of rural populations, and industry is the development path that promotes the prosperity of rural populations and the improvement of rural environments. Based on the 3 subsystems of population-land-industry, the evaluation index system of the rural regional system development in the water source area of the middle route of the South-to-North Water Diversion Project was constructed. The spatial and temporal pattern and evolution mechanism of the coordinated development of the rural regional system from 2000 to 2020 were explored, and the development types were divided by the average trend line. The results show that: 1) The rural regional system development level and coordination level has improved in 2000—2020, both present “East and west high, low in the middle, high and low values staggered distribution” spatial pattern, gradually developed into the northern county of Hanzhong City, Hanbin District of Ankang City, the Danjiangkou Reservoir area surrounding counties as the core of high level concentrated area, and has formed the Hantai District-Hanbin District-Dengzhou City horizontal development axis. 2) Rural regional system coordination type can be divided into 4 types: low coordination level-population development leading, coordination level-population development leading, coordination level-land development leading, high coordination level-industry development leading, and land and industrial development is the main driving force of spatial differentiation to promote the rural regional coordination level improvement. 3) Resource and environmental conditions determine the spatial pattern of rural regional coordination in the water source area in the initial stage, and under the regulation of industrial development and regional policies, the reconstruction of human activities, resources allocation and economic pattern in the water source area is continuously promoted. This study reveals the interaction and mutual influence between human activity intensity, land use change, and industrial and economic integration development in rural areas of water source regions. It can provide methodological and theoretical references for the implementation of rural revitalization strategies in ecologically fragile and extremely poor areas, as well as for the sustainable development of rural areas.

The rural transformation and development is a crucial lever for achieving rural revitalization, and the population migration of rural areas directly impacts the trajectory of rural transformation and development. This paper constructs a theoretical analytical framework for examining the relationship between the population migration of rural areas and the rural transformation and development, considering 3 migration types in terms of the migration-in, migration-out, and migration return. Based on a questionnaire survey of the population migration of rural areas and the rural transformation and development in Zhejiang Province, this article adopts the GIS analysis, the spatial autocorrelation, and multiple linear regression models to investigate spatial characteristics of the population migration of rural areas and its effects on the transformation and development. First, the result shows that the population migration of rural areas in Zhejiang Province has a significant spatial differentiation with 3 patterns in terms of the migration-in, migration-out, and migration return. A spatial pattern characterized by a gradual enhancement from south to north and from east to west is observed. Second, both the migration-out and migration return in Zhejiang Province show positive spatial autocorrelations, which indicates the presence of high-value clustering or low-value clustering. High-value clustering areas of population migration of rural areas are observed around the Hangzhou Bay urban agglomeration. In contrast, low-value clustering areas are identified in the southwestern periphery of Zhejiang. Third, socioeconomic characteristics of the migration population of rural areas, migration patterns, migration objectives, and their contributions and facilitations of the rural transformation and development are main factors that influence the rural transformation and development. Moreover, the migration-in, migration-out, and migration return exert varying degrees of influence on the rural transformation and development.

Technology-oriented enterprises are the primary drivers of urban innovation spaces and have become a key force for countries to enhance their comprehensive national strength. This study utilizes Point of Interest (POI) geographic big data to quantitatively analyze the spatial distribution characteristics and influencing factors of technology-oriented enterprises in China. The findings are as follows: 1) The overall distribution of technology-oriented enterprises in China exhibits a gradient decline from east to west, conforming to the distribution pattern of the Hu Line, with a micro-scale clustering model of “three cores-two rings-multiple nuclei”; 2) Large enterprises are concentrated north of the Yangtze River, mainly in the Yangtze River Delta and Beijing-Tianjin-Hebei region, while medium-sized enterprises dominate in the Pearl River Delta and the southern part of the Yangtze River Delta, and micro-enterprises prevail in the central and western regions; 3) Manufacturing, scientific research and technical services, information transmission, software, and information technology services are the three leading industries among technology-oriented enterprises. Manufacturing is characterized by high-density core areas in the Yangtze River Delta and the Pearl River Delta, scientific research, and technical services form high-value clusters in the east, dumbbell diffusion in the middle, and single-core block distribution in the west. Information transmission, software, and information technology services are primarily distributed along the eastern coastal area , Yellow River Basin and the Yangtze River Economic Belt; 4) Market environment, industrial policy, and economic strength significantly impact these enterprises, with larger enterprises relying more on human capital and small and micro enterprises depending more on government policy support, market environment, and economic conditions. All industries are most affected by policies, with manufacturing also influenced by market structure and industrial layout, and the service industry highly dependent on talent, economic level, and industrial environment.

In the process of plant ecological construction in sandy areas, it is still necessary to conduct in-depth scientific research on what kind of herbaceous plants can achieve the best windbreak effect. This article uses a mobile wind tunnel to plant herbaceous plant communities (Astragalus laxmannii and Leymus chinensis “Zhongke No.1”) in the wild. The effects of two plant communities on sediment transport rate and wind speed profile were explored under four different vegetation cover levels (10%, 20%, 30%, and 40%) to clarify the windbreak and sand fixation ability The wind tunnel field test results show that Astragalus laxmannii has significantly stronger windbreak and sand fixation efficiency than Leymus chinensis, manifested as a significantly lower sediment transport rate than Leymus chinensis under the same vegetation cover and wind speed conditions, but a significantly higher wind speed reduction rate and wind erosion inhibition efficiency than Leymus chinensis. The optimal windproof coverage of Astragalus laxmannii is 30%, which can suppress about 90% of the sediment transport within 20 cm on the surface. When the vegetation coverage of Leymus chinensis is 32%, it can suppress 75% of the sediment transport within 20 cm on the surface. From the perspective of plant morphology and structure, plants with harder stems and multiple clustered stems have better windbreak and sand fixation effects.

In the era of digitalization, it is of great practical significance to explore the spatial network structure of digital economy and its driving factors for promoting the construction of “digital China”. The research applied the modified CRITIC evaluation method to measure the development level of digital economy of China from 2013 to 2020, and explored the evolution characteristics and causes of the spatial network structure of the digital economy by social network analysis. The results show that: 1) The overall level of digital economy development has shown a steady upward trend, and the spatial pattern is characterized by high in the east and low in the west. 2) During the study period, the spatial connection network of the provincial digital economy in China shows a complex situation of multi-threaded and dense networking. The network density is improved, and there is no hierarchical spatial structure as a whole. 3) The economically developed regions have a significant advantage in the spatial network structure, and the connections between the western and border regions and other regions needs to be improved; the condensed subgroup spatial distribution gradually forms an orderly agglomerated distribution. 4) The spatial correlation network of digital economy is affected by the joint action of multiple factors. The level of scientific and technological innovation, government support and geographical distance have always played a significant role, while the effects of economic development level, industrial structure level and urbanization level reflect the stage characteristics by strong first and weak later. The above factors together drive the optimization and restructuring of the provincial digital economy spatial network structure in China.

Optimizing land use objectives offers effective tools for judicious resource allocation. Simulating future land use and carbon stock changes is vital for formulating regional sustainability policies and enhancing terrestrial ecosystem carbon storage. This article analyzed the spatial and temporal differences in land use and carbon storage from 2000 to 2020, predicted the spatial and temporal differences in land use changes and carbon storage under four development patterns, namely, “natural development pattern (BAU), urban development pattern (RED), cultivated land protection pattern (CPS), and ecological protection pattern (EPS)” in 2030 by constructing the coupled land use model (PLUS-InVEST), and estimated the economic value of the carbon storage by combining the formula of compounded present value and compounded terminal value in Lanzhou City from 2000 to 2030. The results showed that land use changes were dominated by the decrease of grassland area and the increase of building land area in Lanzhou City from 2000 to 2020, with a loss of 61.77×10⁴ t of carbon storage during the 20-year period. With the exception of the EPS, in which the carbon storage increased by 5.09 ×10⁴ t, all other scenarios showed different degrees of carbon loss compared to the 2020, with the largest loss of carbon in RED at 56.46×10⁴ t. In this study, the economic value of carbon storage increased by 10.3×10⁸ yuan in Lanzhou City from 2000 to 2020 through the compound present value method, which is mainly attributed to the significant increase in carbon price on the time scale. Compared to the economic value of carbon storage in 2020, the EPS has the highest economic value of carbon storage at 34.58×10⁸ yuan, which is the optimal development model for the study area. This study has important practical significance for the low carbon development of land resources and scientific decision-making of ecosystem management in Lanzhou.

Refinement governance is the future governance direction of the city, and it is also an important challenge for Shanghai to build an outstanding global city. Most of the existing research is based on the innovative mode and management mechanism of urban grid management, but the analysis and mining of grid management event data are still insufficient, and there is a lack of regular analysis of meteorological conditions on the occurrence of events. From the meteorological perspective, this paper uses spatiotemporal feature analysis and natural language processing methods to analyze the features of grid management event data, and uses correlation analysis and frequent pattern mining algorithms to obtain the association rules between meteorological conditions and urban management events. On this basis, the typical meteorological conditions that trigger grid management events are obtained, and the typical event knowledge graph covering meteorological conditions is constructed. The results show that the events are highly correlated with the characteristics of residents’ activities, the occurrence time of events is highly consistent with the working time, and the occurrence area also coincides with the densely populated areas of the city. There is a phenomenon of “concentrated head and long tail distribution” in the category, and a clear clustering structure can be formed in the event word segmentation, forming a co-occurrence term relation network with citizen activities as the main body. Analysis with meteorological data, municipal facilities and sanitation categories have obvious correlations with air temperature, wind-vulnerable structures are greatly affected by wind, and some illegal behaviors are also highly correlated with meteorological conditions. In addition, under specific weather conditions, some events will show an obvious tendency to occur easily. For example, events such as foundation pits, disputes, high-altitude parabolas, and river greening occur under specific weather conditions such as precipitation, low temperature, high temperature and strong wind, and strong winds will also have an amplified effect on environmental problems such as river pollution, open burning and the distribution of leaflet. On this basis, the knowledge graph technology is used to summarize and express the relationship between meteorology and urban operation, so as to form a knowledge framework for urban operation signs triggered by meteorological conditions, which is beneficial for urban operation managers to respond and deal with specific weather conditions in advance, and provide certain decision-making references for Shanghai to improve refined management measures and optimize the urban governance system.

A striking warming trend has triggered extreme weather and events in the regions worldwide. Snowfall is an important indicator of climate change, changes in snowfall at different levels reflect the integrated response of global warming to the trends in key climate variables and extreme events, and significant geographical variation exists in the response pattern of snowfall in different levels across regions. Mountainous areas are highly sensitive to climate change. As an important geographical demarcation line between the north and south of China, the Qinling Mountains belongs to China’s snow-frequent belts, a sensitive area under climate change. Based on the daily data from the 72 meteorological stations, we analyzed the spatio-temporal variation of heavy snowfall and light snowfall during November-March of the following year in the south and north of Qinling Mountains from 1970 to 2018, using the method of the wet bulb temperature dynamic threshold and trend analysis. Moreover, we assessed the relationship between the snowfall and temperature in three sub-regions (Guanzhong Plain, south slope of Qinling Mountains and Hanjiang Valley). The results show that: 1) From 1970 to 2018, the number of rainless days increased, and the solid precipitation decreased obviously during November-March of the following year in the south and north of the Qinling Mountains. The number of snowfall days decreased from 8.5% to 5.2% in the previous period after 1998, and the proportion of sleet days decreased from 1.2% to 0.6%. 2) The response patterns of snowfall to climate change varies from snowfall in different levels. The snowfall amount and snowfall days of light snowfall decreased significantly, as well as intensity of light snowfall increased significantly. However, there was no clear linear increasing tendency in the snowfall amount, snowfall days and intensity of heavy snowfall, which indicates that the change of snowfall in the transition zone between north and south of China is dominated by the decrease of light snow. 3) In terms of the response to the temperature, the snowfall amount and days of light snowfall in the three sub-regions were significantly negatively correlated with temperature changes (P<0.05). Similar results were observed in the snowfall amount and days of heavy snowfall days in Guanzhong Plain and Hanjiang Valley. Meanwhile, three indexes of heavy snowfall were weakly correlated with the temperature of the south piedmont of the Qinling Mountains. Both Guanzhong Plain and Hanjiang Valley are the sensitive area the response of heavy snowfall to the temperature. The findings of this research can provide a theoretical basis for better understanding the winter climate response pattern in winter in the transitional zone between the north and south of China.

Based on the data of the national censuses and 1% population sampling surveys from 1990 to 2020, this study investigates the characteristics and dynamic changes of the amount, source and destination selection of population outflow in the three northeastern provinces, namely Heilongjiang, Jilin, and Liaoning, from the perspective of structural and comparative analysis. The results show that population loss in the three northeastern provinces is a long-term phenomenon which could be dated back to the 1990s. However, the intensity of the population outflow in Northeast China is weaker than other regions with population loss such as central and western provinces according to the outflow amount and rate. The major cause of long-lasting population loss for Northeast China is not the huge amount of population outflow, but the combined effects of the low population inflow rate, the high outflow rate of registered residents, and the simultaneous outflow of urban and rural population. Compared with other provinces with population outflow such as Guizhou, Sichuan, Henan, and Anhui, the destinations of outflowed population from Northeast China are more diversified and decentralized. Economic factors are the common leading factors for the destination choice of the outflow population from Northeast China and other provinces. The outflow population from Northeast China prefer places that are geographically closer and have high-quality employment and public services, but no obvious evidence was found for their salient climate preference as previous studies claimed. The permanence and comprehensiveness of the lack of regional population attraction in Northeast China indicate that the regional governments not only should take measures to delay the outflow of population as much as possible, but also should actively explore new paths of high-quality development for better coping with population loss.

Resource-based cities (RBCs) are a unique type of cities in China that have significantly contributed to China’s national economy. However, the development of RBCs has encountered challenges, and their transformation and development require urgent attention due to the depletion of resources in RBCs and the need to comply with national ecological environment protection requirements in the 21st century. To explore the hotspots and frontiers of RBCs’ transformation and development, this paper analyzes 602 CSSCI journal papers published between 1999 and 2021. The research distribution was analyzed from 3 aspects: time, journal and author distribution, using software such as CiteSpace, VOSviewer, Data source, scientific knowledge graph. The paper explores the overall situation from evolution, hotspots and frontier. The conclusions are as follows: 1) The transformation and development of RBCs has gradually entered the Chinese government’s and academia’s vision since the 1980s, but substantive research only began in 2004. 2) From a time distribution perspective, research on the transformation and development of RBCs in China can be roughly divided into 3 periods: slow exploration period (1980s—2003), high-speed growth period (2004—2013) and steady progress period (2014—); In terms of journal distribution, China Population, Resources and Environment, Economic Geography, Journal of Natural Resources, Geographical Research and Scientia Geographica Sinica are notable journals that significantly supported and led studies on RBCs. Regarding authorship, the study found that Yu Jianhui, Qiu Fangdao, Zhang Wenzhong and Jiao Huafu have had great influence and contribution in this field. 3) The study also identified 6 research frontier trends: green transformation and development of RBCs, population shrinkage of RBCs, carbon emissions of RBCs, spatial reconstruction of RBCs, innovation and transformation development of RBCs, comprehensive management of two special difficult areas of coal mining subsidence areas and independent industrial and mining areas. Finally, the study proposed future research directions in four aspects: high-quality development of RBCs, smart development of RBCs, carbon emission reduction of RBCs and spatial development of RBCs.

Under the new round of technological revolution and industrial transformation, it is crucial to vigorously develop digital technology. This paper defines digital technologies using patent data and employs them as a measure of urban digital technology innovation. Spatial statistical and econometric methods are used to reveal the temporal evolution, spatial evolution, and influencing factors of digital technology innovation in Chinese cities. The results are as following: 1) The scale of digital technology innovation in China presents a monotonically increasing trend driven by multiple factors such as demand and policies, with the innovation scale of 7 subdivided fields maintaining a relatively stable hierarchical pattern. 2) There are significant spatial differences in digital technology innovation across Chinese cities. Its evolution exhibits a notable trend of agglomeration and diffusion coexistence, forming a digital technology innovation pattern with the Beijing-Tianjin-Hebei (BTH), the Yangtze River Delta (YRD), the Pearl River Delta (PRD) urban agglomerations as the core drivers, provincial capitals and sub-provincial cities as multi-point supporters. The spatial evolution of innovation in the 7 digital technology fields shows a similar process of diffusion from core cities to peripheral cities. The outputs of all types of digital technologies are highly concentrated in leading cities like Beijing, Shanghai, and Shenzhen, but the main city compositions for different types of digital technologies differ slightly. Some cities with overall middle-to-high rankings demonstrate relative comparative advantages in specific digital technology fields. 3) The influencing factors of digital technology innovation in Chinese cities exhibit significant spatial spillover characteristics. Knowledge bases and technology introduction are important factors promoting local digital technology innovation. However, the role of institutional factors in driving local digital technology innovation remains to be highlighted. Neighboring regions’ explanatory variables usually have a reverse impact on local digital technology innovation due to the suction effect or a unidirectional impact due to the spillover effect. The heterogeneity analysis shows that the influencing factors for the 7 digital technologies exhibit both similarities and heterogeneities.

The urban thermal environment is deteriorating due to global warming and urbanization. Scholars worldwide are focused on lowering the land surface temperature (LST) to mitigate this issue. Building morphology significantly influences LST by affecting thermal radiation and wind speed. While numerous studies have examined the impact of 2D and 3D building morphologies on LST, many have lacked comprehensive consideration of relevant indicators and thorough analysis based on zoning schemes for various architectural features. In this study, the central urban area of Tianjin was used as a case study, and Landsat-8 remote sensing imagery was utilized to retrieve LST using the radiative transfer equation to characterize the urban thermal environment. Concurrently, building data were employed to quantify 17 building morphology indicators and identify 9 typical urban form prototypes. Subsequently, the differences in LST among the various urban form prototypes were analyzed, and the thermal environmental effects of building morphology within these prototypes were examined using multiple linear stepwise regression. The findings indicate that: 1) significant differences in summer LST were observed among different urban form prototypes, with the highest mean LST in low-rise, high-density blocks (42.58℃) and the lowest in high-rise, low-density blocks (38.16℃); 2) building morphology within different urban form prototypes had a significant impact on summer LST, although the degree of influence varied. The high-rise, high-density blocks had the greatest influence on LST (explanation degree of 45.9%), while the multi-rise, low-density blocks had the smallest influence (17.6%); 3) within typical urban form prototypes, there was a coexistence of differences and consistency in the response of summer LST to various building morphology indicators. Across all urban form prototypes, the direction of influence of each building morphology factor on LST was consistent, but its relative importance varied. Therefore, future urban planning and urban renewal should avoid low-rise, high-density configurations and prioritize high-rise, low-density ones to enhance the thermal environment. Adjusting and regulating building morphology within limited land and space resources can improve the urban thermal environment. Differentiated building morphology planning and design strategies should be implemented with targeted solutions based on the thermal effects of various urban form prototypes.

In recent years, the focal point of policy attention has revolved around how to adapt to new circumstances and implement regional optimization and spatial governance. Chinese government has recently emphasized the construction of a series of well-connected suburban new cities, aiming to drive the development of polycentric urban centers and suburbanization. Consequently, the current policy emphasis is on advancing the rational layout of internal urban spaces through planning and governance, optimizing the internal spatial structure of cities. From a practical perspective, over the past two decades, the internal spatial structure of most Chinese cities has indeed undergone a transition from a single center to multicenter. While factors influencing the internal spatial structure encompass aspects like natural endowments, economic levels, public services, and transportation conditions, the research on these influencing factors has been insufficient due to data limitations and the complexity of real-world issues. Simultaneously, significant transformations have occurred in China’s highways over the past two decades. Numerous studies have confirmed their crucial role in promoting regional coordinated development and reshaping urban systems. However, there has been a prolonged neglect of the impact of highways on the internal spatial structure of cities. Against this backdrop, this study, utilizing the analysis framework of the urban polycentric spatial structure model, specifically elucidates the impact mechanism of highways on the polycentric spatial structure. Simultaneously, based on high-resolution global population distribution data from LandScan from 2001 to 2019, we constructed multiple sets of indicators for the polycentric spatial structure. Employing historical transportation routes, planned routes, and other instrumental variables, we systematically identified the impact effects of highways on the polycentric spatial structure. The results show that: 1) Polycentric spatial structure has become a primary development trend within China’s urban areas. 2) Ray-shaped or peripheral transit highways can enhance the regional advantages of peripheral areas and accelerate the transformation of spatial structure from monocentric to polycentric by agglomeration of land, development zones, enterprises, and population factors. 3) Large cities, eastern and southern cities and cities with low government financial capacity are more effective in shaping polycentric spatial structure of highways. The approach provides insights into using spatial big data technology for researching urban economics issues. The study’s conclusions supplement empirical evidence on the role of transportation infrastructure construction in promoting factor mobility and shaping spatial structures. The study holds significant policy implications for further optimizing the layout of transportation infrastructure, exploring the advantages of polycentric spatial structures, and enhancing urban economic and population carrying capacity.

Taking the metro station domains in the main urban area of Hefei as an example, this study analyzed its spatial vitality characteristics based on multi-source data such as the Baidu heat index. A random forest model was used to measure the correlation of six built environment elements, including geographic location, land use, functional facilities, development intensity, accessibility, and environmental quality, with the spatial vitality of metro station domains. Furthermore, combined with the K-means clustering algorithm, this study classified the types of metro station domains and identified the deficiencies of typical station domains, and then proposed spatial vitality optimization strategies for different types of metro station domains in a targeted manner. The results show that: 1) The spatial vitality of the metro station domains in the main urban area of Hefei was higher in the evening peak than in the morning peak, and higher in the evening peak on weekdays than in the evening peak on weekends. Spatial vitality also showed a differentiation pattern of “high inside and low outside”, with vitality values decreasing from the second ring road to the outside; 2) The relative importance of each built environment variable on the spatial vitality of metro station domains during the morning, evening, and off-peak hours on weekdays and weekends was ranked in descending order: functional facilities>development intensity>geographic location>land use>accessibility>environmental quality; 3) The metro station domains in the main urban area of Hefei were divided into 4 types: mature, growing, low maturity and breeding, showing the significant clustering characteristic of “circle structure”. The mature-type station domains were clustered within the second ring road, the growing-type station domains were distributed in a circular pattern along the second ring road, the low maturity-type station domains were mainly scattered outside the second ring road in the Binhu District and the Economic Development District, and the breeding-type station domains were mainly distributed outside the second ring road in various districts of the city with a wide range. The study aims to provide policy insights for the enhancement of spatial vitality of metro station domains and low-carbon transportation strategies from a built environment perspective.

Taking Shandong Province as an example, this article uses the net carbon sequestration accounting model and Kuznets Curve and other methods to calculate the net carbon sequestration of cultivated land use in 16 cities in Shandong Province from 2001 to 2020, and clarifies the relationship between the spatiotemporal changes of net carbon sequestration of cultivated land use and the economic and social benefits of cultivated land. The results show that: 1) From 2001 to 2020, cultivated land in Shandong Province played a huge role as a carbon sequestration, and the net carbon sequestration showing a fluctuating upward trend. In 2020, the net carbon sequestration of cultivated land use in Shandong Province increased by 33.052%, which has exceeded one-ninth of the national total. 2) The net carbon sequestration of cultivated land use in Shandong Province has exhibited significant spatial heterogeneity characteristics, presenting a spatial pattern of high in southwest and low in northeast. In 2020 alone, the high-value area of net carbon sequestration was 13.743 times that of the low-value area. 3) There exists an upward-downward-upward “N” shaped Kuznets curve relationship between the net carbon sequestration of cultivated land use and the output value of cultivated land per unit area in 16 cities in Shandong Province. 4) There is a significant inverted “U” shaped Kuznets curve relationship between the net carbon sequestration of cultivated land use and the per capita disposable income of rural residents in 16 cities in Shandong Province. The spatiotemporal changes of net carbon sequestration in cultivated land use have a significant responsive relationship with the economic and social benefits of cultivated land use.

Snow cover changes in the middle (2035—2064) and end (2070—2099) of 21st century are investigated over the Qinghai-Tibet Plateau based on the Historical data and ScenarioMIP data of the Coupled Model Intercomparison Project Phase 6 (CMIP6). Compare with the reference period (1985—2014), the mean annual snow cover days and mean snow duration decrease during the middle and end of the 21st century over the Qinghai-Tibet Plateau, and the overall reduction are more pronounce with the increase of greenhouse gas emission concentration; the reduction in the late-21st century is more pronounced compare to the mid-21st century except for the low emission scenario; Spatially, the decrease in the southeast of the Qinghai-Tibet Plateau is more severe than that in the northwest. The snow onset date is delayed and the snow end date is advanced in the middle and late 21st century, the days of former is 1.5-2.0 times that of the latter; The more greenhouse gas emissions the more days the snow onset (end) date is delayed (advanced); The changes of the snow onset date and the snow end date are more pronounced in the late 21st century. Snowfall (temperature) is positively (negatively) correlated with the annual snow cover days; Generally ,the relative contribution rate of snowfall to the annual snow cover days increases with the increase of greenhouse gas emission concentration; Spatially, snowfall (temperature) contributes more to the annual snow cover days in the southern and northern (east and west) parts of the Qinghai-Tibet Plateau. Decrease in snowfall from July to December is greater than from January to June, which may be the reason why the days of snow onset date is delayed more than the days of snow end date is advanced. There are great differences in the future snow cover changes over the Qinghai-Tibet Plateau under different scenarios, so controlling greenhouse gas emissions is crucial to slowing down the future snow cover reduction rate over Qinghai-Tibet Plateau.

The traditional flood risk evaluation takes into account the effects of regional flood risk solely, based on proximity to water or the number of partial river network systems, without considering the impact of river network system connectivity. To address the problem, this study proposes a three-dimensional comprehensive evaluation system for assessing the connectivity within a river network system. The flood risk assessment index system, which considers the connectivity of river systems, is developed based on this framework. Furthermore, a more objective game-theoretic combination weighting method is employed to determine the optimal weights, and the cartographic method for thematic mapping is applied by integrating natural and administrative boundaries. In the case of the Xiangjiang River Basin, this study investigates the feasibility and superiority of the system compared to the conventional flood risk assessment approach. The findings indicate that: 1) The structural connectivity and comprehensive connectivity of the Xiangjiang River Basin increase from south to north, and the functional connectivity of the northern rivers is stronger than that of the southern rivers. The hydraulic connectivity of the main stream is higher than that of the tributaries, and the hydraulic connectivity of the downstream tributaries is higher than that of the upper and middle reaches. 2) The overall flood risk in the Xiangjiang River Basin increases from south to north, and the medium risk area and the lower risk area account for 59.25%. The medium risk area is concentrated in the northern part of the Xiangjiang River Basin, and the southwest and central part of the south of the Xiangjiang River Basin, accounting for 25.01%. The low and lower risk areas are concentrated in the southern mountainous area and the western edge area, accounting for 22.48% and 34.24% respectively. The high and higher risk areas are concentrated in the lower reaches of the Xiangjiang River, accounting for 9.34% and 8.94% respectively. 3) The traditional flood risk assessment underestimates the flood risk in the upstream area of the basin and overestimates the flood risk in the downstream area. However, combined with the actual flooding situation in the Xiangjiang River Basin in recent years, we found that considering the river system connectivity is more objective and delicate than the traditional way to portray the flood risk evaluation results. The results of this study can further improve the scientificity of flood risk evaluation.