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.

The hypsometric index (HI) plays a crucial role in quantitatively evaluating tectonic activity, geomorphic features, and the evolutionary stages of watersheds. In this study, the global spatial autocorrelation Moran’s I index was utilized to assess the spatial distribution pattern of HI, with calculations performed on a 2 km × 2 km grid. Through hotspot analysis and GIS spatial analysis, the characteristics and control factors of HI in the Lanping Basin were investigated. The results reveal the following: 1) HI in the Lanping Basin exhibits an aggregated distribution pattern. Across all grid cells, HI values range from 0.198 to 0.746, with an average of 0.495. This indicates that the basin has reached a mature stage of geomorphic evolution. High HI regions are predominantly located in the Chongshan Mountain, the Longma Mountain, and Xuebangshan uplift belt, while low HI ones are mainly found in the river valleys of the Lancang River, the Bijiang River, and the Yangbi River. Overall, HI gradually decreases from west to east. 2) The correlation between HI and the size of the grid area is weak. However, HI exhibits significant correlations with topographic indexes such as elevation, relief, slope, roundness index, and precipitation. This suggests that HI is not scale-dependent and is unaffected by the grid size. Instead, it is influenced by the shape of the basin, topography, and climate. 3) The order of HI in different lithological areas of the Lanping Basin is as follows: igneous rock area (0.532) > metamorphic rock area (0.503) >sand shale area (0.494)>carbonate area (0.470)>loose rock area (0.446). This indicates that areas with stronger rock corrosion resistance tend to exhibit more mature geomorphic development and evolution. The correlation between HI and structural factors highlights the distance from the main fault as an important factor controlling the spatial differentiation of HI. As the distance from the main fault increases, structural activity weakens, leading to a gradual decrease in HI. In conclusion, the geomorphic evolution of the study area is the result of the interplay between tectonic uplift, precipitation erosion, and lithology resistance. This study provides important support for future endeavors focused on natural disaster prevention, water conservancy project construction, and ecological environment conservation.

Global warming causes change in tree growth and even the whole forest ecosystem. The Jigong Mountain is located in the transitional zone between north subtropical zone and warm temperate zone. Tree growth in the Jigong Mountain is sensitive to climate change. However, the relationship between the radial growth of Pinus massoniana at the edge of the distribution and climate change is not clear. In this paper, the response and stability of whole ring (RW), early-wood (EW) and late-wood (LW) radial growth data of Pinus massoniana to climate change at peak of Jigong Mountain were analyzed. The results showed that the residual chronologies of RW, EWW and LWW of Pinus massoniana in the Jigong Mountain contained more climate information, and there was a high correlation between RW and EWW and LWW chronologies. The response of RW and EWW chronologies to climate was better than that of LWW chronology. There was also a certain difference between RW and EWW and LWW chronologies and the climatic factors. RW and EWW were mainly affected by the temperature and precipitation in the early and late growing season of last year, and the combination of water and heat in the spring was the main limiting factor affecting the radial growth of Pinus massoniana in this area. LWW was mainly affected by the precipitation in last autumn and the combination of water and heat in the early growing season. After the abrupt change of annual mean temperature in the Jigong Mountain in 1994, the annual ring index of RW and EWW increased obviously, but the annual ring index of LWW did not change visibly. The response of RW and EWW to temperature was enhanced while the response of LWW to temperature was not obvious. Therefore, through the analysis of different indexes of the annual rings of Pinus massoniana at Peak of the Jigong Mountain, we can deeply understand the different responses of radial growth of Pinus massoniana to temperature changes, and provide a scientific basis for forest renewal, protection and management in the western Dabie Mountains.

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.

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.

As the primary industry, agriculture bears essential responsibilities such as ensuring food security, maintaining social stability, and protecting the environment. High-quality agricultural development is the cornerstone of high-quality economic development in China, and it is also necessary to accelerate the construction of agricultural power. Comprehensively measuring the high-quality development level of agriculture in the Yellow River Basin will help to exert the ecological protection barrier of the Yellow River Basin, ensure national food security, promote agricultural modernization, and accelerate the construction of agricultural power. An evaluation index system for high-quality agricultural development was constructed based on the new development concept. Taking 76 prefecture-level cities in 8 provinces (regions) in the Yellow River Basin as research objects, the AHP-entropy method was used to measure the high-quality agricultural development level in the Yellow River Basin from 2016 to 2020. Dagum Gini coefficient and Kernel density estimation describe its spatiotemporal evolution law. The results shows that: 1) The overall high-quality agricultural development in the Yellow River Basin shows an upward trend but is still at a low level between 0.25 and 0.35. The high-quality agricultural development in the upper, middle, and lower reaches shows a spatial distribution pattern of “high on both sides and low in the middle”. The development level of each dimension is generally low, especially the innovation dimension, which has the lowest development level, indicating that agricultural development in the Yellow River Basin lacks endogenous growth momentum. 2) The Dagum Gini coefficient shows that the intra-regional differences in the upper, middle, and lower reaches of the Yellow River Basin are constantly shrinking, showing an overall downward trend, and the inter-regional differences generally appear as upstream-downstream>upstream-middle reaches>midstream-downstream. Inter-regional differences are the primary source of differences in high-quality agricultural development levels in the Yellow River Basin, followed by intra-regional differences, with hypervariable density differences making the most negligible contribution. 3) Kernel density estimation shows that the center of the distribution curve of the entire basin moves to the right, indicating that the level of high-quality agricultural development in the Yellow River Basin is gradually improving. The distribution curves in the upper, middle, and lower reaches all have a right tail, indicating that the level of high-quality agricultural development in some regions is significantly higher than that in other cities in the same area. The distribution curves changed from single peak to bimodal or multi-peak after 2018, indicating that the high-quality agricultural development in the Yellow River Basin has polarized and stratified characteristics after 2018, showing a pronounced gradient effect. Accordingly, it is necessary to adhere to the development strategy of promoting agriculture through science and technology, assisting agriculture through science and technology, and empowering agriculture through science and technology. Downstream regions must fully play their role as “leaders” and promote the construction of systematic projects for agricultural innovation-driven development and high-quality development. Taking the coordinated development of agriculture in the upper, middle, and lower reaches as an essential focus to promote the high-quality development of agriculture in the Yellow River Basin, each region must capitalize on its unique advantages, adapt to local conditions, and coordinate as a whole to promote the overall high-quality development of agriculture in the Yellow River Basin. Central regions should leverage the spillover effect and radiation driving role on the surrounding areas, smooth the channels and mechanisms for cooperation and exchanges between regions, and truly achieve “point-to-area” assistance.

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.

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.

Under the dual influence of climate change and human activities, grassland degradation and land desertification in the Mongolian Plateau are intensifying, posing a threat to the sustainable economic development and regional ecological security of China and Mongolia. Based on the long-term series remote sensing data of nearly 40 years, the pattern dynamics and influencing factors of grassland net primary productivity (NPP) in typical areas of the Mongolian Plateau were discussed, and the comparative analysis between China and Mongolia was further carried out considering the differences in development models and policy orientations between China and Mongolia. The results showed that: 1) From 1982 to 2020, the average annual NPP of grassland in typical areas of Mongolia was significantly lower than that of China, about 4/5 of that of China; grassland NPP in typical areas of Mongolia showed an increasing trend, grassland was mainly restored, and the growth rate of NPP in China was about twice that of Mongolia, and showed significant performance. 2) From the first stage (1982—1999) to the second stage (2000—2020), the proportion of NPP increase in grassland NPP in typical areas of China and Mongolia increased by 19% and 22.1%, and the proportion of significance increased by about 19.7% and 10.8%, respectively. 3) The increase of grassland NPP in typical areas of China and Mongolia is mainly caused by climate change, followed by the combined effect of climate change and human activities, while the decrease of grassland NPP is almost entirely caused by human activities. The differences are: the increase of grassland NPP in Mongolia shows the driving evolution characteristics from two-factor to single-factor, while China shows continuous three-factor driving characteristics. The decrease of grassland NPP in Mongolia was manifested as a single-factor feature driven by human activities, while in China, it was manifested as a three-factor driving feature dominated by human activities. 4) Precipitation is the main factor affecting grassland recovery in China, human activities are the main drivers of grassland degradation in the two countries, but due to the differences in policies between the two countries, Mongolia’s regional grassland NPP reduction area accounts for a much larger proportion than China’s. Therefore, China needs to continuously strengthen ecological engineering construction and policy implementation to promote grassland restoration; Mongolia needs to learn from China’s experience, improve the industrial structure and management measures of animal husbandry, and accelerate grassland ecological protection and governance with policy guidance.

Immersion tourism context are crucial to the formation of memorable and extraordinary tourism experiences, and interaction is an important dimension in immersion tourism context, so it is valuable to explore the tourist multiple interactions and their group influence paths in immersion tourism context. This study analyzes the concepts and dimensions of tourist multiple interactions in immersion tourism context based on “context-cognitive-affective unit-behavioral unit” combined with CAPS theory, and explores the grouping paths of the influencing factors of tourist multiple interactions by using fuzzy-set qualitative comparative analysis. Firstly, the concept of immersion tourism context is proposed according to the existing literature, which is a virtual or real space integrating technology, narrative and interaction that deeply integrates culture and tourism. Second, based on the CAPS theory and the combination of qualitative and quantitative methods, a scale of 3 dimensions and 15 question items of tourist multiple interactions including cognition interaction, emotion interaction and behavior interaction was developed. Finally, among the 6 influencing factors of destination elements, destination charm, consumer innovativeness, tourist involvement, place identity and place dependence, a total of 11 grouping paths were extracted using the fsQCA, which were categorized into 4 types of logics, including place attachment logic, situational participation logic, image cognition logic and deep experience logic. This study provides an effective reference for future academic research related to tourist immersion experience, tourism context, and tourist interaction, as well as practical insights for immersion scene development, management, and marketing in tourism destinations.

With the promotion of high-quality development in China, high-quality export development has become an important direction for foreign trade. As an important carrier of China’s ‘going global’ strategy, overseas cooperation zones (OCZs) have gradually become an important measure to promote high-quality export development. This article takes the establishment of the OCZs as a quasi-natural experiment and uses a multi-period difference-in- difference model to study the impact and mechanism of the establishment of the OCZs on China’s export product structure. The results show that the establishment of the OCZs significantly reduces the export share of non-technical products and increases the import share of technical products, and improves the ratio of technical products to non-technical products, optimizing China’s product structure; after parallel trends and a series of robustness tests, this conclusion still holds; Mechanism testing shows that OCZs mainly expand the technology spillover effects of outward foreign direct investment, improve infrastructure construction, enhance institutional quality, in order to achieve the optimization of China’s export product structure. Heterogeneity analysis found that: compared with developed countries (areas), the OCZs have a higher degree of optimization in China’s export product structure in developing countries (areas); The OCZs dominated by Chinese private enterprises have a more significant optimization of China’s export product structure to the host countries (areas); The optimization effect of open cooperation zones on China’s export product structure is mainly reflected in developing countries (areas) and cooperation zones dominated by private enterprises. Further analysis shows that the OCZs have significantly increased the export share of China’s low tech and medical technology products, but have no significant impact on the export proportion of high-tech products. This article provides policy recommendations for China to promote the optimization of its export product structure by establishing OCZs, and also provides empirical evidence for China to accelerate its ‘going global’ strategy to achieve high-quality export development.

The Hobq Desert is the seventh largest desert in China, and it is also the most distributed desert in the east. In China, the water and soil resources are better in the desert, and it is the area with the most work in the practice of sand prevention and control in China. The information of desertification land change is one of the core index pieces of information of ecological restoration project effectiveness evaluation. In order to deeply analyze the change of desertification land in this area, the remote sensing images from 1987 to 2022 were selected. Based on 3S technology, the NDVI pixel dichotomy model was used to calculate the temporal and spatial dynamic changes of desertified land. Quantitative analysis of the influencing factors affecting the spatial change of desertification land was carried out by using geographic detector. Finally, the PLUS model was selected to predict the distribution pattern of desertified land in 2032. The results showed that: 1) During the 35 years, the vegetation coverage of the Hobq Desert showed an overall growth trend, from 13.59% to 30.43% ; from west to east, there is a clear division from mobile sandy land to fixed sandy land, and the vegetation in the north is mostly along the yellow section. 2) The area of mobile sand decreased by 50%; the area of semi-fixed sandy land increased by 10%, mainly from the management of mobile sandy land. Fixed sandy land increased by 89.80%, with the largest increase of 2.3% in 2017. 3) The desertification cultivated land increased from 13.98% to 17.56% in 35 years, with an average increase of about 1%-2% every 5 years. 30.83% of the increase in the area of desertified cultivated land came from semi-fixed sandy land, and 6.49% came from mobile sandy land. Abandonment or encroachment of desertified farmland led to an increase of 18.97% in grassland area. 4) Social and economic factors are the dominant factors of land use change in the Hobq Desert. In the future, the Hobq Desert will develop in the direction of increasing semi-fixed sand, fixed sand, forest land, grassland, cultivated land and other resource-based land. The reduction of desertification land and the increase of vegetation cover in the study area can provide reference and data reference for the follow-up desert control and desert project implementation along the Yellow River.