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.

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.

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.

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.

New quality productive forces are the core driver for building a modern tourism sector and also support the development of a strong tourism nation. In order to deeply understand the scientific connotation of new quality productive forces and clarify the theoretical logic and strategic path of new quality productive forces empowering the high-quality development of tourism industry, several experts on regional economic development, the digital economy, tourism management and geography were interviewed. The interviews were based on the logic of "problem orientation-innovative thinking-path mechanism", focusing on the background, opportunities and challenges, core meanings, drivers, and innovations empowering the high-quality development of tourism new quality productive forces. There were three main conclusions from this research. The first was the strategic opportunities and risks of promoting the high-quality development of tourism through new quality productive forces against a background of rapid and continuous technological change. New quality productive forces optimize resource allocation through scientific and technological innovation, and improve the production efficiency and growth quality of tourism. They also produce new tourism development models and forms of business, constantly generating momentum to drive high-quality economic and social improvements. Especially in rural tourism, the role of new quality productive forces is particularly significant. It promotes the gradual improvement of the rural tourism production network, enhances cooperation among rural tourism stakeholders, helps rural tourism participants evolve new production initiatives, and ultimately reinvigorates rural areas with increased prosperity. However, given globalization and rapid digitalization, innovation in tourism faces a series of challenges. In particular, the lack of systematic development of tourism software and hardware, risks of data security and privacy protection, resistance to change and innovation in traditional tourism, and the "growing pains" brought by the transformation to new technologies deserve attention. The development of new quality productive forces in future tourism must focus on technological innovation, find and prepare a quality tourism workforce, optimize and more closely integrate products and services with human talent, and significantly improve the total factor productivity of tourism. Second, the process of empowering the high-quality development of tourism must encompass technological innovation leading to industrial modernization. Innovation plays a leading role in new quality productive forces and is the core driver of the high-quality development of tourism. The new quality productive forces empowering this tourism development have several specific features. Technological innovation leads the modernization of the tourism sector and is a prerequisite for the high-quality development of tourism. Factor integration and supply-demand matching are the intrinsic requirements for the high-quality development of tourism. Other critical ingredients are digitization, greening and artificial intelligence. The significant improvement of total factor productivity must be the core goal for the high-quality development of tourism. Third, the guidance of national strategy is crucial to the progress and prospects for new quality productive forces empowering the high-quality development of tourism. New quality productive forces are receiving widespread emphasis since their inception and have become a core issue highly valued by the tourism sector in China. In the New Era, modern technology has become a key production factor in tourism. Additionally, the transformation and upgrading of tourism is dynamically advancing, growth is strengthening, and the ability of tourism to serve national economic and social-cultural strategies is becoming more noticeable. However, it should not be overlooked that high-quality tourism development is still faced with serious problems such as weak technological innovation capabilities, uneven regional development, inadequate circulation of factor resources, and insufficient human talent for tourism. To further enhance the beneficial impacts of new quality productive forces in stimulating the high-quality development of tourism, it is necessary to focus on deepening the reform of the system and operations in tourism, optimizing the creative allocation of tourism production factors, accelerating the development of a modern tourism sector, and improving the quality of professional tourism talent. These three recommendations will not only enhance the understanding and application of new quality productive forces to a certain extent, but also provide decision-making support for building China into a leading tourism nation in the world.

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.

Achieving a balance between environmental conservation and tourism development, and addressing the negative effects of seasonal fluctuations are crucial for the high-quality development of ecotourism destinations. Touristification serves as a prevalent strategy to address these challenges in these areas. However, existing research on touristification lacks effective analytical tools and explanatory mechanisms, making it difficult to delineate the evolution stages, causal mechanisms, and spatiotemporal impacts of tourism development over time. This paper extends the application of touristification theory through a case study, constructing a spatiotemporal model of touristification. Employing observation methods, interviews, and the collection of secondary data, we have obtained multi-source data to delineate the over-two-decade touristification process of the Sayram Lake scenic area in Xinjiang, from an ecological pasture to a mature ecotourism destination. Our study elucidates the multidimensional evolution of touristification and the developmental logic of its various stages. The findings reveal that Sayram Lake has undergone four distinct stages of touristification: government-led internal spatial touristification, government-led external spatial touristification, enterprise-led in-depth spatiotemporal touristification and government and enterprise-led off-peak temporal touristification. In general, tourism destination exhibited an initial internal then external touristification; the focus shifted from the high season and daytime first, then touristification to night-time and off-peak periods. Each stage of touristification at Sayram Lake intersects with the others, each with distinct key issues and driving forces at play. Internal touristification is key to building attractions, while external touristification is crucial for enhancing accessibility, and both should be harmonized. This paper introduces a new touristification model that provides analytical tools to understand the evolutionary patterns of touristification, the causal relationships at different stages, and their impacts. The model offers guidance for balancing conservation and development in ecotourism destinations, resolving the supply and demand contradictions between peak and off-peak seasons, and promoting high-quality development. By providing case studies and pathways, this model serves as a reference for stakeholders in ecotourism to effectively navigate the complexities of touristification.

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.