The Biome-BGC model is a classic forest ecosystem carbon cycle model driven by remote sensing and plant trait data, and it has been widely applied in various regions of China over the years. However, does the Biome-BGC model have good applicability in all regions of China? This question implies that the rationality of some applications of the Biome-BGC model in China might be questionable. To quantitatively assess the overall spatial applicability of the Biome-BGC model in China’s vegetation ecosystems, this study selected ten representative forest and grassland ecosystem sites, all of which have publicly available carbon flux data. In this study, we first used the EFAST method to identify the sensitive ecophysiological parameters of the Biome-BGC model at these sites. Subsequently, we calibrated the optimal values of these sensitive parameters through a literature review and the PEST method and then used these to drive the Biome-BGC model to simulate the productivity (including GPP and NEP) of these ten forest and grassland ecosystems in China. Finally, we compared the simulation accuracy of the Biome-BGC model at these ten sites in detail and established the spatial pattern of the model’s applicability across China. The results show that the sensitive ecophysiological parameters of the Biome-BGC model vary with spatial distribution, plant functional types, and model output variables. After conducting parameter sensitivity analysis and optimization, the simulation accuracy of the Biome-BGC model can be significantly improved. Additionally, for forest ecosystems in China, the model’s simulation accuracy decreases from north to south, while for grassland ecosystems, the accuracy increases from north to south. This study provides a set of localized ecophysiological parameters and advocates that the use of the Biome-BGC model should be based on parameter sensitivity analysis and optimization.

Vegetation, as a crucial carbon sink, is facing extensive degradation under the mounting pressures of urbanization and excessive resource exploitation, exacerbating the imbalance between carbon sources and sinks. In response, the ecological spatial network has emerged as a comprehensive conservation strategy to establish and maintain connectivity and interactions among diverse ecosystems, ensuring the continuous provision of ecological services and preservation of biodiversity. A pivotal indicator in this context is vegetation carbon use efficiency (CUE), which elucidates the relationship between CO2 assimilation through photosynthesis and biomass growth. In this study, utilizing remote sensing data, the Yellow River Basin (YRB) was selected as a case study to analyze vegetation CUE through the Carnegie Ames Stanford Approach (CASA) and Light Use Efficiency-Normalized Difference Water Index (LUE-NDWI) models. Furthermore, the Morphological Spatial Pattern Analysis (MSPA) method and Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model were employed to delineate ecological sources for woodlands, shrublands, and grasslands, while the Minimum Cumulative Resistance (MCR) model was used to identify ecological corridors, forming an ecological spatial network within the YRB. Subsequently, the interrelationship between topological metrics and vegetation CUE was analyzed, and optimization strategies were proposed based on the significance of the structure and function. The findings revealed that: (1) Vegetation CUE exhibited a spatially decreasing trend from the western to eastern regions, with spatial patterns correlated with vegetation types, temperature, and precipitation distribution; (2) The ecological spatial network demonstrated a denser configuration in the upper and middle reaches and a sparser and shorter pattern in the lower reaches, with seamless connectivity among all regions; (3) Significant correlations were observed between topological metrics and vegetation CUE, prompting the adoption of ecological construction and protection measures when functional importance is deficient, and enhancing the connectivity of ecological sources through the augmentation of ecological corridors when structural importance is lacking. The implementation of these strategies is expected to bolster the ecological functions of the network, facilitate carbon cycling, and exert a positive and far-reaching impact on the sustainable development of the ecological environment in the YRB.

Amid China's high-speed urbanization, the lack of urban landscape and homogenization of urban image are becoming increasingly serious, prompting discussion on how to preserve the natural urban landscape. This study extends the existing landscape assessment model to evaluate the urban hillscape of the Lishui urban area of Nanjing over the past 50 years. First, a landscape quality analysis is conducted based on raster images, and a landscape quality map of the study area is drawn. Then, a visibility analysis of the site is conducted, key observation points (KOPs) are screened using a digital surface model, and the visual magnitude of each KOP is quantitatively calculated. Finally, the visual impact scores of the mountains in the site are synthesized. The results show that the model can accurately quantify the visual impact of historical mountain views. The following conclusions are drawn (1) The visual quality of the landscape in the area has improved as the urban area has spreads out. (2) The viewshed and magnitude of the area are trending downward over time. (3) The Chengnan area's visual impact score increased by 25.7%, but that of the Tuanshan and Chengzhong areas decreased by 48% and 26.9%, respectively. This study presents a spatial decision support system that can obtain better landscape assessment results with more accessible open-source data, and can be used for historical landscape evaluation. By comparing the characteristics and trends of urban hillscapes in different historical periods, this study's landscape assessment model can help managers better understand the impact of development on urban intentions and make better decisions.

The sequestration of carbon in forests plays a crucial role in mitigating global climate change and achieving carbon neutrality goals. Carbon use efficiency (CUE) is an essential metric used to evaluate the carbon sequestration capacity and efficiency of Vegetation. Previous studies have emphasized the importance of assessing CUE at specific regions and times to better understand its spatiotemporal variations. The southeastern region of Tibet in the Qinghai-Tibet Plateau is recognized as one of the most biodiverse areas in China and globally, characterized by diverse vegetation types ranging from subtropical to temperate. In this study, we focused on Nyingchi, which is the largest virgin forest area in southeast Tibet, to explore the spatial-temporal dynamic characteristics of regional CUE based on MODIS remote sensing products. The following results were obtained: (1) On a monthly scale, regional CUE exhibits significant seasonal variations, with varying patterns among different vegetation types. Specifically, the fluctuation of CUE is the lowest in high-altitude forest areas and the greatest in grasslands and barrens. On an annual scale, forests exhibit higher fluctuations than areas with sparse vegetation and the overall volatility of CUE increased over the past 11 years. (2) There are regional differences in the trend of CUE changes, with a substantial downward trend in the Himalayan region and a significant upward trend in the residual branches of the Gangdise Mountains. More than 75% of the regions exhibit no persistent trend in CUE changes. (3) Vegetation type is the main determinant of the range and characteristics of vegetation CUE changes, while the geographical location and climatic conditions affect the variation pattern. CUE in the southern and northern regions of Nyingchi at 28.5°N exhibits different responses to temperature and precipitation changes, with temperature having a more significant impact on CUE.

Promoting green production behavior among farmers is crucial for enhancing income, improving industrial efficiency, and ensuring ecological security. This study analyzes the determinants of green production behavior among farmers in Cheifeng, China, Using a binary logistic model on a sample of 860 microdata. The result of empirical analysis pointed out that the implementation rate of rural households was low at 54.5 %, highlighting the need for intervention. The results suggest that age, party members, total income, total area, irrigation conditions, market, policy, social factors, and knowledge transfer ability significantly influence green production behavior. Among these factors, age, party member, total area, irrigation conditions, and knowledge transfer ability have negative effects, while other factors positively affect green production. To promote green production behavior, this study recommends improving access to information, developing a training system, enhancing quality and safety information disclosure, establishing traceability mechanisms, and providing policy subsidies. This stud also highlights the need to strengthen publicity, technical guidance, and media persuasion for promoting the green production of Chinese medicinal materials.

There are some inefficient turbines in active wind farms, which affect the performance and normal benefits of wind farms. In view of the above problems, a wind farm post assessment was proposed, in which the low-efficiency turbines are defined and determined with the low efficacy factors been analyzed. The proposed method was applied in the actual wind farm in Hebbei. First of all, the low-efficiency turbines are determined based on the wind speed and power generation. The data shows that there are 3 low-efficiency turbines out of the 19 turbines in the wind farm. Then, the wind shear formula is used to calculate the converted wind speed of each turbine, and the relative error between the converted wind speed and the measured wind speed is judged to be consistent with the relative height of the turbine and the wind tower. The results show that two inefficient turbines are affected by the wake. Finally, the wake model was used to verify the two turbines, and the calculated values were in good agreement with the measured values, proving the effectiveness of the method. This study can provide a certain post-field evaluation for in-service wind farms and provide guidance for the operation and maintenance strategies of wind farms.

In recent years, the accelerated pace of urbanization has increased patch fragmentation, which has had a certain impact on the structure and ecological environment of forest–grass ecological networks, and certain protection measures have been taken in various regions. Therefore, studying the spatiotemporal changes and correlations of ecological service functions and forest–grass ecological networks can help to better grasp the changes in landscape ecological structure and function. This paper takes the Wuding River Basin as the research area and uses the windbreak and sand fixation service capacity index, soil conservation capacity, and net primary productivity (NPP) to evaluate the ecological service capacity of the research area from the three dimensions of windbreak and sand fixation, soil conservation, and carbon sequestration. The Regional Sustainability and Environment Index (RSEI) is used to extract ecological source areas, and GIS spatial analysis and the minimum cumulative resistance (MCR) model are used to extract potential ecological corridors. Referring to complex network theory, topology metrics such as degree distribution and clustering coefficient are calculated, and their correlation with ecological service capacity is explored. The results show that the overall ecological service capacity of sand fixation, soil fixation, and carbon sequestration in the research area in 2020 has increased compared to 2000, and the ecological flow at the northern and northwest boundaries of the river basin has been enhanced, but there are still shortcomings such as fragmented ecological nodes, a low degree of clustering, and poor connectivity. In terms of the correlation between topology indicators and ecological service functions, the windbreak and sand fixation service capacity index have the strongest correlation with clustering and the largest grasp, while the correlation between soil conservation capacity and eigencentrality is the strongest and has the largest grasp. The correlation between NPP and other indicators is not obvious, and its correlation with eccentricity and eigencentrality is relatively large.

The transformation of the landscape structure in mining cities is influenced by a combination of various factors, resulting in a decline in the ecological quality of the landscape and an increased vulnerability of the ecological environment. The quality of landscape ecology directly impacts the flow and transmission of ecosystem functions, underscoring the need for careful consideration in the establishment of landscape ecological networks. In this study, an analysis was conducted using land-use data from 2005 to 2020 in Linfen City, Shanxi Province, China. A network optimization strategy was established, focusing on the capacity for ecosystem self-restoration and the connectivity of ecological patches. Additionally, the spatiotemporal characteristics of landscape ecological risk between 2005 and 2020 were examined. Taking into account the influence of the ecological value of the landscape on the resilience of the ecosystem, the Minimum Cumulative Resistance (MCR) model was employed to construct the landscape ecological network. The structural characteristics of the landscape ecological network were explored using complex network methods. In addition, an optimization strategy based on ecosystem functionality and connectivity (EC) was adopted, and by comparing the connectivity and robustness of the network before and after the optimization, it was found that the method enhanced the smoothness of energy transfer and interconnectivity among nodes of the network, and significantly enhanced the stability of the ecological network. Within the study area, local levels of risk increased due to coal mining and urban expansion. (The maximum value in 2005 was 335.) However, overall risk levels improved with the progress of land reclamation efforts. (The maximum value in 2020 was 325.) Higher risks were observed in the vicinity of urban construction and mining areas, while forested and grassland areas exhibited relatively lower risks. The changes in risk within the study area were primarily influenced by factors such as mining activities, urban expansion, government policies, changes in land use types, and village relocation. The findings of this study provide theoretical support for the optimization of the landscape structure in mining cities, the construction of systems of ecological security as well as the restoration of ecosystems.

The leachate generated from the long-term storage of waste residue and waste liquid produced in mining mineral resources diffuses into the soil, which is easy to cause the surrounding soil to be polluted by heavy metals and affects its crop growth. After human beings eat fruits containing heavy metals through the food chain, they will cause neurasthenia of the nervous system, numbness of hands and feet, indigestion of the digestive system, blood poisoning, kidney injury and other symptoms. Then it will pollute and damage the ecological environment and personal safety, therefore, how to quickly find out the situation of soil pollution is particularly critical. With the development of remote sensing technology, multispectral remote sensing has great potential in breaking through the vegetation barrier to monitor heavy soil metals because of its high spectral resolution and real-time non-destructive and large-area monitoring advantages. This study takes peach trees, the main crop in Pinggu District, as the research object. Using hyperspectral data of peach leaves and field soil sampling data, the response characteristics of peach leaf spectral curves were analyzed. The reflectance spectra of peach leaves were transformed by first-order/second-order differentiation, standard normal transformation and continuous de unification. The characteristic variables are determined by correlation analysis and multiple linear regression model, construction of vegetation index HMSVI, the correlation between HMSVI and Cd, As and Pb content is higher than that of common vegetation index. After modeling element content and vegetation index HMSVI by linear regression method, selecting the model with good fitting, the statistical modeling of leaf hyperspectral reflectance spectrum and soil heavy metal content was realized, and the spatial distribution of heavy metal content was retrieved from sentinel-2 remote sensing image, and the results were verified. The results show that : the average spectral reflectance of leaves under heavy metal stress was higher than that of normal leaves, and the phenomenon of "blue shift" occurred. 780, 945 and 1 375 are the most sensitive to heavy metal pollution. The inversion model established by using the vegetation index constructed in three bands can be better used to predict the content of heavy metal elements in peach forest soil. The prediction models are y = 0. 44x+ 0. 193, y = 7. 4361nx +13. 161, y = - 15. 359x+ 13. 583x(2) + 23. 541 respectively. The spatial inversion results show that the high-value areas of the three heavy metals are widely distributed near the liujiadian tailings pond, Wanzhuang tailings pond and Jinhai Lake tailings pond in Pinggu District. Heavy metal pollution is more serious in the West than in the East. The mapping results can provide basic data support for preventing and treating heavy metal pollution in Taolin, Pinggu District, Beijing.

With the development of wind resources under flat terrain, wind farms in extreme wind conditions are developed, and the size of the WT’s rigid-flexible coupling components increases. Therefore, accurately understanding the load characteristics and transmission mechanism of each component plays an important scientific role in improving the reliability of WT (WT) design and operation. Through the collation and analysis of the literature, this review summarizes the research results of large-scale WT load under source–grid coupling. According to the classification of sources, the variation characteristics of different loads are analyzed, and different research methods for different loads are summarized. In addition, the relative merits of the existing improvement schemes are analyzed, and the existing problems are pointed out. Finally, a new research idea of ‘comprehensively considering the coupling effects of source and network factors, revealing WT load characteristics and transmission mechanism’ is summarized. This paper provides important implications for the safety design and reliable operation research of large WTs with complex terrain.

A random wind speed time history model based on Weibull distribution is constructed. According to the known mean and variance of wind speed, the time fitting curve of random wind speed is obtained through MATLAB simulation method. According to the wind speed range of the actual operation of the wind turbine, the obtained wind speed curve is transformed, and then according to the obtained relationship between the wind speed of the wind turbine and the input torque, the input time-varying torque of the actual operation of the wind turbine is obtained. Establish the rigid shaft model of the wind turbine gearbox, calculate the gearbox transmission ratios at all levels, and then obtain the torque output curves of the gearbox planetary stage, medium-speed stage, and high-speed stage under random wind speed input to study wind power the torque transmission law of the machine drive system. The ability to better analyse the transmission structure of the gearbox and the torque transmission law of key components has a certain significance for the design and manufacture of the wind turbine transmission chain.

The feasibility and benefits of a novel biomass-fired cogeneration system are demonstrated. Different from the traditional heating mode, in the new concept proposed, three heat sources are selected as heating energy through a Li-Br absorption heat pump. Based on a 35MW biomass-fired combined heat and power plant, conduct thermodynamic analysis of the new system to clarify the internal mechanism of its efficiency improvement. The results show that due to the coupling and optimization, the net power generation of the novel system is increased by 1.36MW. Moreover, the total system efficiency and effective electrical efficiency can reach 41.88 and 34.56 percentage points. In terms of overall exergy damage, compared with the reference system, it was reduced by 1.36MW. The exergy efficiency of this heating process increased by 33.05 percentage points. In short, the proposed novel system is proven to be advanced.

Permafrost and alpine vegetation are widely distributed in Tibet, which is a sensitive area for global climate change. In this study, we inverted the surface deformation from 22 May 2018 to 9 October 2021 in a rectangular area within the city of Linzhi, Tibet, using the Sentinel1-A data and two time-series interferometric system aperture radar (InSAR) techniques. Then, the significant features of surface deformation were analyzed separately according to different vegetation types. Finally, multiple machine learning methods were used to predict future surface deformation, and the results were compared to obtain the model with the highest prediction accuracy. This study aims to provide a scientific reference and decision basis for global ecological security and sustainable development. The results showed that the surface deformation rate in the study area was basically between ±10 mm/a, and the cumulative surface deformation was basically between ±35 mm. The surface deformation of grassland, meadow, coniferous forest, and alpine vegetation were all significantly correlated with NDVI, and the effect of alpine vegetation, coniferous forest, and grassland on permafrost was stronger than that of the meadow. The prediction accuracy of the Holt–Winters model was higher than that of Holt′s model and the ARIMA model; it was expected that the ground surface would keep rising in the next two months, and the ground surface deformation of alpine vegetation and the coniferous forest was relatively small. The above studies indicated that the surface deformation in the Tibetan permafrost region was relatively stable under the conditions of alpine vegetation and coniferous forest. Future-related ecological construction needs to pay more attention to permafrost areas under grassland and meadow conditions, which are prone to surface deformation and affect the stability of ecosystems.

Enhancing and protecting biodiversity is a prerequisite for achieving "harmony between human beings and nature". With the disturbance of human activities, the landscape pattern is constantly changing, changing the flow of species, information, and energy within the landscape, which in turn has a significant impact on biodiversity. Therefore, based on the InVEST model, this paper obtained the spatial distribution of biodiversity maintenance function and established a Chinese biodiversity conservation empowerment network through GIS spatial analysis. Based on the complex network theory, the dynamic evolution characteristics of the ecological spatial network structure were analyzed, and the relationship between its topological structure and the conservation of terrestrial biodiversity was revealed. The results show that: biodiversity maintenance function is high in south, southwest, and northeast China and low in the west, and ecological corridors also show a dense distribution in the east and sparse in the west. Over the past 20 years, the average biodiversity maintenance function has grown slowly, and the ecological community of the network has increased, the structure is more stable, and the connectivity is stronger. The weight degree, betweenness centrality, PageRank, and eigenvector centrality were significantly correlated with biodiversity maintenance function, indicating that ecological network structure plays a huge role in maintaining biodiversity. By focusing on building significant correlation sources, and constructing corridors to adjust network connections, the integrity and connectivity of important habitats will be gradually restored, so that ecological land biodiversity function can exert greater ecological benefits.

In the context of strengthening the construction of ecological civilization and accelerating the “carbon peak” in China, the regional ecological pattern and its connection with carbon sink capacity have become an urgent topic. Given that Inner Mongolia is a large carbon emission province and the conflict between economic development and ecological protection is particularly prominent, we took Inner Mongolia as an example to extract its ecospatial network, then calculated the integrity index, topological indices, and recovery robustness of the network and evaluated integrity and other properties of the ecospatial network structure by combining them with the ecological background. In addition, we analyzed the relationship between the topological indices and net primary productivity (NPP). The results showed that the network was scale-free and heterogeneous, with low integrity, connectivity and stability, which were the focus of future optimization. The nodes with important functions were mainly distributed in the farm-forest ecotone, grasslands, and the agro-pastoral ecotone; under the simulation attack, the node recovery robustness was stronger than the corridor recovery robustness, and NPP was negatively and significantly correlated with the woodland nodes and grassland nodes. In terms of ecological restoration, the unused land in the west is a key area, and it is necessary to add new ecological nodes and corridors. In terms of enhancing carbon sequestration capacity, under the premise of ensuring network connectivity, the appropriate and rational merging of ecological nodes and corridors within woodlands and grasslands is a particularly effective means. This study provides a reference for evaluating and optimizing the ecological pattern of areas with prominent ecological problems and improving the carbon sink of ecosystems in terms of their ecospatial network structure.

Aiming at the problem where the current engineering wake model does not describe the wind speed distribution of the wake in the complex terrain wind farm completely, based on the three-dimensional full wake model (3DJGF wake model), this paper proposed a wake model that can predict the three-dimensional wind speed distribution of the entire wake region in the complex wind farm, taking into account the Coanda effect, wind shear effect, and wake subsidence under the Gaussian terrain. Two types of Doppler lidar were used to conduct wind field experiments, and the inflow wind profile and three-dimensional expansion of the wake downstream of the wind turbine on the Gaussian terrain were measured. The experimental results showed that the wake centerline and terrain curve showed similar variation characteristics, and the near wake profile was similar to a super-Gaussian shape (asymmetric super-Gaussian shape) under low-wind-speed conditions, while the near wake profile presented a bimodal shape (asymmetric bimodal shape) under high-wind-speed conditions. The predicted profiles of the Gaussian terrain wake model were compared with the experimental data and the three typical wake models. The comparison results showed that the newly proposed Gaussian terrain wake model fit well with the experimental data in both near wake and far wake regions, and it had better performance in predicting the wake speed of the Gaussian terrain wind farm than the other three wake models. It can effectively predict the three-dimensional velocity distribution in the whole wake region of complex terrain.

Heavy metal pollution constitutes one of the most urgent problems in soil environmental pollution, as plants become enriched in heavy metals through the soil, which endangers human health and poses a great potential danger to the ecological environment. The monitoring over heavy metal pollution in soil by traditional chemical methods is time-consuming and laborious and limited in scope. However, the method for monitoring heavy metal in soil leveraging hyperspectral vegetation technology is capable of quickly and accurately obtaining the heavy metal content in the soil, breaking through the vegetation barrier, and making the monitoring more efficient. Providing an important reference for the monitoring over and early warning of heavy metal elements in soil, this method matters for achieving the goal of constructing ecological civilization into a higher level and improving the quality of arable land. In this study, peach trees, the dominant economic fruit tree in Beijing, were research targets. 50 sampling points were evenly set up in the study area, and the spectral data of peach tree leaves were measured by using FieldSpec 4 portable ground wave spectrometers, while soil samples were collected and brought back to the laboratory for testing and analysis to obtain the data of heavy metal content in the soil. Efforts were made to analyse the leaf spectral characteristics of peach tree leaves under the stress of heavy metals in soil in different kinds of pollution and investigate how different soil heavy metals are correlated with leaf spectra through calculation. It was determined that element As in soil had a higher correlation with spectral reflectance. As a result, we calculated the correlation coefficients between element As in soil and vegetation indices, and construct a prediction model for elements As in soil using the appropriate vegetation indices. The results show that the spectral reflectance of peach leaves in the polluted area was generally higher than that in the background area and was more sensitive to heavy metals in soil in the wavelength range of 760 similar to 1 300 nm. The heavy metals in soil did not interfere considerably with the position of the red, blue and yellow edges of the leaves and were sensitive to the slope of the red, blue and yellow edges, and all of them were positively correlated. Spectral reflectance was weakly correlated with elements Cr, Cu and Hg in soil, and 0. 1 level of significant correlation was reached with elements As, Pb and Cd in some wavelength ranges. The overall correlation curve trend was the same, with the correlation magnitude ranked as As>Pb>Cd in order. According to the above studies, it is found that As elements in soil have the strongest correlation. Therefore, we performed correlation analysis using As elements in soil and vegetation index, which showed that As elements were significantly correlated with both PRI1 and PRI3. The regression analysis was performed using SPSS data analysis software with PRI1 and PRI3 as independent variables and As in soil as a dependent variable. The test results show that the index prediction model of PRI3 (y=e(43,644x)-39. 386, R-2 = 0. 937, RMSE=0. 161) rendered the best results and was more stable.

There exists serious heavy metal contamination of agricultural soils in China. It is not only time- and labor-intensive to monitor soil contamination, but it also has limited scope when using conventional chemical methods. However, the method of the heavy metal monitoring of soil based on vegetation hyperspectral technology can break through the vegetation barrier and obtain the heavy metal content quickly over large areas. This paper discusses a highly accurate method for predicting the soil heavy metal content using hyperspectral techniques. We collected leaf hyperspectral data outdoors, and also collected soil samples to obtain heavy metal content data using chemical analysis. The prediction model for heavy metal content was developed using a difference spectral index, which was not highly satisfactory. Subsequently, the five factors that have a strong influence on the content of heavy metals were analyzed to determine multiple regression models for the elements As, Pb, and Cd. The results showed that the multiple regression model could better estimate the heavy metal content with stable fitting that has high prediction accuracy compared with the linear model. The results of this research provide a scientific basis and technical support for the hyperspectral inversion of the soil heavy metal content.

Since the financial system has illustrated an increasingly prominent characteristic of inextricable connections, information theory is gradually utilized to study the financial system. By collecting the daily data of industry index (2005-2020) and region index (2012-2020) listed in China as samples, this paper applies an innovative measure named partial mutual information on mixed embedding to generate directed networks. Based on the analysis of nonlinear relationships among sectors, this paper realizes the accurate construction of “time-varying” financial network from the perspective of risk spillover. The results are presented as follow: (1) interactions can be better understood through the nonlinear networks among distinct sectors, and sectors in the networks could be classified into different types according to their topological properties connected to risk spillover; (2) in the rising stage, information is transmitted rapidly in the network, so the risk is fast diffused and absorbed; (3) in the declining stage, the network topology is more complex and panic sentiments have long term impact leading to more connections; (4) The US market, Japan market and Hongkong market have significant affect on China’s market. The results suggest that this nonlinear measure is an effective approach to develop financial networks and explore the mechanism of risk spillover.

Achieving carbon neutrality is a necessary effort to rid humanity of a catastrophic climate and is a goal for China in the future. Ecological space plays an important role in the realization of carbon neutrality, but the relationship between the structure of vegetation ecological space and vegetation carbon sequestration capacity has been the focus of research. In this study, we extracted the base data from MODIS products and other remote sensing products, and then combined them with the MCR model to construct a vegetation ecospatial network in the Yellow River Basin in 2018. Afterward, we calculated the topological indicators of ecological nodes in the network and analyzed the relationship between the carbon sequestration capacity (net biome productivity) of ecological nodes and these topological indicators in combination with the Biome-BGC model. The results showed that there was a negative linear correlation between the betweenness centrality of forest nodes and their carbon sequestration capacity in the Yellow River Basin (p < 0.05, R2 = 0.59). On the other hand, there was a positive linear correlation between the clustering coefficient of grassland nodes and their carbon sequestration capacity (p < 0.01, R2 = 0.49). In addition, we briefly evaluated the vegetation ecospatial network in the Yellow River BASIN and suggested its optimization direction under the background of carbon neutrality in the future. Increasing the carbon sequestration capacity of vegetation through the construction of national ecological projects is one of the ways to achieve carbon neutrality, and this study provides a reference for the planning of future national ecological projects in the Yellow River Basin. Furthermore, this is also a case study of the application of remote sensing in vegetation carbon budgeting.