spatial planning research papers

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 22 August 2024

Spatial and temporal changes of ecosystem service value and its influencing mechanism in the Yangtze River Delta urban agglomeration

• Yugui Lu 1 , 2 ,
• Jiacong Wang 3 &
• Xiaokun Jiang 1  

Scientific Reports volume  14 , Article number:  19476 ( 2024 ) Cite this article

254 Accesses

Metrics details

• Ecosystem services
• Environmental economics
• Sustainability

As the mainstream and trend of urban development in China, deeply exploring the spatiotemporal patterns and influencing mechanisms of ecosystem service value in the Yangtze River Delta urban agglomeration is of great significance for achieving sustainable development goals in urban agglomerations. This paper uses the normalized difference vegetation index and net primary productivity as dynamic adjustment factors to measure the ecosystem service value of the Yangtze River Delta urban agglomeration and analyze its spatiotemporal evolution characteristics. Furthermore, a panel quantile regression model is constructed to explore the response differences of ecosystem service value at different levels to various influencing factors. The results show that: (1) From 2006 to 2020, the ecosystem service value of the Yangtze River Delta urban agglomeration decreased by 37.086 billion yuan, with high-value areas mainly concentrated in the southern part of the urban agglomeration. (2) The value structure of various land type ecosystems and primary ecosystem sub-services in the Yangtze River Delta urban agglomeration is stable. (3) The number of grid units with reduced ecosystem service value is continuously increasing, mainly distributed in the eastern coastal areas. (4) The degree of interference of various types of land on ecosystem service value varies, and the response of ecosystem service value at different levels to the same influencing factor also shows heterogeneity. In summary, exploring the spatiotemporal patterns of ecosystem service value in the Yangtze River Delta urban agglomeration and analyzing its influencing mechanisms is conducive to adjusting the intensity of human utilization and protection methods of ecosystems, which is of great significance for enhancing the value of ecosystem products in urban agglomerations.

Similar content being viewed by others

Spatial–temporal evolution of ESV and its response to land use change in the Yellow River Basin, China

The impact of China's urbanization on ecosystem service value from the perspective of gross ecosystem product: a case study of Beijing-Tianjin-Hebei region

Unveiling the dynamics of urbanization and ecosystem services: insights from the Su-Xi-Chang Region, China

Introduction.

Ecosystems play a crucial role in providing humans with essential biological resources, including food and raw materials. Additionally, they also provide vital services for climate regulation, hydrology management, gas regulation, and waste disposal from human activities 1 , serve as a fundamental cornerstone for ecological security, human living environments, and sustainable economic and social development 2 . According to the United Nations Environment Programme's 2021 Annual Report, humanity still faces a significant journey in addressing the global crisis and improving the “human-earth relationship”. Promoting harmony between people and nature has become increasingly crucial. There is a global consensus on the importance of protecting and enhancing the ecology to ensure a healthy living environment for humans 3 . The conflict between the degradation of ecosystem service capacity and the growing demands of human beings is intensifying. The increasingly complex relationship between humans and land has brought the research on Ecosystem service value (ESV) to the forefront of the academic community 4 , 5 , 6 . China's urbanization level continues to rise due to rapid economic development in recent years 7 , 8 , 9 . However, this trend is accompanied by the occupation of significant amounts of arable land, wetlands, and forestland 10 , 11 . Changes in ecological land use patterns pose a serious threat to the balance of urban ecological environments 12 . Serve as the primary form and crucial driver of urbanization in China, urban agglomeration experience frequent changes in ecological land patterns during urbanization and exhibit a significant positive spatial spillover effect on urban land urbanization. This implies that an increase in the urbanization level of central cities will drive a corresponding increase in the urbanization level of adjacent cities 13 . As a new form of urbanization, the construction of urban agglomerations not only promotes regional economic development but also has a profound impact on the local ESV 14 . The Yangtze River Delta (YRD) urban agglomeration, known as the most economically developed and highly urbanized region in China 15 . The rapid urban expansion and economic growth have attracted widespread attention 16 . The significant increase in urbanization level is accompanied by a substantial consumption of land and water resources, placing enormous pressure and challenges on the ecological environment, which is critical to the region's sustainable development and the quality of life for its residents 17 . The unique position of the YRD urban agglomeration, along with its core role in the economic pattern of China and even the world, provides an ideal case for studying the spatiotemporal pattern evolution and influence mechanisms of ESV in rapidly urbanizing areas.

The concept of ESV was first proposed by Daily (1997) and gives a numerical indicator based on monetary units, which is a quantitative estimate of the capacity of ecosystem services 18 . Costanza et al. 19 first summarized the functional types of ecosystem services and quantified the global natural asset value, laying the foundation for quantitative estimation of ESV. Since then, academics have conducted extensive research on ecosystem services. In terms of calculating ESV, the approach is based on two main dimensions: economic value and material quality. From the economic value per spective, there are the equivalent factor method 20 and the remote sensing model method 21 respectively. The material quality assessment 22 method is the perspective based on the material quality. In addition, some researches have also evaluated regional ESV by Meta-regression using the results of the available literature 23 , which is a rigorous statistical approach with strong objectivity 24 . Li et al. 25 conducted an assessment of China's terrestrial ESV from the perspective of economic value, finding a downward trend over time in the country's terrestrial ESV. Additionally, they observed spatial heterogeneity in ESV changes. Chen et al. 26 assessed the ESV of the urban agglomeration in the middle reaches of the Yangtze River and found that forests contributed the largest proportion to the ESV, primarily providing hydrological regulation services. They also noted that the ESV in urban areas and plains was significantly lower than in other regions. Hou et al. 27 discovered that the ESV of Xi’an in China increased along the urban–rural gradient. Scholars have extensively studied the temporal and spatial characteristics of ESV at various administrative scales, which could aid regional ecosystem management. However, to manage different ecosystems precisely, assessments need to be conducted on the ESV of specific ecosystems. Song et al. 28 found that in the wetlands of the Northeast China region, the reduction in natural wetland area was mainly converted to farmland, resulting in an overall increase in ESV. Kibria et al. 29 . conducted research on Cambodia's national forest park and identified that the value of this forest ecosystem was underestimated and faced threats from population pressure and illegal extraction.

The assessment of ESV is a way to identify ecological problems and is not sufficient for optimal regional ecosystem service management. Therefore, it is particularly important to study the influence mechanism of ESV in order to solve regional ecological problems. The research methods on exploring the impact mechanism of ESV are mainly divided into whether to consider the spatial correlation of variables. To be specific, the methods that consider the spatial correlation of variables include geographic weighted regression 30 , 31 and geographic detector 32 . The methods that do not consider the spatial correlation of variables mainly include grey correlation analysis 33 and logistic regression 34 . It has been shown that the factors affecting ESV can be summarized as both natural environmental and human factors 35 with natural factors as direct drivers and human factors as indirect drivers 36 . Natural factors mainly include vegetation growth, climate conditions and terrain conditions 37 . Vegetation can affect ESV by maintaining soil, sequestration of carbon and other ecological services 38 . For example, climatic factors represented by thermal and water conditions (temperature, rainfall) 39 , topographic relief, elevation 40 , and so on, mainly affect ESV by influencing vegetation growth. Human factors include land use, population density and economic density 41 . With the development of cities, population agglomeration and urban expansion 42 have changed the pattern of regional land use 43 as well as the accompanying socio-economic activities 44 , which indirectly affect ESV by generating pollutants 45 . Natural and human factors are intertwined, and human beings will selectively and systematically develop land use according to natural conditions 46 . However, inappropriate over-exploitation 47 and excessive population density 48 can exceed the carrying capacity of the ecosystem for human activities, resulting in serious consequences such as environmental damage (soil erosion, desertification) and ecological collapse (For example, the ecosystem service capacity is declining, and the service capacity of food provision, climate regulation and hydrological regulation is insufficient). Therefore, the investigation of ESV impact mechanisms can help humans improve the efficiency of ecosystem management and contribute to the improvement of human-land relations. Therefore, the exploration of ESV impact mechanisms can help humans to decode regional ecological problems, improve the efficiency of ecosystem management, as well as contribute to the improvement of human-earth relations.

In summary, there is an abundance of research results on the estimation of ESV and its influencing mechanisms, which lays a solid theoretical and methodological foundation for this study. By taking the Yangtze River Delta urban agglomeration as the entry point, this paper will analyze the characteristics and influencing mechanisms of ESV’s spatiotemporal pattern evolution. This can provide insight for good ecosystem management in urban agglomeration across China. Additionally, it offers theoretical guidance for promoting sustainable socio-economic development and is crucial for formulating effective ecological conservation and urban planning policies, contributing to the sustainable development of the region. It also has the potential to point out new paths for the synergistic coexistence of economic development and ecological environment protection in other regions globally. The possible contributions of this paper include: (1) Calculating ESV by subdividing grid units and analyzing the ESV spatiotemporal pattern evolution from multiple perspectives, which is beneficial for more refined ecosystem management. (2) Using normalized difference vegetation index (NDVI) and net primary productivity (NPP) as dynamic adjustment factors in the ESV accounting, making the assessment results more consistent with regional characteristics. (3) Building a panel quantile regression model and utilizing “segmented effects” to reveal the underlying mechanisms of ESV influence in depth, emphasizing the heterogeneity of various influencing factors at different ESV levels on its dynamic changing trend, providing a new perspective for the study of ESV influence mechanisms.

Study area and data sources

Study area overview.

The YRD urban agglomeration is located in the downstream region of the Yangtze River in China, with a total area of 211,700 km 2 , occupying approximately 2.2% of China's land area (Fig.  1 ). It is composed of 26 cities including Shanghai, Nanjing, Hangzhou, and Hefei. The YRD urban agglomeration is low-lying in the south and high in the north, with an elevation below 10 m and scattered remnants of some solitary mountains. The YRD urban agglomeration is the region with the highest density of river network in China, with a predominantly subtropical monsoon climate and a significant increase in average annual temperature and average annual maximum and minimum temperatures in recent years. The YRD urban agglomeration has maintained high growth since 2006. According to the data of the municipal statistics bureau, the average urbanization rate of the YRD urban agglomeration reached 75.01% by 2020. As a core region, the YRD urban agglomeration had a GDP of over 20 trillion yuan in 2020, concentrating one-fourth of China's total economic output within its 2.2% regional area.

Source : Based on the standard map GS(2019)1825 from the Standard Map Service website of the Ministry of Natural Resources of China ( http://bzdt.ch.mnr.gov.cn/ ), it was clipped and produced using Arcgis10.2 software. The base map boundaries have not been modified, same as below.

Location map of the Yangtze River Delta urban agglomeration.

Data sources

Since China proposed in 2006 to take urban agglomeration as the main form of advancing urbanization, this study chooses the period from 2006 to 2020 to investigate the spatiotemporal pattern evolution of ESV since the construction of urban agglomeration. The data used in this paper are mainly divided into two categories: remote sensing data and regional statistical data, the specific types and sources of which are shown in Table 1 . All remote sensing data are processed through geographical operations such as mask extraction, resampling, grid projection, and spatial statistics using ARCGIS 10.2 software. To meet the needs of this study and based on the current standards of “Land Use Status Classification” issued by the Ministry of Land and Resources of China in 2017, the land use types are reclassified into six categories: farmland, forest, grassland, water bodies, desert, and construction land. This classification provides the input data for calculating the ESV of the YRD urban agglomeration. To improve the spatial differentiation resolution and the reliability of quantile regression, this paper studies the spatiotemporal pattern evolution of ESV in the YRD urban agglomeration at a grid scale. Previous research experience suggests that if the grid assessment unit is close to the resolution of the land use data, i.e., the unit is too small, it will destroy the relative consistency of the adjacent units 49 ; while if the grid assessment unit is close to the scale of administrative divisions, i.e., the unit is too large, it will greatly ignore the spatial differences of ESV 50 . Taking into account the actual situation of the study area and after repeated experiments, this paper divides the YRD urban agglomeration into 5 km × 5 km grids, resulting in 8764 basic assessment units for related research on ESV in the Yangtze River Delta urban agglomeration.

Research methodology

This paper unfolds around the research approach of "posing questions—analyzing problems—solving problems," primarily focusing on analyzing the spatio-temporal pattern evolution and its influencing mechanisms of the ESV of the YRD urban agglomeration from 2006 to 2020. Consequently, it proposes effective ecological conservation and urban planning policies to promote regional sustainable development. The research framework is shown in Fig.  2 .

Research framework.

Remote sensing quantitative ESV accounting model

Building upon the previous work, Xie et al. 52 derived an ESV equivalent factor table specific to China through a combination of literature research, expert knowledge, and model operation. In this study, we adopt this table as the fundamental basis for calculating the ESV of the YRD urban agglomeration 52 . Additionally, taking into account the influence of social and economic factors, we estimated the average market value of grain yield per unit area in the YRD urban agglomeration. This was done by calculating the arithmetic average of the sown area and yield of major crops, such as corn, wheat, and rice, in Shanghai, Zhejiang, Jiangsu, and Anhui provinces within the YRD urban agglomeration for the years 2006, 2011, 2016, and 2020. We also considered the annual prices in the Chinese agricultural market for the year 2020. This paper conducted a preliminary adjustment of the ESV coefficients for the YRD urban agglomeration based on the definition of the ESV equivalent factor. Specifically, this adjustment assumes that one seventh of the average market value of grain yield in the YRD urban agglomeration for a given year represents the economic value of one equivalent factor 53 . The calculation formula is shown in the followed equation:

Let \(V\) represent the economic value of one equivalent factor. Here, \(i\) refers to the crop type, while \(n\) represents the total number of crops \(n=3\) . The planting area of the \(i\) th crop is denoted as \({s}_{i}\) , the annual average market price as \({p}_{i}\) , and the yield per unit area as \(q\) . The total planted area of the \(n\) crops is represented by \(m\) . Additionally, \({V}_{j}\) represents the value coefficient of ecosystem service for category \(j\) , and \({\beta }_{j}\) represents the current value of type \(j\) ecosystem services. The calculated market value of grain output per unit area in the YRD urban agglomeration is 17317.02 yuan/hm 2 . Consequently, using Eq. ( 1 ), the economic value of per unit equivalent in the YRD urban agglomeration is determined to be 2473.86 yuan/hm 2 . The ESV coefficients per unit area of the YRD urban agglomeration were derived from Eq. ( 2 ) (Table 2 ).

The same land use type will serve significantly different ecosystem services due to the difference of vegetation density in each land use type 54 . In addition, the net carbon sequestration by green plants in different regions through photosynthesis represents the carbon sequestration capacity of surface vegetation as well as the differences in regional ecosystem service capacity 55 . Therefore, this paper uses the NDVI to reflect the plant growth status in the region and the NPP of vegetation to reflect the carbon sequestration capacity of regional vegetation, and revises the ESV of the study area to make the evaluation results more in line with regional characteristics and ensure the credibility of the results. Finally, the formulas for calculating the ESV for each land category, each sub-service, and the ESV per unit area for each city are obtained:

Here, \({EVS}_{i}\) represents the ecosystem services value of the i-th city, and the ESV value of the YRD urban agglomeration is the cumulative value of ESV of the 26 cities in the region; \({ESV}_{im}\) represents the ESV of the mth land type of the ith city; \({ESV}_{ik}\) represents the value of the kth type of sub-service in the ith city. \({AESV}_{i}\) represents the ESV per unit area of the \(i\) th city, reflecting the abundance of natural capital in the city. \({S}_{mn}\) represents the area of the \(m\) th type of ecosystem in the \(n\) th grid unit, \({V}_{mk}\) represents the value coefficient of the \(k\) th ecosystem service of the \(m\) th type of ecosystem; \({R}_{n}\) is the dynamic adjustment factor of ESV calculated based on the spatiotemporal variation of NDVI and NPP for the \(n\) th grid unit. m represents the type of ecosystem, and since the ESV coefficient of construction land is 0, this paper does not consider the ESV of construction land, so \(a=5\) ; k represents the type of ecosystem service, \(b=9\) ; \(n\) represents the grid unit number, and the value of \(c\) is determined based on the size of the area of each city region, with a total of 8764 grid units in the YRD urban agglomeration.

The formula for the dynamic adjustment factor \({R}_{n}\) is:

In the above equation,

In the equation, \({NPP}_{mean}\) and \({f}_{mean}\) are the mean values of \(NPP\) and vegetation cover \(f\) ; \({NPP}_{n}\) and \({f}_{n}\) are the \(NPP\) and \(f\) of the \(n\) th grid unit; the calculation of vegetation cover \(f\) is obtained using the pixel-based binary model, where \({NDVI}_{veg}\) and \({NDVI}_{mean}\) represent the \(NDVI\) values of pixels that are completely covered by vegetation and pixels that have no vegetation cover, respectively. These values can be replaced by the maximum and minimum \(NDVI\) values of grid units within the region. Other variables are the same as above.

Cold hotspot analysis model

The cold-hot spot analysis method is used to identify high and low value areas in different spatial locations, that is, to explore the spatial distribution characteristics of hotspots and cold spots of ESV ecological resilience 56 . The calculation formula is as follows:

Equation ( 7 ) is normalized.

where \({W}_{ij}\) is the spatial weight matrix. \({X}_{i}\) and \({X}_{j}\) represent ESV of \(i\) and \(j\) , respectively. \(E\left({G}_{i}^{*}\right)\) and \(Var({G}_{i}^{*})\) are the mathematical expectation and coefficient of variation of \({G}_{i}^{*}\) , respectively. If \(Z({G}_{i}^{*})\) is significantly positive, it indicates that the ESV around this area is high and it belong to the hotspot area. If \(Z({G}_{i}^{*})\) is significantly negative, it indicates that the ESV around this area is low and it belong to the cold pot area.

Land use intensity index

Land use intensity reflects the degree of human interference on land resources and becomes an important driving force of environmental change. Different land use types represent the characteristics of different degrees of land use, which is an important indicator of sustainable land use 57 . According to Chen 58 ,this paper assigns values to the intensity of human use of various types of land and calculates the land use intensity index for each research unit. The specific values are: 4 for construction land; 3 for farmland; 2 for forestland, grassland and water area; 1 for desert. The calculation formula is as follows.

where \({LUI}_{j}\) represents the land use intensity index of the \(j\) th grid cell. \({S}_{i}\) is the area of category \(i\) land use type. \({D}_{i}\) is the assigned value of land use intensity of category \(i\) .

Contribution rate of ESV change

The data form of land use types could not be used in the panel quantile regression model. However, considering its most direct and dominant influence on the ESV 59 , the contribution of ecosystem services of each type of land is applied in this paper to analyze the interference extent of land use types with changes in the ESV. The contribution rate of ESV change can reveal the main influencing factors of ESV change in each city of the YRD urban agglomeration. It signifies the proportion of ESV change for different land use types in each city to the total ESV change of the city during the study period 60 , with the following formula:

where \({F}_{i}\) is the contribution rate of category \(i\) land to the change in ESV of urban agglomeration during the study period. \(i\) is the land use type. \(\Delta {ESV}_{i}\) is the change in ESV of the \(i\) th land during the study period.

Variable selection

The selection of influencing factors variables is based on the above review and analysis of influencing factors of ESV. The intensity of human activities on land use indirectly affects the natural ecosystem. In this paper, LUI was used as a variable to influence the ESV. Average annual rainfall (AAR) and average annual temperature (AAT) were selected as climate factor variables affecting the value of ecosystem services. Population density (PD) and economic density are the main socio-economic factors, which can reflect the overall characteristics of the development and utilization level of regional land resources. PD is considered to be the main driver of land use change 61 . Economic density reflects land use efficiency and regional social production development intensity 62 . Both of them affect the ESV by influencing the utilization of land resources. Therefore, the two variables were selected as the socio-economic factors affecting the ESV. Under certain conditions, night light brightness (NLB) can be used as a proxy variable of GDP, so this paper uses the regional average NLB as a proxy variable of economic density 63 . The topographic factors do not vary significantly over the years and are affected by the deficiencies of the year of monitoring data, which could not meet the data conditions of the panel regression. Therefore, topographic factors are not included in this paper.

Panel quantile regression model

The panel quantile regression model has the advantages of both the panel model and the quantile regression model, providing more information and weaker collinearity between variables. Moreover, it has strong resistance to estimation in the presence of non-normal distribution or outliers, and can provide more complete information and clearer explanation of the heterogeneity of the response of independent variables under different distributions of dependent variables 64 . This method differs from the traditional least squares regression as it minimizes the weighted absolute residuals to fit conditions at various quantiles, thereby revealing the effect of the independent variables on the dependent variable across different distributions 65 . By modeling different quantiles, it is possible to analyze the effect of the independent variables at various quantile values of the dependent variable, which helps to unveil the variable sensitivity and response patterns under different conditions 66 . Panel data has both time and cross-sectional properties, greatly increasing the sample size of data, and can improve the reliability and accuracy of regression results. Due to the large amount of data and non-normal distribution with outliers in the study of ESV impact mechanisms at the grid scale, this paper overcomes the non-normal distribution and outlier problems based on the characteristics of the data, using panel data of 8764 grid units for four periods in 2006, 2011, 2016, and 2020, and selects the panel quantile regression model to explore the “segmentation effect” of the response of ESV in the YRD urban agglomeration to various influencing factors. The commonly used panel regression models include fixed effects and random effects. The Hausman test shows that the fixed effects model is more suitable for this study. The basic model is as follows:

where \({ESV}_{kt}\) is the explained variable; \(c\) is the constant term; \(\alpha\) is the regression coefficient; \({LUI}_{kt}\) is the land use intensity index; \({AAR}_{kt}\) is the average annual rainfall; \({AAT}_{kt}\) is the average annual temperature; \({PD}_{kt}\) is the population density; \({NLB}_{kt}\) is the night light brightness; The value of \({\mu }_{k}\) does not change over time as an individual effect; \({\tau }_{kt}\) is the disturbance term.

Panel fixed effects model studies the influence of each explanatory variable \(X\) on the conditional expectation \(E(ESV|X)\) of \({ESV}_{kt}\) . This is a mean reversion 67 . In the case of asymmetric distribution of \(ESV|X\) , the regression results only reflect part of the relationship between \(X\) and \(ESV\) . Quantile regression model can be used to explore the linear relationship between \(X\) and \(ESVESV\) quantiles, which can depict the conditional distribution in more detail. Therefore, regression results that are not affected by extreme values are more robust 68 . The corresponding regression coefficient estimates can be obtained for \({\varvec{X}}\) and \({\varvec{ESV}}\) at different quartiles. To sum up, this paper uses panel quantile regression model to study the influence mechanism of ESV. The model design is as follows:

where \({\varvec{\beta}}\) is the quantile, \({\varvec{\beta}} \in \left( {0,1} \right)\) ; \({\varvec{Z}}_{{{\varvec{ESV}}_{{{\varvec{kt}}}} }}\) is the \({\varvec{\beta}}\) conditional quantile of ESV; \({\varvec{c}}\left( {\varvec{\beta}} \right)\) is the constant term at the \({\varvec{\beta}}\) quartile; \({\varvec{\alpha}}\left( {\varvec{\beta}} \right)\) is the influence coefficient at the quartile of \({\varvec{\beta}}\) ; \({\varvec{\mu}}_{{\varvec{k}}} \left( {\varvec{\beta}} \right)\) is the individual effect at the quartile of \({\varvec{\beta}}\) , and other variables are the same as above.

Results analysis

Spatial and temporal patterns of esv evolution in yrd urban agglomeration.

According to Table 2 and Eq. ( 4 ), the ESV of different land categories in each city were measured by grid cells using the raster calculator tool of ARCGIS, and then summed up using the spatial statistics tool to obtain the ESV of each land category in the YRD urban agglomeration (Table 3 ). The total ESV in the YRD urban agglomeration was 8220.23, 8189.37, 8016.77 and 784.937 billion yuan in 2006, 2011, 2016 and 2020 respectively, showing a monotonically decreasing trend, but the average annual decline rate gradually stabilized. According to the land use data, the construction land of the YRD urban agglomeration that does not generate ESV has expanded by 9343.18 km 2 since 2006. The area of land types generating ESV have all decreased, leading to a decline in ESV and degradation of ecosystem service capacity in the YRD urban agglomeration. It can be seen that the spatial urbanization of the YRD urban agglomeration has brought enormous pressure to the construction of ecological civilization, and the contradiction between the degradation of ecosystem service capacity and the increasing human demand has become increasingly intense. The structure of ESV in the YRD urban agglomeration is stable, mainly dominated by forestland, farmland and water area. These three together generate more than 98% of the overall value, and their respective proportion do not change significantly over time. During this study period, the average annual decline rate of ESV generated by farmland and desert ecosystems was gradually decreasing, while that generated by grassland and water area ecosystems was gradually increasing. Stress on grassland and water area ecosystems in the YRD urban agglomeration is gradually increasing. Therefore, efforts to protect grassland and water area ecosystems should be strengthened.

Similarly, according to Table 2 and Eq. ( 5 ), the value of each sub-service for each city is obtained, and then aggregated to yield the overall value of each ecosystem sub-service for the YRD urban agglomeration. (Table 4 ). Overall, the value structure of Tier 1 subservices is stable, mainly providing adjustment services and support services, which together account for more than 82% of the overall value, with insignificant changes over time. Ecosystems regulate the pollution caused by human activities and maintain the stable operation of ecosystems through hydrological regulation, climate regulation, soil conservation and biodiversity maintenance. Among them, the capacity of supply and support service has been continuously degraded, however, the degradation rate has slowed down and the trend has been controlled since 2016. The value of regulating services and cultural services is also declining, and their average annual decline rate is expanding. The ability of ecosystems to play a role in gas regulation, hydrological regulation, climate regulation, and provision of aesthetic landscapes is continuously degrading, which may lead to a series of ecological and environmental problems in the YRD urban agglomeration, such as intensified climate change, water pollution, air pollution, ultimately resulting in the degradation of the living environment.

By combining Eqs. ( 3 ) and ( 6 ), the per unit area ESV for each city can be calculated. To visually analyze the composition of the ESV and the level of natural capital abundance in each city, a dual y-axis graph can be drawn as shown in Fig.  3 . The composition of ESV for each city is represented by a bar chart, with different colors indicating the ESV of different land categories, and their values corresponding to the y-axis on the left, unit is billion yuan. The per unit area ESV for each city is represented by a line graph, with its values corresponding to the y-axis on the right, unit is ten thousand yuan /km 2 . At the same time, since the ESV generated by grasslands and deserts accounts for a very small proportion, it is not easy to show it in the bar chart. Therefore, we take Figure a as an example and zoom in on it to illustrate the ESV generated by grasslands and deserts. Overall, the ESV and ESV per unit area in Hangzhou are the highest, which are 97.082 billion yuan and 5.7764 million yuan /km 2 respectively by 2020. Hangzhou has the strongest ecosystem service capacity and the highest degree of natural capital abundance. Although Zhoushan City has the lowest ESV, it has the highest ESV per unit area of 4.1546 million yuan/km 2 , indicating a high degree of natural capital abundance. During the study period (2006–2020), only Anqing city had a positive growth trend of ESV, while the rest of cities had a negative growth. The cities of Shanghai, Zhoushan, Changzhou, Suzhou, Jiaxing and Zhenjiang suffered serious degradation of ecosystem service capacity, and their ESV decreased by more than 10% per year compared with 2006. According to data from the regional statistical bureaus, the level of urbanization in these cities has increased significantly since 2006, which easily shows that rapidly urbanizing areas inevitably come at the cost of destroying ecosystem services. According to the analysis of land category, the urban ESV was mainly generated by farmland, forestland and water area ecosystems, while grassland and desert ecosystems ESV accounted for a small proportion. Anqing, Nantong, Hefei, Chuzhou and Yancheng, as the main food producers in the YRD urban agglomeration, have high values of farmland ecosystem services.

Yangtze River Delta urban agglomeration ecosystem service value and its structure.

The ESV of the grid cells of the YRD urban agglomeration were precisely spatially located, and its spatio-temporal evolution characteristics were analyzed (Fig.  4 ). For ease of analysis, classification is carried out at intervals of 40 million yuan. The numbers in parentheses in the legend represent the quantity of grid cells, and the same applies below. Firstly, overall, ESV in the YRD urban agglomeration presents the characteristics of spatial imbalance and time instability. The spatial divergence between high and low values of ESV is obvious, and the north–south divergence of ESV becomes more obvious as time goes on. The number of high-value grid cells gradually decreases and the number of low-value grid cells gradually increases over time, and the low-value space gradually spreads to the south of the YRD urban agglomeration. Second, counting statistical analysis of grid cells with different value levels. The number of grid cells with ESV less than 40 million yuan is increasing year by year, while the number of grids with ESV greater than 80 million RMB is decreasing year by year, and there are constantly new grid cells with decreasing ESV. Third, looking at the spatial distribution of grid units with different value levels across cities. The low-value spatial units with ESV less than 80 million yuan are mainly distributed in the northern cities of the YRD urban agglomeration. The low-value space is spread out from the center of Shanghai, Hefei, Nanjing and Hangzhou. High-value spaces worth more than 160 million yuan are mainly distributed in the southern cities of the urban agglomeration, forming a solid ecological barrier for the YRD urban agglomeration.

Spatial and temporal pattern evolution of ecosystem service value in Yangtze River Delta urban agglomeration.

The spatial agglomeration characteristics of ESV in the YRD urban agglomeration from 2006 to 2020 were further explored through a cold hotspot analysis model (Fig.  5 ). Here, “HS” stands for hotspots, “CS” stands for coldspots, and “99”, “95”, “90” respectively represent significance levels of 99%, 95%, and 90%. “NS” indicates non-significant. Counting statistical analysis shows that half of the regional ESV spatial agglomeration characteristics are not significant from 2006 to 2020, and the insignificant areas are on the rise. The number and spatial distribution of agglomeration areas are more stable, and the number of high-value agglomeration areas is higher than the number of low-value agglomeration areas. High-value agglomerations are mainly located in the eastern coastal regions, while low-value agglomerations are mainly located in the northeastern part of the YRD urban agglomeration and the eastern coastal region. The high cold spot areas spatially overlap with the central city areas with high urbanization levels. It can be found from Fig.  5 that the cold spot region in northeast China, namely the agglomeration area of low ESV value, decreases year by year and transforms from the cold spot region to the insignificant region, indicating that the ecosystem service capacity of this region has changed significantly and gradually transformed from contiguous distribution to discrete distribution. Figures  3 and 4 both show that the ESV of the eastern coast and the northern region along the Yangtze River in the YRD urban agglomeration is relatively low, which has become the frontier position of urbanization construction. The southern region of the urban agglomeration builds the logistics support area of the YRD urban agglomeration.

Spatial agglomeration characteristics of ecosystem service value in Yangtze River Delta urban agglomeration.

In the same way, spatial positioning was carried out for the grid cells with ESV changes in the YRD urban agglomeration (Fig.  6 ). Analysis of their spatial and temporal pattern evolution enables accurate monitoring to identify the changing status of ESV in the region. The spatial units of ESV changes in each research stage have obvious spatial differentiation and are discretely distributed in urban agglomeration. From Fig.  6 a, b, and c, the number of grid cells with increasing (decreasing) ESV decreases (increases) with time. The percentage of the number of areas with decreasing ESV in each specific phase is 61.86%, 79.01%, and 82.98%, showing a gradual increasing trend. From Fig.  6 d, the spatial percentage of ESV reduced is 82.78%, which mainly concentrated in the contiguous areas centered on Shanghai, Nanjing, Suzhou and Hangzhou, as well as the central part of Jinhua and the central part of Hefei. The spatial proportion of units with a increase in ESV is only 17.22%, which is much smaller than the number of units with a decrease in ESV. The above analysis further explains the reasons for the decrease of ESV in the YRD urban agglomeration from a spatial perspective, and the spatial positioning of the units with changed ESV can provide a reference for local governments to make decisions on targeted protection of regional ecology.

Spatial and temporal evolution of ecosystem service value changes in the Yangtze River Delta urban agglomeration.

Degree of disturbance to changes in ESVs by land type

According to Eq. ( 12 ), the contribution rates of ESV changes in various land categories in each city to the overall ESV change are calculated, thereby revealing the main influencing factors of ESV change in the cities of the YRD urban agglomeration (Fig. 7 ). The overall impact of each land ecosystem on ESV change in the same city varies from period to period; the impact of each land on ESV change in different cities in the same period also varies. In this paper, the contribution rate of all types of land to ESV change exceeding 50% is defined as the main influencing factor of ESV change in the city. During the study period, the farmland ecosystem was identified as the primary influencing factor for ESV variations in Taizhou, Wuhu, Jinhua, Xuancheng, Suzhou, Anqing, and Shaoxing. Forestland ecosystem was identified as the primary influencing factor for ESV variations in Chizhou, Nantong, Nanjing, Taizhou, Chuzhou, Yangzhou, Zhenjiang, Changzhou, Hefei and Ma 'anshan. Water area ecosystem was identified as the primary influencing factor for ESV variations in Tongling, Shanghai, Wuxi and Yancheng. From Fig.  6 , it can be observed that the degree of interference from agricultural ecosystems on ESV shows an increasing trend in Ma'anshan, Huzhou, and Jinhua. The disturbance degree of forestland ecosystem to ESV in Xuancheng City and Changzhou city also showed an increasing trend. The disturbance of water area ecosystems to ESV changes in the majority of cities showed an oscillating upward or strict upward trend, and even the disturbance to ESV changes in Shaoxing, Zhoushan, Xuancheng, Taizhou, Tongling and Wuxi cities has reached 90% during 2016–2020. Based on the above results, local governments should strengthen the monitoring and management of various kinds of ecosystems to ensure the stability of regional ecosystems and enhance the ability of regional ecosystem services.

Contribution rate of ecosystem service value change in the Yangtze River Delta urban agglomeration.

Segmentation effect of influencing factors of ESV

Quantile regression can identify the degree of response of different levels of ESV to each influencing factor, i.e., the "segmentation effect" of various influencing factors on different levels of ESV, which can enable us to understand the influence mechanism of ESV in a deeper level. To precisely identify this "segmentation effect", nine quartiles from 0.1 to 0.9 are selected in the model for regression, and the results reveal the changes in the elasticity coefficients of each influencing factor in the conditional distribution of ESV in the YRD urban agglomeration (Table 5 ). The baseline regression results show that LUI, AAR, AAT, PD, and NLB which represents economic density, have significant effects on ESV in the YRD urban agglomeration. The regression coefficients of LUI and AAR are significantly negative, indicating that land development and rainfall have a significant constraining effect on the enhancement of ecosystem service capacity in the YRD urban agglomeration. The regression coefficients of AAT, PD and NLB, which represent economic activities, are significantly positive, indicating that the heat conditions provided by high temperature for plant growth, human activities and appropriate economic activities have obvious positive promoting effects on the improvement of ecological environment and the promotion of ESV capacity.

The following analyzes the "segmentation effect" of the impact factors on the influence of ESV at different levels. First, observing the quantile regression results for LUI, the regression coefficients for each quantile are significantly negative, indicating that the reduction in ESV is coerced by the exploitation of land at different ESV levels. The absolute value of the regression coefficient at the 0.1 quantile was significantly smaller than the rest of the quantile, indicating that areas with low levels of ESV were less stressed by land development at the same land use intensity. Second, the regression coefficients of AAR at different quartiles are significantly negative, probably due to the sufficient water resources and rainfall in the YRD urban agglomeration itself, which implies that increased rainfall will instead reduce ESV 69 . The overall stress effect on ESVs in the YRD urban agglomeration was characterized by an "inverted U-shape", with the 0.5 quantile as the boundary. At the 0.1 to 0.5 quantile, the lower the ESV, the stronger the stress effect of rainfall on the region, while the result was opposite at the 0.5 to 0.9 quantile. Third, the regression coefficients of AAT were not significant at the 0.6, 0.7 and 0.8 quartiles, while the rest of the quartiles were significantly positive. The promotion effect of average temperature on ESV showed a U-shaped characteristic of marginal decrease at first and then marginal increase, with a cut-off at the 0.6 quantile. Fourth, the regression coefficients of both PD and NLB were insignificant only at the 0.1 quantile, and the rest of the quantile regression coefficients were significantly positive at the 1% level and monotonically increasing with quantile. This indicates that in the natural context, if land is not developed and utilized, certain population and economic activities in the region may provide aesthetic landscape value by improving the cultural services of the ecosystem, and thus enhancing ecosystem services.

Ecosystem provides important ecological resources for human production and life, serves as a bridge and link for the harmonious development of man and nature 69 . Ecological civilization construction is the fundamental plan for China's development, and the contradiction between the degradation of ecosystem services and the ever-increasing needs of human beings is becoming increasingly intense. The quantitative accounting of ecosystem service capacity of urban agglomeration in monetary form and the spatial identification of ESV change status translate ecological and environmental issues into economic issues that is easily understandable for the public 70 , which helps the general public to understand ecological issues intuitively and aids in the monitoring of regional ecosystem dynamics 71 . The ESV was divided into different levels by quantile, and the heterogeneity of the dynamic change trend of the same influencing factor under different ESV levels was emphasized, so as to obtain a more comprehensive explanation of ESV in the affected region. It can also provide reference for local governments to manage regional ecosystems, solve ecological problems, and target policies. This article takes the YRD urban agglomeration as the research object, divides the study area into detailed grid units, and revises the ESV assessment model again with NDVI and NPP. It analyzes the spatiotemporal pattern evolution of the ESV in the YRD urban agglomeration from different perspectives. Finally, it applies panel quantile regression to analyze the mechanisms influencing ESV. The following discussions are conducted:

regarding the discussion of research methods

Costanza et al. accounted for the value of global ecosystem services and natural capital 19 , which quantified the measurement of ESV, but their results also contain biases and are not entirely applicable to ESV research in China. Xie Gaodi and others, based on Costanza et al.'s research, derived ESV equivalent factor tables suitable for China, which this article uses as the basis for measuring the ESV of the YRD urban agglomeration 52 . Based on this, the economic value per equivalent unit was first revised using the yield, planting area, and market prices of the main crops in the YRD urban agglomeration. Then, on the basis of previous work using NDVI to reflect the vegetation growth status within a region 72 , NPP was added to reflect the carbon sequestration capacity of regional vegetation for a second revision. The revised model with added NPP can measure ecosystem productivity more accurately. More importantly, NPP directly reflects the ability of ecosystems to convert CO2 into biomass, which can to some extent reflect the carbon sink potential of the region. This is of great significance in the assessment of regional ESV under China's "dual carbon" goals and conforms to the development concept that "lucid waters and lush mountains are invaluable assets." The ESV assessment model, after two rounds of revisions, can ensure that the ESV assessment results conform to regional characteristics and the characteristics of the era. It can provide decision-making references for local governments to manage regional ecosystems, solve ecological problems, and target policies. Additionally, this paper uses the panel quantile regression model to explore the mechanisms influencing ESV. Previous studies often used geodetectors 73 , geographic weighted regression models 74 , piecewise linear regression 75 , etc., to examine the mechanisms influencing ESV, ignoring the differential response levels of different ESV levels to influencing factors. This paper effectively solves this problem by using the panel quantile regression model. By dividing ESV into different levels using quantiles, it emphasizes the heterogeneity in the dynamic trends of the same influencing factor at different ESV levels, leading to a more comprehensive and scientific understanding of regional ESV influences, providing a new perspective for the study of ESV mechanisms.

In terms of the discussion of research findings

From 2006 to 2020, the ESV of the YRD urban agglomeration continuously declined, which is consistent with the results of Ding et al. 76 , confirming the reliability of the calculations in this study 77 . Moreover, the grid cells with decreased ESV have primarily expanded from coastal cities to inland areas and from urban built-up areas to the periphery. This indicates that rapid urbanization and high-intensity land development are the main reasons for the ESV decline in the YRD urban agglomeration 78 . According to the ESV grid distribution map, cities such as Hangzhou and Jinhua in the southern part of the urban agglomeration have higher ESVs, because the dominant land use type in the southern part is forest land. The study finds that the ESV of forests accounts for a relatively large proportion, which explains the higher ESV in the cities of the southern urban agglomeration. This also shows that the ESV of cities is closely related to their natural resource endowment, and during the practice of ecological civilization construction, cities should carry out targeted restoration and protection based on their local natural resource endowment. Observing the degree of disturbance of each land type on ESV changes, it's apparent that farmland, forest, and water ecosystems are the main influencing factors of ESV changes in most cities. This also suggests that in the process of ESV decline, a considerable amount of these ecosystems have been destroyed 79 . In the future, protection and restoration of the ecosystems of farmland, forests, and water areas should be strengthened in all cities, and the boundaries of urban development should be controlled 80 . Especially in shrinking cities, land development should be stopped or restricted, and existing built-up areas should be used rationally for urbanization construction 81 . The segmented effects of each influencing factor on ESV suggest that areas with higher ESV levels have a stronger negative response to land use intensity. This might be because these areas, with high ESV, initially lacked land use and development, and once they undergo land use and development, the damage to ESV is extremely severe 82 . This also tells us that when developing land, we should as much as possible adopt a contiguous development model instead of an enclave-style development model.

Future research prospects

There are also some limitations in this paper. Firstly, the accounting model of ESV has room for improvement, and the dynamic factors of spatial and temporal changes are not selected comprehensively enough, and future studies can consider more human economy, regional natural conditions and other factors. Secondly, due to the limitations of data availability and method models, the research on the influence mechanism of ESV is not comprehensive enough. For example, soil conditions, light and other factors are not considered in the analysis. In the future, new research methods can be actively explored to carry out more completed research on these aspects.

Conclusions and policy recommendations

Based on the multi-source remote sensing data, this paper combines the equivalent factor method and the quantitative remote sensing method, and integrates the NDVI and NPP factors that represent the regional habitat quality with spatial and temporal changes, to measure the ESV of the YRD urban agglomeration at the grid scale and analyze its spatial agglomeration and spatio-temporal evolution characteristics. The contribution rate model was used to explore the interference of each land use type on ESV changes. A panel quantile regression model is used to reveal the degree of difference in the response of different spatial regions of ESV to the influencing factors at different levels. The following conclusions were obtained.

First, from 2006 to 2020, the ESV of the YRD urban agglomeration continuously decreased, with a total reduction of 37.086 billion yuan. The ESV declines were more severe in Shanghai, Zhoushan, Changzhou, Suzhou, Jiaxing, and Zhenjiang.

Second, the value structure of various land-type ecosystems and primary ecosystem services in the YRD urban agglomeration is stable. The ESV is mainly generated from farmland, forest land, and water bodies, primarily providing regulation services and support services.

Third, the number of grid cells with reduced ESV is continuously increasing, mainly distributed in the eastern coastal areas. From 2006 to 2020, over 80% of the grid cells experienced a decrease in ESV, primarily located in contiguous areas centered around Shanghai, Nanjing, Suzhou, and Hangzhou.

Fourth, the degree of disturbance to ESV varies among different land types, and there is heterogeneity in the response of ESV levels to the same influencing factors. Farmland, forest land, and water bodies have a greater degree of disturbance to the ESV of the urban agglomeration. LUI exerts more stress on areas with higher ESV levels, while population and economic activities within the region have a greater promoting effect on these high ESV areas. AAR and AAT have stress and promoting effects on the ESV of the YRD urban agglomeration, respectively, exhibiting "inverted U-shaped" and "U-shaped" characteristics.

Policy recommendations

In this paper, through the study of ESV in the YRD urban agglomeration, the following policy recommendations are obtained.

Firstly, under the background of integrated urban agglomeration construction, it is crucial to establish an integrated ecosystem management mechanism centered on urban agglomerations. Facing the severe fact that many cities are experiencing a decline in ESV, it is important for urban agglomerations to coordinate and jointly demarcate ecological land use red lines with local natural resource departments that are adapted to the sustainable economic and social development of the urban agglomeration, especially the red lines for construction land use. The same applies to other urban agglomerations; they need to establish urban development boundaries and ecological land use red lines, clearly defining areas that are off-limits to development and the boundaries of ecological resources that can be used sustainably. This can ensure that the pursuit of economic development does not excessively sacrifice the health and service capacity of ecosystems.

Secondly, in the urbanization construction of urban agglomerations, the concept "to protect in development, and to develop in protection" should be practiced. Implementing ecosystem protection measures such as "compensation for occupation"—where for every piece of ecological land occupied by urbanization construction, measures such as artificial afforestation and lake creation are taken to maintain the total ESV. Actively construct urban green spaces to achieve ecological urbanization. Promoting rooftop greening, city parks, green belts, and other urban greening projects can not only enhance the ecological functions of cities but also improve residents' quality of life and the city's attractiveness.

Thirdly, the management of forest, water, and farmland ecosystems should be highly emphasized. Since the ecosystem services of the YRD urban agglomeration are primarily provided by forests, water, and farmlands, increased protection and restoration of these ecosystems is necessary. Scientific management methods and restoration technologies, such as ecological agricultural practices and wetland restoration, can be applied to enhance the natural resilience and service functions of these ecosystems. This is critical for increasing the regional ecosystem service capacity and ensuring the self-regulation ability of the regional ecosystem, maintaining stability while meeting human demands.

Fourthly, it is essential to improve the land-use efficiency of urbanized areas. According to the exploration of factors affecting ESV, in any area, land development behavior greatly damages the ecosystem service capacity. Therefore, during the urbanization process, contiguous development should be adopted to avoid “enclave-style” development as much as possible and reduce the damage to ecologically vulnerable areas. Optimize the city layout and land use to ensure that development activities are coordinated with ecological protection goals. Promote land-intensive use and contiguous development strategies to minimize the negative impact of urban expansion on ecosystems.

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Westman, W. E. How much are nature’s services worth?. Science (New York, N.Y.) 197 , 960–964 (1977).

Article   ADS   CAS   PubMed   Google Scholar  

Liu, C. et al. Global trends and characteristics of ecological security research in the early 21st century: A literature review and bibliometric analysis. Ecol. Indic. 137 , 108734 (2022).

Article   Google Scholar  

Klarin, T. The concept of sustainable development: From its beginning to the contemporary issues. Zagreb Int. Rev. Econ. Bus. 21 , 67–94 (2018).

Wang, B., Zhang, Q. & Cui, F. Scientific research on ecosystem services and human well-being: A bibliometric analysis. Ecol. Indic. 125 , 107449 (2021).

Zhao, J. et al. The impact of land use and landscape pattern on ecosystem services in the Dongting lake region, China. Remote Sens. 15 , 2228 (2023).

Article   ADS   Google Scholar  

Ogbodo, U. S., Liu, S., Feng, S., Gao, H. & Pan, Z. Trade-offs and synergies among 17 ecosystem services in Africa: A long-term multi-national analysis. Remote Sens. 15 , 3588 (2023).

Wang, S. et al. Urbanization can benefit agricultural production with large-scale farming in China. Nat. Food 2 , 183 (2021).

Article   PubMed   Google Scholar  

Ning, Y. et al. Urban growth rates, trajectories, and multi-dimensional disparities in China. Cities 126 , 103717 (2022).

Ning, Y. et al. Dynamic multi-dimensional scaling of 30+year evolution of Chinese urban systems: Patterns and performance. Sci. Total Environ. 863 , 160705 (2023).

Article   CAS   PubMed   Google Scholar  

Chang, J. et al. Assessment of net ecosystem services of plastic greenhouse vegetable cultivation in China. Ecol. Econ. 70 , 740–748 (2011).

Wang, J., Lin, Y., Glendinning, A. & Xu, Y. Land-use changes and land policies evolution in China’s urbanization processes. Land Use Policy 75 , 375–387 (2018).

Pan, Z. & Wang, J. Spatially heterogeneity response of ecosystem services supply and demand to urbanization in China. Ecol. Eng. 169 , 106303 (2021).

Xu, D. & Hou, G. The spatiotemporal coupling characteristics of regional urbanization and its influencing factors: Taking the Yangtze River Delta as an example. Sustainability 11 , 822 (2019).

Wang, J. et al. Spatio-temporal changes in ecosystem service value: Evidence from the economic development of urbanised regions. Technol. Forecast. Soc. Change 193 , 122626 (2023).

Zhou, X., Yang, L., Gu, X., Zhang, L. & Li, L. Scarcity value assessment of ecosystem services based on changes in supply and demand: A case study of the Yangtze River Delta City cluster, China. Int. J. Environ. Res. Public Health 19 , 11999 (2022).

Article   PubMed   PubMed Central   Google Scholar  

Tian, Y. & Mao, Q. The effect of regional integration on urban sprawl in urban agglomeration areas: A case study of the Yangtze River Delta, China. Habitat Int. 130 , 102695 (2022).

Luo, Q., Zhou, J., Zhang, Y., Yu, B. & Zhu, Z. What is the spatiotemporal relationship between urbanization and ecosystem services? A case from 110 cities in the Yangtze River Economic Belt, China. J. Environ. Manag. 321 , 115709 (2022).

Wang, Z. & Cao, J. Assessing and predicting the impact of multi-scenario land use changes on the ecosystem service value: A case study in the upstream of Xiong’an new area, China. Sustainability 13 , 704 (2021).

Costanza, R. et al. The value of the world’s ecosystem services and natural capital. Nature 387 , 253–260 (1997).

Article   ADS   CAS   Google Scholar  

Costanza, R. et al. Changes in the global value of ecosystem services. Glob. Environ. Change Hum. Policy Dimens. 26 , 152–158 (2014).

Li, J. et al. Quantitative evaluation of ecological cumulative effect in mining area using a pixel-based time series model of ecosystem service value. Ecol. Indic. 120 , 106873 (2021).

Tiemann, A. & Ring, I. Towards ecosystem service assessment: Developing biophysical indicators for forest ecosystem services. Ecol. Indic. 137 , 108704 (2022).

Thompson, S., Vehkaoja, M., Pellikka, J. & Nummi, P. Ecosystem services provided by beavers Castor spp. Mamm. Rev. 51 , 25–39 (2021).

Woodward, R. T. & Wui, Y.-S. The economic value of wetland services: A meta-analysis. Ecol. Econ. 37 , 257–270 (2001).

Li, G., Fang, C. & Wang, S. Exploring spatiotemporal changes in ecosystem-service values and hotspots in China. Sci. Total Environ. 545 , 609–620 (2016).

Article   ADS   PubMed   Google Scholar  

Chen, W., Chi, G. & Li, J. Ecosystem services and their driving forces in the middle reaches of the Yangtze River urban agglomerations, China. Int. J. Environ. Res. Public Health 17 , 3717 (2020).

Hou, L., Wu, F. & Xie, X. The spatial characteristics and relationships between landscape pattern and ecosystem service value along an urban-rural gradient in Xi’an city, China. Ecol. Indic. 108 , 105720 (2020).

Song, F., Su, F., Mi, C. & Sun, D. Analysis of driving forces on wetland ecosystem services value change: A case in Northeast China. Sci. Total Environ. 751 , 141778 (2021).

Kibria, A. S. M. G., Behie, A., Costanza, R., Groves, C. & Farrell, T. The value of ecosystem services obtained from the protected forest of Cambodia: The case of Veun Sai-Siem Pang National Park. Ecosyst. Serv. 26 , 27–36 (2017).

Wang, S., Liu, Z., Chen, Y. & Fang, C. Factors influencing ecosystem services in the Pearl River Delta, China: Spatiotemporal differentiation and varying importance. Resour. Conserv. Recycl. 168 , 105477 (2021).

Liu, W. et al. Spatio-temporal variations of ecosystem services and their drivers in the Pearl River Delta, China. J. Clean. Prod. 337 , 130466 (2022).

Article   CAS   Google Scholar  

Wei, X., Zhao, L., Cheng, P., Xie, M. & Wang, H. Spatial-temporal dynamic evaluation of ecosystem service value and its driving mechanisms in China. Land 11 , 1000 (2022).

Chen, M. et al. Drivers of changes in ecosystem service values in Ganjiang upstream watershed. Land Use Policy 47 , 247–252 (2015).

Chen, J. et al. Precipitation dominants synergies and trade-offs among ecosystem services across the Qinghai-Tibet Plateau. Glob. Ecol. Conserv. 32 , e01886 (2021).

Google Scholar  

Wang, S., Wu, B. & Yang, P. Assessing the changes in land use and ecosystem services in an oasis agricultural region of Yanqi Basin, Northwest China. Environ. Monit. Assess. 186 , 8343–8357 (2014).

He, Y., Wang, W., Chen, Y. & Yan, H. Assessing spatio-temporal patterns and driving force of ecosystem service value in the main urban area of Guangzhou. Sci. Rep. 11 , 3027 (2021).

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Li, Z., Xia, J., Deng, X. & Yan, H. Multilevel modelling of impacts of human and natural factors on ecosystem services change in an oasis, Northwest China. Resour. Conserv. Recycl. 169 , 105474 (2021).

Teng, Y. et al. Spatial heterogeneity of ecosystem services trade-offs among ecosystem service bundles in an alpine mountainous region: A case- study in the Qilian Mountains, Northwest China. Land Degrad. Dev. 33 , 1846–1861 (2022).

Ma, S., Wang, L.-J., Jiang, J., Chu, L. & Zhang, J.-C. Threshold effect of ecosystem services in response to climate change and vegetation coverage change in the Qinghai-Tibet Plateau ecological shelter. J. Clean. Prod. 318 , 128592 (2021).

Ma, S., Qiao, Y.-P., Wang, L.-J. & Zhang, J.-C. Terrain gradient variations in ecosystem services of different vegetation types in mountainous regions: Vegetation resource conservation and sustainable development. For. Ecol. Manag. 482 , 118856 (2021).

Fang, L. et al. Identifying the impacts of natural and human factors on ecosystem service in the Yangtze and Yellow River Basins. J. Clean. Prod. 314 , 127995 (2021).

Li, S. et al. Impacts of urban expansion forms on ecosystem services in urban agglomerations: A case study of Shanghai-Hangzhou Bay urban agglomeration. Remote Sens. 13 , 1908 (2021).

Deng, C. et al. How trade-offs between ecological construction and urbanization expansion affect ecosystem services. Ecol. Indic. 122 , 107253 (2021).

Ligate, E. J., Chen, C. & Wu, C. Evaluation of tropical coastal land cover and land use changes and their impacts on ecosystem service values. Ecosyst. Health Sustain. 4 , 188–204 (2018).

Kumar, R. et al. Impacts of plastic pollution on ecosystem services, sustainable development goals, and need to focus on circular economy and policy interventions. Sustainability 13 , 9963 (2021).

Houghton, R. A. The worldwide extent of land-use change. BioScience 44 , 305–313 (1994).

Wang, X. Managing land carrying capacity: Key to achieving sustainable production systems for food security. Land 11 , 484 (2022).

Wei, Y., Huang, C., Li, J. & Xie, L. An evaluation model for urban carrying capacity: A case study of China’s mega-cities. Habitat Int. 53 , 87–96 (2016).

Donaldson, D. & Storeygard, A. The view from above: Applications of satellite data in economics. J. Econ. Perspect. 30 , 171–198 (2016).

Sannigrahi, S. et al. Ecosystem service value assessment of a natural reserve region for strengthening protection and conservation. J. Environ. Manag. 244 , 208–227 (2019).

Yang, J. & Huang, X. The 30 m annual land cover dataset and its dynamics in China from 1990 to 2019. Earth Syst. Sci. Data 13 , 3907–3925 (2021).

Xie, G., Zhang, C., Zhen, L. & Zhang, L. Dynamic changes in the value of China’s ecosystem services. Ecosyst. Serv. 26 , 146–154 (2017).

Xiao, Y., Xie, G. & An, K. Economic value of ecosystem services in Mangcuo Lake drainage basin. J. Appl. Ecol. 14 , 676–680 (2003).

Peng, J. et al. Simulating the impact of grain-for-green programme on ecosystem services trade-offs in Northwestern Yunnan, China. Ecosyst. Serv. 39 , 100998 (2019).

Yan, Y. et al. Ecosystem health assessment of the Liao River Basin upstream region based on ecosystem services. Acta Ecol. Sin. 36 , 294–300 (2016).

Zhang, G. et al. Mapping changes in the value of ecosystem services in the Yangtze River Middle Reaches Megalopolis, China. Ecosyst. Serv. 48 , 101252 (2021).

Wiggering, H. et al. Indicators for multifunctional land use: Linking socio-economic requirements with landscape potentials. Ecol. Indic. 6 , 238–249 (2006).

Chen, W., Chi, G. & Li, J. The spatial association of ecosystem services with land use and land cover change at the county level in China, 1995–2015. Sci. Total Environ. 669 , 459–470 (2019).

Rotich, B., Kindu, M., Kipkulei, H., Kibet, S. & Ojwang, D. Impact of land use/land cover changes on ecosystem service values in the Cherangany hills water tower. Kenya. Environ. Chall. 8 , 100576 (2022).

Fenglian, L. & Renyi, Y. Evolution of Land use patterns in national central cities and its impact on ecosystem service values: A case study of Wuhan city. Res. Soil Water Conserv. 28 , 177–183 (2021).

Du, X., Jin, X., Yang, X., Yang, X. & Zhou, Y. Spatial pattern of land use change and its driving force in Jiangsu province. IJERPH 11 , 3215–3232 (2014).

van der Sluis, T., Pedroli, B., Kristensen, S. B. P., Lavinia Cosor, G. & Pavlis, E. Changing land use intensity in Europe—Recent processes in selected case studies. Land Use Policy 57 , 777–785 (2016).

Mellander, C., Lobo, J., Stolarick, K. & Matheson, Z. Night-time light data: A good proxy measure for economic activity?. PLoS ONE 10 , e0139779 (2015).

Galvao, A. F. Jr. Quantile regression for dynamic panel data with fixed effects. J. Econom. 164 , 142–157 (2011).

Article   MathSciNet   Google Scholar  

Das, K., Krzywinski, M. & Altman, N. Quantile regression. Nat. Methods 16 , 451–452 (2019).

Duan, K., Ren, X., Shi, Y., Mishra, T. & Yan, C. The marginal impacts of energy prices on carbon price variations: Evidence from a quantile-on-quantile approach. Energy Econ. 95 , 105131 (2021).

Heckman, J. J., Ichimura, H. & Todd, P. Matching as an econometric evaluation estimator. Rev. Econ. Stud. 65 , 261–294 (1998).

Chernozhukov, V. & Hansen, C. Instrumental variable quantile regression: A robust inference approach. J. Econom. 142 , 379–398 (2008).

Yang, Y. et al. Modulations of surface thermal environment and agricultural activity on intraseasonal variations of summer diurnal temperature range in the Yangtze River Delta of China. Sci. Total Environ. 736 , 139445 (2020).

Wang, J., Zhai, T., Lin, Y., Kong, X. & He, T. Spatial imbalance and changes in supply and demand of ecosystem services in China. Sci. Total Environ. 657 , 781–791 (2019).

Crossman, N. D. & Bryan, B. A. Identifying cost-effective hotspots for restoring natural capital and enhancing landscape multifunctionality. Ecol. Econ. 68 , 654–668 (2009).

Hu, M. et al. Spatio-temporal changes in ecosystem service value in response to land-use/cover changes in the Pearl River Delta. Resour. Conserv. Recycl. 149 , 106–114 (2019).

Han, X., Yu, J., Shi, L., Zhao, X. & Wang, J. Spatiotemporal evolution of ecosystem service values in an area dominated by vegetation restoration: Quantification and mechanisms. Ecol. Indic. 131 , 108191 (2021).

Liu, C. et al. Spatiotemporal differentiation and mechanism of anthropogenic factors affecting ecosystem service value in the Urban Agglomeration around Poyang Lake, China. Ecol. Indic. 154 , 110733 (2023).

Shi, J. et al. How socioeconomic factors affect ecosystem service value: Evidence from China. Ecol. Indic. 145 , 109589 (2022).

Ding, T., Chen, J., Fang, Z. & Chen, J. Assessment of coordinative relationship between comprehensive ecosystem service and urbanization: A case study of Yangtze River Delta urban agglomerations, China. Ecol. Indic. 133 , 108454 (2021).

Dai, X. et al. Spatio-temporal variations of ecosystem services in the urban agglomerations in the middle reaches of the Yangtze River, China. Ecol. Indic. 115 , 106394 (2020).

Meng, F. et al. Urban ecological transition: The practice of ecological civilization construction in China. Sci. Total Environ. 755 , 142633 (2021).

Shao, M., Wu, L., Li, F. & Lin, C. Spatiotemporal dynamics of ecosystem services and the driving factors in urban agglomerations: Evidence from 12 National Urban Agglomerations in China. Front. Ecol. Evol. 10 , 804969 (2022).

Liu, Y., Gao, Y., Liu, L., Song, C. & Ai, D. Nature-based solutions for urban expansion: Integrating ecosystem services into the delineation of growth boundaries. Habitat Int. 124 , 102575 (2022).

Xie, M., Feng, Z., Song, Y., Guan, H. & Wang, S. How does urban shrinkage affect land use efficiency? A case study of shrinking cities in Northeast China. Chin. Geogr. Sci. 34 , 34–51 (2024).

Wu, Q., Yang, L. & Mi, J. Detecting the effects of opencast mining on ecosystem services value in arid and semi-arid areas based on time-series remote sensing images and Google Earth Engine (GEE). BMC Ecol. Evol. 24 , 28 (2024).

Download references

Guangxi Philosophy and Social Sciences Planning Research Project, NO.22CJL004.

Author information

Authors and affiliations.

School of Mathematics and Physics, Hechi University, Hechi, 546300, Guangxi, China

Yugui Lu & Xiaokun Jiang

School of Economy and Trade, Guangxi Vocational Normal University, Nanning, 530007, Guangxi, China

School of Economics, Guangxi Minzu University, Nanning, 530006, Guangxi, China

Jiacong Wang

You can also search for this author in PubMed   Google Scholar

Contributions

Conceptualization, Y.L., X.J.; methodology, X.J., J.W.; software, X.J.; validation, Y.L., J.W.; formal analysis, X.J, J.W.; investigation, X.J., Y.L.; resources, Y.L.; data gathering, X.J.; writing—original draft preparation, Y.L.; writing—review and editing, J.W.; visualization, Y.L.; supervision, Y.L.; project administration, X.J.; funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Xiaokun Jiang .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ .

Reprints and permissions

About this article

Cite this article.

Lu, Y., Wang, J. & Jiang, X. Spatial and temporal changes of ecosystem service value and its influencing mechanism in the Yangtze River Delta urban agglomeration. Sci Rep 14 , 19476 (2024). https://doi.org/10.1038/s41598-024-70248-2

Download citation

Received : 13 December 2023

Accepted : 14 August 2024

Published : 22 August 2024

DOI : https://doi.org/10.1038/s41598-024-70248-2

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Yangtze River Delta urban agglomeration
• Ecosystem service value
• Multi-source remote sensing data
• Panel quantile regression
• Spatial and temporal pattern evolution

By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Information

• Author Services

Initiatives

You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader.

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess .

Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.

Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

Original Submission Date Received: .

• Active Journals
• Find a Journal
• Proceedings Series
• For Authors
• For Reviewers
• For Editors
• For Librarians
• For Publishers
• For Societies
• For Conference Organizers
• Open Access Policy
• Institutional Open Access Program
• Special Issues Guidelines
• Editorial Process
• Research and Publication Ethics
• Article Processing Charges
• Testimonials
• Preprints.org
• SciProfiles
• Encyclopedia

Journal Menu

• Sustainability Home
• Aims & Scope
• Editorial Board
• Reviewer Board
• Topical Advisory Panel
• Instructions for Authors
• Special Issues
• Sections & Collections
• Article Processing Charge
• Indexing & Archiving
• Editor’s Choice Articles
• Most Cited & Viewed
• Journal Statistics
• Journal History
• Journal Awards
• Society Collaborations
• Conferences
• Editorial Office

Journal Browser

• arrow_forward_ios Forthcoming issue arrow_forward_ios Current issue
• Vol. 16 (2024)
• Vol. 15 (2023)
• Vol. 14 (2022)
• Vol. 13 (2021)
• Vol. 12 (2020)
• Vol. 11 (2019)
• Vol. 10 (2018)
• Vol. 9 (2017)
• Vol. 8 (2016)
• Vol. 7 (2015)
• Vol. 6 (2014)
• Vol. 5 (2013)
• Vol. 4 (2012)
• Vol. 3 (2011)
• Vol. 2 (2010)
• Vol. 1 (2009)

Find support for a specific problem in the support section of our website.

Please let us know what you think of our products and services.

Visit our dedicated information section to learn more about MDPI.

Spatial Planning and Analysis in Urban Sustainability

• Print Special Issue Flyer

Special Issue Editors

Special issue information, benefits of publishing in a special issue.

• Published Papers

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section " Sustainable Urban and Rural Development ".

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 15966

Share This Special Issue

Dear Colleagues,

We would like to invite you to publish an article in this Special Issue. The thematic scope of this series is based on important issues of socio-economic and spatial development, and how it relates to urban sustainability. The development of cities and the urbanization of peri-urban areas is never harmonious and sustainable. On the one hand, plans, strategies, and development policies are prepared for the analyzed areas, and on the other hand, we observe often irrational or chaotic changes in geographical space. This causes many conflicts and disproportions in the urbanized space and its surroundings. This is where spatial planning plays a role, geared towards urban sustainability. The purpose of this Special Issue is to try to bridge the gap between planning theory and space management practice. This can be achieved by continuing to search for good practices and theoretical solutions, but also by creating analyses that improve on the existing methods of spatial management. To meet the problem of the global COVID-19 pandemic, we would also like to publish articles showing the relationship between sustainable urban development, the consequences of the pandemic, and spatial planning.

We invite articles whose analyses are based on both quantitative and qualitative methods.

In this Special Issue, we are interested in the following:

• spatial planning in problem and conflict areas
• spatial analyzes in terms of determining typical, but also characteristic directions of spatial development
• indicating good practices in urban spatial planning
• analyses of urban space presenting the dynamics of change
• analyses of spatial structures of sustainable cities
• spatial planning on derelict lands.

Prof. Dr. Robert Krzysztofik Dr. Dragan Weronika Dr. Solarski Maksymilian Dr. Spórna Tomasz Guest Editors

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website . Once you are registered, click here to go to the submission form . Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• urban planning
• spatial development
• spatial analysis
• urban sustainability
• urbanisation
• Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
• Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
• Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
• External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
• e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here .

Published Papers (6 papers)

Further Information

Mdpi initiatives, follow mdpi.

Subscribe to receive issue release notifications and newsletters from MDPI journals

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

•  We're Hiring!
•  Help Center

GIS and Spatial Planning

• Most Cited Papers
• Most Downloaded Papers
• Newest Papers
• Strategic Spatial Planning Follow Following
• Planning Theory Follow Following
• HISTORY OF URBAN DEVELOPMENT Follow Following
• Health Planning Follow Following
• Public Participation GIS Follow Following
• Landslide Susceptibility and Hazard Modeling Follow Following
• Method and Theory in the Study of Religion Follow Following
• Informatics and Spatial Analysis Follow Following
• Geo spatial modelling for urban sprawl mapping and urban hazard Follow Following
• Urban Spatial Development and Urban Sprawl Follow Following

Enter the email address you signed up with and we'll email you a reset link.

• Academia.edu Journals
•   We're Hiring!
•   Help Center
• Find new research papers in:
• Health Sciences
• Earth Sciences
• Cognitive Science
• Mathematics
• Computer Science
• Academia ©2024

arXiv's Accessibility Forum starts next month!

Help | Advanced Search

Computer Science > Robotics

Title: simpnet: spatial-informed motion planning network.

Abstract: Current robotic manipulators require fast and efficient motion-planning algorithms to operate in cluttered environments. State-of-the-art sampling-based motion planners struggle to scale to high-dimensional configuration spaces and are inefficient in complex environments. This inefficiency arises because these planners utilize either uniform or hand-crafted sampling heuristics within the configuration space. To address these challenges, we present the Spatial-informed Motion Planning Network (SIMPNet). SIMPNet consists of a stochastic graph neural network (GNN)-based sampling heuristic for informed sampling within the configuration space. The sampling heuristic of SIMPNet encodes the workspace embedding into the configuration space through a cross-attention mechanism. It encodes the manipulator's kinematic structure into a graph, which is used to generate informed samples within the framework of sampling-based motion planning algorithms. We have evaluated the performance of SIMPNet using a UR5e robotic manipulator operating within simple and complex workspaces, comparing it against baseline state-of-the-art motion planners. The evaluation results show the effectiveness and advantages of the proposed planner compared to the baseline planners.

Submission history

Access paper:.

• HTML (experimental)
• Other Formats

References & Citations

• Google Scholar
• Semantic Scholar

BibTeX formatted citation

Bibliographic and Citation Tools

Code, data and media associated with this article, recommenders and search tools.

• Institution

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .

Health & Environmental Research Online (HERO)

• Learn about HERO
• Search HERO
• Projects in HERO
• Risk Assessment
• Transparency & Integrity

Research on real-time trajectory optimization methods for stratospheric airships based on deep learning

• Original Paper
• Published: 29 August 2024

Cite this article

• Tianshu Wang 1 ,
• Zhiqiang Peng 1 &
• Quanbao Wang   ORCID: orcid.org/0000-0001-8990-6439 1  

Stratospheric airships are a type of large aircraft capable of operating for extended periods in the stratosphere. This paper focuses on real-time trajectory planning for stratospheric airships. It constructs an optimization path dataset based on the Gauss pseudospectral method and utilizes deep learning neural networks to solve the real-time path planning problem for stratospheric airships. The article first establishes a six-degree-of-freedom airship spatial motion model. It uses the Gauss pseudospectral method to transform the original optimization problem into a parameter optimization problem, which is then solved using sequential quadratic programming. During the ascent phase, based on the airship's speed, yaw angle, and pitch angle when transitioning from the troposphere to the stratosphere, a total of 26,901 optimized paths are generated using the Gauss pseudospectral method, and the influence of different initial states on the optimized paths is analyzed. During the level flight phase, 3960 optimized paths are generated based on different initial speeds and yaw angles, and an analysis of the impact of the initial yaw angle on the optimized paths is conducted. Finally, the dataset generated by the Gauss pseudospectral method is divided into training and testing sets. Long short-term memory (LSTM) networks and Transformer networks are employed to learn and generate optimized paths from the dataset. Comparison results show that the neural network model is highly consistent with the optimized paths obtained using the Gauss pseudospectral method. Furthermore, the path generation time is reduced from hundreds of seconds to seconds, leading to a significant improvement in generation time stability.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Explore related subjects

• Artificial Intelligence

Data availability

The datasets generated during the current study are not publicly available due to their proprietary nature and are kept within the laboratory for further analysis. However, the methods used to generate and analyze the data are described in detail in the Data generation based on the Gauss pseudospectral method section of this manuscript.

Yang YN, Xu X, Zhang B et al (2020) Bionic design for the aerodynamic shape of a stratospheric airship. Aerosp Sci Technol 98:105664

Article   Google Scholar  

Morante D, Sanjurjo RM, Soler M (2021) A survey on low-thrust trajectory optimization approaches. Aerospace 8:88

Vinh NX (1990) Optimal multiple-pass aero-assisted plane change. Acta Astronaut 21:749–758

Wu D, Jiang FH, Li JF (2021) Warm start for low-thrust trajectory optimization via switched system. J Guid Control Dyn 44(9):1700–1706

Bianco N, Bertolazzi E, Biral F et al (2018) Comparison of direct and indirect methods for minimum lap time optimal control problems. Veh Syst Dyn 57:1–32

Google Scholar  

Lee S, Bang H (2007) Three-dimensional ascent trajectory optimization for stratospheric airship platforms in the jet stream. J Guid Control Dyn 30(5):1341–1352

Jing Y, Wu Y, Tang J et al (2022) Receding horizon trajectory generation of stratospheric airship in low altitude return phase. Aerospace 9:670

Wang JJ, Jian QY (2014) Study of the optimal design for the gliding trajectory. Appl Mech Mater 568–570:1063–1067

Muller JB, Zhao YJ, Garrard WL (2009) Optimal ascent trajectories for stratospheric airships using wind energy. J Guid Control Dyn 32(4):1232–1245

Zhang LX, Wang ZW, Yang XX et al (2013) Ascent trajectory planning for stratospheric airship based on gauss pseudospectral method. J Shanghai Jiao Tong Univ 47(8):1205–1209

Zheng BJ, Zhu M, Guo X et al (2024) Path planning of stratospheric airship in dynamic wind field based on deep reinforcement learning. Aerosp Sci Technol 150:109173

Guo X, Zhu M (2013) Ascent trajectory optimization for stratospheric airship with thermal effects. Adv Space Res 52(6):1097–1110

Wang J, Meng XY, Li CC (2021) Recovery trajectory optimization of the solar-powered stratospheric airship for the station-keeping mission. Acta Astronaut 178:159–177

Ashraf MZ, Choudhry MA (2013) Dynamic modeling of the airship with Matlab using geometrical aerodynamic parameters. Aerosp Sci Technol 25:56–44

Ollero A, Maza I (2007) Multiple heterogeneous unmanned aerial vehicles. Springer, Berlin

Book   Google Scholar  

Li YW, Nahon M (2007) Modeling and simulation of airship dynamics. J Guid Control Dyn 30(6):1691–1700

Tuckerman LB (1923) Notes on aerodynamic forces on airship hulls, NACA report No 129

Muller JB, Paluszekt AMA, Zhao Y (2013) Development of an aerodynamic model and control law design for a high altitude airship. In: AIAA 3rd “Unmanned Unlimited” Technical Conference, Workshop and Exhibit

Zhu YX, Zhao KG, Li HL et al (2020) Trajectory planning algorithm using gauss pseudo-spectral method based on vehicle-infrastructure cooperative system. Int J Automot Technol 21(4):889–901

Li YM, Wang ZD, Yang H et al (2023) energy-optimal planning of robot trajectory based on dynamics. Arab J Sci Eng 48(3):3523–3536

Boggs PT, Tolle JW (1995) Sequential quadratic programming. Acta Numer 4(1):1–51

Article   MathSciNet   Google Scholar  

Gill PE, Murray W, Saunders M (2005) Snopt: an SQP algorithm for large-scale constrained optimization. SIAM Rev 47(1):99–131

Solodov MV (2009) Global convergence of an SQP method without boundedness assumptions on any of the iterative sequences. Math Program 118(1):1–12

Vaswani A, Shazeer N, Parmar N et al (2017) Attention is all you need. Adv Neural Inf Process Syst 30:6000–6010

Chen L, Wang Y, Miao Z (2023) Transformer-based imitative reinforcement learning for multirobot path planning. IEEE Trans Industr Inf 19(10):10233–10243

Wang ZB, Guo JY, Hu ZM et al (2023) Lane transformer: a high-efficiency trajectory prediction model. IEEE Open J Intell Transp Syst 4:2–13

Suvarna S, Chung H, Sinha A et al (2021) Trajectory planning using nonlinear receding horizon optimization for an autonomous airship. In: 2021 Seventh Indian Control Conference, pp 99–104

Download references

Funding was provided by The Fundamental Research Funds for Central Universities (Grant No. 23X010201110).

Author information

Authors and affiliations.

Shanghai Jiao Tong University, Shanghai, China

Tianshu Wang, Zhiqiang Peng & Quanbao Wang

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Quanbao Wang .

Ethics declarations

Conflict of interest.

The authors have no conflicts of interest that will influence the content of this article.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Wang, T., Peng, Z. & Wang, Q. Research on real-time trajectory optimization methods for stratospheric airships based on deep learning. AS (2024). https://doi.org/10.1007/s42401-024-00315-z

Download citation

Received : 17 May 2024

Revised : 18 July 2024

Accepted : 29 July 2024

Published : 29 August 2024

DOI : https://doi.org/10.1007/s42401-024-00315-z

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Stratospheric airship
• Trajectory optimization
• Gauss pseudo-spectral method
• Deep learning
• Find a journal
• Publish with us
• Track your research