Evaluation of Ecosystem Service Value in Kenli District of Dongying

Evaluation of Ecosystem Service Value in Kenli District of Dongying | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Evaluation of Ecosystem Service Value in Kenli District of Dongying Junwei Zhang, Yue-Wei Ma This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3846949/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This paper analyzes the ecosystem service function and its value in the Kenli District of Dongying City, a major town situated at the Yellow River estuary, using the Xiegaodi classification system and evaluation method. The spatio-temporal variations of four ecosystem service values—habitat quality, carbon storage, water conservation, and soil conservation—are examined from 2000 to 2020 employing the InVEST model and a grid-based approach. The findings reveal declining trends in habitat quality, carbon storage, and soil conservation, contrasted with an upward trend in water conservation from 2000 to 2020 in Dongying. Spatially, the habitat quality in the Kenli District of Dongying City displays a pattern of high values in the southeast and low values in the northwest. Higher carbon storage areas are primarily concentrated in the western, central, and northern regions of the Kenli District, with an observed eastward expansion. Water conservation transitions from low to high. Soil conservation values are higher, and the low-value areas shift from the central and southern parts to the southern regions, along with some parts of the northeast moving to the north. The value of ecosystem services in the Kenli District of Dongying City increases from 1.911 billion yuan in 2000 to 3.597 billion yuan in 2010 and further to 3.747 billion yuan in 2020. The impacts of cultivated land, wetland, and water bodies on the ecosystem service value in the region are more apparent. Specifically, the impacts of cultivated land on the ecosystem service value in 2000, 2010, and 2020 amount to 70.29%, 25.54%, and 25.63%, respectively, while those of wetland are 4.77%, 32.39%, and 31.07%, and for water bodies, the percentages are 33.96%, 45.51%, and 49.92%, respectively. From the perspective of ecological service functions, waste treatment, water conservation, and climate regulation exhibit greater importance. Over the study period, waste treatment contributes 29.25%, 32.19%, and 32.97% to the total value, whereas water conservation accounts for 15.51%, 25.86%, and 26.84%, and climate regulation constitutes 10.66%, 12.68%, and 12.05% of the total value, respectively. land use/cover change Ecosystem services Kenli district Yellow River Delta Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Eecosystems provide essential environmental conditions and material resources for human survival and development across multiple dimensions (Teixeira H M et al., 2021). They constitute a crucial support system for human habitation, encompassing not only provisions such as food, medicine, and raw materials for industrial and agricultural needs, but also serving as the foundation for Earth's life support system. This includes vital functions like climate regulation, maintenance of atmospheric chemical balance and stability, biogeochemical and water cycles, preservation of genetic and species diversity, mitigation of drought and flood impacts, facilitation of plant pollination and seed dispersal, biological control, soil formation, and environmental purification(Zheng et al., 2003).Over the past century, intensified industrialization, urbanization, and human exploitation of nature have expedited the degradation of the natural environment(Jing et al., 2018), leading to significant changes in global ecosystem structure and function(Chatanga P et al., 2020). This has brought forth a host of environmental challenges, including deteriorating air quality, soil erosion, degraded land quality, water pollution, and biodiversity loss, causing severe disruptions to ecosystem stability and posing considerable challenges to human life and production(Vitousek P M et al., 1997). This predicament is a shared concern of humanity(Zheng H et al., 2003). It is evident that ongoing alterations in ecosystem composition, structure, and function due to human activities have substantially weakened ecosystem service capabilities(Villa F et al., 2014). Consequently, the study of ecosystem services has emerged as a prominent area of ecological research in the 21st century. Addressing the intricate balance between the development needs of human society and ecological protection, while harmonizing the relationship between humans and the land, is a complex and pressing challenge, rapidly evolving into a strategic imperative for global development(Liu et al., 2022). The Yellow River holds profound significance as the mother river of the Chinese nation, serving as its birthplace and cradle. The Yellow River basin stands as a vital ecological shield and a significant economic zone within China, playing a pivotal role in the country's economic and social progress as well as ecological security. With the highest sediment load globally, the river transports millions of tons of sediment annually, culminating in the formation of the Yellow River Delta(Liu et al., 2006). Within Shandong, the only river-sea confluence region in the Yellow River basin, lies Dongying Province, featuring the world's youngest delta and the most recent wetland ecosystem. Estuarine wetlands are paramount within coastal zones, characterized by their swift landforming process, limited vegetation coverage, and high population density. It is within this context that we analyze the land use/cover changes in the Kenli District of Dongying City from 2000 to 2020 and compute its ecosystem service value. 2. Data and methods 1.1 Overview of the study area Kenli District falls under the administrative jurisdiction of Dongying City, situated within Shandong Province. The district acquired its name from its past designations as the Reclamation District and Lijinwa. Positioned at the estuary of the Yellow River and the heartland of the Shengli Oilfield, its geographical coordinates range from 118°15' to 119°19' east longitude and 37°24' to 38°10' north latitude. The district spans a southwest-to-northeast direction, measuring 96.2 kilometers horizontally from east to west and 55.5 kilometers vertically from north to south. Its eastern boundary fronts the Bohai Sea, while its northwest section borders Lijin County along the Yellow River. It adjoins Dongying District in the southern part of Dongying City and shares a border with Hekou District in the northeast. The total land area encompasses 2204.07 square kilometers. Kenli District holds a significant role as a crucial ecological protection zone in Eastern China. Concurrently, it grapples with the severe issue of soil salinization. Furthermore, it serves as the backdrop for the Shengli Oilfield, a quintessential location characterized by the intricate interplay between natural and human factors. This interaction of river, sea, land, and human activities, while bearing ecological significance, does pose certain threats to ecological security, leading to the emergence of diverse ecological challenges. This research carries immense importance in maintaining the ecological security framework of the lower Yellow River region and propelling the sustainable development of this area. 1.2 Data Sources The data used in this study mainly include land use/cover data, DEM data, NDVI data, basic geographic data and soon in Kenli District of Dongying City. In this paper, WGS 1984 coordinate system is adopted, and the spatial resolution is 30m. The data sources areas follows: The data of Land Use/Cover Change (LUCC) in Kenli District of Dongying City are derived from the 2000, 2010 and 2020 datassets of globeland30 Global Geographic Data product ( http://globeland30.org/).Th e data includes 10 first-level classes with a spatial resolution of 30m. Basic geographic data such as administrative boundaries were obtained from the Resources and Environmental Sciences and Data Center of the Chinese Academy of Sciences ( https://www.resdc.cn/ ). The wetland data was derived from National Data Center for Earth System Science -- Geographic Resources Sub-Center, National Platform for Basic Conditions of Science and Technology ( http://gre.geodata.cn ). 1.3 Research Methods 1.3.1 Habitat quality service assessment model In this study, we employed physical measures to evaluate habitat quality, utilizing the habitat quality module within the InVEST model. This module quantifies regional habitat quality primarily based on factors such as land cover type, extent, and the degree of degradation within a specific area. The calculation formula is outlined as follows: $${Q}_{xj}={H}_{j}\left[1-\frac{{D}_{xj}^{2}}{{D}_{xz}^{z}+{k}^{z}}\right]$$ 1 In formula (3), Qxj is the habitat quality index of grid x in habitat type j; Hj is the habitat suitability of habitat type j, the range is [0,1]; k is the semi-saturation sum constant, taking half of the maximum habitat degradation degree, and the default value is 0.05; z is a normalized constant with a default value of 2.5. The parameters in this model are determined according to the study and case site characteristics of Huang et al(Huang et al., 2018 ). Table 1 Threats weight value and maximum influencing distance Threat Sources Maximum coercion distance /km Weights Spatial decay mode Arable Land 3 0.6 Linear Grassy 5 0.8 Index Wetlands 4 0.6 Index Bodies of water 4 0.5 Linear Artificial surface 8 0.3 Index Bare ground 2 0.4 Linear Ocean 5 1 Linear Table 2 Habitat suitability and sensitivity to threat sources Land use Habitat Threat factors Types suitability Arable land Grass Wetlands Bodies of water Artificial surface Bare ground Ocean Arable land 0.3 0 0.7 0.7 1 0.6 0.3 0.3 Grass 0.7 0.6 0 0.6 0.8 0.6 0.4 0.4 Wetlands 0.75 0.5 0.7 0 0.2 0.6 0.3 0 Bodies of water 0.7 0.45 0.6 0.2 0 0.8 0.4 0 Artificial surface 0 0 0.5 0.1 0 0.2 0.1 0.6 Bare land 0 0 0 0 0 0.2 0 0 Ocean 0 0 0 0 0.2 0.2 0 0 2.3.2 Carbon storage service evaluation model In this study, the InVEST model's carbon storage module was employed to assess ecosystem carbon storage, primarily integrating land use/cover data for the calculations. The ecosystem's carbon storage was categorized into four primary carbon pools: (1) the above-ground carbon pool, which primarily encompasses carbon within the living vegetation on the land surface; (2) the underground carbon pool, primarily comprising carbon in underground plant roots; (3) the soil carbon pool, mainly constituted by organic carbon within the soil; and (4) the dead organic carbon, mainly comprising organic carbon from deceased vegetation and fallen leaves. Additionally, the InVEST model encompasses harvested wood products or related wood product patches (HWPs). However, this specific carbon pool was not considered in this analysis due to the challenges in obtaining data for this particular component, and its overall impact on the carbon stock is minimal(LAI L et al., 2018). The formula utilized for the model calculation is as follows: $${C}_{i}={C}_{i,above}+{C}_{i,below}+{C}_{i,soil}+{C}_{i,dead}$$ 2 \({C}_{total}\) = \(\sum _{i=1}^{n}{C}_{i}\times {S}_{i}\) (3) In formula (2) ~ (3), i is the iland use type; Ci is the total carbon density of soil and biomass of land use type i, Mg·hm-2 ; Ci, above is aboveground biomass carbon density of land use type i, Mg·hm-2 ; Ci, below is the underground biomass carbon density of land use type, Mg·hm-2 ; Ci, dead is carbon density of litter organic matter of land use type i, Mg·hm-2 ; Ci, soil refers to the carbon density of soil organic matter in 0 ~ 30 cm depth of land use type i, Mg·hm-2 ; Ctotal is the total carbon storage, Mg; Si is the total area of land use type i, hm2 ; n is the number of land use types, which takes the value 7. Table 3 Carbon storage table Land Use Types Aboveground biomass carbon density Subsurface biomass carbon density Soil organic matter carbon density Litter organic matter carbon density cultivate 11 3 17.8 0.4 grass 12.6 5.6 15.8 1.4 wetlands 7 2.5 20 0.5 water 1.8 0.75 22 0 artificil 0 0 14 0 bare 0.1 0 13 0 sea 2 1 26 0 2.3.3 Water conservation service evaluation model The evaluation of water conservation is pivotal for maintaining regional ecological security. This paper employs a grid-based approach to estimate the water conservation quantity within Kenli District. The central technique involves utilizing a grid system resembling a fishing net, carefully selecting based on geographical location, removing excess portions, and extracting the area corresponding to distinct land types for each grid based on their attributes. In this study, the extensive expanse of Kenli District within Dongying City has been subdivided into multiple 300m×300m small units, and the equivalent method is employed for calculating its soil conservation. 2.3.4 Soil conservation service evaluation model Soil and water conservation constitutes a pivotal environmental concern within the context of river basin sustainability. This study employs a grid-based methodology to estimate soil conservation levels within the Kenli area. The primary technique involves the utilization of a grid system, akin to a fishing net, wherein selection is based on geographical location, with the removal of redundant components. The extraction of the area for various land types within each grid is determined based on their respective attributes. Within this research, the expansive Kenli District of Dongying City has been subdivided into multiple small areas, each measuring 300m × 300m. Moreover, the equivalent method is employed to compute its water conservation measures. 2.3.5 Ecosystem service value The equivalent factor for the ecosystem service value within the study area was computed by referencing the equivalent factor table for China's ecosystem service value, as adapted from Xie Gaodi by Wu Daqian (adjusted by a correction coefficient of 1.38 for Shandong Province). It is important to note that this scale solely represents the ecosystem service value of terrestrial ecosystems and does not incorporate the ecosystem service value of the ocean in this context(Wu et al., 2009). Table 4 The value of ecosystem services The ecosystem services function cultivated grass wetlands water artificial Bare Ocean Gas conditioning 610.5 976.9 1592.7 0 0 0 0 Climate regulation 1086.8 1099.3 15130.9 561.7 0 0 0 Water conservation 732.6 976.9 13715.2 24885.8 -9215.6 36.6 0 Soil formation and protection 1782.8 2381.2 1513.1 12.1 0 24.4 0 Waste disposal 2002.7 1599.7 16086.6 22199.5 -3000.3 12.1 0 Biodiversity conservation 866.9 1331 2212.2 3040.6 0 415.1 0 Food production 1221.2 366.4 265.5 122.1 0 12.1 0 Ingredients 122.1 61 61.9 12.1 0 0 0 Entertainment culture 12.1 48.9 6383.0 5299.5 0 12.1 0 Total 8437.7 8841.3 56961.1 56133.4 -12215.9 512.4 0 Calculate the service value of each land use type, the value of each service function and the total value of ecosystem services according to formula (4), (5) and (6). $${ESV}_{k}=\sum _{f}{A}_{k}\times {VC}_{kf}$$ 4 \({ESV}_{f}\) = \(\sum _{k}{A}_{k}\times {VC}_{kf}\) (5) ESV= \(\sum _{k}\sum _{f}{A}_{k}\times {VC}_{kf}\) (6) In formula (4), (5) and (6), ESVfk, ESV and ESV are respectively the service value of type k, the value of service function of item f and the total service value; Ak is the land area of type k;VCkf (value coefficient) is the service value of the service unit area of item f of type k. 2.3.6 Ecosystem service value flow to profit and loss model The ecosystem service value (ESV) profit and loss model, built upon a land use transfer matrix, provides a comprehensive approach that simultaneously considers both the quantitative changes in ESV within the study area and the spatial-temporal transfer trends. This model effectively depicts the origins and destinations of ESV gains and losses resulting from the spatial shifts in land use types across different periods within the study area(Zhu et al., 2023). Using this land use transfer matrix as a foundation, this paper computes the ESV fluctuations stemming from alterations in land use types within the study area. The calculation for the ESV flow direction profit and loss is as follows: P ij =(V j -V i ) * A ij (7) In formula (7), Pij is the ESV profit and loss caused by the change of land use type from i to j, Vi and Vj are the ESV coefficient of Class i land use type and ESV coefficient of class j land use type, and Aij is the area of land use type from i to j. 3. Results and analysis 3.1 Ecosystem service value assessment A diverse array of ecosystem services is present. Based on the ecological characteristics of the study area, we have selected four distinct types of ecosystem services for thorough investigation: habitat quality, carbon storage, water conservation, and soil conservation. Table 5 Types of ecosystem services MA Services Types of ecosystem services Physical geographic elements Ecological value Supply services Regulating service Water conservation Carbon storage Hydrology Climate Water supply Regulating climate Support services Habitat quality Soil conservation Biodiversity Soil Maintaining biodiversity Reducing soil erosion 3.1.1 Habitat quality In this phase of the study, we assessed the habitat quality within Kenli District utilizing the habitat quality module of the InVEST model, and the results are depicted in Fig. 2 . From a spatial perspective, Kenli District in Dongying City exhibits a high-quality habitat pattern in the southeast, contrasting with lower quality in the northwest. Predominantly, areas with elevated ecosystem service value are concentrated in Huanghekou Town and Yongan Town to the east, with smaller portions located in Shengtuo Town, Kenli Street, and Xinglong Street in the central-southern region. In terms of temporal distribution, the habitat quality experienced considerable changes from 2000 to 2010, highlighting a phase of poor ecological preservation and heightened risk of habitat quality degradation. Notably, the western southern region exhibited the most prominent decline. Conversely, from 2010 to 2020, the habitat quality remained relatively stable, indicating a well-maintained ecological environment during this period. In 2000, high-quality habitats were primarily concentrated in the middle of the Kenli area, whereas habitats in other regions demonstrated relatively elevated and evenly distributed quality.From 2010 to 2020, except for specific sections in the central area, the eastern region displayed improved habitat quality. Analysis of the data spanning 2000 to 2020 revealed that the habitat quality in Shengtuo Town, Dongji Town, and Haojia Town, situated in the northern and southwestern parts of Kenli District, was relatively lower, with evident habitat quality deterioration. The degradation observed in some areas can be attributed to the transformation of cultivated land to other land types, rapid population growth, and the expanding urban footprint. The enhancement of habitat quality near the Yellow River estuary can be attributed to the conversion of a significant man-made surface area to wetland, a relationship closely tied to wetland ecological preservation efforts in the Yellow River estuary. 3.1.2 Carbon storage This portion of the study employs the carbon storage module within the InVEST model to assess the carbon storage in Kenli District, with the outcomes presented in Fig. 3 . In terms of temporal trends, the carbon storage in 2000 amounted to approximately 5.71 × 10^6 tons, decreasing to around 5.49 × 10^6 tons in 2010, and further reducing to about 5.39 × 10^6 tons in 2020, signifying an overall decline. During the period from 2000 to 2010, the carbon storage experienced a reduction of about 2.18 × 10^5 tons, equating to a decrease of approximately 3.8%. Similarly, from 2010 to 2020, the carbon storage witnessed a decrease of about 9.81 × 10^4 tons, indicating a decline of about 1.8%. From a spatial perspective, regions with high carbon reserves predominantly concentrate in the western and central portions of Kenli District, extending to the central and northwestern areas of Huanghekou Town to the north, exhibiting an eastward expansion trend. Conversely, areas with lower carbon reserves are scattered across Shengtuo Town, Dongji Town, and Haojia Town in the south. They are concentrated in Yongan Town to the east and certain sections of Huanghekou Town in the northeast, displaying an expansion trend. The high-value zones for carbon storage are primarily concentrated within cultivated land, while low-value areas are prominent in coastal regions, artificial surfaces, and bare land. The carbon storage capacity of wetlands and water bodies is below the average. These changes can be attributed to the transition of cultivated land to other land types from 2000 to 2010, while from 2010 to 2020, the expansion of the artificial surface area in the southwest, driven by population growth, contributed to the observed decrease in carbon storage. 3.1.3 Water conservation This segment of the study employs the grid method to assess water conservation in Kenli District, and the findings are depicted in Fig. 4 . The spatial distribution analysis reveals significant differences in water source conservation in Kenli District, Dongying City, from 2000 to 2020. In 2000, areas with high water source conservation values in Kenli District were primarily situated in Kenli Street and a portion of Yongan Town in the central region, resulting in relatively low overall water source conservation levels. By 2010, the high-value water source conservation areas shifted to Xinglong Street and Yongan Town in the southern part, along with select areas in Huanghekou Town, leading to relatively high overall water source conservation levels. In 2020, high-value water conservation areas were mainly concentrated in Huanghekou Town, with scattered high-value areas in the central and southern regions of the district, resulting in elevated overall water conservation levels. During the period from 2000 to 2010, some high-value water conservation areas displayed a notable expansion trend. From 2010 to 2020, substantial changes were observed in the water conservation of Huanghekou Town in the north and Yongan Town in the southeast of Kenli District. Specifically, the water conservation in Huanghekou Town increased significantly, while the water conservation in Yongan Town decreased. In terms of temporal distribution, the water conservation in Kenli District of Dongying City for 2010 and 2020 was notably higher than that in 2000. This increase can be attributed to the significantly greater precipitation in 2010 and 2020 compared to 2000, combined with the complex array of local land use types. The evaporation of cultivated land in these areas was smaller than that of water bodies and wetlands, and the evaporation from construction land was also reduced. This condition allowed more water retention on the surface, resulting in higher regional water conservation for cultivated and construction land. 3.1.4 Soil conservation This section of the study employed the grid method to assess the soil conservation and erosion status in the Kenli area, and the outcomes are illustrated in Fig. 5 . Analyzing the spatial distribution, the soil conservation dynamics in Kenli District, Dongying City, underwent significant changes, displaying a decreasing trend from 2000 to 2020. In 2000, the overall soil conservation quantity in Kenli District was higher, with low-value areas predominantly distributed in the central and southern parts of the district. The construction land in Shengtuo Town, Kenli Street, and Xinglong Street exhibited low soil conservation quantities. By 2010, the low-value soil conservation areas in Kenli District shifted primarily to Xinglong Street and Yongan Town in the south, with some sections in Huanghekou Town, resulting in a relatively higher overall soil conservation level. In 2020, the low-value soil conservation areas in Kenli District were mainly concentrated in Huanghekou Town to the north, leading to an elevated overall soil conservation amount. From 2000 to 2010, certain regions with high water conservation values experienced a decline, while areas with low soil conservation values extended. Between 2010 and 2020, notable changes occurred in the soil conservation quantity in Huanghekou Town to the north and Yongan Town in the southeast of Kenli District, with Huanghekou Town witnessing a particularly evident decreasing trend in soil conservation quantity. Regarding temporal distribution, the soil conservation in Kenli District of Dongying City exhibited a decreasing trend from 2000 to 2020. Soil conservation is directly influenced by factors such as vegetation cover, human activities, topography, slope, slope direction, and surface structure. Within the study area, the soil conservation quantity is larger in grassland, wetland, and cultivated land, whereas some artificial surface and bare land have smaller soil conservation amounts. 3.2 Calculation of ecological service value As shown in Table 6 , the ecosystem service value of Kenli District in Dongying City exhibits a continuous growth trend from 2000 to 2020. In 2000, the total ecosystem service value in the region was 1.911 billion yuan per year. By 2010, this value had risen to 3.597 billion yuan per year, further increasing to 3.747 billion yuan per year by 2020. Arable land, wetland, and water bodies are the three primary land use types contributing to the composition of the ecosystem service value, accounting for over 90% of the overall ecosystem service value. Between 2000 and 2010, the area of cultivated land decreased, while the area of wetland and water bodies increased, with a significant expansion of wetland area contributing to the rise in the ecosystem service value. From 2010 to 2020, the area of cultivated land continued to grow, wetland area remained relatively stable, and water body areas increased, leading to a further increase in the ecosystem service value, although at a slower growth rate. This phenomenon is attributed to the conversion of cultivated land into wetlands and water bodies from 2000 to 2010, and the conversion of wetlands and other areas into cultivated land from 2010 to 2020. In the short term, land use type conversion can enhance the value of ecosystem services, but over time, frequent conversions within a short period can undermine regional development stability. Therefore, land use planning should fully consider local factors such as gas, land, water, and health, adhering to the principle of adaptability to maintain regional stability and sustainable development. As evident from Tables 7 and 8 , each individual service exhibits distinct proportions during the three studied periods. Waste treatment service holds the largest share, followed by soil protection, water conservation, and climate regulation services. Collectively, these four individual services contribute to over 75% of the total ecosystem service value. Notably, the single item of climate regulation initially rises but subsequently declines, whereas waste treatment and water conservation services continue to increase, and soil protection services show a continuous decline. This pattern can be attributed to changes in land area, including an increase in grassland, wetland, water body, and bare land from 2000 to 2010, and a decrease in cultivated land, grassland, and bare land from 2010 to 2020. Grassland, wetland, and water body play pivotal roles in the ecosystem service value, especially in functions such as climate regulation, water source conservation, and waste treatment within the region. The influence of cultivated land, wetland, and water body on the unit ecosystem service value in the region is particularly evident. In 2000, 2010, and 2020, cultivated land's impact on ecosystem service value accounted for 70.29%, 25.54%, and 25.63%, respectively, and wetland contributed 4.77%, 32.39%, and 31.07%, while water body impact reached 33.96%, 45.51%, and 49.92%. In terms of ecological service functions, the value of waste treatment, water conservation, and climate regulation holds greater significance. Over the study period, waste treatment contributed 29.25%, 32.19%, and 32.97% of the total value, while water conservation accounted for 15.51%, 25.86%, and 26.84% of the total value. Climate regulation accounted for 10.66%, 12.68%, and 12.05% of the total value, respectively. Table 6 Ecosystem service value and its change by land use type from 2000 to 2020 Ecosystem service value (billion yuan /a) Proportion of total ecological service value (%) Change in the value of ecological services (billion yuan /a) 2000 2010 2020 2000 2010 2020 2000–2010 2010–2020 2000–2020 cultivate 13.43 9.2 9.6 70.29 25.54 25.63 -4.24 0.41 -3.83 grass 0 0.960 0 0 2.67 0 0.96 -0.96 0 wetlands 0.91 11.65 11.64 4.77 32.39 31.07 10.74 -0.01 10.73 water 6.49 16.37 18.70 33.96 45.51 49.92 9.88 2.33 12.22 artificil -1.72 -2.21 -2.48 -9.01 -6.13 -6.62 -0.48 -0.27 -0.76 bare 0.00004 0.003 0.00003 0.0002 0.007 0.00008 0.003 -0.003 -0.00005 sea 0 0 0 0 0 0 0 0 0 cultivate 19.11 35.97 37.47 100 100 100 16.86 1.49 18.36 Table 7 Value of individual ecosystem services in Kenli District from 2000 to 2020 Gas regulation Climate regulation Water conservation Soil formation and protection Waste disposal Biodiversity conservation Food production Raw materials Entertainment culture 2000 99.71 203.66 296.24 286.31 558.75 176.66 196.20 19.67 73.38 2010 109.69 456.22 930.36 251.37 1157.83 243.03 145.97 15.58 286.99 2020 102.02 451.58 1005.72 234.21 1235.40 245.18 148.47 15.56 308.39 Table 8 The proportion of individual ecosystem service value in Kenli District from 2000 to 2020 Gas regulation Climate regulation Water conservation Soil formation and protection Waste disposal Biodiversity conservation Food production Raw materials Entertainment culture Total 2000 5.22 10.66 15.51 14.98 29.25 9.25 10.27 1.03 3.84 100 2010 3.05 12.68 25.86 6.99 32.19 6.76 4.06 0.43 7.98 100 2020 2.72 12.05 26.84 6.25 32.97 6.54 3.96 0.42 8.23 100 4. Discussion 4.1 Ecosystem service evaluation based on InVEST model and grid method The assessment of ecosystem services using the InVEST model and the grid method represents a well-established and mature evaluation approach, built upon the empirical findings of numerous scholars. It can be likened to building upon the valuable contributions of our predecessors. Among the available methods, the InVEST model stands out as the most mature and is distinguished by its ease of spatial representation and visualization. Simultaneously, the grid method offers an approach that closely approximates actual values and exhibits flexibility over time. Currently, a variety of methods exist for evaluating ecosystem service value. In future research, it is advisable to employ multiple methods to assess the same ecosystem services value within a given region, facilitating the comparison of differences. Furthermore, combining multiple models for comprehensive analysis is a promising avenue for advancing our understanding of ecosystem services. 5. Conclusion Within the context of rapid urbanization, achieving a harmonious equilibrium between ecological security and economic development holds paramount importance for regional progress. In this study, Kenli District of Dongying City, a pivotal locale in the Yellow River Estuary, serves as a case in point. Employing the InVEST model and grid method, we meticulously analyze four key ecosystem service values—habitat quality, carbon storage, water conservation, and soil conservation—across Kenli District from 2000 to 2020. Furthermore, we examine the spatio-temporal evolution patterns and calculate the overall ecosystem service value within the study area. Our findings indicate the following: Over the examined period, habitat quality, carbon storage, and soil conservation in Kenli District exhibit a downward trajectory, whereas water conservation demonstrates an upward trend. When considering spatial and temporal distribution, the habitat quality in Kenli District showcases a distinctive pattern, being higher in the southeast and lower in the northwest. Notably, regions with elevated carbon storage primarily cluster in the western, middle, and northern segments of Kenli District, displaying an eastward expansion trend. Conversely, areas of lower value are dispersed in the south while exhibiting concentrated clusters in the east and northeast, similarly expanding. The water conservation in 2000 is relatively modest, with high-value pockets mainly situated in the middle; in 2010, water conservation values increase, particularly in the southern and northeastern areas. By 2020, overall water conservation in Kenli District is relatively high, with the highest-value water conservation regions concentrated in the northeast, along with scattered high-value zones in some central and southern portions. The overall soil conservation remains high; low-value regions in 2000 are predominantly distributed in a banded manner across the central and southern areas; in 2010, low-value areas shift to the south and part of the northeast; in 2020, low-value regions concentrate primarily in the reclamation area to the north. Ecosystem service value for Kenli District indicates an upward trajectory, rising from 1.911 billion yuan in 2000 to 3.597 billion yuan in 2010 and reaching 3.747 billion yuan in 2020. Land use changes significantly impact ecosystem service value, with cultivated land, grassland, wetland, and water bodies exerting the most pronounced influence. Particularly, wetlands, water bodies, and cultivated land notably impact ecosystem service value. 6. Deficiencies and prospects Kenli District, situated at the Yellow River estuary and encompassing the Shengli Oilfield, holds significant importance. It represents an ecologically fragile region, characterized by high population density in the eastern part, considerable land demand, and prevalent soil salinization. Nonetheless, this study reveals certain limitations. Specifically, the evaluation of ecosystem service values only focuses on those services exhibiting typical ecological characteristics, thus enabling visual analysis. Moving forward, a comprehensive visual analysis encompassing all diverse ecosystem services is essential, a step that requires further refinement to enhance practical applicability. Declarations Author Contribution J.Z. is responsible for questionnaire survey, data collection and collation, data analysis and paper writing, Y.M. Responsible for checking data and revising articles. There is no conflict of interest between the authors. 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Zheng H, Ouyang ZY, Zhao TQ, Li ZX, Xu W H.(2003). The impact of human activities on ecosystem services. Journal of Natural Resources, 18(1): 118-126. Villa F, Voigt B, Erickson J D.(2014). New perspectives in ecosystem services science as instruments to understand environmental securities. Philosophical Transactions of the Royal Society B: Biological Sciences, 369(1639): 20120286. Liu Jinhua, Yang Shuo & Lv Yongqiang.(2022). Identification and diagnosis of key areas for ecological restoration based on ecological security pattern and ecological vulnerability assessment: a case study of Wenshang County. Chinese Journal of Environmental Sciences (07),3343-3352. Liu G H, Liu Q S, Ye Q H & Chang J.(2006). Dynamic monitoring of land use and integrated coastal zone management in the Yellow River Delta. Resource Science (05),171-175. Huang X F, Yang Y J, Wu Y, Gao Y H, Gu Y Y & Yuan Z M.(2018). Impacts of land use change on habitat quality in karst nature reserves from 1990 to 2017. Bulletin of soil and water conservation (6), 345-351. The doi: 10.13961 / j.carol carroll nki STBCTB. 2018.06.052. LAI L, HUANG X J, YANG H.(2016). Carbon Emissions From Land-use Change and Management in China Between 1990 and 2010. Science Advances, 2(11): e1601063. Wu Daqian, Liu Jian, He Tongli, Wang Shujun & Wang Renqing.(2009). Profit and loss analysis of ecological service value in Yellow River Delta based on land use change. Chinese Journal of Agricultural Engineering (8),256-261. Zhu Linna, Zhao Mudan, Li Yunfei, Fan Yi & Wang Jian.(2022). Temporal and spatial relationship between ecosystem service value and human activity intensity in Xi 'an metropolitan area. Journal of Ecology and Rural Environment. doi:10.19741/j.issn.1673-4831.2022.1078. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3846949","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":266318896,"identity":"0e5817f3-024b-47d5-b6a9-dfbad13c9d0d","order_by":0,"name":"Junwei Zhang","email":"","orcid":"","institution":"Southwest Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Junwei","middleName":"","lastName":"Zhang","suffix":""},{"id":266318897,"identity":"36109687-58d0-46c6-b5c9-f6755fb1d887","order_by":1,"name":"Yue-Wei Ma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIiWNgGAWjYBADHjZm/o8PEipsSNDCz95gbPDgTBoJ1kj2HDCTfNh2iLBK3fYzZhIf22xkDG4kpFUksB1g4G/vTsCrxexMjpnkzLY0HqCWYzcSeO4wSJw5uwG/lgM5ZtK82w4DtSS23UiQeMZgIJFLQMv5N2bSf7f9B2pJZitIMDhMhJYbQFsYtx3gkew5xsaQkECUlmfFlr3/koGB3MMskXAgjYewX84nb7zx44ydPRszD+PHn/9s5Pjbe/FrYWDgMEDh8hBQDgLsD4hQNApGwSgYBSMaAAAnQUtmPc26bQAAAABJRU5ErkJggg==","orcid":"","institution":"Southwest Forestry University","correspondingAuthor":true,"prefix":"","firstName":"Yue-Wei","middleName":"","lastName":"Ma","suffix":""}],"badges":[],"createdAt":"2024-01-09 02:29:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3846949/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3846949/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49460249,"identity":"31615585-5f36-4999-bb7f-9113ef46081a","added_by":"auto","created_at":"2024-01-11 07:50:29","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":515773,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of the research area\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3846949/v1/1c1b8bea3b388a1f90d94903.png"},{"id":49460250,"identity":"1f92f4e8-4ccc-4ebd-a2b9-de17aa56331e","added_by":"auto","created_at":"2024-01-11 07:50:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":294363,"visible":true,"origin":"","legend":"\u003cp\u003eChanges of habitat quality from2000 to 2020 in Kenli\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3846949/v1/9c9d8be0c7ad6237f444e5d4.png"},{"id":49460248,"identity":"19738f82-0e0b-44c9-a62c-d2e6555555a8","added_by":"auto","created_at":"2024-01-11 07:50:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":168334,"visible":true,"origin":"","legend":"\u003cp\u003eChanges of carbon storage from 2000 to 2020 in Kenli\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-3846949/v1/c7586e8dd397e33678b789f6.png"},{"id":49460498,"identity":"b758c47c-a432-412a-9632-a0a8edbfb104","added_by":"auto","created_at":"2024-01-11 07:58:29","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":294909,"visible":true,"origin":"","legend":"\u003cp\u003eChanges of water conservation from 2000 to 2020 in Kenli\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-3846949/v1/1b3c8f91dea81585c25e7ce1.png"},{"id":49460252,"identity":"492dc902-3ccc-47ab-a2ad-c95716f36b92","added_by":"auto","created_at":"2024-01-11 07:50:29","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":290243,"visible":true,"origin":"","legend":"\u003cp\u003eChanges of soil conservation from 2000 to 2020 in Kenli\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-3846949/v1/4265f5fd58db3ebc421c9f3d.png"},{"id":55397458,"identity":"029b3a07-c215-4b8b-8321-a5e37c336390","added_by":"auto","created_at":"2024-04-26 17:27:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2223180,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3846949/v1/4207e256-e926-4861-b9cb-a0ab0a66386f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eEvaluation of Ecosystem Service Value in Kenli District of Dongying\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eEecosystems provide essential environmental conditions and material resources for human survival and development across multiple dimensions\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(Teixeira H M et al., 2021). They constitute a crucial support system for human habitation, encompassing not only provisions such as food, medicine, and raw materials for industrial and agricultural needs, but also serving as the foundation for Earth\u0026apos;s life support system. This includes vital functions like climate regulation, maintenance of atmospheric chemical balance and stability, biogeochemical and water cycles, preservation of genetic and species diversity, mitigation of drought and flood impacts, facilitation of plant pollination and seed dispersal, biological control, soil formation, and environmental purification(Zheng et al., 2003).Over the past century, intensified industrialization, urbanization, and human exploitation of nature have expedited the degradation of the natural environment(Jing et al., 2018), leading to significant changes in global ecosystem structure and function(Chatanga P et al., 2020). This has brought forth a host of environmental challenges, including deteriorating air quality, soil erosion, degraded land quality, water pollution, and biodiversity loss, causing severe disruptions to ecosystem stability and posing considerable challenges to human life and production(Vitousek P M et al., 1997). This predicament is a shared concern of humanity(Zheng H et al., 2003). It is evident that ongoing alterations in ecosystem composition, structure, and function due to human activities have substantially weakened ecosystem service capabilities(Villa F et al., 2014). Consequently, the study of ecosystem services has emerged as a prominent area of ecological research in the 21st century. Addressing the intricate balance between the development needs of human society and ecological protection, while harmonizing the relationship between humans and the land, is a complex and pressing challenge, rapidly evolving into a strategic imperative for global development(Liu et al., 2022).\u003c/p\u003e\n\u003cp\u003eThe Yellow River holds profound significance as the mother river of the Chinese nation, serving as its birthplace and cradle. The Yellow River basin stands as a vital ecological shield and a significant economic zone within China, playing a pivotal role in the country\u0026apos;s economic and social progress as well as ecological security. With the highest sediment load globally, the river transports millions of tons of sediment annually, culminating in the formation of the Yellow River Delta(Liu et al., 2006). Within Shandong, the only river-sea confluence region in the Yellow River basin, lies Dongying Province, featuring the world\u0026apos;s youngest delta and the most recent wetland ecosystem. Estuarine wetlands are paramount within coastal zones, characterized by their swift landforming process, limited vegetation coverage, and high population density. It is within this context that we analyze the land use/cover changes in the Kenli District of Dongying City from 2000 to 2020 and compute its ecosystem service value.\u003c/p\u003e"},{"header":"2. Data and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e1.1 Overview of the study area\u003c/h2\u003e\n \u003cp\u003eKenli District falls under the administrative jurisdiction of Dongying City, situated within Shandong Province. The district acquired its name from its past designations as the Reclamation District and Lijinwa. Positioned at the estuary of the Yellow River and the heartland of the Shengli Oilfield, its geographical coordinates range from 118\u0026deg;15\u0026apos; to 119\u0026deg;19\u0026apos; east longitude and 37\u0026deg;24\u0026apos; to 38\u0026deg;10\u0026apos; north latitude. The district spans a southwest-to-northeast direction, measuring 96.2 kilometers horizontally from east to west and 55.5 kilometers vertically from north to south. Its eastern boundary fronts the Bohai Sea, while its northwest section borders Lijin County along the Yellow River. It adjoins Dongying District in the southern part of Dongying City and shares a border with Hekou District in the northeast. The total land area encompasses 2204.07 square kilometers.\u003c/p\u003e\n \u003cp\u003eKenli District holds a significant role as a crucial ecological protection zone in Eastern China. Concurrently, it grapples with the severe issue of soil salinization. Furthermore, it serves as the backdrop for the Shengli Oilfield, a quintessential location characterized by the intricate interplay between natural and human factors. This interaction of river, sea, land, and human activities, while bearing ecological significance, does pose certain threats to ecological security, leading to the emergence of diverse ecological challenges. This research carries immense importance in maintaining the ecological security framework of the lower Yellow River region and propelling the sustainable development of this area.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003e1.2 Data Sources\u003c/h3\u003e\n\u003cp\u003eThe data used in this study mainly include land use/cover data, DEM data, NDVI data, basic geographic data and soon in Kenli District of Dongying City. In this paper, WGS 1984 coordinate system is adopted, and the spatial resolution is 30m. The data sources areas follows:\u003c/p\u003e\n\u003cp\u003eThe data of Land Use/Cover Change (LUCC) in Kenli District of Dongying City are derived from the 2000, 2010 and 2020 datassets of globeland30 Global Geographic Data product (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://globeland30.org/).Th\u003c/span\u003e\u003c/span\u003ee data includes 10 first-level classes with a spatial resolution of 30m.\u003c/p\u003e\n\u003cp\u003eBasic geographic data such as administrative boundaries were obtained from the Resources and Environmental Sciences and Data Center of the Chinese Academy of Sciences (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.resdc.cn/\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe wetland data was derived from National Data Center for Earth System Science -- Geographic Resources Sub-Center, National Platform for Basic Conditions of Science and Technology (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://gre.geodata.cn\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003e1.3 Research Methods\u003c/h3\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e1.3.1 Habitat quality service assessment model\u003c/h2\u003e\n \u003cp\u003eIn this study, we employed physical measures to evaluate habitat quality, utilizing the habitat quality module within the InVEST model. This module quantifies regional habitat quality primarily based on factors such as land cover type, extent, and the degree of degradation within a specific area. The calculation formula is outlined as follows:\u003c/p\u003e\n \u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e$${Q}_{xj}={H}_{j}\\left[1-\\frac{{D}_{xj}^{2}}{{D}_{xz}^{z}+{k}^{z}}\\right]$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eIn formula (3), Qxj is the habitat quality index of grid x in habitat type j; Hj is the habitat suitability of habitat type j, the range is [0,1]; k is the semi-saturation sum constant, taking half of the maximum habitat degradation degree, and the default value is 0.05; z is a normalized constant with a default value of 2.5. The parameters in this model are determined according to the study and case site characteristics of Huang et al(Huang et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThreats weight value and maximum influencing distance\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eThreat Sources\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMaximum coercion\u003c/p\u003e\n \u003cp\u003edistance /km\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWeights\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSpatial decay mode\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArable Land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLinear\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGrassy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndex\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWetlands\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndex\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBodies of water\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLinear\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArtificial surface\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndex\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBare ground\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLinear\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOcean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLinear\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eHabitat suitability and sensitivity to threat sources\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLand use\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHabitat\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003eThreat factors\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTypes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003esuitability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArable\u003c/p\u003e\n \u003cp\u003eland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGrass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWetlands\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBodies\u003c/p\u003e\n \u003cp\u003eof\u003c/p\u003e\n \u003cp\u003ewater\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArtificial\u003c/p\u003e\n \u003cp\u003esurface\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBare\u003c/p\u003e\n \u003cp\u003eground\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOcean\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArable land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGrass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWetlands\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBodies of\u003c/p\u003e\n \u003cp\u003ewater\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArtificial\u003c/p\u003e\n \u003cp\u003esurface\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBare land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOcean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3.2 Carbon storage service evaluation model\u003c/h2\u003e\n \u003cp\u003eIn this study, the InVEST model\u0026apos;s carbon storage module was employed to assess ecosystem carbon storage, primarily integrating land use/cover data for the calculations. The ecosystem\u0026apos;s carbon storage was categorized into four primary carbon pools: (1) the above-ground carbon pool, which primarily encompasses carbon within the living vegetation on the land surface; (2) the underground carbon pool, primarily comprising carbon in underground plant roots; (3) the soil carbon pool, mainly constituted by organic carbon within the soil; and (4) the dead organic carbon, mainly comprising organic carbon from deceased vegetation and fallen leaves. Additionally, the InVEST model encompasses harvested wood products or related wood product patches (HWPs). However, this specific carbon pool was not considered in this analysis due to the challenges in obtaining data for this particular component, and its overall impact on the carbon stock is minimal(LAI L et al., 2018). The formula utilized for the model calculation is as follows:\u003c/p\u003e\n \u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e$${C}_{i}={C}_{i,above}+{C}_{i,below}+{C}_{i,soil}+{C}_{i,dead}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({C}_{total}\\)\u003c/span\u003e\u003c/span\u003e=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\sum _{i=1}^{n}{C}_{i}\\times {S}_{i}\\)\u003c/span\u003e\u003c/span\u003e (3)\u003c/h2\u003e\n \u003cp\u003eIn formula (2) ~ (3), i is the iland use type; Ci is the total carbon density of soil and biomass of land use type i, Mg\u0026middot;hm-2 ; Ci, above is aboveground biomass carbon density of land use type i, Mg\u0026middot;hm-2 ; Ci, below is the underground biomass carbon density of land use type, Mg\u0026middot;hm-2 ; Ci, dead is carbon density of litter organic matter of land use type i, Mg\u0026middot;hm-2 ; Ci, soil refers to the carbon density of soil organic matter in 0\u0026thinsp;~\u0026thinsp;30 cm depth of land use type i, Mg\u0026middot;hm-2 ; Ctotal is the total carbon storage, Mg; Si is the total area of land use type i, hm2 ; n is the number of land use types, which takes the value 7.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCarbon storage table\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLand Use Types\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAboveground biomass carbon density\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSubsurface biomass carbon density\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSoil organic matter carbon density\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLitter organic matter carbon density\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ecultivate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003egrass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ewetlands\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ewater\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eartificil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ebare\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003esea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3.3 Water conservation service evaluation model\u003c/h2\u003e\n \u003cp\u003eThe evaluation of water conservation is pivotal for maintaining regional ecological security. This paper employs a grid-based approach to estimate the water conservation quantity within Kenli District. The central technique involves utilizing a grid system resembling a fishing net, carefully selecting based on geographical location, removing excess portions, and extracting the area corresponding to distinct land types for each grid based on their attributes. In this study, the extensive expanse of Kenli District within Dongying City has been subdivided into multiple 300m\u0026times;300m small units, and the equivalent method is employed for calculating its soil conservation.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3.4 Soil conservation service evaluation model\u003c/h2\u003e\n \u003cp\u003eSoil and water conservation constitutes a pivotal environmental concern within the context of river basin sustainability. This study employs a grid-based methodology to estimate soil conservation levels within the Kenli area. The primary technique involves the utilization of a grid system, akin to a fishing net, wherein selection is based on geographical location, with the removal of redundant components. The extraction of the area for various land types within each grid is determined based on their respective attributes. Within this research, the expansive Kenli District of Dongying City has been subdivided into multiple small areas, each measuring 300m \u0026times; 300m. Moreover, the equivalent method is employed to compute its water conservation measures.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3.5 Ecosystem service value\u003c/h2\u003e\n \u003cp\u003eThe equivalent factor for the ecosystem service value within the study area was computed by referencing the equivalent factor table for China\u0026apos;s ecosystem service value, as adapted from Xie Gaodi by Wu Daqian (adjusted by a correction coefficient of 1.38 for Shandong Province). It is important to note that this scale solely represents the ecosystem service value of terrestrial ecosystems and does not incorporate the ecosystem service value of the ocean in this context(Wu et al., 2009).\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe value of ecosystem services\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eThe ecosystem services function\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ecultivated\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003egrass\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ewetlands\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ewater\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eartificial\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBare\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOcean\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGas conditioning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e610.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e976.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1592.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClimate regulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1086.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1099.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15130.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e561.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWater conservation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e732.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e976.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13715.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24885.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9215.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSoil formation and protection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1782.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2381.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1513.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWaste disposal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2002.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1599.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16086.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22199.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3000.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBiodiversity conservation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e866.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1331\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2212.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3040.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e415.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFood production\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1221.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e366.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e265.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e122.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIngredients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e122.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEntertainment culture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6383.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5299.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8437.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8841.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e56961.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56133.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-12215.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e512.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eCalculate the service value of each land use type, the value of each service function and the total value of ecosystem services according to formula (4), (5) and (6).\u003c/p\u003e\n \u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e$${ESV}_{k}=\\sum _{f}{A}_{k}\\times {VC}_{kf}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({ESV}_{f}\\)\u003c/span\u003e\u003c/span\u003e=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\sum _{k}{A}_{k}\\times {VC}_{kf}\\)\u003c/span\u003e\u003c/span\u003e (5)\u003c/h2\u003e\n \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\n \u003ch2\u003eESV=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\sum _{k}\\sum _{f}{A}_{k}\\times {VC}_{kf}\\)\u003c/span\u003e\u003c/span\u003e (6)\u003c/h2\u003e\n \u003cp\u003eIn formula (4), (5) and (6), ESVfk, ESV and ESV are respectively the service value of type k, the value of service function of item f and the total service value; Ak is the land area of type k;VCkf (value coefficient) is the service value of the service unit area of item f of type k.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3.6 Ecosystem service value flow to profit and loss model\u003c/h2\u003e\n \u003cp\u003eThe ecosystem service value (ESV) profit and loss model, built upon a land use transfer matrix, provides a comprehensive approach that simultaneously considers both the quantitative changes in ESV within the study area and the spatial-temporal transfer trends. This model effectively depicts the origins and destinations of ESV gains and losses resulting from the spatial shifts in land use types across different periods within the study area(Zhu et al., 2023). Using this land use transfer matrix as a foundation, this paper computes the ESV fluctuations stemming from alterations in land use types within the study area. The calculation for the ESV flow direction profit and loss is as follows:\u003c/p\u003e\n \u003cp\u003eP\u003csub\u003eij\u003c/sub\u003e=(V\u003csub\u003ej\u003c/sub\u003e-V\u003csub\u003ei\u003c/sub\u003e) * A\u003csub\u003eij\u003c/sub\u003e (7)\u003c/p\u003e\n \u003cp\u003eIn formula (7), Pij is the ESV profit and loss caused by the change of land use type from i to j, Vi and Vj are the ESV coefficient of Class i land use type and ESV coefficient of class j land use type, and Aij is the area of land use type from i to j.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results and analysis","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1 Ecosystem service value assessment\u003c/h2\u003e\n \u003cp\u003eA diverse array of ecosystem services is present. Based on the ecological characteristics of the study area, we have selected four distinct types of ecosystem services for thorough investigation: habitat quality, carbon storage, water conservation, and soil conservation.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTypes of ecosystem services\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMA Services\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTypes of ecosystem services\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePhysical geographic elements\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEcological value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSupply services\u003c/p\u003e\n \u003cp\u003eRegulating service\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWater conservation\u003c/p\u003e\n \u003cp\u003eCarbon storage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHydrology\u003c/p\u003e\n \u003cp\u003eClimate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWater supply\u003c/p\u003e\n \u003cp\u003eRegulating climate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSupport services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHabitat quality\u003c/p\u003e\n \u003cp\u003eSoil conservation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBiodiversity\u003c/p\u003e\n \u003cp\u003eSoil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaintaining biodiversity\u003c/p\u003e\n \u003cp\u003eReducing soil erosion\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1.1 Habitat quality\u003c/h2\u003e\n \u003cp\u003eIn this phase of the study, we assessed the habitat quality within Kenli District utilizing the habitat quality module of the InVEST model, and the results are depicted in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. From a spatial perspective, Kenli District in Dongying City exhibits a high-quality habitat pattern in the southeast, contrasting with lower quality in the northwest. Predominantly, areas with elevated ecosystem service value are concentrated in Huanghekou Town and Yongan Town to the east, with smaller portions located in Shengtuo Town, Kenli Street, and Xinglong Street in the central-southern region. In terms of temporal distribution, the habitat quality experienced considerable changes from 2000 to 2010, highlighting a phase of poor ecological preservation and heightened risk of habitat quality degradation. Notably, the western southern region exhibited the most prominent decline. Conversely, from 2010 to 2020, the habitat quality remained relatively stable, indicating a well-maintained ecological environment during this period. In 2000, high-quality habitats were primarily concentrated in the middle of the Kenli area, whereas habitats in other regions demonstrated relatively elevated and evenly distributed quality.From 2010 to 2020, except for specific sections in the central area, the eastern region displayed improved habitat quality. Analysis of the data spanning 2000 to 2020 revealed that the habitat quality in Shengtuo Town, Dongji Town, and Haojia Town, situated in the northern and southwestern parts of Kenli District, was relatively lower, with evident habitat quality deterioration. The degradation observed in some areas can be attributed to the transformation of cultivated land to other land types, rapid population growth, and the expanding urban footprint. The enhancement of habitat quality near the Yellow River estuary can be attributed to the conversion of a significant man-made surface area to wetland, a relationship closely tied to wetland ecological preservation efforts in the Yellow River estuary.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1.2 Carbon storage\u003c/h2\u003e\n \u003cp\u003eThis portion of the study employs the carbon storage module within the InVEST model to assess the carbon storage in Kenli District, with the outcomes presented in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. In terms of temporal trends, the carbon storage in 2000 amounted to approximately 5.71 \u0026times; 10^6 tons, decreasing to around 5.49 \u0026times; 10^6 tons in 2010, and further reducing to about 5.39 \u0026times; 10^6 tons in 2020, signifying an overall decline. During the period from 2000 to 2010, the carbon storage experienced a reduction of about 2.18 \u0026times; 10^5 tons, equating to a decrease of approximately 3.8%. Similarly, from 2010 to 2020, the carbon storage witnessed a decrease of about 9.81 \u0026times; 10^4 tons, indicating a decline of about 1.8%.\u003c/p\u003e\n \u003cp\u003eFrom a spatial perspective, regions with high carbon reserves predominantly concentrate in the western and central portions of Kenli District, extending to the central and northwestern areas of Huanghekou Town to the north, exhibiting an eastward expansion trend. Conversely, areas with lower carbon reserves are scattered across Shengtuo Town, Dongji Town, and Haojia Town in the south. They are concentrated in Yongan Town to the east and certain sections of Huanghekou Town in the northeast, displaying an expansion trend. The high-value zones for carbon storage are primarily concentrated within cultivated land, while low-value areas are prominent in coastal regions, artificial surfaces, and bare land. The carbon storage capacity of wetlands and water bodies is below the average. These changes can be attributed to the transition of cultivated land to other land types from 2000 to 2010, while from 2010 to 2020, the expansion of the artificial surface area in the southwest, driven by population growth, contributed to the observed decrease in carbon storage.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1.3 Water conservation\u003c/h2\u003e\n \u003cp\u003eThis segment of the study employs the grid method to assess water conservation in Kenli District, and the findings are depicted in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. The spatial distribution analysis reveals significant differences in water source conservation in Kenli District, Dongying City, from 2000 to 2020. In 2000, areas with high water source conservation values in Kenli District were primarily situated in Kenli Street and a portion of Yongan Town in the central region, resulting in relatively low overall water source conservation levels. By 2010, the high-value water source conservation areas shifted to Xinglong Street and Yongan Town in the southern part, along with select areas in Huanghekou Town, leading to relatively high overall water source conservation levels. In 2020, high-value water conservation areas were mainly concentrated in Huanghekou Town, with scattered high-value areas in the central and southern regions of the district, resulting in elevated overall water conservation levels.\u003c/p\u003e\n \u003cp\u003eDuring the period from 2000 to 2010, some high-value water conservation areas displayed a notable expansion trend. From 2010 to 2020, substantial changes were observed in the water conservation of Huanghekou Town in the north and Yongan Town in the southeast of Kenli District. Specifically, the water conservation in Huanghekou Town increased significantly, while the water conservation in Yongan Town decreased. In terms of temporal distribution, the water conservation in Kenli District of Dongying City for 2010 and 2020 was notably higher than that in 2000. This increase can be attributed to the significantly greater precipitation in 2010 and 2020 compared to 2000, combined with the complex array of local land use types. The evaporation of cultivated land in these areas was smaller than that of water bodies and wetlands, and the evaporation from construction land was also reduced. This condition allowed more water retention on the surface, resulting in higher regional water conservation for cultivated and construction land.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1.4 Soil conservation\u003c/h2\u003e\n \u003cp\u003eThis section of the study employed the grid method to assess the soil conservation and erosion status in the Kenli area, and the outcomes are illustrated in Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. Analyzing the spatial distribution, the soil conservation dynamics in Kenli District, Dongying City, underwent significant changes, displaying a decreasing trend from 2000 to 2020. In 2000, the overall soil conservation quantity in Kenli District was higher, with low-value areas predominantly distributed in the central and southern parts of the district. The construction land in Shengtuo Town, Kenli Street, and Xinglong Street exhibited low soil conservation quantities. By 2010, the low-value soil conservation areas in Kenli District shifted primarily to Xinglong Street and Yongan Town in the south, with some sections in Huanghekou Town, resulting in a relatively higher overall soil conservation level. In 2020, the low-value soil conservation areas in Kenli District were mainly concentrated in Huanghekou Town to the north, leading to an elevated overall soil conservation amount.\u003c/p\u003e\n \u003cp\u003eFrom 2000 to 2010, certain regions with high water conservation values experienced a decline, while areas with low soil conservation values extended. Between 2010 and 2020, notable changes occurred in the soil conservation quantity in Huanghekou Town to the north and Yongan Town in the southeast of Kenli District, with Huanghekou Town witnessing a particularly evident decreasing trend in soil conservation quantity. Regarding temporal distribution, the soil conservation in Kenli District of Dongying City exhibited a decreasing trend from 2000 to 2020. Soil conservation is directly influenced by factors such as vegetation cover, human activities, topography, slope, slope direction, and surface structure. Within the study area, the soil conservation quantity is larger in grassland, wetland, and cultivated land, whereas some artificial surface and bare land have smaller soil conservation amounts.\u003c/p\u003e\n \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2 Calculation of ecological service value\u003c/h2\u003e\n \u003cp\u003eAs shown in Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e, the ecosystem service value of Kenli District in Dongying City exhibits a continuous growth trend from 2000 to 2020. In 2000, the total ecosystem service value in the region was 1.911\u0026nbsp;billion yuan per year. By 2010, this value had risen to 3.597\u0026nbsp;billion yuan per year, further increasing to 3.747\u0026nbsp;billion yuan per year by 2020. Arable land, wetland, and water bodies are the three primary land use types contributing to the composition of the ecosystem service value, accounting for over 90% of the overall ecosystem service value. Between 2000 and 2010, the area of cultivated land decreased, while the area of wetland and water bodies increased, with a significant expansion of wetland area contributing to the rise in the ecosystem service value. From 2010 to 2020, the area of cultivated land continued to grow, wetland area remained relatively stable, and water body areas increased, leading to a further increase in the ecosystem service value, although at a slower growth rate. This phenomenon is attributed to the conversion of cultivated land into wetlands and water bodies from 2000 to 2010, and the conversion of wetlands and other areas into cultivated land from 2010 to 2020. In the short term, land use type conversion can enhance the value of ecosystem services, but over time, frequent conversions within a short period can undermine regional development stability. Therefore, land use planning should fully consider local factors such as gas, land, water, and health, adhering to the principle of adaptability to maintain regional stability and sustainable development.\u003c/p\u003e\n \u003cp\u003eAs evident from Tables \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e, each individual service exhibits distinct proportions during the three studied periods. Waste treatment service holds the largest share, followed by soil protection, water conservation, and climate regulation services. Collectively, these four individual services contribute to over 75% of the total ecosystem service value. Notably, the single item of climate regulation initially rises but subsequently declines, whereas waste treatment and water conservation services continue to increase, and soil protection services show a continuous decline. This pattern can be attributed to changes in land area, including an increase in grassland, wetland, water body, and bare land from 2000 to 2010, and a decrease in cultivated land, grassland, and bare land from 2010 to 2020. Grassland, wetland, and water body play pivotal roles in the ecosystem service value, especially in functions such as climate regulation, water source conservation, and waste treatment within the region. The influence of cultivated land, wetland, and water body on the unit ecosystem service value in the region is particularly evident. In 2000, 2010, and 2020, cultivated land\u0026apos;s impact on ecosystem service value accounted for 70.29%, 25.54%, and 25.63%, respectively, and wetland contributed 4.77%, 32.39%, and 31.07%, while water body impact reached 33.96%, 45.51%, and 49.92%. In terms of ecological service functions, the value of waste treatment, water conservation, and climate regulation holds greater significance. Over the study period, waste treatment contributed 29.25%, 32.19%, and 32.97% of the total value, while water conservation accounted for 15.51%, 25.86%, and 26.84% of the total value. Climate regulation accounted for 10.66%, 12.68%, and 12.05% of the total value, respectively.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEcosystem service value and its change by land use type from 2000 to 2020\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eEcosystem service value (billion yuan /a)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eProportion of total ecological service value (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eChange in the value of ecological services (billion yuan /a)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2000\u0026ndash;2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2010\u0026ndash;2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2000\u0026ndash;2020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ecultivate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003egrass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.960\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ewetlands\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ewater\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eartificil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ebare\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003esea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ecultivate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable id=\"Tab7\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eValue of individual ecosystem services in Kenli District from 2000 to 2020\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGas regulation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eClimate regulation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWater conservation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSoil formation and protection\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWaste disposal\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eBiodiversity conservation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eFood production\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eRaw materials\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eEntertainment culture\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e203.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e296.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e286.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e558.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e176.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e196.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e19.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e73.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e109.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e456.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e930.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e251.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1157.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e243.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e145.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e15.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e286.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e451.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1005.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e234.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1235.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e245.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e148.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e15.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e308.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable id=\"Tab8\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe proportion of individual ecosystem service value in Kenli District from 2000 to 2020\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGas regulation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eClimate regulation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWater conservation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSoil formation and protection\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWaste disposal\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBiodiversity conservation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFood production\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRaw materials\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEntertainment culture\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":" \u003ch2\u003e4.1 Ecosystem service evaluation based on InVEST model and grid method\u003c/h2\u003e\n \u003cp\u003eThe assessment of ecosystem services using the InVEST model and the grid method represents a well-established and mature evaluation approach, built upon the empirical findings of numerous scholars. It can be likened to building upon the valuable contributions of our predecessors. Among the available methods, the InVEST model stands out as the most mature and is distinguished by its ease of spatial representation and visualization. Simultaneously, the grid method offers an approach that closely approximates actual values and exhibits flexibility over time. Currently, a variety of methods exist for evaluating ecosystem service value. In future research, it is advisable to employ multiple methods to assess the same ecosystem services value within a given region, facilitating the comparison of differences. Furthermore, combining multiple models for comprehensive analysis is a promising avenue for advancing our understanding of ecosystem services.\u003c/p\u003e"},{"header":"5. Conclusion","content":" \u003cp\u003eWithin the context of rapid urbanization, achieving a harmonious equilibrium between ecological security and economic development holds paramount importance for regional progress. In this study, Kenli District of Dongying City, a pivotal locale in the Yellow River Estuary, serves as a case in point. Employing the InVEST model and grid method, we meticulously analyze four key ecosystem service values\u0026mdash;habitat quality, carbon storage, water conservation, and soil conservation\u0026mdash;across Kenli District from 2000 to 2020. Furthermore, we examine the spatio-temporal evolution patterns and calculate the overall ecosystem service value within the study area. Our findings indicate the following: Over the examined period, habitat quality, carbon storage, and soil conservation in Kenli District exhibit a downward trajectory, whereas water conservation demonstrates an upward trend. When considering spatial and temporal distribution, the habitat quality in Kenli District showcases a distinctive pattern, being higher in the southeast and lower in the northwest. Notably, regions with elevated carbon storage primarily cluster in the western, middle, and northern segments of Kenli District, displaying an eastward expansion trend. Conversely, areas of lower value are dispersed in the south while exhibiting concentrated clusters in the east and northeast, similarly expanding. The water conservation in 2000 is relatively modest, with high-value pockets mainly situated in the middle; in 2010, water conservation values increase, particularly in the southern and northeastern areas. By 2020, overall water conservation in Kenli District is relatively high, with the highest-value water conservation regions concentrated in the northeast, along with scattered high-value zones in some central and southern portions. The overall soil conservation remains high; low-value regions in 2000 are predominantly distributed in a banded manner across the central and southern areas; in 2010, low-value areas shift to the south and part of the northeast; in 2020, low-value regions concentrate primarily in the reclamation area to the north. Ecosystem service value for Kenli District indicates an upward trajectory, rising from 1.911\u0026nbsp;billion yuan in 2000 to 3.597\u0026nbsp;billion yuan in 2010 and reaching 3.747\u0026nbsp;billion yuan in 2020. Land use changes significantly impact ecosystem service value, with cultivated land, grassland, wetland, and water bodies exerting the most pronounced influence. Particularly, wetlands, water bodies, and cultivated land notably impact ecosystem service value.\u003c/p\u003e"},{"header":"6. Deficiencies and prospects","content":"\u003cp\u003eKenli District, situated at the Yellow River estuary and encompassing the Shengli Oilfield, holds significant importance. It represents an ecologically fragile region, characterized by high population density in the eastern part, considerable land demand, and prevalent soil salinization. Nonetheless, this study reveals certain limitations. Specifically, the evaluation of ecosystem service values only focuses on those services exhibiting typical ecological characteristics, thus enabling visual analysis. Moving forward, a comprehensive visual analysis encompassing all diverse ecosystem services is essential, a step that requires further refinement to enhance practical applicability.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJ.Z. is responsible for questionnaire survey, data collection and collation, data analysis and paper writing, Y.M. Responsible for checking data and revising articles. There is no conflict of interest between the authors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eTeixeira H M, Bianchi F J J A, Cardoso I M, Tittonell P, Pena-Claros M.(2021). Impact of agroecological management on plant diversity and soil-based ecosystem services in pasture and coffee systems in the Atlantic forest of Brazil. Agriculture, Ecosystems \u0026amp; Environment, 305: 107-171.\u003c/li\u003e\n \u003cli\u003eZheng Hua, Ouyang Zhiyun, Zhao Tongqian, Li Zhenxin, Xu Weihua.(2003). 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Journal of Ecology and Rural Environment. doi:10.19741/j.issn.1673-4831.2022.1078.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"land use/cover change, Ecosystem services, Kenli district, Yellow River Delta","lastPublishedDoi":"10.21203/rs.3.rs-3846949/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3846949/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper analyzes the ecosystem service function and its value in the Kenli District of Dongying City, a major town situated at the Yellow River estuary, using the Xiegaodi classification system and evaluation method. The spatio-temporal variations of four ecosystem service values\u0026mdash;habitat quality, carbon storage, water conservation, and soil conservation\u0026mdash;are examined from 2000 to 2020 employing the InVEST model and a grid-based approach. The findings reveal declining trends in habitat quality, carbon storage, and soil conservation, contrasted with an upward trend in water conservation from 2000 to 2020 in Dongying. Spatially, the habitat quality in the Kenli District of Dongying City displays a pattern of high values in the southeast and low values in the northwest. Higher carbon storage areas are primarily concentrated in the western, central, and northern regions of the Kenli District, with an observed eastward expansion. Water conservation transitions from low to high. Soil conservation values are higher, and the low-value areas shift from the central and southern parts to the southern regions, along with some parts of the northeast moving to the north. The value of ecosystem services in the Kenli District of Dongying City increases from 1.911\u0026nbsp;billion yuan in 2000 to 3.597\u0026nbsp;billion yuan in 2010 and further to 3.747\u0026nbsp;billion yuan in 2020. The impacts of cultivated land, wetland, and water bodies on the ecosystem service value in the region are more apparent. Specifically, the impacts of cultivated land on the ecosystem service value in 2000, 2010, and 2020 amount to 70.29%, 25.54%, and 25.63%, respectively, while those of wetland are 4.77%, 32.39%, and 31.07%, and for water bodies, the percentages are 33.96%, 45.51%, and 49.92%, respectively. From the perspective of ecological service functions, waste treatment, water conservation, and climate regulation exhibit greater importance. Over the study period, waste treatment contributes 29.25%, 32.19%, and 32.97% to the total value, whereas water conservation accounts for 15.51%, 25.86%, and 26.84%, and climate regulation constitutes 10.66%, 12.68%, and 12.05% of the total value, respectively.\u003c/p\u003e","manuscriptTitle":"Evaluation of Ecosystem Service Value in Kenli District of Dongying","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-11 07:50:24","doi":"10.21203/rs.3.rs-3846949/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0a8a7483-2a3a-4834-a1f3-6ac7cc754451","owner":[],"postedDate":"January 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-26T17:26:32+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-11 07:50:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3846949","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3846949","identity":"rs-3846949","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}