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Article

Research on Local Ecosystem Cultural Services in the Jiangnan Water Network Rural Areas: A Case Study of the Ecological Green Integration Demonstration Zone in the Yangtze River Delta, China

by
You Zuo
and
Lin Zhang
*
College of Architecture and Urban Planning, Tongji University, Shanghai 200092, China
*
Author to whom correspondence should be addressed.
Land 2023, 12(7), 1373; https://doi.org/10.3390/land12071373
Submission received: 6 June 2023 / Revised: 4 July 2023 / Accepted: 7 July 2023 / Published: 9 July 2023
(This article belongs to the Special Issue Current and Future Trends of Socio-Economic Values in Terrestrial Ecosystem Services)
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Abstract

:
Ecosystem cultural services (CESs) are crucial for rural revitalization and sustainable development. As research on monitoring and mapping CESs continues to increase, there is a relative lack of research perspectives that effectively combine spatial modeling techniques with the local context of CESs in rural areas. Rural areas in China face challenges such as the encroachment of ecological service spaces and the displacement and relocation of their own cultural characteristics during the urbanization process. It is crucial to enhance our understanding of the relationship between CES characterization and rural locality. This paper established a framework for the quantitative research and spatial optimization of local CESs in rural areas. We selected the Ecological Green Integrated Development Demonstration Zone (EGIDZ) in the Yangtze River Delta as the research subject, considering its representativeness of the Jiangnan Water Network Area and the characteristics of integrated ecological development across regions. The Maxent model was utilized to integrate environmental variables with the locations of services, facilitating spatial mapping and quantitative evaluation of CESs, as well as determining the influence of each variable. Additionally, K-means clustering was employed to analyze CES combination patterns. The results indicated significant variations in mean values and spatial distribution within each CES category. The natural environment, spatial distance, and human activities factors all exhibited significant effects on shaping local CESs. Furthermore, the CES clusters were classified into three categories: CES-developed cluster, CES-developing cluster, and CES-potential cluster, accounting for 9.34%, 49.23%, and 41.44%, respectively. Based on these comprehensive findings, we provided insights into optimizing local CESs in the Jiangnan Water Network rural areas.

1. Introduction

Cultural ecosystem services (CESs) are a crucial component of the existing conceptual frameworks for ecosystem service groups [1]. According to the Common International Classification of Ecosystem Services (CICES), CESs refer to “the non-material benefits that humans obtain from ecosystems”, including spiritual satisfaction, aesthetic experiences, recreation, education, and cultural heritage values [2,3].
As research advances, numerous empirical studies on CESs have been conducted, mainly focusing on CESs in urbanized areas [4], such as urban parks [5], green infrastructure, and large-scale blue-green spaces in urban and suburban areas, paying close attention to cultural entertainment, recreational activities, cognitive development, well-being, and social participation in ecosystem management for urban planning and sustainable development [6]. However, there has been a relative lack of research on CESs in traditional rural settlements and agricultural spaces [7]. CESs in rural areas are intricately connected to the optimization of the rural living environment, livelihood support, cultural significance, and the local perceptions of rural residents. Existing studies have mainly focused on the diverse components and combinations of rural CESs, exhibiting significant value diversity and local characteristics, showcasing nested features across multiple scales and structural variations across different regions. However, in contrast to urban CESs, the standardization of cultural services in rural areas is relatively weak, evaluation indicators tend to be more flexible [8], and there is considerable heterogeneity in assessments by various social actors [9]. Meanwhile, rural CESs face the challenge of coexisting external and internal pressures, including the encroachment of ecosystem service space and the re-localization of cultural characteristics in the urbanization process [10]. Therefore, it is vital to expand the efficiency of service supply and reconstruct the relationship between rural spatial locations and the characterization of CESs from local perspectives.
Locality refers to the distinctive identities that distinguish one place from another, and it is relational, contextual, and historical [11,12]. The composition of locality has multiple layers [13], derived from both objective geographical elements and subjective cultural connotations [14]. However, the role of locality in understanding the contribution of ecosystems to cultural services for humans has not been fully considered [15]. Indeed, ecosystem cultural services are typically regional, community-based, and individual-specific [16]. Consequently, locality plays a significant role in the long-term transmission of rural cultural characteristics and the survival of community spirit, ultimately shaping the provision of CESs. At the objective level, physical geography, climate, landforms, and biodiversity provide the foundation for human interactions with the environment. Subjective cultural interpretations, values, traditions, and belief systems shape the unique cultural identity of rural areas. These elements collectively constitute the spatial structure and organizational patterns of CESs, ensuring the effective delivery of multiple functions of CESs. In the process of globalization and rural modernization construction, rural CESs in China face challenges such as ecological space encroachment, settlement relocation, changes in industrial structure, and the loss of cultural continuity. Locality can assist rural areas in utilizing external resources and conditions that contribute to their development and maintaining their distinct localization [14]. Additionally, the reproduction of locality can preserve the significance of local culture [17], which is crucial for emphasizing the role of CESs. Therefore, a context-specific approach rather than a generalized perspective to improve our cognition of locality towards rural cultural ecosystem services is crucial [18].
The diverse attributes, elements, characteristics, and combinations found in rural CESs offer a broader and more flexible research dimension. Popular research methodologies for studying rural CESs include empirical research [19], value estimation [20], conceptual mapping [21], and model simulation [22]. Some researchers have used semi-structured interviews and questionnaire surveys [23,24] to assess the perceived level of CESs at the micro level of individuals, but most of them have issues such as data redundancy and high subjectivity. The value estimation methods can monetize the value of various CESs through willingness-to-pay research [25] but have numerous input parameters and complicated processes. Modeling and simulation methods have gained widespread recognition as effective tools for predicting the spatial distribution of CESs, such as providing a multi-scale social indicator framework (MSIF) to assess the ability of rural agrarian areas to provide CESs [26] and developing relevant indicators and mapping approaches that highlight ecosystem value via in-depth engagement with local stakeholders [27]. Meanwhile, existing research scales have mainly focused on administrative units at the district level [28], or geographical units at the regional level [8], resulting in research result inaccuracies and prediction uncertainties. In this study, we selected typical rural areas located within the Yangtze Delta Basin and crossing provincial administrative boundaries, attempting to construct a cross-regional research framework for rural locality-specific CESs, which is significant for integrating the quantification of CESs at the regional and watershed scale, as well as mapping and identifying specific issues of CESs at the village level.
The main methods of rural CESs mapping include direct/empirical mapping, simulation and process models, and hybrid mapping methods [29]. Specific models consist of the MIMES, InFOREST, and ARIES models [30,31], and most of them face challenges such as limited applicability to small-scale research areas, extensive data requirements, operational complexity, and potential inaccuracies in predictions and assumptions. The InVEST model [32], which has commonly been used in this research field, may still have limitations in fully incorporating cultural context, perceived value, and local knowledge. Among them, the Maxent model may utilize limited sample data to simulate the relationship between environmental variables and location points, geographically map the CESs, and quantify the impact of various environmental variables on each type of service [33]. The data sources of existing research are participatory mapping, big data analysis, self-mapping, crowdsourced imagery, and geo-tagged pictures [34]. In this study, a combination of Points of Interest (POI) data and social media data was selected as the data acquisition method, which can cost-effectively provide detailed information and a spatial representation of CES perception. POI datasets derived from digital moving maps [35] may provide a substantial amount of rural CESs’ location information, such as farmland, lakes, wetlands, farms, ports, fairs, and cultural centers, as well as respondents’ preferences [36]. Therefore, it provides opportunities for rural CESs evaluation in terms of efficient management of geographic datasets, convenience of spatial visualization, and suitability for certain processing platforms. Relevant studies include Shan He et al., who used POI data in conjunction with the Maxent model to estimate and map the supply, demand, and flow of farmlands for CESs in the Hangzhou metropolitan area [37].
The majority of previous studies focused on separate service types, with little exploration of multiple services and the combined effects of rural CESs. CES clusters are composite patterns of multiple CESs that co-occur across different scales [38]. In this study, we utilized the K-means clustering algorithm, an unsupervised clustering method [39], for the spatial modeling of combination patterns of CESs. This approach facilitated accurate measurements of the geographic distinctiveness of CES clusters and determined the trade-off relationships between various services, which promote the development of locally targeted strategies for optimizing and enhancing rural CESs.
The rural areas of the Jiangnan Water Network are situated in the middle and lower reaches of the Yangtze River plain. They are bordered to the east by the East China Sea and to the south by the Qiantang River. To the west, they are delineated by mountainous regions such as Tianmu Mountain and Mao Mountain. With an intricate network of waterways and rich historical contexts, these areas possess abundant ecological resources and serve as a concentrated embodiment of the cultural significance found in the Jiangnan region [40]. Previous research has predominantly focused on rural areas in southern Jiangsu Province and the Hangjiahu Plain in northern Zhejiang Province [41]. These studies have addressed aspects such as the spatial patterns of rural settlements, the preservation of cultural landscapes, and the continuity of historical characteristics. However, there is a relative scarcity of research that quantitatively assesses the Cultural Ecosystem Services (CESs) of these rural areas from a holistic regional perspective.
The Ecological Green Integration Demonstration Zone (EGIDZ) in the Yangtze River Delta is situated within the saucer-shaped depression of the Taihu Lake basin, characterized by low-lying terrains and a complex hydrological system, presenting a typical Jiangnan Water Network landscape. It is strategically positioned at the intersection of Jiangsu Province, Zhejiang Province, and Shanghai City, showcasing unique institutional differences and cross-regional integration development characteristics. This makes it an excellent case study for investigating the influence of rural CESs on optimizing the living environment and fostering coordinated development in the context of rapid urbanization and rural re-localization.
The rural areas in the EGIDZ are encompassed by Taihu Lake, Dianshan Lake, Yuandang Lake, Taipu River, and the Beijing–Hangzhou Canal, boasting abundant natural and cultural resources. “The Territorial Spatial Master Plan for the Yangtze River Delta Ecological Green Integrated Development Demonstration Zone (2021–2035)” emphasizes the importance of rural areas as a crucial component of the functional system within the demonstration area. While existing studies have primarily focused on rural landscape ecological patterns and sustainable development potential [42,43], there is a relative lack of research on the cultural services provided by rural ecosystems. Therefore, this study focused on 246 villages in the EGIDZ with the aim of acquiring a profound comprehension of the local CES characteristics and establishing a framework for the quantitative evaluation and spatial optimization of CESs in rural areas.

2. Materials and Methods

This study analyzed the characteristics, combinations, and influencing factors of rural local CESs. We tried to develop a comprehensive assessment framework for rural CESs that combines top-down objective environmental factors with bottom-up subjective perceptual preferences (Figure 1). Based on the Millennium Ecosystem Assessment (MA) classification system [1] and the local cultural characteristics of the Jiangnan Water Network Rural Areas, we classified CESs into six distinct categories including the aesthetic experience features of the natural ecological environment (native aesthetic services [44,45] and recreational entertainment services [46,47]), the resident activity features of production, construction, and local knowledge inheritance (livelihood support services [48] and knowledge education services [28]), and the traditional spiritual features of historical humanistic heritage (historical culture [49] and spiritual connotations [50]), providing a local perspective for CES investigation and evaluation. Then we collected and preprocessed environmental variables and POI datasets representing each category of CES separately and inputted them into the Maxent model for the spatial mapping and quantitative assessment of CESs. Through variable contribution, permutation importance, and Jackknife tests, we assessed the influence of environmental variables in predicting CESs. Response curves were utilized to quantify the impact mechanism of different environmental variables. Additionally, K-means clustering was applied to explore the combination patterns of CESs in rural areas, and the resulting CES clusters were mapped and partitioned using ArcGIS.

2.1. Study Area

The Ecological Green Integration Demonstration Zone (EGIDZ) in the Yangtze River Delta (30°45′36″–31°17′24″ E, 120°21′36″–121°19′48″ N) is located at the intersection of Jiangsu, Zhejiang, and Shanghai, and includes Wujiang District, Jiashan County, and Qingpu District. By retrieving the urban-rural classification codes of village- and community-level administrative areas via the official website of the National Bureau of Statistics, the study obtained information on 246 villages in the EGUDZ, including 60 villages in Wujiang District, 76 villages in Jiashan County, and 110 villages in Qingpu District (Figure 2). The rural areas covered 1337.8 km2, with 177.26 km2 of water area accounting for 13.25%. Since the establishment of the EGIDZ in 2019 [51], the rural development areas have increasingly encroached on natural spaces such as rivers and woodlands, jeopardizing the ecological environment of the traditional Jiangnan Water Network. Rapid economic growth has resulted in the renewal and reorganization of the characteristic industrial structure, and the influx of a non-local population has also impacted the traditional culture. Therefore, it is urgently necessary to evaluate and optimize the local CESs of rural areas in the EGIDZ.

2.2. Data Sources and Processing

2.2.1. Ecosystem Cultural Services POI Data

The POI datasets offer a wealth of information and precise location details [36]. POI data corresponding to CESs were supposed to match the local natural and human conditions in rural areas of EGIDZ, yet there may be logical causal relationships and overlapping values among subdivided services [52]. To ensure accurate classification, we referred to the related literature to obtain descriptions for the six categories of CESs and establish classification criteria for the POIs (Table 1). To gather data on service locations, we trawled evaluation tags and keywords of rural CES points from the city open data platform, government official websites, and various social media platforms [35] (Table S1). After preliminary screening, a total of 2156 POIs were obtained. Subsequently, we conducted data cleaning, classification, screening, and preprocessing procedures, resulting in a final collection of 1951 points. We utilized the Gaode map API to acquire the position information of the POIs [34] and employed coordinate conversion in ArcGIS 10.8 to accurately map their spatial locations. The screening and processing of the POI datasets were conducted from 25 November 2022 to 5 December 2022.

2.2.2. Environmental Variable Data

The composition of different local elements leads to a differentiation between native and constructed localities in the modern adaptation of rural areas [53], subsequently influencing the divergence of regional cultural ideologies. According to a 2010 World Conservation Monitoring Centre (WCMC) study, the primary indicators for quantifying CESs are natural condition indicators, intermediate service indicators, and human well-being indicators [54]. Therefore, we referred to the research literature that represented the important environmental variables in accessing local CESs [9,55,56] and chose twelve environmental variables for this study, comprising five natural environment factors, six geographical distance factors, and one human activity factor (Table 2). Among them, natural environment factors [57] represented the distinctive topographic environment, which forms the foundation for the ecosystem to provide various services. Geographical distance factors captured the influence of traffic accessibility [58], infrastructure development, and landscape availability [59] on the development of rural CESs. Human activity factors [60] encompassed the patterns of artificial production and the intricate relationship between humans and the land within the original habitat.
Among the natural environmental factors, the elevation was collected from the Geospatial Data Cloud by downloading the GDEMDEM data with a resolution of 30 m, the slope and aspect were calculated based on the elevation data in ArcGIS. The annual average temperature was obtained via the National Qinghai-Tibet Plateau Data Center. The geographical distance factors were obtained by downloading the original vector data from the Open Street Map (OSP) platform and calculating the Euclidean distance in ArcGIS. Land use and land cover were obtained via the PIE-Engine (Table S2). The environmental elements were spatially displayed in ArcGIS (Figure 3). Collinearity diagnosis was carried out for environmental variables to avoid multicollinearity, and the statistics indicated that the variance inflation factor (VIF) values of each variable were less than 5, indicating no significant collinearity. The environmental variables datasets used in this study were all from the year 2022, which was consistent with the data of POIs.
We also considered other environmental factors such as annual precipitation (PRE), annual evapotranspiration (ET), population density (POP), and annual gross domestic product (GDP). However, the highest available resolution for these factors did not meet the required accuracy of 30 m × 30 m. Through repeated simulation experiments and factor screening, we ultimately selected the aforementioned 12 environmental variables as input parameters to ensure the accuracy of the prediction results and to account for the significant influence of various environmental factors on different CESs.

2.3. Research Methodology

2.3.1. The Maxent Model

Maxent is an ecological locus model based on the maximum entropy theory used to estimate the probability distribution of unknown information based on incomplete known indications, established by Phillips et al. [33]. The principle is to maximize the entropy of the information distributed in the unknown region while satisfying all known constraints. Assuming the probability distribution of the unknown region x is P(x) and the finite set of x in the study region is X, the entropy in the Maxent operation is given by
H ( P ) = − ∑ x ∈ X P ( x ) ln ⁡ P ( x ) .
The CESs evaluation was conducted in Maxent 3.4.1. First, the latitude and longitude data of POI datasets, as well as the environmental factors, were printed into the species distribution and environmental variables modules, respectively. A total of 75% of the distribution points were randomly selected as the training data set for model building, and the remaining 25% of the distribution points were used as the test data set for model validation. The parameters, such as the regularization multipliers = 1, max number of background points = 10,000, maximum iterations = 500, convergence threshold = 0.00001, and default prevalence = 0.5, were set to the default settings as suggested by previous studies [61,62]. A total of 10 bootstrap replications were subsequently performed and the variable contribution, permutation importance, and Jackknife tests were used to determine the influence of diverse environmental variables on CESs [28].
The Maxent model generates Receiver Operating Characteristic (ROC) curves in each simulation, where the false-positive rate is plotted on the horizontal axis and the true-positive rate is plotted on the vertical axis [63]. The Area Under the Curve (AUC) could be used as an accuracy indicator to determine the effectiveness of the simulation. It is divided into Test AUC and Training AUC. The former represents the model’s prospective ability to be applied to value transfer, while the latter demonstrates the model’s degree of fit. The closer the AUC value is to 1, the better the simulation effects [64].

2.3.2. K-Means Clustering

K-means clustering is an unsupervised clustering method [39] with a clear clustering structure and a simple clustering process. It has become a widely used clustering method in geographical spatial pattern research and is capable of measuring the combination pattern and overall service quality of different ecological services, which is calculated as follows:
d ( x i , x j ) = ∑ r = 1 p x i r − x r j 2 1 2 ,
C ( l ) = argmin 1 ≤ l ≤ k ⁡ d ( x i , v i ) , i = 1,2 , . . . , N ,
v l = argmin v ⁡ ∑ i ∈ C l d ( x i , v ) , i = 1,2 , . . . , N ,
where d (xi, xj) represents the Euclidean distance calculated from the samples during clustering, xi is the ith sample, xir is the rth feature parameter of the ith sample, C(l) is the set of samples contained in category l, and vl is the center of gravity of category l. The preceding steps are repeated iteratively and the clustering process is terminated until the iteration termination condition is reached [65].
This study utilized K-means clustering to perform a spatial overlay of six types of CESs, aiming to identify representative categories of CES clusters in the rural areas of the EGIDZ. Furthermore, spatial visualization of the clustering results enabled the exploration of the layout patterns of different CES clusters.

3. Results

3.1. Model Accuracy Verification

Spatial distribution models for different CESs were constructed using Maxent 3.4.1. The average test AUC and training AUC of the prediction results were 0.886 and 0.853, respectively. These values were significantly higher than the simulated value of random distribution (0.5), indicating that the Maxent model was suitable for simulating the spatial distribution of local CESs in the Jiangnan Water Network rural areas.

3.2. Spatial Distribution and Quantitative Assessment of Local CESs

Based on the simulation results (Figure 4), the values of each CES were quantitatively evaluated and reclassified into high, medium, and low levels (Figure 5, Table 3).
According to the findings, the mean value of native aesthetic services was 0.31. Rural areas with high service values accounted for 15.13% and were primarily concentrated near the lacustrine zone in the northeast of Tongli Town, the Dianshan Lake zone in the middle of Zhujiajiao Town, the Suzhou River zone in the northeast of Baihe Town, and the intersection zone of lakes and rivers in Ganyao Town and Dayun Town. Examples of such areas included Beilian Village, Dianhu Village, Santang Village, Jin Xiang Village, Hongqi Village, Nanzhu Village, and Yangqiao Village. Rural areas with medium native aesthetic service values accounted for 40.95% and were mainly concentrated in the rural belt of Changyang in the central part of Wujiang District, as well as the rural settlements in the river network area of Liantang Town and the field-wetland area of Jiashan County. Rural areas with low aesthetic service values accounted for 43.93% and were primarily concentrated in the marginal villages in southeastern Wujiang District, western Qingpu District, and northwestern Jiashan County.
The mean value of livelihood support services was 0.40. Rural areas with high and medium service values accounted for 69.57% collectively, and their spatial distribution indicated a trend of concentration towards the town centers, showcasing the radiation effect of urban built-up areas on rural economic development, infrastructure construction, and transportation connections. Examples of such areas included rural areas near the Hi-tech Zone in Wujiang district, the Industrial Base and Service Cluster Area in Qingpu district, and the Economic Development Zone in Jiashan County. Meanwhile, rural revitalization policies have played a crucial role in promoting local livelihood support services [66]. For instance, Liming Village in Gangyao Town, Jiashan County, has effectively seized the opportunity of rural revitalization to pursue sustainable development by integrating the local brick kiln industry with the region’s vernacular culture.
The mean value of historical culture services was relatively low at 0.12, with the majority of rural areas (74.45%) categorized as having low service value. However, specific regions, including traditional villages [67], historical and cultural towns, relic protection units, intangible cultural heritage protection units, and the Jiangnan Canal Cultural Belt [68], emerged as key locations with medium- and high-value historical culture services. Notable examples of such areas included Xigang Village in Pingwang Town, renowned for its preservation of tangible cultural treasures such as the Donglin Ancestral Hall and Zouma Hall Building, and Zhanxing Village in Yaozhuang Town, known for preserving the 6000-year-old Dawang Ploder and being the origin of the intangible cultural heritage, Jiashan Field Song.
The knowledge education services exhibited a generally high quality, as indicated by a mean value of 0.41. Rural areas with high and medium service values collectively accounted for 68.51% of the total. These areas were predominantly found in Zhenze Town, Pingwang Town, and Lili Town in Wujiang District, Zhujiajiao Town and Xianghuaqiao Street in Qingpu District, as well as Xitang Town, Taozhuang Town, and Tianning Town in Jiashan County. Notably, the establishment of integrated cultural centers, ecological museums, science education bases, and cultural practice stations has improved significantly with the implementation of the rural revitalization strategy [69]. This progress has created more opportunities for the transmission of traditional skills and local knowledge, as well as facilitating greater accessibility to education services across regions.
With a mean value of 0.37, the recreational entertainment services exhibited a relatively high level of quality. Rural areas with high and medium service values accounted for 63.75% of the total. The geographical distribution was generally homogeneous, with the majority located in the Changyang Featured Pastoral Rural Belt in Wujiang District, the Beautiful Rural Cluster in Qingpu District, as well as the A-class Scenic Countryside Group and the Agriculture-Culture-Tourism Integrated Rural Cluster in Jiashan County. In the eastern half of Wujiang District, low-service areas were primarily distributed where cultivated land, forest land, and water bodies converged. These areas have mainly been designated as permanent basic farming areas, limiting the potential for recreational or leisure activities.
The spiritual connotation services areas primarily encompassed traditional folklore activity venues such as local clan shrines and memorial halls, as well as locations designated for the conservation of rural intangible cultural heritage, including the Xuan scrolls in Tongli Town, the Xiaoman opera in Shengze Town, the pyrograph in Jinze Town, and the art of ancient residential architecture in Xitang Town. The research revealed that the quality of spiritual connotation services was relatively low, with a mean value of 0.27. Spatial differentiation is evident, with areas of high and medium service values collectively accounting for 56.26% of the total. These areas are primarily concentrated in Hengfan Street, Pingwang Town, and Tongli Town in Wujiang District, Zhujiajiao Town and Jinze Town in Qingpu District, as well as Tianning Town, Xitang Town, and Ganyao Town in Jiashan County.

3.3. Impacts of Environmental Variables on Local CESs

The contribution, permutation importance, and Jackknife tests of each environmental variable to the prediction results were obtained by the Maxent model and the results are shown in Table 4.
According to the investigation, CESs were significantly impacted by the natural environment, spatial distance, and human activity factors. Among them, the primary affecting elements are land use and land cover (LULC), distance to national roadways (DTN), distance to township centers (DTT), and distance to water (DTW).
The influence mechanisms of the main environmental variables were assessed using response curves, as depicted in Figure 6. The response curves were re-drawn by inputting the numerical data files of the average response curves for the six categories of CESs with respect to LULC, DTN, DTT, and DTW, generated from 10 simulations using the MAXENT model, into Origin software for organization and plotting. It was noticeable that the values of spiritual and historical cultural services exhibited an upward trend along with the increase in DTN, and a downward trend with the increase in DTW, highlighting the distinctive value of the water network system for rural areas in the Yangtze River Delta. The values of all CESs demonstrated an upward trend with the increase in DTT within a 4-km radius, while showing a downward trend beyond that radius, indicating the continuous influence of transportation connectivity and township development on the core functions of CESs. Notably, LULC exhibited the highest contribution rate to most CESs, underscoring the significant impact of land use and landscape composition [60].

3.4. Local CES Clustering

As shown in Figure 7, this study conducted a K-means clustering analysis of local CESs in the EGIDZ of the Yangtze River Delta and classified them into three categories of CES clusters: CES-developed clusters with a higher average value of services, CES-developing clusters with a medium average value of services, and CES-potential clusters with a lower average value of services. The spatial distribution of different CES clusters in rural areas was mapped (Figure 8) and the raster layers of the clustering results were tabulated by village domain (Table 5).
The average rankings of services in the CES-developed cluster were as follows: native aesthetic > knowledge education > historical culture > recreational entertainment > livelihood support > spiritual connotation. Rural areas with a CES-developed cluster covered 124.9 km2, accounting for 9.34% of the total. They were primarily situated in rural settlements near the water network area of Wujiang District, the rural belt around Dianshan Lake in Qingpu District, and the rural cluster close to the polder-wetland area of Jiashan County. Due to the eco-primitive background, natural landscape features, and the humanistic historical styles of the Jiangnan Water Network, these areas possess distinctive values in terms of CESs. Moreover, their proximity to major scenic spots and urban built-up areas makes it convenient for promoting folk culture and eco-agricultural practices, which fully reflect the integration of urban and rural areas in the EGIDZ. Furthermore, the implementation of the Beautiful Rural Construction policy [70] and the integration of agriculture, culture, and tourism have significantly enhanced the values of various CESs. Some notable representative villages include Hanshang Village, Xinchi Village, Dianhu Village, Santang Village, Shanwan Village, and Yuanjia Village.
The average rankings of services in the CES-developing cluster were as follows: livelihood support > knowledge education > recreational entertainment > native aesthetic > spiritual connotation > historical culture. Rural areas with a CES-developing cluster covered 658.57 km2, accounting for 49.23% of the total. In Wujiang District, they were mainly located in the Changyang Featured Pastoral Rural Belt and Yuandang Beautiful Rural Cluster, which rely on the cross-regional linkage effect and the agricultural development model [71]. Combined with the characteristic resources to conduct natural science education and eco-agricultural tourism, they have become demonstration areas for integration development. Representative villages include Yinghu Village, Zhonganqiao Village, and Xinxing Village. In Qingpu District, they were primarily situated in the rural belt around Dianshan Lake in Zhujiajiao Town and the rural settlements surrounding the Industrial Base and Service Cluster Area, which take up the demand for vacation, research, and recreation and have extensive advantages in economic revitalization and infrastructure services [72]. These areas primarily include Xuejian village, Qingfeng village, and Anzhuang village. In Jiashan County, they were mainly located in the Beautiful Rural Cluster to the north of the Economic Development Zone. Due to the implementation of the rural revitalization strategy, rural areas in Jiashan County have developed significantly in terms of industry upgrading and environment improvement. The representative villages are Changxiu Village, Xitang Village, Fanjing Village, Dong Village, and Taozhuang Village.
The average rankings of services in the CES-potential cluster were as follows: recreational entertainment > knowledge education > livelihood support > spiritual connotation > native aesthetic > historical culture. Rural areas with a CES-potential cluster covered 554.33 km2, accounting for 41.44% of the total, and were mainly located in the key areas of comprehensive land improvement in Fenhu Town in the eastern part of Wujiang District and the key area of agricultural land improvement in Liantang Town in the southern part of Qingpu District, where urgent renovation is required for adaptive development models that meet the requirements of permanent basic farmland protection and construction [46]. Representative villages are Xingyi Village, Chuanxingang Village, and Jinqian Village. CES-potential clusters were also located in the key areas of Dayun-Huimin construction land remediation in the southern part of Jiashan County, where the rural areas have suffered from inefficient development and are adjacent to manufacturing pollution risk sources [73], resulting in low values of local CESs, mainly including Damao Village and Xinrun Village in Huimin Street and Caojia Village in Dayun Town.

4. Discussion

4.1. Quantitative Evaluation and Spatial Mapping of Rural Local CESs

The ranking of mean values for the different services in the study was as follows: knowledge education (0.41) > livelihood support (0.40) > recreational entertainment (0.37) > native aesthetic (0.31) > spiritual meaning (0.27) > historical culture (0.12). It is worth noting that a significant portion of these services fell into the low-value category, with all of them exceeding 30%. This finding underscored the significant challenges faced by local CESs in rural areas, particularly the insufficient attention given to historical culture, spiritual connotations, and native aesthetic services. Based on these findings, it is necessary to embrace a systematic integration approach that combines both the natural and cultural components of rural localities. This approach aims to preserve the distinctive Jiangnan Water Network settlements style and reconstruct the contextual framework of spiritual culture within the broader context of implementing the rural revitalization strategy.
The spatial divergence of distinct CESs was visible. The spatial aggregation of native aesthetic services was strong, with high-quality areas primarily concentrated near scenic spots and rural belts around Dianshan Lake, Taipu River, and the Jiangnan Canal, while low-quality areas were primarily concentrated on the rural outskirts, where arable land dominated the landscape. Livelihood support and knowledge education services demonstrated comparable spatial distributions, influenced by the expansion of town centers and the development of township industries, resulting in a neighborhood effect with urban built-up regions [71]. Historical culture services generally exhibited low quality throughout the entire area, except for areas radiating from traditional villages, cultural relic protection units, and historical landscape regions. The spatial distribution of recreational entertainment services showed relatively consistent high service quality, with the exception of the agricultural land concentration area in Wujiang District. The spatial variations of spiritual connotation services formed a cross-domain linkage in the central part of the rural areas. However, the capacity of services coverage was inadequate.

4.2. The Impact of Environmental Variables on Rural Local CESs

According to the investigation of the contributions and mechanisms of environmental variables, CESs were significantly impacted by the natural environment, spatial distance, and human activity factors. The primary factors were identified as land use and land cover (LULC) [60], distance to national roadways (DTN), distance to township centers (DTT) [58], and distance to water (DTW) [57]. The contribution rates of LULC were above 8% to all types of CES. Most CESs showed a decreasing trend in service value with increasing DTN, indicating that long-distance rapid transportation has a positive impact on rural industrial development and local cultural promotion. The service values of native aesthetic, knowledge education, and recreational entertainment exhibited an initially increasing trend followed by a reduction with increasing DTT, demonstrating the continuous influence of the urban-rural integration strategy on CESs. The service values of spiritual connotation, historical culture, and native aesthetic decreased with increasing DTW, indicating that the water network persistently influenced local CESs due to its distinctive landscape pattern and cultural significance.

4.3. Rural Local CES Clusters

According to the results of K-means clustering, three CES clusters were identified and their spatial distribution was mapped. Rural areas with CES-developed clusters, accounting for 9.34% of the areas, had a mean service value of 0.50. They were mainly situated in the rural areas surrounding Dianshan Lake and the southern belt of the Taipu River. Rural areas with CES-developing clusters, covering 49.23% of the areas, had a mean service value of 0.34. They were primarily located in the Changyang Featured Pastoral Rural Belt, the Yuandang Beautiful Rural Cluster, and the rural areas adjacent to the Industrial Base Area and Economic Development Zone. Additionally, rural areas with CES-potential clusters, representing 41.44% of the areas, exhibited a mean service value of 0.22. They were mainly concentrated in the comprehensive land improvement key areas, agricultural land improvement key areas, and construction land improvement key areas.
These findings indicated that the quality of local CESs in rural areas within the EGIDZ was below satisfactory. Several issues were identified, including low spatial share and inadequate service coverage of the CES-developed cluster, unbalanced spatial distribution, and insufficient service quality of the CES-developing cluster, as well as complex spatial problems and a prolonged timeline for rectification of the CES-potential cluster.

4.4. Recommendations for Optimizing Rural Local CESs

4.4.1. Improving the Overall Values of CESs

To optimize rural local CESs, it is recommended to prioritize improving the values of CESs. This can be achieved by strategically integrating the spatial distribution of knowledge education, livelihood support, and recreational entertainment services, with a particular focus on regions with homogeneous and inefficient service provision. Additionally, emphasis should be placed on enhancing the values of historical culture, spiritual connotation, and native aesthetic services.
Practical approaches can be taken to realize these objectives. For instance, intangible cultural heritage crafts and traditional construction techniques can be integrated with the promotion of rural agricultural tourism and ecological science education [74]. Existing rural cultural practice sites can be creatively leveraged to combine historical heritage and folk festivals [75], facilitating the continuity of cultural traditions. Furthermore, restoring the trade function of the Jiangnan water street and optimizing the commercial layout of rural areas can contribute to promoting residents’ neighborhood communication and emotional connections.

4.4.2. Constructing the Coordination Mechanism of CESs

To optimize rural local CESs, it is recommended to construct a robust coordination mechanism that facilitates collaboration among different services. This can be achieved by leveraging the water network substrate. The water network provides opportunities for ecological enhancement and cultural intercourse, which can improve the overall service quality. Additionally, the spatial layout of CESs should be strategically allocated, taking advantage of urban-rural integration and cross-regional linkages. By capitalizing on these opportunities, synergistic interactions among CESs can be facilitated, resulting in a collective positive impact that surpasses the contributions of individual services. In addition, environmental variables such as land use patterns, natural resources, and cultural heritage should be considered to balance and optimize rural local CESs, based on the unique characteristics of each region.

4.4.3. Enhancing the Service Capacity of the Rural Areas with CES-Potential Clusters

To enhance the service capacity of CES-potential areas, it is crucial to implement effective strategies. According to “The Territorial Spatial Master Plan for the Yangtze River Delta Ecological Green Integrated Development Demonstration Zone (2021–2035)”, several key measures will be taken by 2035. These include implementing permanent basic farmland protection on 665,400 mu of land, delineating an ecological protection red line covering 143.32 km2, and establishing an urban development boundary spanning 647.6 km2. Therefore, optimizing the structure of rural land use [76], improving service performance on inefficient construction lands, and implementing whole-life cycle management are essential strategies for achieving stable and sustainable growth of local CESs, promoting long-term environmental and socio-economic benefits.

4.5. Subsequent Research

Future research will concentrate on optimizing the research framework for rural local CESs by incorporating theories of human-land interaction, Landscape Character Assessment (LAC) [77], and the local residential environment system [78]. This will help expand the scope and index framework of CES studies, enabling a more comprehensive understanding of rural local CESs.
In addition, future research will focus on expanding the data sources for rural local CESs by additional indicators such as rural resident population and gross township enterprise product. This will provide insights into local economic and social activities as environmental variables are acquired [79]. Furthermore, efforts will be made to establish the correlation between a spot’s spatial information and respondents’ preferences, aiming to incorporate individual action practices into the evaluation system for optimal management of POI datasets.
Furthermore, the focus will be on examining the combined effects of multiple environmental variables on local CES clusters, including investigating trade-offs and synergistic relationships using geographic detectors [80]. This approach will provide a scientific pathway for optimizing CESs in rural areas by understanding the complex interactions among environmental factors.

5. Conclusions

In this study, we spatially mapped and quantitatively analyzed six categories of ecological cultural services (CESs) in the rural areas of the EGIDZ of Yangtze River Delta and investigated the impact of environmental variables on each type of CES. The K-means clustering algorithm was used to determine the differentiation of rural CES clusters. The findings are shown below.
Mean values of local CESs were ranked as knowledge education (0.41) > livelihood support (0.40) > recreational entertainment (0.37) > native aesthetic (0.31) > spiritual connotation (0.27) > historical culture (0.12).
The natural environment, spatial distance, and human activity factors all had significant effects on shaping the quality and distribution of rural CESs. Among these factors, key environmental impact variables included land use and land cover (LULC), distance to national roadways (DTN), distance to township centers (DTT), and distance to water (DTW).
The CES clusters were classified into three categories: CES-developed clusters, CES-developing clusters, and CES-potential clusters. Rural areas with these clusters accounted for 9.34%, 49.23%, and 41.44%, respectively.
This study established a framework for the quantitative evaluation and spatial optimization of local CESs, provided insights into the distribution and potential of local CESs in Jiangnan Water Network rural areas, and served as a foundation for targeted interventions to promote sustainable rural development.
There are still some limitations to this study. The first is the lack of comprehensive consideration of the perceived characteristics of rural CESs by different stakeholders [81]. In fact, different audiences, such as rural indigenous people, planning managers, local governments, and tourists [66], have different preferences, cognitive attitudes, and participation patterns towards CESs. Therefore, by quantifying the perceived differences among different local groups, we can better understand the diversities of rural local CESs in terms of the social value system and cultural capital ability.
The second limitation is that this study primarily focused on the supply capacity and service quality of rural CESs, lacking the perspective of demand and flow. Understanding the multiple relationships between ecosystem services (ES) supplies and socio-economic demands is a prerequisite for spatial sustainability [82]. Therefore, future research should complement the content related to supply-demand interactions of CESs and utilize the concept of CES flows to quantify the spatial distributions of service mismatches [83], thus providing a more comprehensive research framework for the assessment and exploration of the effect mechanisms of rural CESs.
The third is that the mechanisms underlying the dynamic evolution of rural local CESs at different temporal scales have not been fully considered. This study primarily focused on the quantitative assessment of rural CESs after the implementation of rural revitalization policies and the establishment of EGIDZ. However, the characteristics of rural CESs evolve in conjunction with habitat construction, socio-economic development, and government policy promulgation, leading to continuous changes in their constituent elements, spatial structure, and organizational relationships. Therefore, the study should pay more attention to the characterization and mechanisms of the evolution of rural CESs under spatio-temporal dynamics and propose planning strategies to promote the coordinated development of ecology, economy, and culture in local rural habitat construction.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/land12071373/s1, Table S1: POI data sources; Table S2: Environmental Variable Data Sources.

Author Contributions

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

Funding

This research was funded by the China National Natural Science Foundation: Theory and Method of the Ecological Planning of Urban and Rural Landscape with Locality for Livability (52130804).

Data Availability Statement

Data for POIs and environmental variables are available in publicly accessible repositories, including government official websites, data platforms for Points of Interest (POIs), and geographic information data centers. The links are listed in the Supplementary Materials.

Acknowledgments

This work has been supported by the National Natural Science Foundation and has received significant guidance and assistance from Binyi Liu’s research team. The research started in September 2021 and after theoretical exploration, field investigations, and data accumulation, the research foundation has been established. In addition, fruitful collaborations have been established with Tsinghua University and Peking University, expanding the scope of research.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Millennium Ecosystem Assessment. Ecosystem and Human Wellbeing: Synthesis; World Resources Institute: Washington, DC, USA, 2005. [Google Scholar]
  2. Dickinson, D.C.; Hobbs, R.J. Cultural ecosystem services: Characteristics, challenges and lessons for urban green space research. Ecosyst. Serv. 2017, 25, 179–194. [Google Scholar] [CrossRef]
  3. Plieninger, T.; Bieling, C.; Fagerholm, N.; Byg, A.; Hartel, T.; Hurley, P.; López-Santiago, C.A.; Nagabhatla, N.; Oteros-Rozas, E.; Huntsinger, L. The role of cultural ecosystem services in landscape management and planning. Curr. Opin. Environ. Sust. 2015, 14, 28–33. [Google Scholar] [CrossRef] [Green Version]
  4. Gugulica, M.; Burghardt, D. Mapping indicators of cultural ecosystem services use in urban green spaces based on text classification of geosocial media data. Ecosyst. Serv. 2023, 60, 101508. [Google Scholar] [CrossRef]
  5. Song, X.P.; Richards, D.R.; He, P.; Tan, P.Y. Does geo-located social media reflect the visit frequency of urban parks? A city-wide analysis using the count and content of photographs. Landsc. Urban Plan. 2020, 203, 103908. [Google Scholar] [CrossRef]
  6. La Rosa, D.; Spyra, M.; Inostroza, L. Indicators of Cultural Ecosystem Services for urban planning: A review. Ecol. Indic. 2016, 61, 74–89. [Google Scholar] [CrossRef]
  7. Plieninger, T.; Dijks, S.; Oteros-Rozas, E.; Bieling, C. Assessing, mapping, and quantifying cultural ecosystem services at community level. Land Use Policy 2013, 33, 118–129. [Google Scholar] [CrossRef] [Green Version]
  8. Yang, S.Q.; Zhao, W.W.; Pereira, P.; Liu, Y.X. Socio-cultural valuation of rural and urban perception on ecosystem services and human well-being in Yanhe watershed of China. J. Environ. Manag. 2019, 251, 109615. [Google Scholar] [CrossRef]
  9. Bing, Z.H.; Qiu, Y.S.; Huang, H.P.; Chen, T.Z.; Zhong, W.; Jiang, H. Spatial distribution of cultural ecosystem services demand and supply in urban and suburban areas: A case study from Shanghai, China. Ecol. Indic. 2021, 127, 107720. [Google Scholar] [CrossRef]
  10. Marsden, T.; Sonnino, R. Rural development and the regional state: Denying multifunctional agriculture in the UK. J. Rural Stud. 2008, 24, 422–431. [Google Scholar] [CrossRef]
  11. Appadurai, A. The production of locality. In Counterworks; Routledge: Abingdon, UK, 2003; pp. 208–229. [Google Scholar]
  12. Augé, M. Non-Places: An Introduction to Supermodernity; Verso Books: New York, NY, USA, 2020. [Google Scholar]
  13. Pred, A. Place as historically contingent process: Structuration and the time-geography of becoming places. Ann. Assoc. Am. Geogr. 1984, 74, 279–297. [Google Scholar] [CrossRef]
  14. Escobar, A. Culture sits in places: Reflections on globalism and subaltern strategies of localization. Political Geogr. 2001, 20, 139–174. [Google Scholar] [CrossRef]
  15. Parsons, M.; Fisher, K. Indigenous peoples and transformations in freshwater governance and management. Curr. Opin. Env. Sust. 2020, 44, 124–139. [Google Scholar] [CrossRef]
  16. Polishchuk, Y.; Rauschmayer, F. Beyond “benefits”? Looking at ecosystem services through the capability approach. Ecol. Econ. 2012, 81, 103–111. [Google Scholar] [CrossRef]
  17. Harvey, D. The condition of postmodernity. In The New Social Theory Reader; Routledge: Abingdon, UK, 2020; pp. 235–242. [Google Scholar]
  18. Dou, Y.H.; Liu, M.X.; Bakker, M.; Yu, X.B.; Carsjens, G.J.; Groot, R.D.; Liu, J.G. Influence of human interventions on local perceptions of cultural ecosystem services provided by coastal landscapes: Case study of the Huiwen wetland, southern China. Ecosyst. Serv. 2021, 50, 101311. [Google Scholar] [CrossRef]
  19. Tao, Y.; Wang, H.N.; Ou, W.X.; Guo, J. A land-cover-based approach to assessing ecosystem services supply and demand dynamics in the rapidly urbanizing Yangtze River Delta region. Land Use Policy 2018, 72, 250–258. [Google Scholar] [CrossRef]
  20. de Groot, R.S.; Wilson, M.A.; Boumans, R.M.J. A typology for the classification, description and valuation of ecosystem functions, goods and services. Ecol. Econ. 2002, 41, 393–408. [Google Scholar] [CrossRef] [Green Version]
  21. Kearney, A.R.; Bradley, G. Human dimensions of forest management: An empirical study of stakeholder perspectives. Urban Ecosyst. 1998, 2, 5–16. [Google Scholar] [CrossRef]
  22. Nedkov, S.; Burkhard, B. Flood regulating ecosystem services-Mapping supply and demand, in the Etropole municipality, Bulgaria. Ecol. Indic. 2012, 21, 67–79. [Google Scholar] [CrossRef]
  23. Stürck, J.; Poortinga, A.; Verburg, P.H. Mapping ecosystem services: The supply and demand of flood regulation services in Europe. Ecol. Indic. 2014, 38, 198–211. [Google Scholar] [CrossRef]
  24. Brown, G.; Fagerholm, N. Empirical PPGIS/PGIS mapping of ecosystem services: A review and evaluation. Ecosyst. Serv. 2015, 13, 119–133. [Google Scholar] [CrossRef]
  25. Sherrouse, B.C.; Semmens, D.J.; Clement, J.M. An application of social values for ecosystem services (SolVES) to three national forests in Colorado and Wyoming. Ecol. Indic. 2014, 36, 68–79. [Google Scholar] [CrossRef]
  26. Carvalho-Ribeiro, S.; Correia, T.P.; Paracchini, M.L.; Schüpbach, B.; Sang, A.O.; Vanderheyden, V.; Southern, A.; Jones, P.; Contreras, B.; O′Riordan, T. Assessing the ability of rural agrarian areas to provide cultural ecosystem services (CES): A multi scale social indicator framework (MSIF). Land Use Policy 2016, 53, 8–19. [Google Scholar] [CrossRef] [Green Version]
  27. van Berkel, D.B.; Verburg, P.H. Spatial quantification and valuation of cultural ecosystem services in an agricultural landscape. Ecol. Indic. 2014, 37, 163–174. [Google Scholar] [CrossRef]
  28. Li, Y.J.; Zhang, D.Y.; Wang, K.; Huang, L. Supply-demand relationships of cultural ecosystem services in rural areas: A case study of Huzhou City, Zhejiang Province. Acta Ecol. Sin. 2022, 42, 6888–6899. [Google Scholar]
  29. Chen, C.D.; Wang, Y.C.; Jia, J.S.; Mao, L.F.; Meurk, C.D. Ecosystem services mapping in practice: A Pasteur’s quadrant perspective. Ecosyst. Serv. 2019, 40, 101042. [Google Scholar] [CrossRef]
  30. Boumans, R.; Roman, J.; Altman, I.; Kaufman, L. The Multiscale Integrated Model of Ecosystem Services (MIMES): Simulating the interactions of coupled human and natural systems. Ecosyst. Serv. 2015, 12, 30–41. [Google Scholar] [CrossRef]
  31. Bagstad, K.J.; Semmens, D.J.; Waage, S.; Winthrop, R. A comparative assessment of decision-support tools for ecosystem services quantification and valuation. Ecosyst. Serv. 2013, 5, 27–39. [Google Scholar] [CrossRef]
  32. Sharp, R.; Tallis, H.; Ricketts, T.; Chaplin-Kramer, R.; Wood, S.A.; Guerry, A.; Nelson, E.J.; Ennaanay, E.D.; Olwero, N.; Vigerstol, K.; et al. InVEST User’s Guide; The Natural Capital Project: Stanford, CA, USA, 2014. [Google Scholar]
  33. Phillips, S.J.; Anderson, R.P.; Schapire, R.E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 2006, 190, 231–259. [Google Scholar] [CrossRef] [Green Version]
  34. Li, Y.G.; Xie, L.; Zhang, L.; Huang, L.Y.; Lin, Y.; Su, Y.; AmirReza, S.; He, S.; Zhu, C.M.; Chen, X. Understanding different cultural ecosystem services: An exploration of rural landscape preferences based on geographic and social media data. J. Environ. Manag. 2022, 317, 115487. [Google Scholar] [CrossRef]
  35. Casado-Arzuaga, I.; Onaindia, M.; Madariaga, I.; Verburg, P.H. Mapping recreation and aesthetic value of ecosystems in the Bilbao Metropolitan Greenbelt (Northern Spain) to support landscape planning. Landsc. Ecol. 2014, 29, 1393–1405. [Google Scholar] [CrossRef]
  36. Wood, S.A.; Guerry, A.D.; Silver, J.M.; Lacayo, M. Using social media to quantify nature-based tourism and recreation. Sci. Rep. 2013, 3, 2976. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  37. He, S.; Su, Y.; Shahtahmassebi, A.R.; Huang, L.Y.; Zhou, M.M.; Gan, M.Y.; Deng, J.S.; Zhao, G.; Wang, K. Assessing and mapping cultural ecosystem services supply, demand and flow of farmlands in the Hangzhou metropolitan area, China. Sci. Total Environ. 2019, 692, 756–768. [Google Scholar] [CrossRef] [PubMed]
  38. Zoderer, B.M.; Tasser, E.; Carver, S.; Tappeiner, U. Stakeholder perspectives on ecosystem service supply and ecosystem service demand bundles. Ecosyst. Serv. 2019, 37, 100938. [Google Scholar] [CrossRef]
  39. Wang, X.J.; Leeser, M. K-means Clustering for Multispectral Images Using Floating-point Divide. In Proceedings of the 15th Annual IEEE Symposium on Field-Programmable Custom Computing Machines, Napa, CA, USA, 23–25 April 2007; pp. 151–162. [Google Scholar]
  40. UNESCO. The Ancient Waterfront Towns in the South of Yangtze River. Available online: https://whc.unesco.org/en/tentativelists/5328 (accessed on 24 March 2023).
  41. Yang, S.; Wan, R.R.; Yang, G.H.; Li, B.; Dong, L.F. Combining historical maps and landsat images to delineate the centennial-scale changes of lake wetlands in Taihu Lake Basin, China. J. Environ. Manag. 2023, 329, 117110. [Google Scholar] [CrossRef]
  42. Chen, G.; Sun, X.; Yu, W.; Wang, H. Analysis Model of the Relationship between Public Spatial Forms in Traditional Villages and Scenic Beauty Preference Based on LiDAR Point Cloud Data. Land 2022, 11, 1133. [Google Scholar] [CrossRef]
  43. Zhang, X.D.; Wang, X.Y.; Zhang, C.Y.; Zhai, J. Development of a cross-scale landscape infrastructure network guided by the new Jiangnan watertown urbanism: A case study of the ecological green integration demonstration zone in the Yangtze River Delta, China. Ecol. Indic. 2022, 143, 109317. [Google Scholar] [CrossRef]
  44. Brandt, P.; Abson, D.J.; DellaSala, D.A.; Feller, R.; von Wehrden, H. Multifunctionality and biodiversity: Ecosystem services in temperate rainforests of the Pacific Northwest, USA. Biol. Conserv. 2014, 169, 362–371. [Google Scholar] [CrossRef]
  45. Schirpke, U.; Tappeiner, G.; Tasser, E.; Tappeiner, U. Using conjoint analysis to gain deeper insights into aesthetic landscape preferences. Ecol. Indic. 2019, 96, 202–212. [Google Scholar] [CrossRef]
  46. Brown, G.; Montag, J.M.; Lyon, K. Public participation GIS: A method for identifying ecosystem services. Soc. Nat. Resour. 2012, 25, 633–651. [Google Scholar] [CrossRef]
  47. Csurgó, B.; Smith, M.K. The value of cultural ecosystem services in a rural landscape context. J. Rural Stud. 2021, 86, 76–86. [Google Scholar] [CrossRef]
  48. Clemente, P.; Calvache, M.; Antunes, P.; Santos, R.; Cerdeira, J.O.; Martins, M.J. Combining social media photographs and species distribution models to map cultural ecosystem services: The case of a Natural Park in Portugal. Ecol. Indic. 2019, 96, 59–68. [Google Scholar] [CrossRef]
  49. Marrero-Rodríguez, N.; Peña-Alonso, C.; García-Romero, L.; Sánchez-García, M.J.; Pérez-Chacón Espino, E. Historical social relevance of ecosystem services related to long term land uses in a coastal arid aeolian sedimentary system in Lanzarote (Canary Islands, Spain). Ocean Coast. Manag. 2021, 210, 105715. [Google Scholar] [CrossRef]
  50. Daniel, T.C.; Muhar, A.; Arnberger, A.; Aznar, O.; Boyd, J.W.; Chan, K.M.; Costanza, R.; Elmqvist, T.; Flint, C.G.; von der Dunk, A.; et al. Contributions of cultural services to the ecosystem services agenda. Proc. Natl. Acad. Sci. USA 2012, 109, 8812–8819. [Google Scholar] [CrossRef] [PubMed]
  51. National Development and Reform Commission. A Plan for Building a Demonstration Area in the Yangtze River Delta on Ecologically Friendly Development; National Development and Reform Commission: Beijing, China, 2019. [Google Scholar]
  52. Zhao, Y.Q.; You, W.B.; Lin, X.E.; He, D.J. Assessing the supply and demand linkage of cultural ecosystem services in a typical county-level city with protected areas in China. Ecol. Indic. 2023, 147, 109992. [Google Scholar] [CrossRef]
  53. Wang, F.; Zhao, X.G.; Qiu, Y.X.; Dong, S.R.; Wang, S.Y.; Xue, P.C. A study on native and constructed localities in the modern adaptation of villages. Habitat Int. 2023, 138, 102849. [Google Scholar] [CrossRef]
  54. Yoshimura, N.; Hiura, T. Demand and supply of cultural ecosystem services; use of geotagged photos to map the aesthetic value of landscapes in Hokkaido. Ecosyst. Serv. 2017, 24, 68–78. [Google Scholar] [CrossRef]
  55. Schirpke, U.; Timmermann, F.; Tappeiner, U.; Tasser, E. Cultural ecosystem services of mountain regions: Modelling the aesthetic value. Ecol. Indic. 2016, 69, 78–90. [Google Scholar] [CrossRef] [Green Version]
  56. Tieskens, K.F.; Van Zanten, B.T.; Schulp, C.J.; Verburg, P.H. Aesthetic appreciation of the cultural landscape through social media: An analysis of revealed preference in the Dutch river landscape. Landsc. Urban Plan. 2018, 177, 128–137. [Google Scholar] [CrossRef]
  57. Pan, J.F.; Ma, Y.W.; Cai, S.Q.; Chen, Y.; Chen, Y.M. Distribution patterns of lake-wetland cultural ecosystem services in highland. Environ. Dev. 2022, 44, 100754. [Google Scholar] [CrossRef]
  58. Ala-Hulkko, T.; Kotavaara, O.; Alahuhta, J.; Helle, P.; Hjort, J. Introducing accessibility analysis in mapping cultural ecosystem services. Ecol. Indic. 2016, 66, 416–427. [Google Scholar] [CrossRef]
  59. Westerink, J.; Opdam, P.; Van Rooij, S.; Steingröver, E. Landscape services as a boundary concept in landscape governance: Building social capital in collaboration and adapting the landscape. Land Use Policy 2017, 60, 408–418. [Google Scholar] [CrossRef]
  60. Chen, Y.; Ke, X.; Min, M.; Cheng, P. Disparity in perceptions of social values for ecosystem services of urban green space: A case study in the East Lake Scenic Area, Wuhan. Front. Public Health 2020, 8, 370. [Google Scholar] [CrossRef] [PubMed]
  61. Lin, J.Y.; Li, H.; Zeng, Y.J.; He, X.Y.; Zhuang, Y.Y.; Liang, Y.R.; Lu, S.Y. Estimating potential illegal land development in conservation areas based on a presence-only model. J. Environ. Manag. 2022, 321, 115994. [Google Scholar] [CrossRef]
  62. Phillips, S.J.; Dudík, M. Modeling of species distributions with Maxent: New extensions and a comprehensive evaluation. Ecography 2008, 31, 161–175. [Google Scholar] [CrossRef]
  63. Fielding, A.H.; Bell, J.F. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ. Conserv. 1997, 24, 38–49. [Google Scholar] [CrossRef]
  64. Swets, J.A. Measuring the accuracy of diagnostic systems. Science 1988, 240, 1285–1293. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  65. Bai, X.J.; Chen, H.; Liu, D.; Shi, Q.Q.; Li, T. Spatial differentiation of ecosystem services demand and its bundles based on MaxEnt model: A case study of Mizhi County, Shanxi Province, China. Acta Ecol. Sin. 2023, 43, 2745–2755. [Google Scholar]
  66. Liu, Y. The basic theory and methodology of rural revitalization planning in China. Acta Geogr. Sin. 2020, 75, 1120–1133. [Google Scholar]
  67. Liu, J.; Song, Q.; Wang, X. Spatial Morphology Evolution of Rural Settlements in the Lower Yellow River Plain: The Case of Menggang Town in Changyuan City, China. Land 2023, 12, 1122. [Google Scholar] [CrossRef]
  68. Feng, S.; Lu, Q.; Wu, Z.; Guo, Z. The Distribution Model and Spatial Structure of Market Towns in the Pearl River Delta during the Ming, Qing, and Min-Guo Periods: A Case Study of Taishan County. Land 2022, 11, 1354. [Google Scholar] [CrossRef]
  69. Ye, C.; Ma, X.Y.; Gao, Y. Laurel Johnson, The lost countryside: Spatial production of rural culture in Tangwan village in Shanghai. Habitat Int. 2020, 98, 102137. [Google Scholar] [CrossRef]
  70. Ye, C.; Ma, X.; Cai, Y.; Gao, F. The countryside under multiple high-tension lines: A perspective on the rural construction of Heping Village, Shanghai. J. Rural Stud. 2018, 62, 53–61. [Google Scholar] [CrossRef]
  71. Fan, L.Y. Evolution and Reflection on Rural Planning of City-cluster Areas from the Perspective of Urban-rural Relationship: A Case Study of Suzhou. Urban Plan. Forum 2015, 226, 106–113. [Google Scholar]
  72. Crouch, D. Popular culture and what we make of the rural, with a case study of village allotments. J. Rural Stud. 1992, 8, 229–240. [Google Scholar] [CrossRef]
  73. Xue, J.Y.; Wang, Q.R.; Zhang, M.H. A review of non-point source water pollution modeling for the urban–rural transitional areas of China: Research status and prospect. Sci. Total Environ. 2022, 826, 154146. [Google Scholar] [CrossRef]
  74. Hølleland, H.; Skrede, J.; Holmgaard, S.B. Cultural heritage and ecosystem services: A literature review. Conserv. Manag. Archaeol. Sites 2017, 19, 210–237. [Google Scholar] [CrossRef] [Green Version]
  75. Rossetti, G.; Quinn, B. Understanding the cultural potential of rural festivals: A conceptual framework of cultural capital development. J. Rural Stud. 2021, 86, 46–53. [Google Scholar] [CrossRef]
  76. Long, H.; Li, Y.; Liu, Y.; Woods, M.; Zou, J. Accelerated restructuring in rural China fueled by ‘increasing vs. decreasing balance’ land-use policy for dealing with hollowed villages. Land Use policy 2012, 29, 11–22. [Google Scholar] [CrossRef]
  77. Trop, T. From knowledge to action: Bridging the gaps toward effective incorporation of Landscape Character Assessment approach in land-use planning and management in Israel. Land Use Policy 2017, 61, 220–230. [Google Scholar] [CrossRef]
  78. Liu, B.Y.; Gao, W.L. Systematic Construction of Human Settlement Locality inYangtze River Delta and its Evoluttion Assessment. Chin. Landsc. Archit. 2023, 39, 6–13. [Google Scholar]
  79. Burkhard, B.; Crossman, N.; Nedkov, S.; Petz, K.; Alkemade, R. Mapping and modelling ecosystem services for science, policy and practice. Ecosyst. Serv. 2013, 4, 1–3. [Google Scholar] [CrossRef]
  80. Bi, Y.Z.; Zheng, L.; Wang, Y.; Li, J.F.; Yang, H.; Zhang, B.W. Coupling relationship between urbanization and water-related ecosystem services in China’s Yangtze River economic Belt and its socio-ecological driving forces: A county-level perspective. Ecol. Indic. 2023, 146, 109871. [Google Scholar] [CrossRef]
  81. Kim, N.; Kim, M.; Lee, S.; Oh, C.-O. Comparing Stakeholders’ Economic Values for the Institution of Payments for Ecosystem Services in Protected Areas. Land 2023, 12, 1332. [Google Scholar] [CrossRef]
  82. Sun, Y.; Zhao, T.Y.; Cotella, G.; Liu, Y.S. Ecosystem services supply and demand mismatches and effect mechanisms in the mixed landscapes context. Sci. Total Environ. 2023, 885, 163909. [Google Scholar] [CrossRef] [PubMed]
  83. Bagstad, K.J.; Johnson, G.W.; Voigt, B.; Villa, F. Spatial dynamics of ecosystem service flows: A comprehensive approach to quantifying actual services. Ecosyst. Serv. 2013, 4, 117–125. [Google Scholar] [CrossRef]
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Figure 1. The research framework of local CESs in the Jiangnan Water Network Rural Areas.
Figure 1. The research framework of local CESs in the Jiangnan Water Network Rural Areas.
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Figure 2. Rural areas of the Ecological Green Integration Demonstration Zone in the Yangtze River Delta.
Figure 2. Rural areas of the Ecological Green Integration Demonstration Zone in the Yangtze River Delta.
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Figure 3. Environmental variables in rural areas of EGIDZ.
Figure 3. Environmental variables in rural areas of EGIDZ.
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Figure 4. Spatial distribution of local CESs in rural areas of EGIDZ.
Figure 4. Spatial distribution of local CESs in rural areas of EGIDZ.
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Figure 5. Classification of local CESs in rural areas of EGIDZ.
Figure 5. Classification of local CESs in rural areas of EGIDZ.
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Figure 6. Response curves of environmental variables on rural local CESs: (a) Response curve of DTN to CESs; (b) Response curve of DTW to CESs; (c) Response curve of DTT to CESs; (d) Response diagram of LULC to CESs.
Figure 6. Response curves of environmental variables on rural local CESs: (a) Response curve of DTN to CESs; (b) Response curve of DTW to CESs; (c) Response curve of DTT to CESs; (d) Response diagram of LULC to CESs.
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Figure 7. CES clusters of rural areas: (a) Average services value of the CES-developed cluster; (b) Average services value of the CES-developing cluster; (c) Average services value of the CES-potential cluster.
Figure 7. CES clusters of rural areas: (a) Average services value of the CES-developed cluster; (b) Average services value of the CES-developing cluster; (c) Average services value of the CES-potential cluster.
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Figure 8. Spatial mapping and village distribution of local CES clusters: (a) Rural areas with different local CES clusters; (b) Villages of different local CES clusters; (c) Spatial images of different local CES clusters.
Figure 8. Spatial mapping and village distribution of local CES clusters: (a) Rural areas with different local CES clusters; (b) Villages of different local CES clusters; (c) Spatial images of different local CES clusters.
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Table
Table 1. Descriptions and classification criteria of POI data.
Table 1. Descriptions and classification criteria of POI data.
CategoryDescriptionClassification CriterionQuantityReferences
Native aestheticNatural and cultural places with unique aesthetic significanceTags: scenic spots, natural places, common places
Keywords: farmland, lake, river, stream, spring, waterfall, wetland, cave, park, garden, etc.		299[44,45]
Livelihood supportProduction and commercial locations that offer indigenous people with subsistence and material securityTags: life services, production materials, commercial markets
Keywords: transaction service station, elderly care service station, operation service station, convalescent station, comprehensive service center, wholesale market, etc.		504[48]
Historical cultureLocal places which hold historical heritage with humanistic and traditional relevanceTags: cultural relics protection units, folklore activity venues, heritage monuments
Keywords: cultural site, former residence, museum, pagoda, bridge, shrine, ancestral hall, monument, pavilion, well, etc.		118[49]
Knowledge educationLocal places for natural science research and ecological science educationTags: natural popularization bases, science education places
Keywords: innovation training base, ecological museum, science technology research center, education practice park, civilization practice station, etc.		387[28]
Recreational entertainmentLocal places for entertainment experience and recreational activitiesTags: Leisure venues, hotel caterings, vacation retreats, consumer places
Keywords: tourism orchard, farmhouse, pier, amusement park, hotel, restaurant, sightseeing tower, etc.		502[46,47]
Spiritual connotationLocal carriers of spiritual civilization that have significance in inheriting folk cultureTags: Intangible Cultural Heritage Protection Units
Keywords: location for preserving intangible cultural heritage		141[50]
Table
Table 2. Environmental Variables.
Table 2. Environmental Variables.
TypeVariableCodeUnitVIF
Natural
environment
factors		ElevationELEVm1.430
SlopeSLOPE°1.361
AspectASP°1.018
Annual average temperatureTEM°C1.186
Distance to waterDTWkm1.777
Geographical
distance
factors		Distance to railwaysDTRkm2.690
Distance to highwaysDTHkm1.487
Distance to national roadwaysDTNkm1.351
Distance to provincial highwaysDTPkm1.196
Distance to A-level scenic spotsDTSkm3.381
Distance to township centersDTTkm1.312
Human
activity factors		Land use and land coverLULC-1.038
Table
Table 3. Statistics of local CESs in rural areas of the EGIDZ.
Table 3. Statistics of local CESs in rural areas of the EGIDZ.
CES CategoryMean ValveClassArea (km2)Proportion (%)
Native aesthetic0.31High 218.5915.13
Medium591.6240.95
Low634.6843.93
Livelihood support0.4High 387.4326.81
Medium617.7942.76
Low439.6730.43
Historical culture0.12High 95.026.58
Medium274.0918.97
Low1075.7774.45
Knowledge education0.41High 368.925.53
Medium620.9342.97
Low455.0631.49
Recreational entertainment0.37High 293.3720.3
Medium627.8143.45
Low523.7136.25
Spiritual connotation0.27High 298.2220.64
Medium514.7135.62
Low631.9643.74
Table
Table 4. Contribution, permutation importance, and training gain (with only the variable) of each variable to rural local CESs.
Table 4. Contribution, permutation importance, and training gain (with only the variable) of each variable to rural local CESs.
Environmental VariableELEVSLOPEASPTEMDTWDTRDTHDTNDTPDTSDTTLULC
Native aestheticcontribution (%)6.922.026.719.347.517.318.2410.235.625.4511.2319.81
Importance (%)5.134.515.124.815.325.7214.515.716.034.4825.7413.23
Training gain0.02 0.04 0.04 0.11 0.09 0.04 0.06 0.08 0.02 0.04 0.11 0.13
Livelihood supportcontribution (%)3.386.7312.922.337.536.246.0223.228.717.537.258.34
Importance (%)4.5110.2414.031.068.099.065.7316.6410.2211.125.823.72
Training gain0.03 0.03 0.06 0.02 0.03 0.03 0.04 0.03 0.03 0.02 0.04 0.02
Historical culturecontribution (%)2.721.474.610.435.815.313.169.453.653.944.0355.54
Importance (%)3.691.915.231.426.1210.221.5312.872.356.135.5843.22
Training gain0.09 0.06 0.09 0.04 0.10 0.10 0.06 0.15 0.06 0.08 0.13 0.55
Knowledge educationcontribution (%)4.243.8217.461.6610.335.988.5114.059.215.989.828.93
Importance (%)3.534.7414.683.4411.046.828.9613.9613.927.729.232.06
Training gain0.01 0.02 0.05 0.01 0.02 0.03 0.04 0.07 0.01 0.02 0.02 0.03
Recreational entertainmentcontribution (%)4.717.6313.243.2411.137.7212.538.425.542.918.3414.92
Importance (%)2.126.4412.734.7114.758.1412.718.135.868.149.816.63
Training gain0.02 0.03 0.03 0.03 0.02 0.04 0.05 0.04 0.02 0.02 0.05 0.06
Spiritual connotationcontribution (%)2.536.457.817.4111.868.6311.6212.2410.738.254.428.15
Importance (%)3.8911.022.137.3913.6211.5317.739.6111.346.083.432.28
Training gain0.04 0.11 0.05 0.13 0.15 0.06 0.06 0.17 0.06 0.09 0.03 0.07
Table
Table 5. Statistics on CES cluster area, proportion, and village quantity.
Table 5. Statistics on CES cluster area, proportion, and village quantity.
CES ClusterArea (km2)Proportion (%)Village Quantity
CES-developed cluster124.909.3410
CES-developing cluster658.5749.23150
CES-potential cluster554.3341.4490
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Zuo, Y.; Zhang, L. Research on Local Ecosystem Cultural Services in the Jiangnan Water Network Rural Areas: A Case Study of the Ecological Green Integration Demonstration Zone in the Yangtze River Delta, China. Land 2023, 12, 1373. https://doi.org/10.3390/land12071373

AMA Style

Zuo Y, Zhang L. Research on Local Ecosystem Cultural Services in the Jiangnan Water Network Rural Areas: A Case Study of the Ecological Green Integration Demonstration Zone in the Yangtze River Delta, China. Land. 2023; 12(7):1373. https://doi.org/10.3390/land12071373

Chicago/Turabian Style

Zuo, You, and Lin Zhang. 2023. "Research on Local Ecosystem Cultural Services in the Jiangnan Water Network Rural Areas: A Case Study of the Ecological Green Integration Demonstration Zone in the Yangtze River Delta, China" Land 12, no. 7: 1373. https://doi.org/10.3390/land12071373

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