Impact of Urbanization on Ecosystem Services in the Yangtze River Delta: An Analysis from Explicit and Implicit Perspectives

Abstract

Rapid urbanization has profoundly impacted regional ecosystem services. However, most current studies have not paid enough attention to the implicit quality-of-life dimensions of urbanization, and few studies have been published on the dynamic interactions between urbanization and the evolution of ecosystem services. This study investigated the temporal and spatial dynamics of urbanization and ecosystem services value (ESV) in the Yangtze River Delta (YRD) region from 2010 to 2020 and their correlation. We conceptualized and measured the level of urbanization in two dimensions: Urbanization I (population, economy, and landscape) and Urbanization II (public services, education and spiritual life, and habitation environment construction). ESV was quantitatively evaluated by the equivalent factor method. The global and local spatial autocorrelation analysis was used to reveal the influence of urbanization dynamic evolution on ESV change. The results show the following: (1) the level of Urbanization I rose steadily, while the level of Urbanization II, though starting from a lower base, grew at a significantly faster rate, especially after 2017; (2) total ESV declined, with the largest decline in regulating services; (3) a significant negative spatial correlation was found between urbanization and ESV, with Urbanization I exerting a greater negative impact than Urbanization II; (4) spatially, “high-low” clusters (high urbanization, low neighboring ESV) dominate in the eastern coastal areas, while “low-high” clusters dominate in the western inland areas. The findings are of great significance for regional sustainable development and can provide a reference for other rapidly urbanizing regions in the world.

1. Introduction

Urbanization is a global socio-economic development trend that has profound impacts on regional ecological environments. The urbanization process has a complex impact on regional ecosystem services by changing land use patterns, disrupting biodiversity, and influencing the material and energy cycle [1]. Ecosystem services refer to the various benefits that humans receive from the ecosystem, including provisioning services, regulating services, cultural services, and supporting services [2]. With the rapid advancement of urbanization, ecosystem services in urban areas are constantly challenged [3]. Urban expansion occupies a large amount of cropland, leading to the decline of food production and other provisioning services [4,5]. The increase in urban impervious area weakens the regulating services, such as rainwater storage [6]. The reduction of urban green space reduces cultural services such as recreation and aesthetics [7]. Increased urban pollution impairs supporting services such as soil formation and nutrient cycling [8]. Clarifying the relationship between urbanization and ecosystem services is therefore of great significance for promoting sustainable urban development.

At present, scholars have mainly explored the complex relationship between urbanization and ecosystem services from the following perspectives: (1) based on the equivalent factor method of ecosystem services value (ESV) proposed by Costanza et al. [9], the impact of land use change on ESV in the process of Urbanization I was quantitatively assessed [10,11,12]; (2) the trade-off and synergistic relationship between different ecosystem services in the context of urbanization was analyzed, and the path of optimal allocation of urban multifunctional landscape patterns was clarified [13,14]; (3) the multi-scale effects of urbanization on ecosystem services were revealed, and the spatial differentiation characteristics of ecosystem services within cities and on the urban–rural gradient in regions were explored [15,16,17]. In terms of research methods, spatial analysis and spatial statistics are adopted in most studies, and ArcGIS, FRAGSTATS, GeoDa, and other software are used to describe the impact of urbanization on the spatial pattern of ecosystem services from the perspective of landscape pattern and spatial autocorrelation. The Global Moran’s I and Anselin Local Moran’s I methods in GeoDa can effectively reveal the spatial agglomeration characteristics and spatial heterogeneity of ecosystem services, and are favored by more and more scholars [18,19,20].

According to the “World City Hypothesis” by the American geographer Friedman, cities can play different roles in the economic hierarchy, and he divides the urbanization process into “Urbanization I” and “Urbanization II” [21]. He believes that Urbanization I refers to population urbanization, economic urbanization, and landscape urbanization, showing the explicit characteristics of urbanization. Urbanization II mainly refers to the change in people’s life concept and lifestyle, showing the implicit characteristics of urbanization. This study argues that “Urbanization II” profoundly reflects the intrinsic quality of urban development and further operationalizes it into three core dimensions: public services, education and spiritual life, and human settlement environment construction. However, while existing studies focus on explicit indicators such as population and economy, they pay insufficient attention to the implicit characteristics of urbanization. Moreover, most studies only focus on the static relationship between urbanization and ecosystem services at a certain point in time and lack a systematic description of the dynamic evolution process of the relationship between urbanization and ecosystem services [22].

The Yangtze River Delta (YRD) region is one of the most urbanized and economically developed regions in China, and the coordination between urbanization and ecosystem services is facing great challenges. In recent years, scholars have carried out a series of studies on urbanization and ecosystem services in the YRD region. For example, Li et al. found that the coupling coordination degree between urbanization and ESV in the YRD region showed a slow upward trend during the study period, indicating a preliminary coordination state [23]. Mao and Niu showed that Jiangsu Province is the main ecosystem services provider, while the YRD region as a whole belongs to the ecological deficit area and needs to pay a lot of ecological compensation [24]. Yang et al. proved that the impact of urbanization on ecosystem services in the YRD region has significant spatial heterogeneity, especially in the urban–rural gradient [25]. However, these studies also ignore the recessive characteristics of urbanization and lack the description of the dynamic evolution process of urbanization, making it difficult to fully understand the impact mechanism of urbanization on ecosystem services.

To narrow the research gaps, this study aims to reveal the spatiotemporal interactions between urbanization and ecosystem services in the YRD region from 2010 to 2020. We adopt a dynamic perspective and differentiate between explicit (Urbanization I) and implicit (Urbanization II) processes to address three key questions: (1) What are the dynamic trends and spatiotemporal patterns of different urbanization processes in the YRD over the past decade? (2) How has ESV evolved during the same period, and what are the spatial characteristics of these changes? (3) What is the nature of the spatial correlation between the dynamics of urbanization and the changes in ESV, and where do significant clusters of their interaction occur? Unlike researchers who focused on coupling coordination or static assessments, this study uniquely differentiates between explicit (Urbanization I) and implicit (Urbanization II) dimensions and employs dynamic spatial statistics to reveal the decoupling trajectories between urbanization and ecosystem degradation. This study has the potential to provide scientific support for the sustainable utilization of ecosystem services in the YRD, and the methodology can be applied to other rapidly urbanizing regions worldwide.

2. Materials and Methods

2.1. Study Area

The YRD region, which includes the Shanghai, Jiangsu, Zhejiang, and Anhui provinces, is one of the most economically developed and urbanized regions in China (Figure 1). Located on the east coast of China, the region comprises 41 cities, including Shanghai, Nanjing, Suzhou, Wuxi, Changzhou, Hangzhou, Ningbo, Wenzhou, Hefei, and Wuhu, forming a huge city cluster (

Table 1. City codes in the Yangtze River Delta region.

Province	City	Code	Population in 2020 (One Thousand)	Area (km2)
Anhui	Hefei	A1	8005	11,434
	Wuhu	A2	3740	6188
	Bengbu	A3	3164	5952
	Huainan	A4	2334	2536
	Maanshan	A5	1366	1702
	Huaibei	A6	1900	2737
	Tongling	A7	716	1113
	Anqing	A8	4580	15,440
	Huangshan	A9	1470	9807
	Chuzhou	A10	3937	13,400
	Fuyang	A11	7599	9887
	Suzhou (AH)	A12	6633	9606
	Lu’an	A13	4967	18,286
	Bozhou	A14	4850	8394
	Chizhou	A15	1430	8285
	Xuancheng	A16	2815	12,340
Jiangsu	Nanjing	J1	9314	6587
	Wuxi	J2	7636	4628
	Xuzhou	J3	8580	11,258
	Changzhou	J4	4592	4375
	Suzhou (JS)	J5	10,721	8488
	Nantong	J6	7283	8001
	Lianyungang	J7	4393	7777
	Huaian	J8	4787	10,072
	Yancheng	J9	7260	17,000
	Yangzhou	J10	4459	6663
	Zhenjiang	J11	3113	3844
	Taizhou (JS)	J12	4618	5793
	Suqian	J13	4808	8555
Zhejiang	Hangzhou	Z1	10,360	16,847
	Ningbo	Z2	8202	9816
	Wenzhou	Z3	9122	11,784
	Jiaxing	Z4	4501	4223
	Huzhou	Z5	2893	5818
	Shaoxing	Z6	4912	8256
	Jinhua	Z7	5361	10,926
	Quzhou	Z8	2176	8844
	Zhoushan	Z9	1121	1440
	Taizhou (ZJ)	Z10	5968	9411
	Lishui	Z11	2122	17,298
Shanghai	Shanghai	S1	24,870	6341

). Although its area only accounts for 3.73% of China’s territory (about 358,000 square kilometers), the region is home to about 16 percent of the country’s population, about 240 million people, and its GDP (Gross Domestic Product) in 2020 accounts for 24.1 percent of the country’s total GDP (about 24.47 trillion yuan) [26]. Over the past four decades, the YRD region has experienced significant urbanization, with an annual growth rate of 1.2 percent, from 20.6 percent in 1980 to 83.4 percent in 2020 [24]. The YRD region is not only China’s economic center but also the core region of scientific and technological innovation and opening up. It is home to a large number of high-tech industries and world-class enterprises and is an important engine driving China’s economic development.

Within the YRD region, the eastern coastal area refers to the highly developed cities along the coast and the Yangtze River, centered on Shanghai. This includes major urban centers like Shanghai, Southern Jiangsu (Nanjing and Suzhou), and Northern Zhejiang (Hangzhou), known for their advanced infrastructure and high economic activity. Conversely, the western inland area includes the less developed arc around the western edges of Anhui and Zhejiang provinces, where cities such as Hefei and Wuhu generally have lower levels of urbanization and economic development but offer substantial potential for future growth.

2.2. Data Sources

The data used in this study mainly include land use data and statistical data, as shown in

Table 2. Data sources of this study.

Data Name	Data Source URL
China Statistical Yearbook	https://www.stats.gov.cn/sj/ndsj/, accessed on 20 March 2025
China Urban Statistical Yearbook	https://www.stats.gov.cn/zs/tjwh/tjkw/tjzl/202302/t20230220_1913734.html, accessed on 22 March 2025
Jiangsu Provincial Statistical Yearbook	http://www.jiangsu.gov.cn/col/col84736/, accessed on 2 April 2025
Zhejiang Provincial Statistical Yearbook	http://tjj.zj.gov.cn/col/col1525563/index.html, accessed on 5 April 2025
Anhui Provincial Statistical Yearbook	http://tjj.ah.gov.cn/ssah/qwfbjd/tjnj/index.html, accessed on 8 April 2025
Shanghai Municipal Statistical Yearbook	https://tjj.sh.gov.cn/tjnj/index.html, accessed on 10 April 2025
30 m annual land cover datasets and their dynamics in China from 1990 to 2020	https://zenodo.org/records/5210928#.YeqApch1Mvd, accessed on 15 April 2025

. The socio-economic data of this study are mainly from China Statistical Yearbook, China Urban Statistical Yearbook, Jiangsu Provincial Statistical Yearbook, Zhejiang Provincial Statistical Yearbook, Anhui Provincial Statistical Yearbook, and Shanghai Municipal Statistical Yearbook from 2010 to 2020. The data cover multiple indicators of socioeconomic status and urbanization. To ensure the uniformity and comparability of the data, a standardized method is adopted to process the data.

The land use data are derived from the 30 m annual land cover datasets and their dynamics in China from 1990 to 2020 data sets (Table 2). Land use types include cropland, forest, shrub, grassland, water body, ice and snow, bare land, impervious surface, and wetland. In data processing, ArcGIS 10.8 software was used to reclassify land use data by using the lookup table tool.

2.3. Measurement of Urbanization Level

2.3.1. Construction of a Comprehensive Evaluation Index System

One of the core innovations of this study is the differentiation of two distinct dimensions of the urbanization process. Following Friedmann’s classic division of “Urbanization I” (the economic-geographical dimension) and “Urbanization II” (the socio-cultural dimension) [21], we aim to achieve a more comprehensive understanding of the impact of urbanization on ecosystems. Urbanization I, also known as explicit urbanization, is primarily manifested through changes in material forms, such as population agglomeration, economic growth, and land expansion. This study selected corresponding indicators from three levels: population, economy, and landscape (

Table 3. Index system used for assessing the level of urbanization.

	Category	Dimension	Indicator	Unit
Comprehensive urbanization	Urbanization I (Explicit perspective)	Population	Population density	persons/hm2
			Proportion of urban population	%
		Landscape	Proportion of urban construction land	%
			Actual road area at the end of the year	hm2
		Economy	Per capita GDP	CNY
			Proportion of secondary industry in regional GDP	%
			Proportion of tertiary industry in regional GDP	%
	Urbanization II (Implicit perspective)	Public services	Number of hospitals	Unit
			Number of public buses per 10,000 people	Unit
		Education and spiritual life	Number of general higher education institutions	Unit
			Number of theaters and cinemas	Unit
			Number of books in libraries	Unit
		Habitation environment construction	Real estate development investment completed	100 million CNY

Note: The comprehensive urbanization includes both Urbanization I and Urbanization II; the comprehensive urbanization index is the sum of Urbanization I and Urbanization II. All indicators in this system are treated as positive indicators (+), meaning that higher values represent a higher level of urbanization intensity.

). Urbanization II, also known as implicit urbanization, reflects more profoundly the transformation of residents’ lifestyles, behavioral patterns, and values toward modern urban civilization. To this end, this study constructed an “Urbanization II” evaluation system consisting of three dimensions, and selected the most suitable proxy variables for each dimension based on the availability of data:

(1)

In the “public services” dimension, we selected “the number of hospitals” and “the number of public buses per 10,000 people”. We believe that these two indicators reflect the city’s ability to provide basic public services, such as health and transportation, and are important cornerstones of modern urban quality of life.

(2)

In the dimension of “education and spiritual life”, we adopted “the number of general higher education institutions”, “the number of theaters and cinemas”, and “the number of books in libraries”. The inclusion of these indicators is grounded in the framework of cultural ecosystem services, which posits that physical cultural spaces (e.g., libraries and theaters) are the essential “environmental settings” that enable residents to derive non-material benefits such as spiritual fulfillment, aesthetic experiences, and knowledge acquisition [27]. Furthermore, within the context of China’s “New-type Urbanization” policy, the focus has shifted from mere land expansion to “people-oriented” development. According to Chen et al., the availability of public cultural facilities is a critical metric for assessing the quality of urbanization and the satisfaction of residents’ higher-level spiritual needs [28]. Therefore, these indicators serve as robust proxies for the “implicit” quality of urban life.

(3)

In the dimension of “habitation environment construction”, we selected “real estate development investment completed” as a proxy indicator. Theoretically, this choice is grounded in the unique “bundled public goods” characteristic of China’s housing market [29,30]. Unlike in many Western contexts, real estate development in China is the primary vehicle for urban renewal and infrastructure upgrading. High levels of real estate investment typically mandate the simultaneous construction of high-standard community amenities, including green spaces, sanitation facilities, and internal road networks, which directly replace dilapidated urban villages or rural settlements [31]. Therefore, this indicator serves as a robust proxy for the intensity of physical environment improvement and the modernization of living conditions.

Through this distinction, we are able to more precisely distinguish the differential impact of different types of urbanization processes on ESV. For an explanation of the various indicators used to evaluate urbanization, please refer to Table S1 in the Supplementary Materials.

2.3.2. Entropy Method

To determine the weight of each index scientifically and reasonably, the entropy method is adopted in this study. The application of the entropy method is based on the principle of information theory. It was first proposed by Shannon and introduced into economics by Theil to solve the problem of weight allocation in multivariable systems [32,33]. Compared to principal component analysis (PCA) or factor analysis, the entropy method does not rely on data dimensionality reduction, which may lead to information loss. It objectively determines weights solely based on the discrete degree of the data by calculating the information entropy of each index. This approach avoids subjective bias and the assumption of linear correlations between variables, thereby enhancing the scientific nature of weight distribution. The calculation steps are as follows:

Indicator Standardization:

$$r_{ij}={\displaystyle \frac{x_{ij}-x_{imin}}{x_{imax}-x_{imin}}}$$

(1)

$$r_{ij}={\displaystyle \frac{x_{imax}-x_{ij}}{x_{imax}-x_{imin}}}$$

(2)

where $x_{ij}$ is the value of indicator $j$ in observation $i$, $x_{imin}$ and $x_{imax}$ are the minimum and maximum values of indicator $j$ across all observations. These equations normalize the indicator values between 0 and 1, where Equation (1) is used when higher values indicate better performance and Equation (2) is used when lower values are preferable. This normalization helps to mitigate scale differences among indicators.

Proportion of the $j$ indicator in the $i$ year:

$$p_{ij}={\displaystyle \frac{r_{ij}}{{\sum }_{i=1}^m{r}_{ij}}}$$

(3)

where $r_{ij}$ is the normalized value of indicator $j$ in year $i$, and the denominator is the sum of $r_{ij}$ for all years. This equation calculates the proportion of indicator $j$ for year $i$, reflecting the relative importance of each observation within the dataset for that particular indicator.

The Information entropy of the $j$ indicator:

$$e_j=-k\sum _{i=1}^m{p}_{ij}\mathit{ln}{p}_{ij}$$

(4)

where $p_{ij}$ is the proportion of indicator $j$ in year $i$, and $k$ is a constant typically set to 1/log ($m$) to ensure that entropy values are scaled between 0 and 1, with $m$ being the number of observations. This equation measures the entropy or unpredictability of indicator $j$, quantifying the amount of information or diversity contained in the indicator’s distribution.

The Information redundancy of the $j$ indicator:

$$d_j=1-e_j$$

(5)

where $e_j$ is the entropy of indicator $j$. This equation calculates the redundancy, or the complement of entropy, representing the amount of order or predictability of indicator $j$. A higher redundancy value indicates that the indicator has less uncertainty and potentially carries more intrinsic information relevant to the analysis.

The weight of the $j$ indicator:

$$w_j={\displaystyle \frac{d_j}{{\sum }_{i=1}^m{d}_j}}$$

(6)

where $d_j$ is the redundancy of indicator $j$, and the denominator is the sum of redundancies for all indicators. This equation assigns weights to each indicator based on their redundancy, ensuring that indicators with less entropy (more predictability) have a greater influence on the overall assessment.

The Comprehensive evaluation index for the $i$ year, that is, the level of urbanization:

$$\sum _{j=1}^n{w}_j\times r_{ij}$$

(7)

where $w_j$ is the weight of indicator $j$ and $r_{ij}$ is the normalized value of indicator $j$ in year $i$. This equation computes the comprehensive evaluation index for year $i$ by summing the products of each indicator’s weight and its normalized value for that year, providing a singular measure of urbanization based on multiple weighted indicators.

To ensure the reliability of the calculated weights, we conducted a sensitivity analysis by introducing perturbations (±5%, ±10% and ±15%) to the original data. The results show that the weights remain stable with minimal fluctuation, confirming the robustness of the entropy method (Supplementary Materials Figure S1).

2.4. Measurement of ESV

Based on the global ESV assessment method proposed by Costanza et al., Xie et al. have adapted and refined the approach to suit China’s national conditions [34], making it more applicable to various local ecosystems. This method, by quantifying the contribution of ecosystems to human well-being, has been widely utilized in environmental policy and resource management decisions. Xie et al. propose that the economic value of natural food production from 1 hectare of cropland serves as an effective proxy due to its traceability through efficient markets [34].

To enhance the accuracy and temporal representativeness of the evaluation, we recalculated the standard equivalent factor (D) based on the multi-year average net profit of grain production, following the improved method proposed by Xie et al. [35]. We collected data on sown areas, yields, market prices, and production costs of the three major crops (paddy, wheat, and corn) in the YRD region from 2010 to 2020 (see Table S3 in Supplementary Materials). By using the 11-year average data, we minimized the potential bias caused by annual fluctuations in prices or climate. The D value was finally determined as the area-weighted average of the net profits, resulting in a localized factor of USD 408.74/hm².

Subsequently, the value coefficient for each specific ecosystem service was derived by multiplying this localized D value by the equivalent weight factors proposed by Xie et al. [34]. Finally, the total ESV was calculated by summing the product of these localized coefficients and the area of each land use type. The calculation formula is as follows:

$$V_{ES}=\sum _{j=1}^n\sum _{i=1}^n{A}_i\times E_{i,j}$$

(8)

$V_{ES}$ is the total ESV; $E_{i,j}$ is the value coefficient of ecosystem services for type $j$ of ecosystem type $i$; $A_i$ is the area of ecosystem type $i$. Currency converted to USD based on the 2020 average exchange rate: 1 USD ≈ 6.9 CNY.

2.5. Dynamic Measurement of Urbanization and ESV

Unlike most previous studies, which only focused on the static relationship between urbanization and ecosystem services at a certain point, this study attempts to explore the dynamic relationship between urbanization evolution and ecosystem services change in 11 consecutive years. Using the linear regression analysis method, we quantitatively measure the dynamic changes of urbanization development level and ESV in the YRD region from 2010 to 2020. The formula is as follows:

$$Y={\beta }_0+{\beta }_{1}X+\phi$$

(9)

In this context, $Y$ represents the score of urbanization development level or ESV, $X$ is the year, ${\beta }_0$ is the intercept, ${\beta }_1$ is the slope, and $\phi$ is the error term. By ${\beta }_1$ calculating the annual change rates of urbanization development levels and ESV, we assess their dynamic trends during the study period. Combining the rates of change in urbanization processes with the changes in ESV, we comprehensively evaluate the impact of urbanization on ecosystem services in the YRD region.

2.6. Correlation Analysis

To reveal the dynamic relationship between urbanization and ESV, this paper explores the spatial correlation between urbanization and ESV based on geographical location. Therefore, this study first attempted to clarify the positive or negative correlation between urbanization and ESV, based on data from 41 units in the study area. Then, GeoDa software (version 1.18.0.16) was employed to quantify spatial autocorrelation, including the Global Moran’s I (global scale) and the Local Bivariate Moran’s I (a type of Local Indicator of Spatial Association, LISA, for local scale) [36] (Figure 2). A first-order Queen Contiguity spatial weight matrix was selected to define the spatial relationships, as it captures neighbors sharing both common boundaries and vertices, suitable for the irregular administrative units in the YRD region.

3. Results

3.1. Spatiotemporal Dynamics of Urbanization

From a temporal perspective, the overall urbanization level in the YRD region showed a significant upward trend from 2010 to 2020 (Figure 3). During this period, the level of Urbanization I steadily improved. In contrast, although Urbanization II started at a lower level, its growth rate has significantly accelerated, especially after 2017. The increase in Comprehensive Urbanization coincided with the rapid growth of Urbanization II.

From a spatial perspective, the distribution of urbanization levels in 2010 and 2020 consistently showed a pattern of higher levels in the eastern coastal areas and lower levels in the western inland areas (see Supplementary Materials, Figure S2). This study, however, focuses on the dynamic rates of change, which reveal the nuanced trajectories of regional development (Figure 4). The dynamic change maps reveal a dominant spatial pattern common to all indices: a clear development gradient, with higher growth rates in the eastern coastal areas and lower growth rates in the western inland areas. The regions with the highest rate of change are concentrated in major coastal hubs such as Shanghai (S1), cities in southern Jiangsu (such as Suzhou, J5; Wuxi, J2), and cities in northern Zhejiang (such as Hangzhou, Z1; Ningbo, Z2). On the contrary, several cities in Anhui, such as Wuhu (A2) and Tongling (A7), have experienced stagnation or even regression in their development process. Spatially, impervious surface expansion was most aggressive in the eastern coastal cities (e.g., Shanghai, Suzhou), whereas inland cities like Anqing exhibited a more fragmented and slower expansion pattern.

In addition to this overall trend, the dynamic pattern also exhibits significant regional differences. For example, the development of Urbanization II in Jiangsu Province shows a significant north-south gap, with southern cities developing much faster than northern cities. Anhui’s development, especially in terms of comprehensive urbanization, has a strong dominant pattern: the capital city Hefei (A1) is growing rapidly, while many surrounding cities are clearly lagging behind. These different patterns highlight the spatial heterogeneity of quantitative and qualitative urban development in the YRD region.

3.2. Temporal and Spatial Changes in ESV

Over the study period, the total ESV in the YRD region showed a downward trend, with an overall decrease of USD 18.27484 billion. This decline was evident in all service categories, especially in regulating services, which accounted for the largest share of total losses (Figure 5). From a provincial perspective, Anhui’s total ESV consistently maintained at the highest level, followed by Zhejiang, Jiangsu, and Shanghai (see Supplementary Materials, Table S3).

Spatially, the static distribution of ESV in 2010 and 2020 showed significant spatial heterogeneity, with higher values typically concentrated in the southern mountainous and western regions, as well as around major water bodies, while lower values were mainly distributed in the eastern regions (see Supplementary Materials, Figure S3). Our research focuses on the dynamic rate of change of ESV and reveals its degrading spatial patterns (Figure 6). The regions with the fastest decline in total ESV were concentrated in the most economically developed and densely populated areas, namely Shanghai (S1) and southern Jiangsu Province. In contrast, Anhui Province had the slowest overall decline, with some cities even experiencing a slight increase in ESV.

The overall pattern of “eastern decline, western stability” varies depending on the type of service. The decline in provisioning services value was most severe in Shanghai and southern Jiangsu, which are highly urbanized cities, reflecting the extensive loss of high-quality cropland. The spatial pattern of the decreasing value of regulating services and total ESV was very similar. The values of supporting and cultural services in Zhejiang Province decreased the most significantly, especially in cities such as Shaoxing (Z6) and Jinhua (Z7). Among all ESV categories, the regions with the slowest decline rate were consistently located in northern Anhui and northwestern Jiangsu.

3.3. Spatial Correlation Between Urbanization and ESV

The analysis results of Global Bivariate Moran’s I indicated that there was a significant negative spatial correlation between the urbanization rate and the ESV change rate throughout the YRD region (Figure 7). The Moran’s I statistics of the three urbanization indices were all negative. Among them, the dynamic change of Urbanization I had the strongest negative spatial correlation with the change of ESV (Moran’s I = −0.282), while the negative correlation of Urbanization II was relatively the weakest (Moran’s I = −0.206). The above results confirmed that although the urbanization process was generally spatially associated with ESV degradation, the intensity of this relationship varied depending on the different urban development models.

To identify the specific locations of these spatial patterns, this study further conducted a Local Bivariate Moran’s I analysis (Figure 8). The results revealed significant differences in the geographical distribution of the two related patterns. The “high-low” (H-L) clusters were mainly concentrated in the highly developed eastern coastal regions, especially around Shanghai. This indicated that cities with a high rate of urbanization themselves were significantly associated with the rapid decline of ESV in their surrounding areas. On the contrary, the “low-high” (L-H) clusters were mainly distributed in the western inland areas, especially in the western part of Anhui Province. This pattern showed that cities with a lower urbanization rate themselves were associated with more stable (i.e., slow decline or slight growth) ESV performance in their surrounding areas.

Beyond this common geographical differentiation, the analysis also identified some notable exceptions. Hefei, the provincial capital, was characterized by a “high-high” (H-H) cluster, which indicated that in this rapidly urbanizing city, the ESV performance of its surrounding areas was relatively stable. In addition, a few “low-low” (L-L) clusters were also identified, such as Taizhou (J12) in Jiangsu Province and Jiaxing (Z4) in Zhejiang Province. This indicated that the overall ESV performance of the regions where these cities with slower urbanization processes were located was also poor. These local-level results highlighted the profound spatial heterogeneity in the interaction between different urbanization paths and changes in ecosystem services within the Yangtze River Delta.

4. Discussion

4.1. Evolution Trends and Differentiation Between Urbanization and ESV

This study reveals that the urbanization process and ecosystem services in the YRD region presented two distinct yet closely related dynamic trajectories during the period from 2010 to 2020. A core trend was the internal transformation of urban development patterns. The results showed that although Urbanization I, characterized by material expansion, was still growing steadily, Urbanization II, centered on quality connotation, showed a significantly accelerating trend, especially after 2017. This discovery was in line with the view of Grimm et al. that mature urbanized areas will undergo a transformation from external expansion to internal improvement [3], indicating that the urban development of the YRD is entering a new stage that pays more attention to “soft power”. The acceleration of Urbanization II after 2017 closely aligns with China’s national strategic shift towards “High-Quality Development” proposed at the 19th National Congress in 2017. This policy shift incentivized local governments to prioritize public services, cultural facilities, and ecological civilization over mere economic expansion.

However, in sharp contrast to the improvement in the quality of urban development, the regional ecosystem service was continuously deteriorating. Throughout the entire research period, the total ESV of the region showed a clear downward trend, among which the regulating services that were crucial to the ecological stability declined the most sharply. This phenomenon is not an isolated case and is highly consistent with the trends observed in other highly urbanized regions around the world. For instance, urban expansion in the Mediterranean region of Europe has been shown to have led to the degradation of key regulating services [37]; in the United States, urban land expansion has also caused a net loss of carbon storage and water purification [38]; and in Japan, the abandonment of rural land related to urbanization causes the degradation of supply and support services [39]. These international cases jointly confirm that rapid urbanization poses a huge and widespread pressure on regional ecosystem services [40].

A comprehensive analysis of these two dynamic trends can clearly reveal the inherent contradictions between them. Our dynamic rate of change analysis confirmed that the regions with the fastest urbanization rate (such as southern Jiangsu and Shanghai) were precisely the areas where ESV declined most sharply. This once again validated the conclusion drawn from many studies that faster urbanization is often accompanied by a faster decline in ecosystem services [41]. Therefore, it is precisely this parallelism and differentiation between the “improvement of urban development quality” and the “decline in the total amount of ecosystem services” that constitutes the core of understanding the challenges of sustainable development in the YRD region.

4.2. Impacts of Urbanization on ESV

One of the core findings of this study is that there are significant differences in the impacts of urbanization processes on ecosystem services across different dimensions. The results indicate that compared to Urbanization II, Urbanization I has a greater and more direct negative impact on ESV. The impact mechanism of Urbanization I is clear. The primary driver of ESV decline was the substantial conversion of high-value ecological lands (cropland and water bodies) into impervious surfaces during Urbanization I. This process directly and severely weakens the supply capacity of regional ecosystem services [42]. The impact of Urbanization II is more complex. On the one hand, the development of certain dimensions may exacerbate environmental pressures. For example, the “habitation environment construction” characterized by large-scale investment, while improving living conditions, also implies significant natural resource consumption and potential environmental disturbance during the construction process [43]. On the other hand, improvements in other dimensions may bring positive ecological effects. For example, the improvement of “public services” and the enhancement of “education and spiritual life” levels are usually positively correlated with residents’ overall quality, the improvement of environmental awareness, and the pursuit of a desirable ecological environment [44]. This helps to form a consensus and action at the social level to promote green development and ecological protection, thereby indirectly mitigating or even offsetting some of the negative impacts of development.

The results of the Local Bivariate Moran’s I analysis further revealed significant spatial heterogeneity in the interaction between urbanization and ESV. This study found that there are two typical spatial correlation patterns in the YRD region: the first is a “high-low” cluster centered around Shanghai. This pattern showed that Shanghai’s own high-speed urbanization process was spatially significantly related to the sharp decline of ESV in its surrounding areas. This spatially validates the enormous pressure that the siphon effect of core cities as “growth poles” exerts on their hinterland ecosystems. The second pattern is represented by the “low-high” clusters in western Anhui. The relatively mild urbanization process in these regions was associated with relatively stable ESV changes (i.e., slow decline or slight improvement) in their neighboring areas. This reflects a regional ecological coexistence state under a low-intensity development model, which may be related to the natural geographical pattern of mountainous areas in these regions. These findings are consistent with the view of Peng et al. that urbanization exhibits spatial heterogeneity in its impact on ecosystems and requires differentiated management strategies [5].

Beyond these two typical patterns, the “high-high” cluster in the provincial capital city of Hefei provides a thought-provoking exception. The high-speed urbanization of Hefei spatially coincides with the relatively good ESV performance of its surrounding areas. It suggests a potential synergy that aligns with the implementation of the ‘Chaohu Lake Ecological Demonstration Zone’ strategy. In recent years, this state-led initiative has invested heavily in wetland restoration and green infrastructure, likely mitigating the ecological pressure typically exerted by urban expansion. This is similar to international experiences, such as Portland’s urban growth boundary management [45] and Leipzig’s ecological urban renewal [46]. These examples indicate that through scientific regional planning and active ecological intervention, it is possible to alleviate or even change the traditional spatial relationship between “growth” and “degradation” [40]. How to handle the development and protection relationship between the core city and its hinterland well, rather than just focusing on the interior of the city, is the key to achieving regional sustainable development.

4.3. Implications

The three spatial correlation patterns revealed by Local Bivariate Moran’s I analysis in this study—the “growth-degradation” model represented by Shanghai, the “conservation-stability” model represented by western Anhui, and the “synergistic development” model represented by Hefei—provide profound inspiration for the sustainable development governance of the YRD region. The coexistence of these models highlights that a single, “one-size-fits-all” environmental protection policy is ineffective, and a shift towards a collaborative governance framework based on spatial heterogeneity is necessary.

Firstly, for the “high-low” clusters centered on Shanghai, the key lies in establishing and improving the cross-regional ecological compensation mechanism. This pattern clearly reveals that during the rapid development of the core growth pole, there is a negative spillover effect of its ecological cost to the surrounding areas. Therefore, it is crucial to establish a cost-sharing and benefit-sharing mechanism that takes ESV into account to restrain the traditional development path of “pollute first, govern later”.

Secondly, for the “low-high” clusters represented by western Anhui, the policy focus should be on consolidating their function as the “supply source” of ecosystem services and exploring sustainable paths to realize the value of ecological products. These regions serve as the strategic rear for maintaining the ecological security pattern of the entire YRD region. Policies should focus on identifying and protecting key ecosystem service flow paths [19,47] while vigorously supporting green industries such as eco-tourism and organic agriculture, providing these regions with a sustainable development option different from the traditional industrialization path.

Finally, Hefei’s “high-high” cluster provides compelling evidence that the synergistic development of rapid urbanization and regional ecosystem stability is entirely possible. Its outstanding performance is not coincidental but stems from a series of large-scale and systematic strategic ecological engineering investments. Among them, the most representative one is the construction of the “Chaohu Lake Ecological Demonstration Zone”. Through comprehensive governance, this project has built ten major wetlands around the lake with a total area of 100 square kilometers, greatly enhancing the water purification capacity and biodiversity [48]. Due to its remarkable achievements, this project has been recognized by the United Nations Environment Programme (UNEP) as a flagship project for the UN Decade on Ecosystem Restoration, earning widespread international acclaim. These successful practices demonstrate that through forward-looking regional planning and large-scale, continuous ecological engineering investment, high-growth regions can fully achieve the transformation from “sucking on the periphery” to “feeding back to the region”, providing valuable experience for handling the development and protection relationship between core cities and their hinterlands.

4.4. Limitations

This study revealed the spatio-temporal evolution characteristics and interaction mechanism of urbanization and ESV in the YRD region. The research conclusions can not only provide inspiration for the sustainable development of the YRD region but also offer a reference for the development of other rapidly urbanizing regions around the world. However, there are still some deficiencies in this study, which await further deepening in future research.

(1)

Although the operationalization of the “Urbanization II” index has a theoretical basis, it still relies on proxy variables. For instance, “real estate development investment” was used as a proxy for “habitation environment construction”. Although we have provided a strong argument for this choice in light of China’s development background, this indicator mainly reflects the scale of investment rather than the ecological quality or livability of the built environment. Future research could introduce more direct indicators, such as per capita green space area, public transportation accessibility, or air quality indices, to measure the quality of urbanization more precisely.

(2)

The regionalization of the ESV assessment could be further refined. Although we calibrated the standard equivalent factor using local crop data to reflect the region’s agricultural productivity, the weighting coefficients for cultural and regulating services relied on the national average table proposed by Xie et al. [34]. We acknowledge that this may underestimate values in affluent coastal cities where residents’ willingness-to-pay is typically higher. However, to address this concern, we conducted a sensitivity analysis (see Supplementary Materials Figure S4–S6) by adjusting these coefficients upward. The results confirmed that this potential valuation bias does not alter the overall declining trend of ESV in the YRD region.

(3)

Using linear regression slopes β₁ effectively captures the overall directional trend over the decade. However, we acknowledge this approach assumes a monotonic change and may smooth out short-term inter-annual fluctuations.

(4)

This study focused on analyzing the impact of urbanization on ESV, but the feedback of ESV on urbanization remains an issue to be explored. For instance, would a high-quality ecological environment (high ESV) in turn attract investment and high-quality talent, thereby influencing the trajectory of “Urbanization II”? Exploring this reciprocal relationship will be a highly valuable and promising research direction. Future studies can adopt methods such as spatial regression models or panel vector autoregression (PVAR) to reveal these complex feedback mechanisms.

5. Conclusions

This study takes the YRD region as the research area, comprehensively considers the dominant and recessive characteristics of urbanization, and adopts a dynamic perspective to explore the spatiotemporal evolution model and correlation between the urbanization process and ecosystem services. Through the analysis of data from 41 cities for 2010–2020, the following key conclusions are drawn.

First, over the past decade, the urbanization process in the Yangtze River Delta region has accelerated significantly. In particular, the development speed of Urbanization II, represented by the construction of public services, cultural education, and living environments, has been significantly faster than that of Urbanization I, which is characterized by population, economy, and landscape expansion. This finding indicates that the urban development in the YRD region has shifted from mere scale expansion to a new stage that focuses more on improving internal quality. Urbanization I presents a pattern of concentration along the eastern coast and the Yangtze River, while Urbanization II presents a spatial organization pattern of the provincial capital radiating to the surrounding cities.

Second, the regional total ESV showed a downward trend, and the regulating services value showed the most significant decline. The distribution of ESV showed obvious spatial heterogeneity, with the high–value area mainly concentrated around the water area, while the rapidly decreasing ESV area was mainly located in the highly urbanized areas such as Jiangsu and Shanghai.

Third, a significant negative correlation is found between urbanization and ESV, and the negative impact of Urbanization I on ESV is greater than that of Urbanization II. The results of global and local spatial autocorrelation analysis show that the eastern coastal area is dominated by high urbanization–low ESV clusters, while the western inland area is dominated by low urbanization–high ESV clusters. Notably, the provincial capital Hefei emerges as an exception, forming a “high-high” cluster. This suggests a potentially more synergistic development model, where rapid urban growth coexists with a relatively stable ecosystem in its hinterland.