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Article

The Relationship Between Ecosystem Provisioning Services and Urban Economic Resilience in the Pearl River Delta Urban Agglomeration, China

by
Qiongrui Zhang
1,
Songjun Xu
2 and
Wei Feng
3,*
1
School of Tourism Management, Chaohu University, Hefei 238024, China
2
School of Geography, South China Normal University, Guangzhou 510631, China
3
School of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, China
*
Author to whom correspondence should be addressed.
Land 2025, 14(9), 1731; https://doi.org/10.3390/land14091731
Submission received: 12 July 2025 / Revised: 22 August 2025 / Accepted: 25 August 2025 / Published: 26 August 2025
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Abstract

Ecosystem services and economic development are equally important for urban sustainability, and exploring the relationship between ecosystem provisioning services (EPSs) and economic resilience (ER) can provide the key for achieving sustainable synergy between ecology and economy. Taking the Pearl River Delta Urban Agglomeration (PRD) as an example, this paper explores the relationship between EPSs and ER. Four types of EPSs were evaluated using the InVEST model and the statistical yearbook data, and ER was evaluated based on three dimensions: economic structure, economic vitality and economic innovation. The results show that (1) in the PRD, the total water yield was 57,284.04 × 106 m3, the total grain production was 3,042,988 tons, the total vegetable production was 13,890,149 tons, and the total forestry output value was CNY 11,293.04 million. High-value water yield areas and low-value grain and forestry production areas lie in the PRD core area, while each prefecture-level city has high-value vegetable production areas. (2) The average ER value of the PRD 2020 is 0.32; the ER in the core areas of the PRD and the central urban areas of cities is relatively high. (3) ER was significantly negatively correlated with grain production, vegetable production, and forestry production in PRD and its core area and was positively correlated with water yield. Finally, this study puts forward suggestions for balancing ecological and economic development in urban agglomerations from the aspects of strengthening water conservancy regulation, valuing ecological products, and regional industrial coordination.

1. Introduction

Ecosystem services (ESs) encompass both tangible and intangible benefits that humans derive from nature, including essential goods for production and daily life, as well as a healthy and safe living environment [1]. At present, the most widely used classification is the four classifications of supporting services, provisioning services, regulating services, and cultural services in the Millennium Ecosystem Services Assessment [2]. Of particular note are ecosystem provisioning services (EPS), which furnish critical material resources, including food, water, timber, etc. An EPS constitutes the irreducible foundation of human existence; it is inseparable from both individual survival and quality of life and is inextricably linked to all human activity and the long-term viability of our cities [3]. Yet, numerous studies indicate that global ecosystem services are showing a downward trend, and that global efforts are being made to protect and manage ES [4,5,6].
Existing studies about ESs mostly focused on their trade-offs and synergies, the flow of ESs, and the factors that affect ESs [1,4,6]. Such investigations, however, remain largely confined to the internal dynamics of ES systems. Previous studies found that urbanization processes caused a general decline in ESs by time-scale studies [7]. For instance, a social–ecological system is used to analyze the trade-offs and synergy of ESs in Shanxi Province, China, and it is found that socio-economic factors play a dominant role in long-term ES changes [8]. At the same time, urbanization can affect the temporal and spatial patterns of ESs, such as carbon sequestration, water yield, soil conservation, and water purification [9]. Although many studies have focused on the relationship between water yield, food supply, and their driving socio-economic factors [7,10], few studies focus on EPSs. Given EPSs’ dual characteristics as both economically valuable and economically influenced [3], their relationship with economic resilience demand urgent scholarly attention.
The term “resilience” originated from engineering and refers to the ability of an object to return to its original state after being disturbed by an external force [11]. Reggiani introduced “resilience” into economic geography [12], and with the intensification of economic fluctuations and frequent uncertain events, regional economic resilience (ER) has gradually become a hot topic. Urban ER refers to the ability of the economic system to absorb, transform, and regulate crises after external disturbances and strive to promote regional economic stability or transformational development [13]. Contemporary ER research mainly includes concept analysis [14], evolutionary mechanism studies [15], investigations into factors influencing ER [16], and ER evaluation studies. The evaluation methods for ER mainly include the indicator system method [17] and the core variable method [14], and there is currently no unified consensus on indicators.
As a quintessential social–ecological system, the urban economy and its ecosystems are both dynamic and complex adaptive systems [18]. Multiple ecosystem services underpin urban resilience while simultaneously providing a biophysical foundation for ER [1,2]. Existing research indicates that from 2012 to 2022, the ecosystem service value and ER in 286 cities in China showed a dynamic coupling characteristic [19]. Studies in the Yangtze River Delta region have shown that cities with a stronger ER have a greater ability to cope with flood disasters, that is, they have a stronger capacity for regulating services [20]. Many scholars have conducted macro-level analyses of the relationship between ESs and urban resilience, but the specific relationship between EPSs and ER remains empirically underexplored. For sustainable urban development, both ER and EPSs are indispensable and closely related. Although the expansion of the urban scale brought by economic growth has put enormous pressure on the ecological environment, some studies have found that ER can enhance the ability and speed of urban ecological governance [21,22]. EPSs provide material and energy input to the economic system, which, in turn, interacts with the ecosystem through technology and capital [2,18]. ER is the reflection of a city’s ability to respond and evolve, reflecting its rapidity and resourcefulness in the face of shocks [23]. Therefore, considering the sustainable development of cities and their close relationship with humans, it is necessary to explore the relationship between ER and EPSs.
Chinese urban agglomerations, now constituting the primary urbanization mode, contribute 80% of the country’s gross domestic product (GDP) while generating 75% carbon emissions [24]. Meanwhile, the ER of urban agglomerations has a spatial spillover effect among neighboring cities [25]. By strengthening economic connections and cooperation between cities, urban agglomerations can better diversify risks and share resources in the face of external shocks, thereby enhancing overall ER [26]. The Pearl River Delta urban agglomeration (PRD), one of the three major urban agglomerations in China, was selected as the case study area due to its high urbanization rate, rapid economic development level, and emphasis on ecological protection [27].
The aims of this study are as follows: (1) to evaluate the EPS across the PRD; (2) to assess the ER of cities in the PRD; (3) to analyze the relationship and differences between EPSs and ER at different scales in the PRD, intending to provide effective proportions for the sustainable development of the urban economy and ecology.

2. Materials and Methods

2.1. Study Area

The Pearl River Delta urban agglomeration (21.17–23.55° N, 111.59–115.25° E) occupies the central and southern part of Guangdong Province, China (Figure 1). The PRD consists of nine cities and is an important part of the Guangdong–Hong Kong–Macao Greater Bay Area. Its annual rainfall is 1600–2000 mm, with over 70% of the land being forest and cultivated land, and it is surrounded by mountains on three sides. This study focused on PRD and the core area of the PRD, including Guangzhou, Shenzhen, Zhuhai, Foshan, Dongguan, and Zhuhai Cities [28]. The GDP of the core area accounts for about 85% of the PRD.
There are three main reasons for choosing the PRD. First, the PRD is one of the most developed urban agglomerations in China, with close connections between cities, efficient and convenient flow of people and materials, and significant regional economic integration. The GDP in 2024 is CNY 11.5 trillion. Second, the urbanization rate in the PRD is high, reaching 86.5% in 2024, far higher than the average level in China [29]. Third, the PRD is China’s first forest urban agglomeration with an important ecological location, and it has paid great attention to ecological protection; for example, ecological corridors have been constructed [27]. What is more, the PRD is an important node in the migration routes of migratory birds in China, with 3 out of the 8 major global migration routes passing through the PRD. Therefore, analyzing the relationship between EPSs and ER in the PRD can offer guidance to other urban agglomerations on balancing ecological protection with economic development.

2.2. Quantification of Ecosystem Provisioning Services

Based on the importance of and direct impact on human production and life, water yield, grain production, vegetable production, and forestry production were selected to characterize EPSs [30]. Water yield ensures basic human survival needs and can also serve as irrigation water and hydropower to provide a healthy environment for agricultural production [31]. The number of large cities susceptible to water resource pressure in the next 25 years will increase from 35% to 45% [32]. Regional food security is of great significance for local development [33], and vegetables and grains are important sources of food and nutrition for humans [34]. The selection of forestry production as the main indicator is due to the high forest coverage in Guangdong Province and the fact that the PRD is China’s first forest urban agglomeration (Table 1).
The water yield module in InVEST 3.8.9 was used to evaluate water yield services. This module is based on Budyko’s water–heat balance assumption and uses interpolation of precipitation and evapotranspiration to determine the water production for each grid, without distinguishing between surface water and groundwater [35]. The calculation formula is as follows, and the required data and processing procedures are shown in Table 1.
Y i = ( 1   −   A E T i P i ) × P i
Yi is the water yield of pixel i (m3/hm); Pi and AETi represent the annual actual precipitation and evapotranspiration on pixel i (mm), respectively.
Table 1. Data source and preparation process.
Table 1. Data source and preparation process.
DataData SourceNote
Land use/land coverGeographic Data Sharing Infrastructure, Resource and Environment Science and Data Center (http://www.resdc.cn)Divided into cultivated land, forest land, grassland, water bodies, wetlands, construction land, and bare land (Figure 1); grid resolution is 30 m × 30 m.
Annual average precipitationChina Meteorological Data Center (http://data.cma.cn)Interpolated and clipped of average values of surrounding stations in Guangdong, Guangxi, and Fujian Province from 2000 to 2020. The grid resolution is 30 m × 30 m.
Reference evapotranspirationChina Meteorological Data Center (http://data.cma.cn)Calculated by modified Hargreaves formula [36]; the grid data are interpolated to 30 m × 30 m.
Depth to root restricting layerNational Tibetan Plateau Data Center (http://data.tpdc.ac.cn)Derived from the Harmonized World Soil Database 1 km × 1 km.
Biophysical tableRefer to existing research [10].
Z parameter12
The grain production and vegetable production data were obtained from the Guangdong Provincial Statistical Yearbook 2021 and the Guangdong Rural Statistical Yearbook. The data in each district and county were selected as basic data; rice, potato, and soybean are the main grains in the PRD. Forestry production adopts the forestry output value data of each district and county in the Guangdong Rural Statistical Yearbook. The statistical values of forestry production mainly include three aspects of data: (1) tree breeding and seedling cultivation, and planting and collection of economic forest products, such as wood, bamboo, fruits, nuts, etc.; (2) the processing of forest related industries, such as wood, bamboo, and rattan, as well as the processing and manufacturing of non-wood forest products, such as woody oilseeds and fruit tea; (3) forestry production services and forestry tourism and leisure services. The third part belongs to cultural ecosystem services, which had little impact on the spatial distribution of forestry production during the COVID-19 pandemic.

2.3. Evaluating ER of the PRD

The evaluation framework for ER is rooted in the development of resilience theory, which has undergone interdisciplinary evolution through engineering resilience, ecological resilience, and evolutionary resilience [17,37]. Engineering resilience emphasizes stability and efficiency [37], reflected in urban economic vitality as short-term responsiveness. Ecological resilience emphasizes the diversification of economic structure, and the proportion of the tertiary industry can better reflect industrial diversification [15,17]. Evolutionary resilience emphasizes the system’s ability to adapt to long-term shocks, and economic innovation determines the city’s ability to transform under long-term pressure [15]. Therefore, the three dimensions of economic structure, economic vitality, and economic innovation are used to evaluate ER (Table 2). This evaluation framework not only reflects the evolution process of resilience theory but also reflects the risk resistance foundation, short-term response ability, and long-term adaptation ability of urban economy.
The economic structure is represented by the proportion of the tertiary industry to the GDP. The tertiary industry plays a significant role in promoting people’s living standards and industrial modernization [38]. Per capita GDP and total retail sales of consumer goods are used to characterize economic vitality, which are the most intuitive indicators of urban economic development and can reflect the vitality of the urban economy [38]. The ratio of education and technology to fiscal investment is used to represent economic innovation. According to Wagner’s Law, education can improve the quality of the labor force, and scientific research can promote technological innovation. The greater the investment in these two aspects, the stronger the learning and innovation ability of the urban system to cope with changes [39].
The data are sourced from the statistical yearbooks and bulletins of prefecture-level cities in the research area, with districts and counties as the basic statistical units. The final ER of each district and county was calculated using a hierarchical analysis method, with economic structure, economic vitality, and economic innovation accounting for 30%, 45%, and 25%, respectively, based on an existing study [40]. Before calculating ER, the Min–Max standardization method (Equation (2)) was used to perform linear ratio changes on the raw data for the unit difference.
Y i   =   X i − Min i Max i − Min i   ( X i   −   Min i ) / ( Max i   −   Min i )
Yi represents the standardized value of i, Xi represents the value before standardization, Maxi represents the maximum value, and Mini represents the minimum value.

2.4. The Correlation Analysis

Spearman’s rank correlation coefficient was employed to quantify the relationship between ER and various EPS indicators in this study. It can effectively evaluate the monotonic relationship between variables by calculating the rank correlation coefficient. The coefficient has a value range of −1 to 1. Spearman correlation analysis can evaluate nonlinear monotonic relationships and is widely used in fields such as social sciences, psychology, economics, and medicine [36].
In this study, we analyzed and compared the correlation between the entire PRD, the PRD core area, and other regions. The mean values of water yield, grain production, vegetable production, and forestry production per unit area were calculated for each district and county during the analysis. Before the analysis, all the variables were normalized in SPSSAU to eliminate unit heterogeneity.

3. Results

3.1. EPSs of the PRD

In 2020, the total water yield of the PRD was 57,284.04 × 106 m3. In terms of the total water yield of each city, Huizhou, Jiangmen, and Zhaoqing have higher annual water production, with a total volume exceeding 10,000 × 106 m3, while the annual water yield in Zhuhai and Zhongshan is relatively low. The water yield of the PRD shows a trend of higher values in the central region and lower values in the periphery (Figure 2). Except for Zhaoqing, where the water production is less than 1000 mm per grid, there is not much difference in unit grid water yield among the other cities.
The total grain production in the PRD is 3,042,988 tons, with Zhaoqing City having the highest total grain production. The grain production in Huidong, Kaiping, Gaoyao District, Fengkai County, and Huaiji County all exceeded 200,000 tons. The grain production in the PRD core area is mostly below 6500 tons in each district, especially in several districts of Guangzhou and Shenzhen, where there is no grain production (Figure 2). This is mainly because these areas are the central urban areas, and there is no cultivated land in the built-up areas (Table 3).
The total vegetable production in the PRD is 13,890,149 tons, with over 3 million tons produced in Guangzhou, Huizhou, and Zhaoqing. Each prefecture-level city has its high-value vegetable production area. With the exception that the vegetable production in Shenzhen is in a low-value area, various regions in the PRD can basically achieve self-sufficiency in vegetables.
The forestry production services in the PRD present a layout with lower levels in the middle counties and higher levels in the surrounding areas. The total forest production value of the case site in 2020 was CNY 11,293.04 million, with Zhaoqing City having the highest forestry production value and the highest forest coverage rate (70.7%), while Zhuhai and Zhongshan trail at the lower end of the range (Table 3).

3.2. ER of the PRD

The average proportion of tertiary industry GDP in the PRD in 2020 reached 0.53, with a standard deviation of 0.20, indicating good development of the tertiary industry in the entire study area. From the box plot of per capita GDP in cities (Figure 3), it can be seen that the median per capita GDP in Shenzhen, Guangzhou, Zhuhai, and Foshan is all above CNY 100,000/(person·year), and Shenzhen has the highest degree of dispersion in per capita GDP. Huizhou and Shenzhen have relatively high consumption vitality. The financial investment in technology and education in various cities is similar to the per capita GDP. Some new districts in cities have rapidly developed education after a slight improvement in their economy, such as Huangpu District and Zengcheng District in Guangzhou, with a higher proportion of scientific and educational investment than other urban areas in Guangzhou.
The average ER value of each county and district in the PRD 2020 is 0.32 (SD = 0.15). Values rise sharply toward the core and the southern corridor, then taper toward the periphery (Figure 4). The high-value areas of ER in the research area are mainly located in Guangzhou and Shenzhen. Guangzhou, as the capital city of Guangdong Province, has relatively low ER in only the Huadu District and the Conghua District. Shenzhen, as an economic special zone in China, has been operating at a high level since the reform and opening up policy, with an overall high per capita GDP level.
From the perspective of various prefecture-level cities, the ER of the central urban area and some new districts is significantly higher than that of other areas. The central cities of prefecture-level cities are often political, economic, and educational centers, such as Duanzhou District and Dinghu District in Zhaoqing City. Some newly built urban areas in various cities, such as Huangpu District in Guangzhou, started late but have a fast economic operation speed due to the policy support.

3.3. The Correlation Between EPSs and ESs

Across the PRD, ER is negatively correlated with grain production, vegetable production, and forestry production (Table 4). Water yield is positively correlated with economic resilience. Except for ER and forestry production, other relationships exist in the core area of the PRD. It can be seen that regions with stronger ER have lower grain and vegetable production (Figure 3 and Figure 4). In the non-core areas of the PRD, economic resilience is only negatively correlated with forest services and has no significant relationship with other EPSs.

4. Discussion

4.1. The Spatial Distribution of EPSs in the PRD

Four EPSs that are critical to human survival were evaluated in this study. This study used the localized Hargreaves–Samani equation to calculate radiation data when evaluating water yield [36], and the evaluation results were closer to the current situation in the study area. The results show that the water yield of the PRD exhibited a layout of high in the core area and low in the periphery, which is consistent with the water yield of the PRD from 2010 to 2022 in the existing study by InVEST [41]. It is easy to find that the high-value areas overlapped more with the construction land. On the one hand, the land use change in the built-up area leads to an increase in precipitation through thermodynamics and water vapor transmission; on the other hand, the evapotranspiration of the artificial surface is lower than that of the vegetation-covered land [28,42]. Regional grain production is tightly coupled with the availability of cultivated land. The core area of the PRD is vigorously developing its economy, and some central urban areas no longer have arable land, such as Liwan District and Yuexiu District in Guangzhou and across most of Shenzhen. Consequently, the current self-sufficiency rate of food in the PRD is less than 25%.
Although the cultivated land area is limited, every prefecture contains high-yield pockets of vegetable production. The relatively balanced layout of vegetable production is related to policy and economic factors. Since the launch of the “Vegetable Basket” platform in the Guangdong–Hong Kong–Macao Greater Bay Area in 2019, each city attaches great importance to the construction of the “Vegetable Basket” project and regards it as an important lever for rural revitalization. What is more, the economic profit of vegetable planting is significantly higher than that of grain planting, and their growth cycle is shorter [34]. With policy support, vegetable production has become an important part of agriculture in the PRD. Forestry production was quantified through gross forestry output value, an aggregate metric that captures multiple timber and non-timber services while avoiding the unit-conversion challenges posed by raw and processed forest products. The spatial layout of forestry production is not only related to forest area, forest species, etc., but also to the focus of local industrial development, which confirms the influencing factors of changes in the forestry industry structure in Kentucky, USA, analyzed by previous researchers through dynamic panel regression models [43].

4.2. The Spatial Distribution of ER in the PRD

Economic structure, economic vitality, and economic innovation are important components that are interrelated and interact with each other in the economic system. The use of these three dimensions to evaluate ER reflects the emphasis on the overall and collaborative nature of the economic system. This evaluation framework is based on the development history of resilience theory and can reflect the ability of the urban economy to cope with short-term or long-term shocks [15,17,44]. The results show that the ER of the PRD core area is significantly higher than that of other regions (Figure 3). Within the core area, Guangzhou is the capital city of Guangdong Province and posts uniformly high ER values, except in the outlying districts of Huadu and Conghua. Shenzhen, established as China’s first special economic zone, has had a sustained elevated per capita GDP since the 1980s and likewise registers uniformly high ER. At the same time, the central urban area of each prefecture-level city is an ER high-value area. The central urban area developed early, with relatively complete facilities, equipment, and various systems. For some new urban areas, such as Huangpu District in Guangzhou, although the development started late, the economic development and construction progress are relatively fast because of the abundant policy support. Therefore, the ER of these two types of places is higher than that of other urban areas. Foshan, Dongguan, Zhongshan, and other cities also exhibit high ER. On the one hand, these core cities in the PRD region have basically completed their industrial restructuring. On the other hand, the proximity effect within the urban agglomeration has promoted mutual stimulation of economic development [25,45].

4.3. Correlation Analysis Between EPSs and ER

This study explores the correlation between EPSs and ER, breaking through the current research on ecosystem service clusters, which is limited to the trade-offs and coordination within ESs. Whereas earlier work remained largely conceptual, we provide an empirical analysis of urban agglomerations [2,18]. This study focuses on the PRD in China and makes a comparative analysis of its core areas. The urbanization and economic degree of the region are very high, and attention is paid to the construction of ecological civilization. The newly increased domestic sewage treatment pipe networks in Guangzhou, Shenzhen, and Dongguan since 2018 account for 78.6% of the newly increased domestic sewage treatment pipe networks in the province. The research on the correlation between EPSs and ER in this region not only has important theoretical significance but also can provide useful references for other similar developed regions.
This study found that ER and most EPSs are trade-offs, especially within the PRD core area. Although previous studies have proposed that ER can enhance the governance ability and speed of the urban ecological environment [21,22], its ecological feedback effect is still poor in this study; the ecological impacts of economic development remain unverified. Economic development and urban expansion have occupied a large amount of farmland and forest land [10]. The results verify the previous result that the more human activities, the higher the risk of local ecosystem service supply [5]. In the non-core area of the PRD, ER has no significant correlation with some EPSs negatively correlated with forest production. On the one hand, this negative correlation is due to the fact that in areas with high forest coverage, the area of land available for development and utilization is limited, and ecological protection policies limit the diversity of local economic development. On the other hand, the added value of the primary industry dominated by agriculture and forestry is small [46], the economic effect is relatively low, and it is greatly affected by natural conditions and market price fluctuations [34].

4.4. Policy Implications

In terms of EPSs, human survival needs and ecological protection are equally important. High-value areas for water yield are concentrated in construction land, which reminds us to build a cross-regional water resources management infrastructure. Building a “sponge city” combines an urban green space system and technology, improving water resources recycling and reducing urban flooding [47]. At present, the self-sufficiency rate of grain in the PRD is very low. To ensure food security and coordinated economic development, every prefecture-level city should designate and permanently protect a minimum area of prime cultivated land [33]. The PRD should improve the compensation for basic cultivated land protection and develop and promote excellent seeds and planting methods to improve the yield per unit area.
As for the negative correlation between ER and most EPSs, we should implement spatially targeted policies to optimize the ES relationship. Long-term ecosystem management must couple ecological restoration with social–economic incentives [8]. First, enhance the regional economic driving effect of urban agglomerations, and implement industrial transfer and division of labor cooperation strategies. Transfer some industries from the core area of the PRD to non-core areas, and guide the development of industries that match the core industries in non-core areas. This industrial re-balancing will raise the overall regional ER without further squeezing natural capital. Second, we should attach importance to the development of ecological and economic integration and enhance the ER of non-core areas. For example, given Zhaoqing’s high forest coverage, we should strengthen the ways and means of forest land utilization, diversify development, and balance ecological protection and economic development by establishing an ecological compensation mechanism. Finally, increasing economic investment in ES conservation constitutes a long-term strategy for mitigating the trade-off between EPSs and ER [15]. Therefore, governments should improve the education investment and technology investment to enhance ecological protection technologies.

4.5. Limitations

Several limitations merit explicit acknowledgment. First, this study is confined to a cross-sectional analysis within the PRD, without longitudinal comparison at different periods. Future work should incorporate multi-year data to track the influence of ER on ecosystem services. Second, EPSs encompass far more than the four EPSs discussed here. Future efforts should integrate aquatic products, energy carriers, and natural ornamental resources to capture the full spectrum of economic reliance on ecosystems. Finally, four types of ecosystem services all affect urban economic development; extending the analysis to cultural and regulating ecosystem services would provide a more comprehensive picture of the trade-offs and synergies shaping regional sustainability.

5. Conclusions

This study focuses on the relationship between ecosystem services and the economy. Multi-source data were used to evaluate the EPSs and ER of the PRD based on the InVEST model and the analytic hierarchy process. The relationship between EPSs and ER in the core area and non-core area of the PRD was analyzed. This study found that the high-value areas of water yield of EPSs in the PRD were concentrated in the middle of the study area, the high-value areas of forestry production were mainly concentrated in the peripheral areas with high forest coverage, and the grain production was also high in the peripheral areas. Due to the implementation of the Vegetable Basket project, each city had its high-value areas of vegetable production. The ER of the core area of the PRD is significantly higher than that of the peripheral areas. The ratio of the tertiary industry to GDP and per capita GDP in the core area of the PRD is high. Except for water yield, ER was negatively correlated with other EPSs, which was significant in the core area of the PRD. Based on the research results, we propose strategies such as building an urban drainage system and protecting basic cultivated land to ensure the sustainability of EPSs. At the same time, we put forward the strategy of coordinated development of regional industries and diversified development of forest land to reduce the negative correlation between EPSs and ER.

Author Contributions

Conceptualization, data curation, and formal analysis, Q.Z.; funding acquisition, Q.Z. and W.F.; investigation, S.X.; methodology, W.F.; supervision, S.X.; validation, Q.Z.; writing—original draft, Q.Z.; writing—review and editing, S.X. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China, grant number 42376226; Anhui Provincial Department of Education Key Research Project for Universities, grant number 2024AH052866; Anhui Provincial Philosophy and Social Science Planning Project, grant number AHSKO2024D020; and Chaohu University for the Start-UP, grant number KYQD-2023068.

Data Availability Statement

All data used in this study are cited in the text and are available upon reasonable request from the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

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Land 14 01731 g001
Figure 1. (a) Location of the study area; (b) land use types of the PRD.
Figure 1. (a) Location of the study area; (b) land use types of the PRD.
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Figure 2. The spatial distribution of EPSs in the PRD. WY: water yield; GP: grain production; VP: vegetable production; FP: forestry production.
Figure 2. The spatial distribution of EPSs in the PRD. WY: water yield; GP: grain production; VP: vegetable production; FP: forestry production.
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Figure 3. Boxplot of ER indicators of each city.
Figure 3. Boxplot of ER indicators of each city.
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Figure 4. Distribution of ER in the study area.
Figure 4. Distribution of ER in the study area.
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Table 2. Indicators of economic resilience.
Table 2. Indicators of economic resilience.
DimensionIndicatorQuantification of IndicatorsSelection Reason
Economic structure
(Risk resistance foundation)		Proportion of tertiary industry to GDPOutput value of tertiary industry/local GDP (%)The proportion of the tertiary industry can reflect the degree of improvement in the urban industrial structure [38].
Economic vitality
(Short-term response capability)		Per capita GDPRegional gross domestic product/Regional permanent populationPer capita GDP is the most intuitive reflection of regional economic conditions (Wagner’s Law).
Proportion of comprehensive consumer goods in GDPTotal retail sales of consumer goods/regional GDPConsumption not only drives economic development but also serves as a pillar driving economic growth.
Economic innovation
(Long-term adaptation potential)		Educational investmentEducational financial investment/total local financial investmentEducation investment has a significant impact on long-term economic growth, and technology investment can continuously stimulate urban innovation. The larger these two investments, the stronger the learning ability and innovation ability of urban systems to cope with changes [39].
Technology investmentTechnology financial investment/total local financial investment
Table 3. The results of EPSs in each prefecture-level city.
Table 3. The results of EPSs in each prefecture-level city.
CityWater YieldGrain ProductionVegetable ProductionForest CoverageForestry Production
×106 m3tt%104 CNY
Guangzhou9060.30142,2484,038,20341.60 42,758
Shenzhen2437.037349124,46739.40 14,585
Zhuhai1738.7328,876143,86832.20 273
Foshan4717.0047,221850,80921.40 16,811
Huizhou11,136.39604,3833,270,96761.60 77,495
Dongguan3262.448177396,98336.40 3214
Zhongshan2116.2913,378358,33523.10 707
Jiangmen10,357.90979,1671,705,82245.10 99,995
Zhaoqing12,457.961,212,1893,000,69570.70 873,466
Table 4. Spearman’s correlation coefficient between EPSs and ER.
Table 4. Spearman’s correlation coefficient between EPSs and ER.
ER—WYER—GPER—VPER—FP
PRD0.594 **−0.633 **−0.354 *−0.513 **
Core PRD0.494 **−0.522 **−0.516 **−0.292
Other regions0.416−0.3090.087−0.524 *
** represents a significant correlation at the level of 0.01 (double-tailed); * represents a significant correlation at the level of 0.05 (double-tailed). ER: economic resilience; EPSs: ecosystem provisioning services; WY: water yield; GP: grain production; VP: vegetable production; FP: forest production.
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Zhang, Q.; Xu, S.; Feng, W. The Relationship Between Ecosystem Provisioning Services and Urban Economic Resilience in the Pearl River Delta Urban Agglomeration, China. Land 2025, 14, 1731. https://doi.org/10.3390/land14091731

AMA Style

Zhang Q, Xu S, Feng W. The Relationship Between Ecosystem Provisioning Services and Urban Economic Resilience in the Pearl River Delta Urban Agglomeration, China. Land. 2025; 14(9):1731. https://doi.org/10.3390/land14091731

Chicago/Turabian Style

Zhang, Qiongrui, Songjun Xu, and Wei Feng. 2025. "The Relationship Between Ecosystem Provisioning Services and Urban Economic Resilience in the Pearl River Delta Urban Agglomeration, China" Land 14, no. 9: 1731. https://doi.org/10.3390/land14091731

APA Style

Zhang, Q., Xu, S., & Feng, W. (2025). The Relationship Between Ecosystem Provisioning Services and Urban Economic Resilience in the Pearl River Delta Urban Agglomeration, China. Land, 14(9), 1731. https://doi.org/10.3390/land14091731

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