Introduction

Water, energy, and food are crucial strategic resources for social development and human survival. Ensuring a balance between the supply and demand of water, energy, and food is vital for regional sustainability and high-quality development1. As crucial carriers of resources like water, energy, and food, ecosystems engage in close information and material exchanges with the water-energy-food (WEF) system. The coupling relationship between the ecosystem and the WEF is critical to regional stability and development2, making it necessary to integrate ecosystem into the WEF nexus3,4. Furthermore, the land system serves as the carrier of natural resources, providing basic conditions for the existence and development of water, energy, food, and ecosystems. Natural characteristics such as soil fertility, soil type, and topography directly influence the distribution and utilization of water resources, the extraction and conversion of energy, the production and supply of food, as well as the stability and diversity of ecosystems. With the acceleration of urbanization and increasing population density, the demand for urban construction land has risen significantly, leading to increasingly scarce cultivated land resources. This directly results in increased consumption of water resources and energy, while also encroaching on food cultivation areas and affecting the regulatory mechanisms of ecosystems. Therefore, there exists a complex relationship between water, energy, food, ecology, and land. The water-energy-food-ecology-land system (WEFELS) is an interdependent and mutually restrictive composite system. Studying the coupling and coordination of this system is of great significance for the sustainable development of regional resources. As one of the important regions of Chinese social and economic development, the YRD is facing multiple pressures on water, energy, food, ecology, and land resources driven by rapid population growth and extreme climate change. Specifically, the uneven spatial and temporal distribution of water resources and the problem of water pollution are typical, energy consumption depends on external input, and industrialization and urbanization led to a sharp contradiction between land supply and demand, as well as the significant conflict between the expansion of construction land and the protection of agricultural and ecological land5. Therefore, it is necessary to study the coupling and coordinated development level of WEFELS in the YRD.

In 2011, at the Bonn Conference in Germany, it was first proposed that there was a complex feedback relationship among water, energy and food, and the correlation between the three was defined as the water-energy-food (WEF) nexus6,7. Subsequently, scholars have widely responded and actively engaged in the research of the WEF8. Preliminary research on the WEF system mainly focuses on security assessment9, synergistic development10, and optimal management11. Due to the complexity and interconnectedness of the system, changes in one subsystem often lead to changes in the structure of other subsystems and the stability of the overall system. Therefore, research based on coupling rather than a single perspective can not only solve practical problems, but also promote the sustainable development of resources and society. In recent years, with the rapid growth of the global population and the rapid development of the economy and society, problems such as water scarcity, energy depletion, tight food supply, climate change, and environmental pollution have emerged12. Scholars’ research on the WEF system is not limited to the system itself, but incorporates factors such as climate change13,14 and sustainable development15. Rasul and Sharma16 put forward a comprehensive framework linking water resources, energy, and food security to better adapt to climate change, promoting the coordinated development of WEF and climate. Chamas et al.17 deeply analyzed the interaction between the WEF and carbon emissions by building a nonlinear optimization model, so as to achieve a sustainable optimal allocation of resources. Núñez-López et al.18 proposed a systematic method to assess the resilience of WEF relations in arid and semi-arid regions, and evaluated the impact of different types of natural disasters on Mexico’s WEF system. Chai et al.19 constructed the causality diagram of the water-energy-food-economy-society-environment system by using the Bayesian network model, and studied and quantified the correlation between water, energy, food and economy, society, and environment in China. With the impact of global population, socio-economic and climate change, the rapid development of the world has not only increased the consumption of natural resources, but also caused increasing pressure on the ecosystem. At the same time, the utilization of water, energy and food is also inseparable from the carrier of the ecological environment20. Therefore, there is a close relationship between WEF and ecology. In order to achieve sustainable development, scholars have constructed the water-energy-food-ecology system (WEFES) framework to analyze the interaction between water, energy, food and ecology, and consider the feedback regulation of ecological environment on WEF. Shi et al.21 comprehensively considered the correlation between the systems composed of water resources, energy, food and ecology, and combined with the Bayesian network method, an in-depth analysis of water resource utilization in the Syr Darya River Basin was conducted. Qin et al.22 conducted a comprehensive assessment of WEFES pressure based on the projection pursuit model and the concept of virtual water trade, and revealed the transmission laws of pressure. Song et al.23 analyzed the complex dynamic relationships in the WEFES linkages in Uzbekistan using an integrated approach and proposed pathways to promote the sustainable development of WEFES in Uzbekistan and Central Asia. Liu et al.24 used the PSR framework, random forest model and data envelopment analysis to estimate the vulnerability of WEFES. In addition, as a key element in the process of water resources storage and recycling, energy development and utilization, food production and transportation, land is also facing great pressure with the rapid development of urbanization. In order to ensure the stability and sustainable development of the WEF system, it is essential to strengthen the protection and management of land resources, utilize them rationally, and promote the coordinated development of these systems. Therefore, more and more scholars have carried out a series of studies on water-energy-food-land system (WEFLS). Jing et al.25 evaluated and analyzed the coupling coordination relationship and spatio-temporal dynamic evolution of WEFLS along the Yangtze River Economic Belt by using the comprehensive Benefit Evaluation Index (CBEI), Coupled Coordination Degree (CCD) model and Obstacle Factor Diagnosis (OFD) model. He et al.26 studied the changes of coupling and coordinated development of WEFLS in Henan Province, China. Durán-Díaz27 proposes a holistic framework that considers multi-dimensional interactions of land with WEF to help effectively address the complex challenges of rural and peri-urban rehabilitation in the Global South. Li28 quantified the interactions and feedback between water, land, food and energy subsystems by establishing an optimal model frame for sustainable management of limited water-land-food-energy resources in agroforestry systems under climate change conditions.

Presently, research on the WEF system mainly focuses on the relationship between WEF, collaborative security, and the analysis of a single influencing factor in the system. However, ecology and land are fundamental to the operation of the WEF system and are crucial for the sustainable development of the social economy, resources, and the environment. There are deficiencies in the research on the formation of WEFELS, and there are few analyses on the mechanism of information exchange and material exchange among water, energy, food, ecology and land. Concurrently, the existing relevant research findings mostly focus on the description of the current status of spatio-temporal characteristics, and further analysis of the causes behind spatio-temporal differences remains to be supplemented. Therefore, this paper integrates the subsystems of ecology and land into the WEF system. It constructs the mechanism framework of WEFELS, discusses the comprehensive evaluation index and the coupling coordinated development of regional WEFELS, and establishes the obstacle degree model to comprehensively identify impact factors in the study area. This aims to provide a reference for promoting the development of WEFELS and optimizing resource allocation in the YRD. The innovation of the research is mainly reflected in the following aspects: (1) The interaction between water resources, energy, food, ecology and land is analyzed. On this basis, the two elements of ecology and land are incorporated into the WEF system, and the action mechanism diagram of the WEFELS is drawn, which helps to show the correlation between the systems. (2) This paper studies the WEF system from the perspective of multi-factor, multi-system, and multi-regional integration, and more comprehensively considers the importance of ecology and land as the basic guarantee of system operation and the sustainable development of resources and environment. (3) Based on the analysis of the spatio-temporal evolution of WEFELS, the relevant obstacle factors are further identified, which will help to relieve the pressure of regional resources more comprehensively and promote the rational allocation of resources.

Results

Comprehensive evaluation index analysis

According to the entropy weight method, the weights of each indicator in the indicator system are determined, as shown in Table 1. The weights of the five subsystems of water, energy, food, ecology, and land are 0.1864, 0.1816, 0.2263, 0.1948, and 0.2108, respectively. Among them, the weights of per capita GDP, energy self-sufficiency rate, and ecological water use proportion are relatively large, which are 0.084, 0.076, and 0.053, respectively. On this basis, the comprehensive evaluation index model was used to calculate the comprehensive evaluation index (CEI) of the WEFELS in the YRD from 2005 to 2022, as well as the contribution proportion of each subsystem, as shown in Fig. 1.

Fig. 1: The CEI of WEFELS and contribution ratio of each subsystem in the YRD.
figure 1

Colors of each bar are as follows: blue color is water subsystem, orange color is energy subsystem, gray color is food subsystem, yellow color is ecology subsystem and green color is land subsystem. Red line is comprehensive evaluation index.

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Table 1 Weight of each evaluation index
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As shown in Fig. 1, the trend of the CEI of the WEFELS in the YRD from 2005 to 2022 can be divided into three stages. From 2005 to 2012, it experienced a fluctuating upward trend. Between 2013 and 2021, it was in an overall upward trend. Since 2022, it has been in a downward trend. This characteristic of change is closely related to the changes in the comprehensive evaluation index of each subsystem. The contribution of the food system, ecology system, and land system to the CEI of WEFELS is significant, with an average annual proportion of 22.15%, 21.42%, and 19.40%, respectively. The water system and energy system contribute relatively little to the CEI of the system, accounting for 18.25% and 18.78% respectively. The contribution ratio of the CEI of the land system has increased the most, from 17.02% in 2005 to 21.59% in 2022, followed by the food system. The contribution ratio of the CEI of ecology system has decreased the most, from 24.58% in 2005 to 19.24% in 2022, followed by water systems. The primary reason lies in the comprehensive policy support for agricultural development in the YRD. These policies actively promote modernization and innovation, rigorously enforce farmland protection measures, and emphasize balancing socioeconomic development with ecological conservation. However, the region’s rapid growth has exposed challenges such as imbalances in resource supply and demand, an imperfect regional coordination mechanism, and notably, acute conflicts between hydropower development and ecological protection. Overall, the CEI of the WEFELS in the YRD showed a wave like upward trend from 2005 to 2022.

As can be seen from Fig. 2, the floating range of the CEI of each subsystem in Shanghai is small, ranging from 0.1 to 0.6, showing an overall upward trend. Among them, the land system, food system, and energy system experienced the largest growth rates, with 177.21%, 95.18%, and 71.65%, respectively. However, the CEI of the food and energy systems remains lower than that of other systems. The growth rate of water and ecology systems were slow, 15.43% and 9.86% respectively.

Fig. 2: The CEI of WEFELS in Shanghai City.
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Colors of each line are as follows: blue color is water subsystem, gray color is energy subsystem, green color is food subsystem, yellow color is ecology subsystem and orange color is land subsystem.

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As shown in Fig. 3, the CEI of each subsystem in Jiangsu Province ranges from 0.1 to 0.8. Among these, the CEI of the energy, food, and land systems demonstrates a clear upward trend. Notably, the food system’s CEI increased from 0.53 in 2005 to 0.77 in 2022, while the energy and land systems’ CEIs rose from 0.21 and 0.36 in 2005 to 0.33 and 0.54 in 2022, respectively. However, the CEIs of the water system and ecosystem show significant fluctuations.

Fig. 3: The CEI of WEFELS in Jiangsu Province.
figure 3

Colors of each line are as follows: blue color is water subsystem, gray color is energy subsystem, green color is food subsystem, yellow color is ecology subsystem and orange color is land subsystem.

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As can be seen from Fig. 4, the CEI of each subsystem in Zhejiang Province fluctuates from 0.2 to 0.7, among which the CEI of water system shows obvious fluctuation, while the CEI of energy system, food system and land system shows a slight fluctuation upward trend. On the whole, the CEI of other systems shows an increase in varying degrees except the CEI of water system and ecosystem decreases.

Fig. 4: The CEI of WEFELS in Zhejiang Province.
figure 4

Colors of each line are as follows: blue color is water subsystem, gray color is energy subsystem, green color is food subsystem, yellow color is ecology subsystem and orange color is land subsystem.

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As can be seen from Fig. 5, the CEI of each subsystem in Anhui Province has the largest floating range, ranging from 0.1 to 0.9. The CEI of the food system has the largest increase in 18 years, rising from 0.50 in 2005 to 0.85 in 2022, with an increase of 69.78%, followed by the land subsystem, with an increase of 48.40%. However, the CEI of energy system and ecology system decreased slightly by 6.91% and 3.02%, respectively.

Fig. 5: The CEI of WEFELS in Anhui Province.
figure 5

Colors of each line are as follows: blue color is water subsystem, gray color is energy subsystem, green color is food subsystem, yellow color is ecology subsystem and orange color is land subsystem.

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Spatio-temporal evolution of coupling coordination degree

Based on the Coupling Coordination Degree (CCD) model, the Degree of Coupling Coordination (DCC) of WEFELS in the YRD and each region from 2005 to 2022 are calculated, as shown in Figs. 6 and 7. It can be seen from Fig. 6 that the DCC of WEFELS in the YRD showed a wave-like upward trend from 2005 to 2022, rising from 0.561 in 2005 to 0.653 in 2022, with a relative growth rate of 16.40% and an average annual value of 0.607. The coupling coordination type was in the transitional category from 2005 to 2013, and shifted to the coordinated development category from 2014 to 2022. Overall, the DCC of WEFELS in the YRD is developing in a good direction. This reflects the improvement of resource utilization efficiency and resource allocation level in the YRD. The main reason is that the integration policy of the YRD promotes the coordinated management of different resources, while adhering to technological innovation and focusing on the green transformation development of various industries.

Fig. 6
figure 6

The DCC level of WEFELS in the YRD from 2005 to 2022.

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Fig. 7: The DCC levels of WEFELS in each region of the YRD from 2005 to 2022.
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In the picture, a stands for DCC in Shanghai, b stands for DCC in Jiangsu, c stands for DCC in Zhejiang, d stands for DCC in Anhui.

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According to Fig. 7, the DCC of WEFELS in Shanghai has increased from 0.47 in 2005 to 0.60 in 2022, with a growth rate of 27.97% and an average annual value of 0.53. The DCC in Shanghai has progressed rapidly, from near imbalance in 2005–2011 to barely coordinated in 2012–2020, and then to primary coordination in 2021. The DCC of WEFELS in Jiangsu Province fluctuates greatly from 2005 to 2013, and shows a significant upward trend from 2014 to 2022, rising from 0.57 in 2005 to 0.66 in 2022, with a relative growth rate of 15.77% and a multi-year average of 0.61. The DCC development of Jiangsu Province was barely coordinated from 2005 to 2013, and in primary coordination from 2014 to 2022. The DCC of WEFELS in Zhejiang Province has shown a wave-like upward trend, increasing from 0.57 in 2005 to 0.64 in 2022, with an increase of 11.20% and a multi-year average of 0.60. The development of DCC in Zhejiang Province first shifted from barely coordinated to primary coordinated in 2015, decreased to barely coordinated in 2017, and rose again to primary coordinated in 2018. The DCC of WEFELS in Anhui Province increases from 0.63 to 0.71 during the research period, with a relative growth rate of 12.66% and a multi-year average of 0.61. Anhui Province has a higher DCC than the other two provinces and Shanghai City in the YRD, and its growth trend is similar to that of Zhejiang Province. The type of coupling coordination has gradually shifted from primary coordination from 2005 to 2014 to intermediate coordination from 2015 to 2022. Overall, the DCC of WEFELS in three provinces and one city in the YRD has shown an upward trend to varying degrees, and is developing in a good direction.

Analysis of the influencing factors of the coupled coordinated development

Through the obstacle degree model, 35 obstacle factors affecting the DCC of WEFELS in the YRD from 2005 to 2022 were identified, and the results are shown in Fig. 8. There are seven major barriers in Shanghai, which are W5, E1, E2, F2, S5, L2, and L5. Among them, E2, F2 and S5 are the three most significant long-term factors affecting the DCC of WEFELS in the region. It can be seen that Shanghai needs to focus on energy, food and ecology to promote the improvement of the overall DCC of the WEFELS. The major obstacle factors in Jiangsu Province include W5, E1, E2, S1 and L5. Among them, W5, E2 and L5 are the three most significant long-term factors affecting the DCC of WEFELS in Jiangsu Province, indicating that water resources system, energy system and land system are important factors affecting the DCC of the overall system in the region. The major obstacles in Zhejiang include E1, E2, F2, S5, L2, L5, among which E2, S5 and L5 are the three most significant long-term factors affecting the DCC of WEFELS in the region. Therefore, the focus of work to maintain the DCC of WEFELS in Zhejiang Province should be on the energy system, ecology system and land system. W4, W5, S1, S5 and L5 are the most influential factors in Anhui Province. Among them, W4, W5 and L5 have the most significant impact on the DCC of WEFELS in the region. It can be concluded that water system and land system are the key factors restricting the DCC of WEFELS in Anhui Province.

Fig. 8: Obstacle degree results of various impact factors in the YRD.
figure 8

In the picture, the more pronounced red color indicates the greater degree of influence of the obstacle factor on DCC. a Represents the DCC obstacle factors affecting Shanghai, b represents the DCC obstacle factors affecting Jiangsu, c represents the DCC obstacle factors affecting Zhejiang, d represents the DCC obstacle factors affecting Anhui.

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In summary, E2 and L5 are the key factors that restrict the DCC of WEFELS in the YRD. The main reason is that the rapid development of industry and agriculture in the region consumes abundant resources, but the energy supply depends on external transportation, and the low rate of energy self-sufficiency leads to the continuous expansion of regional energy area and the increasing intensity of land development. As a result, some ecological land is crowded out, and with the continuous growth of GDP, high GDP density causes frequent problems of land resource premium, which decreases the coupling coordination ability of WEFELS. On the whole, the energy system and land system are the long-term factors that affect the DCC of WEFELS in the YRD.

Discussion

The Yangtze River Delta (YRD), as one of China’s most economically dynamic and innovative regions, faces significant challenges in balancing rapid development with resource scarcity and ecological sustainability. This study incorporated ecology and land into the WEF system to form WEFELS. On the basis of analyzing the interaction of five kinds of resources, entropy weight method, comprehensive index evaluation model and entropy weight method were used to comprehensively analyze the comprehensive development level and coupling coordination of WEFELS, and to identify the obstacle factors affecting the improvement of coupling coordination level in the YRD.

The study found that the CEI of WEFELS in the YRD demonstrated an upward trend from 0.35 in 2005 to 0.45 in 2022, reflecting incremental improvements in resource management. However, the CEI of each subsystem varies significantly across different regions, with the CEI of the energy system and food system in Shanghai being notably lower. In the provinces of Jiangsu and Zhejiang, the CEI of water system and ecology system fluctuated greatly. In the province of Anhui, the CEI of food system showed a significant upward trend, but the CEI of energy system and ecology system showed a downward trend. These conclusions are similar to those of Ding et al.29 The DCC of WEFELS in the YRD showed a slight upward trend, among which the DCC of Anhui Province was higher than that of other regions. During the study period, the critical obstacles to DCC improvement were identified as W5, E1, E2, S5, and L5. Notably, 40% of these factors belong to the energy system, while L5 emerged as the most recurrent obstacle, reflecting the tension between economic growth and land-use efficiency. For different regions, the energy system and ecology system are long-term factors constraining the DCC of WEFELS in Shanghai and Zhejiang. The water resource system and land system are crucial factors influencing the DCC of the WEFELS in Jiangsu and Anhui.

To promote sustainable and green development of resources and economy in YRD, the three provinces and one city should take corresponding improvement measures according to regional characteristics. Specifically, Shanghai should focus on the synergy of energy, food and ecology, use its economic advantages to accelerate the construction of energy industry structure, establish a digital grain logistics platform to enhance the resilience of the food supply chain, and at the same time conduct rigid management and control of ecological space, focusing on protecting key nodes such as the Yangtze River estuary wetland. Jiangsu needs to solve the contradiction between water, energy and land resources, improve the utilization efficiency of water resources and energy in the region through intensive use of water resources and deep adjustment of energy structure, and strengthen land management and planning. Zhejiang should strengthen the synergy among the three resources of energy, ecology and land, and improve the DCC of WEFELS by upgrading the structure of clean energy system, giving priority to supporting eco-tourism and organic agriculture, and forcing the withdrawal of inefficient industrial land. Anhui needs to focus on the development and utilization of water and land resources, and can establish a smart water transport system to improve the optimal allocation of water resources across the basin, while improving the innovation ability of ecological compensation, and encouraging the multi-functional use of land30. On the whole, the YRD should actively promote the composite use of land, achieve industrial upgrading and efficient land allocation by eliminating inefficient industries and developing emerging industries, and pay attention to regional coordination of resources and policy innovation, so as to improve the overall coupling coordination level of WEFELS.

However, this study has certain limitations. Firstly, the evaluation index system requires refinement. The WEFELS has a complex boundary, and its coupling and coordinated development is affected by many factors. In this paper, the selection of indicators is limited by the availability of data, which may make the evaluation index system incomplete, especially in ecology and land subsystems. Secondly, research method innovation should be strengthened. The current research methods for system coordination development mainly rely on existing literature and previous studies, lacking mature methods for feedback adjustment inside the system, simulation and prediction of future coupling and coordination development. Therefore, in future research, the index system should be enhanced for greater research universality, and research methods need to be innovated to better understand and predict the dynamic changes of WEFELS.

Methods

System relationships

Water, energy, and food are the basic resources for human survival and sustainable development, and maintaining a long-term balance between them is crucial31. Ecology, as the source and carrier of resources such as water, energy, and food, has a close interrelationship with the WEF system, forming a dynamic water-energy-food-ecology composite system. With the rapid economic development and sharp population growth, not only has the consumption of water, energy, food, and land resources been intensified32, but also the negative impact of other related driving factors such as urbanization, land degradation, and climate change on the ecological environment has been amplified33. This is causing the water, energy, food, ecology, and land resources to be intertwined and gradually forming the water-energy-food-ecology-land composite system. The quantity, quality, distribution, and spatiotemporal configuration combination of water, energy, food and ecology, and land resources development and utilization in the region are influenced by the dynamic relationship between human activity demand and resource supply26. From the perspective of resource interaction, the system as a whole can be divided into 10 pairs of subsystems: water-energy, water-food, water-ecology, water-land, energy-food, energy-ecology, energy-land, food-ecology, food-land, and ecology-land. Any change in a subsystem will cause a “chain reaction” in other subsystems, which in turn will have a certain impact on the stability of the overall system. For example, the increased demand for construction land not only reduces arable land, encroaching on agricultural and ecological areas, but also heightens the consumption of regional water resources and energy. Land is a key element in energy production, and energy extraction consumes significant amounts of water and land resources, affecting the quality and yield of food and damaging ecosystem structures and functions. Grain production depends on the supply of water, energy, and land resources, as well as a healthy ecological environment. Additionally, the acquisition and processing of water resources consume other resources (As shown in Fig. 9). Therefore, it is necessary to consider the optimal allocation and cooperation of various resources within the region. Reasonable resource arrangement and layout can promote the utilization efficiency and sustainable development of various resources, providing ideas for the comprehensive use and coordinated development of regional resources.

Fig. 9
figure 9

Mechanism of WEFELS.

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Based on the constructed evaluation index system, this study employs the entropy weight method to assign weights to each index. The comprehensive evaluation index model is then utilized to determine the overall development level of the individual subsystems of water, energy, food, ecology, and land. On this basis, the coupled coordination degree model is used to analyze the coupled coordinated development level of WEFELS, and the obstacle degree model is used to identify the influencing factors. The research framework of this paper is shown in Fig. 10.

Fig. 10
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Research framework.

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Construct index system and determine weight

The indicators of water subsystem are selected from the perspective of total resource, consumption structure and utilization benefit. The indicators of energy subsystem are selected from the view point of resource supply and consumption, environmental safety. The indicators of food subsystem are selected from the perspective of production, consumption and circulation. The indicators of ecology subsystem are selected taking into account ecological status, ecological pressure and ecological governance. The indicators of land subsystem are selected from the vantage point of human life, economic society, and land carrying capacity34,35,36,37. There are a total of 35 evaluation indicators, as shown in Table 2.

Table 2 Evaluation index system of coupling coordination degree of WEFELS
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Due to the varying dimensions and significant differences in the magnitudes of different indicators, all data must be standardized before calculation. Based on their overall impact, the indicators are categorized as positive or negative. Referring to the methods in relevant studies, the calculation formula is as follows:

Positive indicators:

$$Y=\frac{X-{X}_{\min }}{{X}_{\max }-{X}_{\min }}$$
(1)

Negative indicators:

$$Y=\frac{{X}_{\max }-X}{{X}_{\max }-{X}_{\min }}$$
(2)

Where \(Y\) is the value of standardized data of WEFELS evaluation indicators. X is the original value of WEFELS evaluation indicators. \({X}_{\max }\) and \({X}_{\min }\) represent the maximum and minimum values of each indicator, respectively.

To eliminate subjective factors and reduce the impact of human factors on the data, the entropy weight method is used to objectively weight each indicator. The smaller the entropy value, the higher the degree of difference between each indicator, and the greater the weight of the indicator in each subsystem38,39. The calculation formula is as follows:

$${P}_{{ij}}=\frac{{Z}_{{ij}}}{\mathop{\sum }\nolimits_{i=1}^{n}{Z}_{{ij}}}$$
(3)
$${E}_{j}=-\frac{1}{\mathrm{ln}n}\mathop{\sum }\limits_{i=1}^{n}{P}_{{ij}}\mathrm{ln}{P}_{{ij}}$$
(4)
$${G}_{j}=1-{E}_{j}$$
(5)
$${W}_{j}=\frac{{G}_{j}}{\mathop{\sum }\nolimits_{j=1}^{m}{G}_{j}}$$
(6)

Where \(Z\) is the standardized matrix formed after the standardization of each index in the WEFELS evaluation index system, and \({P}_{{ij}}\) is the proportion of the value of the \({i\_th}\) object under the \({j\_th}\) index to that index. \({E}_{j}\) and \({G}_{j}\) represent the entropy and difference coefficient of the \({j\_th}\) index, respectively. \({W}_{j}\) is the weight of the \({j\_th}\) index in WEFELS evaluation index system.

Comprehensive evaluation index

Using the comprehensive evaluation index system and the assigned weights for each index, the comprehensive evaluation index (CEI) of each subsystem is calculated through the weighted sum method. The specific calculation formula is:

$${F}_{w}=\mathop{\sum }\limits_{j=1}^{n}{W}_{\alpha }{X}_{{wj}}$$
(7)
$${F}_{e}=\mathop{\sum }\limits_{j=1}^{n}{W}_{\beta }{X}_{{ej}}$$
(8)
$${F}_{f}=\mathop{\sum }\limits_{j=1}^{n}{W}_{\gamma }{X}_{{fj}}$$
(9)
$${F}_{s}=\mathop{\sum }\limits_{j=1}^{n}{W}_{\delta }{X}_{{sj}}$$
(10)
$${F}_{l}=\mathop{\sum }\limits_{j=1}^{n}{W}_{\theta }{X}_{{lj}}$$
(11)

In the formula, \({F}_{w}\), \({F}_{e}\), \({F}_{f}\), \({F}_{s}\), and \({F}_{l}\) represent the comprehensive evaluation indices of water, energy, food, ecology, and land subsystems, respectively; \({W}_{\alpha }\), \({W}_{\beta }\), \({W}_{\gamma }\), \({W}_{\delta }\), and \({W}_{\theta }\) respectively represent the weights of various indicators in the subsystems of water, energy, food, ecology, and land; \({X}_{{wj}}\), \({X}_{{ej}}\), \({X}_{{fj}}\), \({X}_{{sj}}\), and \({X}_{{lj}}\) are the standardized values of each indicator.

Coupling and coordination degree

The coupling and coordination degree model measures the degree of coupling and coordinated development between internal elements of a system40. The coupling degree reflects the degree of closeness of the interconnection and interaction between elements or systems, while the coordination degree reflects the level of synchronous development and collaborative progress between elements41,42. Therefore, this article adopts a coupled coordination degree model to reflect the coordinated development level of WEFELS. The specific formula is as follows:

$$C=\frac{n\root{n}\of{{u}_{1}\times {u}_{2}\times {u}_{3}\times \cdots \times {u}_{n}}}{{u}_{1}+{u}_{2}+{u}_{3}+\cdots +{u}_{n}}$$
(12)

Where \(C\) represents the coupling degree value; \(n\) is the number of subsystems; \({u}_{i}\) is the comprehensive evaluation index of each subsystem. The greater the value of \(C\), the smaller the degree of dispersion between subsystems and the higher the degree of coupling. The classification criteria are shown in Table 33.

$$T=\mathop{\sum }\limits_{i=1}^{n}{\alpha }_{i}\times {u}_{i}$$
(13)
Table 3 The classification of the degree of coupling and coordination (DCC)
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\(T\) is the comprehensive coordination index; \({\alpha }_{i}\) is the weight of the \(i\)-th subsystem, \(\mathop{\sum }\nolimits_{i=1}^{n}{\alpha }_{i}=1\). The water, energy, food, ecology, and land subsystems provide important resources and spatial environments for human activities and socio-economic development, and are the foundation for the smooth progress of all activities. This article believes that the five subsystems have equal importance in overall coupling and coordination, so the weights of each subsystem are the same.

$$D=\sqrt{C\times T}$$
(14)

Where \(D\) represents the coupling coordination degree value, which reflects the degree of coordination between systems. The division criteria are shown in Table 343.

Obstacle degree model

To understand the key factors hindering the development of the coupling coordination degree of WEFELS, this paper introduces an obstacle degree model to objectively analyze the WEFELS. The obstacle degree model uses factor contribution degree, indicator deviation degree, and obstacle degree of each indicator for calculation44,45.

$${F}_{j}={W}_{j}{{W}^{{\prime} }}_{j}$$
(15)
$${I}_{j}={1-x}_{{ij}}$$
(16)
$${P}_{j}=\frac{{F}_{j}{I}_{j}}{\mathop{\sum }\nolimits_{j=1}^{n}{F}_{j}{I}_{j}}$$
(17)

Where \({F}_{j}\) represents the factor contribution degree, indicating the influence degree of a single factor on the overall goal, \({W}_{j}\) represents the weight of the j-th single indicator, and \({{W}^{{\prime} }}_{j}\) represents the weight of the subsystem to which the \(j\)-th individual indicator belongs; \({I}_{j}\) is the index deviation degree, representing the gap between the single index and the target value, while \({x}_{{ij}}\) is the quantified indicator value; \({P}_{j}\) is the obstacle degree, indicating the influence degree of single index on the overall coupling coordination degree of the system.

Study area and data

The Yangtze River Delta (YRD) region is located in the eastern part of China, covering three provinces (Jiangsu, Zhejiang, and Anhui) and the city of Shanghai, as illustrated in Fig. 11. As urbanization and population growth have progressed, the YRD has faced serious challenges in water resources, energy, food production, and ecological environment. In terms of water resources, the total water resources in the YRD in 2020 were 21,414 billion cubic meters, but the per capita water resources were less than 1/2 of the national average and only 1/8th of the world average. In terms of energy, except for Anhui, Jiangsu, Zhejiang, and Shanghai have insufficient primary energy production and are highly dependent on external imports and imports. As an important granary in the Jianghuai River Basin, although agriculture in the YRD is developed, the per capita grain output is only equivalent to 78% of the national average level. Concurrently, high-intensity regional development has resulted in reduced ecological land use, significant consumption of resources and energy, and prominent issues of water and air pollution31. In addition, the rapid urbanization process in the YRD has also intensified the tension of land resources. Between 1990 and 2018, the urbanization rate in the YRD increased from 21.4% to 66.8%, and the average annual growth area of construction land exceeded 700 km2, making it a typical area for rapid urbanization and resource and environmental overload46. Therefore, to achieve sustainable development of resources in the Yangtze River Delta (YRD), it is essential to reasonably plan the use of water, energy, food, ecology, and land resources, and to deeply analyze the coupling and coordinated development status of the water-energy-food-ecology-land composite system in the region.

Fig. 11: Location and regions of the YRD of China.
figure 11

The black lines are city cluster boundaries, the white areas show different provinces in China and the blue area (a shaded part of a map of China) represents the Yangtze River Delta region.

Full size image

The data are sourced from the Annual Statistical Yearbook of China, the Annual Report on Soil and Water Conservation of China, as well as the statistical yearbooks, water resources bulletins, and environmental protection situation bulletins of each region from 2005 to 2022. Missing data for certain indicators in some years were filled in using the linear interpolation method.