Spatiotemporal dynamics and sustainable pathways of water ecological footprint in northeast Sichuan, China

Given the characteristics of water resources in Northeast Sichuan, a systematic evaluation system for water resources utilization was developed based on the water resources ecological footprint model and calculation methods. This system was applied to analyze the ecological footprint of water resources and the sustainable development capacity of Northeast Sichuan from 2013 to 2022. The results showed the following: (1) The ecological footprint of water resources in Northeast Sichuan increased by a total of 1.35 × 10⁶ ha(hectare) from 2013 to 2022, with the largest per capita ecological footprint of water resources in each city being Guangyuan and the smallest being Bazhong. (2) The ecological carrying capacity of water resources in the region varies greatly from year to year, mainly affected by the total amount of water resources and the amount of precipitation in that year, with large spatial differences between districts and counties, and a general downward trend. (3) The ecological footprint of water quality shows a trend of first increasing and then continuously decreasing, indicating that the treatment rate of wastewater is continuously improving. The value of water resources’ ecological footprint of 10,000 yuan GDP (Gross Domestic Product) in each district and county basically stays around 0.10 ha, and the utilization efficiency of water resources is improving. (4) Water resource utilization and economic growth in Northeast Sichuan are in a weakly decoupled state, and economic growth is still dependent on water resource consumption, and benign development is not stable enough. In the future, attention will be paid to the ecological protection of water resources to avoid possible ecological risks and water scarcity. This will ensure the sustainable development of water resources.

1 Introduction

The uneven distribution of water resources, as a key resource of the earth, has led to shortages and deterioration of water quality in some regions, while other regions have experienced wastage and pollution due to mismanagement (Wang et al., 2019). Global industrialization and urbanization have exacerbated the impacts of climate change in the form of melting glaciers, fluctuating river runoff, and increased water pollution, putting systemic pressure on all parts of the water cycle, from water sources (Cheng et al., 2021). The current core challenges of water resources management include pollution, groundwater overdraft, and inefficient utilization (Jing et al., 2024), which not only threaten human health and socio-economic development but also pose significant risks to ecosystem sustainability (Zheng et al., 2022). In order to realize the coordination of the “social-economic-ecological” system, it is urgent to promote the sustainable use of water resources through innovative concepts, which has become a major issue of international consensus (Feng, 2018).

The Ecological Footprint (EF), a pivotal metric for quantifying anthropogenic impacts on Earth’s ecosystems, was initially proposed by William Rees and subsequently refined by his student Wackernagel and Rees (1997) (Rees, 1996; Rees, 1992). This concept was introduced to China by Xu and Chen (2001) (Xu et al., 2002) and progressively applied to sustainability research domains such as water resources (Xu et al., 2003). Domestic scholars have substantially advanced its applications, manifested through: (1) Deepening of research contents–Duan et al. (2012) incorporated freshwater and pollution accounts into the original model, aiming to address its limitations in water resource assessment and environmental carrying capacity quantification; Li et al. (2015) conducted empirical analysis of China’s water resource ecological footprint and carrying capacity in 2012. (2) Innovation in methodologies–Ouyang et al. (2023) integrated grey water footprint to quantify water required for pollution assimilation, coupled with system dynamics simulation for sustainable water utilization regulation; Xiong et al. (2022) developed an enhanced three-dimensional EF model resolving fossil energy carrying capacity deficits while optimizing land-use equivalence factors. This methodological framework originated from Niccolucci et al. (2011), who first defined the stock-flow relationship in ecological capital accounting. From regional scales to provincial, municipal, and county scales, many scholars have analyzed the development and utilization of water resources in regions such as Beijing (Yue et al., 2021; Qin et al., 2023; Bin et al., 2023), Tianjin (Yue et al., 2022; Liu and Song, 2021), Shaanxi (Zhang et al., 2021), and Sichuan (Zhao et al., 2020; Li et al., 2023), and put forward targeted suggestions.

One of the most complex aspects of water resource accounting is assessing point source and non-point source pollution caused by various human activities. Scholars at home and abroad have mainly followed the water footprint theoretical framework proposed by Hoekstra (2017). In their research on water quality ecological footprint, and gradually incorporated the accounting of pollutant assimilation capacity. In recent years, researchers have taken the Grey Water Footprint as a core indicator, whose development serves as one of the methods for quantifying water pollution. By quantifying the degree of water resource occupation during the pollutant assimilation process, the Grey Water Footprint provides an important methodological tool for evaluating the impact of human activities on the water environment (Ene and Teodosiu, 2011).

Northeast Sichuan, as a comprehensive transportation hub for eastward and northward Sichuan, is a key area to support national strategies such as the Western Development and the Yangtze River Economic Belt. The site is located in the ecological barrier zone of the upper reaches of the Yangtze River in China, with well-developed water systems but uneven spatial and temporal distribution of water resources. Accompanied by rapid urbanization, water demand for industry and agriculture continues to grow, while high water-consuming industrial agglomeration and agricultural water inefficiency lead to increased resource wastage, posing a serious challenge to integrated water resources management (Wang, 2024). Given this, it is crucial for this study to quantitatively analyze the spatial and temporal evolution patterns and influence mechanisms of the water resources ecological footprint in Northeast Sichuan from 2013 to 2022 based on the water resources ecological footprint theory, using the water resources ecological footprint, ecological carrying capacity, water quality ecological footprint, and the Tapio decoupling model.

2 Materials and methods

2.1 Overview of the study area

Northeast Sichuan includes five prefecture-level cities, Nanchong, Guang’an, Dazhou, Bazhong, and Guangyuan, with a total land area of 64,004.4 km² (accounting for 0.66% of China’s total area) as of 2021, and a total resident population of 19.071 million (with an urbanization rate of 48.85%) at the end of 2022. Figure 1 shows an overview map of the geographic location of the study area. The region is bounded by the parallel ridge and valley of east Sichuan in the east, with high terrain in the north and low terrain in the south, with large topographic ups and downs, and it is the southern edge of China’s north-south transition zone and the key zone for the extension of the mountainous areas of the Sichuan Basin circumference to the center of the basin. The climate type of this area belongs to the su btropical humid monsoon climate, rain and heat at the same time, the southern low mountainous area is cold in winter and hot in summer, the northern middle mountainous area is cold in winter and cool in summer, precipitation is mainly concentrated from June to September, with large seasonal and interannual variations, the Qiujiang River and the Jialing River flow through the area, the amount of water is plentiful, the environment is superior, but there is an imbalance of water resources in the distribution of space and time (Pan et al., 2020).

Figure 1

Figure 1. Overview of the study area. Note: This map is based on the standard map downloaded from the Standard Map Service Website of the Ministry of Natural Resources of the People’s Republic of China, with map review number GS (2019)1822. The base map boundaries have not been modified in any way, and subsequent maps follow the same principle.

As shown in Figure 2, the interannual variation trends of total water resources, surface water resources and precipitation in Northeast Sichuan are consistent, while the groundwater resources remain stable with a small variation range. Affected by the significant monsoon climate, the region’s water resources are mainly replenished by atmospheric precipitation, which is concentrated in June-September. From 2013 to 2022, the annual average precipitation in the region was 76.229 billion m³, and the annual average total water resources was 35.225 billion m³. The maximum values of both precipitation and total water resources occurred in 2021, and the minimum values in 2016, indicating that the total water resources are significantly affected by precipitation. Specifically, 2019 and 2021 were wet years; 2013, 2017, 2018 and 2020 were normal flow years; and 2015, 2016 and 2022 were dry years.

Figure 2

Figure 2. Trend of water resources in Northeast of Sichuan Province from 2013 to 2022.

By the end of 2022, the total precipitation in Northeast Sichuan was 65.973 billion m³, the surface water resources were 21.528 billion m³ (accounting for 9.75% of Sichuan Province’s total surface water resources), and the groundwater resources were 4.012 billion m³ (accounting for 7.33% of Sichuan Province’s total groundwater resources). Affected by the severe drought in Sichuan Province in 2022 (the average precipitation in the province was 842.7 mm, the fourth lowest rainfall year since 1956, a decrease of 12.4% compared with the multi-year average), the total precipitation in the region in 2022 decreased by 41.418 billion m³ compared with 2021 (a year-on-year decrease of 38.6% and a decrease of 13.5% compared with the multi-year average), and the total water resources decreased by 40.178 billion m³ compared with 2021 (a year-on-year decrease of 61.1% and a decrease of 27.5% compared with the multi-year average). Only Bazhong City saw a slight increase in precipitation by 1.4%, while the other four cities experienced varying degrees of decrease.

2.2 Data sources

The datasets employed in this study encompass three categories: water resources, socio-economic indices, and geospatial information. Water resources data comprise annual precipitation, total water resources, surface water, groundwater, and sectoral water consumption (agricultural, industrial, domestic, ecological), alongside wastewater discharge, ammonia nitrogen emissions, and chemical oxygen demand. These data are sourced from the Water Resources Bulletin of Sichuan Province, Ecological Environment Status Bulletin of Sichuan Province, and the Statistical Yearbooks of Sichuan Province and its prefecture-level cities for the period 2013–2022. Socio-economic data (including gross regional product, permanent resident population, population density, and urbanization rate) are sourced from the Statistical Yearbooks at both provincial and municipal levels for the period 2013–2022. Geospatial datasets (administrative boundaries, administrative centers, digital elevation models, river networks) were obtained from the Resource and Environment Science and Data Center (RESDC) of the Chinese Academy of Sciences and the Geospatial Data Cloud platform.

2.3 Research method

2.3.1 Water ecological footprint model

The ecological footprint of water resources is used to measure the per capita water consumption in a region and convert it into the corresponding land area (Sun et al., 2024), by converting the water resources consumed by various types of production, living and ecological activities in the region into the corresponding ecological area, to show the consumption of water resources in a quantitative form (Guan, 2017). The formula for its calculation is as follows:

$E{F}_W=N\times {\text{ef}}_{\mathrm{w}}=N\times {\gamma }_w\times \left(W/P_W\right)(1)$

Where $E{F}_W$ is the total ecological footprint of water resources (ha); ${\text{ef}}_{\mathrm{w}}$ is the ecological footprint of water resources per capita (ha/person); N represents the number of resident population at the end of the year (persons); ${\gamma }_w$ is the global equilibrium factor of water resources; W represents the total consumption of water resources (cubic meters); and $P_W$ is the global average production capacity of water resources (m³/ha).

2.3.2 Water ecological carrying capacity model

The concept of ecological carrying capacity of water resources refers to the upper limit of water resources in a region to meet the water demand for domestic, productive, and ecological use (Xu et al., 2013). It measures the maximum capacity of a natural system to provide resources and services within a sustainable range (Zhang et al., 2012). The formula is as follows:

$E{C}_W=N\times {\text{ec}}_{\mathrm{w}}=0.4\times \psi \times {\gamma }_w\times Q/P_W(2)$

Where, $E{C}_W$ is the ecological carrying capacity of water resources (ha); ${\text{ec}}_{\mathrm{w}}$ is the ecological carrying capacity of water resources per capita (ha/person); $\psi$ represents the regional water resources production factor; and Q represents the total amount of water resources (cubic meters). A region consumes 60% of its water resources to maintain regional biodiversity and ecosystems themselves, so the ecological carrying capacity of water resources needs to be multiplied by.

2.3.3 Water quality ecological footprint model

The ecological footprint of water quality is the area of water resource land required to absorb water pollutants discharged during urban production and living processes (Zhang et al., 2013). Chemical oxygen demand (COD) and ammonia nitrogen (NH₃) emissions are key indicators characterizing the emission patterns of organic pollutants and nitrogen pollution in industrial wastewater: COD reflects the total amount of reducing organic matter, and ammonia nitrogen directly indicates the degradation degree of nitrogen-containing pollutants. According to China’s “Action Plan for Water Pollution Prevention and Control”, COD and NH₃ are binding indicators for total pollutant control, which are of direct significance for practical supervision. Among the monitoring data of the study area, the continuity and integrity of these two parameters are also better than those of other indicators (e.g., total phosphorus, heavy metals). In addition, many core papers have selected COD and NH₃ emissions to analyze the dynamic changes of water quality ecological footprint. Therefore, these two indicators were prioritized in this study (Huang et al., 2008). Their calculation formulas are as follows:

$E{F}_{\mathrm{q}}=E{F}_{COD}+E{F}_{N{H}_3}=\gamma {}_W\times \left[\left(U_{COD}/P_{COD}\right)+\left(U_{N{H}_3}/P_{N{H}_3}\right)\right](3)$

Where $E{F}_{\mathrm{q}}$ is the ecological footprint of water quality (ha); $E{F}_{COD}$, $E{F}_{N{H}_3}$ respectively, is the ecological footprint of water quality of COD, NH₃ (ha); $U_{COD}$, $U_{N{H}_3}$, respectively, represents the emissions of COD, NH₃ (cubic meters); $P_{COD}$, $P_{N{H}_3}$, respectively, represents the purification capacity of COD, NH₃ per unit area of water (m³/ha).

2.3.4 The efficiency of water resource ecological footprint

The water resources ecological footprint of 10,000 Yuan GDP, which is calculated as a standardized ratio of water resources ecological footprint to regional gross domestic product (GDP), constructs a quantitative relationship between economic output and water resources consumption, and can assess the efficiency of regional water resources use. The calculation formula is as follows:

$EFwper10,000YuanGDP=E{F}_W/GDP(4)$

2.3.5 Tapio decoupling model

Decoupling models are often used to explore the interrelationships between economic growth and the resource environment and their changes. In this study, the Tapio decoupling model is introduced to calculate the decoupling elasticity of water resource use and economic growth in Northeast Sichuan, and the decoupling status of Northeast Sichuan is evaluated and analyzed. The calculation model is as follows:

$T=\Delta E{F}_W/\Delta GDP=\left[\left(E{F}_{W_{\mathrm{t}}}-E{F}_{W_{\mathrm{t}-1}}\right)/E{F}_{W_{\mathrm{t}-1}}\right]/\left[\left(GD{P}_{\mathrm{t}}-GD{P}_{\mathrm{t}-1}\right)/GD{P}_{\mathrm{t}-1}\right](5)$

Where $\Delta E{F}_W$, $\Delta GDP$ denotes the growth rate of water resources ecological footprint, economic growth rate, respectively; $T$ denotes the decoupling index. In this paper, based on the research of Jia Li (Jia et al., 2018), Pan Yuanquan (Pan et al., 2020), and others, the evaluation standard of decoupling status is determined. When $\Delta E{F}_W$ is lower than $\Delta GDP$, the two are decoupled and the development is more coordinated; on the contrary, when $\Delta E{F}_W$ is higher than $\Delta GDP$, it indicates that the two are not decoupled, and the economic growth is still dependent on the consumption of water resources, and the benign development is not stable enough (Liu et al., 2025).

The index factor decomposition method is used to assess the degree of influence of each factor by decomposing the indicators of the influencing factors into simple indicators that are easy to study (Zhu et al., 2022). Currently, Laspeyres and Divisia are commonly used exponential decomposition methods, but they suffer from the problem of residual terms and “0” values. To address these issues, the LMDI (Logarithmic Mean Divisia Index) model was chosen in this study to analyze the drivers of water resources ecological footprint changes in Northeast Sichuan, as follows (Zuo et al., 2020):

${\text{ef}}_{\mathrm{t}}=\sum _{\mathrm{i}=1}^3\left[\left({\text{ef}}_{\text{it}}/{\text{ef}}_{\mathrm{t}}\right)·\left({\text{ef}}_{\mathrm{t}}/{\mathrm{r}}_{\mathrm{t}}\right)·\left({\mathrm{r}}_{\mathrm{t}}/{\mathrm{p}}_{\mathrm{t}}\right)·{\mathrm{p}}_{\mathrm{t}}\right](6)$

$\Delta {\text{ef}}_t=e{f}_t-e{f}_0=\Delta e{f}_s+\Delta e{f}_i+\Delta e{f}_r+\Delta e{f}_p(7)$

Where $e{f}_t$ is the ecological footprint of water resources per capita in year t for category i (ha/person); $e{f}_0$ is the ecological footprint of water resources per capita in year t (ha/person); ${\mathrm{r}}_t$, $p_t$ are the regional GDP (in millions of yuan) and the number of resident population (in ten thousand) in year t, respectively; $e{f}_0$ is the ecological footprint of water resources per capita (ha/person) in 2013; $e{f}_s$, $e{f}_i$, $e{f}_r$, $e{f}_p$ represent structural effects, technological effects, economic effects, and demographic effects; $\Delta e{f}_s$, $\Delta e{f}_i$, $\Delta e{f}_r$, $\Delta e{f}_p$ denote the amount of change in the ecological footprint of water resources per capita (ha/person) caused by each factor, respectively.

$\Delta {\text{ef}}_{\mathrm{s}}=\sum _{i=0}^3\left\{\left[\left({\text{ef}}_{\text{it}}-{\text{ef}}_{\mathrm{i}0}\right)/\left(Ine{f}_{it}-Ine{f}_{i0}\right)\right]·In\left(s_t/s_0\right)\right\}(8)$

$\Delta {\text{ef}}_{\mathrm{i}}=In\left(i_t/i_0\right)\sum _{i=0}^3\left(\left({\text{ef}}_{\text{it}}-{\text{ef}}_{\mathrm{i}0}\right)/\left(Ine{f}_{it}-Ine{f}_{i0}\right)\right)(9)$

$\Delta {\text{ef}}_{\mathrm{r}}=In\left(r_t/r_0\right)\sum _{i=0}^3\left(\left({\text{ef}}_{\text{it}}-{\text{ef}}_{\mathrm{i}0}\right)/\left(Ine{f}_{it}-Ine{f}_{i0}\right)\right)(10)$

$\Delta {\text{ef}}_{\mathrm{p}}=In\left(p_t/p_0\right)\sum _{i=0}^3\left(\left({\text{ef}}_{\text{it}}-{\text{ef}}_{\mathrm{i}0}\right)/\left(Ine{f}_{it}-Ine{f}_{i0}\right)\right)(11)$

Where $s_{it}=e{f}_{it}/e{f}_t$; $i_t=e{f}_t/{\mathrm{y}}_{\mathrm{t}}$; ${\mathrm{r}}_t={\mathrm{y}}_{\mathrm{t}}/{\mathrm{p}}_{\mathrm{t}}$, $s_{it}$ is the share of the ecological footprint of water resource category i to the total ecological footprint of water resource in year, t representing the structural effect; $i_t$ is the ecological footprint of water resources per unit of GDP in year, t representing the technology effect; $r_t$ is GDP per capita in year, t representing the economic effect; $p_t$ is the number of permanent residents in year, t representing the demographic effect.

3 Results and analysis

3.1 Temporal variation analysis of water ecological footprint

3.1.1 Change characteristics of water ecological footprint

Calculated using the water resources ecological footprint model, the results are shown in Table 1. The total water resources ecological footprint of Northeast Sichuan’s current situation shows a trend of growth, but the growth rate is small, and the overall tendency is benign. The fluctuation of the total ecological footprint of water resources from 2013 to 2022 remained between 6.256 × 10⁶ ha and 7.652 × 10⁶ ha, with the maximum value occurring in 2018 and the minimum value in 2013. The average ecological footprint of water resources was calculated to be 7.304 × 10⁶ ha over the 10 years, with 80% of the years (2015–2022) being above this baseline level, while only 20% of the years (2013–2014) were below the average. The ecological footprint of water resources of each water account remained relatively stable during the study period, but still showed some degree of fluctuation.

Table 1

Table 1. Northeast of Sichuan province water resources ecological footprint summary.

During 2013–2022, the four water resource ecological footprint (WEF) accounts in Northeast Sichuan exhibited significant proportional disparities. Agricultural WEF dominated the accounts, with a multi-year mean of 4.27 × 10⁶ ha, demonstrating a persistent upward trend that peaked at 4.77 × 10⁶ ha in 2022. This predominance stems from regional agricultural expansion driven by the topographic advantages of deep hills-low mountains in the Sichuan Basin and the Jialing River system, sustaining elevated water demand. Industrial WEF averaged 1.15 × 10⁶ ha, showing a gradual decline throughout the period and reaching its minimum (0.74 × 10⁶ ha) in 2022—a trend directly attributable to the area’s underdeveloped industrial infrastructure and lagging growth. Domestic WEF remained stable at 1.72 × 10⁶ ha annually with modest growth, reflecting routine urban and rural water needs. Ecological WEF constituted the smallest proportion (multi-year mean: 0.17 × 10⁶ ha), its growth effectively constrained through water-saving technology promotion, public awareness enhancement, and optimized allocation measures, maintaining consistently low levels.

3.1.2 Variation signatures of water ecological carrying capacity

The ecological carrying capacity of water resources in northeastern Sichuan was calculated using by Equation 2. Water resource ecological carrying capacity (WRECC) in Northeast Sichuan declined from 2.10 × 10⁷ to 1.69 × 10⁷ ha during 2013–2022, representing a modest 19.52% reduction (Table 2). Despite this gradual decrease, the decadal mean remained substantial at 2.31 × 10⁷ ha. Notable interannual fluctuations included: a minimum of 1.40 × 10⁷ ha (2016) and maximum of 4.34 × 10⁷ ha (2021), with the most extreme interannual changes being +82.35% (2020–2021) and −61.06% (2021–2022). The per capita ecological carrying capacity of water resources corresponds to specific years, with the highest value of 2.27 ha per capita in 2021 and the lowest of 0.70 ha per capita in 2016. The factors influencing the changes in per capita ecological carrying capacity of water resources are mainly the total water resources and total precipitation of the corresponding year. According to the data from the Sichuan Provincial Water Resources Bulletin, the total water resources in 2021 reached 65.718 billion cubic meters, and the total precipitation was 107.391 billion cubic meters—both of which were the highest during the study period, and the ecological carrying capacity of water resources also reached the highest level in the historical years. Therefore, the change in per capita ecological carrying capacity of water resources is closely related to hydrometeorological conditions: years with high carrying capacity are wet years, characterized by sufficient regional water resource recharge; while years with low carrying capacity are mainly characterized by persistent low precipitation and prolonged drought, belonging to dry years. Sustained drought conditions will exacerbate the contradiction between water supply and demand, ultimately leading to a decline in the per capita ecological carrying capacity of water resources.

Table 2

Table 2. Northeast of Sichuan province, total ecological carrying capacity of water resources.

3.1.3 Temporal dynamics of water quality ecological footprint

According to China’s Action Plan for Water Pollution Prevention and Control, chemical oxygen demand (COD) and ammonia nitrogen (NH₃) are binding indicators for the total amount control of pollutants, and they have direct significance for practical supervision. Among the monitoring data of the study area, the continuity and integrity of these two parameters are also better than those of other indicators (such as total phosphorus and heavy metals); therefore, they are prioritized for selection. The water quality ecological footprint data calculated by Formula 3 are illustrated in Figures 3A,B, from 2016 to 2022, COD emissions in Northeast Sichuan exhibited a general downward trend, with NH₃ emissions also demonstrating a decline. The stabilization of COD and NH₃ emissions indicates that Northeast Sichuan is taking measures to address the issue of water environmental pollution. Furthermore, there has been an observed increase in sewage discharge, with a rise from 52,200 tons to 62,900 tons, indicating a growth rate of 20.40%. This represents an average annual growth rate of approximately 3.41%.

Figure 3

Figure 3. Emissions of major indicators of water pollution and Water quality ecological footprint changes in the Northeast of Sichuan Province. (A) Water quality ecological footprint from 2016 to 2022. (B) Emissions of major indicators of water pollution from 2016 to 2022.

This underscores the necessity for enhanced oversight and strict regulation of high water consumption, substantial emissions resulting from enterprise production and business activities, and sustained endeavors to enhance water environment management. These measures are imperative to ensure fundamental improvement in the quality of the regional water environment and to achieve harmonization between economic development and environmental protection.

The water quality ecological footprint of Northeast Sichuan can be calculated from 2016 to 2022. This calculation is based on COD and NH₃ emissions and the water quality ecological footprint model. As demonstrated in Figures 3A,B, the water quality ecological footprint of Northeast Sichuan exhibited an initial increase, followed by a subsequent decline during the period from 2016 to 2022. The water quality ecological footprint exhibited an increasing trend during the period from 2016 to 2017, with a rise from 0.390 × 10⁸ ha in 2016 to 0.418 × 10⁸ ha in 2017, representing a 7.18% increase. This period also marked the attainment of the maximum value for the regional water quality ecological footprint. However, from 2018 to 2022, the water quality ecological footprint demonstrated a persistent downward trend, declining from 0.317 × 10⁸ ha in 2018 to 0.168 × 10⁸ ha in 2022, marking a substantial 47% decrease. A comprehensive analysis reveals that the water quality ecological footprint of Northeast Sichuan has undergone a substantial decline, with a 56.92% decrease observed in 2022 compared to the 2016 baseline. On September 22, 2017, the Sichuan Province promulgated the Regulations on Environmental Protection of Sichuan Province, which took effect on January 1, 2018. Consequently, Northeast Sichuan has exhibited a favorable response to the policy call by implementing stringent environmental protection policies, augmenting environmental protection enforcement, fortifying the treatment of water pollution, safeguarding the regional water environment, sustaining the fundamental functions of the water ecosystem, and progressively diminishing the ecological footprint of water quality.

3.1.4 Evolutionary signatures of water use efficiency

The ecological footprint of water resources of 10,000 yuan GDP is a critical metric for evaluating the coordination between regional water resources utilization and economic development. It effectively reflects the utilization efficiency of regional water resources management. The ecological footprint of water resources of 10,000 yuan GDP is calculated by Equation 4. As demonstrated in Figure 4A, the ecological footprint of water resources associated with 10,000 yuan of GDP exhibited a persistent downward trend from 2013 to 2022, diminishing from a peak of 0.1520 ha in 2015 to 0.0893 ha in 2022, marking a cumulative decrease of 40.68%. The mean value of 0.1218 ha during the study period demonstrates stability below the mean level after 2018. As illustrated in Figure 4B, the Gross Domestic Product (GDP) generated per cubic meter of water exhibited an increase from $108.75 in 2015 to $185.04 in 2022, with an average annual growth rate of 6.2%. This phenomenon aligns with the observed decline in the ecological footprint of water resources of 10,000 yuan GDP, thereby substantiating the systematic enhancement in the region’s water resource utilization efficiency. Consequently, it can be concluded that the region has achieved notable success in its endeavor to promote the development of a water-saving society. This initiative has not only enhanced the economic efficiency of water resources but also established a substantial foundation for the sustainable management of regional water resources.

Figure 4

Figure 4. The ecological footprint of water resources of 10,000 Yuan GDP and Water use efficiency index changes in the Northeast of Sichuan Province. (A) Water resource ecological footprint of 10,000 yuan GDP from 2013 to 2022. (B) Water use efficiency index from 2013 to 2022.

3.2 Geospatial differentiation signatures of aquatic ecosystem footprint

3.2.1 Geospatial differentiation of municipal water footprint per capita

Figure 5 depicts the spatiotemporal evolution of per capita water ecological footprint (PWEF) across five cities in Northeast Sichuan (2013–2022). Bazhong maintained the region’s lowest PWEF (0.24–0.33 ha/capita) due to: (i) topographic constraints in the Daba Mountains limiting agricultural land; (ii) underdeveloped industries reducing water demand; and (iii) abundant Jialing River tributary flows under humid subtropical monsoon climate ensuring water balance. Conversely, Guangyuan exhibited the highest PWEF (0.394–0.456 ha/capita), attributable to its mountain-basin transitional position where: (a) 75% agricultural population sustains 6.5-million-acre croplands; (b) water-intensive industries (textiles, machinery, chemicals) dominate. Guang’an, Nanchong, and Dazhou showed moderate PWEF variability (0.271–0.426 ha/capita) with stable total water resources.

Figure 5

Figure 5. Northeast of Sichuan Province spatial distribution of per capita ecological footprint of water resources by municipalities. (a) Spatial distribution of per capita water resource ecological footprint by municipalities in Northeast of Sichuan Province (Year 2013), (b) Spatial distribution of per capita water resource ecological footprint by municipalities in Northeast of Sichuan Province (Year 2016), (c) Spatial distribution of per capita water resource ecological footprint by municipalities in Northeast of Sichuan Province (Year 2019), (d) Spatial distribution of per capita water resource ecological footprint by municipalities in Northeast of Sichuan Province (Year 2022).

3.2.2 Inter-city spatial heterogeneity analysis of per capita water ecological carrying capacity

Figure 6 demonstrates that the spatial distribution of per capita ecological carrying capacity of water resources in the five cities in northeast Sichuan exhibits unevenness across the four periods of 2013, 2016, 2019, and 2022, thereby manifesting a trend of “high in the north and low in the south”. The ecological footprint of water resources per capita in northeastern Sichuan is maintained at approximately 1.35 ha/person across the four periods. Guangyuan City has the highest per capita water resources carrying capacity, with an average of 2.468 ha/person over 10 years and a change interval of 1.365–2.468 ha/person. Bazhong City follows with an average of 1.765 ha/person over 10 years and a change interval of 0.779–3.605 ha/person. These two cities are located in the Qinba Mountain area. They are influenced by southwest airflow and have high precipitation, a developed regional water system, abundant runoff resources, and a high ecological carrying capacity of water resources. Nanchong City has had the lowest per capita water resources carrying capacity in northeast Sichuan for 9 years. This is mainly due to low precipitation in Nanchong City compared to other areas in northeast Sichuan. The continuous development of agriculture and industry, as well as the gradual increase in population, has put great pressure on water resources. Therefore, adjusting the water use structure is inevitable.

Figure 6

Figure 6. Northeast of Sichuan Province, spatial distribution of per capita water resource ecological carrying capacity of municipalities. (a) Spatial distribution of per capita water resource ecological carrying capacity of municipalities in Northeast of Sichuan Province (Year 2013), (b) Spatial distribution of per capita water resource ecological carrying capacity of municipalities in Northeast of Sichuan Province (Year 2016), (c) Spatial distribution of per capita water resource ecological carrying capacity of municipalities in Northeast of Sichuan Province (Year 2019), (d) Spatial distribution of per capita water resource ecological carrying capacity of municipalities in Northeast of Sichuan Province (Year 2022).

3.2.3 Spatial analysis of water use efficiency by municipality

Figure 7 illustrates the systematic decline in water ecological footprint per 10,000 Chinese Yuan GDP (WEF/10⁴CNY) across five cities in Northeast Sichuan (2013–2022), revealing pronounced spatial heterogeneity. Southern Nanchong (decadal mean: 0.1168 ha/10⁴CNY), Dazhou (0.1173), and Guang’an (0.1156) formed a water-efficient cluster, successively achieving regional minima during 2013–2014, 2015–2017, and 2018–2022 respectively. This progression demonstrates synergistic optimization between economic development and intensive water resource utilization. Conversely, northern Bazhong (0.1439) and Guangyuan (0.1356) consistently exceeded regional averages, with Bazhong recording the highest values in 9 of 10 years. These disparities stem from delayed industrial restructuring and inadequate technological innovation capacity, necessitating institutional reforms and technology diffusion to enhance water use efficiency.

Figure 7

Figure 7. Northeast of Sichuan Province spatial distribution of water resource ecological footprint of 10,000 Yuan GDP by municipalities. (a) Spatial distribution of water resource ecological footprint of 10,000 Yuan GDP by municipalities in Northeast of Sichuan Province (Year 2013), (b) Spatial distribution of water resource ecological footprint of 10,000 Yuan GDP by municipalities in Northeast of Sichuan Province (Year 2016), (c) Spatial distribution of water resource ecological footprint of 10,000 Yuan GDP by municipalities in Northeast of Sichuan Province (Year 2019), (d) Spatial distribution of water resource ecological footprint of 10,000 Yuan GDP by municipalities in Northeast of Sichuan Province (Year 2022).

3.3 Analysis of ecological footprint drivers for water resources

3.3.1 Tapio decoupling model coordination relationship analysis

The Tapio decoupling index is calculated by Equation 5. Tapio decoupling analysis reveals that elevated water ecological footprint (WEF) growth intensifies water resource pressure during economic expansion, resulting in weaker decoupling (60% of years exhibiting weak decoupling in Northeast Sichuan, 2013–2022). Table 3 and Figure 8 illustrate the dynamics of decoupling water use and economic development in the northeast Sichuan region from 2013 to 2022. Conversely, diminished or stabilized WEF growth facilitates stronger resource-economic decoupling by reducing water dependency. The region manifested three decoupling states: expansive negative decoupling in 2015 (WEF growth > GDP growth), strong decoupling in 2017/2019/2020 (WEF declines of −1.85% to −1.61%), and predominant weak decoupling in remaining years (WEF growth < GDP growth). Although 90% of years achieved decoupling—confirming effective water conservation amid economic growth—the prevalence of weak decoupling indicates persistent resource dependence and systemic instability. Strategic optimizations are imperative: implementing water-saving policies, upgrading industrial structures, refining urban-water system integration, and advancing sustainable water-economy coordination mechanisms.

Table 3

Table 3. Decoupling index for northeast Sichuan from 2013 to 2022.

Figure 8

Figure 8. Dynamics of decoupling water use and economic development in the Northeast of Sichuan Province from 2013 to 2022.

3.3.2 Analysis of LMDI model drivers

The LMDI decomposition quantifies drivers of water ecological footprint dynamics in Northeast Sichuan (2014–2022), where positive/negative decomposed effects respectively promote/inhibit WEF growth (Lin et al., 2017; Sun et al., 2024). Calculations based on Equations 6–11, categorized by structural, technological, economic, and population effects (2013 base year; Table 4), results reveal a mean total effect of +7.09 million ha—consistent with observed WEF increases. Crucially, structural and economic effects consistently exhibited positive contributions, while technological effects remained negative (except 2015) alongside persistently inhibitory population effects, demonstrating multi-dimensional drivers of regional water resource pressure.

Table 4

Table 4. Northeast of Sichuan Province decomposition values of water resources ecological footprint factors.

During 2014–2022, the economic effect consistently exerted a positive driving force on the water resource ecological footprint (WEF) in Northeast Sichuan, constituting the dominant growth factor. Its decomposition value increased persistently from 30.1 million ha in 2014 to 274.4 million ha in 2022. Concurrently, the regional gross domestic product (GDP) rose from 453.765 billion yuan to 851.798 billion yuan, reflecting an average annual growth rate of 9.75%. Economic expansion triggered water demand escalation, thereby propelling a sustained increase in WEF and intensifying regional water stress.

The structural effect characterizes how industrial composition and water conservation measures influence water use efficiency. During 2014–2022, this effect remained positive but exhibited a low contribution rate with decelerating growth, indicating limited positive driving force on the water resource ecological footprint (WEF) in Northeast Sichuan. This trajectory reflects synergistic effects from regional industrial restructuring—transitioning water-intensive industries to low-water-consumption, high-technology sectors—and widespread adoption of water-saving technologies. By enhancing water use efficiency and curbing excessive resource consumption, these measures effectively alleviated growth pressure on the WEF.

The uncertainty of the LMDI model in this study mainly stems from slight fluctuations in input data (such as total water consumption and GDP), with relatively small errors.

The technological effect exhibited consistently negative values during 2014–2022 (except 2015), indicating a significant inhibitory effect on water resource ecological footprint (WEF) growth. The core mechanism involves reducing water consumption per unit GDP through technological innovation and application, thereby curbing the ecological footprint. With iterative advancements in green technologies, intelligent water management systems, and water conservation techniques, this effect will continue strengthening its constraining influence on the ecological footprint. Such progression fosters synergistic development between economic growth and sustainable water resource utilization.

From the perspective of the population effect, the decomposition value of the population effect has always remained negative during the period of 2014–2022, which effectively inhibits the increase of the ecological footprint of water resources to a certain extent. The phenomenon under scrutiny can be attributed primarily to the stringent enforcement of the national family planning policy, which has been implemented to effectively regulate the population base. Concurrently, the rising expenditure on child education, healthcare, and other forms of support has led to a decline in the region’s birth rate, thereby reducing the natural population growth rate. Consequently, the population effect has hindered the expansion of the ecological footprint of water resources.

4 Discussion

The predominant nature of produced water in total water consumption underscores the pivotal role of enhancing its utilization efficiency in mitigating regional water stress (Tao and Guo, 2018). The ecological footprint of production water constituted a substantial proportion of each water use account in northeast Sichuan, a finding that aligns with the results of the water resources ecological footprint study of Sichuan Province by Li et al. (2023). The optimization of production water management has been demonstrated to be an effective strategy for reducing water consumption and providing substantial support for the sustainable utilization of regional water resources.

Water resource ecological carrying capacity demonstrates a significant correlation with regional precipitation, a finding consistent with Wang et al. (2025) in the Three-River Source Region. As the primary replenishment source of water resources, precipitation governs regional water availability through its interannual variability and seasonal distribution patterns, directly regulating surface runoff and groundwater recharge. Furthermore, alterations in precipitation regimes influence ecosystem stability indirectly by modulating ecological factors such as vegetation coverage and soil moisture content.

Water resource ecological carrying capacity (WRECC) in Northeast Sichuan exhibits marked spatial heterogeneity, a characteristic reflecting regional natural water endowment that aligns with Yang et al.'s (Yang et al., 2024) findings for China’s seven major river basins. The higher WRECC in northern areas is primarily attributed to the Daba Mountains’ subtropical monsoon climate, characterized by extended flood seasons and abundant precipitation that generates richer surface water resources. To enhance regional water sustainability, priority should be given to optimizing production water management, strengthening regulatory control over domestic water use, and promoting eco-environmental water allocation practices.

The findings of this study align with the research results of scholars such as Qian and He (2011) and Gao et al. (2020), which demonstrate that regional industrial structure, economic development level, and the degree of water resource pollution have a more significant impact on the water resource utilization efficiency. In order to enhance the efficacy with which regional water resources are utilized, government regulation of water usage behavior is one potential solution. Such regulation would take the form of institutional constraints. Conversely, the conditions of regional water resources endowment mandate the formulation of a scientific reserve strategy tailored to local circumstances, thereby ensuring the long-term sustainable utilization of regional water resources.

5 Conclusion

1. From 2013 to 2022, the total water resources ecological footprint in Northeast Sichuan increased by 1.35 × 10⁶ hectares. The driving force behind this growth lies in the rising water demand caused by population expansion and the development of urban areas, industry, and agriculture. Among the four water resource categories, agricultural water has the highest proportion of ecological footprint but the lowest ecological water consumption. In terms of spatial distribution, it presents a “high in the west and low in the east” pattern: the western region shows gentle changes due to the balance between economic development and ecological benefits, while the eastern region has the phenomenon of excessive water consumption accompanying economic growth.

2. The annual average value of regional water resources ecological carrying capacity is 2.31 × 10⁷ hectares, showing an overall fluctuating downward trend. The inter-annual fluctuations are mainly affected by the total precipitation, and it is necessary to strengthen water storage projects to ensure stable water supply. In terms of spatial differences, the per capita carrying capacity of Guangyuan and Bazhong in the northern part is significantly higher than that of Nanchong and Guang’an in the southern part.

3. From 2016 to 2022, the water quality ecological footprint showed a trend of “first increasing and then decreasing”. Although the emissions of COD and NH₃ in the region decreased, the wastewater discharge still showed an upward trend. This indicates that the current environmental protection policies have improved the wastewater treatment rate, but it is necessary to further strengthen the control of wastewater discharge to reduce pollution risks. During the same period, the water resources ecological footprint per 10,000 yuan of GDP decreased by 40.68% from 2013 to 2022. Among them, the indicator levels of Nanchong, Dazhou, and Guang’an are relatively low, and the improvement effect of water resources utilization efficiency is more significant.

4. The water resources utilization and economic growth in the region are in a state of weak decoupling, and the dependence of economic growth on water consumption has not been fundamentally changed. The LMDI index decomposition shows that both the economic effect and the structural effect promote the expansion of the water resources ecological footprint, but the growth rate has slowed down significantly; the technological effect and the demographic effect effectively inhibit the growth of the footprint, and have become key driving factors supporting the sustainable development of water resources, providing a direction for the subsequent optimization of water resources management.

Data availability statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material.

Author contributions

AP: Writing – review and editing. LC: Writing – original draft. CS: Writing – review and editing. YY: Writing – original draft. HZ: Writing – original draft. RC: Writing – original draft.

Funding

The author(s) declare that financial support was received for the research and/or publication of this article. This project was supported by Study on the Coordinated Development of Population-Economy-Space in Northeastern Sichuan Cities, China (473527), Study on the Ecological Vulnerability Characteristics and Management of the Dry and Hot River Valley in Southwestern Sichuan, China (412886), Study on the Ecologically Sensitive Areas of the Jialing River Basin, China (463083).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declare that no Generative AI was used in the creation of this manuscript.

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References

Bin, L., Jiang, R., Cao, Y., Xu, K., Han, Z., et al. (2023). Dynamic change and prediction of water resources ecological footprint in Beijing-Tianjin-Hebei region. Water Resour. Prot. 39 (5), 32–38. doi:10.3880/j.issn.1004-6933.2023.05.005

CrossRef Full Text | Google Scholar

Cheng, J., You, Q., Zhou, Y., Cai, M., Pepin, N., Chen, D., et al. (2021). Increasing cloud water resource in a warming world. Environ. Res. Lett. 16 (12), 124067. doi:10.1088/1748-9326/ac3db0

CrossRef Full Text | Google Scholar

Duan, J., Kang, M. Y., and Jiang, Y. (2012). An improved ecological footprint model based on freshwater resource account and pollution account. J. Nat. Resour. 27 (6), 953–963.

Google Scholar

Ene, S., and Teodosiu, C. (2011). Grey water footprint assessment and challenges for its implementation. Environ. Eng. Manag. J. 10 (3), 333–340. doi:10.30638/eemj.2011.049

CrossRef Full Text | Google Scholar

Feng, X. Y. (2018). The evolution of China's sustainable development theory in the 40 years of reform and opening up. Mod. Manag. Sci. 2018(6), 27–29.

Google Scholar

Gao, M. F., Yu, H., and Zheng, J. (2020). Study on green water efficiency and spatial drivers in the Yellow River Basin. Ecol. Econ. 36 (7), 44–50.

Google Scholar

Guan, G. G. (2017). Study on ecological footprint of water use and ecological footprint of polluted water in Shanxi Province. J. Shanxi Normal Univ. 11.

Google Scholar

Hoekstra, A. Y. (2017). Water footprint assessment: evolvement of a new research field. Water Resour. Manag. 31 (10), 3061–3081. doi:10.1007/s11269-017-1618-5

CrossRef Full Text | Google Scholar

Huang, L. N., Zhang, W. X., Jiang, C. L., and Fan, X. Q. (2008). Methodology for calculating the ecological footprint of water resources. Acta Ecol. Sin. (3), 1279–1286. doi:10.3321/j.issn:1000-0933.2008.03.044

CrossRef Full Text | Google Scholar

Jia, L., Liu, Y. H., and Shen, Y. J. (2018). Decoupling analysis of water resources utilization and economic development - a case study of beibu gulf economic zone of Guangxi. Yangtze River 49 (2), 40–45. doi:10.16232/j.cnki.1001-4179.2018.02.009

CrossRef Full Text | Google Scholar

Jing, Y. Y., Zheng, D. F., and Zhang, X. R. (2024). Interprovincial water resources efficiency and resilience measures and their co-evolutionary analysis in China. Pearl River 45 (9), 47–55. doi:10.3969/j.issn.1001-9235.2024.09.006

CrossRef Full Text | Google Scholar

Li, X. Z., Lin, A. W., Yang, Q., Ji, Y., and Qiu, L. J. (2015). Supply-demand balance analysis of ecological carrying capacity of water resources in China based on ecological footprint. Hubei Agric. Sci. 54 (20), 5008–5014. doi:10.14088/j.cnki.issn0439-8114.2015.20.021

CrossRef Full Text | Google Scholar

Li, F. G., Liu, W., Dong, Z. F., Jia, Z., and Li, Q. (2023). Comprehensive evaluation of ecological footprint and sustainable utilization in Sichuan Province. Environ. Pollut. Control 45 (2), 245–249. doi:10.15985/j.cnki.1001-3865.2023.02.019

CrossRef Full Text | Google Scholar

Lin, Y. N., Jin, X. B., Yang, X. H., Long, Y., and Zhou, Y. K. (2017). Estimation and spatial reconstruction of the amount of urban and rural construction land in Jiangsu Province in the past two centuries. J. Geogr. Sci. 72 (3), 488–506. doi:10.11821/dlxb201703010

CrossRef Full Text | Google Scholar

Liu, Z. J., and Song, Y. J. (2021). Analysis of ecological footprint model for sustainable utilization of water resources in Tianjin. J. Changchun Normal Univ. 40 (8), 109–115. doi:10.3969/j.issn.1008-178X.2021.04.020

CrossRef Full Text | Google Scholar

Liu, J., Xu, Y., and Wang, H. (2025). Evaluation of the decoupling of water utilization analysis and economic development in megacities - a case study of Shanghai Municipality. J. China West Normal Univ. 46 (2), 178–184. doi:10.16246/j.issn.1673-5072.2025.02.010

CrossRef Full Text | Google Scholar

Niccolucci, V., Galli, A., Reed, A., Neri, E., Wackernagel, M., and Bastianoni, S. (2011). Towards a 3D national ecological footprint geography. Ecol. Model. 222 (16), 2939–2944. doi:10.1016/j.ecolmodel.2011.04.020

CrossRef Full Text | Google Scholar

Ouyang, X. T., Liao, H. Y., Jiang, Q. X., and Wang, Z. L. (2023). Simulation and regulation of sustainable water resources utilization in China based on an improved ecological footprint model of water resources. J. Environ. Sci. 44 (3), 1368–1377. doi:10.13227/j.hjkx.202204267

PubMed Abstract | CrossRef Full Text | Google Scholar

Pan, Y. Q., Zhang, Q. L., and Xu, L. Y. (2020). Exploring the coordinated relationship between water resources utilization and economic development. Jiangsu Water Resour. (5), 18–22. doi:10.16310/j.cnki.jssl.2020.05.004

CrossRef Full Text | Google Scholar

Qian, W. J., and He, C. F. (2011). Study on regional differences in water resource utilization efficiency and influencing factors in China. Chin. J. Popul. Resour. Environ. 21 (2), 54–60. doi:10.3969/j.issn.1002-2104.2011.02.010

CrossRef Full Text | Google Scholar

Qin, H. H., Huang, L. X., and Wang, J. Q. (2023). Prediction and evaluation of water resources ecological footprint in Beijing under the influence of changing environment. Pearl River 44 (8), 25–35. doi:10.3969/j.issn.1001-9235.2023.08.004

CrossRef Full Text | Google Scholar

Rees, W. E. (1992). Ecological footprints and appropriated carrying capacity: what urban economics leaves out. Environ. Urbanization 4 (2), 66–77. doi:10.1177/2455747117699722

CrossRef Full Text | Google Scholar

Rees, W. E. (1996). Revisiting carrying capacity: area-based indicators of sustainability. Popul. Environ. 17 (3), 195–215. doi:10.1007/BF02208489

CrossRef Full Text | Google Scholar

Sun, J. X., Qi, P., Zhang, G. X., Du, C., and Li, Z. (2024). Carrying capacity analysis of agricultural soil and water resources based on the ecological footprint method - a case study of Youyi farm in the Sanjiang plain. Sci. Geogr. Sin. 44 (6), 1060–1068. doi:10.13249/j.cnki.sgs.20221284

CrossRef Full Text | Google Scholar

Tao, Q. J., and Guo, C. X. (2018). Quantitative evaluation of the ecological footprint of water resources in Guangdong Province and its influencing factors. Water Resour. Prot. 34 (2), 28–33. doi:10.3880/j.issn.1004-6933.2018.02.06

CrossRef Full Text | Google Scholar

Wackernagel, M., and Rees, W. E. (1997). Perceptual and structural barriers to investing in natural capital: economics from an ecological footprint perspective. Ecol. Econ. 20 (1), 3–24. doi:10.1016/S0921-8009(96)00077-8

CrossRef Full Text | Google Scholar

Wang, Y. L. (2024). Evaluation of water resources carrying capacity in Chinese provinces: based on the support-pressure coupling mechanism. Statistics Decis. 40 (8), 68–73. doi:10.13546/j.cnki.tjyjc.2024.08.012

CrossRef Full Text | Google Scholar

Wang, Y. X., Wang, Y., Su, X. L., Qi, L., and Liu, M. (2019). Evaluation of the comprehensive carrying capacity of interprovincial water resources in China and the spatial effect. J. Hydrology 575, 794–809. doi:10.1016/j.jhydrol.2019.05.076

CrossRef Full Text | Google Scholar

Wang, S. X., Zhou, Y., Yang, M. X., Li, C. X., Ning, X. C., Wang, Y. H., et al. (2025). Spatial and temporal dynamics of ecological carrying capacity in Sanjiangyuan area and its zoning study. J. China Agric. Univ. 30 (3), 218–231. doi:10.11841/j.issn.1007-4333.2025.03.20

CrossRef Full Text | Google Scholar

Xiong, Y., Ai, G. X., Zhou, C., Yao, Y., and Xie, Q. (2022). Spatial and temporal evolution of ecological sustainability in Dongting Lake area based on an improved three-dimensional ecological footprint model. Acta Ecol. Sin. 42 (3), 1165–1179. doi:10.5846/stxb202106161595

CrossRef Full Text | Google Scholar

Xu, Z. M., and Chen, D. J. (2001). Application and discussion of ecological footprint theory in China's arid Zones--Taking Xinjiang as an example. Arid. Land Geogr. (4), 305–309. doi:10.3321/j.issn:1000-6060.2001.04.003

CrossRef Full Text | Google Scholar

Xu, Z. M., Chen, D. J., Zhang, Z. Q., and Cheng, G. D. (2002). Ecological footprint analysis of China in 1999. Acta Pedol. Sin. (3), 441–445. doi:10.3321/j.issn:0564-3929.2002.03.021

CrossRef Full Text | Google Scholar

Xu, Z. M., Zhang, Z. Q., Cheng, G. D., and Chen, D. J. (2003). China's ecological footprint calculation and development capacity analysis in 1999. Chin. J. Appl. Ecol. 14 (2), 280–285. Available online at: http://ir.casnw.net/handle/362004/9965.

PubMed Abstract | Google Scholar

Xu, S., Xia, L. H., Chen, Z. B., and Zhou, X. Z. (2013). Analysis of sustainable utilization of water resources in Guangdong Province based on the ecological footprint method. South-to-North Water Transfers Water Sci. and Technol. 11 (5), 11–15. doi:10.3724/SP.J.1201.2013.05011

CrossRef Full Text | Google Scholar

Yang, D. L., Li, Y. B., Li, X. M., Hu, Y., and Shu, S. M. (2024). Water resources supply/demand balance and its sustainability in seven major river basins in China. Bull. Soil Water Conservation 44 (6), 269–277. doi:10.13961/j.cnki.stbctb.2024.06.028

CrossRef Full Text | Google Scholar

Yue, C., Liu, F., Yang, L., Gao, Y., and Guo, S. (2021). Ecological footprint and ecological carrying capacity of water resources in Beijing from 2010 to 2019. Bull. Soil Water Conservation 41 (3), 291–295. doi:10.13961/j.cnki.stbctb.2021.03.038

CrossRef Full Text | Google Scholar

Yue, C., Li, F., Fu, L., Gong, Z., Qian, Y., Cui, X., et al. (2022). Study on water resources ecological footprint in Tianjin. J. Hydrology 42 (6), 56–60. doi:10.19797/j.cnki.1000-0852.20210241

CrossRef Full Text | Google Scholar

Zhang, J., Zhang, R. Z., and Zhou, D. M. (2012). Evaluation of water resources carrying capacity of Shule River Basin based on ecological footprint method. Acta Prataculturae Sin. 21 (4), 267–274. doi:10.11686/cyxb20120432

CrossRef Full Text | Google Scholar

Zhang, Y., Zhang, H. P., Li, F. S., and Guo, L. (2013). Dynamic analysis of ecological footprint of water resources in Guangxi based on improved modeling. Resour. Sci. 35 (8), 1601–1610. doi:10.5846/stxb201209231344

CrossRef Full Text | Google Scholar

Zhang, S., Zhang, H., Zhang, H., An, B., and Huang, Z. (2021). Analysis of water resources ecological footprint and its sustainable utilization in Shaanxi Province. Yangtze River 52 (4), 130–136. doi:10.16232/j.cnki.1001-4179.2021.04.020

CrossRef Full Text | Google Scholar

Zhao, M., Xu, Y., and Li, W. (2020). Spatiotemporal change analysis of water resources ecological footprint in the urban agglomeration of Northeast Sichuan. Yangtze River 51 (6), 73–78. doi:10.16232/j.cnki.1001-4179.2020.06.014

CrossRef Full Text | Google Scholar

Zheng, D. F., Wang, M., and An, Z. Y. (2022). Research on water-energy-food nexus system and its analysis. J. Econ. Water Resour. 40 (6), 8–15. doi:10.3880/j.issn.1003-9511.2022.06.002

CrossRef Full Text | Google Scholar

Zhu, Z. R., Zhan, Y. Q., Cao, Y. Q., and Jiang, J. C. (2022). Spatial and temporal characterization of the ecological footprint of water resources in Liaoning Province and its influencing factors. J. Yangtze River Sci. Res. Inst. 39 (11), 29–34. doi:10.11988/ckyyb.202106902022

CrossRef Full Text | Google Scholar

Zuo, Q. T., Jiang, L., Feng, Y. K., and Diao, Y. X. (2020). Spatial and temporal characterization of the ecological footprint of water resources in provinces along the Yellow River. J. Irrigation Drainage 39 (10), 1–8. doi:10.13522/j.cnki.ggps.2019479

CrossRef Full Text | Google Scholar