Spillover of Water Scarcity Risk through Virtual Water Trade in Rapidly Urbanizing Drylands

Abstract

Virtual water trade plays a pivotal role in alleviating water scarcity in rapidly urbanizing drylands, and accurately assessing the spillover of local water scarcity pressure to other regions through such trade is essential for sustainable development in these areas. However, systematic research on the spillover of water scarcity risks through virtual water trade and its transmission pathways in arid and semi-arid regions remains relatively limited. Taking the Hohhot-Baotou-Ordos-Yulin (HBOY) urban agglomeration as an example, this study integrated the multi-regional input-output model and structural path analysis to assess the spillover of water scarcity risk through virtual water trade and trace key transmission paths. We found that over 90% of HBOY’s water scarcity risk was transferred to regions experiencing severe or extreme water stress. Spatially, Inner Mongolia and Ningxia were the primary recipients, absorbing 37.2% and 14.5% of HBOY’s total spillover of water scarcity risk, respectively. Sectorally, 62.0% of the risk spillover originated from HBOY’s agriculture, light industry, and construction sectors and was passed to the agricultural sector in external regions. The most important risk transmission path was from HBOY’s agriculture to Inner Mongolia’s agriculture, accounting for 18.3% of HBOY’s total risk spillover. Additionally, potential loss due to insufficient external virtual water supply constituted nearly one-third of HBOY’s total economic loss from water scarcity. We recommend that rapidly urbanizing drylands and their trade partners should actively develop a cross-regional collaborative management system to mitigate the adverse effects of risk spillover.

Introduction

The spillover of water scarcity risk through virtual water trade refers to transferring local water scarcity pressure to other regions along the supply chain when a region imports virtual water through commodity trade to meet its own consumption demands (Hu et al. 2019; Zhang, Fan, et al. 2020). Rapidly urbanizing drylands (RUDs) are regions undergoing rapid urbanization, characterized by water shortages that constrain productivity and ecosystem function (MEA 2005). Due to insufficient local water supply, these regions often rely on importing large quantities of water-intensive products to sustain socioeconomic development and alleviate water stress (Allan 2005; Zheng et al. 2024). However, large-scale virtual water imports can transfer water scarcity risk to exporting regions along the supply chain, potentially exacerbating water crises in those areas (Zhang, Fan, et al. 2020). If these regions reduce virtual water exports due to intensified water shortages, it can negatively impact the sustainability of RUDs. Therefore, accurately assessing the spillover of water scarcity risk through virtual water trade is crucial for the sustainable development of RUDs.

The concept of virtual water, first introduced by Allan (1993), refers to the total freshwater required to produce goods and services. Virtual water trade describes the process of transferring water resources between regions through the exchange of these goods and services, thereby linking local consumption to water resource utilization in other regions. It is regarded as an effective strategy to address the uneven distribution of water resources (Mekonnen et al. 2024). However, virtual water does not always flow from water-rich regions to water-scarce regions, and the impact of virtual water trade on regional water stress has attracted widespread attention (Du et al. 2022). To quantify this impact, several derivative indicators and methods have been proposed and refined, including the stress-weighted water footprint (Ridoutt and Pfister 2010), virtual scarce water (Feng et al. 2014), scarce water saving accounting framework related to virtual water trade (Zhao et al. 2018), impact-oriented virtual water trade (Wu et al. 2022), and virtual water for comprehensive water pressures (Zhi et al. 2022). Over the past two decades, there has been a growing body of literature exploring interregional water stress transfer through the lens of virtual water. For instance, Zhao et al. (2015) found that interprovincial virtual water trade in China not only failed to play a major role in alleviating water stress in water-receiving regions but also exacerbated water stress in water-supplying regions. Du et al. (2022) reported an 18.9% increase in the global virtual water trade vulnerability from 2005 to 2015, indicating a deviation of global virtual water transfer from an idealized spontaneous entropy-increasing process. Increasing evidence suggests that virtual water trade may transfer water scarcity pressures to more vulnerable trade partner regions, further exacerbating water resource shortages and exhibiting significant spillover effects (Zhang, Fan, et al. 2020; Wei et al. 2023).

Recently, the spillover of water scarcity risk through virtual water trade has drawn growing attention because of its unexpected socioeconomic and environmental consequences (Feng et al. 2014). In terms of geographical scale, most existing research has concentrated on the national and provincial levels, with lesser emphasis on the city level (Chen et al. 2021). Zhang, Fan, et al. (2020) developed a risk spillover index by combining a multi-regional input-output (MRIO) model with a water stress index to analyze the spatial spillover of water scarcity risks through virtual water trade between the three northeastern provinces and 27 other provinces. Chen et al. (2022) defined water scarcity risk as the economic output loss due to water shortages and examined its spread across 187 countries in the global virtual water trade system. Li et al. (2023) quantified the spillover patterns of water scarcity risk in interprovincial agricultural virtual water trade in China. However, these studies primarily focused on the spatial or sectoral transfer of water scarcity risks, lacking further examination of the intermediate transmission process, thus failing to provide a comprehensive depiction of the specific pathways of virtual water trade risk spillover. Moreover, despite the significant impact of virtual water trade risk spillover on the sustainable development of RUDs, relevant studies for these regions are still limited and require further exploration.

Combining the MRIO model with structural path analysis (SPA) provides an effective approach for quantitatively characterizing water scarcity risk transmission in virtual water trade. The MRIO model is widely used to quantify virtual resource transfers and their spillover effects along supply chains (Zheng et al. 2021). It depicts economic interaction networks across regions and sectors, capturing the flow of virtual resources and supporting risk spillover assessments (Meng et al. 2013; Zhang et al. 2019; Malik et al. 2021). However, while the MRIO model focuses on measuring risk flows between source and recipient units, it does not fully capture the complex intermediate transmission mechanisms of risk. As an effective complementary tool, SPA can trace specific risk conduction paths in virtual water trade by decomposing water consumption across supply chain into numerous flow paths (Liu and Song 2024). Therefore, combining the MRIO model and SPA allows for a more comprehensive and precise assessment of risk spillover and transmission paths in virtual water trade in RUDs.

The Hohhot-Baotou-Ordos-Yulin (HBOY) urban agglomeration, located in the central part of the drylands of northern China and undergoing significant urbanization, is an ideal case for investigating spillover of water scarcity risk through virtual water trade in RUDs (Qi et al. 2023). This region is not only a typical water-scarce area, with an average annual precipitation of 320 mm—half the national average—but also a key resource-rich area, providing around 40% of China’s energy supply (Yang and Zhang 2023). Against the backdrop of rapid urbanization, the region’s resource-based economy and water scarcity are intertwined, creating a complex virtual water trade pattern. While large virtual water imports alleviate local water pressure, the region also exports water scarcity risks to other areas. Thus, the HBOY area exhibits both typical and unique characteristics, and may provide valuable insights for other RUDs.

The purpose of this study was to assess the spillover of water scarcity risk through virtual water trade in RUDs, using the HBOY area as an example. First, we compiled a sectoral water withdrawal inventory and a MRIO table for HBOY. Then, we quantified risk spillover using a MRIO model. Finally, we identified the critical risk transmission paths through SPA. Our findings enhance the understanding of the spillover of water scarcity risk through virtual water trade in RUDs and offer policy recommendations to prevent systemic risk of water scarcity in these regions.

Study Area and Data

This section offers a detailed description of the geographical setting and key characteristics of the study area, and outlines the data sources used, including the MRIO table, data for compiling the water withdrawal inventory, and sector aggregation.

Study Area

The HBOY area is located in the central part of the drylands of northern China (36°48′50″–42°44′5″N, 106°28′16″–122°18′7″E) and is composed of four cities: Hohhot, Baotou, Ordos, and Yulin (Fig. 1). According to the water resources bulletin (Water Resources Department of Inner Mongolia Autonomous Region 2023; Yulin Water Conservancy Bureau 2023), the per capita water resources in HBOY were only 631.77 m³ in 2022, less than one-third of China’s average and far below the international threshold for water scarcity (1,000 m³/person), highlighting the severe water scarcity in the region. Over the past few decades, the area has experienced rapid urbanization (Tang et al. 2023). The urban land area in HBOY expanded from 151 km² in 1990 to 1,231 km² in 2017 and is projected to increase by an additional 978–1,594 km² by 2050 (Song et al. 2020). Meanwhile, its urbanization rate rose from 63.3% in 2010 to 76.3% in 2022, significantly higher than the national average.

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Fig. 1

The study area—Hohhot-Baotou-Ordos-Yulin (HBOY) urban agglomeration. (a) Geographical location; (b) Water use structure; (c) Water supply structure; (d) Urbanization rate

Situated in the middle of the northern agropastoral ecotone, HBOY is also an important resource-based region in China, supplying abundant energy and mineral products to other regions (Li et al. 2024). The intersection of agricultural, industrial, and urban development in the region has formed a complex virtual water trade pattern. Through virtual water imports along the supply chain, HBOY meets its development needs while increasingly facing the spillover of water scarcity risk. In the future, HBOY is expected to become a national high-end energy and chemical industrial base, which means that as urbanization and industrialization accelerate, the water resource challenges in the region will further intensify (NDRC 2018; Lu et al. 2024). Alleviating local water pressure and mitigating the systemic risks posed by water shortages have become priorities for HBOY’s sustainable development.

Data

The 2017 city-level MRIO table for China was sourced from the China Emission Accounts and Datasets (CEADs).1 It provides the latest input-output data, covering 313 regions and 42 economic sectors in China’s mainland, with the exception of Hong Kong, Macau, and Taiwan (Zheng et al. 2021). These data have been extensively applied to assess various virtual resource flows and their environmental impacts triggered by economic activities (Jin et al. 2024; Shu et al. 2024). Based on this dataset, a nested MRIO table focused on HBOY was constructed for this study.

This study compiled a water withdrawal inventory for 61 sectors across the four cities in HBOY and 31 provinces in China’s mainland, using a series of statistical data (Table S12). The total water withdrawal for five major sectors (agriculture, industry, urban public, household, and environment) was obtained from 2017 municipal and provincial water resources bulletins published by water conservancy departments. Farmland irrigation intensity and water use indicators for urban and rural residents were also derived from these bulletins. The irrigated farmland area, industrial output, housing floor space, service industry employees, urban population, and rural population data were collected from municipal and provincial statistical yearbooks, as well as the China Statistical Yearbook, China Industry Statistical Yearbook, and China City Statistical Yearbook published by the National Bureau of Statistics. Water withdrawal intensities for industry, construction, and service were sourced from Zhang, Liu, et al. (2020) and Zhang et al. (2024).

Due to differences in sector classification between the MRIO table and the water withdrawal inventory, we consolidated the sectors in the water withdrawal inventory based on the Industrial Classification for National Economic Activities to align with the sectors of the MRIO table. To facilitate subsequent analysis, the final results were merged into the following 10 industry categories: agriculture (S1), mining (S2), light industry (S3), heavy industry (S4), high-tech (S5), electricity, gas, and water supply (S6), construction (S7), wholesale, retail, hotels, and catering (S8), transport and telecommunication (S9), and service (S10). The detailed sector integration plan is shown in Table S2.²

Methods

In interregional virtual water trade, when one region indirectly consumes water resources from another region through supply chains, the local water scarcity risk is transferred to the suppliers, leading to risk spillover. To accurately assess the spillover of water scarcity risk through virtual water trade in HBOY, this study followed four key steps (Fig. 2). First, a detailed sectoral water withdrawal inventory was compiled for HBOY and 31 provinces of China’s mainland. Second, a city-province nested MRIO table was constructed. Third, the virtual water trade risk spillover in HBOY was quantified using an environmentally extended MRIO model. Finally, SPA was applied to trace the critical transmission paths of risk spillover of the area’s virtual water trade.

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Fig. 2

Flowchart of this study. HBOY Hohhot-Baotou-Ordos-Yulin area, MRIO Multi-regional input-output, CEADs China Emission Accounts and Datasets

Preparing Water Withdrawal Inventory

Establishing sectoral water withdrawal accounts is a prerequisite for quantifying and tracking virtual water trade risk spillover. Generally, the water resources bulletins published by China’s water conservancy departments include five types of water withdrawals: agriculture, industry, urban public, household, and environment. Referring to the methodology of Zhang et al. (2024), we disaggregated total regional water withdrawals into 61 sectors (2 agricultural sectors, 41 industrial sectors, 1 construction sector, 14 service sectors, 2 household sectors, and 1 environmental sector). Detailed sector codes are provided in Table S1.² The specific disaggregation process is as follows:

For agricultural water, if the water resources bulletin separates irrigation from forestry, animal husbandry, and fishery, we used the values in the bulletin. Otherwise, irrigation water was determined by the irrigated area and irrigation intensity (that is, water consumption per unit of farmland), with the remainder allocated to forestry, animal husbandry, and fishery.

1

$$\begin{array}{c}{\mathit{Water}}_{r,1}={\mathit{Irriareas}}_{r,1}\times {\mathit{Intensity}}_{r,1}\end{array}$$

2

$$\begin{array}{c}{\mathit{Water}}_{r,2}={\mathit{Water}}_{Agriculture,r}-{\mathit{Water}}_{r,1}\end{array}$$where ${\mathit{Water}}_{r,1}$, ${\mathit{Irriareas}}_{r,1}$, and ${\mathit{Intensity}}_{r,1}$ represent the irrigation water withdrawal, irrigated area, and irrigation intensity in region r, respectively. ${\mathit{Water}}_{r,2}$ is the water withdrawal of forestry, animal husbandry, and fishery in region r, and ${\mathit{Water}}_{Agriculture,r}$ is the total agricultural water in region r.

For industrial water, we allocated the total regional industrial water according to the economic output and water use intensity (that is, water used per unit of economic output) of each sub-sector.

3

$$\begin{array}{c}{W}_{r,i}={\mathit{Intensity}}_{r,i}\times {\mathit{Output}}_{r,i}/{\sum }_{i=3}^{43}\left({\mathit{Intensity}}_{r,i},\times ,{\mathit{Output}}_{r,i}\right)\end{array}$$

4

$$\begin{array}{c}{\mathit{Water}}_{r,i}=W_{r,i}\times {\mathit{Water}}_{Industry,r},i\in \left[\text{3,43}\right]\end{array}$$where $W_{r,i}$, ${\mathit{Intensity}}_{r,i}$, and ${\mathit{Output}}_{r,i}$ represent the water withdrawal share, water withdrawal intensity, and economic output of industrial sub-sector i in region r, respectively. ${\mathit{Water}}_{Industry,r}$ is the total industrial water in region r, and ${\mathit{Water}}_{r,i}$ is the water withdrawal of sector i in region r. ${\mathit{Intensity}}_{r,i}$ is based on point-source data from 161,598 enterprises recorded in the national pollution source census and the periodic reporting system of the Ministry of Ecology and Environment (Zhang et al. 2024).

Urban public water includes water consumption from construction and service industries. The water withdrawal of construction was determined based on housing floor space and water consumption per unit area. For service industry, water withdrawal was estimated based on the number of employees in each sub-sector and water withdrawal per employee (Zhang, Liu, et al. 2020).

5

$$\begin{array}{c}{\mathit{Water}}_{r,44}\prime ={\mathit{Flospac}}_{r,44}\times {\mathit{Intensity}}_{r,44}\end{array}$$

6

$$\begin{array}{c}{\mathit{Water}}_{r,i}\prime ={\mathit{Employment}}_{r,i}\times {\mathit{Intensity}}_{r,i},i\in \left[\text{45,58}\right]\end{array}$$

7

$$\begin{array}{c}{\mathit{Water}}_{r,i}=\frac{{\mathit{Water}}_{r,i}\prime }{{\sum }_{k=44}^{58}{\mathit{Water}}_{r,i}\prime }\times {\mathit{Water}}_{UrbanPublic,r}\end{array}$$where ${\mathit{Flospac}}_{r,44}$ and ${\mathit{Intensity}}_{r,44}$ represent the housing floor space and water withdrawal per unit area in region r, respectively. ${\mathit{Employment}}_{r,i}$ and ${\mathit{Intensity}}_{r,i}$ are the number of employees and water withdrawal per employee of service sub-sector i in region r, respectively. ${\mathit{Water}}_{UrbanPublic,r}$ is the total urban public water in region r. According to the First Water Resources Census Bulletin, water withdrawal per unit of housing floor area is 0.86 m³, while water intensity is 718 L/day⋅capita for accommodation and catering and 291.8 L/day⋅capita for other service sectors (Zhang, Liu, et al. 2020).

For household water, we used urban population and per capita urban water withdrawal to estimate urban household water withdrawal. A similar method was applied for rural household.

8

$$\begin{array}{c}{\mathit{Water}}_{r,i}\prime ={\mathit{Population}}_{r,i}\times {\mathit{Intensity}}_{r,i},i\in \left[\text{59,60}\right]\end{array}$$

9

$$\begin{array}{c}{\mathit{Water}}_{r,i}=\frac{{\mathit{Water}}_{r,i}\prime }{{\sum }_{i=59}^{60}{\mathit{Water}}_{r,i}\prime }\times {\mathit{Water}}_{Household,r}\end{array}$$where ${\mathit{Population}}_{r,i}$ and ${\mathit{Intensity}}_{r,i}$ represent the urban or rural population and their per capita water withdrawal in region r, respectively. ${\mathit{Water}}_{Household,i}$ is the total household water in region r. The per capita domestic water withdrawal in urban and rural areas was sourced from the water resources bulletins.

The water withdrawal for environmental purposes was acquired directly from the water resources bulletins. Ultimately, we constructed a water withdrawal inventory covering 35 regions and 61 sectors, totaling 2,135 sub-sectors. Similar to previous studies, this inventory primarily includes blue water withdrawals (including surface water and groundwater) and excludes green and grey water (Zhang, Fan, et al. 2020; Du et al. 2022; Shen et al. 2024). The reason for this is that green water, stored in soil and consumed through vegetation evaporation, is part of the natural water cycle and is typically not directly affected by human consumption or competitive use (Zhang and Anadon 2014). Grey water refers to the amount of freshwater needed to absorb pollutants and meet environmental water quality standards, and is generally monitored and managed through environmental indicators (Vanham et al. 2019). While green and grey waters are significant in specific studies, such as agricultural production and environmental governance, blue water remains the primary focus in water resource management and virtual water trade due to its direct consumption by human activities (Lu et al. 2022; Zhu et al. 2022). The statistical systems of water conservancy departments typically include only blue water data, which is another reason that green and grey waters are not included. Additionally, due to a lack of detailed sectoral water monitoring data, the water withdrawal inventory only shows total withdrawals by sector (including both surface water and groundwater) without distinguishing the specific extraction of each within different sectors (Zhang, Liu, et al. 2020).

Constructing the Multi-Regional Input-Output (MRIO) Table

The MRIO table serves as a vital basis for quantifying the spillover of water scarcity risk through virtual water trade. Researchers from CEADs compiled a city-level MRIO table for China in 2017 using the entropy model, which includes input-output data for 313 administrative units (309 cities and 4 provinces) and 42 sectors (Zheng et al. 2021). To tailor this MRIO table to our study, we followed the techniques of Xing et al. (2022) and Zhu et al. (2022) to construct a nested MRIO table focused on HBOY through regional aggregation. The nested MRIO table captures trade relationships between 42 sectors across 35 regions (4 cities in HBOY and 31 provinces in external regions). It comprises eight components: intermediate inputs, total input, total output, value added, final demand, exports, imports, and error terms (Table S3²).

Assessing Risk Spillover through Virtual Water Trade

Quantifying virtual water trade risk spillover provides valuable insights into how a water-scarce region transfer its water scarcity risk to other regions through import activities (Li, Tian, et al. 2021). Referring to Zhang, Fan, et al. (2020), the risk spillover index of virtual water trade is expressed as:

10

$$\begin{array}{c}{\mathit{RSI}}_{\mathit{rs}}=\frac{{\mathit{VW}}_{\mathit{rs}}}{{\mathit{TVW}}_r}\times {\mathit{WSI}}_r\end{array}$$where ${\mathit{RSI}}_{\mathit{rs}}$ denotes the water scarcity risk spilled from region s to region r, ${\mathit{VW}}_{\mathit{rs}}$ is the virtual water exported from region r to region s, ${\mathit{TVW}}_r$ is the total virtual water of region r, including the virtual water exported and consumed by local production. ${\mathit{WSI}}_r$ is the water stress index of region r, calculated as the ratio of water withdrawal (WW) to water availability (Q), that is, ${\mathit{WSI}}_r={\mathit{WW}}_r/Q_r$. According to the WSI, water stress in a region is typically classified into four levels: no stress (WSI ≤ 0.2), moderate stress (0.2 < WSI ≤ 0.4), severe stress (0.4 < WSI ≤ 1.0), and extreme stress (WSI > 1.0) (Zhao et al. 2015).

The total water scarcity risk transferred from region s to other regions is obtained by summing up the risk spillover indices from region s:

11

$$\begin{array}{c}\mathit{RSI}_{\ast s}^{}={\sum }_{r,r\ne s}{\mathit{RSI}}_{\mathit{rs}}\end{array}$$

The proportion of water scarcity risk transferred from region s to a specific region r' relative to the total risk spillover of region s is calculated as:

12

$$\begin{array}{c}{\mathit{Proportion}}_{r\prime s}=\frac{{\mathit{RSI}}_{r\prime s}}{{\sum }_{r,r\ne s}{\mathit{RSI}}_{\mathit{rs}}}\times 100\%\end{array}$$

Assuming that there are m regions, each region has n sectors and k kinds of final demand, the following formula is derived based on the horizontal balance of the MRIO table (Liu et al. 2021):

13

$$\begin{array}{c}{x}_i^r={\sum }_{s=1}^m{\sum }_{j=1}^n{z}_{\mathit{ij}}^{\mathit{rs}}+{\sum }_{s=1}^m{\sum }_{t=1}^k{f}_{\mathit{it}}^{\mathit{rs}}+e_i^r-{\epsilon }_i^r\end{array}$$where $x_i^r$, $e_i^r$, and ${\epsilon }_i^r$ represent the total output, exports, and statistical errors of region r’s sector i, respectively. $z_{\mathit{ij}}^{\mathit{rs}}$ denotes the intermediate input from region r’s sector i to region s’s sector j, $f_{\mathit{it}}^{\mathit{rs}}$ signifies the t-th final demand of region s that is satisfied by goods and services produced by region r’s sector i. The technical coefficient $a_{\mathit{ij}}^{\mathit{rs}}$ refers to the input of goods or services required for each unit of output of region s’s sector j from region r’s sector i, calculated as $a_{\mathit{ij}}^{\mathit{rs}}=z_{\mathit{ij}}^{\mathit{rs}}/x_j^s$.

Equation 13 can be further expressed as:

14

$$\begin{array}{c}X=AX+F+E-\epsilon \end{array}$$

Solving for X, we get:

15

$$\begin{array}{c}X={\left(\mathbf{I},-,\mathbf{A}\right)}^{-1}\left(\mathbf{F},+,\mathbf{E},-,\mathbf{\epsilon }\right)=L\left(\mathbf{F},+,\mathbf{E},-,\mathbf{\epsilon }\right)\end{array}$$where I denotes the identity matrix, A represents the technical coefficient matrix, and $\mathbf{L}={\left(\mathbf{I},-,\mathbf{A}\right)}^{-1}$ is known as the Leontief inverse matrix. X, F, E, and ε are the total output, final demand, export, and error matrices, respectively. Notably, the final demand includes five categories: rural household consumption, urban household consumption, government consumption, fixed capital formation, and inventory changes, with exports excluded (Zheng et al. 2021; Qian et al. 2022). In other words, the final demand matrix F and the export matrix E are independent of each other, which does not affect the calculation of the technical coefficient matrix A. Furthermore, considering that the share of international trade in HBOY is negligible (only 0.1% of China’s total trade), this study primarily focused on the spillover of water scarcity risk through domestic virtual water trade in the region (Li et al. 2024).

Obviously, the interregional virtual water transfer can be calculated as:

16

$$\begin{array}{c}VW=\widehat{\mathbf{W}}{\left(\mathbf{I},-,\mathbf{A}\right)}^{-1}F\end{array}$$where VW represents the virtual water transfer matrix driven by final demand, and its element ${\mathit{VW}}_{\mathit{ij}}^{\mathit{rs}}$ denotes the virtual water transferred from region r’s sector i to region s’s sector j. $\widehat{\mathbf{W}}$ is the diagonal matrix of direct water consumption coefficient, whose element $w_i^r$ signifies the water withdrawal per unit of output for region r’s sector i. It is calculated as $w_i^r={\mathit{ww}}_i^r/x_i^r$, where ${\mathit{ww}}_i^r$ is water withdrawal of region r’s sector i.

The water footprint refers to the total volume of water demanded by the goods and services consumed in a region over a specific period (Feng et al. 2012). It includes both virtual water from regional consumption and direct water uses by households and the environment (Zhu et al. 2022). It can be calculated as follows:

17

$$\begin{array}{c}{\mathit{WF}}^r=\mathit{VW}_{\mathit{in}}^{\ast r}+{\mathit{VW}}^{\mathit{rr}}+{\mathit{HW}}^r+{\mathit{EW}}^r\end{array}$$where ${\mathit{WF}}^r$ is the water footprint of region r, $\mathit{VW}_{\mathit{in}}^{\ast r}$​ is the virtual water imported by region r from other regions, ${\mathit{VW}}^{\mathit{rr}}$ is the virtual water generated from local consumption in region r, and ${\mathit{EW}}^r$ and ${\mathit{HW}}^r$ represent the direct water consumption by the environment and households in region r, respectively.

Identifying the Critical Paths of Water Scarcity Risk Transmission

Structural path analysis can decompose the water usage associated with particular industries or products throughout the entire production chain into a series of interconnected flow paths by analyzing the complex production relationships between industries in the economic system (Zhang et al. 2018; Li, Liang, et al. 2021). Thus, SPA is an effective approach for tracking the spillover of water scarcity risk through virtual water trade.

The expansion of the Leontief inverse matrix can be derived via the power series approximation method.

18

$$\begin{array}{c}L={\left(\mathbf{I},-,\mathbf{A}\right)}^{-1}=I+{\mathbf{A}}^1+{\mathbf{A}}^2+{\mathbf{A}}^3+\cdots \end{array}$$

Based on Eqs. 10, 16, and 18, the layer-by-layer transmission process of water scarcity risk in virtual water trade can be expressed as:

19

$$\begin{array}{c}SRI=\delta {\left(\mathbf{I},-,\mathbf{A}\right)}^{-1}F=\delta IF+\delta AF+\delta {\mathbf{A}}^{2}F+\delta {\mathbf{A}}^{3}F+\cdots \end{array}$$where $\delta$ is determined by the direct water consumption coefficient, the water stress index, and the total virtual water consumption. $\mathrm{\delta }{\mathbf{A}}^{\text{p}}\mathbf{F}$ indicates the risk transmission occurring at the p-th production layer. For instance, $\mathrm{\delta }\mathbf{I}\mathbf{F}$ is the risk transmission in the 0th production layer, $\delta AF$ in the 1st production layer, $\mathrm{\delta }{\mathbf{A}}^2\mathbf{F}$ in the 2nd production layer, and so forth. To gain key insights into the water scarcity risk transmission of virtual water trade in HBOY, we calculated the first five layers and identified the top 30 critical paths, referring to previous research (Wang et al. 2021).

Results

This section presents the main findings based on the MRIO and SPA methods. First, it reveals the spatial distribution of the spillover of water scarcity risk through virtual water trade in HBOY. Then, it analyzes the sectoral characteristics of risk spillover. Finally, it identifies the key transmission pathways of the risks along the supply chain.

Spatial Pattern of Risk Spillover in Hohhot-Baotou-Ordos-Yulin (HBOY)’s Virtual Water Trade

The cumulative risk spillover index from HBOY to other regions was 0.19. Inner Mongolia and Ningxia were the primary recipients of risk spillover from HBOY’s virtual water trade, absorbing 37.2% and 14.5%, respectively (Fig. 3). This was related to the amount of virtual water these regions exported to HBOY and their water stress index. Inner Mongolia was the largest recipient of risk spillover, which supplied over one-third of HBOY’s virtual water. Although Ningxia’s virtual water supply was relatively small, its water stress index of 6.12 made it the second-largest recipient. Shanghai, Tianjin, Jiangsu, Gansu, Xinjiang, Hebei, Heilongjiang, Beijing, Liaoning, Henan, Shaanxi, Jilin, Anhui, and Shandong were moderate provincial level recipients, with risk absorption ranging from 1.3% in Shandong to 8.0% in Shanghai. Shanxi, Guangdong, Zhejiang, Hainan, Jiangxi, Fujian, Hunan, Guangxi, Chongqing, Guizhou, Hubei, Yunnan, Sichuan, Qinghai, and Tibet were small recipients, each absorbing less than 0.6% of HBOY’s total risk spillover. On the one hand, they exported limited virtual water to HBOY, accounting for less than one-fifth of HBOY’s total imports. On the other hand, they were abundant in water resources and had almost no water stress. Overall, HBOY transferred 97.1% of its water scarcity risk to water-scarce regions, with 37.6% going to regions under extreme water stress, 53.2% to regions under severe water stress, and 6.3% to regions under moderate water stress.

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Fig. 3

Spatial distribution of risk spillover in Hohhot-Baotou-Ordos-Yulin (HBOY)’s virtual water trade. The risk spillover index was categorized into three levels by natural breakpoint method: High (> 0.02), Medium (0.002–0.02), and Low (< 0.002)

Sectoral Characteristics of Risk Spillover in Hohhot-Baotou-Ordos-Yulin (HBOY)’s Virtual Water Trade

The risk spillover in HBOY’s virtual water trade was primarily concentrated in agriculture, light industry, and construction (Fig. 4). Most of these risks flowed into the agriculture of external regions through the supply chain. In terms of HBOY’s spilling sectors, agriculture, light industry, and construction were the top three sectors, accounting for 28.6%, 23.4%, and 22.8% of HBOY’s total risk spillover, respectively. Service, heavy industry, and wholesale, retail, hotels, and catering followed, contributing 11.6%, 7.4%, and 4.4%, respectively. Transport and telecommunication, electricity, gas, and water supply, high-tech, and mining had the lowest risk spillover, each accounting for less than 1% of HBOY’s total risk spillover. From the perspective of the receiving sectors in external regions, agriculture absorbed the most transferred risk from HBOY, accounting for 74.7% of the total. Heavy industry followed, absorbing 12.9% of HBOY’s risk spillover. Notably, the risk spillover received by Shanghai and Jiangsu was primarily concentrated in heavy industry, comprising 66.0% and 43.2% of their respective received risk. The risk spillover received by Beijing was concentrated in service, making up 33.4% of its received risk. The risk spillover received by the remaining provincial units was concentrated in agriculture, with the proportion of received risk ranging from 50.6% in Anhui to 97.9% in Xinjiang.

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Fig. 4

Sectoral distribution of risk spillover in Hohhot-Baotou-Ordos-Yulin (HBOY)’s virtual water trade. Note: S1: agriculture; S2: mining; S3: light industry; S4: heavy industry; S5: high-tech; S6: electricity, gas, and water supply; S7: construction; S8: wholesale, retail, hotels, and catering; S9: transport and telecommunication; S10: service

The risk spillover in HBOY’s virtual water trade exhibited complex intersectoral interactions. Agriculture, as a fundamental sector, struggles to meet the growing demand for agricultural raw materials driven by population growth and industrial development due to water scarcity. As a result, HBOY imported significant amounts of agricultural products from regions such as Inner Mongolia, Ningxia, and Xinjiang. These imports not only satisfied local agricultural needs but also provided critical raw materials for downstream sectors like light industry and construction. For example, external agricultural sectors supplied HBOY’s light industry (for example, textiles and food processing) with essential materials such as cotton and grains and provided the construction sector with timber and paper-based products, accounting for 28.7% and 16.5% of the risk spillover received by external agriculture, respectively. This underscores the close linkages between agriculture and light industry, as well as agriculture and construction. Rapid urbanization drove the growth of HBOY’s construction, resulting in substantial imports of steel, cement, and machinery from external heavy industry, which accounted for 48.2% of the risk spillover received by external heavy industry. This indicates a strong connection between heavy industry and construction. As the terminal sector of economic activities, the service sector exhibited a more diversified pattern of virtual water trade risk spillover, with the majority spilled over to external agriculture (51.9%), heavy industry (15.1%), service (14.5%), and electricity, gas, and water supply (12.7%). This is because the service sector depends on external agriculture for basic consumer goods such as food and beverages, on the electricity, gas, and water supply sectors for stable energy and water resources, on heavy industry for production equipment and communication infrastructure, and on other service sectors for leasing and business support.

Critical Paths of Risk Spillover in Hohhot-Baotou-Ordos-Yulin (HBOY)’s Virtual Water Trade

The top 30 critical paths of water scarcity risk transmission in HBOY’s virtual water trade accounted for 49.3% of its total risk spillover (Fig. 5). Among them, there were 10 direct transmission paths occurring at Layer 0, contributing 54.4% to the total risk spillover in the top 30 paths. These direct paths primarily transmitted risk from HBOY’s agriculture to the agricultural sectors of Inner Mongolia, Ningxia, Xinjiang, Gansu, Shaanxi, Heilongjiang, and Beijing. In indirect transmission paths, 17 paths occurred at Layer 1, accounting for 42.5% of the total risk spillover in the top 30 paths. Three paths occurred at Layer 2, representing 3.0% of the total risk spillover, and they ultimately transmitted to agriculture in Inner Mongolia. Notably, the path with the highest risk spillover in HBOY’s virtual water trade was from HBOY’s agriculture to Inner Mongolia’s agriculture, accounting for 37.0% of the total risk spillover in the top 30 paths. The path with the lowest risk spillover was from HBOY’s light industry to Heilongjiang’s light industry, and then to Heilongjiang’s agriculture, contributing only 0.7% of the total risk spillover in the top 30 paths.

[See PDF for image]

Fig. 5

Critical paths of water scarcity risk transmission in Hohhot-Baotou-Ordos-Yulin (HBOY)’s virtual water trade. The sector codes S1–S10 are shown in Fig. 4

From the perspective of transmission starting points, agriculture, light industry, and construction were the main drivers of water scarcity risk transmission in HBOY’s virtual water trade. Agriculture and light industry were responsible for the most transmission paths, with 10 each, accounting for 56.1% and 19.7% of the total risk spillover in the top 30 paths, respectively. Construction followed with 5 paths, contributing 16.2%. Heavy industry and service each drove 2 paths, accounting for 4.7% and 2.3% of the total risk spillover, respectively. Wholesale, retail, hotels, and catering drove the fewest paths, with only 1 path, contributing 1.0% of the total. From the perspective of path endpoints, Inner Mongolia’s agriculture was the largest recipient of risk spillover, with 12 paths converging on this sector, accounting for 69.9% of the total risk spillover in the top 30 paths. As for intermediate nodes, HBOY’s construction and Inner Mongolia’s agriculture were the most active transit points, each involved in 4 paths, representing 14.2% and 8.3% of the total risk spillover, respectively.

Discussion

This section first assesses the accuracy and reliability of the results. Subsequently, it explores the dependence of rapidly urbanizing drylands on external virtual water and its potential impacts. Next, it proposes policy recommendations, emphasizing the importance of strengthening cross-regional collaboration. Finally, it discusses the limitations of this study and outlines directions for future research.

Accuracy and Credibility of the Results

The results of this study are reliable and robust. First, we used the most up-to-date and reliable data. The uncertainties in this study primarily stem from the MRIO table and the water withdrawal inventory. Recognizing the absence of inter-city trade data, CEADs researchers developed a city-level MRIO table for China utilizing entropy theory (Zheng et al. 2021). Although the modeling process includes some inherent idealized assumptions, these have been proven to be necessary and reasonable in practice, particularly when faced with significant data scarcity (Li et al. 2024). In recent years, this dataset has been widely used in various virtual resource studies, demonstrating its reliability and validity (Xia et al. 2022; Zhang, Zhang, et al. 2023; Fu et al. 2024). This study utilized the latest city-level MRIO table published by CEADs and conducted regional aggregation operations similar to previous studies (Xing et al. 2022). The main uncertainties in water withdrawal inventory are related to water withdrawal intensity and activity data. The industrial water withdrawal intensity was based on point-source survey data from 161,598 enterprises, offering localized characteristics and overcoming the homogenization assumption in existing literature, thereby accurately reflecting actual conditions (Zhang et al. 2024). For the service sector’s water withdrawal intensity, we employed a Monte Carlo method with 200,000 stochastic simulations, following the procedure outlined in Zhang, Liu, et al. (2020), which revealed an uncertainty of 8.62% for the service sector. Despite some unavoidable biases, given the lack of sectoral water data, this approach is considered reasonable and feasible, and it has been validated in related studies (Li, Zhou, et al. 2021; Zhang, Niu, et al. 2023). To systematically evaluate the impact of parameter changes, a sensitivity analysis was performed following Sun et al. (2019). A ±10% perturbation was applied to the direct water use coefficients of each sector, and the resulting virtual water trade volumes between HBOY and various provinces were compared to the baseline scenario. The total virtual water imports of HBOY changed by ±1.36%, confirming the robustness of the results. Additionally, the water withdrawal activity data in this study were sourced from official Chinese statistics, which have higher credibility than other data sources, thereby lowering the uncertainty.

To further verify the reliability of our results, we compared the estimated water footprint of China in this study with that from existing literature (Fig. 6). In 2017, China’s water footprint in this study was 535.28 Gm³, closely matching the result of Fu et al. (2024), with a difference of only 5.3 Gm³. Another estimate by Long et al. (2021) was 5.0% higher than this study, while estimates by Shen et al. (2023) and Hou et al. (2022) were 4.6% and 6.5% lower, respectively. These disparities were mainly due to the differences in the scopes of water footprint. This study focused on HBOY and its virtual water trade with 31 provinces, covering all of China’s mainland in the water footprint calculation. In contrast, Shen et al. (2023) and Hou et al. (2022) excluded Tibet, resulting in lower estimates. Long et al. (2021) included international virtual water trade with 51 countries, leading to a higher estimate. Another factor was the use of different MRIO tables. This study used the 2017 city-level MRIO table, while Shen et al. (2023) and Hou et al. (2022) employed provincial MRIO tables. Despite using the same MRIO table as Fu et al. (2024), slight differences in calculation results may have arisen from variations in in the compilation of sectoral water withdrawal inventory. Overall, our calculations showed high consistency with previous studies and reliability.

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Fig. 6

Comparison of China’s water footprint in 2017 between this study and previous studies

Sustainable Development of Rapidly Urbanizing Drylands Relies on External Virtual Water

Rapidly urbanizing drylands spill water scarcity risk to other regions by importing virtual water, but this also creates economic dependence on those supplying regions. When the virtual water supply from these regions is insufficient, the importing regions may experience economic loss due to disrupted supply chains. In the case of HBOY, the potential economic loss due to inadequate external virtual water supply was RMB 11.06 billion yuan,3 accounting for 31.3% of the total economic loss from water shortages, based on the method outlined by Qu et al. (2017). A significant positive correlation was found between HBOY’s potential economic loss due to insufficient external virtual water and its risk spillover (Table 1). This not only confirms the reliability of the risk spillover assessment, but also highlights HBOY’s strong dependence on regions bearing a large share of its transferred risk. Specifically, HBOY was most dependent on Inner Mongolia, where an insufficient virtual water supply would reduce HBOY’s economic output by RMB 4,274 million yuan, representing 38.7% of the total potential loss from inadequate external virtual water supply (Fig. 7). Jiangsu followed, where inadequate virtual water would have resulted in a decrease of 1,414 million yuan in HBOY’s economic output, or 12.8% of the total potential loss. The dependence on Hebei, Gansu, Shandong, Henan, Shanghai, Xinjiang, and Heilongjiang was moderate, with potential economic loss to HBOY due to inadequate virtual water supply ranging from 442 million yuan in Heilongjiang to 737 million yuan in Hebei. The dependence on Liaoning, Anhui, Ningxia, and Shaanxi was relatively small, with potential loss to HBOY from insufficient virtual water supply ranging from 195 million yuan in Shaanxi to 381 million yuan in Liaoning. The dependence on the remaining 18 provinces was less than 100 million yuan.

Table 1. Pearson correlation between potential economic loss due to inadequate virtual water to Hohhot-Baotou-Ordos-Yulin (HBOY) and risk spillover of HBOY

		Potential Economic Loss Due to Inadequate Virtual Water to HBOY	Risk Spillover of HBOY
Potential economic loss due to inadequate virtual water to HBOY	Pearson correlation	1	0.898**
	Significance (2-tailed)		0
	Sample size	31	31
Risk spillover of HBOY	Pearson correlation	0.898**	1
	Significance (2-tailed)	0
	Sample size	31	31

^** The correlation is significant at the 0.01 level (2-tailed).

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Fig. 7

Potential economic loss to Hohhot-Baotou-Ordos-Yulin (HBOY) caused by insufficient virtual water supply from external regions

Sectorally, HBOY was most dependent on the agricultural sector in external regions (Fig. 8). If the virtual water supply from external agriculture had been insufficient, HBOY’s economic output would have decreased by RMB 9.70 billion yuan, accounting for 87.7% of the total potential loss due to inadequate external virtual water supply. This reliance stemmed from HBOY’s heavy import of virtual water-rich agricultural products to support its light industry, agriculture, construction, and service sectors. After agriculture, HBOY was also dependent on external heavy industry, where a disruption in virtual water supply would result in a 682 million yuan loss to HBOY, accounting for 6.2% of the total potential loss. This is because HBOY’s large-scale infrastructure and housing development during rapid urbanization required importing industrial products such as steel, cement, and machinery equipment from other regions. The electricity, gas, and water supply sector followed, where insufficient virtual water would have reduced HBOY’s economic output by 375 million yuan, representing 3.4% of the total potential loss. The dependence of HBOY on the remaining seven sectors was minimal, with combined losses from these sectors contributing less than 3.0% of the total potential loss.

[See PDF for image]

Fig. 8

Potential economic loss to Hohhot-Baotou-Ordos-Yulin (HBOY) caused by insufficient virtual water supply from external sectors. The sector codes S1–S10 are shown in Fig. 4.

Policy Implications

Water scarcity not only disrupts the economic system but also triggers a series of knock-on effects such as groundwater overexploitation, land degradation, ecological damage, and further spreads through the supply chain, posing systemic risks to regional sustainable development (Feng et al. 2014; Huang et al. 2024). However, the spillover of water scarcity risk through virtual water trade has not received adequate attention in current policy framework. Existing water policies mainly focus on managing physical water, aiming to address worsening water scarcity by controlling total water usage and reducing water intensity. For example, China has adopted the most rigorous water resource management framework since 2012 and proposed further measures to strengthen water conservation and intensive use in 2023 (GOSC 2012; NDRC 2023). Although these measures have effectively curbed water wastage and pollution, they have struggled to balance regional disparities in water stress. In particular, trade activities in RUDs have transferred water scarcity risk to water-stressed areas, weakening sustainable supply capacity of virtual water in those regions, exacerbating regional unsustainability, and further threatening China’s progress towards the sustainable development goals (SDGs) 6 and 10.

We found that HBOY transferred over 90% of its water scarcity risk to regions experiencing severe or extreme water stress. This phenomenon of transferring water scarcity risk to areas with comparable or more severe water stress has also been observed in other water-stressed regions (for example, Beijing, Tianjin) (Feng et al. 2014; Jiang et al. 2015). Therefore, we recommend that RUDs should actively develop cross-regional governance strategies to reduce risk spill to water-scarce areas. First, adjust the direction of virtual water imports to reduce risk transfers to water-scarce regions. Rapidly urbanizing drylands should classify trading partners based on water resource endowments to establish clear priorities for virtual water imports. Meanwhile, a bottom-up approach should be used to develop a list of key imported products, considering sectoral water demand and industrial linkages to optimize the import structure. To support this transition, policy incentives such as tax breaks, financial subsidies, and green credit should be introduced to encourage enterprises to procure more water-intensive products from water-abundant southern regions (for example, Guangdong, Guangxi, Hainan). Furthermore, local governments should lead multilateral negotiations with key trading partners to establish specialized virtual water trade agreements, ensuring long-term stability and sustainability in cooperation.

Second, establish a fair compensation mechanism for unavoidable risk spillover to ensure that water-scarce regions bearing these risks receive necessary economic or technological compensation. There are several successful cross-regional water ecological compensation practices in China. For example, Henan and Shandong Provinces signed the first horizontal ecological compensation agreement in the Yellow River Basin in 2021, establishing a compensation framework based on the principle of “those who benefit should compensate, those who protect should receive compensation.” After two years of implementation, Shandong Province, as the beneficiary of improved water conditions, paid Henan Province RMB 126 million yuan in compensation (MOF 2024). Similar cases include the Xin’an River and Chishui River Basins (Yu et al. 2024). Drawing on these experiences, RUDs and their trading partners can explore a compensation mechanism for virtual water trade risk spillover. This requires clearly defining responsible parties based on the direction and scale of risk transfers. Compensation standards should follow the “those who benefit should compensate” principle, considering dynamic changes in water stress and potential economic losses from insufficient virtual water supply. Additionally, a horizontal transfer payment and monitoring system should be implemented to track and evaluate the compensation process, ensuring timely adjustments for greater effectiveness.

Third, implement cross-regional water diversion projects to reduce dependence on virtual water. Since nearly three-quarters of HBOY’s water footprint comes from external virtual water, promoting physical water diversion projects can help alleviate water stress in RUDs and reduce their reliance on virtual water from water-scarce regions by directly supplementing physical water supplies.

Fourth, strengthen interregional cooperation in water management and water-saving technologies. Given that agriculture is the main source of water scarcity risk spillover through virtual water trade, with over one-quarter of HBOY’s total risk spillover originating from this sector, RUDs should enhance interregional cooperation in agricultural water-saving technologies, boost investment and promotion of efficient irrigation systems and water-saving facilities, improve overall water utilization efficiency, and reduce the spillover of water scarcity risk at source.

Additionally, attention should be given to the interconnections and feedback of risk spillover across sectors, such as between agriculture and light industry or between construction and heavy industry. By fostering intersectoral collaboration, RUDs can more effectively manage the virtual water trade risk spillover and reduce the unintended consequences of water scarcity on multiple sectors.

Future Perspectives

Taking HBOY as an example, this study analyzed the spillover of water scarcity risk through virtual water trade and its transmission paths in RUDs using the MRIO and SPA methods. The findings indicate that HBOY transferred its local water scarcity risk to already water-stressed areas through the supply chain. Similar pattern is also observed in other RUDs (Feng et al. 2014; Jiang et al. 2015). However, compared with previous research, this study further employed SPA to uncover the stepwise transmission process of risk spillover. The results show that the water scarcity risks of HBOY were predominantly directly transmitted, accounting for 54.4% of the total risk spillover in the top 30 critical paths, with the most important spillover path being agriculture in HBOY → agriculture in Inner Mongolia. Additionally, HBOY’s construction and Inner Mongolia’s agriculture served as key transit hubs in indirect transmission paths, facilitating the cross-sectoral and cross-regional diffusion of risk spillover—an aspect not captured in existing literature. By combining MRIO and SPA, this study not only revealed the risk spillover pattern but also identified critical transmission paths, offering new insights into the systemic understanding of virtual water trade risk spillover in RUDs and providing a scientific basis for cross-regional water resource collaboration.

However, there are still several potential limitations in this study. First, the risk spillover index used does not fully capture all dimensions of water scarcity risk, such as social, economic, and environmental impacts, leading to a more conservative estimate. Second, although the MRIO and SPA methods provide a snapshot of virtual water trade risk spillover in HBOY, static models are limited in predicting future risk changes driven by factors such as technological progress, consumption shifts, and policy adjustments. Given that these factors typically evolve gradually, however, this study still offers valuable insights (Li et al. 2024). Third, despite using the latest available data, the lag in the MRIO table means that the results may not reflect the most recent dynamics of virtual water trade risk spillover, which is a common issue in virtual water trade research (Liu et al. 2021; Yang et al. 2024). Finally, the lack of product-level data has prevented an exploration of how product quality differences influence virtual water trade and risk spillover.

In the future, the analytical framework used in this study can be further expanded to provide a more comprehensive assessment of water scarcity risk spillover through virtual water trade. For example, the risk spillover index should be extended to include factors from social, economic, and environmental dimensions. In research focused on agricultural production and water environment governance, information on green and grey water should also be incorporated (Cao et al. 2023; Sturla et al. 2024). Meanwhile, the city-level MRIO table should be updated to the latest year to provide more timely insights into virtual water trade risk spillover and policy recommendations. More detailed sectoral water monitoring data should also be provided to quantify the different roles of surface water and groundwater, thereby offering more precise and comprehensive assessment results (Cai et al. 2024). Furthermore, future studies could consider integrating refined product-level data and the life cycle assessment method to supplement information on product-level virtual water trade and risk spillover (Zhao et al. 2021; Ji et al. 2022). Finally, the computable general equilibrium (CGE) model should be incorporated to simulate the impact of various policy changes, such as technological progress, changes in consumption patterns, water price fluctuations, and industrial structure adjustments, on virtual water trade and its risk spillover (Sun et al. 2022; Wen et al. 2023).

Conclusion

More than one-third of HBOY’s water scarcity risk of virtual water trade was transferred to regions experiencing extreme water stress, while over half was transferred to regions under severe water stress. Spatially, HBOY primarily spilled these risks to Inner Mongolia and Ningxia, accounting for 37.2% and 14.5% of the total risk spillover, respectively. Sectorally, 62.0% of HBOY’s water scarcity risk was transferred from its agriculture, light industry, and construction sectors to the agricultural sector in external regions. The transmission was primarily direct, with the most important spillover path being agriculture in HBOY → agriculture in Inner Mongolia, contributing 18.3% of HBOY’s total risk spillover. Additionally, HBOY’s construction and Inner Mongolia’s agriculture served as key transit hubs in risk transmission.

HBOY’s sustainable development heavily relied on external virtual water. The potential economic loss from insufficient external virtual water supply was estimated at RMB 11.06 billion yuan, representing approximately 31.3% of HBOY’s total loss due to water shortages. Spatially, HBOY was most reliant on Inner Mongolia, and sectorally, its greatest reliance was on external agriculture.

Rapidly urbanizing drylands transfer water scarcity risks through virtual water trade to regions facing comparable or even more severe water stress, presenting a significant challenge to regional water security and sustainable development. Therefore, policymakers should establish an effective policy framework and foster cross-regional collaboration to minimize the transfer of risks to water-scarce areas. First, the direction of virtual water imports should be optimized by forming long-term, stable partnerships with water-rich southern provinces. Specialized virtual water trade agreements should prioritize imports from these regions, reducing reliance on northern water-scarce areas. Second, cross-regional water diversion projects and rainwater harvesting facilities should be developed to supplement physical water resources. This would reduce reliance on external virtual water and enhance regional water self-sufficiency. Third, a risk-sharing mechanism should be explored, and appropriate compensation schemes for risk transfer should be devised. This could include establishing a virtual water risk transfer fund, promoting water resource insurance, and using other financial instruments to ensure that water-scarce regions receiving risk spillover are compensated with economic support, technical assistance, and necessary infrastructure. Finally, collaboration on water-saving technologies, particularly in key sectors like agriculture, should be strengthened. Promoting efficient irrigation techniques and water-saving infrastructure will improve water resource utilization and reduce the transfer of water scarcity risks at the source.

Acknowledgments

This study was supported by the National Natural Science Foundation of China (grant No. 42371296 and 42361144859) and BNU-FGS Global Environmental Change Program (grant No. 2023-GC-ZYTS-08).

1

https://www.ceads.net.cn/

2

https://doi.org/10.5281/zenodo.15856731

3

USD 1 = RMB 6.75 yuan (annual average in 2017).

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