Effects of trade liberalization policies on water resource management through the concept of water shadow price: application of the DCGE model Open Access

Water resources constitute fundamental natural assets that are indispensable for both societal development and ecosystem sustainability. In recent decades, however, escalating anthropogenic pressures have led to severe freshwater overexploitation, substantially diminishing both the quantity and quality of these crucial resources (Zafar et al. 2021). This growing water crisis – particularly acute water scarcity – has emerged as a paramount global challenge, generating significant socioeconomic and environmental consequences that parallel twentieth-century development patterns (Naderi et al. 2024). Multiple interconnected factors drive this crisis: burgeoning water demands from population growth and urbanization (Yang et al. 2019; Zolghadr-Asli et al. 2023), climate change impacts (Siddiqui et al. 2023), and intensifying agricultural needs. Current consumption trajectories threaten to exacerbate water scarcity, potentially escalating inter-sectoral conflicts among water users (Sivakumar 2011; Biswas & Tortajada 2019). These urgent challenges underscore the critical need for developing comprehensive policy frameworks to promote water conservation and sustainable resource management across all economic sectors (Mirzaei & Zibaei 2021).

Water scarcity is a crucial factor affecting economic production and poses a substantial risk to local economic growth. As global economic integration intensifies, the impacts of economic activities extend beyond national boundaries, amplifying the challenges associated with water scarcity. The integration of local and global trade has created a highly interconnected system in which regional economies can significantly influence global dynamics networks. This complex interdependence requires a broader focus on how its effects are transmitted and amplified across trade networks (Zhu et al. 2023). This issue is highlighted by various studies, emphasizing its growing significance in both local and global contexts (Mazzoni et al. 2023; Wang et al. 2024). Therefore, any change in international trade policy will be accompanied by a change in the real value of water resources. This is while the World Trade Organization (WTO) and regional trade agreements aim to reduce or eliminate tariff and non-tariff barriers to facilitate the exchange of goods between countries (Duarte et al. 2019; Delbourg & Dinar 2020; Raimondi & Scoppola 2022). Meanwhile, trade liberalization can affect the issue of virtual water trade and, consequently, water resource management (Chen et al. 2024). Therefore, the trade of products is a factor that influences water resource management (Konar et al. 2011). To better understand this, the concept of virtual water was first introduced by Allan (1997, 1998). In this definition, virtual water represents the volume of water used throughout the entire production chain. According to this definition, the trade of products, considering the volume of water used in their production, can impact water resource management. Hence, the relationship between import tariffs, especially for agricultural and industrial products due to their high water consumption in production, and virtual water trade is undeniable (Chen et al. 2024).

The relationship between import tariffs and virtual water trade has been examined in various studies. For example, studies by Fracasso (2014), Xu (2018), Fan et al. (2019), and Chen et al. (2024) have demonstrated the negative impact of import tariffs on virtual water trade. Reducing tariffs can make the export of relatively water-intensive products economically viable and improve virtual water trade. Conversely, increasing import tariffs can lead to a reduction in virtual water trade and motivate countries to focus on producing products with lower water requirements. However, some studies have found no significant relationship between import tariffs and virtual water trade (e.g., Raimondi & Scoppola 2022). Nonetheless, none of these studies have considered the effects of trade liberalization on the real economic value of water as an efficient tool for managing water resource consumption in various economic sectors.

Trade liberalization policies, especially regional trade agreements (RTAs), can impact the economic value of water in addition to changing the final price of goods. By reducing tariff barriers, these policies can lead to more efficient consumption and allocation of water resources, reflecting the real economic value of water (Grafton et al. 2023). Understanding the real economic value of water is particularly important in water-scarce regions, and the concept of the shadow price of water is crucial in this context (Gramlich & Ohlsen 2023). The shadow price of water has multiple definitions in the literature (He et al. 2007). In the most common definition, the shadow price of water refers to the price that includes all costs (including opportunity costs and external environmental and economic effects) and represents the maximum price users are willing to pay for an additional unit of water consumption (Young & Loomis 2014; Bierkens et al. 2019). Therefore, the shadow price of water can be considered a comprehensive indicator of the real value of water, taking into account stakeholders and external environmental and economic effects, while the water price reflects the current conditions of water trade in economic markets (Bierkens et al. 2019).

Various studies have shown that the biggest challenge in water resource management is the market price of water being lower than its real economic value (Gramlich & Ohlsen 2023). Water pricing in the agricultural, industrial, and service sectors is proposed as an economic incentive and policy tool to conserve and store water resources and to encourage the use of water for higher economic value purposes (Dinar et al. 2015; Bierkens et al. 2019). Water prices lead to the allocation of this resource among different uses and users (Zetland, 2021; Gramlich & Ohlsen 2023). Low water prices can easily lead to water wastage, while high water prices can increase production costs and challenge producers. Therefore, water prices should be set within a reasonable range for each economic activity to achieve sustainable water use (Wang et al. 2018). However, due to inadequate and unclear property rights, government monopoly on water supply, external effects, and government price controls, the water market has failed, and competitive water pricing does not exist (Jia et al. 2017). As a result, the price that stakeholders in various economic sectors pay for water consumption does not reflect its scarcity (Bierkens et al. 2019). Therefore, estimating the shadow price of water for different economic activities and the impact of trade liberalization policies on it can pave the way for efficient water resource management.

To evaluate the effects of trade policies on various economic, social, and environmental variables, different types of computable general equilibrium (CGE) models have been widely used (Kuik & Gerlagh 2003; Diao et al. 2008; and Diao & Roe 2003; Liu et al. 2020; Vellinga & Tanaka 2024). For example, Liu et al. (2020) used a CGE model to examine the effects of increasing import tariffs on tradable products between China and the USA on environmental variables and found that with increased tariffs, greenhouse gas emissions could decrease by up to 5%. Vellinga & Tanaka (2024) investigated the effects of rice import liberalization in Japan on household welfare using a dynamic-stochastic CGE model and concluded that household welfare in the long term would be higher than in the short term due to rice import liberalization. A review of the studies shows that none of them have examined the effects of trade liberalization on the real economic value or shadow price of water.

Therefore, this study aims to evaluate the impact of trade liberalization policies on the shadow price of water as an indicator of the real economic value of water in various economic sectors using a dynamic computable general equilibrium (DCGE) model. Based on this evaluation, the study seeks to implement necessary policies for water resource management, a topic that has not been previously explored. Iran has been chosen for this study for two main reasons. First, over the past 23 years, Iran has experienced a severe water crisis due to rising temperatures, decreased rainfall, reduced surface water resources, and the degradation of groundwater resources (National Climate Change Office 2010). Second, the price of water in Iran, especially for agricultural production, is very low and significantly below its real economic value (Arabpour et al. 2023). Overall, this study addresses the following three key questions:

• 1. How has the shadow price of water in the agricultural, mining, industrial, and services sectors changed over time?

• 2. What are the effects of trade liberalization policies on the shadow price of water in the agricultural, mining, industrial, and services sectors over time?

• 3. Which sectors are most affected by pricing policies in managing water resource consumption?

Based on the research questions, the hypotheses of this study are formulated as follows:

• H1: The shadow price of water has increased over time in all sectors due to increasing water scarcity and increasing demand.

• H2: The agricultural sector will show the smallest increase in the shadow price of water under trade liberalization, as it is less integrated into global trade networks compared to other sectors.

• H3: The agricultural sector will be most affected by water pricing policies due to its high water intensity and reliance on subsidized water.

By conducting this study, it is possible to identify the trend of the shadow price of water, examine the impact of trade liberalization on the shadow price of water, and assess the responsiveness of different sectors to pricing policies.

The DCGE model captures the complex interactions between different sectors and factors in the economy and provides valuable insights into how trade policies can affect the shadow price of water and, consequently, water resources management. This section develops a DCGE model to simulate the effects of trade liberalization policies on the shadow price of water resources. The social accounting matrix (SAM) derived from Iran's input–output tables (years 2001 and 2006) was used as the model data. This section is divided into three parts: (1) The basic structure of the DCGE model. (2) Sensitivity analysis of the DCGE model and evaluation of the effects of trade liberalization policies. (3) The model solution algorithm. Accordingly, the present study thoroughly examines these aspects by developing a DCGE base model, establishing equilibrium conditions, calculating the shadow price of water using data from the SAM, altering the values on the right side of the constraints, and evaluating the policy in question.

This study conducts a sensitivity analysis by altering the right-hand side values of the constraints associated with the water consumption coefficient. This approach is innovative for several reasons:

(1) It systematically examines the impact of modifications in water consumption constraints on the optimal solution of a linear programming (LP) problem within the DCGE framework.

(2) By varying these constraints, the study evaluates the stability and robustness of the shadow price of water under different scenarios.

(3) The sensitivity analysis provides policymakers with insights into the range of possible outcomes and potential impacts of trade liberalization and water pricing policies.

From this perspective, the algorithm begins by solving an initial LP problem using the output structure matrix, integrating constraints and equilibrium conditions to ensure accurate results. Additionally, the algorithm calculates the shadow price of water for each year, illustrating its evolution over time and across different sectors. Finally, the algorithm performs a sensitivity analysis on the shadow price, examining how changes in water consumption constraints influence the outcomes. Therefore, the heuristic algorithm enhances the methodological rigor of the study, making it a valuable tool for future research in water resources management and economic policy analysis. Furthermore, it provides a detailed framework for replicating the study's findings in other contexts or for analyzing various policy scenarios.

In order to use the DCGE model, a SAM is required as input data for the model. SAMs are used to analyze the impact of economic policies on different sectors and institutions. They help in understanding how changes in one part of the economy affect the rest (Mainar-Causapé et al. 2018). Technically, SAM is a square matrix where each account is represented by both a row and a column. Each cell in the matrix indicates a payment from the account in its column to the account in its row. Therefore, accounts are displayed as income along the rows and expenditure along the columns. The fundamental principle of double-entry accounting ensures that, for each account in the SAM, the total revenue (row total) equals the total expenditure (column total) (Lofgren et al. 2002). It is important to note that the use of the 2001 and 2006 SAM matrix, may not fully capture recent economic, technological, and environmental changes in Iran. The dated SAM data may not reflect current economic structures, sectoral shifts (e.g., industrialization or urbanization), or advancements in water-saving technologies and policies. These limitations could lead to inaccuracies in the model's predictions, particularly regarding water consumption patterns and the shadow price of water. To address these issues, the study employs sensitivity analyses to enhance the robustness of its findings despite the constraints of the older data.

DCGE models are built on microeconomic principles, where the behavior of individual agents (households, firms, government) is modeled based on optimization and rational expectations. DCGE model is a method to overcome the difficulties in the calculation of dynamic shadow price in water resource project evaluation. The model proposed in this paper is different from traditional analysis because it is based on large LP in a specific time frame. The shadow price is calculated as well as a balance of economic system results. Two advantages of this model are given as follows:

• 1. The shadow price is in accord with the dynamic global optimal solution reflecting the dynamic order of the resource's optimal allocation.

• 2. The DCGE model can be modified to calculate the shadow price of a certain year easily. Solution sets of shadow prices are useful to balance the development of an economic system.

In general, the DCGE model captures the intricate interdependencies among various sectors (e.g., agriculture, industry, and services) and factors of production (e.g., labor, capital, and water). This model facilitates a comprehensive analysis of how trade liberalization policies influence the shadow price of water across the economy. Unlike static models, the DCGE model incorporates temporal dynamics, allowing for the examination of how the shadow price of water evolves over time. This feature is particularly valuable for understanding the long-term effects of policies such as trade liberalization.

The model explicitly calculates the shadow price of water, reflecting its true economic value under different policy scenarios. This is crucial for comprehending the actual cost of water consumption and for devising efficient pricing mechanisms. Furthermore, the DCGE model enables sensitivity analysis, assessing the robustness of results to variations in key parameters or assumptions. This enhances the reliability of findings and provides policymakers with a broader range of potential outcomes. Moreover, the DCGE model integrates detailed data from sources such as the SAM, ensuring that the analysis is grounded in real-world economic structures and relationships. Consequently, the DCGE model is well-suited for simulating the effects of specific policy changes, such as reducing import tariffs. It offers insights into how these policies impact the true economic value of water, which is essential for effective water resource management. However, the DCGE model's reliance on high-quality, detailed data – such as SAM tables – poses a limitation. In many instances, such data may be outdated, incomplete, or unavailable, restricting the model's applicability. Additionally, the complexity of the DCGE model can hinder the interpretation and communication of results to non-experts. Most importantly, the model's outcomes are sensitive to assumptions regarding economic behavior, market structures, and policy scenarios. If these assumptions are unrealistic or incorrect, the results may be misleading (He et al. 2007; Jia & Lin 2022).

Despite these challenges, the DCGE model provides a robust framework for analyzing the impact of trade liberalization on the price of water, offering valuable insights into sectoral and economic impacts. Policymakers should interpret the model results with caution, complementing them with additional analyses and considering them within a broader socio-economic and environmental context.

Equations (1)–(7) indicate the calculated shadow prices based on the DCGE model with parameters and constraints. In this study, the objective function included the maximization of GDP, Where is output matrix in tth year, is direct input coefficients matrix in the specific Tth year, is output matrix in the specific Tth year, is output matrix in the specific Tth year, is investment coefficient matrix in tth year, is output amount matrix in the specific (T + 1)th year, is output structure matrix in (T + 1)th year, is depreciation coefficient matrix in tth year, is final demand coefficient vector in tth year, is investment coefficient matrix in the specific (T + 1)th year, is depreciation coefficient matrix in the specific T_th year, is final demand coefficient vector in the specific Tth year, is direct water input coefficient matrix in the specific Tth year, is total water input in the specific Tth year, is the maximum production capacity vector in sector ith, is the labor-working rate vector in sector ith, L is the available labor amount, is the input coefficient vector of the kth resource in sector ith, is the available resource amount vector of the kth resource in sector ith, is input coefficient of the import commodity vector in sector ith, is input coefficient of the export commodity vector in sector ith, is income of dwellers in the property sector, is income of non-property sector, U is non- commercial payout of dwellers, is supply of consumable, W is import consumable, is consumption amount in sector ith in the previous term, is capital forming amount vector in the previous term, is average fund occupying amount per person in sector ith, is number of the employees in the previous term and is number of employees in the present term.

Equation (8) represents constraints of production capacity, Equation (9) represents constraints of labor capacity, Equation (10) represents other constraints of resources or constraints of the balance between the amount of water resource available in sector i and amount of source input kth to sector ith, Equation (11) represents constraints of the equilibrium between import and export, Equation (12) represents constraints of the equilibrium between the reward of the income and the consumable, Equation (13) represents the equilibrium between accumulation and consumption, Equation (14) represents an equilibrium between forming and occupying.

where is the water resource constraint in the original LP in Equation (1).

• Step 1: Solve the primary LP using the output structure matrix that in this part must that the constraints and balance conditions can be also added and reduced to get the right results.

• Step 2: To calculate the shadow price for each year.

• Step 3: To do a sensitivity analysis of the shadow price with the DCGE model.

In this section, first, the general schematic of the SAM matrix is explained, divided into the agricultural, mining, industrial, and service sectors. Then, the results of calculating the shadow price of water are presented. Next, the impact of trade liberalization scenarios on the shadow price of water is explained by the relevant sectors.

According to the results in Table 1, the agricultural sector had the highest water capital, with values of 58.19% in 2001 and 50.13% in 2006. Also, the lowest amount of water capital is related to the services and mining sectors with numerical values of 3.77 and 2.49% for each sector, respectively. Additionally, the services sector in 2001 had the lowest water capital at 3.77%, and the mining sector in 2006 had the lowest at 2.49%. The utilization of water capital in the agricultural sector decreased by about 8% in 2006 compared to 2001. Meanwhile, the industrial sector, which is the second-largest consumer of water capital after agriculture, saw an increase of more than 10% in this production factor from 2001 to 2006. Therefore, it can be inferred that the main competition for the utilization of water capital is between the agricultural and industrial sectors.

After solving the model, the shadow price of water for the period from 2001 to 2023 was calculated, and the results are presented in Table 2. According to the results in Table 2, during the 2001–2024 period, the highest and lowest shadow prices of water correspond to the mining and agricultural sectors, respectively. The shadow price of 1 m³ of water for the mining and agricultural sectors in 2001 was estimated at 29,640 and 1,548 Rials, respectively. According to the UNESCO World Water Development Report (2021), the lower shadow price of water in the agricultural sector compared to other sectors has also been confirmed. In a study by Esmaeili & Shahsavari (2015), the shadow price of agricultural water in 2011 for the Doroodzan basin in Iran was evaluated based on monthly periods, and their results showed that the average shadow price of one cubic meter of irrigation canal water was about 0.141$ (equivalent to 2,674 Rials).

The results of Table 2 showed that the trend of the shadow price of one cubic meter of water for all economic sectors increased from 2001 to 2024. Among the various sectors, the highest and lowest growth in the shadow price of water during the years under review corresponds to the services and agricultural sectors, respectively. The shadow price of 1 m³ of water for the services sector increased about 6.38 times from 2001 to the average price in the period 2021–2024, reaching 33,240 Rials. Meanwhile, the shadow price of 1 m³ of water for the agricultural sector increased about 4.17 times from 2001 to the average price in the period 2021–2024, reaching 6,458 Rials. Therefore, it can be concluded that the economic return of the agricultural sector has grown less compared to other sectors, while the services sector in Iran has experienced higher profitability growth compared to other sectors. The decrease in the share of value added by the agricultural sector and the increase in the share of the services sector in Iran's GDP is supported by statistics provided by the World Bank (2023).

To better understand the status of water resources in various sectors, it is necessary to compare the shadow price of water as its real economic value with the water tariff rate. Therefore, the information in Table 3 for the base year 2001 showed that the highest and lowest ratios of shadow price to water tariff rate correspond to the agricultural and services sectors, with ratios of 15.48 and 1.24, respectively. This finding indicates that the water tariff rate in the agricultural sector is much lower than the actual value of water consumption, and a policy of increasing water prices seems necessary for better water resource management in this sector. Meanwhile, the water tariff rate in the services sector is close to the real price, and increasing water prices in this sector may not be an effective and efficient policy for water resource management. The significant discrepancy between the real economic value of water and the water tariff rate in the agricultural sector has been proven in various studies (Falahati et al. 2012; Seyedan & Morab 2019; Ravasizadeh et al. 2023).

The results also showed that for all sectors from 2001 to 2024, the increase in the difference between the shadow prices of water occurs under policies of reducing tariff barriers. In other words, during the period 2021–2024, the shadow price of water will increase at a higher rate due to the reduction of tariff barriers. For example, in 2001, trade liberalization could not create a significant difference in the shadow price of water. This finding reflects the fact that over time, the impact of trade policies on virtual water trade and the real economic value of water, and consequently on water resource management, will become more pronounced. The impact of trade policies on virtual water trade and water resource management has also been proven in various studies (Ramirez-Vallejo 2005; Xu 2018; Chen et al. 2024).

The comparison of the effects of trade liberalization policies on the shadow price of water in various sectors also showed that the greatest impact of these policies is on the industrial sector, while the least impact is on the services sector. This finding indicates that the economic value of water as a production factor in the industrial sector will increase more sharply under trade liberalization policies compared to other sectors. For instance, the shadow price of 1 m³ of water consumed in the industrial sector will increase from 37,255 Rials to 90,469 Rials during the period 2021–2024 due to the policy of complete removal of import tariffs. Meanwhile, the rate of increase in the economic value of water in the services sector under trade liberalization policies is lower compared to other sectors. For example, the shadow price of one cubic meter of water consumed in the services sector will increase from 33,240 Rials to 38,566 Rials during the period 2021–2024 due to the policy of complete removal of import tariffs. Therefore, it can be said that trade policies may not be very promising for managing water resource consumption in the services sector. However, for the industrial sector, the role of trade policies in water resource management can be considered very significant. The impact of trade policies, both directly and indirectly, on water resource consumption in the industrial sector has also been proven in some studies (Lu 2018; Chen et al. 2021). However, Shirkhani et al. (2022) concluded that the amount of trade and economic growth in the industrial sector does not have a significant effect on water consumption in Iran's industrial sector. The industrial sector faces the most significant impact of trade liberalization policies on the shadow price of water, attributed to its high water consumption, export-driven growth, and integration into global markets. As trade liberalization enhances industrial production and exports, the demand for water in this sector rises substantially, leading to an increase in the shadow price of water. In contrast, the services sector is less affected due to its lower water consumption and minimal dependance on physical water resources for its economic activities. These findings highlight the need for sector-specific water management strategies, particularly in the industrial sector, to address the challenges posed by trade liberalization and promote sustainable water use.

Given the intensifying water resource crisis and the necessity of managing water consumption globally, especially in arid and semi-arid countries, and on the other hand, the increasing pressure on water resources due to economic growth and trade liberalization policies, it is essential to evaluate the effects of trade liberalization policies on the management of these resources. Since the effects of trade liberalization policies vary across different production sectors, this study aims to simulate the impact of trade liberalization policies in the form of import tariff reductions on the shadow price of water as an indicator of its real economic value and to assess the issue of water resource management through pricing policies. To achieve this goal, Iran was considered as the study area, and the impact of trade liberalization policies was examined using a DCGE model on the shadow price of water resources in the agricultural, mining, industrial, and services sectors. Therefore, a summary of the results obtained to answer the research questions is presented: (I) The highest and lowest economic value of water corresponds to the mining and agricultural sectors, respectively. (II) The real economic value of water has increased over time for all sectors. (III) The greatest discrepancy between the water tariff rate and the real economic value of water is in the agricultural sector, while the smallest discrepancy is in the services sector, and this discrepancy has increased over time. (IV) Trade liberalization policies in the form of import tariff reduction scenarios will increase the real economic value of water in all economic sectors, and this impact will intensify over time. (V) The greatest and smallest impact of trade liberalization policies on the real economic value of water corresponds to the industrial and services sectors, respectively. Given the largest gap between water tariff rates and their real economic value in the agricultural sector, consequently, an increase in the shadow price of water due to trade liberalization can disproportionately impact the livelihoods of those in the agricultural sector. Water pricing reforms tend to affect small-scale agriculture more severely, as small-scale farmers often rely heavily on subsidized water for irrigation. Hence, the rise in water prices can lead to a decrease in agricultural production and an increase in costs for these farmers. To address these challenges, policymakers can consider targeted subsidies or support programs for small-scale farmers. By integrating these social considerations, effective water efficiency management can be achieved.

Based on the results obtained, it seems necessary to focus on the issue of water resource management in various sectors, especially the agricultural sector, through gradual water price increase policies. However, increasing water prices due to higher production costs requires improving production and sales conditions and consequently increasing the financial power of producers. In this regard, removing tariff barriers and facilitating trade liberalization, while improving production and sales conditions and the financial income of producers, especially in the industrial sector, can result in water resource management through gradual water price increases. The model of this study can also be implemented as an innovative model for other countries. Additionally, future studies can incorporate other environmental issues into the model and assess the impact of trade liberalization policies on the shadow price of carbon, chemicals, or water pollution. Furthermore, incorporating advanced modeling techniques such as interval linear programming, fuzzy linear programming, or stochastic linear programming into the study would significantly enhance its comprehensiveness and realism. These methods address uncertainties and complexities that are often overlooked in traditional deterministic models like the standard DCGE model. Although the presented model adjusts for trends, unforeseen shocks (e.g., severe droughts, sudden policy changes) may not be fully accounted for. Therefore, the results of the present study are conditional on current trends and policy environments, and this modeling structure could be re-checked if newer SAMs become available. For example, if irrigation efficiency has improved (e.g., with the adoption of drip irrigation), the model could consider water productivity coefficients to reflect the reduction in water intensity in agriculture. The sensitivity analysis in this study examines variations in water consumption coefficients. Future research could extend this approach by incorporating multivariate sensitivity analyses that account for interactions with other key constraints, such as labor and capital shortages. For instance, if labor scarcity limits industrial expansion, the resulting reduction in water demand may dampen upward pressure on shadow prices. Such an analysis would provide a more comprehensive understanding of the dynamic tradeoffs between water pricing and broader economic constraints, offering policymakers a refined tool for resource management under real-world conditions. While this study adopts full tariff elimination as a benchmark scenario to assess water pricing dynamics, future research could enhance policy realism by incorporating (1) sector-heterogeneous liberalization timelines – particularly gradual phase-outs for sensitive sectors like agriculture – and (2) concurrent reductions in non-tariff barriers, whose persistence may significantly modulate water scarcity effects. Such refinements would better align simulations with real-world trade agreements, such as Iran's WTO accession framework, where transitional protections for strategic sectors are commonplace.

Data cannot be made publicly available; readers should contact the corresponding author for details.

The authors declare there is no conflict.