Water Rights Trading and Agricultural Water Use Efficiency: Evidence from China

Abstract

Inefficient agricultural water use is a significant factor exacerbating global water scarcity. Water rights trading (WRT) offers a new governance paradigm to address this issue. Initiated by China in 2014, the WRT policy provides a case for researching formal water markets in developing countries. This paper uses a sample of 30 Chinese provinces from 2007 to 2022 and employs the difference-in-differences method to evaluate the impact of WRT on agricultural water use efficiency (AWUE). The findings suggest that AWUE in pilot areas increased by an average of 48.1% compared to non-pilot areas. Heterogeneity analysis reveals a stronger WRT impact on AWUE in regions with developed markets, abundant water, and high agricultural dependence. Subsequent analysis identifies that WRT enhances AWUE mainly by incentivizing water-saving innovation, promoting cross-industry factor mobility, and optimizing crop structures. This study thus offers empirical evidence supporting China’s water marketization reform and explores WRT policy as a pathway to enhance AWUE.

1. Introduction

Water scarcity has become a critical constraint to global sustainable development [1]. In emerging economies such as China, it has evolved into multiple governance challenges [2]. Despite its position as the sixth-largest global water resource holder, China’s per capita availability of water is significantly below the global average [3]. Agriculture is the most water-consuming sector in China, primarily due to the irrigation of farmland [4,5]. Nevertheless, challenges such as flood irrigation and channel leakage have led to a significant disparity in irrigation water efficiency between China and developed countries [6,7]. The “three red lines” policy in 2011 strictly controlled agricultural water quotas. China’s ever-increasing industrial water demand has further squeezed the space for agricultural water use [8,9]. The agricultural sector faces a dilemma between ensuring food security and transitioning to water conservation.

Under the premise of limited and highly developed water resources, agricultural water use efficiency (AWUE) has become a prominent topic [10]. Conceptually, AWUE’s scope has broadened beyond direct agricultural water use to include water productivity and rainfall resources [4]. From a methodological perspective, since DEA’s introduction [11], advancements like the non-radial directional distance function model and super-efficiency slacks-based measure (SBM) model have enabled more precise AWUE measurement [12,13,14,15]. Regarding influencing factors, research primarily examines natural conditions and technological progress [16,17], with some studies also highlighting the role of institutional factors, particularly legal frameworks in water rights trading (WRT) [18].

To address the water use efficiency dilemma, global water governance is shifting from engineering solutions to institutional innovation. In this context, the WRT system has emerged. The system has been developed to facilitate the market-based reallocation of water resources, thereby offering a theoretical basis for enhancing governance effectiveness [19]. In developed countries, formal water markets have achieved notable success [20]. And most developing countries still rely on informal water markets, whose efficiency and sustainability need further exploration [21,22]. This is primarily attributable to the difficulties in acquiring data from the least developed countries, in conjunction with the absence of well-defined WRT markets.

The WRT policy implemented by China serves as a paradigmatic exemplar of the development of formal water markets in developing countries. In July 2014, China initiated WRT pilot projects in seven provinces and regions, including Guangdong, Henan, Hubei, Jiangxi, Inner Mongolia, Ningxia, and Gansu. Policy priorities centered on registering water rights, enabling their trading, and establishing institutional frameworks [23]. Zhejiang, Shandong, and Hebei subsequently launched provincial pilots, establishing a joint central-local reform framework [24]. Following a period of over three years spent in exploration, the initial group of national-level WRT pilots has now been successfully completed. In water-scarce Ningxia Hui Autonomous Region [25], farmland irrigation efficiency rose from 0.464 to 0.511 (10.1% increase). And the water consumption per ten thousand yuan of GDP and per ten thousand yuan of industrial added value decreased by 28.7% and 29.8%, respectively. It is noteworthy that despite a decrease of 720 million cubic meters in total water consumption, the regional GDP maintained an average annual growth rate of 7.6%.

This paper employs the difference-in-differences (DID) method to examine the impact of WRT on AWUE. WRT policy significantly enhanced AWUE in pilot areas, with results robust across multiple tests. Furthermore, the initial batch of national-level pilots has been found to demonstrate a stronger policy influence in comparison to subsequent provincial-level pilot projects. Heterogeneity analysis reveals that differences in regional resource endowments and economic development characteristics significantly affect policy implementation outcomes. Specifically, in regions with more developed marketization, abundant water resources, and a higher agricultural dependency, the effects of WRT policy are stronger. WRT enhances AWUE through three pathways: incentivizing water-saving innovation, promoting agricultural water reallocation, and facilitating crop structure adjustments. However, inter-regional water transfer effects remain limited, likely due to underdeveloped WRT markets.

This study makes three key contributions: (1) bridging the gaps between policy and research by verifying the effects of China’s WRT policy through a quasi-natural experiment; (2) revealing regional heterogeneity via a triple-difference model accounting for marketization, water endowment, and agricultural dependency differences, enabling region-specific policy design; and (3) by advancing mechanism analysis through technological innovation, factor allocation, and crop structure optimization, this study transcends single-mechanism limitations and strengthens the theoretical foundations of water rights systems.

The remainder of this paper is organized as follows: The Section 2 introduces the institutional background of China’s WRT policy and presents the research hypotheses. The Section 3 covers the research design, including the model specification and variable description. The Section 4 presents the baseline estimation results, along with a series of robustness checks. The Section 5 provides further discussion, including heterogeneity analysis and mechanism analysis. Finally, the conclusion outlines the research findings, policy implications, and directions for future research.

2. Institutional Background and Research Hypotheses

2.1. Institutional Background

The practice of WRT in China can be traced back to 2000. Zhejiang’s Dongyang and Yiwu signed China’s first WRT agreement, contractually transferring water use rights between regions [26]. However, water resource allocation dominated by administrative orders struggles to promptly respond to the demands for water use rights from different regions and industries. The imbalance in the supply and demand of water resources has led to market-oriented water rights reform [24]. Since 2014, China’s Ministry of Water Resources (MWR) has launched WRT pilots in seven provinces, building a modern water rights system through institutional innovation. The policy establishes three core tasks, including water rights registration, trading, and institutional development [27].

The confirmation and registration of water use rights is a prerequisite for WRT. Following the principle of “total control, household allocation,” the process involves four steps: (1) gradually breaking down the total water use volume; (2) allocating water use quotas to various departments; (3) confirming the share of water for economic use after meeting basic needs; and (4) issuing agricultural water rights certificates and industrial water intake permits [27]. In water-scarce regions, such as Ningxia and the Shule River Basin in Gansu, issuing these certificates imposes binding usage caps that alleviate water scarcity constraints.

It is evident that the confirmation of rights constitutes merely the initial phase in the process of WRT. The crux of the discourse surrounding WRT pertains to the notion of the exchange of water resource utilization rights. In the course of implementing policies pertaining to the trade of water rights, a variety of pilot regions have explored and established four major trading paradigms: inter-regional, inter-sectoral, among water users, and upstream-downstream WRT [28,29]. In particular, Bayannur City in the Inner Mongolia Autonomous Region conserved 120 million cubic meters of WRT quotas through agricultural water conservation. Through inter-regional WRT, water quotas were provided for 35 new industrial projects in Ordos City. This approach ensures food security while also meeting the water needs of industrial development.

The successful advancement of WRT policies is contingent upon the establishment of institutional frameworks. The promulgation of normative documents, including the “Interim Measures for the Administration of Water Rights Trading,” has established the framework for transaction types and procedures. In order to further facilitate the market-based allocation of water use rights via standardized trading platforms, the MWR of China and the Beijing Municipal Government jointly initiated the establishment of the China Water Exchange (CWE) in 2016 [30]. By the close of 2019, the CWE had successfully executed a total of 227 transactions, with a traded water volume of 2.823 billion cubic meters and a transaction value of CNY 1.711 billion. In summary, WRT facilitates the transfer of water resources from low-efficiency to high-efficiency sectors and promotes a shift among water users from relying on the government for water to seeking water in the market [31].

To provide a clearer understanding of the development of WRT, we have compiled a timeline of its key milestones from 2000 to 2019 [24,26,27,28,29,31]. For details, see

Table 1. Key milestones in the development of water rights trading (WRT) in China.

Years	Event	Description
2000	The Water Rights Transfer Case between Dongyang and Yiwu, Zhejiang Province.	China’s first water rights transaction.
2005	MWR Issues “Several Opinions on the Transfer of Water Rights” and the “Framework for the Construction of the Water Rights System.”	For the first time, rules for WRT have been established, thereby providing an institutional framework for reform.
2011	The Decision of the Central Committee of the Communist Party of China (CPC) and the State Council on Accelerating the Reform and Development of Water Resources.	Explicitly call for the establishment of a water rights system and incorporate water rights reform into the national strategy.
2014	MWR Issues the “Notice on Launching Pilot Programs for Water Rights.”	Seven provinces in China officially launch nationwide water rights pilot program.
2016	1. MWR issued the “Interim Measures for the Administration of Water Rights Trading”; 2. China Water Rights Exchange Established in Beijing.	1. The first nationwide regulation for WRT; 2. Marks the launch of a unified national trading platform.

.

2.2. Research Hypotheses

2.2.1. WRT and AWUE

The enhancement of water use efficiency can be regarded as a dynamic process of optimally allocating water resources [32]. Spatial water allocation patterns in Chinese agriculture emerge from regional resource endowment heterogeneity interacting with consumption practices [33]. By converting water rights into tradable assets via market pricing, WRT offers an institutional alternative to inefficient administrative water allocation [19]. As demonstrated by Razzaq et al. [22], agricultural producers have the opportunity to obtain residual water rights through the implementation of water-saving practices. These rights can then be sold, with the objective of generating benefits. At this stage, both supply and demand sides facilitate the transfer of water resources from less efficient to more efficient areas through market transactions, ultimately achieving a Pareto improvement in resource allocation. The following theoretical hypothesis is hereby proposed on the basis of the analysis presented above:

Hypothesis 1.

WRT contributes to the improvement of AWUE.

2.2.2. Mediating Mechanism: Technological Innovation

The WRT policy establishes a channel for converting the economic value of water-saving behaviors. Within the framework of the hierarchical allocation of initial water rights, the usage rights acquired by water users have property rights attributes that are both storable and tradable [34]. However, when the financial burden of acquiring external water rights becomes excessively high, agricultural producers tend to transition from reliance on WRT to the adoption of water-saving technologies. The WRT policy establishes a dual incentive pathway for technological innovation. WRT establishes dual innovation incentives. Quota constraints clarify responsibilities, compelling proactive water-saving technology adoption. And the trading profits convert conservation into economic returns, financially motivating farmers and aligning with studies [24,35]. Consequently, agricultural producers are witnessing a substantial shift in their behavioral patterns within the framework of the WRT policy. The following theoretical assumptions are proposed on the basis of the analysis above:

Hypothesis 2.

WRT improves AWUE by promoting technological innovation.

2.2.3. Mediating Mechanism: Factor Mobility

The fundamental principle of WRT is predicated on the delineation of property rights and the establishment of market pricing. This endows water resources with unambiguous economic attributes and consequently facilitates the optimal allocation of water resources in accordance with value principles. In China, the exchange of water rights is predominantly observed to take place between the agricultural and industrial sectors. This transaction has been shown to engender substantial economic advantages for agricultural producers, who often benefit from the transfer of surplus water rights [24]. Specifically, WRT revenue boosts farmers’ water-saving technology adoption and funds irrigation upgrades, alleviating agricultural conservation financing constraints. As the scale of WRT expands, the resulting positive externalities of water saving will exhibit marginally increasing characteristics, thus promoting the continuous optimization of AWUE. The following research hypotheses are thus proposed on the basis of the analysis above:

Hypothesis 3.

WRT improves AWUE by promoting factor mobility.

2.2.4. Mediating Mechanism: Optimizing Crop Planting Structures

WRT establishes a mechanism that establishes a correlation between the economic implications of water usage and the decisions made regarding planting [36]. By instituting market-driven water rights allocation, WRT achieves reallocation flexibility unmatched by conventional approaches. In instances where the demand for water for specific crops exceeds the allocated quota, the cost of acquiring additional water rights has the potential to influence the relative economic profitability of different crops. Excess water usage costs are converted into explicit production factor expenditures, compelling farmers to reassess the water income ratio of different crops under marginal cost constraints. The adaptation of planting structures in accordance with the elasticity of factor substitution can be regarded as a rational response to the increased scarcity of water resources. Land resources are shifting to crops with higher water productivity, establishing a “de-grain” trend dominated by less water-intensive varieties [37]. In accordance with the preceding analysis, the subsequent theoretical hypothesis is hereby proposed:

Hypothesis 4.

WRT enhances AWUE by adjusting the cropping structure.

In summary, WRT policies have a positive effect on AWUE. This effect is primarily realized by promoting technological innovation, the flow of production factors, and adjustments in cropping patterns. Figure 1 presents a conceptual diagram of the related analysis.

3. Methodology and Variables

3.1. DID Model

China’s 2014 WRT pilot was a quasi-natural experiment. The staggered implementation of the program satisfies the exogeneity condition, thereby indicating the absence of systematic trends in AWUE between the treatment and control provinces prior to the initiation of the pilot program. The DID model is

$${AWUE}_{it}=\alpha +\beta {WRT}_{it}{+Control}_{it}+{\theta }_i+{\mu }_t+{\epsilon }_{it}$$

(1)

where ${WRT}_{it}=1$ if province i is treated in year t (post-2014 for initial 7 provinces). ${\theta }_i$ and ${\mu }_t$ represent province and year fixed effects, respectively; and standard errors cluster at the province level. All of our estimates were performed using R (version 4.1.3).

3.2. Variable Description

3.2.1. Dependent Variable

Our AWUE indicator system (

Table 2. Input–output indicator system.

Types	Indicators	Description (Unit)
Input variables	Water resources input	Total agricultural water use (10,000 cubic meters)
	Labor input	Number of employees in primary industry (10,000 people)
	Land input	Cropped area of crops (thousand hectares)
	Technology input	Total power of agricultural machinery (ten thousand kilowatts)
	Material capital input	Net application of chemical fertilizers (ten thousand tons)
Output variable	Expected output	Total agricultural output value (billion yuan)

) integrates agricultural water use features and prior research [38,39]. To overcome traditional DEA’s failure in efficiency discrimination and non-radial treatment, we applied Tone’s [13] super-efficiency SBM model. This model offers significant advantages over traditional DEA models and the stochastic frontier approach, and it is widely used in related research [38,40].

3.2.2. Independent Variable

The independent variable in this study is the policy dummy variable, which is indicative of whether province $i$ was included in the WRT policy pilot in year $t$. According to the “Notice on Carrying Out Water Rights Pilot Work” issued by the MWR of China, 2014 is considered the year when the policy began implementation. Guangdong, Henan, Hubei, Jiangxi, Inner Mongolia, Ningxia, and Gansu, the seven pilot provinces, are classified as the experimental group, while the remaining provinces are designated as the control group [24].

3.2.3. Control Variables

In order to address the endogeneity problem caused by omitted variables, several key control variables are introduced: (1) Population density (PD). In high population density areas, there is often a greater demand for water resources for domestic and industrial use, which exacerbates competition for water resources. Consequently, agricultural water use is more likely to face shortages or increased costs. These pressures have the potential to incentivize farmers to conserve water and improve AWUE [10]. (2) Water resource density (WRD). Generally, regions with high water resource density tend to have relatively developed economies. Their stronger economic strength allows them to invest more funds in efficient irrigation facilities and farmland water conservancy projects. Therefore, higher AWUE is observed in regions with high water resource density, a result of combined technological investment and infrastructure development [41]. (3) Farmland water conservancy construction (FWCC). During the process of urbanization, the development of water conservancy infrastructure can effectively mitigate its adverse effects on AWUE [17,42]. (4) Industrial structure (IS). Water use efficiency is a vital component of sustainable agricultural development. Research by Zhang et al. [43] indicates that agricultural industry agglomeration and industrial structure upgrading have a positive impact on sustainable agricultural development. (5) Financial support for agriculture (FSA). Public funding drives agricultural technology innovation and farmer adoption, thus elevating AWUE [17]. (6) Environmental regulation (ER). A robust correlation has been demonstrated between environmental regulation and water use efficiency, characterized by a non-linear U-shaped relationship [44].

This paper has selected 30 provinces in China as the subjects of investigation, with the period under review spanning from 2007 to 2022. The data for each variable is primarily derived from the “China Statistical Yearbook,” the “China Water Resources Bulletin,” and the “China Rural Statistical Yearbook.” All the above data were sourced from the “China Economic and Social Big Data Research Platform” (https://data.cnki.net/). The relevant variables and their descriptive statistics are presented in

Table 3. Descriptive statistics of variables.

Variables	Abbr.	Description	Mean	SE	Min	Max
Agricultural water use efficiency	AWUE	Calculated by the super-efficiency SBM	0.350	0.188	0.079	1.185
Water rights trading	WRT	Policy dummy variable	Binary variable 0/1
Population density	PD	Population per square kilometer (person/km2)	460.289	684.752	7.642	3925.868
Water resource density	WRD	Water resources per square kilometer (10 billion m3/km2)	0.440	0.369	0.013	1.701
Farmland water conservancy construction	FWCC	Natural logarithm of effective irrigated area	7.267	1.032	4.694	8.805
Industrial structure	IS	Ratio of agricultural output to regional GDP (%)	0.096	0.056	0.003	0.297
Financial support for agriculture	FSA	Ratio of expenditure on agriculture, forestry, and water affairs to total fiscal expenditure (%)	0.110	0.033	0.029	0.204
Regulatory intensity	ER	Natural logarithm of chemical oxygen demand	3.624	0.994	0.678	5.290

.

4. Results

4.1. Baseline Regression

The baseline regression results based on Equation (1) are presented in

Table 4. Baseline regression results.

	(1) AWUE	(2) AWUE	(3) LN (AWUE)
WRT	0.185 *** (0.023)	0.097 *** (0.026)	0.393 *** (0.084)
PD		0.001 * (0.000)	0.003 * (0.002)
WRD		−0.016 (0.063)	0.094 (0.130)
FWCC		0.002 *** (0.000)	1.175 ** (0.481)
IS		−1.521 * (0.882)	−4.261 (2.973)
FSA		0.533 (0.644)	5.570 ** (2.265)
ER		0.033 *** (0.012)	0.082 *** (0.024)
Constant	0.326 *** (0.003)	−0.521 (0.345)	−0.278 (0.852)
${\theta }_i$	YES	YES	YES
${\mu }_t$	YES	YES	YES
N	480	480	480
R2	0.795	0.838	0.933

Notes: Robust standard errors in parentheses, *** p < 0.01, ** p < 0.05, * p < 0.1.

. Column (1) includes only region and time fixed effects, while Column (2) adds control variables to the specification in Column (1). In Column (1), WRT is positively significant at the 1% level, suggesting that the WRT policy effectively enhanced AWUE in the pilot regions. Column (2) shows WRT remains positively significant at the 1% level, thus supporting Hypothesis 1. Additionally, to meet the needs of policymakers, we further consider its economic significance. The results in Column (3) show that, compared to non-pilot regions, the average AWUE in pilot regions increased by 48.1%.

We also examined the impact of control variables on AWUE. The population density, farmland water conservancy infrastructure, and environmental regulations all demonstrated significant positive effects. The pressure on food demand resulting from population growth has driven regions to improve AWUE as a means to alleviate water resource constraints [10]. Concurrently, infrastructure such as farmland water conservancy serves as a pivotal foundation for enhancing AWUE [17,42]. It is noteworthy that the positive effect of environmental regulations is most significant in economic terms. This suggests that appropriately stringent environmental regulations can effectively promote improvements in AWUE.

However, the industrial structure exerts a negative effect on AWUE. One potential explanation for this phenomenon is that the negative correlation between the proportion of agricultural output and AWUE reflects a development stage trap. In the short term, agricultural expansion is characterized by the utilization of substantial resource inputs, which curtails the capacity for efficiency enhancements. Long-term breakthroughs require marketizing water rights to transform resources into value-added assets, achieving coordinated gains in agricultural output and AWUE.

4.2. Robustness Test

4.2.1. Parallel Trend Test

Prior to the implementation of the DID method, it is imperative that the parallel trends assumption is fulfilled [45]. Based on relevant studies, we employ the event study method for testing [46]. The specific model is set as follows:

$${AWUE}_{it}=\alpha +\sum _{k\ge -7}^8{\beta }_k{WRT}_{it}^k+{Control}_{it}+{\theta }_i+{\mu }_t+{\epsilon }_{it}$$

(2)

where ${WRT}_{it}^k$ is employed to represent the WRT policy, with the coefficient ${\beta }_k$ serving to quantify the trend differences between the treatment and control groups. The confidence interval for the coefficient ${\beta }_k$ is 95%. Moreover, in order to circumvent issues of multicollinearity, the year preceding policy implementation is designated as the baseline period [47]. The definitions and estimation methods for the remaining variables are consistent with those in Equation (1).

Figure 2 validates parallel pre-trends and reveals dynamic effects. Prior to the implementation of the WRT policy, the estimated values of ${\beta }_k$ did not achieve statistical significance, with their confidence intervals entirely encompassing zero. Following the implementation of WRT, AWUE rose immediately and continued to increase over time, indicating that policy learning has a cumulative effect. This pattern is consistent with the findings of Chen et al. [29].

4.2.2. Placebo Test

In order to eliminate the potential influence of unobservable confounding factors on the identification of causality, we conducted a placebo test based on the counterfactual causal inference framework [48] of 500 placebo regressions with randomly selected pseudo-pilot provinces using Model (1). Meanwhile, anticipatory effect estimations artificially advance WRT implementation by 1–3 years. Figure 3’s placebo distribution (mean $\beta \approx 0$) and falsified treatment timing (

Table 5. Robustness test results.

	One Year Ahead (1) AWUE	Two Years Ahead (2) AWUE	Three Years Ahead (3) AWUE	Recalculate (4) AWUE-DEA	Exclude Policy Interference (5) AWUE	Exclude Provincial Pilot (6) AWUE	Exclude Provincial Pilot (7) AWUE
WRT	−0.032 (0.025)	−0.033 (0.024)	−0.030 (0.028)	0.131 *** (0.031)	0.067 ** (0.031)	0.153 *** (0.031)	0.113 (0.035)
Control	YES	YES	YES	YES	YES	YES	YES
Constant	0.351 * (0.183)	0.359 (0.184)	1.106 ** (0.500)	−0.609 (0.405)	−0.306 (0.290)	−0.624 * (0.319)	−0.581 (0.378)
${\theta }_i$	YES	YES	YES	YES	YES	YES	YES
${\mu }_t$	YES	YES	YES	YES	YES	YES	YES
N	480	480	480	480	480	480	480
R2	0.838	0.838	0.800	0.901	0.840	0.839	0.854

Notes: Robust standard errors in parentheses, *** p < 0.01, ** p < 0.05, * p < 0.1.

, Columns (1)–(3)) rule out spurious effects. The true coefficient ($\beta =0.097$) lies outside the 95% placebo interval, reinforcing causality.

4.2.3. Replace the Dependent Variable

In order to control for the impact of differences in measurement methods on baseline estimation results, AWUE was re-evaluated using DEA. The input–output indicator system and the assumption of constant returns to scale are kept consistent with the previous section to ensure comparability across different measurement methods [49]. Following the replacement of the dependent variable with the AWUE derived from Data Envelopment Analysis, Equation (1) was utilized once more for regression estimation, with the results displayed in column (4) of Table 5. The estimated coefficient of the WRT policy is 0.131, indicating a positive effect on AWUE at the 1% significance level. The difference between this result and the baseline regression coefficient (0.097) is 0.034, accounting for 35.05% of the baseline effect. However, the two coefficients are fully consistent in both directions (positive effect) with statistical significance (p < 0.01). Therefore, we consider that the core conclusions of the baseline regression remain unaffected.

4.2.4. Eliminate Policy Interference

Since 2016, pilot reforms of the water resource tax have been successively launched in China. The objective of the water resource tax reform is to improve water use efficiency through a differentiated tax rate design [50]. It is evident that there is a considerable overlap in both the time frame and geographical scope between the WRT policy and the water resource tax reform. The baseline regression model may be susceptible to estimation bias resulting from the omission of policy interaction effects. Therefore, we introduced a policy dummy variable for the water resource tax reform and incorporated it into Equation (1) for regression estimation. As demonstrated in column (5) of Table 5, the WRT policy maintains its positive influence on AWUE at the 1% significance level. And the coefficient of the WRT policy demonstrates minimal deviation from the baseline regression outcomes, substantiating the reliability of the baseline estimation.

4.2.5. Excluding Provincial Pilot

Following the establishment of the national WRT, provinces such as Zhejiang, Shandong, and Hebei subsequently initiated provincial-level pilots. These provincial pilots, drawing on the experience of the national pilot, developed differentiated WRT policies based on their own circumstances. The provincial pilots may influence the identification of the policy effects of the national pilot through horizontal competition [51]; therefore, in order to eliminate the influence of provincial pilots, we included the provincial pilots in the treatment group for regression. Then, we excluded the provincial pilots from the control group for regression. The findings in columns (6) and (7) of Table 5 suggest that, excluding the provincial pilots, the national WRT pilot demonstrates a more pronounced policy effect. A possible explanation is that authoritative top-level design breaks local protectionism and reduces institutional friction. In conclusion, the inclusion of the provincial pilots in the treatment group or their exclusion does not affect the direction or significance of the core conclusions.

5. Further Analysis

5.1. Heterogeneity Analysis

The estimates obtained from the DID method reflect only the average effect of the policy and do not account for variations in effects among different groups. The policy recommendations based on average effects may not be applicable. In order to identify the heterogeneous characteristics of policy effects, this paper employs a triple difference model for extended analysis. The specific model is set as follows:

$${AWUE}_{it}=\alpha +{\beta }_1{(WRT}_{it}\times H_i)+{\beta }_2{WRT}_{it}+{H_i+Control}_{it}+{\theta }_i+{\mu }_t+{\epsilon }_{it}$$

(3)

In Equation (3), $H_i$ represents the moderating variables of heterogeneity, specifically the level of marketization (${ML}_i$), water resource endowment (${WRE}_i$), and agricultural dependency (${AD}_i$). The level of marketization is based on the marketization index compiled by Xin and Xin [52]. The concept of water resource endowment is represented by per capita water resources, while agricultural dependency is represented by per capita agricultural water usage. The remaining settings are consistent with Equation (1).

5.1.1. Marketization Level

There are significant variations in the level of marketization across different regions of China [53]. And the effective operation of market mechanisms constitutes an essential institutional foundation for WRT [54]. Consequently, the divergent institutional environments that have arisen as a consequence of China’s market-oriented transformation may have a bearing on the policy outcomes of WRT. The estimation results presented in column (1) of

Table 6. Heterogeneity analysis results.

	Marketization Level (1) AWUE	Water Resource Endowment (2) AWUE	Agricultural Dependence (3) AWUE
${WRT}_{it}\times {ML}_i$	0.014 *** (0.003)
${ML}_i$	−0.051 (0.007)
${WRT}_{it}\times {WRE}_i$		0.013 *** (0.004)
${WRE}_i$		0.004 (0.076)
${WRT}_{it}\times {AD}_i$			0.020 *** (0.006)
${AD}_i$			−0.243 (0.171)
Control	YES	YES	YES
Constant	0.329 (0.286)	−0.532 (0.388)	0.506 (0.955)
${\theta }_i$	YES	YES	YES
${\mu }_t$	YES	YES	YES
N	480	480	480
R2	0.837	0.841	0.839

Notes: Robust standard errors in parentheses, *** p < 0.01.

demonstrate that the coefficient of the interaction term ${WRT}_{it}\times {ML}_i$ is 0.014, which is significant at the 1% level. This finding indicates that in regions characterized by a higher degree of marketization, the impact of WRT policies on AWUE is more pronounced. One possible reason is that a higher degree of marketization leads to more sensitive price signals and more reliable contract enforcement, thereby reducing transaction costs. Furthermore, marketization is frequently intertwined with government governance capabilities [55], and local governments further strengthen the policy effects of WRT by implementing targeted policies.

5.1.2. Water Resource Endowment

China’s water resources demonstrate both temporal and spatial disparities [33], which have the capacity to affect the efficiency of policy transmission through the elasticity of agricultural water use. Due to the rigid demand for water resources, WRT policies are less effective in incentivizing agricultural water-saving behaviors in regions with limited water resources. Conversely, regions with abundant water resources can leverage their surplus to achieve water-saving and efficiency gains by optimizing water use structures and reducing the proportion of water used for agricultural irrigation. The estimation results presented in column (2) of Table 6 demonstrate that the coefficient of the interaction term ${WRT}_{it}\times {WRE}_i$ is 0.013, which is significant at the 1% level. In comparison to regions characterized by limited water resources, WRT policies exert a more pronounced influence on AWUE in regions with abundant water resources. Water-rich regions benefit from structural flexibility, while arid regions face rigid demand [56].

5.1.3. Agricultural Dependence

As Reeve [57] argues, differences in factor endowments across regions have led to heterogeneous paths of industrial structure evolution. Regional agricultural dependency critically shapes resource allocation priorities and potentially affects WRT policy efficacy under resource constraints. The regression results in column (3) of Table 6 demonstrate that the interaction coefficient between WRT and agricultural dependence is 0.020. This finding suggests that, at the 1% significance level, agricultural dependence exerts a significant positive moderating effect on the policy implications of WRT pilots. This empirical finding reveals the heterogeneity of policy effects, specifically that the intensity of policy effects in regions with high agricultural dependence is significantly greater than in regions with low agricultural dependence. The main reason for this phenomenon is that economies of scale facilitate the adoption of technology.

5.2. Mechanism Analysis

While WRT exerts a direct influence on AWUE, it may also have indirect effects through various factors. Therefore, an understanding of this mechanism is imperative for elucidating the relationship. We use theoretical assumptions to explore the mechanism by which WRT affects AWUE through three aspects, including technological innovation, factor mobility, and cropping structure.

5.2.1. Technological Innovation

Unlike conventional technologies, a water-saving focus now targets household and community systems. These increasingly replace flood irrigation, enhancing farmers’ water-use efficiency [24]. In order to verify the mechanism of technological innovation, the number of patent authorizations and the number of sprinkler facilities per unit area were selected as proxy variables. In the empirical strategy, the dependent variable ${AWUE}_{it}$ in Equation (1) is replaced with ${TI}_{it}$ and ${SIM}_{it}$ in order to examine the hypothesis that WRT has enhanced AWUE through the promotion of technological innovation. The estimation results are displayed in columns (1) and (2) of

Table 7. Mechanism analysis results.

	(1) TI	(2) SIM	(3) IFF	(4) RFF	(5) APS
WRT	0.859 *** (0.212)	0.400 *** (0.162)	0.828 ** (0.344)	0.011 (0.009)	−0.016 ** (0.006)
Control	YES	YES	YES	YES	YES
Constant	5.294 *** (1.231)	7.858 *** (0.656)	−5.360 ** (2.449)	−0.050 (0.087)	0.820 *** (0.044)
${\theta }_i$	YES	YES	YES	YES	YES
${\mu }_t$	YES	YES	YES	YES	YES
N	480	480	480	480	480
R2	0.930	0.601	0.416	0.203	0.239

Notes: Robust standard errors in parentheses, *** p < 0.01, ** p < 0.05.

. Independent variables show positive 1% significance, indicating higher water-saving tech innovation and more sprinkler deployment in pilot versus non-pilot areas, aligning with Fang & Zhang [58]. These results verify Hypothesis 2, namely that the WRT policy effectively promotes investment in research and development and the deployment of new irrigation technologies through the establishment of a market-oriented water-saving incentive mechanism, thereby achieving an improvement in AWUE.

5.2.2. Factor Mobility

The WRT in China comprises three primary components: regional water rights trading, water withdrawal rights trading, and irrigation water user rights trading [29]. The extant research indicates that even after farmers are granted water rights, trading among individual farmers is rare [59]. Thus, we primarily focus on regional WRT and cross-industry trading within water withdrawal rights trading, particularly on the agricultural and industrial sectors. To verify this mechanism, we introduce two variables, regional factor flow and inter-industry factor flow. These are represented by the annual change rate in regional agriculture’s share of national water use and the primary-to-secondary sector water use ratio, respectively. Columns (3) and (4) of Table 7 present the estimated results of WRT on inter-regional and inter-industry factor flows, respectively. The results indicate that the policy dummy variable representing WRT is positive at the 1% level, suggesting that WRT facilitates the transfer of water rights from agriculture to industry, which is consistent with the conclusions of Zhang et al. [24]. But inter-regional flows are stagnant, likely due to administrative fragmentation. This partial support for Hypothesis 3 suggests the need for cross-province trading platforms.

5.2.3. Cropping Structure

In the majority of developing countries, the allocation of irrigation water is typically determined through administrative means to ensure food security. This approach does not take into account the economic cost of water use in crop production, resulting in significant water wastage and low efficiency [60]. Conversely, WRT has been demonstrated to enhance the efficiency of irrigation water use and optimize water resource allocation through price signals. In order to verify this mechanism, the time variation ratio of grain crop planting area was used as a proxy variable for planting structure. The time variation ratio of grain crop planting area is defined as the ratio of the current year’s to the previous year’s grain planting area. Column (5) of Table 7 presents the regression results of WRT on planting structure. The coefficient of the independent variable is −0.016, which is significant at the 1% level. This finding indicates that WRT reduces grain area, as farmers shift to high-value, low-water crops [60]. While this confirms Hypothesis 4, policymakers must balance efficiency gains against food security risks.

6. Conclusions and Policy Implications

6.1. Conclusions

This paper utilizes the DID method to examine the impact of WRT on AWUE, drawing upon balanced panel data from 30 provinces in China spanning from 2007 to 2022. The study findings are as follows: (1) WRT policy significantly enhances AWUE in pilot areas, and this conclusion remains robust after multiple tests. Furthermore, the policy effect of national-level WRT pilots is greater than that of provincial-level pilots; (2) the policy effect is significantly influenced by regional endowment conditions, with better outcomes in areas with higher levels of marketization, water resource endowment, and agricultural dependency; and (3) WRT affects AWUE through three channels: technological innovation, inter-industry factor mobility, and adjustment of cropping structure. However, the mediating effect of inter-regional factor mobility did not achieve statistical significance, suggesting the presence of market segmentation and institutional barriers in cross-regional trade.

6.2. Policy Implications

The research conclusions offer significant policy insights for refining WRT and boosting AWUE. Firstly, implement institutional transplantation of national pilot programs. Spearheaded by the MWR and the National Development and Reform Commission, with active collaboration from provincial governments, prioritize extending national pilot program experiences to provinces heavily reliant on agriculture. Secondly, dismantle barriers to cross-regional transactions and establish a unified trading market. Led by the MWR, collaborate with provincial departments of ecology and environment to integrate all provincial trading platforms into the CWE, overcoming administrative fragmentation. Finally, spearheaded by the Ministry of Agriculture and Rural Affairs and the Ministry of Science and Technology, strategically activate three mechanisms. Establish a dedicated fund for water rights reform to provide subsidies for upgrading water-saving facilities and cultivating food crops.

6.3. Limitations

The present study is subject to certain limitations that necessitate further in-depth investigation. The policy effects derived from the DID method are essentially average treatment effects at the national level, which may not accurately reflect the policy impacts in specific regions. It is recommended that future research endeavors incorporate the synthetic control method in order to construct a counterfactual framework. Furthermore, with the continued rollout of other policies, future research could examine the effects of policy mixes, such as WRT combined with tiered pricing. We also note that the impact of environmental regulation on AWUE may have a U-shaped effect, which warrants further in-depth analysis in future research.