Space-Time Pattern of Coupling Coordination between Environmental Regulation and Green Water Resource Efficiency in China

Abstract

Based on the coupling coordination model, this paper intends to use the panel data of 31 provinces in China from 2000 to 2019, analyze the coupling and coupling coordination relationship between environmental regulation and green water resource efficiency (GWRE), and explore the space-time pattern of coupling coordination. The results show that: (1) The overall level of environmental regulation in China is showing increasingly stringent characteristics, and the overall GWRE is showing an upward trend. Both show spatial differences, and there is a strong correlation between the spatial spillover of environmental regulation and the improvement of GWRE. (2) The environmental regulation and GWRE in China have not yet achieved the benign resonance goal of high coupling. It is overall manifested as a medium-coupled run-in and a medium-coupled coordination. (3) The space-time evolution characteristics are prominent, and the coupling coordination degree of different provinces (regions) shows a more significant difference, showing the spatial pattern of higher in the eastern region and lower in the west region. Therefore, some policy suggestions are put forward on how to break through the bottleneck of environmental regulation for the improvement of GWRE and how to optimize the external environment in which the government optimizes environmental regulation to inhibit the efficiency of green water resources.

1. Introduction

1.1. Study Background

As an important resource for economic and social development, water resources are the basic guarantee for maintaining national economic development and social progress. With the excessive consumption of water resources, serious water environment problems in the world have arisen, which will inevitably affect the quality of life and the improvement of the happiness index of the global population. Currently, 72% of the Earth’s surface is covered by water, but only 0.75% of the Earth’s surface is available. Additionally, there are very few areas that can be directly used for people’s production and daily life. From the perspective of regional distribution, the freshwater resources of Brazil, Russia, Canada, China, the United States, Indonesia, Colombia, and the Congo account for 60% of the world’s freshwater resources. What is even more frightening is that, by 2025, more than 3 billion people in the world will face water shortages, and 40 countries and regions will have serious shortages of freshwater resources. The Food and Agriculture Organization of the United Nations (FAO) recently released the State of Food and Agriculture 2020 report, which focuses on the challenges of global water scarcity and improves water efficiency, productivity, and sustainability. In recent years, water resources [1,2] and water environment issues [3,4,5] have become the focus of discussion in all walks of life and have also become the focus of global attention. In 1995, Falkenmark proposed the concept of green water for agriculture and food security, arguing that precipitation is divided into blue water resources and green water resources in terrestrial ecosystems. Blue water is the part of precipitation that forms surface water and groundwater, that is, traditional water resources. Green water systems, which include the water consumption of forests, grasslands, wetlands, and crops, support both terrestrial ecosystems and rain-fed crop production and play an important role in maintaining terrestrial ecosystem security and agricultural food security. The green water resources proposed in this paper mainly include five parts: agricultural water, industrial water, domestic water, urban water, and ecological water.

In 2016, the Ministry of Water Resources issued the Guiding Opinions on Strengthening the Management of Water Resources Utilization, which proposed that, by 2030, China will comprehensively establish an institutional system for water resource utilization, comprehensively improve the efficiency of water resources, and ensure the water demand of all industries in society. The report of the 19th National Congress of the Communist Party of China clearly proposed accelerating the reform of the ecological civilization system and build a beautiful China, aiming to solve outstanding problems such as water shortage and water pollution. The Resource Tax Law of the People’s Republic of China was promulgated in August 2019, which clearly states that: in accordance with the needs of national economic and social development and the principles of this Law, the State Council levies water resource taxes on units and individuals that use surface water or groundwater. Where water resources taxes are levied, the collection of water resources fees shall be stopped [6]. It provides an institutional and legal guarantee for the “change of fees to taxes” of water resources. In this context, how to efficiently allocate and utilize water resources has become a top priority in China.

1.2. Literature Review

Green water resources efficiency is an important concept derived from the dual background of green development and ecological civilization construction [7]. It is defined as the ability to minimize water resource input under green output, taking into account the ecological and economic value functions of water resources [8]. At present, scholars at home and abroad have explored the relationship between environmental regulation and GWRE from different perspectives, and at the beginning of the study, scholars focused on the measurement and evaluation of water resources efficiency, mainly using the index system evaluation method [9] (factor analysis method [10], entropy method [11], etc.), stochastic frontier analysis method (SFA) [12], and data envelopment analysis method (DEA) [13,14].

Over the course of research based on water efficiency, industrial water consumption and industrial output value were considered [15], and when Abhilash and Rani et al. (2020) studied companies, they found that the amount of water used by enterprises is directly proportional to their size [16]. At the same time, some scholars have used the super-efficient DEA-Tobit two-stage model to evaluate water resource efficiency and analyzed the regional differences in water resources efficiency and its influencing factors [17]. Some scholars have also explored the measurement and effect of environmental regulation, the research areas on the efficiency or effect of environmental regulation are relatively wide, and the research areas mainly include green development efficiency [18], innovation ability [19], green technology innovation [20], industrial ecological efficiency [21], and industrial structure adjustment [22].

In addition, some scholars have found that environmental regulation has an impact on GWRE. The main examination is the efficiency of agricultural water use, industrial water use efficiency, and water resources comprehensive efficiency. At the level of agricultural water efficiency, Yang Qian et al. (2015) used the Bootstrap fractured regression model to empirically find that environmental regulation has significantly improved the efficiency of agricultural water resources in China [23], and Abhilash et al. (2020) believed that improving water use efficiency (WUE) and improving the scope of agricultural water productivity have become priority areas of concern; therefore, it is necessary to improve irrigation methods or instill multi-sensor-based technologies to automate irrigation systems or take agronomic measures in the field. At the level of industrial water use efficiency, Sun et al. (2018) used the SBM model to measure the industrial water efficiency of urban agglomeration of the Yangtze River Delta [24] and found that the industrial water-use efficiency was low. Shi et al. (2021) used dynamic SBM models to measure China’s industrial water use efficiency from 2013 to 2017 and found that the overall industrial water use efficiency showed a downward trend [25]. At the level of comprehensive efficiency of water resources, Xu et al. (2019) found that the impact of environmental regulation on green water efficiency is non-linear [26], and there is spatial heterogeneity in the threshold effect of command, market, and voluntary environmental regulation on GWRE. Rouissat B et al. (2021) applied the principles of systems engineering to explore and evaluate the performance of the water resources system in Bokhlara, northwestern Algeria, taking into account a variety of factors such as water mobilization, treatment, purification, and transfer [27].

In summary, although most scholars at home and abroad have studied the measurement and evaluation of water resources efficiency and the influencing factors, the research objects are relatively general, and few scholars have studied the relationship between environmental regulation and the coordinated development of GWRE alone.

2. Aims of the Study

Based on the theoretical idea of environmental regulation and water resource integration, this paper constructs a coordinated evaluation index system for the coupling between environmental regulation and GWRE, calculates the comprehensive evaluation index of the two systems respectively, uses the coupling coordination degree model [28,29,30,31,32] to measure the coupling coordination degree of China and 31 provinces in China, and analyzes in depth the spatial correlation characteristics of the coordinated development of environmental regulation and GWRE. Therefore, it is of great significance to study the space-time pattern of coupling coordination between environmental regulation and GWRE. Compared with previous studies, the innovations of this paper are mainly reflected in three aspects. Firstly, the mechanisms of both are sorted out. Secondly, this paper empirically examines the space-time pattern of coupling coordination in China. Thirdly, the study results may provide a reference for the coordinated development of water resources and environmental regulation in China and even the world.

3. Coupling Coordination Mechanism and Model Specification

3.1. Mechanism Analysis of Coupling Coordination

Coupling is generally a measure of the degree of association between two or more different systems, and it is used to examine the coordinated development of two or more systems [33]. The degree of coupling coordination can reflect whether the systems have a good level and can also reflect the harmonious and consistent relationship between the systems [34,35]. As two important means of environmental protection and sustainable development of water resources in China, environmental regulation and GWRE directly affect the coordinated development of environmental protection and high-quality development in China.

Firstly, the system between environmental regulation and GWRE is not closed [36], both belong to the open system in which the exchange of matter, energy, and information with the outside world is always in a dynamic state. For example, the system and the outside world are exchanged for various professionals, funds, and basic equipment. At the same time, it also exports environmental emissions to society [37].

Secondly, the system between environmental regulation and GWRE is still in an orderly system far from equilibrium [38], the supply and demand relationship of the elements in the system (such as labor, capital, land, and technology) is not always balanced, and there is still room for further improvement.

Thirdly, the mechanism of improvement system between environmental regulation and GWRE is a non-linear relationship, and it exhibits random volatility; that is to say, the changes in funds, materials, information, and energy inside and outside of the two systems are not strictly in accordance with a specific law, but there are more random fluctuations. According to the theory of systems science, the system between environmental regulation and GWRE cross a certain behavioral critical point, it will gradually derive a new “dissipative structure” [39,40] after the new dissipative structure has evolved, and may produce a favorable “dissipative structure” under random conditions, which makes the efficiency increase and may also produce harmful dissipative structures that may reduce efficiency. Therefore, when exploring the coordinated development of improvement systems between environmental regulations and GWRE, it is necessary to correctly grasp and make good use of the development law of “dissipative structure”.

3.2. Model Specification

3.2.1. Coupling Degree Model

In order to measure the correlation between environmental regulation and GWRE, a coupling degree model [41] between environmental regulation and GWRE is constructed, which is combined with the principles of balance theory and benefit theory, as shown in Formula (1).

(1)$\mathrm{C}={\left\{\raisebox{1ex}{$f(x)\times g(x)$}\!\left/ \!\raisebox{-1ex}{${\left[\frac{f(x)+g(x)}{2}\right]}^2$}\right.\right\}}^{\frac{1}{2}}$

In Formula (1), C represents the coupling degree between environmental regulation and GWRE, f(x) represents the comprehensive index of environmental regulation, and g(x) represents the comprehensive index of GWRE.

3.2.2. Coupling Coordination Degree Model

From Formula (1), it can be seen that the value of the coupling degree C is between 0 and 1, and we divide the coupling degree into three levels: low coupling, moderate coupling, and high coupling, as shown in Table 1.

The degree of coupling reflects the degree of interdependence between the two systems to a certain extent, but it is difficult to reflect the degree of coordination between the two systems. We start from the indicators of environmental regulation and GWRE and quantitatively analyze the degree of coordination between the two systems. The degree of coupling coordination is constructed, as shown in Formulas (2) and (3).

(2)$\mathrm{T}=\alpha \mathrm{f}(x)+\beta g(x)$

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

In the formula, T, α and β denote the comprehensive development level of each subsystem, the weight of environmental regulation and the weight of GWRE, respectively. We believe that environmental regulation and GWRE are in the same position in the coupling coordination study, so we determined that α = β = 0.5; D measures the coupling coordination degree. From Formulas (2) and (3), it can be found that the coupling coordination degree can reflect both the comprehensive level of coordinated development and the coordinated development between the two systems, so we used this formula to evaluate the coupling and coordination relationship between environmental regulation and GWRE. The value range of the coupling coordination degree is a closed interval [0, 1], and the size of the coupling coordination degree value is proportional to the degree of coordinated development of the two systems. This paper classifies it into three levels: primary coordination, moderate coordination, and high coordination. The specific divisions are shown in Table 2.

3.2.3. Coupling Coordination Criterion

Coupling coordination degree is an important basis for measuring the coupling coordination level between environmental regulation and GWRE. According to the judgment standard of coupling degree and coupling coordination degree, the coupling coordination of environmental regulation and GWRE is divided into nine types, including high coupling coordination type and medium coupling coordination type, as shown in Table 3.

3.2.4. Index System Construction

Environmental regulation indicators are selected with reference to the method of Pan et al. (2020) [42]. Environmental regulations are constructed that include three aspects: command-type, market-type, and autonomous-type. Command-type environmental regulation selects projects that focus on the environment with the government, and these indicators reflect the government’s policy orientation towards the environment; therefore, the command-type environmental regulation selects the number of environmental protection acceptance projects completed in the year in which the government pays attention to the environment, the total number of administrative departments and supervision agencies, the number of administrative punishment environmental cases, and the number of governance projects completed in this year; the indicators selected by market-type environmental regulation are the market embodiment of the environmental regulations faced by enterprises, thus, it selects environmental protection acceptance projects environmental protection investment, prevention and control project investment, sewage charges, and pollution control projects to complete investment this year. The selection of indicators for autonomous-type environmental regulation reflects the importance attached to environmental regulation by the subjective will of the government and enterprises, so it selects the number of proposals made by the People’s Congress and the CppcC, the number of scientific research institutions concluded by the Letter and Petition Office, and the number of project registrations independently prepared for examination and approval of construction. The specific environmental regulatory classification and index value description are shown in Table 4.

GWRE is measured using the SE-SBM model [43,44]. Let us assume that there are N decision units (DMUn, n = 1, 2, 3, …, N); each decision-making unit is composed of three parts: M inputs (x_i ∈ R₊^M), G undesirable outputs (b_g ∈ R₊^G), and R expected outputs (y_r ∈ R₊^R). X, Y, and B are all matrices, where X = [x₁, …, x_n], Y = [y₁, …, y_r], and B = [b₁, …, b_g]. Based on the case where the scale remuneration is variable, the set of production possibilities is T = {(x, y, b)|x ≤ X^θ, y ≤ Y^θ, b ≤ B^θ}, where θ is the weight vector. The specific model is built as follows:

(4)$\begin{array}{c}\mathrm{M}\mathrm{i}\mathrm{n}{\rho }_{\mathrm{s}\mathrm{e}}=\frac{1+\frac{1}{\mathrm{M}}{\displaystyle {\sum }_{\mathrm{m}=1}^M\frac{{\mathrm{s}}_m^x}{x_{mk}}}}{1-\frac{1}{R+G}({\displaystyle {\sum }_{r=1}^R\frac{s_r^y}{y_{rk}}}+{\displaystyle {\sum }_{g=1}^G\frac{s_g^b}{b_{gk}}})}\\ s.t.\left\{\begin{array}{l}{\displaystyle \sum _{j=1,j\ne k}^n{x}_{mj}{\theta }_j-s^x=x_{mk}}\\ {\displaystyle \sum _{j=1,j\ne k}^n{y}_{rj}{\theta }_j+s^y=y_{rk}}\\ {\displaystyle \sum _{j=1,j\ne k}^n{\mathrm{b}}_{gj}{\theta }_j+s^b=b_{gk}}\\ \\ 1-\frac{1}{\mathrm{R}+\mathrm{G}}({\displaystyle {\sum }_{r=1}^{\mathrm{R}}\frac{s_r^y}{y_{rk}}}+{\displaystyle {\sum }_{g=1}^G\frac{s_g^b}{b_{gk}}})>0\\ m=1,2,3\cdots M;r=1,2,3,\cdots R\\ g=1,2,3,\cdots G;j=1,2,3,\cdots ,n(j\ne k)\\ \theta ,s^y,s^b>0\\ \end{array}\right.\end{array}$

In the model, k denotes the evaluated unit, x_mj is the input of the m item of the j decision unit; b_gj denote the g_th item of the j decision unit; y_rj is the expected output of the r item of the j decision unit; s^x, s^b, and s^y are the slack variables corresponding to the input variable, the undesirable output, and the expected output, respectively; and the molecule and denominator in the formula ρ_se indicate the average reducible ratio or average expandable ratio of the actual input and output of the production decision unit relative to the common production frontier, respectively. This reflects situations where inputs are inefficient or outputs are inefficient. If ρ_se ≥ 1, this means that the DMU is in an efficient state, and if ρ_se < 1, this means that the DMU is in an inefficient state.

In this study, based on the basic principles of the SE-SBM model, we designed a calculation method for GWRE, and the input indicators we assume include capital stock [45], labor force [46], and water resources [47], with capital stock using the capital perpetual law, the labor force adopting the number of employees at the end of the year, and water resources using water footprint values [48], and output indicators including GDP and sewage discharge.

4. Coupling and Coordination Analysis between Environmental Regulation and GWRE

There is an interactive phenomenon between environmental regulation and GWRE, and a certain agglomeration pattern has gradually formed in space [49]. In fact, the coupling and coordination between environmental regulation and GWRE not only reflects the differences in space and driving mechanisms, but also shows a certain degree of spatial regularity. Based on the statistics of 31 provinces in China from 2000 to 2019, the coupling degree model and coupling coordination degree model are used to calculate the coupling degree and coupling coordination degree between environmental regulation and GWRE, and the coupling coordination analysis is carried out.

4.1. Timing Analysis of Coupling Coordination

The coupling degree model is used to calculate the coupling degree of environmental regulation and GWRE in each province, and the following is the coupling degree of China and four regions, as shown in Figure 1.

It can be seen from Figure 1 that the average coupled value of national environmental regulation and GWRE ranges from 0.5461 to 0.6142 from the national situation, and its timing change characteristics show a volatility increase. From the average situation of the four regions, the coupling degree of environmental regulation and GWRE in the eastern region was relatively stable, where the minimum value was 0.5469 in 2002 and the maximum value was 0.6207 in 2019, and the coupling degree between environmental regulation and GWRE in the central region first increased slowly, and then the coupling degree showed a downward trend, where the highest value appeared in 0.5895 in 2019, and the minimum value appeared in 0.5547 in 2008. The overall upward trend of coupling between environmental regulation and GWRE in the western region is the most obvious. From 0.5342 in 2000 to 0.5999 in 2019, the minimum value for the period was 0.5295 in 2006, and the fastest increase was in 2011.The coupling value of environmental regulation and GWRE in northeast China is also obvious, from 0.5443 in 2000 to 0.6166 in 2019. The minimum value appeared at 0.5211 in 2008. The maximum value appeared at 0.6166 in 2019. In the study period from 2000 to 2019, the coupling values of environmental regulation and GWRE in each province were calculated according to the coupling degree model ranging between 0.1879 and 0.6704, and from the data of each province, most of the coupling degrees of each province were concentrated above 0.5000, mainly concentrated in the two stages of moderate coupling and high coupling, indicating that the interaction between provinces is strong.

Using the degree of coupling coordination to judge the sustainability of the coupling relationship between environmental regulation and GWRE is conducive to a deep understanding of the degree of correlation between them. The higher the degree of coupling and coordination between environmental regulation and GWRE, the higher the correlation and the stronger the benign promotion effect between them, otherwise, it may cause a vicious circle due to low correlation and they may hinder the development of each other. According to the coupling coordination degree model, the timing change graph of the coupling coordination degree in China and four regions is obtained, as shown in Figure 2.

It can be seen from Figure 2 that the improvement average coupling and coordination degree between national environmental regulation and GWRE is between 0.3868 and 0.6424, and the coupling coordination degree between environmental regulation and GWRE in various provinces in the region is obviously different.

Firstly, between 2000 and 2019, the coupling coordination degree of Beijing, Tianjin, Shanghai, Guangdong, Jiangsu, Fujian, and Sichuan was within the range of 0.6000 to 0.8401, and from the mean level, the average level of coupling coordination in Beijing was the highest, reaching 0.6956, which was at the forefront of the country. Shanghai, Jiangsu, Fujian, Guangdong, Tianjin, and Sichuan were the next six provinces, with an average of above 0.6215, 0.6174, 0.6170, 0.6153, 0.6145, and 0.6082, respectively, and Shanghai, Tianjin, Guangdong, and other provinces remained at a high level for a long time. The coupling coordination degree is above 0.5000 every year, indicating that the improvement system between them in these areas has been in the middle coordination stage for a long time and the effect of environmental regulation is relatively obvious. Further, under the effective support and drive of environmental regulation, China’s water resources have been effectively allocated and effectively utilized.

Secondly, the provinces with the mean degree of coupling coordination within the range from 0.5000 to 0.6000, including Hebei, Shandong, Zhejiang, Anhui, Shanxi, Henan, Hubei, Hunan, Inner Mongolia, Chongqing, Guangxi, Yunnan, Guizhou, Shanxi, Liaoning, Jilin, and Heilongjiang, which contained the largest number of provinces in this range according at 17 provinces. The mean coupling coordination degrees were 0.5967, 0.5848, 0.5494, 0.5500, 0.5953, 0.5943, 0.5757, 0.5804, 0.5584, 0.5774, 0.5664, 0.5886, 0.5148, 0.5411, 0.5996, 0.5018, and 0.5416, and the level of coupling and coordination between environmental regulation and GWRE in these provinces has been improved in the development of more than 10 years.

Finally, the provinces with the average coupling coordination degree within the range from 0.1000 to 0.5000 include seven provinces, including Hainan, Jiangxi, Gansu, Qinghai, Ningxia, Tibet, and Xinjiang, with values of 0.3869, 0.4811, 0.4628, 0.3400, 0.3815, 0.2767, and 0.4643, respectively. This shows that the level of regional environmental regulation and GWRE improvement is uneven, and the implementation effect of relevant policies is not as good as that in the eastern region; therefore, the matching degree between environmental regulation and GWRE improvement capacity is not high, and it is difficult for environmental regulation and GWRE to form a mutual promotion effect, and a benign level of interaction and coordination has not been achieved. However, from the perspective of regional development potential, there is still great potential for the coupling and coordinated development of these provinces. Therefore, according to the actual situation of the region, the government departments of each region should formulate policies that are in line with the coupling and coordinated development of the region and promote the coupling and coordination of environmental regulation and GWRE in the region as soon as possible to move to a higher level.

4.2. Spatial Analysis of Coupling Coordination

The average calculation of the coupling coordination degree between the factors in the three time periods of 2000–2009, 2010–2019, and 2000–2019 is calculated, as shown in Table 5.

It can be seen from the relevant data in Table 5 that the regional difference in the average coupling coordination degree between environmental regulation and GWRE at the provincial level in China is not too obvious; the average coupling coordinations of the three time periods in the eastern region from 2000 to 2009, 2010 to 2019, and 2000 to 2019 were 0.5716, 0.5996, and 0.5854, respectively, and the average values of the same three time periods in the central region were 0.5685, 0.5754, and 0.5725, respectively; the average values of the same three time periods in the western region were 0.5464, 0.5865, and 0.5633, respectively; and the average values of the same three time periods in the northeast region were 0.5376, 0.5546, and 0.5464, respectively. The national averages for the three same time periods were 0.5571, 0.5837, and 0.5721, respectively; the average values are basically in the range of 0.5000 to 0.6000, and at the same time, the overall trend of the average of coupling coordination is rising, but not too high. From 2000 to 2009, except for Beijing, Shanghai, Tianjin, Chongqing, and Guizhou provinces, the average coupling coordination degrees were 0.6321, 0.6032, 0.6168, 0.6072, and 0.6063, respectively. Their average values exceeded 0.6000, the average values of other provinces were basically maintained in the range of 0.5000 to 0.6000, and the coupling coordination showed an upward trend, although the coupling coordination value decreased in some years, such as in Hainan where the coupling coordination values were 0.4658 and 0.4072 in 2001 and 2002, below 0.5000. From 2010 to 2019, the provinces with an average coupling degree of coordination of more than 0.6000 included Beijing, Shanghai, Tianjin, Chongqing, Jiangxi, Guangxi, Guizhou, and Shaanxi, and the average of other provinces remained basically within the range from 0.5000 to 0.6000.

In general, between the years of 2000 and 2019, the regions where China’s coupling coordination improved more rapidly were in the eastern and central regions. Between the years of 2010 and 2019, most provinces in the eastern region continued to improve, while the coupling coordination degree in most parts of China’s western region and northeast region also increased significantly. Tibet and Qinghai in the western region and Liaoning in the northeast were particularly prominent. The mean coupling coordination degree increased from 0.2637, 0.4735, and 0.4946 in 2000–2009 to 0.5463, 0.5743, and 0.5082 in 2010–2019, respectively, exceeding 0.5000. The coupling coordination degree shows a certain gradient change trend in China’s space, and the coupling coordination degree of the whole country shows a spatial pattern of “step-by-step decline in the east, central, northeast and west”, but the polarization is not very obvious. From the perspective of the trend of coupling and coordinated changes in China, the scientific research level, high-quality talents and the concentration of science and technology industries in the eastern coastal areas were relatively high compared with the inland areas and have superior resources endowments and geographical location advantages. Under the spillover of technical knowledge, technological innovation can be effectively carried out at the same time. Under the tilt of the macro policy of the national and environmental science and technology department policies, environmental regulation, and GWRE improvement have seen benign resonance, which is conducive to promoting the improvement of coupling and coordinated development of environmental regulation and GWRE in China.

4.3. Convergence Analysis of Coupling Coordination

According to the coupling coordination degree theory, the convergence of the coupling coordination degree between environmental regulations and GWRE is examined, and the convergence between the two is analyzed. Absolute β convergence indicates that the coupling coordination degree in the model shows the same convergence trend, and the conditional β convergence indicates that the trend is not exactly the same and there are specific steady-state conditions. According to the treatment method of Barro et al. (1992) [50], the absolute β convergence model is designed as the Formula (5):

(5)$(\mathrm{I}\mathrm{n} {\mathrm{d}}_{\mathrm{it}}-In d_{i0})/T=\alpha +\beta \mathrm{I}n d_{\mathrm{i}0}+{\epsilon }_{\mathrm{t}}$

In Formula (5), d_i0 and d_it represent the coupling coordination degree at the beginning and end of the period i (i = 1, 2, …, 31) in each province, T represents time, α represents the constant term, ε_t represents the random perturbation term, β represents the convergence coefficient, β < 0 represents the coupling coordination degree of environmental regulation and GWRE tending to converge, and β > 0 represents that there is no convergence. In convergence detection, if it is found that the test results do not exist for β convergence, then some control variables can be appropriately increased or decreased, and then the convergence test is performed. For β < 0, it can be considered that there is a condition of β convergence. At the same time, for the condition of the β convergence detection model, Formula (6) is constructed:

(6)$\mathrm{I}\mathrm{n} {\mathrm{d}}_{\mathrm{i}\mathrm{t}}-In d_{\mathrm{i}(\mathrm{t}-1)}=\alpha +\beta \mathrm{I}n d_{\mathrm{i}(\mathrm{t}-1)}+{\epsilon }_{\mathrm{t}}$

In Equation (6), d_it and d_i(t−1) represent the level of coupling coordination between environmental regulation and the GWRE improvement system in phase t and phase t − 1, α, β and ε_t explained in the same Formula (5). If β < 0, this indicates that the level of improvement of coupling and coordination between environmental regulation and GWRE tends to converge, and oppositely, there is no convergence. According to Formulas (5) and (6), the convergence test and analysis of the coupling coordination level above were carried out, and the results are shown in Table 6.

Under the test of absolute β convergence, it is shown that the β value is greater than zero, and it passes the t-test at the significance level of 1%, which shows that there is no absolute β convergence at the level of coupling coordination between environmental regulation and GWRE. During the study period, and under the assumption that the initial conditions are the same, the coupling and coordination level discussed above in China is not a homogeneous development, but a differentiated development trend. Under the test of conditional β convergence, the regression coefficient of convergence coefficient β is also greater than zero, and the t test under the significance level of 1% is passed, indicating that there is no conditional β convergence of the two, and further conclusions are obtained. The coupling level of environmental regulation and GWRE in China shows a differentiated development trend.

5. Discussion, Conclusions, and Policy Recommendations

5.1. Discussion and Main Conclusions

The coordinated development assessment of environmental regulation and GWRE represents a critical issue, which has been addressed in this work with an integrated approach based on the coupling coordination model for a set of representative contexts of China.

The World Water Forum, held every three years and jointly organized by the World Water Council and host governments, aims to implement international resolutions on water and sustainable development and to promote exchanges and cooperation among countries in the sustainable use of water resources.

In order to facilitate further observation and analysis of the evolution trajectory of the coupling degree and coupling coordination degree between environmental regulation and GWRE, the next step can be to examine global data and use models for in-depth exploration, so that the spatial pattern of the world may be obtained.

According to the environmental regulation index and calculation method of GWRE, this paper calculated the intensity of environmental regulation and the level of GWRE. A coupled coordination degree model is constructed; the coupling coordination between both is analyzed, and the conclusions are as follows.

Firstly, according to the principle of coupling and coupling coordination, the coupling and coordination of environmental regulation and GWRE is divided into nine types, and the coordinated development of the coupling of environmental regulation and GWRE in China is analyzed from the time series and spatial dimensions of national and provincial (regional) countries. The space-time evolution characteristics of coupling and coordination between both factors are more prominent, and there are six main types, namely high coupling coordination type, high coupling run-in type, medium coupling coordination type, medium coupling run-in type, medium coupling antagonism type, and low coupling antagonism type, and there are types, such as high coupling antagonism type, low coupling coordination type, and low coupling run-in type. The coordination between both factors in China is generally in the run-in type, mainly showing a spatial pattern of high in the east and low in the west. At present, China’s environmental regulation and GWRE have not yet achieved the benign resonance goal of high coupling, and the overall situation of medium coupling run-in and medium coupling coordination is presented.

Secondly, according to the three time periods of 2000–2009, 2010–2019, and 2000–2019, and the average analysis of China and 31 provinces (regions), it was found that the coupling and coordination in China from 2000 to 2009 are in the eastern region and the central region. Between the years of 2000 and 2019, most provinces in the eastern region continued to improve, while the coupling coordination degree in most parts of the western region and northeast China also increased significantly, especially in the western regions of Tibet and Qinghai, and in Liaoning Province in the northeast. By calculating the absolute and conditional β convergence analysis between them, it is found that they are not significant, which further illustrates the differentiated development trend of the coupling level between environmental regulation and GWRE in 31 provinces (regions) in China.

Thirdly, based on the regional characteristics of the coupling coordination model, the coupling coordination type in the national region is mainly presented as the medium coupling run-in type and the medium coupling coordination type. The types of coupling coordination in the eastern region are mainly presented as medium coupling run-in type, medium coupling coordination type, and medium coupling coordination type. Coupling coordination in the central region is mainly presented as the medium coupling run-in type and medium coupling coordination type, the western region coupling coordination type is mainly presented as medium coupling antagonistic type and medium coupling run-in type, and the type of coupling coordination type in northeast China continues to appear as the medium coupling run-in type. From the perspective of spatial dimension, the eastern region has a high degree of coupling and the best degree of coupling co-ordinations, such as Beijing, Tianjin, and other provinces (cities) have reached a high coupling coordination for many years. In the central region, there are two main types of coupling degrees, medium coupling and high coupling, and coupling coordination is mainly the medium coupling run-in type. In the western region and the northeastern region, the coupling degree is relatively high, and the coupling coordination degree mainly exists in the run-in type and antagonistic type.

5.2. Policy Recommendations

According to the research conclusions, at present, there are still large differences and room for improvement in the coupling and coordination of environmental regulation intensity and GWRE. Therefore, the policy suggestion of this study for China are as follows.

Firstly, the central and local governments should attach importance to the heterogeneous characteristics and the development of environmental regulations and water resources, strengthen the great importance attached to environmental issues in all regions, run through the concept of green development, and improve the awareness of environmental protection and water resources protection of all citizens. According to the results obtained above, all provinces in China should consider local conditions, adapt to local conditions fully, and adopt scientific and reasonable water use policies. With a large span of the whole country and large differences in the natural environment and socio-economic conditions in the eastern, central, western, and northeastern regions, each province thus needs to comprehensively consider the influencing factors of all aspects, improve the existing measures that are not conducive to environmental improvement and water resource efficiency improvements, and adopt environmental regulations and water resource policies suitable for their own provinces. When formulating environmental governance policies and water resources protection policies, the government is required to comprehensively assess the economic and social effects of environmental policies, not just for a single short-term prevention and control goal, but in order to pay more attention to the sustainability of their policies. Before the implementation of the policy, we should work well in the preliminary preparation and long-term planning plan. In the process of policy implementation, we should carry out governance work in strict accordance with standards and programs to ensure the implementation effect of the policy, which in the later stage of the implementation of the policy, we should summarize the policy effect, real-time adjustment, revision, and improvement of the original policy to ensure the coherence of policy implementation and the sustainability of economic and social activities.

Secondly, the construction of water resource infrastructure should be accelerated. Through the comprehensive promotion of capital investment in water facilities and technological transformation, as well as the vigorous training of technical talents, we will provide a solid facility foundation and technical support for the improvement of green water resource efficiency. Administrative barriers should be broken down, and government officials should enhance exchanges and cooperation between provinces, learn from each other, and learn from methods conducive to environmental improvement and GWRE improvement, so as to promote the sharing of relevant technical experience among provinces. In addition, transaction costs may be reduced nationwide, and the various correlations between geographical, economic, and policy units should be strengthened so as to build a new pattern of opening up at the national and even global level.

Thirdly, it is preferable to promote scientific and technological innovation, since the GWRE will benefit from environmental regulation based on our results to some extent, and the market competition mechanism should also be improved accordingly. Under the green development theory, we should reduce the barriers to interregional technology spillover and improve the ability to absorb knowledge, so as to promote the efficiency of transforming knowledge and new technologies into new means. Under the new development theory, we should adhere to water conservation, Scientific use of water [51], and systematic governance, build a modern green water resources competition market system under the requirements of environmental regulations, optimize the distribution efficiency of water resources, and minimize the dependence of economic development on water resources so as to improve the efficiency of green water resources.

Author Contributions

Z.P. designed the research and drafted the manuscript; Z.W. reviewed and commented on the manuscript; X.L., J.L. and Y.Z. conducted the model simulation and revised the manuscript. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

All authors agree to cooperate on this paper.

Data Availability Statement

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Conflicts of Interest

The authors declare that they have no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures and Tables

Figure 1. Coupling relationship between environmental regulation and GWRE from 2000 to 2019.

Figure 2. Coupling coordination degree between environmental regulation and regional GWRE from 2000 to 2019.

Table 1

Classification of coupling degrees between environmental regulation and GWRE.

The Range of Coupling Degrees	Coupling Level
[0.0000, 0.4000]	Low degree of coupling
[0.4000, 0.6000]	Moderate coupling
[0.6000, 1.0000]	Highly coupled

Table 2

Evaluation criteria for the coupling coordination of environmental regulation and GWRE.

Coupling Coordination	Hierarchical Classification	Illustration
[0.0000, 0.4000]	Primary coordination	The coordination between environmental regulation and GWRE is low
[0.4000, 0.6000]	Intermediate coordination	The degree of coordination between environmental regulation and GWRE is relatively high
[0.6000, 1.0000]	Highly coordinated	Environmental regulation and GWRE develop together

Table 3

Types of coupling coordination between environmental regulation and GWRE.

Degree of Coupling	Coupling Coordination	Coupling Coordination Type
High	High	High coupling coordination type
Medium	High	Medium coupling coordination type
Low	High	Low coupling coordination type
High	Medium	High coupling run-in type
Medium	Medium	Medium coupling run-in type
Low	Medium	Low coupling run-in type
High	Low	Highly coupled antagonistic type
Medium	Low	Medium coupled antagonistic type
Low	Low	Low coupling antagonistic type

Table 4

Classification of environmental regulations and selection of indicators.

Target Layer	Indicators	Description of the Values of the Indicators
Environmental regulation	command-type	The number of environmental protection acceptance projects completed in the current year/regional GDP
		Total number of administrative and supervisory bodies/gross regional product
		Number of administrative penalties environmental cases/regional GDP
		The number of governance projects completed in the current year/regional GDP
	market-type	Environmental acceptance project environmental protection investment/regional GDP
		Investment in the treatment of industrial pollution sources/regional industrial added value
		The amount of pollutant discharge paid into the warehouse/the added value of regional industry
		The pollution control project completed the investment/regional GDP this year
	autonomous-type	Number of proposals made by the National People’s Congress and the Chinese People’s Political Consultative Conference/Number of Regional Populations
		Total number of petitioning offices/number of regional populations
		Number of scientific institutions/population of the region
		Examine and approve the number of project registrations for construction independently compiled/the number of regional populations

Table 5

Average coupling coordination degree between environmental regulation and GWRE in provinces from 2000 to 2009, 2010 to 2019, and 2000 to 2019.

Province (Region)	2000–2009	2010–2019	2000–2019	Province (Region)	2000–2009	2010–2019	2000–2019
Beijing	0.6321	0.6412	0.6345	Inner Mongolia	0.5933	0.5648	0.5813
Tianjin	0.6032	0.6420	0.6209	Guangxi	0.5737	0.6053	0.5886
Hebei	0.5743	0.5691	0.5716	Chongqing	0.6072	0.6327	0.6192
Shanghai	0.6168	0.6396	0.6268	Sichuan	0.5645	0.5821	0.5737
Jiangsu	0.5579	0.5886	0.5716	Guizhou	0.6063	0.6336	0.6184
Zhejiang	0.5401	0.5932	0.5653	Yunnan	0.5986	0.5853	0.5953
Fujian	0.5824	0.5875	0.5856	Tibet	0.2637	0.5463	0.3964
Shandong	0.5413	0.5645	0.5521	Shanxi	0.5843	0.6008	0.5901
Guangdong	0.5423	0.5841	0.5615	Gansu	0.5775	0.5954	0.5863
Hainan	0.5247	0.5875	0.5536	Qinghai	0.4735	0.5743	0.5184
Eastern region	0.5716	0.5996	0.5854	Ningxia	0.5363	0.5762	0.5556
Shanxi	0.5585	0.5619	0.5598	Xinjiang	0.5447	0.5541	0.5415
Anhui	0.5963	0.5897	0.5937	Western region	0.5464	0.5865	0.5633
Jiangxi	0.5746	0.6066	0.5884	Liaoning	0.4946	0.5254	0.5082
Henan	0.5236	0.5275	0.5261	Jilin	0.5635	0.5657	0.5644
Hubei	0.5843	0.5857	0.5826	Heilongjiang	0.5564	0.5743	0.5663
Hunan	0.5751	0.5743	0.5754	Northeast	0.5376	0.5546	0.5464
Central region	0.5685	0.5754	0.5725	Nationwide	0.5571	0.5837	0.5721

Table 6

Convergence analysis of the coupling coordination degree between environmental regulation and GWRE.

Convergence	Parameter	Numeric Value	Convergence	Parameter	Numeric Value
Absolute β convergence	α	−0.012 ***	Conditional β convergence	α	−0.052 ***
		(−5.75)			(−5.10)
	β	0.028 ***		β	0.116 ***
		(13.97)			(8.30)
	Adjusted R2	0.2388		Adjusted R2	0.1036
	F	197.15		F	68.93

Note: () is the t statistic, *** indicate the level of 1%.

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