The interactive relationship between water environmental pollution and the quality of economic growth in China: An empirical research based on the dynamic simultaneous equations model

Abstract

As the Chinese economy enters the stage of new normal, high-quality economic development has become the current priority. Economic development should not be achieved at the cost of environment and resources. Clarifying the interactive relationship between water environment pollution and the quality of economic growth can help realize water environment protection and sustainable economic development. Based on the data of 30 provinces and cities in China, this paper studies the bidirectional mechanism between water environmental pollution and the quality of economic growth with the dynamic simultaneous equations model. The paper concludes that environmental pollution and the quality of regional economy growth have strong hysteretic nature; And that water environmental pollution and the development of regional economy display the relationship of bidirectional feedback. Discharge of pollutants into the water environment can inhibit high-quality development of regional economy. As the intensity of water environmental pollution increases by 1%, the quality of regional economic growth decreases by 0.0230%; Hence as the quality of regional economic growth increases by 1%, the intensity of water environmental pollution increases by 0.148%. Therefore, sustainable economic development should be attached with due significance, by removing the factors of environmental pollution, timely adjusting environmental policies, and promoting harmony between ecological environment and economic growth.

Keywords

Water environmental pollution the quality of economic growth dynamic simultaneous equations

1. Introduction

Having developed at top speed for decades, the Chinese economy, the volume of which ranks second in the world, has achieved significant progress and greatly contributed to the development of world economy. However, the achievement is at the cost of environmental pollution. Environmental quality, especially the quality of water environment, is closely related to public life. Abundant nitrogen, sulphur, phosphorus and other organic matters in the sewage can inhibit biological growth and even threaten people’s lives. While the Chinese economy enters the stage of “new normal”, the original resource-intensive growth pattern becomes unsustainable and the focus of economic growth shifts from quantity to quality. Believing that lucid waters and lush mountains are invaluable assets, the Chinese government lays emphasis on environmental protection, setting the top priority in fulfilling social responsiblity to be energy conservation, emission reduction and environmental protection. Yet despite the great imput of capital and human resources, environmental protection remains to be strengthened. Therefore, to clarify the relationship between environmental protection and the quality of economic growth is of great practical and theoretical significance for China to deliver appropriate environmental policies, to realize high-quality development of the economy, and to avoid repeating the pattern of “treatment after pollution” that has been adopted by many developed countries.

Here we introduce our research background and research purpose. The current research progress is shown in the literature review and theoretical framework. The idea of considering GDP as the sole priority still exists, while political achievements and promotion seem to be the main concern of some government officials. They are bound to ignore the relationship between environmental protection and economic growth. Changing those backward ideas and promoting the coordinated development of environment protection and economic growth will help to achieve green and sustainable development, balance material demands and improve the environment.

The remaining sections of this essay are briefed as follows: Section two is the literature review on the relationship between water environmental pollution and regional economic growth; Section three describes the construction, variable selection and data source of the model; Section four analyzes empirically the interactions between water environmental pollution and the quality of regional economic growth and carries out the robustness test; Section five summarizes the major research conclusions, offering countermeasures and suggestions.

2. Literature review and theoretical framework

2.1 Literature review

At present, many academic researches have been conducted on the relationship between economic development and water environmental pollution, which mainly fall into the following three categories:

Studies on the influences of economic development upon water environmental pollution: Grossman and Krueger found that as the economy develops, environmental pollution tends to aggravate before being alleviated [1]. Zhang and Wang reviewed the data of the period from 2000 to 2014 in Shandong province and found through calculation that the emission of industrial wastewater kept decreasing along with economic growth, while urban domestic wastewater kept increasing [2]. Wu et al. thought that there was complicated relationship between regional economic growth and the intensity of environmental pollution [3].

Studies on the influences of water environmental pollution upon economic development: Li found that the linear relationship of the industrial wastewater discharge value and per capita GDP followed an inverted U-shaped EKC curve, while that of the domestic wastewater and per capita GDP showed a positive U-shaped EKC curve [4]. Xu constructed a regression model of industrial wastewater discharge, urban domestic wastewater discharge and regional economic development, and his results showed that the industrial wastewater discharge and regional economic development followed an N-shaped EKC curve, while urban domestic wastewater discharge and per capita GDP followed a positive U-shaped EKC curve [5]. Zhang and Shen thought that inverted U-shaped curve relationship existed between per capita gross regional product of Jiangsu province and the three water environmental pollution indicators, namely industrial wastewater discharge, urban domestic wastewater discharge and agricultural surface pollution [6]. Luo analyzed the reasons and variations of economic development and water pollution discharge of Hunan province with the input-output model [7].

Studies on the relationship between economic development and water environmental pollution: Wu and Wu analyzed the differences between economic growth and environmental pollution based on the VAR model [8]. Liu et al. studied the relationship between the quality of water environment and economic growth in Shandong Province using the environmental Kuznets curve [9]. The results showed that there were significant correlations between water environmental pollution and economic development in Shandong Province and that EKC curves featuring chemical oxygen demand (COD) and ammonia nitrogen emissions showed a downward trend. Feng et al. used the provincial panel data of environmental pollution and economic development to construct a simultaneous equations model featuring their interactions. Research results showed that interactive relationship existed between China’s economic development and water environmental pollution, and that water pollution and per capita GDP followed the N-shaped curve [10]. Fan and Luo tested the relationship between economic growth and water pollution with various methods, such as co-integration test and impulse response [11]. The results showed that there was bidirectional relationship between economic growth and water pollution. According to study of Wang et al., agricultural economy and water environmental pollution had long-term stable co-integration relationship [12].

The current research on the relationship between water pollution environment and economic growth mainly adopts panel econometric analysis and complete decomposition model [13, 14]. The former studies the existence of EKC curve and whether the “pollution paradise” hypothesis is tenable, while the latter studies the impact of economic scale on water environment pollution and the differences among Suzhou, Wuxi and Changzhou.

To conclude, there are abundant researches on the relationship between water environmental pollution and economic development. However, due to differences in research perspectives and theories, the studies lack concerns on endogeneity and dynamics. At different stages of economic development, most studies focus on quantity instead of quality. In terms of research methods, most of them adopt VAR, hence the relationship between water environmental pollution and economic development cannot be estimated accurately [15]. Taking the above factors into consideration, this essay attempts to study the interactions between water environmental pollution and the quality of economic growth systematically under the simultaneous framework, so as to assess the interactions between water environmental pollution and the quality of economic growth. Our ultimate goal is to promote the coordinated development of ecological environment and economic quality. The innovation of this paper is that it is not an isolated study of the relationship between the quality of economic growth and environmental pollution or the relationship between environmental pollution and the quality of economic growth. It shows that in the situation, the two are cause and effect, and this relationship is not considered in the literature at home and abroad, but the two are organically combined. While improving pollution and economic development, we should consider the dynamic correlation between them.

2.2 Theoretical framework

Part of the academia agrees with the Environmental Kuznets curve suggesting that economic development will aggravate environmental pollution. With the upgrading of economic development, environmental pollution will be gradually improved. However, in terms of China’s national conditions, economic development has not reached the inflection point and is still on the left side of the inverted U-shaped curve. China’s economic development affects the quality of water environment through the main scale effect. The improvement of people’s living standards leads to the increase in material demands, met by vigorous industrial development. The increase in economic volume leads to the exacerbation of water pollution. As a result, economic development has aggravated water environmental pollution.

Hypothesis 1: economic growth increases water pollution.

When the discharge of water pollutants increases, water pollution will directly affect human health and the quality of life, possibly for a long time. Therefore, information of polluters and pollution damage should be constantly collected. Strong environmental pressure will urge the government to protect the environment through strict environmental regulation. For a qualified enterprise, compliance with environmental regulations will inevitably increase cost. Pollution discharge and innovation increase expenditure, which will lead to poorer performance in the short term. Yet pollution is not in line with sustainable economic development, thus quality growth should prioritize efficiency, achieving growth at a very small environmental cost.

Hypothesis 2: water pollution discharge is not conducive to improving the quality of economic growth.

3. Model building and index selection

At present, studies on the relationship between regional economic growth and water environmental pollution are mainly unidirectional, namely conducting one-way analysis of regional economic growth and water environmental pollution while neglecting their interactions. There are great defects in the results. Thus, this study adopts dynamic simultaneous equation to overcome the disadvantage of traditional models. Basic principle: The simultaneous equation model shows that there is a two-way correlation between economic variables, an economic variable is affected by other economic variables and also acts on other economic variables. Combined with all the exogenous variable information of the model, a structural equation group of the correlation between different economic variables can be obtained by fitting, evaluating and estimating several endogenous variables in the simultaneous equation model, which can provide reference for relevant economic actions. Therefore, the simultaneous equations model can more comprehensively reflect the rules of economic system operation. The disadvantage is that the single equation model is used to study the relationship between core variables, often neglecting the problems caused by the endogeneity of variables and resulting in inconsistent parameter estimation. On the other hand, the dynamic simultaneous equation model takes into account the lag of the explained variables. Economic development and water environment pollution are affected at an early stage. The endogenous variables in the model are determined by the system, while the exogenous variables have nothing to do with the system. The interaction between endogenous variables and identifiability is the premise for the model to produce a feasible solution.

There are many estimation methods for simultaneous equation models, mainly including single equation estimation method, such as indirect least squares (2SLS), instrumental variable method, two-end least squares method, etc. system estimation methods, including three-stage least squares method, generalized moment estimation, etc. In this paper, system estimation method is used to estimate all parameters in system equations. The idea is to consider the covariance of residuals based on 2SLS. The specific steps are as follows: first estimate each equation with 2SLS and estimate the simplified model of simultaneous equation system, then use the fitting values of all endogenous variables to obtain the 2SLS estimation of each equation in simultaneous equation, estimate the variance and covariance between equations, and finally obtain the parameter estimator of generalized minimum two multiplication. The advantage of the model is that each equation is incorporated into the system and all equations are regarded as a whole. The analysis framework is shown in Fig. 1.

Figure 1.

Analysis framework chart.

The basic structural formula of simultaneous equation is as follows:

$\displaystyle BY=\tau X+U$ (1) $\displaystyle Y=\left({\begin{array}[]{c}Y_{1}\\ \vdots\\ Y_{g}\\ \end{array}}\right)\quad X=\left({\begin{array}[]{c}X_{1}\\ \vdots\\ X_{k}\\ \end{array}}\right)\quad U=\left({\begin{array}[]{c}U_{1}\\ \vdots\\ U_{g}\\ \end{array}}\right)\quad B=({b_{ij}})_{g\ast g}\quad\tau=({\gamma_{ij}})_{g% \ast k}$ (2)

The limiting conditions of the basic formula include: ⟀ The number of endogenous variables is the number of equations; ⟁ The diagonal element of $B$ matrix is 1 and $B$ is reversible.

Identification and discrimination conditions for modularity of simultaneous equations:

The necessary and sufficient condition for the recognizability of the equation is that the rank of the first equation is equal to $g-1$ , and the non recognizable condition is that the rank is less than $g-1$ . At this time, the solution of the equation system is not unique, while the first equation is unique.

Taking

$\displaystyle Y_{it}=b_{ij}Y_{it}+\cdots+\gamma_{im}X_{mt}+\cdots+U_{it}$ (3)

as an example, the solution of the equation is divided into two steps: ⟀ Take each endogenous explanatory variable y in the structural equation as the explained variable and all the signed variables in the model as the explanatory variables, and carry out the ordinary least square estimation in the first stage to obtain each $\hat{Y}_{j}$ .

$\displaystyle\hat{Y}_{j}=a_{j1}X_{1}+\cdots+a_{jk}X_{k}+\varepsilon_{j}\to\hat% {Y}_{j}=\hat{a}_{j1}X_{1}+\cdots+\hat{a}_{jk}X_{k}$ (4)

⟁ Replace $Y_{j}$ with $\hat{Y}_{j}$ , carry out the second stage least square estimation of the structural equation, and obtain the estimation of the structural parameters of the equation.

$\displaystyle\hat{Y}_{i}=b_{ij}\hat{Y}_{j}+\cdots+\gamma_{im}X_{m}+\cdots+({b_% {ij}\omega_{j}+\cdots+U_{i}})\to\hat{Y}_{i}=\hat{b}_{ij}\hat{Y}_{j}+\cdots+% \hat{\gamma}_{im}X_{m}$ (5)

Given the research object of this paper, the simultaneous equations are established as follows:

$\displaystyle\ln\textit{pol}_{i,t}=c_{1i,t}+\alpha_{1}\ln\textit{pol}_{i,t-1}+% \alpha_{2}\ln\textit{tfp}_{i,t}+\alpha_{3}\ln\textit{hum}_{i,t}+\ln\textit{fdi% }_{i,t}+\ln\textit{urb}_{i,t}{}+\mu_{i}+\upsilon_{t}+\varepsilon_{i,t}$ (6) $\displaystyle\ln\textit{tfp}_{i,t}=c_{2i,t}+\beta_{1}\ln\textit{tfp}_{i,t-1}+% \beta_{2}\ln\textit{pol}_{i,t}+\beta_{3}\ln\textit{pop}_{i,t}+\beta_{4}\ln% \textit{gov}_{i,t}+\beta_{5}\ln ec_{i,t}{}+\mu_{i}+\upsilon_{t}+\varepsilon_{i% ,t}$ (7)

Equation (6) is the water environmental pollution equation, used to analyze the influences of economic growth quality ( $\textit{tfp}_{i,t}$ ) of region i in the year t upon environmental pollution ( $\textit{pol}_{i,t}$ ) of the same year. The second explanatory variable is human capital, the third foreign direct investment and the fourth urbanization level. Equation (7) is the equation of economic growth quality, used to test the influences of environmental pollution on regional economic growth quality. The second explanatory variable is population density ( $\textit{pop}_{i,t}$ ), the third government scale ( $\textit{gov}_{i,t}$ ), and the fourth energy consumption ( ${ec}_{i,t}$ ). $\mu_{i}$ represents individual effect, $\upsilon_{t}$ time effect, $\varepsilon_{i,t}$ the error term, $\textit{pol}_{i,t-1}$ the first-order lag item of environmental pollution and $\textit{tfp}_{i,t-1}$ the quality of regional economic growth.

Index selection and data source: this paper adopts the panel data of 30 provinces and cities of China from 2004 to 2018. Detailed explanation of the variables are as follows:

Endogenous variable: The quality of economic growth (tfp). There is still no unified definition on indexes of economic growth. Referring to the study of Sun et al., this paper adopts the total factor productivity of various regions to evaluate the quality of regional economic growth, the capital stock and quantity of employment as the input variable and GDP as the output variable. Capital stock is evaluated with perpetual inventory method [16].

Intensity of water environmental pollution (pol): Industrial wastewater is an important source of water environmental pollution. To eliminate the changes caused by demographic factor, it is evaluated as discharge of industrial wastewater/permanent population. Robustness test is evaluated as discharge of industrial wastewater/GDP.

Table 1

Descriptive statistics

Variable	Obs	Mean	Std.Dev.	Min	Max
Pol	450	15.687	8.94	3.306	48.901
Pol1	450	7.000	5.533	0.419	34.743
Hum	450	8.738	1.014	6.378	12.555
Urb	450	0.5260	0.139	0.146	0.896
Tfp	450	0.5250	0.235	0.175	1.085
Pop	450	440.700	645.647	7.457	3825.692
Ec	450	1.319	0.751	0.354	4.323
Gov	450	0.215	0.096	0.0790	0.627
Fdi	450	0.025	0.019	0.000	0.086

Exogenous variables:

Human capital (hum): per capita degree of education. Referring to the study of Wen et al., the duration of education is categorized as follows to evaluate regional human capital: 0 year for illiteracy and semi-illiteracy, 6 years for elementary education, 9 years for junior middle school, 12 years for senior middle school and technical secondary school, and 15 years for junior college and above [17].

Urbanization level (urb): the proportion of urban population in total population [18].

Foreign direct investment (fdi): the ratio of foreign direct investment to gross regional domestic product.

Population density (pop): the ratio of the permanent resident population of each province to the total area of the region.

Government size (gov): government expenditure/gross regional domestic product.

Energy consumption ( $e c$ ): total energy consumption/permanent population.

All data come from China Statistical Yearbook, Chinese Environmental Statistical Yearbook and the statistical yearbooks from 2005 to 2019. The missing values are calculated with the averaging method. To make the data comparable, all data adopted in this essay are deflated with the year 2004 as the base period. The original data are processed into logarithms before being analyzed. Descriptive statistical results of the variables are shown in Table 1.

4. Empirical test and analysis

4.1 Stability test

In order to avoid pseudo regression, it is necessary to carry out unit root test on the data before the empirical analysis. The data used in this paper involve the past 11 years. Three test methods, namely LLC, IPS and HADRI, are used to test the sample data of each variable, as shown in Table 2. When more than two methods pass the test, the original assumption that there is a unit root of the rejected variable can be estimated in the next step.

Table 2
Stability test

Variable	LLC(p)	IPS(p)	HADRI(p)	Conclusion
lntfp	$-$ 6.8685 (0.0000)	$-$ 2.5212 (0.0058)	9.7982 (0.0000)	Stable
lnpol	$-$ 3.9366 (0.0000)	$-$ 1.8954 (0.0290)	8.1264 (0.0000)	Stable
lnpol1	$-$ 4.1515 (0.0000)	$-$ 1.6390 (0.0506)	8.1511 (0.0000)	Stable
lnhum	$-$ 5.7429 (0.0000)	$-$ 3.9519 (0.0000)	8.2510 (0.0000)	Stable
lnfdi	$-$ 4.4255 (0.0000)	0.5525 (0.7907)	9.1578 (0.0000)	Stable
lngov	$-$ 4.6187 (0.0000)	$-$ 1.3451 (0.0893)	8.9593 (0.0000)	Stable
lnpop	$-$ 2.7031 (0.0034)	$-$ 2.7721 (0.0028)	7.8764 (0.0000)	Stable
lnurb	$-$ 1.3e+02 (0.0000)	$-$ 33.8181 (0.0000)	10.5343 (0.0000)	Stable
lnec	$-$ 10.7421 (0.0000)	$-$ 4.9437 (0.0000)	8.0004 (0.0000)	Stable

4.2 Empirical analysis and results

Before the simultaneous equation model is estimated, it is necessary to ensure that all the simultaneous equation models are recognizable, so that the overall parameters can be estimated. Two stage least square (2SLS) and three stage least square (3SLS) can be used to estimate the parameters. However, compared with the former, 3SLS can estimate all the equations as a whole, evaluating the relationship among the equations with higher efficiency. Therefore, 3SLS is adopted to estimate the model. The estimation results of the simultaneous equations model are shown in Table 3, in which R ${}^{2}$ value of tfp equation is 0.9903 and R ${}^{2}$ value of pol equation is 0.9608, indicating that the equation set is highly suitable for modeling.

Table 3
Results of model estimation

Variable name	Tfp	Pol
L.Lntfp	0.903 ${}^{\ast\ast\ast}$ [72.16]	/
L.Lnpol	/	0.676 ${}^{\ast\ast\ast}$ [19.94]
Lnpol	$-$ 0.0230 ${}^{\ast\ast}$ [ $-$ 2.23]	/
Lntfp	/	0.148 ${}^{\ast\ast\ast}$ [2.70]
Lnhum	$-$ 0.00166[ $-$ 0.02]	/
Lnfdi	0.00779 ${}^{\ast\ast}$ [2.07]	/
Lngov	/	$-$ 0.163[ $-$ 1.51]
Lnpop	/	$-$ 0.585 ${}^{\ast\ast\ast}$ [ $-$ 5.42]
Lnurb	0.0891 ${}^{\ast\ast}$ [2.45]	/
Lnec	/	0.255 ${}^{\ast\ast}$ [2.40]
_Cons	$-$ 0.0118[ $-$ 0.06]	5.356 ${}^{\ast\ast\ast}$ [5.68]
R-sq	0.9903	0.9608
RMSE	0.0403414	0.1486547
Chi2	42925.98	10292.02

Note: t statistics in brackets. ${}^{\ast\ast\ast}$ , ${}^{\ast\ast}$ , and ${}^{\ast}$ are significant at 1%, 5%, and 10% respectively.

Hypothesis 2 is verified as the influence coefficient of environmental pollution on the quality of economic growth is negative and passes the test at 5% significance. Hypothesis 1 is verified as the influence factor of the quality of regional economic growth on environmental pollution is positive, significant at 1% significance, indicating that there is bidirectional causal relationship between environmental pollution and regional economic growth. That is to say, every 1% increase in the intensity of environmental pollution is associated with a 0.0230% decrease in the quality of regional economic growth. Hence every 1% increase in the quality of regional economic growth increasing is associated with a 0.148% increase, in environmental pollution. One possible reason is that water environmental pollutants will seriously endanger human health, thereby damaging human capital and reducing labor productivity, while problematic water quality caused by environmental pollution can result in increase of environmental protection cost. On the other hand, imbalance between resources and environment needs to be compensated with much capital and human power, which indirectly hinders the development of regional economy.

The influences of economic growth quality on environmental pollution is greater than those of water environmental pollution on regional economy quality. In the post-industrialization era, the contribution of industrial development is significant, since the quality of economic growth cannot be divorced from industrial support. Yet in the process of economic development, abundant pollutants are generated, inevitably damaging the environment quality. Water environmental pollution is a byproduct of economic growth.

At the same time, it is worth noting that both the quality of regional economic growth and water environmental pollution can be affected positively by the quality of regional economic growth and water environmental pollution of the previous stage. If the quality of regional economic growth and the intensity of water environmental pollution increase, those of the next year will increase by 0.903 and 0.676 units respectively.

Control variables of economic growth quality model: The estimation coefficient of human capital to the quality of economic growth is not significant, indicating that human capital is irrelevant with the quality of economic growth. One possible reason is that human capital requires a transformation process to drive economic growth. In the information age, human capital can only function after it is transformed into information and technology-based human capital and applied in practice. Although most scholars believe that human capital contributes to economic growth, it only has an impact on the quantity of economic growth. At present, there are still few areas with high human capital level in China, and most provinces and cities are in areas with medium or low human capital level, which should have a “crowding out effect” on the effect of Technology Spillover and inhibit the transformation of economic growth to an intensive mode. At present, most regions have not overcome this disadvantage. At the same time, the growth of human capital attracts the inflow of human capital through the “siphon effect”, which also makes most provinces and cities unable to enjoy the knowledge and technology advantages brought by human capital. The estimation coefficient of foreign direction investment to the quality of economic growth is positive and significant at 5%. Foreign direct investment is an important channel for local government to satisfy capital demands [19]. While meeting the demands for capital, FDI promotes China’s technological progress and industrial upgrading, thereby driving economic growth [20]. The estimation coefficient of urbanization level to the quality of economic growth is also positive. As a development strategy, urbanization not only boosts employment, but also plays significant roles in labor consumption, industrial upgrading as well as transformation of the economic development mode [21].

Control of water environmental pollution model: The estimation coefficient of population aggregation to water environmental pollution is negative and passes 1% significance test, indicating that population aggregation can help alleviate water environmental pollution. This is possibly because population aggregation drives resource integration, facilitating water pollution control and lowering the cost of pollution control, which in turn reduces water environmental pollution [22]. Energy consumption has aggravated water environmental pollution, as fossil fuel dominates energy consumption in the industry-oriented growth model. Utilization of traditional energy resources produces mercury and other micro-elements, which pollute the water environment.

4.3 Robustness test

To guarantee the reliability and robustness of the above research results, variable substitution method (the explained variable is replaced by industrial wastewater discharge/GDP) is adopted for the robustness test. The results show that only the coefficients differ slightly, which do not change the basic conclusion of this. Therefore, the research results boast high reliability, as shown in Table 4.

Table 4
Robustness results

Variable name	Tfp	Pol
L.Lntfp	0.906 ${}^{\ast\ast\ast}$ [73.51]	/
L.Lnpol	/	0.756 ${}^{\ast\ast\ast}$ [23.20]
Lnpol	$-$ 0.0429 ${}^{\ast\ast\ast}$ [ $-$ 4.12]	/
Lntfp	/	0.127 ${}^{\ast\ast}$ [2.47]
Lnhum	$-$ 0.0139[ $-$ 0.16]	/
Lnfdi	0.00691 ${}^{\ast}$ [1.86]	/
Lngov	/	$-$ 0.166[ $-$ 1.63]
Lnpop	/	$-$ 0.0281[ $-$ 0.29]
Lnurb	0.0730 ${}^{\ast\ast}$ [2.01]	/
Lnec	/	0.227 ${}^{\ast\ast}$ [2.23]
_Cons	0.0100[0.05]	0.216[0.26]
R-sq	0.9935	0.9704
RMSE	0.0330818	0.1292026
Chi2	64050.39 (0.0000)	13768.49 (0.0000)

Note: t statistics in brackets. ${}^{\ast\ast\ast}$ , ${}^{\ast\ast}$ , and ${}^{\ast}$ are significant at the level of 1%, 5%, and 10% respectively.

5. Conclusion and policy enlightenment

Based on the bidirectional interactions between water environmental pollution and the quality of economic growth, this paper constructs a simultaneous system of equations that contains two endogenous factors. The paper evaluates panel data of 30 provinces and cities from 2004 to 2018 are estimated with 3SLS and empirically analyzes the relationship between water environmental pollution and the quality of economic growth. Research findings include: both water environmental pollution and the quality of regional economic growth have strong hysteresis quality; Water environmental pollution and regional economic development are in reciprocal causation, that is, water environmental pollution hinders the improvement of regional economic growth quality, which in turn exacerbates environmental pollution.

Based on analysis of the empirical results, we reach the following conclusions:

First, we should be more aware of the complex relationship between water environmental pollution and the quality of economic growth. The quality of economic development should be attached with more importance. As water environmental pollution keeps increasing, it is necessary to lay emphasis on the application of pollution reduction technology and green technology. Second, we should be fully aware that economic growth cannot come at the cost of environmental pollution. We should reduce pollution-intensive industries, change the GDP-oriented standard for weighing political achievements, timely adjust environmental regulation policies and establish long-term policy mechanism, so as to realize stable and sustainable development. Third, economic policies should focus on green economy, emissions trading and ecological compensation, and form an environmental policy system featuring “prevention first and combination of prevention and control” for water pollution. We shall improve the water price mechanism, attach due importance to the degree of water shortage and ecological value of water resources, reduce the wasting of water resources, impose water pollution reduction, and raise the utilization rate of water resources among some industries in particular. Targeting different groups of population, we shall give full play to the effect of reasonable water pricing in allocating water resources, so as to reduce water pollution. Fourth, continue to launch new national energy policies, develop new and renewable energy such as hydroenergy and wind energy, reduce dependence on fossil fuel, rely on the marketization of energy prices, further improve the energy taxation mechanism, and strengthen energy laws and policies. Building a resource-saving and environment-friendly society is defined as a strategic goal in the medium and long-term planning of national economic and social development. Fifth, although the current water environment pollution situation has improved, the overall form is still severe. PO4-P and some heavy metals in seven major basins such as the Yangtze River and the Yellow River in China exceed the standard seriously. It is necessary to strengthen sewage collection, gradually improve nitrogen and phosphorus removal processes and improve the removal rate of nitrogen, phosphorus and other nutrients on the basis of comprehensive consideration of water quality requirements, operation and maintenance and economic level. Through strict environmental protection approval to control new pollution sources, focus on supervising the sewage discharge of chemical, pharmaceutical, cement, steel and other industries, strictly implement the project “environmental assessment” and “three Simultaneities” system, and focus on monitoring different water pollution sources according to different industries, so as to achieve the effect of water pollution control.

This paper has its limitations. Space, economic development and water pollution do not exist in isolation, and may be affected by adjacent areas. In data selection, this paper focuses on provincial data, while the county economy, deeply affecting the regional division of labor, is the basic unit of the national economy. The research on the county level has more practical significance. In future research, county shall be taken as the research object, and spatial perspective shall be integrated into the dynamic simultaneous equations for new discoveries.

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The interactive relationship between water environmental pollution and the quality of economic growth in China: An empirical research based on the dynamic simultaneous equations model

Abstract

Keywords

1. Introduction

2. Literature review and theoretical framework

2.1 Literature review

2.2 Theoretical framework

3. Model building and index selection

4.1 Stability test

Table 2 Stability test

Table 3 Results of model estimation

Table 4 Robustness results

References

Table 2
Stability test

Table 3
Results of model estimation

Table 4
Robustness results