Decoupling of Economic Growth and Industrial Water Use in Hubei Province: From an Ecological–Economic Interaction Perspective

Abstract

Rational water use is the basis for sustainable development. The issue of how to use limited water resources to satisfy the high rate of economic development has attracted a great deal of attention from society. This paper presents a quantitative analysis of the intrinsic relationship between economic growth and industrial water use changes in Hubei Province based on panel data from 2004 to 2019. With the help of the Tapio decoupling model, the problem of decoupling the economic growth of Hubei Province and the water use of the three industries in 15 years was discussed. On the basis of Kaya’s extended identity, the Logarithmic Mean Divisia Index (LMDI) index decomposition method is used to evaluate the driving factors and steady state changes in the three industries’ water use. The results show that, with regard to the decoupling state, there are three decoupling states between economic growth and industrial water use in Hubei province: negative decoupling, strong decoupling, and weak decoupling, which showed a phase characteristic. From the decomposition of the factors, the industrial structure effect and the water intensity effect are the key factors that determine the decoupling of economic growth and industrial water use in Hubei Province, as well as the core driving force to promote the decoupling state. According to the development trend, Hubei Province needs to take into account the efficiency and affordability of water resources in the process of promoting social and economic development. Therefore, in line with the research outcomes, this study provides effective and feasible recommendations for promoting sustainable economic and social development in Hubei Province.

1. Introduction

As a basic natural resource, water carries the guaranteed function of human survival. At present, China’s industrial water consumption is large and relatively concentrated, but the overall level of industrial water efficiency is low and generates a large amount of industrial wastewater. In 2019, the total amount of water resources in Hubei Province was 61.37 billion cubic metres (The year 2019 was a dry year. The annual average temperature in Hubei Province was 0.7 °C higher than normal, with 10 counties and cities breaking historical records for the number of sustained high temperature days. The total amount of water resources in the province was 40.8% less than normal.) The total amount of water resources in China reached 2904.10 billion cubic metres, of which Hubei Province only accounted for 2% [1]. However, the per capita comprehensive water use of Hubei Province is 512 cubic metres, which is 81 cubic metres higher than that of the national use. In addition, the GDP growth of Hubei Province in that year was 646.176 billion yuan, and the nominal GDP growth rate reached 16.41%, ranking it seventh in the country. As the major driving region of Central China, Hubei Province’s level of GDP has increased almost six-fold over the past 15 years. During the period from 2004 to 2019, although the economic level of Hubei Province improved significantly with the increase of industrial water use, the low efficiency of water resources utilization, and the unreasonable structure of industrial water use, etc., the contradiction between the supply and demand of water resources in Hubei Province has been disturbed indirectly [2].

Hubei Province is located in the middle of China. The Yangtze River—the largest river in China, which passes through Hubei Province Danjiangkou Reservoir, the water source of the middle route project of the South to North Water Transfer Project—is also located in Hubei Province. Therefore, Hubei Province is rich in water resources and should reasonably have sufficient industrial water. However, the reality shows that Hubei Province also has the problem of unreasonable use of water resources. From 2004 to 2019, the proportion of water consumption in Hubei Province by primary industry reached 50.11% of the total water use on average, but the economic benefit has accounted for only 12.33% of the total economic benefit. However, the proportion of economic benefits brought by the secondary industry and the tertiary industry reached 47.11% and 41.41%, respectively, approximately 3–4 times that of the primary industry. Influenced by the lagging infrastructure, poor sales channels, and unreasonable production structure, the benefits brought by the primary industry in Hubei Province are far less than those of the secondary and tertiary industries. Although the vigorous development of the secondary and tertiary industries has promoted economic and social progress, it has inevitably brought about a series of negative effects that curb sustainable development [3]. It could be found that the total amount of water resources is limited. In the long run, increased water use may exacerbate the contradiction between social development and the ecological environment [4]. Today, people are profoundly aware of the close relationship between resource conservation and social sustainability [5]. Therefore, how to use scarce resources to respond to the rapid development of the social economy has been of great concern to society [6].

Internationally, the dynamic relationship between economic development and the sustainability of resources and environment is one of the hot topics in academic research. In previous studies, there has been rich advanced econometric modeling for empirical analysis, such as cross-section dependence (CD) analysis, second-generation panel unit-root analysis, cross-sectional ISP (CIPS), and cross-sectional ADF (CADF). They were adapted to investigate the dynamic relationship among urbanization [7], economic growth [8,9,10], energy investments [11], clean energy [12,13], and environmental sustainability [14,15] thus to make feasible suggestions for policy makers. In the 1960s, the hot problem of economic development and resource environmental decoupling was first proposed [16], and the decoupling theory was created as a result. This theory is often used to describe a state in which the relationship between economic growth and environmental resource consumption is broken [17]. The Organization for Economic Cooperation and Development (OECD) [18] divided the decoupling theory into two categories in 2002, absolute decoupling and relative decoupling, and applied it to the study of the changing trend between agricultural trade and market equilibrium. On the basis of the OECD, Vehmas et al. [19] divided the decoupling states into six types, namely, strong decoupling, weak decoupling, declining decoupling, expansive coupling, weak coupling, and strong coupling. Tapio [20] explored the developing relationship between economic growth and transport-related CO₂ emissions using the three variables of GDP, transport volume, and CO₂ emissions in 15 EU countries between 1970 and 2001. Furthermore, the perfect decoupling state is divided into 8 categories, including weak decoupling, strong decoupling, weak negative decoupling, strong negative decoupling, declining coupling, declining decoupling, expanding negative decoupling, and expanding coupling [21]. The above is the evolution of decoupling theory.

With the development of decoupling theory, scholars try to determine the driving factors affecting economic growth and environmental resources [22,23]. Ang et al. [24] found that compared with other factor decomposition analysis methods, Logarithmic Mean Divisia Index (LMDI) decomposition method can be studied from the micro level with higher accuracy.

In recent years, increasingly more studies have been carried out by using LMDI. Through existing relevant studies, it can be found that the topic about the relationship between resources and economic growth practically revolves around energy consumption [25] and carbon emissions [22]. Many scholars adopted LMDI to analyze the contribution of driving factors from the perspectives of regional [26], national [27], provincial [28], and municipal [29] views, and put forward effective and targeted solutions based on the research results. In terms of carbon emissions and energy consumption, some scholars pointed out that LMDI can be used to predict the potential of reducing carbon dioxide emissions [30,31]. It contributes to timely institutional innovation and the formulation of effective policy plans [32]. Some scholars regard the LMDI method as an important measurement tool to evaluate the internal relationship between energy consumption and economic income [33,34] and to explore whether economic development can be less dependent on energy consumption [35], so as to determine the key driving factors [36].

The study of the use of LMDI methods on water resource and social development mainly includes the following aspects. First, many scholars have studied the close relationship between economic development and water resource consumption from the perspective of industrial water environment [37], agricultural economic development [38], urban economic output [39], and food production [40]. Furthermore, the driving factors influencing the change of environmental pressure and its changing trend are explored in depth. Second, some scholars have conducted research on the influence of social development on water resource utilization efficiency. It is studied for water resources, both city [39] and provincial [41]. On the basis of the scenario and the case analysis [42], they gauged whether the decision is achieved between economic growth and water consumption. Thirdly, some scholars evaluated water resources and environmental carrying capacity and discussed the limiting effect of water resources consumption on economic development [43]. The research shows that in order to avoid the high consumption of water resources constraining the development of society, promoting the process of becoming a water-saving society has become the consensus of global green development [44].

From the above analysis, we can learn that the relationship between resource conservation and economic development is an important hotspot for academic research. The in-depth development of decoupling theory and the increasing sophistication of the LDMI have provided good measurement tools for exploring the relationship between resources and economic growth. By comparison of the accuracy of the decoupling state and the drivers, it is undeniable that the relationship between economic development and water resources has been richly explored and studied by many scholars, but there is a lack of analysis of the spatial and temporal evolution of the decoupling relationship between water consumption in the three industries and economic development and the mechanisms that shape it. In addition, there is little research on economic growth and water consumption of the three industries. Therefore, this paper constructs a decoupling model by the Tapio Elastic Analysis Method to analyze the decoupling degree between industrial water use and economic development in Hubei Province from 2004 to 2019 and to explore the characteristics and evolution process in different stages of development. On the basis of the Kaya identity and the LMDI, this study conducted an analysis about the driving factors affecting the decoupling between economic growth and industrial water use in Hubei Province, mainly answering the following questions: What affects the relationship between economic growth and industrial water use in Hubei Province? Which factors play a key role in the decoupling the relationship between economic growth and industrial water use? How to control the driving factors from the policy aspect and promote the process of decoupling?

By answering these questions, this paper further enriches the study about the relationship between economic growth and environmental sustainability based on industrial water use. Meanwhile, by considering Hubei as an example, this paper explores the factors driving the decoupling of economic growth and industrial water use and provides recommendations for government governance, aiming to improve the healthy development of Hubei’s economy and thus provide Chinese wisdom for the sustainable development of the world economy. Overall, this study seeks to address the topic of high-quality economic development in Hubei Province from the perspective of water resource sustainability. Due to all these reasons, we expect that this research will prove a significant contribution to the literature at the intersection of water resources, environmental sustainability, and economic growth.

The remainder of the study is organized as follows: The second part presents the research design of this study, including the constructed decoupling model, Kaya identity, LMDI index decomposition method, and research technical route; the third part carries out the empirical research on the decoupling relationship between economic growth and industrial water use and analyzes the relationship between industrial water use and economic growth as well as the key driving factors of decoupling and its contribution value; the fourth part provides the conclusion and policy implications. The conclusion of the paper is summarized and the relevant suggestions are given to provide theoretical reference for sustainable social construction in Hubei Province.

2. Research Design

2.1. Research Methods

2.1.1. Tapio Decoupling Elasticity Analysis Method

The concept of decoupling was put forward by the Organization for Economic Cooperation and Development in 2002 when verifying the relationship between economic development and environmental pollution in various countries. The Tapio decoupling model is the result of further improvement based on the decoupling model. At present, Tapio decoupling model is a scientific tool used to measure the relationship between ecological environment and economic development, aiming to measure whether economic development is sustainable. In this study, we used the Tapio decoupling model to measure the relationship between industrial water use and economic development, which can fully explore the relationship between the two and analyze the driving factors. Therefore, we constructed the decoupling relationship between Hubei Province’s economic growth and the water use of the three industries as Equation (1):

$$\mathsf{\epsilon }\left(W,G\right)=\frac{∆W/W}{∆G/G}$$

(1)

In Equation (1), ε(W, G) represents the elastic coefficient of decoupling between the regional GDP of Hubei Province and the water use of the three industries in Hubei Province; ∆W represents the change in water use of the three industries in Hubei Province; and ∆G represents the change in the regional GDP of Hubei Province. According to Tapio’s research, the decoupling state can be divided into three categories: decoupling, negative decoupling, and coupling, and it can be further divided into eight subcategories: weak decoupling (WD), strong decoupling (SD), weak negative decoupling (WND), strong negative decoupling (SND), recessive coupling (RC), recessive decoupling (RD), expanding negative decoupling (END), and expansive coupling (EC), as shown in

Table 1. The decoupling types and corresponding state division.

Decoupling Category	Decoupling Type	∆W	$\mathbf{∆}\mathit{G}$	$\mathsf{\epsilon }\left(\mathit{W},\mathit{G}\right)$	State
Decoupling	WD	>0	>0	0 < ε < 0.8	Better
	SD	<0	>0	ε < 0	Best
	RD	<0	<0	ε > 1.2	Worse
Negative decoupling	END	>0	>0	ε > 1.2	Normal
	SND	>0	<0	ε < 0	Worst
	WND	<0	<0	0 < ε < 0.8	Bad
Coupling	EC	>0	>0	0.8 < ε < 1.2	Normal
	RC	<0	<0	0.8 < ε < 1.2	Worse

.

2.1.2. Kaya Identity

To facilitate the investigation of the driving factors of changes in carbon dioxide emissions, Professor Kaya of Japan created the Kaya identity in 1989, using simple mathematical formulas to analyze the complex relationships among various influencing factors [45]. This study expands the application of Kaya identities and applies them to the detection of driving force of economic growth and water use in the three industries as Equation (2):

$$W={\displaystyle \sum }_i{W}_i={\displaystyle \sum }_i\frac{W_i}{G_i}\frac{G_i}{G}\frac{G}{P}P=I_{i}S{I}_{nc}P$$

(2)

In this study, W represents the total water use of Hubei Province; W_i represents the total water use of the i industry in Hubei Province; G_i is the added value of the i industry in Hubei Province; G represents the GDP of Hubei Province; and P represents the total population of Hubei Province. By constructing a factor decomposition model of economic growth and water use, the driving factors are decomposed into four parts: water intensity (I_i), industrial structure (S), economic income (I_nc), and population size (P).

2.1.3. LMDI Index Decomposition Method

On the basis of the research of the IDA method, B.W. Ang et al. proposed the LMDI [46]. This method consists of two decomposition modes: multiplication and addition. From the calculation results, there is no difference between the two. The LMDI index decomposition method gives an almost perfect decomposition, which effectively solves the unexplainable residual items after the decomposition factors, and it is widely used [47]. According to the additive decomposition form of the LMDI, the influence intensity of factors in Equation (2) is decomposed, the results are as Equation (3):

$$∆W=∆W_I+∆W_S+∆W_{Inc}+∆W_P$$

(3)

Among them, ∆W_I represents the effect of water intensity; ∆W_S represents the effect of industrial structure; ∆W_Inc represents the effect of economic income; and ∆W_P represents the effect of population scale. The additive decomposition calculation formula of the above 4 driving factors is as Equations (4)–(7):

$$∆W_I={{\displaystyle \sum }}^{ }\frac{W_i^t-W_i^0}{\ln W_i^t-\ln W_i^t}\ln \left(\frac{I_i^t}{I_i^0}\right)$$

(4)

$$∆W_S={{\displaystyle \sum }}^{ }\frac{W_i^t-W_i^0}{\ln W_i^t-\ln W_i^t}\ln \left(\frac{S_i^t}{S_i^0}\right)$$

(5)

$$∆W_{Inc}={{\displaystyle \sum }}^{ }\frac{W_i^t-W_i^0}{\ln W_i^t-\ln W_i^t}\ln \left(\frac{I_{nc}^t}{I_{nc}^0}\right)$$

(6)

$$∆W_P={{\displaystyle \sum }}^{ }\frac{W_i^t-W_i^0}{\ln W_i^t-\ln W_i^t}\ln \left(\frac{P_i^t}{P_i^0}\right)$$

(7)

The $W_i^t$ and $W_i^0$ in the formula represent the change in the total water use of the i-th industry in Hubei Province during the base period and the t-th period, respectively. When the effect value of the driving factor is positive, it means that the driving factor will cause an increase in water use of the i-th industry to a certain extent. Similarly, when the effective value of the driving factor is negative, it means that the driving factor has an inhibitory impact on the water use of the i-th industry, which means that the driving factor will cause a reduction in the water use of the i-th industry to a certain extent. If the effective value of the driving factor is exactly equal to 0, it means that the driving factor has no significant impact on the water use of the i-th industry.

2.2. Data Sources

This study is based on the data of Hubei Province’s economic growth and water use of the three industries from 2004 to 2019. Among them, the data of Hubei Province’s GDP and the number of permanent residents from 2004 to 2019 were collected from the Hubei Statistical Yearbook. The GDP of each year is converted to comparable prices in 2005 (after eliminating the influence of price factors), and the population indicator is from the end of the year number; the data of GDP and added value of each industry were collected from the Statistical Bulletin of Hubei Province’s National Economic and Social Development; and the total amount of water resources and water use of the three industries were collected from Hubei Water Resources Bulletin.

3. Results and Discussion

3.1. Variable Change Trend Analysis

To clarify the decoupling relationship between the economic growth of Hubei Province and the water use of the three industries in the past 15 years, this study analyzed the changes in the industrial added value and industrial water use in Hubei Province from 2004 to 2019, as shown in Figure 1. In the figure, water use n represents the water use of the n-th industry in that year, and value-added n represents the value added by the n-th industry in that year. If Hubei Province’s industrial water use maintains a good state while its economy is growing rapidly, it indicates that a sustainable development pattern between economic growth and industrial water use is achieved in Hubei Province. During this 15-year period, the total water use of the three industries in Hubei Province showed a fluctuating upward trend. It rose from 24.3 billion cubic metres in 2004 to 30.3 billion cubic metres in 2019, with an average annual increase of 0.56%.

According to the time series, this study used 5-year classifications as a stage to further study the growth characteristics of the total industrial water use in Hubei Province. In the first stage (2004–2009), the total water use of Hubei’s aquaculture industry rose from 24.3 billion cubic metres to 28.1 billion cubic metres. Among them, the water use of the primary industry accounted for a large proportion, all above 50%. The proportion of the tertiary industry was relatively small, measuring only approximately 10%. In 2008, due to the impact of natural disasters and financial crisis, the economic development of Hubei Province suffered to a certain extent, which also led to a rapid decline of 1 billion cubic metres in the total industrial water use. In order to combat this continuous worsening trend, Hubei government insisted on industrial transformation and vigorously developed the secondary and tertiary industries. As a result, the total industrial water use in Hubei province rapidly increased by 3.8 billion cubic metres during 2008–2009, with a growth rate of 15.6%. In the second stage (2009–2014), the rising trend of total industrial water use in Hubei Province was more moderate. It rose from 28.1 billion cubic metres in 2009 to 28.8 billion cubic metres in 2014. After the strategic adjustment of the economic structure, the overall economic development of Hubei Province at this stage was good, and the quality and efficiency of economic growth were also improved. In 2010–2012, the proportion of water consumed by the primary industry dropped below 50%. However, the proportions of water use and economic growth of the secondary industry and the tertiary industry were significantly improved, and the GDP increased by 693.1 billion yuan and 634.4 billion yuan, respectively. In the third stage (2014–2019), the total amount of industrial water use in Hubei province still showed a fluctuating upward trend which rose from 28.8 billion cubic metres in 2014 to 30.3 billion cubic metres in 2019. Compared with the first stage, the water use of the tertiary industry in the third stage increased by nearly two times. With the accelerated pace of industrial transformation, the rapid development of the service industry enhanced the vitality of economic development in Hubei Province. The gross domestic product of the tertiary industry grew by 115.71 billion yuan during this phase, and its share increased by nearly 10%.

3.2. Decoupling Analysis

3.2.1. Analysis of the Overall Decoupling between Economic Growth and Total Industrial Water Use

∆W and ∆G are the key indicators to explore the decoupling relationship between economic growth and industrial water use in Hubei Province. In this study, the relevant data was processed according to Formula (1) to obtain the decoupling index ε(W, G), and the results were classified according to the decoupling type and status in Table 1, as shown in

Table 2. The Decoupling state of total industrial water use during 2004–2019.

Period	$\mathbf{∆}\mathit{G}$	$\mathbf{∆}\mathit{W}$	$\mathsf{\epsilon }$0	State
2004–2005	2.60	4.41	1.70	END
2005–2006	15.62	2.14	0.14	WD
2006–2007	22.05	−2.09	−0.09	SD
2007–2008	23.83	−4.22	−0.18	SD
2008–2009	13.25	15.96	1.20	END
2009–2010	23.18	3.89	0.17	WD
2010–2011	23.97	1.48	0.06	WD
2011–2012	13.55	0.87	0.06	WD
2012–2013	10.87	−2.50	−0.23	SD
2013–2014	10.94	−1.19	−0.11	SD
2014–2015	7.98	4.48	0.56	WD
2015–2016	10.54	−6.41	−0.61	SD
2016–2017	11.81	2.94	0.25	WD
2017–2018	7.79	2.28	0.29	WD
2018–2019	16.41	2.12	0.13	WD

.

The $\mathsf{\epsilon }$0 in Table 2 represents the decoupling elastic coefficients between economic growth and total industrial water use and water use of the industry in Hubei Province, respectively. Table 2 shows the decoupling relationship between the economic growth of Hubei Province and the total industrial water use from 2004 to 2019. During these 15 years, there were three decoupling relationships between economic growth and industrial water use in Hubei Province, including expansion negative decoupling (END), strong decoupling (SD), and weak decoupling (WD). Only during 2004–2005 and 2008–2009, the unsustainable decoupling state of expansion negative decoupling (END) appeared, and the remaining years showed a relatively stable decoupling state.

• Expansion Negative Decoupling (END)

In 2004–2005 and 2008–2009, the elastic coefficients of decoupling between economic growth and the total water use of the three industries in Hubei Province reached 1.7 and 1.2, respectively, indicating a negative decoupling of expansion, which is an unsustainable state. The characteristic is that when the economic growth rate (∆G) and the growth rate of the total water use of the three industries (∆W) are both positive, the economic growth rate is slow, and the growth rate of industrial water use is greater than the economic growth rate, showing a somewhat inefficient development trend. According to the survey, during this period, the global financial crisis broke out and the Chinese economy faced the challenge of economic slowdown. The economic growth of Hubei Province has also suffered to a certain extent. In terms of agriculture, the central government issued a series of policies to benefit farmers, invested a large number of agricultural production factors, and increased efforts to promote agricultural development. In 2008, the water use of the primary industry in Hubei Province increased by 16.61% over the previous year; in terms of industry, Hubei, as an old industrial base, transformed itself from an extensive economy to a new type of industrialization with scientific development. The growth rate of water use in the secondary industry reached 16.05% that year.

2.

Strong Decoupling (SD)

Strong decoupling means that while the economic growth rate maintains a steady upward trend (∆G > 0), the growth rate of total industrial water use (∆W < 0) and the industrial water use per unit economic growth are simultaneously decreasing and are negative. In 2006–2008, 2012–2014, and 2015–2016, the elastic coefficients of decoupling between economic growth and total industrial water use in Hubei Province were −0.09, −0.18, −0.23, −0.11, and −0.61, respectively, which has realized a most favorable and ideal state for sustainable economic and social development. To reduce the pressure on the water environment and improve the efficiency of water resources development and utilization, Hubei government vigorously advocated the development of efficient, safe, and characteristic high-quality agriculture. With the continuous optimization of the economic structure, economic development has maintained good momentum.

3.

Weak Decoupling (WD)

Weak decoupling is an ideal and positive decoupling state. The characteristic is that when both the economic growth rate (∆G) and the total industrial water use growth rate (∆W) are positive, the growth rate of the industrial water use rate is significantly smaller than the economic growth rate. During the periods of 2005–2006, 2009–2012, 2014–2015, and 2016–2018, the decoupling relationship between economic growth and the total amount of water used by three industries in Hubei province maintained a good state of weak decoupling. The elastic coefficients between economic growth and industrial water use were 0.14, 0.17, 0.06, 0.06, 0.56, 0.25, and 0.29, respectively. After 2013, a series of related water resources management systems were put in place. By further advocating water-saving industries and adjusting the industrial structure, the coordinated development of economic growth and industrial water use has achieved certain results.

In addition, strong negative decoupling (SND) means that the growth rate (∆W) of total industrial water use does not fall but rises, and the economic growth rate (∆G) is growing negatively. This state means that the socio-economic level is in a recession stage, which is the most negative state among the eight states. Although the remaining four decoupling states did not appear in this study, we note that they are not conducive to the sustainable development of the economy and society.

3.2.2. Decoupling Relationship between Economic Growth and Water Use of the Three Industries

The supply of industrial water in Hubei Province is a fundamental requirement for economic growth. On the basis of the rate of change in water use in the three industries and GDP, and after data processing calculation and analysis, the decoupling relationship between the economic growth of Hubei Province and the water use of the three industries from 2004 to 2019 were computed, and the results are shown in

Table 3. The decoupling the state of economic growth and water use for the three industries during 2004–2019.

Period	$\mathbf{∆}\mathit{G}$	$\mathbf{∆}\mathit{W}$	$\mathsf{\epsilon }$1	State1	$\mathbf{∆}\mathit{G}$	$\mathbf{∆}\mathit{W}$	$\mathsf{\epsilon }$2	State2	$\mathbf{∆}\mathit{G}$	$\mathbf{∆}\mathit{W}$	$\mathsf{\epsilon }$3	State3
2004–2005	+	+	0.8237	EC	+	+	0.0021	WD	+	+	0.9806	EC
2005–2006	+	+	0.1097	WD	+	+	0.2934	WD	+	+	0.0384	WD
2006–2007	+	−	−0.4358	SD	+	+	0.3789	WD	+	−	−0.0015	SD
2007–2008	+	−	−0.0485	SD	+	−	−0.3526	SD	+	−	−0.2488	SD
2008–2009	+	+	18.6000	END	+	+	0.8385	EC	+	+	0.8508	EC
2009–2010	+	−	−0.3813	SD	+	+	0.7178	WD	+	+	0.2556	WD
2010–2011	+	+	0.1460	WD	+	−	−0.0340	SD	+	+	0.2261	WD
2011–2012	+	−	−0.1118	SD	+	+	0.0730	WD	+	+	0.6787	WD
2012–2013	+	+	1.3883	END	+	−	−4.3544	SD	+	+	1.1643	EC
2013–2014	+	−	−0.2054	SD	+	+	2.1579	END	+	−	−3.1594	SD
2014–2015	+	+	0.0789	WD	+	−	−3.0969	SD	+	+	9.3576	END
2015–2016	+	−	−1.1369	SD	+	−	−0.3027	SD	+	+	0.1179	WD
2016–2017	−	+	−2.1540	SND	+	−	−0.7391	SD	+	+	0.1944	WD
2017–2018	+	+	8.8539	END	+	−	−0.0406	SD	+	+	0.0368	WD
2018–2019	+	+	0.2157	WD	+	+	0.3750	WD	+	+	0.0022	WD

.

The $\mathsf{\epsilon }$1, $\mathsf{\epsilon }$2, and $\mathsf{\epsilon }$3 in Table 3 represent the decoupling elastic coefficients between economic growth and total industrial water use and water use of the industry in Hubei Province, respectively. The “+” and “−” in the table represent greater than zero and less than zero respectively. On the whole, there is no weak negative decoupling, recessionary coupling, or recessionary decoupling in Hubei Province from 2004 to 2019. In terms of distribution, the decoupling relationship between economic growth and water use of the primary industry in Hubei Province experienced five decoupling states from 2004 to 2019: expansion coupling (EC), weak decoupling (WD), strong decoupling (SD), strong negative decoupling (SND), and expansion negative decoupling (END). The decoupling relationship with the water use of the secondary industry experienced four states: weak decoupling (WD), strong decoupling (SD), expansive coupling (EC), and expansion negative decoupling (END); the decoupling relationship with the water use of the tertiary industry experienced four states: expansive coupling (EC), weak decoupling (WD), strong decoupling (SD), and expansion negative decoupling (END). On the whole, the primary industry and the tertiary industry were in a relatively unstable development trend, and the state of decoupling fluctuated, but most of them were in a good state of strong decoupling and weak decoupling. It is worth mentioning that the decoupling relationship between the water use and the secondary industry gradually transitioned from weak decoupling to strong decoupling and gradually stabilized in the ideal state of the strong decoupling.

3.3. Analysis of Driving Factors for Decoupling

Based on the Tapio decoupling model, Kaya extended identity, and LMDI index decomposition method, this study divided the decoupling driving factors of economic growth and industrial consumption into four major factors: water intensity effect (∆W_Ii), industrial structure effect (∆W_Si), economic income effect (∆W_Inc), and population-scale effect (∆W_P). In the process of factor decomposition, the income effect (∆W_Inc) and population size effect (∆W_P) of the three industries remain unchanged. Therefore, this study focuses on the decomposition of the water intensity effects and industrial structure effects of the primary, secondary, and tertiary industries. After data analysis, the assignment of driving factors for the decoupling of economic growth and water use in the three industries in Hubei Province were compiled and are shown in

Table 4. Decoupling driving factors between economic growth and water use in the three industries.

Period	∆WI1	∆WS1	∆WI2	∆WS2	∆WI3	∆WS3	∆WInc	∆WP
2004–2005	0.18	3.05	6.35	−8.42	0.26	2.94	3.26	0.29
2005–2006	−8.27	−11.57	−11.94	4.03	−3.42	−0.57	21.11	−0.43
2006–2007	−43.15	3.00	−10.24	−0.25	−5.61	−0.16	27.02	0.14
2007–2008	−30.75	1.32	−25.08	−1.92	−7.82	0.55	27.31	0.27
2008–2009	11.10	−7.04	−2.40	4.68	0.93	−1.08	17.02	0.22
2009–2010	−27.52	−13.60	−9.60	7.15	−3.77	−1.44	29.88	0.10
2010–2011	−21.22	−4.95	−29.47	2.40	−5.24	−0.46	29.30	0.84
2011–2012	−16.33	−3.37	−14.57	0.44	−1.57	0.12	17.46	0.51
2012–2013	0.12	−2.82	−38.15	−2.04	3.67	1.33	14.63	0.51
2013–2014	−5.34	−11.98	12.84	−5.10	−26.87	3.00	15.35	0.45
2014–2015	−5.72	−5.42	−22.55	−2.68	25.33	1.51	10.72	0.92
2015–2016	−26.18	−6.36	−7.63	−3.48	−6.23	1.36	13.48	0.81
2016–2017	0.30	−5.53	−14.67	1.04	−6.11	1.33	15.09	0.40
2017–2018	15.26	−19.60	−4.74	−2.21	−7.13	3.03	10.67	0.37
2018–2019	−8.40	−12.28	−6.08	−3.64	−11.85	2.94	22.82	0.26
Total	−83.93	−92.95	−137.02	−3.44	−38.84	11.64	196.42	5.39

.

In this paper, the promoting factor refers to the key factor that promotes the decoupling relationship between economic growth and industrial water use. On the contrary, it belongs to an inhibiting factor. It could be found in Figure 2 that the main driving factor in 2004–2019 which promoted the decoupling between Hubei’s economic growth and the water use of the primary industry was ∆W_Si, with the total decoupling factor reaching −92.95. The main factor of restraining decoupling is the economic income effect, with the total decoupling factor of 196.42; the restraining effect is approximately 36 times that of ∆W_P. From a horizontal comparison, compared with the secondary and tertiary industries, the industrial structure effect of the primary industry played a significant role in the decoupling between the economic growth and industrial water use. Among them, the water intensity effect and industrial structure effect of the primary industry were mostly negative.

Furthermore, it can be seen in Figure 3 and Figure 4 that the main driver of decoupling economic growth from water use in the secondary and tertiary industries in Hubei Province was ∆W_Ii, and the total decoupling factor for ∆W_Ii was −137.02 and −38.84, respectively. In terms of the water intensity effect, the decoupling is more significant in the secondary industry than in the primary and tertiary industries.

The economic income effect is the main factor restraining decoupling. Since the 12th five-year plan period, the main keynote of economic development in Hubei Province is to pursue progress while ensuring stability, following the concept of “lucid waters and lush mountains are invaluable assets” to maintain a slow momentum of development. Compared with 2011, the economic income effect has weakened to a certain extent. However, the slowdown in economic growth is not only a manifestation of the increase in the economic base but also an important opportunity to adjust the industrial structure and promote industrial upgrading, which will benefit the overall sustainable development of Hubei Province. On the whole, the value of the decoupling factor of the population size effect was relatively small, and the fluctuation trend was almost horizontal. Among them, the maximum population size effect appeared in 2014–2015, which was 0.92. The minimum value appeared in 2005–2006 and was −0.43. It can be seen that the size of the population had no obvious effect on the decoupling of economic growth and industrial water use in Hubei Province. In general, the decoupling relationship between economic development and industrial water use in Hubei Province in 2019 comprehensively improved compared with 2004.

4. Conclusions and Policy Implications

4.1. Conclusions

On the basis of the panel data of Hubei Province from 2004 to 2019, and with the help of the Tapio decoupling model, Kaya extended identity, and the LMDI, this study explores the decoupling relationship between economic growth in Hubei Province and the water use of the three industries and the driving factors of decoupling throughout 15 years. Through the analysis of this research, the main results are as follows:

Firstly, in terms of decoupling status, the decoupling relationship between economic growth and total industrial water consumption in Hubei Province from 2004 to 2019 has obvious stage characteristics. It can be seen that in the initial stage of social development and transformation, the growth rate of total industrial water consumption is significantly higher than that of economic growth. The weak decoupling state will continue, which belongs to the ideal sustainable development state. By comparison, it is not difficult to see that the decoupling state of the secondary industry is ahead of the primary and tertiary industries, and gradually stabilizes in the ideal state of strong decoupling. This indirectly proves that the innovative water-saving technology of the secondary industry in Hubei province has been improved to a certain extent and the water consumption has been greatly improved.

Second, from the decomposition results of driving factors, the driving factors promoting decoupling are mainly industrial structure effect and water intensity effect, while the main inhibiting factor is economic income effect. Among them, the effect of population size and economic income is always positive, while the effect of industrial structure and water intensity of primary industry is negative. It can be seen that the decline of water intensity and the optimization and upgrading of industrial structure have a particularly obvious decoupling effect on economic growth and water intensity of primary industry in Hubei Province. Furthermore, the industrial structure effect of secondary industry and tertiary industry is also negative. It shows that the effective control of water intensity in Hubei province during this period strengthened the relationship between economic growth and water decoupling of secondary and tertiary industries. In addition, in the primary industry, the contribution of industrial structure effect is the largest, far ahead of the second and third industries. The result of industrial structure effect is closely related to the speed of industrial structure adjustment and the development of efficient agriculture in Hubei Province. In the secondary and tertiary industries, the contribution degree of water intensity effect is the largest. It can be seen that water intensity effect and industrial structure effect are the key factors driving economic growth and industrial water decoupling in Hubei Province.

Third, in terms of the development trend, Hubei province still needs to make efforts in the management and control of industrial water consumption. In 2004–2019, there was a certain increase in water efficiency in the three industries in Hubei Province. Among them, the relationship between water use in the primary industry and economic growth is still in a relatively unstable state. The secondary sector has gradually maintained a strong decoupling. In addition, the tertiary sector has transitioned to weak decoupling and remains stable. It is worth noting that economic growth in the secondary sector has been effectively decoupled from water use. As an old industrial province, Hubei province needs to fully consider the utilization efficiency and endurance of water resources to promote social and economic development. Therefore, taking full account of resource conditions and improving water efficiency of the primary industry are necessary measures to promote the early decoupling of the primary industry and economic growth in Hubei Province.

4.2. Policy Implications

In order to speed up the process of economic growth and industrial water decoupling in Hubei Province and achieve the ideal state of decoupling as soon as possible, on the basis of the above conclusions, this study has the following main policy implications.

First, the Hubei Provincial Government Department can continually accelerate the development of water-saving technology transformation through industry technology innovation; actively promote the research, development, and promotion of water-circulating utilization technology; actively explore the industrial water circulation mode; strictly control the water and high water consumption industry; and accelerate the development of low-cost water and water-saving industries.

Second, Hubei Province should fully consider the quality of regional green development and promote water-saving measures at the local level. In the process of development, we should pay attention to the development of the green industry but not ignore the rise in the economy. We should consider water and the quantity of water, while adhering to the transformation of economic development and reasonably decompose the dual-control target in order to strengthen the total amount of water consumption and intensity of industrial.

Third, Hubei Province must actively promote the process of industrial structure adjustment, accelerating the elimination of backward production capacity and low-end industry. At the same time, it must focus on environmental protection and improve the green utilization efficiency of industrial water. Through the optimization and upgrading of industrial structure, the first industrial water saving, the second industrial water-saving emission reduction, and the development of water-saving technological innovation are introduced. In this way, green development is promoted to lay a solid foundation for the sustainable development of the economic and social development in Hubei Province.

This paper discusses the decoupling relationship between water use and economic growth of three industries in Hubei Province, and the research results can provide reference for the high-quality economic development of Hubei Province. At the same time, it also provides reference experience for the coordinated development of water resource utilization and economy in other areas of China.

However, there are still some shortcomings in this paper. For example, a set of existing index systems is used to measure the relationship between water use in the three industries and economic development, and the results will have some deviations. In particular, whether the consumption of water in the domestic sector is included in the consumption of water in the tertiary industry will have a certain influence on the calculation results. Therefore, in order to ensure the accuracy of research results, the method of index selection needs to be further optimized. Future studies could bring into play other economic and political sustainability factors such as urbanization, technology innovation capacity, and environmental regulation, to make the research more exciting. Moreover, in the process of revealing the relationship between water use and economic development of the three industries in Hubei Province, there is no horizontal comparison with other provinces, which is not conducive to finding the similarities and differences of the characteristics of inter-provincial decoupling. The decoupling of the 13 cities in Hubei province has not been discussed in detail from the micro level. Subsequent studies can be analyzed from the perspectives of time and space to make the results more accurate. Apart from this, more advanced econometric methodologies, such as panel co-integration analysis to indicate long-run relationships of the variables for empirical analysis, could be adapted to make the result more accurate. In summary, the problem of spatial evolution of water in industrial water has not been discussed in this paper. It will be the subject of future research.