China’s Inequality in Urban and Rural Residential Water Consumption—A New Multi-Analysis System

Abstract

This paper presents a multivariate analysis of urban and rural residential water consumption from 2010 to 2020 using an input–output model considering consumption and income. We employed structural decomposition analysis (SDA) and structural path analysis (SPA) to identify the main drivers and pathways. The Water-Gini (W-Gini) coefficient was used to quantify inequalities in water consumption. The results showed that rural water consumption exceeded urban consumption starting in 2012, reaching 1.8 times the urban level by 2020, with Agriculture (S1) being the largest contributor. SDA indicated that the decrease in urban consumption was primarily due to the intensity effect. In SPA, the first-order path accounted for over 70% of total consumption, with urban contributions linked to “residential income → S2-Health care and medical services (M7)”. For rural areas, “residential income → F1-Food (M1)” contributed to 40% of water consumption in the first-order path, reflecting increased consumption in the middle sector. The W-Gini coefficient rose to 0.4 in 2020, driven by the income side, particularly in Agriculture (S1), which had a W-Gini of 0.61. These variations in water consumption highlight the need for policy considerations, especially regarding rural income.

1. Introduction

Water is an indispensable resource for human life and productivity. China’s accessible water resources per person are barely 25% of what they are worldwide [1]. As socioeconomic development, population increases, and urbanization accelerate in China, the consumption of water resources is also increasing [2]. One of the goals of the SDGs is to ensure that everyone has access to water and that it is sustainably managed [3]. ESG is in a period of rapid development [4]. Water must therefore be sustainably usable. Therefore, rational water resource usage across various sectors and the development of sustainable consumption methods are crucial to alleviate the current water stress situation in China. The social water cycle is significantly impacted by residential water consumption [5]. Because residents consume water as a commodity in their daily activities [6], residents’ income can serve as an initial input and consumption as the final output, affecting the water supply chain from two perspectives. One way to look at it is that rising consumption leads to an increase in the need for products and services [7], which either directly or indirectly increases water consumption. On the other hand, the boost in local people’s income encourages the development of downstream businesses [8]. As people’s living standards improve, the linkages between sectors become more diverse [9], and thus the water consumed by residents in the supply chain will also become more complex. Moreover, water consumption is closely tied to the industrial sectors in the supply chain. Climate change [10], population density [11], changes in consumption and income [12] and even the COVID-19 pandemic [13] impact water consumption in terms of both direction and intensity over time. These factors will also affect the distribution of water resources across sectors through the supply chain. If every sector adopts a uniform standard to reduce water consumption, ignoring the linkages between the industries and the most important drivers could lead to inefficient efforts [14]. Therefore, in the context of increasingly complex sectoral relationships, it is imperative to implement a new accounting system that focuses on water consumption under the interactions of various sectors. This mechanism should analyze the key factors influencing water consumption and the pathways of shutdowns and reductions, which are of great significance.

While water is essential to every resident, access to water resources and their benefits are not equally distributed [15]. There are differences between urban and rural households in terms of population, eating habits [16], production activities [17], and consumption preferences [18]. Residential water consumption inequality should thus also be explored to understand the differences in water resource use between urban and rural areas. A major contributing factor to water inequality is the disparities in water consumption behaviors between urban and rural communities. For example, agriculture is the largest water-consuming sector [19] and provides most of the income for rural residents. This implies that a higher environmental cost could arise from an increase in the income of rural populations [20]. Therefore, water consumption must be broken down at the sectoral level, and the reasons behind the observed factors should be investigated. It is important to consider that rising incomes will change consumption patterns and tendencies [21], leading to an increase in inequality, thus widening the differences in residential consumption. This increase in inequality in residential water consumption may suggest variations in the availability and accessibility of water resources, potentially leading to inequalities in economic status, affluence, and overall welfare [22]. It is certain that cutting back on water consumption at the expense of equity will have a negative effect on how water resources are used in society. Therefore, the inequities in urban and rural water consumption need to be analyzed, and understanding urban–rural differences from a sectoral perspective is crucial for water resource management.

In this paper, Section 3 constructs a new accounting method to estimate the residential water consumption from 2010 to 2020. The method combines SDA and SPA, where influencing factors are longitudinally identified through SDA, and SPA is used to analyze the residential water consumption paths horizontally. Finally, a quantitative analysis is conducted on the disparity in water consumption between urban and rural inhabitants. Section 4 introduces the results regarding the residential water consumption from 2010 to 2020, as well as the decomposition analysis and an explanation of the observed inequity. Section 5 presents a summary of the findings, as well as policy recommendations.

2. Literature Review

Residential activities are a primary driver of global energy consumption [23]. These daily activities not only directly use energy but also consume water resources indirectly. Experts from developed countries have observed that rising economic levels lead to higher energy use and, in turn, higher water resource consumption [24]. The input–output method of environmental expansion is one of the approaches used to account for water consumption from top to bottom between sectoral transactions [25]. Current studies have focused on accounting for water consumption from the consumption side to emphasize consumption guidance and management [26,27,28]. Few researchers have considered residential water consumption from multiple perspectives to make relevant recommendations, even though residential income is a crucial source of livelihood security. In addition, input–output tables with a release cycle of three to five years must be used in accordance with China’s input–output model-based accounting system for residential water consumption. However, socioeconomic developments such as technological advances [29], population size increases [30], and individual consumption and income on a per capita basis [31] are all driving changes in the volume and the composition of water usage [32]. Few articles have examined the time series of domestic water consumption, making it challenging to understand the law of continuous changes.

The socioeconomic aspects of water resources have been effectively analyzed through the application of structural decomposition analysis [33]. Some articles have utilized SDA to investigate the factors influencing water consumption. One paper divided water consumption into five drivers in the energy sector: water quantity, economic production framework, consumption structure, per capita consumption, and population [34]. Other papers have utilized SDA to evaluate the drivers behind trade-induced changes in the blue and gray water prints, presenting technological innovations that reduce water consumption intensity and alleviate water scarcity [35]. Structural path analysis is often used to describe the upstream and downstream industrial relationships of products. By identifying different production chains, SPA provides a deeper understanding of the interactions between economic systems. When analyzing water consumption, SPA can help unravel the propagation paths of final consumption and the exogenous influences on various water consumption pathways [36]. One paper tries to use SPA to investigate the critical paths of the Autonomous Water Orbit [37]. Research is being conducted to look at the patterns brought about by trade flows between provinces and regions [38]. These ongoing studies examine the trajectory of consumption patterns and income allocation. Most existing literature uses either SDA or SPA alone as the decomposition method, with a single dimension. This approach may not fully capture the current complexities of residential water consumption in China.

Economic development leads to changes in income levels and consumption patterns, which in turn affects resource utilization. Duarte et al. [39] analyzed the relationship between consumption patterns across various countries and income inequality. One result indicates that the depletion of natural resources in urban areas disproportionately contributes to indirect growth, exacerbating inequities within the ecosystem of services [40]. Correspondingly, water, as a natural resource, is also affected by income increases, and its consumption varies among different groups. The urban–rural gap further creates new inequities stemming from natural resource use. In China, the process of economic development and urbanization has widened the gap between urban and rural populations. This divide shows up in variations in consumption and income distribution, in addition to disparities in the earning capacities of urban and rural populations. This will affect the disparities in water consumption among urban and rural populations. In 2017, the average urban water consumption in China was 2.1 times that of rural water use. Existing studies have shown that income differences can exacerbate water resource challenges, hindering equitable distribution among various income groups [41]. Some studies suggest that an increase in income level can reduce such inequalities [42]. However, most studies focus on analyzing the inequity or the components of inequity brought about by the gap between income levels, or focus on the differences between urban and rural communities. Existing studies have demonstrated how socioeconomic variables contribute to variations in water consumption [43], and others have decomposed the drivers of inequity into structural effects, the effects of water footprint changes, and interaction effects [44]. Some articles have even calculated the inequalities in urban and rural dietary water footprints separately [45]. Few articles directly examine the unequal distribution of water resources in China’s urban–rural dual economy or employ a quantitative representation of the differences within urban and rural regions.

Therefore, it is necessary to explore the historical water consumption pattern by considering a time series in urban and rural regions, focusing on consumption and income. At the same time, the trend is decomposed and analyzed to find out the primary elements influencing water consumption. This enables an examination of the disparities between urban and rural communities, along with an exploration of the factors contributing to these shifts through a pathway framework.

3. Methodology

3.1. Framework

As shown in Figure 1, we constructed a new system—the multi-dimensional analysis water accounting method for residential consumption—to compute the water resource use from the consumption and income sides between 2010 and 2020. SDA is used to examine the factors affecting water consumption. SPA tracks the variations, on the consumption and income sides, between urban and rural communities in the supply chain. To assess the variation in individual water consumption, we consider the W-Gini coefficients and identify the sectors in which discrepancies between urban and rural water consumption are most pronounced.

3.2. Account for Residential Water Consumption

In an input–output model, the ultimate output from the production sector is what drives residents’ water consumption, whereas the initial input determines residents’ water consumption depending on income [46]. Thus, the basic economic relationships reflecting the final output ${\rm Y}$, total output ${{\rm T}}_j$, initial inputs $\Psi$ and total output ${{\rm T}}_k$ can be obtained using Equations (1) and (2), respectively:

$${{\rm T}}_j={\left({\rm I}-\alpha \right)}^{-1}{\rm Y}$$

(1)

$${{\rm T}}_k=\Psi {\left({\rm I}-\beta \right)}^{-1}$$

(2)

where ${\rm I}$ is the identity matrix and $\alpha$ denotes the input coefficient matrix, which means the amount of input money required to produce one unit of output in one sector and one unit of output in another sector. The output coefficient matrix $\beta$ is expressed in the opposite sense of $\alpha$, ${\rm Y}$ and $\Psi$ are the final output and intermediate input, respectively.

A sector’s energy consumption is typically thought to be linearly proportional to both its total inputs and outputs. Therefore, the consumption-side residential water consumption $Q_{{\rm M}}$ and the income-side residential water consumption $Q_{{\rm N}}$ can be extended by the underlying input–output model and are expressed by Equations (3) and (4), respectively:

$$Q_{{\rm M}}=W·{\left(I-\alpha \right)}^{-1}·{\rm Y}=W·\rho ·{\rm Y}$$

(3)

$$Q_{{\rm N}}=\Psi ·{\left(I-\beta \right)}^{-1}·W=\Psi ·\sigma ·W$$

(4)

where ${\rho =\left(I-\alpha \right)}^{-1}$ is the Leontief inverse matrix. The Ghosh inverse matrix is represented by ${\sigma =\left(I-\beta \right)}^{-1}$. The water intensity is denoted by $W$. In this paper, the final output ${\rm Y}$ and the intermediate input $\Psi$ are the consumption and income of residents.

The consumption-based residential water consumption, $Q_M$, is the upstream use brought by final demand. The income-based residential water consumption $Q_N$ is the downstream use generated by primary inputs. To differently express consumption-based and income-based residential water consumption, they must be taken into consideration separately, as shown in Equation (5):

$$\begin{array}{cc}Q\hfill & =Q_{{\rm M}}+Q_{{\rm N}}=(W·\rho ·{\rm Y})+(\Psi ·\sigma ·W)\hfill \\ \hfill & =\left(Q_{uM}+Q_{rM}\right)+\left(Q_{rN}+Q_{rN}\right)\hfill \\ \hfill & =\left[W\rho \left({\epsilon }_{u}P{p}_u\right)+\left({ϵ}_{u}P{p}_u\right)\sigma W\right]+[W\rho \left({\epsilon }_{r}P{p}_r\right)+\left({ϵ}_{r}P{p}_r\right)\sigma W]\hfill \end{array}$$

(5)

where $\epsilon$ and $ϵ$ represent the per capita consumption and income. For urban residents, these can be indicated as urban consumption ${\epsilon }_u$ and urban income ${ϵ}_u$; for rural residents, consumption ${\epsilon }_r$ and income ${ϵ}_r$ are used. $P$ is the total population, where $p_u$ and $p_r$ denote the proportion of urban and rural populations, respectively.

3.3. Identify the Key Drivers

According to the input–output model, SDA breaks down the influencing drivers that drive changes in water consumption and measures their contributions individually [47]. Here, we set a basic SDA model to help better understand the rationale of SDA, as shown in Equations (6) and (7) below. The two factors are represented by $A$ and ${\rm B}$. The comparative quantity between year $0$ to next year $t$ is denoted by $∆$.

$$x=A\ast {\rm B}$$

(6)

$$\begin{array}{cc}∆x\hfill & =x_t-x_0\hfill \\ \hfill & =\left(A_t\ast {{\rm B}}_t\right)-\left(A_0\ast {{\rm B}}_0\right)\hfill \\ \hfill & ={\displaystyle \frac{\left[\left(∆A\ast {{\rm B}}_t\right)+\left(∆A\ast {{\rm B}}_0\right)\right]}{2}}+{\displaystyle \frac{\left[\left(A_t\ast ∆{\rm B}\right)+\left(A_0\ast ∆{\rm B}\right)\right]}{2}}\hfill \end{array}$$

(7)

The influence of $x$ can be no greater than two. Therefore, we applied SDA to decompose the influence of industry characteristics and social development factors, both economic indicators of water consumption. The consumption-side change in residential water consumption in volume in two adjacent years can be represented by Equations (8) and (9):

$$∆Q_M={∆Q}_{uM}+{∆Q}_{rM}$$

(8)

where

$$\begin{array}{cc}∆Q_{uM}\hfill & =Q_{uM\left(t\right)}-Q_{uM\left(0\right)}\hfill \\ \hfill & =W_t{\rho }_t\left({\epsilon }_{ut}{P}_t{\eta }_{ut}\right)-W_0{\rho }_0\left({\epsilon }_{u0}{P}_0{\eta }_{u0}\right)\hfill \\ \hfill & =\left(∆W\right)\rho {\epsilon }_{u}P{\eta }_u+W\left(∆\rho \right){\epsilon }_{u}P{\eta }_u+W\rho \left(∆{\epsilon }_u\right)P{\eta }_u+W\rho {\epsilon }_u\left(∆P\right){\eta }_u+W\rho {\epsilon }_{u}P\left(∆{\eta }_u\right)\hfill \\ \hfill & =[\left(∆W\right){\rho }_t{\epsilon }_{ut}{P}_t{\eta }_{ut}+\left(∆W\right){\rho }_0{\epsilon }_{u0}{P}_0{\eta }_{u0}+W_t\left(∆\rho \right){\epsilon }_{ut}{P}_t{\eta }_{ut}+W_0\left(∆\rho \right){\epsilon }_{u0}{P}_0{\eta }_{u0}\hfill \\ \hfill & +W_t{\rho }_t\left(∆{\epsilon }_u\right)P_t{\eta }_{ut}+W_0{\rho }_0\left(∆{\epsilon }_u\right)P_0{\eta }_{u0}+W_t{\rho }_t{\epsilon }_{ut}\left(∆P\right){\eta }_{ut}+W_0{\rho }_0{\epsilon }_{u0}\left(∆P\right){\eta }_{u0}\hfill \\ \hfill & +W_t{\rho }_t{\epsilon }_{ut}{P}_t\left(∆{\eta }_u\right)+W_0{\rho }_0{\epsilon }_{u0}{P}_0(∆{\eta }_u)]/2\hfill \\ \hfill & ={\Gamma }_W+{\Gamma }_{\rho }+{\Gamma }_{u\epsilon }+{\Gamma }_P+{\Gamma }_{u\eta }\hfill \end{array}$$

(9)

Consequently, urban water consumption on the consumption side is decomposed into the water consumption intensity effect ${\Gamma }_W$, intermediate input effect ${\Gamma }_{\rho }$, per capita consumption effect ${\Gamma }_{u\epsilon }$, total population effect ${\Gamma }_P$, and urbanization effect ${\Gamma }_{u\eta }$ (expressed as a proportion of the urban population ${\eta }_u$, and the rural proportion is expressed by ${\eta }_r$).

The corresponding water consumption of residents on the income side is decomposed as follows in Equations (10) and (11), being decomposed into the water consumption intensity effect ${\Gamma }_W$, intermediate output effect ${\Gamma }_{ϵ}$, per capita income effect ${\Gamma }_{ϵ}$, total population effect ${\Gamma }_P$, and urbanization effect ${\Gamma }_{\eta }$, respectively:

$$∆Q_N={∆Q}_{uN}+{∆Q}_{rN}$$

(10)

where

$$\begin{array}{cc}{∆Q}_{uN}\hfill & ={∆Q}_{uN\left(t\right)}-{∆Q}_{uN\left(0\right)}\hfill \\ \hfill & =\left({ϵ}_{ut}{P}_t{\eta }_{ut}\right){\sigma }_t{W}_t-\left({ϵ}_{u0}{P}_0{\eta }_{u0}\right){\sigma }_0{W}_0\hfill \\ & =\left({∆ϵ}_u\right)P{\eta }_u\sigma W+{ϵ}_u(∆P){\eta }_u\sigma W+{ϵ}_{u}P\left({∆\eta }_u\right)\sigma W+{ϵ}_{u}P{\eta }_u(∆\sigma )W+{ϵ}_{u}P{\eta }_u\sigma (∆W)\hfill \\ \hfill & =\left[{(∆ϵ}_{ut}\right)P_t{\eta }_{ut}{\sigma }_t{W}_t+∆{ϵ}_{u0}{P}_0{\eta }_{u0}{\sigma }_0{W}_0+{ϵ}_{ut}\left(∆P\right){\eta }_{ut}{\sigma }_t{W}_t+{ϵ}_{u0}\left(∆P\right){\eta }_{u0}{\sigma }_0{W}_0\hfill \\ \hfill & +{ϵ}_{ut}{P}_t\left({∆\eta }_u\right){\sigma }_t{W}_t+{ϵ}_{u0}{P}_0\left({∆\eta }_u\right){\sigma }_0{W}_0+{ϵ}_{ut}{P}_t{\eta }_{ut}\left(∆\sigma \right)W_t+{ϵ}_{u0}{P}_0{\eta }_{u0}\left(∆\sigma \right)W_0\hfill \\ \hfill & +{ϵ}_{ut}{P}_t{\eta }_{ut}{\sigma }_t\left(∆W\right)+{ϵ}_{u0}{P}_0{\eta }_{u0}{\sigma }_0\left(∆W\right)]/2\hfill \\ \hfill & ={\Gamma }_{uϵ}+{\Gamma }_P+{\Gamma }_{u\eta }+{\Gamma }_{\sigma }+{\Gamma }_W\hfill \end{array}$$

(11)

3.4. Identify the Critical Paths

Structural path analysis (SPA) allows for an exhaustive description of the resource consumption flows in the supply chain. Thus, SPA is a tool able to identify the prominent paths and sectors within them in the network of products. Based on SPA, the sectoral water consumption is decomposed: the Leontief inverse matrix links the demand relations between sectors, and the aggregate supply is linked by the Ghosh inverse matrix [48]. These are expanded in Equations (12) and (13) below:

$$\rho ={\left(I-\alpha \right)}^{-1}=I+\alpha +{\alpha }^2+{\alpha }^3+\cdots +{\alpha }^{\mathcal{c}}(\mathcal{C}\to \mathcal{\infty })$$

(12)

$$\sigma ={\left(I-\beta \right)}^{-1}=I+\beta +{\beta }^2+{\beta }^3+\cdots +{\beta }^{\mathcal{c}}(\mathcal{c}\to \mathcal{\infty })$$

(13)

Each item on the right side of Equations (12) and (13) expresses a different production layer. Thus, the two inverse matrices are decomposed into direct and indirect links, so the sectoral relationships upstream and downstream of residential water consumption can be further analyzed. Therefore, Equation (3) can be expanded to Equation (14) to calculate the consumption-side water consumption:

$$\begin{array}{cc}{Q}_M\hfill & =W·{\left(I-\alpha \right)}^{-1}·\gamma \hfill \\ \hfill & =W·I·\gamma +W·\alpha ·\gamma +W·{\alpha }^2·\gamma +W·{\alpha }^3·\gamma +\cdots \hfill \end{array}$$

(14)

The first item of Equation (12) is the first-order path of water consumption, which represents water consumption directly. The second-order path, which does not pass through an intermediate sector directly related to the final output and initial input, is shown in the second item; the third item of the equation is the third-order path, which can be interpreted as the induced demand and supply that passes through an intermediate sector. The above items require more intermediate sectors [49]. As the number of supply path sectors increases, the amount of water flowing through each sector decreases, weakening the impact of each path [50,51]. There is no need to pay attention to paths that are too long. Instead, we pay greater attention to paths that actually have an impact.

Similarly, the income-side water consumption can be expressed as Equation (15):

$$\begin{array}{cc}{Q}_N\hfill & =\Psi ·{\left(I-\beta \right)}^{-1}·W\hfill \\ \hfill & =\Psi ·I·W+\Psi ·\beta ·W+\Psi ·{\beta }^2·W+\Psi ·{\beta }^3·W+\cdots \hfill \end{array}$$

(15)

The SDA and SPA models decompose the drivers and pathways of water consumption to show urban–rural differences. However, they cannot intuitively quantify the imbalance and inequity present in the urban–rural water consumption structure. Therefore, we introduce the W-Gini coefficient to calculate the urban–rural differences.

3.5. Quantifying Inequities in Water Consumption

The indices that can be used to evaluate water inequity are the Gini index, Theil index [52], and Atkinson index [53]. As stated in the Introduction, there is a gap in income and consumption between urban and rural residents. The Gini coefficient is an indicator of the income gap [54]. It can be used as a useful regional indicator to evaluate differences in annual water consumption based on consumption and income, thus balancing ecological and economic concerns. The calculated Gini coefficient is a ratio, and the absolute value of the result is in the interval [0, 1], where a value closer to 1 means that the inequality is also greater. The formula for the Gini coefficient $G$ is shown in Equation (16):

$$G=\sum _{k=1}^n{D}_{\tau }{V}_{\tau }+2\sum _{k=1}^n{D}_{\tau }\left(1-T_{\tau }\right)-1$$

(16)

where $\tau$ is the different income groups, $D_{\tau }$ is the proportion of residents in each group, $V_{\tau }$ is the proportion of income in each group, and $T_{\tau }$ is the cumulative split of each group’s income.

Therefore, the Gini coefficient for per capita residential water consumption ($G_W)$ is expanded from the base Equation (16) to Equation (17):

$$G_W=\sum _{k=1}^n{\eta }_{\tau }{q}_{\tau }+2\sum _{k=1}^n{d}_{\tau }\left(1-{\phi }_{\tau }\right)-1$$

(17)

Equations (18) and (19) account for urban and rural annual water consumption:

$$q_{\tau u}=W·\rho ·u\epsilon +uϵ·\sigma ·W$$

(18)

$$q_{\tau r}=W·\rho ·r\epsilon +rϵ·\sigma ·W$$

(19)

where $\tau$ denotes urban and rural groups, ${\eta }_{\tau }$ is the population share of urban and rural residents, $q_{\tau }$ is the water consumption for each person, and ${\phi }_{\tau }$ is the cumulative ratio of average water consumption.

3.6. Data

In this work, we concentrate on water consumption in China by sector. The data on water consumption in different industries were collected from different sources. An important source of data on the agricultural and industrial sectors’ water consumption is the annual water resources briefing [55,56,57,58,59,60,61,62,63,64,65]. The secondary sector-by-sector data are determined based on the proportion of additional value in each sector [66]. Since there are no direct figures available for the service sectors, the difference between domestic and residential water consumption is used to calculate the tertiary sector’s overall water consumption [67]. Finally, each sector’s part is determined using the annual intermediate inputs used in the production and supply of water [68], and the water consumption in the service sector is obtained.

Due to the lag and discontinuity, the National Bureau of Statistics has only issued input–output tables for 2010, 2012, 2015, 2017, 2018, and 2020; therefore, we updated the input–output table for 2011, 2013, 2016, 2017, and 2019 based on the RAS method [69] to achieve continuity in the data over time. The price differences in each year for reasons such as inflation [70] are also taken into account to determine the price index for each year. We use 2010 as the base year, and the nominal prices of the remaining years are modified. The table of sectors obtained from the National Bureau of Statistics is different every year. For the purposes of this article, the merging and splitting of sectors from year to year is considered, and the impact of imports on domestic goods is excluded by considering China as a closed country [71]. Finally, we compile the 42 noncompetitive sectors into input–output tables for 2010–2020, encompassing data on urban and rural residential consumption and income. The Statistical Yearbook is used to retrieve data on population counts, consumption, and income [72,73,74,75,76,77,78,79,80,81,82].

In the SPA analysis, we further compare the 42 sectors’ input–output with categories published by the NBS, including Food (M1), Clothing (M2), Housing (M3), Household equipment and services (M4), Transportation and communications (M5), Education, culture and recreation (M6), Health care and medical services (M7), and Other (M8) [70,83]. This provides a more straightforward depiction of the disparities in consumption and income distribution between urban and rural communities. An examination shows the outcomes, revealing that 42 sectors and 8 residential consumption categories align with the patterns in

Table 1. Input–output sectors’ correspondence with consumption categories.

Number	Sector	Consumption Categories
S1	Agriculture	M1
S2	Mining and washing of coal	M4
S3	Extraction of petroleum and natural gas	M4
S4	Mining and processing of metal ores	M4
S5	Mining and processing of nonmetal and other ores	M4
S6	Food and tobacco processing	M1
S7	Textile industry	M2
S8	Manufacture of leather, fur, feather and related products	M2
S9	Processing of timber and furniture	M4
S10	Manufacture of paper, printing and articles for culture, education and sport activity	M6
S11	Petroleum processing, coking and nuclear fuel processing	M5
S12	Manufacture of chemical products	M4, M6
S13	Manufacture of non-metallic mineral products	M3, M4
S14	Smelting and processing of metals	M4
S15	Manufacture of metal products	M3, M4
S16	Manufacture of general-purpose machinery	M1, M2, M7
S17	Manufacture of special-purpose machinery	M1, M2, M7
S18	Manufacture of transport equipment	M5
S19	Manufacture of electrical machinery and equipment	M4, M5, M6
S20	Manufacture of communication equipment, computers and other electronic equipment	M5
S21	Manufacture of measuring instruments	M4, M6
S22	Other manufacturing and waste resources	M3, M4, M6, M7, M8
S23	Waste products and materials	\
S24	Repair of metal products, machinery and equipment	M3, M4
S25	Production and distribution of electric power and heat power	M3
S26	Production and distribution of gas	M3
S27	Production and distribution of tap water	M3
S28	Construction	M3
S29	Wholesale and retail trades	M1, M2, M3, M4, M5, M6, M7, M8
S30	Transport, storage and postal services	M5
S31	Accommodation and catering	M1, M6
S32	Information transfer, software and information technology services	M5, M6
S33	Finance	M7
S34	Real estate	M3
S35	Leasing and commercial services	M5, M6
S36	Scientific research and polytechnic services	M6, M8
S37	Administration of water, environment and public facilities	M3, M6
S38	Resident, repair and other services	M4, M8
S39	Education	M6
S40	Health care and social work	M7
S41	Culture, sports and entertainment	M6, M7
S42	Public administration, social insurance and social organizations	M7

. It is worth noting that Waste products and materials (S23) differs from the consumption categories, and therefore no sectoral matching has been carried out.

In this article, to separate the sectors along distinct pathways, we provide the following explanations: sectors marked with “F” that are directly tied to initial supply and final demand are known as first-order sectors; “S” stands for a first-order sector, which is closely related to a second-order sector. Similarly, “T” indicates the third-order sector, e.g., “S3-M1” means that M1 is the second-order sector in the third-order path. We use “${\mathrm{M}}_{\mathrm{I}}$ → M_II → residential consumption” to denote the water consumption on the consumption side. As indicated by the first arrow symbol, category $\mathrm{I}$ provides items for category II, and category II; creates goods for consumption, as indicated by the last arrow. Similarly, the path at the income side can be expressed as “residential income → ${\mathrm{M}}_{\mathrm{I}}$ → M_II”.

4. Result and Discussion

4.1. Temporal Trends in Residential Water Consumption and Driving Factors

Figure 2 illustrates the long-term trends in water consumption on the consumption and income sides for urban and rural residents and their share in general water consumption. Although the peak during these 11 years is reached in 2016 and drops off in 2017 and 2018, residential water consumption rises again in 2019 and 2020. The figure shows that the income-side residential water consumption of rural areas plays a major role in total water consumption. Simultaneously, water consumption first decreases and then increases. Notably, water consumption for rural residents is 1.8 times higher than that for urban residents in 2020. At first, more water was consumed by urban residents. However, since 2012, rural residential water consumption has surpassed urban water consumption, even though the percentage of water consumed by rural residents declined in 2017. It is nevertheless greater than urban residential water consumption. This finding suggests that the change in rural consumption is largely to blame for the fluctuations in overall water consumption. The residential water consumption on the income side has consistently been greater than that on the consumption side, and its share has maintained an upward trend, reaching 77% in 2020. However, consumption-side water consumption has not solely risen. It has followed a pattern of rising, declining, and rising again. The water consumption in 2010 and 2019 exceeds that in 2016. Specifically, during the period under study, it is the consumption side that plays a major role in urban residential water consumption, whereas in rural areas, it is the income side. On the other hand, there is a stark contrast between the trends on the urban consumption side and the rural income side. Urban water consumption on the consumption side maintains a downward trend, even though there is a brief rebound in 2015 and 2016. Despite this, it did not change its downward trend, and the rate of decline reached 13% in 2020. In contrast, the rural residential water consumption on the income side is the largest, and it has been maintaining an upward trend. Specifically, the growth rate reached 25% in 2018–2019. Correspondingly, the water consumption of urban residents on the income side has not changed significantly every year, and its proportion is around 20%. Water consumption and the proportion of rural residents on the consumption side are decreasing year by year. Thus, from these analyses, it can be determined that the water consumption of the rural income side contributes more significantly to the overall water consumption. And there is a discernible contrast between the amount of water consumption. During the research period, China experienced rapid urbanization. Like urban areas, those living in rural areas experienced a rapid rise in their incomes. Urban and rural residents have different consumption patterns and standards of living. Therefore, to comprehend the contrasts in water distribution throughout urban and rural areas, the major sectors of both types of water consumption from the perspective of consumption and income must be defined.

In Figure 3, urban residential water consumption is decomposed into a water consumption intensity effect, an intermediate input (output) effect, a per capita consumption (income) effect, a population effect, and an urbanization effect. This decomposition is performed separately for (a) the consumption side and (b) the income side. The most significant factor contributing to the negative impact on the consumption side is the water consumption intensity effect. In 2010–2011, it has the strongest negative effect. The starkly negative impact is vividly represented by the decline in 2011 compared to 2010. The positive effect on the consumption side is different before and after 2016. Prior to 2016, the per capita consumption effect exhibited the most pronounced positive impact, while after this year, the urbanization effect emerged as the factor with the greatest influence. The per capita consumption effect does not have a large impact on the consumption side, but it was extremely strong in 2015–2016. This caused water consumption on the consumption side for urban residents to peak in 2016. Although the urbanization effect after this period is the most significant among the factors that have a positive impact, it has a small overall impact. This means that the total effect shown by these influences after 2016 is negative. The negative effect could be attributed to a drop in the growth rate of urban residents’ per capita consumption, as the per capita consumption effect became negative after 2016. On the income side, the per capita income effect is the same as the per capita consumption effect. It is positive in some years and negative in other years. Nevertheless, there is no association between per capita consumption and the per capita income effects. It may be true that the influence of an increasing per capita income on changing water consumption patterns is directly tied to the speed of urban per capita wealth growth. This is because in the years when the per capita income effect is negative, the growth pace decreases. The water consumption intensity effect on the income side is as negative as that on the consumption side. They both have a significant negative effect. In other words, urban water consumption does not increase with economic development but rather decreases, owing to technological advances, changes in lifestyles, and so on. The urbanization effect still has a large beneficial impact on water consumption, even on the income side. Expanding urban populations are correlated with rising water consumption. However, the water consumption of urban residents has not been dramatic because of the strong water consumption intensity effect. A key reason for this lies in the transfer of rural residents to urban areas as a result of urbanization.

Correspondingly, rural residential water consumption is decomposed, as shown in Figure 4. Also, this is carried out separately for both (a) the consumption side and (b) the income side. For rural residents, the most influential factor is the water consumption intensity effect. The adverse impact is comparable to that for urban residents, suggesting that inhabitants exhibit greater efficiency in water consumption. However, the adverse effect of water intensity is not as strong as it is in urban areas. This could be because rural areas rely more on rainwater, groundwater, and other renewable water resources and do not rely on the same water supply systems as urban areas. The per capita consumption effect on the rural consumption side is a positive factor in all years except 2016–2017. This differs from the performance of urban water consumption. It indicates that the consumption patterns of residents in urban and rural areas may yield different outcomes in terms of consumption. It is a matter that requires additional analysis. In contrast, in rural areas, the per capita consumption effect in 2016–2017 shows a significant negative effect, leading to a significant decline in water consumption on the consumption side in 2017. However, the rural residential per capita consumption did not decline. A change in the composition of individual consumption behaviors may have had a major impact on water consumption. We hypothesize that this negative effect is caused by the change in their water consumption structure. Therefore, in the subsequent path analysis, we decompose the consumption categories. On the income side, the per capita income effect has always shown a positive effect. This is different from urban residents. The decrease in water consumption is somewhat impacted by an urbanization effect as well. The population shift in rural areas does have a greater influence on water consumption when combined with the urbanization effect among urban residents. At the same time, the intermediate input and output effects are almost similar between urban and rural areas. These effects show how residential income from downstream businesses and their consumption of upstream enterprises affect their water consumption. This suggests that urban and rural residential consumption and income structure have a similar impact on the supply chain. Though intermediate input and output effects have less of an impact, there are still stark gaps between urban and rural residents, making the analysis of these distinctions crucial.

4.2. Sectoral Water Consumption Change Caused by Driving Factors

When breaking down the factors influencing changes, the water consumption intensity effect plays an essential role. Therefore, a sectoral decomposition of water consumption intensity effects is necessary when decomposing sectors further in order to analyze the key sectors that exhibit it. Figure 5 illustrates the sectoral decomposition of water consumption intensity on the (a) consumption side and (b) income side for urban residents. Almost all sectors on the urban consumption and income sides show negative growth in water consumption intensity in all years. This makes the water consumption intensity effect a noteworthy factor in the decline in overall water consumption. Agriculture (S1) is a dominant factor and always has a negative impact. This implies that this is the key sector to address when aiming to minimize water consumption with regard to the water consumption intensity effect. In other words, the water consumption of Agriculture (S1) does not increase along with the increase in GDP. In contrast, the water efficiency is improving. This may be related to the restructuring of the sector. The sector restructures toward crops that consume less water and occupy less arable land, or water-efficient livestock farming. Thus, it reduces the demand for water in urban areas. Moreover, the decline in water consumption on the consumption side is much larger than on the income side. This suggests that the decline in urban water consumption on the consumption side is closely linked to this sector. In addition to this, Food and tobacco processing (S6) has a major effect on the decline on the consumption and income sides. However, Other manufacturing and waste resources (S22) changes positively throughout the study period. The water consumption intensity effect in this sector is declining, but it has no meaningful influence on the overall picture. Meanwhile, the services industry is of concern. The total reduction in Finance (S33) is more pronounced in the services sector on both the consumption and income sides, especially in 2011–2012. In the above analysis, it is found that the service sector has a high rate of change despite its small water consumption and shows a negative increase in the water consumption intensity effect. However, this does not mean that the service sectors’ water consumption intensity effect is not improving. It is just not as significant as that of the agricultural and secondary industries.

According to the sectoral decomposition of the water consumption intensity effect of rural residents on the (a) consumption side and (b) income side, as shown in Figure 6, it is clear that the water consumption intensity effect in rural areas is indeed significantly different from the that in urban areas. Agriculture (S1) is still the sector with the largest reduction. However, on the income side, the effect is larger than that on the consumption side, which is different from urban areas. This shows that the dependence of rural areas on Agriculture (S1) has resulted in a notable decline in water consumption efficiency due to technological progress. The following are several examples illustrating advancements in water consumption efficiency: China has achieved a coverage of 38.2% for high-efficiency drip irrigation, which can enhance the water use efficiency (WUE) on sloping cultivated land by 49.8% [84]. Additionally, a model that integrates photovoltaic panels with rainwater harvesting has demonstrated potential water savings of 57.74% [85]. Consequently, these technological advancements have led to a reduction in the water intensity in Agricultural (S1) practices among rural residents, resulting in improved water consumption efficiency. The performance of other sectors is also different from that of urban areas. The decline in water consumption from Agriculture (S1) is particularly significant on the consumption side of urban areas, while the decline in other sectors is not so obvious. However, in rural areas, there is no “single outstanding” situation. Manufacture of metal products (S15) and Manufacture of special-purpose machinery (S17) have both declined more significantly. Even in the case of a more even decline in all sectors, the water consumption intensity effect in rural areas is smaller, showing that Agriculture (S1) makes an important contribution to the decline in water consumption in urban areas. When comparing the rural consumption side with the income side, although the consumption side demonstrates an overall negative effect, some industrial sectors offset the increase in certain years. For instance, Processing of petroleum, coking and processing of nuclear fuel (S11) in 2014–2015 and Manufacture of measuring instruments (S21) in 2011–2012 both show significant increases. The other sectors on the income side are similar to the urban case, with no significant declines in other sectors. It is additionally crucial to note that the water consumption intensity effect of Other manufacturing and waste resources (S22) is favorable for both consumption and income sides. Construction (S28) on the consumption side also decreases significantly. This is because rural residents are more sensitive to economic issues, especially in household buildings, and with advances in technology, such as energy-efficient water heaters, and increased educational attainment, the water consumption intensity has reduced in rural households. Compared with urban areas, the amount of change in the service sector in rural areas is not significant in terms of the water consumption intensity effect. Finance (S33) still shows a downward trend but has only declined by 7.3 × 10⁹ m³, decreasing by 1.7 × 10¹⁰ m³ from that in urban areas. At the same time, it is difficult to achieve regularity in the yearly patterns of other sectors.

In the decomposition of water consumption for urban residents, the per capita consumption effect and income effect are different, as is the performance in rural regions. Therefore, the effect needed to be decomposed, with the results shown in Figure 7. The per capita consumption effect is shown in Figure 7a, and Figure 7b represents the per capita income effect. Between 2010 and 2016, the most vital factor having a positive impact was the per capita consumption effect. Almost all sectors during this period show an increase in per capita consumption, except for Agriculture (S1), Manufacture of paper, printing and articles for culture, education and sport activity (S10), and Manufacture of transport equipment (S18). These sectors make a significant contribution to the increase in per capita consumption. However, the per capita consumption effect does not show a positive effect after 2016, mainly because water consumption declines in all sectors during that year. Even though water consumption in Agriculture (S1) rises in 2018–2019, it rises much less than in previous years. In contrast, the corresponding per capita income has a left- and right-swinging role, and does not significantly influence variations on the income side. Through sectoral decomposition, it is possible to determine which secondary industry sector’s overall impact is most responsible for the per capita income effect. The key sectors engaged include Processing of petroleum, coking and processing of nuclear fuel (S11), Manufacture of metal products (S15), and Smelting and processing of metals (S14). The per capita consumption effect and per capita income effect on the overall water consumption side are not visually apparent. But from the above analyses, we conclude that changes in consumption and the income structure have a greater impact on urban water consumption than changes in value.

Figure 8 illustrates the sectoral decomposition of the impact of per capita (a) consumption and (b) income effects in rural areas. Both the per capita consumption and income effects significantly influence the increase. In rural areas, the sectors with greater impacts include Agriculture (S1), Food and tobacco processing (S6), Textile industry (S8) and Manufacture of paper, printing, and articles for culture, education and sport activity (S10). The difference also shows that rural residential consumption and income are more inclined to basic industries. The largest changes in water consumption due to per capita rural consumption and income are all in Agriculture (S1). There is a decline in 2016–2017, but it is unable to neutralize the impact of the rise in subsequent years. The per capita income effect and the noteworthy growth in income for rural residents are closely related. The increase in water consumption caused by all years and sectors has a large positive effect. Among the decreasing sectors in 2016–2017, the main contributors are Textile industry (S7) and Manufacture of leather, fur, feather and related products (S8). This shows that the rural areas are still dependent on traditional handicrafts. However, water consumption efficiency is rising again, marking the technological advancement in this industry. Moreover, the per capita consumption effect in rural areas shows a rising trend related to water consumption in all sectors except Food and tobacco processing (S6) in 2017–2018, which shows a significant decline. In 2019–2020, Agriculture (S1) and Food and tobacco processing (S6) declined to a lesser extent than urban residents. Nonetheless, the rural residential per capita consumption effect is still less than that of the urban effect, and it is strongly correlated with the degree of economic growth.

4.3. Path Analysis of Residential Water Consumption Structures

In order to compare and contrast the differences in urban and rural water consumption, SPA is performed to obtain their paths. To more clearly illustrate the differences between the main sectors in the water consumption footprint, we reallocate all the sectors again to create a Sankey diagram, as shown in Figure 9. This figure shows the top 20 key category paths with the largest implied water consumption. These top 20 supply paths account for more than 80% of all water consumption. For urban residents, both on the consumption side and on the income side, the first-order paths have the largest contribution to water consumption. The contribution of the first-order paths on the consumption side is more than 70%, and in 2010 and 2012, their share was 78%. The same is true on the income side. Thus, the first-order path’s water consumption is also relatively high even among the top 20 paths with the largest contribution, which highlights the significance of the first-order path. However, it is worth noting that in our above analysis, urban residents’ water consumption on the consumption side mainly comes from Agriculture (S1). Meanwhile, on the income side, it comes from several sectors and use types. The first-order path that contributes the most to the amount of water consumed is the “F1-Food (M1) → residential consumption”, which is always the largest. In the first-order path, the proportion is more than 30%. The annual trend in its proportion is closely related to the trend on the urban consumption side. This not only shows that this path is the main contributing path on the urban consumption side, but also illustrates the important role of Food (M1) in overall water consumption. For the income side, the paths of “residential income → Household equipment and services (M4)”, “residential income → Education, culture and recreation (M6)”, and “residential income → Health care and medical services (M7)” have the largest contributions, and most of them flow to Food (M1). The income of urban residents is more diversified. It covers a variety of fields such as Household equipment and services (M4), Education, culture and recreation (M6), Health care and medical services (M7), and so on. Therefore, the sources on the income side will also be more varied. In the second-order path, the consumption side and the income side differ from one another as well. On the consumption side, the supply sector that contributes the most to the initial consumption of residents is S2-Household equipment and services (M4). And the path of its larger water consumption is “S2-Household equipment and services (M4) → F2-Housing (M3) → residential consumption”. However, over time, the role of S2-Household equipment and services (M4) has gradually decreased. This is mainly because the water consumption of the “S2-Household equipment and services (M4) → F2-Clothing (M2) → residential consumption” pathway has been transferred to other paths. It does not mean, however, that the water consumption of this path has declined, but rather that the proportion of its water consumption is far lower than before. On the income side, S2-Health care and medical services (M7) plays a prominent role. Among the 12 major paths other than the first-order path, there are four paths in which the demand-implied water consumption of S2-Health care and medical services (M7) brought about by residential income is stable from 2010 to 2020. This is “residential income → F2-Food (M1) → S2-Health care and medical services (M7)”, “residential income → F2-Clothing (M2) → S2-Health care and medical services (M7)”, “residential income → F2-Household equipment and services (M4) → S2-Health care and medical services (M7)”, and “residential income → F2-Health care and medical services (M7) → S2-Health care and medical services (M7)”. The share of water consumption among these four paths has also increased over time. This shows that S2-Health care and medical services (M7) has significantly contributes to the path on the urban income side. Urban residential consumption and income are analyzed as the initial input and final output of the supply path. From the figure, we can see that in the top 20 paths with the largest water consumption at both ends, the consumption of supply water caused by income is larger than the demand caused by consumption. But this does not mean that the consumption side is smaller than the income side. Therefore, the path decomposition of urban residential water consumption indicates that the path playing a major role is the first-order path. Food (M1) is the key category on the consumption side. On the income side, there is no exact category. But “residential income → S2-Health care and medical services (M7)” has a stable contribution.

Figure 10 shows the top 20 paths that contribute the most to the water consumption of rural residents. The main contributor to the water consumption paths is the first-order path. Though it is above 45% on the income side, the first-order path on the consumption side makes up over 60% of the overall amount of water consumed and even reaches 76% in 2019. This pattern mirrors that of urban areas, indicating that a significant portion of the water consumed by residents comes from direct water consumption [86]. The contribution of the first-order path on the rural income side is lower than that on the consumption side. The path of “residential income → F1-Food (M1)” plays the most important role in the first-order path, contributing more than 40%. The first-order path’s lower contribution to rural income can be attributed to the rise in water consumption predicted by the intermediate sector in the path that leads to Food (M1). For example, the second-order path of “residential income → S1-Food (M1) → F1-Food (M1)” grows by 105% from 2010 to 2020. In 2020, among the top 20 paths contributing the most to the rural income side, the third-order path “residential income → F3-Food (M1) → S3-Food (M1) → T3-Food (M1)” is added, with its water consumption reaching 4.09 × 10⁸ m³. Thus, we can determine that Food (M1) is the main flow direction of rural residential income. No matter how many sectors pass through, the demand for Food (M1) will eventually increase. Therefore, it is worth paying attention to the Food (M1) sector on the rural income side, as it creates a difference in water consumption on the urban income side. Urban areas show multiple flows, while the rural areas focus on Food (M1), leading to an inequitable difference between urban and rural water consumption on the income side. Regarding the first-order paths on the consumption side, the implicit water consumption of “F1-Food (M1) → residential consumption”, “F1-Household equipment and services (M4) → residential consumption” and “F1-Eduction, culture and recreation (M6) → residential consumption” is more prominent. It is not surprising that rural residents consume food. It is worth noting that rural residents consume household equipment services and education and entertainment. More rural populations are becoming migrants, most likely as a result of economic developments and advancements in transportation technology [87,88]. This shows a certain difference in urban residential water consumption. Even if urban residents can earn more money than those in rural regions, this is not a sign that urban residential consumption categories are more diverse. Nor does it mean that their income has reached a certain level and makes their consumption more even in each category. In addition, in the second-order path on the consumption side, S2-Household equipment and services (M4) has a significant role, the same as in urban water consumption. The contribution value of “F2-Clothing (M2) → S2-Household equipment and services (M4) → residential consumption” is also transferred to other paths. This demonstrates the increasing similarity between the consumption patterns of residents in different regions, indicating a convergence in their consumption habits. Therefore, the variation between the consumption and income paths points out that the income side differs more than the consumption side. The main difference between the income sides of rural and urban water consumption is as follows: the water consumption path of the income side of the rural residents focuses on Food (M1). It does not matter how many intermediate sectors it passes through. The path of the income side of urban residents flows in multiple directions.

4.4. Inequity in Water Consumption Between Urban and Rural Populations

In Figure 11, the W-Gini coefficients in the sectors on the consumption side are shown. The W-Gini coefficients for the sectors in each year are concentrated below 0.35. This falls within a reasonable range. Moreover, the W-Gini coefficients for nearly all sectors decrease over time. In this case, similar to the above, Agriculture (S1) is the key sector for urban–rural reductions. The urban–rural differences in this sector are not large, and the distribution of water consumption is mostly even. This also suggests the need to focus not only on the magnitude of water consumption but also on urban–rural differences. However, the urban–rural differences in Production and distribution of tap water (S27) and Construction (S28) are very prominent in 2010 and 2011 and contribute significantly to the overall variability. In particular, the urban–rural difference in Production and distribution of tap water (S27) has consistently decreased, but it did not decrease below 0.1 in 2020 like in the other sectors. This shows that the urban and rural distribution of this sector is improving, but not by much. It indicates the persistence of urban–rural distribution on the consumption side of the sector, which is a cause for concern. In addition, the service sector does not have significantly larger water use or changes in the above statistics. However, through the W-Gini coefficient, it is found that the differences in the service sector have a large impact on the overall water consumption. Most of the service sector W-Gini coefficients and the overall inequality in the changes are consistent. However, some sectors in the figure show very prominent performances. In particular, Health and social work (S40)’s W-Gini coefficients were 0.41 and 0.39 in 2010 and 2011. The Administration of water, environment and public facilities (S37) is also consistently high. This has to do with the widening gap in per capita water consumption. Regarding Accommodation and catering (S31), it is one of the service sectors that contributes to more water consumption both overall and per person. And the inequality related to it has remained steady at 0.25. This means that the inequality is not too big and has decreased, but that it has maintained a certain urban–rural difference. This is mostly the case when the urban per capita water consumption is higher, and the reason for this is easy to understand. The differences in dietary structure [89] and accommodation habits [90] between urban and rural residents result in notable differences in their water consumption patterns. It is therefore necessary to begin to focus on the performance of the tertiary sector in the analysis of inequality.

The inequality is more complicated on the income side than on the consumption side, as Figure 12 illustrates. Most sectors show a rise in the allocation. The W-Gini coefficient for Agriculture (S1) in particular peaks at 0.61 in 2020. The substantial imbalance in income in this sector is the primary cause of the already noticeable divide between urban and rural water consumption. Urban residents are no longer dependent on agriculture for their source of income. However, rural residents cannot afford to lose their income from family agricultural businesses. Thus, the income-side gap is becoming increasingly wider. In other sectors, although the distribution of water consumption between urban and rural residents is more reasonable, industrial sectors such as the Processing of petroleum, coking and processing of nuclear fuel (S11) show a downward and then upward trend. And the W-Gini coefficient reaches its highest point in 2020. On the income side, the decline in the W-Gini coefficient not only represents a reduction in inequality, it is also due to the fact that rural per capita water consumption does not increase at the same rate as the rural population. Before the decline in inequality, the rural per capita water consumption was less than the urban per capita water consumption, and so there was an urban–rural difference that led to greater inequality. Compared to the consumption side, the inequality on the income side gradually deviates from the population distribution. Inequality diminishes as the discrepancy in per capita water consumption approaches the population distribution ratio. Afterward, due to increases in rural water consumption, there is again a significant difference between rural and urban water consumption, leading to an increase in the W-Gini coefficient. In addition, the W-Gini coefficients for the Textile industry (S7, S8), Food and tobacco processing (S6), and Processing of timber and furniture (S9) are gradually increasing. This signals that inequality is becoming more pronounced and that urban and rural differences are increasing. This is probably because these traditional industries mainly provide income for rural residents, while urban residents receive less and less income from them. As for the service industry, although the overall inequality impact is smaller than that of the primary and secondary industries, there are still a few sectors whose performances are worth discussing. Accommodation and catering (S31) continues to see a rise in W-Gini. On average, urban residents consume more water than rural residents in this sector. Although rural residents are increasingly participating in the services of this sector, they do not obtain more income as a result. Moreover, the inequity in Education (S39) is declining. This implies that while rural residents have been earning more in this sector, the urban per capita water consumption has increased almost at the same time as the population has grown. In contrast, the consumption side of Administration of water, environment and public facilities (S37) still exhibits significant urban–rural differences, with the W-Gini coefficient being 0.4 in 2020.

5. Conclusions and Policy Implications

5.1. Conclusions

Through multivariate analysis, our research examines the water consumption of residents in China between 2010 and 2020, broken down into urban and rural and considering both the consumption and income sides. We decompose the influencing factors, including the water consumption intensity effect, per capita consumption (income) effect, intermediate input (output) effect, total population effect, and urbanization effect, using SDA. This allows us to identify the key driving factors and sectors of residential water consumption. For a more comprehensive analysis, SPA was used to decompose the composition of water consumption and identify the key pathways and consumption categories. We also quantify the inequality in average water consumption within urban and rural communities. Commonalities and distinctions in the main sectors and drivers of the consumption and income sides are found when analyzing the water consumption in urban and rural communities. The outcome demonstrates that rural water consumption is gradually becoming higher than urban water consumption. The main contributor to this trend is rural income-side water consumption. The water consumption intensity effect is a major inhibitor. Water consumption in urban and rural areas is influenced differently by the per capita consumption and income effects. Growth in rural residential water consumption is primarily driven by the rural residential per capita income, with urban residents having little effect. Therefore, measures that reduce water consumption on the income side should be supplemented, and measures to optimize water consumption on the urban consumption side should be maintained. In the SPA, it was discovered that the first-order paths are significant. It was also found that the suggested water consumption paths between the consumption side and the income side in urban and rural areas are different yet comparable. In particular, the urban–rural differences on the income side are greater than those on the consumption side. The path pattern for urban residents is difficult to summarize, but the patterns for rural residents were always dominated by Food (M1), especially on the income side. In addition, urban and rural water consumption needs to be differentiated in order to reduce it, and determining the W-Gini coefficients reveals that differences on the income side are the main source, with Agriculture (S1) being the key sector.

This article focuses on the disparities in residential water consumption between urban and rural areas in China. However, China is characterized by vast regional disparities in economic development and water resource availability. This study, therefore, may have overlooked regional gaps in consumption practices driven by external economic factors. Thus, it could be further improved by delving into the significant variations that exist not only between urban and rural settings, but also among different provinces.

5.2. Policy Recommendations

China’s residential water consumption will keep rising as the country becomes more urbanized and the financial divide between its urban and rural populations grows. The analysis indicates that, beyond aligning the rate of consumption growth with economic expansion to promote sustainable social development, the government must take appropriate economic steps to guarantee that China’s economy keeps growing healthily. Thus, in light of the aforementioned conclusions, the following policy recommendations are made:

(1)

Adopt differentiated water management policies

To tackle the differences in water consumption across China, it is important to create specific water management policies that reflect the needs of urban and rural areas, as well as the unique situations in each province. Policymakers should assess local water resources, usage habits, and economic activities to ensure that strategies fit each region’s reality. By paying attention to the gaps between urban and rural water needs and involving local communities in decision-making, authorities can design solutions that ensure fair resource use. Additionally, policies should be flexible and adapt to changes like population growth and climate effects, promoting effective water management that supports economic growth and addresses local challenges.

(2)

Accelerate technological progress

The most significant factor is that the water consumption intensity effect does not work as well as it should. As a result, it is imperative to accelerate technological advancement, support the building of water conservation systems, improve water consumption efficiency, and minimize the depletion of water reserves from their natural settings.

(3)

Incentives for sustainable practices

The government should consider giving financial support to help urban and rural residents adopt water-saving technologies and practices. This could include subsidies for water-efficient appliances, rainwater collection systems, and farming methods that resist drought. However, there are some challenges associated with the implementation of these strategies, such as high initial costs and a lack of awareness. To address these issues, it is important to implement education and training programs that show communities the benefits of saving water and ways to do so.

(4)

Bridging the water divide

To address water consumption inequalities, policies should focus on improving rural income and consumption patterns. Higher incomes enable better investments in water systems and technologies, enhancing public health and agriculture. Strengthening water resource allocation between urban and rural areas is key. This includes prioritizing basic water needs and integrated water supply projects. Monitoring the equality of water consumption could help adjust strategies, reducing social and environmental impacts. This approach supports public health, food security, and rural livelihoods, forming a solid foundation on which to address water consumption inequalities.