Provincial water footprint in China and its critical path

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Original Articles

Provincial water footprint in China and its critical path ⁎

Guomei Zhaoa, Chao Gaoa, Rui Xiea, , Mingyong Laia, Ligao Yangb a b

School of Economics and Trade, Hunan University, No. 109 Shi Jia Chong Road, Changsha 410079, China Changsha University of Science & Technology, No. 45 Chi Ling Road, Changsha 410076, China

A R T I C LE I N FO

A B S T R A C T

JEL Codes: Q25 R58 L95 Q53

The key path analysis of water footprint (WF) is essential to formulate water-saving plans, and improve the related allocation of resources. Based on the inter-provincial input-output table in 2012, this study constructs a WF estimation model and a structural path analysis model. Specifically, we estimate the WF of each Chinese province, and identify the complete industry path of this WF. The results show that coastal provinces, such as Guangdong, Jiangsu, and Shandong, exhibit a higher number of WFs, while economically less-developed provinces, such as Tibet and Qinghai, have lower WFs. The WFs of provinces characterized by water scarcity, such as Beijing and Tianjin, are mainly composed of external water footprints (EWFs), primarily from Hebei, Xinjiang, and Heilongjiang, while the WFs of water-rich provinces, such as Guangxi, Jiangxi, and Sichuan, mainly consist of internal water footprints (IWFs). Except for the provinces of Tibet and Qinghai, the amount of water consumed per household is the most important part of the WF. The inner-province industrial path of Agriculture → Household is the most crucial path of all provinces’ WFs, except for Shanghai and Chongqing. This path contributes more than 20% to the WF of most provinces with larger IWFs, but has less impact on Beijing and Tianjin, with larger EWFs. The inner-province industrial paths of Agriculture → Food and beverage sector → Household, Agriculture → Investment, and Steam and hot water supply sector → Household are the main paths of most provinces’ WFs. A similar result is obtained for the inter-provincial industrial path of some provinces’ WFs. For provinces with larger IWFs, such as Xinjiang and Heilongjiang, there is no inter-provincial industrial path that contributes more than 0.5% to the WF.

Keywords: Provincial analysis Water footprint (WF) Structural path analysis (SPA) Critical path

1. Introduction Water is crucial for the sustainable development of a country, as its shortage might severely constrain economic growth. China's per capita water resources are less than one-third of the world average1, and water resources are more abundant in South China. With the separation of production and consumption, inter-province virtual water trade has the potential to optimize water resources’ allocation, and solve the spatial distribution imbalance (Chen et al., 2017). Moreover, the adjustment of the import–export product structure can ease the pressure in provinces with water resource shortage (Allan, 1994; Hoekstra and Chapagain, 2007). In this situation, the water footprint (WF) of a single province measured from the consumer side can more accurately reflect the actual national water resources than the direct production water in the province (Hoekstra and Hung, 2002; Okadera et al., 2015; Hoekstra, 2016). In this context, the accurate calculation of the actual water

consumption in each province and the identification of its complete industrial path become urgent issues to be solved for the effective implementation of a national water-saving plan2 under the precondition of optimal allocation of water resources. In extant literatures, the study of the impact of human activities on water resources’ allocation mainly involves two basic concepts: virtual water (VW) and WF. Allan (1994) first defined VW as the water used in the production process of goods and services, that is, the water embodied in products, rather than the real water (Hoekstra and Chapagain, 2007). Based on VW, Hoekstra and Hung (2002) proposed the concept of provincial WF as the sum of inner-province water production and net virtual water imports3. By definition, VW is water consumption from the production perspective, while WF is water consumption from the consumer perspective. Thus, WF can be considered the evolution and extension of VW, which is why most follow-up studies on it involve the study of VW. In actual calculations, VW is mainly used to measure the

⁎

Corresponding author. E-mail address: [email protected] (R. Xie). 1 Source: World Water Development Report. 2 The report of the 19th Chinese Communist Party National Congress clearly states the importance of promoting comprehensive resource conservation and recycling, implementing national water conservation initiatives, reducing energy and material consumption, and achieving a circular link between production and living systems. 3 This definition is similar to the one of “ecological footprint.” https://doi.org/10.1016/j.ecolind.2018.06.058 Received 4 July 2017; Received in revised form 23 June 2018; Accepted 27 June 2018 1470-160X/ © 2018 Published by Elsevier Ltd.

Please cite this article as: Zhao, G., Ecological Indicators (2018), https://doi.org/10.1016/j.ecolind.2018.06.058

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an SPA model to quantify the environmental impact of the entire supply chain based on a multi-region IO table. Using input–output analysis and the SPA method, Zhang et al. (2017) linked the entire supply chain of China’s economy from energy extraction to final consumption. Thus, this study aims to construct an SPA model from the inter-provincial IO tables to examine the complete industrial path of China’s provincial WFs. The results will provide important insights to implement national water-saving actions, and improve water resources allocation in Chinese provinces. The main contributions of this study are two-fold. First, starting from China’s inter-provincial IO table in 2012 and the direct water consumption data on each province and sector, we build the interprovincial water resources–economic IO table. Then, we use the IO analysis to separately quantify the composition of the Chinese provinces’ WF in 2012. Second, we use the SPA method to analyze the industrial path of the provincial WF from upstream to downstream, and find key linkages among provincial departments. This method enables the development of a water-saving plan or the optimal allocation of water resources with respect to a particular provincial sector or to a particular type of demand. The rest of the paper is organized as follows. Section 2 outlines the theoretical derivation of the WF indicator and the WF SPA model; it also briefly describes the data. Section 3 reports and discusses the results of the WF of each province in China, while section 4 reports and discusses the results of the SPA. The last section presents the conclusions and policy indications of the study.

water consumed to produce a good, which is equal to the direct and indirect water intensity multiplied by the value of the product. For example, the water consumption for export and import products in a province is called VW export and VW import, respectively (Guan and Hubacek, 2007; Mubako et al., 2013; Zhao et al., 2015; Zhang et al., 2016; Chen et al., 2018; Huang et al., 2017). WF measures the actual water consumption in a province, and is equal to the inner-province internal water footprint (IWF is equal to the amount of water consumption to produce goods and services within the province minus the amount of water consumption to meet exports, that is, the water consumed by production in the province minus VW exports) plus the province’s external water footprint (EWF is equal to the water consumption to produce imported intermediate and final products, that is, VW imports) (Zhao et al., 2009). The input-output (IO) analysis method makes it possible to quantify both the direct and indirect economic and technological linkages among inter-provincial sectors. Constructing a WF estimation model based on the IO table is the main method used in recent years to study the WF from a quantitative perspective. For example, when the research area is a single country, Zhao et al. (2009) measured the Chinese WF in 2002 and its intensity in 23 sectors based on the 2002 IO table. Then, they used it to measure China’s VW trade, and defined the WF as the sum of the IWF and EWF. When the research area comprises different regions of a country, Feng et al. (2012) assessed the flow of VW between four regions by constructing an IO model (MRIO), and distinguishing rural and urban households’ WFs. Feng and Chen (2016) analyzed the WF and the VW flows in China’s regional energy production and trade using a hybrid multi-region IO model. The results showed that the electricity sector had the largest water consumption per unit of energy produced, suggesting that non-electricity production was concentrated in water-scarce regions. When the research area comprises different provinces of a country, Dong et al. (2014) focused on China’s WF and the inter-provincial VW trade employing an interregional IO (IRIO) methodology for 2007. Using China’s 2007 IO tables for 13 sectors in 30 provinces, Chen et al. (2017) measured the WF in various provinces and the related inter-provinces’ VW trade. When the research area is a city of a country, Zhang et al. (2011) used the 2002 IO table for China that comprised 30 sectors and 30 provinces in order to estimate the WF of Beijing in 2002 at the sectoral and inter-provincial levels. The results showed that Beijing’s WF was 4498.4 million tons, with 51% of EWFs obtained through VW imports. Wang et al. (2013) measured the direct, indirect, and overall WF intensity of different sectors, as well as the total WF in Beijing in 2002 and 2007, based on an IO model and inter-sector water flows. The results showed that Beijing was a net VW importer, and the WF of the agricultural and industrial sectors had reduced. They claimed that both industrial restructuring and VW imports should be prioritized as water-saving strategies in Beijing. In summary, extant research mainly focuses on measuring the WF of each Chinese province. In addition, some studies have also investigated the dominant sectors and pathways for water, embodied water or energy circulation, and the mutual relationships between pairwise sectors with the use of network analysis (Fang et al., 2014; Fang and Chen, 2015, 2017). Furthermore, Llop and Ponce-Alifonso (2015) adopted structural path analysis (SPA) to analyze the dissemination of exogenous impacts into various channels of water consumption, while the number of studies on its specific industrial path is limited. Given the imbalance in water savings and the existence of VW trade among Chinese provinces, it would be important to identify the specific industrial path of the provincial WF in order to provide a sound basis to implement water-saving plans, and then, allocate water resources. The SPA method proposed by Defourny and Thorbecke (1984) considers final demand to analyze the complete industrial path of an economic variable from upstream to downstream WF. Lenzen (2007) described the application of SPA to IO technologies to measure the flows in ecosystem and eco-economic networks. Hong et al. (2016) constructed

2. Data and methods 2.1. Data sources This study employs Chinese data from the inter-provincial IO table (2012) and on water consumption in various provinces by sector. This inter-provincial IO table (2012) is compiled by the Institute of Policy and Management of the Chinese Academy of Sciences; it includes 31 major provinces (excluding Hong Kong, Macau, and Taiwan) subdivided into 42 sectors. In order to match the water consumption of the 31 provinces by sector, 42 sectors of the inter-provincial IO table are merged into 30 (see Table 1). Water consumption of the first industry in each province is derived from information on water consumption in agriculture published by the 2013 China Statistical Yearbook (National Bureau of Statistics of China, 2013a), which corresponds to the first sector of China’s inter-provincial IO table (2012) and includes agriculture, forestry, animal husbandry, and fishery. To obtain the industrial water consumption by province and sector, several estimation steps are followed. First, the total amounts of industrial water consumption for each province are retrieved from the 2013 China Statistical Yearbook (National Bureau of Statistics of China, 2013a). Second, the water use by industry according to each sector in six provinces (Jiangxi, Anhui, Henan, Xinjiang, Hebei, and Chongqing) for 2012 are estimated based on the data from the Statistics Yearbooks in 2013 or Economic Census Yearbook in 2012 of these provinces. Third, the amounts of water use and output value in industry for the provinces of Tianjin, Shanxi, Guangxi, Guangdong, Gansu, and Shaanxi for 2013 and Hunan for 2010 are respectively obtained from the Economic Census Yearbook of these provinces in 2013 and the Hunan Energy Statistical Yearbook in 2010. And then, adjust in accordance with the total amounts of industrial water use in the seven provinces indicated in the 2013 China Statistical Yearbook. Thus, the adjusted coefficients of water use are obtained, together with the industrial water use by sector in 2012. Finally, owing to the similarity in water use coefficients of provinces geographically adjacent or located in the same water resources basin, the coefficients of 13 provinces by sector and the output value of the other 18 provinces in 2012 (obtained from the China’s inter-provincial IO table) are considered in order to measure water consumption by sector in the latter 18 provinces. Then, water 2

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Table 1 The form of Inter-provincial Water Resource-Economy Input-Output Table in China. Intermediate Demands P1

…

S1 Intermediate inputs

P1

⋮ Pn

S1 ⋮ Sm ⋮ S1 ⋮ Sm

Imports Value-added Total inputs Water consumption

Sm

Final Demands

…

Pn

…

S1

P1

…

Sm

…

Pn

H

G

I

…

H

G

I

Exports

Total Outputs

Z11

…

Z1n

H11

G11

I11

…

H1n

G1n

I1n

E1

X1

⋮ Zn1

⋱ …

⋮ Znm

Hn1

⋮ Gn1

In1

⋱ …

Hnm

⋮ Gnm

Inm

⋮ En

⋮ Xn

M1 V1 X1' W1

… … … …

Mn Vn Xn' Wn

Note: P represents Province, S represents Sector, H is household consumption, G is government consumption, and I is investment. n is the number of provinces, m is the number of sectors, the superscript (’) indicates that the vector or matrix is transposed.

the consumption demands from households, government, and investn ment for all provinces in each sector in province i, with H i = ∑ j H ij ,

consumption is adjusted in line with the total amounts of water use by sector (China Statistical Yearbook, 2013)). For the water use in construction and tertiary industries in each province, this study proceeds as follows. First, in order to obtain the total amount of water use in these sectors for 2012, we assume that the proportion of households, tertiary industry, and construction in water consumption related to households and services in 2012 is the same as that in 2011, obtained from Bulletin of First National Census for Water (National Bureau of Statistics of China, 2013b). Second, according to the proportions of the output value in the construction sector for each province to the national output value in 2012, we allocate the total amount of water use in the sector. Third, in order to obtain the water use in the tertiary industry by sector, we assume that its proportion in 2012, obtained from the IO Analysis of Water Resources Consumption and Water Input Coefficient in National Economic Sectors (Researching Group of Chinese Input-Output Association, 2007), is the same as that in 2002. Finally, according to the output proportion in each province for each sector in the tertiary industry corresponding to the national output, we allocate water use in the tertiary industry by sector, and obtain the amount for each province. The matrices in Table 1 are defined as follows: Zij is the intermediate demand matrix for each sector in province i for the production in each sector in province j, and its dimension is m × m; Hij, Gij, and Iijrepresent, respectively, the vector of consumption demands from households, government, and investment of province j for each sector in province i, with dimension m × 1. Xi and Ei are the total output and the export vectors of province i, respectively, and their dimension is m × 1. Mi, Vi, and Wi are imports, value added, and water consumption vectors in province i, respectively, with dimension 1 × m.

expressed as Aij = Z ij /(uX j ′) , represents the direct input from each sector in province i for the production of one unit of product in each sector in province j. Then, Eq. (1) can be rewritten as: n

Xi =

n j

+

∑ j

Ii

+

(2)

X = AX + H + G + I + E

n

H ij +

∑ j

n

Gij +

1 1 1 1 ⎡G ⎤ ⎡I ⎤ ⎡X ⎤ ⎡H ⎤ , H=⎢ ⋮ ⎥ , G=⎢⋮⎥ , I = ⎢⋮⎥ , E X=⎢ ⋮ ⎥ n n n n ⎢ ⎢ ⎢ ⎢ ⎣I ⎥ ⎦mn × 1 ⎣X ⎥ ⎦mn × 1 ⎣H ⎥ ⎦mn × 1 ⎦mn × 1 ⎣G ⎥ 1

⎡E ⎤ =⎢⋮⎥ n ⎢ ⎦mn × 1 ⎣E ⎥ 11 1n ⎛A … A ⎞ A=⎜ ⋮ ⋱ ⋮ ⎟ n1 nn ⎝ A ⋯ A ⎠mn × mn

T ij + E i =

j

∑

11 1n 11 1n ⎛ I −A … − A ⎞ ⎛ B … B ⎞ B=⎜ ⋮ ⋱ ⋮ ⎟=⎜ ⋮ ⋱ ⋮ ⎟ n1 nn n1 nn ⎝ − A ⋯ I −A ⎠ ⎝ B ⋯ B ⎠mn × mn

i

(6)

(7)

Moreover, water consumption can be expressed as:

W = wX = wB (H + G + I + E ) [wki ]1 × mn

(8)

where w = is the water consumption coefficients’ vector for each sector in all provinces, with i = 1, 2, ⋯, n and k = 1, 2, ⋯, m . Based on Eq. (8), we can measure the water consumption of a single province at the horizontal level, that is, the water consumption for production in province r (excluding those that meet the needs of foreign countries) 4can be expressed as:

Z iju + H i + Gi (1)

i

(5)

In Eq. (6), B is the Leontief inverse matrix, that is, the complete demand coefficient matrix:

j

Ei

(4)

Eq. (3) can be further rewritten as:

n

∑

(3)

Here, X, H, G, I, and E are the total output, household consumption, government consumption, investment, and export matrix in each sector in all provinces, respectively, while A is the direct consumption coefficient matrix for each sector in all provinces. They can be written as follows:

X = (I −A)−1 (H + G + I + E ) = B (H + G + I + E )

n

Z iju +

Aij X j + H i + Gi + I i + E i

Eq. (2) can be written in matrix form:

2.2.1. The basic input–output model The models built in this study are based on the inter-provincial water resource–economy IO table in China presented in Table 1. According to the equilibrium relationship between the row direction in the first quadrant and the second quadrant in Table 1, the output value for each sector in province i comprises three parts: intermediate demand, final demand (including household consumption, government consumption, investment, and export), which can be written as follows:

∑

∑ j

2.2. Methods

Xi =

n

n

Gi = ∑ j Gij , I i = ∑ j I ij . The direct consumption coefficient matrix Aij,

i

where u is an m × 1 column vector; H , G , and I represent, respectively, 3

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Fig. 1. Water consumption for production and WF of the Chinese provinces.

WC r = w rBr (H + G + I )

(9)

2.2.2. Model for the SPA Defourny and Thorbecke (1984) were the first to propose the SPA methodology. The basic underlying idea is to decompose the Leontief inverse matrix using the direct and indirect consumption coefficient matrix (Waugh, 1950). Similarly, we employ the SPA method to identify the critical path of the WF. Eq. (10) can be used to estimate the WF of each province in China. Thus, the SPA method analyzes the key industrial paths of the WFs in China’s provinces, which can then be used to discuss the sources of actual water use. According to approximation theory of power series proposed by Waugh (1950), the Leontief inverse matrix B can be further extended to:

Similarly, we can measure the WF of each province. Specifically, the WF of province r equals the amount of water needed to produce goods and services (excluding the imports) consumed in the province. This WF is calculated as:

WF r

1r 1r ⎫ ⎧ ⎡ H1r ⎤ ⎡G ⎤ ⎡I ⎤ ⎪ ⎪ ⎢ H 2r ⎥ 2r ⎥ 2r ⎥ ⎢ ⎢ G I + + = wB ⎢ ⋮ ⎥ ⎢⋮⎥ ⎬ ⎨⎢ ⋮ ⎥ ⎢ nr ⎥ ⎢ nr ⎥ nr ⎥ ⎪ ⎪⎢ ⎩ ⎣ H ⎦mn × 1 ⎣G ⎦mn × 1 ⎣ I ⎦mn × 1 ⎭

= wB (FHr + FGr + FIr )

(10)

B = I + A + A2 + A3 + ⋯

In Eqs. (9) and (10), r represents the province, with r = 1,⋯,n. WCr and WFr are the water consumption for production and the WF of province r, respectively. w r = (w1r , w2r , ⋯, wmr)1 × m is the water consumption coefficients’ vector of province r by sectors. Br is a part of the Leontief inverse matrix B, with Br = (Br1, Br 2, ⋯, Brn )m × mn . Fdr represents 1r ⎡d ⎤ 2r ⎢ d r the d type final demand of province r, with Fd = ⎢ ⎥ and d = H, ⋮ ⎥ ⎢ nr ⎥ ⎣ d ⎦mn × 1 G, I, while dir represents the d type final demand of province r for each sector in province i, with dimension m × 1. According to Eq. (10), the internal WF (IWFr) and the external WF (EWFr) of province r defined by Zhao et al. (2009) can be expressed as follows:

⎫ ⎧⎡ 0 ⎤ ⎡ 0 ⎤ ⎡0⎤ ⎪ ⎪⎢ ⋮ ⎥ ⎢ ⋮ ⎥ ⎢⋮⎥ r rr rr rr + ⎢G ⎥ + ⎢I ⎥ IWF = wB ⎢ H ⎥ ⎬ ⎨⎢ ⋮ ⎥ ⎢ ⋮ ⎥ ⎢⋮⎥ ⎪ ⎪⎢ ⎢ 0 ⎥ ⎥ ⎢0⎥ 0 ⎦mn × 1 ⎣ ⎦mn × 1 ⎣ ⎦mn × 1 ⎭ ⎩⎣

(11)

EWF r = WF r −IWF r

(12)

(13)

As per Eq. (13), Eq. (10) can thus be rewritten as:

WF r = wB (FHr + FGr + FIr ) = w (I + A + A2 + A3 + ⋯)(FHr + FGr + FIr ) = wFHr + wFGr + wFIr + wAFHr + wAFGr + wAFIr 1st − order   2nd − order + wA2FHr + wA2FGr + wA2FIr +⋯    3rd − order

(14)

On the right-hand side of Eq. (14), the first part is the amount of WF that originates from the first-order effect, corresponding to the amount of water consumed, by sector, for producing goods and services to directly meet the final demands without involving any intermediate sector, such as the amount of water consumed for the direct needs of the agricultural sector for some types of final demand. The second part is the amount of WF caused by the second-order path, which explains the amount of water consumed, by sector, for producing goods and services to indirectly meet the final demands through one intermediate sector, such as the amount of water consumed by the sector of nonmetallic mineral products for producing goods and services to indirectly meet the final demands of household consumption through the construction industry. Finally, the third- and higher-order paths involve, respectively, two and more intermediate sectors.

According to the definitions of the VW and WF provided in the Introduction (Section 1), the VW import of each province is equal to the EWF of the province, while the VW export of each province is equal to the water consumption for production minus the IWF of the province. In other words, the net VW exports in each province are equal to the difference between the VW imports and exports in the province.

3. Analysis of the provincial WF in China In this section, we use the equations in section 2.2.1 to estimate the WF, IWF, EWF, and net export of VW for each sector in China’s provinces in 2012. 3.1. Provincial WF in China

4

This study mainly focuses on the provincial WF in China. It does not consider the international exports and imports of each province, and thus, water consumption for production in each province is defined as the amount of water consumed in the province to produce domestic goods and services, that is, the water consumption of each province from the producer’s perspective. Moreover, the WF of each province considered here refers to the amount of water embodied in the goods and services consumed in the province.

Although the WF that reflects actual water consumption in the Chinese provinces does not exhibit a positive correlation with water consumption for production, it exhibits a positive correlation with GDP and population. Fig. 1 shows the water consumption in 2012 from the producer's perspective—Xinjiang is the province with the highest water 4

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Fig. 2. GDP and population of the Chinese provinces in 2012. Source of data: National Bureau of Statistics of China (2013a).

consumption for production in 2012, with a total volume of 48.63 billion tons, accounting for 10.76% of the total water consumption for production in China, followed by Jiangsu, Guangdong, Heilongjiang, Hunan, Hubei, Guangxi, and Anhui. All of them are characterized by water consumption exceeding 20 billion tons for production. The provinces with less water consumption for production are Tianjin, Qinghai, Beijing, Tibet, and Hainan. Specifically, the amount of water consumption for production in these provinces does not exceed 5 billion tons, overall accounting for only 2.71% of the national consumption. From the perspective of actual water consumption, the province with the largest WF is Guangdong, reaching 43.744 billion tons (9.68% of the country’s total), followed by Jiangsu, Shandong, Zhejiang, Shanghai, Xinjiang, and Sichuan. The WF in these provinces exceeds 20 billion tons. The provinces with low WFs (no more than 5 billion tons, that is, 2.58% of the country’s total) include Tibet, Qinghai, and Hainan. Fig. 2 shows that some provinces, such as Guangdong, Jiangsu, Shandong, and Zhejiang, are characterized by higher GDP and population, while other provinces, such as Tibet, Qinghai, and Hainan, exhibit lower levels. This implies that an exaggerated economic and population growth will likely lead to an increase in the WF (Chen et al., 2017).

Table 2 The three sectors with the highest proportion of water consumption for production and WF in all provinces (unit: %). Province

Beijing Tianjin Hebei Shanxi InnerMongolia Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Tibet Shaanxi Gansu Qinghai Ningxia Xinjiang

3.2. WF of the major sectors in each province of China Whether from water consumption for production or actual water consumption, the agriculture sector (S1 hereafter) has the largest production water consumption and WF in all Chinese provinces. Thus, improving the efficiency of water consumption in this sector will definitely help China achieve its water conservation goals. Regarding water consumption for production, Table 2 shows that the sectors with the largest water consumption in each province and the corresponding share of total consumption are very different. S1 has the largest water consumption for production in China, except for the provinces of Shanghai and Chongqing, where such consumption accounts for more than 50% of the total. “Steam and hot water supply” (S23) has the largest water consumption for production in these two provinces, accounting for 56% and 37% of the total, respectively. In addition, large quantities of water are also consumed in “Mining of nonmetals” (S5), “Production and distribution of electricity and heat” (S22), “Hotels and restaurants” (S27), “Leasing and commercial services” (S28), and “Other service activities” (S30). In terms of real water consumption, S1, S23, and S22 have the largest WFs in all provinces, but the corresponding shares of their totals differ significantly. Specifically, S1 has the largest water consumption for production in all Chinese provinces, accounting for more than 50% of the total. S23 (Steam and hot water supply) and S22 (Production and

Sector (ratio) Water consumption from the producer's perspective

Water footprint

S1(33) S1(52) S1(82) S1(69) S1(83) S1(74) S1(72) S1(87) S23(56) S1(58) S1(52) S1(59) S1(51) S1(73) S1(77) S1(65) S1(53) S1(65) S1(61) S1(78) S1(86) S23(37) S1(69) S1(54) S1(75) S1(93) S1(76) S1(85) S1(88) S1(92) S1(97)

S1(71) S1(71) S1(72) S1(72) S1(75) S1(73) S1(61) S1(79) S1(71) S1(63) S1(72) S1(57) S1(65) S1(75) S1(77) S1(60) S1(54) S1(68) S1(76) S1(69) S1(62) S1(54) S1(73) S1(66) S1(72) S1(71) S1(65) S1(77) S1(65) S1(84) S1(89)

S30(17) S5(9) S14(4) S23(9) S22(7) S5(6) S5(7) S5(5) S1(17) S23(21) S23(26) S23(21) S23(31) S22(10) S30(3) S23(12) S22(20) S22(11) S23(28) S23(13) S23(8) S1(36) S23(19) S22(26) S23(17) S6(1) S22(3) S22(6) S22(5) S22(3) S23(1)

S28(14) S27(8) S5(2) S14(4) S14(2) S27(3) S22(3) S22(2) S28(8) S12(4) S27(4) S22(4) S6(2) S10(3) S27(3) S22(4) S23(8) S23(7) S27(2) S6(1) S27(2) S12(5) S27(2) S23(5) S27(1) S13(1) S3(3) S14(3) S14(2) S12(1) S22(1)

S23(4) S23(5) S23(6) S23(9) S22(5) S23(6) S23(8) S22(4) S23(15) S23(19) S23(13) S23(24) S23(19) S22(6) S23(6) S23(14) S23(15) S23(9) S23(10) S23(17) S23(13) S23(25) S23(12) S22(9) S23(14) S23(6) S23(10) S23(5) S23(9) S22(3) S22(3)

S22(4) S22(5) S22(4) S22(3) S23(3) S22(4) S22(6) S23(3) S22(2) S22(3) S22(2) S22(4) S22(3) S23(5) S22(4) S22(7) S22(13) S22(8) S22(2) S22(2) S22(6) S22(4) S22(3) S23(8) S22(3) S22(6) S22(5) S22(5) S22(7) S23(3) S23(2)

Note: The ratio in brackets is equal to the amount of water that the corresponding departments should bear, divided by the gross water consumption for the corresponding provinces. S represents sectors, the meanings of the sector codes are defined in footnote. S1-Agriculture, S3-Mining of oil and gas, S5-Mining of nonmetal, S6-Food and beverage, S10-Paper and products for culture, education, and sports, S12Chemicals and chemical products, S13-Nonmetallic mineral products, S14-Basic metals, S22-Production and distribution of electricity and heat, S23-Steam and hot water supply, S27-Hotels and restaurants, S28-Leasing and commercial services, S30-Other service activities.

distribution of electricity and heat) also exhibit large WFs in all provinces. Thus, S1, S22, and S23 have the largest amounts of real water consumption in each province. Consequently, increasing water use 5

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Fig. 3. The proportion of IWF and EWF in the total WF in 2012.

originate. In Fig. 5, the EWFs of Beijing and Tianjin are shown to be mainly from Hebei, Xinjiang, and Heilongjiang, with the three provinces contributing 30% to the WFs of Beijing and Tianjin. Shanghai’s EWF mainly comes from Heilongjiang, with a contribution rate of 15.47%, followed by Jiangsu (10.35%) and Xinjiang (8.79%). The WF of Zhejiang mainly originates from Jiangsu (9.44%), Xinjiang (8.34%), and Heilongjiang (8.27%), while that of Shanxi is from Heilongjiang (7.22%), Hebei (6.43%), and Jiangsu (6.32%). The WFs of other provinces, such as Qinghai, Shandong, Shaanxi, and Hainan, mainly derive from Xinjiang. Overall, Xinjiang, a water-rich resource region, is an important WF source in most provinces in China.

efficiency in these sectors would help achieve water-saving targets in all provinces. At the same time, provinces should optimize the allocation of water resources according to their own production and consumption structure. For example, in provinces with scarce water resources, the pressure from water shortages could be alleviated by moving the sectors that require heavy water consumption to other provinces. 3.3. Composition of the provincial WF in China The analyses in sections 3.1 and 3.2 clearly examine the WFs of China’s provinces in 2012. However, its composition is not discussed. To do this, the WF is first decomposed into IWF and EWF. Then, the source of the EWFs is refined at the provincial level. This section investigates the composition of the WFs at the internal, external, and inter-provincial levels. Fig. 3 shows that the WFs of water-rich provinces in 2012 are mainly composed of IWFs, while those of water-scarce provinces mainly consist of EWFs. The ratio of IWF to WF for Beijing, Tianjin, Shanghai, Qinghai, Zhejiang, and Shandong does not exceed 30%. That is, the ratio for Beijing and Tianjin is only 1/10. However, the IWFs of Xinjiang, Guangxi, Heilongjiang, and Anhui account for more than 2/3 of their total WFs. In particular, about 5/6 of the WF in Xinjiang comes from the IWF. At the same time, comparing provincial WFs and water resources (Fig. 4), we find that water-scarce provinces, such as Beijing, Tianjin, and Shanghai, alleviate water pressure mainly through VW imports (i.e., EWFs). On the contrary, the WF of water-rich provinces, including Jiangxi, Sichuan, and Hunan, mainly comes from the IWF. Nonetheless, despite abundant water resources, the EWF in Tibet and Guangdong accounts for more than 55% of the total WF. In this part, we analyze the provinces from which the EWFs

3.4. Net export of provincial VW in China In Fig. 6, the 31 Chinese provinces are roughly classified into two categories. The first category comprises the provinces in which the VW export is larger than the VW import, that is, the provinces that are net exporters of VW. Such provinces mainly include Xinjiang, Heilongjiang, Hunan, Guangxi, Hubei, Anhui, Jiangxi, Hebei, Inner Mongolia, Jilin, and Gansu. Moreover, except for Jiangxi, Guangxi, and Hunan, the total amount of water resources in other provinces in 2012 is not larger than 100 billion tons. These provinces are characterized by water scarcity. For example, water resources in Hebei are particularly low (less than 25 billion tons). The second category includes the provinces that are net importers of VW (that is, VW exports are less than VW imports). Such provinces are mainly consumption-oriented, and economically developed provinces, such as Guangdong, Shanghai, Zhejiang, Shandong, Beijing, and Tianjin. This indicates that the actual amount of water required far exceeds that of water consumption for production. Therefore, the calculated WF reflects more accurately the actual water

Fig. 4. Water resources in the Chinese provinces in 2012. Source of data: National Bureau of Statistics of China (2013a,c). 6

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Fig. 5. The main souces of China’s provincial EWF in 2012 at the provincial level.

sources of the WF, with a contribution rate of about 50%. Fig. 7 shows that, as the path order increases, the contribution rate of higher-order paths to the total WF decreases. This suggests that all provinces should give priority to low-order paths when optimizing water resources allocation and implementing water-saving plans. The contribution rates of different paths to the WF clearly differ across provinces. Specifically, Fig. 7 shows that the contribution rates of the first-order path to the WF of Xinjiang and Ningxia are 56% and 50%, respectively. However, those of Hubei and Beijing are only 24% and 21%, respectively. The contribution rates of the second-order path to the WF of Hubei and Shandong reach 31% and 30%, respectively, while those of Qinghai, Hainan, and Tibet are only 20%, 20%, and 18%, respectively. Therefore, water conservation planners should give priority to low-order paths when designing water-saving goals through the path of the WFs. Moreover, they should fully consider the impact of each path on the WFs.

consumption in Chinese provinces. 4. SPA of WF in China The WF of each Chinese province in 2012 and its composition (at the internal, external, and inter-provincial levels) are discussed in section 3. However, the specific path of the WF is not highlighted. Therefore, this section further investigates the complete industrial path from the final demand to both the upstream and downstream WF in China. 4.1. Overview The amount of water resources consumed in the first six-order paths in various provinces in China contributes to 90% to the total WF. Among them, the first-order and second-order paths are the main

Fig. 6. VW import, VW export, and net exports of VW in China’s provinces in 2012. 7

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Fig. 7. The contribution rate of the first six order paths to the total WF of China’s provinces in 2012.

4.2. Analysis of the WF induced by the domestic final demand

indirectly. This section is devoted to the investigation of the complete industrial path from the final demand to both the upstream and downstream WF in China. From Section 4.1, it emerges that third- and higher-order paths have less impact on the provincial WFs, and involve more intermediate sectors. At the same time, Section 3.3 shows that there are substantial differences in the composition of provincial WFs. Therefore, this study focuses on the first three critical paths of both the IWF (inner-provincial, that is, within the province) and the EWF (inter-provincial). The detailed results are shown in Tables 3 and 4. More specifically, in Table 3, “path” shows the path of the IWF in each province. The number in parentheses indicates the contribution rate of this path to the province’s total WF. For example, P31S1 → P31H (44.17) in the second row and second column indicates that the contribution rate of the WF from the direct demand of P31H (household consumption in Xinjiang) inP31S1 (agricultural sector of Xinjiang) to the total WF of Xinjiang is 44.17%. Moreover, P31S1 → P31S6 → P31H (6.81) in the second row and sixth column indicates that the contribution rate of the WF from indirect demand of P31H (household consumption in Xinjiang) in P31S1 (agricultural sector of Xinjiang) through P31S6 (Food and beverage sector of Xinjiang) to the total WF of the province is 6.81%. In a similar way, in Table 4, “path” indicates the path of the EWFs across provinces. In terms of IWF paths for each province, Inner-province S11 → Inner-province H is the most inner-provincial path for the WFs in all provinces, except for Shanghai and Chongqing. Inner-province S11 → Inner-province S66 → Inner-province H is also a main path for most

Looking at the types of final demand, the WF from household consumption provides the largest contribution to the total WF in all provinces except for Tibet and Qinghai. The contribution rate highly differs across different types of final demand. Fig. 8 shows that the WF from household consumption accounts for 83% of the WF in Guangdong, followed by Sichuan (81%), Zhejiang (81%), and Shanghai (81%). However, the contribution rates of household consumption in Tibet and Qinghai are only 32% and 31%, respectively. The contribution rates to the total WF from investment in Tibet and Qinghai are 65% and 61%, respectively, while those of Sichuan, Zhejiang, and Guangdong are around 14%, 14%, and 13%, respectively. There are thus marginal differences in the contribution rates from government consumption, fluctuating between 5% and 15% in all provinces. Overall, the WF from these two types of final demand (household consumption and investment) is the major component of Chinese provinces’ WF. The amount of water actually consumed in the provinces is mainly to meet household consumption and investment. Therefore, policymakers in all provinces could implement water-saving plans more effectively by explicitly adjusting for the final demand structure. 4.3. Critical path analysis of the WF in China In sections 4.1 and 4.2, the contributions of each path and type of final demand to the provincial WFs are discussed by considering whether the final demand affects the WF through sectors directly or

Fig. 8. The contribution rates of different domestic final demands to the total WF in 2012. 8

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Table 3 Critical paths of the IWF in the Chinese provinces (unit: %). Province

Path(ratio)

Xinjiang Guangxi Heilongjiang Hunan Anhui Jiangxi Jiangsu Sichuan Yunnan InnerMongolia Hubei Hebei Gansu Ningxia Henan Fujian Liaoning Jilin Guizhou Guangdong Chongqing Tibet Hainan Shaanxi Shanxi Shandong Zhejiang Qinghai Shanghai Tianjin Beijing

P31S1 → P31H (44.17) P20S1 → P20H (24.95) P8S1 → P8H (26.32) P18S1 → P18H (22.12) P12S1 → P12H (15.07) P14S1 → P14H (25.97) P10S1 → P10H (9.98) P23S1 → P23H (19.37) P25S1 → P25H (25.49) P5S1 → P5S6 → P5H (11.77) P17S1 → P17S6 → P17H (9.89) P3S1 → P3H (20.79) P28S1 → P28H (22.22) P30S1 → P30H (26.26) P16S1 → P16H (9.03) P13S1 → P13H (10.62) P6S1 → P6H (13.33) P7S1 → P7H (9.10) P24S1 → P24H (17.35) P19S1 → P19H (17.38) P22S23 → P22H (13.02) P26S1 → P26I (15.52) P21S1 → P21H (17.25) P27S1 → P27H (9.48) P4S1 → P4H (14.42) P15S1 → P15H (7.91) P11S1 → P11H (6.95) P29S1 → P29I (7.88) P9S23 → P9H (8.34) P2S1 → P2S6 → P2H (1.23) P1S1 → P1H (1.50)

P31S1 → P31I (8.01) P20S1 → P20S6 → P20H (8.10) P8S1 → P8S6 → P8H (9.55) P18S1 → P18S6 → P18H (7.59) P12S23 → P12H (12.44) P14S1 → P14S6 → P14H (7.54) P10S23 → P10H (9.96) P23S1 → P23S6 → P23H (8.38) P25S23 → P25H (6.67) P5S1 → P5H (10.16) P17S1 → P17H (6.32) P3S1 → P3S6 → P3H (6.08) P28S1 → P28S6 → P28H (6.36) P30S1 → P30I (7.78) P16S1 → P16S6 → P16H (6.79) P13S23 → P13H (10.24) P6S1 → P6S6 → P6H (7.97) P7S1 → P7S6 → P7H (5.01) P24S1 → P24S6 → P24H (3.04) P19S23 → P19H (5.32) P22S1 → P22H (7.97) P26S1 → P26H (14.83) P21S1 → P21I (4.10) P27S1 → P27S6 → P27H (4.62) P4S23 → P4H (2.40) P15S1 → P15S6 → P15H (7.07) P11S23 → P11H (6.46) P29S1 → P29H (7.06) P9S1 → P9H(2.12) P2S27 → P2H (0.87) P1S30 → P1G (0.90)

P31S1 → P31S6 → P31H (6.81) P20S23 → P20H (7.18) P8S1 → P8I (4.20) P18S23 → P18H (3.00) P12S1 → P12S6 → P12H (7.42) P14S1 → P14I (3.03) P10S1 → P10S6 → P10H (6.58) P23S23 → P23H (7.01) P25S1 → P25S6 → P25H (5.07) P5S1 → P5I (4.71) P17S23 → P17H (3.85) P3S1 → P3S1 → P3H (2.42) P28S1 → P28I (4.56) P30S1 → P30S6 → P30H (3.46) P16S23 → P16H (4.75) P13S1 → P13S6 → P13H (5.44) P6S1 → P6I (1.63) P7S1 → P7I (3.91) P24S1 → P24I (2.69) P19S1 → P19S6 → P19H (3.46) P22S1 → P22I (1.00) P26S1 → P26S1 → P26I (0.87) P21S23 → P21H (2.07) P27S1 → P27I (2.50) P4S1 → P4S6 → P4H (2.40) P15S1 → P15I (0.80) P11S1 → P11S6 → P11H (1.94) P29S1 → P29G (2.27) P9S23 → P9S23 → P9H (0.61) P2S1 → P2I (0.73) P1S27 → P1H (0.88)

Note: The ratio in brackets is the contribution rate of water consumption caused by the path to water footprint of the corresponding province. P represents provinces, the corresponding relationship shown in Table 3. S represents sectors, the meanings of the sector codes are defined in footnote. H, I and G represents the demand of household consumption, investment and government consumption, respectively. S1-Agriculture, S6-Food and beverage, S23-Steam and hot water supply, S27-Hotels and restaurants, S30- Other service activities.

Table 4 Critical paths of the EWF in the Chinese provinces (unit: %). Province Beijing Tianjin Shanghai Qinghai Zhejiang Shandong Shanxi Shaanxi Hainan Tibet Chongqing Guangdong Guizhou Jilin Liaoning Fujian Henan Ningxia Gansu Hebei Hubei Inner Mongolia Yunnan Sichuan Jiangsu Jiangxi Anhui Hunan Heilongjiang Guangxi Xinjiang

Path (ratio) P3S1 → P1H (1.76) P3S1 → P2H(2.92) P8S1 → P9H(6.36) P31S1 → P29I (3.81) P31S1 → P11H (2.41) P31S1 → P15H (2.35) P3S1 → P4H(1.24) P31S1 → P27H (1.67) P31S1 → P21H (1.26) P31S1 → P26I (2.28) P22S6 → P21S1 → P22H (2.21) P31S1 → P19H (4.14) P31S1 → P24H (3.04) P8S1 → P7H(0.71) P8S1 → P6H(1.18) P31S1 → P13H (1.60) P31S1 → P16H (0.76) P31S1 → P30H (7.80) P31S1 → P28H (1.96) P31S1 → P3H (0.53) P19S23 → P17H (1.68)

P31S1 → P1H (1.75) P1S1 → P2H(2.14) P31S1 → P9H (3.77) P31S1 → P29H (3.41) P8S1 → P11H (1.84) P31S1 → P15S6 → P15H (2.10) P8S1 → P8S6 → P4H (1.06) P19S23 → P27H (0.90) P19S23 → P21H (1.08) P31S1 → P26H (2.18) P31S1 → P22H (2.19) P20S1 → P19H (2.53) P23S1 → P24H (0.56) P10S23 → P7H (0.53) P31S1 → P6H (0.95) P31S1 → P13S6 → P13H (0.82) P18S23 → P16H (0.67) P31S1 → P30I (2.31) P31S1 → P31S6 → P28H (0.84)

P5S1 → P6H(0.79) P8S1 → P13H (0.67) P31S1 → P16S6 → P16H (0.57) P31S1 → P30S6 → P30H (1.03) P31S1 → P28S6 → P28H (0.56)

P31S1 → P17S6 → P17H (0.59)

P20S23 → P17H (0.53)

P31S1 → P25H (3.50) P31S1 → P23H (2.52) P8S1 → P8S6 → P10H (0.64) P31S1 → P14H (0.96) P10S23 → P12H (0.63) P31S1 → P18H (0.97)

P31S1 → P25S6 → P25H (0.70) P31S1 → P23S6 → P23H (1.09) P8S1 → P10H (0.58)

P28S1 → P25H (0.63) P20S1 → P23H (0.53) P31S1 → P10H (0.57)

P31S1 → P12H (0.57) P19S23 → P18H (0.54)

P19S23 → P12H (0.55)

P19S23 → P20H (1.43)

P31S1 → P20H (0.78)

9

P8S1 → P1H (1.05) P31S1 → P2H (2.13) P10S1 → P9H (3.12) P31S1 → P29G (1.10) P10S1 → P11H (1.74) P14S1 → P15H (0.71) P5S1 → P4H(1.03) P31S1 → P27S6 → P27H (0.81) P20S1 → P21H (0.67) P19S23 → P22H (1.71) P18S1 → P19H (1.66) P25S1 → P24H (0.55)

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provinces in China. In addition, Inner-province S11 → Inner-province Investment I is one of the key paths in some provinces. Moreover, Table 3 shows that Inner-province S11 → Inner-province H is the most critical IWF path for most provinces with large amounts of IWF, such as Xinjiang, Guangxi, Heilongjiang, Jiangxi, Yunnan, Hunan, Ningxia, and Gansu, with a contribution rate exceeding 20%. However, this path has a lower impact in Zhejiang, Shandong, Beijing, and Tianjin, where the amounts of EWF are very large. The most critical IWF path for Shanghai and Chongqing is Inner-province S233 → Inner-province H, with a contribution rate of 13.02% in Chongqing. Moreover, Inner-province S11 → Inner-province S66 → Inner-province H is an important IWF path in all provinces, except for Chongqing, Hainan, Qinghai, and Beijing, accounting for 11.77% of Inner Mongolia’s WF. The contribution rate of Inner-province S11 → Inner-province I to the WFs of Tibet, Xinjiang, and Qinghai is relatively large, showing that the development of agricultural sector in these provinces entails large amount of water resources. Besides, Inner-province S30 → Inner-province G is a particularly crucial industrial IWF path for Beijing. So far, we have considered the contribution rate of water consumption from key IWF paths, but we did not discuss the critical EWF path across provinces. Therefore, we further analyze the impact of key inter-provincial paths on WFs to identify the critical EWF path for each province. The inter-provincial paths are the critical industrial paths for provinces with large EWF amounts, such as Guangdong, Shanghai, Zhejiang, Shandong, Beijing, and Tianjin, but not the most important ones for provinces with large IWF amounts, such as Xinjiang, Heilongjiang, Guangxi, Hebei, and Inner Mongolia. In Table 4, the top three key inter-provincial industrial paths (P8S1 → P9H with 6.36%, P31S1 → P9H with 3.77%, and P10S1 → P9H with 3.12%) contribute up to 13.25% of the WF in Shanghai. As for Inner Mongolia, Xinjiang, and Heilongjiang, there is no inter-provincial industrial path that contributes more than 0.5% to the corresponding WFs. Key inter-provincial paths mainly involve geographically or economically close provinces. In this respect, S1 in provinces geographically adjacent to or economically close to the local province → Inner-province H is the most important inter-provincial industrial path of the WF for most provinces. Moreover, Other provinces’ S1 → Innerprovince I, Other provinces’ S23 → Inner-province H, Other provinces’ S1 → Inner-province S6 → Inner-province H, and Other provinces’ S1 → Other provinces’ S6 → Inner-province H are also relevant interprovincial industrial paths for some provinces. Furthermore, Hebei S1 → Beijing H (P3S1 → P1H) is the most important path of the WF in Beijing, followed by P31S1 → P1H and P8S1 → P1H. The key interprovincial industrial paths for Tianjin’s WF are P3S1 → P2H, P1S1 → P2H, and P31S1 → P2H, while the most important one for Hubei and Guangxi is S23 in provinces geographically adjacent to or economically close to the local province → Inner-province H, and that for Anhui is S23 in provinces geographically adjacent to the local province → Innerprovince H. The two major inter-provincial paths for Zhejiang are P31S1 → P11H (2.41%) and P8S1 → P11H (1.84%). The contribution rate of P31S1 → P19H and P20S1 → P19H to Guangdong’s WF is 6.67%. Moreover, for Fujian, Jiangxi, Hainan, Sichuan, Guizhou, Yunnan, and Shaanxi, the most crucial inter-provincial industrial path is P31S1 → Inner-province H, with a contribution rate in the range 1%–4%. For Qinghai, Tibet, and Ningxia, the path S1 in P31 economically close to the local province → Inner-province I has a relatively large impact on their WFs, with contribution rates higher than 4%. As for Shandong, Shaanxi, Fujian, Henan, Ningxia, Hubei, Yunnan, and Sichuan, S1 in P31 economically close to the inner province → Inner-province S6 → Inner-province H is the important inter-provincial industrial path, while for Shanxi, Gansu, and Jiangsu, it is S1 in the province geographically adjacent to the local province → S6 in the province geographically adjacent to the inner province → Inner-province H. It is also worth noting that both this section and section 3.3 point out that Xinjiang

(P31) is a key source of EWFs for all other provinces in China. We further conclude that the VW trade is conducted between Xinjiang and other provinces mainly through the agricultural sector (S1) 5. Conclusions and policy indications Based on the Chinese inter-provincial water resource–economy IO table for 2012, the IO analysis method is used to calculate the WF and its composition in each province. Then, the SPA method is used to analyze the industrial path of the provincial WF going from the final demands to the industrial sectors. The results of this study provide a reference to all provinces for the optimization of water resources’ allocation and the implementation of water-saving plans based on a deep understanding of the crucial path of their own WF. In light of this, the following conclusions can be outlined. First, the WF could better reflect the amount of water actually used in each province. In this respect, policymakers should focus on provinces such as Guangdong, Jiangsu, Shandong, Zhejiang, and Shanghai when implementing water-saving plans and allocating water costs, since such provinces are characterized by developed economies, large WFs, and populations. The agriculture sector (S1) has the largest amounts of WF in all Chinese provinces, accounting for more than 50% of the provincial WF, followed by Steam and hot water supply (S23), and Production and distribution of electricity and heat (S22). Second, there are substantial differences in the WF composition across provinces. Water-scarce provinces, such as Beijing, Tianjin, and Shanghai, alleviate water pressure mainly through VW imports (i.e., EWF). On the contrary, the WF of water-rich provinces, including Jiangxi, Sichuan, and Hunan, mainly comes from IWFs. Moreover, the EWF of Beijing and Tianjin mainly derives from Hebei, while the WFs of Shanghai and Zhejiang mainly come from Heilongjiang. Furthermore, the WFs of Qinghai, Shandong, and other provinces mainly originate from Xinjiang. Xinjiang, Heilongjiang, Guangxi, Hebei, and Inner Mongolia are net VW exporters, while Guangdong, Shanghai, Zhejiang, Shandong, Beijing, and Tianjin are net importers. Third, as the WF from household consumption exhibits the largest contribution rate to the total WF for all provinces except for Tibet and Qinghai, policymakers in all provinces could implement effective water-saving plans by properly adjusting the final demand structure. In terms of IWF paths, Inner-province S1 → Inner-province H is the most inner-province path for the WFs in all provinces, except for Shanghai and Chongqing. For the latter, the most critical IWF path is Inner-province S23 → Inner-province H. In addition, Inner-province S1 → Innerprovince S6 → Inner- province H is an important IWF path in most provinces, and Inner-province S1 → Inner-province I is also very crucial for some provinces. Inter-provincial paths are critical industrial paths in provinces with large EWF amounts, such as Guangdong, Shanghai, Zhejiang, Shandong, Beijing, and Tianjin. However, this is not the case in provinces with large IWF amounts, such as Xinjiang, Heilongjiang, Guangxi, Hebei, and Inner Mongolia. Key inter-provincial paths mainly involve geographically or economically close provinces. Specifically, S1 in provinces geographically adjacent to or economically close to the Inner-province → Inner-province H is the most important inter-provincial industrial path of the WF for most provinces. Moreover, Other provinces’ S1 → Inner-province I, Other provinces’ S23 → Inner-province H, Other provinces’ S1 → Inner-province S6 → Inner-province H, and Other provinces’ S1 → Other provinces’ S6 → Inner-province H are also relevant inter-provincial industrial paths for some provinces. Based on the above conclusions, we propose the following indications for policy. First, the provinces with scarce water resources and large net imports of VW, such as Beijing, Tianjin, and Shanghai, should provide ecological compensation to major source provinces for their WFs. If the major sources provinces use this compensation to increase the efficiency of water use for production, the WF from the imported goods and services will be reduced. In this way, the target of saving 10

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water from both the production and the consumption side could be reached. In addition, the critical paths identified through the SPA method should be prioritized in water conservation programs. For most provinces, the improvement in water use efficiency of the agriculture sector in provinces geographically adjacent to or economically close to the inner-province is beneficial to water conservation in inner-provinces. Therefore, geographically or economically neighboring provinces can choose to cooperate in the implementation of a water-saving plan. On the one hand, the critical inner-provincial paths identified through the SPA method should be prioritized in water conservation programs for the inner-province. On the other hand, the critical interprovincial paths identified by the SPA method could be viewed as important channels for transferring high water consumption industries in provinces with water shortages to provinces that are water-rich. Second, provinces with scarce water resources and large net exports of VW, such as Hebei and Gansu, should aim to increase their water use efficiency. Their water-intensive sectors should be moved to water-rich provinces. The provinces of Guangdong and Sichuan, with their rich water resources and net imports of VW, could produce more waterintensive products and services, thus easing the pressure on water resources in other provinces and boosting the efficiency of water use. Finally, the amounts of WF from household consumption and investment comprise the major WF component for almost all provinces. In light of this, the provincial departments of water resources should take their production and final demand structures into account in order to optimize water resources’ allocation and water conservation. It is also worth mentioning that, while formulating water-saving plans, policymakers should prioritize low-order WF paths in each province in order to better consider the impact of different paths on the provincial WFs.

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