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Spatial analysis of dual-scale water stresses based on water footprint accounting in the Haihe River Basin, China
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Spatial analysis of dual-scale water stresses based on water footprint accounting in the Haihe River Basin, China Chunhui Lia,d, Meng Xua,b, Xuan Wanga,d, Qian Tanc,
⁎
a
Ministry of Education Key Lab of Water and Sand Science, School of Environment, Beijing Normal University, Beijing 100875, PR China School of Public Administration, Zhejiang University of Finance & Economics, Hangzhou 310018, PR China c College of Water Resources & Civil Engineering, China Agriculture University, Beijing 100083, PR China d State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, PR China b
A R T I C L E I N F O
A B S T R A C T
Keywords: Blue water footprint Gray water footprint Haihe River Basin Exploratory spatial data analysis Water footprint accounting
Water scarcity and degradation of water quality in river basins are among the major issues addressed by water resources management authorities. Moreover, two typical challenges associated with water resources management include naturally unbalanced distribution and administrative disparities under multiple jurisdictions at the watershed scale. Effective accounting and management methods are thus desired to deal with such challenges. Water footprint accounting is widely used for assessing natural water resources availability and supporting optimal allocation among multiple jurisdictions, representing a useful tool for improving watershed sustainability. Not only will this enable successful revealing of the direct water consumption by relevant agents, but also indirect water consumption by concerning users. Such a method is particularly useful for addressing waterrelated issues in numerous watersheds of developing countries, which are subject to diverse water stressors. Hence, this study aimed at conducting water footprint accounting under city/regional and basin scales for the investigation of the amounts of blue and gray water in the Haihe River Basin (HRB), China. The Blue Water Footprint Index (BWFI) and the Gray Water Footprint Capacity Coefficient (K) were introduced to comprehensively evaluate water scarcity in terms of quantity and quality. The results revealed that all cities of the HRB suffered from extreme water scarcity. The industrial sector was identified as the primary contributor to the blue water footprint at the watershed. The agricultural gray-water footprint of the HRB accounted for 54% of the total. The exploratory spatial data analysis (ESDA) was adopted to analyze the spatial auto-correlation features of blue and gray water in the HRB. The results indicated that Dezhou and Binzhou had the highest water deficiency in both of the water stresses, and that Xinxiang, Puyang, Anyang and Jiaozuo had the highest water problem predominantly due to water quality stress.
1. Introduction Limited availability of fresh water jeopardizes human well-being, hampers economic growth, and contributes to losses of ecosystem functions and biodiversities (Launiainen et al., 2014). Currently, onethird of the world population are facing water scarcity (Manzardo et al., 2014). According to the UNEP (2008), the turning point will arrive in 2025 when almost half of the world’s population would be living in declining situations of water stress due to increased water use (Sultana et al., 2014). One of the prominent problems of water scarcity in some areas is the severe discrepancy of the spatial distribution among the water resources, population and the economic development (Zhang and Anadon, 2014). The spatial imbalance and mismatch of the water endowments and demands have led to significant adverse ecological
⁎
impacts, posing an immense challenge for the sustainable development of cities and river basins in arid regions (Foster et al., 2004). The increasing pressures on water resources have created the need for critical techniques and strategies related to sustainable water use and management (Sultana et al., 2014). Currently, virtual water (VW) and water footprint (WF) are identified as important indications for sustainable water management. The concept of VW was proposed by Tony Allan in 1993 (Allan, 2003) to reveal the amount of embedded freshwater used to produce agricultural and industrial goods. In the early 2000s, this concept was further extended by the idea of “water footprint” (WF) by Hoekstra (Chapagain and Hoekstra, 2007; Hoekstra and Chapagain, 2007; Hoekstra and Hung, 2005; Ma et al., 2006) to uncover the hidden link between consumption and water use, which can be set as the basis for
Corresponding author. E-mail address: [email protected] (Q. Tan).
http://dx.doi.org/10.1016/j.ecolind.2017.02.046 Received 28 November 2016; Received in revised form 31 January 2017; Accepted 13 February 2017 1470-160X/ © 2017 Published by Elsevier Ltd.
Please cite this article as: Li, C., Ecological Indicators (2017), http://dx.doi.org/10.1016/j.ecolind.2017.02.046
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standards (Hoekstra et al., 2011). Based on these concepts, researches have been conducted to assess regional blue, green, and gray water footprints. Two main shortcomings remain for the WF assessment. First, most of the previous studies mainly concentrated on agricultural and livestock productions, but few touched industrial and residential aspects due to limitations in accounting methods and difficulties in data collection (Chapagain and Hoekstra, 2007, 2008; Chapagain et al., 2006; Hoekstra and Chapagain, 2007; Mekonnen and Hoekstra, 2011; Zhao et al., 2010; Yan et al., 2013). With the expansion of urban areas and the prosperity of the modern life style, industrial production and residential activities developed into the fundamental cause of the current ecological crises. Thus, the study of the WF for industrial productions and residential activities is a significant field of research, which can scientifically reveal the multiple impacts on freshwater resources, caused by industrial and residential processes, ultimately improving sustainable and equitable water use in these fields (Yan et al., 2013). Secondly, few current studies have analyzed the three types of water footprints at a river basin scale, in particular, the assessment of these three types of WFs in different economic sectors of the cities level. Such studies would be quite helpful for water allocation in river basins, as there are wide variations in water and other natural resource endowments among different cities/regions inside river basins. In addition, it will make it feasible with a more detailed WFs accounting based on the city scale for the further optimal allocation of the water resources among river basins. Therefore, the objective of this study was to conduct an in-depth WF accounting for different economic sectors and cities at river basins, and thus to reveal the spatial distributions of water stress and water pollution stress within the study area. The study would improve the evaluation accuracy of WFs for river basins via accounting efforts downscaled to cities, and enhance the WF-based methods of water stress analyses through applying an explore spatial distribution analysis (ESDA) model. With the Haihe River Basin (HRB) being the study area, the research entails the following tasks: (a) to evaluate blue and gray WFs in different sectors of the cities within HRB as well as for the entire basin; (b) to conduct blue and gray WF analyses based on indicators of both water stress and water pollution stress; and (c) to analyze the spatial distribution correlation of the cities based on the water stress and water pollution stress analyses indicators. In detail: (i) the blue and gray WFs of the three main sectors for 26 cities in the HRB will be analyzed, respectively, via the Environmental Input-Output Model; (ii) the water stress and water pollution stress will be analyzed via the Water Footprint Index and the Gray Water Footprint Capacity Coefficient at the city and basin scales; (iii) the ESDA Model will be applied to identify the spatial cluster properties of the cities in terms of the water stress and water pollution stress indicators. The results of this study reveal the water stress on both the quantity and quality of the river basin in various economic sectors. Thus, the further concerning instructions on water allocation strategies can be formulated accordingly.
the formulation of new strategies of water governance (Hoekstra et al., 2011). From the perspective of water footprint, final consumers, retailers, food industry, and traders of water-intensive products could enter the scenario as potential ‘change agents’. They can be addressed not only for their role as direct water users, but also for their role as indirect ones (Hoekstra et al., 2011). In recent years, a large number of studies have been carried out on WF assessment at different scales. At a global level, O'Bannon et al. (2014) have reconstructed the network of agricultural pollution based on international trade records, commodity, and nation-specific gray WFs for the period between 1986 and 2010. Ercin and Hoekstra (2016) have developed forecasts of global WF scenarios for 2050 based on a number of drivers of change: population growth, economic growth, production/trade patterns, consumption patterns, and technological development to understand the changes in the WF of production and consumption for possible future developments by region, thus elaborating main drivers of this change. At a national level, several scholars have tried to compile the life cycle assessment (LCA) concept in the WF accounting for a sound analysis on the VW contents in various agricultural or livestock products. For instance, Lamastra et al. (2014) assessed the WF of Italian wine production throughout its entire life cycle. Haro et al. (2014) estimated the WF of sugarcane in Mexico. Zonderland-Thomassen et al. (2014) assessed the WF of beef cattle and sheep produced in New Zealand and compliant with LCA principles. Besides WF accounting for the products, the WF assessment approach was predominantly used in the VW accounting for analyzing national policies on water conservation via VW trade. For example, Mekonnen and Hoekstra (2014) conducted research on water resources conservation via the VW trade of Kenya. Winter et al. (2014) highlighted incongruent terms in VW balance by comparing the indexes denoting the ratio of virtual water exchanged during the swap and the ratio of the economic values of the swapped products for an illustration of the swap-product trade. Feng et al. (2014) incorporated a water stress index as well as an indicator for ecosystem damage into the assessment of interregional virtual water flows across 30 Chinese provinces. Cazcarro et al. (2014) complemented the WF estimations for Spanish tourism via an input-output analysis. El-Sadek (2010) introduced VW trade as a solution for the water scarcity in Egypt. In recent years, researches have also been conducted on the WF assessment at the regional level. The WF accounting is no longer restricted to the agricultural products, and some studies on industrial products have been undertaken by applying an input-output analysis. Such analyses help to assess annual changes in the WF of a region, identify the key economic sectors and factors leading to the changes, and quantitatively evaluate the contributions of those sectors and factors to the changes. Consequently, policy-makers can take water-saving actions focusing on the key economic sectors or factors. Zhang et al. (2011) quantitatively evaluated the water footprint and virtual water trade of Beijing by applying a provincial-level, interregional input–output model. Mubako et al. (2013) applied an input–output analysis to evaluate water use and to quantify virtual water transfers between California and Illinois. Relevant studies have also been undertaken in semi-arid and arid areas of European, South American, and African regions and other semi-arid and arid areas (Ene et al., 2013; Mekonnen et al., 2012; Nana et al., 2014; Pena and Huijbregts, 2014; Perez-Blanco and Thaler, 2014; Yan et al., 2013; Zhang and Anadon, 2014; Zoumides et al., 2014). WF is a multidimensional indicator of freshwater consumption volumes by sources as well as of polluted water volumes by types of pollution. It is further analyzed as blue water footprint (blue WF), green water footprint (green WF), and gray water footprint (gray WF). The blue WF refers to the consumption of blue water resources (surface and groundwater) along the supply chain of a product. The green WF refers to the consumption of green water resources (rainwater insofar as it does not become run-off). The gray WF is defined as the volume of freshwater that is required to assimilate the load of pollutants, given natural background concentrations and existing ambient water quality
2. Methodology 2.1. Environmental input-output (IO) model 2.1.1. Internal & external water footprint The general IO model was set up by Leontief (1941) to represent the monetary trade of intermediate products between different sectors in an economic system. This IO model was further developed into the Water IO model by Zhang et al. (2011) This model portrays how the production of an economy depends upon interactions between different sectors and final demand (Zhi et al., 2014). Water is a primary input into economic production, and this relationship is reflected through freshwater use coefficients for each economic sector (Mubako et al., 2013). Thus, the water IO model can be summarized in Eq. (1) (Zhang and Anadon, 2014): 2
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Xi =
∑
xij + yi
re-export of imported products in sector i. It can be derived from Eq. (14) as follows:
(1)
j =1
n
where Xi is the gross economic output of the sector i; xij represents the inputs from the sector i to the sector j; n is the number of economic sectors of an economy; yi is the final demand of sector i. From a transactions table, technical coefficients of production aij can be derived by dividing the inter-sectoral flows from sector i to j (xij ) via total input of sector j (Xj):
aij = xij / X j
EVWi re − ex = νi × ( ∑ GVWIi × mij ) i
where νi is the adjusting coefficient derived as the proportion of the import amount value from oversea sector i and then re-exported to abroad over the outcome of final demand in each sector i, mij is the import from the oversea sector i to the domestic sector j. Thus, the total WF of sector i is:
(2)
n
Xi =
∑
a ij X j + yi
j =1
WFi = IWFi + EWFi (3)
X = AX + Y
(4)
X = ( I − A )−1 Y
(5)
GVWIi =
∑
DVWIi × bij
2.1.2. Water stress index (WSI) and water self-sufficiency (WSS) The water stress index (WSI) is defined as the ratio of the total WF over the total water availability in the local area (Ma et al., 2015; Metulini et al., 2016; Zhao et al., 2015) and can be expressed as:
WSI =
−1
IVWi = GVWI i N × MIi
IWF WF
WSS =
(7)
where IWF is the internal water footprint of the study area. For the evaluation of the WSS, five WSS levels were set: self-sufficient (> 1), barely self-sufficient (0.8–1), comparatively self-insufficient (0.4–0.8), self-insufficient (0.2–0.4), and extremely self-insufficient (< 0.2). When WSS equals 1, this means that the local water resource can only just meet the need of regional water consumption. Whereas, a WSS below 1 represents the different margins of insufficiency of the local water supply (Ma et al., 2015).
(8)
(10)
(11)
where EVWi is the exported VW for sector i, GVWI i is the VW intensity of sector i in the local region, ME i is the export value of sector i from the local region. The internal WF (IWF) refers to the amount of water resources consumed for production by the local residents. Thus, it equals to the total freshwater consumption minus the total exported VW. The external WF (EWF) is defined as the amount of water resources used in other regions for the production of the goods consumed by local inhabitants. The corresponding calculations lead to the following equations:
IWFi = Qc i − EVWi
(12)
EWFi = IVWi − EVWi re − ex
(13)
(17)
2.1.3. Location quotient for substitution In most cases, IO data is merely available at the provincial level from official statistics. However, IO data at a city or county scale is necessary for analyzing the WFs of a local area through the Environmental IO method. With the assumption that the proportions of intermediate products and economic contributions in a local area is similar to those at the corresponding provincial level, the location quotient could be applied for WF assessment at local areas (Jensen et al., 1979; Zhao et al., 2010; Feng et al., 2012). The substitution quotient can be obtained from the provincial IO data via the follow equation (Zhi et al., 2014):
(9)
where IVWi is the imported VW for sector i; GVWIiN is the average national VW intensity of sector i, as it is for the import amount, MI i is the import value for the final demand of sector i to the local region.
EVWi = GVWIi × MEi
(16)
(6)
Thus, the VW imports and exports are expressed as:
IVWIi = GVWIi − DVWIi
WF WA
where WF represents the WF of the area and WA refers to local water supply availability. In this study, we classified the WSI into four degrees for the evaluation of the water scarcity levels: extreme (> 1), severe (0.4–1), moderate (0.2–0.4), and no stress (< 0.2)(Ma et al., 2015; Metulini et al., 2016; Zhao et al., 2015). The water self-sufficiency (WSS) refers to the ratio of the internal WF over the total WF, representing the local water resource supply capability in the region. It is expressed as:
where DVWI is the direct virtual water intensity. Qci is the freshwater consumption by sector i. GVWIi represents the gross water consumed by sector i for generating one monetary unit of final demand in sector j, bij can be derived from Eq. (8), which bridges the final demand of a product with both direct and indirect water use.
[ bij ] = [ I − A ]
(15)
where WFi is the total WF of sector i in the local region.
where X is the vector of total outputs, A is the technical coefficients matrix, Y is the vector of final demands. When solved for X, Eq. (4) can be further written as Eq. (5), where (I − A)−1is the Leontief inverse matrix. The original IO table has been extended by adding a row vector of freshwater use coefficients for each sector (Mubako et al., 2013). The freshwater use coefficient is denoted as the direct water consumed by sector i for generating one monetary unit of final demand in sector j. It can be expressed as the direct virtual water intensity in the extended IO model. Thus, via the extended IO table, the direct virtual water intensity (DVWI), gross virtual water intensity (GVWI), and indirect virtual water intensity (IVWI) could be calculated (Guan and Hubacek 2007):
DVWIi = Qci / Xi
(14)
pi / Li =
∑
pi
i
piP /
∑ i
piP
(18)
where Li is the location quotient of sector i, pi is the output of sector i in the studied area, while piP is the provincial output of sector i for the studied area. 2.1.4. Gray WF and gray WF carrying capacity coefficient (K) Chemical Oxygen Demand (COD) is the major composite indicator for pollutants in wastewater discharge. Hence, COD is chosen to calculate the gray WFs.
where IWFi is the internal WF for sector i, EWFi is the external WF for sector i, EVWi re − ex is the exported VW to the other regions as a result of 3
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WFgray =
L Cmax − Cnat
2.2.2. Local indicators of spatial association (LISA) Local Indicators of Spatial Association (LISA), also called Anselin local Moran's I, is utilized to identify local hot spots or clusters that signify heterogeneity in one or more regions of the study area (Wang et al., 2016). It is defined as:
(19)
where WFgray is the gray WF, L is the pollutant load (kg/a), Cmax is the maximum acceptable concentration of the pollutant (kg/m3), and Cnat is the natural concentration in the receiving water body (kg/m3). In this study, Cmax was chosen for the standard concentration of the pollutant set according to Class III of the Chinese Environmental Quality Standard of Surface Water (GB 3838—2002), andCnat took 0. In fact, WFgray only reflects the volume of freshwater required to assimilate pollution. The water pollution stress at an area can be better illustrated by the gray WF Carrying Capacity Coefficient (K), which is the ratio of WFgrayto the total water supply of the region. A higher K value suggests a higher water pollution stress in the area.
n
Ii = zi
where zi and zj are the standardized values of the target attribute for the study area. The local Moran’s Ii can be visualized in the LISA cluster map. According to the statistic values, clusters and outliers of spatial data would be classified as clusters of high value zones (H-H), low value zones (L-L), and outlier zones which are relatively isolated low value zones (H-L) or high value zones (L-H). 3. Study site & data
where Tws refers to the total water supply of the area (m3).
The HRB, located in northern China (112–120°E, 35–43°N), is one of the key national industrial and agricultural centers. It covers an area of 318,200 km2, encompassing Beijing, Tianjin, the vast majority of Hebei Province, the eastern part of Shanxi Province, the northern parts of Henan and Shandong provinces, as well as a small part of the Inner Mongolia Autonomous Region and Liaoning Province (Fig. 1) (Zhao et al., 2010; Zheng et al., 2015; Zou et al., 2015). The population of the HRB is 143 million, accounting for 9.7% of the total Chinese population. The average annual water resource of the HRB is 41.9 billion m3, approximately 1.3% of the total, whereas the gross domestic product (GDP) of the HRB shares 12% of the nation’s total. The average water consumption of the HRB is 335 m3, which is merely 7% of the nation’s average (Haihe River Water Conservancy Commission, 2014). The dense population, low precipitation, and high development levels of the agricultural and industrial sectors have driven the HRB to become one of the most water scarce regions in China. At present, this water scarce region still produces water intensive goods to be consumed in other areas, which further amplifies the serious water shortage of the river basin. Currently, the exploitation level of surface water at the HRB is more than 90%, while that of groundwater is more than 100%. Although several water diversion projects have been conducted in the region, it still faces a severe water shortage. To cope with water scarcity problems, the virtual water strategy is considered as an viable option (Zhao et al., 2010). Previous studies on WF accounting for the HRB were mainly conducted at provincial scales (Zhi et al., 2014; White et al., 2015; Zhao et al., 2010). However, the provincial boundaries do not match those of the river basin. Thus, the WF accounting results at the provincial scale differ greatly from those at the basin scale. In this study, WF evaluations were undertaken at the city scales (26 cities). Table 2 provides a more detailed breakdown of the evaluation scope. Moreover, the relevant analysis of WFs would be made in dual scales, directed at cities and the whole basin. The data required in the IO models (including the input-output matrix, consumption, import, and export) were obtained from the IO table of the National Bureau of Statistics (2007) and the regional IO tables of local statistic bureaus (2007). The data of sectoral blue water used for IO analysis at each region in HRB was collected from the Haihe River Water Resource Bulletin 2008 (Haihe River Water Conservancy Commission, 2008). Green water was not considered because blue water was the only water supply in most sectors, except agriculture and the sectors depending on agricultural raw materials (Zhang et al., 2012; Zhao et al., 2010; Zhi et al., 2014). The data of wastewater discharges from different sectors in each study areas as well as their relevant COD levels were obtained from the Chinese Water Resources Bulletin of 2007 (Ministry of Water Resources, 2008), the Haihe River Water Resource Bulletin 2008 (Haihe River Water Conservancy Commission, 2008), the China’s Environmental Statistical Yearbook 2007, the Water Resources
2.2. Exploratory spatial data analysis (ESDA) ESDA is a spatial analysis method that focuses on the description and interpretation of spatial structures and correlations in an area (Dou et al., 2016). The ESDA method is also used to describe and visualize spatial distributions, and to identify spatial association patterns and spatial heterogeneity (Deng et al., 2016). 2.2.1. Global spatial auto-correlation Global spatial auto-correlation is measured via the Global Moran Index (Moran’s I) in this study. It is used to identify the overall spatial auto-correlation for quantifying the degree of clustering or dispersion (Deng et al., 2016; Wang et al., 2016). The statistic model is written as follows: n
∑ ∑ I =
i
Wij ( xi − x ) ( xj − x )
j≠i n
S2
x =
1 n 1 n
n
∑ ∑ i
S2 =
j≠i
Wij (21a)
n
∑
( xi − x )
2
i
(21b)
n
∑
xi
(j )
i (j )
(21c)
where xi and xj are the geographical attributes of regions i and j, Wij is the spatial weight between region i and j. When the two regions are adjacent, Wij = 1; otherwise, Wij = 0. n is the amount of the total regions included in the study. Zscore should be calculated for testing whether there is a statistically significant spatial auto-correlation:
I − E (I ) Var (I )
(22a)
E (I ) = −1/ (n − 1)
(22b)
Var (I ) = E [I 2] − E [I ]2
(22c)
Zscore =
(23)
(20)
Tws
n
Wij zj ( i ≠ j )
j =1
WFgray
K =
∑
where the statistically significance level α is set for 0.05. Accordingly, Zscore should be bigger than 1.96. Namely, when Zscore > 1.96 (α = 0.05), there is significant spatial correlation. Otherwise, the analyzed attribute is randomly distributed in the study area. The Global Moran's I takes values between −1 to 1. When I is positive and close to 1, the attribute is statistically auto-correlated and suitable for clustering analysis; an I value close to −1 indicates a dispersed distribution; and when I takes zero, the attribute has no spatial correlation. 4
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Fig. 1. The location of HRB in China.
industries such as electric power and heat power production, smelting, and the pressing and manufacture of metals. Hence, the internal industrial blue WFs for the HRB were comparatively higher among the three sectors. For the external blue WFs, industrial WFs occupied 66.92% and the amounts for agricultural and residential WFs were 28.7% and 4.61%, respectively. The gray WFs of the HRB amounted to 862.99 × 108 m3, which was 2.245-fold of the total annual water supply. Given the blue water deficit of 561.68 × 108 m3, compensation of water supply for the pollution assimilation in the river basin was impossible. Hence, the HRB had a high load of gray WFs accumulation. Consequently, the whole basin continuously suffers from severe water quality problems.
Bulletin 2007, and the Environmental Statistical Yearbook 2007 of each study area (Beijing Statistics Bureau, 2008; Tianjin Statistics Bureau, 2008; Hebei Statistics Bureau, 2008; Shanxi Statistics Bureau, 2008; Shandong Statistics Bureau, 2008; Henan Statistics Bureau, 2008; National Bureau of Statistics, 2007).
4. Results & discussion 4.1. Total WFs for the whole HRB Table 1 showed the total, blue, and gray WFs of the entire river basin as well as the WFs of its three major economic sectors. The total WF of the HRB was 1809.15 × 108 m3, among which the total blue WF was 946.16 × 108 m3, accounting for approximately 52.30% of the total. To compare with the water supply of the HRB in 2007, which was 384.48 × 108 m3, there was a water deficit of 561.68 × 108 m3. Consequently, the water supply could only meet 40.64% of the blue water demand. Hence, the total river basin remained in a severe water deficiency. The internal blue WFs of the river basin was 807.69 × 108 m3, accounting for 85.37% of the total blue WFs. This indicated that the blue water consumption of the HRB mainly depended on local water supply. Among the internal blue WFs, the industrial WFs had the largest proportion, sharing approximately 76.49% of the total, while agricultural and residential WFs accounted for only 23.42% and 0.09%, respectively. According to the statistics report (Haihe River Water Resource Conservancy Commission, 2008), the HRB was one of the most important industrial bases in China, especially for water intensive
4.2. WFs for cities in HRB The WFs of 26 cities of the HRB were evaluated in this study as well. The blue WFs of each city are illustrated in Figs. 2–4. The figures revealed that in 2007, Beijing, Tianjin, and Tangshan had the largest internal as well as external WF, occupying approximately 27.04%, 15.64%, and 9.00% of the total internal WF and 19.75%, 10.72%, and 12.05% of the external WF, respectively. The three cities have formed one of the most competitive and fast-developing economic zones in China (the Beijing-Tianjin-Tangshan Urban Economic Zone). Taking the advantage of its regional superiority and resource endowments, this economic zone has become the base for the processing of China’s agricultural products, automobile manufacturing, raw material and power generation, and the metallurgy industry. Most of these industries are water-intensive. Yangquan, Xinzhou, Shuozhou and Yangquan, Datong, Xinzhou had the least internal and external WFs, respectively. The per capita WFs of the 26 cities in the HRB were ranging from 10.61 m3 to 159.38 m3 (see Fig. 5). The top three cities with the highest per capita WFs were also Beijing, Tianjin, and Tangshan. As the capital and a megalopolis, Beijing is not only the political centre, but also the economic centre of China. Its industries range from automobile manufacturing, high-tech manufacturing, electricity and power generation to communication and other electronic equipment manufacturing. Moreover, as the cultural and social centre, the living conditions of Beijing are also of high standard. Consequently, the per capita WF of
Table 1 WFs of the HRB. WF (108m3) Blue WFs
Gray WFs Total
Internal Blue WFs External Blue WFs Total Blue WFs
Agricultural
Industrial
Residential
Total
189.16 39.42 228.58 466.02 694.60
617.83 92.67 710.5 366.67 1077.17
0.70 6.38 7.08 436.28 443.36
807.69 138.47 946.16 862.99 1809.15
5
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Table 2 WFs & per capita WFs of cities in the HRB. Cities
Blue WF (108m3)
Gray WF (108m3)
Total WF (108m3)
Water Supply (108m3)
Population (105)
Per Cap. Blue WF (m3)
Per Cap. Gray WF (m3)
K
Beijing Tianjin
251.98 144.73
53.25 68.65
305.23 213.38
34.81 23.38
1581.00 1075.00
159.38 134.63
33.68 63.86
1.53 2.94
Hebei Province Shijiazhuang Chengde Zhangjiakou Qinhuangdao Tangshan Langfang Baoding Cangzhou Hengshui Xingtai Handan
62.58 19.02 16.10 15.88 92.00 42.28 37.88 38.95 18.65 30.39 47.36
64.85 21.06 31.50 10.70 48.25 20.50 34.25 26.53 28.05 24.75 22.95
127.43 40.08 47.60 26.58 140.25 62.78 72.13 65.48 46.70 55.14 70.31
4.60 1.62 1.50 1.57 10.24 2.15 2.30 0.90 1.60 1.52 2.49
978.00 339.00 420.00 293.00 739.00 406.00 1085.00 693.00 426.00 688.00 876.00
63.98 56.11 38.34 54.21 124.49 104.15 34.91 56.21 43.78 44.18 54.06
66.31 62.11 75.00 36.52 65.29 50.49 31.57 38.28 65.85 35.97 26.20
16.34 13.84 21.53 7.96 4.75 10.13 16.81 32.55 18.17 16.47 10.27
Shanxi Province Datong 4.94 Yangquan 2.47 Changzhi 6.94 Shuozhou 5.07 Xinzhou 3.26
36.12 9.10 21.82 27.72 30.35
41.06 11.57 28.76 32.79 33.60
2.34 1.26 2.07 1.65 2.12
315.97 131.37 326.93 152.66 307.26
15.64 18.78 21.22 33.21 10.61
114.32 69.28 66.74 181.59 98.76
16.28 7.61 11.29 17.37 14.95
Henan Province Anyang 19.69 Hebi 12.75 Xinxiang 14.36 Jiaozuo 15.05 Puyang 10.09
34.52 11.43 38.07 29.86 13.84
54.21 24.18 52.44 44.90 23.93
1.60 1.04 1.48 1.48 0.48
539.00 145.00 558.00 345.00 361.00
36.53 87.95 25.74 43.62 27.95
64.04 78.82 68.23 86.54 38.35
22.85 11.42 26.37 74.47 22.85
Shandong Province Dezhou 22.47 Liaocheng 21.50 Binzhou 15.51
63.47 53.40 38.01
85.94 74.90 53.52
0.60 3.38 0.53
545.28 553.66 367.12
41.21 38.83 42.25
116.40 96.44 103.53
110.31 16.09 74.60
chemical industries. Thus, the per capita WF of Tianjin also ranked higher in the total HRB. The three cities, which had the least per capita WFs are all located in the Shanxi Province. Shanxi is among the most water deficient provinces of China with a per capita water supply of 17.16% of the
Beijing was comparatively higher. Tianjin, as a municipality as well as a megalopolis, has the advantage of a harbor and is one of the most important international navigation centers of China. It has highly developed industries, such as aerospace manufacturing, ship building industry, petrochemical industry, iron and steel manufacturing, and
Fig. 2. External blue WFs of 26 cities in HRB. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
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Fig. 3. Internal blue WFs of 26 cities in HRB. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
cities with the lowest gray WFs were Yangquan, Qinhuangdao, and Hebi. Among all 26 cities, Shuozhou had the largest gray WF per capita, followed by Dezhou and Datong (see Table 2). Comparing the blue WFs and gray WFs with the water supply of each city, we found that the water supply of none of the cities could meet the requirements of either blue WFs or gray WFs. This revealed that all cities in the HRB were in lack of water resources both in quantity and quality (see Fig. 7). Table 3 listed the blue and gray WFs of each city in the HRB in different sectors. The results revealed that Beijing, Tianjin, and
national average. Hence, in the year 2000, the provincial authority issued the Urban Water Supply & Conservation Regulation of Shanxi Province for water conservation in the province. Moreover, since 2006, four cities in the Shanxi province have been selected as the national water conservation pilot cities. Those effects on water resource conservation finally resulted in the observed low per capita WFs of the cities in Shanxi province. The total gray WFs of the cities in the HRB were ranging from 9.10 × 108 m3 to 68.65 × 108 m3 (see Fig. 6). The three cities with the highest gray WFs were Tianjin, Shijiazhuang, and Dezhou, while the
Fig. 4. Total blue WFs of 26 cities in HRB. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
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Fig. 5. Per capita blue WFs of 26 cities in HRB. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
cities had more industrial gray WFs than agricultural and residential gray WFs, which were mainly industrial cities located in Henan and Hebei provinces. The remaining 38.46% of the cities had more agricultural gray WFs, the majority of which were cities from agricultural-based provinces like Shandong and Hebei.
Tangshan had the highest blue WFs in the agricultural sector. Moreover, the cities in Shandong province also had comparatively elevated blue WFs in the agricultural sector, as Shandong is the biggest agricultural base of China. The cities with the highest industrial and residential blue WFs were Beijing and Tianjin, respectively. Figs. 8–11 revealed the detailed information on gray WFs of each city in three sectors. Among them, approximately 38.46% of the cities had more residential gray WFs than industrial and agricultural WFs, which were mainly megalopoli such as Beijing and Tianjin or tourist cities such as Chengde and Qinhuangdao. Approximately 23.08% of the
4.3. Water stress index, water self-sufficiency & gray WF carrying capacity coefficient The results revealed that the WSI of all the cities were above 1,
Fig. 6. Total gray WFs of 26 citie.
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Fig. 7. Comparisons of Blue & Gray WFs with water supplies in 26 cities. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Table 3 Blue & gray WFs by different sectors of cities in the HRB. Cities
Blue WF (108 m3)
Gray WF (108 m3)
Agricultural
Industrial
Residential
Agricultural
Industrial
Residential
Beijing Tianjin
38.20 23.44
209.05 113.51
0.02 2.19
6.91 12.98
3.31 15.37
49.94 53.28
Hebei Province Shijiazhuang Chengde Zhangjiakou Qinhuangdao Tangshan Langfang Baoding Cangzhou Hengshui Xingtai Handan
15.61 5.11 5.24 4.38 19.81 12.17 11.67 9.36 6.66 8.51 11.74
44.93 13.50 10.39 10.87 70.11 28.62 25.27 28.90 11.68 21.44 34.71
1.00 0.24 0.28 0.27 1.34 0.27 0.42 0.23 0.14 0.25 0.40
23.91 4.87 4.96 6.30 18.71 8.25 21.66 15.72 12.75 16.42 22.38
38.76 9.02 8.95 4.60 37.75 7.78 13.25 15.23 10.55 10.55 7.46
26.09 12.04 22.55 6.10 10.50 12.72 21.00 11.30 17.50 14.20 15.49
Shanxi Province Datong Yangquan Changzhi Shuozhou Xinzhou
0.78 0.15 0.95 0.77 0.77
4.08 2.28 5.95 4.25 2.41
0.00 0.00 0.00 0.00 0.00
13.56 2.31 18.32 14.61 21.94
13.45 3.39 8.67 10.32 8.65
22.67 5.71 13.15 17.40 21.69
Henan Province Anyang Hebi Xinxiang Jiaozuo Puyang
0.93 0.32 1.10 0.90 0.60
16.97 10.18 11.18 12.66 7.54
0.00 0.00 0.00 0.00 0.00
16.85 2.91 21.85 10.40 12.58
20.68 6.16 23.26 18.29 8.35
13.84 5.27 14.81 11.57 5.50
Shandong Province Dezhou Liaocheng Binzhou
18.85 18.39 12.17
3.61 3.09 3.34
0.01 0.01 0.01
63.47 53.40 38.01
26.42 16.23 20.22
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Fig. 8. Industrial gray WFs of 26 cities.
with to a level of self-sufficiency. 30.77% of the cities (Langfang, Shuozhou, Xinzhou, Anyang, Hebi, Xinxiang, Dezhou, and Liaocheng) were comparatively insufficiently equipped with the water supplies of the local area and were dependent on external water resources. The remaining 69.23% of the cities could predominantly depend on their own water resources for the local water demands, and their reliance on external water resources was comparatively lower (see Fig. 13). The results of gray WF carrying capacity coefficient (K) have been
which meant that all cities of the HRB fell in the category of extreme water scarcity. The western part of the HRB (mainly the cities in Shanxi Province) had comparatively less water scarcity stress. However, the eastern part (Cangzhou, Dezhou and Binzhou) suffered from extreme severe water scarcity, and the local water supplies could merely meet 2.30% to 3.44% of their WFs (Fig. 12). The evaluation of the WSS indicated that among the 26 cities, none of their local water resources could meet the local water consumptions
Fig. 9. Agricultural gray WFs of 26 cities.
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Fig. 10. Residential gray WFs of 26 cities.
respectively. Both of the results ranged within [-1,1], and were bigger than 0. The corresponding ZWSI and ZK were 3.589 and 2.906, respectively, which both passed significance tests, revealing the results to be statistically correlated. Figs. 14 and 15 are the LISA cluster maps of the cities in the HRB based on WSI and K values. Fig. 14 revealed that the cities (Dezhou, Binzhou, and Cangzhou) in the eastern part were located in the H-H clusters. In this area, the total water deficiencies were highest among all regions in the HRB. More cities fell into the L–L cluster areas in the northern and western parts of the HRB as shown in the map. The cities in the mid-part of the HRB, mainly Beijing, Tianjian, Xingtai, and Liaocheng were in the cluster of
listed in Table 2. The cities with the top three largest K were Dezhou, Binzhou, and Jiaozuo. Their gray WF carrying capacity coefficients were 110.31, 74.60, and 74.37, respectively. This indicated that these three cities had comparatively higher water pollution stresses. The city with the smallest K was Beijing. 4.4. Spatial correlation analysis The results of the WSI and K were further analyzed via the ESDA model for the spatial distribution attributes among the cities in the HRB. The Moran’s I for the WSI and K were 0.2742 and 0.2890,
Fig. 11. Gray WFs in different sector-oriented.
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Fig. 12. WSI of cities in HRB.
Fig. 13. WSS of cities in HRB.
distribution features, Beijing, Tianjian, and most cities in Shanxi and Hebei provinces had comparatively less water deficits due to water quality problems. However, cities in Shandong and Henan provinces should remedy their water quality problems. A comparison of Figs. 14 and 15 revealed that Dezhou and Binzhou had suffered from the highest water deficiency on both their water quantity and quality. Jiaozuo, Xinxiang, Anyang, and Puyang should put more emphasis on their water problem resulting from waste water discharge.
L-H, which indicated that compared to most of the cities in Shandong and Hebei, these cites had less water stresses. The reasons could be the relatively higher water utility rates or more high-tech industries that had been applied in these more advanced cities. Fig. 15 showed the LISA cluster map of cities in HRB based on the gray WF carrying capacity coefficient. The H-H cluster mainly focused on the southern and southeastern parts of the HRB. These cities are predominantly situated in Shandong and Henan provinces. The L–L clusters distributed in the northern and western parts of the HRB, mainly cities situated in Hebei and Shanxi provinces. Hence, from the 12
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Fig. 14. LISA cluster map of cities in HRB based on the WSI.
5. Conclusion
evaluations of the water stress in terms of quantity and quality were conducted at both the river basin and city levels. Additionally, the spatial correlation analysis based on their relevant water stress indexes and gray WF carrying capacity coefficients were conducted via the ESDA model for the illustration of the spatial correlation distributions of the water stresses for the cities of the HRB. The major results of this study included: (1) Beijing, Tianjin, and Tangshan had the largest internal and external blue WFs. These three cities were also the top cities with the highest per capita WFs in the HRB. The top three cities for gray WFs were Tianjin, Shijiazhuang, and
Water resources management at river basins faces the challenges of unbalanced water distributions among different regions and economic sectors. The involvement of multiple regional jurisdictions further complicates the optimal allocation and sustainable management of water resources at river basins. Water footprints have been widely applied to assess sustainable utilization of water resources. In this study, based on the blue and gray WFs accounting of different economic sectors, as well as an extended Environmental IO Analysis, the
Fig. 15. LISA cluster map of cities in HRB based on the K.
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Dezhou whereas the cities with the lowest gray WFs were Yangquan, Qinhuangdao and Hebi; (2) The iindustrial sector represented approximately 75.09% of the total blue WFs in the HRB. The agricultural sector accounted for 54% of the total gray WFs. The city with the highest agricultural and industrial blue WFs was Beijing; and (3) All cities of the HRB suffered from extreme water scarcity. Dezhou and Binzhou had water stress problems in both quantity and quality, while Jiaozuo, Anyang, Xinxiang and Puyang mainly suffered from high-level water pollution stress. The results of this study would be useful for assessing water endowments and supporting decision-making in water allocation among the cities and economic sectors within the river basin. In future studies, an optimization model could be developed on the basis of these regional virtual water flow analyses for allocating water resources to various sectors. Acknowledgements This research was supported by the National Key Research and Development Program (2016YFC0401302), the Zhejiang Social Science Program (12JCJJ10YB), and the Social Science Project of Education Department of Zhejiang Province (Sustainable Development of Coastal Areas based on Ecosystem Analysis). In addition, we also acknowledge the editor and anonymous reviewers for their valuable suggestions and comments on our manuscript. References Allan, J.A., 2003. Virtual water − the water, food, and trade nexus useful concept or misleading metaphor? Water Int. 28, 106–113. Beijing Water Resources Bureau, 2008. Beijing Water Resource Bulletin. China Water Power Press, Beijing (in Chinese). Cazcarro, I., Hoekstra, A.Y., Choliz, J.S., 2014. The water footprint of tourism in Spain. Tour. Manage. 40, 90–101. Chapagain, A.K., Hoekstra, A.Y., 2007. The water footprint of coffee and tea consumption in the Netherlands. Ecol. Econ. 64, 109–118. Chapagain, A.K., Hoekstra, A.Y., 2008. The global component of freshwater demand and supply: an assessment of virtual water flows between nations as a result of trade in agricultural and industrial products. Water Int. 33, 19–32. Chapagain, A.K., Hoekstra, A.Y., Savenije, H.H.G., Gautam, R., 2006. The water footprint of cotton consumption: an assessment of the impact of worldwide consumption of cotton products on the water resources in the cotton producing countries. Ecol. Econ. 60, 186–203. Deng, X.J., Xu, Y.P., Han, L.F., Yang, M.N., Yang, L., Song, S., Li, G., Wang, Y.F., 2016. Spatial-temporal evolution of the distribution pattern of river systems in the plain river network region of the Taihu Basin, China. Quat. Int. 392, 178–186. Dou, Y., Luo, X., Dong, L., Wu, C.T., Liang, H.W., Ren, J.Z., 2016. An empirical study on transit-oriented low-carbon urban land use planning: exploratory Spatial Data Analysis (ESDA) on Shanghai, China. Habitat Int. 53, 379–389. El-Sadek, A., 2010. Virtual water trade as a solution for water scarcity in Egypt. Water Resour. Manage. 24, 2437–2448. Ene, S.A., Teodosiu, C., Robu, B., Volf, I., 2013. Water footprint assessment in the winemaking industry: a case study for a Romanian medium size production plant. J. Clean. Prod. 43, 122–135. Ercin, A.E., Hoekstra, A.Y., 2016. European water footprint scenarios for 2050. Water 8. Feng, K.S., Hubacek, K., Pfister, S., Yu, Y., Sun, L.X., 2014. Virtual scarce water in China. Environ. Sci. Technol. 48, 7704–7713. Foster, S., Garduno, H., Evans, R., Olson, D., Tian, Y., Zhang, W.Z., Han, Z.S., 2004. Quaternary aquifer of the North China Plain − assessing and achieving groundwater resource sustainability. Hydrol. J. 12, 81–93. Guan, D., Hubacek, K., 2007. Assessment of regional trade and virtual water flows in China. Ecol. Econ. 61, 159–170. Haihe River Water Conservancy Commission, Haihe River Water Resource Bulletin 2008, 2008, (in Chinese). Haihe River Water Conservancy Commission, 2014. Haihe River Water Resource Bulletin 2014. (in Chinese). Haro, M.E., Navarro, I., Thompson, R., Jimenez, B., 2014. Estimation of the water footprint of sugarcane in Mexico: is ethanol production an environmentally feasible fuel option? J. Water Clim. Change 5, 70–80. Hebei Water Resources Bureau, 2008. Hebei Water Resource Bulletin. (in Chinese). Henan Water Resources Bureau, 2008. Henan Water Resource Bulletin. (in Chinese). Hoekstra, A.Y., Chapagain, A.K., 2007. Water footprints of nations: water use by people as a function of their consumption pattern. In: Integrated Assessment of Water Resources and Global Change. Bonn, Germany. pp. 35–48. Hoekstra, A.Y., Hung, P.Q., 2005. Globalisation of water resources: international virtual water flows in relation to crop trade. Glob. Environ. Change-Hum. Policy Dimens. 15, 45–56.
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Ecological Indicators journal homepage: www.elsevier.com/locate/ecolind
Spatial analysis of dual-scale water stresses based on water footprint accounting in the Haihe River Basin, China Chunhui Lia,d, Meng Xua,b, Xuan Wanga,d, Qian Tanc,
⁎
a
Ministry of Education Key Lab of Water and Sand Science, School of Environment, Beijing Normal University, Beijing 100875, PR China School of Public Administration, Zhejiang University of Finance & Economics, Hangzhou 310018, PR China c College of Water Resources & Civil Engineering, China Agriculture University, Beijing 100083, PR China d State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, PR China b
A R T I C L E I N F O
A B S T R A C T
Keywords: Blue water footprint Gray water footprint Haihe River Basin Exploratory spatial data analysis Water footprint accounting
Water scarcity and degradation of water quality in river basins are among the major issues addressed by water resources management authorities. Moreover, two typical challenges associated with water resources management include naturally unbalanced distribution and administrative disparities under multiple jurisdictions at the watershed scale. Effective accounting and management methods are thus desired to deal with such challenges. Water footprint accounting is widely used for assessing natural water resources availability and supporting optimal allocation among multiple jurisdictions, representing a useful tool for improving watershed sustainability. Not only will this enable successful revealing of the direct water consumption by relevant agents, but also indirect water consumption by concerning users. Such a method is particularly useful for addressing waterrelated issues in numerous watersheds of developing countries, which are subject to diverse water stressors. Hence, this study aimed at conducting water footprint accounting under city/regional and basin scales for the investigation of the amounts of blue and gray water in the Haihe River Basin (HRB), China. The Blue Water Footprint Index (BWFI) and the Gray Water Footprint Capacity Coefficient (K) were introduced to comprehensively evaluate water scarcity in terms of quantity and quality. The results revealed that all cities of the HRB suffered from extreme water scarcity. The industrial sector was identified as the primary contributor to the blue water footprint at the watershed. The agricultural gray-water footprint of the HRB accounted for 54% of the total. The exploratory spatial data analysis (ESDA) was adopted to analyze the spatial auto-correlation features of blue and gray water in the HRB. The results indicated that Dezhou and Binzhou had the highest water deficiency in both of the water stresses, and that Xinxiang, Puyang, Anyang and Jiaozuo had the highest water problem predominantly due to water quality stress.
1. Introduction Limited availability of fresh water jeopardizes human well-being, hampers economic growth, and contributes to losses of ecosystem functions and biodiversities (Launiainen et al., 2014). Currently, onethird of the world population are facing water scarcity (Manzardo et al., 2014). According to the UNEP (2008), the turning point will arrive in 2025 when almost half of the world’s population would be living in declining situations of water stress due to increased water use (Sultana et al., 2014). One of the prominent problems of water scarcity in some areas is the severe discrepancy of the spatial distribution among the water resources, population and the economic development (Zhang and Anadon, 2014). The spatial imbalance and mismatch of the water endowments and demands have led to significant adverse ecological
⁎
impacts, posing an immense challenge for the sustainable development of cities and river basins in arid regions (Foster et al., 2004). The increasing pressures on water resources have created the need for critical techniques and strategies related to sustainable water use and management (Sultana et al., 2014). Currently, virtual water (VW) and water footprint (WF) are identified as important indications for sustainable water management. The concept of VW was proposed by Tony Allan in 1993 (Allan, 2003) to reveal the amount of embedded freshwater used to produce agricultural and industrial goods. In the early 2000s, this concept was further extended by the idea of “water footprint” (WF) by Hoekstra (Chapagain and Hoekstra, 2007; Hoekstra and Chapagain, 2007; Hoekstra and Hung, 2005; Ma et al., 2006) to uncover the hidden link between consumption and water use, which can be set as the basis for
Corresponding author. E-mail address: [email protected] (Q. Tan).
http://dx.doi.org/10.1016/j.ecolind.2017.02.046 Received 28 November 2016; Received in revised form 31 January 2017; Accepted 13 February 2017 1470-160X/ © 2017 Published by Elsevier Ltd.
Please cite this article as: Li, C., Ecological Indicators (2017), http://dx.doi.org/10.1016/j.ecolind.2017.02.046
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standards (Hoekstra et al., 2011). Based on these concepts, researches have been conducted to assess regional blue, green, and gray water footprints. Two main shortcomings remain for the WF assessment. First, most of the previous studies mainly concentrated on agricultural and livestock productions, but few touched industrial and residential aspects due to limitations in accounting methods and difficulties in data collection (Chapagain and Hoekstra, 2007, 2008; Chapagain et al., 2006; Hoekstra and Chapagain, 2007; Mekonnen and Hoekstra, 2011; Zhao et al., 2010; Yan et al., 2013). With the expansion of urban areas and the prosperity of the modern life style, industrial production and residential activities developed into the fundamental cause of the current ecological crises. Thus, the study of the WF for industrial productions and residential activities is a significant field of research, which can scientifically reveal the multiple impacts on freshwater resources, caused by industrial and residential processes, ultimately improving sustainable and equitable water use in these fields (Yan et al., 2013). Secondly, few current studies have analyzed the three types of water footprints at a river basin scale, in particular, the assessment of these three types of WFs in different economic sectors of the cities level. Such studies would be quite helpful for water allocation in river basins, as there are wide variations in water and other natural resource endowments among different cities/regions inside river basins. In addition, it will make it feasible with a more detailed WFs accounting based on the city scale for the further optimal allocation of the water resources among river basins. Therefore, the objective of this study was to conduct an in-depth WF accounting for different economic sectors and cities at river basins, and thus to reveal the spatial distributions of water stress and water pollution stress within the study area. The study would improve the evaluation accuracy of WFs for river basins via accounting efforts downscaled to cities, and enhance the WF-based methods of water stress analyses through applying an explore spatial distribution analysis (ESDA) model. With the Haihe River Basin (HRB) being the study area, the research entails the following tasks: (a) to evaluate blue and gray WFs in different sectors of the cities within HRB as well as for the entire basin; (b) to conduct blue and gray WF analyses based on indicators of both water stress and water pollution stress; and (c) to analyze the spatial distribution correlation of the cities based on the water stress and water pollution stress analyses indicators. In detail: (i) the blue and gray WFs of the three main sectors for 26 cities in the HRB will be analyzed, respectively, via the Environmental Input-Output Model; (ii) the water stress and water pollution stress will be analyzed via the Water Footprint Index and the Gray Water Footprint Capacity Coefficient at the city and basin scales; (iii) the ESDA Model will be applied to identify the spatial cluster properties of the cities in terms of the water stress and water pollution stress indicators. The results of this study reveal the water stress on both the quantity and quality of the river basin in various economic sectors. Thus, the further concerning instructions on water allocation strategies can be formulated accordingly.
the formulation of new strategies of water governance (Hoekstra et al., 2011). From the perspective of water footprint, final consumers, retailers, food industry, and traders of water-intensive products could enter the scenario as potential ‘change agents’. They can be addressed not only for their role as direct water users, but also for their role as indirect ones (Hoekstra et al., 2011). In recent years, a large number of studies have been carried out on WF assessment at different scales. At a global level, O'Bannon et al. (2014) have reconstructed the network of agricultural pollution based on international trade records, commodity, and nation-specific gray WFs for the period between 1986 and 2010. Ercin and Hoekstra (2016) have developed forecasts of global WF scenarios for 2050 based on a number of drivers of change: population growth, economic growth, production/trade patterns, consumption patterns, and technological development to understand the changes in the WF of production and consumption for possible future developments by region, thus elaborating main drivers of this change. At a national level, several scholars have tried to compile the life cycle assessment (LCA) concept in the WF accounting for a sound analysis on the VW contents in various agricultural or livestock products. For instance, Lamastra et al. (2014) assessed the WF of Italian wine production throughout its entire life cycle. Haro et al. (2014) estimated the WF of sugarcane in Mexico. Zonderland-Thomassen et al. (2014) assessed the WF of beef cattle and sheep produced in New Zealand and compliant with LCA principles. Besides WF accounting for the products, the WF assessment approach was predominantly used in the VW accounting for analyzing national policies on water conservation via VW trade. For example, Mekonnen and Hoekstra (2014) conducted research on water resources conservation via the VW trade of Kenya. Winter et al. (2014) highlighted incongruent terms in VW balance by comparing the indexes denoting the ratio of virtual water exchanged during the swap and the ratio of the economic values of the swapped products for an illustration of the swap-product trade. Feng et al. (2014) incorporated a water stress index as well as an indicator for ecosystem damage into the assessment of interregional virtual water flows across 30 Chinese provinces. Cazcarro et al. (2014) complemented the WF estimations for Spanish tourism via an input-output analysis. El-Sadek (2010) introduced VW trade as a solution for the water scarcity in Egypt. In recent years, researches have also been conducted on the WF assessment at the regional level. The WF accounting is no longer restricted to the agricultural products, and some studies on industrial products have been undertaken by applying an input-output analysis. Such analyses help to assess annual changes in the WF of a region, identify the key economic sectors and factors leading to the changes, and quantitatively evaluate the contributions of those sectors and factors to the changes. Consequently, policy-makers can take water-saving actions focusing on the key economic sectors or factors. Zhang et al. (2011) quantitatively evaluated the water footprint and virtual water trade of Beijing by applying a provincial-level, interregional input–output model. Mubako et al. (2013) applied an input–output analysis to evaluate water use and to quantify virtual water transfers between California and Illinois. Relevant studies have also been undertaken in semi-arid and arid areas of European, South American, and African regions and other semi-arid and arid areas (Ene et al., 2013; Mekonnen et al., 2012; Nana et al., 2014; Pena and Huijbregts, 2014; Perez-Blanco and Thaler, 2014; Yan et al., 2013; Zhang and Anadon, 2014; Zoumides et al., 2014). WF is a multidimensional indicator of freshwater consumption volumes by sources as well as of polluted water volumes by types of pollution. It is further analyzed as blue water footprint (blue WF), green water footprint (green WF), and gray water footprint (gray WF). The blue WF refers to the consumption of blue water resources (surface and groundwater) along the supply chain of a product. The green WF refers to the consumption of green water resources (rainwater insofar as it does not become run-off). The gray WF is defined as the volume of freshwater that is required to assimilate the load of pollutants, given natural background concentrations and existing ambient water quality
2. Methodology 2.1. Environmental input-output (IO) model 2.1.1. Internal & external water footprint The general IO model was set up by Leontief (1941) to represent the monetary trade of intermediate products between different sectors in an economic system. This IO model was further developed into the Water IO model by Zhang et al. (2011) This model portrays how the production of an economy depends upon interactions between different sectors and final demand (Zhi et al., 2014). Water is a primary input into economic production, and this relationship is reflected through freshwater use coefficients for each economic sector (Mubako et al., 2013). Thus, the water IO model can be summarized in Eq. (1) (Zhang and Anadon, 2014): 2
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Xi =
∑
xij + yi
re-export of imported products in sector i. It can be derived from Eq. (14) as follows:
(1)
j =1
n
where Xi is the gross economic output of the sector i; xij represents the inputs from the sector i to the sector j; n is the number of economic sectors of an economy; yi is the final demand of sector i. From a transactions table, technical coefficients of production aij can be derived by dividing the inter-sectoral flows from sector i to j (xij ) via total input of sector j (Xj):
aij = xij / X j
EVWi re − ex = νi × ( ∑ GVWIi × mij ) i
where νi is the adjusting coefficient derived as the proportion of the import amount value from oversea sector i and then re-exported to abroad over the outcome of final demand in each sector i, mij is the import from the oversea sector i to the domestic sector j. Thus, the total WF of sector i is:
(2)
n
Xi =
∑
a ij X j + yi
j =1
WFi = IWFi + EWFi (3)
X = AX + Y
(4)
X = ( I − A )−1 Y
(5)
GVWIi =
∑
DVWIi × bij
2.1.2. Water stress index (WSI) and water self-sufficiency (WSS) The water stress index (WSI) is defined as the ratio of the total WF over the total water availability in the local area (Ma et al., 2015; Metulini et al., 2016; Zhao et al., 2015) and can be expressed as:
WSI =
−1
IVWi = GVWI i N × MIi
IWF WF
WSS =
(7)
where IWF is the internal water footprint of the study area. For the evaluation of the WSS, five WSS levels were set: self-sufficient (> 1), barely self-sufficient (0.8–1), comparatively self-insufficient (0.4–0.8), self-insufficient (0.2–0.4), and extremely self-insufficient (< 0.2). When WSS equals 1, this means that the local water resource can only just meet the need of regional water consumption. Whereas, a WSS below 1 represents the different margins of insufficiency of the local water supply (Ma et al., 2015).
(8)
(10)
(11)
where EVWi is the exported VW for sector i, GVWI i is the VW intensity of sector i in the local region, ME i is the export value of sector i from the local region. The internal WF (IWF) refers to the amount of water resources consumed for production by the local residents. Thus, it equals to the total freshwater consumption minus the total exported VW. The external WF (EWF) is defined as the amount of water resources used in other regions for the production of the goods consumed by local inhabitants. The corresponding calculations lead to the following equations:
IWFi = Qc i − EVWi
(12)
EWFi = IVWi − EVWi re − ex
(13)
(17)
2.1.3. Location quotient for substitution In most cases, IO data is merely available at the provincial level from official statistics. However, IO data at a city or county scale is necessary for analyzing the WFs of a local area through the Environmental IO method. With the assumption that the proportions of intermediate products and economic contributions in a local area is similar to those at the corresponding provincial level, the location quotient could be applied for WF assessment at local areas (Jensen et al., 1979; Zhao et al., 2010; Feng et al., 2012). The substitution quotient can be obtained from the provincial IO data via the follow equation (Zhi et al., 2014):
(9)
where IVWi is the imported VW for sector i; GVWIiN is the average national VW intensity of sector i, as it is for the import amount, MI i is the import value for the final demand of sector i to the local region.
EVWi = GVWIi × MEi
(16)
(6)
Thus, the VW imports and exports are expressed as:
IVWIi = GVWIi − DVWIi
WF WA
where WF represents the WF of the area and WA refers to local water supply availability. In this study, we classified the WSI into four degrees for the evaluation of the water scarcity levels: extreme (> 1), severe (0.4–1), moderate (0.2–0.4), and no stress (< 0.2)(Ma et al., 2015; Metulini et al., 2016; Zhao et al., 2015). The water self-sufficiency (WSS) refers to the ratio of the internal WF over the total WF, representing the local water resource supply capability in the region. It is expressed as:
where DVWI is the direct virtual water intensity. Qci is the freshwater consumption by sector i. GVWIi represents the gross water consumed by sector i for generating one monetary unit of final demand in sector j, bij can be derived from Eq. (8), which bridges the final demand of a product with both direct and indirect water use.
[ bij ] = [ I − A ]
(15)
where WFi is the total WF of sector i in the local region.
where X is the vector of total outputs, A is the technical coefficients matrix, Y is the vector of final demands. When solved for X, Eq. (4) can be further written as Eq. (5), where (I − A)−1is the Leontief inverse matrix. The original IO table has been extended by adding a row vector of freshwater use coefficients for each sector (Mubako et al., 2013). The freshwater use coefficient is denoted as the direct water consumed by sector i for generating one monetary unit of final demand in sector j. It can be expressed as the direct virtual water intensity in the extended IO model. Thus, via the extended IO table, the direct virtual water intensity (DVWI), gross virtual water intensity (GVWI), and indirect virtual water intensity (IVWI) could be calculated (Guan and Hubacek 2007):
DVWIi = Qci / Xi
(14)
pi / Li =
∑
pi
i
piP /
∑ i
piP
(18)
where Li is the location quotient of sector i, pi is the output of sector i in the studied area, while piP is the provincial output of sector i for the studied area. 2.1.4. Gray WF and gray WF carrying capacity coefficient (K) Chemical Oxygen Demand (COD) is the major composite indicator for pollutants in wastewater discharge. Hence, COD is chosen to calculate the gray WFs.
where IWFi is the internal WF for sector i, EWFi is the external WF for sector i, EVWi re − ex is the exported VW to the other regions as a result of 3
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WFgray =
L Cmax − Cnat
2.2.2. Local indicators of spatial association (LISA) Local Indicators of Spatial Association (LISA), also called Anselin local Moran's I, is utilized to identify local hot spots or clusters that signify heterogeneity in one or more regions of the study area (Wang et al., 2016). It is defined as:
(19)
where WFgray is the gray WF, L is the pollutant load (kg/a), Cmax is the maximum acceptable concentration of the pollutant (kg/m3), and Cnat is the natural concentration in the receiving water body (kg/m3). In this study, Cmax was chosen for the standard concentration of the pollutant set according to Class III of the Chinese Environmental Quality Standard of Surface Water (GB 3838—2002), andCnat took 0. In fact, WFgray only reflects the volume of freshwater required to assimilate pollution. The water pollution stress at an area can be better illustrated by the gray WF Carrying Capacity Coefficient (K), which is the ratio of WFgrayto the total water supply of the region. A higher K value suggests a higher water pollution stress in the area.
n
Ii = zi
where zi and zj are the standardized values of the target attribute for the study area. The local Moran’s Ii can be visualized in the LISA cluster map. According to the statistic values, clusters and outliers of spatial data would be classified as clusters of high value zones (H-H), low value zones (L-L), and outlier zones which are relatively isolated low value zones (H-L) or high value zones (L-H). 3. Study site & data
where Tws refers to the total water supply of the area (m3).
The HRB, located in northern China (112–120°E, 35–43°N), is one of the key national industrial and agricultural centers. It covers an area of 318,200 km2, encompassing Beijing, Tianjin, the vast majority of Hebei Province, the eastern part of Shanxi Province, the northern parts of Henan and Shandong provinces, as well as a small part of the Inner Mongolia Autonomous Region and Liaoning Province (Fig. 1) (Zhao et al., 2010; Zheng et al., 2015; Zou et al., 2015). The population of the HRB is 143 million, accounting for 9.7% of the total Chinese population. The average annual water resource of the HRB is 41.9 billion m3, approximately 1.3% of the total, whereas the gross domestic product (GDP) of the HRB shares 12% of the nation’s total. The average water consumption of the HRB is 335 m3, which is merely 7% of the nation’s average (Haihe River Water Conservancy Commission, 2014). The dense population, low precipitation, and high development levels of the agricultural and industrial sectors have driven the HRB to become one of the most water scarce regions in China. At present, this water scarce region still produces water intensive goods to be consumed in other areas, which further amplifies the serious water shortage of the river basin. Currently, the exploitation level of surface water at the HRB is more than 90%, while that of groundwater is more than 100%. Although several water diversion projects have been conducted in the region, it still faces a severe water shortage. To cope with water scarcity problems, the virtual water strategy is considered as an viable option (Zhao et al., 2010). Previous studies on WF accounting for the HRB were mainly conducted at provincial scales (Zhi et al., 2014; White et al., 2015; Zhao et al., 2010). However, the provincial boundaries do not match those of the river basin. Thus, the WF accounting results at the provincial scale differ greatly from those at the basin scale. In this study, WF evaluations were undertaken at the city scales (26 cities). Table 2 provides a more detailed breakdown of the evaluation scope. Moreover, the relevant analysis of WFs would be made in dual scales, directed at cities and the whole basin. The data required in the IO models (including the input-output matrix, consumption, import, and export) were obtained from the IO table of the National Bureau of Statistics (2007) and the regional IO tables of local statistic bureaus (2007). The data of sectoral blue water used for IO analysis at each region in HRB was collected from the Haihe River Water Resource Bulletin 2008 (Haihe River Water Conservancy Commission, 2008). Green water was not considered because blue water was the only water supply in most sectors, except agriculture and the sectors depending on agricultural raw materials (Zhang et al., 2012; Zhao et al., 2010; Zhi et al., 2014). The data of wastewater discharges from different sectors in each study areas as well as their relevant COD levels were obtained from the Chinese Water Resources Bulletin of 2007 (Ministry of Water Resources, 2008), the Haihe River Water Resource Bulletin 2008 (Haihe River Water Conservancy Commission, 2008), the China’s Environmental Statistical Yearbook 2007, the Water Resources
2.2. Exploratory spatial data analysis (ESDA) ESDA is a spatial analysis method that focuses on the description and interpretation of spatial structures and correlations in an area (Dou et al., 2016). The ESDA method is also used to describe and visualize spatial distributions, and to identify spatial association patterns and spatial heterogeneity (Deng et al., 2016). 2.2.1. Global spatial auto-correlation Global spatial auto-correlation is measured via the Global Moran Index (Moran’s I) in this study. It is used to identify the overall spatial auto-correlation for quantifying the degree of clustering or dispersion (Deng et al., 2016; Wang et al., 2016). The statistic model is written as follows: n
∑ ∑ I =
i
Wij ( xi − x ) ( xj − x )
j≠i n
S2
x =
1 n 1 n
n
∑ ∑ i
S2 =
j≠i
Wij (21a)
n
∑
( xi − x )
2
i
(21b)
n
∑
xi
(j )
i (j )
(21c)
where xi and xj are the geographical attributes of regions i and j, Wij is the spatial weight between region i and j. When the two regions are adjacent, Wij = 1; otherwise, Wij = 0. n is the amount of the total regions included in the study. Zscore should be calculated for testing whether there is a statistically significant spatial auto-correlation:
I − E (I ) Var (I )
(22a)
E (I ) = −1/ (n − 1)
(22b)
Var (I ) = E [I 2] − E [I ]2
(22c)
Zscore =
(23)
(20)
Tws
n
Wij zj ( i ≠ j )
j =1
WFgray
K =
∑
where the statistically significance level α is set for 0.05. Accordingly, Zscore should be bigger than 1.96. Namely, when Zscore > 1.96 (α = 0.05), there is significant spatial correlation. Otherwise, the analyzed attribute is randomly distributed in the study area. The Global Moran's I takes values between −1 to 1. When I is positive and close to 1, the attribute is statistically auto-correlated and suitable for clustering analysis; an I value close to −1 indicates a dispersed distribution; and when I takes zero, the attribute has no spatial correlation. 4
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Fig. 1. The location of HRB in China.
industries such as electric power and heat power production, smelting, and the pressing and manufacture of metals. Hence, the internal industrial blue WFs for the HRB were comparatively higher among the three sectors. For the external blue WFs, industrial WFs occupied 66.92% and the amounts for agricultural and residential WFs were 28.7% and 4.61%, respectively. The gray WFs of the HRB amounted to 862.99 × 108 m3, which was 2.245-fold of the total annual water supply. Given the blue water deficit of 561.68 × 108 m3, compensation of water supply for the pollution assimilation in the river basin was impossible. Hence, the HRB had a high load of gray WFs accumulation. Consequently, the whole basin continuously suffers from severe water quality problems.
Bulletin 2007, and the Environmental Statistical Yearbook 2007 of each study area (Beijing Statistics Bureau, 2008; Tianjin Statistics Bureau, 2008; Hebei Statistics Bureau, 2008; Shanxi Statistics Bureau, 2008; Shandong Statistics Bureau, 2008; Henan Statistics Bureau, 2008; National Bureau of Statistics, 2007).
4. Results & discussion 4.1. Total WFs for the whole HRB Table 1 showed the total, blue, and gray WFs of the entire river basin as well as the WFs of its three major economic sectors. The total WF of the HRB was 1809.15 × 108 m3, among which the total blue WF was 946.16 × 108 m3, accounting for approximately 52.30% of the total. To compare with the water supply of the HRB in 2007, which was 384.48 × 108 m3, there was a water deficit of 561.68 × 108 m3. Consequently, the water supply could only meet 40.64% of the blue water demand. Hence, the total river basin remained in a severe water deficiency. The internal blue WFs of the river basin was 807.69 × 108 m3, accounting for 85.37% of the total blue WFs. This indicated that the blue water consumption of the HRB mainly depended on local water supply. Among the internal blue WFs, the industrial WFs had the largest proportion, sharing approximately 76.49% of the total, while agricultural and residential WFs accounted for only 23.42% and 0.09%, respectively. According to the statistics report (Haihe River Water Resource Conservancy Commission, 2008), the HRB was one of the most important industrial bases in China, especially for water intensive
4.2. WFs for cities in HRB The WFs of 26 cities of the HRB were evaluated in this study as well. The blue WFs of each city are illustrated in Figs. 2–4. The figures revealed that in 2007, Beijing, Tianjin, and Tangshan had the largest internal as well as external WF, occupying approximately 27.04%, 15.64%, and 9.00% of the total internal WF and 19.75%, 10.72%, and 12.05% of the external WF, respectively. The three cities have formed one of the most competitive and fast-developing economic zones in China (the Beijing-Tianjin-Tangshan Urban Economic Zone). Taking the advantage of its regional superiority and resource endowments, this economic zone has become the base for the processing of China’s agricultural products, automobile manufacturing, raw material and power generation, and the metallurgy industry. Most of these industries are water-intensive. Yangquan, Xinzhou, Shuozhou and Yangquan, Datong, Xinzhou had the least internal and external WFs, respectively. The per capita WFs of the 26 cities in the HRB were ranging from 10.61 m3 to 159.38 m3 (see Fig. 5). The top three cities with the highest per capita WFs were also Beijing, Tianjin, and Tangshan. As the capital and a megalopolis, Beijing is not only the political centre, but also the economic centre of China. Its industries range from automobile manufacturing, high-tech manufacturing, electricity and power generation to communication and other electronic equipment manufacturing. Moreover, as the cultural and social centre, the living conditions of Beijing are also of high standard. Consequently, the per capita WF of
Table 1 WFs of the HRB. WF (108m3) Blue WFs
Gray WFs Total
Internal Blue WFs External Blue WFs Total Blue WFs
Agricultural
Industrial
Residential
Total
189.16 39.42 228.58 466.02 694.60
617.83 92.67 710.5 366.67 1077.17
0.70 6.38 7.08 436.28 443.36
807.69 138.47 946.16 862.99 1809.15
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Table 2 WFs & per capita WFs of cities in the HRB. Cities
Blue WF (108m3)
Gray WF (108m3)
Total WF (108m3)
Water Supply (108m3)
Population (105)
Per Cap. Blue WF (m3)
Per Cap. Gray WF (m3)
K
Beijing Tianjin
251.98 144.73
53.25 68.65
305.23 213.38
34.81 23.38
1581.00 1075.00
159.38 134.63
33.68 63.86
1.53 2.94
Hebei Province Shijiazhuang Chengde Zhangjiakou Qinhuangdao Tangshan Langfang Baoding Cangzhou Hengshui Xingtai Handan
62.58 19.02 16.10 15.88 92.00 42.28 37.88 38.95 18.65 30.39 47.36
64.85 21.06 31.50 10.70 48.25 20.50 34.25 26.53 28.05 24.75 22.95
127.43 40.08 47.60 26.58 140.25 62.78 72.13 65.48 46.70 55.14 70.31
4.60 1.62 1.50 1.57 10.24 2.15 2.30 0.90 1.60 1.52 2.49
978.00 339.00 420.00 293.00 739.00 406.00 1085.00 693.00 426.00 688.00 876.00
63.98 56.11 38.34 54.21 124.49 104.15 34.91 56.21 43.78 44.18 54.06
66.31 62.11 75.00 36.52 65.29 50.49 31.57 38.28 65.85 35.97 26.20
16.34 13.84 21.53 7.96 4.75 10.13 16.81 32.55 18.17 16.47 10.27
Shanxi Province Datong 4.94 Yangquan 2.47 Changzhi 6.94 Shuozhou 5.07 Xinzhou 3.26
36.12 9.10 21.82 27.72 30.35
41.06 11.57 28.76 32.79 33.60
2.34 1.26 2.07 1.65 2.12
315.97 131.37 326.93 152.66 307.26
15.64 18.78 21.22 33.21 10.61
114.32 69.28 66.74 181.59 98.76
16.28 7.61 11.29 17.37 14.95
Henan Province Anyang 19.69 Hebi 12.75 Xinxiang 14.36 Jiaozuo 15.05 Puyang 10.09
34.52 11.43 38.07 29.86 13.84
54.21 24.18 52.44 44.90 23.93
1.60 1.04 1.48 1.48 0.48
539.00 145.00 558.00 345.00 361.00
36.53 87.95 25.74 43.62 27.95
64.04 78.82 68.23 86.54 38.35
22.85 11.42 26.37 74.47 22.85
Shandong Province Dezhou 22.47 Liaocheng 21.50 Binzhou 15.51
63.47 53.40 38.01
85.94 74.90 53.52
0.60 3.38 0.53
545.28 553.66 367.12
41.21 38.83 42.25
116.40 96.44 103.53
110.31 16.09 74.60
chemical industries. Thus, the per capita WF of Tianjin also ranked higher in the total HRB. The three cities, which had the least per capita WFs are all located in the Shanxi Province. Shanxi is among the most water deficient provinces of China with a per capita water supply of 17.16% of the
Beijing was comparatively higher. Tianjin, as a municipality as well as a megalopolis, has the advantage of a harbor and is one of the most important international navigation centers of China. It has highly developed industries, such as aerospace manufacturing, ship building industry, petrochemical industry, iron and steel manufacturing, and
Fig. 2. External blue WFs of 26 cities in HRB. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
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Fig. 3. Internal blue WFs of 26 cities in HRB. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
cities with the lowest gray WFs were Yangquan, Qinhuangdao, and Hebi. Among all 26 cities, Shuozhou had the largest gray WF per capita, followed by Dezhou and Datong (see Table 2). Comparing the blue WFs and gray WFs with the water supply of each city, we found that the water supply of none of the cities could meet the requirements of either blue WFs or gray WFs. This revealed that all cities in the HRB were in lack of water resources both in quantity and quality (see Fig. 7). Table 3 listed the blue and gray WFs of each city in the HRB in different sectors. The results revealed that Beijing, Tianjin, and
national average. Hence, in the year 2000, the provincial authority issued the Urban Water Supply & Conservation Regulation of Shanxi Province for water conservation in the province. Moreover, since 2006, four cities in the Shanxi province have been selected as the national water conservation pilot cities. Those effects on water resource conservation finally resulted in the observed low per capita WFs of the cities in Shanxi province. The total gray WFs of the cities in the HRB were ranging from 9.10 × 108 m3 to 68.65 × 108 m3 (see Fig. 6). The three cities with the highest gray WFs were Tianjin, Shijiazhuang, and Dezhou, while the
Fig. 4. Total blue WFs of 26 cities in HRB. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
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Fig. 5. Per capita blue WFs of 26 cities in HRB. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
cities had more industrial gray WFs than agricultural and residential gray WFs, which were mainly industrial cities located in Henan and Hebei provinces. The remaining 38.46% of the cities had more agricultural gray WFs, the majority of which were cities from agricultural-based provinces like Shandong and Hebei.
Tangshan had the highest blue WFs in the agricultural sector. Moreover, the cities in Shandong province also had comparatively elevated blue WFs in the agricultural sector, as Shandong is the biggest agricultural base of China. The cities with the highest industrial and residential blue WFs were Beijing and Tianjin, respectively. Figs. 8–11 revealed the detailed information on gray WFs of each city in three sectors. Among them, approximately 38.46% of the cities had more residential gray WFs than industrial and agricultural WFs, which were mainly megalopoli such as Beijing and Tianjin or tourist cities such as Chengde and Qinhuangdao. Approximately 23.08% of the
4.3. Water stress index, water self-sufficiency & gray WF carrying capacity coefficient The results revealed that the WSI of all the cities were above 1,
Fig. 6. Total gray WFs of 26 citie.
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Fig. 7. Comparisons of Blue & Gray WFs with water supplies in 26 cities. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Table 3 Blue & gray WFs by different sectors of cities in the HRB. Cities
Blue WF (108 m3)
Gray WF (108 m3)
Agricultural
Industrial
Residential
Agricultural
Industrial
Residential
Beijing Tianjin
38.20 23.44
209.05 113.51
0.02 2.19
6.91 12.98
3.31 15.37
49.94 53.28
Hebei Province Shijiazhuang Chengde Zhangjiakou Qinhuangdao Tangshan Langfang Baoding Cangzhou Hengshui Xingtai Handan
15.61 5.11 5.24 4.38 19.81 12.17 11.67 9.36 6.66 8.51 11.74
44.93 13.50 10.39 10.87 70.11 28.62 25.27 28.90 11.68 21.44 34.71
1.00 0.24 0.28 0.27 1.34 0.27 0.42 0.23 0.14 0.25 0.40
23.91 4.87 4.96 6.30 18.71 8.25 21.66 15.72 12.75 16.42 22.38
38.76 9.02 8.95 4.60 37.75 7.78 13.25 15.23 10.55 10.55 7.46
26.09 12.04 22.55 6.10 10.50 12.72 21.00 11.30 17.50 14.20 15.49
Shanxi Province Datong Yangquan Changzhi Shuozhou Xinzhou
0.78 0.15 0.95 0.77 0.77
4.08 2.28 5.95 4.25 2.41
0.00 0.00 0.00 0.00 0.00
13.56 2.31 18.32 14.61 21.94
13.45 3.39 8.67 10.32 8.65
22.67 5.71 13.15 17.40 21.69
Henan Province Anyang Hebi Xinxiang Jiaozuo Puyang
0.93 0.32 1.10 0.90 0.60
16.97 10.18 11.18 12.66 7.54
0.00 0.00 0.00 0.00 0.00
16.85 2.91 21.85 10.40 12.58
20.68 6.16 23.26 18.29 8.35
13.84 5.27 14.81 11.57 5.50
Shandong Province Dezhou Liaocheng Binzhou
18.85 18.39 12.17
3.61 3.09 3.34
0.01 0.01 0.01
63.47 53.40 38.01
26.42 16.23 20.22
9
13.69 10.51 7.76
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Fig. 8. Industrial gray WFs of 26 cities.
with to a level of self-sufficiency. 30.77% of the cities (Langfang, Shuozhou, Xinzhou, Anyang, Hebi, Xinxiang, Dezhou, and Liaocheng) were comparatively insufficiently equipped with the water supplies of the local area and were dependent on external water resources. The remaining 69.23% of the cities could predominantly depend on their own water resources for the local water demands, and their reliance on external water resources was comparatively lower (see Fig. 13). The results of gray WF carrying capacity coefficient (K) have been
which meant that all cities of the HRB fell in the category of extreme water scarcity. The western part of the HRB (mainly the cities in Shanxi Province) had comparatively less water scarcity stress. However, the eastern part (Cangzhou, Dezhou and Binzhou) suffered from extreme severe water scarcity, and the local water supplies could merely meet 2.30% to 3.44% of their WFs (Fig. 12). The evaluation of the WSS indicated that among the 26 cities, none of their local water resources could meet the local water consumptions
Fig. 9. Agricultural gray WFs of 26 cities.
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Fig. 10. Residential gray WFs of 26 cities.
respectively. Both of the results ranged within [-1,1], and were bigger than 0. The corresponding ZWSI and ZK were 3.589 and 2.906, respectively, which both passed significance tests, revealing the results to be statistically correlated. Figs. 14 and 15 are the LISA cluster maps of the cities in the HRB based on WSI and K values. Fig. 14 revealed that the cities (Dezhou, Binzhou, and Cangzhou) in the eastern part were located in the H-H clusters. In this area, the total water deficiencies were highest among all regions in the HRB. More cities fell into the L–L cluster areas in the northern and western parts of the HRB as shown in the map. The cities in the mid-part of the HRB, mainly Beijing, Tianjian, Xingtai, and Liaocheng were in the cluster of
listed in Table 2. The cities with the top three largest K were Dezhou, Binzhou, and Jiaozuo. Their gray WF carrying capacity coefficients were 110.31, 74.60, and 74.37, respectively. This indicated that these three cities had comparatively higher water pollution stresses. The city with the smallest K was Beijing. 4.4. Spatial correlation analysis The results of the WSI and K were further analyzed via the ESDA model for the spatial distribution attributes among the cities in the HRB. The Moran’s I for the WSI and K were 0.2742 and 0.2890,
Fig. 11. Gray WFs in different sector-oriented.
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Fig. 12. WSI of cities in HRB.
Fig. 13. WSS of cities in HRB.
distribution features, Beijing, Tianjian, and most cities in Shanxi and Hebei provinces had comparatively less water deficits due to water quality problems. However, cities in Shandong and Henan provinces should remedy their water quality problems. A comparison of Figs. 14 and 15 revealed that Dezhou and Binzhou had suffered from the highest water deficiency on both their water quantity and quality. Jiaozuo, Xinxiang, Anyang, and Puyang should put more emphasis on their water problem resulting from waste water discharge.
L-H, which indicated that compared to most of the cities in Shandong and Hebei, these cites had less water stresses. The reasons could be the relatively higher water utility rates or more high-tech industries that had been applied in these more advanced cities. Fig. 15 showed the LISA cluster map of cities in HRB based on the gray WF carrying capacity coefficient. The H-H cluster mainly focused on the southern and southeastern parts of the HRB. These cities are predominantly situated in Shandong and Henan provinces. The L–L clusters distributed in the northern and western parts of the HRB, mainly cities situated in Hebei and Shanxi provinces. Hence, from the 12
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Fig. 14. LISA cluster map of cities in HRB based on the WSI.
5. Conclusion
evaluations of the water stress in terms of quantity and quality were conducted at both the river basin and city levels. Additionally, the spatial correlation analysis based on their relevant water stress indexes and gray WF carrying capacity coefficients were conducted via the ESDA model for the illustration of the spatial correlation distributions of the water stresses for the cities of the HRB. The major results of this study included: (1) Beijing, Tianjin, and Tangshan had the largest internal and external blue WFs. These three cities were also the top cities with the highest per capita WFs in the HRB. The top three cities for gray WFs were Tianjin, Shijiazhuang, and
Water resources management at river basins faces the challenges of unbalanced water distributions among different regions and economic sectors. The involvement of multiple regional jurisdictions further complicates the optimal allocation and sustainable management of water resources at river basins. Water footprints have been widely applied to assess sustainable utilization of water resources. In this study, based on the blue and gray WFs accounting of different economic sectors, as well as an extended Environmental IO Analysis, the
Fig. 15. LISA cluster map of cities in HRB based on the K.
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