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Does urbanization intensify regional water scarcity? Evidence and implications from a megaregion of China
Journal Pre-proof Does urbanization intensify regional water scarcity? Evidence and implications from a megaregion of China
Weifeng Li, Xia Hai, Lijian Han, Jingqiao Mao, Mingming Tian PII:
S0959-6526(19)33462-6
DOI:
https://doi.org/10.1016/j.jclepro.2019.118592
Reference:
JCLP 118592
To appear in:
Journal of Cleaner Production
Received Date:
28 August 2018
Accepted Date:
24 September 2019
Please cite this article as: Weifeng Li, Xia Hai, Lijian Han, Jingqiao Mao, Mingming Tian, Does urbanization intensify regional water scarcity? Evidence and implications from a megaregion of China, Journal of Cleaner Production (2019), https://doi.org/10.1016/j.jclepro.2019.118592
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Journal Pre-proof
Does urbanization intensify regional water scarcity? Evidence and implications from a megaregion of China Weifeng Li1, Xia Hai1, Lijian Han1, Jingqiao Mao2, Mingming Tian2 1. Research Center for Eco-environmental Sciences, Chinese Academy of Sciences, Shuangqing Road 18, Beijing 100085, China;
2. College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China
Abstract: Water scarcity as a serious global issue has been challenging the human beings. Despite broad research on water scarcity from global to local scale, there is lack of comprehensive understanding of water scarcity in urban areas, particular in the megaregion which is a cluster of a number of cities with dense human activities and close interactions between each other cities. In this study, we took one important megaregions of China, the Beijing-Tianjin-Hebei (BTH) megaregion, as a case study to analyze the spatial-temporal trajectories of water scarcity and the driving forces. First, we developed a water scarcity index system that could separately assess the spatial variation of physically- and human-induced water scarcity. Second, we explored the relations between water scarcity and the multi-manifestation of urbanization in terms of demographic, social, landscape, and economic aspects. The results showed: 1) the overall water scarcity varied spatially and temporally across different cities in the BTH megaregion; the physically- and human-induced water scarcity was not spatially coincident; most the variation in water scarcity was due to human-induced water scarcity; 2) urbanization primarily affected the human-induced water scarcity; the economic urbanization had the strongest negative impacts, whereas the landscape urbanization had positive effects; non-urban water utilization, such as agricultural water use, strongly competed for water use with regional urbanization. Overall, the results highlight the importance of optimizing economic development mode and controlling agriculture water use to alleviate the water scarcity of megaregion. The general framework used in this study can be applied to other megaregions in China and to some other developing countries that have similar water scarcity problems. 1
Journal Pre-proof Keywords: Physically-induced water scarcity; Human-induced water scarcity; Urbanization; Megaregion
1. Introduction Water scarcity as one of the most serious issues, is becoming a threat to global sustainable development, due to the increasing population (Mekonnen and Hoekstra, 2016). In general, the fundamental concept of water scarcity refers to the imbalance between water availability and demand, and long-term water scarcity would have severe consequences, such as river driving up, groundwater depletion, and water table falling (Kummu et al., 2016; Jiang 2009). Well understanding of the driving forces of water scarcity can provide a basis for targeting actions to alleviate water scarcity. Due to the basic concept, water scarcity is often interpreted from the perspectives of supply and demand sides in terms of drives (Martin-Carrasco et al., 2013; Kummu, et al., 2010). Taking into account of supply-side impact, water scarcity is induced by limited water resource relative to large population. Environmental factors such as climate change (i.e. aridity and drought) and population are the major elements of water scarcity (Iglesias et al., 2007). By contrast, given demand-side impact, water scarcity is caused by high water use relative to water availability, and humankinds are the major influential forces, which can be seen as the human-driven factors of water scarcity (Liu et al., 2017). Many methods have been developed to assess the status of water scarcity, among which the index-based approaches are commonly used (Liu, et al., 2017; Kummu, et al., 2016). These indexes are mainly based on the drivers of water scarcity, and usually focus on some particular characteristics, such as physical, population or social-economic factors. The well-used water scarcity indexes such as Falkenmark (Falkenmark et al., 1989), IWMI (Seckler et al., 1998), Basic Human Water Requirements (Gleick, 1996) and Social Water Stress Index (Ohlsson, 2000), emphasized the impact of water availability. Meanwhile, some other water scarcity 2
Journal Pre-proof indexes such as Water Poverty Index (Sullivan et al., 2003), Water Resources Vulnerability Indices (Raskin, et al., 1997) and Water Supply Stress Index (McNulty et al., 2010), focused on demand side. Moreover, there are also some other certain synthesized water scarcity indexes to incorporate more influential factors of water scarcity such as water availability, environment, life style, infrastructure and economic factors (Liu et al., 2017). Yet, few indices paid attention to separating different forces such as physical and humankind effects on water scarcity. The status of water scarcity has been extensively assessed across global, national and sub-national scales (Mekonnen and Hoekstra, 2016). For example, the global-scale concerns of water scarcity are mainly its continental or sub-continental distribution, affected population, and climate change (Krummu et al., 2016; Mekonnen and Hoekstra, 2016). The national-scale study on water scarcity usually focused on the distribution of water availability and population, and mismatch between them (Brown et al., 2019; Jiang, 2009). Additionally, the regional-scale research on water scarcity mainly paid attention to human activities’ impacts (i.e. urbanization or agricultural development) (Gober and Kirkwood, 2010; Ren et al., 2018). Despite broad research on water scarcity across different scales, the trajectory and causes of water scarcity of the urbanized areas remains less understood. The rapid urbanization, where fast population and social-economic growth brings about increasing demand for water resource, commonly faces the problem of resource shortage including water scarcity, and this in turn restricts the farther development (Fan, et al., 2017; McDonald et al., 2011). Thus, deepen understanding the dynamic of water scarcity in urbanized region and its relation with urbanization would help adapt mitigation solutions, particularly in large-scale urbanized area such as megaregions, which currently is the dominant trend in urbanization cross the world duo to their enormous social-economic advantages but also puts more stress on the environment than individual cities (Han, et al., 2016; Marull, et al., 2013). In this study, we used the Beijing-Tianjin-Hebei (BTH) megaregion which faces sever water scarcity problem and typifies other megaregions in eastern China, as a 3
Journal Pre-proof case study to assess water scarcity and identify the drivers (Yang and Zehnder, 2001; Zhao, et al., 2017). We first used a dataset that spanned from 2001 to 2015 to develop a water scarcity index system which could separate the physically- and human-induced effects on water scarcity. Second, we explored the relations between the trajectories of water scarcity and multi-manifestation of urbanization in terms of demographic, social, economic, and landscape aspects. Finally, we provided recommendations for water scarcity mitigation of megaregions for a more cooperative water resource management. The finding and implication of this study is not only limited to the BTH megaregion but also to other megaregions beyond.
2. Data and methods 2.1. Study area The BTH megaregion lies on the shores of the Bohai Sea in the eastern part of north China. The cities in the megaregion range from latitude 36°01’N to 42°37’N and from longitude 113°04’E to 119°53’E (Fig. 1). The total area of the BTH is 22.0 × 106 ha, which represents 2.35% of the country. The mean annual temperature ranges from 4 to 13°C and the annual precipitation is 300–800 mm per year. The BTH megaregion has 13 cities, including two megacities and eleven prefecture-level cities (Fig.1). By the end of 2015, the total population of the BTH megaregion was 116 million and accounted for 8.12% of China’s population (NBSC, 2015). The average demographic urbanization rate was up to 62.6%, which was higher than the national average of 56.1%. But there were great differences in the urbanization rates among the cities, which ranged from 46.6% to 86.5%. The average per capita GDP of the BTH megaregion was 6.23×104 RMB in 2015, which was higher than the 5.0×104 RMB average for the country of (NBSC, 2015). However, the GDP also varied from 2.4 to 10.7×104 RMB among the different cities (NBSC, 2015).
4
Journal Pre-proof During the research period of 2001 to 2015, the average water resource per capita in the region was less than 200 m3 and ranged from 120 to 547 m3 among different cities (BWA, 2001-2015; TWA, 2015; DWRHP, 2001-2015). Currently, a large quantity of the water used by the BTH megaregion comes from outside and is transferred to the region by water transfer projects, such as the South-to-North Water Transfer Project, the Jin-to-Jing Water Transfer Project, and the Huang-to-Jing Water Transfer Project.
Fig. 1. Spatial locations of the Beijing-Tianjin-Hebei (BTH) megaregion 2.2. Methods and data 2.2.1 Water scarcity assessment index system We developed a two-level index system to assess water scarcity of the BTH megaregion, by separating the contribution of physically- and human-induced influence (Table 1). Basically, we developed two sub-level indexes to respectively assess physically- and human-induced water scarcity. To assess the physical water scarcity, we developed an index by combing two major environmental indicators (total water resource availability and precipitation) as previous discussion (Iglesias et 5
Journal Pre-proof al., 2007; Long and Pijanowski, 2017); to assess the human water scarcity, we developed an index by combining three indicators (water resource per capita, water use per unit of GDP and water utilization rate), which could generally reflect the effects of population and social-economic activities that are particularly related to urbanized area (Kummu et al., 2016; Liu et al., 2017). Then, we created a composite water scarcity index by combing the above two sub-level indexes to reflect the overall status of water scarcity. Detailed explanations of the indexes are given in Table 1. The data used to calculate water scarcity were collected from the Water Resource Bulletin and City Statistical Yearbook. Specially, the data of annual precipitation and total water resource were collected from the City Water Resource Bulletin of Beijing, Tian and the other 11 prefecture-level cities belonging to Hebei province (BWA, 2001-2015; TWA, 2001-2015; DWRHP, 2001-2015). Additionally, the data of population and water utilization rate of each city in the BTH megaregion were collected from the City Statistical Yearbook (NBSC, 2001-2015). All the data set spanned the period from 2001 to 2015. Since the basic indicators of water scarcity have different dimensions and are not be directly accumulated, we need to normalize the data and calculate the index by weighting set. The details of data normalization and weights setting are explained in the following 2.2.3 and 2.2.4 sections. Table 1. Index system used to assess water scarcity within the BTH megaregion of China Composite index
Sub-indexes
Indicators
Affected
Weights
direction
indicators
–
0.31
of
Weights of sub-indexes
Total amount of water Physical-induced water scarcity
resource (100 million m3)
0.36
Precipitation
Composite
(100 million m3)
water
Water resource per
scarcity
capita Human-induced water
(m3
scarcity
Water use per unit of
–
0.69
–
0.03
per capita)
GDP
0.64 +
(t/104RMB) 6
0.45
Journal Pre-proof Water utilization rate (%)
+
0.52
2.2.2 Urbanization evaluation index system Although the demographic urbanization level is commonly used to measure the degree of urbanization, urbanization is a multi-faceted phenomenon, particularly in megaregions where both urbanization levels and modes vary among different cities (Bai, et al., 2012). In this study, we attempted to differentiate the urbanization within the BTH megaregion in terms of demographic, landscape, social, and economic aspects (Hai, et al., 2018). We established a two-level index system (Table 2). Basically, we developed four sub-level indexes to separately evaluate demographical, social, economic and landscape urbanization. To assess demographical urbanization, we integrated two indicators of urban population ratio and urban population size; to assess social urbanization, we integrated three indicators of retail sales of consumer goods of the city, urban per capital consumption expenditure and number of college students in universities; to assess the economic urbanization, we integrated three indicators of per capital of GDP, proportion of secondary production and proportion of tertiary industry; to assess the landscape urbanization, we integrated two indicators of built-up area and per capita built-up area. Then, we developed a composite urbanization index by combing the four sub-level indexes to assess the composite characteristics of urbanization. All data used to calculate urbanization of the BTH megaregion were collected from the City Statistical Yearbook. The data set spanned the period from 2001 to 2015 (NBSC, 2001-2015). Similarly to water scarcity index calculation, we need to normalize the data and calculated the index by weighting set. The details of data normalization and weights setting are explained in the following 2.2.3 and 2.2.4 sections. Table 2. Index system for evaluating urbanization within the BTH megaregion of China 7
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Combined index
Sub-indices
Indicators Urban population ratio
Demographic
(%)
urbanization
Urban population size (ten thousand persons)
Affected
Weights
direction
indicators
+
0.40
of
Weights of sub-indexes
0.23 +
0.60
+
0.41
+
0.29
+
0.29
+
0.10
+
0.41
+
0.49
+
0.66
+
0.34
Retail sales of consumer goods of the city (100 million RMB) Urban per capital Social urbanization
consumption expenditure
0.29
(RMB) Composite
Number of college students in
urbanization
universities
index
(ten thousand persons) Proportion of secondary production (%) Economic
Proportion of tertiary industry
urbanization
(%) Per capital of GDP (104 per capita) Built-up area
Landscape
(km2)
urbanization
Per capita built-up area (km2/104 persons)
0.18
0.30
2.2.3 Normalization of indicators We utilized the min-max normalization method to standardize different indicators. This method has been commonly used because it could preserve all the relationship of the original data values without introducing any potential bias (Li and Liu, 2011; Schenatto, et al., 2017). This method is bounded by 10.0 and 1.0 with at least observed value at each of these data sets, and the influence direction of different indicators could be also considered, as the following formulas.
Positive indicator: 𝑋'𝑖𝑗𝑘 = (𝑋𝑖𝑗𝑘 ― 𝑚𝑖𝑛{𝑋𝑗})/(𝑚𝑎𝑥{𝑋𝑗} ― 𝑚𝑖𝑛{𝑋𝑗})
(1)
Negative indicator: 𝑋'𝑖𝑗𝑘 = (𝑚𝑎𝑥{𝑋𝑗} ― 𝑋𝑖𝑗𝑘)/(𝑚𝑎𝑥{𝑋𝑗} ― 𝑚𝑖𝑛{𝑋𝑗})
(2)
8
Journal Pre-proof Where 𝑋𝑖𝑗𝑘 represents the value of indicator j in year i of city k, and max{𝑋𝑗} and min{𝑋𝑗} indicate the minimum and maximum values of the indicator among all years and all cities within the megaregion. 2.2.4 Weights setting of indexes The entropy-weight method is adopted to calculate the weights of criteria both for the indexes of water scarcity and urbanization. By comparison with subjective fixed weight methods, the entropy-weight method is based on the objective information of indexes, and the weights of the criteria are determined according to the difference between the objects for a criterion. The criteria with a larger difference between objects have greater weights, while the criteria with a less difference among objects have smaller weights. Thus, the entropy-weight method rests on the objective basis of criteria and makes results more consistent with facts (Li et al., 2012; Chen et al., 2012; Delgado and Romero, 2016). The criteria weighting steps are as following: The proportion of indicator j in year i: 𝑚 𝑋' = 1 𝑖𝑗
𝑌𝑖𝑗 = 𝑋'𝑖𝑗/∑𝑖
(3)
Information entropy for the indicator: 1
𝑚 𝑌 = 1 𝑖𝑗
𝑒𝑗 = ― ln 𝑚∑𝑖
(4)
× ln 𝑌𝑖𝑗 (0 ≤ 𝑒𝑗 ≤ 1)
Entropy redundancy: 𝑑𝑗 = 1 ― 𝑒𝑗
(5)
Weight of the indicator: 𝑛
𝑊𝑗 = 𝑑𝑗/∑𝑗
𝑑 =1 𝑗
(6)
Where n is the number of indicators for each index, and m is the number of years. Then the water and urbanization indices for the BTH megaregion for the period 2001–2015 were calculated. The weights of different indicators and indexes for water scarcity and urbanization of the BTH megaregion were calculated (Table 1 and 2).
9
Journal Pre-proof 2.2.5 Statistical analysis We used simple Pearson correlation analysis to analyze the relationship between water scarcity and urbanization. Then, we used the stepwise regression method to construct multi-linear regression models to analyze the relationship between water scarcity and different aspects of urbanization, and identified the main drivers of water scarcity.
3 Results 3.1. Water scarcity in the BTH megaregion 3.1.1. Spatial-temporal variation in water scarcity The composite water scarcity within the BTH megaregion varied strongly among cities within the BTH megaregion (Figs. 2(a)). Spatially, the composite water scarcity values ranged from 0.1 to 0.94 among all cities over the period from 2001 to 2015, which meant that the severity of water scarcity among different cities were quite different. Generally, the water scarcity values for all the cities decreased over the period of 2001 to 2015. The variation in physically- and human-induced water scarcity was not spatially concordant among cities (Figs. 2(a), (b)). Except for Chengde and Zhangjiakou, the physically-induced water scarcity for most cities was severe, with the index values greater than 0.6 during 2001 to 2015. Furthermore, there was no obvious change of physically-induced water scarcity from 2001 to 2015. In contrast, the human-induced water scarcity generally decreased from 2001 to 2015 for most cities, and the largest reductions in human-induced water scarcity were in Hengshui, Xingtai, and Baoding.
10
Journal Pre-proof
1.0 Hengshui
0.74
0.94
0.57
0.61
0.65
0.73
0.72
0.58
0.5
0.59
0.53
0.46
0.44
0.69
0.49
Langfang
0.58
0.59
0.51
0.48
0.53
0.56
0.45
0.42
0.44
0.46
0.43
0.34
0.42
0.47
0.41
Cangzhou
0.61
0.72
0.42
0.44
0.44
0.41
0.43
0.36
0.31
0.36
0.36
0.29
0.32
0.47
0.3
0.8
Chengde
0.39
0.5
0.34
0.31
0.2
0.28
0.26
0.16
0.27
0.13
0.14
0.1
0.15
0.19
0.16
0.7
Zhangjiakou
0.44
0.41
0.32
0.31
0.37
0.35
0.34
0.25
0.33
0.2
0.3
0.24
0.21
0.26
0.2
Baoding
0.61
0.53
0.5
0.36
0.46
0.49
0.37
0.27
0.37
0.34
0.26
0.2
0.26
0.35
0.28
Xingtai
0.68
0.71
0.5
0.5
0.5
0.49
0.5
0.46
0.44
0.45
0.42
0.39
0.36
0.47
0.45
0.9
0.6 0.5
Handan
0.57
0.59
0.39
0.45
0.42
0.47
0.43
0.39
0.38
0.42
0.39
0.4
0.39
0.42
0.45
Qinhuangdao
0.51
0.55
0.46
0.43
0.44
0.47
0.43
0.4
0.41
0.35
0.36
0.21
0.35
0.38
0.37
Tangshan
0.48
0.55
0.41
0.38
0.4
0.42
0.37
0.33
0.37
0.34
0.32
0.18
0.31
0.35
0.33
0.3
0.6
0.52
0.43
0.39
0.46
0.44
0.4
0.34
0.36
0.41
0.36
0.34
0.34
0.44
0.38
0.2
Tianjing
0.49
0.58
0.4
0.36
0.4
0.41
0.39
0.31
0.34
0.4
0.33
0.21
0.35
0.38
0.35
Beijing
0.39
0.4
0.37
0.32
0.31
0.31
0.29
0.22
0.31
0.28
0.26
0.18
0.28
0.31
0.25
Shijiazhuang
0.4
0.1 0
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
(a) 1.0 Hengshui
0.92
0.98
0.83
0.92
0.94
0.96
0.95
0.9
0.83
0.93
0.91
0.85
0.84
0.98
0.9
Langfang
0.96
0.98
0.93
0.92
0.96
0.99
0.92
0.9
0.94
0.95
0.93
0.81
0.93
0.98
0.93
Cangzhou
0.87
0.93
0.72
0.77
0.82
0.79
0.83
0.76
0.68
0.77
0.77
0.65
0.73
0.91
0.69
0.8
Chengde
0.24
0.51
0.29
0.31
0.17
0.39
0.4
0.2
0.5
0.17
0.26
0.15
0.28
0.37
0.29
0.7
0.6
0.56
0.38
0.43
0.55
0.54
0.55
0.39
0.62
0.34
0.61
0.46
0.41
0.53
0.41
Baoding
0.73
0.64
0.64
0.45
0.64
0.72
0.55
0.36
0.61
0.57
0.42
0.32
0.49
0.68
0.53
Xingtai
0.87
0.89
0.71
0.79
0.81
0.82
0.86
0.82
0.79
0.83
0.81
0.78
0.74
0.88
0.86
Zhangjiakou
1.0
0.9
0.6 0.5
Hengshui
0.64
0.92
0.42
0.44
0.49
0.61
0.59
0.39
0.32
0.4
0.32
0.24
0.21
0.53
0.26
0.9
Langfang
0.36
0.37
0.27
0.23
0.28
0.31
0.18
0.15
0.17
0.18
0.15
0.08
0.13
0.18
0.12
Cangzhou
0.46
0.6
0.26
0.25
0.22
0.19
0.2
0.14
0.11
0.12
0.12
0.09
0.09
0.22
0.09
0.8
Chengde
0.48
0.49
0.37
0.31
0.21
0.22
0.18
0.13
0.14
0.11
0.08
0.07
0.07
0.09
0.08
0.7
Zhangjiakou
0.36
0.32
0.29
0.25
0.26
0.24
0.22
0.16
0.16
0.12
0.13
0.11
0.09
0.1
0.09
Baoding
0.54
0.48
0.42
0.31
0.36
0.37
0.27
0.22
0.24
0.21
0.17
0.14
0.13
0.17
0.13
Xingtai
0.57
0.6
0.38
0.34
0.33
0.3
0.3
0.26
0.24
0.23
0.19
0.18
0.15
0.24
0.22
Handan
0.41
0.43
0.26
0.26
0.22
0.25
0.21
0.16
0.15
0.18
0.16
0.17
0.16
0.18
0.21
0.3
0.33
0.23
0.19
0.2
0.21
0.17
0.14
0.14
0.11
0.1
0.05
0.09
0.1
0.1
0.6 0.5
Handan
0.85
0.89
0.63
0.8
0.77
0.87
0.84
0.79
0.78
0.84
0.81
0.81
0.8
0.83
0.88
Qinhuangdao
0.89
0.94
0.86
0.84
0.87
0.92
0.89
0.87
0.88
0.79
0.84
0.5
0.82
0.89
0.86
Tangshan
0.75
0.86
0.69
0.67
0.73
0.77
0.7
0.65
0.74
0.71
0.67
0.37
0.68
0.75
0.73
0.3
Tangshan
0.32
0.37
0.25
0.21
0.21
0.22
0.19
0.14
0.16
0.14
0.12
0.07
0.1
0.13
0.12
0.3
Shijiazhuang
0.84
0.76
0.68
0.65
0.77
0.76
0.72
0.64
0.68
0.78
0.7
0.68
0.7
0.87
0.77
0.2
Shijiazhuang
0.46
0.39
0.29
0.24
0.28
0.26
0.23
0.17
0.17
0.2
0.17
0.15
0.13
0.2
0.16
0.2
Tianjing
0.87
0.92
0.78
0.74
0.8
0.83
0.8
0.7
0.74
0.84
0.74
0.5
0.79
0.84
0.78
Tianjing
0.28
0.39
0.18
0.15
0.18
0.17
0.16
0.1
0.11
0.16
0.1
0.05
0.1
0.12
0.12
Beijing
0.68
0.73
0.69
0.62
0.65
0.67
0.63
0.47
0.67
0.62
0.58
0.39
0.62
0.69
0.56
Beijing
0.23
0.21
0.18
0.15
0.12
0.11
0.1
0.07
0.1
0.09
0.08
0.06
0.08
0.1
0.07
0.4
Qinhuangdao
0.1 0
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
(b)
0.4
0.1 0
(c)
Fig. 2. Spatial-temporal variations in water scarcity among cities within the BTH megaregion: (a) spatial-temporal variation in overal water scarcity, (b) spatial-temporal variation in physical-induced water scarcity, (c) spatial-temporal variation in human-induced water scarcity.
3.1.2. Determinants of water scarcity The physical causes were primary contributors to water scarcity compared to the human causes (Fig. 3). For instance, during 2001 to 2015, the average contribution of physically impact to overall water scarcity increases from 51.24 to 74.93 %. Especially after 2011, the physical impact has been the major force of water scarcity for all the cities in the BTH megaregion. Hengshui
55.4
62.6
52.4
54.3
52.1
53.5
52.4
55.9
59.8
56.7
61.8
66.5
68.7
51.1
66.1
Langfang
59.6
59.8
65.6
69
65.2
63.6
73.6
77.1
76.9
74.3
77.9
85.8
79.7
75.1
81.7
Cangzhou
51.3
53.3
61.7
63
67.1
69.4
69.5
76
79
77
77
80.7
82.1
69.7
82.8
Chengde
78.8
62.7
69.6
64
67.2
50.1
55.4
52
66.7
54.2
66.9
54
67.2
70.1
65.3
Zhangjiakou
52.4
50
58
51.6
53.5
55.5
58.2
56.2
67.6
61.2
73.2
69
70.3
73.4
73.8
Baoding
56.7
58
53.8
55.1
50.1
52.9
53.5
52.1
59.4
60.4
58.2
57.6
67.8
69.9
68.1
Xingtai
53.6
54.1
51.1
56.9
58.3
60.2
61.9
64.2
64.6
66.4
69.4
72
74
67.4
68.8
Handan
53.7
54.3
58.2
64
66
66.6
70.3
72.9
73.9
72
74.8
72.9
73.8
71.1
70.4
Qinhuangdao
62.8
61.5
67.3
70.3
71.2
70.5
74.5
78.3
77.3
81.3
84
85.7
84.3
84.3
83.7
Tangshan
56.3
56.3
60.6
63.5
65.7
66
68.1
70.9
72
75.2
75.4
74
79
77.1
79.6
Shijiazhuang
50.4
52.6
56.9
60
60.3
62.2
64.8
67.8
68
68.5
70
72
74.1
71.2
72.9
Tianjing
63.9
57.1
70.2
74
72
72.9
73.8
81.3
78.4
75.6
80.7
85.7
81.3
79.6
80.2
Beijing
62.8
65.7
67.1
69.7
75.5
77.8
78.2
76.9
77.8
79.7
80.3
78
79.7
80.1
80.6
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
physical dominant water scarcity
human dominant water scarcity
Fig. 3. Contributions of different drivers to water scarcity among cities within the BTH 11
Journal Pre-proof megaregion. The values in symbols with different colors denoted the contribution (%) of this type of water scarcity to overall water scarcity.
The in-depth single factor analysis identified the primary determinants of both physically- and human-induced water drivers (Fig.4). Except for Chengde, rainfall was the primary factor that influenced physical water scarcity. In contrast, the primary determinants of human-induced water scarcity varied among the cities. These included water utilization per unit of GDP and the overall water utilization rate. Between 2001 and 2005, the primary determinant of human-induced water scarcity for most cities was water utilization per unit of GDP, but this changed to overall water utilization rate after 2005.
(a) (b) Fig.4. Primary driving factors of water scarcity among cities within the BTH megaregion: (a) primary driving factors of physically-induced water scarcity; (b) primary driving factors of human-induced water scarcity.
3.2. Relationship between water scarcity and urbanization 3.2.1. Spatial-temporal variations in urbanization The composite urbanization level varied internally within the BTH megaregion, and showed unequal development and polarization among cities (Fig. 5 (a)). For instance, during 2001 to 2015, the composite urbanization levels of two megacities (Beijing and Tianjing) were evidently higher than that of the other cities at the same stage, and yet temporally the cities with lower composite urbanization values (such as Cangzhou, Langfang, Chengde, and Baoding) developed faster. For example, the relative overall urbanization level for Beijing increased 2.06 times from 0.47 to 0.97 during the period of 2001 to 2015, and Tianjin increased 2.15 times from 0.32 to 0.69. 12
Journal Pre-proof By contrast, the overall urbanization levels for Hengshui and Chengde increased 3.75 times (from both 0.04 to 0.15) and 4.25 times (from 0.04 to 0.17). The spatial-temporal variation of urbanization in different aspects was obviously different (Fig. 5(b-e)). Generally, the two megacities of Beijing and Tianjin were distinctly ahead of the other cities in every aspect of urbanization. The greatest difference existed in landscape urbanization, for instance, and by 2015 the average landscape urbanization level of Beijing and Tian was 0.79, while that of the other cities was 0.10. But, the growth rates of urbanization in demographic, social and landscape of cities in Hebei province were faster than the megacities, except in economic urbanization which showed the disparity of economic development level between megacities and others increased. 1.0 Hengshui
0.04
0.05
0.06
0.06
0.07
0.08
0.08
0.09
0.09
0.1
0.11
0.13
0.12
0.13
0.15
0.9
Langfang
0.05
0.06
0.08
0.08
0.09
0.09
0.1
0.11
0.15
0.16
0.16
0.16
0.2
0.2
0.25
Cangzhou
0.04
0.04
0.05
0.06
0.07
0.08
0.09
0.1
0.12
0.14
0.16
0.19
0.21
0.22
0.21
0.8
Chengde
0.04
0.04
0.05
0.05
0.07
0.08
0.09
0.11
0.12
0.13
0.15
0.17
0.17
0.18
0.17
0.7
Zhangjiakou
0.07
0.08
0.08
0.08
0.09
0.1
0.1
0.12
0.13
0.14
0.15
0.15
0.15
0.16
0.19
Baoding
0.06
0.06
0.08
0.09
0.09
0.11
0.12
0.13
0.14
0.16
0.17
0.18
0.19
0.2
0.23
Xingtai
0.07
0.07
0.08
0.08
0.08
0.09
0.11
0.12
0.12
0.13
0.13
0.14
0.14
0.15
0.17
Handan
0.06
0.06
0.09
0.11
0.12
0.13
0.13
0.14
0.15
0.16
0.17
0.18
0.19
0.2
0.21
Qinhuangdao
0.11
0.12
0.14
0.14
0.15
0.16
0.17
0.17
0.18
0.19
0.19
0.19
0.23
0.23
0.26
0.1
0.11
0.14
0.14
0.16
0.17
0.18
0.19
0.24
0.27
0.28
0.31
0.31
0.32
0.32
0.3
Shijiazhuang
0.11
0.12
0.15
0.15
0.18
0.2
0.2
0.23
0.25
0.26
0.29
0.31
0.34
0.37
0.37
0.2
Tianjing
0.32
0.34
0.36
0.36
0.4
0.41
0.44
0.47
0.5
0.53
0.57
0.59
0.62
0.64
0.69
Beijing
0.47
0.54
0.59
0.58
0.65
0.69
0.72
0.74
0.77
0.76
0.8
0.83
0.85
0.91
0.97
Tangshan
0.6 0.5 0.4
0.1 0
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
(a) 1.0
1.0 Hengshui
0.08
0.09
0.1
0.11
0.12
0.13
0.14
0.15
0.16
0.17
0.18
0.18
0.19
0.2
0.22
Langfang
0.02
0.03
0.09
0.09
0.09
0.1
0.09
0.1
0.2
0.22
0.21
0.21
0.22
0.23
0.27
Cangzhou
0.03
0.03
0.06
0.07
0.07
0.11
0.13
0.13
0.2
0.22
0.29
0.37
0.41
0.45
0.34
0.8
0
0.01
0.02
0.03
0.05
0.07
0.08
0.09
0.1
0.12
0.15
0.17
0.18
0.19
0.21
0.7
Zhangjiakou
0.04
0.06
0.07
0.09
0.1
0.11
0.13
0.14
0.16
0.18
0.2
0.21
0.22
0.23
0.26
Baoding
0.04
0.04
0.08
0.11
0.11
0.13
0.14
0.14
0.19
0.22
0.22
0.22
0.22
0.23
0.3
Xingtai
0.19
0.2
0.2
0.21
0.21
0.22
0.22
0.22
0.23
0.23
0.24
0.24
0.25
0.25
0.27
Handan
0.03
0.03
0.12
0.19
0.21
0.22
0.24
0.25
0.27
0.28
0.28
0.29
0.29
0.3
0.33
Qinhuangdao
0.08
0.09
0.15
0.17
0.17
0.17
0.17
0.17
0.21
0.22
0.21
0.21
0.21
0.23
0.25
Tangshan
0.11
0.12
0.14
0.15
0.15
0.15
0.15
0.16
0.28
0.33
0.33
0.32
0.33
0.36
0.37
0.3
Shijiazhuang
0.11
0.14
0.19
0.22
0.23
0.24
0.25
0.26
0.3
0.33
0.37
0.44
0.47
0.5
0.46
0.2
Tianjing
0.54
0.54
0.55
0.56
0.57
0.59
0.6
0.63
0.65
0.68
0.7
0.73
0.75
0.77
0.78
Beijing
0.69
0.7
0.72
0.73
0.77
0.81
0.82
0.84
0.86
0.93
0.95
0.97
0.98
0.99
1
Chengde
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
0.9
Hengshui
0.02
0.13
0.13
0.15
0.15
0.16
0.17
0.19
0.17
0.18
0.19
0.26
0.23
0.27
0.29
0.9
Langfang
0.15
0.16
0.17
0.18
0.17
0.19
0.21
0.24
0.27
0.29
0.31
0.29
0.37
0.41
0.47
Cangzhou
0.15
0.16
0.16
0.17
0.21
0.23
0.25
0.27
0.28
0.3
0.31
0.32
0.34
0.35
0.39
0.8
Chengde
0.15
0.16
0.15
0.14
0.15
0.16
0.16
0.18
0.23
0.24
0.26
0.31
0.27
0.28
0.31
0.7
Zhangjiakou
0.21
0.2
0.19
0.18
0.19
0.2
0.21
0.22
0.25
0.26
0.27
0.22
0.28
0.28
0.3
Baoding
0.15
0.16
0.17
0.17
0.16
0.17
0.18
0.2
0.19
0.21
0.22
0.3
0.23
0.27
0.28
Xingtai
0.11
0.11
0.12
0.12
0.11
0.13
0.15
0.16
0.17
0.18
0.19
0.21
0.22
0.24
0.26
Handan
0.16
0.16
0.16
0.18
0.2
0.21
0.22
0.24
0.24
0.24
0.27
0.26
0.29
0.3
0.32
Qinhuangdao
0.27
0.27
0.28
0.28
0.3
0.32
0.33
0.34
0.33
0.35
0.37
0.27
0.38
0.38
0.4
Tangshan
0.17
0.18
0.19
0.22
0.25
0.28
0.31
0.36
0.38
0.42
0.47
0.61
0.53
0.53
0.52
0.3
Shijiazhuang
0.21
0.22
0.22
0.23
0.24
0.26
0.19
0.3
0.31
0.33
0.36
0.32
0.41
0.42
0.44
0.2
Tianjing
0.3
0.31
0.32
0.33
0.36
0.38
0.41
0.44
0.51
0.56
0.62
0.66
0.7
0.73
0.76
Beijing
0.38
0.4
0.41
0.42
0.55
0.58
0.62
0.65
0.7
0.71
0.76
0.79
0.82
0.86
0.9
0.6 0.5 0.4
0.1 0
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
(b)
(c)
13
0.6 0.5 0.4
0.1 0
Journal Pre-proof
1.0
1.0 Hengshui
0.01
0.01
0.01
0.02
0.03
0.04
0.04
0.05
0.06
0.07
0.09
0.1
0.09
0.11
0.13
Langfang
0.04
0.04
0.05
0.06
0.08
0.08
0.09
0.09
0.14
0.15
0.14
0.16
0.21
0.2
0.28
0.01
0.02
0.02
0.03
0.04
0.04
0.06
0.08
0.09
0.1
0.11
0.12
0.15
0.17
0.19
0.8
Chengde
0.02
0.02
0.02
0.03
0.03
0.04
0.05
0.1
0.07
0.08
0.1
0.1
0.11
0.12
0.15
0.7
Zhangjiakou
0.01
0.02
0.02
0.02
0.03
0.04
0.05
0.08
0.08
0.09
0.09
0.11
0.11
0.12
0.16
0.04
0.06
0.07
0.06
0.1
0.11
0.12
0.14
0.15
0.17
0.19
0.19
0.26
0.26
0.24
Xingtai
0.01
0.01
0.02
0.02
0.03
0.04
0.07
0.1
0.09
0.1
0.11
0.12
0.12
0.12
0.14
0.01
0.02
0.02
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.02
0.03
0.04
0.04
0.04
0.05
0.05
0.05
0.05
0.05
0.05
0.06
0.06
0.06
0.06
Cangzhou
0
0
0
0
0.01
0.01
0.01
0.01
0.01
0.01
0.03
0.03
0.03
0.03
0.04
0.8
Chengde
0.03
0.03
0.03
0.03
0.07
0.1
0.11
0.11
0.11
0.14
0.15
0.16
0.15
0.15
0.08
0.7
Zhangjiakou
0.07
0.07
0.07
0.07
0.07
0.07
0.07
0.08
0.08
0.09
0.09
0.09
0.08
0.08
0.09
Baoding
0.03
0.03
0.03
0.05
0.05
0.05
0.05
0.07
0.07
0.07
0.07
0.08
0.08
0.09
0.14
Xingtai
0.01
0.01
0.02
0.02
0.02
0.02
0.04
0.04
0.04
0.04
0.04
0.04
0.05
0.06
0.06
Handan
0.06
0.06
0.06
0.06
0.06
0.06
0.06
0.06
0.07
0.07
0.07
0.07
0.07
0.08
0.08
Qinhuangdao
0.12
0.13
0.13
0.13
0.13
0.13
0.14
0.14
0.14
0.14
0.14
0.15
0.15
0.16
0.21
0.1
0.13
0.16
0.16
0.17
0.18
0.18
0.19
0.2
0.21
0.2
0.21
0.22
0.22
0.17
0.3 0.2
Hengshui
Cangzhou
Baoding
0.01
Langfang
0.9
0.6 0.5
Handan
0.03
0.04
0.04
0.04
0.06
0.07
0.08
0.08
0.1
0.11
0.14
0.15
0.17
0.18
0.18
Qinhuangdao
0.04
0.04
0.05
0.03
0.07
0.08
0.09
0.09
0.11
0.12
0.13
0.18
0.23
0.21
0.24
Tangshan
0.04
0.05
0.07
0.07
0.1
0.12
0.13
0.12
0.17
0.18
0.2
0.23
0.24
0.28
0.31
0.3
Tangshan
Shijiazhuang
0.08
0.1
0.13
0.1
0.19
0.21
0.24
0.26
0.29
0.3
0.34
0.36
0.38
0.42
0.45
0.2
Shijiazhuang
0.07
0.07
0.09
0.11
0.12
0.12
0.13
0.13
0.14
0.14
0.15
0.15
0.15
0.19
0.19
Tianjing
0.14
0.16
0.19
0.19
0.26
0.29
0.32
0.36
0.4
0.45
0.5
0.55
0.59
0.64
0.66
Tianjing
0.33
0.36
0.39
0.39
0.42
0.42
0.44
0.48
0.48
0.49
0.5
0.5
0.5
0.49
0.61
Beijing
0.28
0.33
0.37
0.3
0.47
0.51
0.54
0.59
0.63
0.69
0.74
0.8
0.84
0.95
1
Beijing
0.55
0.7
0.82
0.82
0.81
0.83
0.86
0.86
0.88
0.74
0.76
0.77
0.79
0.84
0.97
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
0.4
0.1 0
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
0.9
0.6 0.5 0.4
0.1 0
(d) (e) Fig.5. Spatial-temporal variations in urbanization levels from different aspects among cities within the BTH megaregion: (a) spatial-temporal variation in overall urbanization, (b) spatial-temporal variation in demographic urbanization, (c) spatial-temporal variation in economic urbanization, (d) spatial-temporal variation in social urbanization, (e) spatial-temporal variation in landscape urbanization.
3.2.2. Relationship between water scarcity and urbanization The correlation analysis showed that composite water scarcity was negatively related to the urbanization level (R = –0.44, P < 0.01) (Table 3). However, the relations of composite water scarcity and urbanization varied among different aspects of urbanization. Economic urbanization was found to have the strongest negative correlation with the composite water scarcity (R = –0.52, P < 0.01) (Table 3). Unlike the negative relationships between composite water scarcity and economic, social and landscape urbanization, the composite water scarcity was positively related to demographic urbanization, indicating the complex water scarcity driving mechanism in the BTH megaregion. In addition, the relations of physically- and human-induced water scarcity with urbanization were different (Table 3). The physically-induced water scarcity was only negatively related with landscape urbanization (R =-0.14, P<0.01), whereas the human-induced water scarcity was negatively related to all four aspects of urbanization, among which the relationship between human-induced water scarcity and economic urbanization was the strongest (R =-0.59, P<0.01). Table 3. Pearson correlation coefficients between water scarcity and the factors affecting urbanization within the BTH megaregion of China 14
Journal Pre-proof
Composite
Composite
Demographic
Social
Economic
Landscape
urbanization
urbanization
urbanization
urbanization
urbanization
–0.44**
0.40**
–0.45**
–0.52**
–0.38*
–0.10
–0.03
–0.10
–0.08
–0.14*
–0.50**
–0.47**
–0.50**
–0.59**
–0.40**
water scarcity Physically-ind uced water scarcity Human-induce d water scarcity * Correlation is significant at the P < 0.05 level (two-tailed) ** Correlation is significant at the P < 0.01 level (two-tailed)
Results from the multi-linear regression analysis indicated that only economic urbanization was finally included in the regression model and it explained approximately 27% of the variation in composite water scarcity across the cities (Table 4). The regression analysis indicated that most of the urbanization variables had no significant relationship with the physically-induced water scarcity and that the variation explained by urbanization was mainly associated with human-induced water scarcity. It revealed that the joint effects of the economic development and landscape urbanization may explain 38% of the variation in human-induced water scarcity, and that the economic urbanization level explained the highest proportion of the variation (Table 4). After controlling the interactions between other aspects of urbanization, landscape urbanization was found to have positive effects on the variation in human-induced water scarcity. Table 4: Multiple-linear stepwise regression models for water scarcity (dependent variable) and the factors affecting urbanization (independent variables) in the BTH megaregion Constant Composite
Demographic
Social
Economic
Landscape
urbanization
urbanization
urbanization
urbanization
0.46**
-0.41**
water scarcity Human-induced
R2 0.27
(–0.52) 0.41**
water scarcity
** P < 0.01 * P < 0.05 15
–0.71**
0.18**
(–0.84)
(0.29)
0.38
Journal Pre-proof 4. Discussion 4.1. Causes of water scarcity in the BTH megaregion The two-level water scarcity assessment index system can separate the different contribution of physically- and human-induced impacts to overall water scarcity. The variation in degree of water scarcity among cites within the BTH megaregion, suggests that the causes of water scarcity of the megaregion might be different and underlines that the importance of conducting city-scale water scarcity analysis. The spatial inconsistency of physically- and human-induced water scarcity indicates that alleviating water scarcity of the whole megaregion will require adopting integrated water resource management. The results also showed that most cities in the BTH megaregion faced serious physically-induced water scarcity, and evidencing that the environmental constraint is an important reason of water scarcity. For the 15 year period during 2001 to 2015, the average annual precipitation for all the 13 cities within the BTH megaregion was less than 800 mm (NBSC, 2001-2015). Conversely, the distinct spatial-temporal variation in human-induced water scarcity across cities indicates that different city development mode might have significant effects on water scarcity. Despite the rapid increase in urbanization, human-induced water scarcity in all the cities had notably decreased, suggesting rapid regional development of the BTH megaregion took account of saving water. During 2001 to 2015, the total annual water utilization of the BTH megaregion decreased from 269.31 to 244.37 billion m3, and the main contribution to the decreased water use was from agricultural water use by decreasing from 180.86 to 138.00 billion m3 (BWA, 2001-2015; TWA, 2001-2015; DWRHP, 2001-2015). In contrast, the residential water use increased from 39.6 to 49.58 billion m3, with a bit increase of average residential water use per capita from 119.34 to 121.64 liter per capita per day (BWA, 2001-2015; TWA, 2001-2015; DWRHP, 2001-2015). The amount of industrial water use generally remained unchanged, about 31.27 billion m3 16
Journal Pre-proof both in 2001 and 2015 (BWA, 2001-2015; TWA, 2001-2015; DWRHP, 2001-2015). Therefore, the water scarcity of the megaregion has been affected by the interaction of physical and human factors. Thus, alleviating water scarcity of large-scale urbanized region require integrated consideration of different causes, and take corresponding actions to different kind of causes. 4.2. Impacts of urbanization on water scarcity and policy implications Our research found a strong relationship between urbanization and human-induced water scarcity, not between physically-induced water scarcity. This implies that large-scale rapid urbanization in the BTH megaregion had little impact on the physical restraints over a short period, such as total water availability and precipitation. Most cities within the BTH megaregion face serious physically-induced water scarcity due to the innate shortage of water resource, which is the inherent obstacle of water scarcity mitigation. Therefore, in the long run, the sustainable megaregion development still requires considerable amounts of direct or indirect water diversion from outside the region, such as the South-North Water Transfer Project and the Virtual Water Trades, which have played major roles in supplying water to the BTH megaregion (Distefano and Kelly, 2017; Holland, et al., 2015; Wichelns, 2004; Zhao, et al., 2010, 2015). These projects will help alleviate water scarcity of the BTH megaregion. Results of our study highlight the significance of urbanization on water scarcity in particularly the higher the economic urbanization level of the city, the lower the overall water scarcity, whereas increasing the built-up area increased water scarcity. This finding indicates that the economic development modes adopted in the BTH megaregion might be the primary factor affecting water scarcity (Distefano and Kelly, 2017). In the BTH megaregion and others beyond in China, the development of economy is the extremely important impetus of urbanization, which usually includes many water-intensity industries such as steel, manufacturing, textiles, electricity and so on. On the one hand, large number of industry requires large amount of water, and 17
Journal Pre-proof on the other the average industry water use efficiency was low. For instance, during 2001 to 2015, the average water utilization per 10,000 RMB GDP of the BTH megaregion reduced 86.4 % from 256.62 to 34.91 m3 per 10000 RMB GDP, but varied from 104.02 (15.54 in 2015) to 565.42 m3 in 2001 (126.12 in 2015) among different cities, underscoring there is still great potential to narrow the variation in water scarcity by balancing water use efficiencies between the cities (NBSC, 2001-2015). The water use efficiency of megacities Beijing and Tianjin was obviously higher than the other cities in Hebei province. Therefore, to further promote the synergetic urbanization of the BTH megaregion, it is critical to emphasize efficient water-saving actions in cities with low water use efficiency such as Hebei Province. Especially by 2030 according to the BTH Integration Strategy (SCC, 2015), many industries will have moved from Beijing to Hebei province, and this will exacerbate water scarcity. Thus, particular attention should be paid to controlling industrial water use of the cities of Hebei province by improving water utilization efficiency to help mitigate water scarcity (Iglesias, et al., 2007; Long and Pijanowski, 2017). Notably, the positive relation between human-induced water scarcity and landscape urbanization suggests that increasing the urban landscape area, mainly represented by the increase in the built-up area, and would intensify water scarcity due to the large water consumption associated with the development of constructed land. The building construction process of modern urban development needs a large amount of direct and indirect water (Han et al., 2016). The building sector has been reported to be account for about 30% of global fresh water consumption, and China’s annual construction account for almost half of the newly-built floor area in the world (Hong et al., 2019). Thus, this finding highlights the importance of promoting effective water-saving measures of building construction sector of large-scale urbanized area. The regression analysis showed that the differences in the urbanization variables only explain part of variation in overall water scarcity (27%) across the BTH 18
Journal Pre-proof megaregion, and therefore there must be other factors affecting water scarcity. One main reason was that large amount of water was consumed by agricultural sector in the BTH megaregion. Despite the BTH megaregion is the most urbanized region in northern China, it is also an important agricultural production area (Wang, et al., 2017). Agriculture has been the biggest water consuming sector in the BTH megaregion, accounting for 67.16% of the total water use in 2001 and decreasing to 56.47% in 2015 (BWA, 2001-2015; TWA, 2001-2015; DWRHP, 2001-2015). Especially in most cities of Hebei province, the proportion of agricultural water used was over 80% (DWRHP, 2001-2015). Therefore, from a regional synergy perspective, it is important to cooperatively allocate water resources between urban and agricultural development if water scarcity in the BTH megaregion is to be reduced (Jiang, 2009; Wang, et al., 2017). This is particularly pertinent in Hebei Province where the urbanization level is relative low, but the water scarcity is more serious than the two megacities of Beijing and Tianjin. Another efficient way is to pay more attention on improving agricultural water utilization efficiency, such as optimizing the agricultural planting structure by replacing high water consuming crops with low water consuming ones and adopting innovative agricultural water-saving technologies (Calzadilla, et al., 2011). Furthermore, there still has some limitation, for the focus of this study was on water quantity induced water scarcity, and didn’t consider water quality which also had important impacts on water scarcity. Further research is needed to assess the effects of water quality on water scarcity.
5. Conclusions This study used the BTH megaregion in China as a pilot case to analyze the spatial-temporal dynamics of water scarcity within the megaregion and to identify the impacts of large-scale urbanization on water scarcity. The results led to the following conclusions. 19
Journal Pre-proof We suggest a new approach to quantitatively assessing internal water scarcity within the megaregion by distinguishing between physically- and human-induced water scarcity. The results suggested that physically-induced water scarcity was serious and the variation between cities was lower than for human-induced water scarcity. Not only the urbanization levels but also urbanization types had significant impacts on water scarcity. The higher the level of urbanization, the lower the level of human-induced water scarcity faced. The economic urbanization level within the BTH megaregion had the most significant impacts on water scarcity, and this underscored the importance of improving the industrial structures of cities to save water. These results also show that coordinating water resource allocation by balancing urban and agricultural demand on water is important. The analytical framework used in this study can be applied to other megaregions in China and to some other developing countries that have similar water scarcity problems.
Acknowledgement This research was funded by the National Key Research and Development Program (2017YFC0505701); the National Natural Science Foundation of China (Grant 41590841).
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Journal Pre-proof Environ. Sci. Technol. 44, 9150–9156.
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Highlights: Water scarcity severity of megaregions is dually influenced by physical and human factors Physically- and human-induced water scarcity of cities within megaregions is spatially inconsistent Urbanization has significant impacts on human-induced water scarcity Economic development pattern of cities within megaregions is the key to alleviate water scarcity
Weifeng Li, Xia Hai, Lijian Han, Jingqiao Mao, Mingming Tian PII:
S0959-6526(19)33462-6
DOI:
https://doi.org/10.1016/j.jclepro.2019.118592
Reference:
JCLP 118592
To appear in:
Journal of Cleaner Production
Received Date:
28 August 2018
Accepted Date:
24 September 2019
Please cite this article as: Weifeng Li, Xia Hai, Lijian Han, Jingqiao Mao, Mingming Tian, Does urbanization intensify regional water scarcity? Evidence and implications from a megaregion of China, Journal of Cleaner Production (2019), https://doi.org/10.1016/j.jclepro.2019.118592
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Does urbanization intensify regional water scarcity? Evidence and implications from a megaregion of China Weifeng Li1, Xia Hai1, Lijian Han1, Jingqiao Mao2, Mingming Tian2 1. Research Center for Eco-environmental Sciences, Chinese Academy of Sciences, Shuangqing Road 18, Beijing 100085, China;
2. College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China
Abstract: Water scarcity as a serious global issue has been challenging the human beings. Despite broad research on water scarcity from global to local scale, there is lack of comprehensive understanding of water scarcity in urban areas, particular in the megaregion which is a cluster of a number of cities with dense human activities and close interactions between each other cities. In this study, we took one important megaregions of China, the Beijing-Tianjin-Hebei (BTH) megaregion, as a case study to analyze the spatial-temporal trajectories of water scarcity and the driving forces. First, we developed a water scarcity index system that could separately assess the spatial variation of physically- and human-induced water scarcity. Second, we explored the relations between water scarcity and the multi-manifestation of urbanization in terms of demographic, social, landscape, and economic aspects. The results showed: 1) the overall water scarcity varied spatially and temporally across different cities in the BTH megaregion; the physically- and human-induced water scarcity was not spatially coincident; most the variation in water scarcity was due to human-induced water scarcity; 2) urbanization primarily affected the human-induced water scarcity; the economic urbanization had the strongest negative impacts, whereas the landscape urbanization had positive effects; non-urban water utilization, such as agricultural water use, strongly competed for water use with regional urbanization. Overall, the results highlight the importance of optimizing economic development mode and controlling agriculture water use to alleviate the water scarcity of megaregion. The general framework used in this study can be applied to other megaregions in China and to some other developing countries that have similar water scarcity problems. 1
Journal Pre-proof Keywords: Physically-induced water scarcity; Human-induced water scarcity; Urbanization; Megaregion
1. Introduction Water scarcity as one of the most serious issues, is becoming a threat to global sustainable development, due to the increasing population (Mekonnen and Hoekstra, 2016). In general, the fundamental concept of water scarcity refers to the imbalance between water availability and demand, and long-term water scarcity would have severe consequences, such as river driving up, groundwater depletion, and water table falling (Kummu et al., 2016; Jiang 2009). Well understanding of the driving forces of water scarcity can provide a basis for targeting actions to alleviate water scarcity. Due to the basic concept, water scarcity is often interpreted from the perspectives of supply and demand sides in terms of drives (Martin-Carrasco et al., 2013; Kummu, et al., 2010). Taking into account of supply-side impact, water scarcity is induced by limited water resource relative to large population. Environmental factors such as climate change (i.e. aridity and drought) and population are the major elements of water scarcity (Iglesias et al., 2007). By contrast, given demand-side impact, water scarcity is caused by high water use relative to water availability, and humankinds are the major influential forces, which can be seen as the human-driven factors of water scarcity (Liu et al., 2017). Many methods have been developed to assess the status of water scarcity, among which the index-based approaches are commonly used (Liu, et al., 2017; Kummu, et al., 2016). These indexes are mainly based on the drivers of water scarcity, and usually focus on some particular characteristics, such as physical, population or social-economic factors. The well-used water scarcity indexes such as Falkenmark (Falkenmark et al., 1989), IWMI (Seckler et al., 1998), Basic Human Water Requirements (Gleick, 1996) and Social Water Stress Index (Ohlsson, 2000), emphasized the impact of water availability. Meanwhile, some other water scarcity 2
Journal Pre-proof indexes such as Water Poverty Index (Sullivan et al., 2003), Water Resources Vulnerability Indices (Raskin, et al., 1997) and Water Supply Stress Index (McNulty et al., 2010), focused on demand side. Moreover, there are also some other certain synthesized water scarcity indexes to incorporate more influential factors of water scarcity such as water availability, environment, life style, infrastructure and economic factors (Liu et al., 2017). Yet, few indices paid attention to separating different forces such as physical and humankind effects on water scarcity. The status of water scarcity has been extensively assessed across global, national and sub-national scales (Mekonnen and Hoekstra, 2016). For example, the global-scale concerns of water scarcity are mainly its continental or sub-continental distribution, affected population, and climate change (Krummu et al., 2016; Mekonnen and Hoekstra, 2016). The national-scale study on water scarcity usually focused on the distribution of water availability and population, and mismatch between them (Brown et al., 2019; Jiang, 2009). Additionally, the regional-scale research on water scarcity mainly paid attention to human activities’ impacts (i.e. urbanization or agricultural development) (Gober and Kirkwood, 2010; Ren et al., 2018). Despite broad research on water scarcity across different scales, the trajectory and causes of water scarcity of the urbanized areas remains less understood. The rapid urbanization, where fast population and social-economic growth brings about increasing demand for water resource, commonly faces the problem of resource shortage including water scarcity, and this in turn restricts the farther development (Fan, et al., 2017; McDonald et al., 2011). Thus, deepen understanding the dynamic of water scarcity in urbanized region and its relation with urbanization would help adapt mitigation solutions, particularly in large-scale urbanized area such as megaregions, which currently is the dominant trend in urbanization cross the world duo to their enormous social-economic advantages but also puts more stress on the environment than individual cities (Han, et al., 2016; Marull, et al., 2013). In this study, we used the Beijing-Tianjin-Hebei (BTH) megaregion which faces sever water scarcity problem and typifies other megaregions in eastern China, as a 3
Journal Pre-proof case study to assess water scarcity and identify the drivers (Yang and Zehnder, 2001; Zhao, et al., 2017). We first used a dataset that spanned from 2001 to 2015 to develop a water scarcity index system which could separate the physically- and human-induced effects on water scarcity. Second, we explored the relations between the trajectories of water scarcity and multi-manifestation of urbanization in terms of demographic, social, economic, and landscape aspects. Finally, we provided recommendations for water scarcity mitigation of megaregions for a more cooperative water resource management. The finding and implication of this study is not only limited to the BTH megaregion but also to other megaregions beyond.
2. Data and methods 2.1. Study area The BTH megaregion lies on the shores of the Bohai Sea in the eastern part of north China. The cities in the megaregion range from latitude 36°01’N to 42°37’N and from longitude 113°04’E to 119°53’E (Fig. 1). The total area of the BTH is 22.0 × 106 ha, which represents 2.35% of the country. The mean annual temperature ranges from 4 to 13°C and the annual precipitation is 300–800 mm per year. The BTH megaregion has 13 cities, including two megacities and eleven prefecture-level cities (Fig.1). By the end of 2015, the total population of the BTH megaregion was 116 million and accounted for 8.12% of China’s population (NBSC, 2015). The average demographic urbanization rate was up to 62.6%, which was higher than the national average of 56.1%. But there were great differences in the urbanization rates among the cities, which ranged from 46.6% to 86.5%. The average per capita GDP of the BTH megaregion was 6.23×104 RMB in 2015, which was higher than the 5.0×104 RMB average for the country of (NBSC, 2015). However, the GDP also varied from 2.4 to 10.7×104 RMB among the different cities (NBSC, 2015).
4
Journal Pre-proof During the research period of 2001 to 2015, the average water resource per capita in the region was less than 200 m3 and ranged from 120 to 547 m3 among different cities (BWA, 2001-2015; TWA, 2015; DWRHP, 2001-2015). Currently, a large quantity of the water used by the BTH megaregion comes from outside and is transferred to the region by water transfer projects, such as the South-to-North Water Transfer Project, the Jin-to-Jing Water Transfer Project, and the Huang-to-Jing Water Transfer Project.
Fig. 1. Spatial locations of the Beijing-Tianjin-Hebei (BTH) megaregion 2.2. Methods and data 2.2.1 Water scarcity assessment index system We developed a two-level index system to assess water scarcity of the BTH megaregion, by separating the contribution of physically- and human-induced influence (Table 1). Basically, we developed two sub-level indexes to respectively assess physically- and human-induced water scarcity. To assess the physical water scarcity, we developed an index by combing two major environmental indicators (total water resource availability and precipitation) as previous discussion (Iglesias et 5
Journal Pre-proof al., 2007; Long and Pijanowski, 2017); to assess the human water scarcity, we developed an index by combining three indicators (water resource per capita, water use per unit of GDP and water utilization rate), which could generally reflect the effects of population and social-economic activities that are particularly related to urbanized area (Kummu et al., 2016; Liu et al., 2017). Then, we created a composite water scarcity index by combing the above two sub-level indexes to reflect the overall status of water scarcity. Detailed explanations of the indexes are given in Table 1. The data used to calculate water scarcity were collected from the Water Resource Bulletin and City Statistical Yearbook. Specially, the data of annual precipitation and total water resource were collected from the City Water Resource Bulletin of Beijing, Tian and the other 11 prefecture-level cities belonging to Hebei province (BWA, 2001-2015; TWA, 2001-2015; DWRHP, 2001-2015). Additionally, the data of population and water utilization rate of each city in the BTH megaregion were collected from the City Statistical Yearbook (NBSC, 2001-2015). All the data set spanned the period from 2001 to 2015. Since the basic indicators of water scarcity have different dimensions and are not be directly accumulated, we need to normalize the data and calculate the index by weighting set. The details of data normalization and weights setting are explained in the following 2.2.3 and 2.2.4 sections. Table 1. Index system used to assess water scarcity within the BTH megaregion of China Composite index
Sub-indexes
Indicators
Affected
Weights
direction
indicators
–
0.31
of
Weights of sub-indexes
Total amount of water Physical-induced water scarcity
resource (100 million m3)
0.36
Precipitation
Composite
(100 million m3)
water
Water resource per
scarcity
capita Human-induced water
(m3
scarcity
Water use per unit of
–
0.69
–
0.03
per capita)
GDP
0.64 +
(t/104RMB) 6
0.45
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+
0.52
2.2.2 Urbanization evaluation index system Although the demographic urbanization level is commonly used to measure the degree of urbanization, urbanization is a multi-faceted phenomenon, particularly in megaregions where both urbanization levels and modes vary among different cities (Bai, et al., 2012). In this study, we attempted to differentiate the urbanization within the BTH megaregion in terms of demographic, landscape, social, and economic aspects (Hai, et al., 2018). We established a two-level index system (Table 2). Basically, we developed four sub-level indexes to separately evaluate demographical, social, economic and landscape urbanization. To assess demographical urbanization, we integrated two indicators of urban population ratio and urban population size; to assess social urbanization, we integrated three indicators of retail sales of consumer goods of the city, urban per capital consumption expenditure and number of college students in universities; to assess the economic urbanization, we integrated three indicators of per capital of GDP, proportion of secondary production and proportion of tertiary industry; to assess the landscape urbanization, we integrated two indicators of built-up area and per capita built-up area. Then, we developed a composite urbanization index by combing the four sub-level indexes to assess the composite characteristics of urbanization. All data used to calculate urbanization of the BTH megaregion were collected from the City Statistical Yearbook. The data set spanned the period from 2001 to 2015 (NBSC, 2001-2015). Similarly to water scarcity index calculation, we need to normalize the data and calculated the index by weighting set. The details of data normalization and weights setting are explained in the following 2.2.3 and 2.2.4 sections. Table 2. Index system for evaluating urbanization within the BTH megaregion of China 7
Journal Pre-proof
Combined index
Sub-indices
Indicators Urban population ratio
Demographic
(%)
urbanization
Urban population size (ten thousand persons)
Affected
Weights
direction
indicators
+
0.40
of
Weights of sub-indexes
0.23 +
0.60
+
0.41
+
0.29
+
0.29
+
0.10
+
0.41
+
0.49
+
0.66
+
0.34
Retail sales of consumer goods of the city (100 million RMB) Urban per capital Social urbanization
consumption expenditure
0.29
(RMB) Composite
Number of college students in
urbanization
universities
index
(ten thousand persons) Proportion of secondary production (%) Economic
Proportion of tertiary industry
urbanization
(%) Per capital of GDP (104 per capita) Built-up area
Landscape
(km2)
urbanization
Per capita built-up area (km2/104 persons)
0.18
0.30
2.2.3 Normalization of indicators We utilized the min-max normalization method to standardize different indicators. This method has been commonly used because it could preserve all the relationship of the original data values without introducing any potential bias (Li and Liu, 2011; Schenatto, et al., 2017). This method is bounded by 10.0 and 1.0 with at least observed value at each of these data sets, and the influence direction of different indicators could be also considered, as the following formulas.
Positive indicator: 𝑋'𝑖𝑗𝑘 = (𝑋𝑖𝑗𝑘 ― 𝑚𝑖𝑛{𝑋𝑗})/(𝑚𝑎𝑥{𝑋𝑗} ― 𝑚𝑖𝑛{𝑋𝑗})
(1)
Negative indicator: 𝑋'𝑖𝑗𝑘 = (𝑚𝑎𝑥{𝑋𝑗} ― 𝑋𝑖𝑗𝑘)/(𝑚𝑎𝑥{𝑋𝑗} ― 𝑚𝑖𝑛{𝑋𝑗})
(2)
8
Journal Pre-proof Where 𝑋𝑖𝑗𝑘 represents the value of indicator j in year i of city k, and max{𝑋𝑗} and min{𝑋𝑗} indicate the minimum and maximum values of the indicator among all years and all cities within the megaregion. 2.2.4 Weights setting of indexes The entropy-weight method is adopted to calculate the weights of criteria both for the indexes of water scarcity and urbanization. By comparison with subjective fixed weight methods, the entropy-weight method is based on the objective information of indexes, and the weights of the criteria are determined according to the difference between the objects for a criterion. The criteria with a larger difference between objects have greater weights, while the criteria with a less difference among objects have smaller weights. Thus, the entropy-weight method rests on the objective basis of criteria and makes results more consistent with facts (Li et al., 2012; Chen et al., 2012; Delgado and Romero, 2016). The criteria weighting steps are as following: The proportion of indicator j in year i: 𝑚 𝑋' = 1 𝑖𝑗
𝑌𝑖𝑗 = 𝑋'𝑖𝑗/∑𝑖
(3)
Information entropy for the indicator: 1
𝑚 𝑌 = 1 𝑖𝑗
𝑒𝑗 = ― ln 𝑚∑𝑖
(4)
× ln 𝑌𝑖𝑗 (0 ≤ 𝑒𝑗 ≤ 1)
Entropy redundancy: 𝑑𝑗 = 1 ― 𝑒𝑗
(5)
Weight of the indicator: 𝑛
𝑊𝑗 = 𝑑𝑗/∑𝑗
𝑑 =1 𝑗
(6)
Where n is the number of indicators for each index, and m is the number of years. Then the water and urbanization indices for the BTH megaregion for the period 2001–2015 were calculated. The weights of different indicators and indexes for water scarcity and urbanization of the BTH megaregion were calculated (Table 1 and 2).
9
Journal Pre-proof 2.2.5 Statistical analysis We used simple Pearson correlation analysis to analyze the relationship between water scarcity and urbanization. Then, we used the stepwise regression method to construct multi-linear regression models to analyze the relationship between water scarcity and different aspects of urbanization, and identified the main drivers of water scarcity.
3 Results 3.1. Water scarcity in the BTH megaregion 3.1.1. Spatial-temporal variation in water scarcity The composite water scarcity within the BTH megaregion varied strongly among cities within the BTH megaregion (Figs. 2(a)). Spatially, the composite water scarcity values ranged from 0.1 to 0.94 among all cities over the period from 2001 to 2015, which meant that the severity of water scarcity among different cities were quite different. Generally, the water scarcity values for all the cities decreased over the period of 2001 to 2015. The variation in physically- and human-induced water scarcity was not spatially concordant among cities (Figs. 2(a), (b)). Except for Chengde and Zhangjiakou, the physically-induced water scarcity for most cities was severe, with the index values greater than 0.6 during 2001 to 2015. Furthermore, there was no obvious change of physically-induced water scarcity from 2001 to 2015. In contrast, the human-induced water scarcity generally decreased from 2001 to 2015 for most cities, and the largest reductions in human-induced water scarcity were in Hengshui, Xingtai, and Baoding.
10
Journal Pre-proof
1.0 Hengshui
0.74
0.94
0.57
0.61
0.65
0.73
0.72
0.58
0.5
0.59
0.53
0.46
0.44
0.69
0.49
Langfang
0.58
0.59
0.51
0.48
0.53
0.56
0.45
0.42
0.44
0.46
0.43
0.34
0.42
0.47
0.41
Cangzhou
0.61
0.72
0.42
0.44
0.44
0.41
0.43
0.36
0.31
0.36
0.36
0.29
0.32
0.47
0.3
0.8
Chengde
0.39
0.5
0.34
0.31
0.2
0.28
0.26
0.16
0.27
0.13
0.14
0.1
0.15
0.19
0.16
0.7
Zhangjiakou
0.44
0.41
0.32
0.31
0.37
0.35
0.34
0.25
0.33
0.2
0.3
0.24
0.21
0.26
0.2
Baoding
0.61
0.53
0.5
0.36
0.46
0.49
0.37
0.27
0.37
0.34
0.26
0.2
0.26
0.35
0.28
Xingtai
0.68
0.71
0.5
0.5
0.5
0.49
0.5
0.46
0.44
0.45
0.42
0.39
0.36
0.47
0.45
0.9
0.6 0.5
Handan
0.57
0.59
0.39
0.45
0.42
0.47
0.43
0.39
0.38
0.42
0.39
0.4
0.39
0.42
0.45
Qinhuangdao
0.51
0.55
0.46
0.43
0.44
0.47
0.43
0.4
0.41
0.35
0.36
0.21
0.35
0.38
0.37
Tangshan
0.48
0.55
0.41
0.38
0.4
0.42
0.37
0.33
0.37
0.34
0.32
0.18
0.31
0.35
0.33
0.3
0.6
0.52
0.43
0.39
0.46
0.44
0.4
0.34
0.36
0.41
0.36
0.34
0.34
0.44
0.38
0.2
Tianjing
0.49
0.58
0.4
0.36
0.4
0.41
0.39
0.31
0.34
0.4
0.33
0.21
0.35
0.38
0.35
Beijing
0.39
0.4
0.37
0.32
0.31
0.31
0.29
0.22
0.31
0.28
0.26
0.18
0.28
0.31
0.25
Shijiazhuang
0.4
0.1 0
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
(a) 1.0 Hengshui
0.92
0.98
0.83
0.92
0.94
0.96
0.95
0.9
0.83
0.93
0.91
0.85
0.84
0.98
0.9
Langfang
0.96
0.98
0.93
0.92
0.96
0.99
0.92
0.9
0.94
0.95
0.93
0.81
0.93
0.98
0.93
Cangzhou
0.87
0.93
0.72
0.77
0.82
0.79
0.83
0.76
0.68
0.77
0.77
0.65
0.73
0.91
0.69
0.8
Chengde
0.24
0.51
0.29
0.31
0.17
0.39
0.4
0.2
0.5
0.17
0.26
0.15
0.28
0.37
0.29
0.7
0.6
0.56
0.38
0.43
0.55
0.54
0.55
0.39
0.62
0.34
0.61
0.46
0.41
0.53
0.41
Baoding
0.73
0.64
0.64
0.45
0.64
0.72
0.55
0.36
0.61
0.57
0.42
0.32
0.49
0.68
0.53
Xingtai
0.87
0.89
0.71
0.79
0.81
0.82
0.86
0.82
0.79
0.83
0.81
0.78
0.74
0.88
0.86
Zhangjiakou
1.0
0.9
0.6 0.5
Hengshui
0.64
0.92
0.42
0.44
0.49
0.61
0.59
0.39
0.32
0.4
0.32
0.24
0.21
0.53
0.26
0.9
Langfang
0.36
0.37
0.27
0.23
0.28
0.31
0.18
0.15
0.17
0.18
0.15
0.08
0.13
0.18
0.12
Cangzhou
0.46
0.6
0.26
0.25
0.22
0.19
0.2
0.14
0.11
0.12
0.12
0.09
0.09
0.22
0.09
0.8
Chengde
0.48
0.49
0.37
0.31
0.21
0.22
0.18
0.13
0.14
0.11
0.08
0.07
0.07
0.09
0.08
0.7
Zhangjiakou
0.36
0.32
0.29
0.25
0.26
0.24
0.22
0.16
0.16
0.12
0.13
0.11
0.09
0.1
0.09
Baoding
0.54
0.48
0.42
0.31
0.36
0.37
0.27
0.22
0.24
0.21
0.17
0.14
0.13
0.17
0.13
Xingtai
0.57
0.6
0.38
0.34
0.33
0.3
0.3
0.26
0.24
0.23
0.19
0.18
0.15
0.24
0.22
Handan
0.41
0.43
0.26
0.26
0.22
0.25
0.21
0.16
0.15
0.18
0.16
0.17
0.16
0.18
0.21
0.3
0.33
0.23
0.19
0.2
0.21
0.17
0.14
0.14
0.11
0.1
0.05
0.09
0.1
0.1
0.6 0.5
Handan
0.85
0.89
0.63
0.8
0.77
0.87
0.84
0.79
0.78
0.84
0.81
0.81
0.8
0.83
0.88
Qinhuangdao
0.89
0.94
0.86
0.84
0.87
0.92
0.89
0.87
0.88
0.79
0.84
0.5
0.82
0.89
0.86
Tangshan
0.75
0.86
0.69
0.67
0.73
0.77
0.7
0.65
0.74
0.71
0.67
0.37
0.68
0.75
0.73
0.3
Tangshan
0.32
0.37
0.25
0.21
0.21
0.22
0.19
0.14
0.16
0.14
0.12
0.07
0.1
0.13
0.12
0.3
Shijiazhuang
0.84
0.76
0.68
0.65
0.77
0.76
0.72
0.64
0.68
0.78
0.7
0.68
0.7
0.87
0.77
0.2
Shijiazhuang
0.46
0.39
0.29
0.24
0.28
0.26
0.23
0.17
0.17
0.2
0.17
0.15
0.13
0.2
0.16
0.2
Tianjing
0.87
0.92
0.78
0.74
0.8
0.83
0.8
0.7
0.74
0.84
0.74
0.5
0.79
0.84
0.78
Tianjing
0.28
0.39
0.18
0.15
0.18
0.17
0.16
0.1
0.11
0.16
0.1
0.05
0.1
0.12
0.12
Beijing
0.68
0.73
0.69
0.62
0.65
0.67
0.63
0.47
0.67
0.62
0.58
0.39
0.62
0.69
0.56
Beijing
0.23
0.21
0.18
0.15
0.12
0.11
0.1
0.07
0.1
0.09
0.08
0.06
0.08
0.1
0.07
0.4
Qinhuangdao
0.1 0
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
(b)
0.4
0.1 0
(c)
Fig. 2. Spatial-temporal variations in water scarcity among cities within the BTH megaregion: (a) spatial-temporal variation in overal water scarcity, (b) spatial-temporal variation in physical-induced water scarcity, (c) spatial-temporal variation in human-induced water scarcity.
3.1.2. Determinants of water scarcity The physical causes were primary contributors to water scarcity compared to the human causes (Fig. 3). For instance, during 2001 to 2015, the average contribution of physically impact to overall water scarcity increases from 51.24 to 74.93 %. Especially after 2011, the physical impact has been the major force of water scarcity for all the cities in the BTH megaregion. Hengshui
55.4
62.6
52.4
54.3
52.1
53.5
52.4
55.9
59.8
56.7
61.8
66.5
68.7
51.1
66.1
Langfang
59.6
59.8
65.6
69
65.2
63.6
73.6
77.1
76.9
74.3
77.9
85.8
79.7
75.1
81.7
Cangzhou
51.3
53.3
61.7
63
67.1
69.4
69.5
76
79
77
77
80.7
82.1
69.7
82.8
Chengde
78.8
62.7
69.6
64
67.2
50.1
55.4
52
66.7
54.2
66.9
54
67.2
70.1
65.3
Zhangjiakou
52.4
50
58
51.6
53.5
55.5
58.2
56.2
67.6
61.2
73.2
69
70.3
73.4
73.8
Baoding
56.7
58
53.8
55.1
50.1
52.9
53.5
52.1
59.4
60.4
58.2
57.6
67.8
69.9
68.1
Xingtai
53.6
54.1
51.1
56.9
58.3
60.2
61.9
64.2
64.6
66.4
69.4
72
74
67.4
68.8
Handan
53.7
54.3
58.2
64
66
66.6
70.3
72.9
73.9
72
74.8
72.9
73.8
71.1
70.4
Qinhuangdao
62.8
61.5
67.3
70.3
71.2
70.5
74.5
78.3
77.3
81.3
84
85.7
84.3
84.3
83.7
Tangshan
56.3
56.3
60.6
63.5
65.7
66
68.1
70.9
72
75.2
75.4
74
79
77.1
79.6
Shijiazhuang
50.4
52.6
56.9
60
60.3
62.2
64.8
67.8
68
68.5
70
72
74.1
71.2
72.9
Tianjing
63.9
57.1
70.2
74
72
72.9
73.8
81.3
78.4
75.6
80.7
85.7
81.3
79.6
80.2
Beijing
62.8
65.7
67.1
69.7
75.5
77.8
78.2
76.9
77.8
79.7
80.3
78
79.7
80.1
80.6
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
physical dominant water scarcity
human dominant water scarcity
Fig. 3. Contributions of different drivers to water scarcity among cities within the BTH 11
Journal Pre-proof megaregion. The values in symbols with different colors denoted the contribution (%) of this type of water scarcity to overall water scarcity.
The in-depth single factor analysis identified the primary determinants of both physically- and human-induced water drivers (Fig.4). Except for Chengde, rainfall was the primary factor that influenced physical water scarcity. In contrast, the primary determinants of human-induced water scarcity varied among the cities. These included water utilization per unit of GDP and the overall water utilization rate. Between 2001 and 2005, the primary determinant of human-induced water scarcity for most cities was water utilization per unit of GDP, but this changed to overall water utilization rate after 2005.
(a) (b) Fig.4. Primary driving factors of water scarcity among cities within the BTH megaregion: (a) primary driving factors of physically-induced water scarcity; (b) primary driving factors of human-induced water scarcity.
3.2. Relationship between water scarcity and urbanization 3.2.1. Spatial-temporal variations in urbanization The composite urbanization level varied internally within the BTH megaregion, and showed unequal development and polarization among cities (Fig. 5 (a)). For instance, during 2001 to 2015, the composite urbanization levels of two megacities (Beijing and Tianjing) were evidently higher than that of the other cities at the same stage, and yet temporally the cities with lower composite urbanization values (such as Cangzhou, Langfang, Chengde, and Baoding) developed faster. For example, the relative overall urbanization level for Beijing increased 2.06 times from 0.47 to 0.97 during the period of 2001 to 2015, and Tianjin increased 2.15 times from 0.32 to 0.69. 12
Journal Pre-proof By contrast, the overall urbanization levels for Hengshui and Chengde increased 3.75 times (from both 0.04 to 0.15) and 4.25 times (from 0.04 to 0.17). The spatial-temporal variation of urbanization in different aspects was obviously different (Fig. 5(b-e)). Generally, the two megacities of Beijing and Tianjin were distinctly ahead of the other cities in every aspect of urbanization. The greatest difference existed in landscape urbanization, for instance, and by 2015 the average landscape urbanization level of Beijing and Tian was 0.79, while that of the other cities was 0.10. But, the growth rates of urbanization in demographic, social and landscape of cities in Hebei province were faster than the megacities, except in economic urbanization which showed the disparity of economic development level between megacities and others increased. 1.0 Hengshui
0.04
0.05
0.06
0.06
0.07
0.08
0.08
0.09
0.09
0.1
0.11
0.13
0.12
0.13
0.15
0.9
Langfang
0.05
0.06
0.08
0.08
0.09
0.09
0.1
0.11
0.15
0.16
0.16
0.16
0.2
0.2
0.25
Cangzhou
0.04
0.04
0.05
0.06
0.07
0.08
0.09
0.1
0.12
0.14
0.16
0.19
0.21
0.22
0.21
0.8
Chengde
0.04
0.04
0.05
0.05
0.07
0.08
0.09
0.11
0.12
0.13
0.15
0.17
0.17
0.18
0.17
0.7
Zhangjiakou
0.07
0.08
0.08
0.08
0.09
0.1
0.1
0.12
0.13
0.14
0.15
0.15
0.15
0.16
0.19
Baoding
0.06
0.06
0.08
0.09
0.09
0.11
0.12
0.13
0.14
0.16
0.17
0.18
0.19
0.2
0.23
Xingtai
0.07
0.07
0.08
0.08
0.08
0.09
0.11
0.12
0.12
0.13
0.13
0.14
0.14
0.15
0.17
Handan
0.06
0.06
0.09
0.11
0.12
0.13
0.13
0.14
0.15
0.16
0.17
0.18
0.19
0.2
0.21
Qinhuangdao
0.11
0.12
0.14
0.14
0.15
0.16
0.17
0.17
0.18
0.19
0.19
0.19
0.23
0.23
0.26
0.1
0.11
0.14
0.14
0.16
0.17
0.18
0.19
0.24
0.27
0.28
0.31
0.31
0.32
0.32
0.3
Shijiazhuang
0.11
0.12
0.15
0.15
0.18
0.2
0.2
0.23
0.25
0.26
0.29
0.31
0.34
0.37
0.37
0.2
Tianjing
0.32
0.34
0.36
0.36
0.4
0.41
0.44
0.47
0.5
0.53
0.57
0.59
0.62
0.64
0.69
Beijing
0.47
0.54
0.59
0.58
0.65
0.69
0.72
0.74
0.77
0.76
0.8
0.83
0.85
0.91
0.97
Tangshan
0.6 0.5 0.4
0.1 0
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
(a) 1.0
1.0 Hengshui
0.08
0.09
0.1
0.11
0.12
0.13
0.14
0.15
0.16
0.17
0.18
0.18
0.19
0.2
0.22
Langfang
0.02
0.03
0.09
0.09
0.09
0.1
0.09
0.1
0.2
0.22
0.21
0.21
0.22
0.23
0.27
Cangzhou
0.03
0.03
0.06
0.07
0.07
0.11
0.13
0.13
0.2
0.22
0.29
0.37
0.41
0.45
0.34
0.8
0
0.01
0.02
0.03
0.05
0.07
0.08
0.09
0.1
0.12
0.15
0.17
0.18
0.19
0.21
0.7
Zhangjiakou
0.04
0.06
0.07
0.09
0.1
0.11
0.13
0.14
0.16
0.18
0.2
0.21
0.22
0.23
0.26
Baoding
0.04
0.04
0.08
0.11
0.11
0.13
0.14
0.14
0.19
0.22
0.22
0.22
0.22
0.23
0.3
Xingtai
0.19
0.2
0.2
0.21
0.21
0.22
0.22
0.22
0.23
0.23
0.24
0.24
0.25
0.25
0.27
Handan
0.03
0.03
0.12
0.19
0.21
0.22
0.24
0.25
0.27
0.28
0.28
0.29
0.29
0.3
0.33
Qinhuangdao
0.08
0.09
0.15
0.17
0.17
0.17
0.17
0.17
0.21
0.22
0.21
0.21
0.21
0.23
0.25
Tangshan
0.11
0.12
0.14
0.15
0.15
0.15
0.15
0.16
0.28
0.33
0.33
0.32
0.33
0.36
0.37
0.3
Shijiazhuang
0.11
0.14
0.19
0.22
0.23
0.24
0.25
0.26
0.3
0.33
0.37
0.44
0.47
0.5
0.46
0.2
Tianjing
0.54
0.54
0.55
0.56
0.57
0.59
0.6
0.63
0.65
0.68
0.7
0.73
0.75
0.77
0.78
Beijing
0.69
0.7
0.72
0.73
0.77
0.81
0.82
0.84
0.86
0.93
0.95
0.97
0.98
0.99
1
Chengde
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
0.9
Hengshui
0.02
0.13
0.13
0.15
0.15
0.16
0.17
0.19
0.17
0.18
0.19
0.26
0.23
0.27
0.29
0.9
Langfang
0.15
0.16
0.17
0.18
0.17
0.19
0.21
0.24
0.27
0.29
0.31
0.29
0.37
0.41
0.47
Cangzhou
0.15
0.16
0.16
0.17
0.21
0.23
0.25
0.27
0.28
0.3
0.31
0.32
0.34
0.35
0.39
0.8
Chengde
0.15
0.16
0.15
0.14
0.15
0.16
0.16
0.18
0.23
0.24
0.26
0.31
0.27
0.28
0.31
0.7
Zhangjiakou
0.21
0.2
0.19
0.18
0.19
0.2
0.21
0.22
0.25
0.26
0.27
0.22
0.28
0.28
0.3
Baoding
0.15
0.16
0.17
0.17
0.16
0.17
0.18
0.2
0.19
0.21
0.22
0.3
0.23
0.27
0.28
Xingtai
0.11
0.11
0.12
0.12
0.11
0.13
0.15
0.16
0.17
0.18
0.19
0.21
0.22
0.24
0.26
Handan
0.16
0.16
0.16
0.18
0.2
0.21
0.22
0.24
0.24
0.24
0.27
0.26
0.29
0.3
0.32
Qinhuangdao
0.27
0.27
0.28
0.28
0.3
0.32
0.33
0.34
0.33
0.35
0.37
0.27
0.38
0.38
0.4
Tangshan
0.17
0.18
0.19
0.22
0.25
0.28
0.31
0.36
0.38
0.42
0.47
0.61
0.53
0.53
0.52
0.3
Shijiazhuang
0.21
0.22
0.22
0.23
0.24
0.26
0.19
0.3
0.31
0.33
0.36
0.32
0.41
0.42
0.44
0.2
Tianjing
0.3
0.31
0.32
0.33
0.36
0.38
0.41
0.44
0.51
0.56
0.62
0.66
0.7
0.73
0.76
Beijing
0.38
0.4
0.41
0.42
0.55
0.58
0.62
0.65
0.7
0.71
0.76
0.79
0.82
0.86
0.9
0.6 0.5 0.4
0.1 0
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
(b)
(c)
13
0.6 0.5 0.4
0.1 0
Journal Pre-proof
1.0
1.0 Hengshui
0.01
0.01
0.01
0.02
0.03
0.04
0.04
0.05
0.06
0.07
0.09
0.1
0.09
0.11
0.13
Langfang
0.04
0.04
0.05
0.06
0.08
0.08
0.09
0.09
0.14
0.15
0.14
0.16
0.21
0.2
0.28
0.01
0.02
0.02
0.03
0.04
0.04
0.06
0.08
0.09
0.1
0.11
0.12
0.15
0.17
0.19
0.8
Chengde
0.02
0.02
0.02
0.03
0.03
0.04
0.05
0.1
0.07
0.08
0.1
0.1
0.11
0.12
0.15
0.7
Zhangjiakou
0.01
0.02
0.02
0.02
0.03
0.04
0.05
0.08
0.08
0.09
0.09
0.11
0.11
0.12
0.16
0.04
0.06
0.07
0.06
0.1
0.11
0.12
0.14
0.15
0.17
0.19
0.19
0.26
0.26
0.24
Xingtai
0.01
0.01
0.02
0.02
0.03
0.04
0.07
0.1
0.09
0.1
0.11
0.12
0.12
0.12
0.14
0.01
0.02
0.02
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.02
0.03
0.04
0.04
0.04
0.05
0.05
0.05
0.05
0.05
0.05
0.06
0.06
0.06
0.06
Cangzhou
0
0
0
0
0.01
0.01
0.01
0.01
0.01
0.01
0.03
0.03
0.03
0.03
0.04
0.8
Chengde
0.03
0.03
0.03
0.03
0.07
0.1
0.11
0.11
0.11
0.14
0.15
0.16
0.15
0.15
0.08
0.7
Zhangjiakou
0.07
0.07
0.07
0.07
0.07
0.07
0.07
0.08
0.08
0.09
0.09
0.09
0.08
0.08
0.09
Baoding
0.03
0.03
0.03
0.05
0.05
0.05
0.05
0.07
0.07
0.07
0.07
0.08
0.08
0.09
0.14
Xingtai
0.01
0.01
0.02
0.02
0.02
0.02
0.04
0.04
0.04
0.04
0.04
0.04
0.05
0.06
0.06
Handan
0.06
0.06
0.06
0.06
0.06
0.06
0.06
0.06
0.07
0.07
0.07
0.07
0.07
0.08
0.08
Qinhuangdao
0.12
0.13
0.13
0.13
0.13
0.13
0.14
0.14
0.14
0.14
0.14
0.15
0.15
0.16
0.21
0.1
0.13
0.16
0.16
0.17
0.18
0.18
0.19
0.2
0.21
0.2
0.21
0.22
0.22
0.17
0.3 0.2
Hengshui
Cangzhou
Baoding
0.01
Langfang
0.9
0.6 0.5
Handan
0.03
0.04
0.04
0.04
0.06
0.07
0.08
0.08
0.1
0.11
0.14
0.15
0.17
0.18
0.18
Qinhuangdao
0.04
0.04
0.05
0.03
0.07
0.08
0.09
0.09
0.11
0.12
0.13
0.18
0.23
0.21
0.24
Tangshan
0.04
0.05
0.07
0.07
0.1
0.12
0.13
0.12
0.17
0.18
0.2
0.23
0.24
0.28
0.31
0.3
Tangshan
Shijiazhuang
0.08
0.1
0.13
0.1
0.19
0.21
0.24
0.26
0.29
0.3
0.34
0.36
0.38
0.42
0.45
0.2
Shijiazhuang
0.07
0.07
0.09
0.11
0.12
0.12
0.13
0.13
0.14
0.14
0.15
0.15
0.15
0.19
0.19
Tianjing
0.14
0.16
0.19
0.19
0.26
0.29
0.32
0.36
0.4
0.45
0.5
0.55
0.59
0.64
0.66
Tianjing
0.33
0.36
0.39
0.39
0.42
0.42
0.44
0.48
0.48
0.49
0.5
0.5
0.5
0.49
0.61
Beijing
0.28
0.33
0.37
0.3
0.47
0.51
0.54
0.59
0.63
0.69
0.74
0.8
0.84
0.95
1
Beijing
0.55
0.7
0.82
0.82
0.81
0.83
0.86
0.86
0.88
0.74
0.76
0.77
0.79
0.84
0.97
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
0.4
0.1 0
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
0.9
0.6 0.5 0.4
0.1 0
(d) (e) Fig.5. Spatial-temporal variations in urbanization levels from different aspects among cities within the BTH megaregion: (a) spatial-temporal variation in overall urbanization, (b) spatial-temporal variation in demographic urbanization, (c) spatial-temporal variation in economic urbanization, (d) spatial-temporal variation in social urbanization, (e) spatial-temporal variation in landscape urbanization.
3.2.2. Relationship between water scarcity and urbanization The correlation analysis showed that composite water scarcity was negatively related to the urbanization level (R = –0.44, P < 0.01) (Table 3). However, the relations of composite water scarcity and urbanization varied among different aspects of urbanization. Economic urbanization was found to have the strongest negative correlation with the composite water scarcity (R = –0.52, P < 0.01) (Table 3). Unlike the negative relationships between composite water scarcity and economic, social and landscape urbanization, the composite water scarcity was positively related to demographic urbanization, indicating the complex water scarcity driving mechanism in the BTH megaregion. In addition, the relations of physically- and human-induced water scarcity with urbanization were different (Table 3). The physically-induced water scarcity was only negatively related with landscape urbanization (R =-0.14, P<0.01), whereas the human-induced water scarcity was negatively related to all four aspects of urbanization, among which the relationship between human-induced water scarcity and economic urbanization was the strongest (R =-0.59, P<0.01). Table 3. Pearson correlation coefficients between water scarcity and the factors affecting urbanization within the BTH megaregion of China 14
Journal Pre-proof
Composite
Composite
Demographic
Social
Economic
Landscape
urbanization
urbanization
urbanization
urbanization
urbanization
–0.44**
0.40**
–0.45**
–0.52**
–0.38*
–0.10
–0.03
–0.10
–0.08
–0.14*
–0.50**
–0.47**
–0.50**
–0.59**
–0.40**
water scarcity Physically-ind uced water scarcity Human-induce d water scarcity * Correlation is significant at the P < 0.05 level (two-tailed) ** Correlation is significant at the P < 0.01 level (two-tailed)
Results from the multi-linear regression analysis indicated that only economic urbanization was finally included in the regression model and it explained approximately 27% of the variation in composite water scarcity across the cities (Table 4). The regression analysis indicated that most of the urbanization variables had no significant relationship with the physically-induced water scarcity and that the variation explained by urbanization was mainly associated with human-induced water scarcity. It revealed that the joint effects of the economic development and landscape urbanization may explain 38% of the variation in human-induced water scarcity, and that the economic urbanization level explained the highest proportion of the variation (Table 4). After controlling the interactions between other aspects of urbanization, landscape urbanization was found to have positive effects on the variation in human-induced water scarcity. Table 4: Multiple-linear stepwise regression models for water scarcity (dependent variable) and the factors affecting urbanization (independent variables) in the BTH megaregion Constant Composite
Demographic
Social
Economic
Landscape
urbanization
urbanization
urbanization
urbanization
0.46**
-0.41**
water scarcity Human-induced
R2 0.27
(–0.52) 0.41**
water scarcity
** P < 0.01 * P < 0.05 15
–0.71**
0.18**
(–0.84)
(0.29)
0.38
Journal Pre-proof 4. Discussion 4.1. Causes of water scarcity in the BTH megaregion The two-level water scarcity assessment index system can separate the different contribution of physically- and human-induced impacts to overall water scarcity. The variation in degree of water scarcity among cites within the BTH megaregion, suggests that the causes of water scarcity of the megaregion might be different and underlines that the importance of conducting city-scale water scarcity analysis. The spatial inconsistency of physically- and human-induced water scarcity indicates that alleviating water scarcity of the whole megaregion will require adopting integrated water resource management. The results also showed that most cities in the BTH megaregion faced serious physically-induced water scarcity, and evidencing that the environmental constraint is an important reason of water scarcity. For the 15 year period during 2001 to 2015, the average annual precipitation for all the 13 cities within the BTH megaregion was less than 800 mm (NBSC, 2001-2015). Conversely, the distinct spatial-temporal variation in human-induced water scarcity across cities indicates that different city development mode might have significant effects on water scarcity. Despite the rapid increase in urbanization, human-induced water scarcity in all the cities had notably decreased, suggesting rapid regional development of the BTH megaregion took account of saving water. During 2001 to 2015, the total annual water utilization of the BTH megaregion decreased from 269.31 to 244.37 billion m3, and the main contribution to the decreased water use was from agricultural water use by decreasing from 180.86 to 138.00 billion m3 (BWA, 2001-2015; TWA, 2001-2015; DWRHP, 2001-2015). In contrast, the residential water use increased from 39.6 to 49.58 billion m3, with a bit increase of average residential water use per capita from 119.34 to 121.64 liter per capita per day (BWA, 2001-2015; TWA, 2001-2015; DWRHP, 2001-2015). The amount of industrial water use generally remained unchanged, about 31.27 billion m3 16
Journal Pre-proof both in 2001 and 2015 (BWA, 2001-2015; TWA, 2001-2015; DWRHP, 2001-2015). Therefore, the water scarcity of the megaregion has been affected by the interaction of physical and human factors. Thus, alleviating water scarcity of large-scale urbanized region require integrated consideration of different causes, and take corresponding actions to different kind of causes. 4.2. Impacts of urbanization on water scarcity and policy implications Our research found a strong relationship between urbanization and human-induced water scarcity, not between physically-induced water scarcity. This implies that large-scale rapid urbanization in the BTH megaregion had little impact on the physical restraints over a short period, such as total water availability and precipitation. Most cities within the BTH megaregion face serious physically-induced water scarcity due to the innate shortage of water resource, which is the inherent obstacle of water scarcity mitigation. Therefore, in the long run, the sustainable megaregion development still requires considerable amounts of direct or indirect water diversion from outside the region, such as the South-North Water Transfer Project and the Virtual Water Trades, which have played major roles in supplying water to the BTH megaregion (Distefano and Kelly, 2017; Holland, et al., 2015; Wichelns, 2004; Zhao, et al., 2010, 2015). These projects will help alleviate water scarcity of the BTH megaregion. Results of our study highlight the significance of urbanization on water scarcity in particularly the higher the economic urbanization level of the city, the lower the overall water scarcity, whereas increasing the built-up area increased water scarcity. This finding indicates that the economic development modes adopted in the BTH megaregion might be the primary factor affecting water scarcity (Distefano and Kelly, 2017). In the BTH megaregion and others beyond in China, the development of economy is the extremely important impetus of urbanization, which usually includes many water-intensity industries such as steel, manufacturing, textiles, electricity and so on. On the one hand, large number of industry requires large amount of water, and 17
Journal Pre-proof on the other the average industry water use efficiency was low. For instance, during 2001 to 2015, the average water utilization per 10,000 RMB GDP of the BTH megaregion reduced 86.4 % from 256.62 to 34.91 m3 per 10000 RMB GDP, but varied from 104.02 (15.54 in 2015) to 565.42 m3 in 2001 (126.12 in 2015) among different cities, underscoring there is still great potential to narrow the variation in water scarcity by balancing water use efficiencies between the cities (NBSC, 2001-2015). The water use efficiency of megacities Beijing and Tianjin was obviously higher than the other cities in Hebei province. Therefore, to further promote the synergetic urbanization of the BTH megaregion, it is critical to emphasize efficient water-saving actions in cities with low water use efficiency such as Hebei Province. Especially by 2030 according to the BTH Integration Strategy (SCC, 2015), many industries will have moved from Beijing to Hebei province, and this will exacerbate water scarcity. Thus, particular attention should be paid to controlling industrial water use of the cities of Hebei province by improving water utilization efficiency to help mitigate water scarcity (Iglesias, et al., 2007; Long and Pijanowski, 2017). Notably, the positive relation between human-induced water scarcity and landscape urbanization suggests that increasing the urban landscape area, mainly represented by the increase in the built-up area, and would intensify water scarcity due to the large water consumption associated with the development of constructed land. The building construction process of modern urban development needs a large amount of direct and indirect water (Han et al., 2016). The building sector has been reported to be account for about 30% of global fresh water consumption, and China’s annual construction account for almost half of the newly-built floor area in the world (Hong et al., 2019). Thus, this finding highlights the importance of promoting effective water-saving measures of building construction sector of large-scale urbanized area. The regression analysis showed that the differences in the urbanization variables only explain part of variation in overall water scarcity (27%) across the BTH 18
Journal Pre-proof megaregion, and therefore there must be other factors affecting water scarcity. One main reason was that large amount of water was consumed by agricultural sector in the BTH megaregion. Despite the BTH megaregion is the most urbanized region in northern China, it is also an important agricultural production area (Wang, et al., 2017). Agriculture has been the biggest water consuming sector in the BTH megaregion, accounting for 67.16% of the total water use in 2001 and decreasing to 56.47% in 2015 (BWA, 2001-2015; TWA, 2001-2015; DWRHP, 2001-2015). Especially in most cities of Hebei province, the proportion of agricultural water used was over 80% (DWRHP, 2001-2015). Therefore, from a regional synergy perspective, it is important to cooperatively allocate water resources between urban and agricultural development if water scarcity in the BTH megaregion is to be reduced (Jiang, 2009; Wang, et al., 2017). This is particularly pertinent in Hebei Province where the urbanization level is relative low, but the water scarcity is more serious than the two megacities of Beijing and Tianjin. Another efficient way is to pay more attention on improving agricultural water utilization efficiency, such as optimizing the agricultural planting structure by replacing high water consuming crops with low water consuming ones and adopting innovative agricultural water-saving technologies (Calzadilla, et al., 2011). Furthermore, there still has some limitation, for the focus of this study was on water quantity induced water scarcity, and didn’t consider water quality which also had important impacts on water scarcity. Further research is needed to assess the effects of water quality on water scarcity.
5. Conclusions This study used the BTH megaregion in China as a pilot case to analyze the spatial-temporal dynamics of water scarcity within the megaregion and to identify the impacts of large-scale urbanization on water scarcity. The results led to the following conclusions. 19
Journal Pre-proof We suggest a new approach to quantitatively assessing internal water scarcity within the megaregion by distinguishing between physically- and human-induced water scarcity. The results suggested that physically-induced water scarcity was serious and the variation between cities was lower than for human-induced water scarcity. Not only the urbanization levels but also urbanization types had significant impacts on water scarcity. The higher the level of urbanization, the lower the level of human-induced water scarcity faced. The economic urbanization level within the BTH megaregion had the most significant impacts on water scarcity, and this underscored the importance of improving the industrial structures of cities to save water. These results also show that coordinating water resource allocation by balancing urban and agricultural demand on water is important. The analytical framework used in this study can be applied to other megaregions in China and to some other developing countries that have similar water scarcity problems.
Acknowledgement This research was funded by the National Key Research and Development Program (2017YFC0505701); the National Natural Science Foundation of China (Grant 41590841).
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Bai, X. M., Chen, J., Shi, P. J. 2012. Landscape urbanization and economic growth in China: positive feedbacks and sustainability dilemmas. Environ. Sci. Technol. 46, 132–139. Brown, T.C., Mahat, V., Ramiiez, J.A. 2019. Adaptation to Future Water Shortages in the United States Caused by Population Growth and Climate Change. Earths Future. 7(3), 219– 234. BWA (Beijing Water Authority). 2001-2015. Beijing Water Resource Bulletin. Beijing Water Authority, Beijing, China. Calzadilla, A., Rehdanz, K., Tol, R.S.J., 2011. Water scarcity and the impact of improved irrigation management: a computable general equilibrium analysis. Agr. Eco. 42, 305–323. Chen, J., Liu, Q., Qian, H. 2012. Application of improved Nemerow index method based on entropy weight for groundwater quality evaluation. International Journal of Environmental Sciences, 2(3), 1284–1290. 20
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Journal Pre-proof Environ. Sci. Technol. 44, 9150–9156.
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Highlights: Water scarcity severity of megaregions is dually influenced by physical and human factors Physically- and human-induced water scarcity of cities within megaregions is spatially inconsistent Urbanization has significant impacts on human-induced water scarcity Economic development pattern of cities within megaregions is the key to alleviate water scarcity