An alternative model for evaluating the balance of carrying capacity between functional urban infrastructures

Environmental Impact Assessment Review 79 (2019) 106304

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An alternative model for evaluating the balance of carrying capacity between functional urban infrastructures

T

⁎

Jinhuan Wanga,b, Liyin Shena,b, , Yitian Rena,b, Xiaoxuan Weia,b, Yongtao Tanc, Tianheng Shua,b a

School of Construction Management and Real Estate, Chongqing University, Chongqing, PR China International Research Center for Sustainable Built Environment, Chongqing University, Chongqing, PR China c School of Engineering, RMIT University, Melbourne, VIC 3001, Australia b

A R T I C LE I N FO

A B S T R A C T

Keywords: Functional urban infrastructures Infrastructure carrying capacity Mean-Variance Analysis (MVA) Catastrophe Progression Method (CPM) Sustainable urban development

Urban infrastructures commonly include four types of functional infrastructures: traditional infrastructures, greenspace infrastructures, water infrastructures, and connective infrastructures. They work as an integrated system for supporting sustainable urban development. It is therefore important for having a proper method to help understand whether there is a balance between functional urban infrastructures' carrying capacities (FUICC). This paper introduces an alternative model named FUICC Catastrophe Model (FCM) to evaluate the balance of carrying capacity between various urban infrastructures. The development of the model FCM adopts Mean-Variance Analysis (MVA) technique and Catastrophe Progression Method (CPM) collectively. The application of FCM is demonstrated by using the empirical data collected from 35 cities in China. The research findings suggest that: 1) the difference in carrying capacity of each functional infrastructure between the sample cities is significant; 2) the difference in the degree of balance between FUICC is also significant between the sample cities; 3) the sample cities are classified into four categories, namely, acceptably balanced, less balanced, poorly balanced, and unbalanced. 4) the balance performance between FUICC among Chinese cities is characterized with polarization; 5) the balance performance of carrying capacity between functional urban infrastructures is generally poor in China, 40% of the sample cities achieving acceptable performance.

1. Introduction Urban infrastructures are commonly defined as an integrated ecosystem consisting of four typical types of functional infrastructures: traditional infrastructures, greenspace infrastructures, water infrastructures, and connective infrastructures (Breuste et al., 2015; Li et al., 2017; Silva and Wheeler, 2017; Ashley et al., 2018). Traditional infrastructures are usually referred as transportation, education, medical services, water supply, gas supply, electricity supply, and other utilities for providing public services (Carroli, 2018). Greenspace infrastructures include urban parks, forests, farmland, green roofs, and other natural or artificial green spaces (Li et al., 2014). Water infrastructures include wetlands, lakes, ponds, streams, rivers, and other flowing and fluctuating water (Gunawardena et al., 2017). Connective infrastructures are the facilities that connect the above three types of infrastructures, include facilities of drainage, sewerage, garbage disposal (Li et al., 2017). These functional urban infrastructures assume various essential roles (Liu et al., 2018), such as protection of soil fertility and soil biodiversity (Eijsackers et al., 2017; Artmann, 2016), nutrient

⁎

retention (Koskiaho and Puustinen, 2019), water supply (Bediako et al., 2018), water recycling (Wanjiru and Xia, 2017), energy supply (Sonnenschein et al., 2015), heat dissipation (Wang et al., 2019b), metabolism or breakdown of wastes (González et al., 2013; Wu et al., 2016; Wu et al., 2019), resources regeneration (Sairinen and Kumpulainen, 2006), runoff reduction (Lindholm and Nordeide, 2000), and provisions of productive, living, educational, and medical services (Geneletti, 2013; Cui and Sun, 2019; Ives M et al., 2019). These roles are essential for promoting sustainable urban development (Liu et al., 2018). As these functional infrastructures work collectively as a system to contribute to sustainable urban development, the capacity of each type of infrastructure should be attended and protected to a proper level. In other words, these functional infrastructures can only make contributions to sustainable urban development if their capacities are effective and balanced. Such balance will contribute to urban habitability, safety, resilience, and sustainability (Li et al., 2017). Previous studies have appreciated that different urban infrastructures have to work collectively in making contributions to urban development, and the ways how various infrastructures function have also been

Corresponding author at: School of Construction Management and Real Estate, Chongqing University, Chongqing, PR China. E-mail address: [email protected] (L. Shen).

https://doi.org/10.1016/j.eiar.2019.106304 Received 9 March 2019; Received in revised form 10 May 2019; Accepted 15 August 2019 0195-9255/ © 2019 Elsevier Inc. All rights reserved.

Environmental Impact Assessment Review 79 (2019) 106304

J. Wang, et al.

balance thus sustainable urban development can be achieved. Previous scholars presented various methods for conducting balance evaluation in various research fields. For example, Zaree et al. (2017) evaluated the balance between effort and reward for understanding the impact of psychosocial factors on nurses' medication errors by conducting a cross-sectional descriptive analysis method. In using this method, the balance between effort and reward is measured by the ratio between cost and gain on drug delivery of sample nurses. Chen et al. (2016) investigated the inter-provincial balance in the process of developing high-grade highway in China by decomposing the Gini coefficient, the decomposed Gini coefficients are used to measure the balance between different types of high-grade highway in Chinese inland and coastal areas. Shu and Xiong (2018) investigated the balance of regional development in China by using both the non-grouped Gini index and the multigroup overall Gini index. Wu et al. (2018b) constructed a hierarchical structure framework to evaluate the imbalance of research funding in China, in which a three-staged nested Theil index is used to measure the imbalance in its study. Several previous studies have also been conducted in the discipline of evaluating urban carrying capacity in the context of the Chinese cities. For example, Wei et al. (2016) evaluated the urban carrying capacity for Chinese mega-cities from five perspectives including urban infrastructures, natural resources, social service capacity, institutional structure, and public participation. Sun et al. (2018) assessed the urban carrying capacity of Yangtze River Economic Belt (YREB) in China by considering four subsystems, namely, transport infrastructure, ecological environment, industrial economy, and factor market. Wang et al. (2017) evaluated the carrying capacity of wetlands infrastructure of Beijing by adopting system dynamics (SD) model. Ding et al. (2015) evaluated the water ecological carrying capacity of lake infrastructure in Wuhan City in China using a multi-objective model. Jia et al. (2018) evaluated the carrying capacity of water infrastructure in referring to 34 provincial regions in China based on catastrophe progression method. However, it appears that none of the methods used in previous studies for balance evaluation is for evaluating the balance of carrying capacity between various functional urban infrastructures. Therefore, this paper aims to develop an alternative model to evaluate the balance between FUICC. The application of the model will be demonstrated by using the data collected from 35 cities in China.

examined. For example, grassed ways of greenspace infrastructures and detention ponds of water infrastructures have been adopted collectively to reduce stormwater runoff in Australia and North America since the 1970s (Hannam and Hicks, 1980; Hannam, 1979; Robinson and Spieker, 1978; Berkooz, 2011). According to the study by Alderlieste and Langeveld (2005), infiltration devices in greenspace infrastructures and drainage pipes in traditional infrastructures have been installed together to reduce the silting up of drains in Djenne and Mali of West Africa. Liu and Jensen (2018) demonstrated that artificial green areas built in greenspace infrastructures and water-supply systems in traditional infrastructures have been adopted simultaneously to help save potable water for consumption in Melbourne and Sino-Singapore Tianjin Eco-city. However, rapid urbanization process, accompanied with population concentration in urban areas, has brought various urban problems globally, typically water resources shortage, greenspace destruction, high energy consumption, traffic congestion, air and water pollution, and waste pollution (Wang et al., 2019a; Zhang et al., 2019; Zhao et al., 2019). Factors contributing to these problems are multiple across technical and management horizons. A major concern among these reasons is the overlooking of the balance between different functional urban infrastructures. It seems that the dramatic urbanization program in those large developing countries such as China has been driving the explosive construction of traditional infrastructures, such as roads, subways, houses, schools and hospitals (Iojă et al., 2018; Nahrin, 2018), but less construction for other functional infrastructures (Artmann, 2015; Artmann, 2016; Haaland and Bosch, 2015; Wan and Shen, 2015; Yu et al., 2017; Forman, 2016; Kabisch et al., 2015). Since the early 1980s, the urbanization process in China has experienced an unprecedented development era, with the increase of urbanization rate from 17.92% in 1978 to 58% in 2017, giving an average annual increase rate of 1.03% (Ren et al., 2018; Shen et al., 2018). According to Sun et al. (2015), the rapid changes of land use for urbanization in China have resulted in a reduction of 3.4 million hectares in wetlands and 8.3 million hectares in cultivated land during the period of 2000–2010. The reduction of green spaces and wetlands in turn leads to the decrease and insufficiency of carrying capacity of greenspace and water infrastructures. Consequently, the carrying capacity of traditional infrastructures is much advanced position in comparing to other types of infrastructures such as greenspace and water infrastructures. The imbalance of carrying capacity between functional urban infrastructures has induced various urban problems, such as decreased species richness (Mckinney, 2008), degraded water quality (Seilheimer T et al., 2007), increased heat island effect (Wu et al., 2018a), urban flooding (Kessler, 2011), haze effects (Latif et al., 2018), habitat loss (Kjølle et al., 2012), energy shortage (Avni and Blázquez, 2011), and increased air pollution (Xia et al., 2014). It appears, nevertheless, that little concern is given on whether there is a balance of carrying capacity or cohesion between different types of urban infrastructures whilst ambitious urbanization programs are implemented globally. Therefore, it is important to introduce an evaluation method to help understand whether the functional urban infrastructures' carrying capacities (FUICC) in a given urban area are balanced. Without this evaluation method, it is difficult to understand properly whether these urban infrastructures work as an integrated system, thus to identify which functional urban infrastructure is in shortage. In fact, the shortage in any type of urban infrastructures can induce urban problems, thus affect sustainable urban development accordingly. For example, according to the study by Wu et al. (2018a), the shortage of greenspace and water infrastructures has induced heat island effect in many cities in China. Drinking water shortage in coastal areas of Bangladesh and Sub-Saharan region of Africa has been affecting people's daily life (Shamsuzzoha et al., 2018; Mapunda et al., 2018). Only when a clear understanding on the balance status between FUICC is obtained can measures and policies be formulated and adopted to ensure that all types of functional urban infrastructures are properly developed in

2. Methodology As stated in introduction, this paper aims to develop a model for evaluating the balance of carrying capacity between urban infrastructures. In order to achieve this aim, four research procedures are designed as follows. Firstly, the indicators for measuring the carrying capacity between functional urban infrastructures (FUICC) will be selected. The selection will mainly be conducted through comprehensive literature review and by adopting indicator selection criteria. Literature review also helps understand the implications of the typical urban infrastructures. Previous studies have presented a large number of literatures in the discipline of carrying capacities of urban infrastructures (Wei et al., 2016; Zhang et al., 2018b). Candidate indicators that portray FUICC will be identified from reviewing these literatures. The final list of FUICC indicators will be filtered by considering selection criteria which will be designed to ensure that the characteristics of urban infrastructures can be truly reflected. Secondly, a measurement for calculating the carrying capacities of urban functional infrastructures will be established by employing the Mean-Variance Analysis (MVA) method. Previous studies have provided a wide range of methods for analyzing urban carrying capacity, such as Analytical Hieratical Process (AHP) (Sun et al., 2018), Principle Components Analysis (PCA) (Zhang et al., 2018a), System Dynamics (SD) (Zhou et al., 2017; Wang et al., 2014), Artificial Neural Network (ANN) 2

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including 1) feasibility, 2) rationality, 3) data availability, 4) number limitation, 5) completeness, 6) representativeness, 7) relevance, 8) measurability, 9) and communicability. Furthermore, the definitions of the four types of functional urban infrastructures, as stated in introduction, are referred. Those indicators in Table 1 which cannot reflect the content of the infrastructure definitions will be transformed. For example, the indicator X19, X20, X21 and X22 in Table 2 is transformed from the candidate indicator of “surface water resources” in Table 1 by Jia et al. (2018). Therefore, these indicators in Table 2 are considered representative to spell out the characteristics of carrying capacities between four types of functional urban infrastructures, and will be used for analysis in this study.

(Wang et al., 2014), and Mean-Variance Analysis (MVA) (Wei et al., 2016). This study adopts two criteria for selecting the method for calculating the carrying capacity of functional urban infrastructures. (a) The method should be objective-based instead of subjective-based, this can avoid the effects of subjective opinions from individuals on the assessment results; (b) A comprehensive range of indicators should be included, which can provide holistic perspectives and improve the accuracy of the assessment results. By referring to the criteria (a), the methods of AHP and SD are excluded. In using AHP and SD methods, the opinions of experts are employed, which may subjectively affect the assessment results. By referring to the criteria (b), the methods of PCA and ANN are excluded. In using PCA and ANN methods, only few indicators are adopted. Whilst, the Mean-Variance Analysis (MVA) method has the advantages of being objective and able to employ a large group of indicators, thus the assessment accuracy can be enhanced. Therefore, the method of MVA is adopted in this study. Thirdly, the Catastrophe Progression Method (CPM) will be used to establish the FUICC Catastrophe Model (FCM) for assessing the degree of balance between FUICC. CPM has been widely used in various research fields for evaluating status of a system, which is influenced by the balance performance of multiple types of control variables within the system (Jia et al., 2018; Xu et al., 2017; Chen et al., 2018; Li et al., 2018). A system will run smoothly when the control variables within the system are balanced. Nevertheless, when the balance is broken, a catastrophe against the initial system status will occur. CPM is generally divided into four categories, namely, Folded Catastrophe Model which includes only one control variable (Zhengguo et al., 2018; Feng-qiang et al., 2008), Cusp Catastrophe Model which includes two control variables (Zetong et al., 2018), Swallowtail Catastrophe Model which includes three control variables (Chen et al., 2018), and Butterfly Catastrophe Model (BCM) which includes four control variables (Jia et al., 2018; Xu et al., 2017; Chen et al., 2018). In this study, four types of urban infrastructures are concerned, namely, traditional infrastructures, greenspace infrastructures, water infrastructures, and connective infrastructures. And four control variables are used to describe the carrying capacity of these four types of infrastructures respectively. Therefore, the Butterfly Catastrophe Model (BCM) is used in this study to establish the FCM for evaluating the balance between FUICC. Fourthly, a demonstration is conducted to show the application of the proposed FUICC Catastrophe Model (FCM) for evaluating the degree of balance of the carrying capacity between urban infrastructures. Data for demonstration analysis are collected from 35 sample Chinese cities.

4. Model development 4.1. Measurement for calculating carrying capacity of functional urban infrastructures In line with the methodology addressed in Section 2, Mean-Variance Analysis (MVA) technique will be used to calculate the carrying capacities of functional urban infrastructures. The model of MVA is presented by following equations (Wei et al., 2016). Sample mean:

E (Ykj ) =

1 n

∑ Fij

(1)

Mean square error:

σ (Ykj ) =

∑ (Fij − E (Ykj ))2

(2)

Weight coefficient:

ω (Ykj ) =

(Fij − E (Ykj ))2 /σ(Ykj )

(3)

Estimation results:

Ykj =

1 n

∑ (ω (Ykj ) ∙Fij)

(4)

Where the Ykj is the value of carrying capacities of the functional infrastructure k for the sample city j, n is the total number of indicators for measuring the performance of carrying capacity of the functional infrastructures k, Fij is a normalized value for the specific indicator i in referring to the sample city j. As different indicators present different dimensions and magnitudes, it is necessary to normalize all the indicators for avoiding dimension effects, thus effective comparison can be conducted between indicators. The following Eqs. (5) and (6) are used to normalize original data of indicator i:

3. Indicators for measuring carrying capacities of functional infrastructures In this section, there are two steps to establish the indicators for measuring Functional Urban Infrastructures' Carrying Capacity (FUICC): 1) to identify typical candidate indicators through literature review; 2) to select the final list of indicators by applying certain indicator selection criteria. There are a lot of previous studies addressing urban carrying capacity, those major ones published in good international journals are listed in Table 1. The selection of these literatures is based on three major considerations. Firstly, the literatures are from high-level international journals, which have high impact factors. Secondly, the literatures are published within the recent 5 years from 2014. Thirdly, the term “urban infrastructure” is a key word in the process of literature selection. All the literatures in Table 1 have presented various indicators for measuring urban carrying capacity. It is considered reasonable to apply these indicators as candidate indicators. In general, the studies on urban carrying capacity are within the theme of sustainable urban development (Tian and Sun, 2018a; Jia et al., 2018; Wei et al., 2015a; Wei et al., 2015b; Martire et al., 2015). And the criteria for selecting the indicators to measure the performance of sustainable urban development are proposed by Hák et al. (2016),

Fij =

Fij =

Xij − minj {Xij } max j {Xij } − minj {Xij }

(5)

max j {Xij } − Xij max j {Xij } − minj {Xij }

(6)

Where the variable Fij is the normalized value of the indicator i, the variable Xij is the original value of the indicator i for the sample city j, maxj{Xij} and minj{Xij} represent respectively the maximum and minimum original values of the indicator i across all sample cities. Eq. (5) is used for positive (beneficial) indicators, where a larger value of the indicator represents better performance of functional carrying capacity. Eq. (6) is used for negative (cost) indicators, where a smaller value of the indicator represents better performance of functional carrying capacities. 3

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Table 1 Candidate indicators proposed in typical literatures for measuring urban carrying capacity. Researcher Tian and Sun (2018a)

Indicators recommended water supply (ton) • Per-capita areas greenery coverage rate (%) • Built-up of private cars per 10,000 persons(unit) • Number urban road area (m ) • Per-capita highway mileage (km) • Per-capita of doctors per 1000 persons (unit) • Number of primary and secondary schools(unit) • Number of students of common colleges (unit) • Number treatment concentration rate (%) • Wastewater rate for industrial purpose (%) • Reuse water resources (m ) • Surface coverage (%) • Forest coverage area (ha) • Green sewage treatment rate (%) • Urban water supply (ton) • Per-capita Greenland area (km ) • Per-capita of living garbage harmless disposal (%) • Ratio highway mileage (km) • Per-capita of doctors per 1000 persons (unit) • Number of primary and secondary schools(unit) • Number of students of common colleges (unit) • Number capita water resources (m ) • Per of ecological water consumption (%) • Rate capita park green space (m ) • Per rate of industrial solid waste (%) • Utilization water supply (ton) • Per-capita Greenland area (km ) • Per-capita of living garbage harmless disposal (%) • Ratio of buses per 10,000 persons (ton) • Number Highway mileage (km) • Per-capita of doctors per 1000 persons (unit) • Number of primary and secondary schools(unit) • Number of students of common colleges (unit) • Number capita water supply (ton) • Per capita electricity consumption (kwh) • Per ratio of sewage treated (%) • The capita green area (m ) • Per beds per 10,000 persons (unit) • Hospital access rate (%) • Water of bus per 10,000 persons (unit) • Number urban road areas (m ) • Per-capita of city green areas (%) • Coverage capita water resources (m ) • Per gas connection rate (%) • Urban bed per 1000 persons (unit) • Hospital • Per capita road areas (m ) 2

Jia et al. (2018)

3

Wang et al. (2018)

Sun et al. (2018)

2

Zhang et al. (2018b)

3

2

Tian and Sun (2018b)

2

Wei et al. (2016)

2

2

Wei et al. (2015a)

3

2

ratio of sewage treated (%) • The ratio of living garbage harmless disposal (%) • The Greenland areas (km ) • Per-capita of buses per 10,000 persons (unit) • Number of hospital beds per 10,000 persons (unit) • Number of common colleges (unit) • Number • The average number of teachers' colleges (unit) water rate for industrial purpose (%) • Saving use per capita (m ) • Water water resources (m ) • Influx coverage rate (%) • Water recreational green space (ha) • Public rate of industrial water (%) • Recycling ratio of sewage treated (%) • The areas greenery coverage rate (%) • Built-up urban road area (m ) • Per-capita of hospital beds per 10,000 persons (unit) • Number of common colleges (unit) • Number • The average number of teachers of colleges (unit) treatment rate (%) • Sewage coverage rate of built up area (%) • Green • Harmless disposal rate of municipal solid waste (%) ratio of sewage treated (%) • The areas greenery coverage rate (%) • Built-up of private cars (unit) • Number Urban Road Area (m ) • Per-capita of hospital beds per 10,000 persons (unit) • Number of common colleges (unit) • Number • The average number of teachers of colleges (unit) capita gas supply (m ) • Per ratio of industrial solid waste (%) • The garbage treatment rate (%) • Living coverage rate of urban built-up areas (%) • Green of drainage pipe in built-up areas (km/km ) • Density access rate (%) • Gas • Number of private cars per 10,000 persons (unit) of city forest (%) • Coverage recycling rate of industrial water (%) • The capita public green area (m ) • Per of libraries per 10 thousand people (unit) • Number • Rate of harmless disposal of domestic garbage 2

3

3

2

2

3

2

2

this study are independent (Silva and Wheeler, 2017; Li et al., 2016). Jia et al. (2018) introduced a model for calculating the balance degree between control variables. According to this model, the balance degree between the four types of infrastructure carrying capacities can be written as follows:

4.2. A model for evaluating the balance of carrying capacity between functional urban infrastructures According to the methodology addressed in Section 2, Butterfly Catastrophe Model (BCM) will be used to establish the Catastrophe Model for evaluating the degree of balance between FUICC, which is denoted as FCM, namely, FUICC Catastrophe Model. There are two procedures for establishing the model FCM in using BCM method: 1) to establish the priority between control variables. In this study, there are four control variables, namely, Y1j(the carrying capacity of traditional infrastructures), Y2j(the carrying capacity of greenspace infrastructures), Y3j(the carrying capacity of water infrastructures), Y4j(the carrying capacity of connective infrastructures). Previous studies suggest that the variable Y4j is more important, followed by Y3j, Y2j and Y1j(Li et al., 2017; Li et al., 2014). Li et al. (2017) argues that connective infrastructures are most important to ensure that the all typical functional urban infrastructures work collectively. Furthermore, considering the current serious environmental problems in the Chinese cities, water and greenspace infrastructures are considered more important over traditional infrastructures (Wu et al., 2014; Li et al., 2017); 2) to confirm the independence between control variables. According to previous studies, the four control variables addressed in

β = min { Y4j , 3 Y3j , 4 Y2j , 5 Y1j }

(7)

Where β is the value to indicate the balance degree between the four types of infrastructure carrying capacities. The larger is the value β, the better the balance performance between FUICC is. Ykj, as described before, is the control variable indicating the carrying capacity of the four types of urban functional infrastructures in referring to a specific city j. The novelty of the balance model (7) is that the model enables to evaluate the degree of balance between FUICC. It is the first time to apply Catastrophe Model in the discipline of urban infrastructures' carrying capacity. Catastrophe Model itself is a well-established methodology for evaluating balance status between control variables. Its application in this research extends the value of the methodology to the important field of urban infrastructure carrying capacity, and makes it possible to evaluate the balance between FUICC. The applicability of the model (7) will be demonstrated through case studies in next section. 4

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Table 2 Indicators for measuring carrying capacities between functional urban infrastructures. Functional infrastructures

Indicators for measuring carrying capacities

Attributes

Units

Traditional infrastructures (Y1)

Urban road area per capita (X1) Delay index of peak congestion (X2) Urban rail transit length per 10,000 people (X3) Number of buses per 10,000 people (X4) Number of taxis per 10,000 people (X5) Water supply per capita (X6) Water coverage rate (X7) Density of water supply pipelines in built district (X8) Natural gas supply per capita (X9) Petroleum gas supply per 10,000 people (X10) Gas coverage rate (X11) Electricity consumption per capita (X12) Public library collection per capita (X13) Number of teachers per 10,000 people in primary schools (X14) Number of hospital beds per 10,000 people (X15) Public recreational green space per capita (X16) Green space rate in built district (X17) Forest coverage rate (X18) Coastal wetlands coverage rate (X19) River wetlands coverage rate (X20) Lake-swamp wetlands coverage rate (X21) Constructed wetlands coverage rate (X22) Domestic garbage harmless treatment rate (X23) Centralized treatment rate of wastewater treatment plants (X24) Density of sewers in built district (X25)

+ − + + + + + + + + + + + + + + + + + + + + + + +

m2 / m Unit Unit m3 % km/km2 m3 ton % kwh Volume Unit Unit m2 % % % % % % % % km/km2

Greenspace infrastructures (Y2)

Water infrastructures (Y3)

Connective infrastructures (Y4)

Note: +refers to benefit indicator that is the larger the better; −refers to cost indicator that is the smaller the better.

5. Demonstration

5.1. Sample cities and research data

In this section, a demonstration is used to show the application of the FUICC Catastrophe Model (FCM) expressed by formula (7). The research data used in the demonstration are collected from a set of 35 Chinese sample cities.

The 35 sample Chinese cities and their geographic locations are shown in Fig. 1. The sources of the data for all the indicators listed in Table 2 are described in Table 3. The data are collected for the year of 2016. As the volume of the research data collected is too large, Table 4 only provides the data for the example city of Beijing.

Fig. 1. 35 sample cities and their geographic locations. 5

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Table 3 Data sources for all the indicators in measuring urban infrastructure carrying capacity. Indicators

Data sources

Publication institutions

X1, X6, X7, X8, X12, X16, X17

China Urban Construction Statistical Yearbook (2017)

X2 X3, X4, X5, X9, X10, X11, X13, X14, X15, X23, X24, X25 X18, X19, X20, X21, X22

Traffic Analysis Reports for Major Cities in China (2017) China City Statistical Yearbook (2017)

Ministry of Housing and Urban-Rural Development of China AutoNavi Traffic Big-data of China Urban Survey Department of National Statistical Bureau of China Statistics Department of Cities in China

Statistical Bulletin on National Economic and Social Development in Chinese Cities (2017)

5.2. Calculation results

Table 5 The evaluation results of the balance of FUICC in 35 cities of China.

By applying the research data described in Section 5.1 to Eqs. (1)–(6), the capacity values of Y1, Y2, Y3, and Y4 for all sample cities can be calculated. The results are shown in Table 5. By applying the results of the calculation values of Y1, Y2, Y3, and Y4 to Eq. (7), the value β (degree of balance between functional urban infrastructures' carrying capacities) for 35 cities in China are obtained and ranked, as shown in Table 5 as well. 6. Discussion The demonstration study in previous section suggests that the proposed FUICC Catastrophe Model (FCM) is applicable. In applying the model, the balance between functional urban infrastructures' carrying capacities in individual cities can be assessed. The assessment results are important references to further examine and identify those weak areas of urban infrastructures. City managers and decision-makers can revise or formulate better policy measures to improve balance performance of Functional Urban Infrastructures Carrying Capacity (FUICC) for promoting sustainable urban development. The assessment results of balance value β in Table 5 provide the following interesting findings. Different cities have different carrying capacity between the four types of urban infrastructures. For example, Jinan has the highest carrying capacity in connective infrastructures, Shanghai has the highest carrying capacity in water infrastructures, Guangzhou has the highest carrying capacity in greenspace infrastructures, Shenzhen has the highest carrying capacity in traditional infrastructures. In general, the difference in infrastructure carrying capacity between cities is significant. For example, in referring to water infrastructures, Shanghai has the highest carrying capacity with the Y3 value of 0.2246, whilst Hohhot has the lowest carrying capacity with the Y3 value of 0.0045. The performance of carrying capacity of water infrastructures (Y3) varies hugely between different cities and is the major contributor to the unbalanced status of FUICC between the Chinese cities. At national level, the country has good carrying capacity in connective infrastructures, evidenced by the fact that this type of infrastructure has the largest average value of 0.3415, as shown in Column Y4 in Table 5. The disparity of carrying capacity of traditional urban infrastructures among the sample cities is least, evidenced by the fact that the variation of this type of infrastructure between sample

Cities

Y1

Y2

Y3

Y4

β

Rank (β)

Shenyang Nanchang Shanghai Wuhan Ningbo Chongqing Nanjing Hangzhou Fuzhou Dalian Qingdao Tianjin Changchun Zhengzhou Harbin Xiamen Xian Guangzhou Yinchuan Haikou Chengdu Jinan Shenzhen Beijing Nanning Hefei Urumqi Kunming Shijiazhuang Guiyang Xining Lanzhou Taiyuan Changsha Hohhot Average (μ) Variations (σ2)

0.1139 0.0763 0.1286 0.1226 0.1124 0.0766 0.1325 0.1251 0.0910 0.1083 0.1130 0.1106 0.1080 0.1088 0.1007 0.1030 0.1181 0.1280 0.0938 0.0942 0.1011 0.1165 0.1371 0.1340 0.0999 0.1002 0.1302 0.0942 0.1059 0.0881 0.1051 0.0842 0.1087 0.1192 0.0913 0.1080 0.0003

0.1503 0.2349 0.0944 0.1371 0.2387 0.2964 0.2996 0.4021 0.3182 0.2429 0.3142 0.0649 0.2823 0.1523 0.2061 0.2332 0.2602 0.4058 0.2145 0.2023 0.2523 0.1848 0.3087 0.4011 0.2437 0.2304 0.2114 0.2379 0.3195 0.2990 0.2414 0.0927 0.1873 0.2780 0.3022 0.2440 0.0066

0.1843 0.2048 0.2246 0.1578 0.1526 0.1355 0.2118 0.1259 0.1168 0.1135 0.1085 0.1043 0.0990 0.0981 0.0702 0.0662 0.0640 0.0530 0.0433 0.0400 0.0391 0.0279 0.0225 0.0204 0.0204 0.0167 0.0158 0.0143 0.0139 0.0134 0.0104 0.0086 0.0049 0.0048 0.0045 0.0746 0.0043

0.3178 0.3086 0.3130 0.4346 0.3652 0.3710 0.2592 0.3624 0.3316 0.3288 0.3801 0.3503 0.2536 0.3689 0.1901 0.3056 0.3869 0.2443 0.3186 0.5063 0.3323 0.5191 0.3765 0.2832 0.3419 0.3970 0.1717 0.3442 0.4539 0.2522 0.2435 0.3621 0.4200 0.4424 0.3141 0.3415 0.0060

0.5637 0.5555 0.5544 0.5404 0.5344 0.5136 0.5091 0.5012 0.4888 0.4841 0.4769 0.4708 0.4627 0.4612 0.4125 0.4045 0.4000 0.3757 0.3512 0.3420 0.3394 0.3034 0.2822 0.2735 0.2733 0.2554 0.2509 0.2428 0.2406 0.2375 0.2182 0.2048 0.1693 0.1689 0.1654 0.3725 0.0164

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 – –

cities is the smallest with the value of 0.0003, as shown in Column Y1 in Table 5. In referring to the degree of balance between FUICC, denoted by β, the difference is also significant between the sample cities. Those cities with high rankings in β are mainly located in the areas along the big

Table 4 The data for all the indicators in measuring urban infrastructure carrying capacity in Beijing. Indicators

Value

Indicators

Value

Indicators

Value

X1 X4 X7 X10 X13 X16 X19 X22 X25

7.6200 (m2) 10.4413 (unit) 100 (%) 1158.2222 (ton) 2.8667 (volume) 16.0100 (m2) 0 (%) 2.1327 (%) 10.0800 (km/km2)

X2 X5 X8 X11 X14 X17 X20 X23

2.0330 31.5173 (unit) 19.6700 (km/km2) 100 (%) 23.8331 (unit) 46.0800 (%) 0.8422 (%) 99.8400 (%)

X3 X6 X9 X12 X15 X18 X21 X24

264.1631 (m) 91.6329 (m3) 1120.9868 (m3) 899.4031 (kwh) 50.6333 (unit) 35.8000 (%) 0.0718 (%) 87.9800 (%)

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rivers or the coast areas. For example, Shenyang City (rank first) is located along the Hunhe River of China; Nanchang City (rank second) is located along the Ganjiang River of China; Shanghai City (rank third) is located at the mouth of the Yangtze River. Table 5 also spells out that most of the cities in central and western parts of China present low values in β. For example, Hohhot, ranked the last, is located in the northern frontier of China and the interior of Eurasia. Since those cities located in coastal areas or along rivers have better economic and social development standard, they have better understanding that sustainable urban development requests for balanced investment on various types of urban infrastructures. For example, according to the study by Ji et al. (1998), Shanghai, a coastal city, has invested 41.29 billion yuan in the construction of various urban infrastructures in 1997, and the amount of this investment has maintained an annual growth rate of around 10% since then. In particular, in order to balance the carrying capacity between various urban infrastructures, Shanghai Water Resources Foundation (SWRF), the first foundation in China specified in water resources, has been established to protect finite water resources and reduce water pollution in 2007. For further analysis, the data of β in Table 5 can be expressed in scatter diagram, as illustrated in Fig. 2. The Figure spells out the following three findings. Firstly, catastrophe areas can be observed in Fig. 2. According to the catastrophe theory, catastrophe areas are identified by the adjacent-β values between sample cities. In this study, the β value for 35 cities are calculated, thus 34 adjacent-β values are obtained. The top three catastrophe areas with the adjacent-β values of 0.0487, 0.0360, and 0.0355 are marked in bold red-lines in Fig. 2. The three catastrophe areas in the figure divide the sample cities into four categories, namely, acceptably balanced, less balanced, poorly balanced, and unbalanced, as shown in Fig. 2. This finding shows that the balance performance between functional urban infrastructures' carrying capacities are different significantly among different cities. Secondly, there are 40% (14/35) sample cities located in the acceptably balanced category, and 20% (7/35), 31% (11/35), 9% (3/35) are in less balanced, poorly balanced and unbalanced respectively. As shown in Fig. 2, the distance of β for the balanced category is 0.1025 (0.4612, 0.5637), and that for less balanced, poorly balanced and unbalanced are 0.1218 (0.3394, 0.4612), 0.1346 (0.2048, 0.3394), 0.0394 (0.1654, 0.2048) respectively. If α denotes for the proportion of cities in a specific category and γ denotes the β distance of the category, a catastrophe density (ρ) for a specific category can be calculated as

Unbalanced

Poorly balanced

follows:

ρacceptably balanced =

ρ less balanced =

= 11/35 = 31% Poorly balanced = 0.1346 Poorly balanced = 2.30

= 3/35 = 9% = 0.0394 Unbalanced = 2.28 Unbalanced

0.0360 Second catastrophe area

0.0355 Third catastrophe area 0.2

0.25

0.3

α 31% = = 2.30 γ 0.1346

(10)

α 9% = = 2.28 γ 0.0394

(11)

Acceptably balanced

Shenyang Nanchang Shanghai Wuhan Ningbo Chongqing Nanjing Hangzhou Fuzhou Acceptably balanced = 14/35 = 40% Dalian Qingdao Acceptably balanced = 0.1025 Tianjin Changchun 0.0487 Acceptably balanced = 3.90 Zhengzhou Harbin Xiamen Xian Guangzhou First catastrophe area Yinchuan Haikou Chengdu Jinan Shenzhen Beijing Nanning Hefei Urumqi Kunming Shijiazhuang Guiyang Xining Lanzhou Taiyuan Changsha Hohhot

= 7/35 = 20% Less balanced = 0.1218 Less balanced = 1.64

Less balanced

Unbalanced

0.15

(9)

The results of the catastrophe density (ρ) in Eqs. (8)–(11) suggest that the acceptably balanced category has highest catastrophe density, with the value of ρacceptably balanced = 3.90. The less balanced category has lowest catastrophe density with ρless balanced = 1.64. This finding tells that the Chinese cities tend to aggregate either in acceptably balanced or unbalanced in the performance between FUICC. This further indicates that the balance performance between FUICC among Chinese cities is characterized with polarization. This is due to the disparity in the investment on functional urban infrastructures in different cities in the current China. As argued above, those cities with good economicsocial development have more capital and awareness to balance the development of various functional urban infrastructures. For example, Wuhan has invested 10 million yuan annually to carry out ecological compensation for the wetland nature reserves in the city, and it is reported that this capital keeps a huge percentage in the overall fiscal investment of the city (Yang et al., 2017). Whereas, those cities with poor economic development can only prioritize the development of traditional infrastructure as they have limited capital available. For example, according to the data published by Hohhot City Statistical Yearbook (2018), the city has planned to allocate very limited funds in improving the carrying capacity of greenspace and water infrastructures, whilst large amount of funds has planned for improving the carrying capacity of traditional infrastructures. Therefore, the polarization in the balance performance of FUICC among Chinese cities may remain in the coming future as the gap of economic-social development between cities are too big to be narrowed in a foreseeable period. Thirdly, the overall performance of β is poor across the whole country. For example, although the proportion of cities located in category of acceptably balanced is 40%, their performance value β is just between 0.4612 and 0.5637. The performance of FUICC in other cities

Less balanced

Poorly balanced

(8)

α 20% = = 1.64 γ 0.1218

ρpoorly balanced =

ρunbalanced =

α 40% = = 3.90 γ 0.1025

0.35 0.4 Balance value (

0.45

0.5

)

Fig. 2. The scatter diagram of the balance value β in 35 cities in China. 7

0.55

0.6

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performance of carrying capacity between functional urban infrastructures of cities internationally. It is also recommended to develop measures to improve the carrying capacity of those weak functional urban infrastructures, if they are identified.

are even much worse. This shows that appropriate actions should be taken to improve the poor balance performance between functional urban infrastructures in all Chinese cities. The policy implications from the analysis results is that actions from government departments in China should be taken to reduce the unbalanced performance of FUICC in almost all cities. These actions include, for example, 1) introduce policy regulations to specify proper appropriations between different functional urban infrastructures. The appropriations will continue to narrow the gap of the balance performance of carrying capacity among different Chinese cities; 2) those cities with poor β performance should invest more resources on those weak functional infrastructures by seeking for multiple financial channels, such as Public-Private Partnership (PPP) mechanism (OseiKyei and Chan, 2017; Muhammad and Johar, 2018). In fact, it is reported in the study by Geddes and Reeves (2017), New York City has successfully adopted PPP mechanism in developing its transportation infrastructures. And the investment will change the polarization status of balance performance of FUICC among Chinese cities; 3) introduce an evaluation mechanism to assess the real-time balance performance of FUICC. This mechanism will enable decision-makers to identify dynamically the problems if any existed in the practice. Thus, the lessons learnt in failures or good experiences can be learnt and shared among cities (Shen et al., 2013; Wang et al., 2019a). Consequently, a more balanced status of FUICC can be achieved to support sustainable urban development nationally.

Acknowledgment This paper was supported by the Fundamental Research Funds for the Central Universities (Project No. “2019CDSKXYJSG0041”), and the National Social Science Foundation of China (Grant No. “17ZDA062”, “15AZDO25” and “15BJY038”). References Alderlieste, M.C., Langeveld, J.G., 2005. Wastewater planning in Djenne, Mali. A pilot project for the local infiltration of domestic wastewater. Water Sci. Technol. 51, 57–64. Artmann, M., 2015. Managing urban soil sealing in Munich and Leipzig (Germany)—from a wicked problem to clumsy solutions. Land Use Policy 46, 21–37. Artmann, M., 2016. Urban gray vs. urban green vs. soil protection — development of a systemic solution to soil sealing management on the example of Germany. Environ. Impact Assess. Rev. 59, 27–42. Ashley, R., Gersonius, B., Digman, C., et al., 2018. Including uncertainty in valuing blue and green infrastructure for stormwater management. Ecosyst. Serv. 33, 237–246. Avni, A., Blázquez, M.A., 2011. Can plant biotechnology help in solving our food and energy shortage in the future? Curr. Opin. Biotechnol. 22, 220–223. Bediako, I.A., Zhao, X., Antwi, H.A., et al., 2018. Urban water supply systems improvement through water technology adoption. Technol. Soc. 55, 70–77. Berkooz, C.B., 2011. Green infrastructure storms ahead. Planning 77. Breuste, J., Artmann, M., Li, J., et al., 2015. Special Issue on Green Infrastructure for Urban Sustainability. American Society of Civil Engineers. Carroli, L., 2018. Planning roles in infrastructure system transitions: a review of research bridging socio-technical transitions and planning. Environ. Innov. Soc. Transit. 29, 81–89. Chen, J., Chen, J., Yi, M., et al., 2016. Unbalanced development of inter-provincial highgrade highway in China: decomposing the Gini coefficient. Transp. Res. D 48, 499–510. Chen, H., Zhang, Y., Liu, H., et al., 2018. Cause analysis and safety evaluation of aluminum powder explosion on the basis of catastrophe theory. J. Loss Prev. Process Ind. 55, 19–24. Cui, Y., Sun, Y., 2019. Social benefit of urban infrastructure: an empirical analysis of four Chinese autonomous municipalities. Util. Policy 58, 16–26. Ding, L., Chen, K.L., Cheng, S.G., et al., 2015. Water ecological carrying capacity of urban lakes in the context of rapid urbanization: a case study of East Lake in Wuhan. Phys. Chem. Earth A/B/C 89–90, 104–113. Eijsackers, H., Reinecke, A., Reinecke, S., et al., 2017. Threatened southern African soils: a need for appropriate ecotoxicological risk assessment. Environ. Impact Assess. Rev. 63, 128–135. Feng-qiang, G., Xi-Bing, L., Ke, G., 2008. Catastrophe progression method for stability classification of underground engineering surrounding rock. J. Cent. South Univ. 39, 5. Forman, R.T.T., 2016. Urban ecology principles: are urban ecology and natural area ecology really different? Landsc. Ecol. 31, 1–10. Geddes, R.R., Reeves, E., 2017. The favourability of U.S. PPP enabling legislation and private investment in transportation infrastructure. Util. Policy 48, 157–165. Geneletti, D., 2013. Assessing the impact of alternative land-use zoning policies on future ecosystem services. Environ. Impact Assess. Rev. 40, 25–35. González, A., Donnelly, A., Jones, M., et al., 2013. A decision-support system for sustainable urban metabolism in Europe. Environ. Impact Assess. Rev. 38, 109–119. Gunawardena, K.R., Wells, M.J., Kershaw, T., 2017. Utilising green and bluespace to mitigate urban heat island intensity. Sci. Total Environ. 584, 1040. Haaland, C., Bosch, C.K.V.D., 2015. Challenges and strategies for urban green-space planning in cities undergoing densification: a review. Urban For. Urban Green. 14, 760–771. Hák, T., Janouškov, S., Moldan, B., 2016. Sustainable development goals: a need for relevant indicators. Ecol. Indic. 60, 565–573. Hannam, I., 1979. Urban soil erosion: an extreme phase in the Stewart subdivision, West Bathurst. J. Soil Conserv. Serv. NSW. Hannam, I.D., Hicks, R.W., 1980. Soil conservation and urban land use planning. J. Soil Conserv. Serv. NSW. Iojă, I.-C., Osaci-Costache, G., Breuste, J., et al., 2018. Integrating urban blue and green areas based on historical evidence. Urban For. Urban Green. 34, 217–225. Ives M, C., Hickford A, J., Adshead, D., et al., 2019. A systems-based assessment of Palestine’s current and future infrastructure requirements. J. Environ. Manag. 234, 200–213. Ji, R., Han, S., Zhang, R., 1998. Briefly describe the functions of finance in Shanghai’s urban infrastructure construction investment. Shanghai Financ. Tax. 8, 7–8. Jia, Z., Cai, Y., Chen, Y., et al., 2018. Regionalization of water environmental carrying capacity for supporting the sustainable water resources management and development in China. Resour. Conserv. Recycl. 134, 282–293. Kabisch, N., Qureshi, S., Haase, D., 2015. Human–environment interactions in urban

7. Conclusions This paper establishes the FUICC Catastrophe Model (FCM) for evaluating the balance of carrying capacity between functional urban infrastructures. The application demonstration tells that this model is an effective and applicable model. The data used for the demonstration are from the Chinese context. And the findings suggest that 1) the difference in carrying capacity of each functional infrastructure between cities is significant. Jinan, Shanghai, Guangzhou, and Shenzhen enjoy the highest carrying capacity respectively in connective, water, greenspace, and traditional infrastructures; 2) the difference in the degree of balance between FUICC is also significant between the sample cities. Shenyang, Nanchang, and Shanghai have the high rankings in balance performance; 3) there are four categories of cities in China in terms of the balance performance of FUICC, namely, acceptably balanced, less balanced, poorly balanced, and unbalanced; 4) the Chinese cities tend to aggregate either in acceptably balanced or unbalanced in the performance between FUICC. The balance performance between FUICC among Chinese cities is characterized with polarization; 5) The overall balance performance between functional urban infrastructures is poor in the Chinese cities, with only 40% of the sample cities are acceptably balanced, and others are much worse. The introduction of the model FCM in this paper provides an important theoretical tool for examining the balance status of carrying capacity between functional urban infrastructures in a city. It contributes to the development of the literature in the discipline of urban carrying capacity study. The application of the model can help decisionmakers in a city understand whether the infrastructures in the city are provided in balance. The assessment results are important references to further examine and identify those weak areas of urban infrastructures. By referring to these research results, city managers and decision-makers can revise or formulate better policy measures to improve balance performance of FUICC for promoting sustainable urban development. Without understanding on the balance status between functional urban infrastructures, weak areas of urban infrastructures could be overlooked, which accordingly could not support sustainable urban development. The limitations of the model FCM in its current stage are implicit as the sample cities for the demonstration are only from China. It is recommended for further study to apply FCM in evaluating the balance 8

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