Quantitative models for assessing the human-ocean system's sustainable development in coastal cities: The perspective of metabolic-recycling in the Bohai Sea Ring Area, China

Ocean & Coastal Management 107 (2015) 46e58

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Ocean & Coastal Management journal homepage: www.elsevier.com/locate/ocecoaman

Quantitative models for assessing the human-ocean system's sustainable development in coastal cities: The perspective of metabolic-recycling in the Bohai Sea Ring Area, China Xionghe Qin a, Caizhi Sun a, *, Wei Zou a, b a b

Center for Studies of Marine Economy and Sustainable Development, Liaoning Normal University, 850 Huanghe Road, Dalian 116029, China School of Foreign Languages, Liaoning Normal University, 850 Huanghe Road, Dalian 116029, China

a r t i c l e i n f o

a b s t r a c t

Article history: Received 12 October 2014 Received in revised form 14 January 2015 Accepted 11 February 2015 Available online

Given that nearly half the human population lives in coastal areas, we urgently need new analytical models on how to maintain the ocean's capacity to sustain our progress. This paper introduces quantitative models for assessing the human-ocean system's sustainable development (HOSSD). The quantitative assessment models, consisting of the developmental degree (DD), harmonious degree (HD), and metabolic-recycling degree (MD), are constructed. The models are applied to measure the conditions of HOSSD in 17 coastal cities in the Bohai Sea Ring Area (BSRA) from 2000 to 2011. The kernel density estimation method is employed to analyze the dynamic evolution of HOSSD. The analysis of temporal variation reveals that the sustainable development levels of the cities were beginning to exhibit a polarized tendency by 2005 and that the gaps in sustainable development increased as a whole over the period 2000 to 2011. The spatial patterns for 17 cities are divided into 4 grades: strong, medium, weak, and very weak. The cities with weak or very weak sustainable development levels are mainly located in northwestern BSRA. The main influencing factors of HOSSD differ considerably between the 17 cities and 3 groups. Our approach allows the visualization of how these dimensions of HOSSD differ from city to city, providing important insights into short-term and long-term policies that may help enhance sustainability at a particular location. © 2015 Published by Elsevier Ltd.

Keywords: Human-ocean system sustainable development Quantitative models KDE Metabolic-recycling Analysis of spatial-temporal variation China

1. Introduction The ocean plays many critical roles in supporting human wellbeing, from providing food, livelihoods, and recreational opportunities, to regulating the global climate (Halpern et al., 2012), and it is essential for international trade and cultural activities (Visbeck et al., 2014). However, free acquisition of ocean resources and services, along with human development, have imposed great pressures on marine ecosystems and coastal environments. These pressures range from overfishing and reckless resource extraction to assorted channels of unchecked pollution (Jackson et al., 2001; Halpern et al., 2008; Visbeck et al., 2013). Despite these threats, the mitigation of marine environmental problems and attempts to connect human development with the ocean's capacity to sustain progress have a very low priority in many coastal regions

* Corresponding author. E-mail address: [email protected] (C. Sun). http://dx.doi.org/10.1016/j.ocecoaman.2015.02.003 0964-5691/© 2015 Published by Elsevier Ltd.

€m et al., 2009). Against this background, the development (Rockstro of a comprehensive and quantitative method to measure and monitor the sustainability of the human-ocean system is an important step towards integrated coastal zone management (Gallagher, 2010). Understanding the relationship between human development and the ocean's capacity has long been an important goal in the humanities and ecology (Pandolfi et al., 2003). The concept of the human-ocean system is defined as “all interactions and linkages between humankind and the entire ocean” (Visbeck et al., 2013). Halpern et al. created an index including ten diverse public goals for a healthy coupled human-ocean system (Halpern et al., 2012). Vulnerability analysis of the human-ocean system provides a new paradigm for the study of interactive mechanisms and processes of sustainable development (Li et al., 2012). Moreover, considering the importance of human-ocean relationship coordination, many studies have begun to pay close attention to the sustainability of the human-ocean system and its development.

X. Qin et al. / Ocean & Coastal Management 107 (2015) 46e58

Developing an integrated index system and method for measuring the human-ocean system's sustainable development (HOSSD) requires a discussion of how the term sustainability can be operationalized in the context of humans and the ocean (Visbeck et al., 2014). The Brundtland Commission defines sustainable development as “development that meets the needs of the present without compromising the ability of future generations to meet their own needs” (Brundtland, 1987a, b). Thus, sustainable development is a broad concept without an operational definition, and as researchers attempt to define it more precisely from different perspectives, there is a need to conduct deeper quantitative research in socio-economic, eco-environmental, and scientific terms. (Marques et al., 2009). Within a fisheries context, the concept of development can be summarized as ensuring food security and the long-term viability of the resource, while also maintaining the health and integrity of marine ecosystems for the benefit of other uses and users (Garcia et al., 2000). Zhang (2000) and Jiang and Wang (2000) stressed that marine sustainable development has been studied from the viewpoints of marine economic sustainability, marine ecological sustainability, and social sustainability. Using the theory of field force in the socialeconomic-natural complex ecosystem, Di et al. (2009) quantified a comprehensive evaluation index for the sustainable development of a marine economy. The results demonstrated that coordination between the economic system, social system, and ecosystem is necessary to achieve sustainable development. Other researchers have also assessed the concept of sustainable development from various perspectives, such as separate indicators and composite indices (Bowen and Riley, 2003; Shi et al., 2004; Boyd and Charles, 2006). This paper proposes a pioneering model for an inclusive approach, which considers socio-economic, ecological, and political systems in a cohesive manner. Although many analytical frameworks and methods regarding the assessment of sustainable development have been proposed, the literature still contains some limitations. (1) HOSSD is essentially a dynamic process of maintaining effective relationships, or a “virtuous cycle,” between marine functions and human development. While previous studies have more or less, considered these characteristics, no importance was placed on this essential aspect. (2) Currently, qualitative descriptions regarding the models used to evaluate sustainable development outnumber quantitative research, and most of the evaluation models are linear, which clearly ignores the non-linear relationship between subsystems. (3) Previous related research was less involved in the analysis of spatial distribution patterns, especially in regard to spatial-temporal analysis. Introducing this type of analysis could contribute to decision-makers’ ability to adequately select suitable development strategies and policies. Based on the understanding of the human-ocean system, the connections between the socio-economic subsystem and the resource-environmental subsystem, and the meaning of sustainable development, we believe that HOSSD is not only the rapid development of the social economy while maintaining a healthy coastal eco-environment, but it is also a progression of coordinated resource-environment and socio-economic development. By mutually promoting both subsystems, the dynamic process of the virtuous cycle can be maintained. The relationship between the eco-environment subsystem and the socio-economic subsystem is like an Egg of Well-being, where the eco-environment subsystem surrounds and supports the development of the social economy, just as the egg white surrounds and supports the egg yolk. As long as both the egg white and egg yolk are good, the egg is good, and the same is true with the Egg of Well-being: only when both the socio-economic and eco-environment subsystems are good will the human-ocean system also be healthy and sustainable (Guijt and

47

Moiseev, 2001). What is more, the human-ocean system is a dissipative structure, which cannot provide the energy necessary to support economic and social development. In order to keep the system in a stable and orderly state, it must obtain matter and energy from outside itself. Like a complex organism, it also constantly generates products and waste to achieve the optimization, recycling, and regeneration necessary for healthy marine ecosystem functions. Accordingly, this paper presents a detailed analysis of HOSSD in coastal cities, focusing on the Bohai Sea Ring Area (BSRA) of China. Following the structure of previous studies, we first build an index system for assessing HOSSD from three aspects: socio-economic development, resource-environmental support, and material metabolism and recycling. We then construct interactive quantitative models to measure the degree of these three aspects: developmental degree (DD) (situation of socio-economic development and resource-environmental protection), harmonious degree (HD) (coordination of socio-economic development and resourceenvironmental protection), and metabolic-recycling degree (MD) (metabolic capacity and recycling capability of the human-ocean system). We use the production possibility curves to quantify the DD of the human-ocean system. According to Environmental Kuznets theory, from the perspective of demand for environmental quality, communities with different levels of economic development have different demands for environmental quality (Panayotou, 2003). In the developmental model, the developmental curve simulates this difference as much as possible to quantify the DD of the human-ocean system. The curve also applies to quantitative studies that have a similar inverted U-shaped relationship, such as evaluation of welfare comprising the environment and income. However, the quantifying accuracy of the curve requires to be improved in the future. Finally, we calculate a comprehensive sustainable development level for each city and present visual results of the current situation of HOSSD in this area. Our discussion focuses on spatial and temporal analyses and on how policymakers can visually identify the main influencing factor of HOSSD to help consider policy actions in short-term and long-term policies for enhancing sustainability. 2. Materials and methods 2.1. Study area The Bohai Sea Ring Area (BSRA), located in the extreme north of the Chinese mainland coastline, comprises the whole coastal area

Fig. 1. Location of the study area.

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of the Bohai Sea and a part of the coastal area of the Yellow Sea (Fig. 1). It covers a length of 6924.2 km of coastline and, as of 2011, the area had a population of 85.61 million. In 2012, this area contributed 36.1% of the Chinese marine gross domestic product (GDP). The whole area has 17 coastal cities: Dandong, Dalian, Yingkou, Panjin, Jinzhou, Huludao, Qinhuangdao, Tangshan, Tianjin, Cangzhou, Binzhou, Dongying, Weifang, Yantai, Weihai, Qingdao, and Rizhao. The geographical constraints of the study area are from 35.1
to 42.3
north latitudes and 115.7
e125.7
east longitudes. The Bohai Sea, which is the only semi-closed inland sea in China, enjoys unique geographical and resource-related advantages and is an important supporting system for the Bohai-Rim Economic Circle. Nonetheless, the exchange capacity of seawater in the Bohai Sea is poor, and the ecosystem is extremely fragile. Sustainable development in the Bohai-Rim Economic Circle will depend on a healthy marine eco-environment and sustainable utilization of marine resources in the BSRA. Although economic development in the BSRA began later than that in the Yangtze River Delta region and the Pearl River Delta region in China, over the past decades it has shown a high growth rate, relying on its abundant resources in fisheries, ports, oil and gas, landscapes, and sea salt. However, the extensive pattern of economic growth in this coastal area has resulted in the excessive consumption of resources, deterioration of the environment, and decline of marine ecological carrying capacity, all of which pose a serious threat to HOSSD. 2.2. Models 2.2.1. Harmonious development model In order to assess the DD and HD of the human-ocean system, based on the relationship between the socio-economic subsystem (S1) and the resource-environmental subsystem (S2), we establish a rectangular coordinate diagram (Fig. 2), which shows the possible trends and movements of harmonious developmental conditions of the human-ocean system in the future. Curves A, B, and C in Fig. 2(a) are the projections of the developmental function f (x, y) ¼x3 þ y onto the rectangular coordinate diagram (Zhang et al., 2006). The y-axis represents the value of S1, and the x-axis represents the value of S2. This function is based on the productionepossibility curve (Samuelson and Nordhaus, 1998) and reflects the non-linear interactive relationship between socioeconomic development and resource-environmental conditions. The developmental function reflects the developmental level of the human-ocean system. In Fig. 2, when the DDs corresponding to

curves A, B, and C are 1/2, 3/4, and 1 respectively, the points on the curves A, B, and C respectively represent all possible different combinations of the values of S1 and S2. x and y are greater than 0 and less than 1 respectively, and the closer the value is to 1, the better the performance of the subsystems. According to the traditional linear developmental function, the DD should be linear sum of the value of the socio-economic subsystem and resource-environmental subsystem (traditional linear developmental function: f (x, y) ¼ y þ x) (Yu et al., 2010; Shen et al., 2014; Yang et al., 2014). However, we have noted that during the initial stage of development, people tend to value socio-economic benefits relatively higher than resource-environmental benefits. This tendency leads people to sacrifice resources and environmental benefits in exchange for socio-economic benefits. Therefore, we assign a low weight (x2) to the resource-environmental subsystem while measuring the DD of the human-ocean system in the initial stage of development. On the basis of the traditional linear developmental function, the non-linear developmental function considering the weights can be expressed as f (x, y) ¼ y þ x$x2. The DD is low in the early days of development. For example, if f (x, y) < 1/2, we can calculate that both x and y are less than 1/2. Thus, the value of weight (x2) assigned is less than 0.25, which is a lower weight corresponding to the scant attention typically paid to resources and environmental protection at a low development level. With the improvement of the development level, people's value of resource-environmental benefits also increases. In other words, the improvement of the DD means the value(s) of x or/and y are also higher during this period. Then, the value of the weight (x2) will improve accordingly. This weight is not fixed but rather changes depending on the improvement of the development level. The weights given in this way are fairly in accordance with the actual development situation. The harmonious development model is used to calculate the DD and HD of the human-ocean system. The square area in Fig. 2(a) is divided into four equal parts, from the lower left to the upper right, by the following curves: y ¼ 1/2  x3, y ¼ 3/4  x3, and y ¼ 1  x3. These areas are in turn labeled as the four levels of the DD: low (IV), elementary (III), intermediate (II), and advanced (I) (Gustavson et al., 1999; Zhang et al., 2006). The square area in Fig. 2(b) is divided into four equal parts, from the upper left to the lower right, by the following curves: y ¼ x1/3, y ¼ x, and y ¼ x3. Again, these areas are in turn labeled as the four levels of the HD: disturbed type, resource-environmental subsystem lagging (II); harmonious, socioeconomic subsystem leading (I); harmonious type, resourceenvironmental subsystem leading (I0 ); and disturbed type, socio-

Fig. 2. Quantitative models of DD and HD. (a) Developmental model, (b) Harmonious model.

X. Qin et al. / Ocean & Coastal Management 107 (2015) 46e58

economic system lagging (II0 ). The location of the points in Fig. 1 is determined by the x and y coordinates of the sample cases, such that the points located in the top right region of Fig. 2(a) have a higher DD than those in the bottom left region, and the closer the points in Fig. 2(b) are to the diagonal line (y ¼ x), the higher the HD. The values of S1 (x) and S2 (y) can help us find the location of the points on the rectangular axes, which will visually show the DD and HD levels of the human-ocean system in the development model and the harmonious model respectively. In both models, the points lying along the direction of the dotted lines indicate the achievement of the ideal DD and HD. The curves seen in the divided structure of these models reveal an interactive relationship between humans and the ocean, and the HOSSD must be based on a virtuous cycle of the resources and environment. If the humanocean system loses the material and energy flows created by the resource-environmental subsystem, it cannot run normally, and then, HOSSD is impossible to achieve. According to the Environmental Kuznets curve, only if economic development and environmental protection achieve a “winewin” range can they begin to show a positive correlation and follow a harmonious progression (Stern et al., 1996). 2.2.2. Metabolic-recycling model According to the laws of thermodynamics, the development of the human-ocean system will consume a given amount of resources, but it will also regenerate a given amount of resources through the metabolic subsystem (S3) and the recycling subsystem (S4). In this paper, we consider the whole system as an organism and establish the metabolic-recycling model based on the theories of metabolism and recycling. In the model, the recycling aspect emphasizes regenerating the quantity of resources by waste disposal and reuse for given levels of technology and economic development. That is, recycling focuses on the social succession of quantity. The metabolism aspect places more emphasis on regenerating the quality of resources by reducing resource consumption and waste generation. That is, metabolism focuses on the natural succession of quality in the process of using the resource. The metabolic-recycling model indicates that the quantity of resources can be constantly replenished by recycling and that the quality of resources can continue to be renewed through metabolism. Only by dynamically coupling both the metabolic capacity and recycling capability of the system can the human-ocean system achieve a virtuous cycle and sustainable development.

49

subjective weights are also called weights of the magnitude of value. However, objective weighting methods determine the importance of indicators based on correlation or variation between the values of each indicator. This kind of weight, determined by data, is called the weight of the magnitude of information. Due to absolute objectivity, objective weighting methods may violate the economic significance or technical sense of indicators, while changes in the sample cases may result in variations and instability of weight. Therefore, this study proposes an integrated weighting method that allows for the insights of both the subjective and the objective approaches, and hence, it combines the magnitude of value and the magnitude of information. To ensure the reasonableness of the indicator weight, we combine the advantages and disadvantages of both the AHP and the Entropy methods. The optimization model determining the integrated weight of the indicator is established by the least squares method, as follows. The AHP-determined subjective weighting vector is defined as

n ¼ ðn1 ; n2 ; /; nm ÞT ;

(1)

the Entropy-determined objective weighting vector is defined as

m ¼ ðm1 ; m2 /; mm ÞT ;

(2)

and the integrated weighting vector is

w ¼ ðw1 ; w2 ; /; wm ÞT :

(3)

For all sample cases, the error of the integrated weighting evaluation should be as small as possible. The least squares minimization problem, using the integrated weight wj, is given by

minHðwÞ ¼

n X m n X  2   2 o mj  wj zij þ nj  wj zij

(4)

i¼1 j¼1

s:t:

m X

wj ¼ 1; wj  0ðj ¼ 1; 2; /; mÞ:

j¼1

The optimization model is solved by constructing a Lagrangian function (Liu, 1999).

2.3. Methods 2.3.1. Evaluation of multi-criteria decision-making methods To evaluate the multi-criteria decision-making (MCDM) methods for the development of an integrated weighting method, it is important to consider the existing subjective and objective MCDM weighting methods (Iwaro et al., 2014). There are a variety of successfully developed subjective weighting methods, such as Delphi (Hwang and Lin, 1987), TRADE-OFF € yho €nen and Ha €ma €l a €inen, 2001), and Analytic Hierarchy Pro(Po cess (AHP) (Saaty, 1990; Rao, 2008). These methods subjectively determine the weights by judging practical problems based on expert knowledge and past experience. In contrast, objective weighting methods are also considered very useful since the majority of the subjective methods fail to consider the objective information of actual data. Objective weighting methods include Entropy (Shanian and Savadogo, 2009), Criteria Importance Through Inter-Criteria Correlation (Diakoulaki et al., 1995), and TOPSIS (Deng et al., 2000). Generally, subjective weighting methods are more concerned with the economic and technical significance of indicators. Thus,

2.3.2. Calculation of DD, HD, MD, and SDD We need to calculate x, y, the value of metabolic subsystem (m), value of recycling subsystem (r), DD, HD, MD, and sustainable development degree (SDD). The numerical results of x, y, m, and r are calculated by the following equation:

Im ¼

n X

wi zij :

(5)

i¼1

In Eq. (5), Im represents x, y, m, and r separately, n represents the number of indicators for the corresponding subsystems, zij is the standardized score of each indicator, and wi is the integrated weight of each indicator calculated using Eq. (4). For the developmental model, the DD is calculated by the following equation:

DD ¼ y þ x3 :

(6)

For the harmonious model, the HD is calculated using the following equations:

50

8 < a; HD ¼ 1 : ; a

X. Qin et al. / Ocean & Coastal Management 107 (2015) 46e58

a<1 a1

2.4. Data collection and processing

;

(7)

where a ¼ logx y, the harmonious coefficient, and it is calculated using the following equation:

y ¼ xa :

(8)

The metabolic-recycling model is used to calculate the MD of the human-ocean system. For the metabolic-recycling model, the MD is calculated using the following equation:

MD ¼ ðmrÞ1=2 :

(9)

In Eq. (9), the MD is the geometric average of the metabolic subsystem's composite index value (m) and the recycling subsystem's composite index value (r). According to the definition of HOSSD, the SDD of a human-ocean system should include the DD, HD, and MD, and it is calculated using the following equation:

SDD ¼ 0:4DD þ 0:3HD þ 0:3MD:

(10)

As China is the largest developing country in the world, it is always cognizant of development issues. Indeed, China's Twelfth Five-Year Plan of Marine Economic Development, issued by the State Council (2012), also renewed the country's overall goal for enhancing marine economic development. Therefore, development remains a priority, and the weight assigned to DD in Eq. (10) is a little higher. Thus, the final SDD is the approximate average value of DD, HD, and MD, with their respective weights being 0.4, 0.3, and 0.3 respectively.

2.3.3. Kernel density estimation It is difficult to get the true distribution of small samples using a parametric method, because with such few data points available, there is not enough information to estimate the probability density function. However, kernel density estimation (KDE) does not need as many assumptions and is the most general non-parametric density estimation method (Jones et al., 1996; Zhang et al., 2012). For a given kernel function K, a positive smoothing factor h (bandwidth), and a random sample x1, x2 …, xn, the KDE is given by

  n 1 X x  xi b f h ðxÞ ¼ ; K nh i¼1 h

(11)

where the kernel function K is a weighting function. The common kernel functions are the Gaussian kernel, Epanechnikov kernel, Triangular kernel, and Rectangular kernel. Based on the intensity of the packet data, this study employs the Gaussian kernel, which is given by 1 2 1 pffiffiffiffiffiffie2t : 2p

where S is the standard deviation value of a random variable.

  xij  minj xij    : zij ¼ maxj xij  minj xij When it is a negative value:

zij ¼

  maxj xij  xij    : maxj xij  minj xij

where xij is the original value of indicator i for sample case j. zij is the normalized value of indicator i for sample case j (I ¼ 1,2,3, …,n and j ¼ 1,2,3, …,m). maxj (xij) and minj (xij) respectively are the maximum and minimum values of indicator i for sample case j. Our index system of HOSSD consists of 28 indicators (F1eF28) grouped according to the 4 subsystems (S1eS4). S1 and S2 represent the socio-economic subsystem and the resource-environmental subsystem respectively, while S3 and S4 represent the metabolic subsystem and the recycling subsystem respectively. 2.4.1. Indicators 2.4.1.1. Socio-economic subsystem. Economic and social development is a fundamental goal of HOSSD. The socio-economic subsystem consists of both economic growth indicators and social development indicators. Indicator F1 shows the status of the marine economy in regional development. F2 and F3 reflect the quality of the marine economy and land economy respectively. F4 and F5 reflect the level of development of the main marine industries. F6 reflects the population agglomeration caused by coastal economic growth, which has put great pressure on economic and social development. F7 is an important quantitative indicator of port production and business operations, and its role is to meet the needs of social and economic development. F8 reflects the overall level of marine scientific research. F9 shows the labor efficiency of marine employees. F10 is a measure of the standard of living.

(12)

As Silverman pointed out, in the case of large samples, usually the non-parametric estimation is not sensitive to the choice of kernel, but the choice of bandwidth h has greater influence on the estimator (Silverman, 1986). In this paper, the best bandwidth h is estimated using the versatile method proposed by Silverman, and it is given by, and it is given by

h ¼ 0:9Sn  0:8;

The data gathering process is an important undertaking before beginning any assessment. Good indicators should be easy to understand, sensitive to changes, and relevant among themselves (Nardo et al., 2005). In particular, they will need to be measured to guarantee that they are reasonable and valid in scientific and statistical terms, so as to be capable of providing quantitative information (Yu et al., 2010). An index system for HOSSD was established for this paper, involving a total of 28 indicators and covering the 17 coastal cities of the BSRA over the period 2000 to 2011. The data were obtained from the relevant sources and references indicated in Table 1. Considering that different indicators have different dimensions or magnitudes and/or different effects (positive or negative) on performance evaluation, a normalization process is necessary. By using the Min-Max normalization method, each indicator is normalized according to the following equations. When it is a positive value:

2.4.1.2. Resources-environmental subsystem. The support provided by the resource-environmental subsystem is the foundation of the HOSSD. As the development and utilization of marine resources is important for supporting economic and social development, we select marine resource indicators F11, F12, and F13, which directly reflect the substantial support that marine resources provide to HOSSD. F14 and F15 reflect the dependence of the coastal economy on energy consumption. F16 and F18 show the degree of industrial solid waste discharge and wastewater discharge to the sea, reflecting the pressure that economic and social development put on the regional environment. F17 indicates the level of urban

X. Qin et al. / Ocean & Coastal Management 107 (2015) 46e58

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Table 1 HOSSD performance measurement indicators. Subsystem layer

Indicator layer

References and data sources

Socio-economic subsystem (S1)

F1: Location quotient of marine economy F2: Gross ocean product per capita F3: Coastal zone economic density F4: Earning from coastal tourism F5: Output value of marine fisheries F6: Population density () F7: Volume of freight handled at coastal seaports F8: Population employed in scientific research, technical services, and geological prospecting F9: Labor productivity of marine industry F10: Engel's coefficient () F11: Mariculture area F12: Berths for productive use at coastal seaports above designed size F13: Output of aquatic products F14: Water consumption per 10,000 Yuan of gross ocean product ()

(SOA, 2000e2011) (SOA, 2000e2011; Yang et al., 2014) (SOA, 2000e2011; Yu et al., 2010) (DUSNBSC, 2000e2011) (SOA, 2000e2011; Ou and Liu, 2010) (NBSC, 2000e2011) (SOA, 2000e2011) (NBSC, 2000e2011; Yang et al., 2014)

Resource-environmental subsystem (S2)

F15: Energy consumption per 10,000 Yuan of gross ocean product

Metabolic subsystem (S3)

Recycling subsystem (S4)

F16: F17: F18: F19: F20: F21: F22: F23: F24:

Volume of industrial wastewater discharged directly to sea () Rate of green coverage in established districts Volume of industrial solid wastes discharged () Number of marine-type nature reserves Rate of relative exploitation of ocean resources Proportion of R&D funds accounting for GDP Coefficient of international trade Rate of up-to-standard industrial wastewater discharge Rate of wastewater recycled and reused

F25: Reuse rate of industrial water F26: Proportion of environmental investment accounting for regional GDP F27: Rate of industrial sulfur dioxide treatment F28: Comprehensive utilization rate of industrial solid waste

(SOA, 2000e2011) (NBSC, 2000e2011; Yu et al., 2010) (SOA, 2000e2011; Yang et al., 2014) (SOA, 2000e2011; Yang et al., 2014) (DUSNBSC, 2000e2011; Yang et al., 2014) (HPSB, 2000e2011; LPSB, 2000e2011; SPSB, 2000e2011; TPSB, 2000e2011; Yu et al., 2010; Schernewski et al., 2014) (HPSB, 2000e2011; LPSB, 2000e2011; SPSB, 2000e2011; TPSB, 2000e2011; Schernewski et al., 2014) (DUSNBSC, 2000e2011) (NBSC, 2000e2011) (DUSNBSC, 2000e2011) (SOA, 2000e2011; Yu et al., 2010; Schernewski et al., 2014) (DUSNBSC, 2000e2011; SOA, 2000e2011) (DUSNBSC, 2000e2011; NBSC, 2000e2011; Yu et al., 2010) (DUSNBSC, 2000e2011) (DUSNBSC, 2000e2011) (HPSB, 2000e2011; LPSB, 2000e2011; SPSB, 2000e2011; TPSB, 2000e2011) (HPSB, 2000e2011; LPSB, 2000e2011; SPSB, 2000e2011; TPSB, 2000e2011; Zhang et al., 2005) (DUSNBSC, 2000e2011; NBSC, 2000e2011; Zhang et al., 2006) (DUSNBSC, 2000e2011) (DUSNBSC, 2000e2011)

() means that the indicator is a negative value.

greening. F19 reflects, to some extent, the status of the marine ecosystem. 2.4.1.3. Metabolic and recycling subsystems. The metabolic and recycling subsystems are the lifeblood of the human-ocean system. F20 is a measure of resource exploitation, which fundamentally affects land resource consumption. F21 indicates that higher R&D investment results in higher Total Factor Productivity, playing an important role in reducing resource consumption and waste generation. F22 reflects the contribution of international trade to regional economic development. As F22 increases, economic development is less dependent on the regional resources and environment. F23 is an indicator of efforts to reduce wastewater. Indicators of the recycling subsystem can be divided into two groups. The first group includes the indicators F24, F25, and F27 and concerns waste treatment and resource reuse issues. The other group includes indicators F26 and F28 and reflects attempts to control the generation of pollution. 3. Results 3.1. Weights Integrated weights combining the subjective with objective weights are introduced to reflect the contribution extent of each attribute to the overall evaluation on indicators’ performance (Chen et al., 2014). The AHP is used to find the subjective weights of 28 indicators within the 4 subsystems. Based on a consultation with the experts, the pairwise comparison matrixes of the four subsystems are formed as shown in Appendix A. The calculated subjective weights are represented in the third column of Table 2. The objective weights for 28 indicators within the four subsystems are

calculated using the Entropy method (Shen et al., 2014). The integrated weights for each indicator are calculated using Eq. (4). The results of the three kinds of weights are listed in Table 2. Of the ten indicators related to the socio-economic subsystem, the subjective weights of F1, F6, and F10 are markedly higher than those of the other attributes, while the objective weights of F3, F4, and F8 are evidently higher (Table 2). The result shows that there are apparent differences in the focal points between the subjective and objective perspectives. In terms of economic and social development, the experts pay more attention to the location quotient of the marine economy, population density, and Engel's coefficient, while the objective weights tend to focus more on coastal zone economic density, earnings from coastal tourism, and scientific and technical personnel. Of the nine indicators of the resource-environmental subsystem, the subjective weights of F14 to F16 and F18 are markedly higher than the objective weights, while the objective weights of F11 to F13 are higher. In terms of resources and environmental support, the experts are more concerned about energy consumption and environmental pollution, insisting that reductions in either lead to sustainable human development. The objective weights consider the production capacity of marine fisheries and marine transportation to be much more important. Of the nine indicators related to both the metabolic subsystem and the recycling subsystem, the subjective weights of F21 and F26 are relatively higher than those of the other indicators, and F20, F22, F24, and F26 have high objective weights. Both subjective and objective weights put more emphasis on the proportion of environmental investment accounting for the regional GDP than the other elements. Furthermore, experts also focus their attention on capital input for R&D within the metabolic subsystem, and based on the data for the indicators, the objective weights assign less

52

X. Qin et al. / Ocean & Coastal Management 107 (2015) 46e58

Table 2 Subjective weights, objective weights, and integrated weights. Subsystems

Indicators

Type of weights AHP weights

S1

S2

S3

S4

F1: Location quotient of marine economy F2: Gross ocean product per capita F3: Coastal zone economic density F4: Earning from coastal tourism F5: Output value of marine fisheries F6: Population density () F7: Volume of freight handled at coastal seaports F8: Population employed in scientific research, technical services, and geological prospecting F9: Labor productivity of marine industry F10: Engel's coefficient () F11: Mariculture area F12: Berths for productive use at coastal seaports above designed size F13: Output of aquatic products F14: Water consumption per 10,000 Yuan of gross ocean product () F15: Energy consumption per 10,000 Yuan of gross ocean product () F16: Volume of industrial wastewater discharged directly to sea () F17: Rate of green coverage in established districts F18: Volume of industrial solid wastes discharged () F19: Number of marine-type nature reserves F20: Rate of relative exploitation of ocean resources F21: Proportion of R&D funds accounting for GDP F22: Coefficient of international trade F23: Rate of up-to-standard industrial wastewater discharge F24: Rate of wastewater recycled and reused F25: Reuse rate of industrial water F26: Proportion of environmental investment accounting for regional GDP F27: Rate of industrial sulfur dioxide treatment F28: Comprehensive utilization rate of industrial solid waste

a

0.193 0.113 0.105 0.049 0.082a 0.151 0.092 0.049 0.092 0.075a 0.186 0.125 0.065 0.088a 0.088a 0.213a 0.057 0.125a 0.053 0.234 0.393a 0.139 0.234 0.087 0.204 0.364a 0.124 0.221

Entropy weights

Integrated weights

0.026 0.089 0.171b 0.207b 0.096 0.018 0.145 0.145b 0.087 0.016 0.308b 0.348b 0.249b 0.009 0.010 0.009 0.035 0.013 0.020 0.375b 0.142 0.455b 0.027 0.331b 0.048 0.363b 0.193 0.065

0.081 0.100 0.206 0.159 0.096 0.048 0.090 0.099 0.094 0.029 0.235 0.274 0.160 0.037 0.037 0.067 0.106 0.049 0.035 0.260 0.200 0.452 0.088 0.225 0.100 0.421 0.152 0.102

Note. a Indicates that subjective weights for these indicators exert stronger influence. b Indicates that objective weights for these indicators exert stronger influence.

importance to the reuse rate for industrial purposes and the comprehensive utilization rate of industrial solid waste on the recycling subsystem. The above differences between the subjective and objective perspectives reflect the viewpoints of experts regarding HOSSD in the BSRA, namely that it is important to continue focusing on economic development, protection of resources, and the environment as well as progress in science and technology. In its quest to improve the service level and strengthen the sustainable development of the human-ocean system, the proposed integrated weighting method is actually able to reduce the differences among various aspects and provide a more reasonable weight for the evaluation of HOSSD in the BSRA. The weight vectors of the four subsystems in the column on the extreme right in Table 2 are calculated using Eq. (4). F3 gets the final weight of 0.206, the highest weight within the socio-economic subsystem, while F10 gets the final weight of 0.029, a less important value. The foremost indicator of the resource-environmental subsystem, namely, F12, has the weight of 0.274, which means that production capacity of marine transportation is accorded even more attention. F22 is very important as it constitutes more than 40% of the weight among the four indicators of the metabolic subsystem, and F20, F21, and F22 rank second, third, and fourth successively with scores of 0.260, 0.200, and 0.088 respectively. F26 has the weight of 0.421, which indicates that it is far more significant than the other indicators of the recycling subsystem. Thus, each of the other indicators contributes, on average, less than 15%. 3.2. Evaluation of the DD, HD, and MD After the raw statistical data is treated by the Min-Max method, the integrated weights wm are calculated using the AHP, Entropy method, and Eq. (4). Then, the values of S1 (x), S2 (y), S3 (m), and S4

(r) can be obtained using Eq. (6). Finally, the DD, HD, and MD are computed using Eqs. (7) to (10) (Fig. 3). Fig. 3 shows the changing trends of DD, HD, and MD from 2000 to 2011. The DD of all the cities was low in 2000 and increased slowly over time for almost all the cities. On the whole, the HD fluctuated, mostly showing an upward trend, while the MD rose at the beginning and dropped later. The means of DD, HD and MD show the highest value for HD, followed by MD, and DD (Fig. 4). As a result, achieving socio-economic development is crucial in this region under the condition of maintaining a good eco-environment and metabolic-recycling capacity. 3.3. Evaluation of the sustainable development degree of the human-ocean system The mean of HOSSD includes three aspects: development, harmony, and metabolic-recycling of the human-ocean system. According to Eq. (11), the SDD of the human-ocean system of the cities in the BSRA are computed using the scores of the DD, HD, and MD from 2000 to 2011. Almost equal importance is assigned to the DD, HD, and MD, and none of them can be neglected. The higher the SDD, the better the performance. The values of the SDD are shown in Table 3. Overall, the values of the SDD in all the cities show a slowly rising trend (Fig. 5). However, the means of the SDD in most cities of the BSRA were less than 0.5, which is fairly low across the twelve years (Table 3). The mean SDDs at the city level varied from a low of 0.275 (Standard Deviation (SD) ¼ 0.020) and 0.294 (SD ¼ 0.040) in Binzhou and Cangzhou respectively to a high of 0.536 (SD ¼ 0.082) in Tianjin. Weihai recorded a moderate SDD (0.408), but is notable for the considerably higher variation (SD ¼ 0.107) created by differing SDD levels in different years. The SDD of Weihai rose considerably over the period under study, from 0.284 to 0.635,

X. Qin et al. / Ocean & Coastal Management 107 (2015) 46e58

53

Fig. 3. Changing trends of DD, HD, and MD of each city in the BSRA from 2000 to 2011. The vertical coordinates denote the evaluation scores of DD, HD, and MD, and the evaluation scores range 0e1.

which was also the largest increase. Binzhou's SDD showed the slowest increase, from 0.242 to 0.303. 4. Discussions 4.1. Analysis of temporal variation Using the SDD in Table 3, we depict the measured distribution of HOSSD in the BSRA by the KDE method (Fig. 6). The diagram shows

Fig. 4. Means of DD, HD, and MD of each city in the BSRA from 2000 to 2011.

the kernel density distributions from three different years (2000, 2005, and 2011) and explains the evolution of HOSSD in the BSRA. This evolution has several significant features. First, based on the analysis of the shape, the curves for SDD show a significantly skewed distribution, and they are not strictly unimodal in shape. The unimodal distribution appears in 2000, suggesting that the distribution of sustainable development levels in cities was concentrated at that timedbut at a low level. With the passage of time, the curve transforms into a bimodal distribution by 2005, which indicates the sustainable development levels of the cities were beginning to exhibit a polarized tendency. In addition, the distribution of the sustainable development levels in the cities tends to disperse, because differences of economic growth may lead to an increasing gap in sustainable development levels between them. This gap increases not because the weak sustainable development level of cities became weaker and the strong sustainable development level of cities became stronger, but because cities with moderate sustainable development increased their sustainability levels and flowed into the strong sustainable development group. However, the differences in the rate and extent of flow changed the inequality of sustainable development. The proportion of cities with strong sustainable development expanded, while the proportion of cities with weak sustainable development gradually shrank. Second, based on the analysis of curve location, the curve tends to move to the right from 2000 to 2011, and the height of the peak decreasesdthat is, the kernel density corresponding to cities having weak sustainable development levels declines. It shows that the levels of sustainable development in most cities were steadily rising.

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X. Qin et al. / Ocean & Coastal Management 107 (2015) 46e58

Table 3 SDD of the human-ocean system of the cities in the BSRA from 2000 to 2011. Cities

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

Mean

Tianjin Tangshan Qinhuangdao Cangzhou Dalian Dandong Jinzhou Yingkou Panjin Huludao Qingdao Dongying Yantai Weifang Weihai Rizhao Binzhou

0.391 0.263 0.317 0.251 0.392 0.248 0.265 0.243 0.297 0.286 0.323 0.260 0.317 0.227 0.284 0.347 0.242

0.416 0.256 0.335 0.252 0.393 0.270 0.276 0.277 0.308 0.299 0.343 0.279 0.323 0.271 0.287 0.347 0.264

0.452 0.246 0.338 0.249 0.405 0.291 0.270 0.282 0.302 0.298 0.366 0.284 0.334 0.283 0.290 0.328 0.269

0.477 0.246 0.375 0.265 0.405 0.273 0.282 0.269 0.294 0.302 0.372 0.274 0.343 0.299 0.322 0.358 0.309

0.523 0.241 0.357 0.258 0.408 0.305 0.265 0.268 0.297 0.307 0.386 0.304 0.343 0.292 0.348 0.319 0.266

0.614 0.246 0.383 0.279 0.457 0.327 0.311 0.337 0.319 0.321 0.467 0.299 0.409 0.301 0.418 0.317 0.253

0.582 0.292 0.450 0.300 0.455 0.314 0.283 0.296 0.322 0.330 0.425 0.340 0.426 0.296 0.418 0.369 0.269

0.621 0.316 0.474 0.326 0.445 0.291 0.289 0.291 0.343 0.340 0.460 0.371 0.403 0.328 0.454 0.363 0.267

0.597 0.333 0.474 0.337 0.464 0.295 0.313 0.306 0.353 0.346 0.512 0.380 0.428 0.348 0.449 0.374 0.276

0.587 0.344 0.459 0.329 0.534 0.326 0.335 0.331 0.383 0.371 0.519 0.414 0.420 0.347 0.474 0.388 0.280

0.582 0.387 0.472 0.340 0.568 0.372 0.355 0.375 0.411 0.391 0.485 0.432 0.440 0.383 0.514 0.396 0.297

0.593 0.400 0.482 0.349 0.596 0.367 0.360 0.385 0.440 0.375 0.506 0.462 0.462 0.401 0.635 0.437 0.303

0.536 0.298 0.410 0.294 0.460 0.306 0.300 0.305 0.339 0.331 0.430 0.342 0.387 0.315 0.408 0.362 0.275

Finally, the analysis of the peak shows a change from a spikeshaped peak to a more broad-shaped peak over the period 2000 to 2011. The curves show a significant spiked peak characteristic in 2000, and as time passes, the spiked peak flattens. The value of the sustainable development levels corresponding to the peak position, therefore, improved significantly, which indicates that the values of the sustainable development levels in this period increased in the BSRA as a whole. 4.2. Spatial pattern of HOSSD in the BSRA The mean of the SDD in all the cities is 0.359, and the SD is 0.071. The SDDs of the 17 cities are divided into four grades depending on

the mean and SD of the classification statistics (Fig. 7) (Wallenstein et al., 1980). Cities with SDDs that are at least one SD greater than the mean are termed ‘‘strong sustainable development level cities.’’ Those with the SDDs ranging from the mean to the mean plus one SD are termed ‘‘medium sustainable development level cities.’’ Cities with SDDs ranging from the mean minus one SD to the mean are termed ‘‘weak sustainable development level cities.’’ Cities with SDDs less than the mean minus one SD are termed ‘‘very weak sustainable development level cities’’ (strong: 0.429 to 1.000, medium: 0.359 to 0.429, weak: 0.288 to 0.359, very weak: 0.000 to 0.288.) As shown in Fig. 7, the cities with weak or very weak sustainable development levels are mainly located in northwestern BSRA, and

Fig. 5. Trends in SDD for each city in the BSRA from 2000 to 2011.

X. Qin et al. / Ocean & Coastal Management 107 (2015) 46e58

55

maintenance of the ecosystem is an important objective of sustainable development (Turner and Kirkpatrick, 1994). As the Bohai Sea is a typical semi-closed inland sea, the exchange capacity and self-purification capacity of seawater in different bays are different. These capabilities can reduce pollutant concentrations and improve water quality. In the no-wind case, the half exchange-times of seawater in the Bohai Bay and the Liaodong Bay are obviously longer than those in the Laizhou Bay and the Bohai Strait (Sun and Tao, 2006). Most of these cities are located along the Bohai Bay and Liaodong Bay, which have poor seawater exchange capacity, and thus, their marine ecosystems are becoming more vulnerable. Second, as the level of socio-economic development is low in these cities, people tend to concentrate on development while ignoring resource and environmental protection, which can lead to unsustainable economic growth. 4.3. Detailed comparative study on HOSSD of coastal cities in the BSRA Fig. 6. Kernel density distribution of HOSSD in the BSRA. The horizontal axis represents the SDD, and the vertical axis represents the value of the KDE. The blue, red, and green lines represent the kernel density distributions in 2000, 2005, and 2011 respectively. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

the cities with medium or strong sustainable development levels are mainly located in southern BSRA and southeastern BSRA. Tangshan, Cangzhou, Dangdong, Jinzhou, Yingkou, Panjin, Huludao, Dongying, Weifang, and Binzhou have weak or very weak sustainability. More attention should be paid to these cities to promote their HOSSD. It is probable that there are two chief causes for weak or very weak sustainability within these cities. First, the

Understanding what promotes human development alongside the ocean's capacity to sustain progress is a critical task for scientists and decision-makers. Using the recognized framework of HOSSD, which comprises the DD, HD and MD, we present the most detailed comparative and quantitative study on HOSSD of coastal cities in the BSRA to date. We plot three dimensions of HOSSD to help discern the locations of the main factors influencing HOSSD at the city level (Fig. 8). In Fig. 8, we can visually identify the main factors influencing the HOSSD of the cities. In order to better analyze the main factors influencing HOSSD, we form three groups based on the DD, HD, and MD levels (Fig. 4), and we use these to compare the sustainability levels of all the cities in the study area (Fig. 8). As noted previously,

Fig. 7. Spatial patterns of HOSSD in the BSRA.

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(Fig. 4, Fig. 7, and Table 3). For example, even though Dalian had the lowest HD levels, its average DD and MD levels were high, indicating stronger overall sustainability compared to other cities. These distinctions in the main factors influencing HOSSD are important because specific policy tools may be required to address the different dimensions of the human-ocean system. For example, specific policies and flexible measures may be required to improve the quality and level of cities’ economic development on the premise of ensuring a good ecological environment, whereas others may help build metabolic capacity and recycling capability. Importantly, these policies and measures should vary over spatial and temporal scales. In the short term, instead of focusing on all the dimensions, emphasis may be put on improving the dimension that performs the poorest, which has greater potential to enhance the sustainability of the human-ocean system. In the long term, the authorities should focus on promoting coordinated development of the economy, society, resources, and environment, to achieve a balance among all dimensions. 5. Conclusion Fig. 8. Plot of the sustainability of the human-ocean system of the coastal cities in the BSRA. The three indices consist of a suite of indicators that are rated to give performance scores. The scores are plotted as coordinates to provide a visual representation. The x-axis represents the DD, and the y-axis represents the HD. The more sustainable cities are located in the top right region of the graph, and the less sustainable cities, in the bottom left. The third dimension of the human-ocean system, the MD, is represented as the size of the bubble (larger bubble size ¼ higher MD, Low MD: 0.00 to 0.30, Medium MD: 0.30 to 0.40, High MD: 0.40e1.00).

the statistical characteristics of the DD, HD, and MD in these three groups are quite different (Table 4). The mean SDD is the largest for Group G2, and it accurately indicates that the human-ocean system in this region is strongly sustainable as a whole. However, the mean DD is relatively low and becomes a limiting factor for HOSSD in this region. The cities in Group G1 are located toward the bottom right region in Fig. 8. They have larger bubble sizes and the highest DD and MD scores, but their HD levels are the lowest among all three groups. It is obvious that a more effective way to enhance HOSSD is to improve the HD of the human-ocean system. Therefore, development of these coastal cities requires not only a consideration of the conditions necessary for the sustainable use of coastal natural environments and marine natural resources but also those for coordinated growth of the economy and resource environmental protection (Feng et al., 2014). The cities in Group G3 have relatively smaller-sized bubbles, are mainly distributed in the bottom left region in Fig. 8, and have low DD, HD, and MD levels. These types of overall sustainable development measures provide useful information about the relative state of sustainability of the human-ocean system, but the implications of a single quantitative metric can be difficult to interpret, and the measure itself is not particularly informative about what policy actions could help enhance sustainability at a particular location (Cinner et al., 2012). However, the approach we utilize allows one to characterize key determinants of the human-ocean system at a particular location Table 4 Grouping of cities in the BSRA based on their mean DD, HD, MD, and SDD levels. Group

Cities

Mean DD

Mean HD

Mean MD

Mean SDD

G1 G2 G3

Qingdao, Yantai, Weihai, Dalian Tianjin, Qinhuangdao, Panjin Huludao, Jinzhou, Rizhao, Cangzhou, Yingkou, Dongying, Tangshan, Dangdong, Weifang, Binzhou

0.401 0.267 0.154

0.431 0.724 0.545

0.440 0.349 0.293

0.421 0.428 0.313

Based on the understanding of the related theories of HOSSD, we built quantitative models consisting of the DD, HD, and MD, to describe the sustainable developmental capacity of the humanocean system from the perspective of metabolic-recycling. Through empirical research, an index system was created to offer a comprehensive assessment of HOSSD. Analyses of sustainable development have shown that HOSSD is essentially development with metabolic-recycling capacity and comprehensive coordination. As a result, it is important to continue maintaining a good eco-environment and metabolic-recycling capacity in order to achieve socio-economic development in this region. The previous index systems commonly used to evaluate sustainable development not only omitted the non-linear relationship between the subsystems, but they also did not place any significance on the metabolic-recycling capacity of resources. This is mainly why we have proposed analyzing the DD, HD, and MD in our assessment of sustainable development. Establishing an evaluation index system of the human-ocean system from the perspective of metabolic-recycling can not only effectively reflect the health, vitality, and metabolic-recycling capacity of the human-ocean system, but it can also make up, to some extent, for the deficiencies of previous evaluation index systems of sustainable development. Finally, the analysis of temporal variation at three points in time by kernel density estimation revealed that based on the shape of the curves, the sustainable development levels of cities were beginning to exhibit a polarized tendency by 2005, while the sustainable development level in the BSRA increased as a whole over the period 2000 to 2011. The spatial pattern for 17 cities showed that cities with weak or very weak sustainable development levels are mainly located in northwestern BSRA. Our approach provided a means to understand and visualize how key dimensions of HOSSD vary between the 17 cities and 3 groups defined in our study, which provides a strong basis for making short-term and long-term policies aimed at enhancing sustainability. Acknowledgments This research has been funded by the MOE's Project of Key Research Institutes of Humanities and Social Sciences in Universities (No. 12JJD790032) and the Chinese National Natural Science Foundation (No. 41301129). Constructive suggestions and comments on the manuscript from the reviewer(s) and editor(s) are appreciated.

X. Qin et al. / Ocean & Coastal Management 107 (2015) 46e58

References

Appendix A

Table A1 Pairwise comparison matrix of the socio-economic subsystem.

F1 F2 F3 F4 F5 F6 F7 F8 F9 F10

F1

F2

F3

F4

F5

F6

F7

F8

F9

F10

Weight

1

2 1

2 1 1

3 2 2 1

3 2 2 0.5 1

2 1 0.5 0.33 0.5 1

2 2 1 0.5 1 2 1

3 2 2 1 2 3 2 1

2 1 1 0.5 1 2 2 0.5 1

2 1 2 0.5 2 2 1 0.5 2 1

0.193 0.113 0.105 0.049 0.082 0.151 0.092 0.049 0.092 0.075

For a better understanding of the table, consider the following example. The value in the first row and fifth column is 3, which means that F1 is three times as important as F5. The consistency ratio of the pairwise comparison matrix is found to be 0.0240, and as the value is under 0.10, we conclude that the comparison matrix is consistent. Table A2 Pairwise comparison matrix of the resource-environmental subsystem.

F11 F12 F13 F14 F15 F16 F17 F18 F19

F11

F12

F13

F14

F15

F16

F17

F18

F19

Weight

1

2 1

2 2 1

2 2 1/2 1

2 2 1/2 1 1

1 1/2 1/3 1/3 1/3 1

3 2 1 2 2 3 1

2 1 1/2 1/2 1/2 2 1/2 1

2 2 2 2 2 3 1 2 1

0.186 0.125 0.065 0.088 0.088 0.213 0.057 0.125 0.053

The consistency ratio of the pairwise comparison matrix is found to be 0.0188, and as the value is under 0.10, we conclude that the comparison matrix is consistent. Table A3 Pairwise comparison matrix of the metabolic subsystem.

F20 F21 F22 F23

F20

F21

F22

F23

Weight

1

1/2 1

2 2 1

1 2 1/2 1

0.234 0.393 0.139 0.234

The consistency ratio of the pairwise comparison matrix is found to be 0.0226, and as the value is under 0.10, we conclude that the comparison matrix is consistent. Table A4 Pairwise comparison matrix of the recycling subsystem.

F24 F25 F26 F27 F28

57

F24

F25

F26

F27

F28

Weight

1

1/2 1

1/3 1/2 1

1/2 2 3 1

1/3 1 2 1/2 1

0.087 0.204 0.364 0.124 0.221

The consistency ratio of the pairwise comparison matrix is found to be 0.0174, and as the value is under 0.10, we conclude that the comparison matrix is consistent.

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