Evaluation of city sustainability using the deviation maximization method

Sustainable Cities and Society 50 (2019) 101529

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Evaluation of city sustainability using the deviation maximization method ⁎

T

Pingtao Yi, Qiankun Dong , Weiwei Li School of Business Administration, Northeastern University, Shenyang 110167, China

A R T I C LE I N FO

A B S T R A C T

Keywords: City sustainability Sustainability evaluation Multi-attribute decision making Objective indicators weights Deviation maximization method

With the rapid urbanization process, city sustainability is a hot topic for scholars. Effective evaluation of city sustainability is an important aspect of city sustainability. This paper evaluated the sustainability of the 17 cities in Shandong province, China. A set of 21 indicators were selected from economic, social and environmental dimensions. The indicator weights were calculated using the deviation maximization (DM) method to highlight the overall difference among the performance values of alternatives. It is found that the sustainability levels of the cities in Shandong province were not ideal. Only two cities’ average performance values (accounting for 11.76%) were over 3.5 in the year 2012–2016. In addition, cities located in East Shandong had the best performance and in West Shandong showed the worst performance. The average performances of all the cities, except for Dongying, Qingdao and Weihai, were below 1 on economic sustainability. The performances of social sustainability were very uncoordinated, with the highest value being 2.18 and the lowest −1.94. In addition to Dongying and Weihai, all the cities were at same level on environmental sustainability, but 7 cities (accounting for 41.8%) showed negative growth. This case should arouse the attention of local authorities.

1. Introduction City is the product of industrial, commercial and social development. It serves as a "social bond" that transcends family or clan. The city population accounted for only 2% of the world's population in 1800, but it was more than 50% in 2008 (Wu, Xiang, & Zhao, 2014). With the continuous urbanization worldwide, urban sustainability has gradually become an important part of society development and taken a central stage in both science and policy arenas. United Nations Centre for Human Settlements (Habitat) (1997) defined sustainable city as a city where achievements in social, economic, and physical development are made to last and where there is a lasting supply of the natural resources on which its development depends. During the past two decades, many researches have been developed about city sustainability (Bullock, Brereton, & Bailey, 2017; Madu, Kuei, & Lee, 2017; Orazalin & Mahmood, 2018). Cheng and Hu (2010) indicated that the changes in city planning are being made to switch to sustainability, with new cities being designed to be ecologically friendly. The sustainability of natural ecosystems is an important prerequisite and a feasible method for the achievement of regional sustainable development because natural ecosystems provide the material basis and fundamental support for regional sustainable development (Peng, Wang, Wu, Shen, & Pan, 2011). Dempsey, Bramley, Power, and Brown (2011) argued that sustainability no longer was regarded as an environmental issue, but also

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included social dimension that is identified and discussed in equitable access and the sustainability of the community itself. In general, a widely accepted approach used to measure city sustainability should encompass three dimensions: environment, economy and society based on the concept of triple-bottom-line (Elkington, 1998). City sustainability evaluation is an important safeguard for city sustainable development, which help city managers or authorities clearly understand the current sustainable level of cities on which a reasonable development plan is usually based. Many models and frameworks have been developed for evaluating the sustainability level of a city. Hunt, Jefferson, and Rogers (2011) used the ‘Urban Futures’ toolkit that explored the uses of underground space within 4 future scenarios to evaluate the sustainability of the underground space usage in UK. Ding, Zhong, Shearmur, Zhang, and Huisingh (2015) outlined an inclusive model, entitled Trinity of Cities' Sustainability from Spatial, Logical and Time Dimensions (TCS-SLTD), for assessing the sustainability of cities in developing country. Based on the dynamic change of sustainability of urban regeneration and urgency of urban regeneration, a general decision-making framework was developed for dynamic monitoring city regeneration (Peng, Lai, Li, & Zhang, 2015). In addition, another widely used approach is about regarding city sustainability evaluation as a multi-criteria decision-making (MCDM) problem (United Nations, 2007), since the sustainability indicators are generally multi-dimensional and some of them may not be easily measured. Zhang, Xu, Yeh,

Corresponding author. E-mail addresses: [email protected] (P. Yi), [email protected] (Q. Dong), [email protected] (W. Li).

https://doi.org/10.1016/j.scs.2019.101529 Received 24 January 2019; Received in revised form 1 April 2019; Accepted 1 April 2019 Available online 03 April 2019 2210-6707/ © 2019 Elsevier Ltd. All rights reserved.

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method for the evaluation of city sustainability to distinguish the performances of alternatives (cities) as much as possible. The validity of the method was illustrated by comparing with that of the variation coefficient method (VCM), the entropy method and the equal weight method, shown in Section 4. The rest of the paper is organized as follows. Section 2 points out the importance of city sustainable development in China, and introduces the study case in the paper. Section 3 introduces the primary methods used in the paper, including the construction of the indicator system and the DM methods. The evaluation results and the main findings are shown in Section 4. Conclusions and suggestions are outlined in Section 5. Section 6 discusses the problems and solutions in the process of city sustainability in China.

Liu, and Zhou (2016) developed an objective weighting approach equipped with a novel optimization model to determine the weights of independent indicators in the context of MCDM. Mulliner, Malys, and Maliene (2016) presented a comparison of six different MCDM approaches for the purpose of assessing sustainable housing affordability in the case of Liverpoor, England, and demonstrated that none of the MCDM methods was considered to be ‘perfect’. Pohekar and Ramachandran (2004) pointed out that MCDM techniques were widely used in energy sustainability. Abdullahi, Pradhan, and Jebur (2014) applied MCDM and Bayes theorems for overall compactness assessment when analyzing sustainability of Kajang city (Malaysia). Zolfani, Pourhossein, Yazdani, and Zavadskas (2017) proposed a hybrid MCDM model to evaluate environmental sustainability of construction projects of hotels using an MCDM framework. In the MCDM process, even though all of the steps, including normalization, weighting, aggregation (upscaling) and others (Huang, Wu, & Yan, 2015), may affect the evaluation result, the weighting approach seems to be the most important. In the literature, many methods that have been proposed to determine indicator weight. (e.g. Hwang & Lin, 2012; Hwang & Yoon, 1981; Liu, Chan, & Ran, 2016). The methods can be primarily divided into two categories. One is a subjective weighting method. In the weighting process, this method generally considers a decision maker’s subjective judgement given by his/her own experience or preference. Subjective weighting methods primarily include Delphi (Rowe & Wright, 1999; Scheibe, Skutsch, & Schofer, 2002), analytic hierarchy process (AHP) (Saaty, 1990, 2013), radio weighting (Ahn, 2011), etc. The characteristic of the subjective weighting methods is that the information hidden in the data does not need to be considered. In addition, the decision maker’s judgement or attitude may be easily integrated in the weighting process. The other one is an objective weighting method that usually identifies the weight by the indicators values objectively. The typical objective weighting methods mainly includes the principal component analysis method (Wold, Esbensen, & Geladi, 1987; Preisendorfer & Mobley, 1988), the entropy method (Kapur, Sahoo, & Wong, 1985; Zou, Yun, & Sun, 2006), the variation coefficient method (VCM) (Lu et al., 2016), etc. The methods mentioned above emphasize the local difference among the performance values of the alternatives with respect to a certain indicator. In this case, the larger difference among the performance values, the larger weight will be given to the associated indicator. It can be seen that these objective weights reflect the difference among indicators, rather than the overall difference of the alternatives. However, in real world application, the ranking of the alternatives or the selection of the optimal alternative is based on the overall difference of the alternatives. To this problem, Guo (2007) proposed the deviation maximization (DM) method, giving a larger weight to the indicator that makes a greater contribution to enlarge the deviation of the final performances of alternatives. In this paper, the DM method was selected as the basic

2. Study case At present, China is facing many problems, such as lack of resource, ecological damage, increased unemployment, and others, caused by the rapid development of economy. Sustainability development, aiming at coordinating economic and social development with limited resources, is one of the important development strategies in China. To improve the sustainability level of traditional resource-based cities, The State Council of China published the file entitled “Sustainable Development Plan for Resource-Based Cities in China (2013–2020)” (Lu et al., 2016). The sustainable development of city agglomeration in China has regional differences, but overall, city sustainability is increasing. Yang, Xu, and Shi (2017) pointed that the sustainable performances of the eastern cities in mainland China were the best, while the sustainable performances in the west region were the poorest. Liang, Zhang, Chen, and Deng (2016) pointed out that the sustainable development capacity of Suzhou was strongest in Jiangsu province, China, as it had advantages over the other cities in environmental construction, social security and economic development. Li, Yi, and Zhang (2018) presented that the cities in northeast China, expect for Shenyang, Dalian, Changchun and Harbin, had a low level of sustainability. This paper aimed at investigating the sustainable development of the cities in Shandong province, which had the third largest economy and the second largest population in China. Located on the eastern coastal areas of China, Shandong province lies between longitudes 114°47´ E and 122°42´ E and between latitudes 34°22´ N and 38°24´ N. Its total area is 155,800 km2. Shandong had 100,058,300 inhabitants in 2017. The gross regional domestic product was 72,678 billion yuan in 2017, which is 7.4% higher than that in 2016. Shandong faces Japan and North Korea across the sea. It is the birthplace of Confucian culture. Many famous ideologists in Chinese history were born here. Shandong province is in the rapid overall development with the further implementation of China’s reform and opening-up. There are 17 cities located in Shandong province, with two

Fig. 1. Locations of the 17 cities in Shandong province, China. Note: Laiwu merged into Jinan in January 2019. 2

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3. Methods

Table 1 Specifications of the 17 cities in Shandong province, China. City

Population

Area (km2)

Annual average temperature (℃)

Annual rainfall (mm)

Percapita GDP (Yuan)

Jinan Qingdao Zibo Zaozhuang Dongying Yantai Weifang Jining Taian Weihai Rizhao Laiwu Linyi Dezhou Liaocheng Binzhou Heze

7,233,100 9,29,500 4,708,400 3,915,600 2,132,100 7,089,400 9,363,000 8,354,400 5,637,400 2,825,600 2,997,200 1,375,800 11,240,000 5,795,800 6,036,800 3,942,500 8,736,000

8,177 11,282 5,965 4,563 8,243 13,748 15,859 11,187 7,761 5,797 5,359 2,246 17,191 10,356 8,715 9,453 12,238

15.4 13.9 14.2 15.4 14.5 13.3 14.4 15.7 14.5 13.8 14.1 14.4 14.9 14.8 14.1 13.8 15.7

1008.2 484.2 671.5 912.3 572.0 531.1 574.7 833.1 789.3 516.6 669.3 780.5 919.2 525.8 666.8 606.6 735.5

90,999 109,407 94,587 54,984 164,024 98,388 59,275 51,662 59,027 114,220 62,357 51,533 38,803 50,856 47,624 63,745 29,904

The purpose of city sustainability is to achieve harmonious development of urban economy, population, resources and environment, and to ensure the sustainable development of generations (Lau, 2012; Vallance, Perkins, & Dixon, 2011). There are two main factors that influence the level of city sustainability, internal factors and external ones. Internal factors refer to the innate configuration of the city itself, such as geographical location, nature resources, climate, etc., which are the basic conditions for sustainable development of the city. Normally, these “inherences” are hard to adjust. External factors refer to a series of activities and states derived from the city itself, including population density, city size, social welfare, urban transportation, etc., which can be optimized to improve the level of city sustainability. For example, improving resource utilization and reduce pollutant emissions (Checker, 2011); optimizing urban transportation systems to increase personnel turnover (Lee, Quinn, & Rogers, 2016); investing scientifically and reasonably in education, medical treatment and public health, culture, sports, employment, and other social welfare infrastructure (Bugliarello, 2006; Ullah, Noor, & Tariq, 2018). This paper investigated city sustainability from the aspect of weak sustainability that is selecting indicators from external factors without considering the interdependences between indicators.

sub-provincial cities: Jinan and Qingdao. The locations of the cities are shown in Fig. 1, and the brief profiles of these 17 cities are shown in Table 1. Table 2 Indicator system for city sustainability. Dimension

Indicator [Code]

Unit

Property

References

Per capita GDP [C1]

Yuan

Benefit

Per capita investment in fixed asset [C2]

Yuan

Benefit

Amount of foreign capital utilized actually per capita [C3]

Yuan

Benefit

Lu et al., 2016; Zhang et al., 2016; Ding, Shao, Zhang, Xu, & Wu, 2016; Tan & Lu, 2016; Xu, Wang, Zhou, Wang, & Liu, 2016; Li et al., 2018. Lu et al., 2016; Zhang et al., 2016; Liang et al., 2016; Li et al., 2018; Yi, Li, & Li, 2018. Li et al., 2018; Yi et al., 2018.

Proportion of GDP generated by the serve industry [C4]

%

Benefit

Proportion of GDP generated by valueadded of industry [C5] Ratio of total export-import volume and GDP [C6] Growth value of retail sales of consumer goods [C7] Natural growth value of population [C8] Urbanization rate [C9]

%

Benefit

% % % %

Benefit Benefit Benefit Benefit

Registered urban unemployment rate [C10]

%

Cost

Beds of medical institutions per 10,000 people [C11]

Unit

Benefit

Coverage rate of old-age insurance [C12] Proportion of government budgetary expenditure generated by the education [C13] Proportion of government budgetary expenditure generated by the science and technology [C14] Per capita green area [C15] Per capita water resource [C16]

% %

Benefit Benefit

Zhang et al., 2016; Ding et al., 2016; Tan & Lu, 2016; Xu et al., 2016; Li et al., 2018; Yi et al., 2018. Lu et al., 2016; Tan & Lu, 2016; Xu et al., 2016; Li et al., 2018. Lu et al., 2016; Zhang et al., 2016; Tan & Lu, 2016; Li et al., 2018; Yi et al., 2018. Lu et al., 2016; Zhang et al., 2016; Ding et al., 2016; Xu et al., 2016; Li et al., 2018. Lu et al., 2016; Zhang et al., 2016; Li et al., 2018; Yi et al., 2018. Zhang et al., 2016; Ding et al., 2016; Tan & Lu, 2016; Li et al., 2018. Lu et al., 2016; Zhang et al., 2016; Ding et al., 2016; Tan & Lu, 2016; Xu et al., 2016; Li et al., 2018; Yi et al., 2018. Zhang et al., 2016; Ding et al., 2016; Tan & Lu, 2016; Xu et al., 2016; Li et al., 2018; Yi et al., 2018. Liang et al., 2016; Ding et al., 2016; Yi et al., 2018. Lu et al., 2016; Liang et al., 2016; Ding et al., 2016; Xu et al., 2016; Li et al., 2018.

%

Benefit

Lu et al., 2016; Ding et al., 2016; Xu et al., 2016; Li et al., 2018; Yi et al., 2018.

m2 m3

Benefit Benefit

Growth value of energy consumption per 10,000-yuan GDP [C17] Proportion of government budgetary expenditure generated by environmental protection [C18] Ratio of industrial solid wastes comprehensively utilized [C19] Industrial soot and dust emissions [C20]

%

Benefit

Lu et al., 2016; Zhang et al., 2016; Liang et al., 2016; Xu et al., 2016; Li et al., 2018; Zhang et al., 2016; Ding et al., 2016; Tan & Lu, 2016; Xu et al., 2016; Li et al., 2018; Yi et al., 2018. Tan & Lu, 2016.

%

Benefit

Lu et al., 2016; Zhang et al., 2016; Tan & Lu, 2016; Li et al., 2018; Yi et al., 2018.

%

Benefit

Ton

Cost

10,000 Ton

Cost

Zhang et al., 2016; Liang et al., 2016; Ding et al., 2016; Tan & Lu, 2016; Li et al., 2018. Lu et al., 2016; Zhang et al., 2016; Ding et al., 2016; Tan & Lu, 2016; Li et al., 2018; Yi et al., 2018. Lu et al., 2016; Zhang et al., 2016; Ding et al., 2016; Tan & Lu, 2016; Li et al., 2018; Yi et al., 2018.

Economy

Society

Environment

Industrial waste water emissions [C21]

3

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3.1. Indicator system

yi = w1xi1 + w2 xi 2 + ... + wm xim

The purpose of building an indicator system is to decompose an abstract, fuzzy problem into several specific operational sub-problems. And the result of decomposition is to obtain the indicators supported by data. As a MCDM problem, the evaluation of city sustainability is a complicated and contradictory process since the establishment of the indicator system is different when facing with different problems. In this study, the indicator system, including 27 indicators, was developed by combining the literature reviews about indicators investigation for measuring city sustainability with local conditions in Shandong province. Strictly speaking, the selected indicators cannot completely reflect the depth and variety of factors affecting urban sustainability, because the selection process was fettered by the availability of data. The selected indicators were divided into economy, society, and environment dimensions, as shown in Table 2. Economic sustainability serves as the guarantee of city sustainability. It should not only consider the quantity of economic growth, but also the quality of economic development. As one of important indicator of economic sustainability, C1 directly reflects the economic level of an individual city; C2 shows the capability of enlarging reproduction and readjusting the economic structure; C3 and C6 were chosen for reflecting the economic openness of an individual city; C4 and C5 reveal the development of service industry and industry structure, respectively; C7 was selected to reflect the people’s consumption level and purchasing power of social commodities. Social sustainability considers the basic demands of contemporary people as well as the development of future generations. It focuses on the population of cities, the quality of human life, the development of education, etc. C8 represents the trend and speed of natural population growth; C9 shows the degree of people’s concentration in the city; C10 indicates the state of unemployment and social stability; C11 and C12 was selected to measure the situation of people’s health care and old-age insurance, respectively; C13 and C14 indicate the development of education and technology, which promotes the construction of civilized society. Environmental sustainability serves as the basis of city sustainability, which primarily pays attention to environmental protection, urban greening construction, pollution controls and treatment. C15 represents the situation of green area within a city; C16 shows the possession of water source within a city; C17 reflects the efficiency of translation energy consumption to economic profits; C18 represents the funding used for environmental protection; C19 reveals the status of solid waste disposal; C20 and C21 show the emission level of water and air pollution caused by industrial development, respectively.

(1)

where yi is the performance value of the ith alternative. The purpose of determining the indicator weight is to maximize the difference among the performance values of alternatives. The difference can be represented by the variance of the performance values, such as:

1 n

s2 =

n

∑ (yi − y¯)2 = i=1

1 n

n

∑ yi2 − y¯2 i=1

(2)

Based on this, a programming model is developed to determine the indicator weight by maximizing the variance of the performance values. The optimization model is given as: n

1 n

max

∑ (yi − y¯)2

(3)

i=1

m

s. t .

⎧ ⎪∑ wi = 1 , i = 1, 2, 3, ..., m ⎨ i=1 ⎪ min(wi ) ≥ 0 ⎩

(4)

By solving the Model (3)–(4), the individual indicator weight can be obtained. 3.3. Extension of the DM method Without loss of generality, assume that the indicators are all efficiency indicator. Firstly, use the Eq. (5) to normalize the indicator value as follows,

pij =

x ij − x¯ij sj

(5)

where pij is normalized indicator value; x¯ij , sj denote the mean value and the standard deviation of the jth indicator, respectively. Additionally, the Eq. (1) can be written as follows: y = Aw

y x11 ⎡ 1⎤ y2 ⎥, A = ⎡ where y = ⎢ ... ⎢ x...21 ⎢ ⎥ ⎢x ⎣ n1 ⎣ yn ⎦ Putting the Eq. (6) into ns2 = wT AT Aw = wT Hw

(6)

x12 x22 ... x n2 Eq.

... x1m w1 ⎤ ⎤ ... x2m ⎥, w = ⎡ w2 ⎥ . ⎢ ... ... ... ⎢ wn ⎥ ... x nm ⎥ ⎦ ⎣ ⎦ (2), it can get the following formula: (7)

where H = AT A, is a real symmetric matrix. Then, a programming model is defined to obtain the indicator weights:

3.2. The deviation maximization (DM) method Assume that there are n alternatives denoted as s1, s1,…, s1, and each alternative is measured by m indicators, denoted as a1, a2, …, am. The actual performance value of alternative si with respect to indicator aj is recorded as xij (i =1, 2,.., n; j =1, 2,…, m). Let wj denote the weight of indicator aj. From a geometric perspective, the n alternatives can be regarded as n points in the m- dimensional evaluation space composed of m indicators. The process of obtaining the evaluation values of n alternatives is equivalent to projecting these n points into a one-dimensional space. Based on this, Guo (2007) proposed an objective weighting method, the deviation maximization (DM) method. The essence of the method is that giving the appropriate weights to the associated indicators to make the difference among the performance values of alternatives as large as possible. Without loss of generality, assume that the indicators are all efficiency and normalized, then the DM method is described as following. The weighted sum model (WSM), one of the simplest and most commonly used MCDM methods (Liu, Wang, & Zhang, 2009), is selected for the aggregation of indicator values, such that:

max wT Hw

(8)

T

(9)

s.t. w w = 1

By solving the Model (8)–(9), it gets the following conclusions. Conclusion 1: If the w is the eigenvector of the largest eigenvalue of the matrix H, the objective function (8) gets the maximum value. Conclusion 2: If all the elements in matrix H are greater than 0, there is a sole positive maximum eigenvalue λmax and an associated positive eigenvector (if do not consider positive integer multiples). 3.4. Evaluation process The evaluation process represents information flow and information combination among indicators and alternatives, as well as the subjective and objective information integration. Its goal is to provide an evaluation value, choice, sorting and in most cases an order of alternatives, from the most preferred to the least preferred option (Liou & Tzeng, 2012; Zavadskas & Turskis, 2011). The basic procedure involved with solving evaluation problem includes: defining the purpose of the 4

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4.2. Findings Form Table 4 to Fig. 3, the following conclusions can be drawn: (1) The sustainability of the 17 cities in Shandong province was not ideal. Only 7 cities’ performance values were over 0 (accounting for 41.1%), and two cities’ performances (Weihai and Dongying) were above 3.5 (accounting for 11.76%). (2) The top three cities were Weihai, Dongying and Qingdao with performance values of 4.143, 3.903 and 2.771, respectively. In addition, the last three were Heze, Liaocheng and Linyi with performance values of −3.701, −2.415 and −2.056, respectively. (3) The sustainability of the 17 cities showed various degree of increase in 2016 compared with 2012. The sustainability of Rizhao, Qingdao, Yantai, Weihai, Binzhou, Jinan, Zibo and Zaozhuang showed sustainable growth during 2012–2016, whereas the other cities were in a fluctuating growth trend. Dongying, Zibo, Yantai and Heze had the largest average annual growth values, such as 0.697, 0.569, 0.520, and 0.512, respectively. (4) The cities in East Shandong showed the highest sustainability level. The cities with the worst performances located in West Shandong with the average performance values being 1.36, 0.64 and −2.28, respectively.

Fig. 2. City sustainability evaluation process.

evaluation; determining relevant indicators and alternatives; obtaining the indicator weight; choosing or constructing an aggregation model; calculating the evaluation value and sorting. Based on the procedures, the evaluation process of city sustainability was shown in Fig. 2.

4. Results and findings 4.1. Evaluation results The data of the indicators listed in Table 2 were collected from the Shandong province Statistical Yearbook (2013–2017) and China City Statistical Yearbook (2013–2017). By implementing the process shown in the Section 3, it obtained the weights of the indicators and the sustainability performances of the 17 cities in Shandong province, China, as shown in Tables 3 and 4, respectively. To more clearly observe the change of the performance values of the cities in different years, the graphic representation about the sustainability development trend of the cities in the year 2012–2016 is shown in Fig. 3. To illustrate the validity of the method used in this paper, the performances obtained by the DM method were compared with that of the VCM (Lu et al., 2016), the entropy method (Zou et al., 2006) and equal weight methods (the weight of each indicator is 1/21). The results are shown in Table 5. It can be seen from the Table 5: (1) the DM method, the VCM and equal weight method obtained the same optimal selection that is Weihai ranked first and Heze ranked last. The ranking of the other cities obtained by the four methods was not completely the same, but the fluctuation range was not large. It demonstrates the validity of the DM method when it is used for the evaluation of city sustainability. (2) The range of the average values calculated by the DM method was far greater than that calculated by the other three methods, such as 7.844, 0.003, 0.015 and 0.06, respectively.In addition, the standard deviation of the average values obtained by the DM method was 2.261, whereas the values of the other three methods were 0.001, 0.005 and 0.002, respectively. It indicates that the DM highlights the difference among the performance values greatly compared with the other objective weighting methods.

The 17 cities in Shandong province were classified into four clusters by the average values and growth values, shown in Fig. 4. The cities located in Cluster I have higher sustainability levels, with average values over 2.5 and growth values over 0.45. The cities located in Cluster Ⅱ have higher average values but showed lower growth values. The cities located in Cluster Ⅲ have poor sustainability level with the lower growth values and lower average values. The cities located in Cluster Ⅳ have higher growth values but lower average values, which indicates a potential upside of the cities In Fig. 4, only two cities (accounting for 11.76%), Dongying and Weihai, located in Cluster I. In Cluster Ⅱ, there was one city, accounting for 5.88%. Eight cities located in Cluster Ⅲ, among which six cities’ average values were blow 0, but seven cities’ growth values were over 0.2. The cities in Cluster Ⅲ needed to pay more attention to their sustainability development, since they had lower average values and lower growth values. Six cities located in Cluster Ⅳ, accounting for 35.29%. Although the cities in Cluster Ⅳ had lower sustainability levels, they showed comparatively higher growth values and had a better development momentum. In order to further analyze the reasons why sustainability difference among different cities is great, the sustainability performance (average performance values in 2012–2016) of the 17 cities with respect to the economic, social and environmental dimensions were calculated, as shown in Fig. 5. On economic sustainability, coastal cities performed better, with the average performance values of Dongying, Qingdao and Weihai being 2.15, 1.62 and 1.21, respectively. Inland cities had poor performances whose average performance values were generally below 0. Especially for Heze, its average performance value was close to -1.5. The main reason is that the coastal cities are rich in natural resource, balanced in industrial structure, convenient for import and export trade. Social sustainability was the most uneven in Shandong province. Weihai, Jinan, Qingdao and Zibo had comparatively better performance, whereas Heze, Liaocheng, Dezhou, Linyi and Rizhao performed poorly on social sustainability. The environmental sustainability of the cities, except for Dongying and Weihai, was at the same level. These results indicate that the cities with poor sustainability levels (such as Heze, Liaocheng, Dezhou, Linyi and Jining) should firstly give more attention to the development of economic and social sustainability, while the cities with high sustainability (such as Dongying, Yantai, Weihai, Qingdao and Jinan) should further improve the environmental sustainability. The paper further calculated the sustainability growth values of the cities on three dimensions, as shown in Table 6. In terms of economic

Table 3 The weights of indicators for city sustainability. Economy

Weight

Society

Weight

Environment

Weight

C1 C2 C3 C4 C5 C6 C7

0.3889 0.3773 0.2477 0.1432 0.1271 0.1089 −0.1217

C8 C9 C10 C11 C12 C13 C14

−0.1952 0.3491 0.1666 0.3106 −0.3573 −0.1388 0.2571

C15 C16 C17 C18 C19 C20 C21

0.1619 −0.0151 −0.0034 0.0078 −0.0172 0.2049 0.1253

5

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Table 4 The sustainability performances of 17 cities in Shandong province, China. City

Jinan Qingdao Zibo Zaozhuang Dongying Yantai Weifang Jining Taian Weihai Rizhao Laiwu Linyi Dezhou Liaocheng Binzhou Heze

Performance Value

Growth value

2012

2013

2014

2015

2016

1.178 2.078 −0.115 −2.832 1.620 0.293 −1.298 −2.739 −1.779 2.982 −2.117 −0.292 −2.716 −3.014 −3.510 −0.601 −4.819

1.354 2.334 1.124 −1.633 4.042 1.182 −0.894 −1.957 −0.457 3.879 −0.946 0.715 −1.968 −1.763 −2.266 −0.417 −3.605

1.571 2.715 1.337 −1.439 4.452 1.393 0.157 −2.224 −0.638 4.022 −0.699 0.419 −2.192 −2.092 −2.369 −0.424 −4.110

2.158 3.097 1.902 −1.120 4.992 1.971 −0.656 −1.283 −0.251 4.859 −0.239 0.579 −1.877 −1.104 −2.120 0.107 −3.203

2.382 3.633 2.162 −0.950 4.407 2.373 −0.959 −1.150 −0.047 4.972 −0.201 0.821 −1.527 −1.101 −1.810 0.280 −2.770

a

0.301 0.389 0.569 0.471 0.697 0.520 0.085 0.397 0.433 0.498 0.479 0.278 0.297 0.478 0.425 0.220 0.512

Average value

1.729 2.771 1.282 −1.595 3.903 1.442 −0.730 −1.871 −0.635 4.143 −0.840 0.448 −2.056 −1.815 −2.415 −0.211 −3.701

b

Ranking

c

4 3 6 12 2 5 10 14 9 1 11 7 15 13 16 8 17

Note: a Growth value represents the average annual growth of the performance value, which was calculated by (yi (2016) – yi (2012)) / 4; b Average value is the mean of the performance values in the year 2012–2016; c Ranking was obtained by the average values associated.

more reference about the development status and trends of the cities sustainability. The evaluation results indicate that the sustainability of the 17 cities in Shandong was not ideal. Only two cities’ performance values (accounting for 11.76%) were over 3.5 in the year 2012–2016. However, the sustainability of all the cities showed various degree of increase in 2016 compared to 2012, which indicates the city sustainability in Shandong had better development momentum. The performances of the cities located in East Shandong was better than that in Middle Shandong and better than that in the West Shandong. In terms of the sustainable development on three dimensions, the difference of the cities’ performance was larger on economic and social sustainability. The environmental performances of the cities, except for Weihai and Dongying, were at the same level. Based on the findings above, some suggestions were given to improve the economic and social sustainability of Shandong province and to realize the coordinated development of the three dimensions. Moreover, it should increase the growth values, especially the growth values of environmental sustainability, on the premise of coordinated development. There is a phenomenon that city development excessively pursues the economic development with consumption of lots of natural resource and emission of amounts of industrial waste, leading to the disruption of city environment. This case should arouse the attention of local authorities.

sustainability, all of the cities had positive growth values. Especially, Weihai and Dongying had the largest growth values of 0.451 and 0.447, respectively. It indicates that the cities in Shandong province showed sustainable economic development. In terms of social sustainability, only 3 cities (accounting for 17.65%) had negative growth values, that were Qingdao (−0.018), Weifang (−0.012) and Laiwu (−0.021) respectively. However, 7 cities (accounting for 41.18%) had negative growth values of environmental sustainability. One main reason is that the development of some cities’ economy is at the expense of the environmental sustainability. It implies that more attention should be payed to the ecological environment protection when improving cities’ economic development.

5. Conclusions and suggestions This paper evaluated the sustainability of the 17 cities in Shandong province, China. It considered 21 sustainability indicators from economy, society and environment dimensions based on the three-pillar model. The indicator weights were calculated using the deviation maximization (DM) method so as to emphasize the overall difference among the performance values of all cities, rather than the local difference between indicator values. The evaluation results provided us

Fig. 3. Change in the performance values of the 17 cities in the year 2012–2016. 6

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Table 5 The average values obtained by the DM method and other three methods. City

Jinan Qingdao Zibo Zaozhuang Dongying Yantai Weifang Jining Taian Weihai Rizhao Laiwu Linyi Dezhou Liaocheng Binzhou Heze Range a Standard deviation

the DM method

the VCM

the entropy method

equal weight method

Average value

Ranking

Average value

Ranking

Average value

Ranking

Average value

Ranking

1.729 2.771 1.282 −1.595 3.903 1.442 −0.730 −1.871 −0.635 4.143 −0.840 0.448 −2.056 −1.815 −2.415 −0.211 −3.701 7.844 2.261

4 3 6 12 2 5 10 14 9 1 11 7 15 13 16 8 17

0.012 0.012 0.012 0.011 0.013 0.012 0.013 0.011 0.011 0.013 0.012 0.011 0.012 0.011 0.011 0.011 0.010 0.003 0.001

9 5 4 16 2 7 3 14 10 1 6 11 8 13 15 12 17

0.010 0.022 0.010 0.007 0.018 0.015 0.014 0.009 0.009 0.019 0.017 0.008 0.008 0.008 0.009 0.011 0.007 0.015 0.005

8 1 9 17 3 5 6 12 11 2 4 14 15 13 10 7 16

0.011 0.014 0.012 0.010 0.015 0.013 0.013 0.011 0.011 0.015 0.013 0.010 0.011 0.011 0.011 0.012 0.009 0.006 0.002

9 3 7 16 2 6 5 14 13 1 4 15 12 11 10 8 17

b

Note: a Range represents the floating range of the average values, calculated by max(yi)-min(yi); b standard deviation shows the degree of dispersion of the average values.

burden to urban resource and environment, which has a significant impact on social economy, policies and culture. (2) Resource shortage. The current situation in most cities is that per capita amount of resources is small, utilization rate of resources is low and consumption mode of production activities is unreasonable. All of these obstruct the development of cities. (3) Serious environmental pollution. Most cities pursuit of the rapid economic development at the expense of the surrounding ecological environment. Environmental pollution has spread from urban areas to rural areas, with the scope of pollution expanding and the degree of pollution worsening. What should it do when facing these problems? In the process of city development, the government should play a leading role. The government can perfect legal system to punish the behaviors that pollute urban environment and destroy urban ecology. Additionally, there are also policy incentives for environmentalists. In order to alleviate the sharp increase in residents’ survival competition caused by the large population, the government should pay attention to people’s livelihood, improve the social welfare and guarantee the living standards of grassroots people. The ultimate purpose of sustainable development is the sustainable development of human beings. Public participation in city sustainability should be encouraged, which can promote the effective implementation of government policies about city sustainability. The development of city sustainability is not independent and should be linked to other cities or regions, or even other countries. For example, the “South-to-North Water Diversion Project” and the “West-to-East Natural Gas Transmission Project” in China. Two cities with complementary resources can get what they need from each other and use resources more efficiently. Nowadays, the trend of economic globalization and information globalization is also conducive to the sustainable development of cities.

Fig. 4. Classification of cities by the average values and growth values.

Fig. 5. Comparison of the sustainability of the 17 cities on three dimensions.

Acknowledgements

6. Discussion

This research is supported by the National Natural Science Foundation of China (71671031, 71701040), the Humanities and Social Sciences Foundation of Chinese Ministry of Education (17YJC630067), and the Fundamental Research Funds for the Central Universities of China (N170604004). Special thanks to the reviewers for their valuable comments.

In the research process, it is find that the following problems are restricting the sustainable development of cities in China. (1) High population pressure. There is a trend in recent years that rural population has continuously been flowing to city. Large population brings 7

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Table 6 Growth values of cities’ sustainability on three dimensions. City

Jinan Qingdao Zibo Zaozhuang Dongying Yantai Weifang Jining Taian

Growth value

City

Economy

Society

Environment

0.156 0.312 0.301 0.249 0.447 0.336 0.100 0.254 0.239

0.132 −0.018 0.237 0.199 0.167 0.162 −0.012 0.100 0.159

0.013 0.095 0.030 0.022 0.083 0.022 −0.004 0.044 0.036

Weihai Rizhao Laiwu Linyi Dezhou Liaocheng Binzhou Heze

References

Growth value Economy

Society

Environment

0.451 0.209 0.242 0.144 0.237 0.252 0.248 0.211

0.129 0.286 −0.021 0.170 0.261 0.127 0.024 0.310

−0.082 −0.016 0.058 −0.016 −0.020 0.046 −0.052 −0.008

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