Evaluation of City Sustainability from the Perspective of Behavioral Guidance

sustainability

Article Evaluation of City Sustainability from the Perspective of Behavioral Guidance

Ying Zhou 1,*, Weiwei Li 2 , Pingtao Yi 2 and Chengju Gong 3

1 School of Management, Shenyang Jianzhu University, Shenyang 110168, China 2 School of Business Administration, Northeastern University, Shenyang 110168, China; [email protected] (W.L.); [email protected] (P.Y.) 3 School of Economics and Management, Harbin Engineering University, Harbin 150001, China; [email protected] * Correspondence: [email protected]

Received: 8 November 2019; Accepted: 28 November 2019; Published: 30 November 2019

Abstract: High-quality evaluation of city sustainability is an important part of city policy making and development. In this paper, we evaluated the sustainability of the 14 cities in Liaoning, China, from 2015 to 2017. Based on the comprehensive consideration of the interactions among the social, economic and environmental systems, the traditional evaluation indicator system is refined. We incorporate the attitude of decision makers into the evaluation model and propose an objective weighting method by considering data distribution to objectively guide the cities to develop towards the established goals. The empirical research results show that cities located in eastern Liaoning performed the best and in western Liaoning performed the worst. The performances of the 14 cities in Liaoning were not perfect. Both the evaluation values and growth rates of 7 cities (accounting for 50.00%) were lower than the overall average level. The evaluation values of the three systems of the 14 cities were not balanced. The evaluation values of the social, economic and environmental systems fluctuated within the range of [0.0159, 0.0346], [0.0151, 0.0677] and [0.0123, 0.0483], respectively. The social and economic systems of most cities supplied more for the environmental system than for the other system. Cities with higher environmental base rankings offered less supply to other systems. At the same time, we also provide some individualized and concrete suggestions for the guidance of city sustainable development. By comparing the empirical data with the reality, it confirms the credibility of the method and the recommendations in this paper.

Keywords: city sustainability; city sustainability evaluation; data distribution analysis; behavioral guidance weight; multi-criteria decision-making

1. Introduction The emergence of cities is a sign of human maturity and civilization, as well as an advanced form of human social life. Urbanization has become a global trend with the development of social productivity, the advancement of science and technology, and the adjustment of the industrial structure. In fact, urbanization has both positive and negative effects on cities’ social, economic and environmental systems. Therefore, city sustainability is an important part of urbanization. Related research is gaining increasing attention and prevalence worldwide [1–18], such as work on the sustainable city [1,2], smart city [3–5] and smart sustainable city [7–9]. Bibri et al. argue that sustainable cities can be defined as a city environment designed with the aim of contributing to improved environmental quality, social equity and well–being over the longrun [1]. Chourabi et al. define a smart city as a city that strives to become more efficient, livable, equitable and sustainable [3]. Although there are many works of research literature on the smart city, the European Smart Cities

Sustainability 2019, 11, 6808; doi:10.3390/su11236808 www.mdpi.com/journal/sustainability Sustainability 2019, 11, 6808 2 of 17

Ranking developed by Giffinger et al. is widely used [5]. According to D’Auria [6], both concepts of sustainable city and smart city cannot be thought of as contrasting because they share many commonalities. The attention focused on social, economic and environmental issues has framed the debate over sustainability and converged in both concepts. The smart sustainable city is a new techno–urban phenomenon that was widely used in the mid-2010s [7]. It is rapidly gaining momentum as a holistic approach to city development and academic pursuit, especially in ecologically and technologically advanced societies [8,9]. Furthermore, sustainability evaluation is an essential part of city sustainability research, because the evaluation can support the next sustainable development policy while monitoring the status of the objects [10–18]. Yi et al. develop the deviation maximization weighting method to observe the sustainable levels of 17 cities in Shandong Province, China [11]. Several studies on city sustainability evaluation can be found, and the related literature can be grouped into three categories, i.e., evaluation indicators selection [19–28], indicators’ weights setting [29–43] and information aggregation [44–52]. A detailed review of the related work is shown in Section2. Prior studies have made significant contributions to city sustainability. However, existing studies have not considered the interaction among different systems in the construction of evaluation indicators. At the same time, the thoughts of the evaluator on behavioral guidance are not introduced in the objective weighting methods. In this paper, we try to find corresponding solutions based on the above problems. We focus on the evaluation of city sustainability in Liaoning Province from the perspective of behavioral guidance. There are two main research goals of our research. On the one hand, through the evaluation, each city evaluation stakeholder can comprehend the sustainable development level of their city from multiple perspectives, such as the dimension of systems’ sustainability, the sub-dimension of the system base, and its supply to other systems. Simultaneously, it is convenient to compare with other cities or their evaluation values in different years. On the other hand, the idea of behavioral guidance is integrated into the evaluation model, which aims to objectively guide cities to develop towards the goals established. The rest of this paper is organized as follows. Section2 provides an overview of the related work on city sustainability evaluation. Section3 constructs the evaluation indicator system for city sustainability, which considers the base of each system and its interaction with other systems, that is, it refines the traditional evaluation indicator system. Section4 proposes an evaluation method with behavioral guidance. An objective weighting method with behavioral guidance is proposed by considering the data distribution. Section5 conducts an empirical study on the sustainable level of 14 cities in Liaoning Province. In Section6, conclusions and suggestions are outlined.

2. Related Work on City Sustainability Evaluation The research on city sustainability evaluation can be mainly divided into three categories, namely, evaluation indicators selection, indicators’ weights setting and information aggregation. For city sustainability evaluation, the selection of sustainable indicators is a key factor influencing the conclusions of the evaluation. The three-pole or three-pillar model that combines social system, economic system, and environment system together are widely used because of its concise and comprehensive features [19–23]. Ding et al. indicate that the sustainable development of the city is a harmonious and comprehensive development of the three elements [24]. Among them, environmental sustainable development is the foundation, economic sustainable development is the guarantee, and social sustainable development is the ultimate goal. Sun et al. use social, economic and natural indicators to monitor the sustainable development of cities and applied it to data from 2000 to 2010 for 277 Chinese cities, including megalopolises, large cities, small cities and medium-sized cities [25]. Because the data acquisition channels are different, there will be some differences in the evaluation indicators of different countries and cities [26–28]. The determination of the indicators’ weights is also an important part of the rationality of city sustainability evaluation. The weighting method can be divided into three categories: Sustainability 2019, 11, 6808 3 of 17 subjective weighting method [29–32], objective weighting method [33–36] and combined weighting method [37–39]. The subjective weighting method, such as the Delphi method [40], analytic hierarchy process (AHP) method [41], etc., reflects the subjective judgment and intuition of the evaluator. However in these methods the objectivity and reproducibility of the evaluation result are relatively poor. The objective weighting method, such as the entropy method [42], deviation maximization method [43], etc., uses relatively perfect mathematical theories and methods, but does not consider the subjective information of the evaluator. In order to simultaneously embody the advantages of the two kinds of methods mentioned above, the combination weighting method has been proposed and widely applied. The information aggregation method in multi-criteria decision making (MCDM) can be widely used to aggregate the evaluation indicator values and the indicator weights together, so as to obtain the corresponding sustainable performances of cities. There are many methods to aggregate information, among which simple additive weighting (SAW) [44] is commonly used. Other aggregation methods are used in city sustainability evaluation as well, such as the weighted geometric average (WGA) method [45], the order preference by similarity to ideal situation (TOPSIS) method [46–48] and others [49–52]. Different methods embodies different evaluation purposes, such as highlighting the advantage indicator, highlighting the disadvantage indicator, reflecting the distance between the indicator and the optimal or worst indicator, etc. The existing studies have made significant contributions to city sustainability evaluation. In line with the previous studies, we also use the WGA method for information aggregation. However, considering the interaction of social, economic and natural systems, we refine the traditional evaluation indicators system to improve the quality of evaluation. In addition, a new weight method is proposed from the perspective of behavioral guidance, which can match the real problems better.

3. Design of Evaluation Indicator System Based on the evaluation indicator system construction framework, we can design an evaluation indicator system to measure the sustainable level of cities from the perspective of measuring city sustainability.

3.1. Framework of the Evaluation Indicator System Due to the interaction of the social, economic and environmental systems of the city, the sustainability of the three systems should be considered when measuring the city sustainability. It can be seen from Figure1 that the dotted frame contains three interacting systems, namely social system, economic system and environmental system. The corresponding interactions are as follows. The economic system provides corresponding funds and technologies for the sustainability of the social system, while the social system provides the necessary knowledge and services for the economic system. In order to achieve the sustainability of the economic system, it is necessary to obtain the corresponding resources and natural environment from the environmental system. However, the pollutants produced by the economic system have an adverse impact on the environmental system. Therefore, it is necessary to invest the funds into the environmental system to improve its state. The environmental system provides the resources and natural environment for the social system. At the same time, the environmental services provided by the social system promote the sustainability of the environmental system, but the pollutants produced by the social system enter the environmental system. Sustainability 2019, 11, 6808 4 of 17 Sustainability 2019, 11, x FOR PEER REVIEW 4 of 18

Services Pollutants Knowledge Funds Services Pollutants Social Economic Environmental system system system Funds Resources Technologies Natural environment

Resources Natural environment

Social system Economic system Environmental sustainability sustainability system sustainability

· Social base · Economic base · Environmental · Society supply to · Economy supply base economy to society · Environment · Society supply to · Economy supply supply to society environment to environment · Environment supply to economy

City sustainability evaluation indicator framework

FigureFigure 1. 1.Frame Frame diagramdiagram ofof evaluationevaluation indicatorindicator system.system. Therefore, there is no clear boundary between the three systems of the city, and it is diﬃcult Therefore, there is no clear boundary between the three systems of the city, and it is difficult to to quantify it in practical application research. In order to avoid the impact of this situation on quantify it in practical application research. In order to avoid the impact of this situation on the the evaluation results, this paper takes the sustainability of each system as the starting point, evaluation results, this paper takes the sustainability of each system as the starting point, comprehensively considers the supply situation of each system to the other systems in the dotted comprehensively considers the supply situation of each system to the other systems in the dotted frame, and establishes the framework of evaluation indicator system for city sustainability as shown frame, and establishes the framework of evaluation indicator system for city sustainability as shown in Figure1. By constructing this interactive evaluation indicator system framework, the evaluated in Figure 1. By constructing this interactive evaluation indicator system framework, the evaluated objects of each city can realize that the evaluation value of each system is related not only to the basic objects of each city can realize that the evaluation value of each system is related not only to the basic operational level but also the contributions made to the other systems. For example, the sustainability operational level but also the contributions made to the other systems. For example, the of city social system can be decomposed into three aspects: social base, society supply to economy sustainability of city social system can be decomposed into three aspects: social base, society supply and society supply to environment. The social base refers to the operational state of the social system. to economy and society supply to environment. The social base refers to the operational state of the The society supply to economy refers to the knowledge and services provided by the social system for social system. The society supply to economy refers to the knowledge and services provided by the the economic system. The society supply to environment refers to the services and pollutants that social system for the economic system. The society supply to environment refers to the services and the social system exports to the environmental system. It can be seen that the sustainable level of a pollutants that the social system exports to the environmental system. It can be seen that the social system is not only related to the basic state of the system, but also related to the supply to the sustainable level of a social system is not only related to the basic state of the system, but also related economic and environmental system. to the supply to the economic and environmental system. 3.2. Selection of Evaluation Indicators 3.2. Selection of Evaluation Indicators Based on the above indicator framework, we selected 21 evaluation indicators by consulting Based on the above indicator framework, we selected 21 evaluation indicators by consulting recent policy documents issued by China and relevant references on city sustainability evaluation, recent policy documents issued by China and relevant references on city sustainability evaluation, as as shown in Table1. The following content is the reasons for selecting these evaluation indicators. shown in Table 1. The following content is the reasons for selecting these evaluation indicators.

Table 1. City sustainability evaluation indicators.

Dimension Sub-dimension Indicator[Code] Unit Property Natural growth rate of population [C1] % Benefit Social Per capita public library inventory [C2] Volume Benefit Base system sustainability Number of beds in medical institutions per 1,000 Set Benefit population [C3]

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Table 1. City sustainability evaluation indicators.

Dimension Sub-Dimension Indicator [Code] Unit Property Natural growth rate of % Benefit population [C ] Base 1 Per capita public library Volume Benefit inventory [C ] Social 2 Number of beds in medical system Set Benefit institutions per 1000 population [C ] sustainability 3 Proportion of research and % Benefit Supply to economy development (R&D) staff [C4] Proportion of employees [C5] % Benefit

Supply to Per capita wastewater discharge [C6] Ton Cost environment Number of sanitation vehicles per Set Beneﬁt capita [C7]

Per capita GDP [C8] Yuan Benefit Base Total import and export per Dollar Benefit capita [C9] Funds of financial institutions per Yuan Benefit capita [C10] Economic system Average wages of unretired Yuan Benefit sustainability Supply to society employees [C11] Per capita R&D expenditure [C12] Yuan Benefit Proportion of financial expenditure on energy conservation and % Benefit Supply to environmental protection [C ] environment 13 Proportion of fixed assets investment in the environment and facilities % Benefit management industry [C14]

Air quality index [C15] Cost Base Per capita garden and green space m2 Beneﬁt area [C16] Environmental Per capita water resources [C17] m3 Beneﬁt system Dust emissions per unit area [C ] Ton Cost sustainability Supply to society 18 Industrial exhaust emissions per unit m3 Cost area [C19] Dust emissions per unit GDP [C ] Ton Cost Supply to economy 20 Industrial exhaust emissions per unit m3 Cost GDP [C21]

(1) Social system sustainability

The evaluation indicators of social base refer to human living conditions. C1 refers to the ratio of the natural population increase to the average population during the determined period. For most cities, the larger the population, the more attractive the city is. C2 is an important indicator to measure the state of a learning society. The development of C2 is conducive to the social system to adapt to the trend of the knowledge age. C3 is a reference indicator for judging the development level of medical care in the social system. The knowledge and services provided by the society to economy are mainly created by the workers of the social system, and then pass to the economic system, which bring certain economic profits. C4 refers to the proportion of full time research and experimental development personnel to the total population. The growing number of scientific and technological talents in the social system is of great benefit to the economic system that is increasingly driven by innovation. The reason for choosing C5 is that employees can obtain wages or other forms of labor remuneration by providing knowledge Sustainability 2019, 11, 6808 6 of 17 and services in the social system. The incomes can promote the sustainability of the economic system to a certain extent. Services and pollutants created by individuals or businesses from the social system will have a positive and negative impact on the environmental system, respectively. The larger the value of C6, the more serious the pollution to the environment. The basic reason for the shortage of city water resources is that the social system cycle of water exceeds the carrying capacity of the environmental system. C7 refers to the special vehicles and equipment used for environmental sanitation operations or monitoring. The improvement of city sanitation in the social system can provide better services for the city’s appearance in the environmental system. (2) Economic system sustainability The evaluation indicators of the economic base are mainly composed of economic accounting, foreign trade and finance. C8 is an effective tool to control the operation of the economic system and an important indicator for economic accounting. C9 equals the sum of imports and exports goods in proportion to the permanent population. It can reflect the total scale of a city in terms of foreign trade. C10 refers to the amount of deposits and loans from financial institutions at the end of the year. The various funds, provided by the economic system, can bring certain benefits to the social system. C11 reflects the residents’ happiness of the social system and the value created by the employed personnel while embodying the status of the economic system. C12 refers to the per capital expenditures of the entire society for the basic research, applied research and experimental development during the statistical year. The improvement of this indicator can promote the upgrading of traditional industries in social systems and the development of strategic emerging industries. Funds and pollutants output by the economic system are indicators that change the state of the environmental system. C13 is the ratio of energy saving and environmental protection financial expenditure to local general public budget expenditure. C14 is calculated by the ratio of the fixed assets investment in the environmental and facilities management industry to the total investment in the environment and facilities management industry. C13 and C14 are used to reflect the efforts of the local government on environmental protection and environmental management. (3) Environmental system sustainability The evaluation indicators of the environmental base are composed of natural elements such as air, land and water environment. C15 is based on the environmental air quality standards and the impact of various pollutants on human health, ecology and nature. It simplifies the concentration of several air pollutants monitored routinely into a single conceptual index form. C16 includes public green space, residential green space, affiliated green space, protective green space, production green space, road green space and scenic forest land area. C17 is an important indicator reflecting water resource in the cities. The supply of environment to society refers to the use of resources and natural environment exported by the environmental system to change the benefits of the social system. C18 is calculated by dust emissions divided by city area and C19 equals industrial exhaust emissions divided by city area. These two indicators are provided by the environmental system, which indirectly affects the sustainable development of the social system. The supply of environment to the economy refers to the use of resources and natural environment exported by the environmental system to change the profit indicators of the economic system. C20 equals dust emissions divided by regional GDP and C21 equals industrial exhaust emissions divided by regional GDP. These two indicators are provided by the environmental system, which indirectly affects the sustainable development of the social system. Moreover, they are the key indicators that indirectly promote the profits of the economic system. It should be noted that there are two reasons why some indicators are not included in the evaluation indicator table. On the one hand, the climatic conditions and natural resource stocks of some Sustainability 2019, 11, 6808 7 of 17 cities are different and cannot be changed, such as non-renewable energy (oil, natural gas, coal storage), sudden environmental events, natural disasters. Therefore, these relevant indicators are not selected. On the other hand, some evaluation indicators are excluded because their optimal range cannot be quantified accurately. For example, the higher electricity consumption does not necessarily mean that the economy system is better, perhaps because there is no electricity saving. The smaller consumption does not necessarily mean that the city is saving electricity. Perhaps the city has fewer start-up projects, that is why the demand for electricity is small.

4. Evaluation Method Without loss of generality, let x (i = 1, 2, , n; j = 1, 2, , m) denote the actual performance ij ··· ··· value of alternative oi on indicator uj and wj represent the weight of indicator uj. The value of xij can be obtained by consulting the statistical yearbook and the website of the Statistics Bureau. However, it is necessary to preprocess xij in order to improve the comparability of the indicators and the evaluation results. On this basis, we can set the weight wj with behavioral guidance eﬀect by analyzing the distribution of the preprocessed xij.

4.1. Preprocess Data The indicator values in Table1 may be negative, such as the population growth rate indicator and the per capita wastewater discharge indicator, so the negative indicator needs to be first converted into a positive indicator. Let xij∗ denote the positive indicator value, then   xij, xij 0 x =  ≥ (1) ij∗  (x x )/(x+ x ) , x < 0  ij − −j ij − −j ij + + = where xj is the upper limit value of uj and x−j is the lower limit value of uj. Usually, we assume xj 2 and x = 2. Because of the inconsistent property of the indicators in Table1, we transform cost-type −j − indicators into benefit indicators. Suppose xij± is the indicator value of unified property, then   x∗ , i f uj is a bene f it indicator x =  ij (2) ij±   1/xij∗ , i f uj is a cost indicator We can then use the normalization method to range the indicator value in an unit scale, [0, 1], such that X o n x = x±/ x± (3) ij ij i=1 ij o where xij is the preprocessed indicator value. In this way, the larger the value of the indicator, the better it will be, which is convenient for the comparison of the evaluation values of the cities.

4.2. Data Distribution Analysis

We can explore the overall operational status of uj by analyzing the value distribution of each oi with respect to uj. Skewness coefficient is an important statistic variable to measure the asymmetry of data distribution. It was first proposed by statistician Karl Pearson in 1895. Suppose skj represents the skewness coefficient of uj, then

o X x xj n n ij − skj = ( ) (4) (n 1)(n 2) i=1 sj − − o P P o 2 where x = n xo /n and s = n (xo x ) /(n 1). The skewness coeﬃcient can be explained j i=1 ij j i=1 ij − j − as follows: Sustainability 2019, 11, 6808 8 of 17

When skj = 0, the data distribution of the indicator uj is symmetrical. It indicates that the data distribution of each evaluated object with respect to uj is balanced. If skj , 0, it means the distribution is asymmetrical. If sk > 1 or sk < 1, it is considered as a severe skewed distribution. That is, the value j j − of the u is very uneven and a few alternatives develop very well or very poorly. If 0.5 sk 1 or j ≤ j ≤ 1 sk 0.5, it is considered as a moderately skewed distribution. The closer sk is to 0, the lower − ≤ j ≤ − j the degree of skew. In addition, skj < 0 and skj > 0 represent negative skewness and positive skewness, o respectively. The xj of the negative skewness distribution is less than the median, the probability o of data with larger values is higher than that with smaller values. The xj of the positive skewness distribution is less than the median, the probability of data with smaller values is higher than that with larger values.

4.3. Determination of Behavioral Guidance Weight

We use yi to represent the sustainable evaluation value of the alternative oi, such that = Pm o o yi j=1 wjxij. It can be seen that the value of yi is determined by wj and xij. In order to guarantee o the fairness of the evaluation, wj is generally given by the evaluator. xij can be improved by the efforts of alternative oi. Under normal circumstances, alternative oi will choose the indicator corresponding to the larger wj when the effort of oi is constant. Therefore, we can guide the behavior of oi by adjusting wj. For example, for most cities, if a certain indicator has a large value, it means that the indicator is easier to upgrade. Therefore, cities with small values of this indicator should focus on improvement. We should give this indicator a larger weight value in order to guide the low-scoring cities to raise this indicator and the high-scoring cities to maintain this indicator. In contrast, if the overall value of a certain indicator in the system is relatively small, it indicates that it is harder to upgrade. In order to promote the overall improvement of the indicator, encourage high-scoring cities to create differences and low-cost cities to continue their efforts, we should also increase the weight of the indicator. In the above two cases, it can be found that the differences are relatively large when comparing the single indicator skewness coefficient with the average skewness coefficient of all indicators. Under normal circumstances, when the indicator value is close to the normal distribution, its weight is relatively small. That is, when the indicator distribution is normally distributed, the indicator operation is relatively stable. From the perspective of behavioral guidance, the need for improvement is relatively small. At this time, the difference is relatively small when comparing the single indicator skewness coefficient with the average skewness coefficient of all indicators. Therefore we can use the following formula to calculate the behavioral guidance weight, such that

η+ X θ + wj = α skj skj/(η η− + 1) (5) − − j=η−

+ where η− is the initial number of the system and η is the end number of the system. For example, + when the system is a social system, η− = 1 and η = 7. Similarly, economic system corresponds to + + θ η− = 8 and η = 14, environmental system corresponds to η− = 15 and η = 21. α is the adjustment coefficient of the system and the calculating method of it will be introduced later. When θ = 1, θ = 2, θ = 3, the systems are the social system, economic system, environmental system respectively. Considering that the data distribution skewness of different systems may be different, we set three system adjustment factors. Pj=η+ In order to achieve city sustainability, the weights wj of the three systems of society, economy j=η− and environment should be the same, that is

η+ Xj= + Xj= + X Xj=m η η θ + 1 wj = α skj skj/(η η− + 1) = wj (6) j=η− j=η− − − 3 j=1 j=η− Sustainability 2019, 11, 6808 9 of 17

Pj=m = where j=1 wj 1. So we can solve the value of the adjustment coeﬃcient mentioned above, such that 1 αθ = (7) + + η Pj=η P + 3 skj skj/(η η− + 1) j=η− − j=η− − Through the behavioral guidance weighting, each system has the same total weight. Within the same system, there is a certain diﬀerence in indicator weights according to the development status of the indicator.

5. A City Sustainability Evaluation Study

5.1. Study Area Liaoning Province is a provincial administrative region of China. It is located in the northeast of China, with a boundary between 38◦430 and 43◦260 north latitude and 118◦530 to 125◦460 east longitude. Liaoning Province is the only coastal province in northeast China. It has a number of strategic industries that are related to the lifeline of the national economy and national security. As an old industrial province, Liaoning Province once had the best infrastructure in China and a large amount of investment in the era of the planned economy. However, the plight of Liaoning Province has indeed been more obvious in recent years. With the development of China, Liaoning Province has gradually changed from a former population inﬂow place to an outﬂow place. In 2016 and 2017, the natural population growth rates were 0.18% and 0.44%, respectively. In 2016, among the growth rates of − − China’s 31 provinces, only Liaoning Province experienced negative growth. Therefore, sustainable development is an important direction for the current transformation of Liaoning Province. It is necessary to conduct sustainable evaluation and suggestion guidance in Liaoning Province. Liaoning Province has 14 prefecture-level cities, Shenyang is the provincial capital. The brief introductions of these 14 cities are shown in Table2.

Table 2. Introductions of the 14 cities in Liaoning Province, China.

a 2 a Average Location in City Population Area (km ) b Temperature (◦C) Liaoning Province Shenyang 8,294,000 12,860 3~15 North Dalian 6,988,000 12,574 9~16 South Anshan 3,598,000 9263 7~16 South Fushun 2,065,000 11,271 0~14 North Benxi 1,687,000 8414 4~15 East Dandong 2,395,000 15,290 5~15 East Jinzhou 3,050,000 10,048 6~16 West Yingkou 2,438,000 5420 6~15 South Fuxin 1,766,000 10,355 2~16 West Liaoyang 1,837,000 4788 5~16 South Panjin 1,437,000 4103 6~15 West Tieling 2,638,000 12,985 3~15 North Chaoyang 2,949,000 19,698 4~18 West Huludao 2,547,000 10,416 5~16 West a Data from the 2018 Statistical Yearbook of Liaoning Province; b Data from China Weather Website.

5.2. Evaluation Process

We collected the initial data xij associated with indicators listed in Table1 from the Liaoning Province Statistical Yearbook (2016–2018) and China City Statistical Yearbook (2016–2018). It should be noted that there are some missing data in other years corresponding to the indicators in Table1, so we only conduct empirical research on the data for the three years from 2015 to 2017. In addition, the data of C15 is obtained from the China air quality online testing and analysis website since it is Sustainability 2019, 11, 6808 10 of 17

o not involved in the statistical yearbook. By Equations (1)–(3), the preprocessed indicator value xij is o calculated, respectively. Based on xij, the skewness coeﬃcient is calculated by Equation (4). We then obtain the behavioral guidance weight wj by Equations (5)–(7), shown in Table3.

Table 3. Behavioral guidance weights for 2015–2017.

Year C1 C2 C3 C4 C5 C6 C7 2015 0.03744 0.03780 0.08751 0.01829 0.04329 0.06729 0.04172 2016 0.07566 0.04275 0.05156 0.02433 0.05374 0.04585 0.03944 2017 0.07408 0.06727 0.06388 0.00949 0.04985 0.04005 0.02870

Year C8 C9 C10 C11 C12 C13 C14 2015 0.03620 0.10791 0.02316 0.01260 0.07460 0.05587 0.02299 2016 0.00264 0.15323 0.00396 0.01079 0.10917 0.05224 0.00129 2017 0.02239 0.09613 0.02874 0.00322 0.11232 0.05083 0.01970

Year C15 C16 C17 C18 C19 C20 C21 2015 0.07060 0.10842 0.00158 0.09607 0.02571 0.00349 0.02747 2016 0.09300 0.10906 0.00881 0.04208 0.02561 0.02277 0.03199 2017 0.02488 0.12252 0.04415 0.02462 0.02095 0.05696 0.03926

The relationship between indicators’ weights of different years is shown in Figure2. There is a slight change in the position of the same color point in the figure, but the overall change is not large. The colored point represents the X and Y axes of an indicator in different years. Furthermore, we use the weights of each year as a sample to calculate the correlation coefficient between the weights in different years. The correlation coefficients of indicator weights for 2015 and 2016, 2016 and 2017, 2015 and 2017 are 0.7860, 0.7676 and 0.5797, respectively. It can be found that the correlation coefficients of the adjacent years are larger (the scatter plots for 2015 and 2016, 2016 and 2017 are more concentrated as shown in Figure2), and the correlation coe fficient between the years with longer intervals is smaller (the weighted scatter plots for 2015 and 2017 are more discrete in Figure2). This is consistent with the idea of government behavior guiding policy making in reality; that is, the focus of work will gradually change over time, but the overall level must maintain a certain degree of correlation, and the longer the interval,Sustainability the 2019 less, 11, relevant.x FOR PEER REVIEW 11 of 18

FigureFigure 2. 2.Matrix Matrix scatter plot plot of ofindicators’ indicators’ weights weights in different in diﬀ years.erent years.

5.3. Results and Findings We obtained the sustainability performances of the 14 prefecture-level cities in Liaoning m Province (2015–2017) by using y = wxo , where x o and w have been calculated above, ijij j=1 ij j = as shown in Table 4. The average evaluation value was obtained by yi = (yyyiii (2015)+ (2016)+ (2017)) / 3 ; The growth rate was calculated by yi 100%* − (yyii (2017) (2015)) / 3 .Further, Figure 3 shows the change in the sustainability values of the cities in different years.

Table 4. Performance of the 14 cities in 2015–2017.

Evaluation value Ranking Growth City 2015 2016 2017 Average 2015 2016 2017 Average Rate (100%) Shenyang 0.0829 0.0844 0.0928 0.0867 4 4 3 4 0.3307 Dalian 0.1166 0.1328 0.1314 0.1269 1 1 1 1 0.4945 Anshan 0.0546 0.0555 0.0857 0.0653 11 9 4 6 1.0387 Fushun 0.0601 0.0551 0.0512 0.0555 10 11 11 11 − 0.2991 Benxi 0.1043 0.1062 0.1061 0.1056 2 2 2 2 0.0584 Dandong 0.0675 0.0723 0.0764 0.0721 7 5 6 5 0.2965 Jinzhou 0.0517 0.0515 0.0510 0.0514 14 13 12 13 − 0.0240 Yingkou 0.0611 0.0634 0.0618 0.0621 9 7 7 7 0.0212 Fuxin 0.0521 0.0501 0.0480 0.0500 13 14 13 14 − 0.1352 Liaoyang 0.0545 0.0542 0.0530 0.0539 12 12 10 12 − 0.0490 Panjin 0.0856 0.0933 0.0849 0.0879 3 3 5 3 − 0.0207 Tieling 0.0728 0.0645 0.0426 0.0600 5 6 14 10 − 1.0066 Chaoyang 0.0716 0.0551 0.0566 0.0611 6 10 9 9 − 0.4999 Huludao 0.0646 0.0614 0.0584 0.0615 8 8 8 8 − 0.2055

Form Table 4 and Figure 2, the following conclusions can be drawn:

Sustainability 2019, 11, 6808 11 of 17

5.3. Results and Findings We obtained the sustainability performances of the 14 prefecture-level cities in Liaoning Province = Pm o o (2015–2017) by using yi j=1 wjxij, where xij and wj have been calculated above, as shown in Table4. The average evaluation value was obtained by yi =(yi(2015) + yi(2016) + yi(2017))/3; The growth rate was calculated by y = 100% (y (2017) y (2015))/3. Further, Figure3 shows the change in the i ∗ i − i sustainability values of the cities in diﬀerent years. Sustainability 2019, 11, x FOR PEER REVIEW 12 of 18 Table 4. Performance of the 14 cities in 2015–2017. (1) The top three cities were Dalian, Benxi and Panjin with average sustainability evaluation values of 0.1269, 0.1056 Evaluationand 0.0621, Value respectively. While the Ranking last three were Fuxin, Jinzhou and City Growth Liaoyang with evaluation2015 2016values of 2017 0.0500, Average 0.0514 and 2015 0.0539, 2016 respectively. 2017 Average Rate (100%) Shenyang(2) The cities 0.0829 with stable 0.0844 scores 0.0928 in the 0.0867past three 4years were 4 Jinzhou, 3 Liaoyang 4 and 0.3307 Benxi. The differencesDalian between 0.1166 their 0.1328maximum 0.1314 and minimum 0.1269 eval 1uation 1 values 1 were 0.0007, 1 0.0015 0.4945 and 0.0019, respectively.Anshan The 0.0546cities whose 0.0555 scores 0.0857 changed 0.0653 greatly 11 were Anshan, 9 4 Tieling 6 and Chaoyang, 1.0387 and the Fushun 0.0601 0.0551 0.0512 0.0555 10 11 11 11 0.2991 corresponding differences were 0.0312, 0.0302 and 0.0165, respectively. The three cities− Dalian, Benxi and HuludaoBenxi ranked 0.1043 steadily 0.1062 and 0.1061remained 0.1056 first, second 2 and 2 eighth 2 from 2015 2 to 2017, 0.0584 respectively. Dandong 0.0675 0.0723 0.0764 0.0721 7 5 6 5 0.2965 (3)Jinzhou The city in 0.0517 the rising 0.0515 ranking 0.0510 was Anshan, 0.0514 and 14 the city 13 in the 12 declining 13 ranking0.0240 was Tieling. − The citiesYingkou with continuously 0.0611 0.0634 rising 0.0618 evaluation 0.0621 values 9 were 7 Shenyang, 7 Anshan 7 and 0.0212Dandong. By contrast,Fuxin the cities 0.0521 that continue 0.0501 0.0480to decline 0.0500 were Fushun, 13 Fuxin, 14 13Liaoyang, 14 Tieling and0.1352 Huludao. − Liaoyang 0.0545 0.0542 0.0530 0.0539 12 12 10 12 0.0490 Generally speaking, the number of cities with dynamically declining evaluation values− was more Panjin 0.0856 0.0933 0.0849 0.0879 3 3 5 3 0.0207 than that with dynamically increasing values. − Tieling 0.0728 0.0645 0.0426 0.0600 5 6 14 10 1.0066 − Chaoyang(4) The average 0.0716 evaluation 0.0551 values 0.0566 of 0.0611eastern, southern, 6 10 western 9 and 9northern Liaoning0.4999 were − 0.0888,Huludao 0.0771, 0.0624 0.0646 and 0.0614 0.0674, 0.0584 respectively. 0.0615 The 8 cities 8in eastern 8 Liaoning 8 had0.2055 the highest − sustainability level, followed by southern Liaoning, northern Liaoning, and western Liaoning.

0.14 0.1328

0.12 0.1062

0.10 0.0933 0.0928 0.0857 0.0764 0.08 0.0716 0.0728 0.0634 0.0646 0.0601 0.06 0.0545 0.0517 0.0521

0.04

0.02

0.00 2015 2016 2017 2015 2016 2017 2015 2016 2017 2015 2016 2017 2015 2016 2017 2015 2016 2017 2015 2016 2017 2015 2016 2017 2015 2016 2017 2015 2016 2017 2015 2016 2017 2015 2016 2017 2015 2016 2017 2015 2016 2017 Dandong Benxi Dalian Anshan Yingkou Liaoyang Jinzhou Fuxin Chaoyang Huludao Panjin Shenyang Fushun Tieling East South West North

FigureFigure 3. 3.Change Change inin thethe performance performance values values of of the the 14 14 cities cities in in 2015–2017. 2015–2017.

FormWith Tablethe data4 and shown Figure in2 ,Table the following 4, we can conclusions compare the can 14 becities drawn: by average evaluation values and 14 growth(1) Therates, top as three shown cities in were Figure Dalian, 4. Let Benxi yy= and Panjin 14=0.0714 with average denote sustainability the overall evaluation average  i=1 i values of 0.1269, 0.1056 and 0.0621, respectively. While the last three were Fuxin, Jinzhou and Liaoyang 14 evaluation value and yy=14=0 represent the overall average growth rate. We can find with evaluation values of 0.0500,i=1 0.0514i and 0.0539, respectively. that (2)50% The of the cities cities with had stable low average scores inevaluation the past va threelues yearsand negative were Jinzhou, growth Liaoyangrates, such and as Tieling, Benxi. TheChaoyang, diﬀerences Fushun, between Huludao, their maximumFuxin, Liaoyang and minimum and Jinzhou. evaluation The evaluation values were values 0.0007, and 0.0015the growth and 0.0019,rates of respectively. Dalian, Shenyang, The cities Dandong whose and scores Benxi changed (accoun greatlyting for were 28.57%) Anshan, were Tieling higher andthan Chaoyang, the overall average level. Only Panjin had a higher average score but showed negative growth. Anshan and Yingkou had higher growth rates, but their evaluation values were lower. In order to further analyze the specific reasons for the differences of the sustainability performances of the cities, we show the average sustainability evaluation values of social, economic and environmental systems in each city in Figure 5. The social, economic, environmental evaluation 7 14 21 values are calculated by ywxSo= , ywxE = o and ywxE = o , respectively. ijij j=1 ijij j=8 ijij j=15 The corresponding average evaluation values are the weighted average of each system in different years.

Sustainability 2019, 11, 6808 12 of 17 and the corresponding differences were 0.0312, 0.0302 and 0.0165, respectively. The three cities Dalian, Benxi and Huludao ranked steadily and remained first, second and eighth from 2015 to 2017, respectively. (3) The city in the rising ranking was Anshan, and the city in the declining ranking was Tieling. The cities with continuously rising evaluation values were Shenyang, Anshan and Dandong. By contrast, the cities that continue to decline were Fushun, Fuxin, Liaoyang, Tieling and Huludao. Generally speaking, the number of cities with dynamically declining evaluation values was more than that with dynamicallySustainability 2019 increasing, 11, x FOR values.PEER REVIEW 13 of 18 (4) The average evaluation values of eastern, southern, western and northern Liaoning were 0.0888, The following conclusions can be found: The systems’ evaluation values of the 14 cities were 0.0771, 0.0624 and 0.0674, respectively. The cities in eastern Liaoning had the highest sustainability not balanced. For example, Shenyang ranked first in the social system, but seventh in the level, followed by southern Liaoning, northern Liaoning, and western Liaoning. environmental system. Dalian’s economic system ranked first, but its environmental system ranked With the data shown in Table4, we can compare the 14 cities by average evaluation values and sixth. By contrast, Benxi ranked first in theP environmental system and seventh in the social system. growth rates, as shown in Figure4. Let y = 14 y /14 = 0.0714 denote the overall average evaluation The sustainabilityP of the economic system ini= 1 Liaoningi Province differed greatly from that of the value and y = 14 y /14 = 0 represent the overall average growth rate. We can find that 50% of social system. Thei= 1 evaluationi values of the social system, economic system and environmental the cities had low average evaluation values and negative growth rates, such as Tieling, Chaoyang, system fluctuated within the range of [0.0159, 0.0346], [0.0151, 0.0677] and [0.0123, 0.0483], Fushun, Huludao, Fuxin, Liaoyang and Jinzhou. The evaluation values and the growth rates of Dalian, respectively. Most cities had different advantages with respect to various systems. Among the three Shenyang, Dandong and Benxi (accounting for 28.57%) were higher than the overall average level. systems, cities with relatively good social performance were Shenyang, Dalian and Anshan. The Only Panjin had a higher average score but showed negative growth. Anshan and Yingkou had higher economic systems of Dalian, Benxi and Yingkou were relatively good. Benxi, Panjin and Huludao growth rates, but their evaluation values were lower. performed relatively well on environmental systems.

1.5

Anshan 1

Dalian 0.5 Dandong Shenyang

rate Jinzhou Yingkou Benxi 0 Liaoyang Panjin Huludao Fuxin Fushun Growth -0.5 Chaoyang

-1 Tieling

-1.5 0.04 0.05 0.06 0.07 0.08 0.09 0.1 0.11 0.12 0.13 Average evaluation value Figure 4. Average evaluation values and growth rates of the 14 cities in 2015–2017. Figure 4. Average evaluation values and growth rates of the 14 cities in 2015–2017. In order to further analyze the specific reasons for the differences of the sustainability performances of the0.07 cities, we show the average sustainability evaluation values of social, economic and environmental social system economic system environmental system systems0.06 in each city in Figure5. The social, economic, environmental evaluation values are calculated S = P7 o E = P14 o E = P21 o by yi j=1 wjxij, yi j=8 wjxij and yi j=15 wjxij, respectively. The corresponding average evaluation0.05 values are the weighted average of each system in different years. The following conclusions can be found: The systems’ evaluation values of the 14 cities were not 0.04 balanced. For example, Shenyang ranked first in the social system, but seventh in the environmental 1 system.0.03 Dalian’s economic system ranked first, but its environmental system ranked sixth. By contrast, Benxi ranked first in the environmental1 system and seventh in the social system. The sustainability of 0.02 1 2 the economic system2 in3 Liaoning Province differed greatly from that of the social2 system. The evaluation3 5 3 4 4 4 6 5 5 7 6 values0.01of the social system,7 economic7 system8 6 and environmental9 11 8 system10 fluctuated within the range 9 13 10 10 8 12 9 13 11 11 12 14 of [0.0159, 0.0346], [0.0151, 0.0677] and [0.0123, 0.0483],14 respectively.14 Most13 cities had di12fferent 0.00 advantages with respect to various systems. Among the three systems, cities with relatively good social Shenyang Dalian Anshan Fushun Benxi Dandong Jinzhou Yingkou Fuxin Liaoyang Panjin Tieling Chaoyang Huludao

Figure 5. System evaluation values and rankings for the 14 cities.

Similarly, with Table 1, we can calculate the average evaluation values of each sub-dimension, 3 5 such as social system base ywS1 = x o , social supply to economy yS2 = w xo and social ijij  j1 i j4 j ij 7 supply to environment yS3 = w xo , as shown in Figure 6. i j6 j ij

Sustainability 2019, 11, x FOR PEER REVIEW 13 of 18

The following conclusions can be found: The systems’ evaluation values of the 14 cities were not balanced. For example, Shenyang ranked first in the social system, but seventh in the environmental system. Dalian’s economic system ranked first, but its environmental system ranked sixth. By contrast, Benxi ranked first in the environmental system and seventh in the social system. The sustainability of the economic system in Liaoning Province differed greatly from that of the social system. The evaluation values of the social system, economic system and environmental system fluctuated within the range of [0.0159, 0.0346], [0.0151, 0.0677] and [0.0123, 0.0483], respectively. Most cities had different advantages with respect to various systems. Among the three systems, cities with relatively good social performance were Shenyang, Dalian and Anshan. The economic systems of Dalian, Benxi and Yingkou were relatively good. Benxi, Panjin and Huludao performed relatively well on environmental systems.

1.5

Anshan 1

Da lia n 0.5 Dandong Shenyang

rate Jinzhou Yingkou Benxi 0 Liaoyang Panjin Huludao Fuxin Fushun Growth -0.5 Chaoyang

-1 Tieling

Sustainability-1.5 2019, 11, 6808 13 of 17 0.04 0.05 0.06 0.07 0.08 0.09 0.1 0.11 0.12 0.13 Average evaluation value performance were Shenyang, Dalian and Anshan. The economic systems of Dalian, Benxi and Yingkou were relativelyFigure good. 4. Average Benxi, Panjin evaluation and values Huludao and performed growth rates relatively of the 14 wellcities onin 2015–2017. environmental systems.

0.07

social system economic system environmental system 0.06

0.05

0.04

1 0.03

1 0.02 1 2 2 2 3 3 4 4 5 3 5 4 7 6 5 7 6 7 8 6 9 0.01 9 13 10 11 8 10 8 12 9 11 10 11 12 14 13 14 14 13 12 0.00 Shenyang Dalian Anshan Fushun Benxi Dandong Jinzhou Yingkou Fuxin Liaoyang Panjin Tieling Chaoyang Huludao Figure 5. System evaluation values and rankings for the 14 cities. Figure 5. System evaluation values and rankings for the 14 cities. Similarly, with Table1, we can calculate the average evaluation values of each sub-dimension, Similarly, with Table 1, weS1 =canP calculate3 o the average evaluation valuesS2 of= eachP5 sub-dimension,o such as social system base yi j=1 wjxij, social supply to economy yi j=4 wjxij and social S 3 o S 5 o suchSustainability as social 2019 system, 11, x FOR base PEERS3 y= REVIEW1 P= 7 wxo , social supply to economy y 2 = wx and social14 of 18 supply to environment yi ijijj=6 jw=1jxij, as shown in Figure6. ijij j=4 7 supply to environment y S3 = wxo , as shown in Figure 6. 0.06 ijij j=6 Environmental supply to economy Environmental supply to society Environmental base

0.05 6 5 4 0.04 14 1 13 3 0.03 2 1 3 8 2 0.02 10 10 1 7 5 9 4 12 8 9 11 2 11 7 0.01 12 6 3 13 6 14 5 4 9 7 14 10 13 12 8 11 0.070 2 Economic supply to environment Economic supply to society Economic base 0.06

0.05 1

0.04 4 1 5 0.03 14 5 11 13 1 7 10 7 4 0.02 8 2 6 8 9 3 3 12 10 14 9 0.01 2 611 5 3 12 4 13 7 10 6 9 8 13 14 12 11 0 0.06 social supply to environment social supply to economy social base

0.05 11 2 0.04 9 1 13 0.03 2 6 10 3 7 1 5 12 0.02 8 4 3 4 5 10 7 9 6 14 1 2 8 11 13 12 0.01 3 14 4 8 6 7 5 9 12 11 14 10 13 0 Shenyang Dalian Anshan Fushun Benxi Dandong Jinzhou Yingkou Fuxin Liaoyang Panjin Tieling Chaoyang Huludao Figure 6. Sub-dimension evaluation values and rankings for the 14 cities. Figure 6. Sub-dimension evaluation values and rankings for the 14 cities. The conclusions are as follows: From the perspective of social base sub-dimension, the evaluation valuesThe of Dalian,conclusions Anshan are andas Shenyangfollows: From were the higher, perspective while those of ofsocial Jinzhou, base Huludao sub-dimension, and Tieling the wereevaluation lower. values Some cities,of Dalian, such Anshan as Dalian and and Shenyang Anshan, hadwere higher higher, social while base those rankings, of Jinzhou, but their Huludao social systemsand Tieling supply were to lower. other systemsSome cities, ranked such lower. as Dalian The socialand Anshan, system had of most higher cities social supplied base rankings, more for thebut environmentaltheir social systems system supply (accounting to other for systems 64.29%) ra thannked for lower. the economicThe social system system (accounting of most cities for supplied more for the environmental system (accounting for 64.29%) than for the economic system (accounting for 35.71%). Compared with the ranking difference between the largest and smallest sub-dimensions of the social system (the social sub-dimension difference of Chaoyang is 12), the ranking difference of the sub-dimensions of the economic system was relatively small. Similar to the social system, the economic system of most cities supplied more for the environmental system (accounting for 57.12%) than for the social system (accounting for 42.86%). Cities with higher environmental base rankings, such as Benxi, Dandong and Fushun, had a relatively poor social and economic base ranking. The higher-ranking cities offered relatively less supply to other systems than those with lower-rankings, such as Panjin and Jinzhou.

Sustainability 2019, 11, 6808 14 of 17

35.71%). Compared with the ranking difference between the largest and smallest sub-dimensions of the social system (the social sub-dimension difference of Chaoyang is 12), the ranking difference of the sub-dimensions of the economic system was relatively small. Similar to the social system, the economic system of most cities supplied more for the environmental system (accounting for 57.12%) than for the social system (accounting for 42.86%). Cities with higher environmental base rankings, such as Benxi, Dandong and Fushun, had a relatively poor social and economic base ranking. The higher-ranking cities offered relatively less supply to other systems than those with lower-rankings, such as Panjin and Jinzhou.

6. Conclusions and Suggestions In this paper, we evaluated the sustainability of the 14 prefecture-level cities in Liaoning Province, China. We expanded the three-pillar model by considering the bases of social, economic and environmental systems and their interactions, and constructed an evaluation indicator system consisting of 21 indicators. We proposed a weighting method based on the data distribution form. The attitude of decision makers is incorporated in the objective weighting process. Therefore, it is possible to objectively guide the alternatives to develop toward the established goal. The empirical results provide us with more references about the status, trends and development suggestions of the cities sustainability. The sustainability evaluation results showed that the cities in eastern Liaoning (0.0888) had the highest sustainability level, while those in western Liaoning (0.0624) had the lowest level. The sustainable level of southern Liaoning (0.0771) is better than that of northern Liaoning (0.0674). The performances of the 14 cities in Liaoning were not perfect. Both the evaluation values and growth rates of 7 cities (accounting for 50.00%) were lower than the overall average level. In terms of the three dimensions, the systems’ evaluation values of the 14 cities were not balanced. The evaluation values of the social, economic and environmental systems fluctuated within the range of [0.0159, 0.0346], [0.0151, 0.0677] and [0.0123, 0.0483], respectively. There were 10 cities (accounting for 71.43%) with the maximum and minimum ranking differences greater than or equal to 5. From the perspective of sub-dimension, the social system of most cities supplied more for the environmental system (accounting for 64.29%) than for the economic system (accounting for 35.71%). Similarly, the economic system of most cities supplied more for the environmental system (accounting for 57.12%) than for the social system (accounting for 42.86%). Cities with higher environmental base rankings, such as Benxi, Dandong and Fushun, offered less supply to other systems. Based on the above conclusions, the relevant suggestions are as follows: due to the interactions of the systems, we should systematically improve the state of the 14 cities’ vulnerable systems to improve their sustainability. Taking Shenyang as an example, it can be seen from Figure5 that the sustainability of its environmental system was inferior to the other systems, which is an important factor hindering the sustainability of the city. Therefore, priority should be given to improving Shenyang’s air quality index (C15) and per capita garden and green space area (C16). The reason is that these two indicators have a larger behavioral guidance weights in the environmental system (as shown in Table3). In addition, from Figure6, it can be seen that its environmental base ranked ninth, so priority should also be given to improving the indicators under this classification, namely C15 and C16. In fact, as an important industrial city in Liaoning Province, Shenyang has a serious problem with air quality. The problem of coal burning in Shenyang is an important factor affecting the value of indicator C15. The government can strengthen the transformation of industrial and heating boilers, promote the application of new energy-saving technologies, and optimize the fuel supply structure. As a provincial capital, Shenyang has a large population, resulting in a small per capita garden and green space. The government can raise the level of indicator C16 in many aspects, such as gardened city, tree-lined roads, beautiful parks and gardened communities. Furthermore, the city underground space should be effectively exploited and matched with the ground space. The above method and the reality verify each other, which shows the feasibility and credibility of the behavioral guidance method in this paper. Similarly, other cities can use the above train of thought to ascertain strategies for sustainability. Sustainability 2019, 11, 6808 15 of 17

Some implications can be drawn from this research: (1) stakeholders of city sustainability evaluation in Liaoning Province can be acquainted with the state, trend, advantages and disadvantages of each city from multiple perspectives, such as the dimension of systems’ sustainability, the sub-dimension of the system base and its supply to the other systems; (2) due to the diﬀerences among the cities in Liaoning Province, the local authorities can use the above results to analyze and choose their own sustainability pattern. The sustainability pattern for each city is more personalized and detailed; that is, more emphasis is placed on indicators with larger behavioral guidance weights and systems with smaller scores; (3) this method is not only suitable for cities in Liaoning Province, but also for cities in other provinces. It is conducive to improving the sustainability of social, economic and environmental system in each city, and can promote the coordination of the three systems. Due to the limitations of missing data before 2015, the research span of this paper is short. In terms of future study, we plan to develop more convenient algorithms to interpolate missing data. In addition, we will consider how to eﬀectively combine the text information of related website users with the evaluation model.

Author Contributions: Writing—original draft preparation, Y.Z.; writing—review and editing, W.L.; data curation, P.Y.; supervision, C.G. Funding: This research was supported by the National Natural Science Foundation of China (71671031, 71701040, 51678375, 71901079). Acknowledgments: Special thanks to the reviewers for their valuable comments. Conﬂicts of Interest: The authors declare no conﬂict of interest.

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