# Dynamic Evaluation of Urban Sustainability Based on ELECTRE: A Case Study from China

Authors: Huimin Li; Xin Huang; Qing Xia; Zanmei Jiang; Chenchen Xu; Xiang Gu; Hui Long
Journal: Discrete Dynamics in Nature and Society
 (2021)

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## Abstract

Sustainable urban evaluation is an important management tool to grasp the status of urban development in real time and to make policy adjustments. In this study, the evaluation indicator system is constructed from the three dimensions of economy, society, and environment. The ELECTRE (elimination et choice translation reality) model based on information entropy weighting is employed to evaluate urban sustainability. The model applies three-dimensional data to explore the dynamics of sustainable urban development. The spatial measurement model is used to explore the spatial effects of cities. Finally, 17 cities in Henan Province from 2013 to 2017 are used as case studies for urban sustainable development evaluation. The results show that, in 2013–2017, the sustainability of cities such as Zhengzhou, Luoyang, and Sanmenxia was stable at a high level, while the sustainability levels of Kaifeng, Luohe, and Xinyang showed a fluctuating downward trend, and the sustainability levels of Puyang, Nanyang, and Xinxiang showed a fluctuating upward trend. Among the 17 cities, Zhengzhou has the highest sustainability level and its economic and social sustainability levels are significantly better than other cities. Zhoukou is the city with the lowest level of sustainability. In addition, from 2013 to 2016, the level of urban sustainability was not spatially correlated but gradually presented positive spatial correlation and the characteristics of clustering distribution in 2017. The cities such as Zhengzhou, Luoyang, and Jiaozuo are mainly represented by “high-high (H-H)” agglomeration. In contrast, Shangqiu, Zhoukou, and Zhumadian are mainly represented by “low-low (L-L)” agglomeration. This research provides suggestions and decision-making support for promoting urban sustainability.


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## Body

## 1. Introduction

As a carrier of human habitation, cities are closely related to people’s daily lives. It is estimated that, by 2050, 67% of the population in the world will live in cities [1]. Since the reform and opening up in the late 1970s, China has experienced rapid urbanization. Especially, in the past 30 years, China has significantly accelerated urbanization and achieved a massive rural population transferring to cities. The urbanization rate in China increased from 17.4% in 1978 to 59.58% in 2018 [2]. However, with the acceleration of urbanization, while promoting local economic prosperity and improving people’s living standards, which also brings severe ecological and social problems [3]. For example, a series of problems have appeared, such as severe air pollution, water quality degradation, energy consumption, resource scarcity, significantly increased temperature difference between urban and suburban areas, and decreased local plant species [4]. Therefore, how to maximize the resource utilization rate of urban socioeconomic activities, minimize the negative impact on the environment in the process of urban development and solve the problem of sustainable and coordinated development between economy, society, and environment in the process of urbanization are the critical research topics in the world today.In 2015, the United Nations launched the 2030 Agenda for Sustainable Development, which called on countries to take action towards the achievement of 17 sustainable development goals over the next 15 years. Among them, Goal 11 is “to build an inclusive, safe, resilient sustainable city and human settlements”. As a significant concentration of population, the sustainable development of cities has become a top priority. In the processes of sustainable urban construction, the policy decision-makers need to resort to evaluation indicators and methods to assess the status of sustainable urban development and make a comparison against other cities. They can recognize the strengths and weaknesses from perspectives of society, economics, and environment after one period, which supports them in making the development planning and correct policy interventions to guarantee the goal of sustainable urban development [5]. Therefore, the evaluation of urban sustainability is an important aspect and plays a crucial role in sustainable urban development. Many scholars have conducted extensive research on urban sustainability evaluation [5–10]. However, most of these studies adopt a single comprehensive evaluation method for static evaluation, which cannot understand the dynamic status of sustainable urban development. Therefore, to compare the level of sustainable development of each city in different periods, it is necessary to carry out the dynamic evaluation.Realistic urban evaluation of sustainability require the followings: (1) the integration of diverse information concerning economic, social, environmental, and other objectives; (2) the handling of conflicting aspects of these objectives as a function of the views and opinions of the individuals involved in the evaluation process. The evaluation of urban sustainability is, therefore, increasingly regarded as a typical decision-making problem that could be handled by multicriteria decision aid (MCDA) methods. ELECTRE (elimination et choice translation reality) is a multicriteria decision-making tool. The basic idea is to eliminate the inferior schemes by constructing a series of weak dominance relations to gradually reduce the scheme set until the decision-maker can select the most satisfactory scheme from them. ELECTRE is a solution to deal with the multicriteria decision making for finite schemes. Because of its easy understanding principle, concise and straightforward logic, and good interactivity, it has been widely used in many areas [11, 12]. Kaya and Kahraman [13] combined the AHP (analytic hierarchy process) and ELECTRE methods for environmental impact assessment. Hatami-Marbini et al. [14] evaluated the safety and health of hazardous waste recycling facilities using the fuzzy group ELECTRE method. Comanita et al. [15] employed the ELECTRE method to assess the economic and environmental performance of bioplastics. In the ELECTRE method set, ELECTRE II focuses on the problem of ranking solutions with explicit decision-making data. Gao and Chen [16] proposed a simplified ELECTRE II ranking model. This model not only avoided the significant differences caused by the subjectivity of the threshold in the traditional ELECTRE method but also facilitated the ranking results based on the obtaining net superiority index value. Therefore, it avoided the complicated process of ranking solutions based on the strength/weakness relationship diagram and subjectively setting multiple thresholds. Besides, the traditional ELECTRE method is suitable for the static evaluation of two-dimensional data and cannot compare the overall level of multiple systems at different periods. This study builds an urban sustainability evaluation index system, whose data are expressed by three-dimensional data. A dynamic evaluation method based on ELECTRE is proposed to evaluate the urban sustainability of 17 prefecture-level cities in Henan Province from 2013 to 2017. The method can get the evaluation result of each period for objects and the overall evaluation value and the ranking results in a certain period for objects.Henan is a typical landlocked province in China, with a large population and few resources, and rapid urbanization in recent years, highlighting the urban sustainability. Its pattern of development and the problems it encounters in the process of development is very representative of China. Therefore, it is typical to study the urban sustainability in Henan Province. The research methods and findings are typical of the nation.The remainder of the paper is organized as follows:Section 2 provides a literature review of urban sustainability and evaluation methods. Section 3 constructs the urban sustainability evaluation indicator system from three dimensions: economy, society, and environment. Section 4 introduces the material and methods. Section 5 describes the result and discussion of this paper. Section 6 presents the research conclusion, limitations, and future research of this paper.


## 2. Literature Review

### 2.1. Urban Sustainability

Since the early 1990s, the concepts of sustainability and sustainable development have been applied to urban planning and design [17], leading to the emergence of urban sustainability and sustainable urban development. United Nations Centre for Human Settlements defines a sustainable city as that has achieved sustainable development in social, economic, and physical aspects and that possesses the natural resources on which sustainable development depends. Camagni [18] defined sustainable urban development as a process of synergistic integration and co-evolution between the various subsystems (economic, social, natural, and environmental) that make up a city. It is necessary to ensure that the local population’s long-term health does not decline, does not damage the surrounding area’s development potential, and reduces the harmful effects of development on the biosphere. Hamilton et al. [19] believed that urban sustainability refers to “the process of developing a built environment that meets people’s needs while avoiding unacceptable social or environmental impacts”. Zhao [20] argued that a sustainable city can maintain and improve urban ecosystem services and provide sustainable welfare to its inhabitants. Bibri and Krogstie [21] believed that urban sustainable development strategies long-term goals, including environmental protection and integration, economic development and regeneration, and urban social equity and justice. In other words, sustainable urban development should improve the quality of life, reduce resource demand, and environmental impacts by providing healthy, livable, and prosperous human settlements to avoid burdening future generations with potential environmental degradation or ecological deficiencies. Wu et al. [22] defined urban sustainability as an adaptive process that promoted and maintained a virtual cycle between ecosystem services and human well-being by coordinating ecological, economic, and social actions to respond to changes inside and outside the city. Dias et al. [23] argued that urban sustainability was the coordinated development of three critical systems of environment, economy, and society, which provided goals, foundations, and conditions for sustainable urban development. Yang et al. [3] believed that sustainable urban development meant dividing urban systems from ecological inputs, social and economic benefits. It also included adjusting the dynamic balance between ecological investment and urban social, economic and environmental benefits, seeking maximize social, economic and environmental benefits, and pursuing the sustainable development of the city in time and space. In general, urban sustainability aims to improve long-term human well-being by balancing three aspects of sustainability: minimizing resource consumption and environmental damage, maximizing resource efficiency, and ensuring fairness and democracy [24].


### 2.2. Evaluation of Urban Sustainability

Sustainable development has gradually become a concept that governments, organizations, and industries are eager to adhere to. Evaluating sustainability quantitatively is an essential part of sustainability science research [22]. Agenda 21, adopted at the first Earth Summit in Rio DE Janeiro, Brazil, in 1992, called for the assessment methodologies of sustainable development. In the following years, many studies on sustainable development assessment have emerged [25–28].Urban sustainability evaluation can help city managers to clearly understand the current level of sustainable urban development to make reasonable development plans [29]. Simultaneously, the evaluation of urban sustainability is a complex process with a strict system, an extended period, and a broad spatial scale. Therefore, scholars have developed many models and methods for assessing urban sustainability. Dijk and Zhang [30] adopted the Urban Sustainable Development Index to measure the sustainability of four medium-sized cities in China from three aspects: urban status, urban coordination, and urban potential. Ding et al. [31] proposed an inclusive framework for sustainable city assessment in developing countries, entitled “trinity of cities sustainability from spatial, logical and time dimensions”. Zinatizadeh et al. [32] evaluated and predicted urban sustainability in different areas of Kermanshah city of Iran from three aspects of social welfare progress, economic growth, and environmental protection. Yi et al. [29] evaluated the sustainability of 17 cities in Shandong Province from three aspects of the economy, society, and environment using the deviation maximization method.In addition, multicriterion decision-making analysis (MCDA) is considered an appropriate tool for sustainability assessment by considering different sustainability perspectives, including stakeholders, values, uncertainties, and intergenerational and internal considerations [33]. MCDA consists of a set of methods that allow for the explicit consideration of multiple criteria to support individuals or groups in the ranking, selecting, and/or comparing different alternatives (e.g., products, technologies, and strategies) [34]. The most commonly used MCDA methods include AHP (analytic hierarchy process), TOPSIS (technique for order preference by similarity to an ideal solution), ELECTRE (elimination et choice translation reality), and PROMETHEE (preference ranking organization method for enrichment evaluation) [35–38]. The MCDA methods are grouped based on three underlying theories: utility function, outranking relation, and decision rules [39, 40]. Among them, theory-based utility approaches (especially, multiattribute utility theory and AHP) and outranking relation-based approaches (especially ELECTRE and PROMETHEE) are considered the most widely used MCDA tools in sustainability research [41]. In terms of urban sustainability, an increasing number of studies are beginning to evaluate the level of urban sustainability using the MCDA methodology. Ding et al. [7] used the TOPSIS-Entropy method to quantitatively evaluate the sustainable development level of 287 cities in China from three aspects of society, economy, and environment. Lu et al. [38] applied the TOPSIS method to measure and rank the sustainability of the selected 15 typical resource-based cities in northeast China. Liang et al. [8] developed a principal component analysis (PCA) and Grey TOPSIS methodology to measure urban sustainability for 13 cities in Jiangsu province from five aspects: environmental capacity, government supports, cultural entertainment, social security, and economic development. Tang et al. [5]proposed a modified TOPSIS model based on grey relational analysis to assess the sustainability of cities in three dimensions: economic, social, and ecological.From the above literature, it can be seen that the urban sustainability evaluation method based on the indicator system has been extensively studied. At the same time, since urban sustainability evaluation is a multicriteria decision problem, the MCDA approach has been widely used. However, there is a lack of dynamic assessment and spatial agglomeration analysis to test the effects of sustainable policy implementation in cities.


## 2.1. Urban Sustainability

Since the early 1990s, the concepts of sustainability and sustainable development have been applied to urban planning and design [17], leading to the emergence of urban sustainability and sustainable urban development. United Nations Centre for Human Settlements defines a sustainable city as that has achieved sustainable development in social, economic, and physical aspects and that possesses the natural resources on which sustainable development depends. Camagni [18] defined sustainable urban development as a process of synergistic integration and co-evolution between the various subsystems (economic, social, natural, and environmental) that make up a city. It is necessary to ensure that the local population’s long-term health does not decline, does not damage the surrounding area’s development potential, and reduces the harmful effects of development on the biosphere. Hamilton et al. [19] believed that urban sustainability refers to “the process of developing a built environment that meets people’s needs while avoiding unacceptable social or environmental impacts”. Zhao [20] argued that a sustainable city can maintain and improve urban ecosystem services and provide sustainable welfare to its inhabitants. Bibri and Krogstie [21] believed that urban sustainable development strategies long-term goals, including environmental protection and integration, economic development and regeneration, and urban social equity and justice. In other words, sustainable urban development should improve the quality of life, reduce resource demand, and environmental impacts by providing healthy, livable, and prosperous human settlements to avoid burdening future generations with potential environmental degradation or ecological deficiencies. Wu et al. [22] defined urban sustainability as an adaptive process that promoted and maintained a virtual cycle between ecosystem services and human well-being by coordinating ecological, economic, and social actions to respond to changes inside and outside the city. Dias et al. [23] argued that urban sustainability was the coordinated development of three critical systems of environment, economy, and society, which provided goals, foundations, and conditions for sustainable urban development. Yang et al. [3] believed that sustainable urban development meant dividing urban systems from ecological inputs, social and economic benefits. It also included adjusting the dynamic balance between ecological investment and urban social, economic and environmental benefits, seeking maximize social, economic and environmental benefits, and pursuing the sustainable development of the city in time and space. In general, urban sustainability aims to improve long-term human well-being by balancing three aspects of sustainability: minimizing resource consumption and environmental damage, maximizing resource efficiency, and ensuring fairness and democracy [24].


## 2.2. Evaluation of Urban Sustainability

Sustainable development has gradually become a concept that governments, organizations, and industries are eager to adhere to. Evaluating sustainability quantitatively is an essential part of sustainability science research [22]. Agenda 21, adopted at the first Earth Summit in Rio DE Janeiro, Brazil, in 1992, called for the assessment methodologies of sustainable development. In the following years, many studies on sustainable development assessment have emerged [25–28].Urban sustainability evaluation can help city managers to clearly understand the current level of sustainable urban development to make reasonable development plans [29]. Simultaneously, the evaluation of urban sustainability is a complex process with a strict system, an extended period, and a broad spatial scale. Therefore, scholars have developed many models and methods for assessing urban sustainability. Dijk and Zhang [30] adopted the Urban Sustainable Development Index to measure the sustainability of four medium-sized cities in China from three aspects: urban status, urban coordination, and urban potential. Ding et al. [31] proposed an inclusive framework for sustainable city assessment in developing countries, entitled “trinity of cities sustainability from spatial, logical and time dimensions”. Zinatizadeh et al. [32] evaluated and predicted urban sustainability in different areas of Kermanshah city of Iran from three aspects of social welfare progress, economic growth, and environmental protection. Yi et al. [29] evaluated the sustainability of 17 cities in Shandong Province from three aspects of the economy, society, and environment using the deviation maximization method.In addition, multicriterion decision-making analysis (MCDA) is considered an appropriate tool for sustainability assessment by considering different sustainability perspectives, including stakeholders, values, uncertainties, and intergenerational and internal considerations [33]. MCDA consists of a set of methods that allow for the explicit consideration of multiple criteria to support individuals or groups in the ranking, selecting, and/or comparing different alternatives (e.g., products, technologies, and strategies) [34]. The most commonly used MCDA methods include AHP (analytic hierarchy process), TOPSIS (technique for order preference by similarity to an ideal solution), ELECTRE (elimination et choice translation reality), and PROMETHEE (preference ranking organization method for enrichment evaluation) [35–38]. The MCDA methods are grouped based on three underlying theories: utility function, outranking relation, and decision rules [39, 40]. Among them, theory-based utility approaches (especially, multiattribute utility theory and AHP) and outranking relation-based approaches (especially ELECTRE and PROMETHEE) are considered the most widely used MCDA tools in sustainability research [41]. In terms of urban sustainability, an increasing number of studies are beginning to evaluate the level of urban sustainability using the MCDA methodology. Ding et al. [7] used the TOPSIS-Entropy method to quantitatively evaluate the sustainable development level of 287 cities in China from three aspects of society, economy, and environment. Lu et al. [38] applied the TOPSIS method to measure and rank the sustainability of the selected 15 typical resource-based cities in northeast China. Liang et al. [8] developed a principal component analysis (PCA) and Grey TOPSIS methodology to measure urban sustainability for 13 cities in Jiangsu province from five aspects: environmental capacity, government supports, cultural entertainment, social security, and economic development. Tang et al. [5]proposed a modified TOPSIS model based on grey relational analysis to assess the sustainability of cities in three dimensions: economic, social, and ecological.From the above literature, it can be seen that the urban sustainability evaluation method based on the indicator system has been extensively studied. At the same time, since urban sustainability evaluation is a multicriteria decision problem, the MCDA approach has been widely used. However, there is a lack of dynamic assessment and spatial agglomeration analysis to test the effects of sustainable policy implementation in cities.


## 3. Urban Sustainability Evaluation Indicator System

It is difficult to evaluate the sustainable development level of a city because we cannot fully grasp its development situation. However, we can have a detailed understanding of the macro situation through some micro and operable fragments of information in the system. The index system construction decomposes a complex and abstract problem into several concrete operable subsystems [29]. Based on the literature review, this study constructs an urban sustainability evaluation index system covering three dimensions of economy, society, and environment. Specific indicators are shown in Table 1.Table 1
Urban sustainability evaluation indicator system.
DimensionCodeIndicatorUnitPropertyReferencesEconomyC1GDP per capitaYuanBenefitYi et al. [29]; Tang et al. [5]; Zhang et al. [10]; Ding et al. [7]; Lu et al. [38]; Xu et al. [9]; Yi et al. [42];C2Per capita investment in fixed assetYuanBenefitYi et al. [29]; Yi et al. [42]; Lu et al. [38]; Li et al. [43];C3Amount of foreign capital utilized actually per capitaDollarBenefitYi et al. [29]; Yi et al. [42]; Li et al. [43];C4Proportion of GDP contributed by tertiary industry%BenefitYi et al. [29]; Zhang et al. [10]; Ding et al. [7];Liang et al. [8]; Lu et al. [38]; Xu et al. [9]; Yi et al. [42];C5Total import and export volume10000 DollarBenefitTang et al. [5]; Lu et al. [38];C6Retail sales of consumer goods10000 YuanBenefitTang et al. [5]; Zhang et al. [10]; Lu et al. [38];Xu et al. [9]; Yi et al. [42];C7Urbanization rate%BenefitYi et al. [29];SocietyC8Per capita disposable income of urban residentsYuanBenefitTang et al. [5]; Ding et al. [7]; Lu et al. [38]; Yi et al. [42];C9Urban unemployment rate%CostYi et al. [29]; Tang et al. [5]; Zhang et al. [10]; Ding et al. [7]; Lu et al. [38]; Xu et al. [9]; Yi et al. [42];C10Natural population growth rate‰BenefitLu et al. [38]; Yi et al. [42];C11Beds of medical institutions for per 10,000 peopleUnitBenefitYi et al. [29]; Liang et al. [8]; Xu et al. [9]; Yi et al. [42];Zhang et al. [10];C12Number of buses for per 10,000 peopleVehicleBenefitDing et al. [7]; Xu et al. [9];C13Per capita green area㎡BenefitYi et al. [29]; Lu et al. [38]; Zhang et al. [10];C14Per capita of water resourcem3BenefitYi et al. [29]; Tang et al. [5]; Zhang et al. [10];C15Coverage rate of old-age insurance%BenefitYi et al. [29]; Liang et al. [8];C16Proportion of government budgetary expenditure in education%BenefitYi et al. [29]; Ding et al. [7]; Liang et al. [8]; Lu et al. [38]; Xu et al. [9];C17Proportion of government budgetary expenditure in science and technology%BenefitYi et al. [29]; Ding et al. [7]; Lu et al. [38]; Xu et al. [9];EnvironmentC18Comprehensive utilization rate of industrial solid wastes%BenefitYi et al. [29]; Tang et al. [5]; Zhang et al. [10]; Ding et al. [7]; Liang et al. [8]; Yi et al. [42]C19Industrial soot and dust emissionsTonCostYi et al. [29]; Zhang et al. [10]; Lu et al. [38]; Yi et al. [42];C20Industrial waste water emissions10,000 TonCostYi et al. [29]; Tang et al. [5]; Zhang et al. [10]; Lu et al. [38]; Yi et al. [42];C21Green coverage rate of built-up areas%BenefitTang et al. [5]; Zhang et al. [10]; Ding et al. [7]; Liang et al. [8]; Lu et al. [38]; Xu et al. [9];C22Annual average concentration of PM2.5Ug/m³CostDing et al. [7];Han et al. [44];He et al. [45]; Xu et al. [9];C23Centralized sewage treatment rate%BenefitDing et al. [7]; Liang et al. [8]; Xu et al. [9];C24Household waste treatment rate%BenefitDing et al. [7]; Liang et al. [8];


### 3.1. Economic Indicators

Economic indicators reflect the economic development of a city. Economic development is the core issue of sustainable development for a city, which is the fundamental guarantee for social development, environmental improvement, and people’s material and cultural life quality. At the same time, economic sustainability is also a guarantee for the sustainable development of cities [29, 43]. Therefore, in this study, GDP per capita, per capita investment in fixed asset, actually per capita of foreign capital utilized, the proportion of GDP contributed by tertiary industry, total import and export volume, retail sales of consumer goods, and urbanization rate are selected as the indicators to measure the sustainable development of the urban economy.


### 3.2. Social Indicators

Social sustainability considers both the basic needs of the present and the development of future generations [29]. It is the ultimate goal of urban sustainable development [43]. Social sustainability aims to promote the continuous improvement of people’s quality of life and social spiritual civilization and provide people with a safe and comfortable living environment, good education opportunities, and social security [46]. Therefore, the establishment of social sustainability indicators should be people-oriented [5]. In this study, per capita disposable income of urban residents, urban unemployment rate, natural population growth rate, beds of medical institutions for per 10,000 people, number of buses for per 10,000 persons, per capita green area, per capita water resource, the coverage rate of old-age insurance, the proportion of government budgetary expenditure in education, and the proportion of government budgetary expenditure in science and technology are selected as the indicators to measure the sustainable social development of urban.


### 3.3. Environmental Indicators

Environmental sustainability is the foundation for urban sustainable development [43]. The low environments not only constrain the healthy development of the economy but also harm human health. Environmentally sustainable cities show a strong awareness and action for environmental protection, urban greening construction, pollution control, and treatment [29]. Therefore, in this study, the comprehensive utilization rate of industrial solid waste, industrial soot and dust emissions, industrial wastewater emissions, green coverage rate of built-up areas, annual average concentration of PM2.5, centralized sewage treatment rate, and household waste treatment rate is selected as environmental sustainability evaluation indicators.


## 3.1. Economic Indicators

Economic indicators reflect the economic development of a city. Economic development is the core issue of sustainable development for a city, which is the fundamental guarantee for social development, environmental improvement, and people’s material and cultural life quality. At the same time, economic sustainability is also a guarantee for the sustainable development of cities [29, 43]. Therefore, in this study, GDP per capita, per capita investment in fixed asset, actually per capita of foreign capital utilized, the proportion of GDP contributed by tertiary industry, total import and export volume, retail sales of consumer goods, and urbanization rate are selected as the indicators to measure the sustainable development of the urban economy.


## 3.2. Social Indicators

Social sustainability considers both the basic needs of the present and the development of future generations [29]. It is the ultimate goal of urban sustainable development [43]. Social sustainability aims to promote the continuous improvement of people’s quality of life and social spiritual civilization and provide people with a safe and comfortable living environment, good education opportunities, and social security [46]. Therefore, the establishment of social sustainability indicators should be people-oriented [5]. In this study, per capita disposable income of urban residents, urban unemployment rate, natural population growth rate, beds of medical institutions for per 10,000 people, number of buses for per 10,000 persons, per capita green area, per capita water resource, the coverage rate of old-age insurance, the proportion of government budgetary expenditure in education, and the proportion of government budgetary expenditure in science and technology are selected as the indicators to measure the sustainable social development of urban.


## 3.3. Environmental Indicators

Environmental sustainability is the foundation for urban sustainable development [43]. The low environments not only constrain the healthy development of the economy but also harm human health. Environmentally sustainable cities show a strong awareness and action for environmental protection, urban greening construction, pollution control, and treatment [29]. Therefore, in this study, the comprehensive utilization rate of industrial solid waste, industrial soot and dust emissions, industrial wastewater emissions, green coverage rate of built-up areas, annual average concentration of PM2.5, centralized sewage treatment rate, and household waste treatment rate is selected as environmental sustainability evaluation indicators.


## 4. Materials and Methods

### 4.1. Evaluation Framework

The evaluation process represents the flow and combination of information between indicators and alternatives, as well as the integration of information between subject and objective. Its goal is to provide an evaluation value, selection, and ranking. In most cases, it is ranking from the best to the least optimal option [47]. The basic procedure to solve the evaluation problem includes clarifying the purpose of evaluation, identifying relevant indicators and alternatives, obtaining indicator weights, selecting or constructing aggregation models, and calculating and ranking evaluation values. Based on this procedure, the urban sustainability evaluation processes are shown in Figure 1.Figure 1
Urban sustainability evaluation processes.


### 4.2. Research Area and Data Source

In this section, 17 cities in Henan Province are taken as the specific research area based on the economic-social-environmental framework. Based on various indices data of each city, the current situation of sustainable urban development in Henan Province is studied, which can provide a reference for the sustainable construction of each city. The distribution of cities is shown in Figure2.Figure 2
Urban distribution map of Henan Province.The rapid urbanization process of Henan Province has not only resulted in a vast increase in the demand for natural resources but also generates environmental problems. Exploring the dynamic development process of urban sustainability is crucial to improve environmental quality during the urbanization process and resultant urban development of these fast-growing regions. For this reason, Henan Province is seen as an appropriate study area to uncover the dynamic process of urban sustainability.The data of the indicators in this study were collected from the Henan Province Statistical Yearbook (2014–2018) and China City Statistical Yearbook (2014–2018). The indicator data of the 17 cities in Henan Province from 2013 to 2017 were derived.


### 4.3. The Indicator Weight Determining Method—Entropy Method

The entropy method was first developed in thermodynamics and was further introduced to measure information or uncertainty in information theory [48]. This particular approach was gradually used in social science for determining the weights in evaluating performance and sustainability [32, 48, 49]. The basic logic behind such an approach is that when the index data contains more effective information, the entropy value is smaller, and the weight would be more extensive. The specific steps of this method are as follows:Step 1. Constructing decision-making matrix: this evaluation system has m cities and n indicators. Let X=xijm×n be the decision-making matrix and xiji=1,2,3,…,m,j=1,2,3,…,n be the index value.(1)X=x11x12⋯x1nx21x22⋯x2n⋮⋮⋱⋮xm1xm2⋯xmn.Step 2. Standardizing the decision matrix: the decision matrix X=xijm×n is standardized according to the 0-1transformation, as matrix Y=yijm×n. The indicators are divided into benefit and cost types.(2)benefit indicesyij=xij−minxijmaxxij−minxij,(3)cost indicesyij=maxxij−xijmaxxij−minxij.Step 3. Calculate the entropy value ej of each indicator:(4)ej=−k∑i=1mpijlnpij.In equation (4), k=1/lnm and pij=yij/∑i=1myij,i=1,2,3,…,m,j=1,2,3,…,n. When pij=0, limpij⟶0pijlnpij=0.Calculate the coefficient of variationhj of each index. For one index, the larger the coefficient of variation h is, the smaller the ej is. The greater the impact of the index on urban sustainability is, and the larger the corresponding weight coefficient is [50]. The formula for calculating the coefficient of variation hj is as follows:(5)hj=1−ej.Determine the weight valuewj of each evaluation index.(6)wj=hj∑j=1nhj,where0≤wj≤1,j=1,2,3,…,n and ∑j=1nwj=1.


### 4.4. Dynamic Evaluation of Urban Sustainability with ELECTRE Method

The evaluation of urban sustainability was carried out using the ELECTRE method, which employs the three-dimensional time-series data to present the dynamic process of the urban sustainability. The specific steps are as follows.It is assumed that there arem alternatives, and each alternative is measured by n indicators. The evaluation value of the j th evaluation indicator of the i th alternative obtained in chronological order is xijtki=1,2,…,m;j=1,2,…,n;k=1,2,…,N.(1)
Constructing the original evaluation indicator value matrixXtk.(7)Xtk=xijtkm×n=x11tkx12tk⋯x1ntkx21tkx22tk⋯x2ntk⋮⋮⋱⋮xm1tkxm2tk⋯xmntk.(2)
Normalizing value matrix. The original evaluation indicator value matrix is normalized to obtain a normalized decision matrixX′.(8)X′tk=xij′tkm×n=x11′tkx12′tk⋯x1n′tkx21′tkx22′tk⋯x2n′tk⋮⋮⋱⋮xm1′tkxm2′tk⋯xmn′tk,Benefit indicesxij′tk=xijtk−minxijtkmaxxijtk−minxijtk,Cost indicesxij′tk=maxxijtk−xijtkmaxxijtk−minxijtk.(3)
Calculating the weighted normalized decision matrix. A weighted normalized decision-making matrix is computed by multiplying the normalized decision-making matrixX′with the weights wjtkj=1,2,…,n. The weighted normalized decision-making matrix Ytk is shown as follows:(9)Ytk=yijtkm×n=xij′tk⋅wjtk=x11′tkx12′tk⋯x1n′tkx21′tkx22′tk⋯x2n′tk⋮⋮⋱⋮xm1′tkxm2′tk⋯xmn′tkw1tk0⋯00w2tk⋯0⋮⋮⋱⋮00⋯wntk=x11′tkw1tkx12′tkw2tk⋯x1n′tkwntkx21′tkw1tkx22′tkw2tk⋯x2n′tkwntk⋮⋮⋱⋮xm1′tkw1tkxm2′tkw2tk⋯xmn′tkwntk=y11tky12tk⋯y1ntky21tky22tk⋯y2ntk⋮⋮⋱⋮ym1tkym2tk⋯ymntk.(4)
Determining the concordance matrixCtk and discordance matrix Dtk.


#### 4.4.1. Determining the Concordance Set and the Discordance Set

Any two different rows are compared in the weighted normalized decision-making matrixY. If the value of y in the i th row and j th column is higher than the value of y in l th row, the indicator represented by j th column would be classified into the concordance set; otherwise, it would be classified into the discordance set.


#### 4.4.2. Calculating the Concordance Matrix

The concordance matrixCtk can be obtained by adding the weights of the indicators in each consistency set.(10)Ctk=ciltkm×m,i=1,2,…,m;l=1,2,…,m;k=1,2,…,N,ciltk=∑j∈Cil′tkwj+0.5∑j∈Cil″tkwj∑j=1nwj,j=1,2,…,n.In equation (10), ciltki=1,2,…,m;l=1,2,…,m;k=1,2,…,N represents the relative advantage index of alternative i over alternative l at tk moment. Cil′tk=j|yijtk>yljtkj=1,2,…,n;k=1,2,…,N means that the value of y in the i th row and the j th column has a higher preference than the value of y in the l th row..Cil″tk=j|yijtk=yljtkj=1,2,…,n;k=1,2,tk,N represents that the preference of y in the i th row and the j th column is equal to the preference of y in the l row.


#### 4.4.3. Determining the Discordance MatrixDtk

The relative disadvantage index of the two alternatives is as follows:(11)Dtk=diltkm×m,i=1,2,…,m;l=1,2,…,m;k=1,2,…,N,diltk=maxj∈Dil′tkyijtk−yljtkmaxl∈Syijtk−yljtk,j=1,2,…,n,Dil′tk=j|yijtk<yljtk,S=1,2,…,n.In equation (11), diltki=1,2,…,m;l=1,2,…,m;k=1,2,…,N represents the relative disadvantage index of alternative i over the alternative l at tk moment. Comparing with ciltk that which only contains the indicator weight information. diltk represents the difference between the weighted indicator values, which not only contains the weight information but also contains the information of the indicator value itself. Therefore, there is no complementarity between the relative advantage index and the relative disadvantage index. The greater the value of diltk is, the more likely i is to be inferior to l.


#### 4.4.4. Revising the Discordance Matrix

According to the idea proposed by Sun [51], the discordance matrix D′tkis revised and shown in the following equation:(12)D′tk=dil′tkm×mdil′tk=1−diltk.The largerdil′tk means that the alternative i may be more inferior to alternative l at tkmoment.


#### 4.4.5. Calculating the Revised Weighted Aggregation MatrixEtk

The elements in the revised discordance matrix are the same as the elements in the concordance matrix. The higher the value is, the higher the degree of preference is. Therefore, the revised weighted aggregate matrixEtk can be obtained by multiplying the elements in the corresponding positions of the concordance matrix. The revised discordance matrix [16] is as follows:(13)Etk=eiltkm×m,i=1,2,…,m;l=1,2,…,m;k=1,2,…,N,eiltk=ciltk⋅dil′tk.


#### 4.4.6. Calculating the Comprehensive Evaluation Valueuitk

This study applies the concept of net advantage value as follows [52]:(14)uitk=∑l=1l≠imeiltk−∑l=1l≠imelitk,i=1,2,…,m.The net advantage valueui is the difference between the sum of the advantage indexes of alternative Xi to other schemes and the sum of the advantage indexes of other alternatives to Xi, which reflects the degree of the advantage of Xi in the alternative set. The larger the ui is, the more advantageous alternatives the Xi will be.


#### 4.4.7. Ranking the Alternatives

The alternatives are ranked according to the overall evaluation value ofuitk, with the greater the uitk meaning of the higher rank.This study determines time weights using the method that the closer to the present, the more important the weight; conversely, the further from the present, the smaller is the weight. Within the time intervalt1,tN, the time weight at tk moment is wk.(15)wk=k∑k=1Nk,∑k=1Nwk=1,wk>0.The comprehensive evaluation value of alternativei in the time interval t1,tN is gi.(16)gi=∑k=1Nwkuitk.Finally, the alternatives are ranked according togi.


### 4.5. The Spatial Econometric Model

In this study, the spatial autocorrelation model is used to study the spatial dependence or spatial correlation between the sustainability level and the geographical location of cities. The spatial autocorrelation model can be divided into global spatial autocorrelation and local spatial autocorrelation. Global spatial autocorrelation adopts the global Moran indexI to test whether there is an interaction between the sustainability levels of cities in a particular area.(17)I=n∑i=1n∑j=1nwijxi−x¯xj−x¯∑i=1n∑j=1nwij∑i=1nxi−x¯2.The value of the global Moran indexI is between −1 and 1. I>0 represents the positive spatial correlation; I<0 represents the negative spatial correlation; I=0 represents spatial randomness. n is the number of cities in Henan Province. xi and xj are the sustainability value of each city.x¯ is the mean value; wij is the spatial weight matrix. If the two spatial units are adjacent to each other, the spatial weight value is 1; otherwise, it is 0.Since the global Moran index can only test whether there is a spatial correlation between all cities in a region, and the specific spatial correlation between cities cannot be obtained [53]. Therefore, the local Moran spatial autocorrelation index Ii is calculated on the basis of the global Moran index.(18)Ii=nxi−x¯∑j=1nwijxj−x¯∑i=1nxi−x¯2.The local spatial autocorrelation is represented by the local Moran scatterplot to verify the existence of spatial clustering characteristics. The local Moran spatial autocorrelation indexIi>0 indicates a high-high or low-low spatial clustering around the spatial unit; and the local Moran spatial autocorrelation index Ii<0 indicates a high-low or low-high spatial clustering around the spatial unit.


## 4.1. Evaluation Framework

The evaluation process represents the flow and combination of information between indicators and alternatives, as well as the integration of information between subject and objective. Its goal is to provide an evaluation value, selection, and ranking. In most cases, it is ranking from the best to the least optimal option [47]. The basic procedure to solve the evaluation problem includes clarifying the purpose of evaluation, identifying relevant indicators and alternatives, obtaining indicator weights, selecting or constructing aggregation models, and calculating and ranking evaluation values. Based on this procedure, the urban sustainability evaluation processes are shown in Figure 1.Figure 1
Urban sustainability evaluation processes.


## 4.2. Research Area and Data Source

In this section, 17 cities in Henan Province are taken as the specific research area based on the economic-social-environmental framework. Based on various indices data of each city, the current situation of sustainable urban development in Henan Province is studied, which can provide a reference for the sustainable construction of each city. The distribution of cities is shown in Figure2.Figure 2
Urban distribution map of Henan Province.The rapid urbanization process of Henan Province has not only resulted in a vast increase in the demand for natural resources but also generates environmental problems. Exploring the dynamic development process of urban sustainability is crucial to improve environmental quality during the urbanization process and resultant urban development of these fast-growing regions. For this reason, Henan Province is seen as an appropriate study area to uncover the dynamic process of urban sustainability.The data of the indicators in this study were collected from the Henan Province Statistical Yearbook (2014–2018) and China City Statistical Yearbook (2014–2018). The indicator data of the 17 cities in Henan Province from 2013 to 2017 were derived.


## 4.3. The Indicator Weight Determining Method—Entropy Method

The entropy method was first developed in thermodynamics and was further introduced to measure information or uncertainty in information theory [48]. This particular approach was gradually used in social science for determining the weights in evaluating performance and sustainability [32, 48, 49]. The basic logic behind such an approach is that when the index data contains more effective information, the entropy value is smaller, and the weight would be more extensive. The specific steps of this method are as follows:Step 1. Constructing decision-making matrix: this evaluation system has m cities and n indicators. Let X=xijm×n be the decision-making matrix and xiji=1,2,3,…,m,j=1,2,3,…,n be the index value.(1)X=x11x12⋯x1nx21x22⋯x2n⋮⋮⋱⋮xm1xm2⋯xmn.Step 2. Standardizing the decision matrix: the decision matrix X=xijm×n is standardized according to the 0-1transformation, as matrix Y=yijm×n. The indicators are divided into benefit and cost types.(2)benefit indicesyij=xij−minxijmaxxij−minxij,(3)cost indicesyij=maxxij−xijmaxxij−minxij.Step 3. Calculate the entropy value ej of each indicator:(4)ej=−k∑i=1mpijlnpij.In equation (4), k=1/lnm and pij=yij/∑i=1myij,i=1,2,3,…,m,j=1,2,3,…,n. When pij=0, limpij⟶0pijlnpij=0.Calculate the coefficient of variationhj of each index. For one index, the larger the coefficient of variation h is, the smaller the ej is. The greater the impact of the index on urban sustainability is, and the larger the corresponding weight coefficient is [50]. The formula for calculating the coefficient of variation hj is as follows:(5)hj=1−ej.Determine the weight valuewj of each evaluation index.(6)wj=hj∑j=1nhj,where0≤wj≤1,j=1,2,3,…,n and ∑j=1nwj=1.


## 4.4. Dynamic Evaluation of Urban Sustainability with ELECTRE Method

The evaluation of urban sustainability was carried out using the ELECTRE method, which employs the three-dimensional time-series data to present the dynamic process of the urban sustainability. The specific steps are as follows.It is assumed that there arem alternatives, and each alternative is measured by n indicators. The evaluation value of the j th evaluation indicator of the i th alternative obtained in chronological order is xijtki=1,2,…,m;j=1,2,…,n;k=1,2,…,N.(1)
Constructing the original evaluation indicator value matrixXtk.(7)Xtk=xijtkm×n=x11tkx12tk⋯x1ntkx21tkx22tk⋯x2ntk⋮⋮⋱⋮xm1tkxm2tk⋯xmntk.(2)
Normalizing value matrix. The original evaluation indicator value matrix is normalized to obtain a normalized decision matrixX′.(8)X′tk=xij′tkm×n=x11′tkx12′tk⋯x1n′tkx21′tkx22′tk⋯x2n′tk⋮⋮⋱⋮xm1′tkxm2′tk⋯xmn′tk,Benefit indicesxij′tk=xijtk−minxijtkmaxxijtk−minxijtk,Cost indicesxij′tk=maxxijtk−xijtkmaxxijtk−minxijtk.(3)
Calculating the weighted normalized decision matrix. A weighted normalized decision-making matrix is computed by multiplying the normalized decision-making matrixX′with the weights wjtkj=1,2,…,n. The weighted normalized decision-making matrix Ytk is shown as follows:(9)Ytk=yijtkm×n=xij′tk⋅wjtk=x11′tkx12′tk⋯x1n′tkx21′tkx22′tk⋯x2n′tk⋮⋮⋱⋮xm1′tkxm2′tk⋯xmn′tkw1tk0⋯00w2tk⋯0⋮⋮⋱⋮00⋯wntk=x11′tkw1tkx12′tkw2tk⋯x1n′tkwntkx21′tkw1tkx22′tkw2tk⋯x2n′tkwntk⋮⋮⋱⋮xm1′tkw1tkxm2′tkw2tk⋯xmn′tkwntk=y11tky12tk⋯y1ntky21tky22tk⋯y2ntk⋮⋮⋱⋮ym1tkym2tk⋯ymntk.(4)
Determining the concordance matrixCtk and discordance matrix Dtk.


### 4.4.1. Determining the Concordance Set and the Discordance Set

Any two different rows are compared in the weighted normalized decision-making matrixY. If the value of y in the i th row and j th column is higher than the value of y in l th row, the indicator represented by j th column would be classified into the concordance set; otherwise, it would be classified into the discordance set.


### 4.4.2. Calculating the Concordance Matrix

The concordance matrixCtk can be obtained by adding the weights of the indicators in each consistency set.(10)Ctk=ciltkm×m,i=1,2,…,m;l=1,2,…,m;k=1,2,…,N,ciltk=∑j∈Cil′tkwj+0.5∑j∈Cil″tkwj∑j=1nwj,j=1,2,…,n.In equation (10), ciltki=1,2,…,m;l=1,2,…,m;k=1,2,…,N represents the relative advantage index of alternative i over alternative l at tk moment. Cil′tk=j|yijtk>yljtkj=1,2,…,n;k=1,2,…,N means that the value of y in the i th row and the j th column has a higher preference than the value of y in the l th row..Cil″tk=j|yijtk=yljtkj=1,2,…,n;k=1,2,tk,N represents that the preference of y in the i th row and the j th column is equal to the preference of y in the l row.


### 4.4.3. Determining the Discordance MatrixDtk

The relative disadvantage index of the two alternatives is as follows:(11)Dtk=diltkm×m,i=1,2,…,m;l=1,2,…,m;k=1,2,…,N,diltk=maxj∈Dil′tkyijtk−yljtkmaxl∈Syijtk−yljtk,j=1,2,…,n,Dil′tk=j|yijtk<yljtk,S=1,2,…,n.In equation (11), diltki=1,2,…,m;l=1,2,…,m;k=1,2,…,N represents the relative disadvantage index of alternative i over the alternative l at tk moment. Comparing with ciltk that which only contains the indicator weight information. diltk represents the difference between the weighted indicator values, which not only contains the weight information but also contains the information of the indicator value itself. Therefore, there is no complementarity between the relative advantage index and the relative disadvantage index. The greater the value of diltk is, the more likely i is to be inferior to l.


### 4.4.4. Revising the Discordance Matrix

According to the idea proposed by Sun [51], the discordance matrix D′tkis revised and shown in the following equation:(12)D′tk=dil′tkm×mdil′tk=1−diltk.The largerdil′tk means that the alternative i may be more inferior to alternative l at tkmoment.


### 4.4.5. Calculating the Revised Weighted Aggregation MatrixEtk

The elements in the revised discordance matrix are the same as the elements in the concordance matrix. The higher the value is, the higher the degree of preference is. Therefore, the revised weighted aggregate matrixEtk can be obtained by multiplying the elements in the corresponding positions of the concordance matrix. The revised discordance matrix [16] is as follows:(13)Etk=eiltkm×m,i=1,2,…,m;l=1,2,…,m;k=1,2,…,N,eiltk=ciltk⋅dil′tk.


### 4.4.6. Calculating the Comprehensive Evaluation Valueuitk

This study applies the concept of net advantage value as follows [52]:(14)uitk=∑l=1l≠imeiltk−∑l=1l≠imelitk,i=1,2,…,m.The net advantage valueui is the difference between the sum of the advantage indexes of alternative Xi to other schemes and the sum of the advantage indexes of other alternatives to Xi, which reflects the degree of the advantage of Xi in the alternative set. The larger the ui is, the more advantageous alternatives the Xi will be.


### 4.4.7. Ranking the Alternatives

The alternatives are ranked according to the overall evaluation value ofuitk, with the greater the uitk meaning of the higher rank.This study determines time weights using the method that the closer to the present, the more important the weight; conversely, the further from the present, the smaller is the weight. Within the time intervalt1,tN, the time weight at tk moment is wk.(15)wk=k∑k=1Nk,∑k=1Nwk=1,wk>0.The comprehensive evaluation value of alternativei in the time interval t1,tN is gi.(16)gi=∑k=1Nwkuitk.Finally, the alternatives are ranked according togi.


## 4.4.1. Determining the Concordance Set and the Discordance Set

Any two different rows are compared in the weighted normalized decision-making matrixY. If the value of y in the i th row and j th column is higher than the value of y in l th row, the indicator represented by j th column would be classified into the concordance set; otherwise, it would be classified into the discordance set.


## 4.4.2. Calculating the Concordance Matrix

The concordance matrixCtk can be obtained by adding the weights of the indicators in each consistency set.(10)Ctk=ciltkm×m,i=1,2,…,m;l=1,2,…,m;k=1,2,…,N,ciltk=∑j∈Cil′tkwj+0.5∑j∈Cil″tkwj∑j=1nwj,j=1,2,…,n.In equation (10), ciltki=1,2,…,m;l=1,2,…,m;k=1,2,…,N represents the relative advantage index of alternative i over alternative l at tk moment. Cil′tk=j|yijtk>yljtkj=1,2,…,n;k=1,2,…,N means that the value of y in the i th row and the j th column has a higher preference than the value of y in the l th row..Cil″tk=j|yijtk=yljtkj=1,2,…,n;k=1,2,tk,N represents that the preference of y in the i th row and the j th column is equal to the preference of y in the l row.


## 4.4.3. Determining the Discordance MatrixDtk

The relative disadvantage index of the two alternatives is as follows:(11)Dtk=diltkm×m,i=1,2,…,m;l=1,2,…,m;k=1,2,…,N,diltk=maxj∈Dil′tkyijtk−yljtkmaxl∈Syijtk−yljtk,j=1,2,…,n,Dil′tk=j|yijtk<yljtk,S=1,2,…,n.In equation (11), diltki=1,2,…,m;l=1,2,…,m;k=1,2,…,N represents the relative disadvantage index of alternative i over the alternative l at tk moment. Comparing with ciltk that which only contains the indicator weight information. diltk represents the difference between the weighted indicator values, which not only contains the weight information but also contains the information of the indicator value itself. Therefore, there is no complementarity between the relative advantage index and the relative disadvantage index. The greater the value of diltk is, the more likely i is to be inferior to l.


## 4.4.4. Revising the Discordance Matrix

According to the idea proposed by Sun [51], the discordance matrix D′tkis revised and shown in the following equation:(12)D′tk=dil′tkm×mdil′tk=1−diltk.The largerdil′tk means that the alternative i may be more inferior to alternative l at tkmoment.


## 4.4.5. Calculating the Revised Weighted Aggregation MatrixEtk

The elements in the revised discordance matrix are the same as the elements in the concordance matrix. The higher the value is, the higher the degree of preference is. Therefore, the revised weighted aggregate matrixEtk can be obtained by multiplying the elements in the corresponding positions of the concordance matrix. The revised discordance matrix [16] is as follows:(13)Etk=eiltkm×m,i=1,2,…,m;l=1,2,…,m;k=1,2,…,N,eiltk=ciltk⋅dil′tk.


## 4.4.6. Calculating the Comprehensive Evaluation Valueuitk

This study applies the concept of net advantage value as follows [52]:(14)uitk=∑l=1l≠imeiltk−∑l=1l≠imelitk,i=1,2,…,m.The net advantage valueui is the difference between the sum of the advantage indexes of alternative Xi to other schemes and the sum of the advantage indexes of other alternatives to Xi, which reflects the degree of the advantage of Xi in the alternative set. The larger the ui is, the more advantageous alternatives the Xi will be.


## 4.4.7. Ranking the Alternatives

The alternatives are ranked according to the overall evaluation value ofuitk, with the greater the uitk meaning of the higher rank.This study determines time weights using the method that the closer to the present, the more important the weight; conversely, the further from the present, the smaller is the weight. Within the time intervalt1,tN, the time weight at tk moment is wk.(15)wk=k∑k=1Nk,∑k=1Nwk=1,wk>0.The comprehensive evaluation value of alternativei in the time interval t1,tN is gi.(16)gi=∑k=1Nwkuitk.Finally, the alternatives are ranked according togi.


## 4.5. The Spatial Econometric Model

In this study, the spatial autocorrelation model is used to study the spatial dependence or spatial correlation between the sustainability level and the geographical location of cities. The spatial autocorrelation model can be divided into global spatial autocorrelation and local spatial autocorrelation. Global spatial autocorrelation adopts the global Moran indexI to test whether there is an interaction between the sustainability levels of cities in a particular area.(17)I=n∑i=1n∑j=1nwijxi−x¯xj−x¯∑i=1n∑j=1nwij∑i=1nxi−x¯2.The value of the global Moran indexI is between −1 and 1. I>0 represents the positive spatial correlation; I<0 represents the negative spatial correlation; I=0 represents spatial randomness. n is the number of cities in Henan Province. xi and xj are the sustainability value of each city.x¯ is the mean value; wij is the spatial weight matrix. If the two spatial units are adjacent to each other, the spatial weight value is 1; otherwise, it is 0.Since the global Moran index can only test whether there is a spatial correlation between all cities in a region, and the specific spatial correlation between cities cannot be obtained [53]. Therefore, the local Moran spatial autocorrelation index Ii is calculated on the basis of the global Moran index.(18)Ii=nxi−x¯∑j=1nwijxj−x¯∑i=1nxi−x¯2.The local spatial autocorrelation is represented by the local Moran scatterplot to verify the existence of spatial clustering characteristics. The local Moran spatial autocorrelation indexIi>0 indicates a high-high or low-low spatial clustering around the spatial unit; and the local Moran spatial autocorrelation index Ii<0 indicates a high-low or low-high spatial clustering around the spatial unit.


## 5. Results

The sustainability of the 17 cities in Henan Province from 2013 to 2017 is evaluated using the mathematical model established in this study.


### 5.1. Calculating the Weight of Each Indicator

According to equations (1)–(3), the decision matrix A is established and normalized. Then, the entropy value ej and weight wj of each indicator are calculated by using equations (4)–(6). The specific calculation results of 2017 are shown in Table 2. Due to limited space, the data of other years are omitted.Table 2
The weight of each indicator.
IndicatorejwjC10.92840.0378C20.88730.0595C30.81390.0983C40.94800.0275C50.91270.0461C60.88230.0621C70.88510.0607C80.97030.0157C90.90960.0477C100.92500.0396C110.87870.0641C120.88050.0631C130.94780.0276C140.96620.0179C150.92200.0412C160.87780.0645C170.83280.0883C180.96810.0168C190.96810.0169C200.95420.0242C210.94400.0296C220.96040.0209C230.97110.0153C240.97200.0148


### 5.2. Dynamic Evaluation of Urban Sustainability Based on ELECTRE

The dynamic evaluation value and ranking of sustainable urban development in Henan Province from 2013 to 2017 can be obtained by implementing the methods shown inSection 4, as shown in Table 3. According to equation (15), the time weight w can be calculated as follows:(19)w=0.0667,0.1333,0.2000,0.2667,0.3333.Table 3
The dynamic evaluation value and ranking of urban sustainable development in Henan Province from 2013 to 2017.
CityAbbreviationValueRanking2013201420152016201720132014201520162017ZhengzhouZZ12.22812.19112.13912.27411.93611111KaifengKF−1.8400.7014.013−4.591−5.26511931516LuoyangLY5.6706.6715.0954.3836.51822232PingdingshanPDS−1.844−3.114−2.350−2.478−2.4701214111112AnyangAY−0.557−1.800−2.033−3.375−1.693911101411HebiHB2.8932.9452.6193.7433.00845644XinxiangXX−1.357−2.576−3.144−3.1102.477101314135JiaozuoJZ1.0681.5090.154−0.4771.55777886PuyangPY−3.514−4.005−2.780−0.521−1.602141513910XuchangXC0.0930.837−0.1040.8281.17988977LuoheLH1.5732.8191.3822.184−1.04666769SanmenxiaSMX5.0915.9383.9854.7596.14833423NanyangNY−2.524−2.370−2.355−2.0020.898131212108ShangqiuSQ−7.007−7.127−7.953−6.670−4.9601716171714XinyangXY1.6643.7872.6813.316−4.124545513ZhoukouZK−6.781−7.277−6.456−5.487−7.2141617161617ZhumadianZMD−4.856−1.128−4.893−2.775−5.0591510151215The comprehensive evaluation value is calculated by considering the time weight. Therefore, the ranking of the sustainability of 17 cities in Henan Province is shown in Figure3.Figure 3
The comparison of the sustainability of cities in Henan Province.Similarly, the resultss can be obtained for 17 cities in Henan Province in the economic, social, and environmental dimensions of sustainable development, as shown in the Tables 4–6.Table 4
The sustainability evaluation value and ranking of each city in the economic dimension.
CityValueRankingConsider the time weighting2013201420152016201720132014201520162017ValueRankingZhengzhou16.00016.00016.00016.00015.9591111115.9861Kaifeng−1.722−4.115−2.655−6.713−3.3651212121413−4.10613Luoyang10.66710.94911.68011.14110.9742222211.1362Pingdingshan−1.305−2.573−2.613−4.858−2.8661111111211−3.20411Anyang0.5671.1731.136−1.014−3.2868991012−0.94410Hebi1.6191.9282.2461.4942.039666761.8926Xinxiang2.5030.5662.3382.1963.4425105652.4435Jiaozuo5.0576.0575.5945.7616.771333446.0574Puyang−7.474−5.833−4.440−2.913−2.6071414131110−3.81012Xuchang0.0942.9941.4392.4731.2551058571.7717Luohe0.3191.8791.4431.0700.040977990.8599Sanmenxia4.3513.3973.8988.1648.552444336.5513Nanyang1.3331.1950.5731.3370.5237810880.8948Shangqiu−10.104−9.632−10.755−10.827−11.7351717171717−10.90817Xinyang−3.771−5.783−7.850−5.760−6.9601313141314−6.44914Zhoukou−8.326−8.680−8.482−8.539−7.6681515151515−8.24215Zhumadian−9.810−9.523−9.551−9.011−11.0701616161616−9.92716Table 5
The sustainability evaluation value and ranking of each city in the social dimension.
CityValueRankingConsider the time weighting2013201420152016201720132014201520162017ValueRankingZhengzhou8.0747.3128.1078.2712.705121156.2421Kaifeng−0.8382.8386.503−0.852−4.79112521316−0.20111Luoyang3.0423.8203.6781.0495.433435513.5393Pingdingshan0.783−1.131−0.286−0.7720.3688128129−0.23912Anyang0.9920.282−1.3380.8381.33261112670.5047Hebi3.5601.3331.4325.6790.3053662102.3174Xinxiang−1.270−2.590−2.936−0.2734.619131414940.4508Jiaozuo0.5370.711−0.767−0.3870.8769791080.16610Puyang−3.349−5.590−2.7450.385−1.443141513711−1.89614Xuchang−0.3640.371−0.909−0.9731.9181010101460.2239Luohe0.8710.5610.6660.371−2.470787812−0.45813Sanmenxia7.1688.2365.5212.1904.761213434.8512Nanyang−0.811−1.249−1.315−2.1665.0001113111520.6056Shangqiu−7.298−9.654−9.157−7.946−4.5231617171615−7.23216Xinyang2.5053.7204.5895.075−2.7395443132.0215Zhoukou−8.832−9.383−7.247−9.988−7.7411716161717−8.53317Zhumadian−4.7700.414−3.798−0.499−3.610159151114−2.35915Table 6
The sustainability evaluation value and ranking of each city in the environmental dimension.
CityValueRankingConsider the time weighting2013201420152016201720132014201520162017ValueRankingZhengzhou−6.494−5.929−4.561−1.585−6.4981715131217−4.72415Kaifeng−5.587−4.8224.919−2.492−2.705151431312−1.59811Luoyang−2.2264.4102.159−3.4111.0119671570.29910Pingdingshan−4.919−3.656−1.9834.9051.65813129360.6497Anyang−4.959−3.571−4.043−3.391−3.6101411121414−3.72314Hebi7.784−0.2177.778−0.0693.7402921043.2744Xinxiang−4.319−3.979−4.632−7.228−6.0551113141616−5.69016Jiaozuo−4.807−5.987−5.305−9.704−4.2921216161715−6.19817Puyang3.3265.915−2.015−0.1080.116641011100.6178Xuchang5.3236.1484.5473.0693.800434434.1693Luohe12.0376.4848.8185.6183.605121256.1312Sanmenxia−2.4730.228−5.5071.083−2.65610817811−1.83212Nanyang−5.617−8.620−4.7591.154−2.854161715713−3.11913Shangqiu−0.8664.8632.7100.1494.675856922.7315Xinyang6.5347.3560.3102.1250.123318692.0866Zhoukou2.261−0.542−2.7493.0640.46271011580.5009Zhumadian5.0031.9194.3126.8199.478575116.4291


### 5.3. Ranking of Urban Sustainability

Figure3 can directly reflect the level of sustainability of cities in Henan Province. Zhengzhou is the city with the highest score in Henan Province, that is, the city with the highest level of sustainability. Luoyang is ranked second place; Zhoukou has the lowest level of sustainability, followed by Shangqiu. Zhengzhou, the capital city of Henan Province, has invested much money in economic, social construction, and environmental protection in recent years to achieve great success. So its sustainability level is significantly higher than other cities in terms of actual development. Luoyang, as a sub−center city in Henan Province and a member of the Zheng−Luo−Xin National Independent Innovation Demonstration Zone, has developed rapidly in recent years to get a high level of sustainability. Zhoukou, Shangqiu, and Zhumadian are located in the regions with relatively concentrated cultivated land in Henan Province. Limited by the red line of agricultural land, it is challenging to develop the industry. The task of grain production restricts the economic development of the cities to some extent, which leads to the relatively backward economic level of these regions. Secondly, the permanent resident population of Zhoukou, Shangqiu, and Zhumadian is ranked in the third, fourth, and fifth place in the province, respectively. Due to the low economic level and large population, as well as the lack of corresponding science and education innovation resources and infrastructure, the level of urban sustainability of this region lags behind that of other cities in Henan Province.Figure4 shows the ranking of the sustainability levels of 17 cities in Henan Province in 2013−2017. It can be seen that Zhengzhou, Luoyang, and Sanmenxia were stable at higher levels during 2013−2017; Kaifeng, Luohe, and Xinyang show downward fluctuations; and Puyang, Nanyang, and Xinxiang show fluctuations upward. From 2013 to 2017, most of the cities are ranked relatively stable in terms of the sustainability level.Figure 4
Ranking of urban sustainability in Henan Province from 2013 to 2017.


### 5.4. Spatial Autocorrelation Analysis

#### 5.4.1. Global Spatial Autocorrelation

The purpose of spatial autocorrelation analysis is to explore whether the urban sustainability level is spatially correlated. If it appears that the closer the geographic location of the city, the more similar the level of urban sustainability is, which means that the urban sustainability level is positively correlated. If it appears that the closer the geographic location of the city, the more different the level of urban sustainability is, which means that the urban sustainability level is negatively correlated. If the urban sustainability level does not appear to be spatially correlated, it shows spatial stochasticity. The adjacency spatial weight matrix is employed to calculate the global Moran index using Data 13.0 software. The global Moran index is shown in Table7, and the Moran scatter plot is shown in Figure 5. As showing in Table 7, the Moran index of urban sustainability level from 2013 to 2016 is not significant. It means that there is no significant spatial correlation between cities from 2013 to 2016. It may be because these cities are in the stage of development transformation. These cities develop independently and are not closely related. The Moran index in 2017 is positive and passes the significance test (Z > 1.65, p<0.1), indicating a positive correlation between the urban sustainability level in 2017.Table 7
Global Moran index of the urban sustainability level.
yearsIZp20130.1451.3280.07420140.1161.1640.12220150.0560.7860.21620160.0470.7350.23120170.3372.6080.005Figure 5
Moran scatter plot of urban sustainability level in 2017.


#### 5.4.2. Local Spatial Autocorrelation

The local spatial autocorrelation in 2017 is analyzed with the Moran scatter plot in Figure5.The numbers in the figure are labeled according to the order of cities in Table3: 1 representing Zhengzhou, 2 representing Kaifeng, and 17 representing Zhumadian. The figure shows that most cities are clustered in the first quadrant (high−high, H–H) and the third quadrant (low−low, L−L), while a few cities are in the second quadrant (low−high, L−H) and the fourth quadrant (high−low, H−L). The result indicates that, in 2017, most cities in Henan Province are in the high−high and low−low clustering, and few are in the low−high and high−low clustering. That is, these cities with higher levels of sustainable development are clustered together, and cities with lower levels of sustainable development are clustered together. Among them, Zhengzhou, Luoyang, Jiaozuo, and Sanmenxia are in the high−high clustering. Shangqiu, Zhoukou, and Zhumadian are in the low−low clustering. The results indicate that the high−high clustering cities are mainly concentrated around Zhengzhou and Luoyang, and the low−low clustering cities are mainly concentrated in the southeast of Henan.


## 5.1. Calculating the Weight of Each Indicator

According to equations (1)–(3), the decision matrix A is established and normalized. Then, the entropy value ej and weight wj of each indicator are calculated by using equations (4)–(6). The specific calculation results of 2017 are shown in Table 2. Due to limited space, the data of other years are omitted.Table 2
The weight of each indicator.
IndicatorejwjC10.92840.0378C20.88730.0595C30.81390.0983C40.94800.0275C50.91270.0461C60.88230.0621C70.88510.0607C80.97030.0157C90.90960.0477C100.92500.0396C110.87870.0641C120.88050.0631C130.94780.0276C140.96620.0179C150.92200.0412C160.87780.0645C170.83280.0883C180.96810.0168C190.96810.0169C200.95420.0242C210.94400.0296C220.96040.0209C230.97110.0153C240.97200.0148


## 5.2. Dynamic Evaluation of Urban Sustainability Based on ELECTRE

The dynamic evaluation value and ranking of sustainable urban development in Henan Province from 2013 to 2017 can be obtained by implementing the methods shown inSection 4, as shown in Table 3. According to equation (15), the time weight w can be calculated as follows:(19)w=0.0667,0.1333,0.2000,0.2667,0.3333.Table 3
The dynamic evaluation value and ranking of urban sustainable development in Henan Province from 2013 to 2017.
CityAbbreviationValueRanking2013201420152016201720132014201520162017ZhengzhouZZ12.22812.19112.13912.27411.93611111KaifengKF−1.8400.7014.013−4.591−5.26511931516LuoyangLY5.6706.6715.0954.3836.51822232PingdingshanPDS−1.844−3.114−2.350−2.478−2.4701214111112AnyangAY−0.557−1.800−2.033−3.375−1.693911101411HebiHB2.8932.9452.6193.7433.00845644XinxiangXX−1.357−2.576−3.144−3.1102.477101314135JiaozuoJZ1.0681.5090.154−0.4771.55777886PuyangPY−3.514−4.005−2.780−0.521−1.602141513910XuchangXC0.0930.837−0.1040.8281.17988977LuoheLH1.5732.8191.3822.184−1.04666769SanmenxiaSMX5.0915.9383.9854.7596.14833423NanyangNY−2.524−2.370−2.355−2.0020.898131212108ShangqiuSQ−7.007−7.127−7.953−6.670−4.9601716171714XinyangXY1.6643.7872.6813.316−4.124545513ZhoukouZK−6.781−7.277−6.456−5.487−7.2141617161617ZhumadianZMD−4.856−1.128−4.893−2.775−5.0591510151215The comprehensive evaluation value is calculated by considering the time weight. Therefore, the ranking of the sustainability of 17 cities in Henan Province is shown in Figure3.Figure 3
The comparison of the sustainability of cities in Henan Province.Similarly, the resultss can be obtained for 17 cities in Henan Province in the economic, social, and environmental dimensions of sustainable development, as shown in the Tables 4–6.Table 4
The sustainability evaluation value and ranking of each city in the economic dimension.
CityValueRankingConsider the time weighting2013201420152016201720132014201520162017ValueRankingZhengzhou16.00016.00016.00016.00015.9591111115.9861Kaifeng−1.722−4.115−2.655−6.713−3.3651212121413−4.10613Luoyang10.66710.94911.68011.14110.9742222211.1362Pingdingshan−1.305−2.573−2.613−4.858−2.8661111111211−3.20411Anyang0.5671.1731.136−1.014−3.2868991012−0.94410Hebi1.6191.9282.2461.4942.039666761.8926Xinxiang2.5030.5662.3382.1963.4425105652.4435Jiaozuo5.0576.0575.5945.7616.771333446.0574Puyang−7.474−5.833−4.440−2.913−2.6071414131110−3.81012Xuchang0.0942.9941.4392.4731.2551058571.7717Luohe0.3191.8791.4431.0700.040977990.8599Sanmenxia4.3513.3973.8988.1648.552444336.5513Nanyang1.3331.1950.5731.3370.5237810880.8948Shangqiu−10.104−9.632−10.755−10.827−11.7351717171717−10.90817Xinyang−3.771−5.783−7.850−5.760−6.9601313141314−6.44914Zhoukou−8.326−8.680−8.482−8.539−7.6681515151515−8.24215Zhumadian−9.810−9.523−9.551−9.011−11.0701616161616−9.92716Table 5
The sustainability evaluation value and ranking of each city in the social dimension.
CityValueRankingConsider the time weighting2013201420152016201720132014201520162017ValueRankingZhengzhou8.0747.3128.1078.2712.705121156.2421Kaifeng−0.8382.8386.503−0.852−4.79112521316−0.20111Luoyang3.0423.8203.6781.0495.433435513.5393Pingdingshan0.783−1.131−0.286−0.7720.3688128129−0.23912Anyang0.9920.282−1.3380.8381.33261112670.5047Hebi3.5601.3331.4325.6790.3053662102.3174Xinxiang−1.270−2.590−2.936−0.2734.619131414940.4508Jiaozuo0.5370.711−0.767−0.3870.8769791080.16610Puyang−3.349−5.590−2.7450.385−1.443141513711−1.89614Xuchang−0.3640.371−0.909−0.9731.9181010101460.2239Luohe0.8710.5610.6660.371−2.470787812−0.45813Sanmenxia7.1688.2365.5212.1904.761213434.8512Nanyang−0.811−1.249−1.315−2.1665.0001113111520.6056Shangqiu−7.298−9.654−9.157−7.946−4.5231617171615−7.23216Xinyang2.5053.7204.5895.075−2.7395443132.0215Zhoukou−8.832−9.383−7.247−9.988−7.7411716161717−8.53317Zhumadian−4.7700.414−3.798−0.499−3.610159151114−2.35915Table 6
The sustainability evaluation value and ranking of each city in the environmental dimension.
CityValueRankingConsider the time weighting2013201420152016201720132014201520162017ValueRankingZhengzhou−6.494−5.929−4.561−1.585−6.4981715131217−4.72415Kaifeng−5.587−4.8224.919−2.492−2.705151431312−1.59811Luoyang−2.2264.4102.159−3.4111.0119671570.29910Pingdingshan−4.919−3.656−1.9834.9051.65813129360.6497Anyang−4.959−3.571−4.043−3.391−3.6101411121414−3.72314Hebi7.784−0.2177.778−0.0693.7402921043.2744Xinxiang−4.319−3.979−4.632−7.228−6.0551113141616−5.69016Jiaozuo−4.807−5.987−5.305−9.704−4.2921216161715−6.19817Puyang3.3265.915−2.015−0.1080.116641011100.6178Xuchang5.3236.1484.5473.0693.800434434.1693Luohe12.0376.4848.8185.6183.605121256.1312Sanmenxia−2.4730.228−5.5071.083−2.65610817811−1.83212Nanyang−5.617−8.620−4.7591.154−2.854161715713−3.11913Shangqiu−0.8664.8632.7100.1494.675856922.7315Xinyang6.5347.3560.3102.1250.123318692.0866Zhoukou2.261−0.542−2.7493.0640.46271011580.5009Zhumadian5.0031.9194.3126.8199.478575116.4291


## 5.3. Ranking of Urban Sustainability

Figure3 can directly reflect the level of sustainability of cities in Henan Province. Zhengzhou is the city with the highest score in Henan Province, that is, the city with the highest level of sustainability. Luoyang is ranked second place; Zhoukou has the lowest level of sustainability, followed by Shangqiu. Zhengzhou, the capital city of Henan Province, has invested much money in economic, social construction, and environmental protection in recent years to achieve great success. So its sustainability level is significantly higher than other cities in terms of actual development. Luoyang, as a sub−center city in Henan Province and a member of the Zheng−Luo−Xin National Independent Innovation Demonstration Zone, has developed rapidly in recent years to get a high level of sustainability. Zhoukou, Shangqiu, and Zhumadian are located in the regions with relatively concentrated cultivated land in Henan Province. Limited by the red line of agricultural land, it is challenging to develop the industry. The task of grain production restricts the economic development of the cities to some extent, which leads to the relatively backward economic level of these regions. Secondly, the permanent resident population of Zhoukou, Shangqiu, and Zhumadian is ranked in the third, fourth, and fifth place in the province, respectively. Due to the low economic level and large population, as well as the lack of corresponding science and education innovation resources and infrastructure, the level of urban sustainability of this region lags behind that of other cities in Henan Province.Figure4 shows the ranking of the sustainability levels of 17 cities in Henan Province in 2013−2017. It can be seen that Zhengzhou, Luoyang, and Sanmenxia were stable at higher levels during 2013−2017; Kaifeng, Luohe, and Xinyang show downward fluctuations; and Puyang, Nanyang, and Xinxiang show fluctuations upward. From 2013 to 2017, most of the cities are ranked relatively stable in terms of the sustainability level.Figure 4
Ranking of urban sustainability in Henan Province from 2013 to 2017.


## 5.4. Spatial Autocorrelation Analysis

### 5.4.1. Global Spatial Autocorrelation

The purpose of spatial autocorrelation analysis is to explore whether the urban sustainability level is spatially correlated. If it appears that the closer the geographic location of the city, the more similar the level of urban sustainability is, which means that the urban sustainability level is positively correlated. If it appears that the closer the geographic location of the city, the more different the level of urban sustainability is, which means that the urban sustainability level is negatively correlated. If the urban sustainability level does not appear to be spatially correlated, it shows spatial stochasticity. The adjacency spatial weight matrix is employed to calculate the global Moran index using Data 13.0 software. The global Moran index is shown in Table7, and the Moran scatter plot is shown in Figure 5. As showing in Table 7, the Moran index of urban sustainability level from 2013 to 2016 is not significant. It means that there is no significant spatial correlation between cities from 2013 to 2016. It may be because these cities are in the stage of development transformation. These cities develop independently and are not closely related. The Moran index in 2017 is positive and passes the significance test (Z > 1.65, p<0.1), indicating a positive correlation between the urban sustainability level in 2017.Table 7
Global Moran index of the urban sustainability level.
yearsIZp20130.1451.3280.07420140.1161.1640.12220150.0560.7860.21620160.0470.7350.23120170.3372.6080.005Figure 5
Moran scatter plot of urban sustainability level in 2017.


### 5.4.2. Local Spatial Autocorrelation

The local spatial autocorrelation in 2017 is analyzed with the Moran scatter plot in Figure5.The numbers in the figure are labeled according to the order of cities in Table3: 1 representing Zhengzhou, 2 representing Kaifeng, and 17 representing Zhumadian. The figure shows that most cities are clustered in the first quadrant (high−high, H–H) and the third quadrant (low−low, L−L), while a few cities are in the second quadrant (low−high, L−H) and the fourth quadrant (high−low, H−L). The result indicates that, in 2017, most cities in Henan Province are in the high−high and low−low clustering, and few are in the low−high and high−low clustering. That is, these cities with higher levels of sustainable development are clustered together, and cities with lower levels of sustainable development are clustered together. Among them, Zhengzhou, Luoyang, Jiaozuo, and Sanmenxia are in the high−high clustering. Shangqiu, Zhoukou, and Zhumadian are in the low−low clustering. The results indicate that the high−high clustering cities are mainly concentrated around Zhengzhou and Luoyang, and the low−low clustering cities are mainly concentrated in the southeast of Henan.


## 5.4.1. Global Spatial Autocorrelation

The purpose of spatial autocorrelation analysis is to explore whether the urban sustainability level is spatially correlated. If it appears that the closer the geographic location of the city, the more similar the level of urban sustainability is, which means that the urban sustainability level is positively correlated. If it appears that the closer the geographic location of the city, the more different the level of urban sustainability is, which means that the urban sustainability level is negatively correlated. If the urban sustainability level does not appear to be spatially correlated, it shows spatial stochasticity. The adjacency spatial weight matrix is employed to calculate the global Moran index using Data 13.0 software. The global Moran index is shown in Table7, and the Moran scatter plot is shown in Figure 5. As showing in Table 7, the Moran index of urban sustainability level from 2013 to 2016 is not significant. It means that there is no significant spatial correlation between cities from 2013 to 2016. It may be because these cities are in the stage of development transformation. These cities develop independently and are not closely related. The Moran index in 2017 is positive and passes the significance test (Z > 1.65, p<0.1), indicating a positive correlation between the urban sustainability level in 2017.Table 7
Global Moran index of the urban sustainability level.
yearsIZp20130.1451.3280.07420140.1161.1640.12220150.0560.7860.21620160.0470.7350.23120170.3372.6080.005Figure 5
Moran scatter plot of urban sustainability level in 2017.


## 5.4.2. Local Spatial Autocorrelation

The local spatial autocorrelation in 2017 is analyzed with the Moran scatter plot in Figure5.The numbers in the figure are labeled according to the order of cities in Table3: 1 representing Zhengzhou, 2 representing Kaifeng, and 17 representing Zhumadian. The figure shows that most cities are clustered in the first quadrant (high−high, H–H) and the third quadrant (low−low, L−L), while a few cities are in the second quadrant (low−high, L−H) and the fourth quadrant (high−low, H−L). The result indicates that, in 2017, most cities in Henan Province are in the high−high and low−low clustering, and few are in the low−high and high−low clustering. That is, these cities with higher levels of sustainable development are clustered together, and cities with lower levels of sustainable development are clustered together. Among them, Zhengzhou, Luoyang, Jiaozuo, and Sanmenxia are in the high−high clustering. Shangqiu, Zhoukou, and Zhumadian are in the low−low clustering. The results indicate that the high−high clustering cities are mainly concentrated around Zhengzhou and Luoyang, and the low−low clustering cities are mainly concentrated in the southeast of Henan.


## 6. Discussion

### 6.1. Economic Dimension

During the period 2013−2017, as showing in Figure6, Zhengzhou has been ranked first in economic sustainability. As the capital city of Henan Province, Zhengzhou is a political, economic, cultural, and transportation center with the best resources in the province and plays a leading role in the development process. The economic sustainability of Luoyang, Sanmenxia, Jiaozuo, Xinxiang, Hebi, and Xuchang has also been maintained at a relatively high level. As cities bordering Zhengzhou, Luoyang, Jiaozuo, Xinxiang, and Xuchang have advantages in transportation, technology, and markets, thus making their economic development more dynamic. Especially in recent years, driven by the strategies of aerodrome economy, Zhengzhou-Europe Shuttle Train, and national central city development, the economy of Zhengzhou is significantly more potent than other regions. As a deputy central city in Henan Province and a member of The National Independent Innovation demonstration zone of Zhengzhou-Luoyang-Xinxiang, Luoyang has experienced rapid economic development in recent years. Xuchang is close to the aviation port area and has experienced remarkable economic growth in recent years. The excellent business environment has brought opportunities for the development of private enterprises in Xuchang. Jiaozuo, as a representative of the successful transformation of a resource−depleted city, is now focusing its economic development on commerce trade and tourism. The rapid development of sunrise industries has made per capita GDP and the proportion of tertiary industry in the national economy perform well. Cities in the eastern and southern regions, such as Shangqiu, Zhumadian, Zhoukou, and Xinyang have limited economic drive and relatively weak industrial manufacturing due to their predominantly agricultural farming areas. Simultaneously, due to the low concentration of high technology and limited market scope, the economic sustainability ranking is significantly lower. A comparative analysis of the indicators of the economic dimension shows that, in order to improve the sustainability of the city, it is necessary to improve the overall economic level [5]. For these cities, the government should focus on providing more policy and financial support, including strengthening investment attraction, creating a favorable market atmosphere and emphasizing the development of the tertiary sector and modern agriculturalization.Figure 6
A comprehensive ranking of the sustainable development of cities in Henan Province in the economic dimension.


### 6.2. Social Dimension

In the social dimension, as showing in Figure7, the highest level of sustainability is still Zhengzhou, the capital of Henan Province. At the same time, its social sustainability level has remained stable at a high level during 2013−2017. In addition, cities such as Sanmenxia, Luoyang, Hebi, Xinyang, and Anyang have relatively high levels of social sustainability. These cities have well-developed public infrastructures and relatively high investment of public resources. From specific indicators, it can be found that these cities have higher expenditures on education, science and technology and medical care, as well as higher pension insurance coverage rates and a more comprehensive social security system. In the southeastern region, social sustainability is at a low level due to a small economy and a large population, and insufficient investment in people’s livelihoods. In particular, cities such as Zhoukou, Shangqiu, and Zhumadian have low per capita disposable income for urban residents and inadequate pension insurance coverage. Financial investment in science and technology, medical care, and other areas is also relatively low. At the same time, the urban registered unemployment rate is high. Therefore, the level of social sustainability is the worst. For these cities, the social security system needs to be further strengthened in the later development process to promote “people-oriented” sustainable development.Figure 7
The comprehensive ranking of the sustainable development of cities in Henan Province in the social dimension.


### 6.3. Environmental Dimension

From 2013 to 2017, as showing in Figure8, the cities with high environmental sustainability levels in Henan Province include Zhumadian, Luohe, Xuchang, Hebi, and Shangqiu. Luohe, Xuchang, and Hebi have relatively good environmental quality due to their excellent environmental foundation and specific economic strength for environmental governance. For cities like Zhumadian and Shangqiu, due to their low industrialization level, the emission of industrial soot and dust and industrial wastewater is less. Cities with poor environmental quality include Jiaozuo, Xinxiang, Zhengzhou, Anyang. Jiaozuo has steel, coal, building materials, textile, and other traditional industries. Most of these industries are raw materials and high energy consumption. Therefore, Jiaozuo’s industrial wastewater emissions and the annual average concentration of MP2.5 are relatively high. In addition, Jiaozuo’s comprehensive utilization rate of industrial solid wastes, the ratio of wastewater centralized treated, and the green coverage rate of built−up areas are low. Therefore, it has always been at the bottom place of urban environmental sustainability in Henan Province from 2013 to 2017. Cities like Xinxiang and Anyang have many heavy industries, such as machinery manufacturing, building materials, and energy. Urban heavy industry has a significant negative impact on environmental sustainability. For example, in the past five years, the average industrial wastewater emissions in Xinxiang ranked first place among 17 cities in Henan. The annual average concentration of PM2.5 ranked third. Anyang has the highest average industrial soot and dust emissions in the past five years, and the annual average concentration of PM2.5 ranks second, after Zhengzhou. With the rapid economic development of Zhengzhou, people’s living standards have been greatly improved. Zhengzhou is the city with the largest number of public vehicles per capita among the 17 cities. As a result, its annual average concentration of PM2.5 is the highest. In addition, the industrial wastewater emissions, industrial soot, and dust emissions of Zhengzhou are also relatively high. Therefore, Zhengzhou has been ranked at the last place of the environmental sustainability level during the period 2013−2017. Research has found that the low level of environmental sustainability in some cities has a great deal to do with their rapid economic development. Therefore, a balance should be struck between economic, social, and environmental sustainable development, avoiding the old path of “pollute first, cure later”. In addition, in the process of urban development, the construction of ecological civilization should be integrated into all areas of society. The “greening” strategy should be used to promote the transformation and upgrading of industries and to realize green industrial production with high technology, high efficiency, low pollution, and low consumption.Figure 8
The comprehensive ranking of the sustainable development of cities in Henan Province in the environmental dimension.


### 6.4. Spatial Autocorrelation Analysis

The results of global spatial autocorrelation analysis show that the spatial correlation between cities in Henan Province from 2013 to 2016 is not significant. This is largely due to the fact that these cities are in a stage of development and transformation. Cities develop independently of each other without close ties. At the end of 2016, the National Development and Reform Commission issued the Development Plan for Central Plains City Clusters, Zhengzhou as the capital of Henan Province, becoming the center to lead the development of Central Plains City Clusters. Under the background of this policy, there is a positive correlation between the level of urban sustainability in Henan Province from 2017.And the local spatial autocorrelation analysis shows that cities with higher levels of sustainable development in Henan Province clustered together in 2017, while cities with lower levels of sustainable development are clustered together. Among them, the high-high clustering cities are mainly concentrated around Zhengzhou and Luoyang, and the low-low clustering cities are mainly concentrated in the southeast of Henan Province. As the capital of Henan Province and the central city of the Central Plains Economic Zone, Zhengzhou ranks first in the Central Plains Economic Zone in terms of urban comprehensive strength, which has a strong attraction to the surrounding areas, prompting the concentration of population, resources and other factors in the surrounding cities to it, strengthening the connection between Zhengzhou and other cities. Zhenzhou has a strong external radiation capacity, driving the development of the surrounding cities. The eastern and southern regions of Henan Province are mainly agricultural, with limited economic capacity, relatively weak industrial manufacturing, and no obvious resource advantages or core industries. Therefore, the overall level of sustainability is relatively weak.


## 6.1. Economic Dimension

During the period 2013−2017, as showing in Figure6, Zhengzhou has been ranked first in economic sustainability. As the capital city of Henan Province, Zhengzhou is a political, economic, cultural, and transportation center with the best resources in the province and plays a leading role in the development process. The economic sustainability of Luoyang, Sanmenxia, Jiaozuo, Xinxiang, Hebi, and Xuchang has also been maintained at a relatively high level. As cities bordering Zhengzhou, Luoyang, Jiaozuo, Xinxiang, and Xuchang have advantages in transportation, technology, and markets, thus making their economic development more dynamic. Especially in recent years, driven by the strategies of aerodrome economy, Zhengzhou-Europe Shuttle Train, and national central city development, the economy of Zhengzhou is significantly more potent than other regions. As a deputy central city in Henan Province and a member of The National Independent Innovation demonstration zone of Zhengzhou-Luoyang-Xinxiang, Luoyang has experienced rapid economic development in recent years. Xuchang is close to the aviation port area and has experienced remarkable economic growth in recent years. The excellent business environment has brought opportunities for the development of private enterprises in Xuchang. Jiaozuo, as a representative of the successful transformation of a resource−depleted city, is now focusing its economic development on commerce trade and tourism. The rapid development of sunrise industries has made per capita GDP and the proportion of tertiary industry in the national economy perform well. Cities in the eastern and southern regions, such as Shangqiu, Zhumadian, Zhoukou, and Xinyang have limited economic drive and relatively weak industrial manufacturing due to their predominantly agricultural farming areas. Simultaneously, due to the low concentration of high technology and limited market scope, the economic sustainability ranking is significantly lower. A comparative analysis of the indicators of the economic dimension shows that, in order to improve the sustainability of the city, it is necessary to improve the overall economic level [5]. For these cities, the government should focus on providing more policy and financial support, including strengthening investment attraction, creating a favorable market atmosphere and emphasizing the development of the tertiary sector and modern agriculturalization.Figure 6
A comprehensive ranking of the sustainable development of cities in Henan Province in the economic dimension.


## 6.2. Social Dimension

In the social dimension, as showing in Figure7, the highest level of sustainability is still Zhengzhou, the capital of Henan Province. At the same time, its social sustainability level has remained stable at a high level during 2013−2017. In addition, cities such as Sanmenxia, Luoyang, Hebi, Xinyang, and Anyang have relatively high levels of social sustainability. These cities have well-developed public infrastructures and relatively high investment of public resources. From specific indicators, it can be found that these cities have higher expenditures on education, science and technology and medical care, as well as higher pension insurance coverage rates and a more comprehensive social security system. In the southeastern region, social sustainability is at a low level due to a small economy and a large population, and insufficient investment in people’s livelihoods. In particular, cities such as Zhoukou, Shangqiu, and Zhumadian have low per capita disposable income for urban residents and inadequate pension insurance coverage. Financial investment in science and technology, medical care, and other areas is also relatively low. At the same time, the urban registered unemployment rate is high. Therefore, the level of social sustainability is the worst. For these cities, the social security system needs to be further strengthened in the later development process to promote “people-oriented” sustainable development.Figure 7
The comprehensive ranking of the sustainable development of cities in Henan Province in the social dimension.


## 6.3. Environmental Dimension

From 2013 to 2017, as showing in Figure8, the cities with high environmental sustainability levels in Henan Province include Zhumadian, Luohe, Xuchang, Hebi, and Shangqiu. Luohe, Xuchang, and Hebi have relatively good environmental quality due to their excellent environmental foundation and specific economic strength for environmental governance. For cities like Zhumadian and Shangqiu, due to their low industrialization level, the emission of industrial soot and dust and industrial wastewater is less. Cities with poor environmental quality include Jiaozuo, Xinxiang, Zhengzhou, Anyang. Jiaozuo has steel, coal, building materials, textile, and other traditional industries. Most of these industries are raw materials and high energy consumption. Therefore, Jiaozuo’s industrial wastewater emissions and the annual average concentration of MP2.5 are relatively high. In addition, Jiaozuo’s comprehensive utilization rate of industrial solid wastes, the ratio of wastewater centralized treated, and the green coverage rate of built−up areas are low. Therefore, it has always been at the bottom place of urban environmental sustainability in Henan Province from 2013 to 2017. Cities like Xinxiang and Anyang have many heavy industries, such as machinery manufacturing, building materials, and energy. Urban heavy industry has a significant negative impact on environmental sustainability. For example, in the past five years, the average industrial wastewater emissions in Xinxiang ranked first place among 17 cities in Henan. The annual average concentration of PM2.5 ranked third. Anyang has the highest average industrial soot and dust emissions in the past five years, and the annual average concentration of PM2.5 ranks second, after Zhengzhou. With the rapid economic development of Zhengzhou, people’s living standards have been greatly improved. Zhengzhou is the city with the largest number of public vehicles per capita among the 17 cities. As a result, its annual average concentration of PM2.5 is the highest. In addition, the industrial wastewater emissions, industrial soot, and dust emissions of Zhengzhou are also relatively high. Therefore, Zhengzhou has been ranked at the last place of the environmental sustainability level during the period 2013−2017. Research has found that the low level of environmental sustainability in some cities has a great deal to do with their rapid economic development. Therefore, a balance should be struck between economic, social, and environmental sustainable development, avoiding the old path of “pollute first, cure later”. In addition, in the process of urban development, the construction of ecological civilization should be integrated into all areas of society. The “greening” strategy should be used to promote the transformation and upgrading of industries and to realize green industrial production with high technology, high efficiency, low pollution, and low consumption.Figure 8
The comprehensive ranking of the sustainable development of cities in Henan Province in the environmental dimension.


## 6.4. Spatial Autocorrelation Analysis

The results of global spatial autocorrelation analysis show that the spatial correlation between cities in Henan Province from 2013 to 2016 is not significant. This is largely due to the fact that these cities are in a stage of development and transformation. Cities develop independently of each other without close ties. At the end of 2016, the National Development and Reform Commission issued the Development Plan for Central Plains City Clusters, Zhengzhou as the capital of Henan Province, becoming the center to lead the development of Central Plains City Clusters. Under the background of this policy, there is a positive correlation between the level of urban sustainability in Henan Province from 2017.And the local spatial autocorrelation analysis shows that cities with higher levels of sustainable development in Henan Province clustered together in 2017, while cities with lower levels of sustainable development are clustered together. Among them, the high-high clustering cities are mainly concentrated around Zhengzhou and Luoyang, and the low-low clustering cities are mainly concentrated in the southeast of Henan Province. As the capital of Henan Province and the central city of the Central Plains Economic Zone, Zhengzhou ranks first in the Central Plains Economic Zone in terms of urban comprehensive strength, which has a strong attraction to the surrounding areas, prompting the concentration of population, resources and other factors in the surrounding cities to it, strengthening the connection between Zhengzhou and other cities. Zhenzhou has a strong external radiation capacity, driving the development of the surrounding cities. The eastern and southern regions of Henan Province are mainly agricultural, with limited economic capacity, relatively weak industrial manufacturing, and no obvious resource advantages or core industries. Therefore, the overall level of sustainability is relatively weak.


## 7. Conclusions

Urban sustainability evaluation plays a crucial role in sustainable urban development, and the evaluation results can provide support for stakeholders and decision-makers to formulate urban development plans and correct policy interventions. This study constructs an urban sustainability evaluation indicator system from the three dimensions of economy, society, and environment. The two-dimensional data with time-series data is extended to three-dimensional data. A dynamic evaluation model based on ELECTRE is employed to evaluate the sustainable development of 17 cities in Henan Province from 2013 to 2017. The research results show that, from 2013 to 2017, cities such as Zhengzhou, Luoyang, and Sanmenxia remained stable at a relatively high level, while the sustainability levels of cities such as Kaifeng, Luohe, and Xinyang showed a trend of downward fluctuations. The cities such as Puyang, Nanyang, and Xinxiang showed a trend of upward fluctuations. The results based on the spatial measurement model showed no spatial correlation in the sustainability level among cities in Henan Province from 2013 to 2016. However, the correlation between urban sustainability began to emerge in 2017. The cities showing high-high clustering are mainly concentrated around Zhengzhou and Luoyang, while those showing low-low clustering are mainly concentrated in the southeastern part of Henan. Specifically, Zhengzhou is the city with the highest level of sustainability among the 17 cities. Its economic and social sustainability levels are significantly higher than any other city. Zhoukou is the city with the lowest level of sustainability among the 17 cities. In the economic dimension, Zhengzhou has the highest sustainability level, and Shangqiu has the lowest level. In the social dimension, Zhengzhou ranks first, and Zhoukou ranks the lowest place. In the environmental dimension, Zhumadian has the highest sustainability level, and Jiaozuo has the lowest.According to the evaluation results, this paper puts forward specific suggestions. Firstly, cities such as Shangqiu and Zhoukou are economically and socially underdeveloped. The government should provide them with more financial and policy support, including strengthening investment attraction, creating a favorable market environment, and emphasizing the development of the tertiary sector and modern agriculture. In addition, the social security system needs to be further strengthened to promote “people-oriented” sustainable development. Secondly, cities such as Zhengzhou, Xinxiang, and Jiaozuo have high economic sustainability levels but low levels of environmental sustainability. In the process of development, a balance should be struck between the economy, society, and the environment. The construction of ecological civilization should be integrated into all areas of society. The “greening” strategy should be used to promote the transformation and upgrading of industries, to achieve high-technology, high-efficiency, low-pollution, and low-consumption green industrial production and to promote the high-quality development of cities.Based on the classic triple bottom line framework of sustainable development theory, this paper considers the characteristics of urban development and constructs an urban sustainability evaluation index system from economic, social, and environmental dimensions. And the ELECTRE method is used to dynamically evaluate urban sustainability based on three-dimensional data. The index system constructed and the research method adopted in this paper provide tools for measuring the sustainability status of a city (not only Henan but also other cities), which has certain theoretical and practical significance.The research contributions of this study are manifested in three aspects. Firstly, the ELECTRE method is used to evaluate the urban sustainability level, and the time weights are considered to obtain the dynamic sustainability level of each city in annual, overall, as well as in the three dimensions of economy, society, and environment. Secondly, a spatial measurement model is used to identify the difference and clustering in the spatial distribution of urban sustainability levels. Finally, the research results provide a useful reference for city managers to clarify the current sustainability level of cities and to formulate reasonable development plans to promote sustainable development further.At the same time, this study also has certain limitations. In the evaluation process, the method used to calculate the weighting system ignored the existence of interaction among indicators. Urban sustainability is a complex system affected by economic, social, and environmental dimensions, with the existence of interaction and conflict among factors. For example, many studies have concluded that there is an inverted “U” relationship between economic growth and environmental pollution in China. Therefore, in the future, we will continue to improve the calculation method of weights and pay more attention to the interaction among indicators.



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*Source: 6659623-2021-02-15.xml*