A research on coordination between economy, society and environment in China: A case study of Jiangsu

Journal of Cleaner Production 258 (2020) 120641

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A research on coordination between economy, society and environment in China: A case study of Jiangsu Mingxue Xu *, Wen-Quan Hu School of Economics, Zhejiang University, 148 Tianmushan Rd., Hangzhou, 310027, China

a r t i c l e i n f o

a b s t r a c t

Article history: Received 24 November 2019 Received in revised form 2 February 2020 Accepted 17 February 2020 Available online 20 February 2020

Environmental stresses with remarkable economic growths in China entail a transformation of development from the traditional path to a coordinated and sustainable one. Based on the connotation of development, this paper constructs a combined model of economic-social-environmental system, coupling with the entropy weight method, to measure the coordinated development of a combined system ESE as well as the subsystems. Via the data of economic growth, social construction and environmental protection in Jiangsu from 2002 to 2017, this study analyzes the current situations and problems faced by Jiangsu, so as to achieve a comprehensive development. Main results show that all the four kinds of coordinated degrees present an upward trend, where the economic-social coordination rises fast, the volatility is insignificant and the gap between different cities is not obvious. The timevariation and space-distribution characteristics of the economy-environment, society-environment and overall development coordination are basically similar. The reason for such fluctuations is the changes in the macroeconomic environment, which have led to an unordered variation in economic development, and the level of social development is mainly affected by the availability of social public service facilities. The fluctuations in environmental systems are mainly due to changes in water resources and sewage discharges, which are also the main factors affecting the overall development coordination level. Therefore, to further promote the sustainable development and optimize the spatial layout in Jiangsu, it is necessary to eliminate such problems as imperfect public construction and insufficient environmental protection and accelerate the economic transformation and upgrading. © 2020 Elsevier Ltd. All rights reserved.

Handling editor: Dr. Govindan Kannan Keywords: Coordination degree Sustainable development Compounding system

1. Introduction As the prospect of global economic growth becomes dimmed, the ecological and social aspects of development are attracting more attention of academic researchers. Although there still are debates over the precise definition of sustainable development (Richard et al., 1998), it has been widely regarded as one of the basic principles of development by countries all over the world (Zhongmin et al., 2002). For the ultimate goal of sustainable development of realizing the well-being of both the human beings and the Planet, the economic growth is just a means, never the target. Countries with strong economies have stronger government finances, which can facilitate to improve or solve social problems or environmental problems such as wide inequalities and pollution through infrastructure constructions and institutional changes

* Corresponding author. E-mail address: [email protected] (M. Xu). https://doi.org/10.1016/j.jclepro.2020.120641 0959-6526/© 2020 Elsevier Ltd. All rights reserved.

(Perkins et al., 2005; Agarwal and Whalley, 2013; Howarth and Kennedy, 2016). And the improvement of social and environmental quality can also provide a better foundation and more possibilities for economic development. Thus, it can be stated that sustainable development is a result of interactions between complex policies and target systems, and that a coordinated development is a process of realizing sustainable development, or a process from disorder to order and from low to high coupling between elements and subsystems. The coordination between different systems usually works in a dynamic and non-linear way (Allen, 1998; Fang et al., 2016), and it needs cooperation across time and space. Since this is a dynamic process and certain sustainable practices may take long to take effect, it is necessary to compare the differences before and after implementations, so as to measure the degree of coordination. Besides, as environmental management and welfare policy have externalities in which the market mechanism may fail, it is also a must to negotiate or arbitrate a compromise if one city or region

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M. Xu, W.-Q. Hu / Journal of Cleaner Production 258 (2020) 120641

has conflicts with others (Zou and Shi, 2004). Given the structure of the bureaucracy system in China, cooperation between different cities is important to realize a coordinated development, which makes spatial analysis necessary. In conclusion, it will have significant meanings to analyze the coordination degree from both time variation and spatial distribution perspectives. Coordination among different indexes or subsystems has been a hot spot, and there exist two main kinds of studies on this topic. The first focuses on the economic and environmental direction and establishes a 3E model (Yi et al., 2017; Yaqin et al., 2019) to discuss the interactions among economy, energy and environment. And the second focuses on the coordination of regional development, with more attention to economic growths and synergies among different economic factors (Wangtu and Ying, 2019). When considering the coordinated development, it is easy to overlook social and environmental subsystems, resulting in biases in determining how to achieve a balanced development in the long run. Libang Ma and his partners (Libang et al., 2019) study the interactions between economic transformation and social transformation to evaluate the comprehensive degree of rural-urban transformation. Also, some researchers (Luhe et al., 2016) investigate the coordinated development of different cities or city clusters from the perspective of social development and the ecological environment under system dynamics, but few literatures consider the economy, society and environment simultaneously (Lin et al., 2016). And over recent years, the sustainable and coordinated development has been measured from diverse dimensions. E.g., in the research of Chuanglin et al. (2019), when constructing a regional sustainable development model, five aspects are taken into consideration: urbanization, GDP growth, water resource, basic farmland and population growth. However, the classical classification is still the basic framework for many scholars to construct the measurement indexes of the synergy degree (Yuantong et al., 2018; Pingtao et al., 2019). For studying the coordinated development among elements and subsystems, it is needed to decide the weight matrix for the indexes. Most traditional assessment methods determine the matrix in a subjective or artificial way (Feng et al., 2009), e.g., the analytic hierarchy process (AHP) (Krajnc and Glavic, 2005; Reza et al., 2011;
n et al., 2012). Veisi et al., 2016) and Delphi method (García-Melo Such methods are divorced from the reality and their results cannot reflect the true situations. In this study, an entropy weight is employed as the index weight. Georgescu-Roegen (1967) took inspiration from physics that the second law of thermodynamics should be a practical tool when assessing the evolutionary process. The law says that the entropy of the universe moves toward a maximum at all times (Clausius and Browne, 1850). Physically, the higher the entropy, the more microscopic states there are. And this theory was borrowed by Shannon (1948) to the information theory, in which the entropy is used as a measure of the information contained in a message. Over time, the entropy law has been enriched to what is known as self-organization (Nicolis and Prigogine, 1977; Prigogine and Stengers, 1984). The relevance of the second law to the position of environment in economic analysis has been discussed by Ayres (1998). As the biggest developing country and the second largest economy in the world, China are facing the same problem, which needs special attention. Over the past decades, the rapid economic growth has been criticized as unsustainable as it does not fully take social and environmental costs into account (Xingqiang et al., 2008). And lack of management in supply chain also leads to low efficiency in resource utilization (Gharaei et al., 2019a). Many studies has carried out about how to optimize maximize profits while optimizing the use of resources (Gharaei et al., 2018; Chaoqun et al., 2018; Gharaei et al., 2019b). The remarkable

economic development in the past has indeed left many problems unsolved, like air pollution, water scarcity and pollution, and soil degradation (Liu et al., 2010; Xueliang et al., 2015; Peiyue and Hui, 2018). If not handled properly, these matters may become obstacles to China’s further development. In this context, this study will be focused on the change characteristic of coordinated development in Jiangsu across time and space. This study makes the following contributions. Firstly, it considers the economic, social and environmental subsystems for the all-around sustainable development. In addition, it tries to select indicators from as many perspectives as possible, while maintaining a balance among different aspects. Secondly, all kinds of coordination degrees between different subsystems are discussed alongside the coordination degree of the combined system under the framework of a composite system, so as to see more clearly which link is weak. And thirdly, this study combines cross-section analyses and a spatial panel model to observe the spatial correlation at the coordinated level in different times, so as to investigate the spillover of collaborative development spatiotemporally. The rest of the paper is organized as follows. Section 2 presents how to build the combined system model with entropy weights and proposes the index framework. Section 3 introduces the study area and data sources. The empirical analysis and discussion are mainly placed in Section 4 and suggestions based on the analysis in Section 5. Finally, conclusions and proposed future work are presented in Section 6. 2. Problems and methods 2.1. Problem definition and assumptions Coordinated development is an organic process involving many aspects and is usually a concept difficult to measure from single index. The consistency of development process in different aspects is the essence of coordination. In this research we regard this problem as a problem of systematics, and tries to evaluate the degree of coordinated development quantitatively and comprehensively. To achieve this goal, it is assumed that the development system contains three aspects of equally importance, economic, social and environmental subsystems. Under the same logic, the development level of subsystems can be calculated by indexes from different perspectives. But the different indicators contain information of different quantities, and the impacts of changes in the values of indicators must be of different extent. To better reflect the reality, these differences should be taken into consideration. Thus, it is necessary to give weight to indicators according to the quantity of information. Besides, explosive spatial data analysis (ESDA) is adopted to analyze the spatial cluster characteristic. In this part, we assume that when two objects are adjacent to each other, the degree of interaction is higher. This is a reasonable and acceptable assumption not only for the regional spillovers in environmental aspect, also considering diffusion of elements in economy and society. 2.2. Coordinated development based on the entropy weight method For any region, the connotation of development should be a multidimensional concept, covering economic growth, social progress and environmental optimization. Therefore, a regional development system S ¼ f (Sx, Sy, Sz) with different dimensions of development shall be constructed, in which Sx indicates the economic subsystem, Sy the social one, and Sz the environmental one. Subsystems not only have spontaneous and irregular independent movements, but also are continuously affected by movements of other subsystems, thus forming a coordinated motion. There are

M. Xu, W.-Q. Hu / Journal of Cleaner Production 258 (2020) 120641

many control variables in such a cooperative motion, which are mainly divided into fast variables and slow ones. The slow variables, also known as order parameters, are the active variables. When describing the system state, the order parameters can measure the motion of micro subsystems and then describe the operation mechanism and state of the system. That is to say, the order parameters are the medium linking the behaviors of subsystems and the evolution process of the system. Order degree is a unit to describe order parameters by measuring the degree of coincidence between the measured value of order parameters and the ideal value. If the order parameter n of subsystem m is emn and the upper and lower bounds of the order parameter are bmn and amn , respectively, then the order degree u(emn) of the order parameter n in subsystem m can be expressed as follows:

uðemn Þ ¼

emn  amn bmn  amn

um ¼

consideration. And the indicators are weighted as shown in equation (2). The greater the amount of information, the greater the degree of impact on the system, and the larger the corresponding weight. Given that different cities have their own different develop methods, different weights are made for each of them. For city C, there are x years’ observations, and every year has y evaluation indicators, so the original evaluation matrix formed by standardizing the data of each indicator can be marked as (rij)x*y. It can be seen that the index value of object i under the index j can be expressed by formula (5).

pij ¼

rij m P rij

(5)

i¼1

(1)

It is clear that u(emn)2[0, 1]. And the greater the degree of order, the greater its contribution to the degree of order of the system. Therefore, a linear weighting method is used to calculate the degree of order of each subsystem, in which the weight is calculated by the entropy weight method. n X

3

!

lmn uðemn

(2)

ej ¼

m 1 X p ,lnpij ln m i¼1 ij

where, ej in (6) represents the information entropy of index rj; and if pij¼ 0, then pij *ln pij ¼ 0. In this study, m equals 14, indicating the length of the observation years. Further, the entropy-weight wj of indicator rj can be calculated in formulate (7).

1

In order to regard the current data and its basic state as a whole and scientifically express the size of the coordination among subsystems, if the initial Sm order degree of subsystems is u0m and the current degree is u1m, then the coordination degree between any two subsystems in a composite system can be expressed by formula (3).

(6)

wj ¼

1  ej m   P 1  ej

(7)

j¼1

  Q  1  e  exp 1  uj  u0j  cðsh ; sk Þ ¼

j¼h;k

(3)

e1

The overall coordination degree D of the system is an integrated expression of the coordination degrees of all subsystems, i.e., it is positively correlated to c and can be expressed by formula (4).

D¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi qY m ci m i¼1

(4)

It is certain that the degree of coordination between subsystems and the overall degree of coordination of the system are both greater than 0 and less than 1, and the grading of the coordination degree is listed in Table 1. And the higher the coordination degree, the stronger the coordination effect among different subsystems. Obviously, in different subsystems, there will be many different indicators for evaluating subsystems, and these different indicators express the behaviors and evolutions of the system from different perspectives. Therefore, different indicators contain information in different qualities and quantities, and the impacts of changes in the values of indicators must be of different extent. In order to reflect the real situation, these differences should be taken into

Table 1 Classification of the coordination degree. Coordination degree

Type

[0.8, 1] [0.6, 0.8) [0.4, 0.6) [0.2, 0.4) [0, 0.2)

Advanced-coordination Moderate-coordination Primary-coordination Nearly-disorder Disorder

2.3. Indexes and data As mentioned above, the evaluation indicator system should comprehensively and veritably reflect all the aspects of development. Thus, based on the principle of rationality, operability and representativeness, the regional development evaluation system constructed in this research includes 12 indicators from three aspects (see Table 2), i.e., economic, social and environmental subsystems. To measure the development of regional economy, GDP is always an effective indicator. At the same time, although China is in a period of economic transition, the traditional troika of “consumption, investment and export” is still an important variable in the economic system. Considering that China’s social system is still under continuous improvement and the main social progresses include urban and rural developments as well as infrastructure and public service upgrades, this research adopts the rate of urbanization, rural Engel coefficient, grade road mileage and local financial expenditure to evaluate the social development in different cities. As there is no serious environmental crisis in Jiangsu, the investigation into the environmental system is not focused on pollution control, but on the local bearing capacity and the industrial pollution generated. So the indicators selected include arable land area per capita, water resource per capita, urban greening coverage, and sewage discharge per unit of industrial output. Among the above twelve indicators, the urbanization rate is obtained by dividing the non-agricultural population by the total population, and the sewage discharge per unit of industrial output value is obtained by dividing the industrial output by the current industrial output.

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M. Xu, W.-Q. Hu / Journal of Cleaner Production 258 (2020) 120641 Table 2 The evaluation indicator system of ESE. Subsystem

Index

Unit

Economic system

GDP Investment in fixed assets Total retail sales of consumer goods Gross export Rate of urbanization Rural Engel coefficient Grade road mileage General budget expenditure Per capita arable land area Per capita water resources Urban greening coverage Sewage discharge per unit of industrial output

RMB RMB RMB RMB % % kilometer RMB hectare/person stere/person % Ton/ten thousand

Social system

Environmental system

3. Materials for the research 3.1. Study area The study area, Jiangsu (116180 -121570 E, 30 450 -35 200 N), lies in eastern coast of China, downstream of the Yangtze River. It borders Shandong in the north, the Yellow Sea in the east, Zhejiang and Shanghai in the southeast, and Anhui in the west. Located in the Yangtze River Economic Belt, Jiangsu has 13 districts and cities under its jurisdiction. In terms of per capita GDP, comprehensive competitiveness and regional development and livelihood index (DLI), Jiangsu ranks first in all Chinese provinces. At the highest comprehensive development level in China, it has entered a grade similar to “middle and upper class” developed countries. With a comprehensive competitiveness of regional economy topping China, it is one of the most active provinces in the nation. 3.2. Data sources Based on the data from the Jiangsu Statistical Yearbook, the China Urban Statistical Yearbook and the statistical yearbooks of 13 cities of Jiangsu, the relevant results are obtained through data processing. The analysis of the coordinated development level in Jiangsu is mainly conducted from two aspects, the characteristics of time variation and the spatial distribution. The spatial characteristics will intercept the coordinated level distribution in three-time sections of 2003, 2010 and 2016, and explain the spatial change law. 4. Results and discussions 4.1. The weight matrix The weights used in the assessment of the coordination degree are listed in Tables 3e5. As explained above, the weights evaluate the importance of the indexes in the development. In other words, the greater the weight, the more important the index. Given the continual features of city development strategies and policies, the weight matrix is calculated based on the panel data of cities from 2002 to 2017. After comparing coefficients of different indexes in different subsystems, it is obvious that the variances of weights of different indexes in the environment subsystem are much greater than those in the social subsystem, and the variances in the economy subsystem are the smallest. Calculation shows that the average variance for environment indexes is nearly 5 times the average variance for the social subsystem, which is also 5 times the average variance for the economy subsystem. That means the structure of the economy subsystem is much more balanced than that of the other

Table 3 The weight matrix for economic system. City

GDP

Investment

Consumption

Gross export

Nanjing Wuxi Xuzhou Changzhou Suzhou Nantong Lianyungang Huaian Yancheng Yangzhou Zhenjiang Taizhou Suqian Average

0.270929 0.248382 0.230004 0.254483 0.253177 0.240713 0.230252 0.251141 0.235977 0.243705 0.233076 0.235718 0.222123 0.242283

0.273859 0.288702 0.268536 0.270101 0.275349 0.325076 0.315779 0.272778 0.304916 0.335459 0.322285 0.344304 0.266688 0.297218

0.296115 0.281307 0.28311 0.284149 0.313928 0.242426 0.255548 0.2538 0.238851 0.234966 0.266642 0.234967 0.2378 0.263355

0.159097 0.181609 0.21835 0.191267 0.157547 0.191785 0.198421 0.222281 0.220256 0.185871 0.177998 0.185012 0.273388 0.197145

two. These differences occur because the emphasis on balances of economic structures has lasted for a rather long time, especially after the Asian economic crisis in 1997. As a result, to establish a sustainable and continual social and environmental development system has long been ignored. From the table for economy system, it can be seen that the coefficients for investment and consumption are generally greater than the other two. Especially, the coefficients for gross export are much lower than others, and the high values gathers along the Xuzhou-Yancheng railway. Jiangsu are equipped with first-class water transportation resources in China, with 1039.7 km of coastlines and 1167.4 km along the Yangtze River; thus, the cities nearby make use of their own traffic resources and geographical advantages to develop extroverted economy. As for social development, increased financial input is the main measure. Although it is well known that China has made remarkable achievements in urbanization and transportation network construction, neither of these two roles has significant effects. But it should be noticed that the rural Engel coefficient has a relatively large weight, which may shows that the urbanization in China has produced tangible results. In the process, Chinese government placed strong emphasis on improving the living standards of rural residents and the consumption structure. Sewage discharge per unit of industrial output plays an important role in the environment construction, mainly because of the advancements in clean production and green production, especially in Xuzhou, Lianyungang and Yancheng. The other three aspects are mainly related to the carrying capacity, which can hardly be changed by human forces in a short time. But it can be seen that Nanjing has a very large weight in arable land areas, for it reclaimed farmlands in 2006 to promote a balanced development of urbanization, industrialization and agricultural production. Urban greening coverage has a rather small weight,

M. Xu, W.-Q. Hu / Journal of Cleaner Production 258 (2020) 120641

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Table 4 The weight matrix for social system. City

Rate of urbanization

Rural Engel coefficient

Grade road mileage

Local financial expenditure

Nanjing Wuxi Xuzhou Changzhou Suzhou Nantong Lianyungang Huaian Yancheng Yangzhou Zhenjiang Taizhou Suqian Average

0.105033 0.095873 0.167352 0.220520 0.179050 0.224822 0.145303 0.198973 0.154904 0.133463 0.161050 0.169420 0.208357 0.166471

0.317050 0.243997 0.250593 0.226477 0.284708 0.221644 0.302343 0.265847 0.275350 0.267572 0.246617 0.357429 0.274966 0.271892

0.132861 0.249342 0.152974 0.199505 0.200118 0.156071 0.16774 0.159362 0.163585 0.150749 0.168686 0.130342 0.140079 0.167032

0.445056 0.410788 0.429081 0.353499 0.336124 0.397463 0.384614 0.375819 0.406161 0.448216 0.423647 0.342809 0.376598 0.394606

Table 5 The weight matrix for environmental system. City

Per capita arable land area

Per capita water resources

Urban greening coverage

Sewage discharge per unit of industrial output

Nanjing Wuxi Xuzhou Changzhou Suzhou Nantong Lianyungang Huaian Yancheng Yangzhou Zhenjiang Taizhou Suqian Average

0.437614 0.375250 0.128074 0.320129 0.128059 0.500883 0.064721 0.210970 0.058818 0.08663 0.087442 0.120417 0.104914 0.201840

0.192348 0.196690 0.096361 0.283839 0.136056 0.190963 0.131182 0.222592 0.092973 0.166924 0.167697 0.173093 0.266500 0.178247

0.066640 0.037910 0.054981 0.061092 0.057347 0.073331 0.049444 0.118622 0.067906 0.173770 0.167214 0.120334 0.138352 0.091303

0.303398 0.39015 0.720584 0.334941 0.678539 0.234823 0.754653 0.447815 0.780303 0.572676 0.577647 0.586157 0.490235 0.528609

because it is much difficult to change the appearances and the distribution characteristics of build-up areas, which always assume the most of the economic functions of cities. Large scale of greening not only incurs the cost of transformation, but also leads to economic losses caused by inconvenience. 4.2. Time series analysis The coordination degree evaluates the synchronicity of developments among different indicators and different subsystems. That is to say, the degree of synergy is a concept relating to relativity. A lower degree usually means that the development processes of different indexes of the object are quite different, rather than those of the object are just backward. The coordination degree between economy and society is the highest, followed by that between economy and environment, with that between society and environment the lowest. The association and interaction between economy and society are the strongest, while those between environment and society are the weakest, a result consistent with the reality. The economic achievements brought by urbanization in China are obvious, but over recent years, scholars have been continuously raising questions about the ecological and environmental problems caused by urbanization. The government also notes that urbanization, if ignoring ecological construction, will not last long, and has already begun to increase economic feedback to ecology. But an effective interaction and connection between social construction and ecology has not yet been established. Also, the variance of the coordination degree between economic and social subsystems is the smallest, followed by that between social and environmental ones; and the variance of

the coordination degree between economic and environmental subsystems is the largest. The average variance among different cities in terms of environment is over 10 times that in terms of economy and society. Due to the small difference in the social system under the provincial framework and the similar progress in the construction of the social system, the social subsystem has an effect of reducing the variance in the degree of coordination. While the environment subsystem has an effect of enlarging the variances among cities, the economy subsystem has the function of enhancing the coordination degree during the advanced process of economic construction. The coordination degree between economy and society subsystems (see Fig. 1(a)) has the strongest uptrend. Most cities had reached the primary coordination level by 2017, except only Taizhou, which is still at a little lower level. The reason is that, compared with other cities, Taizhou’s indexes of social construction have greater improvements while its economic performance is weak. On the contrary is Nanjing, the second-lowest city, whose economy has increased significantly, but with not so many changes in social construction. In other words, its local economic achievements have not fed much back into the society efficiently. Cities with high coordination degrees between economy and social subsystems usually have a rapid growth in the rate of urbanization and a low rural Engel coefficient. Taking Nanjing as an example, which has one of the highest GDP growths, changes happened in the indexes belonging to the social subsystem is relatively small. For one thing, it has a so high start point in urbanization and transportation network that huge changes may be hard to realize. For another, its rural Engel coefficient is higher than other comparable cities. These cases indicate that, with the development of economy, the living

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M. Xu, W.-Q. Hu / Journal of Cleaner Production 258 (2020) 120641

Fig. 1. Coordination degree of development between subsystems.

costs are also rising fast with residents accumulating wealth. Obvious collective slowdowns or even drops in some cities’ coordination degrees in 2009 and 2015 have been observed. This can be attributed to the start and the end of policies for home appliance subsidies for rural areas. These policies could influence the consumption of rural residents. But they barely had any impact on other aspects, thus causing a drawback of coordination. The other two kinds of coordination degrees between subsystems have the similar trends and fluctuations. By 2017, the coordination degree between economy and environment (see Fig. 1(b)) has just reached a primary coordination level only in Zhenjiang, with half of the remaining cities at a nearly-disorder and half-disorder state. The group at nearly disorder includes Nanjing, Changzhou, Nantong, Huaian, Taizhou and Suqian, gathering around the central region of Jiangsu. These cities all have advantages on the environmental aspect, located around the Yangtze River and the Hung-tse Lake with better hydrothermal conditions. And as a result of lake pollution, the cities around Taihu Lake are at a lower level in the province of Jiangsu. But Lianyungang and Yancheng also have a rather good natural ecological environment and even have the world natural heritages, but these conditions fail to promote the local economy very well. As to the coordination degree between society and environment subsystems (see Fig. 1(c)), only Nantong, Zhenjiang and Changzhou were out of disorder by 2017, and they are the part of cities with high coordination degree between economy and environment as mentioned above. As is shown, the characteristics of changes in the socialenvironment and economy-environment relationships also exist in the development trend of overall coordination degree. That

happens because environments serve as the foundation of sustainable development, and a good natural ecological environment is a prerequisite for economic prosperity and social progress. But according to the cask effect, the backwardness in ecology and environment construction will seriously restrict the coordinated development between environment subsystems and other subsystems, and also influence the overall coordination degree. In the early years, because the economic development was the core performance indicator for assessing the officials of local governments, the concerns over environments was relatively rare. After joining WTO in 2002, environmental construction became one of the governments’ work focuses, and then an impulse in economy-environment, social-environment and overall coordination degrees could be seen in 2004. Fluctuations between 2007 and 2012 are mainly caused by the changes in water resources per capita and sewage discharge per unit of industrial output. The overall coordination degree of ESE system is shown in Fig. 1(d), which shows a slow upward trend. By 2017, none city has reached the primary coordination level, let alone a higher level. Seven cities stay at a disorder state, including Suzhou, Wuxi, Yangzhou, Yancheng and Xuzhou. As can be seen, these cities are in typically southernmost and northernmost part of Jiangsu. For the southernmost part, the economic level exceeds the carrying capacity of the local environments and resources, while the northernmost part fails to fully transform the local surplus resources into economic results. The points of fluctuation consist of those from the economy-environment and societyenvironment subsystems.

M. Xu, W.-Q. Hu / Journal of Cleaner Production 258 (2020) 120641

4.3. Spatial structure analysis For easy comparison, the coordination degree of each city is transformed into the standardized Geti’s-Ord value. As equation (8) shows, G statistic is the ratio of the sum of the observations at the adjacent location of element i to the sum of the observations at all spatial locations, mainly used to measure whether there is a local spatial correlation between the observations at each spatial location and the adjacent observations. It can be used to identify the spatial aggregation of high values or low values between a spatial unit and adjacent units. It can be found that the values around location i will be relatively high if they are significantly positive, indicating that they belong to a high-value spatial agglomeration area (hot spot area); on the other hand, they will be relatively low if they are significantly negative, indicating that they belong to a lowvalue one (cold point area).

G*i ¼

n X j¼1

, Wij Xj

n X

Xj

(8)

j¼1

and in this article, we use Jenks natural breaks classification method (NBC) to grade the data clustering level. This method is meant to determine the best arrangement of values into different classes, an arrangement under which each class’ average deviation from the class mean is minimized while each class’ deviation from the means of the other groups is maximized. In this way, the cities are divided into five categories, i.e., very hot, hot, moderate, cold, and very cold. As Figs. 2-5 shows, generally the number of hot and very hot cities is increasing, and so is the number of cold and very cold cities. And as shown in the graphs, the moderate areas shrink obviously, an important fact that leads to the polarization of the coordination degrees. From a trend perspective, the very hot spots continuously move southeast and the cold spots move otherwise. In 2003, the spatial distribution of the four kinds of synergies between different kinds of subsystems was quite different, but after a period of development, the spatial structure of the four synergies tended to be similar, especially when it comes to the coordination degree between the environment subsystem and other systems(see Figs. 3-5). This means that the environment subsystem now has a significant influence on the coordinated development. This phenomenon further illustrates that the development is a comprehensive process, especially when it reaches a certain stage, where the economic growth, social construction and environmental protection have obvious linkage effects and would complement each other. Meanwhile, due to the low degree of coordination degrees in the early stage of development, although the degree of coordination in 2003 shows synergies between cold spots and hot spots in spaces, the absolute values’ gaps between synergies are very small.

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In the later stage, the hot spots in the province continue to spread to the surrounding areas and gather in patches, forming a more obvious block. This also indicates that it is no longer effective and realistic to be outstanding by fighting alone; rather, location factors and appropriate cooperation with surrounding cities are becoming increasingly important in the current development. From 2003 to 2016, the development trends of economic-environmental coordination, social-environmental coordination and overall coordination are very similar. This phenomenon is consistent with the relevant conclusions in time series analysis. Because the lack of attention to environmental construction, when it comes to the coordinated development with environment subsystems, the coordination degrees will be significantly reduced and the fluctuation in environment subsystems will be directly reflected in the degree of coordination. This further illustrates that the environment is a key factor affecting the current balanced development of Jiangsu. Generally speaking, in 2003, the initial hot spots of coordination degrees between economy and society subsystems (see Fig. 2) mainly include Nanjing, Zhenjiang, Changzhou and Lianyungang. In combination of the original data, it can be found that the cold spots in the spatial distribution of coordination degrees in each year often correspond to the regions with a faster urbanization speed in that year, which exposes the problems that the rough urbanization may bring to the long-term development. The main focus of initial urbanization is economic urbanization, which is an incomplete one. In this process, little attention has been paid to the urbanization of citizens migrating from rural areas to cities, causing certain social contradictions; and no attention has been paid to a series of environmental problems that may arise from “land hardening”, a loophole that needs to be slowly remedied at this stage. Later in 2010, the sporadic core hotspot areas gradually merged under the obvious trend of southward migration. Thus, forming a situation where Geti’s-Ord value of four synergies all go down from south to north, which is in line with the economic prosperity. But when we look at the detailed data, we find that in cold area the economy indicators are pulling down the synergies, which indicates these cities has much uncovered potential for economic optimization. In 2016, core hot spots of coordination degrees between economic-social subsystems include Suzhou, Wuxi and Taizhou, and the hot areas and the moderate areas gather in the west and south side of Jiangsu. Only four cities, Lianyungang, Zhenjiang, Yancheng and Changzhou, are classified as cold areas, showing a much-coordinated development state. When it comes to synergies between subsystems of economy and environment in 2016, core hot cities mainly contain Yangzhou, Taizhou and Wuxi. And hot spots include Huaian, Nanjing, Zhenjiang, Changzhou and Nantong, mainly gathering in the central and northern part of Jiangsu. They are also hot spots of synergies in social-environmental subsystems

Fig. 2. Cold hot spot distribution of coordination degree between economy-society Subsystem.

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Fig. 3. Cold hot spot distribution of coordination degree between economy-environment Subsystem.

Fig. 4. Cold hot spot distribution of coordination degree between society-environment Subsystem.

Fig. 5. Cold hot spot distribution of coordination degree of combined ESE system.

and the ESE system, except Zhenjiang and Nantong, both of which are turning moderate. In the northern Jiangsu, there is a gradual change from core hot, hot to cold and core cold in the most northern cities. But in the southern part, only Suzhou is the cold and even the core cold city, which has the shortest distance to Shanghai. This fact may indicate that the unexpected huge economy benefits overflowing from Shanghai could be a kind of overpressure on Suzhou’s local environment and social system. After some periods of time, Suzhou may successfully transform the advantages in economy to the advantages in social construction and environment protection. 5. Managerial implications Based on the analyses above, the following suggestions are put forwards. First, to build a circular economy and effective channels for economic, social and environmental resources to exchange and

interactively develop. The construction mode of sacrificing the environment for the economy should be discarded, and a new way of coordinated development of economy, society and environment be explored. The authorities can include more indicators on environmental protection and social construction in the performance appraisal of government officials. Second, to reduce the loss of environment in economic and social construction, while strengthening the economic and social feedback to the environment. This is the only way to achieve a higher level of development. As elucidated earlier, waste water discharges now have serious influences on the coordinated development, so it is of great significance to introduce a new eutrophication sewage treatment method, like biomass approaches (Sepehri and Sarrafzadeh, 2018; Sepehri et al., 2020). And third, to strengthen regional cooperation and form an efficient platform for resource sharing and win-win cooperation among regions. It is necessary to jump out of the thinking of administrative division, speed up the circulation of

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interregional factors, promote win-win cooperation among regions, and promote a balanced development of regions. This kind of cooperation shall be conducted not only in the aspect of economic construction, but also in the dimension of social development and environmental protection with strong diffusion linkage effects. Moreover, to build a balanced environment, local governments should timely adjust their environmental policies according to the natural capacities for development. And they should make up mind to address the environmental problems left over from the early development, pay attention to the relative consistency of the economic, social and environmental development process in the future development planning, and alleviate the pains caused by the “three-stage overlap” in the “new normal". 6. Conclusion For a high-quality development, the development is the fundamental condition, and the coordination is the key means and fundamental goal of sustainability. This study employs a combined system model with entropy weights to evaluate the coordinated development in Jiangsu from 2003 to 2017, and carries out analyses from both time-based and space-based perspectives. The results show that the overall coordination degrees present an upward trend, the combined coordination degrees in the six cities have come out of disorder, and almost all the cities have reached a primary coordination level between economic and social subsystems, indicating a promising future. However, the coordination degree relating to the environmental subsystem is generally low and shows a trait similar to development and change, meaning that the backwardness of environmental management is the bottleneck for sustainable development. As illegal actions in water resources and sewage discharges can have an arresting influence on the coordination degree, it is of great importance to maintain the stableness of water environment. Furthermore, to extend and enrich the study, a hot-cold spot analysis is use to discuss the spatial distribution of coordination degrees. The four coordination degrees all show a trend of shrinking cold spot areas, gradually joining hot spots, and spreading from the core to the outside. It shows that the development at this stage depends to a certain extent on the location factors and cooperation with the surrounding areas for common development. The main sources of developments and changes are environmental factors, such as water resources per capita and sewage discharges per unit of industrial output. Meanwhile, the cold spots of the coordination degree related to the environment are distributed in the highspeed urbanization areas, indicating that the incomplete urbanization will restrict the further development of the areas in the long run. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. CRediT authorship contribution statement Mingxue Xu: Software, Investigation, Writing - original draft. Wen-Quan Hu: Resources, Writing - review & editing, Visualization. Acknowledgments This research is funded by Zhejiang science and technology project (16NDZB01ZD), a study on countermeasures of

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