Study on the interaction and relation of society, economy and environment based on PCA–VAR model: As a case study of the Bohai Rim region, China

Ecological Indicators 48 (2015) 31–40

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Study on the interaction and relation of society, economy and environment based on PCA–VAR model: As a case study of the Bohai Rim region, China Feifei Tan a , Zhaohua Lu a,b,∗ a b

Institute of Restoration Ecology, China University of Mining & Technology (Beijing), Beijing 100083, China Shandong Provincial Key Laboratory of Eco-Environmental Science for Yellow River Delta, Binzhou university, Binzhou 256600, China

a r t i c l e

i n f o

Article history: Received 12 March 2014 Received in revised form 17 July 2014 Accepted 26 July 2014 Keywords: Society, Economy and environment subsystems Principal component analysis Vector autoregressive model Sustainable development The Bohai Rim region

a b s t r a c t Ongoing success throughout regional development is contingent on maintaining the function, quality and harmony progress in society, economy and environment domains, so exploring the interaction and relation among them should be considerable significant. The model by coupling principal component analysis and vector autoregressive, which relate the aggregated values and dynamic analysis among factors, is proposed to achieve the qualitative and quantitative analysis of interaction and relation among society, economy and environment subsystem, providing a framework to conceptualize the influences among their changes and simulate the future scenarios in the Bohai Rim region. The impulse response analysis and variance decomposition of vector autoregressive method, in particular, permit dynamic interaction between every two systems and display clear decomposition of contribution for each change, respectively. This study results show that: there is a virtuous circle of promotion between economic growth and social progress no matter which is regarded as the endogenous variable during the study period, and yet the negative effect to environmental changes had been produced; what the improvement of social and environmental situation need most might be advancing its own progress; it is also reflected that the entire complicated system walk on the path of unsustainable development due to the evident disequilibrium of three subsystems; and the scenario analysis results obviously tell that in order to attain coordinated development, the annual growth rate of 12% to 16% of overall environmental level should be anticipated along with 8% of social and economic level improve. The study guide future possibilities for relatively more harmonious interconnections among social, economic and the environmental development. © 2014 Elsevier Ltd. All rights reserved.

1. Introduction Since the scientific book, Silent Spring, initiated the reflection of development issue in 1962, numerous exploration and studies had been devoted to appropriate and scientific development pattern, until publication of the report, Our Common Future, which indeed defined that an urgent need to focus explicitly on ecologically sustainable economic development in 1987 (Costantini and Monni, 2008). Thereby human had to introspect upon their behaviors and search for a new development road because the traditional socioeconomic development had been threatened by environment

∗ Corresponding author at: Institute of Restoration Ecology, China University of Mining & Technology, Beijing, Xueyuan Road No. 11, Haidian District, Beijing 100083, China. Tel.: +86 010 62331034. E-mail addresses: [email protected] (F. Tan), [email protected], [email protected] (Z. Lu). http://dx.doi.org/10.1016/j.ecolind.2014.07.036 1470-160X/© 2014 Elsevier Ltd. All rights reserved.

deterioration and resource depletion (McMichael et al., 2003; Ostrom, 2009). As a matter of fact, the reliable approach for dealing with the dilemma should be required well understanding of the significant contributors to whole sustainable development system and of the ways how the environmental management answered to particular policies in decision-making process, especially the interaction and relation among a number of related social, economic and environmental factors when framing effective environmental management regulations and policies for a specific region (Patterson et al., 2004; Ostrom, 2009; Ma et al., 2008). It is generally known that national or regional development should be the eternal theme of entire society, and yet the one in real sense should be diversified, incorporating wealth growth and comprehensive progress of social, political, economic, cultural and ecological environment (Wei and Liefner, 2012; Dai et al., 2013; Wu, 2013). In a huge social-economic-environmental complex system, the society subsystem affords the necessary human resources and infrastructure, while economy subsystem provides fund

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support and environment subsystem endows material foundation to the survive and progress of human society, and thereby the related matters should be viewed as the complicated issues with all subsystems rather than any single subsystem (Ma and Wang, 1984; Bogggia and Cortina, 2010). Since the seminal paper by Grossman and Krueger (1995) there has been considerable academic interest in the relationship between economic development and environmental pollution, and the empirical integrations of environmental issues and economic growth theories have been widely analyzed in many literatures (Bertinelli et al., 2012; Shahbaz et al., 2012; Jorgenson and Clark, 2012). There also has been, from the outset, a large number of literatures in the fields of modeling and analyzing in social and economic aspects (Brock and Taylor, 2010), which commonly select a single indicator to reflect each subsystem, however, it is not great popularity of quantitative study on overall interaction and relation of the social progress, economic growth and environmental changes so far. Exploring the relationship among these variables may be a relatively novel field and considerable significant, especially the specific interaction and relation need to be separately revealed by different technical methods when all the factors in each subsystem can be taken as an entirety. The research outcomes can be employed to help address an integrated paradigm for sustainable development, with endogenous or exogenous treatment to the comprehensive level of each subsystem considered. Principal component analysis (PCA) is frequently considered to be a very common and well-studied data analysis approach that aims to identify some linear trends and simple patterns in a group of samples (Hosseini and Kaneko, 2011; Douka et al., 2012), such as sustainable development indicators (Abou-Ali and Abdelfattah, 2013). The methodology can conduct to convert multiple indexes to a few synthesis and irrelevant indexes from high dimension to low dimension effectively within a given framework, and finally an aggregated value can be gained. Actually, it is very meaningful to provide us with the access to the overall level of national or regional development, so there are increasing literatures on applications in different scales and various aspects (Seema and Lilani, 2006). Meanwhile, vector autoregressive (VAR) model, which is proposed by Christopher Sims and not relied on economic theories (Bagliano and Morana, 2009), is extensively used as an analytical tool in econometrics to make comprehensive and dynamic analysis of several interrelated economic variables in non-stationary time series (Dees et al., 2007; Gao, 2009). Especially, it incorporates some practical operation means and process, such as impulse response analysis and variance decomposition, containing the merits of unique perspective and unambiguous analysis. Thus, an idea of analysis model by coupling VAR and PCA is anticipated to fulfill the clear dissection of the qualitative and quantitative interaction and relation of social, economic and environmental changes. In practice, employing PCA as a pre-processing tool of VAR model can ease the data miscellaneous, embrace the subsystem’s major components and reveal the hidden relationship; in turn, VAR can extend the analysis functions of PCA to realize both dynamic simulation and trend prediction of system behavior. This paper proposes a PCA–VAR model to understand the interaction and relation of the social, economic development and environmental changes in the Bohai Rim region during the period 2001–2010, taking into account interactions and feedback loops among society, economy and environment to the fullest extent, with the future scenario analysis simulated (Swart et al., 2002; Tokimatsu et al., 2013), and pursues the following main objectives: the proposal and construction of a coupling model of analyzing relations among social, economic and environmental changes; the application and simulation of the specific linkages and decompositions of the subsystem changes in the Bohai Rim Region; the presentation of path or direction to

sustainable coordinated development by setting future scenarios. 2. Methods and material 2.1. Study area and data resources The target area of this study (Fig. 1) surrounds the Bohai Sea in China, locates at 34◦ 22N –43◦ 26 N, 113◦ 04 E–125◦ 46 E, and contains two municipalities (Beijing and Tianjin) and three provinces (Hebei, Shandong and Liaoning). The land area, population and total gross domestic product of the Bohai Rim region in 2010 are about 5.18 × 105 km2 , 2.4373 × 107 and 1.0136 × 1012 yuan, accounting for 5.75%, 18.18% and 25.26% of China, respectively. Although economy growth in the region is clearly perceived, the traditional development concept has brought about severe resource shortage and ecological environment problem and restricted the economic and social development in turn. Following the Pearl River Delta and the Yangtze River Delta, it is indispensable to research the interaction and relation of society, economy and environment in regional sustainable development and assess the coordination by simulating the future scenarios. Most of the employed data of each subsystem in the Bohai Rim Region stem from the standard yearbooks, which are compiled by the central government and subordinate ministries (CSY, 2011; BSY, 2002–2011; TSY, 2002–2011; HEY, 2002–2011; SSY, 2002–2011; LSY, 2002–2011). Part of population, economical and educational data are obtained from China Population Statistical Yearbook and China Education Statistical Yearbook of corresponding years (CPSY, 2002–2011; CESY 2002–2011), with the statistical bulletin of district development replenished. Unlike other regions and states, Chinese statistical data from place to place are often available after 2–3 years delay. The Chinese annual statistical yearbooks are published by State Statistical Bureau and cover the enough categories and sectors, so it is helpful to improve credibility of the study. 2.2. Methods This study applies PCA model to aggregate into a compositive value for the society, economy, environment subsystems based on the construction of index system, respectively, and subsequently the VAR model, which is an econometrics approach, is introduced to present the dynamic relationship among three subsystems when the results of PCA are regarded as initial data, and regression equation and the fitting outcome can be used to simulate and predict the future trends by designing several various scenarios. Meanwhile, the impulse response analysis and variance decomposition can play an important role in the analysis process since the specific interaction and relation among variables can be generally expressed by them in econometrics study. In particular, the lagged value of each shock and the contributions to endogenous variable from each structural shock can be used to analyze the interaction and relation among the subsystems quantitatively when adopting impulse response analysis and variance decomposition, respectively. 2.2.1. PCA method PCA is a statistical technique which can utilize the linear transformation of interrelated variables, with the objection of reducing the extended original set to a smaller set of linear combinations that accounts for most of the variations in the former set (AbouAli and Abdelfattah, 2013). It can also be deemed as a variable reduction technique since the number of observed variables can be decreased to avoid a large sample procedure, which has further been utilized to provide better ideas in the assessment of integrated regional development level (Li and Niu, 2010). The

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Fig. 1. Location of the Bohai Rim Region, China.

central theme of PCA is to reduce the dimensionality of a data set but remaining the vast majority of information by aggregating into several new variables, the principal components, which are uncorrelated, orthogonal and ordered and should present the information of original variables. Meanwhile, the comprehensive assessment idea has been concerned broadly to synthesize multiple evaluation indexes into a holistic value, and therefore the compositive value will be gained once the weights of principal components mentioned above are determined (Gómez-Limón and Sanchez-Fernandez, 2010). If considering the variables X1 , X2 , . . ., Xp , a PCA of variables can generate several new ones, known as the principal components (PC), PC1 , PC2 , . . ., PCp are the principal component coefficients. The principal components can be expressed as follow: PC1 = a11 X1 + · · · + a1t Xt = Xa1 .. .

(1)

PCt = at1 X1 + · · · + att Xt = Xat Moreover, some hybrid models of PCA in conjunction with other methodologies have also been proposed, such as geographically weighted regression, data envelopment analysis and so on (Adler and Yazhemsky, 2010; Ghosh, 2010). 2.2.2. VAR model What start as VAR model is the seminal article by Sims (1980), and then this methodology was applied to a vast range of empirical topics and various regions. It is as an approach that can be employed to achieve comprehensive dynamic analysis of multiple interrelated economic variables and has the ability to obtain

predications of relative time series system and dynamic impact analysis to the variable system from the stochastic disturbance (Gao, 2009). The dynamic interaction can be estimated and presented by the regression of lagged terms from one endogenous variable to all endogenous variables of the model in short and long term in detail, by which we can understand the impact from itself and the others. The basic expression is as follow: yt = A1 yt−1 + · · · + Ap yt−p + Bxt + ut (t = 1, 2, . . .T )

(2)

where yt is the endogenous variable vector and xt is the exogenous variable vector; p acts as lagged intervals for endogenous variables; T indicates the number of samples; ut can stand for white noise time series of vectors (Giacinto, 2010). 2.2.2.1. Impulse response. A time series with a unit root is nonstationary with an infinite unconditional variance, and thus it is not possible to be generalized to other time periods. Augmented Dickey Fuller (ADF) is usually employed in examining whether the time series is stationary and a co-integration test should also be required under certain conditions. It is impulse response function that can be modified to general accommodate multivariate autoregressive conditional heteroscedasticity for a primary tool of VAR. As discussed in Hamilton (1994), impulse response function (IFR) aims at the effects of endogenous variables when stochastic disturbances suffer the shocks, and emerges to describe the effect of the current and future values of the shocked endogenous variables after establishment of VAR model (Pesavento and Rossi, 2007). It requires no prior boundary to variables as it is more empirical than theoretical, and therefore the variation of an error term needs to be analyzed rather than the effect of one variable to another. For the expression of VAR model mentioned above, it can easily be transformed to the

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Table 1 The indicators system of the social, economic and environmental subsystem in the Bohai Rim Region. Subsystems Indicators of social subsystem People’s standard of living

Social equity Society development level

Indicators of economic subsystem Economic scale

Economic structure Economic benefits Indicators of environmental subsystem Resource level Ecological index Environmental pollution

Environment protect

Serial number

Names of indicators

C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12

Unemployment rate Engel coefficient in whole society Adult literacy rate Per capita disposable income in whole society Average life expectancy Rate of per capita income of peasant and urban residents Urbanization rate Per capita living space Hospital beds per ten thousand people Number of students in colleges and universities Per capita throughput of post and telecommunications Per capita highway mileage

C13 C14 C15 C16 C17 C18 C19 C20 C21 C22

Per capita GDP Economy density Per capita fiscal revenue Per capita exports Per capita fixed asset investment Per capita retail sale of consumer goods Proportion of primary industry output Proportion of tertiary industry output Average wages of staff and workers Social labor productivity

C23 C24 C25 C26 C27 C28 C29 C30 C31 C32 C33 C34

Per capita water resource GDP energy consumption per ten thousand yuan Per capita forest stocking volume Rate of nature reserves to land area Per capita emissions of SO2 Per capita emissions of COD Per capita emissions of solid wastes Rate of environment protection investment to GDP Industrial solid wastes comprehensive utilization ratio Industrial wastewater discharge compliance rate Industrial dust and smoke emissions compliance rate Industrial SO2 emissions compliance rate

following equation: yt = C + ut + A1 ut−1 + A2 ut−2 + · · · =C+

∞ 

Ai ut−1

(t = 1, 2, . . .T )

(3)

i=0

if the model is covariance stationary. Where the element in row ith and column jth represent the lagged effect of each shock from the ith variable to jth variable, which is also called the impulse response from the ith to jth. Impulse response function describes that the shock on an endogenous variable will make effect on the others in a model, and it could provide more information about the dynamic feature that the variables interact each other. 2.2.2.2. Variance decomposition. The dynamic characteristics of VAR model can be depicted by variance decomposition, while the significance of different structural shocks should be measured by the contributors from every structural shock to endogenous variable. It is necessary to obtain the related important information of random error term and present the relations among variables in the form of variance percentage. Essentially, the basic idea is decomposing the variation of total endogenous variables into several components related to equations and the importance degrees of them, and thereby the contributors of influencing on the endogenous variables from each structure shock can be analyzed (Ghosh, 2010). 2.2.3. PCA–VAR coupling analysis model The coupling analysis model has been constructed through the basic theory and method of vector autoregressive and principal

component analysis. To guarantee rationality of analyzing interaction and relation of regional social, economic and environmental changes, it is inevitably to divide the whole regional complicated system into society, economy and environment subsystems and construct index system for each subsystem in light of the science, maneuverability, hierarchical and dynamic properties (Table 1). In accordance with the data of three subsystems in the Bohai Rim Region, which are acquired by calculating and measuring the related data of five provincial areas, the subsystems can be assessed comprehensively due to the synthesis score or value of each subsystem from several linear combinations of original variables. Afterward, the values should be defined as initial data to establish VAR model because they stand for the actual levels or positions of three subsystems, and then the interaction or relation of the subsystems’ changes are analyzed by impulse response and variance decomposition, with modeling and simulation made. All the process mainly works on SPSS17.0 and EViews6.0. And they can be followed as the below equations: ui = f (x1 , x2 , . . .xn ),

i = SOC, ECO, ENV

v = h (uSOC , uECO , uENV )

(4) (5)

where u and f (x1 , x2 , . . ., xn ) stand for the comprehensive evaluation by PCA; v and h (uSOC , uECO , uENV ) represent the VAR calculation; x1 , x2 , . . ., xn is the original data of index system. The development situations of subsystems should be considered as comprehensive as possible when construct index system with availability and feasibility simultaneously. Among the indicators of social subsystem, C1–C12 reflects the current situation from the aspects of people’s standard of living, social equity and society development level; for the economic subsystem, C13–C22

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Table 2 The results of compositive value of three subsystems in the Bohai Rim Region.

Society Economy Environment

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

0.24 0.09 0.11

0.31 0.30 0.33

0.45 0.42 0.59

0.68 0.50 0.85

1.02 0.86 0.90

1.83 1.22 0.71

2.13 1.58 0.84

2.37 1.98 0.88

2.65 2.28 1.06

2.86 2.76 1.16

outlines the scale or size of economies, economic structure and economic benefits; the environmental subsystem consider resources (C23 and C24), ecological index (C25 and C26), environment pollution (C27–C29) and environmental pollution state (C30–C34), respectively. It is worth mentioning that some indicators have been addressed through some specific process due to the inconsistent statistics caliber of urban and rural residents, such as C2, C5 and C8, which are computed on the basis of living area, per capita income and Engel coefficient of urban and rural resident as weighted by the demographic data. The adult literacy rate stems from the population of illiterates who are aged 15 or older and the total population who are aged 15 or older in the region. 2.2.4. Scenario analysis The objective of scenario analysis is to search the future possibility whether current situation should be redesigned and to evaluate and simulate the potential state of some alternative strategies or measures, which is in favor of expecting some appropriate targets to guide current and future decision making (Ni et al., 2012). For the moment, the major flaw in analytical techniques, such as forecasting, is that models extrapolated from historical data, which usually imply with the assumption that the research object should remain relatively stable. As some supplements, several scenarios at various growth rates will be designed to apply in this study. Based on the specific objectives set of gross domestic product and other indicators in ‘twelve five-year plan’ of China and the five provinces in the Bohai Rim Region, the following three scenario combinations were identified in this study: SOC1 + ECO1 + ENV1(Scenario I), SOC1 + ECO1 + ENV2 (Scenario II) and SOC1 + ECO1 + ENV3 (Scenario III), among which the annual growth rate of environment development level with 8%, 12% and 16% are set as ENV1, ENV2 and ENV3, respectively, while the ones of society and economy subsystem are always set as 8% (SOC1 and ECO1).

In terms of sustainable development level of the Bohai Rim region in different stages (Fig. 2), an illustration of slightly and distinctly progress in the former and latter, respectively, can be observed from the two stages in the tenth five-year plan and eleventh five-year plan of China, among which the more significant progress in the second stage should attribute to the increasing related policy support in the federal and the Bohai Rim region, including the “Five-Point One-line” Strategy of Liaoning, Caofeidian development strategy of Hebei and construction of the Yellow River Delta high-efficiency ecological economic zone of Shandong, and so on. Specially, in the eleventh five-year plan, the related energy saving emission reduction efforts and the total emission control of major air pollutants were stressed and equipped with the performance evaluation system in leading cadres in transforming economic development patterns, which provided with certain room for environmental subsystem level growth, and the improvement of socioeconomic level may be brought about by building a new socialist countryside, promoting industrial structure optimization and upgrading and accelerating service industry development. With respect to the first stage, there should be more dependence on the availability of resources and energy for the production and consumption goods and the sacrifice of ecological environment condition, including natural social capital coastal zone, although some policies were proposed, such as regarding development as major objective and taking structure adjustment as main way. To sum up, the total situation of radar figure, by which we can understand the relationships of complex system, seem from ‘thin’ to ‘fat’ during 2001–2010, revealing that the socioeconomic development level from equal resources occupation and eco-environmental capability is uneven, which may depend on various science technology level, culture and moral quality, ecological environment and policy orientation. 3.2. Results of impulse response analysis

3. Results After determining the index system of society, economy and environment subsystems, the compositive values of each subsystem in Bohai Rim Region are obtained by PCA as the initial data of VAR model, respectively. Thereby the qualitative and quantitative interaction and relation of the social, economic development and environmental changes during the period 2001–2010 are analyzed by the impulse response analysis and variance decomposition. And the regional development state are predicated and simulated by the model established during the period 2011–2020. 3.1. Results of comprehensive evaluation based on PCA From Table 2, the compositive values of each subsystem in Bohai Rim Region have significantly increased during 2001–2010, especially the society and economy subsystems, demonstrating the socioeconomic development and the improvement of living standard are apparent during the period although ecological environment has changed a bit comparatively. The movement of comprehensive level is regarded to agree to the fact since social and economic development requires adequate resource and disposal position.

It is generally known that the unit root test, which is commonly achieved by augmented Dickey Fuller (ADF) test, can be employed to check out the stationary of time series and the integration order of variables before conducting the VAR model. The ADF test results show that SOC, EOC and ENV, which stand for the comprehensive level of society, economy and environment subsystems, respectively, are non-stationary time series at 0.10 significant level, while D(SOC), D(EOC) and D(ENV), which present the values of first-order difference and signify the variation (increment or decrement) of the comprehensive level in the subsystems, are stationary ones. Such that explain the interaction among the variables of three subsystems can be described by VAR model. The dynamic impacts from endogenous variable on stochastic disturbance are depicted by impulse response function and the current and future effect from other subsystems and itself are gained when one subsystem’s changes are regarded as an imposed impact from stochastic disturbance. It is clearly observed that Fig. 3 presents the results of impulse response analysis. The two dotted lines up and down stand for confidence interval, and the abscissa and ordinate indicate the time period from 2001 to 2010 (phase 1 to phase 10) and the effect of the shocked endogenous variables. Fig. 3(a) shows that the positive effect on future economic development would be brought about by

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Fig. 2. Comparison of three subsystems development level of the Bohai Rim region in different years. SOC, ECO and ENV represent the society, economy and environment subsystem, respectively, similarly hereinafter.

advancing the economic growth of current phase; nevertheless, it has decreased at a definite rate from phase 1 to phase 5 and gradually present as a smooth curve and flatten trend after phase 6, revealing that there has been always persistent effect in economic development and actually the accelerating effects have remained in spite of the drop of accelerated velocity. As seen in Fig. 3(b), the improvement of current economic development level have led to short positive effect to environmental subsystem in phase 1, and yet the negative effect appears after phase 2 when the maximum of positive one and negative one are at phase 2 and phase 3, demonstrating that economic development may need to rely on the support and carrying capacity of ecological environment to a large extent. In the later stage of the period, it is also an undoubted actuality that the negative effects gradually decrease and level off because eco-environmental pressure may be eased a bit once the quality of economic growth is improved, such as optimization of economic structure (the growth of tertiary industry and social labor productivity). The response from D(EOC) to D(SOC) (Fig. 3(c)), meanwhile, it is more easily to understand that raising the economic subsystem level has indeed cause positive effect to social development, showing that economic development is available to improve people’s

Fig. 3. Impulse response figure among society, economy and environment subsystems.

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living standards to a certain degree, with a time lag the conversion of achievements harbored. With respect to the response from environmental changes to others and itself, Fig. 3(d) and (f) shows that the negative effects to society and economy subsystems have been induced by the ecological environment improvement during the period, of which the ones in early stage are both larger than later. As a matter of fact, it is possibly deemed to that implementing environment protect measures should require large economic and human consumption. Meanwhile, it is distinctly noted that the impulse response figure of the response from environmental changes to itself (Fig. 3(e)) illustrates that advancing the process of environment protect can produce positive effect in phase1 and transform into the maximum of negative one in phase 2, and yet remain a positive one after phase 3. Consequently, the effect of environment protect strategies are obvious in the beginning and gradually lost its sensitivity due to the accumulated results of high depletion, pollution and consumption in a long term. It can also validated that a progressive and lengthy process should be required to carry ecological environmental protection at any time, and the effect will be more obvious and stable if the positive effect of environment subsystem keeps increasing at a full speed for a long period. According to Fig. 3(g), the variation of curve is roughly similar to Fig. 3(a) except that there is a greater influence from economy rather than from society in the front phases, revealing that social development may also bring economic growth. In terms of the response from social to environmental changes (Fig. 3(h)), the negative effects have been produced and kept a downside before phase 3, because improving the live standard, such as increasing living area and others, may consume more resources and occupy more environment capability. Meanwhile, the Fig. 3(i) is about the impact from society to itself, representing that the minimum and maximum of positive effects are in phase 2 and phase 3, respectively, i.e., social development has accumulating influence on itself. 3.3. Results of variance decomposition Although that, there is explicit analysis of the interaction of three subsystems from impulse response, the contribution to one subsystem’s changes, which is also the specific relation among subsystems, has not yet quantified. Fig. 4(a) demonstrates the distribution and variation of the contributions to economic changes when the changes of three subsystems are regarded as endogenous variables, where 0–12.2984% and 0–24.1688% of economic changes in the region can be explained by environmental and social changes and the rest stem from itself. Fig. 4(c) shows that 0–9.9368% and 0–6.4436% of environmental variation may be separately produced by economic and social changes while the remainder originates from itself. For social changes, it can also be found that besides the contribution from itself, the economic and environmental improvement separately gives rise to 0–3.0949% and 0–1.3978% of entire variation during the period. 3.4. Results of modeling and simulation The VAR model established in EViews 6.0 is expressed by the equation as follows:

Fig. 4. (a) The chart of variance decomposition of economic changes. (b) The chart of variance decomposition of social changes. (c) The chart of variance decomposition of environmental changes.

⎧ ⎨ yeco = 0.6927yeco,t−1 + 0.2499yenv,t−1 + 0.2956ysoc,t−1 + 0.0541 ⎩

yenv = 0.3295yeco,t−1 + 0.4865yenv,t−1 − 0.1802ysoc,t−1 + 0.3702 ysoc = −0.2587yeco,t−1 + 0.7987yenv,t−1 + 1.0324ysoc,t−1 − 0.0421

Fig. 5 shows the compositive values of the subsystems in the Bohai Rim Region during 2001–2010 from the model, presenting that the simulated values are very close to actual one, thus it is feasible to model and simulate the comprehensive level of three

subsystems in this way. It can also be observed that under the same scenario the social and economic level have obvious improvements during 2001–2010 while the increment of the comprehensive level in environmental subsystem is not significant from 2011 to 2020. In summary, the ecological environment will have to undergo a tremendous pressure, which is fatal to the operation of whole

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just keeping pace with the improvement of society and economy subsystem.

4. Discussion and conclusion 4.1. Main achievement

Fig. 5. The comparison between the actual and simulated value of the society, economy and environment subsystems in the Bohai Rim Region.

complex system, leading to an uncoordinated sustainable development state. 3.5. Scenario analysis The national and the regional sustainable development are considered as requiring certain improvement of some fields in this paper in accordance with the specific scenario setting described (Scenario I, Scenario II and Scenario III). Fig. 6 presents the variations of comprehensive level in society, economy and environment subsystems in different scenarios, observing whether the complex system plays well if the compositive values of economy and society subsystems keep a constant growth rate of 8% while ones of environment subsystem remain the growth rate of 8%, 12% and 16%, respectively. Actually, the simulated values of the historical data from PCA–VAR model manifest that the ecological environment level have failed to reach the minimum increase rate set in advance under the current development model. However, it can be found that the entire system may work very well if the comprehensive level of ecological environment can actually improve at the rate of 16% annually and it is also a expectation for coordinated development of three subsystems under the scenario of 12%, with somewhere in between of the environmental level growth anticipated. Therefore, the progress of environment subsystem require more rather than

Fig. 6. The comparison results of the social, economic and environmental subsystems in the Bohai Rim Region among different scenarios.

In this paper we have peered into the interaction and relation of society, economy and environment, seeking answers to the following questions: Is there a connection among the changes of social, economic and environmental subsystems? If so, how they influence one another in qualitative and quantitative way and what cause the changes mostly? Is the established model feasible? And what is the future situation? To answer these questions, various quantitative measures are tried and taken collectively to depict a suitable direction which can confirm future and potential pattern of humans and environment. With two considerations of the comprehensive level of regional society, economy and environment subsystems and the specific interaction and relation among them, the framework of PCA–VAR model was firstly constructed, composed of three modules as three steps in the process of specific analysis. In the first module, the overall development level of three subsystems are emphasized once the selected indicators cover as fully as possible; in the secondary analysis module, we focused on the dynamic influence of the subsystems between each other and the contribution to one subsystem’s changes from the others after determining the stationary of time series; and in modeling and simulation module, the comparison of simulated and actual values, the selection of different scenarios and the investigation of the coordination level are the key points and objectives. Especially, this study gives a new trial idea and approach to tease out the interaction and relation in a huge and complicated system and quantify the contribution to every subsystem from others. It emphasizes that one system in one year has the corresponding development degree after converting a large of historical data, i.e., the overall functions of regional development in every aspect should be considered adequately in analysis process. We need pay some attentions to test the stationary of time series before modeling, which may dominate the rationality and correctness of the model, and the original data require making first or second ordering difference once they are in the range of nonstationary. As a matter of particular importance, the PCA–VAR model is applied in the interaction and relation among social progress, economic growth and environmental changes of the Bohai Rim Region. The results show that there is a virtuous circle of promotion between economic growth and social progress as a whole no matter which is regarded as the endogenous variable, while the negative effect on environmental changes has been produced eventually. Meanwhile, what the improvement of environmental situation need most might be advancing its own progress according to the results of variance decomposition of environmental changes; it is also reflected that the whole complicated system walk on the path of unsustainable development by the evident disequilibrium of three subsystems from the comparison chart of simulation and verification; and the scenario analysis results obviously tell that the annual growth rate of 12% to 16% of environmental changes should be anticipated along with ones of 8% of social and economic changes remained. In conclusion, PCA–VAR model can effectively present the interaction and relation of social, economic and environmental changes, and subsequently the scenarios analysis on the basis of the modeling and simulation can provide advices about what we will do, which is benefit to the regional sustainable development in the real sense.

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4.2. Adaptive considerations 4.2.1. Consideration for model design and test Ecological and environmental change and degradation need to be considered together with both social and economic dimensions, but just one dimension is frequently regarded in relevant research (Costantini and Monni, 2008; Jia et al., 2009; Bertinelli et al., 2012), without fully investigation of variability. The major shortcomings of most models in analyzing the complex relations are confined to pursue the relation between two indicators (Shahbaz et al., 2012), which separately substitute for two systems, especially in the research of economy and environment (Fodha and Zaghdoud, 2010). The contribution degree to the environmental changes from others is usually set down as a hard thing to be quantified, and so is the one to the society and economy changes (Ranis et al., 2000; Lee, 2003). Avoiding the drawbacks above, the PCA–VAR model covers three dimensions of society, economy and environment and the significant applications including assessment of social and economic development, environmental improvement and coordination of sustainable development from overall systematical perspective in study process. With respect to the indicators selection and processing methods, which may directly affect the study results, it can improve the comprehensiveness and reality of the regional situation and give more guarantees of real regional development level and essential feature of some aspects (Ram and Ural, 2014). Especially it may also be an interest or innovation that environment pollution indicators are presented by the per capita value but not total emissions, and so are the post telecommunications and transportation indexes. 4.2.2. Consideration for model practice The interaction and relation of social progress, economic growth and environmental changes are the cores of regional coordinated development, which is commonly focused when assessing regional sustainable development (Chen and Gao, 2011). It is obviously known that their change or variation is a type of intricate and troubling situation and hard to be grasped, which should be symbolized and replaced by a simple and explicit value from summarizing three subsystems, with index system designed carefully. In order to eliminate the difference from various data’s units in PCA module, the maxi–min normalization method, which can ensure the standardized values stay in the interval during 0–1, is adopted instead of the zero-mean normalization, avoiding large deviation among standardized values. It seems to be a fly in the ointment that the standardized values may require to be redefined once there are some variations along with the addition of new data. Nevertheless, this methodology for further investigation should be viewed as a legitimate aggregation approach. Estimations of impulse response and variance decomposition in this study are specific to the comprehensive level changes of society, economy and environment subsystems rather than absolute size on account of the stationary value of first-order difference. And yet, the modeling and simulation aim to the absolute value of three subsystems to predict the future situation in this study. The identification issues of all structural disturbances in specific region have been also addressed explicitly. 4.3. Applications and implications Most researchers proposed Environmental Kuznets Curve (EKC) as a hypothesized relationship between environmental degradation and economic growth (Shahbaz et al., 2012), with other regression, fitting model and system dynamic among multisystems presented (Patterson et al., 2004; Zhan et al., 2012). Though these algorithms and models are intelligent, the matters between parameters or layers are not presented easily and clearly. Our

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empirical study of the PCA–VAR model get clear on the interaction and relation of society, economy and environment subsystems by the techniques such as impulse response, variance decomposition, modeling, and simulation. Meanwhile, this model considers both structural (such as the contributions to one subsystem’s changes and equilibrium of three systems’ development) and functional (such as the response from one subsystem to another subsystem and coordinated development in future scenario) analysis simultaneously; and socioeconomic needs and ecological needs are both fixed on the equal consideration. Hence, this coupling model may be applied in the analysis among other systems, such as energy, economy and environment systems, population, resource, economy and environment systems, and so on, which can also be operated as the supplement of the related mature research and is contingent on the research object in practice. No matter what reality the future scenario will be, there are some limits that should be acknowledged or addressed if we really have a clear scene of what the future holds. While the designed model primarily aim to regional comprehensive level in the systems, it has also proven useful for seeking to the explicit direction to achieve coordinated development. When taken as a group, ongoing success throughout the regional development should be contingent on maintaining the function, quality and harmony of subsystems in social, economic and environmental domains.

Acknowledgments We are grateful for support from the fund projects: the National Natural Science Foundation Programs in China (No. 71273260/G0312) and National Science and “Twelfth Five-Year” Plan National Technology Support Programs in China (No. 2011BAC02B01-05).

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