J. Cent. South Univ. (2017) 24: 1522-1528

DOI: 10.1007/s11771-017-3556-z

Effect of regional ecological carrying capacity on economic transformation

WANG Shuo(王硕), HU Zhen-hua(胡振华)

Business School, Central South University, Changsha 410083, China

Central South University Press and Springer-Verlag Berlin Heidelberg 2017

Abstract: Combined with the actual situation of the western region city of Zunyi, the three subsystems including social, economic and natural environment evaluation index systems of ECC have been built based on the theory of ecological carrying capacity (ECC). In addition, the factor analysis method has been used and the influence factors on the ECC in the economic transition have been gained. The results show that factors of ECC in the three subsystems have different influence: 1) the natural subsystem, which contains factors on the ECC, has obvious limitation, and it has the greatest influence on industrial waste discharge per capita; 2) the social subsystem has a restriction on the ECC, which affects the traffic environment mostly; 3) the economic subsystem has a certain restriction on ECC, which has a large effect on the consumption level per capita.

Key words: economic transition; ecological carrying capacity (ECC); factor analysis; Chinese western region; environmental engineering

1 Introduction

Economic transformation in our country is an effective measure to solve a series of problems on natural environment and social problems caused by the extensive development mode [1-3]. However, there is the great imbalance in the overall economic development of our country among different regions, including the north- south and east-west regional economic differences [4, 5]. Economic differences among these regions did not make the economic transformation process be coordinated [6-11]. The southeast coastal area belongs to the most developed area in our country, outputting low-end industries in the economic transformation, upgrading industry and promoting high-end industry competitiveness [7, 8]. And it also can further improve the quality of the industrial chain, give a help both on the regional economy and the overall economy of our country. On the contrary, the western region will face great challenges in the upgrading industry, and the proportion of low-end industries in regional economic is still very high, and the regional economic contribution rate is still occupying absolute proportion [10-12]. In addition, upgrading the industry will cause serious negative impact to low-end industries, which is just weakly competitive. To some extent, the economic transformation is speeding up quickly in our country under this background. Compared to the poor areas in domestic economy region, the shift of low-end industries in some developed regions is much inclined due to the regional economic development imbalance. From this point of above, the impact on some regional ecological resources and environment under the economic transition will not all the positive role brought by economic transformation [13-15].

Therefore, it needs to pay attention to the positive and negative factors impacting in the process of economic transformation. The impact on environment is obviously phenomenon among those complex factors [5, 16, 17]. For this condition in developed regions, outing low-end industries in the short term will cause economic benefits to decrease. Maybe it can directly reduce the pressure on the ecological environment. On one hand, for a long term, it is undoubted for great help to promote the competitiveness of the industry. On the other hand, for the less developed regions, industry may be gathered much in the low-end, labor-intensive industries and transition process, due to the imperfect industry chain [10, 18, 19]. The status may directly impact supporting pillar of economic development, then cause the regional economy a lot of damage in a short period. Hence, for the ecological environment in underdeveloped region, it is the double pressure of the transformation from local and other area [20-22]. As you know, resources and environment problems are increasingly prominent with the development of social economy, so there is the concept of carrying capacity of resources, environment, and ecology [23, 24]. The difference among these concepts lies in the angle and the starting point of the different understanding to the carrying capacity. Urban ecosystem carrying capacity reflects the urban ecological supporting system in urban development and support functions [25-29]. It is the unity of supply, demand and contradiction between the infinite demand of social economic development and the limited supply determination of ecological support system to determine the threshold of ECC [22, 27, 30]. In addition, the threshold characteristics of ECC are often shown on a bottleneck factor [2, 22, 31]. Therefore，ECC is a measure value to evaluate urban sustainable development capacity. Therefore, analyzing the influence factors of ECC can realize the urban sustainable development in the economic transformation. More specifically, whether regional transformation has achieved reasonable results can be tested.

2 Methodology

2.1 General situation of research area

Zunyi, a famous international tourist city, is located in the hinterland of southwest China. Zunyi is the second largest city in Guizhou Province and has rich natural resources. In the field of water resources, the river is about a total of 9148.5 km and the existing water drainage density is 0.3 km/km², while the inland waters area is 130760 hectares, among which the pond is 4600 hectares. And the surface water resource (rivers runoff) is about 17.972×10¹⁰ m³, producing about 58×10⁴ m³/km², which is about two times the national average [7, 16, 20]. Secondly, Zunyi is rich in mineral resources. At present, there are more than 60 existing proven minerals and in 15 kinds of capacities it is the top one in Guizhou province. It is known that the coal, manganese, bauxite, mercury and pyrite are regarded as “five golden flowers”, while Zunyi has more than 25.761×10¹⁰ t coal resources, which is in the depth of 1500 m above [21, 28, 30]. Thirdly, planting breeding industry is relatively developed nowadays, which is attributed to the rich agricultural resources. Grain, oil, tobacco, livestock, tea, bamboo, medicinal herbs are the important resources of Zunyi city. Additionally, Zunyi is known as the “suck granary” and the food production accounts for more than a quarter of the total in the province. In 2014, annual production was about 2.916×10⁶ t.

2.2 Construction of evaluating indicator system

Zunyi city, a heterotrophic ecosystem, is unable to sustain urban self-organization system. So, its urban operation depends on the constant supply of energy, food, raw materials resources. On one hand, due to the body of main population centers and high intensity economic activity, the city has a quality demand of resources, energy, raw materials and strict ecological environment. On the other hand, some cities subsystem can not provide the required materials because of the dissipative structure and artificial ecosystem. It is high aggregation, incomplete, vulnerability and complexity to achieve sustainable development of urban ecological system. And the city has to depend on a certain ecological support to get materials, energy, output products and so on. Compared with natural ecosystem, the relationship between the carrying object and substrate of urban ecosystem is complex, resulting in a fact that the concept and connotation of urban ecosystem carrying capacity is quite different from the traditional carrying capacity. It is not only limited to how the urban ecological system is able to sustain many people and economic activities, but also based on satisfying human life level and the requirement for the ecological environment.

It is generally recognized that urban ecological carrying capacity usually consists of three parts: resources carrying capacity, environmental carrying capacity and ecological elastic force. The resources carrying capacity depends on the regional resource and the way to exploit and utilize resources. The environmental carrying capacity is concerned with the environmental capacity under a certain environmental standards. And the ecological elastic force refers to the ecological environment in the internal and external disturbance and the elastic limit pressure, which has characteristic of self adjusting and resilience. The size is related with the composition structure, component of the ecological environment and ecosystem elastic degree.

Based on the analysis of the related researches, all of the factors such as nature, society and economy have a great influence on ECC. Thus, an indicator system consisting of nature, society and economy subsystem is constructed, and we try to analyze the factor hidden in these subsystems to find out, which is most influential factor. Indicator system has 28 indicators, which belongs to the three subsystems including nature subsystem (8 indicators), society subsystem (10 indicators) and economy subsystem (10 indicators). After analyzing three subsystem indicators, the most influential factor can be found and the better economic transformation can be developed. The indicators of the subsystem are listed in Table 1.

2.3 Evaluation method

Raw data were from statistical yearbooks of Zunyi. For the selected dimension was not uniform, data normalization was required by the following formula.

Positive index:

             (1)

Table 1 Indicators of subsystems

Negative index:

             (2)

where x_i is the standard value of x_i; a_i and b_i are the maximum and minimum values of every index, respectively. The method of data processing above is the “Max-Min”, which is assuming the attribute values of the minimum and maximum. Through standardization of Min-Max projecting in the values of the interval [0, 1], x_i would be one of the original value. x_i can be divided into the forward and reverse indexes according to the characteristics of index. For greater value of the forward index, the level of the target layer is higher.

3 Results and discussion

3.1 Evaluation processing

Factor analysis method is used under the environment of the SPSS19.0. The main steps are as follows: ① standardizing original variable data to eliminate the influence of each rating index dimension according to the formulas (1) and (2); ② establishing a standardized data correlation coefficient matrix; ③ correlating matrix characteristic value, variance and cumulative variance contribution rate according to principles of the cumulative variance contribution rate of 80% or greater number to determine the extraction factors; ④ using orthogonal rotation of variance in the solution to make the load coefficient of each factor; ⑤ founding out the factor loading scores and comprehensive scores.

3.2 Data processing

In order to find these factors on the ECC, data processing was divided into three parts to achieve the goal of three subsystems data separately.

1) Nature subsystem

Principal component extraction for standardized data of nature subsystem is obtained as follows.

As shown in Table 2, there are three factors’ greater than eigenvalue. And there are three kinds of compositions to be able to explain natural subsystems affecting ECC. In principle, the cumulative contribution rate of 85% can explain the original variables perfectly, but based on the limit of sample size, we can only choose 80%.

As shown in Table 3, for component 1. in N₅ and N₆, the two factors of the discharge of industrial solid waste and industrial waste gas emissions had the larger load coefficient and the explained variance contribution was 37.655%. It reflected the factors influencing the ECC of natural ecological system, and these factors had advantages, which could be named “industrial waste effect”. For Zunyi, therefore, considering the influencing factors of natural ecological system to ECC, management in these two aspects should be strengthened, especially facing the background of economic transition. Reasonable controlling industrial waste pollution was the guarantee of local ecological economic system.

For component 2: in N₄ and N₈, the N₈ had a larger load coefficient, namely, industrial wastewater emissions and land area per capita have large load coefficient, which could be the reason that variance contribution ranked second, 27.775%, and could be named land area Extraction method: Principal component analysis.per capita industrial waste water discharge. The common factor showed that the influencing factor in Zunyi natural subsystem pollutant per capita was also an important factor, which needs to be strengthened to evaluate pollution per person.

Table 2 Explanation of total variance

Table 3 Public factor loading matrix after rotation

For component 3: in N₁ and N₇, forest coverage and industrial waste gas emissions had great load coefficient, of which the variance contribution was 17.415%. It reflected “pressure” and “support” of Zunyi nature subsystem, just a comprehensive impact on the ECC. High coverage forest can reduce waste pollution to improve the atmospheric environment, but to absorb the harmful gas of the type and quantity is limited.

According to the above-mentioned factor score coefficient, build a factor model as

F₁=0.072X₁+0.133X₂+0.233X₃+0.025X₄+0.103X₅+

0.373 X₆-0.161X₇+0.376 X₈

F₂=-0.004X₁-0.0361X₂-0.080X₃+0.426X₄+0.385X₅+

0.02X₆+0.069X₇-0.039X₈

F₃=0.418X₁-0.102X₂+0.234X₃+0.014X₄-0.105X₅-

0.181X₆+0.571X₇-0.149X₈

where F₁ is the estimate of nature subsystem variable calculated from component i (i=1-3); X_j is concrete value of N_j (j=1-8).

As shown in Table 4, the regression equation was obtained above, and then each factor score can be calculated. By calculating the factor score and weighing processing, these factors can be ranked.

Table 4 Factor score coefficient

Weighted weight=common factor characteristic value/the sum of extraction of characteristic value.

Took the original data of the standardized data into formula above, and we obtain respectively: F₁=0.192, F₂=-0.231, F₃=0.523, Z₁=-0.429.

From the score above, Zunyi natural subsystem score is a little low, based on reference standard (ZHANG Hao-hao, 2008), as listed in Table 5.

2) Society subsystem

Principal component extraction for standardized data of society subsystem is obtained as in Table 6.

For component 1: S₅ and S₈ of population density and the number of full-time teachers had a larger load coefficient shown in Table 7. The variance contribution rate was 33.768% which has reflected large influence on social subsystem factors (population and teachers’ number) in Zunyi. These two factors of component factor 1 showed that the demographic factor of Zunyi’s economic net primary ECC was an important factor especially when population was growing, and it would reduce the net primary. Labor was consistent with the ecological carrying capacity, which also reflects the human participated in social activities without other factors’ effect in the ecological system. To some degree, the latter belongs to labor capacity which was influenced by social and economic subsystem of ecological carrying. So, it would reflect the effects on the ecological carrying capacity. The number of teachers was closely related to the local higher education, which was the direct feedback on the impact of labor capacity. The larger the number of colleges and universities is, the more the high-quality level labor force would be provided. The Zunyi labor capacity showed a trend of rising in recent years which was a regional phenomenon, and it could also derived how to improve the quality of personnel from this. Therefore, the component can be named “population and the influence factors of labor”.

Table 5 Factor score range

Table 6 ECC evaluation level

Table 7 Public factor loading matrix after rotation

For component 2: S₆ was named “ten thousand people bus per capita” which had a higher load coefficient, and the variance contribution rate was 19.902%. Bus ownership per capita data reflects the region’s traffic level and this principal component 2 degree is higher, which means that the social system of Zunyi’s ecological carrying capacity region has larger impact on it. Combining the reality of Zunyi area situation, traffic factors and the reflection of the ecological carrying capacity factors, Zunyi was one of Chinese western region which is characterized by high altitude and regional characteristics of terrain are complex, the traffic environment at the point of impacting economic development was relatively higher than other provinces. So, it can be named “regional transportation influence factors”.

For component 3: S₄, per capita road area, had an high load coefficient whose variance contribution rate was 19.224%. And it also reflected the traffic environment factors’ regional influence on the ecological carrying capacity, and the explanation of this factor was different. The per capita road area not only contained the influence on ecological economic system main of body activity service, but also contained the business scope engaged in the activities. What’s more, it can be the effects on the regional ecological economic system. Relatively limited per capita road areas would cause confined economic activity to the fixed area and at a certain point, in continuing economic development time, it would produce excessive development resources, environmental damage, and so on. Compared with other domestic regions, the traffic environment in western region restricted the development of economy, and it was a reason for overall ecological fragile system. So, it can be named “regional energy flow influence factors”. The energy flow as the excessive concentration of ecological footprint, under the flow speed limits, may lead to a regional import and export power imbalanced.

According to the above-mentioned factor score coefficient, build a factor model as

F₁=0.061X₁+0.032X₂+0.200X₃+0.085X₄+0.326X₅+

0.032 X₆+0.002X₇+0.344X₈-0.173X₉+0.234X₁₀

F₂=0.240X₁-0.091X₂+0.081X₃-0.052X₄+0.001X₅+

0.452 X₆+0.438X₇+0.062X₈-0.176X₉-0.197X₁₀

F₃=0.015X₁-0.527X₂-0.098X₃+0.523X₄-0.014X₅-

0.034X₆+0.095X₇+0.095X₈+0.017X₉-0.053 X₁₀

F₄=0.359X₁+0.043X₂+0.216X₃-0.039X₄-0.085X₅+

0.041X₆-0.237X₇-0.165X₈+0.614X₉-0.119X₁₀

where Fi is the estimate of social subsystem variable calculated from component i (i=1-4).

After similar calculation, the test results are as follows: F₁=-0.155, F₂=-0.233, F₃=-0.589, Z₂=-0.033.

From the factor score point above, among the influence factors of social subsystem, only the service level in the composition F₄ is perfect; the rest of the score in the following of evaluation criterion and the overall score lower than 1 show the overall negative impact factor in Zunyi.

3) Economic subsystem

Shown in Table 8, data processing was the same as before, here no longer tautology. We tired out calculating result directly，including the analysis of score factor loading and extraction composition factor score. F₁ factor score is 0.377, at a common and lower level. F₂ factor score is 1.256, at a good level. F₃ score is 0.454, on average, at a common and lower level.

4 Conclusions

The way of factor analysis was used to deal with three subsystems of Zunyi ecological economic system: natural subsystem, social subsystem and economic subsystem. And the factors involved in ecological carrying value evaluation index are analyzed. The results showed that the bearing levels of influence factors in thethree subsystems are different. The results show that factors of ECC in the three subsystems have different influence: The natural subsystem, which contains factors on the ECC, has obvious limitation, and it has the greatest influence on industrial waste discharge per capita; The social subsystem has a restriction on the ECC, which affects the traffic environment mostly; The economic subsystem has a certain restriction on ECC, which has a large effect on the consumption level per capita.

Table 8 Factor score coefficient

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(Edited by YANG Hua)

Cite this article as: WANG Shuo, HU Zhen-hua. Effect of regional ecological carrying capacity on economic transformation [J]. Journal of Central South University, 2017, 24(7): 1522-1528. DOI: 10.1007/s11771-017-3556-z.

Received date: 2017-05-28; Accepted date: 2017-06-29

Corresponding author: WANG Shuo, PhD; Tel: +86-10-13319561020; E-mail: tigerwangshuo@163.com