Evaluation method of urban land population carrying capacity based on GIS—A case of Shanghai, China

Computers, Environment and Urban Systems 39 (2013) 27–38

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Evaluation method of urban land population carrying capacity based on GIS—A case of Shanghai, China Shi Yishao ⇑, Wang Hefeng, Yin Changying College of Surveying and Geo-Informatics, Tongji University, 1239 Siping Road, Shanghai 200092, China

a r t i c l e

i n f o

Article history: Received 21 August 2012 Received in revised form 14 February 2013 Accepted 15 February 2013 Available online 27 March 2013 Keywords: Population carrying capacity GIS spatial analysis Spatial classification evaluation Spatial grading evaluation Shanghai

a b s t r a c t Although research on population carrying capacity has made significant progress, research on urban carrying capacity still has a weak theoretical basis and uses, imperfect regulation mechanisms and estimation methods. This study proposes a new method for evaluating urban population carrying capacity based on spatial analysis with GIS, which utilizes spatial classification and spatial grading of land use. The results demonstrate that urban construction and industrial development subspaces have most of the population, accounting for about 86.4% of the total population carrying capacity, across 40.7% of the total land area. Therefore, urban construction and industrial development subspaces are the centers of the population concentration, industrial agglomeration and wealth concentration in the Shanghai metropolis. The agricultural production and ecological protection subspaces, as noncommercial and ecological conservation areas of the metropolis, should not carry too much industrial development or added-value activities. In addition, under the current conditions of socio-economic and technological development in China, the gross population carrying capacity of Shanghai is estimated to be about 27.1732– 30.3308 million persons, based on 2009 data. The actual population of Shanghai was 22.1028 million persons in 2009; thus, the population can continue to grow before reaching the population carrying capacity. The estimation in this paper takes into account both the internal disparities in carrying capacity of heterogeneous land spaces and composite factors such as natural resources, the environment, economic resources and social resources. Consequently, this method not only addresses defects in the existing research and estimation methods but also improves the credibility of the estimate. Ó 2013 Elsevier Ltd. All rights reserved.

1. Introduction The comprehensive carrying capacity of urban land is defined as the threshold value of the human population and the scale and intensity of various human activities that a city’s land resources can sustain under given economic, social, technological and ecological environment conditions (Guo, 2008). Studies of carrying capacity have made significant progress in the following four aspects: (1) Research on carrying capacity initially focused on biological populations but then shifted toward estimating population carrying capacity by determining the potential agricultural production of arable land (Chen, 1991; Odum, 1953; Park & Burgess, 1921; Price, 1999). However, only a fraction of land is arable. Therefore, research on the carrying capacity of other agricultural land, such as forests and grassland, gradually emerged. Later, the discipline shifted again from examining biophysical carrying capacity based on natural ⇑ Corresponding author. E-mail addresses: [email protected] (Y. Shi), [email protected] (H. Wang), [email protected] (C. Yin). 0198-9715/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.compenvurbsys.2013.02.002

resource endowments to assessing economic carrying capacity. Furthermore, cultural carrying capacity and social carrying capacity including natural resource endowments and the needs of human development have also been studied (Daily & Ehrlich, 1996; Hardin, 1986; Seidl & Tisdell, 1999; Young, 1998). Gradually, research began to focus on the impact of human, cultural and social factors, such as technological progress, lifestyle, social systems, trade, moral and ethical values, cultural acceptance, knowledge and institutional management, on carrying capacity. (2) While initial research focused on investigating a single element of carrying capacity based on scarce resources (such as land, water, mineral and energy resources), more recent research provides a comprehensive study of carrying capacity based on regional resources and environmental restraints. This broad characterization of environmental carrying capacity and ecological carrying capacity has become the focus of much research (Bi, Zheng, Gu, & Guo, 2005; Costanza, 1995; Long, Ren, Li, & Hu, 2010; Lv & He, 2009; Marten & Sancholuz, 1982; Oh, 1998; Oh, Jeong, Lee, & Lee, 2002; Oh, Jeong, Lee, Lee, & Choi, 2005; Onishi, 1994; Rees, 1996; Wang & Mao, 2001; Xu, Yang, & Li, 2005; Yang, Lv, &

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Zheng, 2010; Yu, Min, & Li, 2003; Zhang, Li, Liu, & Wang, 2009; Zao, Cao & Gao, 2005; Zhao, Xiao, Lan, Wang, & Ma, 2005). Unfortunately, the geological environment has been neglected in most studies to date. (3) Studies of carrying capacity have expanded from studying agricultural land to studying urban land and then from focusing on single cities to focusing on regional urban agglomerations (e.g., the Yangtze River Delta, the Pearl River Delta, the Central Plain Urban Agglomeration, the Beijing, Tianjin and Hebei Metropolis Areas, the Chengdu-Chongqing Economic Zone) in recent years (Liu & Fang, 2008; Lv, Sun, & Tan, 2008; Ou & Liu, 2009; Gao, Tu, & Yang, 2010). However, the breadth and depth of research on urban carrying capacity are still insufficient. For instance, research has not investigated how carrying capacity varies depending on the urban land type. In addition, most studies have dealt with the physical carrying capacity of urban areas in material terms based on ability of natural resources and the environment to sustain the population, but few studies have examined pertinent non-material factors, such as economic and social resources. (4) The methods for evaluating carrying capacity have evolved from using only a logistic growth curve equation in initial research to using system indicators (e.g., short board effect method and state spatial method based on the pressurestate-response framework), resource supply-requirement equilibrium (e.g., ecological footprint, virtual water, energy analysis), and model systems (e.g., conventional trend method, fuzzy mathematical evaluation, artificial neural network, data envelopment analysis, system dynamics model) in more recent studies (Brown & Ulgiati, 1997; Cang, 2006; Chen, Zheng, & Ma, 1999; Meyer & Ausubel, 1999; Tan, Shi, & Sun, 2008; Wackernagel & Rees, 1996; Zhang, Liang, Liu, & Zhang, 2005; Zhao et al., 2005). In general, two methodologies are employed for assessing the urban carrying capacity on the material environment plane: system indicators, which are applied by establishing an evaluation indicator system to complete the assessment, and spatial analysis, which is applied by determining the major restricting factors based on GIS (Anselin, 1988, 1995, 1996). In the former method, the most important steps are selecting the evaluation indicators and determining their weights. In the latter method, acquiring sufficient data is crucial. If sufficient data can be acquired, the latter method is better than the former one in scientificity, intuition and management of evaluation results. Natural resources and the environment are not fixed or static factors in a determination of population carrying capacity (Arrow et al., 1995; Cohen, 1997; Daily & Ehrlich, 1992). Although the concept of carrying capacity has been widely used, it has come under heavy criticism due to problems with its operability. Some scholars (Abernethy, 2001) believe that it has an inherent ambiguity and uncertainty and should be discarded. Undeniably, the theoretical basis of carrying capacity has shortcomings and deficiencies in terms of the regulating mechanisms, evaluation methods and other factors (Liu & Fang, 2008). Overall, there are two striking defects in the existing evaluation methods for the carrying capacity of land. First, initial studies concentrated on agricultural land, but urban areas are the centers of the population and economic agglomeration. Therefore, estimating the carrying capacity without including urban areas is imperfect. Second, the evaluation methodology of urban carrying capacity is different from that of agricultural land carrying capacity. Individual cities are usually regarded as homogeneous space in studies of urban carrying capacity. However, diverse urban populations,

various architectural forms, divergent spatial environments and cultural landscapes, and diversified economic activities and land use patterns create a heterogeneous urban space. Because of the remarkable multifunctionality and heterogeneity in urban space, the internal spatial differences should be delineated in any evaluation of urban carrying capacity. In the present study, to overcome the defects of existing evaluation methods of carrying capacity, the authors divide urban land space into four subspaces based on the theories of limits of urban growth, sustainable development, smart growth and intensive land use: urban construction, industrial development, agricultural production and ecological protection. The classification was applied to Shanghai with the help of GIS spatial analysis (Du, 2001). Then, different indicators were adopted to separately evaluate the population carrying capacity of the various subspaces, which varies depending on different factors related to the different subspaces. Both spatial classification and spatial grading are undertaken. Finally, the gross population carrying capacity is obtained, which reflects the integration of the spatial classification evaluation with the spatial grading evaluation as well as the integration of natural resources and the environment with economic and social resources. This method not only addresses the defects of existing estimate methods but also improves the credibility the results. This paper is organized as follows. Section 2 presents the profile of study area and data sources. Research methods are described in Section 3. The selection of the evaluation indicators and the determination of their weights are presented in Section 4. Section 5 explores the specific evaluation process of the population carrying capacity. The conclusion and discussion are presented in Section 6.

2. Study area and data sources Shanghai is an important economic center in China and the primary city of the Yangtze River Delta. Since the economic reform in China and since China opened its markets to the world, the urban and rural economy and society have developed rapidly. Massive urban construction has taken place, and high intensity land development and utilization, groundwater mining and other human activities are becoming more frequent. There has been a rapid expansion in population size: the resident population has increased from 11.52 million in 1980 to 23.03 million in 2010, nearly doubling in the past 30 years. The population density has almost doubled as well, increasing from 1862 persons per square kilometer in 1980 to 3632 persons per square kilometer in 2010 (Shanghai Statistics Bureau, 2011). As a result of the rapid population growth, Shanghai has faced problems such as scarce land resources, a shortage of quality water, an increasing risk of land subsidence and the localized deterioration of the ecological environment. Therefore, this research focuses on estimating Shanghai’s population carrying capacity under multiple constraints, such as land and water resources and ecological and geological factors. This study will provide a basis for efforts that promote the sustainable development of the human population, help to regulate the scale of urban land development and population growth in China and facilitate smart urban growth (Avin & Holden, 2000; Tom, 2001). The main data sources of the present study are as follows: (1) Shanghai Statistical Yearbook in 2010, Shanghai Statistical Yearbook of Districts (County) in 2010, and Shanghai Economic and Social Development Statistical Bulletin of Districts (County) in 2009; (2) Second Shanghai Land Survey Database (2009); (3) Shanghai Water Resources Bulletin in 2009; (4) Shanghai Geological Environment Bulletin in 2009; (5) Contour Map of Shanghai Land Subsidence in 2009; (6) Research Report of Shanghai Geological Environment Survey and Its Impact and Countermeasures on

Y. Shi et al. / Computers, Environment and Urban Systems 39 (2013) 27–38

Urban Safety (Shanghai Geological Survey Institute, 2008); (7) Shanghai Report of Environmental Quality (2006–2010) (Groundwater Section) (Shanghai Geological Survey Institute, 2011); and (8) Atlas of Shanghai Urban Geology (Wei, Zhai, & Yan, 2010). 3. Research methods According to the Second Shanghai Land Survey Database and its classification of land use, the study area (main terrene) is divided into four typical subspaces, i.e., urban construction, industrial development, agricultural production and ecological protection. The urban construction subspace includes urban land, rural residential land, and land devoted to transportation and other construction. The industrial development subspace mainly includes industrial and warehouse areas. The agricultural production subspace includes cultivated land, gardens, aquaculture and pond water surface, and other agricultural land. This subspace includes only nine districts (or counties) of the Shanghai suburbs. Finally,

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the ecological protection subspace includes forests, rivers and lakes, beaches, reed land and other unutilized land. The vector data of the four subspaces are extracted from the database; the subspace vector layers are established and shown in Fig. 1. Deducting the water area, the total area of four typical subspaces is approximately 6763.6 square kilometers (Table 1). The Shanghai administrative boundary is extracted with the help of ArcGIS 10.0 software, and the water area is deducted. Data for various statistical indicators are imported into the district (county) boundary layer attribute table. In addition, three indicators—the suitability of the natural foundation for construction, the potential for geological disaster and the soil environmental quality—are added by digitizing data from other sources. Each of these additional indicators is assigned a value for each district according to the suitability divisions of the natural foundation for construction, the potential geological disaster zoning map and the environmental quality assessment plan for topsoil, respectively (Table 2). Raster layers are obtained for the various indicators by

Fig. 1. Distribution of the four subspaces in Shanghai.

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Table 1 Area statistics of the four subspaces in Shanghai (km2). Urban construction subspace

Industrial development subspace

Agricultural production subspace

Ecological protection subspace

Total area

1855.02

900.77

2115.44

1892.37

6763.6

Table 2 Grades and their values for the 3 digital indicators. Indicators

Grade and value

Suitability of natural foundation for construction Potential geological disaster Soil environmental quality Value

Excellent Safe Excellent 9

Good Low Good 7

Common Middle Common 5

Inferior Liable Inferior 3

Poor High Poor 1

Data source: Wei et al. Atlas of Shanghai urban geology. Beijing: Geology Press; 2010.

Table 3 Specific evaluation indicators for four subspaces and their weights. Urban construction subspace

Agricultural production subspace

Industrial development subspace

Indicators

Weights

Indicators

Weights

Indicators

Weights

Indicators

Weights

Floor area ratio

0.175

0.190

SO2 emission intensity

0.088

0.172

0.209

Percentage of industrial land

0.125

Residential land per capita

0.095

0.141

PM10 emission intensity

0.121

Land subsidence

0.234

0.266

0.094

Rate of good ambient air quality –

0.184

0.076

Value-added of per unit industrial land Rate of industrial waste water up to the discharge standards

0.342

Suitability of natural foundation for construction Economic density Total

Soil environmental quality Output value of per unit agricultural land Agricultural labor productivity

Potential geological disasters Comprehensive index of water quality Green area

0.183

Development land per capita

Cultivated land per capita Grain yield per capita

–

0.230 1.000

– Total

– 1.000

0.248 1.000

– Total

0.194

– 1.000

Industrial employees Total

Ecological protection subspace

0.164 0.469

Notes: (1) Green area includes parks, production greens, affiliated greens and other green area. (2) In China, rate of good ambient air quality is defined that the percentage of days of the ambient air pollution index (API) reaching level II and better than level II account for the year.

raster processing with a grid cell size of 30 m  30 m. At this resolution, Shanghai consists of about 7.5 million cells. These cells are assigned to one of the four subspaces. For each land use type, data on a number of evaluation indicators are obtained. These are weighted using a coefficient of variation. Based on the resulting scores, a cell is graded into five grades ranging from ‘‘excellent’’ to ‘‘poor’’. For each grade and land use, the population carrying capacity is estimated. It is important to note that the authors compared the weighting scheme obtained via the coefficient of variation method to that of the entropy method (see Table 3) and found subtle differences but overall similar trends. The weights from the coefficient of variation method are more in line with Shanghai’s situation and were therefore adopted.

4. Evaluation indicators and weights 4.1. Evaluation indicators selection Under different levels of technology and consumption, the evaluation criteria for the comprehensive urban carrying capacity mostly depend on the endowment of resources, the efficiency of utilization and the constraints of development. Considering the variability among the determinants of population carrying capacity in the four subspaces, this paper uses different carrying capacity indicator systems for each subspace (Table 3). In Shanghai’s urban construction subspace, the main factors in carrying capacity are the scale of construction land, development

intensity, output level and development constraints. Therefore, the following six indicators are chosen for the evaluation index: development land per capita and residential land per capita, which reflect the scale of construction land and living space; floor area ratio, which indicates the strength of urban land development and utilization; economic density, which embodies the scale and strength of economic output; land subsidence, and the suitability of the natural foundation for construction, which indicates the constraints and potential of development. In Shanghai’s industrial development subspace, the main factors in carrying capacity are the scale and structure of potential industrial land, the output performance, and the environmental externalities of further growth or agglomeration. Therefore, the following six indicators are included in the evaluation index: valueadded per unit of industrial land, which reflects the efficiency of industrial land; number of industrial employees, which reflects the intensity of staff input; proportion of industrial waste water that meets discharge standards and emission intensity of SO2 and PM10, which represents the environmental externalities of industrial growth; and percentage of industrial land, which signifies the structural potential. In Shanghai’s agricultural production subspace, the main factors in carrying capacity are arable area, per capita grain availability, agricultural output and labor productivity, and soil environmental constraints. As a metropolis with more than 22 million persons, Shanghai’s arable area accounts for 25.24% of total land area. It is not practical or necessary to meet the grain demands of the entire city, but Shanghai’s arable resource should be able to provide grain to the city’s rural population. This is the base line of cultivated land

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Y. Shi et al. / Computers, Environment and Urban Systems 39 (2013) 27–38 Table 4 Descriptive statistics of the evaluation results of the subspaces. Subspace types

Urban construction subspace Industrial development subspace Agricultural production subspace Ecological protection subspace

Items Maximum

Minimum

Mean

Standard deviation

Total cells

0.71 0.50 0.89 0.75

0.24 0.12 0.11 0.15

0.45 0.36 0.75 0.53

0.04 0.12 0.08 0.11

2,052,820 1,000,487 2,342,207 2,098,380

protection in Shanghai. The following five indicators are included in the evaluation index: cultivated land per capita, which indicates the average coverage of cultivated land by the agricultural labor force; per capita grain availability, which indicates the subsistence level of grain demand; output value per unit of agricultural land and agricultural labor productivity, which reflects the level of agricultural production; and soil environmental quality, which reflects the constraints of soil resources. In Shanghai’s ecological protection subspace, the main factors in carrying capacity are the quality of the atmosphere and water and the constraints of the ecological and geological environment. Therefore, the following four indicators are included in the evaluation index: rate of good ambient air quality, which indicates the quality of the atmosphere; comprehensive index of water quality, which indicates the water environment quality; green area, which reflects the abundance of ecological resources; and potential for geological disaster, which indicates the possibility and spatial distribution of such events. 4.2. Weighting Weights are calculated for each indicator of the subspaces. To avoid the influence of subjective factors, an objective method for calculating weights, the coefficient of variation method, is adopted. The range standardization method is utilized to eliminate the different dimensional effect of the evaluation indicators. For positive indicators, the formula is as follows:

xij ¼

xij  minfxij g ði ¼ 1; 2;    ; m; j ¼ 1; 2;    ; nÞ maxfxij g  minfxij g

ð1Þ

In contrast, for negative indicators, the formula is as follows:

xij ¼

maxfxij g  xij ði ¼ 1; 2;    ; m; j ¼ 1; 2;    ; nÞ maxfxij g  minfxij g

ð2Þ

Based on the normalized values, the coefficient of variation for each indicator is calculated as follows:

Vi ¼

r xi

 ði ¼ 1; 2;    ; nÞ

ð3Þ

where the symbol Vi indicates the coefficient of variation or relative standard deviation; ri is the standard deviation of the i indicator; and xi is the mean of the i indicator. Based on the coefficient of variation of each indicator, the weights are calculated according to the following formula (4), and the results are shown in Table 3.

Wi ¼

vi Rni¼1

Vi

ð4Þ

Table 3 shows that economic density and land subsidence have higher weights in the urban construction subspace, followed by floor area ratio and development land per capita. In the industrial development subspace, the greatest weight is given to the valueadded per unit of industrial land, followed by industrial employees, the percentage of industrial land and PM10 emission intensity. In the agricultural production subspace, the output value of per unit agricultural land has the highest weight, followed by grain yield per capita, agricultural labor productivity and cultivated land per capita. In the ecological protection subspace, the highest weight is for green area, followed by the rate of good ambient air, the potential for geological disasters and the comprehensive index of water quality. 5. Estimation of urban population carrying capacity First, the state of the carrying capacity for the subspaces is evaluated. Second, grades of evaluation results are delineated, and the results for the subspaces are added. The population carrying capacity per unit area is determined for each grade of each subspace, and the ranges of the total population carrying capacities of the different grades in each subspace are calculated. Finally, the gross population carrying capacity of Shanghai is determined by summing the population carrying capacity of the subspaces. 5.1. Evaluation of subspaces In ArcGIS 10, according to the raster data of indicators that is based on the vector layers for each subspace, the raster data of the evaluation indicators for each subspace are selected using the ‘‘Extract by Mask’’ tool and then combined with the weights for the evaluation indicators. The raster calculation is then performed with the ‘‘Map Algebra’’ tool in Spatial Analysis, finally producing the carrying capacity index of each subspace. The evaluation results are shown in Table 4. 5.2. Grading evaluation results To distinguish the internal differences for each subspace, the evaluation result of each subspace is assigned to one of five grades. The natural breaks (Jenks) method determines the range of each grade. This method can identify the grade breaks based on natural groupings inherent in the data. In ArcGIS 10, by means of finding

Table 5 The grade threshold values and grade names for each subspace. Subspace types

Urban construction subspace Industrial development subspace Agricultural production subspace Ecological protection subspace

Grade Excellent

Good

Common

Inferior

Poor

P0.55 P0.44 P0.82 P0.61

(0.55, 0.48] (0.44, 0.35] (0.82, 0.74] (0.61, 0.47]

(0.48, 0.45] (0.35, 0.27] (0.74, 0.67] (0.47, 0.40]

(0.45, 0.39] (0.27, 0.13] (0.67, 0.54] (0.40, 0.30]

<0.39 <0.13 <0.54 <0.30

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the inner grouping and making sure the break points, then the features are divided into five grades. The specific grade threshold values and grade names are shown in Table 5. According to the grade threshold values for each subspace, the results are reclassified by the ‘‘Reclassify’’ tool in Spatial Analysis. The grade results are shown in Figs. 2–5. The areas of the various grades are shown in Table 6. Thus, the general grade of the urban construction subspace is the lowest, in which the areas of the ‘‘Common’’ and ‘‘Inferior’’ grades together account for about 87%, and the area of the ‘‘Excellent’’ and ‘‘Good’’ grades together account for about 10.5%. The general grade of the industrial development subspace is above normal, in which the ‘‘Excellent’’ and ‘‘Good’’ areas account for 55.8% and the ‘‘Inferior’’ and ‘‘Poor’’ areas together account for about 21.3%. The general grade of the agricultural production subspace is relatively low, with the area of the ‘‘Common’’ and ‘‘Inferior’’ grades together accounting for about 53.3%. Finally, the

general grade of the ecological protection subspace is relatively high, with ‘‘Excellent’’ and ‘‘Good’’ areas accounting for 64.3%. 5.3. Estimating population carrying capacity 5.3.1. Estimating program First, the appropriate population carrying number per unit area is determined for each subspace, and this number is then regarded as the reference value of the common grade. The population carrying number per unit area of each of the other grades is adjusted based on the standard of the common grade. Second, according to the areas of the subspaces, the range of the population carrying number is calculated for each of the subspaces. Third, the total population carrying number for each subspace is calculated to obtain the gross population carrying range in Shanghai.

Fig. 2. Grades of carrying capacity in the urban construction subspace in Shanghai.

Y. Shi et al. / Computers, Environment and Urban Systems 39 (2013) 27–38

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Fig. 3. Grades of carrying capacity in the industrial development subspace in Shanghai.

5.3.2. Population carrying capacity number per unit area of different grades 5.3.2.1. Urban construction subspace. Referring to the Code for the Classification of Urban Land Use and Planning Standards of Development Land (GB50137-2011) (Ministry of Housing and Urban-Rural Development of PRC, 2011) and related research on international metropolises, such as New York, London, Paris and Tokyo (Shi, Peng, Chen, Chen H. J., & H., 2010), the paper regards 100 square meters per capita as an appropriate standard for development land (construction land) and sets the appropriate population carrying number at about 10,000 persons per square kilometer in the urban construction subspace, which is also the reference value of the common grade. Taking into account the differences between urban centers and suburbs, the population carrying numbers per square kilometer for the other four grades were then determined. The ranges of population carrying numbers for the other four grades from high

to low are 20,000–15,000 persons, 15,000–10,000 persons, 10,000–8000 persons and 8000–5000 persons per square kilometer (Table 7). 5.3.2.2. Industrial development subspace. Employee densities are known for industrial land in Singapore, Tokyo and other industrialized metropolises. For example, the average number of employees per square kilometer of industrial land has remained at about 7129 persons from 2002 to 2007 in Singapore. In Tokyo, the average number of employees per square kilometer of industrial land was 7377 in 2002 and 7133 in 2007. In this study, the appropriate population carrying number per square kilometer, which is used as the standard of the common grade, was set to 7200 persons in the industrial development subspace. For the other four grades in the subspace, the ranges from high to low are 7500–7350 persons, 7350–7200 persons, 7200–7050 persons and 7050–6900 persons per square kilometer (Table 7).

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Fig. 4. Grades of carrying capacity in the agricultural production subspace in Shanghai.

5.3.2.3. Agricultural production subspace. The average grain yield of the nine districts (counties) in the Shanghai suburbs was 5400 kg/ hm2 in 2009, of which the highest was 8850 kg/hm2 in Baoshan District and the lowest was 4200 kg/hm2 in Fengxian District. If the standard of grain consumption per capita per year is about 400 kg (Guo, 2010), the estimate of the population carrying number per square kilometer in the agricultural production subspace is ranges from about 2212 persons to 1050 persons, with an average of 1350 persons. In this study, the middle value, about 1600 persons per square kilometer, is used as the population carrying number of the common grade. The other four grades, from high to low, are determined to be 2200–1900 persons, 1900–1600 persons, 1600–1300 persons and 1300–1000 persons per square kilometer (Table 7). 5.3.2.4. Ecological protection subspace. Previous research has reported the consumption need per capita of ecological space in

the world. Average production is 1.848 hm2 for the existing quality of life in China, which is greater than China’s availability of ecological space per capita by about 0.65 hm2 (Xie, Cheng, Yu, & Gao, 2002). Taking the amount per capita as a reference value, this study calculates the range of the population carrying number per square kilometer as about 54–153 persons. To facilitate the calculation and grade, the range of the ecological protection subspace is determined to be about 50–150 persons per square kilometer. The middle value, about 100 persons per square kilometer, is used as the standard of the common grade. The other four grades, from high to low, are determined to be 150–125 persons, 125–100 persons, 100–75 persons and 75–50 persons per square kilometer (Table 7). Based on the above analysis, the population carrying numbers per square kilometer for each grade in each subspaces are shown in Table 7. The future land use classification of some cells may change, in which case this methodology can easily reestimate the values.

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Fig. 5. Grades of carrying capacity in the ecological protection subspace in Shanghai.

Table 6 The areas of various grades for the four subspaces (km2). Grade

Space types Urban construction subspace

Poor Inferior Common Good Excellent Total

Industrial development subspace

Agricultural production subspace

Ecological protection subspace

47.32 765.57 848.34 155.10 38.69

84.27 107.70 205.98 101.37 401.45

17.70 269.58 858.51 287.66 682.00

13.20 141.62 521.40 809.11 407.03

1855.02

900.77

2115.44

1892.37

Because this article focuses on the methodology of evaluation, a sensitivity analysis on the ranges by subspace was performed, which is reported in Table 7 (Wen, Song, Cui, & Wen, 2007) (see Table 8).

For the ranges of the base values in Table 7, the uncertainty per unit area for the gross population estimate is as high as 10% for various grades in different subspaces. If the per unit area population carrying capacity varies by 10% in the grades in the urban

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Table 7 The range of population carrying capacity estimated per unit area of various grades in each subspace (persons/km2). Space types

Grades

Urban construction subspace Industrial development subspace Agricultural production subspace Ecological protection subspace

Excellent

Good

Common

Inferior

Poor

[20,000–15,000) [7500–7350) [2200–1900) [150–125)

[15,000–10,000) [7350–7200) [1900–1600) [125–100)

10,000 7200 1600 100

(10,000–8000] (7200–7050] (1600–1300] (100–75]

(8000–5000] (7050–6900] (1300–1000] (75–50]

Table 8 The sensitivity analysis of the value ranges by subspace.

Change of gross population (10 thousand persons) Relative change rate (%) Gross population (10 thousand persons)

Urban construction subspace (±10%)

Industrial development subspace (±10%)

Agricultural production subspace (±10%)

Ecological protection subspace (±10%)

±169.76 ±6.25 2887.08 2547.56

±65.04 ±2.18 2782.36 2652.28

±34.98 ±1.28 2752.30 2682.34

±1.95 ±0.07 2719.27 2715.37

±196.18 ±6.47 3229.26 2836.90

±66.09 ±2.39 3099.17 2967.00

±38.75 ±1.29 3071.83 2994.33

±2.29 ±0.08 3035.38 3030.79

Table 9 Results of population carrying capacity estimation of different grades and subspaces in Shanghai (in thousands of people). Grades

Poor Inferior Common Good Excellent Total

Space types Urban construction subspace

Industrial development subspace

Agricultural production subspace

Ecological protection subspace

236.6–378.6 6124.6–7655.7 8483.4 1551.0–2326.4 580.3–773.7 16975.9–19617.9

581.5–594.1 759.3–775.5 1483.0 729.8–745.1 2950.6–3010.9 6504.3–6608.5

17.7–23.0 350.4–431.3 1373.6 460.3–546.6 1295.8–1500.4 3497.8–3874.9

0.7–1.0 10.6–14.2 52.1 80.9–10.11 50.9–61.1 195.2–229.5

construction subspace and all other subspaces are held constant, the domain of walker of the gross population is between ±6.25% and ±6.47%. Similarly, the domain of walker for the gross population is between ±2.18% and ±2.39% in the industrial development subspace, between ±1.28% and ±1.29% in the agricultural production subspace, and between ±0.07% and ±0.08% in the ecological protection subspace (Table 8). This analysis shows that changes in the urban construction subspace have the largest effect on the on gross population estimate and that changes in the ecological protection subspace have the smallest. 5.3.3. Estimation results of gross population carrying capacity According to the population carrying capacity per square kilometer for the different grades (Table 7), the area of the various grades in each subspace (Table 6), and the ranges for the various grades in the subspaces, the overall range of Shanghai’s population carrying capacity is about 16975.9–19617.9 thousand persons in the urban construction subspace, 6504.3–6608.5 thousand persons in the industrial development subspace, 3497.8–3874.9 thousand persons in the agricultural production subspace, and 195.2–229.5 thousand persons in the ecological protection subspace (Table 9). The urban construction subspace is responsible for most of the population carrying capacity, accounting for 62.47–64.68% of the gross population carrying capacity, with 27.4% of total land area. Next, the industrial development subspace accounts for 21.79– 23.94% of the population, with 13.3% of the total land area. Together, these two subspaces compose about 86.4% of the gross population carrying capacity, with 40.7% of the total land area. The agricultural production and ecological protection subspaces together account for about 13.6% of gross population carrying capacity, with 59.3% of total land area. The gross population carrying capacity of Shanghai is estimated about 27.1732–30.3308 million. According to the 2011 Shanghai

Statistical Yearbook, the resident population of Shanghai was about 22.1028 million in 2009. Therefore, there is still space for potential growth within the population carrying capacity of Shanghai under the current socio-economic conditions and level of technology. 6. Conclusions and discussions Using GIS spatial analysis, this study establishes a system for estimating the population carrying capacity of various land use subspaces using various indicators, integrates an evaluation based on spatial classification with a spatial grading evaluation and effectively reveals the variability in carrying capacity among different land use subspaces, and addresses the defects of the existing research perspective and estimation methods. The evaluation shows that the urban construction and industrial development subspaces hold for most of the population carrying capacity, accounting for about 86.4% of the gross population carrying capacity, with 40.7% of total land area. The agricultural production and ecological protection subspaces together account for about 13.6% of the gross population carrying capacity, with 59.3% of total land area. The urban construction and industrial development subspaces are clearly the centers of population concentration, industrial agglomeration and wealth concentration in the Shanghai metropolis. The agricultural production and ecological protection subspaces, as noncommercial and ecological conservation areas of the metropolis, should not carry too much industrial development and wealth added-value activities. Any development there should compensate appropriately, such as via financial compensation or ecological compensation, to promote the relatively balanced development of the heterogeneous space. At the current levels of technological and socio-economic development, the gross population carrying capacity is about

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27.1732–30.3308 million persons in Shanghai. In 2009, the resident population of Shanghai was about 22.1028 million persons. There is still space for growth in the population in the future. In this post-industrialization era, intensive urbanization should be pursued in Shanghai, with the aim of improving the quality of life of its citizens, enhancing the quality of immigrants, and increasing the population concentration and carrying capacity of construction land in Shanghai’s suburbs. The sprawl of construction land must be reversed and more compact development or smart growth should be encouraged. System engineering experts have concluded that the maximum population carrying capacity of Shanghai will be about 23 million (22.4–23.8 million) persons in 2020. These calculations consider the strategic requirements of Shanghai as an international metropolis for environment, resources, economic efficiency, social life and urban strength. If the overall efficiency, including economic efficiency, social life, level of resource utilization and ecological environment cannot meet the development strategy and strength requirements of Shanghai, the population carrying capacity will be less than 21.8 million persons in 2020. If all factors compensate each other, then the maximum population carrying capacity will reach 25.7 million persons in 2020 (Xu, Mu, & Wang, 2007). The population expert Wu concluded that if the urbanized area can expand up to 2113 km2, the gross population carrying capacity will approach 30 million persons in Shanghai (Wu, 1995). However, his estimation method is rather nebulous, and in fact, the urbanized area already reached 2835 km2 in 2009. The estimate in this article is more accurate, and the method is more detailed, taking into account the disparities in population carrying capacity within different land use spaces. Thus, the total estimation is more credible. This article proposes a methodology for the static evaluation of urban population carrying capacity. However, the factors that affect the carrying capacity of urban land are not static but rather are dynamic. From a historical point of view, the carrying capacity of an urban population generally tends to rise. Apart from the progress of science and technology and rising consumption, this uptrend depends heavily on the expansion of the urban construction and industrial development subspaces and the shrinkage of the agricultural production and ecological protection subspaces. However, this development pattern is limited. Nevertheless, if there are changes in the subspaces because of changing land use, this methodology will easily allow re-estimation. References Abernethy, V. D. (2001). Carrying capacity: The tradition and policy implications of limits. Ethics in Science and Environmental Politics, 1, 9–18. Anselin, L. (1988). Spatial econometrics: Methods and models. Boston: Kluwer Academic Publishers. Anselin, L. (1995). Local Indicators of Spatial Association-LISA. Geographical Analysis, 27(4), 93–115. Anselin, L. (1996). The Moran scatterplot as an ESDA tool to assess local instability in spatial association. In M. Fisher, H. J. Scholten, & D. Unwin (Eds.), Spatial analytical perspectives on GIS. London: Taylor & Francis. Arrow, K., Bolin, B., Costanza, R., Dasgupta, P., Folke, C., Holling, C. S., et al. (1995). Economic growth, carrying capacity, and the environment. Science, 268, 520–521. Avin, U. P., & Holden, D. R. (2000). Does your growth smart? Planning, 1, 26–29. Bi, D. S., Zheng, G. H., Gu, G. W., & Guo, X. J. (2005). The carrying theory of urban ecosystem with a case study in Changjiang Delta. Resources and Environment in the Yangtze Basin, 14(4), 465–469 [in Chinese]. Brown, M. T., & Ulgiati, S. (1997). Energy-based indices and rations to evaluate sustainability: Monitoring economics and technology toward environmentally sound innovation. Ecological Engineering, 9, 51–69. Cang, H. (2006). Carrying capacity, population equilibrium, and environment’s maximal load. Ecological Modelling, 192, 317–320. Chen, B. M. (1991). An outline of the research method of the project ‘‘the productivity and population carrying capacity of the land resource in China’’. Journal of Natural Resources, 6(3), 197–205 [in Chinese]. Chen, C. M., Zheng, C. Y., & Ma, C. X. (1999). System dynamics for Zhengzhou land resource bearing capacity. Journal of Hehai University, 1, 1–6 [in Chinese].

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