Optimal land use allocation of urban fringe in Guangzhou | SpringerLink

J. Geogr. Sci. 2012, 22(1): 179-191 DOI: 10.1007/s11442-012-0920-7 © 2012

Science Press

Springer-Verlag

Optimal land use allocation of urban fringe in Guangzhou GONG Jianzhou1,2, *LIU Yansui1, CHEN Wenli3 1. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China; 2. School of Geographical Sciences, Guangzhou University, Guangzhou 510006, China; 3. Guangdong Party Institute of CCP, Guangdong Administration Institute, Guangzhou 510053, China

Abstract: In response to the strong drive for social and economic development, local governments have implemented urban master plans, providing measures and timeframes to address the continuous demand for land and to alleviate urban problems. In this paper, a multi-objective model was constructed to discuss the problem, including economic benefits and ecological effectiveness, in terms of land use optimization. A genetic algorithm was then adopted to solve the model, and a performance evaluation and sensitivity analysis were conducted using Pareto optimality. Results showed that a set of tradeoffs could be acquired by the allocation of land use. In addition, the Pareto solutions proved the model to be efficient; for example, a limit of 13,500 ha of urban area conformed to plan recommendations. The reduction in crop land, orchard land, grassland, and unused land provided further efficiencies. These results implied that further potential regional land resources remain and that the urban master plan is able to support sustainable local development in the years to come, as well as verified that it is feasible to use land use allocation multi-objective modeling and genetic algorithms. Keywords: optimal allocation; land use; multi-objective modeling; genetic algorithms; fringe area

1

Introduction

As a developing country, it is widely recognized that significant changes have occurred in China’s urban land use, changes that will continue to alter human and natural systems regionally and globally (Foley et al., 2005; Liu et al., 2008; Liu and Li, 2010). These changes have caused several problems, including a scarcity of land resources and the formation of urban heat islands (Stephan et al., 2005; Stevens et al., 2007). It is expected that people will use some method of coordinative development when dealing with ecologic, environmental, and social systems. Urban planning is one example, as it Received: 2011-06-09 Accepted: 2011-08-25 Foundation: National Natural Science Foundation of China, No.41130748; No.41171070; China Postdoctoral Science Foundation, No.200902132; No.20080440511; The Humanities and Social Sciences Project of Ministry of Education, PRC, No.10YJCZH031 Author: Gong Jianzhou (1970–), Ph.D and Associate Professor, specialized in the study of environmental ecology and management, and 3S application. E-mail: [email protected] * Corresponding author: Liu Yansui, Professor, E-mail: [email protected]

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enables the roles and functions of various spaces to achieve both the demands of development and a reduction in the negative impacts of that development (Nuissl et al., 2007; Andreas et al., 2010). It is effective and its importance will be highlighted later in the paper. An urban master plan, the macro development plan of a city, encompasses local economic and social development, allowing, for example, urban spatial distribution within the urban landscape. To sustain strategic planning processes, it is important to acknowledge and incorporate the expectations of local residents in planning policies (Liu, 2010). These expectations—aspirations, needs, and requirements concerning urban spaces (Zoppi and Lai, 2010)—can be included in the planning strategy via a series of quantity indices that determine city size, define the development orientation, and allow for sufficient resources for future development. It is intended that these indices will be referred to in the course of plan implementation. The ultimate aims of the plan are to realize the goals of local economic and social development and to maintain sustainable regional development. This imparts a heavy responsibility on the experts and specialists involved in the process to produce high-quality urban planning, for today and the future. Land use allocation based on multi-objective effectiveness has proven to be helpful (Nuissl, 2006) where the objective comprises economic, social, and environmental aspects. These three aspects could be simultaneously supported in a land use scheme, to become an optimal scheme that contributes significantly to local development; however, simultaneous optimization is often problematic, with the various aspects competing against or contradicting each other. A tradeoff solution is available through the use of genetic algorithms (GAs), which are powerful tools in solving such problems (Matthews et al., 2006). GAs are biologically motivated programming techniques termed “evolutionary computation” (Mitchell, 1996). Presented as an abstraction of biological evolution, GAs are a widely accepted method for moving from one population of “chromosomes” to a new population, by using “natural selection” alongside certain genetic operators such as crossover and mutation. The main advantages of GAs are their parallel searching and stochastic mechanisms for populations without prior knowledge. Using GAs, a set of Pareto-optimal solutions could be returned (also known as a Pareto front when depicted as a curve), which may be especially attractive to decision-makers because it enables them to easily resolve tradeoffs by choosing the preferred alternative (Zhao, 2007; Higgins et al., 2008). Guangzhou, like other Chinese cities, has experienced significant increases (between 30% and 50%) in urban land in the last 15 years (Wu et al., 2007; Yu and Ng, 2007; Gong et al., 2009). As a result, the fringe area of the megacity is most sensitive in terms of land and environment. Yue et al. (2008) stated that land use allocation could be helpful to alleviate the situation. Han (2010) stated that local government planning, concerned with economic growth, has played a major role in such growth. Under an urban master plan, the present paper constructed a multi-objective model, considering economic benefit and ecological effectiveness in terms of land use optimization. GAs were then adopted to solve the model, and performance evaluation and sensitivity analysis were conducted using Pareto optimality. The case study area, Zengcheng, is an administrative district in Guangzhou. The aims of the study are summarized in three points: first, to enable further insight into the effectiveness of the plan and its role in regional eco-

GONG Jianzhou et al.: Optimal land use allocation of urban fringe in Guangzhou

181

nomic and ecological change; second, to enable a perspective on land use and its potential to meet future demands; and finally, to determine the feasibility of the integrated modeling of multi-objectives and GAs in land use allocation.

2

Study area

Zengcheng, an administrative district in Guangzhou, is located in eastern Guangzhou on the Pearl River Delta in China, and has a significant rural–urban fringe zone. It covers an area of approximately 161,600 ha (sourced from the Guangzhou Municipal Land Resources and Housing Administrative Bureau) and extends between a latitude of 23°4'42'' to 23°37'20''N and a longitude of 113°29'4'' to 113°59'44''E (Figure 1).

Figure 1

Location and general view of Zengcheng, Guangzhou, China

Zengcheng has a southern subtropical ocean monsoon climate, with abundant sunshine and rainfall. It has an average annual temperature of 22.2℃ and an average annual precipitation of 1869 mm, which support healthy plant growth. Zengcheng is also known as the 'Town of the Lychee' or the ‘Town of Rice and Fish’, owing to its abundance of natural resources. The city is 60 km from Guangzhou, 120 km from the Shenzhen special zone, and 129.64 km from Hong Kong. Zengcheng is recognized as possessing a ‘Golden Corridor’, because of its proximity to Guangzhou, Hong Kong, and the Shenzhen Special Administrative Zone. In the last 20 years, Zengcheng has experienced sustainable development within the local financial, economic, and social sectors, thereby maintaining a period of prosperity and stability. This development has led to a significant change in the appearance of the city, while at the same time creating a bottleneck in its continuing development as land becomes scarce (Zheng et al., 2007). Local government has taken active steps to deal with this issue. For example, over the past decade (2001–2010), two urban master plans have been developed for Zengcheng, for

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2001–2010 and 2010–2020. Accompanying the first plan, Zengcheng employed three Economic Spatial Development Strategies, for the southern, central, and northern areas. As well, Zengcheng implemented an innovative and appropriate approach to intra-county economic development, termed the ‘Zengcheng Mode’. The latest urban master plan, from which an urban blueprint for 2010–2020 is shown in Figure 1, was drawn up in 2008.

3

Optimal allocation

Land use optimal allocation consists of two parts: a general modeling framework and its application in land systems. The former includes an optimal model, a method of problem-solving and performance evaluation. The latter includes a definition of parameters and their application in selected land systems. 3.1

Optimal modeling framework for land use allocation

Research objects are often regarded as complex systems that refer to multiple influencing factors, as well as two or more conflicting objectives. Optimal modeling is used to derive an optimal scheme via the consideration of tradeoffs within the system (Wang, 2004). Following earlier studies (Rardin, 2007; Wu, 2009), general modeling framework can be expressed as: min(max) f k ( x), s.t

gi ( x) ≤0; (i = 1, 2," , m),

(1)

h j ( x) = 0; ( j = 1, 2," , n),

where fk(x) is the kth objective function, which may be minimal or maximal, gi(x) and hj(x) are inequality and equality constraints, respectively, and x is the decision variable. Letters k, i, and j represent the number of objective functions, inequality constraints, and equality constraints, respectively. This model is also claimed as an example of multi-objective modeling (Huang, 1999). Multi-objective optimization is a process of searching for efficient solutions, which to a designer could acquire acceptable multi-objectives. As mentioned in the introduction, GAs are powerful tools in solving multi-objective optimization problems (Matthews et al., 2006). MATLAB (7.6.0) includes an embedded Genetic Algorithm Toolbox (GAT) to implement the algorithm. When functions and constraints are fed into the activating toolbox, solutions are automatically identified. In modern society, the objectives of economic and ecological returns are often in conflict with regard to land use demands. An effective method to alleviate such conflict is to allocate land resources to specific uses (Wang, 2004). When the integrated consideration of society, economy, environment and the feasibility of quantitative analysis are taken into account, the above two objective functions are subject to a series of constraints. Thus, a general framework for double-objective optimization using GAs in GAT can be described as follows (Wu et al., 2009): n

f1 ( x) = max ∑ c j x j , j =1

(2)


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n

f 2 ( x) = max ∑ d j x j ,

(3)

j =1

s.t

A.x ≤ b,

(4)

Aeq x = beq,

(5)

Ib ≤ x ≤ ub,

(6)

where f1(x) and f2(x) are annual incomes in 10 million yuan, representing economic benefit and ecological effectiveness, respectively. cj is the annual economic benefit for each land use (104 yuan/ha), while dj is the annual ecological effectiveness for each land use, xj is the area of land use j (ha), and n is the total number of land uses (11 are shown in Table 1). The matrices of the constraints of linear inequalities and equalities are represented by A and Aeq, respectively, while b and beq are the corresponding parameters. The lower and upper boundary constraints are denoted by Ib and ub, respectively. Table 1

Land use structure for Zengcheng, 2008 and 2020 Land use

Year 2008 Area (ha)

Year 2020

Percentage (%)

Area (ha)

Percentage (%)

Crop land (x1)

15200

9.4

14508

9.0

Orchard (x2)

36325

22.5

32961

20.4

State forest (x3)

70732

43.8

75512

46.7

Grazing (x4)

3784

2.3

3519

2.2

Fishpond (x5)

5348

1.6

5348

1.6

Urban (x6)

10399

6.4

17826

11.0

Rural resident (x7)

3722

2.3

2133

1.3

Transportation (x8)

4666

2.9

4517

2.8

/

/

/

/

Water conservancy (x9) River (x10)

7834

6.6

5275

5.0

Unused (x11)

3589

2.2

/

/

161600

100

161600

100

Total area

Through the above modeling framework, a feasible solution set can be acquired. Pareto optimality is then used to select the most optimal solution (Altiparmak, 2006; Mattews, 2006). Pareto optimality, being an economic concept of efficiency and income distribution, represents the same marginal rate of substitution for all consumers. Given a set of data and constraints, the above model acquires a Pareto set or a Pareto front by which decision-makers choose an optimal solution. While the marginal rate of substitution in the Pareto frontier is the same for all members, it is always represented by objectives; the performance evaluation of Pareto optimality is achieved by comparing effectiveness among the tradeoffs or objectives. A sensitivity analysis of the model enables the study of the effect that the input parameters or modeling constraints have no outputs or objectives. This can test the model availability and ensure the reliability of the outputs, as well as analyze the influence of the parameters on the outputs.

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3.2 3.2.1


Application of modeling framework for land use allocation

Data and methods

Land use data were sourced from the Guangzhou Municipal Land Resources and Housing Administrative Bureau (http://www.laho.gov.cn/) with a 5-year time horizon from 2004 to 2008. City planning resources were obtained from an online planning website (http://www. zcupb.gov.cn/). The projected land use for 2020 was taken from a blueprint of the urban master plan and is shown in Figure 1. Socio-economic indices were sourced from the Guangzhou Statistics Yearbook at the Government Open Network (http://data.gzstats.gov. cn/gzStat1/chaxun/njsj.jsp), and data regarding additional farmland from 1999 to 2003 were also sourced from this website and included in the area projections for 2020. In addition, Landsat Thematic Mapper images (mesoscale resolution; images taken on 2 January 2009) were used to determine land use at the end of 2008 (Table 1). An urban land use map for 2020 was produced in JPEG format from the urban master plan (2010–2020). The raster-based image was converted into vector-based feature layers via vectorization in GIS; a land use map (Figure 1) was created and the structure of land use in 2020 was projected (Table 1). 3.2.2

Objective functions and coefficients

As used by Xu (2003), dj denotes the ecological service value unit area. Economic benefit, cj, is relatively complicated to calculate. The following is an example using farmland. 1) c1 (farmland). First, the value accredited to land use in GDP is considered. Within each farming year, any increase in value is divided by the land area from the same year; thus, the dynamic economic benefit coefficients (RMB/ha) were calculated from 1999 to 2008 (shown in Table 2, bottom row). Based on these data, c1, shown in Table 2 in bold, was returned using Grey Model GM(1,1). 2) svj (in comparison of significance values for each land use type and farmland). First, the Delphi method was used to obtain any values of important degree that were compared in the paired land use types. Based on those values, an analytic hierarchy process was then used to obtain the weights of the different types. Svj, shown in Table 3 in the first data row, was then returned while each weight was divided by that of the farmland. 3) With the exception of c1, the remaining cj were obtained as follows. (7) c j = c1 ⋅ sv j ( j = 2,3," , n), where cj is the economic benefit coefficient of land use for a particular period (here, 2020), of which c1 is that for farmland. Svj is a compared significance value for each land use type on farmland. The benefit coefficients used a base-price index from 2005 to remove any influence from price increases. As calculated above, all parameters are shown in Tables 2 and 3, and both objective functions can be formulated as follows: (8) f1 ( x) = max(36.24 x1 + 55.88 x2 + 54.38 x3 + " + x11 ), f 2 ( x) = max(6.83 x1 + 7.06 x2 + 8.01x3 + " + 7.19 x11 ).

3.2.3

(9)

Constraints

(1) Unequal constraints The above objectives functions may be subject to the following constraints. With regard to unequal constraints (4), four aspects were considered. Local economic development must

GONG Jianzhou et al.: Optimal land use allocation of urban fringe in Guangzhou Table 2

185

Farmland area and its benefit coefficients from 1999 to 2008, and 2020

Year

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2020

Area

42557

42521

42525

42676

42147

41928

41394

26033

26544

26434

/

Added value 1.44885 1.44863 1.51888 1.70977 1.60950 1.77549 1.72407 1.93504 2.03177 2.23453 Coefficient

34.045

34.069

35.718

40.064

38.188

42.346

41.650

74.331

76.542

/

84.532 432.704

Note: Area and added value indicate farmland area in hectare and farming added value in billion yuan, respectively, with data for both sourced from the Guangzhou Statistics Yearbooks (1999–2008). Coefficients were obtained by dividing added value by area in thousand yuan for each hectare; 432.704 thousand yuan for each hectare (in bold in the last line) is the predicative value using GM(1,1) based on the same line data.

Table 3

Significance values and benefit coefficients for economy and ecology among land use in 2020 x1

x2

x3

x4

x5

x6

x7

x8

x9

x10

x11

Average

svj

1

1.54

1.50

1.54

3.38

7.02

0.67

6.08

1.46

0.55

0

/

cj

36.24

55.88

54.38

55.88

122.63

254.52

24.28

220.36

53.08

19.99

1

89.72

dj

6.83

7.06

8.01

7.19

9.44

0

0

0

7.19

9.44

7.19

5.51

Note: svj is significance values with no unit, units for cj and dj are 104 yuan/ha

ensure that GDP continues to increase. Based on statistical data, the added value of GDP was 11.334477 million yuan in 2008. Therefore, − 36.24 x1 − 55.88 x2 − 54.38 x3 − " − x11 ≤1133.4477. (10)

The development plan states that positive measures must be adopted to ensure the continual adjustment of industrial structure, and as such, the rate of the three industrial structures should be 4.5:55.5:40 in 2015, and 3:52:45 in 2020. Thus, by 2020, the local agricultural value × 52 will be less than or equal to the local industrial value × 3, that is (11) − 628.16 x1 − 968.55 x2 − 942.54 x3 − 968.55 x4 − 2125.55 x5 − 254.52 x6 ≤ 0 . Population is one of the main factors in land use change. Over the next several decades, the urban population in China will increase as much as it has over the last 30 years in controlled growth. In this study, the population of registered residents (807,752) is used as the lower boundary, while the value stated in the plan (1,060,000) is used as the upper boundary. Based on historical population data, areas of farmland and urban land, and urban and rural population density were calculated. The population density was estimated for 2020 using GM(1,1), with 4.24 person/ha for farmland and 21.26 person/ha for urban land. Population constraints can be formulated as follows: (12) − 4.24 (x1 + x2 + x3 + x4 + x5 ) − 21.26 x6 ≤ 807752 . 4.24 (x1 + x2 + x3 + x4 + x5 ) + 21.26 x6 ≤1060000.

(13)

As mentioned above, the matrix of the constraints for linear inequalities (A) is shown in Table 4. The last column shows the values for b, the corresponding constraint parameters. Table 4

Linear inequalities A and parameters b

Linear inequalities (A)

x1

x2

x3

x4

x5

x6

x7

x8

x9

x10

x11

b

GDP

−36.24 −55.88 −54.38 −55.88 −122.63 −254.52 −24.28 −220.36 −53.08 −19.99 −1 −1133.5

Industrial structure

−628.16 −968.55 −942.54 −968.55 −2125.55 254.52

Population

0

0

0

0

0

−4.24

−4.24

−4.24

−4.24

−4.24

−21.26

0

0

0

0

0 −807752

0

4.24

4.24

4.24

4.24

4.24

21.26

0

0

0

0

0 1060000

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(2) Equality constraint All land use should sum to the total study area (161,600 ha), indicating an equality constraint as (14) x1 + x2 + x3 + " + x11 = 161600. According to (5), Aeq represents an 11-dimensional normal vector while beq equals 161,600. (3) Lower and upper boundaries With regard to framework (6), a series of lower and upper constraints were considered based on the master plan and logical social and economic environments. Five decision-making variables, including x1, x3, x4, x6, and x8, can be kept within the current and projected values. The range of x1 was 14508−15200 ha, and 70,732−75,512, 3519−3784, 10,399−17,826, and 4517−4666 ha for x3, x4, x6, and x8, respectively. Orchard area (x2) is important with regard to farming communities and fruit production, and should not reduce in the future according to historical data. Thus, it is assumed that it will, at the very least, remain within the planning area of 32,970 ha, with no upper boundary except for total area. Similarly, unused land (x11) was restricted only within its upper boundary, the current area of 3589 ha, because of an inevitable reduction in area as a response to regional development. With regard to spatial resolution, fishponds (x5) and water bodies (x10) were difficult to distinguish from each other, which resulted in a difference in area in the statistical data. As fishponds and water bodies have been relatively stable during the past decade and have not been specifically defined in the plan, the boundaries can be limited to 3459–7289 and 1918–2160 ha, respectively. Similarly, areas of water conservancy facilities (x9) had a restricted upper boundary of 3044 ha based on historical data. Under China’s “building a new countryside” policy, a long-term development strategy of China’s central government (Long et al., 2009), the area for rural residents (x7) should reduce in the future, and as such, the current area of 3722 ha was used as the upper boundary with no minimum restriction. To illustrate the above, a corresponding simplex boundary table shows the upper and lower boundaries (Table 5). Table 5

Lower boundaries (Ib) and upper boundaries (ub) in hectare x1

x2

x3

x4

x5

x6

x7

x8

x9

x10

x11

Lower (Ib)

14508

32961

70732

3519

3459

10399

0

4517

0

1918

0

Upper (ub)

15200

161600

75512

3784

7289

17826

3722

4666

3044

2160

3589

3.2.4

Genetic Algorithm (GA)

The GAT was used in this paper to solve the model in Matlab7.6.0. Genetic parameters were set, and after repeating the test, the following parameters were adopted: crossover fraction, 0.9; Pareto front population fraction, 0.9; maximum generations, 2000; and fitness limit, 0.001. In addition, defaults were adopted for the remaining parameters. The above discussed functions (8)–(9) and constraints A, b, Aeq, beq, Ib, and up were used to solve the double objectives problem.


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Sensitivity analysis

3.2.5

The future benefit of unused land is uncertain, and as such the parameters remained unchanged while the benefit coefficients of that land were changed. Thus, sensitivity analysis was evaluated to explore changes in Pareto optimality. The average benefit coefficients of all land use (c11 and d11, the final column in Table 3) were taken as the base values for unused land in the sensitivity analysis. Then, both coefficients were reduced to one-and-a-half, one-third, one-quarter, one-fifth, one-tenth, and then one percent of the base value. The remaining coefficients were unaltered, and the model was then run repeatedly, using the altered coefficients. The constraints were then changed to test the model’s sensitivity. While other parameters remained unchanged, the lower boundary for crop constraint and the upper boundary for urban land were changed to 13,563 and 161,600 ha, respectively.

4 4.1

Results and discussion Pareto solutions

A Pareto front for both objectives for land use in 2020 is depicted in Figure 2. Solutions were spread almost uniformly through the line of the front. The trend line for all pairs of solutions highlighted the significantly negative correlation between both functions: f1 and f2 (–0.055 and R2 = 0.971); however, there were still many more solutions, found mainly between f1 (123.76–125.73) (billion yuan) and f2 (10.56–10.68) (billion yuan).

Note: f1 and f2 represent economic and ecological effectiveness, respectively

Figure 2

4.2

Pareto front for formulas (8)–(9) constructed in the model

Performance evaluation

In Figure 2, three points marked A, B, and C, of which A and B were both extreme solutions, were selected and compared with the current land use structure and the effectiveness appended to land use. As shown in Table 6, given that land use structure has not changed since 2008, economic benefit (f1) and ecological effectiveness (f2) would be 109.29 billion yuan and 11.05 billion yuan by 2020, respectively. Using reallocation, f1 could increase to 125.73, 123.76, and 124.28 billion yuan, for schemes A, B, and C, respectively, when they were considered

188


separately, and as expected f2 would decline to 10.56, 10.68, and 10.63 billion yuan, respectively. However, change rates for f1 were 15.04%, 13.24% and 13.71%, and –4.39%, –3.30% and –3.79% for f2, at points A, B, and C, respectively. This indicates that effective value can be acquired with the reallocation of the land use structure. As shown in the figures, none of the schemes completely outperformed the others, with each scheme performing better than others at some point. However, comparison advantages may still be possible and this may be a reason to allocate limited resources such as land. Table 6

Land use structure and its effectiveness in 2008 and 2020 Area of land use (ha) x1

2008

x2

x3

x4

x5

x6

x7

Benefit (billion yuan and rate) x8

15,200 36,325 70,732 3,784 2,513 10,399 3,722 4,666

x9 0

x10

x11

f1

10,668 3,589 109.29

rate1

f2

rate2

11.05

A 14,687 33,069 72,049 3,630 5,846 16,033 3,549 4,518 2,814 1,996 3,409 125.73 15.04 10.56 −4.39 2020 B 14,846 33,822 71,065 3,779 7,236 14,396 3,702 4,665 2,966 2,144 2,979 123.76 13.24 10.68 −3.30 C 14,862 33,253 71,082 3,772 6,676 15,001 3,692 4,659 2,933 2,141 3,530 124.28 13.71 10.63 −3.79 Note: Values for f1 and f2 were calculated based on benefit coefficients (cij from Table 3) in 2020 in billion yuan. rate1 and rate2 were the changed rates of values f1 and f2, in comparison of 2008 values with 2020 values.

In addition, Table 6 proved that the Pareto optimality was also efficient. For example, urban land (x6) experienced significant increases from 2008 to 2020. The calculations at points A, B, and C showed that x6 would increase in area by 16,033, 14,396 and 15,001 ha, respectively, which still conformed, in part, with Zengcheng’s master urban plan of areas of less than 17,831 ha for those areas. In contrast with land use in 2008, agricultural use, including cropland (x1), orchard land (x2), and grassland (x4) had negative growth in area. Changes in area, x1, were –514, –354, –338 ha for A, B, and C, –3256, –2503, –3072 ha for x2, and –154, –5, –13 ha for x4, respectively. Unused land (x11) showed a similar decreasing trend with values of –180, –610, and –59, for A, B, and C. These results may occur because of social and economic development, which would inevitably require greater areas of natural land resources. As discussed above, the area of rural residents (x7) should similarly reduce in the future, which may be a result of China’s “building a new countryside” policy, China’s long-term development strategy (Long et al., 2009). 4.3

Sensitivity analysis

In changing the coefficients and constraints, sensitivity analysis was also performed on benefit maximization object functions, and the results are shown in Figures 3 and 4. In Figure 3, letters a, b, c, d, e, and f represent the benefit coefficients for unused land (c11 and d11), which are measured at one-and-a-half, one-third, one-quarter, one-fifth, one-tenth, and one percent of the average value. In Figure 4, (a) shows the changed lower value of crop constraints and (b) the changed upper value for urban land. The results of the sensitivity analysis (Figures 3 and 4) show a negative correlation between economic benefit and ecological effectiveness where the curve for f1 increases as f2 decreases. Double figures also indicate that both functions were sensitive to any change in either coefficients or constraints. The smaller the value of the coefficients (c11 and d11) was,


Figure 3

Pareto fronts for benefits based on the changed benefit coefficients for unused land

Figure 4

Pareto fronts for benefits based on changed constraints

189

the smaller the value of f1 and f2. When the lower boundary of crop constraints changed, both functions remained negative with the regression coefficients, while the value of R2 changed (Figures 4a and 2). Moreover, the change in the turning point of the curve was explored by comparing Figure 4b with Figure 2, by changing the upper boundary of urban land.

5

Conclusions

In a period of strong social and economic development, the Chinese government has acted to meet the associated challenges by developing urban master plans. These 10-year plans have at their core a land use plan; however, these plans receive limited recognition owing to factors of uncertainty such as the lack of foresight and contextual inefficiencies. The experts and specialists involved in the planning carry a burden of responsibility to create a plan that will work well for today and tomorrow.

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To study land use optimization, the present paper used a rural–urban fringe zone city as the case study. A multi-objective model was constructed, including economic benefit and ecological effectiveness, and a GA was adopted to solve the model. Pareto optimality was used to carry out a performance evaluation and sensitivity analysis of the model. The results from running the model showed that a set of tradeoffs could be acquired by the allocation of land use, which might be especially helpful for decision-makers. The curve of the Pareto front for the two objectives highlighted the significantly negative correlation between both (–0.055 and R2 = 0.971); however, an un-uniformed distribution of 2–3 clusters was found, which explained the exception of the negative correlation. Performance evaluation using Pareto optimality proved the model was efficient. For example, areas of urban land showed a significant increase from 2008 to 2020, while cropland and orchard land, grassland, and unused land all showed decrement in areas at points A, B, and C on the Pareto front, respectively. Thus, an upper limit of 17,831 ha for the total urban area followed the urban master plan guidelines. These also showed that potential regional land resources remain available and that the urban master plan may be an effective tool to secure to sustainable local development in the future.

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