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Integrating ecosystem services value for sustainable land-use management in semi-arid region
Accepted Manuscript Integrating ecosystem services value for sustainable land-use management in semi-arid region
Yingxue Rao, Min Zhou, Guoliang Ou, Deyi Dai, Lu Zhang, Zuo Zhang, Xin Nie, Chun Yang PII:
S0959-6526(18)30780-7
DOI:
10.1016/j.jclepro.2018.03.119
Reference:
JCLP 12377
To appear in:
Journal of Cleaner Production
Received Date:
17 February 2017
Revised Date:
11 March 2018
Accepted Date:
12 March 2018
Please cite this article as: Yingxue Rao, Min Zhou, Guoliang Ou, Deyi Dai, Lu Zhang, Zuo Zhang, Xin Nie, Chun Yang, Integrating ecosystem services value for sustainable land-use management in semi-arid region, Journal of Cleaner Production (2018), doi: 10.1016/j.jclepro.2018.03.119
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ACCEPTED MANUSCRIPT
ACCEPTED MANUSCRIPT Integrating ecosystem services value for sustainable land-use management in semi-arid region
Yingxue Rao1, 2, Min Zhou3, Guoliang Ou4*, , Deyi Dai5, 6, Lu Zhang3, Zuo Zhang7, Xin Nie8, Chun Yang9 1College
of Public Administration, South-Central University for Nationalities, Wuhan, China, 430074; Email: [email protected] 2Research Center of Hubei Ethnic Minority Areas Economic and Social Development, South-Central University for Nationalities, Wuhan, China, 430074 3College of Public Administration, Huazhong University of Science and Technology, Wuhan, China, 430074; Email: [email protected] (Min Zhou); [email protected] (Lu Zhang) 4School of Construction and Environmental Engineering, Shenzhen Polytechnic, Shenzhen, China, 518055; Email: [email protected] 5Center of Hubei Cooperative Innovation for Emissions Trading System (CHCIETS), Wuhan, China, 430205, Email: [email protected] 6School of Logistics and Engineering, Hubei University of Economics, Wuhan, China, 430205 7Collage of Public Administration, Central China Normal University, Wuhan, China, 430079, Email: [email protected] 8School of Public Administration, Guangxi University, Nanning, China, 530004, Email: [email protected] 9College of Public Administration, Central China Normal University, Wuhan 430079, China, Email:[email protected]
*Correspondence:
Guoliang Ou School of Construction and Environmental Engineering, Shenzhen Polytechnic Shenzhen, Number 4089, West Shahe Road, Shenzhen, China, 518055 Email: ouyang8305@ szpt.edu.cn
1
ACCEPTED MANUSCRIPT Abstract: In this study, an inexact multi-objective optimization model integrated with ecosystem service value was developed for supporting sustainable land-use management in Took Mu Qinqi in the northeast of Inner Mongolia Province, China. It was an attempt to incorporate ecosystem service evaluation model within a general modeling framework in typical semi-arid region. The ecosystem service value was modified according to the development of eco-environment and social economy in Took Mu Qinqi based on the study of land ecosystem service value in China. The modified ecosystem service value provided input parameters for the optimization model. The results showed that the ecosystem service value of grassland was the highest among the six land-use categories, with about 97% and 83% of the total value for East and West Took Mu Qinqi, respectively. It indicated that grassland played an important role in ecosystem service. The results also showed that the total economic benefit produced by the optimal land use pattern increased by [11.5, 12.6], [5.66, 6.36] × 1012 RMB ¥ compared with the current land-use pattern for East, West Took Mu Qinqi, respectively. The total ecosystem service value produced by the optimal land-use pattern also increased by [5.9, 9.1], [3.4, 4.5] × 1012 RMB ¥ compared with that produced by the current land-use pattern for East, West Took Mu Qinqi, respectively. It indicated that the optimal land-use pattern generated by the multi-objective model was better than the current land-use pattern either from the point of economic benefit or from the point of ecological benefit. The optimization model of land-use structure based on ecosystem service value could satisfy the social, economical, and environmental development , which provided a new way to solve the key technical problem in land-use planning in typical semi-arid region.
Keywords: Land-use management, ecosystem service value, multi-objective programming, semi-arid.
2
ACCEPTED MANUSCRIPT 1. Introduction There is a growing concern in the science of ecosystem service value and land-use management, especially for the developing country like China with the rapid urbanization (Connor et al., 2015; De Groot et al., 2010; Song et al., 2015; Wu et al., 2015). The urbanization can change land-use pattern, which further influence biogeochemical cycle and climate, making ecological system develop in the direction of dysfunction (Eziz et al., 2016; Langemeyer et al., 2016; Peng et al., 2016; Ping et al., 2015; Van et al., 2015). Meanwhile, the general land-use management pursues economic benefit unilaterally, ignoring ecological benefit, which also intensify the conflict between land resource ecological development and land resource economic benefit (Camacho-Valdez et al., 2014; SawutMamat et al., 2013). This threatens the regional economic and ecological safety. Land-use pattern can directly influence the service kind and strength provided by ecological system, which can cause the change of the area, spatial distribution pattern of ecological system (Guo et al., 2016; Hao et al., 2012; Liu et al., 2012; Schulte et al., 2013; Song, 2017). Therefore, how to fully integrate the concept of ecosystem service value into the general land-use management is desired.
Previously, a number of studies were conducted on ecosystem service valuation. For example, Bolund and Hunhammar (1999) indicated that the locally generated ecosystem services had a substantial impact on the quality-of-life in urban areas and should be addressed in land-use planning. Kreuter et al. (2001) evaluated changes in land-use and ecosystem service values due to urban sprawl in the San Antonia area, Texas. Hein et al. (2006) studied the spatial scales of ecosystem services and examined how stakeholders at different spatial scales attach different values to ecosystem services. Tianhong et al. (2010) suggested that a reasonable land-use plan should be made with emphasis on the highest ecosystem service value including protecting wetland, water body and woodland. Polasky et al. (2011) illustrated the importance of taking ecosystem services into account in land-use and land management decision-making and linking such decision to incentives that accurately reflect social returns. Xue and Luo (2015) found that the dynamic variation of ecosystem service value in response to land-use changes had significant impacts on the sustainability of urban 3
ACCEPTED MANUSCRIPT system. It was an urgent need to lighten the heavy burden on the ecosystem through rational land-use management during rapid urbanization. Fu et al. (2016) addressed the overlap and connections among anthropogenic impacts (land use) with evaluations of societal benefits through ecosystem service value to an environmentally sensitive riparian zone in Northeast China using remote sensing observations and socio-economic data. Sutton et al. (2016) derived the loss of ecosystem service value from land degradation globally. The abovementioned studies identified the influence of ecosystem service value on land-use management. Nevertheless, few of them effectively considered ecosystem service value in the decision-making process of land-use optimization, which is especially important for China as a developing country with the rapid urbanization. In fact, land-use optimization has been investigated by many researchers. For example, Wang et al. (2004) integrated GIS with land-use optimization model to allocate future land uses in the human decision making process related to environmental management and planning. Sadeghi et al. (2009) formulated an optimization model to find out the most suitable land allocation to different land uses targeting soil erosion minimization and benefit maximization. Chuai et al. (2013) optimized land-use structure in 2020 to increase carbon storage in China. The optimized land-use structure presented obvious effect on the entire country. Cao and Ye (2013) developed a comprehensive land-use allocation optimization prototype based on a Coarse-Grained Parallel Genetic Algorithm using Tongzhou Newtown, Beijing, China as the case study. Liu et al. (2015) integrated game theory methods into land-use optimization model to improve the ability of existing land-use spatial optimization model for addressing local land-use competitions. Li and Parrott (2016) proposed an improved Genetic Algorithm to deal with multi-site land-use allocation in the regional district of central Okanagan in Canada. Mohammadi et al. (2016) presented the development and application of hybrid algorithms for solving land use optimization problem. Generally, these studies were individually conducted on ecosystem service valuation, land-use optimization (Wang et al., 2013). Furthermore, the limitation of most existing land-use management models is that the uncertainties are not explicitly taken into account. For example, the economic benefit of land-use pattern, and the ecosystem service value of land-use pattern, as well as the planning area of land-use pattern may appear uncertain. To reflect such uncertainties, inexact system analysis techniques, 4
ACCEPTED MANUSCRIPT including fuzzy mathematical programming, stochastic mathematical programming and interval mathematical programming can be used to assist in developing land-use management. Among of them, interval mathematical programming is widely used due to its low computational requirement, which express the uncertainties as discrete intervals and is effective in situations when little information is available. However, combination of ecosystem service value, land-use optimization, uncertainties handing techniques into a general modeling framework has been seldom reported, which is desired for facilitating sustainable land-use management.
Therefore, the objective of this study is to establish an inexact multi-objective land-use optimization model for sustainable land-use management. The multi-objective model can solve the decision-making problem with conflicting objectives, and gain the reasonable tradeoff among different objectives, which is widely used in land-use management. The typical semi-arid region Took Mu Qinqi in the northeast of Inner Mongolia, China is selected as a case study to determine the optimal land-use spatial pattern. The model maximizes the economic benefit and ecosystem service value of land unit and is subject to actual land-use conditions. The obtained results might help local authorities better understand and address complex land-use systems and develop optimal land-use management strategies that better balance urban expansion and grassland conservation.
2. Materials and methods 2.1 Study area The study area called Took Mu Qinqi is situated in the northeast of Inner Mongolia, China, with a continental semi-arid climate. Took Mu Qinqi grassland is the typical area of Xilingol grassland in Inner Mongolia, which is one of the four biggest grasslands in the world. Its area is 63675.36 km2, and the population is approximate to 128500. The study area includes two administrative regions, East Took Mu Qinqi and West Took Mu Qinqi (as shown in Figure 1). More than 85% of this area belongs to the Wulagai river basin. The length of Wulagai River is 360 km. The average volume of runoff is 113 million cubic meter in this basin. Wulagai River system not only provides valuable water resources, but also plays a vital role 5
ACCEPTED MANUSCRIPT for local ecological benefit. It is the lifeline of Took Mu Qinqi grassland, which is also the important ecological factor for sustaining grassland productivity. However, the monitoring data in recent years indicated that Took Mu Qinqi grassland has shown various degrees of degeneration due to drought and overgrazing, and the degeneration area keep expanding. The total area of various degrees of degeneration has accumulated to one half of the available grassland (Bao et al., 2009). Furthermore, with the development of local economic and the increasing of built-up land, the local land-use structure has changed a lot. And the role of ecosystem service has subsided. On this background, comprehensive analyse of ecosystem service value and land-use structure optimization will be of great importance for land-use policy.
Figure 1 The study area
2.2 Data sources The equivalent factor of ecosystem service value in China has been investigated by Xie et al. (2008) (as shown in Table 1). However, it was an average value, and lacked the evaluation of construction land. This study amended the evaluation method of ecosystem service value according to the research results of Dong et al. (2007) and Jiang et al. (2009). The amended evaluation method was applied in the calculation of ecosystem service value of study area, which provided theoretical basis and data support for ecosystem service value evaluation for 6
ACCEPTED MANUSCRIPT the region with rapid urbanization. Table 1 The equivalent factor of ecosystem service value for unit area in China Kind of service Food production Raw material production Gas regulation Climate regulation hydrological regulation Waste treatment Soil conservation Biodiversity maintenance Entertainment culture Total
Farmland 0.33 2.98 4.32 4.07 4.09 1.72 4.02 4.51 2.08 28.12
Grassland 0.43 0.36 1.50 1.56 1.52 1.32 2.24 1.87 0.87 11.67
Forest land 1.00 0.39 0.72 0.97 0.77 1.39 1.47 1.02 0.17 7.90
Wet land 0.36 0.24 2.41 13.55 13.44 14.40 1.99 3.69 4.69 54.77
River/lake 0.53 0.35 0.51 2.06 18.77 14.85 0.41 3.43 4.44 45.35
Time-series data on existing land use were obtained from general land-use planning of the 12th Five-Year Plan of East Took Mu Qinqi and West Took Mu Qinqi. Statistics of the landuse area were gathered based on nine categories of land, namely farm land, garden land, forest land, grassland, other agriculture land, land for residential areas and mining, traffic land, land for water facilities, and idle land. According to the ecosystem characteristic of Took Mu Qinqi, the kind of ecosystem was determined to be farmland, garden land, forest land, grassland land, idle land, construction land. In detail, farm land and other agriculture land were attributed to farm land ecosystem. Garden land, forest land and grassland belonged to garden land, forest land and grassland ecosystem, respectively. Land for residential areas and mining and traffic land pertained to construction land ecosystem. Land for water facilities and idle land were attributed to idle land ecosystem. A geo-database containing spatial and statistical data has been set up. The statistical data were derived from annually published statistical reports. The year 2015 was chosen as the target year, in line with China’s current land-use master plan.
2.3 Methods 2.3.1 Grey prediction modeling In order to identify optimal strategies for land-use pattern allocation in the study area, the unit economic benefit of various land-use pattern in target year needs to be determined firstly. In this study, grey prediction modeling GM (1, 1) was used to forecast the unit economic benefit 7
Desert 0.02 0.04 0.06 0.13 0.07 0.26 0.17 0.40 0.24 1.39
ACCEPTED MANUSCRIPT of various land-use pattern in target year. When forecasting successive changes of system behaviors, GM (1, 1) could achieve high accuracy (Leephakpreeda, 2008). Consider a time sequence x (0) (k ) that can denote historical series of the unit economic benefit of various land-use pattern as follows:
x (0) (k ) x (0) (1), x (0) (2),..., x (0) (n)
(1)
The grey differential equation is formed by an original time series x (0) (k ) using the accumulated generating operation (AGO) technique. It can be denoted as follows:
x (0) (k ) az (1) (k ) b
(2)
The whitening equation is therefore, as follows (Kayacan et al., 2010): dx (1) (t ) ax (1) (t ) b dt
(3)
where a is the developing coefficient, and b is the control variable. Through the adoption of the least-square method, a and b can be generated as:
a T 1 T b ( A A) A X n
(4)
where z (1) (2) (1) z (3) A (1) z ( n)
1 1 1
(5)
x (0) (2) (0) x (3) Xn x (0) (n)
(6)
where z is the background values and can be calculated by:
z (1) (k ) [ x (1) (k ) x (1) (k 1)] / 2, x (1) (k ) i 1 x (0) (i ) k
(7)
According to Eq. (3), the solution of x (1) (t ) at time k can be presented as follows: b b xˆ (1) (k 1) x (0) (1) e ak a a
(8)
Because the grey forecasting model is formulated using the data of AGO rather than original 8
ACCEPTED MANUSCRIPT data, it is necessary to transfer the data of AGO to actual forecasting value. This technique is called the inverse accumulated generating operation (IAGO) and can be denoted as: b xˆ (0) (k 1) x (0) (1) e ak (1 e a ) a
(9)
where k = 2, 3,…, n, xˆ (0) (1) x (0) (1) . 2.3.2 Ecosystem service value calculation (1) The amendment of ecosystem service value equivalent The adjustment index of willingness-to-pay and ability-to-pay was introduced to amend the equivalent factor of ecosystem service value of farmland, garden land, forest land, grassland land and idle land. The adjustment index was calculated as follows:
AI t Wt At
(10)
where AIt is the adjustment index, Wt is the index of willingness-to-pay, At is the index of ability-to-pay. The logisticeurve model was generally used to describe the index of willingness-to-pay, which is as follows:
Wt
2 (1 ae bm )
(11)
where m is period coefficient of social development, a, b are the constants. The period coefficient of social development was calculated according to the follows: m
1 2.5 Ent
(12)
Ent Entc Ptc Entr Ptr
(13)
where Ent is the total Engel's coefficient of study area in period t; Entc and Entr are the Engel's coefficient of urban and rural area, respectively; Ptc and Ptr are the percentage of population in urban and area of study area in period t. The index of ability-to-pay was described as follows:
At
GDPper
(14)
GDPtotal
where GDPper represents per capita gross domestic product of study area in period t; GDPtotal stands for the total gross domestic product in period t. (2) Ecosystem service value of non-construction land The evaluation model of ecosystem service value of non-construction land was established as follows: 9
ACCEPTED MANUSCRIPT n
ESVn c Ai EFi AI t Et
(15)
i 1
where Ai is the area of land use pattern i in study area (hm2); EFi is the equivalent factor of ecosystem service value of land-use pattern i; Et is the individual event service of food production of farm land. Et
1 Tt 7 St
(16)
where Tt is the total economic value of food production of study area in period t (104 RMB ¥);
St is the total cultivated area of study area in period t (hm2). (3) Ecosystem service value of construction land The influence of construction land ecosystem on eco-environment was the discharge of exhaust, waste water and solid. Land for residential areas and mining and traffic land destroyed hydrological regulating including water retention, filtration, storage and supply. According to Zong et al. (2000) and Dong et al. (2007), the value of freshwater resources consumption was used to evaluate the value of hydrological regulating, and the value of social labor consumption for waste gas, water and solid treatment was used to evaluate the value of waste treatment. a. The service value of hydrological regulating for urban land was calculated as follows: Vhr u
Wwc u Pwr u Su
(17)
where Vc is the service value of hydrological regulating for unit urban area (RMB ¥/hm2); Wwc-u is the annual water consumption for urban residents (ton); Pwr-u is the price of local water resource (RMB ¥/ton); Sc is the area of urban land (hm2). b. The service value of waste treatment for urban land was calculated as follows: Vwt u
Wwwu Pwwu S mwu Pmwu Su Su
(18)
where Wwwu is the annual discharge of municipal wastewater (ton); Pww-u is the price of municipal wastewater treatment (RMB ¥/ton); Smw-u is the number of urban resident households (per family); Pmw-u is the price of municipal solid waste treatment (RMB ¥/per family*month). c. The service value of hydrological regulating for industrial and mining land was calculated as follows: 10
ACCEPTED MANUSCRIPT Vhr ind
Wwc ind Gwc ind Pwc ind Sind
(19)
where Vhr-ind is the service value of hydrological regulating for unit industrial and mining area (RMB ¥/hm2); Wwc-ind is water consumption per ten thousand Yuan GDP (m3/104 RMB ¥); Gwc-ind is total industrial output value (104 RMB ¥); Pwc-ind is the price of industrial water (RMB ¥/m3); Sind is the area of industrial and mining land (hm2). d. The service value of waste treatment for industrial and mining land was calculated as follows:
Vwt ind
Wwwind Pwwind Ewg ind Pwg ind S mwind Pmwind Sind Sind Sind
(20)
where Wwwind is the annual discharge of industrial wastewater (ton); Pww-ind is the price of industrial wastewater treatment (RMB ¥/ton); Ewg-ind is the annual discharge of industrial waste gas (ton); Pwg-ind is the price of local waste gas treatment (RMB ¥/ton); Smw-ind is the annual production of industrial solid waste (ton); Pmw-ind is the price of industrial solid waste treatment (RMB ¥/ton). e. The transport pollution of waste treatment value for unit area of traffic land: Vtrans
a Ptrans Vtrans Strans
(21)
where a is the pollution coefficient (the percentage of pollution fee generated by exhaust gas); Ptrans is transportation expenses per capita (RMB ¥/per capita); Vtrans is the total population (capita); Strans is the area of transport land (hm2). (4) Ecosystem service value of residential area This study analyzed the change of ecosystem service value of residential area from the point of the increase and loss of ecosystem value. Vra ( Aor VCra ) ( Arf VCra )
(22)
where Vra is the ecosystem service value of farmland loss, Aor is the area of residential land transformed by other land type; Arf is the area of farmland transformed by residential land;
VCra is the amended ecosystem service value per unit area.
2.3.3 Interval mathematical programming Interval mathematical programming (IMP) where coefficients of the decision variables in the objective function and constraints are reflected as an interval variation can be expressed as 11
ACCEPTED MANUSCRIPT follows: max f C X
(23)
subject to: A X B
(24)
X 0
(25)
where X
nl
, A
mn
, B
ml
, C
l n
, denotes a set of
interval numbers. According to Huang et al. (1992), the IMP model can be transformed into two deterministic submodels, i.e., f and f , which correspond to the lower and upper bounds of the objective function values. Through solving the two submodels, the relevant , f opt ]. solutions can be acquired as follows: x jopt [ x jopt , x jopt ] , f opt [ f opt
2.3.4 Multi-objective for sustainable land-use optimization Sustainable land-use management is a complicated process as it involves a long-term balance between economic development, environmental protection, efficient resource use, and social equity (Cao and Ye, 2013; Church, 2008; Huang and Zhang, 2014). How to pursue this balance has received great concerns. And attentions should be paid to how to choose suitable land-use patterns which can make a comprehensive consideration among economic benefit, social benefit and ecological benefit (Janssen et al., 2008). In this study, an inexact multiobjective land-use optimization model for sustainable land-use management is developed. The multi-objective land-use optimization model includes the objective of economic benefit, social benefit and ecological benefit. Among them, the objective of ecological benefit is presented as ecosystem service value. The objective of social benefit should be constructed by consciously taking into account the well-being of people and their communities. Such systems strive for distributional equity, adequate provision of social services including living, health and education, gender equity, political participation, etc (Ma and Zhao, 2015). Although it is difficult to quantify the social benefit objective, the notion can be simplified to land-use balance between the supply and the demand in constraints within the land-use optimization model. Therefore, the quantitative modeling of the economic and ecological benefits of land-use is primarily considered when designing the objective functions. Furthermore, it is assumed that the objectives of maximizing economic benefit and maximizing ecological benefit have the same weight coefficient. Then, the multi-objective 12
ACCEPTED MANUSCRIPT optimization model can be transformed into single-objective optimization model by using linear weighting method. Table 2 Simple table of land use optimization in Took Mu Qinqi Objectives Objective 1 Objective 2 Constraints Constraint 1 Constraint 2 Constraint 3 Constraint 4 Constraint 5 Constraint 6 Constraint 7
Maximize economic benefit Maximize ecological benefit Population constraint The total area of land use constraint Agriculture production constraint Forest-related activity constraint Grassland-related activity constraint Industrial activity constraint Technical constraint
(1) Multi-objective land-use optimization model Objective 1: Maximize economic benefit As different land-use patterns yield different economic benefit, optimizing the structure and layout of different land use to maximize economic benefit is crucial. I
J
max f Bij X ij 1
(26)
i 1 j 1
where f1 is the total economic benefit; X ij is the area of land-use pattern i in region j (i = 1, 2,…9, which stands for farmland, garden land, forest land, grassland, other agriculture land, land for residential areas and mining, traffic land, land for water facilities, idle land, respectively; j = 1, 2, which stands for East Took Mu Qinqi and West Took Mu Qinqi, respectively); Bij is the economic benefit of land-use pattern i in region j for unit area. Objective 2: Maximize ecological benefit The ecosystem service value value for different land-use pattern and spatial ecological suitability is considered in this research to ensure the ecological benefit. I
J
max f 2 Cij X ij
(27)
i 1 j 1
where f 2 is the total ecological benefit; Cij is the ecosystem service value of land use pattern i in region j for unit area. Constraints: 13
ACCEPTED MANUSCRIPT The constraint conditions depend on local land use policies, economic and social development plans, and special planning related to land use. Here, we define 2010 as the initial year and assign 2020 as our target prediction year. Constraint 1: The population of target year should be less than the population of regional land carrying capacity. 9
PD
ij
i 1
X ij LCC j , j
(28)
where PDij is the population density of land use pattern i in region j; LCC j is the land carrying capacity in region j. Constraint 2: 9
X i 1
ij
TAj , j
(29)
where TAj is the total area of land-use in region j. Constraint 3: According to the local farmland policy, the area of farmland should be greater than Protection area of farmland in order to ensure the grain security. X 1j PACLj , j
(30)
where PACLj is the Protection area of farmland in region j. Constraint 4: According to the Forestry Development Planning made by local government, forest land should be strictly protected due to its greatest capability to accumulate carbon. X 3j PFLj , j
(31)
where PFLj is the planning area of forest land in region j in 2020. Constraint 5: In order to develop the ecosystem function of grassland and strengthen the protection of the basic ecological land, grassland should be strictly planned according to the Development Planning made by local government. X 4 j PGLj , j
(32)
where PGLj is the planning area of grassland land in region j in 2020. Constraint 6: 14
ACCEPTED MANUSCRIPT With rapid economic development and urbanization, the increasing trend of construction and traffic land is difficult to change. It is imperative to control newly-added construction land according to the state macro-adjustment and control measures, meeting the demand of urban construction and industrial and mining land development under the condition of intensive land use. X 6j PCLj , j
(33)
X 7 j PTLj , j
where PCLj is the planning area of construction land in region j in 2020. PTLj is the planning area of traffic land in region j in 2020. Constraint 7: X ij 0, i, j
(34)
3. Results and discussion 3.1 Land-use pattern prediction in target year The goal of GM (1, 1) model is to extract as much information as possible from the historical data for forecasting time-series unit economic benefit of various land-use patterns over the planning horizon. The data from 2005 to 2010 was used to compare with the prediction values. Two common criteria including the MARE (mean absolute relative error) and the correlation coefficient (R2) can be utilized for evaluating prediction performances of the GM (1, 1) model. If the value of R2 is close to one or the value of MARE is close to zero, then GM (1, 1) mode is consider performing very well in forecasting. The two criteria can be defined as follows: MARE
1 n ( yt y t ) y n t 1 t n
R2 1
(y
t
t 1 n
(y t 1
t
y t )2
yt av )
(35)
100%
(36)
2
where yt denotes the actual values, y t denotes the forecasted values, yt av denotes the average values, t is the time periods (t = 1, 2, …,n), and n is the total observation number. 15
ACCEPTED MANUSCRIPT The values of MARE and R2 through GM (1, 1) model are 8.16% and 93.28%, respectively. It is indicated that the obtained results showed that GM (1, 1) model can well capture the variations of unit economic benefit of various land-use patterns in the future. Table 3 shows the predicted unit economic benefit of various land-use patterns in the study area in target year. The interval number represents the forecasting range, which can be used to deal with uncertainties in land-use optimization model. In order for the kind of ecosystem service analysis that we conducted to become more meaningful for policy formulation, it is imperative to obtain value coefficients for ecosystem service that more accurately reflect local conditions (Zhao et al., 2004). Therefore, the adjustment index in target year was also predicted by grey prediction modeling GM (1, 1). Similarly, the data from 2005 to 2010 was used to compare with the prediction values. Figure 2 displays the forecasted unit ecosystem service value of various land-use patterns in East Took Mu Qinqi. The unit ecosystem service value of land for residential areas and mining was negative due to its negative effectiveness of pollution (e.g. air pollution, water pollution, waste pollution et al). The forecasting parameters provide input data for the following land-use optimization.
Figure 2 The prediction value of unit ecosystem service of various land-use patterns
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Table 3 The predicted unit economic benefits of various land use pattern in the study area in 2020 (104 RMB/hm2) Farmland East Took Mu Qinqi West Took Mu Qinqi
[4.20， 7.20] [4.20， 7.20]
Garden land
Forest land
[2.90，5.00]
[3.90，15.90]
[2.90，5.00]
[3.90，15.90]
Grassland land [286.40， 311.50] [295.30， 334.20]
Other agriculture land
Land for residential areas and mining
Traffic land
[1.80，2.30]
[324.80,342.80]
[116.00，185.80]
[2.00, 3.50]
[389.20, 456.80]
[125.30, 234.50]
17
Land for water facilities [228.90， 335.40] [282.90， 335.40]
Idle land [156.60， 198.60] [156.60， 198.60]
ACCEPTED MANUSCRIPT 3.2 Ecosystem service value evaluation Table 4 lists the adjustment index of ecosystem service value in the study area. The ecosystem service equivalent value of farmland, garden land, forest land and idle land can be obtained based on Table 1 and 4. Based on the unit equivalent value of ecosystem service for food production (as shown in Table 5) and the adjustment index, the unit equivalent value of ecosystem service for farmland, garden land, forest land and idle land can be calculated. Although different ecosystem service valuation methods may lead to different estimation values, which will cause scientific critics and doubts on ecosystem service valuation, it is important to realize that accurate coefficients are often less critical for time series than crosssectional analysis because coefficients tend to affect estimates of directional change less than estimates of the magnitude of ecosystem value at specific points in time (Li et al., 2010). Table 4 The adjustment index of ecosystem service value Region East Took Mu Qinqi West Took Mu Qinqi
Index
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
Willingness-to-pay Ability-to-pay Adjustment index Willingness-to-pay Ability-to-pay Adjustment index
1.22 1.64 2.00 1.24 1.66 2.06
1.24 1.69 2.10 1.25 1.72 2.15
1.25 1.69 2.11 1.23 1.71 2.10
1.24 1.72 2.13 1.27 1.72 2.18
1.28 1.73 2.21 1.29 1.73 2.23
1.30 1.59 2.07 1.31 1.69 2.21
1.28 1.62 2.07 1.28 1.66 2.12
1.33 1.62 2.15 1.35 1.64 2.21
1.30 1.60 2.08 1.31 1.61 2.11
1.31 1.63 2.14 1.33 1.67 2.22
1.32 1.60 2.11 1.34 1.62 2.17
Table 5 The unit equivalent value of ecosystem service for food production in the study area in 2015 Sown area Economic value Economic value per unit Unit equivalent value area (104 RMB/hm2) (104 RMB/hm2) (104 RMB) （hm2） East Took Mu Qinqi West Took Mu Qinqi
3266.68
24909.00
7.63
1.09
2000.00
18956.00
9.48
1.35
3.3 Sustainable land use pattern optimization in semi-arid region Table 6 lists the optimal land-use pattern in target year compared with the current land-use pattern. The area of farmland for East Took Mu Qinqi increased from 37041.30 hm2 to [37027.20, 45527.20] hm2, and for West Took Mu Qinqi increased from 187.1 hm2 to [219.30, 342.30] hm2. The reason was due to the fact that food production generated by farmland should meet the demand of local population growth, which was in line with China’s cultivated land protection policy. It also reflected China's basic state policy of rational land 18
ACCEPTED MANUSCRIPT utilization and earnest protection of cultivated land. The area of forest land for East Took Mu Qinqi increased from 28640.40 hm2 to [30000.00, 32135.00] hm2, and for West Took Mu Qinqi increased from 121350.00 hm2 to [128500.00, 130532.00] hm2. The area of grassland for East Took Mu Qinqi increased from 4001321.70 hm2 to [4271211.40, 4301919.00] hm2, and for West Took Mu Qinqi increased from 1932220.30 hm2 to [1971110.90, 2078562.80]
hm2. Forest coverage has been increasing and ecological environment has been improving for both two administrative regions through afforestation and reforestation. Under the high attention of local government, the implement of ecological forest construction makes great progress, which improves the ability of soil and water conservation. It also plays an important role in controlling water and soil erosion, especially for East Took Mu Qinqi and West Took Mu Qinqi, which has been regarded as the key prevention area of water and soil erosion. The increase of forest coverage and the improvement of ecological environment provide a guarantee for the development of local tourism industry. One obvious characteristic is that the area of traffic land increased from 2992.38 hm2 in 2015 to [3990.00, 4557.43] hm2 for East Took Mu Qinqi, and from 358.89 hm2 to [402.80, 477.55] hm2 for West Took Mu Qinqi in target year, respectively. The area of land for residential areas and mining decreased from 7411.82 hm2 to [4608.21, 5708.16] hm2 for East Took Mu Qinqi, and from 9772.56 hm2 to [3896.50, 5262.09] hm2 for West Took Mu Qinqi, respectively. Under the local governments’ intensive land use policy of both East Took Mu Qinqi and West Took Mu Qinqi, efforts are intensified to use land more economically and intensively, resulting in continued improvement in land-use efficiency. The priority is to meet the demand of urban construction and industrial and mining development. The average land for residential areas was [480.20, 594.60] m2 for East Took Mu Qinqi, and [432.90, 584.70] m2 for West Took Mu Qinqi, which were in line with the development planning of East Took Mu Qinqi and West Took Mu Qinqi, respectively. The idle land was fully utilized. For example, the area of idle land decreased by [273561.40, 313561.40] hm2 for East Took Mu Qinqi and [51821.10, 163187.90] hm2 for West Took Mu Qinqi, respectively. Since a large proportion of idle land would be changed into grassland, and a fraction of idle land be changed into forecast and other agriculture land. 19
Table 6(a) The optimal land-use patterns in the study area in target year compared with the current land-use pattern (upper bound, hm2) Area East Took Mu Qinqi West Took Mu Qinqi
Year
Farmland
Garden land
Forest land
Grassland land
Other agriculture land
Land for residential areas and mining
Traffic land
Land for water facilities
Idle land
2015 2020 2015 2020
37041.30 37027.20 187.10 219.30
1.00 0.00 5.30 0.00
28640.40 30000.00 121350.00 128500.00
4001321.70 4271211.40 1932220.30 1971110.90
10460.50 11460.50 6022.50 16166.00
7411.80 5708.20 9772.60 5262.10
2992.40 4557.40 358.90 477.60
1613.70 3079.40 10.20 11.90
469844.10 196282.70 173524.60 121703.50
Table 6(b) The optimal land-use patterns in the study area in target year compared with the current land-use pattern (lower bound, hm2) Area East Took Mu Qinqi West Took Mu Qinqi
Year
Farmland
Garden land
Forest land
Grassland land
Other agriculture land
Land for residential areas and mining
Traffic land
Land for water facilities
Idle land
2015 2020 2015 2020
37041.30 45527.20 187.10 342.30
1.00 0.00 5.30 0.00
28640.40 32135.00 121350.00 130532.00
4001321.70 4301919.00 1932220.30 2078562.80
10460.50 12786.00 6022.50 19366.10
7411.80 4608.20 9772.60 3896.60
2992.40 3990.00 358.90 402.80
1613.70 2079.40 10.20 11.60
469844.10 156282.70 173524.60 10336.70
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ACCEPTED MANUSCRIPT 3.4 Economic benefits and ecosystem service value analysis Table 7 presents the economic benefit of various land-use patterns under the optimal condition of Economic-Social-Environmental System. The economic benefit of farmland, garden land, forest land, grassland, other agriculture land, land for residential areas and mining, traffic land, land for water facilities, and idle land were [1.91, 2.67], 0, [1.26, 4.77], [12300.00, 13300.00], [0.23, 0.26], [15.00, 19.60], [4.63, 8.47], [4.67, 10.30], [245.00, 390.00] × 109 RMB ¥ for East Took Mu Qinqi, and [0.014, 0.016], 0, [5.10, 20.40], [6140.00, 6590.00], [0.38, 0.56], [15.20, 24.00], [0.51, 1.12], [0.033, 0.040], [16.20, 24.20] × 109 RMB ¥ for West Took Mu Qinqi, respectively. The maximal economic benefit of land-use pattern was grassland, and the minimal economic benefit was garden land for both administrative regions. The reason was that livestock were often the only source of livelihoods for the people living in both East Took Mu Qinqi and West Took Mu Qinqi. Garden land was not incorporated into the relevant overall land-use plan according to land-use planning of both East Took Mu Qinqi and West Took Mu Qinqi. In fact, the total economic benefit of various land-use patterns was influenced by two factors including the unit economic benefit and the area. For example, the unit economic benefit of land for residential and mining was highest, however, the total economic benefit of land for residential area and mining was only [15.00, 19.60] × 109 RMB ¥ for East Took Mu Qinqi, which was lower than that of grassland and idle land. The reason was due to fact that the social and economic characteristics in East Took Mu Qinqi were small population intensity, and the area of land for residential area and mining only accounted for a small fraction of total area in East Took Mu Qinqi. Also, Development Planning made by local government of East Took Mu Qinqi could impact indirectly the total economic benefit of various land-use patterns. Table 8 shows the ecosystem service value of various land-use patterns under the optimal condition of Economic-Social-Environmental System. The ecosystem service value of farmland, garden land, forest land, grassland, other agriculture land, land for residential areas and mining, traffic land, land for water facilities, and idle land were [226, 244], 0, [580, 593], [24400, 27600], [76.2, 76.9], [-243, -230], [-3.12, -2.79], [0.025, 0.073], [36.6, 72.9] × 106 RMB ¥ for East Took Mu Qinqi, and [1.34, 1.84], 0, [2410, 2480], [11800, 12700], [98.1, 101], [-231, -197], [-0.5, -0.48], [0.005, 0.007], [4.4, 70.1] × 106 RMB ¥ for West Took Mu Qinqi, respectively. The maximal ecosystem service value of land-use pattern was grassland, and the minimal ecosystem service value was land for residential areas and mining. Because 21
ACCEPTED MANUSCRIPT of the large coefficient value and the large area, the value of ecosystem service produced by grassland was the highest among the six land-use categories, with about 97% and 83% of the total value for East and West Took Mu Qinqi, respectively. It indicated that grassland played important roles in ecosystem service. Figure 3 displays the total economic benefit of the optimal land-use patterns for East Took Mu Qinqi and West Took Mu Qinqi. It was [12.6, 13.7], [6.18, 6.88] × 1012 RMB ¥ for East Took Mu Qinqi and West Took Mu Qinqi, respectively. The total economic benefit produced by optimal land-use pattern increased by [11.5, 12.6], [5.66, 6.36] × 1012 RMB ¥ compared with that produced by the current land-use pattern for East Took Mu Qinqi and West Took Mu Qinqi, respectively.
Figure 3 The total economic benefits Figure 4 displays the total ecosystem service value of the optimal land-use pattern for East Took Mu Qinqi and West Took Mu Qinqi. It was [25.1, 28.3], [14.1, 15.2] × 109 RMB ¥ for East Took Mu Qinqi and West Took Mu Qinqi, respectively. The total ecosystem service value produced by the optimal land-use pattern increased by [5.9, 9.1], [3.4, 4.5] × 1012 RMB ¥ compared with that produced by the current land use pattern for East Took Mu Qinqi and West Took Mu Qinqi, respectively. This was mainly caused by the increase of forest land and grassland. It indicated that ecological benefit generated by the optimal land-use pattern was better than that by the current land-use pattern.
22
ACCEPTED MANUSCRIPT
Figure 4 The total ecosystem service value The economic growth often seems to be in conflict with ecological protection. Therefore, a compromise between economic development and ecological protection must be considered. It is suggested that a reasonable land-use plan should be made with emphasis on protecting forecast land, grassland, and water body which have high ecosystem service value for Took Mu Qinqi, so as to maintain a balance between economic development and ecosystem health in the future.
4. Conclusion In this study, an inexact multi-objective land-use optimization model integrated with ecosystem service value was developed for supporting sustainable land-use management in the typical semi-arid region, Took Mu Qinqi in the northeast of Inner Mongolia, China. It was an attempt to incorporate ecosystem service evaluation model within a general modeling framework. The change of ecosystem service value in Took Mu Qinqi was evaluated and analyzed based on the study on land ecosystem service value in China. The modified ecosystem service value provided input parameters for the optimization model. The optimized results showed that the total economic benefit of the optimal land-use pattern for East Took Mu Qinqi and West Took Mu Qinqi were [12.6, 13.7], [6.18, 6.88] × 1012 RMB ¥, respectively. The total economic benefit produced by the optimal land-use pattern increased by [11.5, 12.6], [5.66, 6.36] × 1012 RMB ¥ compared with that produced by the 23
ACCEPTED MANUSCRIPT current land-use pattern for East Took Mu Qinqi and West Took Mu Qinqi, respectively. Furthermore, the total ecosystem service value of the optimal land-use pattern for East Took Mu Qinqi and West Took Mu Qinqi were [25.1, 28.3], [14.1, 15.2] × 109 RMB ¥, respectively. The total ecosystem service value produced by the optimal land-use pattern increased by [5.9, 9.1], [3.4, 4.5] × 1012 RMB ¥ compared with that produced by the current land use pattern for East Took Mu Qinqi and West Took Mu Qinqi, respectively. It indicated that the optimal land-use pattern generated by multi-objective model was better than the current land-use pattern either from the point of economic benefit or from the point of ecological benefit. It also indicated that the developed method could also be applicable in other climate or regions by adjusting the corresponding model parameters for helping decision makers identify desired sustainable land-use pattern considering ecosystem service value. Further improvements in modeling and data are needed to increase the reliability of estimates of ecosystem service value. Since the accuracy and reliability of the estimated results are mainly determined by the accuracy of value coefficients. Despite some methodological shortcomings, ecosystem service valuation has the potential to inform policy decisions by highlighting the benefit of sustainable land-use management.
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Table 7(a) The economic benefit of various land-use patterns in target year (Upper bound, 104 RMB) Area East Took Mu Qinqi West Took Mu Qinqi
Farmland
Garden land
Forest land
2.67E+05 0.00E+00 4.77E+05 1.58E+03 0.00E+00 2.04E+06
Grassland land
Other agriculture land
1.33E+09 6.59E+08
2.62E+04 5.59E+04
Land for residential areas and mining
Traffic land
1.96E+06 8.47E+05 2.40E+06 1.12E+05
Land for water facilities
1.03E+06 3.99E+03
Idle land
3.90E+07 2.42E+07
Table 7(b) The total economic benefit of various land-use patterns in target year (Lower bound, 104 RMB) Area East Took Mu Qinqi West Took Mu Qinqi
Farmland
Garden land
Forest land
1.91E+05 0.00E+00 1.26E+05 1.44E+03 0.00E+00 5.10E+05
Grassland land
Other agriculture land
1.23E+09 6.14E+08
2.31E+04 3.78E+04
Land for residential areas and mining
Traffic land
1.50E+06 4.63E+05 1.52E+06 5.05E+04
Land for water facilities
4.76E+05 3.28E+03
Idle land
2.45E+07 1.62E+06
Table 8(a) The ecosystem service value of various land-use patterns in target year (Upper bound, RMB) Area East Took Mu Qinqi West Took Mu Qinqi
Farmland
Garden land
Forest land
2.44E+08 0.00E+00 5.93E+08 1.84E+06 0.00E+00 2.48E+09
Grassland land
Other agriculture land
2.76E+10 1.27E+10
7.69E+07 1.01E+08
Land for residential areas and mining
Traffic land
-2.30E+08 -2.79E+06 -1.97E+08 -4.81E+05
Land for water facilities
7.34E+05 6.75E+03
Idle land
7.29E+07 7.01E+07
Table 8(b) The total ecosystem service value of various land-use patterns in target year (Lower bound, RMB) Area East Took Mu Qinqi West Took Mu Qinqi
Farmland
Garden land
Forest land
2.26E+08 0.00E+00 5.80E+08 1.34E+06 0.00E+00 2.41E+09
Grassland land
Other agriculture land
2.44E+10 1.18E+10
7.62E+07 9.81E+07
25
Land for residential areas and mining
Traffic land
-2.43E+08 -3.12E+06 -2.31E+08 -5.01E+05
Land for water facilities
2.45E+05 5.06E+03
Idle land
3.66E+07 4.40E+06
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ACCEPTED MANUSCRIPT 1. An inexact multi-objective optimization model integrated with ecosystem services value was developed 2. The developed model was applied to a case of sustainable land use management in the typical semi-arid region. 3. The optimal land use pattern generated by the developed model was better than the current land use pattern
Yingxue Rao, Min Zhou, Guoliang Ou, Deyi Dai, Lu Zhang, Zuo Zhang, Xin Nie, Chun Yang PII:
S0959-6526(18)30780-7
DOI:
10.1016/j.jclepro.2018.03.119
Reference:
JCLP 12377
To appear in:
Journal of Cleaner Production
Received Date:
17 February 2017
Revised Date:
11 March 2018
Accepted Date:
12 March 2018
Please cite this article as: Yingxue Rao, Min Zhou, Guoliang Ou, Deyi Dai, Lu Zhang, Zuo Zhang, Xin Nie, Chun Yang, Integrating ecosystem services value for sustainable land-use management in semi-arid region, Journal of Cleaner Production (2018), doi: 10.1016/j.jclepro.2018.03.119
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ACCEPTED MANUSCRIPT
ACCEPTED MANUSCRIPT Integrating ecosystem services value for sustainable land-use management in semi-arid region
Yingxue Rao1, 2, Min Zhou3, Guoliang Ou4*, , Deyi Dai5, 6, Lu Zhang3, Zuo Zhang7, Xin Nie8, Chun Yang9 1College
of Public Administration, South-Central University for Nationalities, Wuhan, China, 430074; Email: [email protected] 2Research Center of Hubei Ethnic Minority Areas Economic and Social Development, South-Central University for Nationalities, Wuhan, China, 430074 3College of Public Administration, Huazhong University of Science and Technology, Wuhan, China, 430074; Email: [email protected] (Min Zhou); [email protected] (Lu Zhang) 4School of Construction and Environmental Engineering, Shenzhen Polytechnic, Shenzhen, China, 518055; Email: [email protected] 5Center of Hubei Cooperative Innovation for Emissions Trading System (CHCIETS), Wuhan, China, 430205, Email: [email protected] 6School of Logistics and Engineering, Hubei University of Economics, Wuhan, China, 430205 7Collage of Public Administration, Central China Normal University, Wuhan, China, 430079, Email: [email protected] 8School of Public Administration, Guangxi University, Nanning, China, 530004, Email: [email protected] 9College of Public Administration, Central China Normal University, Wuhan 430079, China, Email:[email protected]
*Correspondence:
Guoliang Ou School of Construction and Environmental Engineering, Shenzhen Polytechnic Shenzhen, Number 4089, West Shahe Road, Shenzhen, China, 518055 Email: ouyang8305@ szpt.edu.cn
1
ACCEPTED MANUSCRIPT Abstract: In this study, an inexact multi-objective optimization model integrated with ecosystem service value was developed for supporting sustainable land-use management in Took Mu Qinqi in the northeast of Inner Mongolia Province, China. It was an attempt to incorporate ecosystem service evaluation model within a general modeling framework in typical semi-arid region. The ecosystem service value was modified according to the development of eco-environment and social economy in Took Mu Qinqi based on the study of land ecosystem service value in China. The modified ecosystem service value provided input parameters for the optimization model. The results showed that the ecosystem service value of grassland was the highest among the six land-use categories, with about 97% and 83% of the total value for East and West Took Mu Qinqi, respectively. It indicated that grassland played an important role in ecosystem service. The results also showed that the total economic benefit produced by the optimal land use pattern increased by [11.5, 12.6], [5.66, 6.36] × 1012 RMB ¥ compared with the current land-use pattern for East, West Took Mu Qinqi, respectively. The total ecosystem service value produced by the optimal land-use pattern also increased by [5.9, 9.1], [3.4, 4.5] × 1012 RMB ¥ compared with that produced by the current land-use pattern for East, West Took Mu Qinqi, respectively. It indicated that the optimal land-use pattern generated by the multi-objective model was better than the current land-use pattern either from the point of economic benefit or from the point of ecological benefit. The optimization model of land-use structure based on ecosystem service value could satisfy the social, economical, and environmental development , which provided a new way to solve the key technical problem in land-use planning in typical semi-arid region.
Keywords: Land-use management, ecosystem service value, multi-objective programming, semi-arid.
2
ACCEPTED MANUSCRIPT 1. Introduction There is a growing concern in the science of ecosystem service value and land-use management, especially for the developing country like China with the rapid urbanization (Connor et al., 2015; De Groot et al., 2010; Song et al., 2015; Wu et al., 2015). The urbanization can change land-use pattern, which further influence biogeochemical cycle and climate, making ecological system develop in the direction of dysfunction (Eziz et al., 2016; Langemeyer et al., 2016; Peng et al., 2016; Ping et al., 2015; Van et al., 2015). Meanwhile, the general land-use management pursues economic benefit unilaterally, ignoring ecological benefit, which also intensify the conflict between land resource ecological development and land resource economic benefit (Camacho-Valdez et al., 2014; SawutMamat et al., 2013). This threatens the regional economic and ecological safety. Land-use pattern can directly influence the service kind and strength provided by ecological system, which can cause the change of the area, spatial distribution pattern of ecological system (Guo et al., 2016; Hao et al., 2012; Liu et al., 2012; Schulte et al., 2013; Song, 2017). Therefore, how to fully integrate the concept of ecosystem service value into the general land-use management is desired.
Previously, a number of studies were conducted on ecosystem service valuation. For example, Bolund and Hunhammar (1999) indicated that the locally generated ecosystem services had a substantial impact on the quality-of-life in urban areas and should be addressed in land-use planning. Kreuter et al. (2001) evaluated changes in land-use and ecosystem service values due to urban sprawl in the San Antonia area, Texas. Hein et al. (2006) studied the spatial scales of ecosystem services and examined how stakeholders at different spatial scales attach different values to ecosystem services. Tianhong et al. (2010) suggested that a reasonable land-use plan should be made with emphasis on the highest ecosystem service value including protecting wetland, water body and woodland. Polasky et al. (2011) illustrated the importance of taking ecosystem services into account in land-use and land management decision-making and linking such decision to incentives that accurately reflect social returns. Xue and Luo (2015) found that the dynamic variation of ecosystem service value in response to land-use changes had significant impacts on the sustainability of urban 3
ACCEPTED MANUSCRIPT system. It was an urgent need to lighten the heavy burden on the ecosystem through rational land-use management during rapid urbanization. Fu et al. (2016) addressed the overlap and connections among anthropogenic impacts (land use) with evaluations of societal benefits through ecosystem service value to an environmentally sensitive riparian zone in Northeast China using remote sensing observations and socio-economic data. Sutton et al. (2016) derived the loss of ecosystem service value from land degradation globally. The abovementioned studies identified the influence of ecosystem service value on land-use management. Nevertheless, few of them effectively considered ecosystem service value in the decision-making process of land-use optimization, which is especially important for China as a developing country with the rapid urbanization. In fact, land-use optimization has been investigated by many researchers. For example, Wang et al. (2004) integrated GIS with land-use optimization model to allocate future land uses in the human decision making process related to environmental management and planning. Sadeghi et al. (2009) formulated an optimization model to find out the most suitable land allocation to different land uses targeting soil erosion minimization and benefit maximization. Chuai et al. (2013) optimized land-use structure in 2020 to increase carbon storage in China. The optimized land-use structure presented obvious effect on the entire country. Cao and Ye (2013) developed a comprehensive land-use allocation optimization prototype based on a Coarse-Grained Parallel Genetic Algorithm using Tongzhou Newtown, Beijing, China as the case study. Liu et al. (2015) integrated game theory methods into land-use optimization model to improve the ability of existing land-use spatial optimization model for addressing local land-use competitions. Li and Parrott (2016) proposed an improved Genetic Algorithm to deal with multi-site land-use allocation in the regional district of central Okanagan in Canada. Mohammadi et al. (2016) presented the development and application of hybrid algorithms for solving land use optimization problem. Generally, these studies were individually conducted on ecosystem service valuation, land-use optimization (Wang et al., 2013). Furthermore, the limitation of most existing land-use management models is that the uncertainties are not explicitly taken into account. For example, the economic benefit of land-use pattern, and the ecosystem service value of land-use pattern, as well as the planning area of land-use pattern may appear uncertain. To reflect such uncertainties, inexact system analysis techniques, 4
ACCEPTED MANUSCRIPT including fuzzy mathematical programming, stochastic mathematical programming and interval mathematical programming can be used to assist in developing land-use management. Among of them, interval mathematical programming is widely used due to its low computational requirement, which express the uncertainties as discrete intervals and is effective in situations when little information is available. However, combination of ecosystem service value, land-use optimization, uncertainties handing techniques into a general modeling framework has been seldom reported, which is desired for facilitating sustainable land-use management.
Therefore, the objective of this study is to establish an inexact multi-objective land-use optimization model for sustainable land-use management. The multi-objective model can solve the decision-making problem with conflicting objectives, and gain the reasonable tradeoff among different objectives, which is widely used in land-use management. The typical semi-arid region Took Mu Qinqi in the northeast of Inner Mongolia, China is selected as a case study to determine the optimal land-use spatial pattern. The model maximizes the economic benefit and ecosystem service value of land unit and is subject to actual land-use conditions. The obtained results might help local authorities better understand and address complex land-use systems and develop optimal land-use management strategies that better balance urban expansion and grassland conservation.
2. Materials and methods 2.1 Study area The study area called Took Mu Qinqi is situated in the northeast of Inner Mongolia, China, with a continental semi-arid climate. Took Mu Qinqi grassland is the typical area of Xilingol grassland in Inner Mongolia, which is one of the four biggest grasslands in the world. Its area is 63675.36 km2, and the population is approximate to 128500. The study area includes two administrative regions, East Took Mu Qinqi and West Took Mu Qinqi (as shown in Figure 1). More than 85% of this area belongs to the Wulagai river basin. The length of Wulagai River is 360 km. The average volume of runoff is 113 million cubic meter in this basin. Wulagai River system not only provides valuable water resources, but also plays a vital role 5
ACCEPTED MANUSCRIPT for local ecological benefit. It is the lifeline of Took Mu Qinqi grassland, which is also the important ecological factor for sustaining grassland productivity. However, the monitoring data in recent years indicated that Took Mu Qinqi grassland has shown various degrees of degeneration due to drought and overgrazing, and the degeneration area keep expanding. The total area of various degrees of degeneration has accumulated to one half of the available grassland (Bao et al., 2009). Furthermore, with the development of local economic and the increasing of built-up land, the local land-use structure has changed a lot. And the role of ecosystem service has subsided. On this background, comprehensive analyse of ecosystem service value and land-use structure optimization will be of great importance for land-use policy.
Figure 1 The study area
2.2 Data sources The equivalent factor of ecosystem service value in China has been investigated by Xie et al. (2008) (as shown in Table 1). However, it was an average value, and lacked the evaluation of construction land. This study amended the evaluation method of ecosystem service value according to the research results of Dong et al. (2007) and Jiang et al. (2009). The amended evaluation method was applied in the calculation of ecosystem service value of study area, which provided theoretical basis and data support for ecosystem service value evaluation for 6
ACCEPTED MANUSCRIPT the region with rapid urbanization. Table 1 The equivalent factor of ecosystem service value for unit area in China Kind of service Food production Raw material production Gas regulation Climate regulation hydrological regulation Waste treatment Soil conservation Biodiversity maintenance Entertainment culture Total
Farmland 0.33 2.98 4.32 4.07 4.09 1.72 4.02 4.51 2.08 28.12
Grassland 0.43 0.36 1.50 1.56 1.52 1.32 2.24 1.87 0.87 11.67
Forest land 1.00 0.39 0.72 0.97 0.77 1.39 1.47 1.02 0.17 7.90
Wet land 0.36 0.24 2.41 13.55 13.44 14.40 1.99 3.69 4.69 54.77
River/lake 0.53 0.35 0.51 2.06 18.77 14.85 0.41 3.43 4.44 45.35
Time-series data on existing land use were obtained from general land-use planning of the 12th Five-Year Plan of East Took Mu Qinqi and West Took Mu Qinqi. Statistics of the landuse area were gathered based on nine categories of land, namely farm land, garden land, forest land, grassland, other agriculture land, land for residential areas and mining, traffic land, land for water facilities, and idle land. According to the ecosystem characteristic of Took Mu Qinqi, the kind of ecosystem was determined to be farmland, garden land, forest land, grassland land, idle land, construction land. In detail, farm land and other agriculture land were attributed to farm land ecosystem. Garden land, forest land and grassland belonged to garden land, forest land and grassland ecosystem, respectively. Land for residential areas and mining and traffic land pertained to construction land ecosystem. Land for water facilities and idle land were attributed to idle land ecosystem. A geo-database containing spatial and statistical data has been set up. The statistical data were derived from annually published statistical reports. The year 2015 was chosen as the target year, in line with China’s current land-use master plan.
2.3 Methods 2.3.1 Grey prediction modeling In order to identify optimal strategies for land-use pattern allocation in the study area, the unit economic benefit of various land-use pattern in target year needs to be determined firstly. In this study, grey prediction modeling GM (1, 1) was used to forecast the unit economic benefit 7
Desert 0.02 0.04 0.06 0.13 0.07 0.26 0.17 0.40 0.24 1.39
ACCEPTED MANUSCRIPT of various land-use pattern in target year. When forecasting successive changes of system behaviors, GM (1, 1) could achieve high accuracy (Leephakpreeda, 2008). Consider a time sequence x (0) (k ) that can denote historical series of the unit economic benefit of various land-use pattern as follows:
x (0) (k ) x (0) (1), x (0) (2),..., x (0) (n)
(1)
The grey differential equation is formed by an original time series x (0) (k ) using the accumulated generating operation (AGO) technique. It can be denoted as follows:
x (0) (k ) az (1) (k ) b
(2)
The whitening equation is therefore, as follows (Kayacan et al., 2010): dx (1) (t ) ax (1) (t ) b dt
(3)
where a is the developing coefficient, and b is the control variable. Through the adoption of the least-square method, a and b can be generated as:
a T 1 T b ( A A) A X n
(4)
where z (1) (2) (1) z (3) A (1) z ( n)
1 1 1
(5)
x (0) (2) (0) x (3) Xn x (0) (n)
(6)
where z is the background values and can be calculated by:
z (1) (k ) [ x (1) (k ) x (1) (k 1)] / 2, x (1) (k ) i 1 x (0) (i ) k
(7)
According to Eq. (3), the solution of x (1) (t ) at time k can be presented as follows: b b xˆ (1) (k 1) x (0) (1) e ak a a
(8)
Because the grey forecasting model is formulated using the data of AGO rather than original 8
ACCEPTED MANUSCRIPT data, it is necessary to transfer the data of AGO to actual forecasting value. This technique is called the inverse accumulated generating operation (IAGO) and can be denoted as: b xˆ (0) (k 1) x (0) (1) e ak (1 e a ) a
(9)
where k = 2, 3,…, n, xˆ (0) (1) x (0) (1) . 2.3.2 Ecosystem service value calculation (1) The amendment of ecosystem service value equivalent The adjustment index of willingness-to-pay and ability-to-pay was introduced to amend the equivalent factor of ecosystem service value of farmland, garden land, forest land, grassland land and idle land. The adjustment index was calculated as follows:
AI t Wt At
(10)
where AIt is the adjustment index, Wt is the index of willingness-to-pay, At is the index of ability-to-pay. The logisticeurve model was generally used to describe the index of willingness-to-pay, which is as follows:
Wt
2 (1 ae bm )
(11)
where m is period coefficient of social development, a, b are the constants. The period coefficient of social development was calculated according to the follows: m
1 2.5 Ent
(12)
Ent Entc Ptc Entr Ptr
(13)
where Ent is the total Engel's coefficient of study area in period t; Entc and Entr are the Engel's coefficient of urban and rural area, respectively; Ptc and Ptr are the percentage of population in urban and area of study area in period t. The index of ability-to-pay was described as follows:
At
GDPper
(14)
GDPtotal
where GDPper represents per capita gross domestic product of study area in period t; GDPtotal stands for the total gross domestic product in period t. (2) Ecosystem service value of non-construction land The evaluation model of ecosystem service value of non-construction land was established as follows: 9
ACCEPTED MANUSCRIPT n
ESVn c Ai EFi AI t Et
(15)
i 1
where Ai is the area of land use pattern i in study area (hm2); EFi is the equivalent factor of ecosystem service value of land-use pattern i; Et is the individual event service of food production of farm land. Et
1 Tt 7 St
(16)
where Tt is the total economic value of food production of study area in period t (104 RMB ¥);
St is the total cultivated area of study area in period t (hm2). (3) Ecosystem service value of construction land The influence of construction land ecosystem on eco-environment was the discharge of exhaust, waste water and solid. Land for residential areas and mining and traffic land destroyed hydrological regulating including water retention, filtration, storage and supply. According to Zong et al. (2000) and Dong et al. (2007), the value of freshwater resources consumption was used to evaluate the value of hydrological regulating, and the value of social labor consumption for waste gas, water and solid treatment was used to evaluate the value of waste treatment. a. The service value of hydrological regulating for urban land was calculated as follows: Vhr u
Wwc u Pwr u Su
(17)
where Vc is the service value of hydrological regulating for unit urban area (RMB ¥/hm2); Wwc-u is the annual water consumption for urban residents (ton); Pwr-u is the price of local water resource (RMB ¥/ton); Sc is the area of urban land (hm2). b. The service value of waste treatment for urban land was calculated as follows: Vwt u
Wwwu Pwwu S mwu Pmwu Su Su
(18)
where Wwwu is the annual discharge of municipal wastewater (ton); Pww-u is the price of municipal wastewater treatment (RMB ¥/ton); Smw-u is the number of urban resident households (per family); Pmw-u is the price of municipal solid waste treatment (RMB ¥/per family*month). c. The service value of hydrological regulating for industrial and mining land was calculated as follows: 10
ACCEPTED MANUSCRIPT Vhr ind
Wwc ind Gwc ind Pwc ind Sind
(19)
where Vhr-ind is the service value of hydrological regulating for unit industrial and mining area (RMB ¥/hm2); Wwc-ind is water consumption per ten thousand Yuan GDP (m3/104 RMB ¥); Gwc-ind is total industrial output value (104 RMB ¥); Pwc-ind is the price of industrial water (RMB ¥/m3); Sind is the area of industrial and mining land (hm2). d. The service value of waste treatment for industrial and mining land was calculated as follows:
Vwt ind
Wwwind Pwwind Ewg ind Pwg ind S mwind Pmwind Sind Sind Sind
(20)
where Wwwind is the annual discharge of industrial wastewater (ton); Pww-ind is the price of industrial wastewater treatment (RMB ¥/ton); Ewg-ind is the annual discharge of industrial waste gas (ton); Pwg-ind is the price of local waste gas treatment (RMB ¥/ton); Smw-ind is the annual production of industrial solid waste (ton); Pmw-ind is the price of industrial solid waste treatment (RMB ¥/ton). e. The transport pollution of waste treatment value for unit area of traffic land: Vtrans
a Ptrans Vtrans Strans
(21)
where a is the pollution coefficient (the percentage of pollution fee generated by exhaust gas); Ptrans is transportation expenses per capita (RMB ¥/per capita); Vtrans is the total population (capita); Strans is the area of transport land (hm2). (4) Ecosystem service value of residential area This study analyzed the change of ecosystem service value of residential area from the point of the increase and loss of ecosystem value. Vra ( Aor VCra ) ( Arf VCra )
(22)
where Vra is the ecosystem service value of farmland loss, Aor is the area of residential land transformed by other land type; Arf is the area of farmland transformed by residential land;
VCra is the amended ecosystem service value per unit area.
2.3.3 Interval mathematical programming Interval mathematical programming (IMP) where coefficients of the decision variables in the objective function and constraints are reflected as an interval variation can be expressed as 11
ACCEPTED MANUSCRIPT follows: max f C X
(23)
subject to: A X B
(24)
X 0
(25)
where X
nl
, A
mn
, B
ml
, C
l n
, denotes a set of
interval numbers. According to Huang et al. (1992), the IMP model can be transformed into two deterministic submodels, i.e., f and f , which correspond to the lower and upper bounds of the objective function values. Through solving the two submodels, the relevant , f opt ]. solutions can be acquired as follows: x jopt [ x jopt , x jopt ] , f opt [ f opt
2.3.4 Multi-objective for sustainable land-use optimization Sustainable land-use management is a complicated process as it involves a long-term balance between economic development, environmental protection, efficient resource use, and social equity (Cao and Ye, 2013; Church, 2008; Huang and Zhang, 2014). How to pursue this balance has received great concerns. And attentions should be paid to how to choose suitable land-use patterns which can make a comprehensive consideration among economic benefit, social benefit and ecological benefit (Janssen et al., 2008). In this study, an inexact multiobjective land-use optimization model for sustainable land-use management is developed. The multi-objective land-use optimization model includes the objective of economic benefit, social benefit and ecological benefit. Among them, the objective of ecological benefit is presented as ecosystem service value. The objective of social benefit should be constructed by consciously taking into account the well-being of people and their communities. Such systems strive for distributional equity, adequate provision of social services including living, health and education, gender equity, political participation, etc (Ma and Zhao, 2015). Although it is difficult to quantify the social benefit objective, the notion can be simplified to land-use balance between the supply and the demand in constraints within the land-use optimization model. Therefore, the quantitative modeling of the economic and ecological benefits of land-use is primarily considered when designing the objective functions. Furthermore, it is assumed that the objectives of maximizing economic benefit and maximizing ecological benefit have the same weight coefficient. Then, the multi-objective 12
ACCEPTED MANUSCRIPT optimization model can be transformed into single-objective optimization model by using linear weighting method. Table 2 Simple table of land use optimization in Took Mu Qinqi Objectives Objective 1 Objective 2 Constraints Constraint 1 Constraint 2 Constraint 3 Constraint 4 Constraint 5 Constraint 6 Constraint 7
Maximize economic benefit Maximize ecological benefit Population constraint The total area of land use constraint Agriculture production constraint Forest-related activity constraint Grassland-related activity constraint Industrial activity constraint Technical constraint
(1) Multi-objective land-use optimization model Objective 1: Maximize economic benefit As different land-use patterns yield different economic benefit, optimizing the structure and layout of different land use to maximize economic benefit is crucial. I
J
max f Bij X ij 1
(26)
i 1 j 1
where f1 is the total economic benefit; X ij is the area of land-use pattern i in region j (i = 1, 2,…9, which stands for farmland, garden land, forest land, grassland, other agriculture land, land for residential areas and mining, traffic land, land for water facilities, idle land, respectively; j = 1, 2, which stands for East Took Mu Qinqi and West Took Mu Qinqi, respectively); Bij is the economic benefit of land-use pattern i in region j for unit area. Objective 2: Maximize ecological benefit The ecosystem service value value for different land-use pattern and spatial ecological suitability is considered in this research to ensure the ecological benefit. I
J
max f 2 Cij X ij
(27)
i 1 j 1
where f 2 is the total ecological benefit; Cij is the ecosystem service value of land use pattern i in region j for unit area. Constraints: 13
ACCEPTED MANUSCRIPT The constraint conditions depend on local land use policies, economic and social development plans, and special planning related to land use. Here, we define 2010 as the initial year and assign 2020 as our target prediction year. Constraint 1: The population of target year should be less than the population of regional land carrying capacity. 9
PD
ij
i 1
X ij LCC j , j
(28)
where PDij is the population density of land use pattern i in region j; LCC j is the land carrying capacity in region j. Constraint 2: 9
X i 1
ij
TAj , j
(29)
where TAj is the total area of land-use in region j. Constraint 3: According to the local farmland policy, the area of farmland should be greater than Protection area of farmland in order to ensure the grain security. X 1j PACLj , j
(30)
where PACLj is the Protection area of farmland in region j. Constraint 4: According to the Forestry Development Planning made by local government, forest land should be strictly protected due to its greatest capability to accumulate carbon. X 3j PFLj , j
(31)
where PFLj is the planning area of forest land in region j in 2020. Constraint 5: In order to develop the ecosystem function of grassland and strengthen the protection of the basic ecological land, grassland should be strictly planned according to the Development Planning made by local government. X 4 j PGLj , j
(32)
where PGLj is the planning area of grassland land in region j in 2020. Constraint 6: 14
ACCEPTED MANUSCRIPT With rapid economic development and urbanization, the increasing trend of construction and traffic land is difficult to change. It is imperative to control newly-added construction land according to the state macro-adjustment and control measures, meeting the demand of urban construction and industrial and mining land development under the condition of intensive land use. X 6j PCLj , j
(33)
X 7 j PTLj , j
where PCLj is the planning area of construction land in region j in 2020. PTLj is the planning area of traffic land in region j in 2020. Constraint 7: X ij 0, i, j
(34)
3. Results and discussion 3.1 Land-use pattern prediction in target year The goal of GM (1, 1) model is to extract as much information as possible from the historical data for forecasting time-series unit economic benefit of various land-use patterns over the planning horizon. The data from 2005 to 2010 was used to compare with the prediction values. Two common criteria including the MARE (mean absolute relative error) and the correlation coefficient (R2) can be utilized for evaluating prediction performances of the GM (1, 1) model. If the value of R2 is close to one or the value of MARE is close to zero, then GM (1, 1) mode is consider performing very well in forecasting. The two criteria can be defined as follows: MARE
1 n ( yt y t ) y n t 1 t n
R2 1
(y
t
t 1 n
(y t 1
t
y t )2
yt av )
(35)
100%
(36)
2
where yt denotes the actual values, y t denotes the forecasted values, yt av denotes the average values, t is the time periods (t = 1, 2, …,n), and n is the total observation number. 15
ACCEPTED MANUSCRIPT The values of MARE and R2 through GM (1, 1) model are 8.16% and 93.28%, respectively. It is indicated that the obtained results showed that GM (1, 1) model can well capture the variations of unit economic benefit of various land-use patterns in the future. Table 3 shows the predicted unit economic benefit of various land-use patterns in the study area in target year. The interval number represents the forecasting range, which can be used to deal with uncertainties in land-use optimization model. In order for the kind of ecosystem service analysis that we conducted to become more meaningful for policy formulation, it is imperative to obtain value coefficients for ecosystem service that more accurately reflect local conditions (Zhao et al., 2004). Therefore, the adjustment index in target year was also predicted by grey prediction modeling GM (1, 1). Similarly, the data from 2005 to 2010 was used to compare with the prediction values. Figure 2 displays the forecasted unit ecosystem service value of various land-use patterns in East Took Mu Qinqi. The unit ecosystem service value of land for residential areas and mining was negative due to its negative effectiveness of pollution (e.g. air pollution, water pollution, waste pollution et al). The forecasting parameters provide input data for the following land-use optimization.
Figure 2 The prediction value of unit ecosystem service of various land-use patterns
16
Table 3 The predicted unit economic benefits of various land use pattern in the study area in 2020 (104 RMB/hm2) Farmland East Took Mu Qinqi West Took Mu Qinqi
[4.20， 7.20] [4.20， 7.20]
Garden land
Forest land
[2.90，5.00]
[3.90，15.90]
[2.90，5.00]
[3.90，15.90]
Grassland land [286.40， 311.50] [295.30， 334.20]
Other agriculture land
Land for residential areas and mining
Traffic land
[1.80，2.30]
[324.80,342.80]
[116.00，185.80]
[2.00, 3.50]
[389.20, 456.80]
[125.30, 234.50]
17
Land for water facilities [228.90， 335.40] [282.90， 335.40]
Idle land [156.60， 198.60] [156.60， 198.60]
ACCEPTED MANUSCRIPT 3.2 Ecosystem service value evaluation Table 4 lists the adjustment index of ecosystem service value in the study area. The ecosystem service equivalent value of farmland, garden land, forest land and idle land can be obtained based on Table 1 and 4. Based on the unit equivalent value of ecosystem service for food production (as shown in Table 5) and the adjustment index, the unit equivalent value of ecosystem service for farmland, garden land, forest land and idle land can be calculated. Although different ecosystem service valuation methods may lead to different estimation values, which will cause scientific critics and doubts on ecosystem service valuation, it is important to realize that accurate coefficients are often less critical for time series than crosssectional analysis because coefficients tend to affect estimates of directional change less than estimates of the magnitude of ecosystem value at specific points in time (Li et al., 2010). Table 4 The adjustment index of ecosystem service value Region East Took Mu Qinqi West Took Mu Qinqi
Index
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
Willingness-to-pay Ability-to-pay Adjustment index Willingness-to-pay Ability-to-pay Adjustment index
1.22 1.64 2.00 1.24 1.66 2.06
1.24 1.69 2.10 1.25 1.72 2.15
1.25 1.69 2.11 1.23 1.71 2.10
1.24 1.72 2.13 1.27 1.72 2.18
1.28 1.73 2.21 1.29 1.73 2.23
1.30 1.59 2.07 1.31 1.69 2.21
1.28 1.62 2.07 1.28 1.66 2.12
1.33 1.62 2.15 1.35 1.64 2.21
1.30 1.60 2.08 1.31 1.61 2.11
1.31 1.63 2.14 1.33 1.67 2.22
1.32 1.60 2.11 1.34 1.62 2.17
Table 5 The unit equivalent value of ecosystem service for food production in the study area in 2015 Sown area Economic value Economic value per unit Unit equivalent value area (104 RMB/hm2) (104 RMB/hm2) (104 RMB) （hm2） East Took Mu Qinqi West Took Mu Qinqi
3266.68
24909.00
7.63
1.09
2000.00
18956.00
9.48
1.35
3.3 Sustainable land use pattern optimization in semi-arid region Table 6 lists the optimal land-use pattern in target year compared with the current land-use pattern. The area of farmland for East Took Mu Qinqi increased from 37041.30 hm2 to [37027.20, 45527.20] hm2, and for West Took Mu Qinqi increased from 187.1 hm2 to [219.30, 342.30] hm2. The reason was due to the fact that food production generated by farmland should meet the demand of local population growth, which was in line with China’s cultivated land protection policy. It also reflected China's basic state policy of rational land 18
ACCEPTED MANUSCRIPT utilization and earnest protection of cultivated land. The area of forest land for East Took Mu Qinqi increased from 28640.40 hm2 to [30000.00, 32135.00] hm2, and for West Took Mu Qinqi increased from 121350.00 hm2 to [128500.00, 130532.00] hm2. The area of grassland for East Took Mu Qinqi increased from 4001321.70 hm2 to [4271211.40, 4301919.00] hm2, and for West Took Mu Qinqi increased from 1932220.30 hm2 to [1971110.90, 2078562.80]
hm2. Forest coverage has been increasing and ecological environment has been improving for both two administrative regions through afforestation and reforestation. Under the high attention of local government, the implement of ecological forest construction makes great progress, which improves the ability of soil and water conservation. It also plays an important role in controlling water and soil erosion, especially for East Took Mu Qinqi and West Took Mu Qinqi, which has been regarded as the key prevention area of water and soil erosion. The increase of forest coverage and the improvement of ecological environment provide a guarantee for the development of local tourism industry. One obvious characteristic is that the area of traffic land increased from 2992.38 hm2 in 2015 to [3990.00, 4557.43] hm2 for East Took Mu Qinqi, and from 358.89 hm2 to [402.80, 477.55] hm2 for West Took Mu Qinqi in target year, respectively. The area of land for residential areas and mining decreased from 7411.82 hm2 to [4608.21, 5708.16] hm2 for East Took Mu Qinqi, and from 9772.56 hm2 to [3896.50, 5262.09] hm2 for West Took Mu Qinqi, respectively. Under the local governments’ intensive land use policy of both East Took Mu Qinqi and West Took Mu Qinqi, efforts are intensified to use land more economically and intensively, resulting in continued improvement in land-use efficiency. The priority is to meet the demand of urban construction and industrial and mining development. The average land for residential areas was [480.20, 594.60] m2 for East Took Mu Qinqi, and [432.90, 584.70] m2 for West Took Mu Qinqi, which were in line with the development planning of East Took Mu Qinqi and West Took Mu Qinqi, respectively. The idle land was fully utilized. For example, the area of idle land decreased by [273561.40, 313561.40] hm2 for East Took Mu Qinqi and [51821.10, 163187.90] hm2 for West Took Mu Qinqi, respectively. Since a large proportion of idle land would be changed into grassland, and a fraction of idle land be changed into forecast and other agriculture land. 19
Table 6(a) The optimal land-use patterns in the study area in target year compared with the current land-use pattern (upper bound, hm2) Area East Took Mu Qinqi West Took Mu Qinqi
Year
Farmland
Garden land
Forest land
Grassland land
Other agriculture land
Land for residential areas and mining
Traffic land
Land for water facilities
Idle land
2015 2020 2015 2020
37041.30 37027.20 187.10 219.30
1.00 0.00 5.30 0.00
28640.40 30000.00 121350.00 128500.00
4001321.70 4271211.40 1932220.30 1971110.90
10460.50 11460.50 6022.50 16166.00
7411.80 5708.20 9772.60 5262.10
2992.40 4557.40 358.90 477.60
1613.70 3079.40 10.20 11.90
469844.10 196282.70 173524.60 121703.50
Table 6(b) The optimal land-use patterns in the study area in target year compared with the current land-use pattern (lower bound, hm2) Area East Took Mu Qinqi West Took Mu Qinqi
Year
Farmland
Garden land
Forest land
Grassland land
Other agriculture land
Land for residential areas and mining
Traffic land
Land for water facilities
Idle land
2015 2020 2015 2020
37041.30 45527.20 187.10 342.30
1.00 0.00 5.30 0.00
28640.40 32135.00 121350.00 130532.00
4001321.70 4301919.00 1932220.30 2078562.80
10460.50 12786.00 6022.50 19366.10
7411.80 4608.20 9772.60 3896.60
2992.40 3990.00 358.90 402.80
1613.70 2079.40 10.20 11.60
469844.10 156282.70 173524.60 10336.70
20
ACCEPTED MANUSCRIPT 3.4 Economic benefits and ecosystem service value analysis Table 7 presents the economic benefit of various land-use patterns under the optimal condition of Economic-Social-Environmental System. The economic benefit of farmland, garden land, forest land, grassland, other agriculture land, land for residential areas and mining, traffic land, land for water facilities, and idle land were [1.91, 2.67], 0, [1.26, 4.77], [12300.00, 13300.00], [0.23, 0.26], [15.00, 19.60], [4.63, 8.47], [4.67, 10.30], [245.00, 390.00] × 109 RMB ¥ for East Took Mu Qinqi, and [0.014, 0.016], 0, [5.10, 20.40], [6140.00, 6590.00], [0.38, 0.56], [15.20, 24.00], [0.51, 1.12], [0.033, 0.040], [16.20, 24.20] × 109 RMB ¥ for West Took Mu Qinqi, respectively. The maximal economic benefit of land-use pattern was grassland, and the minimal economic benefit was garden land for both administrative regions. The reason was that livestock were often the only source of livelihoods for the people living in both East Took Mu Qinqi and West Took Mu Qinqi. Garden land was not incorporated into the relevant overall land-use plan according to land-use planning of both East Took Mu Qinqi and West Took Mu Qinqi. In fact, the total economic benefit of various land-use patterns was influenced by two factors including the unit economic benefit and the area. For example, the unit economic benefit of land for residential and mining was highest, however, the total economic benefit of land for residential area and mining was only [15.00, 19.60] × 109 RMB ¥ for East Took Mu Qinqi, which was lower than that of grassland and idle land. The reason was due to fact that the social and economic characteristics in East Took Mu Qinqi were small population intensity, and the area of land for residential area and mining only accounted for a small fraction of total area in East Took Mu Qinqi. Also, Development Planning made by local government of East Took Mu Qinqi could impact indirectly the total economic benefit of various land-use patterns. Table 8 shows the ecosystem service value of various land-use patterns under the optimal condition of Economic-Social-Environmental System. The ecosystem service value of farmland, garden land, forest land, grassland, other agriculture land, land for residential areas and mining, traffic land, land for water facilities, and idle land were [226, 244], 0, [580, 593], [24400, 27600], [76.2, 76.9], [-243, -230], [-3.12, -2.79], [0.025, 0.073], [36.6, 72.9] × 106 RMB ¥ for East Took Mu Qinqi, and [1.34, 1.84], 0, [2410, 2480], [11800, 12700], [98.1, 101], [-231, -197], [-0.5, -0.48], [0.005, 0.007], [4.4, 70.1] × 106 RMB ¥ for West Took Mu Qinqi, respectively. The maximal ecosystem service value of land-use pattern was grassland, and the minimal ecosystem service value was land for residential areas and mining. Because 21
ACCEPTED MANUSCRIPT of the large coefficient value and the large area, the value of ecosystem service produced by grassland was the highest among the six land-use categories, with about 97% and 83% of the total value for East and West Took Mu Qinqi, respectively. It indicated that grassland played important roles in ecosystem service. Figure 3 displays the total economic benefit of the optimal land-use patterns for East Took Mu Qinqi and West Took Mu Qinqi. It was [12.6, 13.7], [6.18, 6.88] × 1012 RMB ¥ for East Took Mu Qinqi and West Took Mu Qinqi, respectively. The total economic benefit produced by optimal land-use pattern increased by [11.5, 12.6], [5.66, 6.36] × 1012 RMB ¥ compared with that produced by the current land-use pattern for East Took Mu Qinqi and West Took Mu Qinqi, respectively.
Figure 3 The total economic benefits Figure 4 displays the total ecosystem service value of the optimal land-use pattern for East Took Mu Qinqi and West Took Mu Qinqi. It was [25.1, 28.3], [14.1, 15.2] × 109 RMB ¥ for East Took Mu Qinqi and West Took Mu Qinqi, respectively. The total ecosystem service value produced by the optimal land-use pattern increased by [5.9, 9.1], [3.4, 4.5] × 1012 RMB ¥ compared with that produced by the current land use pattern for East Took Mu Qinqi and West Took Mu Qinqi, respectively. This was mainly caused by the increase of forest land and grassland. It indicated that ecological benefit generated by the optimal land-use pattern was better than that by the current land-use pattern.
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ACCEPTED MANUSCRIPT
Figure 4 The total ecosystem service value The economic growth often seems to be in conflict with ecological protection. Therefore, a compromise between economic development and ecological protection must be considered. It is suggested that a reasonable land-use plan should be made with emphasis on protecting forecast land, grassland, and water body which have high ecosystem service value for Took Mu Qinqi, so as to maintain a balance between economic development and ecosystem health in the future.
4. Conclusion In this study, an inexact multi-objective land-use optimization model integrated with ecosystem service value was developed for supporting sustainable land-use management in the typical semi-arid region, Took Mu Qinqi in the northeast of Inner Mongolia, China. It was an attempt to incorporate ecosystem service evaluation model within a general modeling framework. The change of ecosystem service value in Took Mu Qinqi was evaluated and analyzed based on the study on land ecosystem service value in China. The modified ecosystem service value provided input parameters for the optimization model. The optimized results showed that the total economic benefit of the optimal land-use pattern for East Took Mu Qinqi and West Took Mu Qinqi were [12.6, 13.7], [6.18, 6.88] × 1012 RMB ¥, respectively. The total economic benefit produced by the optimal land-use pattern increased by [11.5, 12.6], [5.66, 6.36] × 1012 RMB ¥ compared with that produced by the 23
ACCEPTED MANUSCRIPT current land-use pattern for East Took Mu Qinqi and West Took Mu Qinqi, respectively. Furthermore, the total ecosystem service value of the optimal land-use pattern for East Took Mu Qinqi and West Took Mu Qinqi were [25.1, 28.3], [14.1, 15.2] × 109 RMB ¥, respectively. The total ecosystem service value produced by the optimal land-use pattern increased by [5.9, 9.1], [3.4, 4.5] × 1012 RMB ¥ compared with that produced by the current land use pattern for East Took Mu Qinqi and West Took Mu Qinqi, respectively. It indicated that the optimal land-use pattern generated by multi-objective model was better than the current land-use pattern either from the point of economic benefit or from the point of ecological benefit. It also indicated that the developed method could also be applicable in other climate or regions by adjusting the corresponding model parameters for helping decision makers identify desired sustainable land-use pattern considering ecosystem service value. Further improvements in modeling and data are needed to increase the reliability of estimates of ecosystem service value. Since the accuracy and reliability of the estimated results are mainly determined by the accuracy of value coefficients. Despite some methodological shortcomings, ecosystem service valuation has the potential to inform policy decisions by highlighting the benefit of sustainable land-use management.
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Table 7(a) The economic benefit of various land-use patterns in target year (Upper bound, 104 RMB) Area East Took Mu Qinqi West Took Mu Qinqi
Farmland
Garden land
Forest land
2.67E+05 0.00E+00 4.77E+05 1.58E+03 0.00E+00 2.04E+06
Grassland land
Other agriculture land
1.33E+09 6.59E+08
2.62E+04 5.59E+04
Land for residential areas and mining
Traffic land
1.96E+06 8.47E+05 2.40E+06 1.12E+05
Land for water facilities
1.03E+06 3.99E+03
Idle land
3.90E+07 2.42E+07
Table 7(b) The total economic benefit of various land-use patterns in target year (Lower bound, 104 RMB) Area East Took Mu Qinqi West Took Mu Qinqi
Farmland
Garden land
Forest land
1.91E+05 0.00E+00 1.26E+05 1.44E+03 0.00E+00 5.10E+05
Grassland land
Other agriculture land
1.23E+09 6.14E+08
2.31E+04 3.78E+04
Land for residential areas and mining
Traffic land
1.50E+06 4.63E+05 1.52E+06 5.05E+04
Land for water facilities
4.76E+05 3.28E+03
Idle land
2.45E+07 1.62E+06
Table 8(a) The ecosystem service value of various land-use patterns in target year (Upper bound, RMB) Area East Took Mu Qinqi West Took Mu Qinqi
Farmland
Garden land
Forest land
2.44E+08 0.00E+00 5.93E+08 1.84E+06 0.00E+00 2.48E+09
Grassland land
Other agriculture land
2.76E+10 1.27E+10
7.69E+07 1.01E+08
Land for residential areas and mining
Traffic land
-2.30E+08 -2.79E+06 -1.97E+08 -4.81E+05
Land for water facilities
7.34E+05 6.75E+03
Idle land
7.29E+07 7.01E+07
Table 8(b) The total ecosystem service value of various land-use patterns in target year (Lower bound, RMB) Area East Took Mu Qinqi West Took Mu Qinqi
Farmland
Garden land
Forest land
2.26E+08 0.00E+00 5.80E+08 1.34E+06 0.00E+00 2.41E+09
Grassland land
Other agriculture land
2.44E+10 1.18E+10
7.62E+07 9.81E+07
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Land for residential areas and mining
Traffic land
-2.43E+08 -3.12E+06 -2.31E+08 -5.01E+05
Land for water facilities
2.45E+05 5.06E+03
Idle land
3.66E+07 4.40E+06
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ACCEPTED MANUSCRIPT 1. An inexact multi-objective optimization model integrated with ecosystem services value was developed 2. The developed model was applied to a case of sustainable land use management in the typical semi-arid region. 3. The optimal land use pattern generated by the developed model was better than the current land use pattern