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1 Landscape and Urban Planning 106 (2012) Contents lists available at SciVerse ScienceDirect Landscape and Urban Planning jou rn al h om epa ge: A coupled model for simulating spatio-temporal dynamics of land-use change: A case study in Changqing, Jinan, China Xin-Qi Zheng a,b,, Lu Zhao a, Wei-Ning Xiang c, Ning Li d, Li-Na Lv a, Xin Yang a a Department of Land Science and Technology, China University of Geosciences, Beijing , PR China b Land Resources Information Development Research Laboratory, Beijing , PR China c Department of Geography and Earth Sciences, University of North Carolina at Charlotte, NC 29223, USA d Lanshan Rural Cooperative Bank, Linyi , Shandong, PR China a r t i c l e i n f o Article history: Received 16 February 2011 Received in revised form 10 February 2012 Accepted 13 February 2012 Available online 8 March 2012 Keywords: Land use Coupled model Spatio-temporal dynamics System dynamic model CLUE-S model Scenario analysis a b s t r a c t General land-use planning in China aims to allocate land use quantitatively on a temporal scale and explicitly on a spatial scale. Therefore, for decision-making, planners need to know the specific land demands under different scenarios and their spatio-temporal dynamics. However, it is not easy to obtain the above knowledge due to the complex and dynamic characteristics of the land use system and the limitations of current models in taking both the temporal and spatial driving factors into consideration. To address these issues, this study coupled the system dynamics model (SD) and the model for the conversion of land use and its effects at small regional extent (CLUE-S) to simulate the land-use change in Changqing District, Jinan, China. The objectives are: (1) to develop the SD model to simulate the landuse demands on a macro-scale as a whole for the period , (2) to improve the presentation of the land use change processes based on the CLUE-S model that will transfer and allocate land demands from the SD model to spatially explicit land use patterns, and (3) to discuss the local land-use dynamics. Our results show that the coupled model could provide information on the specific local land use demands under different developing scenarios, the dynamics of their spatio-temporal changes, and the growth and decline of urban land and farm land. While the results of our study have been applied in local landuse plans, planners should note that the results based on the coupled model are just a scenario under specific conditions Elsevier B.V. All rights reserved. 1. Introduction Changqing is a suburban district of the Jinan Metropolitan Area in China (Fig. 1). Since 1996, along with Jinan s rapid urban development, the district has been undergoing significant growth. Between 1996 and 2006, its urban population quadrupled, from 65,800 to 284,000 (Bureau of Statistics in Jinan, ) and the land for construction increased by 18.74%, from 11,461 ha to 13,609 ha. Along with urban development came the loss of farm land; between 1996 and 2006, the district lost 5% of its arable land (2320 ha) to urban land use. This rapid urban development increased the pressure on the local agriculture sector. More than 90% of the total decrease in farm land area between 1996 and 2006 was through conversion to construction land. In 2006, the total area of local farmland was only the 93.71% of the projected amount (48,840 ha) Corresponding author at: School of Land Science and Technology, China University of Geosciences, 29 Xueyuan Road, Haidian District, Beijing , PR China. Tel.: addresses: zxqsd@126.com (X.-Q. Zheng), zhaolu1985cn@gmail.com (L. Zhao), wxiang@uncc.edu (W.-N. Xiang), @qq.com (N. Li), lvlina.wode@yeah.net (L.-N. Lv), hongyatianshu@yahoo.cn (X. Yang). for 2010 according to the last land-use plan (Jinan Government, 1996). The conflict between urban development and farm land preservation, not unique to Changing, is becoming increasingly contentious in China (Han, Hayashi, Cao, & Imura, 2009; Meng & Zhao, 2007; Wang, Yu, & Huang, 2004). Whether it can be handled correctly will have implications on food security and social security and is related to important issues such as sustainable development of the economy, society, and the environment (Clover & Eriksen, 2009; Erik & Ding, 2006; Steiner, 1989; Whitehead, 2009). The topic has caught the attentions of land-use and urban planners (Bart, 2010; Carsjens & van der Knaap, 2002; Deal & Schunk, 2004; Lee, Huang, & Chan, 2009; Lier, 1998; Morell, 1986; Weber, 2003; Xiang & Clark, 2003). To address the issue, local planners require knowledge of the following. First, how much local land development will take place allowing for the preconditions of urban development in the planning period, and where will it possibly take place? Second, what are the possible impacts of the projected urban land development on farm land? These two questions, however, are not easy to answer due to the complex and dynamic characteristics of the land-use system. The driving mechanism for regional land-use changing is /$ see front matter 2012 Elsevier B.V. All rights reserved. doi: /j.landurbplan

2 52 X.-Q. Zheng et al. / Landscape and Urban Planning 106 (2012) Fig. 1. Location of Changqing District, Jinan City, China. very complicated (Lambin et al., 2001), and the factors involved which are of multiple dimensions (including physical, environmental, social, economic, and political dimensions) (Ding et al., 2007) interact with sub-factors within their own dimensions and with each other between dimensions. It is therefore difficult to identify these complicated relationships quantitatively. Moreover, the influencing factors can be divided into non-spatial factors (land-use policies (Floriane & Jaime, 2008), economy, society, etc.) and spatial ones (for example, the physical characteristics of land). Most existing methods or models are designed to take into account either the non-spatial or spatial factors, and therefore are incapable of handling both (Aaviksoo, 1995; Batisani & Yarnal, 2009; Cai, Liu, Yu, & Verburg, 2004; Cheng, Li, & Zhang, 2004; Li, Yang, & Liu, 2008; Saysel, Barlas, & Yemgun, 2002; Sun, Shen, Yu, Liu, & Mo, 2007; Verburg, Mastura, Veldkamp, & Espaldon, 2002; Voigt & Troy, 2008; Wang & Zheng, 2001; Yang, Li, & Shi, 2008). Thus, taking full consideration of both the macroscopic driving factors and the microcosmic ones for spatial changes is an issue that needs to be resolved (Agarwal et al., 2002; Hubacek & Sun, 2001; Lambin, Rounsevell, & Geist, 2000; Koomen, Stillwell, Bakema, & Scholten, 2007; Schaldach & Priess, 2008). System dynamics (SD) is an approach to understanding the dynamic behavior of complex systems over time. It can deal with internal feedbacks among land-use structures, their functions and behaviors, and gain qualitative insight into the dynamics of the system (Mohapatra, Mandal, & Bora, 1994). Since the publication of the work on SD by Forrester (1961), the method has been widely applied in the research on regionally sustainable development on a temporal scale (Saysel et al., 2002; Sterman, 2001). However, SD could not express the dynamics explicitly on a spatial scale. The CLUE-S model (Conversion of Land Use and its Effects at Small regional extent) is suitable for scenario analysis and simulation of trajectories of land-use change (Batisani & Yarnal, 2009; Cai et al., 2004; Verburg et al., 2002; Veldkamp & Verburg, 2002). The model structure is based on systems theory which allows the integrated analysis of land-use change in relation to socio-economic and biophysical driving factors. As the CLUE-S model is specifically developed for spatially explicit simulation of land-use change, researchers have combined SD and the CLUE-S model to analyze land-use dynamics (Luo, Yin, Chen, Wu, & Lu, 2010). CLUE-S is based on raster data, so there could be some deviations between the statistics based on such data and the SD model s simulations because of the scale effect. This was often ignored in studies which involved both statistical and raster data. With support from local planners in Changqing, we engaged in building a coupled model based on SD and CLUE-S to answer the two questions raised earlier, which the team involved in the ongoing local land-use planning ( ) was faced with. The remainder of the article is organized as follows. Section 2 provides a detailed account of the methodology. Section 3 addresses the implementation issues in Changqing and some results from both the temporal simulation and spatial allocation. Section 4 discusses the evaluation of the model, and Section 5 presents the conclusion. 2. Methodology The flowchart of the methodology used in our study is shown in Fig. 2. It contains two main parts: the land-use scenario analysis and the impact assessment. The land-use scenario analysis is mainly carried out by the coupled model which consists of the SD model (for temporal simulation) and the CLUE-S model (for spatial allocation). Through quantifying and simulating the complicated relationships among the macroscopic driving factors on a temporal scale, the SD model can address the issues of when and how much in specific scenario settings, namely, the issue of land demand. The CLUE-S model can work on the where issue because it can describe the microcosmic and spatial factors very well. Moreover, the balance between temporal land demand and spatial supply bridges the two models since the CLUE-S model allocates each land-use type based on its demand which is simulated by the SD model. Based on the information and knowledge derived from the results of the scenario analysis presented here, planning staff can analyze the impacts of urban land development on farm land to support decision-making.

3 X.-Q. Zheng et al. / Landscape and Urban Planning 106 (2012) Land use scenario analysis Research questions Modeling & outcome Temporal scale: SD model Input factors Driving factors of land use change: When and how much will the land development take place according to different scenarios? Modify / rebuild failed Model calidation pass Different scenarios Temporal simulation Population Land use Economy Land use demand Spatial scale: Land use demand Where will the land development possibly take place? CLUE-S model Spatial allocation Land development scenorios Location suitability Conversion elasticity Conversion matrix Driving factors Logistic of location: regression accessibility, slope, etc. Land use type conversion setting Impact assessment: what are the possible impacts of the projected land development on farm land? Planning support Land use plan Fig. 2. Flowchart of the methodology used in this study The SD model Model structure The SD model aims to simulate the temporal changes of land demand in different scenarios. A land-use system is a complex system which involves complicated interactions among natural, social, and economic factors. Its natural characteristic is the physical basis of land use while the socio-economic ones determine the specific type and pattern of land use. In short term, the most dominant impact on land use on a local level is that of human activities (Lambin et al., 2001). Hence, in this study, we assume that only the factors affecting local land use are the social and economic ones. The SD model in our study consists mainly of three subsystems, namely Population, Economy, and Land Use. As the main social factor, population influences the land use system from many aspects. For one aspect, the non-agricultural land area must increase as the population grows. For the other aspect, the larger the population is, the larger the demand for primary product; thus, the larger the demand for arable land. The subsystem Population consists mainly of factors such as the total population, transitory population, permanent population, urban population, and urbanization rate in the study area. The subsystem Economy involves GDP, the coefficient for the contribution of construction to the economy, and the coefficient for the impacts of increased construction on fixed assets and the investment on it. An increasing of GDP would demand more and more construction at the expense of farm land. An increasing in investment in fixed assets could drive the requirement for additional labor, increase the transitory population, and thus influence the land use structure. In addition, urban development will result in the expansion of the urban area and decrease of farm land, especially farm land in the rural-urban fringe zone. The major factors of the Land Use subsystem include the increment or decrement of the

4 54 X.-Q. Zheng et al. / Landscape and Urban Planning 106 (2012) areas of each land-use type and the changing rate. The model was built using the Powersim8 software (see Appendix A) Variable calculation The behavior patterns are dependent on the structure of the model; thus, it is necessary to confirm the variables involved in the SD model. In this study, the variables are calculated using the following methods. (1) Geometric mean. The geometric mean was used to calculate the average annually changing rate of the land use type. That is, n V = n x i (1) i=1 In Eq. (1), V is the average annually changing rate, x i is the statistic for year i, and n is the number of years. (2) Development trend. This is mainly estimated by building a regression model using historical data in SPSS, including a linear regression model (Eq. (2)) and a non-linear regression model (Eq. (3)). y = at + b (2) 1 y = (1/y m ) + b a t (3) In Eqs. (2) and (3), y is the dependent variable, t is the independent variable denoting the time interval between the initial year and the year considered, y m is the limit point of y and is usually obtained based on the local urban plan, b is a constant, and a is the coefficient of t. Linear regression was used to calculate the coefficient for the impact of increased construction on fixed assets and the investment in it. The urbanization rate, the proportion of fixed assets to GDP, and the urban population were estimated by the non-linear model The CLUE-S model The CLUE-S model was specifically developed for spatially explicit simulation of land use change based on a raster-based system (Veldkamp & Verburg, 2002; Verburg et al., 2002). The spatial allocation is based on a combination of empirical analysis of location probability and spatial analysis, and on dynamic simulation of competition and interactions between the spatial and temporal dynamics of land use systems Logistic regression Generally, conversions of land-use are expected to take place at locations which have the highest preference for a specific type of land use at a given moment. It can be calculated as a probability by logistic regression as follows (Bucini & Lambin, 2002; Gobin, Campling, & Feyen, 2002; Wang & Guo, 2001): { } Pi,u log = ˇ0 + ˇ1X 1 + ˇ2X ˇnX n (4) 1 P i,u where P i,u denotes the probability of a grid cell i to have the selected land-use type u, the parameters X refer to the driving factors, n is the number of driving factors, and the coefficients ˇ will be estimated using the actual land-use pattern as a dependent variable. Relative operating characteristics (referred to as ROC hereafter) are used as a quantitative measure evaluating the fit of the regression model (Pontius & Schneider, 2001). A completely random model gives ROC a value of 0.5 while a perfect fit results in a ROC value of 1.0. If the value of ROC is below 0.7, the accuracy of the model is low; the accuracy will be preferable if an ROC value is above 0.7 (Macmillan & Creelman, 2005) Land-use conversion matrix and elasticity Land-use policies, restrictions and land tenure can influence the pattern of land-use change. There are two sets of such parameters in the CLUE-S model the land-use conversion matrix and elasticity. The land use conversions restricted by these factors can be reflected in a land-use conversion matrix. The rows of the matrix indicate the land-use type at time step t and the columns indicate the landuse type at time step (t + 1). Moreover, each land-use type can be assigned a dimensionless factor that represents the relative elasticity to conversion (Tan, Wu, Mu, Wang, & Yu, 2006; Wassenaar et al., 2007), ranging from 0 (easy conversion) to 1 (irreversible change). The value of this factor is set based on expert knowledge and can be modified during the calibration stage. In our study, we divided the land use of Changqing into six land-use types arable land, garden plots and forest land, other agricultural land, land for construction, unused land, and other land. We assume that the land for construction will not be converted to other land-use types. Based on expert knowledge and observed behavior in the recent past, the conversion elasticities for arable land, garden plots and forest land, other agricultural land, land for construction, unused land, and other land in our study are 0.2, 0.3, 0.4, 0.7, 0.1, 0.2, respectively Allocation procedure Thus, we modified the SD model s simulations through M i (Eq. (5)), and then used the modified results as the input demand in the CLUE-S model. M i = RL i GL i (5) In Eq. (5), M i is the modifying coefficient for land-use type i, RL i is the actual area of land-use type i in 2005, and GL i is the statistical area of i based on the raster data of When all the inputs are provided, the CLUE-S model will simulate the trajectories of land-use change in space in the following steps. First, for each grid cell i which is included in the change, calculate the total probability (TPROP i,u ) for each land-use type u according to Eq. (6) (Duan, Verburg, Zhang, & Yu, 2004; Veldkamp & Verburg, 2002; Zhang, Zhao, & Verburg, 2004). TPROP i,u = P i,u + ELAS u + ITER u (6) In Eq. (6), P i,u is calculated in Eq. (4), ELAS u is the conversion elasticity for u, and ITER u is an iteration variable that is specific to each land-use type u and indicates the relative competitive strength of certain land-use types. Next, the model makes a preliminary allocation for all landuse types with an equal value of ITER by allocating the land-use type with the highest total probability for the considered grid cell. According to the conversion matrix, conversions that are not allowed will not be allocated. Then, the total allocated area of each land use is compared with the demand for that land use type. If the allocated area for a specific land-use type is smaller than the area demanded, the value of the iteration variable will be increased. Otherwise, the value will be decreased since too much land has been allocated to that land-use type. The above steps are repeated until the allocation of land area equals the demand.

5 X.-Q. Zheng et al. / Landscape and Urban Planning 106 (2012) transport and irrigation, the maximum relative error is 2.98%; (4) for permanent population, total population and GDP, the relative errors for each year are all less than 1%. Thus, the SD model could be used to simulate the future land-use demand in an effective way Scenarios setting and temporal simulation Fig. 3. Land-use grid map of Changqing in Grid cell size is 250 m 250 m. 3. Implementation and results 3.1. Data sources and pre-processing The original data comes from the land use data of Jinan for , the relief map of Changqing in 2004, and statistical data of the economy, population and society of Jinan and Changqing from 1996 to The land-use grid map of Changqing in 2006 is shown in Fig SD model calibration and validation Calibration is the process of modifying the input parameters until the output from the model matches an observed set of data, while model validation ensures that the model meets its intended requirements in terms of the methods employed and the results obtained. In this study, historical statistics are used to calibrate and validate the SD model. As this study is a case study for the land-use planning program in Jinan City, we calculated the results of temporal land-use change, population and economy for the whole city from 1997 to 2005 and then extracted the results for Changqing District. Setting 1996 as the initial year, the SD model was calibrated using land-use, population, and economic data from 1997 to 2005, and was validated with the relevant data for The results show that (1) for agricultural land, land for construction and unused land, the relative error in area for each year between the simulated data and the actual data is less than 1%; (2) for agricultural land, garden plots, forest land, and other agricultural land, the maximum value of the relative error between the simulated and actual data is 2.13%; (3) for areas of residential zones, industrial and mining land, and land used for The entire Chinese land-use planning term is fifteen years, where the short-term plan is over five years and the long-term plan covers ten years. For each term, the various local development proposals will bring different land demands, and hence different land-use patterns. Based on the local social-economic statistic from 1996 to 2005, the status quo and the developing trend of the society, economy and land use in Jinan City, we have set three developing scenarios based mainly on the parameters of GDP, total population, and urban development (as shown in Table 1). The first scenario is one of slow development which keeps the development pace close to that of the present one. The second scenario is the medium development one which may speed up the socio-economic development with an 11.05% annual increase in GDP rate during 2006 and The last one is that of fast development, under which the GDP will increase 11.45% annually and the urban area will expanded to 42,000 ha in Taking the year 2006 as the initial year and the year 2020 as the final year, we simulated the land-use demand for Jinan between 2006 and 2020 in an annual time step under the three scenarios. Since the short-term plan is for 5 years and the long-term one is for 10 years, we simulated the land demand in Changqing in 2010, 2015 and 2020 under each scenario based on the distribution plan of the land use among districts in Jinan (Table 2) Driving forces for location Six factors were chosen to analyze the driving forces which affect the location characteristics of land use in Changqing. We generated a grid map for each factor using ArcView3.3 (Fig. 4) with a cell size of 250 m 250 m. The factors of rate of urbanization and population density were evaluated in the urban areas while the other factors were evaluated with the grid cell as the basic unit. The distance to town and to the road for each grid cell is evaluated by buffering, the elevation for each gird can be calculated through the Digital Elevation Model, and the slope is generated by the derive slope function in ArcView. The results from the regression analysis on the factors in SPSS are shown in Table 3. The ROC values are all over 0.70, thus the factors selected perform well in terms of explaining the land-use pattern of Changqing Spatial allocation Taking the modified results of land demand from the SD model as the input for the CLUE-S model, we obtained spatial allocations of land use in Changqing under the three scenarios (Fig. 5). Table 1 Parameter settings for the different scenarios. Parameters Scenario Settings GDP 1: slow growth Increasing by 10.72% annually from 2006 to : medium growth Increasing by 11.05% annually from 2006 to : fast growth Increasing by 11.45% annually from 2006 to Total population Increasing by 2.14% annually from 2006 to Urban development 1: slow development For the whole Jinan City, the urban area will be 40,000 ha in : medium development For the whole Jinan City, the urban area will be 41,000 ha in : fast development For the whole Jinan City, the urban area will be 42,000 ha in Urbanization rate 69.81% for 2010; 73.61% for 2015; 75.86% for In 2005, the GDP of Jinan City was 187,661 million RMB, the urban area of Jinan City was 29,500 ha, total population was 5,974,400, and the urbanization rate was 55.28%.

6 56 X.-Q. Zheng et al. / Landscape and Urban Planning 106 (2012) Table 2 Results of temporal simulation in Changqing (ha). Year Scenario Arable land Garden plots and forest Other agricultural land Land for construction Unused land Other land Fig. 4. Grid maps of the six driving factors which affect the land use location characteristics. Table 3 Beta values and Exp(ˇ) for the results of the regression analysis in SPSS. Arable land Garden plots and forest Other agricultural land Land for construction Unused land Other land ˇ Exp(ˇ) ˇ Exp( ) ˇ Exp(ˇ) ˇ Exp(ˇ) ˇ Exp(ˇ) ˇ Exp(ˇ) Urbanization rate Elevation Distance to town Distance to road Slope Population density Constant ROC value Exp(ˇ) values indicate the change in odds upon a 1-unit change in the independent variable. When Exp(ˇ) > 1, the probability increased upon an increase in the value of the independent variable. When Exp(ˇ) < 1, the probability decreases.

7 X.-Q. Zheng et al. / Landscape and Urban Planning 106 (2012) Fig. 5. Spatial allocations of land use under scenario 1, 2, and Discussions 4.1. Scenario analysis Temporal change dynamics The results in our study show that even though the urban area keeps increasing and the arable land is still decreasing during , the rates of increase and decrease are reduced (Table 4). Taking scenario 2 for the example, the area of agricultural land and unused land decreased by ha and ha from 2006 to 2010, while the construction land increased by ha. However, the change of arable land and construction land becomes smaller. The average reduction of arable land per year is ha in , ha in , and ha in Meanwhile, the increasing rates of construction land in , , and are 7.88%, 7.32%, and 6.43%, respectively Spatial change dynamics The expansion of construction and decrease of arable land are still the main changes in local land use. Most of the increasing construction is due to expansion from the existing urban area through occupation of the surrounding arable land, garden plots, forest, and unused land. It occurs mainly at the urban rural fringe and around major roads. Fig. 6 shows the expanding construction for the three scenarios. The decreased arable land is located mainly along the fringe of the urban area, and in the southern part of the region where the slope of the terrain is steep. The increased arable land is concentrated in the north where the slope is not very steep. The garden

8 58 X.-Q. Zheng et al. / Landscape and Urban Planning 106 (2012) Fig. 6. Expansion of construction, compared for different scenarios. Fig. 7. Decreased/increased arable land under scenario 2 between 2006 and plots, forest land, and unused land are the main sources for the increased arable land. Fig. 7 shows the spatial distribution of the decreased and increased arable land under scenario 2 between 2006 and From the spatial allocations for the three scenarios, we conclude that the main difference among them is the amount of expanding or decreasing land which is dependent on the different land use demands under different social and economic conditions Impact assessment Fig. 8 shows the spatial allocation of the increasing constructions under scenario 2. By comparing Figs. 7 and 8, we can see that the urban rural fringe is the area most sensitive to urbanization. In this area, the agricultural land is more prone to be nibbled by the city, and construction land is becoming the main land use type. The conflict between urban development and farm land preservation has been gradually increasing. Fig. 8. Increased land for construction under scenario 2 between 2006 and 2020.

9 X.-Q. Zheng et al. / Landscape and Urban Planning 106 (2012) Table 4 Changes of land-use structure for different scenarios in Changqing (ha). Scenario Arable land Garden plots and forest Other agricultural land Land for construction Unused land Other land The minus sign means the decrease of the certain land-use type during certain period. However, neither the current comprehensive land use plan nor the urban plan pays much attention to the land use on the urban rural fringe. Thus, in the process of plan-making, the local planners and decision-makers in Changqing need to highlight the issues in this area and strengthen the control of its land use by confirming the amount of urban construction scientifically or by other means, and prohibiting the unplanned expansion of urban area in order to achieve sustainable development of both city and land use Other useful information In our study, we mostly discussed the feasibility of the coupled model in addressing the two issues mentioned in the Introduction. Based on the model, we placed the local urban development in the position of highest priority to simulate the corresponding land development and discuss the possible impacts on farm land. But in future practical applications, planners would be able to get much more essential information through changing or setting some parameters, such as the variables in the SD model and the land-use conversion rules, to assist in their decision-making. For example, they could decide that prime farm land must be preserved or assume that arable land cannot be converted into other land-use types. Through comparing the results without these assumptions, they could point out the spatio-temporal changes of local land use mainly from the aspect of farm land preservation policy. Thus, through the results of land use scenario analysis and impact assessment based on this coupled model, local planners and decision-makers could grasp the different local land use demands under different developing scenarios, the dynamics of their spatiotemporal changes, and the growth and decline in urban land and farm land. In other words, they can get the needed knowledge to assist plan-making. In addition, we considered only the interactions between human activities and the land-use system, while in practice natural factors such as climate and natural resources also play an important role in land-use change. Therefore, the result from the coupled model itself is just a probable scenario under specific conditions and assumptions. Fig. A1. The SD model.

10 60 X.-Q. Zheng et al. / Landscape and Urban Planning 106 (2012) Conclusions Considering the balance between temporal land demand and spatial land supply, we put forward a coupled model based on the SD model and the CLUE-S model to answer the two questions for local planners and decision-makers involved in the process of land use planning in Changqing District, Jinan, China. The balance between local land use demand and physical supply is the bridge linking the two models. Considering the scale effect, we modified the SD simulations to improve the performance of the CLUE-S model. Through the scenario analysis and impact assessment we demonstrated that the results of this model can answer the two questions well. In addition, it provided important information on the spatio-temporal dynamics of local land-use change under different development scenarios and on the influences of urban development on farm land. This information is necessary for planners to make proper plans aiming to balance local land use and urban development. Moreover, the results of scenario 2 in this study have been adopted in the comprehensive land-use planning program of Jinan City for As the result based on the coupled model is just a probable scenario, it describes a probable outcome under specific conditions. Planners or decision-makers should pay attention to this point in the process of decision-making. Acknowledgements This research was supported by the Comprehensive Land-Use Planning Program in Jinan, China ( ), the Science and Technology Innovation Program for Graduate Students at China University of Geosciences, Beijing, China (No ) and by the National Key Technology R & D Program of China (Nos. 2006BAB15B03 and 2006BAB15B06). Appendix A. See Fig. A1. References Aaviksoo, K. (1995). Simulating vegetation dynamics and land use in a mire landscape using a Markov model. 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