3 The LU/GEC Basic Model-II, a Land-use Model Linked with. - An Improved Model from the LU/GEC Basic Model-I -

Transcription

1 1 3 The LU/GEC Basic Model-II, a Land-use Model Linked with the LU/GEC Linkage Model - An Improved Model from the LU/GEC Basic Model-I - Hidenori MORITA* * Kagawa University, 2393 Iketo, Miki-cho, Kida-gun, Kagawa , Japan Abstract A prototype of a land-use model, LU/GEC Basic Model-I (LBM1), was constructed in the first year of the LU/GEC research project (1995). The LBM1 consisted of two sub-models, a land-use model and a driving-force (DF) projection model. The LBM1 s performance was substantiated through case studies, but the DF projection sub-model was incomplete. This study attempted to improve the LBM1 as a LU/GEC Basic Model-II (LBM2) through the addition of a DF projection sub-model. The efficiency and reliability of the LBM2 was substantiated by the case study. 1 Introduction In the first year of the LU/GEC research project (1995), we developed a prototype of a land-use model, the LU/GEC Basic Model-I (hereafter LBM1) 1), 2), and substantiated its performance through case studies in the Kansai district of Japan 3) and the Republic of Indonesia 4). Applying the LBM1 to future land-use projections requires the estimation of the driving forces (DFs) in each sub-zone in a study area. This method was incomplete in the LBM1. This study thus aims to achieve the following: 1) construct a DF projection model as the LU/GEC Linkage Model (LLM), 2) construct a joint model of the LBM1 and the LLM as the LU/GEC Basic Model-II (LBM2), 3) substantiate the performances of the LBM2 through a case study in the Kansai district. 2 Framework of the LBM2 The framework of the LBM2 is shown in Figure-1. It consists of a DF projection sub-model (LLM) and a landuse projection sub-model (LUM) 5), 6). 2.1 Framework of the LLM The LLM is the model that will be used to project the future values of DFs in each sub-zone to provide the input value for the LUM. The LLM presupposes the spatial hierarchy as described in Figure-1. Based on this presupposition, the following inter-hierarchical relation is guaranteed: X i X ( h 1) k = j ihj (1) X ihj, the ith DF in the jth sub-zone in the hth hierarchy; where i = the ith DF, k = the kth sub-zone in the (h-1)th hierarchy, j = the jth sub-zone (within the upper hierarchical sub-zone k),

2 2 h = the hth hierarchy. This self-evident relation leads to the following share model: X ihj Eihj = E j ihj X i( h 1) k ( 2) Eihj, the expected value of the ith DF (in means without the subject of equation (1)). This is the equation to share Xi(h-1)k into Xihj, according to the magnitude of Eihj. Thus if the value of Xihj in the top hierarchy is given, then the value of Xihj in each hierarchy could be estimated as far as the lowest hierarchy through the nest of equation (2). There are several methods that could be used to estimate Eihj, but we used a multiple regression model (equation (3)) in this study, as follows: E ihj = α 0 i + α mih Z mhj (3) m Zmhj, the mth explanatory variable of sub-zone j in hierarchy h, where = a given parameter. 2.2 Framework of the land-use model In the LBM2, the following multi-nominal logit model (MLM) 7) was used as the land-use model (LUM): L kjh = exp( U k kjh exp( U ) kjh ( 4) ) U kjh = βkih X ijh + C i k (5) Lkjh, the share of the kth land-use category in sub-zone j, where k = the land-use category, = a given parameter, C = a constant.lkjh; the share of the k-th land use category in sub-zone j 2.3 Control Variables of the LBM2 Figure-2 represents the outline of the relation of the variables in the LBM2. Land-use L (output of the LBM2) is controlled by input variable Z; these were thus renamed control variables (CVs). If the CVs are given then land use can be projected by the LMB2. To use the LBM2 for policy evaluation, policy alternatives should be included in the CVs. To maintain reliability in future land-use projections, it is better to include some fundamental indices, such as world or national population and GNP, in the CVs. To maintain operational efficiency of the LBM2, small numbers are recommended for CVs. 3 Case Study Methodology The case study was conducted in the Kansai district in Japan, comprising Shiga, Kyoto, and Osaka Prefectures (Figure-3).

3 3 3.1 Calibration of the MLM The spatial hierarchy for applying the MLM was set at the cities, towns, and villages (CTV) level. The data set was prepared at two time points, 1975 and The data sets of the two time points were pooled as a unified data set for calibration. Hence the total number of sub-zone j was 276 (138 CTV times 2 time points). Table-1 lists the variables for the MLM (k=1 to 5, i=1 to 19). The MLM was calibrated by a backward step-wise method. The significance levels of each parameter were estimated on the conditions which were t-statistics at the significance level to 33% (t=1), and the sign (+ or -) of a parameter should be consistent with theoretical considerations. The model was estimated with a very high coefficient of determination (r 2 =0.959). Table-1 lists the estimated parameters. 3.2 Calibration of the LLM Through the calibration of the MLM, the major factors determining the spatial distribution of land use were identified and selected as explanatory variables of the MLM. The number of explanatory variables which cleared the condition t >1 were 8 (Table-1). Three of the 8 were terrain-related indices, whic h can assume a constant value. The remaining 5 variables, regarded as DFs, should be projected by the LLM; these are population, grain production, horticultural production, Agricultural Promotion Area, and Urbanization Zone. In this case study, the LLM was applied at both the prefectural and CTV levels, respectively. At the prefectural level, the LLMs for 2 of 5 DFs, population and paddy production (grain production), were calibrated by equation (3). The remaining 3 DFs were projected by the KSIM method 8). At the CTV level, equation (3) was calibrated on all 5 DFs. The explanatory variables of equation (3) are listed in Table-2 and Table-3. The employment potentials (Zj4 and Zj5) were calculated according to following equation: EMPn Z j4 = ( j n)...(6) d n jn EMPn Z j5 = ( j n)...(7) 2 d n jn EMPn, the number of employees in sub-zone n, where djn = the distance between sub-zones j and n. Equation (3) was calibrated by a backward step-wise method. Each parameter was estimated on the conditions which were t-statistics at the significance level to 5% (t=1.96); the sign (+ or ) of a parameter should be consistent with theoretical considerations. The estimated values of the parameters are listed in Table-2 and Table Results and discussion: Projection of land use in 2050 at the CTV level Land use in 2050 at the CTV level was projected by the LBM2 through following steps: Step1 Prepare the CVs. Step2 Project the DFs of the prefectural level by the LLM and the KSIM. Step3 Project the DFs of the CTV level by the LLM. Step4 Project the land use of the CTV level by the MLM. The operative complexity of the LBM2 depends on the number of CVs. In this study, the CVs, which were selected through the calibrations, were few; these were: national population, national paddy production, and the number of employees at the CTV level. Figure-4 is an example of the output of the LBM2. The output (the land use in 2050 at the CTV level) changes according to the value of the CVs.

4 4 4 Conclusion This study constructed the framework of a land-use model, the LBM2, by building on the LBM1, and attempted to substantiate its performance through a case study in the Kansai district. The main improvement from the LBM1 was in the DF projection sub-model, which was changed to a top-down type of share model, the LLM. The operational efficiency and reliability of the DF projection sub-model was improved and the effects of the change of CVs on land-use change can be evaluated by the LBM2. Acknowledgment I would like to express my sincere thanks to Dr. Kuninori Otsubo (National Institute of Environmental Studies, Japan), the leader of the LU/GEC research project. Furthermore, I thank deeply every member of the LU/GEC research project for their cooperation in this study. References 1) Morita, H, Hoshino, S, et al. In: Otsubo, K, editor. Long term forecast of land use/cover change in Asia and the Pacific Region (I). National Institute for Environmental Studies, Japan [in Japanese]; p ) Kitamura, T, Kagatsume, M, Hoshino, S, Morita, H. A theoretical consideration on the land-use change model for the Japan Case Study Area. IIASA Interim Report IR Laxenburg, Austria; ) Morita, H, Hoshino, S, et al. A substantiative study on the application of the land-use change model for the Kansai District, Japan. IIASA Interim Report IR Laxenburg, Austria; ) Morita, H. Application of LU/GEC Basic Model to the Republic of Indonesia. In: Otsubo, K, editor. Long term forecast of land use/cover change in Asia and the Pacific Region (I). National Institute for Environmental Studies, Japan [in Japanese]; p ) Morita, H. LU/GEC Linkage Model. In: Otsubo, K, editor. Long term forecast of land use/cover change in Asia and the Pacific Region (II). National Institute for Environmental Studies, Japan [in Japanese]; p ) Morita, H. The Kansai model with linkage model. In: Otsubo, K, editor. Long term forecast of land use/cover change in Asia and the Pac ific Region (II). National Institute for Environmental Studies, Japan [in Japanese]; p ) Oota, K. Theoretical development of disaggregate model. Textbook for the 15th lecture course on Dobokukeikakugaku. Doboku-Gakkai [in Japanese]; p ) Sawaragi, Y and Kawamura, K. Participatory systems approach. Chapter 6, KSIM method and its applications. Nikka-giken, Tokyo [in Japanese]; p

5 5 Table 1 Used variables and estimated parameter in MLM at CTV level (r 2 =0.959, t >1.0) *1 employees refers to the number of working people in a working place. *2 The Agricultural Promotion Area Plan is a legal land-use plan authorized by the Agricultural Promotion Area Act. The Agricultural Promotion Area is designed by the Agricultural Promotion Area Plan. *3 The Urbanization Zone is designated in the Urban Planning Zone authorized by the City Planning Act. The Urbanization Zone consists of built-up areas, and non-built-up areas which should be converted to built-up areas within 10 years.

6 6 Table 2 Used variables and estimated parameter in equation (3) at prefecture level ( t >1.96) Table 3 Used variables and estimated parameter in equation (3) at CTV level ( t >1.96)

7 7 Spatial hierarchy Driving force Land use h=1 Earth (Earth level) LLM aggregation h=2 Asia Africa LUM Asia Africa (Continental level) LLM (MLM) Aggregation h=3 Japan China LUM Japan China (National level) LLM (MLM) Aggregation h=4 Kyoto Osaka LUM Kyoto Osaka (Province/ Prefecture level) LLM (MLM) Aggregation Substantiated part Not yet substantiated part h=5 Kyoto Miyazu LUM Kyoto Miyazu (Municipality level) (MLM) Fig.1 Framework of LBM2 Input Endogenous Fig.2 Relation of variables in LBM2 Output Variables Variabels Variables Z LLM X LUM L Control Sift Z and Driving Sift X and Land Use Variables (CVs) Project X Forces (DFs) Project L

8 8 Fig.3 Study area (Kansai district, Japan) Share of forest land in 1990 Increase or decrease of forest land Fig.4 An example of output of LBM2