Land Use Effects on Travel Behavior in Jabotabek ...hicec/coe/products/DP2006/DP2006-4.pdf · the...

Discussion Paper Series Vol.2006-4

Land Use Effects on Travel Behavior in Jabotabek (Indonesia) Metropolitan Area

Metin SENBIL, Junyi ZHANG, Akimasa FUJIWARA

Graduate School of International Development and Cooperation,

Hiroshima University [email protected]

August 2, 2006

No part of this paper may be reproduced in any form or any means without written permission from author.

Abstract: Using person-trips database along with GIS land use data and individual attitudes on certain issues, regression analyses are conducted on certain eleven different household and individual mobility decisions. It is found that medium-term mobility decisions such as private vehicle ownership (private car or motorbike) and commute trip mode are solely affected by socio-economic and demographic characteristics of households or individuals. When the analyses are conducted on short-term mobility decisions, land use and transportation system variables turn out to be effective significantly. Thus it is concluded that for short-term mobility decisions, policies drawing from land use and transportation system might be effective; however their effects on longer term mobility decisions, i.e., private car ownership and commute trip mode are found to be ineffective, further analyses are needed based on more detailed land use and transportation characteristics.

1. INTRODUCTION The long-standing hypothesis that land use affects urban travel behavior still constitutes a source of controversy among researchers. For example, the inverse relationship between density and energy use proposed by Newman and Kenworthy and their colleaques (1, 2, 3) has attracted vigorous criticisms spelling out omission of intermediate (control) variables, and unrealistic policy implications for modal shifts − from private vehicle to public transit or from motorized trip modes to non-motorized trip modes − challenging established behaviors. Among these, intermediate variables such as socio-economic and demographic characteristics are correlated with both density and energy use, thus they would render the correlation among density and energy use spurious (see 4, 5, 6 for discussions).

In this line, at least two recent studies, sought to reevaluate the results proposed by Newman and Kenworthy, maintain the controversy (7, 8). Study by Mindali et al. (7), by using a multivariate technique, finds no direct impact of overall density on energy consumption; however, some density elements are identified to be effective, e.g., CBD density, inner area density. Another recent study by Coevering and Schwanen (8) includes intermediate variables in the regression equations, and reached the same results as those obtained by Newman and Kenworthy, i.e., inverse relationships between density and energy use.

On another footing, one may say that concentrating on macro relationships such as density and energy use while controlling for, say, summary socio-economic and demographic variables shadows travel decision making units, i.e., households and individuals (see 9, 10, 11). Considering travel behavior experienced by individuals and households, mode shift from private car to other modes such as public transit (including adoption of car pooling practices) or non-motorized modes might be found unrealistic when trip making behaviors hinge upon private car (12). In this respect, Kitamura et al. (13), based on their finding that attitudes are stronger than neighborhood characteristics in explaining personal mobility indicators, concludes that land use policies aiming to alter personal mobility, e.g., switch to public transport use, might be ineffective in the face of attitudes not favoring public transport. Coupled with this result, one may think that creating communities characterized with jobs-housing balance or self-containment might also be ineffective in shortening trip distances and promoting travel modes other than private car (11, 14, 15). However they can be made effective if accompanied by other supplementary policies such as provision of integrated transport infrastructure (15).

Notwithstanding these conclusions are controversially deepening, the question whether properly formulated land use policies can serve to direct travel demand is still relevant for developing countries. Because there is a small body of relevant literature conducted in the backdrop of developing countries where both land use and transportation system as well as socio-economic, demographic, and cultural characteristics are significantly different from their counterparts in developed countries.

In developing countries, both urbanization and economic development are stacked not more than several decades after 1950s. This is relatively a short duration for both urban and transportation infrastructure to develop in tandem, and establish at workable proportions. Consequently, the enormous urban change often manifests itself by a noticeable duality between traditional and modern ways of life in cities of the developing world. Adding to these the nature and the intensity of urban transportation problems in developing countries as indicated by WCTR-ITPS (16), Gwilliam (17), Gakenheimer (18), Vasconsellos (19), it is quite possible to obtain different results and reach at different conclusions from the similar studies conducted in developed countries.

By putting the developing country context in the middle, this study proceeds with six

more sections following this introduction. The following two sections are provided with the purpose for setting the research stage. According to this, the second section presents the motivation behind this study, by drawing on the prospects, mainly in energy consumption and its environmental consequences. Besides, in the same section, we supply introductory information about the study area, Jabotabek metropolitan area in Indonesia. In the third section we briefly review the prior research relevant for land use and transportation interactions examined in this study; however, the review supplied in this section is not exhaustive. Instead, as touched upon briefly above, we try to emphasize the controversial nature of conclusions obtained by different studies and pertinent variables for land use and neighborhood characteristics. Remaining sections proceed to the core of this study first by detailing the data used in the study in section four, and then by devising research models followed by estimation results in section five. Lastly, in section six, we present our discussion based on the results obtained in the models.

2. MOTIVATION AND THE STUDY AREA A recent study reports that increase in oil consumption in developing countries will be two thirds of the global increase between 2002-2030; surpassing total energy consumption of developed countries around 2030 (20, pp. 64-66). A significant part of the energy consumption is expected to be consumed by the transportation sector, in which private transportation is estimated to constitute a significant part (20, page 66-67). Depending on time-series data of 26 countries with different economies all over the world, a study by Dargay and Gately (21) finds the effect of income on car ownership highest around US$5000 per-capita GDP level. According to this, relatively low income countries like China, India, Indonesia, Pakistan, Turkey and Mexico, display the highest potential for rapid increases in car ownership levels either nowadays or in the very near future. Another study by Ingram and Liu (22) estimates that in developing countries car ownership is expected to increase at a rate more than income growth. From another perspective, Vasconsellos (19), offering a sociological explanation, highlights social determinism that car ownership in developing countries has exclusively associated with the middle class life styles, and stresses the influence of the social forces on middle class to sustain a mobility level tied to car ownership. Thus it goes from the foregoing that finding ways to diverge from business-as-usual practices towards more sustainable ones in developing countries constitutes a significant importance. This reason along with those mentioned in the introduction part constitute the main motivation for this study.

The study is conducted using data collected in Jabotabek metropolitan area, located on the northern seaboard of Java Island of Indonesia. Jabotabek1 metropolitan area includes the province of DKI2 Jakarta − the capital city of Indonesia− and the surrounding regencies of Bogor and Bekasi in the province of West Java, and Tangerang in the province of Banten (Figure 1). DKI Jakarta encompasses five municipal administrative areas called as Jakarta Barat, Jakarta Pusat, Jakarta Selatan, Jakarta Timur, Jakarta Utara3. The whole Jabotabek area has a population around 21.5 million, which constitutes 10% of the population of Indonesia. The metropolitan population is concentrated on the axes defined by the DKI Jakarta and surrounding city centers. Between 1990 and 2000 the number of households has decreased in some of the center areas in DKI Jakarta and Tangerang while significantly increased in the rest of Jabotabek. The extreme cases concentrate in the areas on the fringe of DKI Jakarta, i.e.,

1 The name Jabotabek (or Jabodetabek as lately called) comes from the first two or three letters of the cities

included in the metropolitan area: Jakarta, Bogor, Tangerang, Bekasi (and Depok). 2 Daerah Khusus Ibukota (DKI): Capital City Special Region. 3 Barat: West, Pusat: Selatan: South; Timur: East; Utara: North; Pusat: Center.

the arc defined by center cities of Kota Bekasi, Kota Depok and Kota Tangerang. This urban sprawl encroaching upon the immediate vicinity of DKI Jakarta is mostly the product of private housing development projects (23, 24). This is part of a long-lasting process that has started during early 1970s − decreasing population density in 0-5 km of the city center, increasing population densities in the rest of the metropolitan area with drastic increases in the 5-15 km ring (25).

The transportation sector in Indonesia is almost entirely dominated by road transport (26). This is partly because of the strong government support to motor vehicle users through intensive road construction projects as well as through subsidies to road users accompanied by underinvestment in public transport and rail systems (26, pp. 80-81). The hotbed of motorization in Indonesia is Jabotabek metropolitan area: with 75 percent of all vehicles owned in Indonesia, Jabotabek house a car population of 2.5 million (as of 2005), and about 140 new cars are sold every day (27).

3. PRIOR RESEARCH Research on the transportation and land use interactions is one of the active fields in transportation circles. This is mostly due to their potential of being a platform for devising policies to curb energy consuming and environmentally polluting travel behavior, e.g., (single occupant) private car use, longer vehicle kilometers traveled, by mobilizing policies that draw on reorganization of land use. However, as outlined in the introduction part, one may come across contradictory results frequently in the literature. Thus drawing results singled-out from any of them (without considering the counter-results) to the policy platform comes with the risk of failure in achieving policy objectives.

Two of the most actively debated land use policies to decrease energy consumption and related side effects are density and mixed land use. High density is generally considered to reduce trip lengths and number of trips (28, 29) and change the mode split in favor of public transit or non-motorized trip modes (30). For example, Newman and Kenworthy (1) reports findings that fuel use in the higher density center areas is higher than the lower density outlying areas; however, due to shorter distances traveled fuel use per capita in the center areas is significantly lower than the outlying areas. These generally constitute the underlying reasons for urban intensification such as compact city development (31). However, increasing densities might turn out to be a single-handed intervention devoid of public support because of household preferences favoring suburban lifestyles (13, 32, 33). There are also policy failures marked by large scale public transit investments aimed at increasing public transit patronage and change land use composition (14, 34, 35).

Similar to high density, mixed land use is also considered to reduce trips lengths and change mode split in favor of public transit and non-motorized modes (36). The very logic behind mixed land use is that different activities in close proximity to each other might normally reduce trip lengths, and increase the propensity to chain trips with different purposes, thus reduce the number of trips. Related to mixed land use, neo-traditional design (37) is promoted as an innovative planning policy concentrating on micro-level design characteristics at the neighborhood level (e.g., continuity of streets, linings of trees on sidewalks, separated bicycle routes) without touching the macro-level land use characteristics at the city or metropolitan level.

Policies concentrated on the transportation generally draw on the public transit systems. By these policies it is intended to increase the patronage of public transportation by giving priority to public transportation or by increasing its attractiveness (38). For this, a necessary (but not sufficient) precondition is to have a sufficient passenger pool with easy access to public transportation. Policy handbooks such as OECD & ECMT (38) supplies many policies for increasing public transport patronage. Transit oriented development is one

of them which foster land use policies complementing public transport: work centers around high accessible public transport nodes, minimum density requirements, etc. (39, 40).

4. DATA AND PREMILIMINARY ANALYSIS The data used in this study are compiled from three data sources. The first data source contains information about person-trips. We have computed variables related to household and individual activity-travel information using this data source. The second data source contains individual attitudes on various issues ranging from very general such as those related to economy and environment, etc. to specific (such as residential environment, traffic congestion, and security). All individuals surveyed for the trip information are asked to supply their attitudes on these issues. The third data source concerns GIS data on land use, transportation infrastructure, population and employment. We use this data source to derive variables related to density, diversity, transportation system and other land use characteristics of the residential location. Some parts of the data are available at different geographical scales, e.g., socio-economic data at traffic analysis zones, thus there will be an aggregation bias induced at various points of data preparation process.

Person-trip data set, compiled from large household person-trip survey conducted in 2003 by JICA4, contains information about one day trips of a total of 433,125 individuals who are members of 158,631 households. An average household in the data consists of 3.66 individuals, and approximately 80 % of the households consists of, at least, three members; yet four or five member households constitute a third within this (their total is equal to about 40 %- twenty per cent each- of all households sampled). In spite of large household sizes, monthly household income stays at lower levels, which is typical of the developing world. As regards individuals, Jabotabek metropolitan area hosts a young population (average age is slightly below 30), which can also be regarded as another developing country characteristic.

During one day, individuals who have participated at least one out-home activity have spent an average of nearly 6 hours for out-of-home activities and 80 minutes for traveling. One day activities are sequenced in a manner that leaves 85 % of individuals with one home-based trip chain. Average number of home-based trip chains is significantly higher at residential locations in city centers than the outlying areas (results are not reported here). Accordingly, travel time per day is higher in center cities than outlying areas. When center cities are differentiated with respect to their locations, i.e., DKI Jakarta or outlying city centers, we detect the number of home-based trip chains is almost the same, while total travel time is approximately 8 minutes longer (82 min. vs. 90 min.) in the outlying city centers.

As regards commute trips, it takes more time to commute in the DKI Jakarta and the centers of satellite cities than outlying areas in Jabotabek. For one, outlying areas are more self-contained in terms of work and residence relationships − in all metropolitan areas, self-contained zones in terms of employment location are generally concentrated in outlying areas, especially in the south, east and north-east of Jabotabek. For another, it might be the existence of traffic congestion in the center cities of Jabotabek. In DKI Jakarta, partly because of small zone sizes, most of the commutes (more than 50 % at least) have destinations in zones other than the origin zone. However, when the larger administrative zones are taken into consideration, e.g., Jakarta Barat, Jakarta Pusat, etc., the ratio of out-commutes decreases only slightly that does not make much difference to originally obtained percentages. Another metropolitan-wide consistency is detected in the usual commute trip mode. In all center cities as well as outlying areas, private trip modes constitute a share between 29.5 %~32.9 % for commute trips; on the other hand, public transit captures slightly more than half of the mode share, i.e., 50.5 % ~56.2 % for commute trips.

4 Japan International Cooperation Agency (JICA).

According to the land-use database, the whole metropolitan area consists of 680,3005 hectares of land (including unurbanized as well as vacant lands), of which 216,876 hectares (around 32%) are put to urban land uses, (i.e., residential, commercial and business, administrative, industrial, educational, parks and recreational uses). 78.38 % of all lands in DKI Jakarta are developed; approximately the same value is obtained for Bekasi where developed lands constitute 78.73 % of all lands. In terms of urban land uses, all outlying cities, i.e., Tangerand, Bekasi, Bogor, and Depok, as well as outlying areas in Jabotabek are predominantly residential with ratios ranging from 81.68% to 95.98 %. This means that most of the commercial activities and businesses as well as government facilities are concentrated in DKI Jakarta.

5. MODEL ESTIMATION AND ANALYSIS OF LAND USE EFFECTS

5.1. Variables Choice related to different dimensions of the travel behavior is a context-dependent phenomenon (41). Zhang et al. (41) classified different contexts effective on travel behavior choice into household and individual-specific, alternative-specific, and circumstantial contexts. Using these different contexts, we develop pertinent variables in the models used in this study. Accordingly, altogether four groups of variables are developed within different contexts. The first group of variables accounts for individual-specific context by drawing from socio-economic and demographic attributes of individual and household. Heterogeneity in individual decision-making process is an outcome of this context. Additionally, as choices of vehicle ownership and residential location preconditions daily travel decisions; they can be considered as part of the individual-specific context. Socio-economic and demographic variables of household and individual include gender, age, occupation, household income, and household size.

Also within household and individual-specific context, the second group of variables refers to individual attitudes on general urban and transportation issues. In this regard, we identify two groups of attitudes (both as individual importance rankings) on general problem areas that society was facing at the moment and on different aspects of the transportation system (Table 1). In this, we follow Kitamura et al. (13) who refer to attitudes as “formed through experience as a result of behavior; attitudes prompt certain types of behavior; and interactive, two way relationships exist between attitudes and behavior” (p.149). According to the results Kitamura et al. (13) obtained, controlling for attitudes might lead us to true effects of land use and transportation system on mobility decisions. However, it will remain unclear which of the attitudes best represent the backstage of decision making on mobility decisions. Notwithstanding this, we manage to derive three factors to be included into the analyses (Table 1).

The third group of variables characterizes residential location, land use and transportation system can be considered within circumstantial context which refers to circumstantial factors that are common to a set of the decision makers. Economic, social and cultural backgrounds can also be considered as examples of the third context. From such standpoint, it is likely that remarkable differences in choice behavior between developed and developing countries may be observed. The last group of variables on household and individual mobility decisions supplies dependent variables to the models. Land use and transportation variables Under land use and transportation system, we collect the observed variables related to the residential neighborhood and transportation system characteristics. Residential neighborhood

5 Slightly less than 10% of Jabotabek is occupied by DKI Jakarta.

is characterized by density and diversity (2D variables). Population and jobs per developed land are the sole density variables used in the study. For diversity, average land use diversity is computed by averaging diversity in residential zone over all hectare grid cells of developed land. Diversity of a grid cell is computed by counting the number of different land uses of hectare grid cells immediately neighboring the grid cell on all sides. Other diversity variables are considered to be ratios of residential, commercial land uses and undeveloped lands in the neighborhood.

Besides, we include variables concerning relative location of the residential neighborhood within the metropolitan area, which is measured by Euclidian distance from a land mark in the center of DKI Jakarta, i.e., Istiqlal Mescid. Transportation system is represented by variables related to the transportation network, e.g., road supply, rail and bus routes passing through traffic zones. Among these, road supply is summarized by the length of all streets as well as all major roads passing through the residential zone. For public transit supply, bus system is summarized with median of total bus lines on street segments; rail system is characterized with ratios of lands within 1 km of rail station in residential zone.

5.2. Models of household and individual mobility decisions Household and individual mobility decisions encompass different elements of one-day individual activity-travel behavior; these variables are used as dependent variables in the regression models. Characterizing household and individual mobility decisions, altogether eight variables are produced (type of the regression model used and observation unit are given in parenthesis): − Private car and motorbike ownership (Bivariate binary Probit model-household) − Private vehicle ownership (car or motorbike) and private motorized (car or motorbike)

commute trip mode (Bivariate Probit-employed person) − Number of home-based trip chains with private car (Poisson regression model - mobile

person) − Number of home-based trips chains with motorbike (Poisson regression model -mobile

person) − Number of trips per day (Poisson regression model - mobile person) − Number of non-work trips per day (Poisson regression model - mobile person) − Ratio of private motorized trips per day (Tobit regression – mobile person) − Ratio of public transit trips per day (Tobit regression – mobile person)

To estimate the models, firstly, we have drawn random samples (approximately 10%) from household and mobile person data files obtained from the original person-trip database. Afterwards, we matched sampled cases with data available in other data sources. For households, variables used in vehicle ownership model is summarized in Table 2. Descriptive statistics of data concerning individuals are presented in Table 3. To account for the validity of these random samples, simple t-tests are conducted on similarity of the ratios of both private car ownership and motorbike ownership as well as most of the household socio-economic and demographic variables in both tables; the results reveal similarity of variables in both random samples and the original data files with probability at least approximately 0.95 (not reported here), thus we conclude to proceed to estimate the models proposed above with the random samples.

As regards the regression models, there are altogether three different models applied to estimate coefficients of explanatory variables. The bivariate binary Probit, the poisson regression and the tobit regression models are shortly explained below before proceeding to present the results of the models. For model explanations presented below, we have referred to Maddala (42), and Greene (43).

The bivariate binary Probit regression model depending on simultaneous observation

of two discrete binary observed-dependent variables, i.e., y, can be expressed as:

22

11

ii

ii

zz

εε+′=+′=

i22

i11

xβxβ

, yij = 1 if zij > 0, yij = 0 otherwise, j = {1, 2} (1)

where i denotes an observation; β and x stand for the vectors of coefficients and the independent variables respectively; εi1 and εi2 are random variates distributed jointly as standard Bivariate Normal with zero means and variances at one along with a free correlation parameter, ρ, i.e., BNV [0,0,1,1,ρ]. Based on the equation given above, the log-likelihood function of a sample of observations can be given as:

[ ]∑ ′′Φ=i

iiii qqqqL ρ2122212 ,,loglog ii11 xβxβ (2)

where Ф2 stands for the standard cumulative Bivariate Normal probability distribution; q is an indicator variable such that qij = 2yij-1, j = {1, 2}.

Tobit model is a censored regression, i.e., the regression is confined to an interval. In our case, the linear regression is conducted for the cases higher than zero at the same time controlling for the cases that are equal to zero such that:

yi = β′xi + εi if yi > 0 (3) yi = 0 else

where i denotes an observation; β and x stand for the vectors of coefficients and the independent variables respectively, εi is distributed as IID Standard Normal , εi ~ N(0, 1) with a common variance σ2. Probability of observing yi below zero is equal to:

( ) ⎟⎟⎠

⎞⎜⎜⎝

⎛⎟⎠⎞

⎜⎝⎛ ′

Φ−=<σ

iiy

xβ10Prob . (4)

While on the other hand, probability of observing a yi value above zero is a conditional probability such that:

( ) ( ) ⎟⎠⎞

⎜⎝⎛ ′−

=>>σ

φ iiiii

yyyfy

xβ00Prob (5)

where φ is the standard normal probability distribution. The resultant log-likelihood function that contains all possible values of yi can be given as follows:

∑∑>=

⎟⎠⎞

⎜⎝⎛ ′−

+⎟⎟⎠

⎞⎜⎜⎝

⎛⎟⎠⎞

⎜⎝⎛ ′

Φ−=00

1loglogii y

ii

y

i yL

σφ

σxβxβ

. (6)

The Poisson regression belongs to the class of count data models. Fully parametric Poisson regression assumes that the conditional frequency of activities, yi, given independent variables, xi, is independently and identically distributed with the probability mass function:

( ) ,...3,2,1,0, ==

−

ii

ii

i

ii yy

eyf

y

!βx

μμ

, (7)

where β is parameter vector of independent variables, xi. The Poisson probability mass function is characterized solely by the mean parameter, μi, which is assumed to be equal to a function of covariates: E(yi|xi,β) = μi = exp(β′xi). The maximum likelihood estimation is the standard method for Poisson regression. The log-likelihood function is:

( ) ( ){ }∑=

−′−′=n

iiiii yyL

1

lnexplog !xβxβ (8)

The estimation results of the regression models are presented in Tables 4 to 9 It is found that the regression models are good enough to describe the long-term and short-term decisions of travel behavior. Land use effects on household vehicle ownership In Table 4, we present the results related to household private car and motorcycle

ownership using the Bivariate Probit model. It is found that socio-economic and demographic variables are significantly effective on household vehicle ownership. However, nearly all of the variables that characterize the residential land use and transportation system turn out to be ineffective on household private car as well as motorbike ownership. Among significant elements of land use and transportation system, the ratio of commercial land use in residential zone (diversity) decreases private car ownership; the length of roads in the neighborhood (road supply), on the other hand, increases private car ownership.

In the Bivariate Probit model, negative correlation between private car and motorbike ownership devalues simultaneous ownership of private car and motorbike by households. Notably, effects of household size on private car ownership and motorbike ownership are with different signs: household size increases the propensity to buy motorbike, decreases the propensity to buy a private car. Another notable result, the effects of income on vehicle ownership for private car far exceeds similar effects on motorbike ownership. As a whole, we can summarize household vehicle ownership as a matter of socio-economic and demographic condition. It is highly probable that increase of household incomes in Jabotabek will lead households to private car ownership, this would also mean gradual decrease of motorbike ownership. Land use effects on individual mobility indicators The results of regression models presented in Tables 5 to 8 targets individual mobility indicators. Table 5 presents the results of the bivariate binary probit model of private vehicle ownership (private car or motorbike; employed individuals as behavioral units) and private motorized commute trip mode. As in the previous models targeting households, this model too reveals highly significant effects of socio-economic and demographic variables. Road supply, i.e., the length of road in the neighborhood, increases both the private vehicle ownership and its use as a commute trip mode. Interestingly, ratio of residential zone within 1 km of rail station increases propensity of both private vehicle ownership and private motorized commute trip mode use.

On the other hand, when we turn to mobility indicators that can be regarded as based on short-term decisions such as number of trips or number of home based trip chains one can see the increase in the number of land use and transportation system variables with significant effects. Two Poisson regression models reported in Table 6 explain the number of home-based trip chains with private car (Poisson 1) and the number of home-based trip chains with motorbike (Poisson 2) by controlling for the same variables used in previous regressions. From the results, it is understood that residing in center cities increase private motorized home-based trips chains, 3.5 times more for private car than motorbike. Land use diversity in a neighborhood has opposite effects on the use of different vehicles: average land use diversity increases private car use, and decreases motorbike use.

Poisson regressions on the number of trips leave us with significant effects of land use and transportation variables on the number of trips along with significant effects of the socio-economic and demographic characteristics − however, household income and household size have no effects on number of trips per day (see Table 7). For both total number of trips (Poisson 3), number of non-work trips (Poisson 4), the center city residing individuals engage more activities than individuals residing in other places in the metropolitan area; however, DKI Jakarta performs slightly lower than the surrounding center cities − Tangerang, Bekasi, Bogor and Depok− in this.

For Tobit regressions on ratio of private vehicle trips and ratio of public transit trips per day (Table 8), all the effects of socio-economic and demographic variables are significant for the results in the Tobit 1 regression, while only age and sex are significant for the Tobit 2 regression.

Factor variables derived from attitudes on general issues as well as transportation

issues are included in the regression analyses when individuals are taken as observation units. Thus, all regressions results reported in Tables 5 to 8 include those for factor variables too. Generally, the results obtained for factors are generally not significant. Of significant results, notably, only transport sensitive individuals are found to use private motorized commute trip mode (Table 5). As indicated above, individuals who use private motorized commute trip modes are more transport sensitive than other commute trip mode users. Behavioral interpretation of land use effects Having assumed that travel behavior is context-dependent, our attempt here is to give some behavioral interpretation about the above-confirmed land use effects. Focusing on land use might mislead us as land use patterns in developing countries are more instable than those in developed countries. It might be expected that decisions based on such instable factors are risky. Thus results obtained for land use effects on long-term mobility indicators might reflect the fact that a decision maker is not likely to reckon with unreliable and risky factors when facing the long-lasting impact of long-term decisions. In contrast, to decision makers rely on more reliable and predictable household and individual-specific context. This might be the reason why land use effects are not influential, but socio-economic and demographic attributes are effective on long-term decisions, reflecting household and individual aspirations (12, 19). On the other hand, land use and transportation systems give rise to heterogeneous accessibility across space, which in turn imposes some constraints on decision-making process of daily travel behavior, hence the significant effects of land us and transportation system.

6. DISCUSSION Jabotabek, Indonesia is the biggest metropolitan area in the South East Asia region, with a fast increasing population and sprawling urban lands along with emerging edge cities around the core of the metropolitan area, DKI Jakarta. As the metropolitan area constitutes the hearth of all economic activities in Indonesia, burgeoning Indonesian economy is directly reflected in the incomes, hence lifestyles of Jabotabek, foremost in Indonesia. As the analyses conducted in this study indicate, socio-economic and demographic variables constitute the single most important group of factors behind private vehicle ownership. This study, along with the results derived from analyses using international data sets (18, 21, 22), indicates gradual increase of automobile ownership in Jabotabek along with gradual decrease of motorbike ownership levels. We have found no significant effects of land use and transportation system on private vehicle ownership, thus it might be concluded that the land use or the transportation system has nearly no effect on medium-term mobility decision such as private vehicle ownership.

However, land use and transportation system becomes effective on short term mobility decisions such as number of trips, number of home-based trip chains, and trips with private car or with public transit. Of special interest, a synopsis of significant effects of variables characterizing neighborhood land use density and diversity can be provided as follows: 1. Effects of density on home-based trip chains:

− Population density decreases home-based trips chains with private car and motorbike − Job density decreases home-based trip chains with motorbike

2. Effects of density on number of trips: − Population density increases number of non-work trips per day

3. Effects of density on ratio of motorized trips: − Both population and job density decreases ratio of private car trips − Residential density decreases ratio of public transit trips

4. Effects of diversity variables on home-based trip chains:

− Average land use diversity along with ratios of commercial, residential and undeveloped lands in the residential neighborhood increase home-based private car trips

− Average land use diversity decreases home-based motorbike trip chains 5. Effects of diversity variables on number of trips:

− Ratio of undeveloped land in the residential neighborhood increases number of trips 6. Effects of diversity variables on motorized trips:

− Ratios of commercial, residential and undeveloped land in the residential neighborhood increase ratio of private car trips

− Average land use diversity and ratio of residential land use in the residential neighborhood increases ration of motorbike trips; ratio of commercial land use and undeveloped land decreases ratio of motorbike trips

As can be recognized from the list provided above, it seems reasonable to concentrate on land use policies to divert private car use to public transit or to non-motorized trip modes. As a further step, it is concluded that land use and transportation system policies to change short term travel behavior might be strategically effective; on the other hand, they will be mostly ineffective if directed at changing long-term travel behavior. However this comes with a caveat that land use data set available in this study is not fine-grained but consists of aggregations at neighborhood level. Thus, further inferences regarding land use effects on short term mobility decisions require more refined data concerning one-to-one interactions of households with surrounding land use and the transportation system.

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LIST OF TABLES AND FIGURES TABLE 1. Attitudes on Various Issues. TABLE 2. Descriptive Statistics of Households (Sample Size: 14,545). TABLE 3. Descriptive Statistics of Individuals (Sample Size: 42,652). TABLE 4. Binary Logit Model of Household Private Car Ownership (Sample Size: 14,545). TABLE 5. Bivariate Binary Probit Model of Household Private Car and Motorbike

Ownership. TABLE 6. Bivariate Binary Probit Model of Household Private Car and Motorbike

Ownership TABLE 7. Poisson Regression of Home-based Private Car Trip Chains (Poisson 1) and

Home-based Motorbike Trip Chains (Poisson 2) TABLE 8. Poisson Regression of Number of Trips per Day (Poisson 3) and Number of

Non-work Trips per Day (Poisson 4) TABLE 9. Tobit Regression of Number of Trips per Day (Tobit 1) and Number of Non-work

Trips per Day (Tobit 2) (Sample Size: 42,652) TABLE 10. Linear Regression of Total Travel Distance (km-day) (Regression 1) and Total

Travel Time (minute-day ) (Regression 2) FIGURE 1 Jabotabek metropolitan area.

TABLE 1. Attitudes on Various Issues. Factor Scores

Group Issue Sample Min. Max. MeanStd.

Dev. Factor 1 Factor 2 Factor 31. Transport 432,915 1 5 4.02 0.92 0.13 0.02 2. Education 432,247 1 5 4.13 0.82 0.14 -0.18 3. Housing 432,361 1 5 3.73 0.82 0.14 0.17 4. Flooding 432,782 1 5 4.12 0.93 0.16 -0.43 5. Garbage 432,946 1 5 4.12 0.90 0.15 -0.41 6. Poverty 432,954 1 5 4.31 0.84 0.15 -0.20 7. Safety 432,901 1 5 4.20 0.89 0.17 0.02 8. Public Facility 432,469 1 5 3.68 0.86 0.15 0.51 9. Environment 432,464 1 5 3.75 0.83 0.16 0.41

General issues

10. Medical Facility 433,050 1 5 4.08 0.80 0.16 0.10 1. Road 432,548 1 5 3.96 0.96 0.24 2. Bus 432,162 1 5 4.09 0.88 0.27 3. Train 430,037 1 5 3.67 1.01 0.27 4. TDM/Enforcement 428,339 1 5 4.21 0.89 0.26

Transportation issues

5. Impact on Environment 429,222 1 5 4.00 0.84 0.26

TABLE 2. Descriptive Statistics of Households (Sample Size: 14,545). Min. Max. Mean Std. Dev.

Household income1 1.00 9.00 3.40 1.96Household size 1.00 9.00 3.66 1.53Average age 5.00 80.00 31.86 11.53

Socio-economic and demographic

Number of males 0.00 7.00 1.47 0.92Center city indicator2 0.00 1.00 0.63 0.48DKI Jakarta indicator 0.00 1.00 0.39 0.49Average land use diversity3 1.00 2.90 1.54 0.42Ratio of commercial land use 0.00 0.16 0.03 0.08Ratio of residential land use 0.00 0.92 0.56 0.28Ratio of undeveloped land 0.00 0.87 0.32 0.29Length of major roads passing through the neighborhood 0.00 8.31 1.21 1.49Length of all roads in the neighborhood 0.06 110.20 25.54 19.90Distance to DKI Jakarta city center 0.36 70.32 23.83 15.34Median of total bus lines on street segments 0.00 915.00 67.96 100.58Ratio of lands within one-kilometers of rail station 0.00 47.18 0.02 0.71Residential density4 9.49 571.76 137.63 82.24

Land use and transportation system (Transportation zones where households have residences)

Job density5 2.49 385.84 37.24 40.63Private car ownership 0.00 1.00 0.19 0.39

Household mobility Motorbike ownership 0.00 1.00 0.36 0.48

1: 1 = less than Rp. 600,000, 2 = Rp.600000-Rp.999999, 3 = Rp.1,000,000-Rp.1,499,999, 4 = Rp.1,500,000-Rp.1,999,999, 5 = Rp.2000000-Rp.2999999, 6 = Rp.3000000-Rp.3999999, 7 = Rp.4000000-Rp.4999999, 8 = Rp.5000000-Rp.7499999, 9 = more than Rp.7500000; 1 US $ = approx. Rp. 9000 Indonesian Rupiahs.

2: DKI Jakarta, cities of Tangerang, Bekasi, Bogor and Depok. 3: For each hectare grid, sum of different urban land uses covering eight neighboring grid cells is averaged over all grid cells with urban land use in a transportation zone. 4: Population/Area of developed land (information is available in large zones which cover more than two transportation zones in DKI Jakarta, fewer than two in other locations). 5: Total number of jobs/Area of developed land (information is available in large zones which cover more than two transportation zones in DKI Jakarta, fewer than two in other locations).

TABLE 3. Descriptive Statistics of Individuals (Sample Size: 42,652).

Min. Max. MeanStd.

Dev.Age 5.00 97.00 29.77 15.63 Sex 0.00 1.00 0.54 0.50 Household income 1.00 9.00 3.37 1.94 Household size 1.00 10.00 3.66 1.56


Employed person indicator 0.00 1.00 0.46 0.50 Center city indicator 0.00 1.00 0.68 0.47 DKI Jakarta indicator 0.00 1.00 0.42 0.49 Average land use diversity 1.00 2.90 1.58 0.42 Ratio of Commercial land use 0.00 0.96 0.03 0.07 Ration of residential land use 0.00 1.00 0.58 0.27 Ratio on undeveloped land 0.00 1.00 0.30 0.28 Length of major roads passing through the neighborhood 0.00 8.31 1.24 1.49 Length of all roads in the neighborhoods 0.09 110.20 26.17 20.12 Distance to DKI Jakarta city center 0.36 70.32 22.46 14.72 Median of total bus lines on street segments 0.00 915.00 68.85 101.17 Ratio of residential zone within 1 km of rail station 0.00 100.00 0.01 0.79 Residential density 9.49 571.76 141.75 83.20

Land use and transportation system

Job density 2.49 385.84 38.25 41.18 Factor 1: attentive citizen -3.195 1.7379 0.01 1.00 Factor 2: priority setter -4.095 2.9074 0.00 1.00 Attitudes Factor 3: transport sensitive -3.187 1.4379 0.00 1.00 Home-based trip chains with private car 0.00 3.00 0.07 0.26 Home-based trip chains with motorbike 0.00 5.00 0.16 0.42 Private vehicle ownership 0.00 1.00 0.50 0.50 Private vehicle commute trip indicator 0.00 1.00 0.15 0.35 Number of trips per day 0.00 11.00 1.50 0.95 Number of non-work trips per day 0.00 6.00 0.43 0.66 Ratio of private motorized trips per day 0.00 1.00 0.18 0.37Ratio of public transit trips per day 0.00 1.00 0.42 0.47Total travel distance (km-day) 0.37 148.89 10.13 14.29

Individual mobility

Total travel time (min-day) 2.00 219.00 78.84 67.44

TABLE 4. Binary Logit Model of Household Private Car Ownership (Sample Size: 14,545)*. Variable Coefficient t-stat Constant -5.77 -15.17

Household income 1.21 55.28 Household size -0.40 -16.69 Average age 0.01 2.01


Number of males 0.12 3.67 Center city indicator 0.13 1.35 DKI Jakarta indicator -0.02 -0.15 Average land use diversity 0.07 0.55 Ratio of Commercial land use -1.13 -1.97 Ration of residential land use -0.36 -1.44 Ratio on undeveloped land 0.33 1.12 Length of major roads passing through the neighborhood -0.01 -0.64 Length of all roads in the neighborhoods 0.01 4.04 Distance to DKI Jakarta city center -0.00 -1.35 Median of total bus lines on street segments -0.00 -1.46 Ratio of residential zone within 1 km of rail station -47.54 0.00 Residential density 0.00 1.36


Job density 0.00 0.76 Degrees of freedom 17 Restricted log-likelihood -7,626.16 Model Log-likelihood -3,959.51

* Significant coefficient values below p = 0.05 are single underlined.

TABLE 5. Bivariate Binary Probit Model of Household Private Car and Motorbike Ownership (Sample Size: 14,545)*. Variable Coefficient t-stat Coefficient t-stat Constant -3.25 -15.61 -0.25 -1.90

Household income 0.63 67.31 0.01 2.34 Household size -0.17 -14.24 0.12 17.09 Average age 0.00 1.66 -0.01 -10.68


Number of males 0.06 3.14 -0.04 -3.17 Center city indicator 0.07 1.19 0.03 0.87 DKI Jakarta indicator 0.01 0.26 -0.09 -2.39 Average land use diversity 0.05 0.65 -0.01 -0.30 Ratio of Commercial land use -0.62 -1.85 0.03 0.13 Ration of residential land use -0.19 -1.41 -0.07 -0.82 Ratio on undeveloped land 0.19 1.15 -0.11 -1.04 Length of major roads passing through the neighborhood (km) 0.00 -0.37 0.02 2.05 Length of all roads in the neighborhoods (km) 0.00 4.44 0.00 1.49 Distance to DKI Jakarta city center (km) 0.00 -0.99 0.00 -3.52 Median of total bus lines on street segments 0.00 -1.28 0.00 2.02 Ratio of residential zone within 1 km of rail station -5.67 0.00 0.05 1.82 Residential density 0.00 1.94 0.00 0.12


Job density 0.00 0.61 0.00 -0.96 ρ （t-stat） -0.25 (-12.61)

Degrees of freedom 34 Restricted log-likelihood -17,724.49 Model Log-likelihood -13,887.55


TABLE 6. Bivariate Binary Probit Model of Private Vehicle Ownership and Private Motorized Commute Trip Mode (Sample Size: 19,481)*.

Variable Coefficient t-stat Coefficient t-stat Constant -1.19 -10.06 -1.61 -12.93

Age 0.01 7.59 Sex 0.78 33.95 Household income 0.33 46.97 0.17 33.32


Household size 0.02 2.97 -0.08 -12.67 Center city indicator 0.06 1.86 0.02 0.70 DKI Jakarta indicator 0.02 0.50 0.06 1.72 Average land use diversity -0.02 -0.34 -0.02 -0.50 Ratio of Commercial land use -0.27 -1.39 -0.32 -1.60 Ratio of residential land use -0.07 -0.89 -0.06 -0.70 Ratio on undeveloped land -0.14 -1.50 -0.03 -0.29 Length of major roads passing through the neighborhood (km) 0.01 1.54 0.01 1.09 Length of all roads in the neighborhood (km) 0.00 2.61 0.00 2.12 Distance to DKI Jakarta city center (km) 0.00 1.35 0.00 0.63 Median of total bus lines on street segments 0.00 0.23 0.00 1.32 Ratio of residential zone within 1 km of rail station 0.07 2.74 0.07 3.10 Residential density 0.00 0.55 0.00 -0.41


Job density 0.00 0.93 0.00 -0.21 Factor 1: attentive citizen 0.01 1.11 Factor 2: priority setter -0.01 -1.20 Attitudes Factor 3: transport sensitive 0.00 2.10

ρ （t-stat） 0.76 (99.84) Degrees of freedom

35

Restricted log-likelihood

-23,007.47

Model Log-likelihood

-20,182.55


TABLE 7. Poisson Regression of Home-based Private Car Trip Chains (Poisson 1) and Home-based Motorbike Trip Chains (Poisson 2) (Sample Size: 42,652)*.

Poisson 1 Poisson 2 Coefficient t-stat Coefficient t-stat Constant -5.96 -21.23 -1.84 -11.92

Age 0.02 13.83 0.02 23.40 Sex 0.25 5.84 0.57 19.43 Household income -0.01 -1.40 -0.01 -1.71 Household size -0.02 -1.31 -0.03 -3.20


Employed person indicator 0.30 6.88 0.37 12.94 Center city indicator 0.56 7.83 0.16 4.06 DKI Jakarta indicator -0.06 -0.86 -0.05 -1.15 Average land use diversity 0.56 6.56 -0.27 -5.03 Ratio of Commercial land use 1.32 3.37 0.21 0.93 Ratio of residential land use 1.90 9.91 0.07 0.74 Ratio on undeveloped land 1.07 4.27 -0.19 -1.54 Length of major roads passing through the neighborhood (km) -0.04 -3.29 -0.02 -2.00 Length of all roads in the neighborhood (km) 0.01 12.68 0.00 -0.39 Distance to DKI Jakarta city center (km) -0.01 -4.03 -0.02 -12.46 Median of total bus lines on street segments 0.00 1.10 0.00 0.22 Ratio of residential zone within 1 km of rail station -45.07 0.00 0.00 0.55 Residential density 0.00 -8.25 0.00 -4.58


Job density 0.00 -0.67 0.00 -4.30 Factor 1: attentive citizen -0.01 -0.46 -0.03 -2.84 Factor 2: priority setter 0.01 0.47 0.03 2.86 Attitudes Factor 3: transport sensitive 0.00 1.11 0.00 -0.05

Degrees of freedom 21 21 Restricted log-likelihood -10,494.65 -19,629.60

Model Log-likelihood -9,690.04 -18,511.45 * Significant coefficient values below p = 0.05 are single underlined.

TABLE 8. Poisson Regression of Number of Trips per Day (Poisson 3) and Number of Non-work Trips per Day (Poisson 4) (Sample Size: 42,652)*

Poisson 3 Poisson 4 Coefficient t-stat Coefficient t-stat Constant 0.15 3.00 -1.52 -16.50

Age 0.00 6.59 0.01 29.81 Sex 0.02 2.49 -0.46 -28.14 Household income 0.00 -1.59 -0.01 -1.63 Household size 0.00 0.13 -0.01 -2.35


Employed person indicator 0.02 2.17 -0.03 -2.00 Center city indicator 0.36 27.04 0.78 28.36 DKI Jakarta indicator -0.05 -3.79 -0.10 -3.73 Average land use diversity -0.02 -0.98 -0.03 -0.85 Ratio of commercial land use 0.06 0.86 0.15 1.19 Ration of residential land use 0.00 -0.08 -0.05 -0.85 Ratio on undeveloped land 0.13 3.19 0.02 0.24 Length of major roads passing through the neighborhood (km) -0.01 -3.04 -0.01 -1.74 Length of all roads in the neighborhood (km) 0.00 2.81 0.00 4.46 Distance to DKI Jakarta city center (km) 0.00 -7.17 0.00 -3.57 Median of total bus lines on street segments 0.00 0.65 0.00 2.23 Ratio of residential zone within 1 km of rail station 0.00 0.31 0.00 0.61 Residential density 0.00 1.61 0.00 4.35


Job density 0.00 0.98 0.00 1.47 Factor 1: attentive citizen 0.00 -1.16 0.00 -0.46 Factor 2: priority setter 0.00 1.17 0.00 0.50 Attitudes Factor 3: transport sensitive 0.00 -0.59 0.00 -0.62

Degrees of freedom 21 21 Restricted log-likelihood -58,094.44 -36,404.14

Model Log-likelihood -57,156.38 -34,344.38 * Significant coefficient values below p = 0.05 are single underlined.

TABLE 9. Tobit Regression of Ratio of Private Vehicle Trips per Day (Tobit 1) and Ratio of

Public Transit Trips per Day (Tobit 2) (Sample Size: 42,652)*

Tobit 1 Tobit 2 Coefficient t-stat Coefficient t-stat Constant -1.62 -16.24 -0.03 -2.49

Age 0.02 28.33 0.00 -7.05 Sex 0.33 18.84 -0.08 -7.48 Household income -0.02 -3.94 0.00 1.38 Household size -0.01 -2.78 0.00 0.66


Employed person indicator 0.20 10.25 0.00 -0.30 Center city indicator 0.08 3.20 0.06 4.18 DKI Jakarta indicator -0.06 -2.32 0.03 2.01 Average land use diversity 0.06 1.60 0.09 4.31 Ratio of commercial land use 0.56 3.73 -0.52 -5.41 Ratio of residential land use 0.46 7.18 0.08 2.07 Ratio on undeveloped land 0.13 1.59 -0.38 -7.96 Length of major roads passing through the neighborhood (km) -0.01 -2.51 0.01 1.87 Length of all roads in the neighborhood (km) 0.00 7.81 0.00 -4.67 Distance to DKI Jakarta city center (km) -0.01 -11.98 0.01 8.23 Median of total bus lines on street segments 0.00 0.48 0.00 7.79 Ratio of residential zone within 1 km of rail station 0.01 0.80 0.01 0.87 Residential density 0.00 -7.77 0.00 -2.26


(Transportation zones where households have residences)

Job density 0.00 -4.43 0.00 0.62 Factor 1: attentive citizen -0.01 -2.45 0.01 1.57 Factor 2: priority setter 0.01 2.48 -0.01 -1.57 Attitudes Factor 3: transport sensitive 0.00 0.27 0.00 0.20

Private motorized commute mode indicator 0.19 8.07 -0.05 -2.90

σ 1.17 110.03 0.87 172.88 Degrees of freedom 22 22

Restricted log-likelihood -28,322.39 -42,253.30 Model Log-likelihood -26,613.73 -41,702.39


TABLE 10. Linear Regression of Total Travel Distance (km-day) (Regression 1) and Total Travel Time (minute-day ) (Regression 2) (Sample Size: 42,652)*

Regression 1 Regression 2 Coefficient t-stat Coefficient t-stat Constant 65.02 6.86 23.22 5.92

Age -0.22 -4.21 0.29 13.18 Sex -3.07 -1.87 9.22 13.58 Household income 0.92 2.25 -0.49 -2.92 Household size 0.00 0.00 -0.11 -0.52


Employed person indicator -3.78 -2.15 8.64 11.86 Center city indicator -1.62 -0.65 16.85 16.40 DKI Jakarta indicator 3.61 1.29 -6.10 -5.26 Average land use diversity -24.67 -7.07 6.23 4.31 Ratio of Commercial land use -25.20 -1.66 11.28 1.80 Ratio of residential land use -40.90 -6.56 39.83 15.45 Ratio on undeveloped land -43.14 -5.61 21.16 6.65 Length of major roads passing through the neighborhood (km) 0.37 0.65 -0.83 -3.53 Length of all roads in the neighborhood (km) 0.00 0.01 0.12 6.60 Distance to DKI Jakarta city center (km) 0.16 1.57 -0.24 -5.77 Median of total bus lines on street segments 0.00 -0.40 0.00 -1.09 Ratio of residential zone within 1 km of rail station 0.68 0.70 0.11 0.27 Residential density 0.05 3.71 -0.02 -2.98


(Transportation zones where households have residences)

Job density -0.21 -7.25 -0.08 -6.82 Factor 1: attentive citizen -0.65 -1.16 -0.20 -0.85 Factor 2: priority setter 0.65 1.18 0.19 0.85 Attitudes Factor 3: transport sensitive -0.01 -1.23 0.00 0.76

Chi-sq [ 21] (prob) 390.75 (.0000) 2612.24 (.0000) R-Squared/Adjusted R-Squared 0.01/0.01 0.06/0.06

F[ 21, 42630] (prob) 18.68 (.0000) 128.21 (.0000) * Significant coefficient values below p = 0.05 are single underlined.

Kabupaten Bogor

Kabupaten Bekasi

Kabupaten Tangerang

Kota Bekasi

Kota Depok

Jakarta Timur

Kota Tangerang

Jakarta Utara

Kota Bogor

Jakarta Selatan

Jakarta BaratJakarta Pusat

DKI JAKARTA

0 8 16 24 324Miles¯

Legend

DKI Jakarta

Sub-centers

Remaining areas

FIGURE 1 Jabotabek metropolitan area.

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Vol.2004-7 本田直子 (広島大学大学院国際協力研究科) 日本の大気汚染問題における社会的環境管

理能力の形成に関する因果構造分析, 2004/10/25.

Vol.2004-8 金原達夫・金子慎治 (広島大学大学院国際協力研究科) 環境パフォーマンスと環境管理行

動の関係, 2005/3/10.

Vol.2004-9 木村宏恒 (名古屋大学大学院国際開発研究科) ジャカルタにおける社会的環境管理能力

形成の現状と展望, 2005/3/10.

Vol.2005-1 Tanaka, K. (Graduate School for International Development and Cooperation, Hiroshima

University) The Role of Environmental Management Capacity on Energy Efficiency: Evidence

from China's Electricity Industry, 2005/9/15.

Vol.2005-2 程雅琴 (中国国際民間組織合作促進会) 中国NGOの活動・役割, 2005/10/1.

Vol.2005-3 村上一真・松岡俊二 (広島大学大学院国際協力研究科) 都市大気汚染政策における社会的

能力の評価, 2005/10/3.

Vol.2005-4 Matsuoka, S., Murakami, K., Aoyama, N., Takahashi, Y., Tanaka, K. (Graduate School for

International Development and Cooperation, Hiroshima University) Capacity Development and

Social Capacity Assessment (SCA), 1st ed., 2005/10/24, 2nd ed., 2005/11/17.

Vol.2005-5 松岡俊二・村上一真・青山直人・高橋与志・田中勝也 (広島大学大学院国際協力研究科) キ

ャパシティ・デベロップメントと社会的能力アセスメント手法, 2005/11/17.

Vol.2005-6 Fujiwara, A., Senbil, M., Zhang, J. (Graduate School for International Development and

Cooperation, Hiroshima University) Capacity Development for Sustainable Urban Transport in

Developing Countries. 2006/1/20.

Vol.2005-7 Yosida, K. (Department of Social Systems and Management, University of Tsukuba) Benefit

Transfer of Stated Preference Approaches to Evaluate Local Environmental Taxes. 2006/1/29.

Vol.2005-8 松岡俊二・淵ノ上英樹 (広島大学大学院国際協力研究科) 開発援助政策の革新とキャパシ

ティ・ディベロップメント, 2006/1/31.

Vol.2005-9 Matsuoka, S., Fuchinoue, H. (Graduate School for International Development and Cooperation,

Hiroshima University) Innovation in Development Aid Policy and Capacity Development

Approach. 2006/3/1.

Vol.2005-10 村上一真・松岡俊二 (広島大学大学院国際協力研究科) 社会的環境管理能力に関する研

究：都市大気汚染対策を事例として, 2006/3/30.

Vol.2006-1 村上一真・松岡俊二 (広島大学大学院国際協力研究科) 都市大気質と経済成長および社会

的環境管理能力の因果構造分析, 2006/7/10.

Vol.2006-2 Nakagoshi, N., Kim, J E., Watanabe S. (Graduate School for International Development and

Cooperation, Hiroshima University) Social Capacity for Environmental Management for

Recovery of Greenery Resources in Hiroshima. 2006/7/28.

Vol.2006-3 Senbil, M., Zhang, J., Fujiwara, A. (Graduate School for International Development and

Cooperation, Hiroshima University) Motorcycle Ownership and Use in Jabotabek (Indonesia)

Metropolitan Area. 2006/8/01.

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