Applied Energy 159 (2015) 449–457

Contents lists available at ScienceDirect

Applied Energy
journal homepage: www.elsevier.com/locate/apenergy

Compact development and energy consumption: Scenario analysis
of urban structures based on behavior simulation
Yanhong Yin a,b,⇑, Shoshi Mizokami c,⇑, Kohei Aikawa daFaculty of Maritime and Transportation, Ningbo University, 818 Fenghua Road, Jiangbei District, Ningbo 315211, China
National Traffic Management Engineering & Technology Research Centre, Ningbo University Sub-center, 818 Fenghua Road, Jiangbei District, Ningbo 315211, Chinac
Department of Civil and Environmental Engineering, Kumamoto University, 2-39-1, Kurokami, Kumamoto 8608555, Japand
Obayashi Corporation, Konan 2-15-2, Minato-ku, Tokyo 1070062, Japanbh i g h l i g h t s
 Individual consumption behaviors are simulated under urban structure scenarios.
 Increased consumption of the non-mobility goods and mass transit trips are found.
 Energy is estimated based on demand of non-mobility and mobility goods.
 Concentrating all population in one city center increases energy consumption.
 Polycentric urban structure shows effect on energy consumption reduction.

a r t i c l e

i n f oArticle history:
Received 20 April 2015
Received in revised form 16 July 2015
Accepted 2 September 2015

Keywords:
Energy consumption
Compact development
Urban structure
Consumption behavior

a b s t r a c t
This study investigates the relationship between compact development and energy consumption by simulating individual consumption behaviors in monocentric and polycentric urban structures in Kumamotoin 2030. A model is developed to estimate individual consumption behaviors and demand of mobility
goods and non-mobility goods at micro level. By considering the economic factor of income, the energy
consumption is calculated based on demand of goods under Business as usual scenario, Central Core City
scenario, and Multi-pole structure scenario. Results indicate the urban structure influences individual
consumption behaviors and energy consumption. Increased consumption of the non-mobility goods
and mass transit trips is shown in Central Core City scenario and the Multi-pole structure scenario.
Monocentric urban structures as shown in Central Core City scenario show less effect on energy consumption than the polycentric urban structure in Multi-pole structure scenario. The findings suggest thatthe multi-pole urban structure is a better choice for compact development in Kumamoto based on lessenergy consumption. The method in this study provides a new approach to analyze the influence of urban
structure on energy consumption at micro level. Findings give good suggestions for planning policy making regarding to compact development in Kumamoto.
Ó 2015 Elsevier Ltd. All rights reserved.

1. Introduction
1.1. Context and literature review
The magnitude and speed of urban growth makes urban
sustainable development a crucial element affecting the long term
⇑ Corresponding authors at: Faculty of Maritime and Transportation, Ningbo
University, 818 Fenghua Road, Jiangbei District, Ningbo 315211, China. Tel.: +86
574 8760 9546 (Y. Yin), Department of Civil and Environmental Engineering,
Kumamoto University, 2-39-1, Kurokami, Kumamoto 8608555, Japan. Tel.: +81 96
342 3541 (S. Mizokami).
E-mail addresses: yinyanhong@nbu.edu.cn (Y. Yin), smizo@gpo.kumamoto-u.ac.
jp (S. Mizokami).
http://dx.doi.org/10.1016/j.apenergy.2015.09.005
0306-2619/Ó 2015 Elsevier Ltd. All rights reserved.

outlook of humanity. High levels of fossil fuel consumption andenvironmental problems have aroused great attention of governments worldwide. Many studies have searching for countermeasures leading to urban sustainable development. Compact city is
recommended [1–3]. Compact city is a relative high density andmixed land use city, which is based on efficient public transport system and dimensions that encourage walking and cycling [1]. It issuggested as an urban form for sustainable development [4]. Two
major environmental benefits from compact city are less private
car dependency and preservation of green fields and arable land[5]. However, limitations have been argued for too compact development, including road congestion, reduced access to green andnatural areas, higher housing prices, and reduced living space [6,7].

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Y. Yin et al. / Applied Energy 159 (2015) 449–457

The debate on sustainable city is fraught with interests on whatkind of urban form should be and how urban living environment
strongly influences residents’ behaviors [8]. It argued that there
does not exist one ideal urban form for all cities, depending on
the local context, existing urban structure, and political possibilities [9]. Based on a review of existing planning strategies throughout cities worldwide, traditional monocentric urban structures
have being substituted by ‘wisely compact cities’ of polycentricurban organization [10]. Considerable attention has been focusedon assessing urban structures for compact development by analyzing energy consumption [11]. The amount of energy consumption
is a very important and effective index for evaluation of urban
structures. First, urban spatial configuration and land use affectthe density and intensity of activities thus energy consumption.
Second, growing environmental awareness requires urban development not only minimize the use of resources and the spatial displacement of environments, but also improve energy efficiency.
There is a wide spectrum of research for evaluating the effect ofurban structures on energy consumption, and exploring opportunities to reduce energy consumption from the viewpoint of compact
development. Broadly speaking, two major streams of studies
could be classified. The first stream of studies investigates therelationship between compact urban structures and energy
consumption by comparing energy consumption in different urban
structures at city and regional scales. Most of them focus on theenergy consumption in transport sector [12–15]. Yao analyzed
the impact of population density and energy consuming density
to space within urban districts of Beijing. Results indicated thatcompact urban structure was the most efficient way to reduceenergy use in urban areas [16]. Liu and Sweeneya investigated
the relationship between household space heating energy use
and urban form for the Greater Dublin Region. The results illustrated that the compact urban structure scenario was likely todecrease the domestic heating energy consumption per householdby 16.2% compared with the dispersed city scenario [17]. Schwanen et al. analyzed the impact of territorial structures upon energy
consumption in the Walloon Region (Belgium). Dense urban settlements were found be more efficient in both mobility and building[18].
Although these studies explored the relationship between
urban form factors and energy consumption, some have heavily
relied on the cross-sectional statistical data of fuel or electricity,which may not satisfactorily address the country or city specificissues, such as different stages of economic development. Academic research investigating into the connections between urban
structures and energy consumption has been inconclusive. The
results obtained differ depending on the methodology used, data
limitations, and spatiotemporal settings [19–21]. Moreover, it fails
to consider social and economic factors and lacks deep investigation on the influence of urban structure on resident behaviors at
micro level. Social economic factor are found influential to household energy consumption behaviors. As income increased the proportion and absolute amount of energy consumption and CO2
emissions decreased for food, and increased for education, culturaland recreation services in China [22].
Increasing studies are tending perform simulation analysis byconsidering social economic attributes. The other stream of studies
developed models to simulate the impact of urban structures on
behaviors thus energy consumption. Feng et al. developed a new
integrated model and applied it in Indonesia. Results suggested
that the polycentric urban structure with proper control of urban
growth and an efficient public transport system would be promising for energy saving in Jabodetabek [22]. Yamagata and Seyadeveloped a land use-transportation model to analyze the energyof future compact and dispersion city scenarios for the year 2050
in Toyo metropolitan area. Results suggested that compact urban

structure may contribute to the reduction of electricity demand
from the residential sector [23]. Besides, other models that integrated land use, transportation, and residential location choice
have been built to analyze the effect of the compact development
policies on resident behaviors thus energy consumption, such as
UrbanSim. It is a software-based simulation system for supportingplanning and analysis of urban development, incorporating theinteractions between land use, transportation, the economy, andthe environment [24]. It allows us to explore the effects of infrastructure and policy choices on housing affordability, greenhousegas emissions, protection of open space, and environmentally sensitive habitats. However, the system requires extensive data which
limit its practical use widely.
Robust suggestions can be drive from model simulation resultsby considering social economic factors. However, most studies
focuses on household energy use in residential and transportation
sectors (space heating and cooling, appliances and lighting, domestic hot water and private cars) [25]. Energy for commercial andpublic services is rarely considered. Actually, it is very important
to consider the energy for commercial and public service becauseof big contribution to all energy use. According to statistic datafrom Agency for Natural Resources and Energy, Ministry of Economy, Trade and Industry, energy use in the commercial sector
accounts for 25.07% of that consumed by all sectors in Kumamoto
prefecture in 1997, compared to 23.07% of residential sector [26].
Furthermore, it has been found that energy requirements for heating, cooking, and driving only increase weakly, while energy
requirements for industrial production and services increasestrongly with rising income [27,28].
On the whole, three main challenges highlighted in the literatures are often neglected or considered individually: (i) the effectof social economic factor on energy consumption; (ii) the importance of investigating the behaviors of citizens at the micro levelin different urban structures; (iii) the consideration of all energy
use that related to the everyday life of residents, including energy
use in transport, residential, and commercial and services sector.
Raising awareness of these challenges in academic studies, andproviding local authorities and policy makers with practical advice
adapted to their own planning situation is crucial. This could leadto successful policy making toward to compact development andsignificant reductions in the total energy consumption of the city.
1.2. Aim of this study
This paper presents an approach dedicated to the energy estimation of transport, residential, and commercial sectors underurban structure scenarios in Kumamoto (Japan) at micro level.
Major objectives of the study are: (i) to identify the methodology
details for estimating individual energy consumption under threekinds of urban structures in Kumamoto by considering the influence of social economic factors; (ii) to investigate the effect ofurban structures on behaviors of residents at micro level; (iii) tosummarize the important findings arising from energy evaluation
and behavior analysis, which could be used to assist policy making
and urban planning in achieving to a sustainable compact
development.
2. Materials and method
2.1. Kumamoto context and urban structure
2.1.1. Kumamoto development context
The Kumamoto metropolitan area is located in the Kumamoto
prefecture on the island of Kyushu (Fig. 1). The population of the
Kumamoto prefecture is 1,812,255 in 2010, ranking 23rd in Japan.

Y. Yin et al. / Applied Energy 159 (2015) 449–457

The Kumamoto metropolitan area is undergoing a transition to
new development because of the promotion of national position
and improvement of traffic accessibility. The bullet train, called
Shinkansen in Japanese, has operated between Kumamoto andTokyo since March, 2012. Moreover, Kumamoto has become a city
designated by government ordinance since April, 2012. It is a citythat has a population greater than 500,000 and has been designated by an order of the cabinet of Japan under Article 252, Section 19 of the Local Autonomy Law. There are 20 government
ordinance cities in Japan. Designated cities are performed manyof the functions normally run by prefectural governments in fields
as public education, social welfare, sanitation, business licensing
and urban planning. The Kumamoto city government is generally
delegated the various minor administrative functions while theKumamoto prefectural government retains authority over majordecisions. For instance, pharmaceutical retailers and small clinics
are licensed by Kumamoto city government, but pharmacies andhospitals are licensed by the Kumamoto prefectural government.
In spite of beneficial news, there are challenges in addressing
the development in Kumamoto, such as decreasing and aging population. A decreasing population has been observed in Kumamoto.
Although the population increased slightly from 2005 to 2010, a
decreasing trend is forecasted over the next thirty years.
Kumamoto city is predicted to lose more than ten thousand of the
population every five years after 2020. Accompanied with a decreasing population, aging is another problem. The ratio of elders (older
than 65) increases year by year. Elders accounted for 18.7% of the
population in 2005. The ratio is projected to increase to 29.7% in
2030. Higher proportion of elders brings more pressure on the financial and the infrastructure system of the city, partly caused by lesstaxpayers and increased welfare output. To ensure the quality of life,easy access to facilities and living services are essential for the growing number of elders. Public transport and car rental become important for the mobility of elders due to the limitation of driving
licenses. In Japan, residents who are older than 75 need retest for
driving licenses. In 2030, more than 17.6% of the population in
Kumamoto are forecasted to be older than 75.
Suburbanization is another challenge. Kumamoto is proceeding
on a process of urban growth and sprawl. Attracted by low rent,
more shopping centers are located in the suburban area.
Meanwhile, cars become affordable for ordinary people due tothe economic development. These factors together attribute to
the booming activities for shopping and recreational in suburban
area. According to a survey by the Kumamoto government,
shopping walkers in the city center decreased sharply in recent
years. The number declined from 797,624 in 2003 to 569,171 in
2008. The city center is losing attraction. To reactivate the city
center, the Kumamoto government has carried out a plan called
the ‘‘Basic plan for activating the Kumamoto city center”. The plan
has been effective, shown as the increasing number of walkers as
585,507 in 2009.
Facing the challenges of declined population and urban sprawl,
the Kumamoto prefecture government made a plan for compact
development for the future planning. Although a compact urban
structure is recommended by scholars, the question as to what
type of compact urban structures is suitable for Kumamoto
remains unanswered. It is expected the right compact urban structure will increase the energy efficiency of the city and preserve
more green land and agricultural land.
2.1.2. Urban structure scenarios
The term urban structure covers aspects of density, geometricshape, use of land (residential, industrial), and infrastructure (road,rail, waterway), it refers to the arrangement of the larger functional
units of a city, reflecting both the historical development of the cityand its more recent planning history [29]. Urban structures are451

summarized as monocentric and polycentric types [30]. Corresponding to the plan of compact development in Kumamoto, threekinds of scenarios are set based on current urban structure anddesired structures, either monocentric or polycentric types. One
scenario is based on the polycentric urban form, called the Multi
pole structure scenario. The Central core city scenario is based ona monocentric strategy. A Business as usual scenario (BAU scenario) is used for comparison.
Although more complex land use indicators would be desirable,
population density can serve as a useful indicator of urban structure [25]. Scenarios of urban structures are set based on different
population distribution strategies. The influence of land use andeconomic activities could be reflected by the population distribution. The total population of the region is assumed to be the samein all scenarios, which is estimated be 942,233 in 2030. The totalenergy consumption of the Kumamoto metropolitan area is estimated in 2030. The base year is 2010.
2.1.2.1. Business as usual scenario (BAU scenario). The urban structure is characterized by one main center and several sub centers,
which is same as the urban structure of 2010. Urban is sprawled
within the urban boundary of 2010. The living area and thelocation of facilities in the community remain stable as the baseyear of 2010. However, the number of population changes. The
population in 2030 is distributed among zones according to thezone population ratio. We use zone population ratio in Personal
Trip Survey (PTS) conducted in 1997 due to the lack of accurate
data of zone population ratio in latest year. The population
distribution result is shown in Fig. 2.
2.1.2.2. Central core city scenario. This classical monocentric urban
structure is characterized by one main city center. The total population is concentrated on the city core by two strategies. In the Central core city scenario (1), the population is distributed inurbanized zones by the zonal population density ratio of 1997. Residents are distributed to living circles in the Central core city scenario (2). Living circles are area mapped at radius of 3, 6, 9, 12,
15 km around the city center (Tsuruya Department Store). The
population is distributed in these living circles with the ratio of
40%, 24%, 18%, 12% and 6%, respectively. Zone population of eachliving circle is estimated by the zonal population ratio of 1997
and the total population of the living circle. Fig. 3a and b presents
the population distribution results of the Central core city scenario.
2.1.2.3. Multi pole structure scenario. A multi-pole urban structure isdesigned as one city core and several living circles (Fig. 4). The city
core marked in red1 is the center of services and facilities of thewhole area. It is connected with the living circles by highways andmass transit lines. Residents live in living circles, which is marked
in green. Regional cores (pink) and living cores (yellow) are includedin the living circles. Facilities and services are supposed located in
the region core. Living cores are dispersed yellow points whichaccommodate residents. The region core and living cores are connected by highways and public transit lines.
Based on the urban structure in Fig. 4, the population is distributed to the poles, which are called cores in Fig. 5a. In theMulti-pole structure scenario, two types of cores are defined as
the center core and the living core. The center core is the centerof the district both politically and economically. It is located within
the bureau of administration, main stations, department stores andother facilities. The living core is the second level core, which provides services for residents’ living, such as supermarkets, schools,
gyms, and community parks. Zones around cores are called living
1
For interpretation of color in Figs. 4 and 5, the reader is referred to the web
version of this article.

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Y. Yin et al. / Applied Energy 159 (2015) 449–457

Fig. 1. The location of the Kumamoto metropolitan area.

[31,32]. Based on this concept, energy consumption could be
estimated through the estimating the good consumption. Utility
theory is chosen as the basic theory for energy estimation
approach for two reasons. Firstly, it is a developed theory that
focuses on consumption behaviors and demand of goods inmicroeconomics. Utility is a term to describe the satisfaction experienced by a person through the consumption of commodities. In
microeconomics, residents are assumed to make decisions basedon their preferences of goods, the cost of goods, and budget constraints to maximize their utility. Solutions of maximum utility
could give answers to the demand of goods. Secondly, social economic factor is included into consideration in utility theory, suchas income. Three sub sections are included to introduce theapproach to estimate individual demand of goods based on utility
theory and calculation method of energy consumption.

2.2. Energy estimation method

2.2.1. Assumptions
All consumption behaviors of residents are classified as consumption behaviors for non-mobility goods and mobility goods.
Mobility goods include car trips and mass transit trips. Nonmobility goods are defined as all other goods except mobility
goods. Specially, non-mobility goods include goods in the Residential and Commercial sectors, such as heating, cooling, food andrecreation. Following assumptions are essential parts for developing the approach to estimate demand of goods: (a) Consumption
preference of the ‘‘representative individual” is applied as a representation of all individuals’ behaviors in each zone. (b) The ‘‘representative individual” is assumed to consume two types of goods:
non-mobility goods and mobility goods. (c) The demand of mobility goods is a function of car trips and mass transit trips. (d) Individuals are supposed to achieve maximum utility and mobility atsame time. The demand of goods on maximum utility and maximum mobility is assumed be equal to the real demand of goods.
(f) All income is spent on consuming without saving.

From a consumption perspective all economic activities and therelated energy use at all the stages of the economic process can beunderstood as being ultimately aimed at final consumption

2.2.2. Mathematical model for demand of goods
A two order Constant Elasticity of Substitution (CES) function
are applied to express the relationship between utility, mobility,

Fig. 2. Population density of the BAU scenario in 2030 (person/km2).

space areas, which accommodate the population. In Fig. 5a, thepopulation is distributed in zones that located in the center corearea (red), living core area (yellow), and living space area (green).
The zone population of the center core area and living core area
is assumed be 1.5 times of population of the same zone in the
BAU scenario. The remaining population is distributed to zones
in the living space area by the zone population ratio in 1997. It is
assumed that there are no residents living in zones outside of thecore and living space areas. The population distribution result isshown in Fig. 5b.

Y. Yin et al. / Applied Energy 159 (2015) 449–457

453

Fig. 3. (a) Population density of the Central core city scenario (1) in 2030 (person/km2). (b) Population density of the Central core city scenario (2) in 2030 (person/km2).

represented by a CES function at the first order (Eq. (1)). Mobility
is determined by car trips and mass transit trips. The substitution
relationship between car trips and mass transit trips is expressedby CES function at the second order (Eq. (2)).

nor1 =ðr11 Þðr 1Þ=r1
ðr 1Þ=r1
ui ðx1i ; x2i Þ ¼ a1 x1i 1
þ a2 x2i 1nor2 =ðr2 1Þ
ðr 1Þ=r2
ðr 1Þ=r2
þ a2M x2Mi2
x2i ðx2Ci ; x2Mi Þ ¼ a2C x2Ci2

Fig. 4. Concept structure of Multi-pole urban structure scenario.

and demand of goods. Utility is determined by the consumption of
non-mobility goods and mobility goods. The constant elasticity of
substitution between non-mobility goods and mobility goods is

ð1Þ
ð2Þ

where ui indicates utility level; x1i, x2i are demand of non-mobility
goods and mobility goods, respectively; x2Ci, x2Mi are demand of car
trips and mass transit trips, respectively; r1 represents substitution
elasticity between non-mobility goods and mobility goods; r2 issubstitution elasticity between car trips and mass transit trips; a1,a2 are expenditure share of non-mobility goods and mobility goods
to income, respectively; a2C, a2M are expenditure share of car trips
and mass transit trips to traffic budget, respectively.
The demand of goods in reality is estimated by solutions ofmaximization problems of utility and mobility. Firstly, maximum
mobility is determined by car trips and mass transit trips subject
to the transportation budget (Eq. (3)). Secondly, maximum utility
is determined by non-mobility goods and maximum mobility
under the income constraint (Eq. (4)).

Fig. 5. (a) Cores and living space area in the Multi-pole structure scenario. (b) Population density of Multi-pole structure scenario in 2030 (person/km2).

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Y. Yin et al. / Applied Energy 159 (2015) 449–457

nor1 =ðr1 1Þ
ðr 1Þ=r1
ðr 1Þ=r1
max : ui ¼ a1 x1i 1
þ a2 x2i 1
x1i ;x2i

2.3. Data source

ð3Þ

s:t: p1i x1i þ p2i x2i 6 Iinor2 =ðr2 1Þ
ðr 1Þ=r2
ðr 1Þ=r2
þ a2M x2Mi2
max : x2i ¼ a2C x2Ci2

x2Ci ;x2Mi

ð4Þ

s:t: p2Ci x2Ci þ p2Mi x2Mi 6 I2i
where in zone i, p1i, p2i, p2Ci, p2Mi represent price of non-mobility
goods, mobility goods, car trips, and mass transit trips, respectively;
Ii is income (person per day); I2i indicates traffic budget (person perday).
The solutions of the maximum mobility problem at the second
stage shows the optimal number of car trips and mass transit trips
as Eqs. (5) and (6). The maximum mobility and corresponding priceof mobility goods are shown in Eqs. (7) and (8), respectively.

 .ð1r Þ
ð1r Þ
2
x2Ci ¼ ða2C =p2Ci Þr2 I2i ar2Ci2 p2Ci 2 þ ar2Mi
p2Mi 2 .
ð1r Þ
ð1r Þ
2x2Mi ¼ ða2M =p2Mi Þr2 I2i ar2Ci2 p2Ci 2 þ ar2Mi
p2Mi 21=ðr2 1Þ
r2
r2
2
x2i ¼ ar2C2 p1
þ ar2M
p1
 I2i
2Ci
2Mi
n
o1=ð1r2 Þð1r Þ
ð1r Þ
2
p2Mi 2p2i ¼ ar2Ci2 p2Ci 2 þ ar2Mi

ð5Þ
ð6Þ
ð7Þ
ð8Þ

The maximum utility problem is solved based on the maximum
mobility and price of mobility goods. The optimal demand of nonmobility goods and mobility goods is shown as Eqs. (9) and (10).
The traffic budget is calculated as Eq. (11). Substituting I2i with Ii,
the optimal demand car trips and mass transit trips on maximum
utility and maximum mobility could be express as Eqs. (12) and(13), respectively.
( ,"
#)

ð1r1 Þr
r1
r1
r2 ð1r2 Þ
r2 ð1r2 Þ ð1r2 Þ
x1i ¼ ða1 =p1i Þ 1 Ii
ar1 1 p1
þ
a
a
p
þ
a
p
ð9Þ
2
1i
2Ci 2Ci
2Mi 2Mi .

r1
r1
þ ar2 1 p1Þx2i ¼ ða2 =p2i Þr1 Ii ðar1 1 p1
1i
2i
 .

r1
r1
þ ar2 1 p1
I2i ¼ p2i x2i ¼ p2i ða2 =p2i Þ Ii ar1 1 p1
1i
2i


r
r2
r2 1r2 ððr2 r1 Þ=ð1r2 ÞÞx2Ci ¼ ða2C =p2Ci Þ 2 ar2 1 ar2C2 p1
2Ci þ a2M p2Mi
n
o1
r1
r2
r2 1r2 ðð1r1 Þ=ð1r2 ÞÞ ar1 1 p1
þ ar2 1 ðar2C2 p1
Ii
1i
2Ci þ a2M p2Mi Þ


r2 r1
r2 1r2
r2 1r2 ððr2 r1 Þ=ð1r2 ÞÞx2Mi ¼ ða2M =p2Mi Þ a2 a2C p2Ci þ a2M p2Mi
n
o1
r1
r2
r2 1r2 ðð1r1 Þ=ð1r2 ÞÞ ar1 1 p1
þ ar2 1 ðar2C2 p1
Ii
1i
2Ci þ a2M p2Mi Þ

ð10Þ
ð11Þ

ð12Þ

ð13Þ

2.2.3. Energy estimation function
Individual energy consumption in zone i is calculated based on
demand of goods, energy unit, and trip time (Eq. (14)). Ei is theenergy consumption of a ‘‘representative individual” in zone i. E
is the total energy consumption of the region, which depends on
Ei (energy consumption of a ‘‘representative individual”) and Pi(population) in zone i. x⁄1i, x⁄1Ci, x⁄1Mi, are demand of nonmobility goods, car trips, and mass transit trips on the maximum
utility (u⁄i ), respectively. e1, e2, and e3, are energy units of nonmobility goods, car trip, and mass transit trip, respectively. Energy
units are used to evaluate the energy needed of each goods, which
are important constants in the function. t2Ci, t2Mi, are trip time ofcar trip and mass transit trip. The trip time is introduced into thefunction to consider the influence of traffic congestions on energy
consumption. Serve traffic congestion means longer travel time,thus more energy consumption.

E¼

X
X





Ei Pi ¼e1 x1i þ e2 t2Ci x2Ci ; x2Mi x2Ci þ e3 t 2Mi x2Ci ; x2Mi x2Miii Pi ui ðx1i ; x2Ci ; x2Mi Þ ¼ ui ; 8i

ð14Þ

2.3.1. Energy unit
According to the data from Agency for Natural Resources andEnergy, Ministry of Economy, Trade and Industry in Japan, theenergy unit of goods in the Residential and Commercial sector is
17.47 kJ/yen in the Kumamoto prefecture in 2010, including
energy for residential, water supply, sewage and waste disposal,
trade and finance service, public service, commercial service, retailservicers [26]. Non-mobility goods include goods in the Residential
and Commercial sector. Energy unit of non-mobility goods isassumed to be same as the energy unit of goods in the Residential
and Commercial sector, which equals 17.47 kJ/yen.
The energy unit of a trip is determined by the running fee unit,
which measures the cost of one vehicle running one kilometer,
including fees for oil, tires, tubes, and vehicle maintenance anddepreciation. Based on the data of the running fee unit from areport titled ‘‘A method to calculate unit value of time and runningfee unit in 2003” from Ministry of Land, Infrastructure and Transport (MLIT) in Japan, the running fee unit in 2003 is used to calculate the energy unit of a car trip and a mass transit trip due to lackof latest data [33]. The energy unit of a trip is calculated as the following: energy unit of trip (kcal/trip min) = the energy unit of trip
distance (kcal/trip min)  average speed (km/min). Based on therunning fee unit of a trip, the trip distance, and the trip time, theenergy unit of a car trip is estimated be 825.4 kJ/trip min (equals
to 197.18 kcal/trip min), and 76.01 kJ/trip min (18.16 kcal/trip min) for a mass transit trip (1 cal = 4.186 J).
2.3.2. Primary trip data
The Personal Trip Survey (PTS) is a person-based travel survey
conducted every ten years by Ministry of Land, Infrastructure
and Transport (MLIT) in Japan. The data are aggregated by traffic
zones. Although the latest PTS was carried in 2012 in Kumamoto,the data is not available. Most of the data used for parameter estimation are from the PTS in 1997, which is provided by the Urban
Transport Planning and Consultation Council of the Kumamoto
metropolitan region. Car trips and mass transit trips are aggregatedfrom the original PTS dataset. Trips only for the purposes of commuting, business, shopping, and returning home, are taken into
consideration.
2.3.3. Price of goods
The price of non-mobility goods is set to one. The price ofmobility goods is determined by the price of car trip and masstransit trip. The price of car trip is measured by the speed of a trip
and the running fee unit. It is calculated as follows: p2Ci (yen/trip)= [(running fee unit (yen/km vehicle)  minimum distance fromzone i to j (km/trip))]/average number of passengers (trip/vehicle).
The number of passengers in a car is 1.21 according to the reportfrom MLIT [26]. The price of a mass transit trip is determined by
the mass transit fare based on the trip assignment result.
2.3.4. Income
It is difficult to estimate the household income in each zone in
Kumamoto in 2030. First, we forecast the total income of allresidents in Kumamoto metropolitan region in 2030 according to
the history income data of each administrative district from thehomepage of the Kumamoto prefecture. The total income of eachadministrative district in 2030 is calculated by multiplying the
average increasing ratio and the income of 2010. The increasing
ratio of income is the averaged value of increasing ratios of income
from 2000 to 2010 [34]. Because a positive relationship between
land price and income is assumed, the zone income ratio is
supposed to be equal to the land price ratio of main roads in eachzone. Then we distribute the total income of each administrative

Y. Yin et al. / Applied Energy 159 (2015) 449–457

district among zones according to the land price ratio of main
roads in 2010. The data of land price of main roads are available
from the Japanese National Tax Agency [35].
2.3.5. Trip time
Trip time is estimated by assigning car trips and mass transit
trips on the maximum utility on the network by software, whichis called JICA System for Traffic Demand Analysis (JICA STRADA).
It is a package system for forecasting transport demand, trafficmode choice, traffic distribution, and traffic assignment. The software was used to estimate the trip time of all zone pair (trips fromzone i to zone j) by assigning estimated demand of trips to the network of Kumamoto. For zone i, there are many trips from zone i tothe different destination zone j. Trip time of all trip pair from zone iis averaged weighted by number of trips. The averaged value of trip
times is used in the energy estimation function for zone i.
2.4. Parameter Estimation
The values of elasticity of substitution, r1 and r2, could be estimated if values of self and cross demand elasticity of car trips andmass transit trips, e2mmi and e2mci, are estimated according to Eqs.
(15) and (16) [36].

e2mmi ¼ ðð@x2mi =x2mi Þ=ð@p2mi =p2mi ÞÞ¼ r2 þ ðr2  r1 Þðp2mi x2mi =I2i Þ þ ðr1  1Þðp2mi x2mi =Ii Þe2mci ¼ ðð@x2mi =x2mi Þ=ð@p2ci =p2ci ÞÞ¼ ðr2  r1 Þ=ðp2ci x2ci =I2i Þ þ r1  1ðp2ci x2ci =Ii Þ

ð15Þ
ð16Þ

An aggregated logit-type model is introduced to estimate thevalues of e2mmi and e2mci, shown as Eqs. (17) and (18). G2Mi andG2Ci are the generalized cost of a car trip and a mass transit trip.
Di is the city center zone dummy variable. It takes the value of 1
for center zone and 0 in other cases. The self and cross demand
elasticity of car trips and mass transit tips, e2mmi and e2mci, are calculated based on Eqs. (19) and (20). The values of e2mmi and e2mci
are calculated zone by zone. The average value of all zones isapplied for model parameters of e2mm and e2mc. It is easy to calibrate the allocation parameters a1, a2, a2C, a2M, if the value of r1
and r2 are determined by Eqs. (21) and (22).

W 2Ci ¼ expðn þ d  Di þ c  G2Ci Þ=fexpðn þ d  Di þ c  G2Ci Þð17Þ
þ expðc  G2Mi Þg.
W 2Mi ¼ expðc  G2Mi Þ fexpðn þ d  Di þ c  G2Ci Þ þ expðc  G2Mi Þg ð18Þ

e2mmi ¼ ðð@W 2Mi =W 2Mi Þ=ð@p2mi =p2mi ÞÞ ¼ c  G2Mi  ð1  W 2Mi Þ
e2mci ¼ ðð@W 2Mi =W 2Mi Þ=ð@p2ci =p2ci ÞÞ ¼ c  G2Ci  W 2Ci


.
r1
r1
r1
aki ¼ pki x1=
þ p2i x1=
Þðk ¼ 1; 2Þðp1i x1=
1i
2i
ki

.

r2
1=r
1=r
p2Ci x2Ci 2 þ p2Mi x2Mi2
ðm ¼ C or MÞ
a2mi ¼ p2mi x1=
2mi

ð19Þ
ð20Þ
ð21Þ
ð22Þ

3. Results
3.1. Parameters and sensitivity analysis
As shown in Eqs. (12) and (13), it is essential to estimate theparameters in utility and mobility CES functions for estimating
the demand of goods, thus energy consumption. r1, the elasticity
of substitution between non-mobility goods and mobility goods,
is estimated be 0.6577. r2, the elasticity of substitution between
car trips and mass transit trips, is 1.0787. The value of the expenditure share parameter, a1, is 0.9995, indicating that residents
spend most of their income on non-mobility goods. In contrast,
a2 is indicated as 0.0005. Eighty percent of the transport fare is455

spent for car trips, shown by the value of 0.8040 of a2C. Less thantwenty percent of the traffic budget is spent on mass transit trips,
shown as the value of a2M as 0.1960.
We also analyzed the sensitivity of input variables on output
variables. In this methodology, the output variable is the amountof individual energy consumption. As parameters and energy
units are fixed constant, input variables include prices of goods,
income, and trip time. Energy consumption is most sensitive toincome. It increases 1.00% as income increases by 1.00%. Priceof non-mobility goods also shows strong influence on energy
consumption. The amount of energy reduces 0.58% as the price of
non-mobility goods improves 1%. Increasing 1% of the price ofcar trips would result in 0.17% reduction of energy consumption.
Energy only increases 0.01% when the price of mass transit trips
improves by 1%. If the trip time of car trips increases 1%, energy
consumption would increase 0.23%. However, there is very limited
effect of trip time of mass transit trips. Energy increases 0.003% as
trips time of mass transit trips increases by 1%.
3.2. Demand of Goods
As shown in Table 1, a total of 6.48 billion units of non-mobility
goods are needed for all residents in Kumamoto each day in the
BAU scenario. Compared to BAU scenario, monocentric and polycentric urban structure scenarios show higher demand of nonmobility goods. A mild increase trend is shown in the Central core
city scenario (1), totally 6.62 billion units of non-mobility goods
are needed. The lowest increase demand of non-mobility goods isshown in the Multi-pole structure scenario, which is 6.51 billion
units, 1.01 times of the BAU scenario’s. The highest demand of
non-mobility goods is found in the Central core city scenario (2).
More than 6.87 billion units of non-mobility goods are expected,
which is 1.1 times of the result of the BAU scenario.
By contrast, the demand of car trips shrinks in monocentric andpolycentric urban structure scenarios. Reduced car trips are foundin all scenarios compared to the BAU scenario. Both Central core
city scenario (1) and (2) show smaller number of car tips, indicatedas 1.71 million and 1.73 million, compared to 1.76 million of the
BAU scenario. The lowest demand of car trips is observed in theMulti-pole structure scenario, which has 1.64 million car trips each
day in Kumamoto. However, an increasing trend is suggested forthe result of mass transit trips. The number of mass transit trips
is expected to increase to 99.9 thousand, 109 thousand, and 102
thousand in the Central core city scenario (1), the Central core city
scenario (2), and the Multi-pole structure scenario, respectively. In
BAU scenario, 88.3 thousand of mass transit trips are estimated per
capita per day.
3.3. Energy Consumption
Table 2 lists the detailed information of energy estimation.
151 billion kJ of energy is needed for all residents per day in Kumamoto in the BAU scenario, compared to 149 billion, 154 billion, and146 billion kJ in the Central core city scenario (1), the Central core
city scenario (2), and the Multi-pole structure, respectively. A
decreasing trend of energy consumption is shown in both the Central core city scenario (1) and the Multi-pole structure scenario.
However, the result of the Central core city scenario (2) indicatesincreased total energy consumption. An increasing trend of energy
use for non-mobility goods is indicated in the results of monocentric and polycentric urban structure scenarios. The highest value ofenergy consumption for non-mobility goods is found in the Central
core city scenario (2), which is 120 billion kJ per day. The lowest
value is shown in the BAU scenario, as 113 billion kJ per day.
Energy consumption for non-mobility goods in the Multi-polestructure scenario increases a little compared to the BAU scenario,

456

Y. Yin et al. / Applied Energy 159 (2015) 449–457

Table 1
Daily demand of goods in Kumamoto.
BAU scenario
Non-mobility goods (yen/day)
Car trips (trip/day)
Mass transit trips (trip/day)9

6.48  10
1.76  106
8.83  104

Central core city scenario (1)9Central core city scenario (2)96.62  10 (1.02)
1.71  106 (0.97)
9.99  104 (1.13)

6.87  10 (1.10)
1.73  106 (0.98)
1.09  105 (1.23)

Multi pole structure scenario
6.51  109 (1.01)
1.64  106 (0.93)
1.02  105 (1.16)

Note: The number in ( ) indicates the increased ratio compared to the data of the BAU scenario.

Table 2Estimated energy consumption in Kumamoto in 2030.

Total energy consumption (kJ/day)
Energy consumption for non-mobility goods (kJ/day)
Energy consumption for car trips (kJ/day)
Energy consumption for mass transit trips (kJ/day)BAU scenario

Central core city scenario (1)Central core city
scenario (2)Multi pole structure scenario

1.51  1011
1.13  1011 (74.72%)
3.75  1010 (24.82%)
6.99  108 (0.46%)

1.49  1011
1.16  1011 (77.61%)
3.26  1010 (21.90%)
7.24  108 (0.49%)

1.54  1011
1.20  1011 (77.98%)
3.30  1010 (21.51%)
7.74  108 (0.50%)

1.46  1011
1.14  1011 (77.66%)
3.19  1010 (21.84%)
7.37  108 (0.50%)

Note: The number in ( ) indicates the energy share.

amounting 114 billion kJ per day. It is of note that more than 20% of
the total energy is used for the consumption of mobility goods inall scenarios. Energy consumption for car trips reaches 37.5 billion kJ per day in the BAU scenario, accounting for 24.82% of thetotal energy consumption. Energy consumption for car trips in
the Central core city scenarios shows lower values of 32.6 billion
and 33 billion kJ per day in the Central core city scenario scenarios(1) and (2), respectively. Energy for car trips in the Multi-polestructure scenario reduces to 31.9 billion kJ. Less than 0.5% of thetotal energy consumption is consumed for mass transit trips. In
the BAU scenario, 699 million kJ of energy is estimated for masstransit trips each day. Other scenarios show increased energy formass transit trips. In the Central core city scenario (2), the value
reaches 774 million kJ.

4. Discussion
Facing the magnitude and speed of urban growth, it is important to evaluate the environmental impacts of urban structures,and to reduce their environmental impacts for urban sustainable
development. Previous works focused on energy efficiency of compact urban structures in the traffic sector. Little attention has been
focused on energy consumption in other sectors. This study analyzed the energy consumption by simulating the consumption
behaviors of residents, both traffic behaviors and non-traffic
behaviors, in urban structure scenarios named Business as usual
scenario, Central Core City scenario and Multi-pole structure scenario from a microeconomic viewpoint.
It found that compact urban structures are influential on energy
consumption behaviors thus energy use. Residents are estimatedto increase the energy use for non-mobility goods in compact
urban structure scenarios. More energy consumption is found in
the monocentric urban structures than polycentric structures.
The energy consumption is effectively reduced in the Multi-polestructure scenario. This polycentric urban structure disperses
activities to several poles, decreasing the complexity of the traffic.
Moreover, shorter trips could be encouraged in dense pole areas
because of high facility availability and employment opportunities.
Concentration of employment around the poles would reduce
commuting trips. Enough services and facilities together withexpanding mass transit infrastructure ensure the high accessibility
and mobility of residents who live in the poles. Individuals haveeasy access to services and goods related to daily life, such as
supermarkets, hospitals, schools, and gyms.

On the other hand, different population distribution methods inmonocentric urban structures show different influence on energy
consumption. Less energy consumption is found in the Central core
city scenario (1), in which the population is distributed in theurbanized area of Kumamoto. The development of the city centerprobably stimulates the use of public transport. The increase ofmass transit usage could reduce the expenditure on mobility
goods, and in return promote the consumption of non-mobility
goods. As a result, small number of trips is found in the Central
core city scenario (1). Even though the strict compact urban structure of the Central core city scenario (2) shows some positive outcomes in terms of travel patterns as less car trips as well as ahigher number of mass transit trips, more energy use is identified
in the results of Central core city scenario (2). It could be explain by
the less beneficial effects on energy saving in the long term. Concentration of residents in the city center would result in longer travel distance and traffic jams in the city center for all residents.
Although most of the living space in the Central core city scenario(2) is urbanized area, there are still residents in the suburban area.
People who live in the suburbs and outside of the city center needto travel far and frequently to obtain services in the city center.
This of course would increase the trips and lead to a more complex
traffic condition in the city center. Consequently, more traffic jams
and more energy consumption are expected.

5. Conclusion
This study showed a quantitative approach to simulate consumption behaviors of residents and estimate energy consumption
under three urban structure scenarios for the Kumamoto
metropolitan region in 2030. The simulation results of the BAU scenario, the Central core city scenario, and the Multi-pole structure
scenario indicate two major findings. Firstly, urban structure is
influential on energy consumption behaviors thus energy use.
More energy use for non-mobility goods is found in Central core
city scenario and the Multi-pole structure scenario. Secondly, moreenergy consumption is found in the monocentric urban structure.
Multi-pole structure is recommended as a better choice of urban
structure for compact development in Kumamoto due to energy
saving.
The study provides deep insights into individual energy use at azone scale, and included a holistic perspective on energy use bygoods. It extends the concept of compact development analysis
to a very small aggregated level and then compares rather extreme

Y. Yin et al. / Applied Energy 159 (2015) 449–457

urban structures in Kumamoto. To the authors’ knowledge, there
are no other models that attempted to quantify total energy consumption for an individual at the zone scale as this work. The
new approach for energy estimation by an innovative idea of utility
enriches the knowledge of energy estimation models at microlevel. Unlike previous studies which took in a limited view ofenergy in transportation or residential sector, this approachenables us to estimate not only the energy consumption for transportation, but also energy for other goods and services. Moreover,the methodology of this paper evaluates the effect of urban structures on energy consumption through personal consumption
behaviors from a new viewpoint. It takes the advantage of consideration of social economic factors. Such results provide a context
for evaluating the impacts of urban structures on energy saving
and allow a more quantitative comparison of energy use across different urban form environments. The study could not only givesuggestions for urban planners in Kumamoto, but also expand
the field of analysis tool of policy making for governments aiming
for compact development.
Our paper provided deeper understanding of consumption
behaviors in the context of compact cities at a micro level, andgive suggestions for the successful implementation of compact
policy in Kumamoto. However, following points should be taken
into consideration for the future work. Firstly, more urban
structure scenarios should be set based on not only the population
distribution but also physical elements of urban structures.
Secondly, the modeling assumptions are still in need of further
refinement as more and better information of consumption
behaviors becomes available. For instance, the paper is not specificin transport goods. The analysis of trips of particular purposes byother transport modes, such as walking, gives more explanationof traffic behaviors of residents. Last but not least, land could beanalyzed as special goods into the model because compact
development is closely related to the use of land. All three points
would benefit future academic study by deeply investigatingon the relationship between consumption behaviors, energy use,and compact development. Useful findings could be drive form togive suggestions for urban planners, transport planner, and energy
saving policy making.
Acknowledgements
The work is funded by the Scientific Research Foundation for
the Returned Overseas Chinese Scholars, State Education Ministry.
The paper was also supported by the National Science Foundationof China (NSFC) (41301171). It is also supported by K.C. Wong
Magna Fund in Ningbo University and the project of ‘‘Subsidy
stimulation mechanism and the influence on the operation ofbus companies” sponsored by the key discipline of ‘‘Transportation
planning and management” in Ningbo city.
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