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Simulating future sustainable city: Case study with Tokyo, Japan
Hajime SEYA
Assistant Professor,
Graduate School for International Development and Cooperation,
Hiroshima University
National Workshop on
Sustainable Urban and industry Development in Mongolia
Ulaanbaatar, 20-21 August 2014 Ulaanbaatar Hotel, Ulaanbaatar
Today, I would like to show you
some future urban-land use scenarios of Tokyo
with considering tradeoffs & co-benefits of climate change mitigation and adaptation policies,
And discuss the possible implications for the urban planning in the UB city.
Before showing future scenarios of the Tokyo,
Let’s take a look at past urban growth of the Tokyo metropolitan area.
2
1914
1975
Urban expansion and agricultural contraction of paddy & crop lands.
1888
1946
Urbanization in the Tokyo metropolitan area
Built-up area Industry area settlement Airport paddy crop land forest other water
1888 to 1975 (Estimated from the GSI Regional Planning Atlas)
Tokyo Yokohama
Urbanization in the Tokyo metropolitan area
4
Around 1890, Tokyo (Just after it had renamed from Edo)
1889, Yokohama (Japan was opened from Yokohama)
1917, Nihonbashi, Tokyo (After westernization)
Tokyo station was completed in 1914
Urbanization in the Tokyo metropolitan area
5
The traffic jam in 1960s (MLIT) http://www.mlit.go.jp/crd/tosiko/zpt/pdf/zenkokupt_gaiyouban_english.pdf
1981, Tama New Town
Dwellings were sold at the same time to
people for similar age groups. Biased age composition: “Old new town”
UN city planned satellite cities
Buyandalai et al.(2013) urban
development issues and challenges
Period of high economic growth
(1960s~1970s)
MLIT “Japan’s sustainable transportation strategy”
Careful design is needed
Depopulation and aging in Japan (Ministry of Land, Infrastructure, Transport and Tourism, MLIT)
6
USA
UK
UK
USA
JAPAN
JAPAN
MLIT “Japan’s sustainable transportation strategy”
Trend of population (1950=1) Trend of Ratio of elderly people
(65>=)
Land use change in the Tokyo metropolitan area in the past 40 years
Correlation between urban land cover change and cropland land cover change (1972~2011)
Urban expansion in the Tokyo metropolitan area 1972 to 2011
(Classification of Landsat imagery
using the subspace method)
1997 1972
2001 2011
9
Bagan and Yamagata, 2014
Development of train networks in Tokyo
11
(Ministry of Land, Infrastructure, Transport and Tourism, MLIT)
MLIT “Japan’s sustainable transportation strategy”
2000s 1950s
Train stations & land prices
12 Changes in de-trended land prices Tsutsumi and Seya (2008) PIRS.
Tsukuba express line Started operation in 2005.
Officially assessed land prices
Are published once in a year.
Spatial interpolation
spatially explicit Land-use model
Indirect utility
(Zonal attractiveness)
Location choice
Floor space
demand Floor space supply
Land market
Income
Rent
House hold
Developer
Land supply
Landlord
Land demand
Floor market
We have modeled economic behaviors of
household, landlord and housing developer.
PV supply-/energy demand
Energy model
Profit maximization
Profit maximization
Utility maximization
Commuting
cost
15
Sustainable urban form in the future
Past
Current
MLIT “Japan’s sustainable transportation strategy”
(Ministry of Land, Infrastructure, Transport and Tourism, MLIT)
Future
Compact
Dispersion
Business as usual: BAU
?
About half of Japanese
City master plan mentions
Compact city ideas in UB city
16
New city center/sub center to decentralize urban
centralization, Ulaanbaatar city
Sources: Ulaanbaatar city Master Plan 2030, 2012
JICA
One of the merits of compact urban form
0
10000
20000
30000
40000
50000
60000
70000
0 50 100 150 200 250 300
Priva
te T
rans
port
Ene
rgy
Use
per
Cap
ita(
MJ)
Urban Density(person/ha)
Sacramento
Los Angeles
Chicago
Toronto
Frankfurt
Brussels
Singapore
Tokyo
Bangkok Seoul Hong
KongLondon
Newman & Kenworthy
Sustainability and cities:
overcoming automobile
dependence, 1999.
Cities in USA
Cities in Asia
Cities in Europe
and Japan
18
Land-use scenario in Tokyo for 2050
Dispersion scenario (BAU)
Compact scenario
- Subsidized by 1200$ /y if moving to near urban centers (Toyama city example) (Zones within 500 meter from city centers)
Black: city centers
Sky blue: subsidized zones
Current urban area
Total number of each
7 types of households
are statistically projected
Consider the
de-population & aging
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Floor / Land (dispersion) Floor / Land (compact)
Fraction of land for forest (dispersion) Fraction of land for forest (compact)
Vacant-lands are re-vegetated Vacant-lands are re-vegetated
Compact city - BAU
Results: Difference in nighttime surface temperature
Surface temp increases/decreases associated with the changes in urban area and AH.
Compact city → - 0.1˚C throughout Tokyo metropolitan area
Vacant areas are re-vegetated
Green area expansion may be
useful policy to reduce cooling demand
In summer season (in case of Tokyo).
Various assessment criteria is needed.
(e.g., ecosystem service)
21
• Demand exceeds PV-supply around the train stations and the center of Tokyo, while PV-supply may exceed demand for all other zones in total (can be calculated hour to hour)
• For the introduction of large PV plants, low-rent land in suburban regions, with high demand potential should be detected.
• Implication[1]: Spatially detailed evaluation of demand and PV-supply potential and data accumulation for that is needed.
Spatial distribution of electricity supply from PV as the ratio to demand in August
(left: BAU, right: compact city)
・ PV panels are installed to the rooftops of all detached houses. (unrealistic under the current technology, but estimation itself may be useful)
22
The Munich Re 2002 (Reinsurance company)
1. Tokyo/Yokohama 710 2. San Francisco 167 3. Los Angeles 100 4. Osaka/Kobe/Kyoto 92 5. ……
Natural hazard risk index
24
Future urban scenario in Tokyo for 2050
Dispersion scenario (BAU)
Compact scenario
Cobenefit scenario + adaptation perspective
- Subsidized by 1200$ /y if moving to near urban centers (Zones within 500 m from city centers)
Black: city centers
Sky blue: subsidized zones
Current urban area
Total number of each
7 types of households
are statistically projected
© MLIT
- Subsidized if moving to near city centers, if whose flood risk is not too high
(< 5m)
25
Compact - Dispersion Combined - Dispersion
Differences in
population
projection
Expected economic risk reduction: –23.2B$
Number
Implications [2]
• The projected mitigation of expected flood loss due to the combined scenarios is –23.2B$. This result suggests that just a careful selection of subsidized area may lead to fairly big differences in expected loss.
• Currently, many Japanese urban master plan mentions the importance of compact city as a future vision of the cities.
• Lacks of the co-benefit view points (especially with natural disaster risk).
26
Considering technological change
• Land-use scenarios: – BAU: dispersed city – Mitigation: Compact city – Adaptation: Flood disaster prevention – Mitigation + Adaptation
• Technological innovation scenarios: Introduction of photovoltaic (PV) panels and Electric vehicles (EVs)
27
© Nissan Motor Co., Ltd.
Do nothing Mitigation (compact)
+ Adaptation (land use regulation by 50% in high flooding risk zones)
Land-use scenarios
BAU :Dispersed city
Mitigation ( Mit. ) : Compact city
+ Climate change adaptation : + Flood risk prevention ( Ad. + )
BAU Mit.
Mit. Ad.
・ Cars are replaced by EVs ・ PV panels are installed to the rooftops of all detached houses
We do not discuss technological aspects in this study.
©MLIT
©MLIT
Photovoltaic (PV) panels
Electric vehicles (EVs) Assumptions
Estimates CO2 emissions.
CO2 emission rate of EVs
Transportation Mode
CO2 Emissions (gCO2/km)
Based on estimated CO2 emission factor for 2050b)
Gasoline car1) 136.1
EV (Lief)2) 74.4
EV (i-MiEV)3) 66.0
1) Fuel consumption: 17.0km/L (MLIT, 2012) 2) AC power consumption rate: 124Wh/km (Nissan, 2012) 3) AC power consumption rate: 110Wh/km (Mitsubishi, 2012) (JC08 mode) a) Source: The Federation of Electric Power Companies of Japan (2012) b) Estimated in this study
Compared with gasoline cars, EVs could make CO2 emissions half. CO2 emissions cannot reduced so much if thermal power will be used in 2050.
28
Depends on the supply mix
Total CO2 emission under different scenarios for the year 2050
68
78
88
98
108
118
128
138
148
Pre
sen
t
BA
U-0
BA
U-1
00
BA
U-5
0
BA
U-3
0
BA
U-2
0
Mit
.-0
Mit
.-1
00
Mit
.-5
0
Mit
.-3
0
Mit
.-2
0
Mit
.+A
d.-
0
Mit
.+A
d.-
10
0
Mit
.+A
d.-
50
Mit
.+A
d.-
30
Mit
.+A
d.-
20C
O2
em
issi
on
(M
tCO
2/y
ear
)
Indirect emission Direct emission
Dispersed city (BAU)
with PVs & EVs
• (1) Without introduction of PVs & EVs
: Mitigation and adaptation could create synergy effect.
• (2) With introduction of PVs & EVs
: Mitigation and adaptation could be trade-off.
(Decrease of Detached house → Decrease of PVs)
Mitigation (compact) Adaptation (land use regulation)
with PVs & EVs
with PVs & EVs
EVs
100% 50
100%
50% PVs
30 20
Direct emission
Indirect emission
29
• When we discuss the urban planning, thinking about possible co-benefits and trade offs, which is sometimes ignored, is important.
• Examples that we showed are:
– [1] Greening & nighttime temperature decrease;
– [2] Compact city & Flood risk reduction;
– [3] Compact city & Land use regulation & PV/EV.
• It can be quantified by using urban models.
• Effective data accumulation strategies for useful quantity urban analyses is fairly important.
30
Recommendation
References • Hasi Bagan, Yoshiki Yamagata (2014) Land-cover change analysis in 50 global cities by using a
combination of Landsat data and analysis of grid cells. Environmental Research Letters, in print.
• Morito Tsutsumi and Hajime Seya (2008) Measuring the impact of large-scale transportation project on land price using spatial statistical models, Papers in Regional Science, 87 (3), 385–401.
• Yoshiki Yamagata, Hajime Seya and Kumiko Nakamichi (2013) Creation of future urban environmental scenarios using a geographically explicit land-use model: a case study of Tokyo, Annals of GIS, 19 (3), 153–168.
• Yoshiki Yamagata and Hajime Seya (2013) Simulating a future smart city: An integrated land use-energy model, Applied Energy, 112, 1466–1474.
• Kumiko Nakamichi, Yoshiki Yamagata and Hajime Seya (2013) CO2 emissions evaluation considering introduction of EVs and PVs under Land-use scenarios for climate change mitigation and adaptation–Focusing on the change of emission factor after the Tohoku earthquake–, Journal of the Eastern Asia Society for Transportation Studies, 10, 1025–1044.
• Yoshiki Yamagata and Hajime Seya (2013) Spatial electricity sharing system for making city more resilient against X-Events, Innovation and Supply Chain Management, 7 (3), 75–82.
• Yoshiki Yamagata and Hajime Seya (2014) Proposal for a local electricity-sharing system: A case study of Yokohama city, Japan, IET Intelligent Transport Systems, in print.
• Sachiho A. Adachi, Fujio Kimura, Hiroyuki Kusaka, Michael G. Duda, Yoshiki Yamagata, Hajime Seya, Kumiko Nakamichi, Toshinori Aoyagi (2014) Moderation of summertime heat-island phenomena via modification of the urban form in the Tokyo metropolitan area, Journal of Applied Meteorology and Climatology, in print.
31
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Simulating the effects of land use
regulation
i
iAVii
pLL
1
Available area of land
0,, biaii ppp
• Land supply by landlord
• Land use regulation resulting in:
b: before, a: after
Simulate the effects on location choice
Land rent
Supply
function
Demand
function
Area
AV
biL ,
AV
aiL ,
i
aip ,
bip ,
biL ,aiL ,
Potential electricity generation from PV panels
34
KKptpcShHhkWhE A )(/ )(
)(hH A : denotes total (solar) irradiance (kWh/m2/h)
: array conversion efficiency (=10%)
S : installation area (m2)
(land area × building to land ratio ×
possible area of installation on the roof (=0.3) × 1/cos30o)
pc : running efficiency of power conditioner (=.945)
Kpt : temperature correction coefficient (=.9221~1)
K : Other performance ratio(=.89)
From our land-use model
We calculate the value of the electric supply by PVs (kWh).
Assumed parameters are as follows;
34