possible approaches for urban carbon mapping · 2016-09-05 · possible approaches for urban carbon...
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Possible Approaches for
Urban Carbon Mapping:
From national (municipality inventory based)
to city (Remote sensing based) level case
studies in Japan
Yoshiki YamagataHead of GCP Tsukuba International Office
Center for Global Environmental Research,
National Institute of Environmental Studies,
Japan
Outline of new Urban Carbon Mapping Project
TokyoSuburbs
Sky Tree
Yoyogi
CO2 monitoring (e.g., Sky Tree)
CO2 emission estimation model
+Dynamic
CO2 mapping+
Urban climate models
CO2 monitoring was started
at Sky Tree from Apr. 2016
Transportation emission
Buildingemission
Absorptionby green
CO2 emissions (Tokyo)
A bottom-up CO2 estimation
Tokyo
Japan
Glo
bal
CO2 monitoring (GOSAT)CO2 emissions(major citiesin Japan) Assessment of
the model accuracy+
Data assimilation with GOSAT data
A top-down CO2 monitoring
Networking major cities through the GCP global research networks Contributions to
IPCC, GEOSS, ...etc.
Municipality inventory based national level Urban Carbon Mapping
Methodology (Direct Emissions)Categorize Energy consumption into energy source and energy use of
each building type Electric power City gas ■ LPG Kerosene(Energy use: Heating, Air conditioning, Refrigerator, Hot water supply, Kitchen, Power energy,
Lighting)
Residential SectorCalculate the CO2 emissions for each municipality:
Total area of floor space (Detached houses; collective houses) * Energy consumption of each energy source and each energy use * Heat value basis
Allocating the figures calculated for each prefecture to each municipality depending on the rate of household of each housing type (detached houses; collective houses)
Commercial SectorCalculate the CO2 emissions for each municipality :
Total area of floor space of each building use * Energy consumption and the rate of each energy source and energy use of each building type
Allocating the figures calculated for each prefecture to each municipality depending on the rate of persons engaged in each business category
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Nakamichi, K., Yamagata, Y., Hanaoka, S. and Wang, X. (2015) Estimation of indirect
emissions in each municipality and comparison to direct emissions, Journal of Japan
Society of Civil Engineers D3, Vol.71, No.5, pp.I_191-I_200. (in Japanese)
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Methodology (Direct Emissions)
Transportation SectorCalculate the CO2 emissions for each municipality and for each
vehicle type, mileage travelled (km/yr) * CO2 emission factor (g-CO2/km) Regions travelled in (direct), or registered in (indirect) Taking into consideration the increase in the amount of the emission
during start and stop of automobiles in addition to the amount of the running emission
Industrial SectorSector :Electricity industry, Heat supply industry, City gas industry
Agriculture and forestry, Marine products industry,
Mining industry, Construction industry, Manufacture,Machinery manufacturing, Waste incineration)
NOx emission data is allocated to each mesh depending on the rate of population,
production value, persons engaged, land use type, etc.
CO2 emission of each sector in Japan is allocated to each mesh depending on the
spatial distribution of NOx emission based on the assumption that NOx emission
correlate roughly with CO2emission7
8
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Methodology (Indirect Emissions)
Data SourceIn order to estimate CO2 emissions from household
consumptions within a zone, we correspond the items of HES to 3EID dataHousehold Expenditure Survey (HES), Japan
performed every month for about 981 consumption items for 8,000 households in 168 villages, towns and cities all over Japan
Embodied Energy and Emission Intensity Data (3EID) Embodied emission intensities on a comsumer's price basis based
on the 2005 Japanese input-output tables (Nansai, Morigushi and Tohno, 2005)
Estimated CO2 emissions of each household is based on the number of 2-type households (single, plural) in each micro zone (National Census in 2005)
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Methodology (Indirect Emissions)
• Indirect CO2 Emission Estimation Model
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𝐶𝐸𝑖 = 𝐻𝑖𝑗 [ 𝐸𝑖𝑗𝑘 𝑖𝑐𝑖𝑘 + 𝑑𝑐𝑖𝑘
𝑘𝑗
]
CEi: annual CO2 emission in each zone i (kg-CO2/year)
Hij: the number of type j households in zone i (household) [National Census]
Eijk: annual expenditure to the item k by type j household type in zone i
(yen/household/year) [HES]
icik: emission intensity of indirect CO2 for the item k in zone i (kg-CO2/yen)
(domestic technology assumption or global extention) [3EID]
dcik: emission intensity of direct combustion CO2 for the item k in zone i
(Gas, kerosene and gasoline) (kg-CO2/yen)
Results
12
13
Minato-Ward and
Shinagawa-Ward, Tokyo
* Area: Sum of sectors
Legend
CO2 emissions per capita(t-CO2/person)
98.0 ≦7.0 ≦6.0 ≦5.0 ≦3.0 ≦< 3.0
PrefecturesMunicipalities
Results (Area Cartogram)
Thermal power plants
■Direct Emissions
Western part (bedroom town) of
TokyoNishi-Ward, Minato-Ward and Chuo-Ward, Osaka 14
* Area: Sum of sectors
Legend
CO2 emissions per capita(t-CO2/person)
5.0 ≦4.0 ≦3.5 ≦3.0 ≦2.5 ≦< 2.5
PrefecturesMunicipalities
Results (Area Cartogram)
■Indirect Emissions (Domestic technology assumption)
Household sector Direct emission
CO2 emission[kg-CO2/yr/m2]
Business sector Direct emission
CO2 emission[kg-CO2/yr/m2]
Transportation sector Direct emission
CO2 emission[kg-CO2/yr/m2]
Remote sensing based city level Urban Carbon Mapping
1972 Landsat MSS
1987 Landsat TM
2002 Landsat ETM+
Urbanization in the Tokyo metropolitan area(40 years)
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Bagan, H., & Yamagata, Y. (2012). Landsat analysis of
urban growth: How Tokyo became the world’s largest
megacity during the last 40 years. Remote Sensing of
Environment, 127, 210-222
Change of urban/built-up in 1-km2 grid cells from 1972 to 2011
Urban Sprawl was the major trend (40 years)
Landsat 8 + ALOS-2 at 30 mLandsat 8 classification
Classification maps: Landsat 8 only, Landsat 8 plus PALSAR-2
Landsat 8 + ALOS-2 at 3 m
Landsat 8 (RGB=6,5,4)
PALSAR 2 (RGB=HV, HH, VV)
Landsat 8 MLC(30 m resolution)
Landsat 8 + PLASAR 2(30 m resolution)
Landsat 8 + PLASAR 2(3 m resolution)
Visualize the differences in the classified maps
Original
images
Land cover
maps
Conclusions:
Combining
PALSAR-2 and
Landsat 8 leads
to increased
urban/built-up
classification
accuracy.
Fusion at 3 m
can extract
detailed urban
structure.
Study area: Center of Tokyo
Tokyo station
PALSAR (HH)Ryogoku
Toyosu
Value65535
0
Around Tokyo station
PALSAR Google Map
Tokyo stationImperial palace
Correlation analysis
• To what extent, does PALSAR explain building heights ?
– Correlation between medians of PALSAR observations in each 500 m grids and medians of building heights, which are estimated from LiDAR, is evaluated.
11 x 17 grids PALSAR Building heights
Value
65535
0
Height(m)
168
60
10
0
Result
– Data in 143 grids with more than 10 buildings are used in this calculation
• Correlation coefficient: 0.480
10000 20000 30000 40000
51
01
52
0
MEDIAN
m_
he
igh
t
Building height (m)
PALSARPALSAR Building heights
Value
40000
8000
Value
22
2
• Building volume (Density × Height)
0.578
Correlation coefficients of PALSAR with
• Building density0.657
PALSAR
Building density(m2)
BuildingVolume(km3)
PALSAR10000 20000 30000 40000
20
00
06
00
00
12
00
00
dd3[dd3[, "FID"] < 176, 5]
dd
3[d
d3
[, "
FID
"] <
17
6, 6
]
10000 20000 30000 40000
01
00
00
00
20
00
00
0
dd3[dd3[, "FID"] < 176, 5]
dd
3[d
d3
[, "
FID
"] <
17
6, 7
]
PALSAR Density PALSAR Volume
0.0
1.0
2
.0
Simulates behaviors of households, landlords and housing developers
Indirect utility
(Zonal attractiveness)
Location choice
Building demand Building supply
Land market
Income
Rent
House hold
Developer
Land supply
Landlord
Land demand
Building market
PV supply-/energy demand
Energy model
Profit maximization
Profit maximization
Utility maximization
Traffic
simulator
Commuting cost
OD trip
distribution
Macro economicModel / Cohort modelTotal # of population(household)
Land use-transport-energy model
Yamagata, Y., Seya, H., 2013. Simulating a future smart city: An integrated land use-energy model. Applied Energy 112, 1466-1474.
• We have developed a Urban Economics Model to simulate Urban Forms
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Urban compaction
Hot spots of employees numbers detected by a spatial clustering method.
→ Subsidized by 1200$/y for people moving within 500m of these districts.
Rates of population density(Compact/BAU)
→ Population increase around business districts, especially along railways.
Urban centers Estimated on population change
Simulation
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Influence on land cover
BAU Compact
Buildingland
Forest
• A simulation was conducted using a spatial compositional data modelfor BAU and Compact scenario.
- Impute: simulated building land amounts in each district.
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Land Use Scenarios for 2050
Business as usual scenario (BAU)
Compact (mitigation) scenario
Compact + Adaptation scenario
- Subsidized by 1200$ /y if moving to near urban centers (Zones less than 500 m)
Current urban form
© MLIT
(< 5m)
- Subsidized by 1200$ /y if moving to near urban centers only when if the flooding risk is not too high
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Compact - BAU [Compact + Adaptation] - BAU
Implications of adaptation to the flood risks
Inundation depth
• The adaptation scenario effectively reduced the flood risk.
Risk reduction: –7.2 B$ Risk reduction: –30.4 B$
Status quo
Compact cityDispersed city
Influence on urban climate
Assessment of RCM and urban scenarios uncertainties in the climate projections for August in the 2050s in Tokyo, H Kusaka, A Suzuki-Parker, T Aoyagi, SA Adachi, Y Yamagata,Climatic Change, 1-12 (2016)
Climate Resilient and Sustainable Urban Design
-
Local community- Help each other- Sharing (e.g., car)- Well-being
Climate resiliency- Mitigation, adaptation
Environmental sustainability- Green recovery- Eco-urbanizm
Urban compaction that achieve high environmental standards as well as improve human well-beings.
A flood in 2015 in Japan
Heatstroke risk in Japan
Low carbon energy- Renewable energy (EV, PV)- Smart grid- Sustainable urbanmetabolism
Building energy demands in NY (Quan et al., 2015)
Urban compaction
Wise-shrink
Trade-off / synergy
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