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

4

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)

5

6

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

9

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

11

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

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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