Possible Approaches for Urban Carbon Mapping: Yoshiki Yamagata

Transcription

1 Possible Approaches for Urban Carbon Mapping: From national (municipality inventory based) to city (Remote sensing based) level case studies in Japan Yoshiki Yamagata Head of GCP Tsukuba International Office Center for Global Environmental Research, National Institute of Environmental Studies, Japan

2 Outline of new Urban Carbon Mapping Project Tokyo Japan Global A bottom-up CO2 estimation CO2 emissions (Tokyo) Building emission Transportation emission CO2 emissions (major cities in Japan) Absorption by green Networking major cities through the GCP global research networks CO2 emission estimation model + Dynamic CO2 mapping + Urban climate models CO2 monitoring (e.g., Sky Tree) Tokyo Assessment of the model accuracy + Data assimilation with GOSAT data Sky Tree Yoyogi Contributions to IPCC, GEOSS,...etc. A top-down CO2 monitoring Suburbs CO2 monitoring was started at Sky Tree from Apr CO2 monitoring (GOSAT)

3 Municipality inventory based national level Urban Carbon Mapping

4 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 Sector Calculate the CO 2 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 Sector Calculate the CO 2 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 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) 4

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7 Methodology (Direct Emissions) Transportation Sector Calculate the CO 2 emissions for each municipality and for each vehicle type, mileage travelled (km/yr) * CO 2 emission factor (g-co 2 /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 Sector Sector :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. CO 2 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 CO 2 emission 7

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10 Methodology (Indirect Emissions) Data Source In order to estimate CO 2 emissions from household consumptions within a zone, we correspond the items of HES to 3EID data Household 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 CO 2 emissions of each household is based on the number of 2-type households (single, plural) in each micro zone (National Census in 2005) 10

11 Methodology (Indirect Emissions) Indirect CO 2 Emission Estimation Model CE i = H ij [ E ijk ic ik + dc ik j k ] CE i : annual CO 2 emission in each zone i (kg-co 2 /year) H ij : the number of type j households in zone i (household) [National Census] E ijk : annual expenditure to the item k by type j household type in zone i (yen/household/year) [HES] ic ik : emission intensity of indirect CO 2 for the item k in zone i (kg-co 2 /yen) (domestic technology assumption or global extention) [3EID] dc ik : emission intensity of direct combustion CO 2 for the item k in zone i (Gas, kerosene and gasoline) (kg-co 2 /yen) 11

12 Results 12

13 Results (Area Cartogram) Direct Emissions Minato-Ward and Shinagawa-Ward, Tokyo Legend Prefectures Municipalities CO 2 emissions per capita (t-co 2 /person) < 3.0 * Area: Sum of sectors Thermal power plants 13

14 Results (Area Cartogram) Indirect Emissions (Domestic technology assumption) Legend Prefectures Municipalities CO 2 emissions per capita (t-co 2 /person) < 2.5 * Area: Sum of sectors Western part Nishi-Ward, Minato-Ward (bedroom town) o 14 and Chuo-Ward, Osaka Tokyo

15 Household sector Direct emission CO2 emission [kg-co2/yr/m 2 ]

16 Business sector Direct emission CO2 emission [kg-co2/yr/m 2 ]

17 Transportation sector Direct emission CO2 emission [kg-co2/yr/m 2 ]

18 Remote sensing based city level Urban Carbon Mapping

19 Urbanization in the Tokyo metropolitan area (40 years) 1972 Landsat MSS 1987 Landsat TM 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, Landsat ETM+ 19

20 Urban Sprawl was the major trend (40 years) Change of urban/built-up in 1-km 2 grid cells from 1972 to 2011

21 Classification maps: Landsat 8 only, Landsat 8 plus PALSAR-2 Landsat 8 classification Landsat 8 + ALOS-2 at 30 m Landsat 8 + ALOS-2 at 3 m

22 Visualize the differences in the classified maps Original images Land cover maps Landsat 8 (RGB=6,5,4 ) Landsat 8 MLC (30 m resolution) Landsat 8 + PLASAR 2 (30 m resolution) PALSAR 2 (RGB=HV, HH, VV) Landsat 8 + PLASAR 2 (3 m resolution) Conclusions: Combining PALSAR-2 and Landsat 8 leads to increased urban/built-up classification accuracy. Fusion at 3 m can extract detailed urban structure.

23 Study area: Center of Tokyo PALSAR (HH) Tokyo station Ryogoku Value Toyosu

24 Around Tokyo station PALSAR Google Map Imperial palace Tokyo station

25 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. Value Height (m) x 17 grids PALSAR Building heights 0 0

26 Result Data in 143 grids with more than 10 buildings are used in this calculation Correlation coefficient: Value Building height (m) m_height Value PALSAR 8000 Building heights PALSAR

27 Correlation coefficients of PALSAR with Building density Building volume (Density Height) Building density (m 2 ) dd3[dd3[, "FID"] < 176, 6] Building Volume (km 3 ) dd3[dd3[, "FID"] < 176, 7] PALSAR Density PALSAR Volume PALSAR PALSAR

28 Land use-transport-energy model We have developed a Urban Economics Model to simulate Urban Forms Macro economic Model / Cohort model Total # of population (household) House hold Indirect utility (Zonal attractiveness) OD trip distribution Location choice Traffic simulator Utility maximization Building demand Commuting cost Income Rent Building market Energy model PV supply-/energy demand Profit maximization Landlord Land supply Land market Land demand Building supply Developer Profit maximization Simulates behaviors of households, landlords and housing developers Yamagata, Y., Seya, H., Simulating a future smart city: An integrated land use-energy model. Applied Energy 112,

29 Urban compaction Urban centers Hot spots of employees numbers detected by a spatial clustering method. Estimated on population change Rates of population density (Compact/BAU) Simulation Subsidized by 1200$/y for people moving within 500m of these districts. Population increase around business districts, especially along railways. 29

30 Influence on land cover A simulation was conducted using a spatial compositional data model for BAU and Compact scenario. - Impute: simulated building land amounts in each district. Building land Forest BAU Compact 30

31 Land Use Scenarios for 2050 Current urban form Compact (mitigation) scenario - Subsidized by 1200$ /y if moving to near urban centers (Zones less than 500 m) Compact + Adaptation scenario Business as usual scenario (BAU) - Subsidized by 1200$ /y if moving to near urban centers only when if the flooding risk is not too high (< 5m) MLIT 31

32 Implications of adaptation to the flood risks The adaptation scenario effectively reduced the flood risk. Inundation depth Compact - BAU [Compact + Adaptation] - BAU Risk reduction: 7.2 B$ Risk reduction: 30.4 B$

33 Influence on urban climate Status quo Dispersed city Compact city 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)

34 Climate Resilient and Sustainable Urban Design Urban compaction Climate resiliency - Mitigation, adaptation - Heatstroke risk in Japan Low carbon energy - Renewable energy (EV, PV) - Smart grid - Sustainable urban metabolism A flood in 2015 in Japan Building energy demands in NY (Quan et al., 2015) Trade-off / synergy Environmental sustainability - Green recovery - Eco-urbanizm Local community - Help each other - Sharing (e.g., car) - Well-being Wise-shrink Urban compaction that achieve high environmental standards as well as improve human well-beings.