Estimating the In use Steel Stock of Ci il Civil Engineering and - - PowerPoint PPT Presentation

Estimating the In‐use Steel Stock of Ci il i i d ildi i Chi Civil Engineering and Building in China via Nighttime Light via Nighttime Light

Feng‐Chi, Hsu (David)1; Feng Chi, Hsu (David) ; Christopher D. Elvidge 2; Yasunari Matsuno 1

1 Department of Materials Engineering, Graduate School of Engineering, The University of Tokyo

APAN31, Hong Kong, 2011.2.21

p g g, g g, y y

2 National Geophysical Data Center, National Oceanic and Atmospheric Administration

Background Background

• Rapid growth in construction sector in China

– Fast urbanization, Population growth

• Steel consumption in China (2004) Hu et al.

Total: 286 Mt Construction sector : 145 Mt ( 50%)

• In‐use steel stock in China (2005) Hatayama et al.

In use steel stock in China (2005) Hatayama et al. Total: 2334Mt Construction sector： 1456Mt ( 65%) Construction sector： 1456Mt ( 65%)

• Steel used in construction sector in China needs more

d t il d t d detailed study

– Material Flow Analysis (MFA)

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M t i l Fl A l i (MFA) Material Flow Analysis (MFA)

• Account for flows of materials in defined system
• Account for flows of materials in defined system.
• System can be geophysical boundaries and/or

product life cycle.

• This study focus on the in‐use stock in China

This study focus on the in use stock in China

Import / Export Import / Export Import / Export Import / Export Produce Produce Transport Transport Use Use Dispose Dispose

Stock Stock

Environment Environment

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Issues in MFA Issues in MFA

• Building stocks and their material contents in China

are estimated by MFA approaches.

– Estimate housing stock using dynamic MFA modeling for 1900‐2100. Hu et al. – Iron and steel stock of the buildings based on estimated housing stock. Hu et al. MFA d l f h i l d fl i – MFA model to account for the material and energy flows in construction sector. Yang et al. Estimation of steel stock regarding multiple end uses in – Estimation of steel stock regarding multiple end uses in different countries. Hatayama et al.; Kakiuchi et al. – Etc – Etc.

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I d Ch ll Issues and Challenges

l l l d

• Mostly country level studies.
• No comprehensive study on sub‐national level

p y (prefectures). L k f b ti l l l t ti ti l d t

• Lack of sub‐national level statistical data.
• Methodology capable without detailed

gy p statistical data?

• Satellite images → Proxy to unknown quantities
• Satellite images → Proxy to unknown quantities

Nighttime Light Nighttime Light

• Anthropological lighting that was observed by

satellite.

• Radiance positively correlated to human

activities activities.

– GDP, Population, Energy consumption, copper and steel stock.

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NGDC, NOAA

P i St d O tli Previous Study ‐ Outline

• Civil engineering/building steel stock

Nighttime light Civil engineering/building steel stock Nighttime light

• Prefectures in Japan

L d id d → C l i fi d

• Land cover considered → Correlation refined

Hsu et al. (2010)

Land Cover

• Indicate coverage properties on

Indicate coverage properties on earth surface

• ISCGM

ISCGM

• 1km grid
• 20 categories

ISCGM

20 categories

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P i St d R lt Previous Study ‐ Result

• Civil engineering steel stock → Total nighttime light
• Civil engineering steel stock → Total nighttime light.
• Building steel stock → Urban nighttime light.
• Land cover is effective in refining the correlation

Building steel stock Building steel stock

50 60 06 t) 50 60

6 t)

g Total Nighttime Light g Urban Nighttime Light

30 40 50 Steel Stock (10 30 40 50 Steel Stock (10 y = 36.786x R² = 0.5458 10 20 50 100 150 Building S

y = 91.342x R² = 0.8227

10 20 Building S 50 100 150 Total Nighttime Light (104 w/cm2/sr) 10 20 30 40 50 60 Urban Nighttime Light (104 w/cm2/sr) 8

Objective Objective

• Use nighttime light image to estimate the civil

engineering/building steel stock for each g g g prefecture in China.

– Civil engineering/building steel stock Nighttime – Civil engineering/building steel stock Nighttime light A i P ifi t i – Asia Pacific countries – Estimate civil engineering/building steel stock in each Chinese prefecture.

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Flow Chart of Methodology Flow Chart of Methodology

[Global] Nighttime light [Global] [Chinese Prefectures] Boundaries Nighttime light [Asian Pac. Countries] [Asian Pac. Countries] Boundaries In-use steel stock [Chinese Prefectures] Total of nighttime light [Asian Pac. Countries] Total of nighttime light Land cover Building steel stock [Asian Pac. Countries] Civil engineering steel stock Land cover [Asian Pac. Countries] Urban nighttime light [A i P C t i ] Linear regression analysis [Chinese Prefectures] Urban nighttime light [Chi P f t ] Gas flare mask [Asian Pac. Countries] Gas flare excluded nighttime light g y [Chinese Prefectures] Gas flare excluded nighttime light f Model of steel stock and nighttime light [Chinese Prefectures] Global datasets Asian Pac. datasets Building steel stock [Chinese Prefectures] Civil engineering steel stock 10 China datasets Analysis & Model

Data Preparation Data Preparation

Sample countries Sample countries

• 16 Asia Pacific Countries

— Restriction of data accessibility

• Russia, China, India, Japan, Korea,

Pakistan, Turkey, Indonesia, Australia, Thailand, Malaysia, Taiwan, Philippine, Thailand, Malaysia, Taiwan, Philippine, New Zealand, Bangladesh, Singapore

Nighttime Light

• F162006

Steel Stock

• Hatayama et al.

F162006

• Radiance Calibrated

– Steel stocks are city concentrated

• In‐use steel stock for multiple

end uses

• Data of year 2006 is used.

– Better NTL data for city center – Avoid saturation

Data of year 2006 is used.

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Ancillary Data Ancillary Data

New

Land Cover (ISCGM)

• Proved valid to refine

Gas Flare Mask (Elvidge et al.)

• Extraordinary bright

correlation between in‐use steel stock and nighttime li ht

• Does not necessary

correlate to steel stock light

• Exclude?

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ISCGM Wikipedia

Li R i l i Linear Regression analysis

y = 19.93x R2 = 0.92

y = 15..57 x R2 = 0.91 y = 82.5 x R2 = 0.85

y = 99.31 x R2 = 0.96

y = 15.71 x R2 = 0.73 y = 21.92 x R2 = 0.87 13

Results

Estimated Civil Engineering/Building Steel Stock in China Estimated Civil Engineering/Building Steel Stock in China

Prefecture

• Civ. Eng.

Bld. Prefecture

• Civ. Eng.

Bld. 1 Anhui 15700 15500 17 Jilin 16400 21600 2 Beijing 21400 63400 18 Lianoning 28800 47100 3 Chongqing 5600 7000 19 Nei Mongol 19700 20000 4 F ji 15400 19300 20 Ningxia 4600 3800 4 Fujian 15400 19300 20 Hui 4600 3800 5 Gansu 8700 9800 21 Qinghai 2400 2700 6 Guangdong 65700 125800 22 Shaanxi 18400 17700 7 Guangxi 11200 10000 23 Shandong 57900 61800 8 Guizhou 5200 2200 24 Shanghai 21400 61600 9 Hainan 3800 3200 25 Shanxi 25000 19300 10 Hebei 35800 30000 26 Sichuan 15700 15200 11 Heilongjiang 32600 41000 27 Tianjin 13500 26700 12 Henan 35900 36900 28 Xinjiang 15000 17600 12 Henan 35900 36900 28 Uygur 15000 17600 13 Hubei 14000 14400 29 Xizang 800 1100 14 Hunan 10600 10100 30 Yunnan 15800 16400

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15 Jiangsu 53500 67500 31 Zhejiang 33000 38400 16 Jiangxi 8700 9100 Unit: kt

Results Results

Density Graph of Steel Stock in China

Beijing Ti ji Tianjin Shanghai Guangzhou

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Discussion Discussion

i d i il i i /b ildi l k

• Estimated civil engineering/building steel stock

– Beijing (2006) : 84.8 Mt

• Reported total steel stock (Rauch et al.)

– Beijing (2000) : 29.4 Mt

Reason?

F t b i ti

• Fast urbanization
• Transition in building styles

– Steel intensity for residential buildings – +13% (2004→2010) (Hu et al.)

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Conclusion Conclusion

C fi d li l i b i il

• Confirmed linear correlation between civil

engineering/building steel stock and nighttime li ht light.

– Civil engineering → Gas flare excluded nighttime light – Building → Urban nighttime light

• Civil engineering/building steel stock estimated

– Highest density, largest quantity in few highly developed prefectures – Eastern China, along coast line

• Seek for ground‐truth in China

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