Estimating the In ‐ use Steel Stock of Ci il Civil Engineering and Building in China i i d ildi i Chi 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 p g g, g g, y y 2 National Geophysical Data Center, National Oceanic and Atmospheric Administration APAN31, Hong Kong, 2011.2.21
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 ( Construction sector ： 1456Mt ( 65% ) 65% ) • Steel used in construction sector in China needs more d t il d t d detailed study – Material Flow Analysis (MFA) 2
M t Material Flow Analysis (MFA) i l Fl A l i (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 3
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 model to account for the material and energy flows in MFA d l f h i l d fl i 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. 4
I Issues and Challenges d Ch ll • Mostly country level studies. l l l d • No comprehensive study on sub ‐ national level p y (prefectures). • Lack of sub ‐ national level statistical data. L k f b ti l l l t ti ti l d t • 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. 6 NGDC, NOAA
P Previous Study ‐ Outline i St d O tli • Civil engineering/building steel stock Civil engineering/building steel stock Nighttime light Nighttime light • Prefectures in Japan • Land cover considered → Correlation refined L d id d → C l i fi d Hsu et al. (2010) Land Cover • Indicate coverage properties on Indicate coverage properties on earth surface • ISCGM ISCGM • 1km grid • 20 categories 20 categories ISCGM 7
P Previous Study ‐ Result i St d R lt • 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 � g g Total Nighttime Light Urban Nighttime Light 60 60 0 6 t) 6 t) 50 50 50 50 Steel Stock (10 Steel Stock (10 40 40 30 30 Building S 20 Building S 20 y = 91.342x 10 y = 36.786x 10 R² = 0.8227 R² = 0.5458 0 0 0 0 50 50 100 100 150 150 Total Nighttime Light (10 4 w/cm 2 /sr) 0 10 20 30 40 50 60 Urban Nighttime Light (10 4 w/cm 2 /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 – Civil engineering/building steel stock Nighttime Nighttime light – Asia Pacific countries A i P ifi t i – Estimate civil engineering/building steel stock in each Chinese prefecture. 9
Flow Chart of Methodology Flow Chart of Methodology [Global] [Global] Nighttime light Nighttime light In-use steel stock [Chinese Prefectures] [Asian Pac. Countries] Boundaries Boundaries [Asian Pac. Countries] Building steel stock [Chinese Prefectures] [Asian Pac. Countries] [Asian Pac. Countries] Total of nighttime light Total of nighttime light Civil engineering steel stock Land cover Land cover Gas flare mask [Chinese Prefectures] [Asian Pac. Countries] Linear Urban nighttime light Urban nighttime light regression analysis g y [Chi [Chinese Prefectures] P f t ] [A i [Asian Pac. Countries] P C t i ] Gas flare excluded Gas flare excluded nighttime light nighttime light Model of steel stock f and nighttime light Global datasets Asian Pac. datasets [Chinese Prefectures] Building steel stock China datasets [Chinese Prefectures] Civil engineering steel stock Analysis & Model 10
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 Steel Stock Hatayama et al. F162006 F162006 • • In ‐ use steel stock for multiple • Radiance Calibrated • end uses – Steel stocks are city concentrated Data of year 2006 is used. Data of year 2006 is used. • – Better NTL data for city center – Avoid saturation 11
Ancillary Data Ancillary Data New Land Cover (ISCGM) Gas Flare Mask (Elvidge et al.) • Proved valid to refine • Extraordinary bright correlation between in ‐ use • Does not necessary steel stock and nighttime correlate to steel stock li ht light • Exclude? ISCGM Wikipedia 12
Li Linear Regression analysis R i l i y = 15..57 x y = 82.5 x y = 19.93x R 2 = 0.91 R 2 = 0.85 R 2 = 0.92 y = 15.71 x y = 21.92 x y = 99.31 x R 2 = 0.73 R 2 = 0.87 R 2 = 0.96 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 Ningxia 4 4 F ji Fujian 15400 15400 19300 19300 20 20 4600 4600 3800 3800 Hui 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 Xinjiang 12 12 Henan Henan 35900 35900 36900 36900 28 28 15000 15000 17600 17600 Uygur 13 Hubei 14000 14400 29 Xizang 800 1100 14 Hunan 10600 10100 30 Yunnan 15800 16400 15 Jiangsu 53500 67500 31 Zhejiang 33000 38400 16 Jiangxi 8700 9100 Unit: kt 14
Results Results Density Graph of Steel Stock in China Beijing Ti Tianjin ji Shanghai Guangzhou 15
Discussion Discussion • Estimated civil engineering/building steel stock i d i il i i /b ildi l k – Beijing (2006) : 84.8 Mt • Reported total steel stock (Rauch et al.) – Beijing (2000) : 29.4 Mt Reason? • Fast urbanization F t b i ti • Transition in building styles – Steel intensity for residential buildings – +13% (2004 → 2010) (Hu et al.) 16
Conclusion Conclusion • Confirmed linear correlation between civil C fi d li l i b i il 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 17
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