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Estimation of in-use steel stock for civil engineering and building using nighttime light images

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  • Hattori, Ryoma
  • Horie, Sadataka
  • Hsu, Feng-Chi
  • Elvidge, Chirstopher D.
  • Matsuno, Yasunari

Abstract

To establish a sustainable society, it is necessary to understand the flows and stocks of materials. However, traditional material flow analysis requires large quantities of data relating to the consumption and trade of materials in the past and the lifetime distributions of end use. To overcome the problem that such data may not be available, we have proposed using nighttime light images to estimate the in-use stocks of materials in countries for which the data are unavailable. In the previous study, in-use steel stock for civil engineering and building in 2006 was estimated using nighttime light images. However, several aspects of the methodology must be improved, and time-series analyses using nighttime light images have not yet been performed. Therefore, in this study, we improved the estimation of in-use steel stock by using new geological information (i.e., LandScan population distribution data) and correcting the pixel area. We prepared radiance-calibrated nighttime light data for 2006 and 2010 and conducted time-series analyses of the in-use steel stock using the nighttime light data. The in-use steel stock for civil engineering and building in 2010 was found to amount to approximately 11.3Gt, which was approximately 21% higher than that in 2006.

Suggested Citation

  • Hattori, Ryoma & Horie, Sadataka & Hsu, Feng-Chi & Elvidge, Chirstopher D. & Matsuno, Yasunari, 2014. "Estimation of in-use steel stock for civil engineering and building using nighttime light images," Resources, Conservation & Recycling, Elsevier, vol. 83(C), pages 1-5.
    • Handle: RePEc:eee:recore:v:83:y:2014:i:c:p:1-5
    DOI: 10.1016/j.resconrec.2013.11.007
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    References listed on IDEAS

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    1. Gang Liu & Colton E. Bangs & Daniel B. Müller, 2013. "Stock dynamics and emission pathways of the global aluminium cycle," Nature Climate Change, Nature, vol. 3(4), pages 338-342, April.
    2. Christopher D. Elvidge & Daniel Ziskin & Kimberly E. Baugh & Benjamin T. Tuttle & Tilottama Ghosh & Dee W. Pack & Edward H. Erwin & Mikhail Zhizhin, 2009. "A Fifteen Year Record of Global Natural Gas Flaring Derived from Satellite Data," Energies, MDPI, vol. 2(3), pages 1-28, August.
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    1. Yi Bao & Zhou Huang & Han Wang & Ganmin Yin & Xiao Zhou & Yong Gao, 2023. "High‐resolution quantification of building stock using multi‐source remote sensing imagery and deep learning," Journal of Industrial Ecology, Yale University, vol. 27(1), pages 350-361, February.
    2. Natthanij Soonsawad & Raymundo Marcos‐Martinez & Heinz Schandl, 2024. "City‐scale assessment of the material and environmental footprint of buildings using an advanced building information model: A case study from Canberra, Australia," Journal of Industrial Ecology, Yale University, vol. 28(2), pages 247-261, April.
    3. Mathieu, Valentin & Roda, Jean-Marc, 2023. "A meta-analysis on wood trade flow modeling concepts," Forest Policy and Economics, Elsevier, vol. 149(C).

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