Modeling In-Use Steel Stock in China’s Buildings and Civil Engineering Infrastructure Using Time-Series of DMSP/OLS Nighttime Lights

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Author(s)

  • Hanwei Liang
  • Hiroki Tanikawa
  • Yasunari Matsuno
  • Liang Dong

Detail(s)

Original languageEnglish
Pages (from-to)4780-4800
Journal / PublicationRemote Sensing
Volume6
Issue number6
Online published27 May 2014
Publication statusPublished - Jun 2014
Externally publishedYes

Abstract

China's rapid urbanization has led to increasing steel consumption for buildings and civil engineering infrastructure. The in-use steel stock in the same is considered to be closely related to social welfare and urban metabolism. Traditional approaches for determining the in-use steel stock are labor-intensive and time-consuming processes and always hindered by the availability of statistical data. To address this issue, this study proposed the use of long-term nighttime lights as a proxy to effectively estimate in-use steel stock for buildings (IUSSB) and civil engineering infrastructure (IUSSCE) at the provincial level in China. Significant relationships between nighttime lights versus IUSSB and IUSSCE were observed for provincial variables in a single year, as well as for time series variables of a single province. However, these relationships were found to differ among provinces (referred to as "inter-individual differences") and with time (referred to as"temporal differences"). Panel regression models were therefore proposed to estimate IUSSB and IUSSCE in consideration of the temporal and inter-individual differences based on a dataset covering 1992-2007. These models were validated using data for 2008, and the results showed good estimation for both IUSSB and IUSSCE. The proposed approach can be used to easily monitor the dynamic of IUSSB and IUSSCE in China. This should be critical in providing valuable information for policy making regarding regional development of buildings and infrastructure, sustainable urban resource management, and cross-boundary material recycling.

Research Area(s)

  • China, DMSP/OLS nighttime lights, In-use steel stock, long-term, Panel regression analysis

Citation Format(s)