High-resolution mapping and evolution of steel stocks and waste in civil buildings: a case study of Changsha, China.

Abstract

Systematic estimation of steel stocks and waste in urban areas and analysis of its historical evolution pattern is crucial for urban buildings steel recycling and environmental sustainability. However, it is a challenging task to collect big data from different sources and estimate accurately with high resolution. In this study, we proposed a novel hybrid approach (GMB model) to estimate building steel stocks and the annual waste rate through combining Geographic Information System, Material Flow Analysis, and Big Data Mining techniques. We estimated the civil-building steel stocks and amount of waste in Changsha urban area from 1985 to 2020 based on the GMB model, and analyzed the historical evolution pattern of steel stocks by using standard deviation ellipse and kernel density. The results showed that the cumulative steel stock in civil buildings grew from 0.66 million tons in 1985 to 8.26 million tons in 2020. The amount of waste increased by 2557 times. The spatiotemporal analysis showed that variations in distribution of the steel stocks are mainly concentrated in the central city, indicating a "central-peripheral" distribution, with a southward trend in the standard deviation ellipse and a southeast-northwest direction in the center of gravity of the steel stocks. There is low-high and high-low spatial aggregation patterns. We also compared the experimental results with the observed data to determine the feasibility of the GMB model. Our study can promote the management of steel resources recycling and aid to achieve the green and low-carbon goals in sustainable development policies.