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 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.
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 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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.