Lulu Song; Shaoqing Dai; Zhi Cao; Yupeng Liu; Wei-Qiang Chen*
Journal of Cleaner Production, 2021,297：126482
High-resolution mapping of steel resources accumulated above ground (referred to as steel stocks) is critical for exploring urban mining and circular economy opportunities. Prior studies have attempted to approximate steel stocks using nighttime light (NTL). Although proven to be a fast estimation technique, the accuracy of the NTL-based approach may be subject to several limitations, and it has not been used for projecting future steel stocks. To fill these gaps, we developed an aggregative downscaling model that fuses multiple large-scale spatial datasets, including gridded population, gross domestic product (GDP), and built-up area. We demonstrated the utility of this model by using it to map steel stocks in mainland China at 1 × 1 km resolution. Our results found the steel stocks increased from 12,873 t/km2 to 33,027 t/km2 during 1995–2015, and four steel stocks clusters (i.e., Beijing-Tianjin-Hebei agglomeration, Yangtze River Delta, Guangdong-Hong Kong-Macao Greater Bay Area, and Chengdu-Chongqing metropolitan) possessed over 40% of the national total in 2015, revealing an unbalanced distribution of steel stocks across China. Moving forward, with the assumed population growth, GDP growth, and built-up area expansion, steel accumulation is expected to climb up to 64,636 t/km2 and cencentrate in larger cities in 2030, such as Beijing, Shanghai, Shenzhen, and Guangzhou. Our analysis highlights the magnitude and pace at which steel resources have been and are expected to be accumulated above ground. Our estimates capture the spatiotemporal dynamics of steel stocks, potentially allowing better policy-making and business decision-making on resource efficiency, waste management, and environmental sustainability on regional or urban scales.
In-use stocks; Steel; Spatiotemporal pattern; Stocks prediction; Mainland China