SummaryChina's mineral resource consumption has gone through multiple increases since 1980, resulting in the inadequacy of important strategic resources and a high level of external dependence. Some developed countries have already reduced primary resources consumption through urban mining. Can China also break through the bottleneck of the resource shortage and continue its economic and social development through strengthening of urban mining? This article selected copper (Cu), aluminum (Al), lead (Pb), and iron (Fe) as case studies and established predictive models for metal demand, recycling, and stock, based on stock analysis, material flow analysis, and a life distribution model, and then analyzed the metabolism of the four resources and compared the environmental effects of three scenarios. The study indicates that the urban mining potential of Cu, Fe, Al, and Pb will attain 8.1, 711.6, 37.0, and 12.1 million tonnes, respectively, in 2040. Compared with 2010, the substitution rate (secondary metals substituting primary metals) of Cu and Fe increase by 25.4% and 59.9%, whereas external dependence decreases by 30.8% and 25.7%. However, substitution is not obvious regarding Al and Pb. The low resource scenario decreases resources use, which will reduce external dependence in the short term, whereas the strengthened recovery scenario increases resource recovery and has a larger effect in reducing external dependence in the long term. So, in line with urban mining in the future, China should change its environment and resource strategy, further strengthen layout and construction of urban mining demonstration bases, and encourage the use of recyclable resources to provide a better foundation for urban mining. Keywords:industrial ecology life cycle metals recycling stock analysis urban mining Supporting information is available on the JIE Web site

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. (2015). "Source apportionment of gaseous and particulate PAHs from traffic emission using tunnel measurements in Shanghai, high-temperature combustion of fuel and gas-to-particle condensation contributed to 32 15-18% and 7-8% of PM10 mass, respectively, but 55-57% of the particle mass was left 33 unexplained. Although the results from the PCA and PMF models were comparable, 34 the PMF method is recommended for source apportionment of PAHs in real traffic 35 conditions. In addition, the combination of multivariate statistical method and 36 bivariate correlation analysis is a useful tool to comprehensively assess sources of 37 PAHs.