Competence Center Sustainability and Infrastructure Systems, Fraunhofer Institute for Systems and Innovation Research ISI, Karlsruhe, Germany.
Environ Sci Technol. 2013 Jun 18;47(12):6564-72. doi: 10.1021/es400069b. Epub 2013 May 31.
We present a dynamic model of global copper stocks and flows which allows a detailed analysis of recycling efficiencies, copper stocks in use, and dissipated and landfilled copper. The model is based on historical mining and refined copper production data (1910-2010) enhanced by a unique data set of recent global semifinished goods production and copper end-use sectors provided by the copper industry. To enable the consistency of the simulated copper life cycle in terms of a closed mass balance, particularly the matching of recycled metal flows to reported historical annual production data, a method was developed to estimate the yearly global collection rates of end-of-life (postconsumer) scrap. Based on this method, we provide estimates of 8 different recycling indicators over time. The main indicator for the efficiency of global copper recycling from end-of-life (EoL) scrap--the EoL recycling rate--was estimated to be 45% on average, ± 5% (one standard deviation) due to uncertainty and variability over time in the period 2000-2010. As uncertainties of specific input data--mainly concerning assumptions on end-use lifetimes and their distribution--are high, a sensitivity analysis with regard to the effect of uncertainties in the input data on the calculated recycling indicators was performed. The sensitivity analysis included a stochastic (Monte Carlo) uncertainty evaluation with 10(5) simulation runs.
我们提出了一个全球铜库存和流动的动态模型，该模型允许对回收效率、使用中的铜库存以及耗散和填埋的铜进行详细分析。该模型基于历史采矿和精炼铜产量数据（1910-2010 年），并结合了由铜业提供的最近全球半成品生产和铜最终用途部门的独特数据集进行了增强。为了使模拟的铜生命周期在封闭的质量平衡方面保持一致，特别是使回收金属流与报告的历史年度生产数据相匹配，我们开发了一种方法来估计每年全球报废（消费后）废料的收集率。基于该方法，我们提供了 8 个不同的时间相关回收指标的估计值。从报废（EoL）废料中回收全球铜的主要效率指标——EoL 回收利用率——在 2000-2010 年期间，由于时间上的不确定性和可变性，平均估计为 45%，±5%（一个标准差）。由于特定输入数据的不确定性较高，主要涉及最终用途寿命及其分布的假设，因此对输入数据的不确定性对计算回收指标的影响进行了敏感性分析。敏感性分析包括了 10（5）次模拟运行的随机（蒙特卡罗）不确定性评估。
