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中国铜的未来——基于铜流和库存分析的视角。

The future of copper in China--A perspective based on analysis of copper flows and stocks.

机构信息

College of Economics and Management, Nanjing Forestry University, Nanjing 210037, PR China.

State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, PR China.

出版信息

Sci Total Environ. 2015 Dec 1;536:142-149. doi: 10.1016/j.scitotenv.2015.07.021. Epub 2015 Jul 21.

Abstract

This study attempts to speculate on the future of copper metabolism in China based on dynamic substance flow analysis. Based on tremendous growth of copper consumption over the past 63 years, China will depict a substantially increasing trend of copper in-use stocks for the next 30 years. The highest peak will be possibly achieved in 2050, with the maximum ranging between 163 Mt and 171 Mt. After that, total stocks are expected to slowly decline 147-154 Mt by the year 2080. Owing to the increasing demand of in-use stocks, China will continue to have a profound impact on global copper consumption with its high import dependence until around 2020, and the peak demand for imported copper are expected to approach 5.5 Mt/year. Thereafter, old scrap generated by domestic society will occupy an increasingly important role in copper supply. In around 2060, approximately 80% of copper resources could come from domestic recycling of old scrap, implying a major shift from primary production to secondary production. With regard to the effect of lifetime distribution uncertainties in different end-use sectors of copper stocks on the predict results, uncertainty evaluation was performed and found the model was relatively robust to these changes.

摘要

本研究试图通过动态物质流分析来推测中国未来的铜代谢情况。基于过去 63 年铜消费的巨大增长,中国在未来 30 年将呈现出铜在用存量显著增加的趋势。最高峰值可能出现在 2050 年,峰值范围在 16300 万至 17100 万吨之间。此后,到 2080 年,总存量预计将缓慢减少 147-154 万吨。由于在用存量需求的增加,中国将继续对全球铜消费产生深远影响,其高度依赖进口将持续到 2020 年左右,进口铜的需求峰值预计将接近 5500 万吨/年。此后,国内社会产生的旧废料将在铜供应中占据越来越重要的地位。到 2060 年左右,大约 80%的铜资源可能来自国内旧废料的回收,这意味着从初级生产向次级生产的重大转变。关于铜存量在不同最终用途部门的寿命分布不确定性对预测结果的影响,我们进行了不确定性评估,发现该模型对这些变化相对稳健。

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