Abstract：
Dysprosium (Dy), as a unique rare earth element of China, is a critical additive that determines the performance of the "super magnet" (Nd-Fe-B permanent magnet). This magnet is an essential component for motors of low-carbon equipment, such as new energy vehicles. As various countries' carbon neutrality targets are approaching, the imbalance between global supply and demand of rare earth is bound to rise. In order to ensure the security of China' dysprosium supply, this research is going to: 1) At a national scale, improve and build the Material System Analysis (MSA) to quantify the flow and stock exploration, mining, processing, manufacturing, use, and collecting processes for eight types of Dy-containing materials or products, such as raw/secondary materials and end-of-life products; 2) Apply deep learning-based natural language processing (NLP) technologies to build a knowledge graph (KG) to track rare-earth related technological trends in real-time; 3) Apply Bayesian network model to dynamically simulate the potential of demand and recycling of Dy in China; 4) Focus on trade conflicts and technological innovation to predict the potential of demand and recycling of Dy in multiple scenarios, evaluate various carbon neutralization paths, formulate a set of strategies suitable for governments, industries, and enterprises; thus provide a vital data basis and decision-making basis for ensuring China's carbon neutrality goals.
Keywords:
Industrial metabolism; Industrial ecology; Material flow analysis; Material metabolism; Substance flow analysis