Circular transformation of the European steel industry renders scrap metal a strategic resource

ArXiv ID: 2406.12098 “View on arXiv”

Authors: Unknown

Abstract

The steel industry is a major contributor to CO2 emissions, accounting for 7% of global emissions. The European steel industry is seeking to reduce its emissions by increasing the use of electric arc furnaces (EAFs), which can produce steel from scrap, marking a major shift towards a circular steel economy. Here, we show by combining trade with business intelligence data that this shift requires a deep restructuring of the global and European scrap trade, as well as a substantial scaling of the underlying business ecosystem. We find that the scrap imports of European countries with major EAF installations have steadily decreased since 2007 while globally scrap trade started to increase recently. Our statistical modelling shows that every 1,000 tonnes of EAF capacity installed is associated with an increase in annual imports of 550 tonnes and a decrease in annual exports of 1,000 tonnes of scrap, suggesting increased competition for scrap metal as countries ramp up their EAF capacity. Furthermore, each scrap company enables an increase of around 79,000 tonnes of EAF-based steel production per year in the EU. Taking these relations as causal and extrapolating to the currently planned EAF capacity, we find that an additional 730 (SD 140) companies might be required, employing about 35,000 people (IQR 29,000-50,000) and generating an additional estimated turnover of USD 35 billion (IQR 27-48). Our results thus suggest that scrap metal is likely to become a strategic resource. They highlight the need for a massive restructuring of the industry’s supply networks and identify the resulting growth opportunities for companies.

Keywords: Electric Arc Furnace (EAF), Scrap Metal Trade, Supply Chain Restructuring, Carbon Emissions, Statistical Modelling, Commodities

Complexity vs Empirical Score

  • Math Complexity: 3.5/10
  • Empirical Rigor: 7.0/10
  • Quadrant: Street Traders
  • Why: The paper employs statistical modeling and linear regression with fixed effects, which is moderately advanced but not highly dense mathematically. It is heavily data-driven, using large trade and business datasets, performing topic modeling, and providing specific numerical forecasts, making it highly backtest-ready and implementation-focused.
  flowchart TD
    A["<b>Research Goal:</b><br>Quantify scrap metal's strategic role<br>in European steel's circular transition"] --> B["<b>Methodology:</b><br>Trade & Business Intelligence Analysis"]
    
    B --> C["<b>Data Inputs:</b><br>Global Scrap Trade Data<br>EU Business Intelligence<br>EAF Capacity Statistics"]
    
    C --> D["<b>Computational Process:</b><br>Statistical Modelling &<br>Causal Extrapolation"]
    
    D --> E["<b>Key Findings:</b><br>1. Scrap imports ↓, exports ↓ with EAF growth<br>2. 1,000t EAF capacity → 550t import increase<br>3. 1 scrap company → 79,000t EU steel production"]
    
    D --> F["<b>Outcomes:</b><br>730 new companies needed<br>35,000 new jobs<br>$35B additional turnover<br>Scrap = Strategic Resource"]