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Predicting CO 2 Emissions in U.S. Ironmaking: A Data-Driven Approach for Long-Term Policy and Process Optimization

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  • Mohammad Meysami

    (Department of Mathematics, Clarkson University, Potsdam, NY 13699, USA)

  • Alex Meisami

    (Department of Accounting and Finance, Judd Leighton School of Business & Economics, Indiana University South Bend, South Bend, IN 46615, USA)

  • Mohammad Merhi

    (Department of Accounting and Finance, Judd Leighton School of Business & Economics, Indiana University South Bend, South Bend, IN 46615, USA)

  • Hassan Dehghanpour

    (Department of Civil and Environmental Engineering, University of Alberta, Edmonton, AB T6G 2C6, Canada)

  • Amirhossein Meysami

    (Department of Materials Science and Engineering, The University of Utah, Salt Lake City, UT 84112, USA)

Abstract

The U.S. ironmaking sector plays a key role in global greenhouse gas emissions, mainly due to long-standing practices such as blast furnaces (BFs) and direct reduction (DR). In this work, we develop a new mathematical approach to estimate future CO 2 emissions from the U.S. ironmaking industry through 2050. Our approach uses historical data from 2005 to 2021 and incorporates economic and energy use indicators to explore how emissions might change over time. According to our results, unless significant technological improvements and stronger energy policies are implemented, the industry is likely to continue producing large amounts of CO 2 . These findings highlight the pressing need to adopt cleaner alternatives—such as hydrogen-based direct reduction—to help meet international climate goals. Supporting the transition to low-emission technologies contributes to broader efforts in sustainable industrial development and long-term climate resilience.

Suggested Citation

  • Mohammad Meysami & Alex Meisami & Mohammad Merhi & Hassan Dehghanpour & Amirhossein Meysami, 2025. "Predicting CO 2 Emissions in U.S. Ironmaking: A Data-Driven Approach for Long-Term Policy and Process Optimization," Sustainability, MDPI, vol. 17(13), pages 1-15, June.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:13:p:5859-:d:1687389
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