IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v293y2024ics036054422400447x.html
   My bibliography  Save this article

A bottom-up assessment of recent (2016–20) energy use by the global iron and steel industry constrained to match a top-down (International Energy Agency) assessment

Author

Listed:
  • Harvey, L.D. Danny

Abstract

This paper presents the first estimates, at the global scale, of energy use by energy type associated with individual steps in the production of fabricated steel products. Estimates are averaged over the period 2016–20, based on a synthesis of literature regarding energy use for (i) preparation of iron ore; (ii) production of iron in a blast furnace (BF) and as direct reduced iron; (iii) production of refined steel in the basic oxygen furnace or electric arc furnace; (iv) steel casting, hot and cold rolling, and coating; (v) fabrication of intermediate steel products; and (vi) processing of scrap. The International Energy Agency provides separate global energy use for coking, transformation energy use by BF, and by the remainder of the iron and steel industry (rISI) including the remaining BF energy use. With re-allocation of the BF portion of rISI back to BF, and a 9% increase in literature-based estimates of energy intensity across all production steps except coking, bottom-up and IEA estimates of total energy use agree. The resulting mean cumulative energy intensity of fabricated steel products is 24.1 GJ/t. The energy content of the lost iron equals about 10% of the total energy used in producing finished steel products.

Suggested Citation

  • Harvey, L.D. Danny, 2024. "A bottom-up assessment of recent (2016–20) energy use by the global iron and steel industry constrained to match a top-down (International Energy Agency) assessment," Energy, Elsevier, vol. 293(C).
    • Handle: RePEc:eee:energy:v:293:y:2024:i:c:s036054422400447x
    DOI: 10.1016/j.energy.2024.130675
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S036054422400447X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2024.130675?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Juntueng, Sirintip & Towprayoon, Sirintornthep & Chiarakorn, Siriluk, 2014. "Energy and carbon dioxide intensity of Thailand's steel industry and greenhouse gas emission projection toward the year 2050," Resources, Conservation & Recycling, Elsevier, vol. 87(C), pages 46-56.
    2. Chen, Wenying & Yin, Xiang & Ma, Ding, 2014. "A bottom-up analysis of China’s iron and steel industrial energy consumption and CO2 emissions," Applied Energy, Elsevier, vol. 136(C), pages 1174-1183.
    3. Yuancheng Lin & Honghua Yang & Linwei Ma & Zheng Li & Weidou Ni, 2021. "Low-Carbon Development for the Iron and Steel Industry in China and the World: Status Quo, Future Vision, and Key Actions," Sustainability, MDPI, vol. 13(22), pages 1-28, November.
    4. Zhang, Hanxin & Sun, Wenqiang & Li, Weidong & Ma, Guangyu, 2022. "A carbon flow tracing and carbon accounting method for exploring CO2 emissions of the iron and steel industry: An integrated material–energy–carbon hub," Applied Energy, Elsevier, vol. 309(C).
    5. Yuan, Yuxing & Na, Hongming & Du, Tao & Qiu, Ziyang & Sun, Jingchao & Yan, Tianyi & Che, Zichang, 2023. "Multi-objective optimization and analysis of material and energy flows in a typical steel plant," Energy, Elsevier, vol. 263(PD).
    6. Hasanbeigi, Ali & Arens, Marlene & Cardenas, Jose Carlos Rojas & Price, Lynn & Triolo, Ryan, 2016. "Comparison of carbon dioxide emissions intensity of steel production in China, Germany, Mexico, and the United States," Resources, Conservation & Recycling, Elsevier, vol. 113(C), pages 127-139.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yang, Honghua & Ma, Linwei & Li, Zheng, 2023. "Tracing China's steel use from steel flows in the production system to steel footprints in the consumption system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 172(C).
    2. Wang, Xiaoyang & Yu, Biying & An, Runying & Sun, Feihu & Xu, Shuo, 2022. "An integrated analysis of China’s iron and steel industry towards carbon neutrality," Applied Energy, Elsevier, vol. 322(C).
    3. Xuan, Yanni & Yue, Qiang, 2017. "Scenario analysis on resource and environmental benefits of imported steel scrap for China’s steel industry," Resources, Conservation & Recycling, Elsevier, vol. 120(C), pages 186-198.
    4. Wang, Chunyan & Wang, Ranran & Hertwich, Edgar & Liu, Yi, 2017. "A technology-based analysis of the water-energy-emission nexus of China’s steel industry," Resources, Conservation & Recycling, Elsevier, vol. 124(C), pages 116-128.
    5. Yuan, Yuxing & Na, Hongming & Chen, Chuang & Qiu, Ziyang & Sun, Jingchao & Zhang, Lei & Du, Tao & Yang, Yuhang, 2024. "Status, challenges, and prospects of energy efficiency improvement methods in steel production: A multi-perspective review," Energy, Elsevier, vol. 304(C).
    6. Meng, Fan & Rong, Guoqiang & Zhao, Ruiji & Chen, Bo & Xu, Xiaoyun & Qiu, Hao & Cao, Xinde & Zhao, Ling, 2024. "Incorporating biochar into fuels system of iron and steel industry: carbon emission reduction potential and economic analysis," Applied Energy, Elsevier, vol. 356(C).
    7. Yuan, Yuxing & Na, Hongming & Du, Tao & Qiu, Ziyang & Sun, Jingchao & Yan, Tianyi & Che, Zichang, 2023. "Multi-objective optimization and analysis of material and energy flows in a typical steel plant," Energy, Elsevier, vol. 263(PD).
    8. Ma, Shuaiyin & Ding, Wei & Liu, Yang & Ren, Shan & Yang, Haidong, 2022. "Digital twin and big data-driven sustainable smart manufacturing based on information management systems for energy-intensive industries," Applied Energy, Elsevier, vol. 326(C).
    9. An, Runying & Yu, Biying & Li, Ru & Wei, Yi-Ming, 2018. "Potential of energy savings and CO2 emission reduction in China’s iron and steel industry," Applied Energy, Elsevier, vol. 226(C), pages 862-880.
    10. Jiang, Sheng-Long & Wang, Meihong & Bogle, I. David L., 2023. "Plant-wide byproduct gas distribution under uncertainty in iron and steel industry via quantile forecasting and robust optimization," Applied Energy, Elsevier, vol. 350(C).
    11. Song, Weiming & Zhou, Jianan & Li, Yujie & Yang, Jian & Cheng, Rijin, 2021. "New technology for producing high-quality combustible gas by high-temperature reaction of dust-removal coke powder in mixed atmosphere," Energy, Elsevier, vol. 233(C).
    12. Li, Nan & Ma, Ding & Chen, Wenying, 2017. "Quantifying the impacts of decarbonisation in China’s cement sector: A perspective from an integrated assessment approach," Applied Energy, Elsevier, vol. 185(P2), pages 1840-1848.
    13. Shao, Tianming & Pan, Xunzhang & Li, Xiang & Zhou, Sheng & Zhang, Shu & Chen, Wenying, 2022. "China's industrial decarbonization in the context of carbon neutrality: A sub-sectoral analysis based on integrated modelling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).
    14. Yue, Xiufeng & Deane, J.P. & O'Gallachoir, Brian & Rogan, Fionn, 2020. "Identifying decarbonisation opportunities using marginal abatement cost curves and energy system scenario ensembles," Applied Energy, Elsevier, vol. 276(C).
    15. Sun, Liang & Chen, Wenying, 2017. "Development and application of a multi-stage CCUS source–sink matching model," Applied Energy, Elsevier, vol. 185(P2), pages 1424-1432.
    16. Feng, Chao & Huang, Jian-Bai & Wang, Miao, 2019. "The sustainability of China’s metal industries: features, challenges and future focuses," Resources Policy, Elsevier, vol. 60(C), pages 215-224.
    17. Liu, Shuhan & Sun, Wenqiang, 2023. "Attention mechanism-aided data- and knowledge-driven soft sensors for predicting blast furnace gas generation," Energy, Elsevier, vol. 262(PA).
    18. Li, Nan & Chen, Wenying, 2018. "Modeling China’s interprovincial coal transportation under low carbon transition," Applied Energy, Elsevier, vol. 222(C), pages 267-279.
    19. Xu, Bin & Lin, Boqiang, 2017. "Assessing CO2 emissions in China's iron and steel industry: A nonparametric additive regression approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 325-337.
    20. Chen, Han & Yang, Lei & Chen, Wenying, 2020. "Modelling national, provincial and city-level low-carbon energy transformation pathways," Energy Policy, Elsevier, vol. 137(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:293:y:2024:i:c:s036054422400447x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.