IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v409y2026ics030626192600111x.html

A coupled material-energy‑carbon flow method for quantifying changes in energy consumption and CO2 emissions of iron and steel sites

Author

Listed:
  • Liu, Shuhan
  • Zhang, Hanxin
  • Sun, Wenqiang

Abstract

The iron and steel sites, being a quintessential energy-intensive and emission-heavy sector, urgently requires precise accounting methodologies to quantify the systemic impacts of material and energy flow variations on energy consumption and CO2 emissions. Current approaches predominantly focus on analyzing localized effects of individual material or energy flows, with synergistic interactions between different flows being oversimplified as linear superimpositions of their respective impacts. This simplification neglects the consequential effects that alterations in material or energy flows exert on associated units' energy consumption and CO2 emissions. To address these limitations, this study decouples the material-energy‑carbon flow models, establishing a relational model that links input material or energy flows to the energy consumption and CO2 emissions of upstream, current, and downstream units. Our investigation systematically examines how modifications in material or energy flows affect energy consumption and CO2 emissions throughout the entire production process. Results demonstrate that increasing scrap ratio, ratio of pellet to sinter, grade of sinter, oxygen enrichment rate, and imported coke can effectively reduce energy consumption and CO2 emissions across the entire production process. Notably, enhancing the scrap ratio yields the most significant improvement - increasing this ratio from 0.14 to 0.30 reduces energy consumption by 93.75 kgce (kilogram of coal equivalent) /tcs (ton of crude steel) and CO2 emissions by 269.76 kg/tcs. Furthermore, when the material and energy flows change simultaneously, the energy conservation and CO2 emissions reduction are not simply the linear sum of the changes caused by each flow. When the aforementioned factors increase together, the energy consumption and CO2 emissions of the on-site power plant decreased by 21.24 kgce/tcs and 7.64 kg/tcs, respectively. In contrast, when the changes induced by these five factors are summed linearly, energy consumption decreases by 70.25 kgce/tcs and CO2 emissions increase by 37.61 kg/tcs.

Suggested Citation

  • Liu, Shuhan & Zhang, Hanxin & Sun, Wenqiang, 2026. "A coupled material-energy‑carbon flow method for quantifying changes in energy consumption and CO2 emissions of iron and steel sites," Applied Energy, Elsevier, vol. 409(C).
  • Handle: RePEc:eee:appene:v:409:y:2026:i:c:s030626192600111x
    DOI: 10.1016/j.apenergy.2026.127459
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S030626192600111X
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2026.127459?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

    for a different version of it.

    References listed on IDEAS

    as
    1. 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).
    2. Liu, Xinmin & Sun, Wenqiang & Chen, Tiantian & Xu, Xiaoyuan & Huang, Tao, 2025. "Energy and environmental performance of iron and steel industry in real-time demand response: A case of hot rolling process," Applied Energy, Elsevier, vol. 389(C).
    3. Chen, Junwen & Gong, Qingshan & Cao, Zhanlong & Liu, Min & Xie, Minchao & Zhao, Gang, 2025. "Three-layer design and optimization of CO2 emission reduction in the iron and steel industry based on ‘BRL’ industrial metabolism," Energy, Elsevier, vol. 315(C).
    4. Feng, Chao & Zhu, Rong & Wei, Guangsheng & Dong, Kai & Xia, Tao, 2023. "Typical case of CO2 capture in Chinese iron and steel enterprises: Exergy analysis," Applied Energy, Elsevier, vol. 336(C).
    5. Zhang, Hui & Dong, Liang & Li, Huiquan & Fujita, Tsuyoshi & Ohnishi, Satoshi & Tang, Qing, 2013. "Analysis of low-carbon industrial symbiosis technology for carbon mitigation in a Chinese iron/steel industrial park: A case study with carbon flow analysis," Energy Policy, Elsevier, vol. 61(C), pages 1400-1411.
    6. Griffin, Paul W. & Hammond, Geoffrey P., 2019. "Industrial energy use and carbon emissions reduction in the iron and steel sector: A UK perspective," Applied Energy, Elsevier, vol. 249(C), pages 109-125.
    7. Wang, Jiayang & Wang, Qiang & Sun, Wenqiang, 2023. "Quantifying flexibility provisions of the ladle furnace refining process as cuttable loads in the iron and steel industry," Applied Energy, Elsevier, vol. 342(C).
    8. Xin Bo & Min Jia & Xiaoda Xue & Ling Tang & Zhifu Mi & Shouyang Wang & Weigeng Cui & Xiangyu Chang & Jianhui Ruan & Guangxia Dong & Beihai Zhou & Steven J. Davis, 2021. "Effect of strengthened standards on Chinese ironmaking and steelmaking emissions," Nature Sustainability, Nature, vol. 4(9), pages 811-820, September.
    9. 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).
    10. Liu, Shuhan & Sun, Wenqiang, 2025. "Knowledge- and data-driven prediction of blast furnace gas generation and consumption in iron and steel sites," Applied Energy, Elsevier, vol. 390(C).
    11. Zeng, Yujiao & Xiao, Xin & Li, Jie & Sun, Li & Floudas, Christodoulos A. & Li, Hechang, 2018. "A novel multi-period mixed-integer linear optimization model for optimal distribution of byproduct gases, steam and power in an iron and steel plant," Energy, Elsevier, vol. 143(C), pages 881-899.
    12. Wu, Junnian & Wang, Ruiqi & Pu, Guangying & Qi, Hang, 2016. "Integrated assessment of exergy, energy and carbon dioxide emissions in an iron and steel industrial network," Applied Energy, Elsevier, vol. 183(C), pages 430-444.
    13. Ren, Lei & Zhou, Sheng & Peng, Tianduo & Ou, Xunmin, 2021. "A review of CO2 emissions reduction technologies and low-carbon development in the iron and steel industry focusing on China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    14. Sun, Wenqiang & Wang, Qiang & Zhou, Yue & Wu, Jianzhong, 2020. "Material and energy flows of the iron and steel industry: Status quo, challenges and perspectives," Applied Energy, Elsevier, vol. 268(C).
    15. Wang, Peng & Jiang, Zeyi & Geng, Xinyi & Hao, Shiyu & Zhang, Xinxin, 2014. "Quantification of Chinese steel cycle flow: Historical status and future options," Resources, Conservation & Recycling, Elsevier, vol. 87(C), pages 191-199.
    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. 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).
    2. 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).
    3. 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).
    4. Ma, Shuaiyin & Huang, Yuming & Liu, Yang & Liu, Haizhou & Chen, Yanping & Wang, Jin & Xu, Jun, 2023. "Big data-driven correlation analysis based on clustering for energy-intensive manufacturing industries," Applied Energy, Elsevier, vol. 349(C).
    5. Xu, Tingting & Huo, Zhaoyi & Wang, Wenjing & Xie, Ning & Li, Lili & Liu, Yingjie & Mu, Lin, 2024. "Evaluation of by-product-gas utilization options for carbon reduction at an integrated iron and steel mill," Energy, Elsevier, vol. 294(C).
    6. Qiu, Ziyang & Sun, Jingchao & Du, Tao & Na, Hongming & Zhang, Lei & Yuan, Yuxing & Wang, Yisong, 2024. "Impact of hydrogen metallurgy on the current iron and steel industry: A comprehensive material-exergy-emission flow analysis," Applied Energy, Elsevier, vol. 356(C).
    7. Sun, Jingchao & Na, Hongming & Yan, Tianyi & Che, Zichang & Qiu, Ziyang & Yuan, Yuxing & Li, Yingnan & Du, Tao & Song, Yanli & Fang, Xin, 2022. "Cost-benefit assessment of manufacturing system using comprehensive value flow analysis," Applied Energy, Elsevier, vol. 310(C).
    8. 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).
    9. 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).
    10. Liu, Shuhan & Sun, Wenqiang, 2025. "Knowledge- and data-driven prediction of blast furnace gas generation and consumption in iron and steel sites," Applied Energy, Elsevier, vol. 390(C).
    11. Sun, Jingchao & Na, Hongming & Yan, Tianyi & Qiu, Ziyang & Yuan, Yuxing & He, Jianfei & Li, Yingnan & Wang, Yisong & Du, Tao, 2021. "A comprehensive assessment on material, exergy and emission networks for the integrated iron and steel industry," Energy, Elsevier, vol. 235(C).
    12. Liu, Xinmin & Sun, Wenqiang & Chen, Tiantian & Xu, Xiaoyuan & Huang, Tao, 2025. "Energy and environmental performance of iron and steel industry in real-time demand response: A case of hot rolling process," Applied Energy, Elsevier, vol. 389(C).
    13. 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).
    14. Shuangping Wu & Anjun Xu, 2021. "Calculation Method of Energy Saving in Process Engineering: A Case Study of Iron and Steel Production Process," Energies, MDPI, vol. 14(18), pages 1-15, September.
    15. Na, Hongming & Sun, Jingchao & Qiu, Ziyang & Yuan, Yuxing & Du, Tao, 2022. "Optimization of energy efficiency, energy consumption and CO2 emission in typical iron and steel manufacturing process," Energy, Elsevier, vol. 257(C).
    16. Ma, Shuaiyin & Huang, Yuming & Liu, Yang & Kong, Xianguang & Yin, Lei & Chen, Gaige, 2023. "Edge-cloud cooperation-driven smart and sustainable production for energy-intensive manufacturing industries," Applied Energy, Elsevier, vol. 337(C).
    17. Wang, Jiayang & Wang, Qiang & Sun, Wenqiang, 2023. "Quantifying flexibility provisions of the ladle furnace refining process as cuttable loads in the iron and steel industry," Applied Energy, Elsevier, vol. 342(C).
    18. Chen, Demin & Li, Jiaqi & Wang, Zhao & Lu, Biao & Chen, Guang, 2022. "Hierarchical model to find the path reducing CO2 emissions of integrated iron and steel production," Energy, Elsevier, vol. 258(C).
    19. Yang, Weijia & Huang, Yuping & Zhang, Tianren & Zhao, Daiqing, 2023. "Mechanism and analytical methods for carbon emission-exergy flow distribution in heat-electricity integrated energy system," Applied Energy, Elsevier, vol. 352(C).
    20. 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).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    JEL classification:

    Statistics

    Access and download statistics

    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:appene:v:409:y:2026:i:c:s030626192600111x. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.