Abstract
The critical role of the iron and steel industry in decarbonizing global energy systems calls for refined strategies of climate mitigation. Here, based on a newly developed database of individual iron and steel facilities worldwide, we explore the distinct differences in age-to-capacity ratio and emissions intensity of primary steelmaking plants. We customize regional cost-effective decarbonization strategies by targeting a certain proportion of plants. We find that the more effective indicator for targeted decarbonization in developing regions is emissions intensity, while for developed countries it is age-to-capacity ratio. Whichever indicator we use to target plants, the strategy of transformation towards secondary steelmaking is generally more cost-effective than efficiency improvement in most cases, although obvious regional priorities exist. Our results emphasize the region-specific priorities of mitigation indicators and strategies in targeting plants, which help with designing short-term, cost-effective strategies for reducing steel-related CO2 emissions.
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Data availability
The GISD data used in this study are publicly available at both https://zenodo.org/record/8237523 and http://gidmodel.org/dataset-gisd (also for future regular updates). The development of the GISD relies on the MCI database, which is used with permission and by licence. The MCI database is available from its original creator, Metal Consulting International (https://www.metalsconsultinginternational.com/), and we have no right to publish the information collected from MCI. Instead, we provided detailed instructions on how to access the database from MCI (for example, contact information, product name and product format) and provided the plant/facility ID in the MCI database that can be used to link with the GISD at facility level. The CISD is built based on the official facility-level statistical data from the Ministry of Ecology and Environment of the People’s Republic of China, and we do not have permission to publish the data. Source data are provided with this paper.
Code availability
The code used to manipulate the data and generate the results is available from the corresponding author on reasonable request.
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Acknowledgements
This work is supported by the National Natural Science Foundation of China (41921005), Tsinghua University Initiative Scientific Research Program (20223080041), the Energy Foundation (G-2009-32416) and the New Cornerstone Science Foundation through the Xplorer Prize. We thank D. Guan for helpful discussion and cooperative development of the GISD.
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Q.Z. and D.T. designed the study. R.X. performed dataset construction and emission estimates with support from J.C., Q.S., X.Q., C.C., L.Y., X.Y., H.W. and D.Z. on data compilation and from Y.L. on analytical approaches. D.T., R.X., Q.Z. and K.H. interpreted the data. R.X. D.T., S.J.D. and Q.Z. wrote the paper with input from all co-authors.
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Extended data
Extended Data Fig. 1 Diagram about major steelmaking processes and routes.
The pretreatment, ironmaking, and steelmaking processes can be combined to generate three main steelmaking routes: BOF/OHF primary steelmaking route, EAF primary steelmaking route, and secondary steelmaking route. The two primary routes convert raw iron ore to crude steel through the pretreatment, ironmaking, and steelmaking processes, among which the BOF/OHF primary steelmaking route mainly relies on pig iron from BF, while the EAF primary steelmaking route generally use DRI-produced iron. Different from primary routes, the secondary steelmaking route uses steel scrap as major input, and converts it to crude steel in EAF.
Extended Data Fig. 2 Steelmaking capacity development.
The marker size is proportional to the total capacity of the region in 1990, 2000, 2010, and 2019. The numbers above the markers and lines represent the capacity of the targeted year and the relative changes between two targeted years, respectively.
Extended Data Fig. 3 Driver decomposition of historic CO2 emissions from global iron and steel industry.
The contribution of drivers to the CO2 emission changes (a) at global scale and in (b) China, (c) India, (d) Rest of Asia, (e) the U.S., (f) Canada and Latin America, (g) Western Europe, (h) Eastern Europe and Russia, and (i) Rest of World. The drivers here include production growth (yellow), production route shift (orange; that is the proportions of primary and secondary steelmaking), and efficiency improvement (deep blue).
Extended Data Fig. 4 Age-to-capacity ratio and emission intensity disproportionalities of primary steelmaking plants.
The same as Fig. 1b,c but the primary steelmaking plants are distinguished by region in 1990 and 2019.
Extended Data Fig. 5 Maps of targeted primary steelmaking plants identified by different percentiles (90th, 75th, and 50th percentiles) of (a) age-to-capacity ratio and (b) CO2 emission intensity indicators, respectively.
The plants are classified by direct CO2 emissions (<100 kt, 100–499 kt, 500–999 kt, 1,000–4,999 kt, and ≥ 5,000 kt) and the largest quantiles according to which they are identified as targeted ones.
Extended Data Fig. 6 The targeted primary steelmaking plants and national net-zero climate goals.
The shares of number and capacity of targeted primary steelmaking plants that are located in countries with net-zero climate goals29 according to age-to-capacity ratio ((a) and (b); top panel) and emission intensity ((c) and (d); bottom panel) indicators. In the left and right columns, the shares are distinguished by the target year (2050 or earlier, 2051–2060, or after 2060) and status (achieved, in law, in policy document, declaration/pledge, proposed/in discussion) of net-zero climate goals, respectively.
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Xu, R., Tong, D., Davis, S.J. et al. Plant-by-plant decarbonization strategies for the global steel industry. Nat. Clim. Chang. 13, 1067–1074 (2023). https://doi.org/10.1038/s41558-023-01808-z
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DOI: https://doi.org/10.1038/s41558-023-01808-z
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