JOURNAL ARTICLE

Construction of an abatement benefit model for the steel industry driven by technological innovation: A perspective on emerging technology factors.

  • Published In: International Journal of Technology Management & Sustainable Development, 2026, v. 25, n. 1. P. 31 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Guo, Xin; Huang, Lucheng; Miao, Hong 3 of 3

Abstract

This article focuses on developing a quantitative abatement benefit model for the steel industry that incorporates an Emerging Technology Factor (ETF) to objectively assess the impact of technological innovation on carbon emission reductions. Using patent and academic literature data, the ETF quantifies the knowledge base and maturity of decarbonization technologies, improving the accuracy of emission reduction potential and cost-effectiveness analyses. Applied to China's steel sector under three policy scenarios, the model estimates emission reductions ranging from 406 to 822 million tons by 2030, with technologies such as bioenergy with carbon capture and storage (BECCS), ammonia-based direct reduced iron-electric arc furnace (Ammonia-DRI-EAF), and hydrogen-based DRI-EAF showing high potential and cost-effectiveness. While decarbonization technologies entail significant costs, the ETF provides a scientific basis for optimizing technology deployment and policy-making to support the steel industry's transition toward carbon neutrality.

Additional Information

  • Source:International Journal of Technology Management & Sustainable Development. 2026/03, Vol. 25, Issue 1, p31
  • Document Type:Article
  • Subject Area:Business and Management
  • Publication Date:2026
  • ISSN:1474-2748
  • DOI:10.1386/tmsd_00116_1
  • Accession Number:192379945
  • Copyright Statement:Copyright of International Journal of Technology Management & Sustainable Development is the property of Intellect Ltd. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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