Exploring carbon reduction pathways in the steel industry from the perspective of emerging technologies for achieving carbon neutrality

Abstract

The iron and steel industry (ISI) plays a pivotal role in global decarbonization efforts, yet achieving carbon neutrality remains a significant challenge due to the sector's high emissions and technological complexity. Strategically identifying emerging technologies is critical for aligning industrial transformation with climate goals, optimizing resource allocation, and mitigating transition risks in a rapidly evolving technological landscape. This study bridges this gap by developing a multi-source data analytical framework that integrates Topmine phrase mining, K-means clustering, and Text2vec similarity analysis. The framework enhances the identification of emerging technologies through a reverse verification mechanism, ensuring the robustness of clustering results. By systematically classifying technologies into hot, growing, mature, and weak-signal categories, this study uncovers key technological pathways shaping the future of ISI decarbonization. Additionally, topic phrase burst analysis is employed to forecast technology evolution trends, revealing key shifts such as multi-technology integration for synergistic effects, intelligent process optimization, and industrial symbiosis. These methodological advancements not only provide a replicable toolkit for strategic decision-making but also empower stakeholders to prioritize investments, foster cross-sector collaboration, and accelerate the ISI's transition to carbon neutrality. The findings offer a transformative roadmap for policymakers and enterprises to navigate technological uncertainties while balancing economic competitiveness and environmental imperatives.