Abstract
As an energy‐intensive sector, the low‐carbon transformation of the steel sector plays a crucial role in achieving China's carbon neutrality goals. Therefore, exploring how learning strategies and incentive mechanisms can synergistically promote the transformation of production and energy structure in the steel sector toward net‐zero emissions is an urgent practical problem that needs to be solved. This study constructed an integrated evaluation framework that combines an improved dynamic computable general equilibrium model with input–output analysis and ecological network analysis, and incorporates learning strategies, carbon taxes, subsidies, carbon trading and other incentive mechanisms into the model to deeply analyze the transformation path of the steel sector in different scenarios. The research not only focuses on the low‐carbon transformation of the steel sector but also comprehensively analyzes the evolution of CO 2 emission metabolism and reveals the complex internal interactions under the background of emission dynamics through a multidimensional network perspective. The results indicate that the steel sector's production and energy structures have significantly changed by implementing incentive mechanisms and introducing learning strategies. In the scenario where carbon taxes, subsidies, and learning strategies work together, it is expected that by 2060, the share of short‐process steelmaking will reach 52.2%, while the proportion of carbon capture and storage equipped in long‐process will exceed 10%. At this time, the short‐process sector's control over other sectors and dependence on the system will increase.