Abstract: As a key carbon-emitting industry, the steel sector urgently requires a carbon emission factor model that can reflect the dynamic operation characteristics of its multi-energy system and the structure of the electricity market for carbon emission optimization. To address these limitations, this paper proposes a time-of-use carbon emission factor modeling method for integrated electricity-heat-hydrogen systems based on energy-carbon flow coupling relationships. The model establishes carbon flow allocation mechanisms for various energy production and storage devices within the industrial park and incorporates the carbon emission attributes of electricity purchased from external markets. By disaggregating the power output into three pathways—electricity, heat, and hydrogen—the number of decision variables on the supply side increases from 120 to 360 under an hourly resolution. Building on this, a bi-level optimization scheduling model with source-load coordination is developed. The upper level minimizes the operational cost of the park, while the lower level minimizes carbon emissions. By leveraging time-varying carbon emission factors to guide the temporal response of multi-energy loads, the model achieves coordinated optimization of economic efficiency and carbon reduction. Case study results demonstrate that the time-of-use carbon emission factor curve can accurately characterize the time-varying characteristics of system carbon intensity. After introducing time-of-use carbon emission factors, the system operation tends to avoid peak and high-carbon periods, with electricity, heat, and hydrogen loads achieving time-shifted adjustments, and the overall carbon emissions significantly reduced. The cumulative carbon emissions of the system are reduced by 10.8%. Meanwhile, the inclusion of purchased electricity carbon emissions can effectively enhance the integrity of carbon optimization, avoiding the underestimation of carbon responsibility and scheduling deviation, thereby verifying the effectiveness and adaptability of the proposed model.