Dynamic Measurement of Power Grid Carbon Emission Factors Based on Carbon Emission Flow Theory

Current carbon accounting in the power sector often relies on annual average emission factors, which suffer from ill-defined system boundaries, update delays, and insufficient temporal granularity. To address these limitations, this study introduces a high-spatiotemporal-resolution dynamic measurement model for grid carbon emission factors, grounded in carbon emission flow theory. Applied to a regional grid in northern China, the model employs nodal carbon–emission–flow balance to construct system-level matrix equations. This approach accurately traces the spatiotemporal transmission paths of carbon emissions, enabling refined, node-level, and hourly carbon accounting. A case study demonstrated that our model significantly outperformed existing static methods based on interprovincial power exchange in both resolution and accuracy. The results revealed pronounced spatiotemporal heterogeneity in grid emission factors: diurnal fluctuations reach up to 45% in maximum deviation, closely coupled with renewable energy output, while spatial disparities between high- and low-emission regions reach a factor of 4.7, highlighting the critical roles of generation mix and grid topology. This study confirms that high-resolution emission factors effectively overcome the biases of traditional methods, providing a critical data foundation for green electricity trading, demand-side response, and regionally differentiated emission-reduction policies. Our approach offers key methodological and policy insights for building new-type power systems and advancing carbon neutrality goals.