The current model for power-grid carbon-embodied emissions is mainly limited by fixed-emission inventory methods, long-computing periods and generalised spatial boundaries; thus, these hindrances significantly obscure the details and evolution characteristics within dynamic multi-energy system networks. In order to theoretically and realistically correct the inherent flaws of traditional aggregated estimates, a high-resolution dynamic carbon emission factor estimation system coupled with embedded IoT edge computing networks is presented in this paper. Systematic boundary revision of the accounts at various stages including transmission substations and distribution stations transforms stationary regional inventories into localised, continuously updating node-level carbon flow matrices. Based on the topological-preserving principle of an advanced quasi-input-output (QIO) system, it is established through rigorous reasoning that the aggregated amount of carbon-equivalent influx at any localised infrastructural node must be a sum of the internal thermodynamic dissipation and operating carbon emissions. Based on this theory, it will be further operationalised by constructing ubiquitous high-frequency electricity consumption monitoring networks to calculate CO2 emissions from power grids with different structures without increasing additional physical measurement equipment. Micro-second level operating data from a regional multi-source substations is used to empirically verify that the embedded measurement technology can accurately reflect short-term changes in carbon intensity triggered by fluctuations of renewable energy power grids and nonlinear loads. Finally, based on this multi-dimensional and layered representation framework of China’s electric vehicle manufacturing industry chain, we propose some countermeasures from the aspects of technological innovation capacity building, market mechanism optimization adjustment, government intervention and support policy improvement to promote continuous optimization and upgrading of the industrial chain.
