Research on energy consumption monitoring and accounting based on carbon emission factors of embedded IoT system

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Provides a method for real-time carbon accounting using IoT edge computing, valuable for researchers in carbon accounting and power system analysis.

🏢実務担当者:Could be used by corporate sustainability teams for more accurate scope 2 tracking and supply chain decarbonization, especially in manufacturing.

🏛政策担当者:Highlights the potential of IoT and edge computing for enhancing carbon monitoring infrastructure, informing policy on data-driven decarbonization.

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.

• semanticscholar https://doi.org/10.65102/is2026819first seen 2026-05-15 21:55:52