Mitigation Drivers of China’s Electricity Grid Revealed by Monthly Variability of Carbon Emission Factors

China
needs rapid electricity system decarbonization to achieve
climate goals, yet planning still relies on annual average grid emission
factors (GEFs) that ignore time variation. In this study, we construct
monthly electricity networks in China from January 2019 to December
2023 using operational grid data and then apply a multiseasonal structural
decomposition of emission changes to quantify driver contributions.
We find pronounced intra-annual variations in GEFs: within provinces,
the month-to-month variability reaches a standard deviation of up
to 0.25 kg CO2/kWh, and the interprovincial dispersion
reaches 0.14 kg CO2/kWh. Using annual-average provincial
GEFs of electricity consumption, which ignore monthly variability
in GEFs and electricity demand, results in a national overestimation
of aggregated emissions, particularly in October, and conceals seasonal
decarbonization signals. The decomposition shows mitigation drivers
shifting from a transmission structure to the carbon intensity of
electricity transfers, especially in the autumn. Incorporating monthly
GEFs into annual carbon target setting and seasonal management could
inform flexible system planning and mitigation strategies, facilitating
demand-side management and carbon capture, utilization, and storage
deployment, and advancing electricity system decarbonization.