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.为实现气候目标，中国亟需快速推进电力系统脱碳，但当前规划仍依赖于忽略时间变化性的年均电网排放因子（grid emission factors, GEFs）。本研究基于运行电网数据，构建了2019年1月至2023年12月中国的月度电力网络，并采用排放变化多季节结构分解法量化驱动因子贡献度。研究发现电网排放因子存在显著的年内变化特征：省内月度波动的标准差最高可达0.25 kg CO₂/kWh，省际离散度达0.14 kg CO₂/kWh。若采用忽略电网排放因子与电力需求月度变化的省级年均用电排放因子，会导致全国总排放量被高估，尤以10月最为显著，同时还会掩盖季节性脱碳信号。结构分解结果显示，减排驱动因子正从输电结构转向电力输送的碳强度，这一变化在秋季尤为明显。将月度电网排放因子纳入年度碳目标设定与季节性管理，可为灵活电力系统规划与减排策略提供参考，助力需求侧管理与碳捕集利用与封存部署，进而推动电力系统脱碳进程。