Accurate estimation of Marginal Emission Factors (MEFs) is critical for evaluating the decarbonization potential of low-carbon technologies and demand-side management. However, canonical methodologies, predominantly relying on linear regression and differencing techniques, fail to capture the structural non-linearities inherent in the merit order, i.e. the marginal technology setting electricity prices. Utilizing Markov switching autoregressive models with exogenous regressors (MS-ARX) and hourly US data (2019-2025), we identify distinct, mutually exclusive regimes governed by fuel-price dynamics. We find that linear models overestimate abatement potential by masking the dichotomy between a gas-driven and coal-driven marginal system. Furthermore, using robust structural break detection, we link regime instability to a specific structural shift in natural gas pricing in May 2022. Our results indicate that post-2022, the grid has transitioned into a correction phase where the coal-driven regime is less persistent but highly volatile, necessitating state-dependent policy metrics rather than static annual averages.

翻译：准确估算边际排放因子（MEFs）对于评估低碳技术和需求侧管理的脱碳潜力至关重要。然而，主要依赖线性回归和差分技术的经典方法，未能捕捉到优先顺序（即决定电价的边际技术）中固有的结构性非线性。利用带有外生回归量的马尔可夫区制转换自回归模型（MS-ARX）和美国小时级数据（2019-2025年），我们识别出由燃料价格动态支配的、截然不同且互斥的区制。我们发现，线性模型通过掩盖天然气驱动与煤炭驱动的边际系统之间的二分性，高估了减排潜力。此外，通过使用稳健的结构突变检测方法，我们将区制不稳定性与2022年5月天然气定价的一次特定结构性转变联系起来。我们的结果表明，2022年后，电网已进入一个修正阶段，其中煤炭驱动区制的持续性减弱但波动性极高，这要求采用状态依赖的政策指标，而非静态的年度平均值。