State-dependent marginal emission factors with autoregressive components

TL;DR

This paper introduces a novel Markov switching autoregressive model to accurately estimate marginal emission factors by capturing regime shifts driven by fuel-price dynamics, revealing significant overestimations by traditional linear models.

Contribution

It develops a state-dependent modeling approach for MEFs that accounts for non-linear regime shifts, improving accuracy over existing linear methods.

Findings

Identifies distinct fuel-driven regimes in US electricity markets.

Links regime shifts to natural gas price structural breaks in 2022.

Shows that post-2022, the grid exhibits volatile, less persistent coal-driven regimes.

Abstract

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.