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State-dependent marginal emission factors with autoregressive components

Antonio Panico, Andrew Burlinson, Luigi Grossi

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.

State-dependent marginal emission factors with autoregressive components

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.
Paper Structure (25 sections, 18 equations, 31 figures, 10 tables)

This paper contains 25 sections, 18 equations, 31 figures, 10 tables.

Table of Contents

  1. Introduction
  2. Review of existing methods
  3. Data sources and description
  4. Stochastic properties and empirical justification for Markov Switching Models
  5. Model specification and estimation methodology
  6. Model specification
  7. Likelihood and estimation strategy
  8. Results and discussion
  9. Model comparison
  10. Temporal dynamics and drivers of regime switching
  11. Robust detection of structural break in natural gas markets
  12. Impact of structural break on daily MEF profiles
  13. Understanding the mechanisms affecting marginal emission factors
  14. MEF regimes and the energy crisis
  15. Conclusion
  16. ...and 10 more sections

Figures (31)

  • Figure 1: Emissions and Generation: Emissions are reported in $\text{Mlbs CO}_2$, while Generation in $\text{MWh}$ refers to the total fossil fuel generation.
  • Figure 2: Temporal evolution of MEFs (2019--2025).(Left) Regime-dependent dynamics: The trajectories of the High-MEF and Low-MEF regimes. Note the convergence of the two states in the end of the considered period. (Right) Benchmark Comparison: Contrast between dynamic (ARIMA) and static (US-FE, HAWKES) linear specifications against the regime-switching baseline.
  • Figure 3: Regime Switching Dynamics (2019--2025). Top: Hourly emissions colored by the most probable regime (Red = High MEF, Blue = Low MEF). Bottom: Smoothed probability $\mathbb{P}(S_t = \text{High MEF} | \mathcal{Y}_T)$. The distinct binary separation confirms that the grid operates in mutually exclusive states rather than a continuum.
  • Figure 4: Temporal Distribution of the High-MEF Regime. Radial plots showing the percentage occurrence of the High-Emission state by Season (Left), Month (Center), and Day of Week (Right).
  • Figure 5: Marginal Fuel Probability by Regime. The bar chart displays the estimated $\beta$ coefficients from Equation (\ref{['eq:marginal_fuel']}). It illustrates that Gas is the dominant marginal fuel in the Low MEF regime, while Coal significantly increases its marginal contribution in the High MEF regime.
  • ...and 26 more figures