State-dependent marginal emission factors with autoregressive components
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866914368637108224 |
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| author | Panico, Antonio Burlinson, Andrew Grossi, Luigi |
| author_facet | Panico, Antonio Burlinson, Andrew Grossi, Luigi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_04260 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | State-dependent marginal emission factors with autoregressive components Panico, Antonio Burlinson, Andrew Grossi, Luigi Applications 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. |
| title | State-dependent marginal emission factors with autoregressive components |
| topic | Applications |
| url | https://arxiv.org/abs/2603.04260 |