Carbon cost pass-through rate in power system: evidence from Italy under the EU ETS

Francesco Lisi; Pierdomenico Duttilo

arxiv: 2604.03076 · v1 · submitted 2026-04-03 · 📊 stat.AP

Carbon cost pass-through rate in power system: evidence from Italy under the EU ETS

Pierdomenico Duttilo , Francesco Lisi This is my paper

Pith reviewed 2026-05-13 18:16 UTC · model grok-4.3

classification 📊 stat.AP

keywords carbon cost pass-throughEU ETSelectricity pricesItaly power marketmarket zoneseconometric estimationphase 3 and 4

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The pith

Carbon costs pass through to Italian electricity prices at a stable 30 percent rate, though incompletely and with regional differences.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper estimates how much of the cost of emitting carbon under the EU ETS ends up in Italian wholesale electricity prices between 2016 and 2024. It finds positive but incomplete transmission, averaging around 30 percent nationally and holding steady across two regulatory phases. Regional market zones show different rates tied to their generation mixes and conditions. This matters because it shows carbon pricing influences power markets without fully shifting costs to consumers, which affects both emission reduction effectiveness and price signals.

Core claim

Using daily data and a linear regression model with autoregressive dynamics, the analysis shows carbon costs are positively and significantly transmitted to electricity prices at a national CPTR of approximately 30 percent across Phases 3 and 4. Pass-through remains below 100 percent overall but increases in the North, Centre-North, and Sardinia during Phase 4 while declining in the Centre-South and Sicily, reflecting differences in generation mix, carbon intensity, and market conditions.

What carries the argument

The carbon cost pass-through rate (CPTR), measured as the coefficient on carbon costs in a linear regression with autoregressive terms applied to daily electricity and carbon price data.

If this is right

Carbon pricing functions as a market driver for electricity prices even when pass-through is only partial.
Regional heterogeneity implies that national carbon policies produce uneven price effects across Italian zones.
Stability of the national rate across phases suggests consistent market responses to the same carbon cost levels.
Quantile regression results indicate that pass-through strength depends on prevailing fuel spread conditions.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Generators appear to absorb part of the carbon cost, which could influence their long-term investment choices in lower-carbon plants.
Zone-specific differences may call for targeted adjustments if policymakers want uniform emission outcomes across Italy.
Incomplete pass-through suggests that achieving a given reduction in emissions may require a higher carbon price than a full-pass-through scenario would need.

Load-bearing premise

The linear regression model with autoregressive dynamics isolates the causal effect of carbon costs on electricity prices without material omitted-variable bias or endogeneity from other market drivers.

What would settle it

If adding controls for fuel prices, demand shocks, or renewable output causes the carbon cost coefficient to become statistically insignificant or drop near zero, the estimated pass-through rates would be undermined.

Figures

Figures reproduced from arXiv: 2604.03076 by Francesco Lisi, Pierdomenico Duttilo.

**Figure 1.** Figure 1: CO2 emissions (in million tCO2e) in the power industry of top emitting economies, 1970-2024. This study aims to examine the impact of the EU ETS on the Italian electricity market. Although part of the existing literature (Levy, 2005; Chernyavs'ka and Gullì, 2008a,b; Sijm et al., 2008; Jouvet and Solier, 2013) focusses on the effects of EU ETS before and after its introduction (Phases 1 and 2), the impacts … view at source ↗

**Figure 2.** Figure 2: reports the total volume of EU allowances, distinguishing between freely allocated and auctioned or sold allowances, for each year (European Environment Agency, 2025). The figure highlights the two important developments in the evolution of the EU ETS: a gradual reduction in the overall cap and a shift in the allocation mechanism from free allocation to auctioning. During the first two phases (2005-2012)… view at source ↗

**Figure 3.** Figure 3: Daily spreads between electricity prices and fuel costs (top panels), carbon costs [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗

**Figure 4.** Figure 4: Autocorrelation (ACF) and partial autocorrelation (PACF) functions of the spread [PITH_FULL_IMAGE:figures/full_fig_p014_4.png] view at source ↗

**Figure 5.** Figure 5: Autocorrelation (ACF) and partial autocorrelation (PACF) functions of the resid [PITH_FULL_IMAGE:figures/full_fig_p017_5.png] view at source ↗

**Figure 6.** Figure 6: Quantile regression estimates for Italy. The estimated coefficients [PITH_FULL_IMAGE:figures/full_fig_p022_6.png] view at source ↗

**Figure 7.** Figure 7: EU ETS carbon spot price p CO2 t vs. switching price p sw t . References Ahamada, I. and Kirat, D. (2015). The impact of Phase II of the EU ETS on wholesale electricity prices. Revue d’Economie Politique, 125(6):887–908. Ahamada, I. and Kirat, D. (2018). Non-linear pass-through of the co2 emission-allowance price onto wholesale electricity prices. Environmental Modeling & Assessment, 23(5):497– 510. 24 [P… view at source ↗

read the original abstract

This paper investigates the impact of carbon pricing under the EU Emissions Trading System (EU ETS) on the Italian electricity market, focusing on the carbon cost pass-through rate (CPTR) across market zones during Phases 3 and 4 (2016-2024). Using daily data, the study applies an econometric framework based on a linear regression model with autoregressive dynamics to estimate the extent to which carbon costs are reflected in wholesale electricity prices. It further incorporates robustness checks and quantile regression to assess how the CPTR varies across different fuel spread levels. The results show that carbon costs are positively and significantly transmitted to electricity prices, confirming the relevance of carbon pricing as a key market driver. However, pass-through is incomplete, with CPTR values consistently below 100%. At the national level, the CPTR remains relatively stable at around 30% across the two phases. Substantial heterogeneity emerges across market zones: pass-through increases in the North, Centre-North, and Sardinia during Phase 4, while it declines in the Centre-South and Sicily, reflecting differences in generation mix, carbon intensity, and market conditions. Overall, the findings highlight the importance of market zones factors in shaping the effectiveness of carbon pricing in electricity markets.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper supplies new zonal estimates of roughly 30% carbon cost pass-through in Italian electricity prices from 2016-2024, but the regressions may not fully isolate the effect from correlated fuel and demand movements.

read the letter

The paper's main contribution is a set of zone-level carbon cost pass-through rates for Italy using daily data across EU ETS phases 3 and 4. Nationally the rate sits around 30 percent and stays fairly stable between phases, while showing clear differences across zones that track generation mix and carbon intensity. The quantile regressions add a check on how the rate changes with fuel spreads. Those numbers are new for this market and period, and the zonal breakdown is a practical extension of earlier pass-through studies. The daily frequency and robustness mentions are also straightforward strengths for an empirical exercise like this. The data work looks solid enough on its face to support the claim of incomplete pass-through. The stress-test point on omitted variables is worth taking seriously. Wholesale prices move with gas and coal prices, renewable output, and load, all of which can covary with allowance prices. The abstract describes an autoregressive linear model but does not flag explicit fuel-price or weather controls, so it is possible the 30 percent figure partly captures those common shocks rather than the marginal carbon effect alone. Zonal heterogeneity could then reflect differing correlations with the omitted factors instead of genuine pass-through differences. Without the full tables and specification details it is hard to judge how much this matters, but the concern is not minor for a causal interpretation. This work is aimed at energy economists and policy analysts who need concrete benchmarks for carbon pricing in European power markets. Readers focused on Italy or on calibrating ETS impacts will find the numbers useful as a reference. It is grounded enough in recent data and a clear policy question to deserve a serious referee, though the identification checks will need close attention in review. I would send it out for peer review.

Referee Report

3 major / 2 minor

Summary. The paper investigates the carbon cost pass-through rate (CPTR) from EU ETS allowance prices to Italian wholesale electricity prices using daily zonal data over 2016-2024 (Phases 3 and 4). It estimates a linear regression model augmented with autoregressive dynamics, reports a national CPTR of approximately 30% that is stable across phases but incomplete (<100%), and documents zonal heterogeneity (increases in North, Centre-North, and Sardinia; declines in Centre-South and Sicily) via robustness checks and quantile regressions that condition on fuel-spread levels.

Significance. If the central estimates survive controls for fuel prices and demand shocks, the work would add to the empirical literature on ETS pass-through by providing high-frequency evidence of incomplete transmission and by linking zonal differences to generation-mix variation. The daily frequency and quantile approach are strengths that could inform policy on carbon pricing effectiveness in liberalized power markets.

major comments (3)

[Abstract and §3] The linear regression specification (described in the abstract and presumably detailed in §3) includes autoregressive lags but does not reference controls for gas/coal prices, renewable output, or load. Because these variables are jointly determined with EU ETS allowance prices, the reported CPTR coefficient is vulnerable to omitted-variable bias and cannot be interpreted as the causal marginal pass-through rate without further identification arguments.
[§4 and §5] The headline national CPTR of ~30% and the claim of stability across phases rest on the AR model isolating the carbon-cost effect. Without explicit fuel-price covariates, instrumental-variables strategies, or zone-by-time fixed effects that absorb common energy-market shocks, the zonal heterogeneity results may simply reflect differential correlations with omitted confounders rather than genuine differences in pass-through.
[Abstract and §4] The quantile-regression exercise is presented as assessing CPTR variation across fuel-spread levels, yet the abstract supplies no coefficient tables, standard errors, or tests for whether the quantile slopes differ statistically from the conditional-mean estimates. This weakens the supporting evidence for the claim that pass-through is incomplete and heterogeneous.

minor comments (2)

[Abstract] The abstract states that 'robustness checks' were performed but does not enumerate them (e.g., alternative lag orders, subsample periods, or placebo tests); a concise list would improve transparency.
[§3] Notation for the CPTR coefficient and the autoregressive terms should be defined explicitly at first use, and the exact lag order selected should be justified (information criteria, residual diagnostics).

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive comments on our manuscript. We address each major point below and describe the revisions we will implement to strengthen the identification and presentation of results.

read point-by-point responses

Referee: [Abstract and §3] The linear regression specification (described in the abstract and presumably detailed in §3) includes autoregressive lags but does not reference controls for gas/coal prices, renewable output, or load. Because these variables are jointly determined with EU ETS allowance prices, the reported CPTR coefficient is vulnerable to omitted-variable bias and cannot be interpreted as the causal marginal pass-through rate without further identification arguments.

Authors: We agree that the baseline AR specification is vulnerable to omitted-variable bias if fuel prices, renewables, and load are not controlled for. While the daily frequency and autoregressive lags capture some persistence and short-run dynamics, they do not fully isolate the carbon-cost effect from correlated energy-market shocks. In the revised manuscript we will augment the main specification with explicit controls for gas and coal prices, renewable generation shares, and zonal load, and we will report the resulting CPTR estimates alongside the original specification as a robustness check. revision: yes
Referee: [§4 and §5] The headline national CPTR of ~30% and the claim of stability across phases rest on the AR model isolating the carbon-cost effect. Without explicit fuel-price covariates, instrumental-variables strategies, or zone-by-time fixed effects that absorb common energy-market shocks, the zonal heterogeneity results may simply reflect differential correlations with omitted confounders rather than genuine differences in pass-through.

Authors: We acknowledge that the current zonal heterogeneity findings could partly reflect differential exposure to omitted confounders. To address this, the revised version will include (i) fuel-price covariates in all regressions, (ii) zone-by-time fixed effects to absorb common shocks, and (iii) a comparison of results with and without these controls. We will also add a brief discussion of why an IV strategy is difficult with daily zonal data but note that the added controls and fixed effects substantially mitigate the concern. revision: yes
Referee: [Abstract and §4] The quantile-regression exercise is presented as assessing CPTR variation across fuel-spread levels, yet the abstract supplies no coefficient tables, standard errors, or tests for whether the quantile slopes differ statistically from the conditional-mean estimates. This weakens the supporting evidence for the claim that pass-through is incomplete and heterogeneous.

Authors: We will revise the abstract to report the key quantile-regression coefficients, their standard errors, and the number of observations at each quantile. In §4 we will add formal Wald-type tests of equality between the quantile slopes and the conditional-mean CPTR, together with a table presenting the full set of quantile results. These additions will provide the statistical evidence currently missing. revision: yes

Circularity Check

0 steps flagged

No circularity: CPTR obtained as direct regression coefficient on observed data

full rationale

The paper estimates the carbon cost pass-through rate (CPTR) via a linear regression model with autoregressive dynamics fitted to daily zonal electricity prices and carbon costs. The reported CPTR values (approximately 30% nationally) are the fitted coefficients themselves, not quantities derived from prior fitted parameters or reduced by construction to earlier results. No self-definitional steps, fitted-input-called-prediction patterns, or load-bearing self-citations appear in the derivation; the central claim rests on standard econometric identification from external market data rather than internal re-use of the paper's own outputs.

Axiom & Free-Parameter Ledger

2 free parameters · 2 axioms · 0 invented entities

The central claim rests on the validity of a linear regression specification that treats carbon cost as an exogenous driver and on the assumption that daily zonal data adequately capture market conditions without major measurement error.

free parameters (2)

CPTR coefficient
The pass-through rate itself is estimated from the data rather than derived from theory.
Autoregressive lag coefficients
Parameters governing price persistence are fitted within the regression.

axioms (2)

domain assumption Carbon allowance price is exogenous to electricity price formation within the daily frequency
Invoked by the regression design; no instrumental-variables strategy is described in the abstract.
domain assumption Linear relationship between carbon cost and electricity price holds across the observed range
Core modeling choice; quantile regression is used as a robustness check but does not replace the linear assumption.

pith-pipeline@v0.9.0 · 5519 in / 1537 out tokens · 30628 ms · 2026-05-13T18:16:16.700436+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

9 extracted references · 9 canonical work pages

[1]

and Kirat, D

Ahamada, I. and Kirat, D. (2015). The impact of Phase II of the EU ETS on wholesale electricity prices.Revue d’Economie Politique, 125(6):887–908. Ahamada, I. and Kirat, D. (2018). Non-linear pass-through of the co2 emission-allowance price onto wholesale electricity prices.Environmental Modeling & Assessment, 23(5):497–

work page 2015
[2]

Determinazioni sui parametri tecnico-economici di impianti rilevanti inclusi nel programma di massimizzazione di cui all’articolo 5-bis del decreto-legge 25 febbraio 2022, n

24 ARERA (2024). Determinazioni sui parametri tecnico-economici di impianti rilevanti inclusi nel programma di massimizzazione di cui all’articolo 5-bis del decreto-legge 25 febbraio 2022, n

work page 2024
[3]

Baumol, W

Technical Report Deliberazione 270/2024/R/EEL, ARERA. Baumol, W. J. and Oates, W. E. (1988).The theory of environmental policy. Cambridge university press. Bertolini, M., Duttilo, P., and Lisi, F. (2025). Accounting carbon emissions from electricity generation: A review and comparison of emission factor-based methods.Applied Energy, 392:125992. Caporin, M...

work page 2024
[4]

Record reduction of 2023 ETS emissions due largely to boost in renewable energy

European Commission (2024). Record reduction of 2023 ETS emissions due largely to boost in renewable energy. Accessed: 2025-10-30. European Commission (2025a). About the EU Emissions Trading System (EU ETS). Ac- cessed: 2025-10-26. European Commission (2025b). EU ETS Emissions Cap.https://climate.ec. europa.eu/eu-action/carbon-markets/eu-emissions-trading...

work page 2024
[5]

Eu ets spot, futures & options.https://www.eex

European Energy Exchange (2025). Eu ets spot, futures & options.https://www.eex. com/en/markets/environmental-markets/eu-ets-spot-futures-options. Accessed: 2025-12-04. European Environment Agency (2025). EU Emissions Trading System (ETS) Data Viewer. Accessed: 2025-07-01. European Parliament (2023). Directive 2003/87/EC of the European Parliament and of ...

work page 2025
[6]

Huisman, R

Monitoraggio del mercato del carbonio. Huisman, R. and Kiliç, M. (2015). Time variation in european carbon pass-through rates in electricity futures prices.Energy Policy, 86:239–249. ISPRA (2025). National inventory of atmospheric emissions.https://emissioni.sina. isprambiente.it/inventario-nazionale/. Accessed: 2025-12-04. Ji, Q., Xia, T., Liu, F., and X...

work page 2015
[7]

Mazzarella, F

Accessed: 2026-03-21. Mazzarella, F. (2017).Electricity Markets and Environmental Policies. PhD Thesis, Roma Tre University. https://arcadia.sba.uniroma3.it/handle/2307/40475. Metcalf, G. E. (2009). Market-based policy options to control u.s. greenhouse gas emissions. Journal of Economic Perspectives, 23(2):5–27. Nazifi, F. (2016). The pass-through rates ...

work page doi:10.7916/d8-w2nc-4103 2026
[8]

Wooldridge, J. M. (2008).Introductory Econometrics: A Modern Approach. Cengage Learn- ing, 4 edition. Zhu, B., Ye, S., Han, D., Wang, P., He, K., Wei, Y.-M., and Xie, R. (2019). A multiscale analysis for carbon price drivers.Energy Economics, 78:202–216. Declaration of interestsThe authors report that financial support was provided by Eu- ropean Union - N...

work page 2008
[9]

31 0 200 400 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 €/MWh Sicily 0 200 400 600 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 €/MWh Sardinia 0 20 40 60 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 tCO2 eq € 0 25 50 75 100 125 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 tCO2 eq € 1500 2000 2500 3000 2016 2017 2018 2019 2020 2021 20...

work page 2016

[1] [1]

and Kirat, D

Ahamada, I. and Kirat, D. (2015). The impact of Phase II of the EU ETS on wholesale electricity prices.Revue d’Economie Politique, 125(6):887–908. Ahamada, I. and Kirat, D. (2018). Non-linear pass-through of the co2 emission-allowance price onto wholesale electricity prices.Environmental Modeling & Assessment, 23(5):497–

work page 2015

[2] [2]

Determinazioni sui parametri tecnico-economici di impianti rilevanti inclusi nel programma di massimizzazione di cui all’articolo 5-bis del decreto-legge 25 febbraio 2022, n

24 ARERA (2024). Determinazioni sui parametri tecnico-economici di impianti rilevanti inclusi nel programma di massimizzazione di cui all’articolo 5-bis del decreto-legge 25 febbraio 2022, n

work page 2024

[3] [3]

Baumol, W

Technical Report Deliberazione 270/2024/R/EEL, ARERA. Baumol, W. J. and Oates, W. E. (1988).The theory of environmental policy. Cambridge university press. Bertolini, M., Duttilo, P., and Lisi, F. (2025). Accounting carbon emissions from electricity generation: A review and comparison of emission factor-based methods.Applied Energy, 392:125992. Caporin, M...

work page 2024

[4] [4]

Record reduction of 2023 ETS emissions due largely to boost in renewable energy

European Commission (2024). Record reduction of 2023 ETS emissions due largely to boost in renewable energy. Accessed: 2025-10-30. European Commission (2025a). About the EU Emissions Trading System (EU ETS). Ac- cessed: 2025-10-26. European Commission (2025b). EU ETS Emissions Cap.https://climate.ec. europa.eu/eu-action/carbon-markets/eu-emissions-trading...

work page 2024

[5] [5]

Eu ets spot, futures & options.https://www.eex

European Energy Exchange (2025). Eu ets spot, futures & options.https://www.eex. com/en/markets/environmental-markets/eu-ets-spot-futures-options. Accessed: 2025-12-04. European Environment Agency (2025). EU Emissions Trading System (ETS) Data Viewer. Accessed: 2025-07-01. European Parliament (2023). Directive 2003/87/EC of the European Parliament and of ...

work page 2025

[6] [6]

Huisman, R

Monitoraggio del mercato del carbonio. Huisman, R. and Kiliç, M. (2015). Time variation in european carbon pass-through rates in electricity futures prices.Energy Policy, 86:239–249. ISPRA (2025). National inventory of atmospheric emissions.https://emissioni.sina. isprambiente.it/inventario-nazionale/. Accessed: 2025-12-04. Ji, Q., Xia, T., Liu, F., and X...

work page 2015

[7] [7]

Mazzarella, F

Accessed: 2026-03-21. Mazzarella, F. (2017).Electricity Markets and Environmental Policies. PhD Thesis, Roma Tre University. https://arcadia.sba.uniroma3.it/handle/2307/40475. Metcalf, G. E. (2009). Market-based policy options to control u.s. greenhouse gas emissions. Journal of Economic Perspectives, 23(2):5–27. Nazifi, F. (2016). The pass-through rates ...

work page doi:10.7916/d8-w2nc-4103 2026

[8] [8]

Wooldridge, J. M. (2008).Introductory Econometrics: A Modern Approach. Cengage Learn- ing, 4 edition. Zhu, B., Ye, S., Han, D., Wang, P., He, K., Wei, Y.-M., and Xie, R. (2019). A multiscale analysis for carbon price drivers.Energy Economics, 78:202–216. Declaration of interestsThe authors report that financial support was provided by Eu- ropean Union - N...

work page 2008

[9] [9]

31 0 200 400 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 €/MWh Sicily 0 200 400 600 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 €/MWh Sardinia 0 20 40 60 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 tCO2 eq € 0 25 50 75 100 125 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 tCO2 eq € 1500 2000 2500 3000 2016 2017 2018 2019 2020 2021 20...

work page 2016