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

Pierdomenico Duttilo, Francesco Lisi

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.

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

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.

Paper Structure

This paper contains 18 sections, 12 equations, 14 figures, 13 tables.

Table of Contents

  1. Introduction
  2. Background
  3. Evolution of the EU ETS
  4. The Italian electricity market
  5. Literature review
  6. Data
  7. Methodology
  8. Stationary and autocorrelation issues
  9. CPTR estimation
  10. Empirical results
  11. The baseline model
  12. The quantile regression model
  13. Concluding remarks and policy implications
  14. Declaration of interests
  15. Acknowledgments
  16. ...and 3 more sections

Figures (14)

  • Figure 1: CO$_2$ emissions (in million tCO$_2$e) in the power industry of top emitting economies, 1970-2024.
  • Figure 2: EU ETS allowances (in million tCO$_2$e) by year.
  • Figure 3: Daily spreads between electricity prices and fuel costs (top panels), carbon costs (middle panels), and electricity demand (bottom panels) for Italy and the North zone, from 1 January 2016 to 31 December 2024.
  • Figure 4: Autocorrelation (ACF) and partial autocorrelation (PACF) functions of the spread for Italy. The top panels show the ACF and PACF of the spread in levels, while the bottom panels show those of the log-differenced spread.
  • Figure 5: Autocorrelation (ACF) and partial autocorrelation (PACF) functions of the residuals for the estimated regression model for Italy.
  • ...and 9 more figures