# Has the EU-ETS Financed the Energy Transition of the Italian Power System?

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## Abstract

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## 1. Introduction

_{2}, is established. Power producers can use the allowances they own either to sell them to other market participants or to cover the greenhouse gas emissions resulting from their power production. Allowances are, therefore, financial products, exchanged in financial markets (Cludius and Betz 2020; Daskalakis et al. 2009; Graham et al. 2016; Harasheh and Amaduzzi 2019). By using the allowances to produce electricity, one bares the opportunity cost of losing their financial value. Rationally, power producers should respond to an increase in costs due to a rise in the value of the allowances by adopting low-emitting technologies. This happens if the rise in costs is perceived as permanent and if prices cannot be raised to shift the burden of the allowances to the final consumers. As a consequence, whenever power producers perceive the cost of the allowances as a relevant component of their production costs and they are not able to rise their bids in the wholesale market, they should progressively respond to an increase in the allowance price by reducing the power production costs. They can do so by minimizing the extent to which they use less efficient (i.e., more emitting) power plants, or by adapting their production portfolio mix to increase the share of plants fueled by renewable energy sources (from now onward, RES-plants). These solutions should increase the share of RES energy in the energy system, lower the amount of emissions, and induce a decreasing wholesale (electricity) price. This is perfectly in line with the “polluters pay” principle when applied to greenhouse gas emission in the power production sector. However, the ability of the EU-ETS to effectively endogenize emissions’ cost relies on the impossibility to shift the burden of the policy to final consumers. The extent to which the CO

_{2}emission costs can be passed to the buyers through an increase in the electricity price is defined as the pass-through rate. It is therefore crucial to evaluate whether and to what extent emission costs of the EU-ETS can be shifted to final consumers. Many empirical studies have tackled such a research question. Most of these studies focus on the first two phases of the EU Emission Trading System (from 2005 to 2007, and from 2008 to 2012, respectively), finding a relevant and high pass-through rate of EUA costs into electricity prices, yet with a large range. Sijm et al. (2006) report a cost pass-through of about 60–117% for Germany, and of 64–81% for the Netherlands. Sijm et al. (2008) extend this analysis and find a positive but incomplete carbon cost pass-through in the Netherlands, Germany, France and Sweden, and a full pass-through in the U.K. market. Fabra and Reguant (2014) measure a pass-through rate for Spain that differs between peak and off-peak hours, and ranges from 70% to 140% for the former and from 28% to 97% for the latter, depending on the model used. Honkatukia et al. (2008) find a pass-through that ranges between 75% and 95% when analyzing the Finish Nord Pool electricity spot prices. Hintermann (2016) establishes a pass-through that ranges between 81% and 111% for the wholesale electricity price in Germany. Jouvet and Solier (2013) consider a set of European countries and, by analyzing both the first and (a part of) the second period of the EU-ETS, they show that the pass-through rates are less relevant during the second phase and even negative in some countries. Other scholars have also found limited pass-through rates. Bariss et al. (2016) find a 55% pass-through rate in the Nordic market and a 65% pass-through rate in Baltic countries. Bunn and Fezzi (2008) show that the pass-through rates can be as low as 42%. For Ahamada and Kirat (2015), the pass-through rates of the French and German electricity baseload prices during the second phase of the EU-ETS are even lower, roughly around 18%. Ahamada and Kirat (2018) further show that the pass-through rate is non-linear and dependent on the threshold levels of the allowances’ prices. Similarly, using futures on electricity, very low pass-through rates are obtained for Germany, France, Belgium and the Netherlands by Lo Prete and Norman (2013). Chernyavs’ka and Gulli (2008) follow a different approach and instead of relying on econometric analysis, they simulate the load duration curve and the merit order supply curve for two zones of the Italian market, showing that the pass-through depends on whether it refers to peak or off-peak hours and on the level of competition across producers.

## 2. Materials and Methods

_{2}prices, which we collected from the Refinitiv Eikon database. Moreover, by averaging daily prices, we control for intraday volatility and intraday price patterns: in this way, we can focus on the determinants of prices, which include emission costs. In addition, by excluding the weekends, we remove the most relevant component of the periodic evolution of electricity prices. Consequently, there is no need to introduce further seasonal adjustments on the electricity prices. Finally, as far as the analyses are concerned, we first consider the whole set of hourly prices, and we later distinguish between peak and off-peak prices. The latter distinction allows to take into consideration the differences in the marginal technology that occur between peak and off-peak hours.

## 3. Results

_{t}are almost equivalent to shocks on the correlated residuals ε

_{t}(see Equation (5)) and the IRFs are very close to those obtained after a one standard deviation shock on the VECM residuals. Given these strong similarities, the IRFs can be interpreted as if they were obtained with a one standard deviation shock on the correlated residuals (i.e., non-orthogonalized). In the tri-variate model, the standard deviations of the shocks are around 0.091 for the energy price, 0.017 for gas and 0.033 for allowances. Differently, in the four-variate model, 0.118 is for peak prices, 0.081 for off-peak prices, and for gas and allowances, they are comparable to the previous ones.

## 4. Conclusions

_{2}emitting technologies with less emitting technologies, or even carbon-neutral ones. However, this depends also on the degree of competitiveness of power markets. The more that players have market power, the less the system marginal price would reflect the cost structure of power generation of the marginal plants. Therefore, the substitution effect can take place only if the market is sufficiently competitive. At first sight, this is what has happened in the structure of the power production in Italy during the third phase of the EU-ETS, which experienced a large penetration of RES-fueled generation capacity that has replaced the share of large CO

_{2}emitters (in particular, oil and coal plants). In particular, the share of power produced by RES in Italy increased from 33.9% in 2013 to 40.83% in 2018, with the final goal for renewables to surpass natural gas as the primary fuel for electric power generation by 2020. In the power market, the increase in electricity produced by RES-fueled plants lowers electricity prices since these plants produce at null or low marginal costs. This is what happened in Italy, which experienced a decrease in the average day-ahead price, which can hardly be explained by market power since the Italian wholesale market is quite competitive, at least in its biggest zone, North (note, however, that a precise assessment of the degree of competitiveness and its impact on the day-ahead price goes beyond the scope of the present paper). However, the reduction in electricity price, captured by a negative pass-through rate and an increasing price of the allowances, is not necessarily caused by a variation in the emissions cost component. The negative pass-through rates do not imply per se that the causality order necessarily goes from the allowances prices to the electricity ones through the technical substitution effect. Indeed, the rise in RES-plants production and in allowances’ prices might be induced by some policy-driven technological changes (such as incentive policies, regulatory changes in the electricity sector, more stringent environmental rules and similar).

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Appendix A

#### Appendix A.1. Summary Statistics and Graphical Representation

Variable | Unit | Mean | St. Dev. | Skewness | Kurtosis |
---|---|---|---|---|---|

PUN | €/MWh | 57.7778 | 12.6281 | 0.5488 | 4.2116 |

PUNp | €/MWh | 60.7118 | 14.7954 | 0.5792 | 4.3579 |

PUNop | €/MWh | 53.8781 | 10.7104 | 0.51488 | 3.9901 |

Gas | €/MWh | 22.0660 | 4.4279 | −0.0837 | 2.1017 |

EUA | €/ton | 7.5589 | 4.3392 | 2.1085 | 6.7950 |

#### Appendix A.2. Stationarity Tests

_{2}emissions and gas prices requires a preliminary evaluation of the time-series features. In fact, different modeling strategies might be adopted depending on the stationarity properties of the observed sequences. We test if the variables of interest are non-stationary in order to exclude spurious results, through the KPSS and the ADF tests. For both tests, we perform the analysis on the series log levels as well as on the first-order difference. Results are reported in Table A2. Note that for the KPSS, the null hypothesis corresponds to the series stationarity, while for the ADF test, the null hypothesis indicates the existence of a unit root. Identifying the presence of non-stationarity in the series log-levels allows to search for cointegration relationships among the variables. KPSS and ADF tests are concordant in suggesting that the EUA and gas log-prices are non-stationary. Differently, when focusing on the electricity prices, the ADF test rejects the null of non-stationary, while the KPSS test rejects the null of stationarity. We are thus in a situation where it is not known with certainty whether the series are trend stationary or non-stationary. As our main interest is the identification of the existence of level relationships between our variables, we adopt the bound testing approach of Pesaran et al. (2001), which introduces a testing framework for identifying the presence of cointegration in cases where there is uncertainty on the stationarity properties of the series of interest. The test builds on an auto-regressive distributed lag model of the form

_{t}is the dependent variable in log-levels, x

_{t}is a vector containing other variables (again in log-levels) that are potentially cointegrated with y

_{t}, and Δ denotes first order differences.

Variable | Test | Lag | Test Statistic | Lag | Test Statistic |
---|---|---|---|---|---|

Log-Levels | Changes in Log-Levels | ||||

PUN | KPSS | 0.547 *** | 0.034 | ||

PUNp | 0.463 *** | 0.030 | |||

PUNop | 0.651 *** | 0.059 | |||

EUA | 0.599 *** | 0.331 | |||

Gas | 0.781 *** | 0.152 | |||

PUN | ADF | 3 | −5.187 *** | 2 | −32.194 *** |

PUNp | 3 | −5.387 *** | 2 | −35.453 *** | |

PUNop | 3 | −5.378 *** | 3 | −26.109 *** | |

EUA | 2 | 0.180 | 3 | −19.920 *** | |

Gas | 2 | −1.826 | 3 | −19.824 *** |

${\mathit{y}}_{\mathit{t}}$ | ${\mathit{x}}_{\mathit{t}}$ | ${\mathbf{\Pi}}_{\mathit{y}}\mathbf{=}\mathbf{0}{\displaystyle \mathbf{\cup}}{\mathbf{\Pi}}_{\mathit{x}}^{\mathbf{\prime}}\mathbf{=}\mathbf{0}$ | ${\mathbf{\Pi}}_{\mathit{y}}\mathbf{=}\mathbf{0}$ |
---|---|---|---|

PUN | GAS, EUA | 18.21 | −7.39 |

PUNP | GAS, EUA | 16.80 | −7.09 |

PUNOP | GAS, EUA | 20.04 | −7.74 |

#### Appendix A.3. IRFs for Model 2 and Model 3

**Figure A2.**Impulse response functions of the VECM model (three variables, peak PUN, one cointegration relation). The shaded area represents the 90% confidence interval.

**Figure A3.**Impulse response functions of the VECM model (three variables, off-peak PUN, one cointegration relation). The shaded area represents the 90% confidence interval.

## Notes

1 | In the considered dataset, there are no negative prices, since in the MGP negative prices are not allowed. |

2 | Note that we are not claiming anything about the theoretical or effective efficiency of the EU-ETS allowances’ markets per se. On this topic, there exists a vast literature. See, for instance, Ibikunle and Gregoriu (2018); Hintermann (2017); and Rannou (2017). |

3 |

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**Figure 1.**Rolling analysis for Models 1, 2 and 3. The graphs show the beta coefficients (and their relative confidence intervals) for allowances (left-hand side) and natural gas (right-hand side) resulting from the rolling analysis.

**Figure 2.**Impulse response functions of the VECM Model 1 (three variables, PUN, and one cointegration relation). The shaded area represents the 90% confidence interval.

**Figure 3.**Impulse response functions of the VECM Model 4 (four variables, peak and off-peak PUN, two cointegration relations). The shaded area represents the 90% confidence interval.

Maximum Rank | Eigenvalue | Trace Statistic | 5% Critical Value | Max Statistic | 5% Critical Value |
---|---|---|---|---|---|

0 | 0.04087 | 68.7412 | 29.68 | 65.1016 | 20.97 |

1 | 0.00232 | 3.6396 * | 15.41 | 3.6296 * | 14.07 |

2 | 0.00001 | 0.0100 | 3.76 | 0.0100 | 3.76 |

Maximum Rank | Eigenvalue | Trace Statistic | 5% Critical Value | Max Statistic | 5% Critical Value |
---|---|---|---|---|---|

0 | 0.05075 | 144.7429 | 47.21 | 81.2428 | 27.07 |

1 | 0.03765 | 63.5001 | 29.68 | 59.8750 | 20.97 |

2 | 0.00231 | 3.6251 * | 15.41 | 3.6141 * | 14.07 |

3 | 0.00001 | 0.0110 | 3.76 | 0.0110 | 3.76 |

Models | ||||||
---|---|---|---|---|---|---|

1 | 2 | 3 | 4 | 4 | ||

Betas | LogPUN | 1 | - | - | - | - |

LogPUNp | - | 1 | - | 1 | 0 | |

LogPUNop | - | - | 1 | 0 | 1 | |

LogGas | −0.729 *** (0.078) | −0.749 *** (0.097) | −0.694 *** (0.065) | −0.745 *** (0.098) | −0.690 *** (0.066) | |

LogEUA | −0.069 * (0.038) | −0.077 * (0.047) | −0.061 ** (0.031) | −0.077 * (0.047) | −0.061 * (0.032) | |

Alphas | PUN | −0.140 *** (0.017) | - | - | - | - |

PUNp | - | −0.151 *** (0.019) | - | −0.193 *** (0.031) | 0.089 ** (0.043) | |

PUNop | - | - | −0.145 *** (0.018) | 0.010 (0.021) | −0.156 *** (0.029) | |

Gas | 0.005 (0.003) | 0.002 (0.005) | 0.010 ** (0.004) | −0.010 ** (0.004) | 0.020 *** (0.006) | |

EUA | 0.003 (0.006) | 0.002 (0.002) | 0.004 (0.007) | −0.003 (0.009) | 0.008 (0.012) |

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**MDPI and ACS Style**

Caporin, M.; Fontini, F.; Segato, S.
Has the EU-ETS Financed the Energy Transition of the Italian Power System? *Int. J. Financial Stud.* **2021**, *9*, 71.
https://doi.org/10.3390/ijfs9040071

**AMA Style**

Caporin M, Fontini F, Segato S.
Has the EU-ETS Financed the Energy Transition of the Italian Power System? *International Journal of Financial Studies*. 2021; 9(4):71.
https://doi.org/10.3390/ijfs9040071

**Chicago/Turabian Style**

Caporin, Massimiliano, Fulvio Fontini, and Samuele Segato.
2021. "Has the EU-ETS Financed the Energy Transition of the Italian Power System?" *International Journal of Financial Studies* 9, no. 4: 71.
https://doi.org/10.3390/ijfs9040071