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Abstract
As the number of instruments applied in the area of energy and climate policy is rising, the issue of policy interaction needs to be explored further. This article analyses the interdependencies between the EU Emissions Trading Scheme (EU ETS) and the German feed-in tariffs (FITs) for renewable electricity in a quantitative manner using a bottom-up energy system model. Flexible modelling approaches are presented for both instruments, with which all impacts on the energy system can be evaluated endogenously. It is shown that national climate policy measures can have an effect on the supranational emissions trading system by increasing emission reduction in the German electricity sector by up to 79 MtCO2 in 2030. As a result, emission certificate prices decline by between 1.9 €/tCO2 and 6.1 €/tCO2 and the burden sharing between participating countries changes, but no additional emission reduction is achieved at the European level. This also implies, however, that the cost efficiency of such a cap-and-trade system is distorted, with additional costs of the FIT system of up to €320 billion compared with lower costs for ETS emission certificates of between €44 billion and €57 billion (cumulated over the period 2013–2020).
Policy relevance
In order to fulfil ambitious emission reduction targets a large variety of climate policy instruments are being implemented in Europe. While some, like the EU ETS, directly address CO2 emissions, others aim to promote specific low-carbon technologies. The quantitative analysis of the interactions between the EU ETS and the German FIT scheme for renewable sources in electricity generation presented in this article helps to understand the importance of such interaction effects. Even though justifications can be found for the implementation of both types of instrument, the impact of the widespread use of support mechanisms for renewable electricity in Europe needs to be taken into account when fixing the reduction targets for the EU ETS in order to ensure a credible long-term investment signal.
Notes
1. The industry sector comprises 14 energy and non-energy intensive subsectors; service/commercial is described by four different building types for heat demand and six additional demand categories, such as lighting or ICT, and also includes agriculture; the residential sector comprises ten different building types for heat and hot water demand and seven additional demand categories, such as electrical appliances or refrigeration; the demand in the transport sector is divided into seven different transport modes.
2. The fixed investment cost assumptions for renewable technologies are an important determinant for the results. Sensitivity analyses have been conducted showing that the results are generally relatively robust, with the exemption of generation based on offshore wind (for which comparatively conservative assumptions have been used) and geothermal energy. Also note that, for solar photovoltaics, relatively conservative cost assumptions are chosen.
3. Note that in the model runs with TIMES PanEU, minimum amounts of electricity generation from renewable sources were set for each country to reflect the national support mechanisms (outside Germany) in an implicit manner.
4. The representation of the cross-border electricity exchange in TIMES PanEU takes into account the currently installed transmission capacity as well as possible future additions based on information from ENTSO-E (European Network of Transmission System Operators for Electricity).
5. For the present analysis it is assumed that the German FIT system is maintained in its current version until 2030. Thus, due to inflation and the annual degression rates, the tariffs are comparatively low towards the end of the time horizon. The electricity generation and associated FIT costs of all existing plants under the system (installed between 2000 and 2012) are bound into the model. The direct marketing schemes introduced in 2012 and the ‘own consumption’ regulation for electricity generated from solar photovoltaics are not taken into account.
6. The FIT surcharge is calculated as the difference between the average FIT tariff and the average annual wholesale electricity price times the percentage share of electricity remunerated through the FIT system in total final electricity consumption.
7. Based on a step function, grid expansion costs of up to 400 €2007/kW of installed capacity of solar photovoltaics as well as onshore and offshore wind energy for the transmission grid and of up to 500 €2007/kW of installed capacity of solar photovoltaics and onshore wind energy for the distribution grid are assumed (based on E-Bridge, BET, & IAEW, Citation2011; dena, Citation2010; EC, Citation2011). Concerning the use of storage capacities, a rule is applied specifying that at all times a maximum of 20% of the electricity supplied to the grid may originate directly from fluctuating sources without the necessity for intermediate storage (Götz et al., Citation2012b).
8. Note that the ETS certificate prices resulting from the present scenario analysis are generally slightly lower than those predicted in other recent studies (e.g. the scenario analyses for the Energy Roadmap 2050 (EC, Citation2011) yield a certificate price of 15 €2008/tCO2 for 2020 and of 32 €2008/tCO2 for 2030).
9. All cost and price data in Section 3 are expressed in real terms (with 2010 as the base year), if not stated otherwise.
10. The costs for ETS emission certificates are calculated as the difference in ETS emissions in Germany multiplied by the respective certificate price, and therefore contain both actual costs (in the case of auctioning of certificates) and opportunity costs (in the case of free allocation of certificates).