Abstract
Subsidies to electricity generation from renewable energy sources (RES-E) implemented next to an emissions trading scheme (ETS) are frequently criticised for producing no additional benefit in terms of mitigating climate change and increasing the costs of emissions abatement. We re-assess the performance of this policy mix in a setting in which electricity generation produces multiple externalities (beyond climate change) and in which these externalities cannot be addressed by first-best policies. Using an analytical partial equilibrium model, we show that the optimal composition of the policy mix depends on the market interactions between the multiple externalities. We complement this analysis by a quantitative policy assessment, combining a top-down, global macro-economic model and a bottom-up, global electricity sector model. The quantitative analysis suggests that RES-E subsidies may be effective in partly reducing externalities from fossil fuel combustion (by crowding out gas- and oil-fired generation) and in mitigating radiation hazards (by crowding out nuclear generation). However, RES-E subsidies are not necessarily suited to address externalities related to the extraction and transportation of fossil fuels or risks of sudden supply interruptions for imported fuels. With respect to these latter externalities, tightening the ETS cap may be a more effective, but politically less feasible approach.
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Notes
This concept is different from the idea of “correlated externalities”, as introduced by (Caplan and Silva 2005). In this case, multiple pollutants are jointly produced by a single source or production process, e.g. CO2 and SOX emissions from fossil fuel combustion, i.e. externalities are technically linked.
A more explicit approach to include the levy in our model would be computing it as l = sz/Q, which would represent the condition for cost-neutrality of the RES-E subsidy. However, the multiplicative functional relationship would make the following comparative-static analysis significantly more complex and produce analytically results that are not readily interpretable anymore. Therefore, we choose the simpler and more general functional form of Eq. (6).
This assumption is plausible if the RES-E levy can be specified as \(l = sz/Q\) (see Footnote 3). In this case, \(L_{s} = z/Q < 1\).
Certainly, such assumption has to be treated with due care since emissions of air pollutants are a function of the characteristics of the specific fuels used, the combustion process as well as the means of pollution control employed. Moreover, the damages produced by air pollution are location-specific and therefore crucially dependent on the spatial patterns of substitution between coal-fired and gas-fired generation.
In reality, however, allocation is more complicated as the electricity sector in ten Eastern European countries is allowed to receive a (declining) share of its EUAs for free while some industries—not regarded to be exposed to the risk of carbon leakage—have to buy a (growing) share of their EUAs at an auction. For specific details on EUA allocation issues, see the website of DG CLIMA (European Commission 2013a).
Besides the baseline scenario, the PRIMES 2009 projections also cover a so-called ‘reference scenario’ which includes additional policies adopted between April 2009 and December 2009. This reference scenario assumes that the two binding EU targets for 2020 will be met (i.e. the 20% renewable energy target and the 20% GHG reduction target). The results for the reference scenario turn out that only half of the third, non-binding target will be achieved (i.e. 9.5% rather than the target of 20% energy savings by 2020). For more details on the PRIMES 2009 baseline and reference scenario projections, see European Commission (2010a).
These price patterns conform largely to the PRIMES projection of the so-called ‘EU Reference Scenario 2013’, which gives an ETS carbon price of 5, 10 and 35 €/tCO2 in 2015, 2020 and 2030, respectively (European Commission 2013a).
The major reason why the EU ETS carbon price in 2015 and 2020 is higher in the ETS scenario (and the other policy scenarios) than in the BASE scenario—while the EU ETS cap in these years is similar in all scenarios—is that the assumed policy changes up to 2030, and the resulting ETS carbon prices up to 2030, lead to different EUA banking patterns over the years 2015–2020 and, therefore, to other CO2 price patterns over these years.
Note that, in modelling terms, this RES-E policy is exactly similar to setting an EU-wide RES-E quota (of 40% by 2030) with an EU-single green certificate trading system in which the certificate price is similar to the uniform feed-in subsidy level.
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Acknowledgements
We are grateful to two anonymous referees. Research for this paper was supported by the German Helmholtz Association (Grant number HA-303). Paul Lehmann additionally received funding from the German Ministry of Education and Research (Grant number 01UU1703).
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Appendix
Appendix
Totally differentiating Eqs. (1)–(6) and (8)–(10) yields:
Substituting (21) into (22) and (24), we obtain:
Substituting (19) into (23) gives:
Substituting (28)–(30) into (25)–(27) yields:
Solving the equations system (31)–(33) for \({\text{d}}x\), \({\text{d}}y\) and \(dz\) gives Eqs. (11)–(13). Substituting Eqs. (11)–(13) into (19)–(21) yields Eqs. (14), (16) and (18). Substituting (14) into (22) and (16) into (23) gives Eqs. (15) and (17).
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Lehmann, P., Sijm, J., Gawel, E. et al. Addressing multiple externalities from electricity generation: a case for EU renewable energy policy beyond 2020?. Environ Econ Policy Stud 21, 255–283 (2019). https://doi.org/10.1007/s10018-018-0229-6
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DOI: https://doi.org/10.1007/s10018-018-0229-6