5. Principles for the Public Promotion of Photovoltaic Generation

In turn, this contribution depends on how the instruments and design elements are implemented. Policies that are easy to understand, transparent in terms of eligibility and compliance and stable in duration and statute (Sovacool 2010) are more likely to achieve those goals.

Obviously, given their qualitative differences, different technologies contribute differently to those goals and their contribution can be evaluated with the assessment criteria.

This section heavily draws from work performed in EU-funded projects (BEYOND2020, TOWARDS2030 and AURES) See http://​www.​res-policy-beyond2020.​eu/​, http://​towards2030.​eu/​ and http://​auresproject.​eu/​. See del Río et al. ( 2012), del Río and Peñasco ( 2014) and del Río et al. ( 2015) for further details. Beyond 2020 and towards were funded by the “Intelligent Energy—Europe” (IEE) programme of the European Commission (operated by EACI and EASME). AURES has been funded under the Horizon 2020 programme of the European Commission.

See, e.g. Schmalensse ( 2011), Green and Yatchew ( 2012), Heal ( 2010). For example, Schmalensee ( 2011: 10) argues that “The notion of ex-post efficiency […] involves taking detailed policy goals as given and asking whether they are likely to be attained at minimum cost or anything close to it. In the case of renewable energy this mainly requires production at the best sites, given the technologies required or allowed to be employed. […] Ex post efficiency as regards the top-line twenty percent target requires E.U.-wide equalization of the marginal cost of producing electricity from renewable energy”. There are other authors which take the other extreme, i.e. they only look at the costs of support and disregard the minimization of generation costs (i.e. Verbruggen and Lauber 2012), including the very influential IPCC report on renewables (Mitchell et al. 2011).

See, e.g. Huber et al ( 2004), EC ( 2008), Ragwitz et al. ( 2007), IEA ( 2008a, 2011), Mitchell et al. ( 2011), among others. Note, however, that policy costs mostly refer to distributional issues between RES-E generators, electricity consumers and, eventually, taxpayers.

See del Río and Cerdá ( 2014) for further explanations regarding the concept of minimization of generation costs and minimization of support costs.

In the responses to the Green Paper (EC 2013b), many EU countries underline that a greater focus on innovation is essential to ensure the feasibility and security of the EU energy system and for the further development of a portfolio of cost-effective and sustainable energy options RES-E policy in a broad sense should promote innovation. RES-E policy in this context encompasses not only support for diffusion (deployment), but also support for RD&D. However, it should be taken into account that deployment policies have innovation effects. As recently put by the European Commission itself, “support scheme design should also reflect the need to address longer term goals of fostering technological innovation, economies of scale, cost-reduction and spillover effects that facilitate reaching 2020 targets and reaching 2050 decarbonisation goals sustainably. Member States may also have a clear objective of promoting technology innovation in renewables to ensure the cost-effective medium term transition to a sustainable energy system” (EC 2013b: 8).

The need for a large-scale deployment of renewables to reduce CO ₂ emissions is common in the projections made with simulation models (see, e.g. IEA 2014a, b; Edenhofer et al. 2009).

The importance of these dynamic efficiency effects is shown by both renewable energy models and climate change models (see, e.g. Stern 2007).

See, for example, EC ( 2013a).

The Directive also set non-legally binding trajectories towards the national target.

Member States have developed their own tailor-made energy policies, which include different goals, ambitions and preferences. Not all Member States share a comparable ambition towards renewable energy, and they are not willing to transfer the required competences to a European level (Resch et al. 2013: 15).

Different types of learning effects have been considered in the literature, including learning-by-doing, learning-by-using, learning-by-interacting and learning-from-suppliers.

Notwithstanding, the competition for engineering, material and human resources may offset costs reductions due to economies of scale and learning effects (IEA 2009).

Greater deployment accelerates technological progress and provides economies of scale in manufacturing the associated equipment. The extent of the reductions depends on the maturity of the technology (IEA 2009).

For example, the diffusion of solar PV technologies depends on improvements in complementary technologies such as transmission and energy storage that will facilitate spread (Barrett 2009).

The economics of innovation has been particularly relevant in this context in so far as it has stressed the need to open the black box of technology, traditionally locked for economists. Opening the black box of low-carbon technologies (and sources of low-carbon technological change) may prove a fruitful approach to propose appropriate policy measures. Too much abstraction with respect to technologies might be of limited policy relevance.

This section strongly draws on del Río ( 2014, 2015).

See also Malueg ( 1989), Driesen ( 2003) and del Río ( 2015) for a detailed discussion on the innovation impacts of an ETS.

During the first compliance period (2005–2007), the EU allowance price reached a peak near 30€ in mid 2006, declining gradually to near 0€ in February 2007 and remaining at such level for the rest of 2007. In the second compliance period (2008–2012), the price reached a peak near 30€ in early 2008 and then stabilized at around 15€ for most of the rest of the period, declining to around 8€ by the end of it.