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Can Technology-Specific Deployment Policies Be Cost-Effective? The Case of Renewable Energy Support Schemes

Environmental and Resource Economics volume 71, pages 475–505 (2018)Cite this article

Abstract

While there is relatively limited disagreement on the general need for supporting the deployment of renewable energy sources for electricity generation (RES-E), there are diverging views on whether the granted support levels should be technology-neutral or technology-specific. In this review paper we question the frequently stressed argument that technology-neutral schemes will promote RES-E deployment cost-effectively. We use a simple partial equilibrium model of the electricity sector with one representative investor as a vehicle to synthesize the existing literature, and review potential rationales for technology-specific RES-E support. The analysis addresses market failures associated with technological development, long-term risk taking, path dependencies as well as various external costs, all of which drive a wedge between the private and the social costs of RES-E deployment. Based on analytical insight and a review of empirical literature, we conclude that the relevance of these market failures is typically heterogeneous across different RES-E technologies. The paper also discusses a number of possible caveats to implementing cost-effective technology-specific support schemes in practice, including the role of various informational and politico-economic constraints. While these considerations involve important challenges, neither of them suggests an unambiguous plea for technology-neutral RES-E support policies either. We close by highlighting principles for careful RES-E policy design, and by outlining four important avenues for future research.

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Fig. 1

Source: Own figure based on data from BMWi (2015)

Fig. 2

Source: Own figure based on data from Ofgem (2015)

Fig. 3
Fig. 4

Notes

  1. The choice between a generic (technology-neutral) and a more specific innovation policy is discussed also in Aalbers et al. (2013) and Nordhaus (2011), but in both these cases with a focus on the diversification of public R&D support.

  2. One potential reason for this is put forward by Budish et al. (2015). These authors note that while patents award innovating firms a certain period of market exclusitivity, the effective term may be considerably shorter since some firms choose to file patents at the time of discovery rather than at first sale. This implies that the patent system may provide meager incentives for firms to engage in learning about technologies that have a long time between invention and commercialization.

  3. Analytically, this is shown in multiple more elaborate studies (Kverndokk and Rosendahl 2007; Fischer and Newell 2008; van Benthem et al. 2008; Bläsi and Requate 2010; Kalkuhl et al. 2012; Lehmann 2013).

  4. Kverndokk and Rosendahl (2007) and Lehmann (2013) also confirm this finding in more complex modeling settings.

  5. Empirically it remains very difficult to separate learning-by-doing from exogenous technological change and economies of scale (see also, Söderholm and Sundqvist 2007). For instance, significant advances in solar PV technology have resulted as a result of investments made outside the RES-E sector, such as in the semi-conductor industry (Nemet 2006).

  6. Certainly, not all successive patent citations can be assumed to be external to innovating firms. In this sense, the actual market failures are likely less profound than suggested by the results reported in Noailly and Shestalova (2013). This notwithstanding, the figures presented clearly illustrate the heterogeneity in terms of spillovers.

  7. Certainly, the values provided in Table 1 do not necessarily indicate a probabilistic variance of possible learning rates. Instead, the different results may also be strongly driven by differences in the methodological approach, the data used as well as the specific context of the empirical case study (Lindman and Söderholm 2012). This notwithstanding, a significant share of learning is likely to be driven by global developments in technological change. So the figures provided in Table 1 do at least to some extent illustrate the general uncertainty regarding technological learning. What is more, the fact that observed learning rates may also be driven by methodology, data and context only increases the uncertainty on the learning rates that should be considered for specific investments.

  8. Certainly, myopic behavior may also be due to other factors such as the fact that firm managers are only appointed for short periods of time (e.g., 4–5 years). Stein (1989) argues that firms may be acting inefficiently with a bias towards short-term payoffs due to agency problems within the firm, thus suggesting myopic behavior also in the presence of efficient capital markets.

  9. Differences in terms of ownership and traditions may affect future pathways. For instance, in his assessment of the electricity regimes in the Nordic countries, Thue (1995) notes that while the Danish electricity system has largely been organized in a bottom-up manner with cooperative organizations and municipalities as owners of power stations, the Swedish system has been more hierarchical. The latter led to a relative lack of experience of investment in small-scale plants in Sweden, and instead a focus on large state-supported hydropower and nuclear energy. During the 1990s this contributed to a relative lack of Swedish interest in wind power, while the tradition of local ownership enabled high level of penetration of small-scale wind power in Denmark.

  10. See, for example, §35 of the German Building Code, which privileges the use of wind power and bioenergy; corresponding plants can be installed in areas for which no formal development plan exists, i.e., in areas where other types of developments are ruled out.

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Acknowledgements

Research for this article has been funded by the German Helmholtz Association under Grant HA-303 as well as the Swedish Research Council Formas. We are particularly grateful to Editor David Popp and two anonymous referees for their numerous constructive comments, which have helped to improve the article significantly. Moreover, the article has benefited from discussions with Erik Gawel and Alexandra Purkus. All remaining errors reside solely with the authors.

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  1. Helmholtz Centre for Environmental Research – UFZ, Permoserstr. 15, 04318, Leipzig, Germany

    Paul Lehmann

  2. Economics Unit, Luleå University of Technology, 97187, Luleå, Sweden

    Patrik Söderholm

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Lehmann, P., Söderholm, P. Can Technology-Specific Deployment Policies Be Cost-Effective? The Case of Renewable Energy Support Schemes. Environ Resource Econ 71, 475–505 (2018). https://doi.org/10.1007/s10640-017-0169-9

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  • DOI: https://doi.org/10.1007/s10640-017-0169-9

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