In fact, in case the superior technology becomes dominant early on, societal costs are reduced. It is important to note that premature technological lock-in is not per se economically inefficient. An example is the carbon lock-in of the current energy system which not only is the main source of climate change but also leaves the economy vulnerable to fossil-fuel price shocks, such as the 1970s oil crises (Unruh 2000, 2002). Second, premature technological lock-in, all else equal, inherently reduces long-term diversity and hence the resilience of the energy system against external shocks (van den Bergh et al 2006, Stirling 2010). As predicting future learning curves of early-stage technologies is difficult, encouraging technological diversity is regarded a viable alternative to achieve long-term economic efficiency (Arthur 1989, Sandén and Azar 2005, del Río González 2008, Schmidt et al 2016). Thus, long-term economic cost of deployment would be higher because faster learning technologies would not have been deployed in their early stage. These arise from locking out technologies which, though more expensive at the outset, would offer superior learning rates. First, it can result in long-term inefficiencies (Schmidt et al 2016). Lock-in may be undesirable in early-stage technologies for two reasons. For novel technologies, the early phase of market deployment is pivotal to drive down their learning curve and thus determining their future competitiveness with other technologies (Hoppmann et al 2013). This phenomenon arises from self-reinforcing and path-dependent processes which reduce the cost of the adopted technology, such as economies of scale, learning-by-doing, learning-by-using, and network externalities, and hence give it a sustained advantage over competing technologies (Dosi 1982, David 1985, Arthur 1989, Sandén and Azar 2005, van den Bergh 2008). Yet deployment policies may drive unintended and premature technological lock-in, a situation where one technology is almost exclusively selected at the expense of other technologies (Unruh 2000, Zeppini and van den Bergh 2011, Battke et al 2016). Technology deployment policies are also important for new technologies necessary for the deep decarbonisation of energy systems, such as battery storage (Trancik 2014, Landry and Gagnon 2015, Malhotra et al 2016). As a result, today, solar PV is cost competitive with conventional technologies in many markets (Creutzig et al 2017). Solar photovoltaics (PV), for instance, has experienced a steep increase in global deployment to 300 GW in 2016 (REN21 2017) and a sharp decrease in cost of over 99% in the past four decades (Trancik et al 2015). Deployment policies have been crucial in triggering capacity additions and thus inducing technological learning and cost reductions, particularly for renewable energy technologies (del Río González 2008, Fouquet and Johansson 2008, Couture and Gagnon 2010, Huenteler et al 2016). Besides carbon pricing and R&D support, technology deployment policies play a central role in inducing such transitions by addressing market failures associated with learning by doing (Sandén and Azar 2005, van Benthem et al 2008, van den Bergh 2013, Bertram et al 2015). To avoid this, policymakers can leverage the fact that different technologies are competitive in different applications and, by designing application-specific deployment policies, effectively offer a level playing field for competing technologies.Ĭlimate change mitigation requires a technological transition of our energy systems (IPCC 2014, Erickson et al 2015). Policies that fail to consider these effects can unintendedly lock in or lock out technologies. Our results show that both features are highly important in technology selection and that spillover effects between applications exist. We focus on Germany's solar photovoltaics feed-in tariff policy between 20 and analyse two design features, technology specificity and application specificity. Here we develop an empirically calibrated agent-based model to analyse how deployment policy design influences which technologies are selected by investors. Yet deployment policies may drive unintended and premature lock-in of currently leading technologies. Technology deployment policies can play a key role in bringing early-stage energy technologies to the market and reducing their cost along their learning curves.
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