Association for Public Policy Analysis & Management

Policies aiming to address the societal challenges associated with energy production and use, such as climate change, often intend to influence the speed and direction of technological change. One key principle for policy intervention is to internalize the negative externality that underlies the societal challenge. In the case of climate change, the “optimal” policy intervention is seen as (i) putting a price on carbon emissions and (ii) providing incentives for R&D (to address positive externalities involved in R&D). However, many technologies profit from learning by doing (LBD), i.e., learning that cannot be generated in a laboratory, which also involves positive externalities. While many governments have introduced technology-specific deployment policies to enable LBD, most of these policies do not consider differences of the targeted low-carbon technologies (other than their incremental cost). Recent empirical studies demonstrate that technological learning can differ strongly between technologies. Fostering innovation in technologies may therefore require technology-specific policy interventions, since the types of learning processes (and hence measures needed to support them) depend on the stage of the technology’s life cycle and the inherent characteristics of the technology.

This study intends to provide insights into how deployment policies can be designed in order to reflect technologies’ varying learning patterns, focusing on the locus of learning and learning feedbacks within technological supply chains. Three different technologies (solar photovoltaics, wind turbines, and lithium-ion batteries) are analyzed in two steps. First, we carry out semi-structured interviews (~60) with actors involved in the innovation, manufacturing and use in order to identify where in the supply chain learning through interactions and feedback takes place, and what kind of knowledge (process or product) is involved. Second, we analyze the content of the key patents over the life cycle of the technologies in order to triangulate with the results of the interview data analysis, and to verify the suitability of using patent data in identifying the locus of learning. The patent data is used to identify (i) the inventor’s role in the technological innovation system, (ii) the type of innovation (process or product), and (iii) the year.

The three technologies are chosen because they are key technologies for low-carbon energy systems, and because all three technologies’ supply chains are relatively large, allowing for the possibility of an iterative process of problem solving between various actors.

We find strong differences in the pattern of innovation and learning feedbacks across the three analyzed technologies: Learning in the wind industry is driven by a higher degree of interaction and knowledge feedbacks between the original equipment manufacturers and the technology users. The solar PV industry shows a higher degree of interaction between the original equipment manufacturers, production equipment suppliers and material suppliers during manufacturing phase. The lithium-ion battery industry shows a high degree of interaction between the mentioned actors during both the manufacturing and use of the technology. We discuss the implications for policy makers, proposing policy designs for deployment policies that reflect these differences to foster innovation more cost effectively.