Knowledge spillovers from renewable energy technologies: Lessons from patent citations

Abstract

This paper studies the knowledge spillovers generated by renewable energy technologies, unraveling the technological fields that benefit from knowledge developed in storage, solar, wind, marine, hydropower, geothermal, waste and biomass energy technologies. Using citation data of patents in renewable technologies at 17 European countries over the 1978-2006 period, the analysis examines the relative importance of knowledge flows within the same specific technological field (intra-technology spillovers), to other technologies in the field of power-generation (inter-technology spillovers), and to technologies unrelated to power-generation (external-technology spillovers). The results show significant differences across various renewable technologies. While wind technologies mainly find applications within their own technological field, a large share of innovations in solar energy and storage technologies find applications outside the field of power generation, suggesting that solar technologies are more general and, therefore, may have a higher value for society. Finally, the knowledge from waste and biomass technologies is mainly exploited by fossil-fuel power-generating technologies. The paper discusses the implications of these results for the design of R&D policies for renewable energy innovation.

Green regions and local firms' innovation

“…They are more “general” than other patents, hence a broader set of actors is influenced by such knowledge (Popp & Newell, ), especially because they are used as input to production in almost all industries. Noailly and Shestalova () find that energy patents have high knowledge spillovers, and that, in particular, storage and solar technologies find applications outside the field of power generation. The applications of a new technology that uses energy more efficiently tend to be used in firm production processes.…”

“…While taking into account firm‐level determinants of innovation, we consider both traditional inputs to regional knowledge production (Acs, Anselin, & Varga, ) and indicators of green regions identified along three dimensions, drawing on established determinants of EI. The first is purely related to the technologies that are at the base of the transition to more environmentally‐friendly mode of production and consumption (Ghisetti & Quatraro, ; Noailly & Shestalova, ; Popp & Newell, ). The second one is about the regulation landscape, extensively recognized as a major driver of EI (Porter & van der Linde, ).…”

Crowding Out or Knowledge Spillovers from the Wind Power Industry: The Effect on Related Energy Machinery

“…Technological change is frequently, but not always, shaped by problem solving within a technological field; building on and expanding knowledge from the same technology. However, knowledge spillovers across technologies also occur (Cohen and Levinthal, 1990;Lazear, 2004;Lettl et al 2009;Nemet, 2012;Nemet and Johnson, 2012;Noailly and Shestalova 2013).…”

“…However, knowledge spillovers across seemingly unrelated technologies also occur, i.e., technologies can benefit from knowledge that originated in other distant technologies (Cohen and Levinthal, 1990;Lazear, 2004;Lettl et al 2009;Nemet 2012;Nemet and Johnson, 2012;Noailly and Shestalova 2013;Schoenmakers and Duysters, 2010). Hence, intra-technology spillovers motivate support for technological variety where unforeseen effects can have a big and positive impact on the general development and can therefore be one potential goal of technology policy (Battke, Schmidt, Stollenwerk, and Hoffmann, 2016).…”

The empirics of enabling investment and innovation in renewable energy

“…Renewable-energy innovation models therefore also include the market size for electricity and energy prices (Noailly and Smeets, 2013;Popp, 2002) to account for market conditions. Further, innovation diffusion and spillovers have to be distinguished from the effect of a country's policies or conditions (Noailly and Shestalova, 2013;Peri, 2005;Dechezleprêtre et al, 2013;Glachant et al, 2010). Additionally, models account for the impact of more general control variables like patent or knowledge stocks, resources, institutions, conditions for adoption of new technologies, innovation capacity of a country, trade and FDI inflows and the general size of the economy (additionally to the above, Spencer et al, 2015;Grübler and Wilson, 2014).…”