Methods and data
Investigating the diffusion of a particular technology and corresponding investments requires a longitudinal research design (Angrist & Pischke, 2008; Cardenas-Rodriguez et al., 2013; Johnstone et al., 2010a; Popp et al., 2011; Wooldridge, Calhoun, Jung, Greber, & Montgomery, 2009). Prior literature applied panel data regressions at the EU level (Bolkesj0 et al., 2014; Marques & Fuinhas, 2012a) with a few comparing OECD or BRIC countries (Aguirre & Ibikunle, 2014; Cardenas-Rodriguez et al., 2013; Johnstone et al., 2010a; Popp et al., 2011).
Building on these methodological approaches we cover a variety of OECD countries, conducting a panel data regression throughout the time period from 2000 to 2011 to explain the influence of policy instruments on the diffusion of clean energy technologies. As policy instruments do not exhibit an immediate effect on technology application and investments, we add a lag procedure which is explained in section 126.96.36.199 (Wooldridge et al., 2009). The time frame is chosen due to the limited availability of high quality data, and as it still covers the most substantial developments in the worldwide renewable energy sector, especially regarding the involvement of institutional investors. Globally the wind sector grew from 18 GW installed capacity in 2000 to 238 GW installed capacity in 2011, while the solar sector grew from 1.5 GW installed capacity in 2000 to 67 GW installed capacity in 2011. The biomass sector, at the same time, grew from 38 GW installed capacity in 2000 to 74 GW installed capacity in 2011 (IEA, 2014).
-  When using data from BNEF, one has to be aware of the fact that the data is being updated after acertain year. Thus we have chosen to limit the data of the dependent variable to 2011, as thistimeframe covers the most reliable data quality.