Abstract
This chapter explores the effects of policies and other factors driving innovation in wind-power technologies in twelve OECD countries over more than two decades. Patent counts are used as an indicator for innovation. The factors considered are generally derived from the systems of innovation literature. Count data econometric model were used for the estimations. The suggest that patenting in wind-power technology is positively related to public R&D in wind power (reflecting supply-side policy), the stock of wind capacity (reflecting learning effects), the number of patents per capita (reflecting a country’s innovative capacity), and the share of Green party voters (reflecting the legitimacy of the technology). In particular, the presence of production or capacity targets for wind power or renewable energy sources and a stable policy environment (reflecting policy process) appear to be favourable for patenting wind-power technologies. These results are robust to various model specifications, distributional assumptions, and alternative classifications of windpower technologies in the patent search.
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Notes
- 1.
Among others, Horbach et al. (2012) note that, since environmental policies are also demand-side innovation policies, environmental and innovation policies should be explored together.
- 2.
Due to a lack of data, private R&D expenditures for wind power technologies could not be used.
- 3.
More specifically, FD03 comprises of: (1) wind motors with rotation axis substantially parallel to the flow of air entering the machine; (2) wind motors with rotation axis substantially at a right angle to the flow of air entering the machine; (3) other wind motors; (4) controlling wind motors; (5) adaptations of wind motors for special use; (6) combinations of wind motors with apparatus driven thereby; and (7) other details, component parts, or accessories of wind motors.
- 4.
See Schleich et al. (2017) for further details. For additional details, we also refer to the IEA ‘Renewable Energy Policies and Measures Database’ (http://www.iea.org/policiesandmeasures/renewableenergy/).
- 5.
FIP means that electricity producers receive a premium payment on top of the electricity wholesale price. To improve the compatibility of RES support systems with the electricity markets, in 2014 the EU adopted the “Environmental and Energy State Aid Guidelines for 2014–2020” (European Commission 2014). Accordingly, FIPs elicited via bidding systems will become the central RES support mechanism in all EU countries.
- 6.
FIT also equals one if a FIP or a PTC was in place, since the incentives for investors are similar to those of FITs.
- 7.
Schleich et al. (2017) also provide details and examples on how these variables were constructed.
- 8.
Findings are quite robust to alternative specifications of the lags. Specifically, lagging all explanatory variables by 1 year (rather than 2 years) hardly changes the results.
- 9.
The average VIF of the explanatory variables in Table 10.3 and all VIFs are below five. Based on the standard cut-off point of ten, collinearity does not appear to be an issue.
- 10.
By “bad controls” we mean control variables that may themselves be outcome variables.
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Acknowledgements
The authors would like to thank Rouven Emmerich for his thorough research assistance, Frank Marscheider-Weidemann for his help in retrieving patent data, and Mario Ragwitz for sharing his insights on renewable energy policies. Special thanks go to Gillian Bowman-Köhler for proofreading our paper.
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Schleich, J., Walz, R. (2018). Effects of Innovation and Domestic Market Factors on OECD Countries’ Exports of Wind Power Technologies. In: Horbach, J., Reif, C. (eds) New Developments in Eco-Innovation Research. Sustainability and Innovation. Springer, Cham. https://doi.org/10.1007/978-3-319-93019-0_10
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