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3. Braun FG, Schmidt-Ehmcke J, Zloczysti P (2010) Innovative activity in wind and solar
technology: empirical evidence on knowledge spillovers using patent data. CEPR Discussion
Papers, no 7865
4. Gurmu S, P é rez-Sebasti á n F (2008) Patents, R&D and lag effects: evidence from exible
methods for count panel data on manufacturing rms. Empirical Economics 35(3):507 - 526
5. Hall B, Griliches Z, Hausman J (1986) Patents and R&D: is there a lag? Int Econ Rev27
(2):265
283
6. Jamasb T, K
-
hler J (2007) Learning curves for energy technology: a critical assessment. In:
Grubb M, Jamasab T, Pollitt MG (eds) Delivering a low carbon electricity system:
technologies, economics and policy. Cambridge University Press, Cambridge
7. Johnstone N, Hascic I, Popp D (2010) Renewable energy policies and technological
innovation: evidence based on patent counts, environmental and resource economics. Eur
Assoc Environ Resour Econ 45(1):133
ö
155
8. Koseoglu NM, van den Bergh JCJM, Subtil Lacerda J (2013) Allocating subsidies to R&D or
to market applications of renewable energy? Balance and geographical relevance. Energy
Sustain Develop 17(5):536 - 545
9. Lindman A, S ö derholm P (2012) Wind power learning rates: a conceptual review and meta-
analysis. Energy Econ 34(3):754 - 761
10. Popp D (2002) Induced innovation and energy prices. Am Econ Rev Am Econ Assoc 92
(1):160 - 180
11. Popp D, Hascic I, Medhi N (2011) Technology and the diffusion of renewable energy. Energy
Econ 33(4):648 - 662
12. Riess AD, Zachmann G, Calthrop E, Kolev A (2012) Investment and growth in the time of
climate change. Bruegel Books, Brussels
13. S
-
derholm P, Sundqvist T (2007) Empirical challenges in the use of learning curves for
assessing the economic prospects of renewable energy technologies. Renew Energy 32
(15):2559
ö
2578
14. Thierry M, Soledad Z (2011) Notes on CEPII
-
s distances measures: the GeoDist database.
CEPII working paper 2011- 25 , December 2011, CEPII
15. Tibshirani R (1996) Regression shrinkage and selection via the lasso. J Royal Stat Soc Series
B (Methodological) 58(1):267
'
288
16. Van Benthem A, Gillingham K, Sweeney J (2008) Learning-by-doing and the optimal solar
policy in California. Energy J 29(3):131
-
151
17. Wiesenthal T, Dowling P, Morbee J, Thiel C, Schade B, Russ P, Simoes S, Peteves S, Schoots
K, Londo M (2012) Technology learning curves for energy policy support. JRC Scienti c and
Policy Reports
-
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