When and How to Support Renewables?—Letting the Data Speak - Green Energy and Efficiency: An Economic Perspective - page 307

Table 8 Deviance ratio for
models explaining the RCA in
wind and solar

(Sparse model) Deviance ratio No. of variables

incl. intercept

Solar RCA ranking 0.29 23

Wind RCA ranking 0.46 23

3.5 From Patents to Competitiveness

The explained variable, number of patents in the narrowly de ne technology, is

only an imperfect proxy for what policy would really care about innovation

leading to sustainable reduction in the total cost of using the technology to replace
existing technologies. 16 To also capture cost-savings that improve the technology
beyond patented innovation we repeat the analysis using the inverse RCA ranking.
This should allow us to understand which policies (deployment, RD&D support or
both) coincide with improvements in the competitiveness of the domestic renewable
energy technology industry (Table 8 ).
Overall, the results for RCA are signi

—

cantly less robust. Obviously, the com-
parative advantage and its development over time is determined by many factors do
not properly control for (labour cost, education, capital cost, etc.). Consequently, the
variation of RCA explained by a relatively sparse model of less than 25 variables is
low if compared to the results obtained in the patents regression. Thus, the results
below should be interpreted with a substantial degree of caution. 17 The major factor
that helps predicting the revealed comparative advantage in wind and solar in a
country, is the logged number of all patents granted in this country in this year
(pat_total_logged, see Tables 9 and 10 in the Appendix). This indicates that a key
driver of export specialisation in renewables is the innovative power of a country.

3.6 Deployment and Competitiveness

The clearest result for competitiveness is that deployment is indeed increasing the
competitiveness of the corresponding technology. A sustained increase in domestic
deployment of wind turbines increases the RCA ranking in wind turbines by about
one position in the case of Germany. For solar panels there is also a clearly positive
impact. Countries which deploy more solar panels are also exporting more of them
in future. The clarity of the results somewhat surprised us, as our prior was that
larger deployment coincides with larger domestic demand and hence more limited
room for exports (Fig. 15 ).

16 Popp [ 11 ] for example argue that the diffusion of renewables is mainly driven by regulation and
less by the knowledge stock.
17 We force the model selection to the subset of models that include 25 explanatory variables or
less, as we noticed a tendency towards models with more than one hundred explanatory variables
when only optimizing on the in-sample predictive power.