When and How to Support Renewables?—Letting the Data Speak - Green Energy and Efficiency: An Economic Perspective - page 289

Literature has identi ed two interacting innovation policies: (i) encouraging

' through government supported deployment of yet uncompeti-

tive technologies and (ii) public RD&D as well as public support to private RD&D.

learning-by-doing '

1.2.1 Deployment Driven Innovation

In recent years, both environment and economic research started focusing on
endogenous technical change in the energy sector using learning curves. Arrow [ 1 ]

acted as a driver to
reduce costs through different channels. 6 Costs of production are modelled as a
function of the cumulated capacity. A learning rate can be derived which estimates
the reduction of cost per doubling of capacity.

rst introduced this theory showing that ' learning-by-doing '

c ¼ a Cap e

2 e

LR ¼ 1

where:
c /KWh)

Cap Deployment (cumulative capacity or production, etc.)
ε

Unit cost ( € /KW or €

Learning elasticity

LR Technology learning rate

Learning rates played a role for of cial policy documents as well as they are

crucial part of a cost bene t analysis for renewable energy support [ 6 ]. Learning

curves can provide a justi cation of subsidies exceeding the direct effect of climate

change mitigation as they decrease the long-term costs of new technologies. That is,
deployment subsidies can lead to innovations in this sector which are more
important than the direct reduction of green house gas (GHG) emissions in terms of
social welfare [ 16 ].

1.2.2 RD&D Driven Innovation

The main purpose of RD&D is to generate innovations. Hence, it is little surprising
that RD&D spending leads to innovations that can be measured in terms of patents.
For example, Gurmu and P

é rez-Sebasti á n( 2008 ) develop a ' patent production

6 James and K ö hler [ 6 ] note that there have been, “ early applications of learning curves, between
1930s and 1960s ” .