Combining Innovation Systems Theory and the Experience curve approach As described in Section 2.1 the focus of experience curves is mainly on the development of the

performance of the technological artefact (e.g. a wind turbine or biomass power plant). A few studies using the experience curve approach in historic analyses pay some attention to the broader frame of strategic niche management and changing regimes to explain mainly why the technology did (or did not) penetrate the market. However, as illustrated in section 2.2 on methodological pitfalls, circumstances such as market developments, knowledge diffusion, sectoral and geographical system boundaries all can have an impact on (the applicability of) the experience curve approach. Thus, it is worthwhile exploring, to what extent general innovation theory could contribute to support the experience curve approach. It should be noted that this a considered an interesting idea for future work rather than a current application of experience curves.

Page 42 of 192 WAB 500102 017

On the other hand, innovations system theory typically aims to describe historical developments in hindsight. Only few attempts have been made to apply this knowledge to make (quantitative) forecasts regarding the market penetration and/or development of production costs. The latter is typically a strong point of the experience curve approach.

In Table 2.2, the strengths and weaknesses of the experience curve approach in comparison to the innovation systems approach are briefly summarized.

The two approaches both have strengths and weaknesses, which are to a certain extent complementary. The experience curve approach and the innovation functions approach could possibly be combined in the following two ways:

(i) In historic studies using the experience curve approach, cost reductions are a sole function of market formation/deployment. While ideally such an analysis should go hand in hand with a description of technological, political, and market developments, a complementary analysis of the entire innovation system and the relevant innovation functions could possibly provide more detailed insights into the drivers for continuous market formation and subsequent cost reductions.

(ii) In prospective studies using the experience curve approach, scenarios for future cumulative production are often based on policy targets (e.g., with respect to the shares of energy produced from renewable resources or the amount of energy saved due to energy efficiency measures) and the accompanying market diffusion rates necessary to reach these policy targets. Exploring the likelihood of reaching the established policy targets is however impossible solely based on the experience curve approach. Here, the systems innovation approach with its innovation functions could provide valuable insight into the extent to which system functions need to be changed to increase the likelihood of achieving the established market diffusion targets.

Table 2.2 Strengths and weaknesses of the experience curve approach in comparison to the innovation systems approach

Criteria Experience curve approach Innovation systems approach

Strengths

• Allows to quantify the dynamics of total production costs as function of cumulative production

• Allows to estimate the costs for policy measures to make a technology competitive compared to the incumbent technology

• Supports the projecting of future trajectories of energy technologies in energy and emission models • Methodology is simple and requires

only limited data input for analyzing future cost reductions

• Takes into account multiple aspects of learning as well as stakeholder and technology interactions, thereby: • Accounting for the entire innovation

system, including also actors, institutions, and their relations.

• Takes into account historical

developments, including past successes and failures in the development of technologies or the innovation systems around them

• Includes different functions of innovation, of which most can be quantified either directly or using proxies

Weaknesses

• Focuses only on the dynamics of production costs (e.g., economics of scale, learning by doing) but excludes other innovation functions

• Does not provide an understanding of drivers for the observed cost

dynamics

• Treats cost dynamics as black box thereby not separating cost

components that depend and do not depend on technological learning • Provides only limited insight into

market potentials of products because only production costs of technologies are quantified

• Provides very limited quantitative information to assessable future market penetration potentials of technologies • Provides limited quantitative analysis for

cost dynamics to support policies in creating, e.g., niche markets

WAB 500102 017 Page 43 of 192

While such a combined analysis so far has not been carried out, such a hybrid approach could particularly be interesting for prospective studies for technologies currently at the beginning of commercial market penetration, such as offshore wind energy, super-critical coal combustion, coal/biomass gasification or various heat pump applications. For these technologies, in general data for experience curves may be available form niche market applications, and all functions of the TSIS can be well-described. Also of interest could be technologies on the verge of (niche) market introduction, such as micro-CHP (combined heat and power), 2nd generation biofuel production, various CCS (carbon capture and sequestration) technologies, advanced pyrolysis / torrefaction concepts and use of fuel cells in transportation vehicles. For these technologies, no or limited data is available for historic cost reduction achieved, but progress ratios could be estimated by comparison with existing technologies, and again, such system can be well- described with the innovation systems approach.

From a methodological point of view, it could be worthwhile exploring whether in historic case studies reduction of production costs can be linked statistically to increasing entrepreneurial activity, market formation and/or resource mobilisation, which could enable the increasing quantification of systems functions.

2.4 The use of experience curves in energy models