El patrimonio como punto de encuentro

In each of these stages, different learning mechanisms play a role in the improvement of the technology, which typically result in a higher conversion efficiency and reliability, easier use and lower investment, operation and maintenance costs. Different learning mechanisms have been described by, amongst others Utterback (1994), Garud (1997), Grübler (1998; Grübler et al., 1999), Kamp (2002) and Dannemand Andersen (2004)16. These authors have developed different approaches to conceptualize knowledge and learning. Most authors identify several of the following mechanisms influencing both the production process and the product itself (Neij et al., 2003) behind technological change and cost reductions:

Learning-by-searching, i.e. improvements due to RD&D, is the most dominant mechanism in the

stages of invention and RD&D, and to some extent also during niche market commercialization. Often also during the stages of pervasive diffusion and saturation, RD&D may contribute to technology improvements.

Learning-by-doing (Arrow, 1962) takes place especially in the production stage after the product

has been designed. Typically, the repetitious manufacturing of a product leads to improvements

14 _{This section is a slightly altered version of review comments given by Schaeffer on an earlier draft of this}

report (Schaeffer, 2008).

15 _{An interesting case where science development (aerodynamics in this case) has followed technology}

development and market penetration is the aviation industry.

16 _{For renewable electricity technologies, different studies have investigated these mechanisms during the}

RD&D and niche market commercialization stage, see for example Kamp (2002) and Garud and Karnøe (2003) for wind energy, Raven and Gregersen (2004) for biogas digestion plants, and Schaeffer et al. (2004) for solar photovoltaics.

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in the production process (e.g. increased labor efficiency, work specialization and production method improvements).

Learning-by-using (Rosenberg, 1982) can occur as a technology is introduced to (niche)

markets. A technology cannot be fully developed inside laboratories and factories. Feedback from user experiences often leads to improvement of the product design.

Learning-by-interacting is related to the increasing diffusion of the technology. During this stage,

the network interactions between actors such as research institutes, industry, end-users and policy makers generally improve, and the above-mentioned mechanisms are reinforced (Kamp, 2002, Lundvall, 1988). In other words, the diffusion of knowledge itself supports the diffusion of the technology17.

Upsizing (or downsizing) and redesigning a technology (e.g. upscaling a gas turbine) may lead

to lower specific unit costs (e.g. the costs per unit of capacity).

Economies of scale (i.e. mass production) can be exploited once the stage of large-scale

production and diffusion is reached. Standardization of the product allows upscaling of production plants, and producing the same product in large numbers.

Often, combinations of these factors occur in each stage, and the contribution of each may change during the development of a technology over time. Also, not all factors may apply to all technologies. Some authors differentiate between effects of (technological) learning (such as the first three factors) and scale effects (such as the last two factors) (Abell and Hammond, 1979).

However, in practice these factors often overlap and are difficult to separate (Neij, 1999a). Also, in most cases both upscaling and mass production of a technology or production process requires many steps18. During each step, experience is gained by learning-by-doing and learning-by-using, which is then incorporated in the next generation of the technology19.

While these factors describe the mechanisms behind cost reductions qualitatively and in hindsight, it is a different matter to quantify the effects of each mechanism separately, and to make projections about their possible contribution in the future when developing a technology. Further knowledge development in this field would be interesting and highly relevant to understand how technological development can be influenced in a cost-effective way. Future projections may be based (at least to some extent) on past achievements, e.g. returns on investment from RD&D expenditures, but RD&D expenditures are no guarantee for cost reductions and returns on RD&D investments may vary. Scaling laws can be used to project potential cost reductions. Yet, upscaling a plant normally requires considerable RD&D expenditures and investments in pilot plants to solve problems arising from the larger scale and to make investment risks known and acceptable. In the end, it is the combination of learning mechanisms causing cost reductions, which makes quantifying the effect of each mechanism separately difficult. A concept, measuring the aggregated effect of these mechanisms is the experience curve approach.

17 _{Somewhat related to this mechanism, Rotmans and Kemp (2003) also mention ‘learning by learning’,}

indicating that the primary learning processes themselves can improve over time. In addition, Schaeffer et al. (2004) distinguish ‘Learning by expanding’, recognizing the fact that more actors, organizational structures and industrial sectors become involved in, focused on, dependent on and adapted to the new technology. Arthur (1988) calls this mechanism ‘increasing returns on adoption’.

18 _{For example, it took over 20 years and over one hundred plants to scale up steel plants from 0.3 to 8 million}

tons of steel output capacity (Grübler, 1998). A similar trend and time span was found for fluidized bed boilers (Koornneef, 2007). Cost reductions due to mass production are of course not all related to learning. Larger production volumes will for example allow manufacturers to negotiate lower prices for raw materials and reduce relative overhead costs. Yet, it is clear that to design, build and operate larger production plants, learning will be required as well.

19 _{This process is documented in detail for the development and upscaling of Danish wind turbines by Neij et al.}

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Finally, it is important to point out that as the learning leading to price decreases in experience curves is a very complex process, it does not only include technology learning (Schaeffer, 2008). It also includes:

• Economic learning (e.g. shifting production to low-wage countries)

• Financial learning (e.g. banks/investors that get confidence in a new technology and reduce their Return on Investment requirements interest rates). This is especially important for experience curve analyses based on Cost of Electricity.

• Social learning (actors that get to know and trust each other better).