Introducción a la desalación

There is strong need to influence both speed and direction of innovation and technological change (e.g., to accelerate energy efficiency improvements). Many concepts have been developed to open up and describe what's inside the black box of innovation and technology development. Innovation is described by various different approaches, including neo-classical economics, evolutionary economics, industrial networks, quasi-evolutionary and large-technical systems approach. Most innovation theories are either very broad in scope (e.g., quasi- evolutionary economics) or narrow in its focus (e.g., neo-classical economics).

Over the last decades, learning theories combined with evolutionary economics have led to the innovation systems theory that expands the analysis of technological innovation, covering the entire innovation system in which a technology is embedded. An innovation system is thereby defined as the network of institutions and actors that directly affect rate and direction of technological change in society. The concept of innovation systems was designed as a heuristic attempt to guide the analysis of complex economic structures and processes. There are multiple innovation systems approaches, i.e. national, regional, sectoral, and technological.

Technology, or the knowledge it embodies, is hardly ever embedded in just the institutional infrastructure of a single nation or region, since - especially in modern society - the relevant knowledge base for most technologies originates from various geographical areas all over the world. We find a similar argument for the relevance of a strictly sectoral delineation. Thus, by taking a specific technology as a starting point, the technological system approach cuts through both the geographical and the sectoral dimensions. This is illustrated in Figure 2.6, which schematically shows how the Technology Specific Innovation System (TSIS) relates to the geographical and sectoral dimensions of respectively the national systems innovation and the sectoral innovation systems approach. It shows that the Technology Specific Innovation System overlaps with parts of various national innovation systems and with various sectoral innovation systems which, in turn, are embedded in national systems of innovations.

20 _{Section 2.3.5 is a brief description of the systems of innovation theory. We thank Roald Suurs for helpful}

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Figure 2.6 Boundary relations between national, sectoral and technology-specific innovation systems (Hekkert et al. 2007) .

The systemic character of technological change explains why technological change is often a very slow process and why it is difficult to influence it. The rate and direction of technological change is in general not so much determined by simple conflicts or complementarities between different technologies, but predominantly by the interactions between various existing innovation systems. There can be inertia between, e.g., technology-innovation systems and the underlying technology or between two technology-innovation systems that may result in relatively rigid technological trajectories. In other words, the key reasons why technological progress often proceeds along certain trajectories are explained by the complex interactions between technology-innovation systems. Understanding technological change implies therefore creating insight in the relations between incumbent technologies and the incumbent (innovation) systems in relation to the emerging technologies and the emerging innovation systems.

The Multi-Level-Models approach pays attention to the context of a technological system and the interactions between its various levels. The Multi-Level-Models approach can be especially suitable to analyze the circumstances under which a niche technology is so successful that it becomes part of the existing technology regime.

The situation can be illustrated using the example of PV development. Advances in PV depend on technological progress made in research institutes and universities all over the world. Thus, the PV innovation system overlaps with those parts of national innovation systems that concentrate on PV research. In turn, global diffusion strongly depends on different national policy regimes that stimulate the adoption of PV by means of investment subsidies or feed-in laws. Again, the PV innovation system overlaps with various national innovation systems in terms of stimulating institutions for solar cell diffusion. Furthermore, the production conditions for PV panels strongly depend on the microelectronics sector due to competition over silicon wafers. Silicon wafers are produced for the microelectronics sector, but the surplus of wafers is sold to solar cell manufacturers. High growth rates in the microelectronics sector lead to silicon shortages and higher prices of solar cells (van Sark, 2007). Furthermore, the application of PV strongly depends on the housing sector, including architecture and ownership characteristics. PV friendly architecture can greatly influence the potential for PV in the building environment and the subsequent energy output. Thus, the technological progress, price, and diffusion of one technology is influenced by the various national and sectoral innovation systems.

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Analyzing innovation systems requires hence a broad scope including the multiple factors and agents influencing innovation on the various spatial and time scales. The use of the innovation system framework to understand technological change has, however, two shortcomings, i.e., (i) in most analyses, innovation systems are treated as quasi-static rather than dynamic in character and (ii) the explanatory power of the framework lies mainly in the part of institutions (macro level), and less on the actions of the entrepreneurs (micro level) (Hekkert et al., 2007). A dynamic innovation systems approach that covers both micro and macro dynamics is hence needed to better understand and guide innovation.

To solve existing shortcomings, Hekkert et al. (2007), proposed the so-called functions of innovations approach. This approach focuses on a number of processes that are highly important for innovation systems. Hekkert et al. (2007) argue that the analysis of technological change should focus on the systematic mapping of activities that take place in innovation systems. Several researchers (e.g., Edquist and Johnson (1997), Hekkert et al. (2007)) identified crucial functions that are required both to map key activities and to describe shifts in technology-specific characteristics in innovation systems. They can be summarized as follows: • Entrepreneurial Activities: At the core of any innovation system are the entrepreneurs. These

risk takers perform the innovative commercial experiments, seeing and exploiting business opportunities.

• Knowledge Development: Technology research and development are prerequisites for innovations, creating variety in technological options. Research and development activities are often performed by researchers, but contributions from other actors are also possible. • Knowledge Diffusion: The typical organisational structure of an emerging innovation system

is the knowledge network, primarily facilitating information exchange.

• Guidance of the Search: Often within a transition trajectory, various technological options exist. This function represents the selection process that is necessary to facilitate a convergence in development, involving for example policy targets, outcomes of technical or economical studies and expectations about technological options.

• Market Formation: New technologies often cannot outperform established ones. In order to stimulate innovation it is necessary to facilitate the creation of (niche) markets, where new technologies have a possibility to grow.

• Resource Mobilisation: Material and human factors are necessary inputs for all innovation system developments, and can be enacted through e.g. investments by venture capitalists or through governmental support programs.

• Support from Advocacy Coalitions: The emergence of a new technology often leads to resistance from established actors. In order for an innovation system to develop actors need to raise a political lobby that counteracts this inertia, and supports the new technology. Based on an analysis of case studies (involving mainly energy technologies), Hekkert et al. (2007) propose that the majority of these functions have to be adequately satisfied to enable an innovation system to succeed.

2.3.6 Combining Innovation Systems Theory and the Experience curve approach