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3.4.5. Understanding and Modeling Technological Change
The future direction and rates of technological change are uncertain and therefore
need to be explored when developing a range of alternative futures (i.e.,
scenarios). However, it would be misleading to resort to simplistic parametric
variations of scenario assumptions without considering some basic elements of
the nature of technological change, briefly reviewed here.
Technological change has often been pictured in linear terms that involve several
sequential steps:
- Scientific discovery - an addition to knowledge.
- Invention - a tested combination of already existing knowledge to a useful
end.
- Innovation - an initial and significant application of an invention.
- Improvement of technology characteristics and reduction of costs.
- Spread of an innovation, usually accompanied by improvement.
However, this model places undue emphasis on the role of basic R&D and scientific
knowledge as precursors and determinants of innovation. It also understates
the role of interactions among different actors and between the five functions
listed above. The emphasis in recent innovation literature is placed more on
a "chain-link" model of innovation, exploiting interactions between firms' R&D
departments, and various stages of production and marketing (Dosi, 1988; Freeman,
1994). Lane and Maxfield (1995) emphasize the role of "generative" relationships
in creativity.
Technological change is linked to the economic and cultural environment beyond
the innovating firm in many ways, as described by Landes (1969), Mokyr (1990),
Rosenberg (1982, 1994, 1997), Rostow (1990), and Grübler (1998a). Innovations
are highly context-specific; they emerge from local capabilities and needs,
evolve from existing designs, and conform to standards imposed by complementary
technologies and infrastructure. Successful innovations may spread geographically
and also fulfill much broader functions. The classic example is the steam engine,
developed as a means of pumping water out of deep mines in Cornwall, England,
but to become the main source of industrial motive power and the key technology
in the rail revolution worldwide.
Numerous examples can be used to demonstrate the messiness, or complexity,
of innovation processes (e.g., Grübler, 1998a; Rosenberg, 1994). But even if
the innovation process is messy, at least some general features or "stylized
facts" can be identified (Dosi, 1988; Grübler, 1998a):
- The process is fundamentally uncertain: outcomes cannot be predicted.
- Innovation draws on underlying scientific or other knowledge.
- Some kind of search or experimentation process is usually involved.
- Many innovations depend on the exploitation of "tacit knowledge" obtained
through "learning by doing" or experience.
- Technological change is a cumulative process and depends on the history
of the individual or organization involved.
These five features render some individuals, firms, or countries better at
innovation than others. Innovators must be willing and able to take risks; have
some level of underlying knowledge; have the means and resources to undertake
a search process; may need relevant experience; and may need access to an existing
body of technology. Many of these features introduce positive feedback into
the innovation process, so that countries or firms that take the technological
lead in a market or field can often retain that lead for a considerable time.
Technological change may be supply driven, demand driven, or both (Grübler,
1998a). Some of the most radical innovations are designed to respond to the
most pressing perceived needs. Many technologies have been developed during
wartime to address resource constraints or military objectives. Alternatively,
some innovation (e.g., television) is generated largely through curiosity or
the desire of the innovator to meet a technical and intellectual challenge.
Market forces (including those anticipated in the future) can act as a strong
stimulus for innovation by firms and entrepreneurs aiming either to reduce costs
or to gain market share. For example, Michaelis (1997a) shows the strong relationship
between fuel prices and the rate of energy efficiency improvement in the aviation
industry; Michaelis (1997b) also discusses the effects of the introduction of
competition on the organizational efficiency of the British nuclear industry.
All innovations require some social or behavioral change (OECD, 1998a). At
a minimum, changes in production processes require some change in working practices.
Product innovations, if they are noticeable by the user, demand a change in
consumer behavior and sometimes in consumer preferences. Some product innovations
- such as those that result in faster computers or more powerful cars - provide
consumers with more of what they already want. Nevertheless, successful marketing
may depend on consumer acceptance of the new technology. Other innovations -
such as alternative fuel vehicles or compact fluorescent lights - depend on
consumers accepting different performance characteristics or even redefining
their preferences. An important perspective on technical change is that of the
end-user or consumer of products and services. Technology can be seen as a means
of satisfying human needs. Several conceptual models have been developed to
describe needs and motivation, although their empirical foundations are weak
(Douglas et al., 1998; Maslow, 1954; Allardt, 1993). In many cases, a
given technology helps to satisfy several different types of need, particularly
evident in two of the most significant areas of energy use: cars and houses.
This tendency of successful technologies to serve multiple needs contributes
to lock-in by making it harder for competing innovations to replace them fully.
Hence, many attempts to introduce new energy efficient or alternative fuel technologies,
especially in the case of the car, have failed because of a failure to meet
all the needs satisfied by the incumbent technology. Different individuals may
interpret the same fundamental needs in different ways, in terms of the technology
attributes they desire (OECD, 1996). Deep-seated cultural values or "metarules"
for behavior can be considered to be filtered through a variety of influences
at the societal, community, household, and individual level (Douglas et al.,
1998; Strang, 1997). Commercial marketing of products usually aims to adjust
the filters, and encourages people to associate their deep-seated values with
specific product attributes (Wilhite, 1997). These associations are likely to
be more flexible than the values themselves, and provide a potential source
of future changes in technology choice.
Technology diffusion is an integral part of technical change. Uptake of a technology
that is locally "new" can be viewed as an innovation. Often, when technology
is adopted it is also adapted in some way, or used in an original way. Just
as technology development is much more complicated than the simple exploitation
of scientific knowledge, Landes (1969), Wallace (1995), Rosenberg (1997), and
others emphasize that technology diffusion is highly complex. Wallace emphasizes
the importance of an active and creative absorption process in the country that
takes up the new technology. The implication of this complexity is that no general
rules define "what works." The process of technology adoption is as context
dependent as that of the original innovation. Rosenberg (1997) also emphasizes
the role of movements of skilled people in the diffusion of technology. Transnational
firms often play a strong role in such movements. Other factors that influence
the technology transfer process include differences in economic developmental,
social and cultural processes, and national policies, such as protectionist
measures.
Grossman and Helpman (1991), Dosi et al. (1990), and others have attempted
to capture some of the complexities in "new growth" and "evolutionary" economic
models. They have been able to demonstrate the flaws in some of the simpler
solutions to technology diffusion often advocated - for example, they show how
free trade might sometimes exacerbate existing gaps in institutions, skills,
and technology.
The complex interactions that underpin technology diffusion may give rise to
regularities at an aggregate level. The geographical and spatial distribution
of successive technologies displays patterns similar to those found in the succession
of biological species in ecosystems, and also in the succession of social institutions,
cultures, myths, and languages. These processes have been analyzed, for example,
in Campbell (1959), Marchetti (1980), Grübler and Nakicenovic (1991), and
Grübler (1998a). An extensive review of the process of international technology
diffusion is available in the IPCC Special Report on Methodological and Technological
Issues in Technology Transfer (IPCC, 2000). That report provides a synthesis
of the available knowledge and experience of the economic, social and institutional
processes involved.
Many attempts to endogenize technical change in economic models rely on a linear
approach in which technical change is linked to the level of investment in R&D
(e.g., Grossman and Helpman, 1991, 1993). More importantly, this linear model
has been the basis of many governments' strategies for technological innovation.
As mentioned above, important additional features of technological change include
uncertainty, the reliance on sources of knowledge other than R&D, "learning
by doing" and other phenomena of "increasing returns" that often lead to technological
"lock in" and hence great difficulties in introducing new alternatives.
These features can be captured to some degree in models and a great deal of
experimentation has taken place with different model specifications. However,
the first feature, uncertainty, means that models cannot be used to predict
the process of technical change. This uncertainty stems partly from lack of
knowledge - the outcomes of cutting-edge empirical research simply cannot be
predicted. It also stems from the complexity of the influences on technological
change, and in particular the social and cultural influences that are extremely
difficult to describe in formal models. Recent attempts to endogenize technical
change in energy and economic models are reviewed by Azar (1996). Optimization
models usually treat technology development as exogenous, but technology deployment
as endogenous and driven by relative technology life-cycle costs. A few GHG
emission projection models (e.g., Messner, 1997) were developed to incorporate
"learning by doing"- the reduction in technology costs and improvement in performance
that can result from experience (Arrow, 1962). Models have also been developed
that explicitly include technological uncertainty to analyze robust technology
policy options (e.g., Grübler and Messner, 1996; Messner et al., 1996).
Other models developed more recently incorporate the effects of investment in
knowledge and R&D (Goulder and Mathai, 1998). Economists and others who study
technological change have developed models that take a variety of dynamics into
account (Silverberg, 1988). Some models focus on technologies themselves, for
example examining the various sources of "increasing returns to scale" and "lock-in"
(Arthur, 1989, 1994). Other models focus on firms and other decision-makers,
and their processes of information assimilation, imitation, and learning (Nelson
and Winter, 1982; Silverberg, 1988; Andersen, 1994). Few of these dynamics,
apart from "increasing returns to scale," have been applied to the projection
of GHG emissions from the energy sector.
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