Working Group III: Mitigation

Other reports in this collection Learning by Doing (LBD)

LBD as a source of technical change was first emphasized by Arrow (1962). Nakicenovic (1996) discussed the importance of LBD in energy technology, and Messner (1995) endogenizes the learning process in energy models. LBD is a happy consequence of those investments in which learning is a result of cumulative experience with new technologies. LBD typically refers to reductions in production cost, in which learning takes place on the shop floor through day-to-day operations, not in the R&D laboratory. The LBD component of change is significant too. Kline and Rosenberg (1986) discuss industry studies that indicate that LBD-type improvements to processes in some cases contribute more to technological progress than the initial process development itself.

LBD models use the installed capacity or cumulative use as an indicator of accumulating knowledge in each sector. The abatement costs are represented by the specific investment costs in US$/kWh. The models are global and therefore the diffusion process is not represented. The optimization problems are non-convex, which raises a difficult computational problem to find an optimum. However, pioneering work at the International Institute for Applied Systems Analysis (IIASA) on the MESSAGE model and additional developments based on models like MARKAL and ERIS; (MATSSON), Kypreos and Barreto (1999), Seebregts et al. (1999a), (SKFB), Tseng et al. (1999), and Kypreos et al. (2000) demonstrate progress in this direction. They show that several technologies are likely to play a prominent role in reducing the cost of abatement, if ITC is indeed taken into account when computing the equilibrium. A problem with modelling endogenous technological change is that the traditional baseline scenario versus optimal policy run argumentation is not feasible. This follows directly from the path dependence. The most important results are: greater consistency of model results with the observed developments of technological change;

  • new technologies first appear in niche markets with rising market shares;
  • the time of breakthrough of new technologies can be influenced by policy measures (taxes and R&D) if they are strong enough;
  • identification of key technologies, like photovoltaic modules or fuel cells, for public R&D investments is difficult; and
  • technological lock-in effects depend on costs.

The most important conclusion for the timing of a mitigation policy is that early emissions-reduction measures are preferable when LBD is considered. This is confirmed unambiguously by a macroeconomic modelling study (van der Zwaan et al., 1999/2000) which finds also lower levels of carbon taxes than those usually advocated.

These findings must be tempered by the fact that the models are not only highly non-linear systems, and therefore potentially sensitive to input assumptions, but also the quantitative values employed by modellers are typically drawn from successful historical examples. Furthermore, the empirical foundations of LBD are drawn from observations of the relationship between cumulative deployment and/or investment in new technology and cost. This relationship is equally consistent with the hypothesis that a third factor reduced costs, in turn leading to increases in demand. The authors restrict their findings to more qualitative assertions, because of the limitations of current models (Messner, 1997; Grübler and Messner, 1998; Barreto and Kypreos, 1999; Seebregts et al., 1999a, 1999b). The research so far has been limited to energy system models and ignored other forms of endogenous, complex changes that are important for emissions, like changes in lifestyles and social institutions. The Distinction Between Action and Abatement

The key message from this discussion about technical change is that a clear distinction has to be made between the timing of action and the timing of abatement. As a result of inertia in technological innovation, short-term action is required to abate more in the future, but a given amount of abatement at a given point in time is not a good measure of the effort. The necessity of this distinction is reinforced by the consideration of inertia in capital stocks. Mitigation costs are influenced by assumptions about the lifespan of existing plants and equipment (e.g., power plants, housing, and transport). Energy-related capital stock is typically long lived and premature retirement is apt to be costly. For example, an effort to change the transportation infrastructure will not reduce carbon emissions significantly for two decades or more. Hence, a drastic departure from the current trend is impossible without high social costs and a delay of action in this sector will require higher abatement costs in the more flexible sectors to meet a given target. Lecocq et al. (1999) found that these costs would be increased by 18% in 2020 for a 550ppmv target and by 150% for a 450ppmv target.

This irreversibility built into technological change is far more critical when the uncertainty about the ultimate target is considered. In this case indeed, many of the parameters that legitimize the postponing of abatement play in the opposite direction. If indeed the concentration constraints turn out to be lower than anticipated, there may be a need for abrupt reduction in emissions and premature retirement of equipment. In other words, even if the permanent costs of an option (in case of perfect expectation) are lower than those of an alternative option, it may be the case that its transition costs are higher because of inertia. For example, two ideal transportation systems can be envisaged, one relying on gasoline, the other on electric cars and railways, both with comparable costs in a stabilized situation; however, a brutal transition from the first system to the second may be economically disruptive and politically unsustainable. These issues are examined in more depth in Chapter 10 because the selection of the ultimate target depends upon the decision-making framework and upon the nature of the damage functions. But, it matters here to insist on the fact that the more inertia is built into the technical system, and the less processes of learning by doing and induced technical change have operated, the more costly corrections of trajectories in hedging strategies will be, for example, moving from a 550ppmv concentration goal to 450ppmv (Ha-Duong et al., 1997; see also Grubb et al., 1995; Grubb, 1997). This possibility of switching from one objective to another is supported by current material regarding climate damages, in particular (Tol, 1996) if the rate of change is considered in the analysis and the delay between symptoms and the response by society (see Chapter 10).

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