Energy supply and demand/Technical learning
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An important aspect of TIMER is the endogenous formulation of technology development, on the basis of learning by doing, which is considered to be a meaningful representation of technology change in global energy models (Azar and Dowlatabadi, 1999; Grubler et al., 1999; Wene, 2000). The general formulation of 'learning by doing' in a model context is that a cost measure Y tends to decline as a power function of an accumulated learning measure:
Y= C * Q-ρ
- Y is cost measure;
- Q the cumulative capacity or output;
- ρ is the learning rate;
- C is a constant.
Often ρ is expressed by the progress ratio P, which indicates how fast the costs metric, Y, decreases with the doubling of Q (P=2-ρ). Progress ratios reported in empirical studies are mostly between 0.65 and 0.95, with a median value of 0.82 (Argotte and Epple, 1990).
In TIMER, learning by doing influences the capital output ratio of coal, oil and gas production, the investment cost of renewable and nuclear energy, the cost of hydrogen technologies, and the rate at which the energy conservation cost curves decline. The actual values used depend on the technologies and the scenario setting. The progress ratio for solar/wind and bioenergy has been set at a lower level than for fossil-based technologies, based on their early stage of development and observed historical trends (Wene, 2000).
There is evidence that, in the early stages of development, P is higher than for technologies in use over a long period of time. For instance, values for solar energy have typically been below 0.8, and for fossil-fuel production around 0.9 to 0.95.
For technologies in early stages of development, other factors may also contribute to technology progress, such as relatively high investment in research and development (Wene, 2000). In TIMER, the existence of a single global learning curve is postulated. Regions are then assumed to pool knowledge and 'learn' together or, depending on the scenario assumptions, are partly excluded from this pool. In the last case, only the smaller cumulated production in the region would drive the learning process and costs would decline at a slower rate.