# Technological change in energy - IMAGE

Model Documentation - IMAGE
Corresponding documentation
Model information
Institution PBL Netherlands Environmental Assessment Agency (PBL), Utrecht University (UU)
Concept The IMAGE framework can best be described as a geographically explicit assessment, integrated assessment simulation model, focusing a detailed representation of relevant processes with respect to human use of energy, land and water in relation to relevant environmental processes.
Solution method Recursive dynamic solution method
Anticipation Simulation modelling framework, without foresight. However, a simplified version of the energy/climate part of the model (called FAIR) can be run prior to running the framework to obtain data for climate policy simulations.

## Technological change in the energy model TIMER

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 123. 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, where n is the learning rate, Q the cumulative capacity or output, and C is a constant:

$\displaystyle $Y = C * Q^{-n}$$

Often n is expressed by the progress ratio p, which indicates how fast the costs metric Y decreases with doubling of Q (p=2-n). Progress ratios reported in empirical studies are mostly between 0.65 and 0.95, with a median value of 0.82 4.

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 3.

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 3. 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.

## Technology substitution in the energy model TIMER

The indicated market share (IMS) of a technology is determined using a multinomial logit model that assigns market shares to the different technologies (i) on the basis of their relative prices in a set of competing technologies (j).

$\displaystyle $MS_{i}=\frac{e^{\lambda x_{i}}}{\sum_je^{\lambda c_{j}}}$$

MS is the market share of different technologies and c is their costs. In this equation, is the so-called logit parameter, determining the sensitivity of markets to price differences.

The equation takes account of direct costs and also energy and carbon taxes and premium values. The last two reflect non-price factors determining market shares, such as preferences, environmental policies, infrastructure (or the lack of infrastructure) and strategic considerations. The premium values are determined in the model calibration process in order to correctly simulate historical market shares on the basis of simulated price information. The same parameters are used in scenarios to simulate the assumption on societal preferences for clean and/or convenient fuels.

## References

1. ^  Christian Azar, Hadi Dowlatabadi (1999). A Review of Technical Change in Assessment of Climate Policy. Annual Review of Energy and the Environment, 24 (), 513-544. http://dx.doi.org/10.1146/annurev.energy.24.1.513
2. ^  A Grubler, N Nakicenovic, D G Victor (1999). Modeling technological change: Implications for the global environment. Annual Review of Energy and the Environment, 24 (), 545-569.
3.   IEA (2010). Experience curves for energy technology policy. Paris, France: OECD/IEA.
4. ^  L. Argote, D. Epple (1990). Learning Curves in Manufacturing. Science, 247 (), 920-924. http://dx.doi.org/10.1126/science.247.4945.920