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

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 (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.

 

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).

MS is the market share of different technologies and c is their costs. In this equation, λ 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.

Technology change in agriculture

The management factor (MF) describes the actual yield per crop group and per socio-economic region as a proportion of the maximum potential yield. This maximum potential yield is estimated taking into account inhomogeneous soil and climate data across grid cells. The MF for the period up to 2005 is estimated as part of the IMAGE calibration procedure, using FAO statistics on actual crop yields and crop areas (FAO, 2013a). The start year for the MF is subsequently taken as point of departure for future projections.

Guidance for future development of yield changes is provided by expert projection such as the assumptions in FAO projections up to 2030 and 2050 (Bruinsma, 2003; Alexandratos and Bruinsma, 2012).The FAO trends are used as exogenous technical development in the MAGNET model, and subsequently adjusted to reflect the relative shortage of suitable land, as part of the model calculation. The combinations of production volumes and land areas from MAGNET are adopted as future MF projections into the future in IMAGE.

Future technological change is dependent on the storyline and needs to be consistent with other scenario drivers. For instance, strong economic growth is typically facilitated by rapid technology development and deployment, rising wages and a labour shift from primary production (agriculture) to secondary (industry) and tertiary (services) sectors. These developments foster more advanced management and technology in agriculture. In order to reflect different trends in exogenous yield increase, FAO trends are combined with projections of economic growth to develop scenario-specific trends of yield changes in multiple-baseline studies, like for the SSPs. Because the MF is such a decisive factor in future net agricultural land area, careful consideration of uncertainties is warranted.

 

A brief overview is presented here, for more information see the IMAGE 3.0 web page.

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