The Scaling Dynamics of Energy Technologies (SD-ET) analysis was developed by Charlie Wilson, initially as a participant in the 2008 Young Scientists Summer Program at IIASA, and subsequently through an ongoing collaboration with Arnulf Grubler, and also Nuno Bento, a IIASA postdoctoral scholar in 2011/12. The SD-ET analysis uses historical examples of scaling at the level of both individual technologies and entire industries for a number of key markets and selected energy supply and end-use technologies over the last 100 years. In turn the SD-ET results can be applied to examine future climate mitigation scenarios in terms of their consistency with observed historical technological scaling and market growth dynamics.
Analyses of historical time series data on energy supply technologies (e.g., refineries, coal power plants, wind turbines) and energy end-use technologies (e.g., jet aircraft, cars, light bulbs) over 100 years provide the empirical basis for the SD-ET analysis.
Common patterns in the historical scaling of different energy technologies include sequential formative, up-scaling and growth phases.
Historical scaling dynamics can be used to "reality check" scenario modeling of low carbon technology diffusion.
The need for deep cuts to greenhouse gas emissions to maintain global temperature rise within "safe" levels will require a huge scaling up in the deployment of energy efficient and low carbon energy supply technologies from current levels. Energy system models typically use cost and performance criteria to assess different technologies’ diffusion and relative market shares under different emission scenarios. But various factors can constrain the projected diffusion of low carbon technologies, including land, finance, manufacturing capacity, resource and material availability SD-ET provides an analytical framework and methodology to explore the impact of such constraints revealed by historical patterns of diffusion.
Technology scaling dynamics are assessed in historical time series data on refineries, power plants (nuclear, coal, gas, wind), jet aircraft, cars, and light bulbs. Wilson’s original data set has been expanded by Bento to include steam machines (ships, locomotives, stationary), motorbikes, e-bikes, washing machines, and cellphones. In cases where S-shaped growth is clearly evidenced, logistic function parameters are used to compare scaling across different technologies. These parameters are estimated using IIASA’s LSM2 model. Scaling operates at two levels: at the level of individual technologies (unit scale), and at the level of entire industries (industry scale).
Three broad findings emerge:
Left-Panel: Industry scaling of energy technologies historically.
Right-Panel: Consistent relationship between extent and duration of industry scaling.
These consistent relationships between scaling parameters historically can be used to test projections of low carbon technologies in future scenarios. An initial finding from this collaborative research between the ENE and TNT Programs is that despite orders of magnitude projected increases by 2100 in the installed capacities of technologies such as nuclear power, carbon capture and storage, and renewable energy, scenarios may actually be conservative in comparison with historical scaling. The SD-ET analytical framework thus provides a useful way of "reality checking" scenario projections of low carbon technologies, and exploring their constraints.
Last edited: 16 March 2015
Emeritus Research Scholar Sustainable Service Systems Research Group - Energy, Climate, and Environment Program
Emeritus Research Scholar Transformative Institutional and Social Solutions Research Group - Energy, Climate, and Environment Program
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