Technology Forecast and Socio-Technical Transitions

Decisions about how and when to decarbonize the global energy system are highly (though not exclusively) influenced by estimates of the likely cost.

In a paper published in Joule, a sister journal to Cell focused on sustainable energy, a research team collaborating at the Institute for New Economic Thinking at the Oxford Martin School, offers an interesting perspective on the bidirectional relationship between technology deployment and cost.

Most energy-economy models have historically underestimated deployment rates for renewable energy technologies and overestimated their costs. These issues have driven calls for alternative approaches and more reliable technology forecasting methods. Here, we use an approach based on probabilistic cost forecasting methods that have been statistically validated by back testing on more than 50 technologies. We generate probabilistic cost forecasts for solar energy, wind energy, batteries, and electrolyzers, conditional on deployment. We use these methods to estimate future energy system costs and explore how technology cost uncertainty propagates through to system costs in three different scenarios. Compared to continuing with a fossil fuel-based system, a rapid green energy transition will likely result in overall net savings of many trillions of dollars—even without accounting for climate damages or co-benefits of climate policy.

Way, Rupert, Matthew C. Ives, Penny Mealy, and J. Doyne Farmer. ‘Empirically Grounded Technology Forecasts and the Energy Transition’. Joule 6, no. 9 (21 September 2022): 2057–82.
Figure 6, op. cit.

The three columns in the figure above represent the three energy system scenarios running from 2021 to 2070, Fast Transition, Slow Transition, and No Transition:

  • A–C annual useful energy provided by each technology as a function of time.
  • D–F annual final energy provided by each technology as a function of time
  • G–I annual electricity generation and storage in gridscale batteries and EV batteries.

Total electricity generation is divided between final electricity delivered to the economy and electricity used to produce P2X fuels for hard-to-electrify applications and for power grid backup.

The key hypothesis in this forecasting exercise is “conditional on deployment”. What does it mean? In contrast to most Integrated Assessment Models used to evaluate policies and generate scenarios, which attempt to project both deployment and costs conditioned on policies, the authors are less ambitious. It is the costs what they forecast, not the rates of deployment.

we only forecast costs conditioned on deployment. Although we have tried to choose deployment scenarios that we think are reasonable, we do not attempt to forecast deployment. Our motivation for taking a less ambitious modelling approach is that this allows us to stay close to the empirical data.

Nevertheless they believe even the Fast Transition Scenario is plausible. In fact, a rapid transition might result in overall net savings of many trillions of dollars.

And here comes what in my humble opinion is the fundamental debate on our current energy transition challenge. It is the whole social, industrial and economic system which we have to transform:

Proponents of the socio-technical transitions (STT) perspective have argued that most energy-economic models used for constructing plausible transition pathways fail to accommodate all the elements vital to understanding the pace and cost of a transition towards a clean energy system. They have contended that the complex web of technologies, infrastructures, organizations, markets, regulations, and user practices required to deliver energy, heat, and transport services to society, must all transition for any meaningful reduction in global emissions. From the STT’s “multi-level perspective” (MLP), any major socio-technical transition will be the outcome of three mutually reinforcing processes: (1) “Increasing momentum of niche innovations, (2) weakening of existing systems; and (3) strengthening exogenous pressures”250. All of these are required for a successful transition to a profoundly new technology. This has typically taken over a century for other major technological transitions, e.g. from sail to steam shipping. Accordingly, they have argued that accelerating technological progress requires not just greater investment in research and development, but enormous political support, widespread market and social acceptance, and a weakening of the existing incumbent regime. Such an approach provides a plausible narrative for the challenges facing the world in orchestrating a transition of the pace implied by the Fast Transition.

In other words, it’s the policy stupid!


Featured Image: Frank W. Geels, Socio-Technical Transitions to Sustainability

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