Energy technology portfolios
We are considering how to build portfolios of clean energy technologies, with the aim of accelerating the transition to a zero carbon economy. Large, targeted investments in just a few promising energy technologies can spur very rapid improvements, with some risk of failure, while diversified investments over many possible technologies can increase the chance of finding superior options in future. This work uses techniques from quantitative finance and statistics to characterise this exploration/exploitation trade-off and find the optimal technically feasible energy technology mix for a fast, low cost transition to a sustainable system.
Predictability of energy transition progress
We are evaluating the predictability of technological progress in energy technologies. Using evidence from a range of different technologies, we are developing methods to produce distributional forecasts, showing not only the median prediction but also the uncertainty surrounding the future costs of energy technologies.
Accelerating the energy transition
We are considering how to build portfolios of clean energy technologies, with the aim of accelerating the transition to a zero carbon economy. We have obtained a quantitative understanding of the degree to which some technologies improve much faster than others, and how exploiting these differences to properly take technological progress into account can accelerate the green energy transition. This work uses techniques from quantitative finance and statistics to characterise this exploration/exploitation trade-off and find the optimal technically feasible energy technology mix for a fast, low cost transition to a sustainable system. This project is supported by Partners For a New Economy (P4NE)
Forecasting technological progress
Innovation is the dominant factor underpinning economic growth, and is essential for sustainability and the clean energy transition. Our goal is to develop models for technological evolution that can provide a foundation for a theory of economic growth and allow us to make clear recommendations regarding the best technological investments for sustainability transitions. To this end, we have developed time series forecasting methods and network models which we use to analyse performance curves and the large historical records of patenting activity to map the evolution of technological ecosystems.
We have found that in many technologies, the rates of progress are sufficiently persistent to be used for forecasting purposes. We have developed methods to quantify uncertainty associated with such predictions, allowing us to design investment portfolios that balance the need to diversify to reduce uncertainty and the need for specialisation to reap the benefits of increasing returns. In contrast to fossil fuels, the prices of key renewable energy sources decline more if we invest in them, so phasing out fossil fuels in favour of clean energy sources may be costly in the short term but will lead to higher benefits in the future.
We have also developed network models that can leverage patent data to understand the relationships between technologies. We found that there are relatively independent clusters of renewable energy sources, suggesting that within a cluster, progress in each technology benefits the others. For instance, patents in hydro energy may also be helpful for further developing wind technologies, but not for nuclear or biofuels. On the forecasting side, we have shown that it is possible to produce better forecasts of future patenting when using models that take into account these interdependencies.
Impact and media coverage:
- Doyne Farmer discusses how learning curves will lead to extremely cheap clean energy on the Volts podcast (September 2022)
- Matthew Ives speaks to the Hear This Idea podcast on Solar Power and Experience Curves
- Research by Doyne Farmer and François Lafond has been used in an article in Bloomberg which discusses President Trump's recent withdrawal from the Paris Climate agreement (June 2017)
- Research by Doyne Farmer and François Lafond on the cost of solar energy quoted in The Guardian (January 2016)
Funders include:
Baillie Gifford, BEIS, IARPA, Partners for a New Economy, Oxford Martin School Programme on Post-Carbon Transition