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.
- Hötte, K., Jee, S.J. & Srivastav, S. (2021). Knowledge for a warmer world: a patent analysis of climate change adaptation technologies. INET Oxford Working Paper No. 2021-19
- Meng, J., Way, R., Verdolini, E., & Diaz Anadon, L. (2021). Comparing expert elicitation and model-based probabilistic technology cost forecasts for the energy transition. Proceedings of the National Academy of Sciences
- Hötte, K., Pichler, A., & Lafond, F. (2021). The rise of science in low-carbon energy technologies. Renewable and Sustainable Energy Reviews, Volume 139.
- Way, R., Mealy, P. & Farmer, J.D. (2020). Empirically grounded technology forecasts and the energy transition. INET Oxford Working Paper No. 2021-01.
- Lafond, F., Greenwald, D. S., & Farmer, J. D. (2020) Can stimulating demand drive costs down? World War II as a natural experiment. INET Oxford Working Paper No. 2020-02.
- Pichler, A., Lafond, F. & Farmer, J. D. (2020). Technological interdependencies predict innovation dynamics. INET Oxford Working Paper, No.2020-04.
- Lafond, F. & Kim, D. (2019) Long-run dynamics of the U.S. patent classification system. Journal of Evolutionary Economics, 631–664.
- Way, R., Lafond, F., Lillo, F., Panchenko, V., & Farmer, J.D. (2019). Wright meets Markowitz: How standard portfolio theory changes when assets are technologies following experience curves. Journal of Economic Dynamics and Control, 101(April), pp.211-238.
- Lafond, F., Bailey, A.G., Bakker, J.D., Rebois, D., Zadourian, R., McSharry, P., & Farmer, J.D. (2018). How well do experience curves predict technological progress? A method for making distributional forecasts. Technological Forecasting and Social Change, 128(March), pp.104-117.
- Mariani, M.S., Medo, M., & Lafond, F. (2018). Early identification of important patents through network centrality. Technological Forecasting and Social Change.
- Farmer, J.D. & Lafond, L. (2016). How predictable is technological progress? Research Policy, 45(3), pp.647-665.
- 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)
Baillie Gifford, BEIS, IARPA, Partners for a New Economy, Oxford Martin School Programme on Post-Carbon Transition