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.

Key findings:

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.

Key publications: