Breakthrough technologies, policy makers are often told, are either lying right beneath our eyes or hiding just around the corner. All it takes is a little push – but what kind of push?
This question, and the near cornelian dilemma it entails, has kept many a regulator awake at night. On the one hand, policy makers need to support innovation in a technology-neutral manner, allowing firms to decide which R&D projects to fund. On the other hand, in some cases supporting the development of a selected set of potentially breakthrough technologies could reduce the overall cost of addressing a particular policy challenge.
Climate change is a case in point: while carbon pricing is a market neutral way to tackle the problem, for example, combining it with R&D policies to deploy technologies like carbon capture and storage could nearly halve the long term costs of keeping temperatures below 2C, according to some estimates . Targeted support, if used, should therefore be directed towards those technologies that have the highest potential to deliver a breakthrough in the future.
Recent academic research on the topic suggests three important ways in which such technologies could be identified.
One method consists in exploiting the wealth of information contained in patent data. “An advantage of patent data is that it enables you to trace the history of an innovation and follow the implications it has had on the building of future ones” says Nick Johnstone, Head of the Structural Policy Division of the OECD’s Directorate for Science, Technology and Industry.
Importantly, with tens of millions of applications upon which to draw from, statistical analysis of patent data could help shed light on how attributes of potentially disruptive technologies (e.g. the extent to which they draw upon a wide knowledge base) can affect their impacts (e.g. diffusion across the economy), and how the link between the two is influenced by policy decisions.
Another way to scout for breakthroughs is to focus on a technology’s potential for cost reductions. Expert consultations have been an important and useful tool in this regard although, by definition, they remain subjective. More recently, researchers at Oxford University have been exploring the use of quantitative modeling to predict the evolution of a technology’s learning curve. Compared to expert consultations, a key advantage of this approach its ability to assess the probability with which costs could decline over a given period of time, as well as that of a given technology becoming more competitive than another selected one. The first test of the method, published earlier this year and based on a generalization of Moore’s law, has yielded promising results.
As promising as they are, the above endeavors are still in their very early stage of research. In the meantime, therefore, governments will have to take a ‘trial-and-error’ approach and continuously track the impact of their support.
Johnstone offers the following advice: “Support, monitor closely, and exit when it’s either failing or succeeding so well you don’t need to be there anymore as a policy maker. The secret is knowing when to fish or cut bait”.
This post first appeared on GE LookAhead. Publication does not imply endorsement of views by the World Economic Forum.
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Author: Dr Elie Chachoua is a contributing writer for GE Look Ahead.
Image: A 12-inch wafer is displayed at Taiwan Semiconductor Manufacturing Company (TSMC) in Xinchu January 9, 2007. REUTERS/Richard Chung.